Publications

Dinh, H, Nguyen, H, Nguyen, D, Hossain, E & Niyato, D 2023, Deep Reinforcement Learning for Wireless Communications and Networking: Theory, Applications and Implementation, IEEE Wiley, New Jersey.

Wang, T, Li, B, Chen, M & Yu, S 2023, Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore.
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Yu, S & Cui, L 2023, Security and Privacy in Federated Learning, Springer Nature Singapore.
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Afsari, M, Shon, HK & Tijing, LD 2023, 'Characterization of nanofibers and nanofiber membranes' in Electrospun and Nanofibrous Membranes, Elsevier, pp. 295-322.
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Alalyan, MS, Jaafari, NA & Hussain, FK 2023, 'Technology Factors Influencing Saudi Higher Education Institutions’ Adoption of Blockchain Technology: A Qualitative Study' in Advanced Information Networking and Applications, Springer International Publishing, pp. 197-207.
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Aldini, S & Liu, D 2023, 'Human-Robot Collaboration in Agriculture' in Encyclopedia of Smart Agriculture Technologies, Springer International Publishing, pp. 1-9.
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Bagirov, A, Seifollahi, S, Piccardi, M, Zare Borzeshi, E & Kruger, B 2023, 'SMGKM: An Efficient Incremental Algorithm for Clustering Document Collections' in Computational Linguistics and Intelligent Text Processing, Springer Nature Switzerland, pp. 314-328.
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Chaczko, Z, Borowik, G, Alsawwaf, M & Kulbacki, M 2023, 'Case Studies in Access Control' in Applied Systemic Studies, Springer International Publishing, pp. 187-198.
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Chen, Y & Jupp, JR 2023, 'Towards a Requirements Co-engineering Improvement Framework: Supporting Digital Delivery Methods in Complex Infrastructure Projects' in Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies, Springer Nature Switzerland, pp. 262-273.
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Cheng, D, Ngo, HH, Guo, W, Pandey, A, Varjani, S, Zhang, Z & Awasthi, MK 2023, 'Sustainability considerations of biochar production in biowaste management' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 41-62.
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Deng, L, Ngo, HH, Ni, B-J, Wei, W, Wang, Q & Guo, W 2023, 'Engineered membrane processes for nutrient removal and microalgae harvesting' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 267-292.
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Deng, S, Ngo, HH, Guo, W, You, N & Peng, S 2023, 'Biological nutrient recovery from wastewater for circular economy' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 355-412.
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Di, X, Qin, J, Sun, Y & Su, QP 2023, 'Visualize the Distribution and Dynamics of Mitochondrial DNA (mtDNA) Nucleoids with Multiple Labeling Strategies' in Methods in Molecular Biology, Springer US, pp. 79-88.
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Dickson-Deane, C, Heggart, K & Vanderburg, R 2023, 'Designing Learning Design Pedagogy: Proactively Integrating Work-Integrated Learning to Meet Expectations' in Design Education Across Disciplines, Springer International Publishing, pp. 125-142.
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Do, QC, Tran, TN, Tran, TH, La, DD, Ngo, HH, Thanh, BX, Chang, SW & Nguyen, DD 2023, 'Sustainable production and application of biochar for energy storage and conversion' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 333-364.
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Do, T-TN, Duong, NMH & Lin, C-T 2023, 'Integrated Sensing Devices for Brain-Computer Interfaces' in More-than-Moore Devices and Integration for Semiconductors, Springer International Publishing, pp. 241-258.
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Fu, H, Liu, C, Qi, X, Lin, B, Li, L, Zhang, L & Yu, X 2023, 'Sign Spotting via Multi-modal Fusion and Testing Time Transferring' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 271-287.
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Hanna, B, Xu, G, Wang, X & Hossain, J 2023, 'Blockchain-Based Energy Efficient Supply Chain Management' in Blockchain in Supply Chain Digital Transformation, CRC Press, pp. 195-218.
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This book explores real-world supply chain use cases from a range of industries to demonstrate the digital transformative capabilities of blockchain and DLT technologies"--

Hanna, B, Xu, G, Wang, X & Hossain, J 2023, 'Blockchain-enabled humanitarian supply chain management: sustainability and responsibility' in Blockchain in a Volatile-Uncertain-Complex-Ambiguous World, Elsevier, pp. 251-276.
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Indraratna, B, Qi, Y & Ngo, T 2023, 'Sustainable Transport Infrastructure Adopting Energy-Absorbing Waste Materials' in Springer Transactions in Civil and Environmental Engineering, Springer Nature Singapore, pp. 159-173.
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Iwanaga, MK, Brennan, MJ, Scussel, O, de Almeida, FCL & Karimi, M 2023, 'On the Pipe Localization Based on the Unwrapped Phase of Ground Surface Vibration Between a Roving Pair of Sensors' in Mechanisms and Machine Science, Springer International Publishing, pp. 1069-1076.
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Jiang, G, Li, X & Song, Y 2023, 'Concrete Sewer Systems and Wastewater Processes Related to Concrete Corrosion' in Microbiologically Influenced Corrosion of Concrete Sewers, Springer International Publishing, pp. 3-20.
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Jiang, G, Liu, Y, Li, X & Sun, X 2023, 'Mathematical Modelling for the Concrete Corrosion of Sewer Systems' in Microbiologically Influenced Corrosion of Concrete Sewers, Springer International Publishing, pp. 159-181.
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Jiang, G, Sun, X, Keller, J, Li, X, Song, Y, Schmid, M & Walenta, G 2023, 'A Systematic Laboratory Testing of Concrete Corrosion Resistance in Sewers' in Microbiologically Influenced Corrosion of Concrete Sewers, Springer International Publishing, pp. 113-137.
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Jiang, G, Sun, X, Li, X, Song, Y & Keller, J 2023, 'Controlling Environmental Factors of Microbiologically Influenced Concrete Corrosion in Sewers' in Microbiologically Influenced Corrosion of Concrete Sewers, Springer International Publishing, pp. 141-158.
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Khwaji, A, Alsahafi, Y & Hussain, FK 2023, 'A Roadmap to Blockchain Technology Adoption in Saudi Public Hospitals' in Advanced Information Networking and Applications, Springer International Publishing, pp. 452-460.
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Lammers, T, Guertler, M, Sick, N & Deuse, J 2023, 'It’s Coming Home Down Under – The Potential of Digital Work to Overcome Australia’s Challenges in Reshoring Manufacturing' in New Digital Work, Springer International Publishing, pp. 161-170.
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AbstractOver the past decades, the world has seen a continuous increase of globalisation and interconnectedness – in part supported by advances in digital communication and production technologies. In the case of industrial production, this trend has led to global, integrated supply chains in order to provide the most competitive and innovative products utilising the most competitive market conditions. In Australia, due to its remote geographic location and socioeconomic conditions, such as high labour costs and negative economics of scale, this has resulted in a loss of domestic manufacturing capabilities. With recent changes in the geopolitical environment (trade wars, actual wars, Covid-19, climate crisis etc.) calls to produce local are becoming louder again. In this article, we therefore explore the potential of digital technologies to overcome Australia’s challenges in reshoring its manufacturing capabilities. Findings indicate that a highly skilled digital workforce is needed to leverage the country’s potential in world-leading niche manufacturing. The Associate Degree of Advanced Manufacturing, developed and delivered by the Centre for Advanced Manufacturing at the University of Technology Sydney (UTS), is presented as an example of how to upskill the manufacturing workforce.

Larpruenrudee, P, Paul, G, Saha, SC, Husain, S, Mortazavy Beni, H, Lawrence, C, He, X, Gu, Y & Islam, MS 2023, 'Ultrafine particle transport to the lower airways: airway diameter reduction effects' in Digital Human Modeling and Medicine, Elsevier, pp. 253-274.
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Lestari, NI, Hussain, W, Merigo, JM & Bekhit, M 2023, 'A Survey of Trendy Financial Sector Applications of Machine and Deep Learning' in Application of Big Data, Blockchain, and Internet of Things for Education Informatization, Springer Nature Switzerland, pp. 619-633.
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Li, B, Guo, T, Wang, Y & Chen, F 2023, 'Data‐Driven Delay Analysis with Applications to Railway Networks', Wiley, pp. 115-143.
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Li, X & Jiang, G 2023, 'Characterization of Corrosion Microbial Communities' in Microbiologically Influenced Corrosion of Concrete Sewers, Springer International Publishing, pp. 99-111.
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Li, X & Jiang, G 2023, 'Surface Treatment for Corroding Concrete Sewers' in Microbiologically Influenced Corrosion of Concrete Sewers, Springer International Publishing, pp. 249-261.
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Li, X, Jiang, G, Grengg, C & Mittermayr, F 2023, 'Mechanisms and Processes of Concrete Corrosion in Sewers' in Microbiologically Influenced Corrosion of Concrete Sewers, Springer International Publishing, pp. 21-34.
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Liang, B, Li, Z, Tian, H, Liang, S, Wang, Y & Chen, F 2023, 'Optimizing Water Quality with Data Analytics and Machine Learning', Wiley, pp. 39-65.
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Lindsay, ED, Hadgraft, RG, Boyle, F & Ulseth, R 2023, 'Disrupting Engineering Education' in International Handbook of Engineering Education Research, Routledge, pp. 115-133.
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Liu, T, Awasthi, SK, Zhou, Y, Varjani, S, Zhang, Z, Pandey, A, Ngo, HH & Awasthi, MK 2023, 'Biochar for sustainable agriculture' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 299-331.
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Liu, X, Deng, L, Chen, Z, Ngo, HH, Guo, W & Wang, D 2023, 'Sustainability assessment of biochar applications' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 415-441.
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Ma, B, Zhao, Y, Wang, X, Liu, Z, Lin, X, Wang, Z, Ni, W & Liu, RP 2023, 'Vehicle Trajectory Obfuscation and Detection' in Advanced Sciences and Technologies for Security Applications, Springer International Publishing, pp. 121-134.
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Manchanda, C, Hussain, W, Rabhi, L & Rabhi, F 2023, 'Towards an API Marketplace for an e-Invoicing Ecosystem' in Lecture Notes in Business Information Processing, Springer International Publishing, pp. 82-96.
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Marynowsky, W, Knowles, J, Bown, O & Ferguson, S 2023, 'Sonic Robotics: Musical Genres as Platforms for Understanding Robotic Performance as Cultural Events' in Springer Series on Cultural Computing, Springer International Publishing, Switzerland, pp. 219-235.
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This edited collection approaches the field of social robotics from the perspective of a cultural ecology, fostering a deeper examination of the reach of robotic technology into the lived experience of diverse human populations, as well as ...

Mishra, DK, Li, L, Hossain, MJ & Zhang, J 2023, 'Controller design for load frequency control: Shortcomings and benefits' in Power System Frequency Control, Elsevier, pp. 17-39.
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Mishra, DK, Li, L, Zhang, J & Hossain, MJ 2023, 'Challenges and viewpoints of load frequency control in deregulated power system' in Power System Frequency Control, Elsevier, pp. 117-132.
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Mishra, DK, Li, L, Zhang, J & Hossain, MJ 2023, 'Fundamentals of load frequency control in power system' in Power System Frequency Control, Elsevier, pp. 1-16.
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Mortazavi, H, Mortazavy Beni, H, Islam, MS & Paul, G 2023, 'Aerosolized airborne bacteria and viruses inhalation: Micro-bioaerosols deposition effects through upper nasal airway inhalation' in Paul, G (ed), Digital Human Modeling and Medicine, Elsevier, pp. 275-288.
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Ngo, HH, Nguyen, TT, Guo, W, Deng, L, Varjani, S & Liu, Y 2023, 'Sustainability assessment of biochar for climate change mitigation' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 443-462.
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Ordibazar, AH, Hussain, O, Chakrabortty, RK, Saberi, M & Irannezhad, E 2023, 'Developing Supply Chain Risk Management Strategies by Using Counterfactual Explanation' in Service-Oriented Computing – ICSOC 2022 Workshops, Springer Nature Switzerland, pp. 53-65.
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Pandey, AK, Gaur, VK, Varjani, S, Pandey, A, Kumar, S, Ngo, HH, Guo, W, Awasthi, MK & Wong, JWC 2023, 'Role of biochar in polyaromatic hydrocarbons remediation and environment management' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 365-385.
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Pathak, N, Badeti, U, Sohn, W, Phuntsho, S & Shon, HK 2023, 'Reverse osmosis (RO) membrane development and industrial applications' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 411-435.
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Polmear, M, Chance, S, Hadgraft, RG & Shaw, C 2023, 'Informal Learning as Opportunity for Competency Development and Broadened Engagement in Engineering' in International Handbook of Engineering Education Research, Routledge, pp. 312-335.
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Rahimi, I, Gandomi, AH, Nikoo, MR & Chen, F 2023, 'Extending Boundary Updating Approach for Constrained Multi-objective Optimization Problems' in Applications of Evolutionary Computation, Springer Nature Switzerland, pp. 102-117.
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Rizwanul Fattah, IM, Ashrafur, RSM & Ahmed, A 2023, 'Black cumin (Nigella sativa L.) essential oil and aroma quality' in Biochemistry, Nutrition, and Therapeutics of Black Cumin Seed, Elsevier, pp. 71-87.
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Seifollahi, S & Piccardi, M 2023, 'Taxonomy-Based Feature Extraction for Document Classification, Clustering and Semantic Analysis' in Computational Linguistics and Intelligent Text Processing, Springer Nature Switzerland, pp. 575-586.
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Shafei, H, Li, L & Aguilera, RP 2023, 'A Comprehensive Review on Cyber-Attack Detection and Control of Microgrid Systems' in Power Systems Cybersecurity, Springer International Publishing, pp. 1-45.
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Shi, Y, Yu, X, Wang, S & Li, H 2023, 'CVLNet: Cross-view Semantic Correspondence Learning for Video-Based Camera Localization' in Computer Vision – ACCV 2022, Springer Nature Switzerland, pp. 123-141.
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Singh, AK & Lin, C-T 2023, 'Content Augmentation in Virtual Reality with Cognitive-Conflict-Based Brain-Computer Interface' in Handbook of Neuroengineering, Springer Nature Singapore, pp. 1901-1922.
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Singh, AK & Zhu, H 2023, 'Human Computer Interface in Smart Agriculture' in Encyclopedia of Smart Agriculture Technologies, Springer International Publishing, pp. 1-8.
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Sun, X, Jiang, G, Keller, J, Bond, P & Li, X 2023, 'Testing of Sulfide Uptake Rate (SUR) and Its Applications' in Microbiologically Influenced Corrosion of Concrete Sewers, Springer International Publishing, pp. 37-58.
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Tian, H, Nghiem, J & Chen, F 2023, 'Electrical Network‐Related Incident Prediction Based on Weather Factors' in Advances in Data Science and Analytics Concepts and Paradigms, Wiley, pp. 233-245.
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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 ...

Tran, VS, Ngo, HH, Guo, W & Nguyen, MK 2023, 'Sustainable application of biochar for storm water reuse' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 259-276.
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Vu, L, Nguyen, QU, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2023, 'A Novel Transfer Learning Model for Intrusion Detection Systems in IoT Networks' in Emerging Trends in Cybersecurity Applications, Springer International Publishing, pp. 45-65.
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Wang, M, Lin, B, Guo, X, Li, L, Zhu, Z, Sun, J, Zhang, S, Liu, Y & Yu, X 2023, 'GaitStrip: Gait Recognition via Effective Strip-Based Feature Representations and Multi-level Framework' in Computer Vision – ACCV 2022, Springer Nature Switzerland, pp. 711-727.
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Wang, T, Li, B, Chen, M & Yu, S 2023, 'Fundamentals of Machine Learning in Data Center Networks' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 9-14.
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Wang, T, Li, B, Chen, M & Yu, S 2023, 'Insights, Challenges and Opportunities' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 101-108.
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Wang, T, Li, B, Chen, M & Yu, S 2023, 'Introduction' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 1-8.
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Wang, T, Li, B, Chen, M & Yu, S 2023, 'Machine Learning Empowered Intelligent Data Center Networking' in Machine Learning Empowered Intelligent Data Center Networking, Springer Nature Singapore, pp. 15-99.
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Xie, M, MA, J, Long, G & Zhang, C 2023, 'Robust Clustered Federated Learning with Bootstrap Median-of-Means' in Web and Big Data, Springer Nature Switzerland, pp. 237-250.
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Ye, Y, Ngo, HH, Guo, W, Kang, J, Jiang, W, Ren, Y & Liu, D 2023, 'Biochar for sustainable remediation of soil' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 277-297.
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Zhou, J & Chen, F 2023, 'Artificial Intelligence in Agriculture' in Encyclopedia of Smart Agriculture Technologies, Springer International Publishing, pp. 1-9.
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Abdollahi, A & Pradhan, B 2023, 'Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model', Science of The Total Environment, vol. 879, pp. 163004-163004.
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Abharian, S, Sarfarazi, V, Marji, MF, Rasekh, H & Sadrekarimi, A 2023, 'Effect of geogrid reinforcement on tensile failure of high-strength self-compacted concrete', Magazine of Concrete Research, vol. 75, no. 8, pp. 379-401.
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In this study, the tensile strength, failure mechanism and ductile behaviour of geogrid-reinforced high-strength self-compacting concrete discs subjected to both the Brazilian tensile strength test and a biaxial compressive test are studied. To determine the combined effects of geogrid layer numbers and inclination angle on the ultimate tensile strength of concrete samples, 21 experiments were conducted with up to three layers of geogrids inclined at angles of 0° to 90°, at increments of 15°. In addition, discrete-element numerical simulations were conducted using two-dimensional particle flow code to examine the failure behaviour of geogrid-reinforced high-strength self-compacting concrete discs. The numerical models were first calibrated by the experimental results and then the failure behaviour of models containing geogrids was investigated. Both experimental and numerical results demonstrate that augmenting the concrete discs with geogrids increases the ductility of specimens, especially after failure. As the number of geogrid layers increased, the tensile strength of specimens also increased, whereas the tensile strength and absorbed energy were the same for specimens with different numbers of geogrid layers and inclination angles of 75° and 90°. The specimen with three horizontal geogrid layers had the highest tensile strength, biaxial compression strength and ductility of all specimens tested.

Abounahia, N, Ibrar, I, Kazwini, T, Altaee, A, Samal, AK, Zaidi, SJ & Hawari, AH 2023, 'Desalination by the forward osmosis: Advancement and challenges', Science of The Total Environment, vol. 886, pp. 163901-163901.
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Abraham, MT, Vaddapally, M, Satyam, N & Pradhan, B 2023, 'Spatio-temporal landslide forecasting using process-based and data-driven approaches: A case study from Western Ghats, India', CATENA, vol. 223, pp. 106948-106948.
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Abu, SM, Hannan, MA, Hossain Lipu, MS, Mannan, M, Ker, PJ, Hossain, MJ & Mahlia, TMI 2023, 'State of the art of lithium-ion battery material potentials: An analytical evaluations, issues and future research directions', Journal of Cleaner Production, vol. 394, pp. 136246-136246.
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Adhikari, S, Thapa, S, Naseem, U, Lu, HY, Bharathy, G & Prasad, M 2023, 'Explainable hybrid word representations for sentiment analysis of financial news', Neural Networks, vol. 164, pp. 115-123.
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Aditya, L, Vu, HP, Nguyen, LN, Mahlia, TMI, Hoang, NB & Nghiem, LD 2023, 'Microalgae enrichment for biomass harvesting and water reuse by ceramic microfiltration membranes', Journal of Membrane Science, vol. 669, pp. 121287-121287.
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Adnan Farooq, M & Nimbalkar, S 2023, 'Novel sustainable base material for concrete slab track', Construction and Building Materials, vol. 366, pp. 130260-130260.
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Afroz, S, Nguyen, QD, Zhang, Y, Kim, T & Castel, A 2023, 'Cracking of limestone calcined clay blended concrete and mortar under restrained shrinkage', Construction and Building Materials, vol. 386, pp. 131599-131599.
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Afroz, S, Zhang, Y, Nguyen, QD, Kim, T & Castel, A 2023, 'Shrinkage of blended cement concrete with fly ash or limestone calcined clay', Materials and Structures, vol. 56, no. 1.
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AbstractThis study investigates the shrinkage of two sustainable aluminosilicate blends with fly ash or limestone-calcined clay (LC3). Paste and concrete were prepared using these SCMs for the highest possible replacement of binder without compromising the strength. The chemical and autogenous shrinkage were assessed for paste samples and further investigation were conducted on hydration by thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR). Opting for an engineering approach, comparison among different segments of shrinkage i.e., autogenous, drying and total shrinkage of concrete having a specific compressive strength were considered. The initial investigation on paste samples highlighted the dissimilarities in shrinkage and hydration of fly ash and calcined clay. LC3 hydrated faster compared to fly ash leading to greater autogenous shrinkage. The high autogenous shrinkage in the LC3 blend was compensated by a low drying shrinkage for a specific compressive strength. Considering the replacement level of cement, shrinkage, and ecological impact, LC3 proved to be a more sustainable and eco-friendly concrete compared to fly ash.

Afsari, M, Li, Q, Karbassiyazdi, E, Shon, HK, Razmjou, A & Tijing, LD 2023, 'Electrospun nanofiber composite membranes for geothermal brine treatment with lithium enrichment via membrane distillation', Chemosphere, vol. 318, pp. 137902-137902.
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Ahmad, K, Abdelrazek, M, Arora, C, Bano, M & Grundy, J 2023, 'Requirements engineering for artificial intelligence systems: A systematic mapping study', Information and Software Technology, vol. 158, pp. 107176-107176.
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Ahmed, F, Afzal, MU, Thalakotuna, DN & Esselle, KP 2023, 'Novel Dual-Band Metascreen for Dual-Band Near-Field Phase Correction', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Ahmed, F, Hayat, T, Afzal, MU, Zhang, S, Esselle, KP & Whittow, WG 2023, '3-D Printable Synthetic Metasurface to Realize 2-D Beam-Steering Antenna', IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 506-519.
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Ahmed, F, Singh, K & Esselle, KP 2023, 'State-of-the-Art Passive Beam-Steering Antenna Technologies: Challenges and Capabilities', IEEE Access, pp. 1-1.
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Ahmed, SF, Debnath, JC, Mehejabin, F, Islam, N, Tripura, R, Mofijur, M, Hoang, AT, Rasul, MG & Vo, D-VN 2023, 'Utilization of nanomaterials in accelerating the production process of sustainable biofuels', Sustainable Energy Technologies and Assessments, vol. 55, pp. 102894-102894.
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Ahmed, SF, Islam, N, Kumar, PS, Hoang, AT, Mofijur, M, Inayat, A, Shafiullah, GM, Vo, D-VN, Badruddin, IA & Kamangar, S 2023, 'Perovskite solar cells: Thermal and chemical stability improvement, and economic analysis', Materials Today Chemistry, vol. 27, pp. 101284-101284.
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Ahmed, SF, Kumar, PS, Ahmed, B, Mehnaz, T, Shafiullah, GM, Nguyen, VN, Duong, XQ, Mofijur, M, Badruddin, IA & Kamangar, S 2023, 'Carbon-based nanomaterials: Characteristics, dimensions, advances and challenges in enhancing photocatalytic hydrogen production', International Journal of Hydrogen Energy.
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Alabdali, SA, Pileggi, SF & Cetindamar, D 2023, 'Influential Factors, Enablers, and Barriers to Adopting Smart Technology in Rural Regions: A Literature Review', Sustainability, vol. 15, no. 10, pp. 7908-7908.
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Smart Technology is a quickly and constantly evolving concept; it has different applications that cover a wide range of areas, such as healthcare, education, business, agriculture, and manufacturing. An effective application of these technologies increases productivity and performance within complex systems. On one side, trends show a lack of appeal for rural environments as people prefer to move to cities, looking for better opportunities and lifestyles. On the other side, recent studies and reports show that the attractiveness of rural areas as places with opportunities is increasing. Sustainable solutions are needed to enhance development in the rural context, and technological innovation is expected to lead and support the stability for people and organizations in rural regions. While Smart City is progressively becoming a reality and a successful model for integrating Smart Technology into different aspects of everyday life, its effective application in a rural context according to a Sustainable Development approach is not yet completely defined. This study adopts comparative and categorial content analysis to address the different applications and the specific characteristics of rural regions, which often present significant peculiarities depending on the country and the context. The main goal is to investigate and discuss how the Smart City model may be adopted and effectively applied within rural contexts, looking at major gaps and challenges. Additionally, because of the complexity of the topic, we provide an overview of the current adoption of Smart Technology in the different applications in rural areas, including farming, education, business, healthcare, and governance. The study highlights the huge difficulties in rural life and the potentiality of Smart Technology to enhance their Sustainable Development, which is still challenging. While the holistic analysis clearly points out a gap, there is no specific strategic roadmap to re-use or adap...

Al-Doghman, F, Moustafa, N, Khalil, I, Sohrabi, N, Tari, Z & Zomaya, AY 2023, 'AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges', IEEE Transactions on Services Computing, vol. 16, no. 2, pp. 1485-1504.
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Alfouneh, M, Hoang, V-N, Luo, Z & Luo, Q 2023, 'Topology optimization for multi-layer multi-material composite structures', Engineering Optimization, vol. 55, no. 5, pp. 773-790.
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Ali, M, Qayoom, A, Ghulam, Z, Imran, S, Yusoff, M, Kalam, MA, Mahmoud, O & El-Shafay, AS 2023, 'Experimental Study on the Cold Flow Behaviour of Azadirachta Indica (NEEM) Biodiesel Blended with Petroleum-based Fuels and Natural Organic Solvents', Journal of Applied Science and Engineering (Taiwan), vol. 26, no. 8, pp. 1153-1167.
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Due to the poor cold flow behaviour of biodiesel in winter, it tends to form solidifying gel in many cold regions across the world, making it challenging to use as an alternative fuel in diesel engines. This research investigation was conducted to investigate the comparative impact on cold flow parameters of biodiesel produced from Azadirachta indica (Neem oil) by blending with petroleum-based fuels and natural organic solvents. The B20 blends with kerosene showed significant improvement in CP, PP, and CFPP to -10 °C, -19 °C, and -20 °C, respectively. B20* blend enhanced the CP, PP and CFPP to 8 °C, 2 °C, and 6 °C respectively, while mixed kerosene/ diesel B20** blend improved CFPP, CP and PP by -4.5 °C, -7 °C and -8 °C respectively. Blend (B20T10) with natural turpentine oil improved CP, PP, and CFPP to 7 °C, 5 °C, and -2 °C, respectively. Diethyl ether and n-butanol did not show substantial improvement in biodiesel cold flow characteristics. The ester functional group in the biodiesel from ATR-FTIR spectral peaks was found at 1740.2 cm−1 denotes C=O, i.e., carbonyl group confirmation in the presence of ester linkage. The cost analysis of B20y** and B20T10 were found to be USD 0.747 and USD 0.782 per L, respectively in comparison to that of petroleum diesel, USD 0.770 per L. Neem biodiesel production showed a positive net energy balance of 14.28%. According to the current study, it is recommended to use blended mixed kerosene/diesel (B20**) and biodiesel blend with turpentine oil (B20T10) in diesel engines with suitable physico-chemical and cold flow properties in compliance with ASTM D6571standard.

AlJaberi, FY, Ahmed, SA, Makki, HF, Naje, AS, Zwain, HM, Salman, AD, Juzsakova, T, Viktor, S, Van, B, Le, P-C, La, DD, Chang, SW, Um, M-J, Ngo, HH & Nguyen, DD 2023, 'Recent advances and applicable flexibility potential of electrochemical processes for wastewater treatment', Science of The Total Environment, vol. 867, pp. 161361-161361.
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Alsobhi, HA, Alakhtar, RA, Ubaid, A, Hussain, OK & Hussain, FK 2023, 'Blockchain-based micro-credentialing system in higher education institutions: Systematic literature review', Knowledge-Based Systems, vol. 265, pp. 110238-110238.
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Alsoibi, I, Agarwal, R, Bharathy, G, Samarawickrama, M, Unhelkar, B & Prasad, M 2023, 'A Systematic Review and Taxonomy of Data Analytics in Non-profit Organizations', Asia Pacific Journal of Information Systems (APJIS), vol. 33, no. 1, pp. 33-68.
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Nonprofit organisations (NPOs) use data analytics and corresponding visualisations to discover and interpret patterns of donations and donor behaviours, predict future funds, and analyse time series to undertake decisions and resolve issues. Further detailed understanding of these activities in the context of NPOs is required for efficient and effective utilisation of data analytics. This article reports a systematic review of available literature on data analytics applications in NPOs to answer three research questions: (1) What are the proposed approaches and frameworks for adopting and applying data analytics in NPOs? (2) What aspects of data analytics are used for NPO activities and missions? (3) What challenges and barriers face NPOs regarding the adoption and application of data analytics for their missions? We answered the three research questions by collecting and examining data and using it to develop a new taxonomy. The results show the utilisation of data analytics applications by NPOs has not been examined in depth, indicating the need for further research. This study contributes to the literature by providing insights on the existing use of data analytics applications in various domains, and their benefits and drawbacks for NPOs. This study also presents future research directions.

Alsolbi, I, Agarwal, R, Bharathy, G, Samarawickrama, M, Unhelkar, B & Prasad, M 2023, 'A Systematic Review and Taxonomy of Data Analytics in Nonprofit Organisations', Asia Pacific Journal of Information Systems, vol. 33, no. 1, pp. 39-68.
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Altaf, T, Wang, X, Ni, W, Liu, RP & Braun, R 2023, 'NE-GConv: A lightweight node edge graph convolutional network for intrusion detection', Computers & Security, vol. 130, pp. 103285-103285.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2023, 'RF-Self-Powered Sensor for Fully Autonomous Soil Moisture Sensing', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 3, pp. 1374-1387.
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An, Y, Lam, HK & Ling, SH 2023, 'Multi-classification for EEG motor imagery signals using data evaluation-based auto-selected regularized FBCSP and convolutional neural network', Neural Computing and Applications, vol. 35, no. 16, pp. 12001-12027.
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AbstractIn recent years, there has been a renewal of interest in brain–computer interface (BCI). One of the BCI tasks is to classify the EEG motor imagery (MI). A great deal of effort has been made on MI classification. What seems to be lacking, however, is multiple MI classification. This paper develops a single-channel-based convolutional neural network to tackle multi-classification motor imagery tasks. For multi-classification, a single-channel learning strategy can extract effective information from each independent channel, making the information between adjacent channels not affect each other. A data evaluation method and a mutual information-based regularization parameters auto-selection algorithm are also proposed to generate effective spatial filters. The proposed method can be used to tackle the problem of an inaccurate mixed covariance matrix caused by fixed regularization parameters and invalid training data. To illustrate the merits of the proposed methods, we used the tenfold cross-validation accuracy and kappa as the evaluation measures to test two data sets. BCI4-2a and BCI3a data sets have four mental classes. For the BCI4-2a data set, the average accuracy is 79.01%, and the kappa is 0.7202 using data evaluation-based auto-selected filter bank regularized common spatial pattern voting (D-ACSP-V) and single-channel series convolutional neural network (SCS-CNN). Compared to traditional FBRCSP, the proposed method improved accuracy by 7.14% for the BCI4-2a data set. By using the BCI3a data set, the proposed method improved accuracy by 9.54% compared with traditional FBRCSP, the average accuracy of the proposed method is 83.70%, and the kappa is 0.7827.

Anaissi, A, Zandavi, SM, Suleiman, B, Naji, M & Braytee, A 2023, 'Multi-objective variational autoencoder: an application for smart infrastructure maintenance', Applied Intelligence, vol. 53, no. 10, pp. 12047-12062.
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AbstractMulti-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets where standard two-way analysis techniques often fail to discover the hidden correlations between variables in multi-way data. We propose a multi-objective variational autoencoder (MO-VAE) method for smart infrastructure damage detection and diagnosis in multi-way sensing data based on the reconstruction probability of autoencoder deep neural network (ADNN). Our method fuses data from multiple sensors in one ADNN at which informative features are being extracted and utilized for damage identification. It generates probabilistic anomaly scores to detect damage, asses its severity and further localize it via a new localization layer introduced in the ADNN. We evaluated our method on multi-way laboratory-based and real-life structural datasets in the area of structural health monitoring for damage diagnosis purposes. The data was collected from our deployed data acquisition system on a cable-stayed bridge in Western Sydney, a reinforced concrete cantilever beam which replicates one of the major structural components on the Sydney Harbour Bridge and a laboratory based building structure obtained from Los Alamos National Laboratory (LANL). Experimental results show that the proposed method can accurately detect structural damage. It was also able to estimate the different levels of damage severity, and capture damage locations in an unsupervised aspect. Compared to the state-of-the-art approaches, our proposed method shows better performance in terms of damage detection and localization.

Arsad, AZ, Hannan, MA, Al-Shetwi, AQ, Begum, RA, Hossain, MJ, Ker, PJ & Mahlia, TMI 2023, 'Hydrogen electrolyser technologies and their modelling for sustainable energy production: A comprehensive review and suggestions', International Journal of Hydrogen Energy.
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Arsad, AZ, Hannan, MA, Al-Shetwi, AQ, Hossain, MJ, Begum, RA, Ker, PJ, Salehi, F & Muttaqi, KM 2023, 'Hydrogen electrolyser for sustainable energy production: A bibliometric analysis and future directions', International Journal of Hydrogen Energy, vol. 48, no. 13, pp. 4960-4983.
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Asadabadi, MR, Saberi, M, Sadghiani, NS, Zwikael, O & Chang, E 2023, 'Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment', Journal of Enterprise Information Management, vol. 36, no. 1, pp. 275-302.
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PurposeThe purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.Design/methodology/approachThis paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.FindingsThe paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.Originality/valueOnline product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.

Asheralieva, A, Niyato, D & Miyanaga, Y 2023, 'Efficient Dynamic Distributed Resource Slicing in 6G Multi-Access Edge Computing Networks with Online ADMM and Message Passing Graph Neural Networks', IEEE Transactions on Mobile Computing, pp. 1-18.
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Ashfaque Ahmed, S, Elahi M. Soudagar, M, Rahamathullah, I, Sadhik Basha, J, Yunus Khan, TM, Javed, S, Elfasakhany, A & Kalam, MA 2023, 'Investigation of ternary blends of animal fat biodiesel-diethyl ether-diesel fuel on CMFIS-CI engine characteristics', Fuel, vol. 332, pp. 126200-126200.
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The experimental study aims at investigation of the ternary blends of animal fat oil biodiesel, diesel and diethyl ether in-terms of performance, emission and combustion analysis in single cylinder four stroke diesel engine. The animal (mono) fat biodiesel was prepared through transesterification process by using alcohol (6:1) and potassium hydroxide (4:1) at 50 °C –55 °C and obtained 90 % of biodiesel. The prepared biodiesel was blended with neat diesel to get the tested blend, animal fat biodiesel (AFB20). To improve the performance parameters of AFB20, the prepared biodiesel blend AFB20 was again blend with diethyl ether (DEE) in various proportions by 10 % and 20 % to obtain the ternary blend AFB20DEE10 and AFB20DEE20. Addition of DEE to AFB20 enhances the physicochemical properties of the biodiesel blends. The first stage of this study was that the neat diesel was examined in the single cylinder four stroke diesel engine to obtain the reference readings. The second and third stage of this investigation was the prepared animal fat biodiesel blend AFB20 and AFB20DEE10 and AFB20DEE20 were examined in the diesel engine. The fourth stage of this investigation was that, the obtained results of neat diesel was compared with the other tested blends. The experimental outcome reveals that, AFB20DEE20 blend perform better than that of the other tested blends. 4.8 % higher fuel is consumed, and 7.1 % lowered brake thermal efficiency and exhaust gas temperature was found in the blend AFB20DEE20 compared to neat diesel. The blend AFB20DEE20 exhibits higher cylinder pressure by 70.43 bar and lower heat release rate by 35.23 J/deg., compared to neat diesel. 0.15 % lower CO emission and 37.8 % lower UBHC emission were found in AFB20DEE20 compared with ND. Reduction of NOx emission by 4.18 % and higher smoke emissions were found in the blend AFB20DEE20 compared to neat diesel.

Azad, TD, Nair, SK, Kalluri, AL, Materi, J, Ahmed, AK, Khalifeh, J, Abu-Bonsrah, N, Sharwood, LN, Sterner, RC, Brooks, NP, Alomari, S, Musharbash, FN, Mo, K, Lubelski, D, Witham, TF, Theodore, N & Bydon, A 2023, 'Delays in Presentation After Traumatic Spinal Cord Injury–A Systematic Review', World Neurosurgery, vol. 169, pp. e121-e130.
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Azizivahed, A, Gholami, K, Li, L & Zhang, J 2023, 'Accurate optimal power flow for active distribution networks via floating tangent surface', Electric Power Systems Research, vol. 217, pp. 109167-109167.
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Bala, D, Hossain, MS, Hossain, MA, Abdullah, MI, Rahman, MM, Manavalan, B, Gu, N, Islam, MS & Huang, Z 2023, 'MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification', Neural Networks, vol. 161, pp. 757-775.
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Bandara, M, Rabhi, FA & Bano, M 2023, 'A knowledge-driven approach for designing data analytics platforms', Requirements Engineering, vol. 28, no. 2, pp. 195-212.
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AbstractBig data analytics technologies are rapidly expanding across all industry sectors as organisations try to make analytics an integral part of their everyday decision-making. Although there are many software tools and libraries to assist analysts and software engineers in developing solutions, organisations are looking for flexible analytics platforms that can address their specific objectives and requirements. To minimise costs, such platforms also need to co-exist with existing IT infrastructures and reuse knowledge and resources already accumulated within the organisation. To address such needs, this paper proposes the Data Analytics Solution Engineering (DASE) framework—a knowledge-driven approach supported by semantic web technologies for requirements engineering, design and development of new data analytics platforms. It includes a meta-model that captures data analytics platform requirements via a Knowledge Base, a set of guidelines that organisations can follow in engineering data analytics platforms and a reference architecture that demonstrates how to use these guidelines. We evaluate the DASE framework through two case studies and demonstrate how it can facilitate knowledge-based and requirements-driven data analytics platform engineering. The resulting data analytics platforms are observed to be user friendly, easy to maintain and flexible in handling changes to requirements. This work contributes to the body of knowledge in knowledge-driven requirements engineering, and data analytics platform engineering by providing a meta-model and a reference architecture that can be tailored to different analytics application domains.

Bao, S, Cui, C, Li, J, Tang, Y, Lee, HH, Deng, R, Remedios, LW, Yu, X, Yang, Q, Chiron, S, Patterson, NH, Lau, KS, Liu, Q, Roland, JT, Coburn, LA, Wilson, KT, Landman, BA & Huo, Y 2023, 'Topological-preserving membrane skeleton segmentation in multiplex immunofluorescence imaging', Medical Imaging 2023: Digital and Computational Pathology.
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Barua, PD, Yildiz, AM, Canpolat, N, Keles, T, Dogan, S, Baygin, M, Tuncer, I, Tuncer, T, Tan, R-S, Fujita, H & Acharya, UR 2023, 'An accurate automated speaker counting architecture based on James Webb Pattern', Engineering Applications of Artificial Intelligence, vol. 119, pp. 105821-105821.
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Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Blaabjerg, F & Siwakoti, YP 2023, 'A Novel Active Neutral Point-Clamped Five-Level Inverter With Single-Stage-Integrated Dynamic Voltage Boosting Feature', IEEE Transactions on Power Electronics, vol. 38, no. 6, pp. 7796-7809.
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Basak, S, Agrawal, H, Jena, S, Gite, S, Bachute, M, Pradhan, B & Assiri, M 2023, 'Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems', Computer Modeling in Engineering & Sciences, vol. 135, no. 2, pp. 1053-1089.
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Baygin, M, Barua, PD, Chakraborty, S, Tuncer, I, Dogan, S, Palmer, E, Tuncer, T, Kamath, AP, Ciaccio, EJ & Acharya, UR 2023, 'CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals', Physiological Measurement, vol. 44, no. 3, pp. 035008-035008.
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Abstract Objective. Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The primary objective of this work is to present a handcrafted model using state-of-the-art technique to detect SZ accurately with EEG signals. Approach. In our proposed work, the features are generated using a histogram-based generator and an iterative decomposition model. The graph-based molecular structure of the carbon chain is employed to generate low-level features. Hence, the developed feature generation model is called the carbon chain pattern (CCP). An iterative tunable q-factor wavelet transform (ITQWT) technique is implemented in the feature extraction phase to generate various sub-bands of the EEG signal. The CCP was applied to the generated sub-bands to obtain several feature vectors. The clinically significant features were selected using iterative neighborhood component analysis (INCA). The selected features were then classified using the k nearest neighbor (kNN) with a 10-fold cross-validation strategy. Finally, the iterative weighted majority method was used to obtain the results in multiple channels. Main results. The presented CCP-ITQWT and INCA-based automated model achieved an accuracy of 95.84% and 99.20% using a single channel and majority voting method, respectively with kNN classifier. Significance. Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals.

Bazli, M, Ashrafi, H, Rajabipour, A & Kutay, C 2023, '3D printing for remote housing: Benefits and challenges', Automation in Construction, vol. 148, pp. 104772-104772.
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Beena, KS, Sandeep, MN, Indraratna, B & Malisetty, RS 2023, 'Near-field vibrations in railway track on soft subgrades for semi high-speed trains', Transportation Engineering, vol. 12, pp. 100176-100176.
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Bennett, NS & Lim, B 2023, 'Assessing the Potential of Heat Pumps to Reduce the Radiator Size on Small Satellites', Energies, vol. 16, no. 10, pp. 4010-4010.
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Future small satellites will demand high-performance on-board electronics, requiring sophisticated approaches to heat rejection beyond simply increasing the radiator surface area. An interesting alternative approach is to increase the surface temperature of the radiator, using a heat pump. In this study, calculations were carried out to compute the theoretical radiator size reduction potential enacted by having a heat pump as part of a satellite’s thermal management system. The practical likelihood of a ‘typical’ vapor compression cycle (VCC) heat pump satisfying theoretical requirements was considered. In agreement with theoretical calculations, employing a ‘typical’ VCC heat pump could either increase or decrease the required radiator surface area. The choice of heat pump and its design is therefore crucial. A heat pump with a large temperature lift is essential for satellite radiator cooling applications, with the coefficient of performance (COP) being less important. Even with a low COP, such as 2.4, a ‘typical’ heat pump providing a large temperature lift, close to 60 °C, could reduce the satellite’s radiator surface area by a factor close to 1.4. This is a significant potential reduction. The decision on whether to pursue this approach compared to alternatives, such as deployable radiators, should consider the relative complexity, cost, weight, size, reliability, etc., of the two options. The focus of this study is VCC heat pumps; however, the results provide performance targets for less mature heat pump technologies, e.g., caloric devices, which could ultimately be applied in space.

Bennett, NS, Hawchar, A & Cowley, A 2023, 'Thermal Control of CubeSat Electronics Using Thermoelectrics', Applied Sciences, vol. 13, no. 11, pp. 6480-6480.
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A feasibility study is presented exploring the possibility of using thermoelectric devices for the thermal control of CubeSat on-board electronics. A simple thermoelectric architecture is devised and an empirical model for how such a system would perform is constructed, using the performance data of a commercially available thermoelectric module. This is used to calculate the temperature to which the system could cool a computer chip, as a function of thermal resistance and heat rejection. As a baseline scenario, the temperature of the system without the thermoelectric device is compared and the benefit, or otherwise, of using a thermoelectric module is calculated. Analysis shows that in some circumstances introducing a thermoelectric device would actually increase the temperature of the electronics being cooled. This is most common when the quantity of heat being removed, or the thermal resistance of the system, is high. Nevertheless, thermoelectric cooling is beneficial for a range of conditions, such as for cooling the computer chip below ambient temperature, however a good quality radiator is required. This constraint could undermine the thermoelectric device’s potential benefit in many cases, due to the need for an unrealistically large radiator.

Beyhan, B, Akcomak, IS & Cetindamar, D 2023, 'How do technology-based accelerators build their legitimacy as new organizations in an emerging entrepreneurship ecosystem?', Journal of Entrepreneurship in Emerging Economies, pp. 1-37.
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Purpose This paper aims to understand technology-based accelerators’ legitimation efforts in an emerging entrepreneurship ecosystem. Design/methodology/approach This research is based on qualitative inductive methodology using ten Turkish technology-based accelerators. Findings The analysis indicates that accelerators’ legitimation efforts are shaped around crafting a distinctive identity and mobilizing allies around this identity; and establishing new collaborations to enable collective action. Further, the authors observe two types of technology-based accelerators, namely, “deal flow makers” and “welfare stimulators” in Turkey. These variations among accelerators affect how they build their legitimacy. Different types of accelerators make alliances with different actors in the entrepreneurship ecosystem. Accelerators take collective action to build a collective identity and simultaneously imply how they are distinguished from other organizations in the same category and the ones in the old category. Originality/value This study presents a framework to understand how accelerators use strategies and actions to legitimize themselves as new organizations and advocate new norms, values and routines in an emerging entrepreneurship ecosystem. The framework also highlights how different accelerators support legitimacy building by managing the judgments of diverse audiences and increasing the variety of resources these audiences provide to the ecosystem.

Bhattacharya, S, Biswas, P, Canning, J & Bandyopadhyay, S 2023, 'Regeneration turn-around-point: A milestone on the way to optimizing regenerated fiber Bragg grating', Optical Fiber Technology, vol. 75, pp. 103146-103146.
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Bhol, P, Patil, SA, Barman, N, Ekambaram Siddharthan, E, Thapa, R, Saxena, M, Altaee, A & Samal, AK 2023, 'Design and fabrication of cobaltx nickel(1-x) telluride microfibers on nickel foam for battery-type supercapacitor and oxygen evolution reaction study', Materials Today Chemistry, vol. 30, pp. 101557-101557.
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Bi, Z, Zhang, L, He, X & Zhai, Y 2023, 'Effect of oblique incidence angle and frequency content of P and SV waves on the dynamic behavior of liquid tanks', Soil Dynamics and Earthquake Engineering, vol. 171, pp. 107929-107929.
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Biswas, K, Shivakumara, P, Pal, U, Lu, T, Blumenstein, M & Lladós, J 2023, 'Classification of aesthetic natural scene images using statistical and semantic features', Multimedia Tools and Applications, vol. 82, no. 9, pp. 13507-13532.
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Blamires, S, Lozano-Picazo, P, Bruno, AL, Arnedo, M, Ruiz-León, Y, González-Nieto, D, Rojo, FJ, Elices, M, Guinea, GV & Pérez-Rigueiro, J 2023, 'The Spider Silk Standardization Initiative (S3I): A powerful tool to harness biological variability and to systematize the characterization of major ampullate silk fibers spun by spiders from suburban Sydney, Australia', Journal of the Mechanical Behavior of Biomedical Materials, vol. 140, pp. 105729-105729.
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Bliuc, D, Tran, T, Chen, W, Alarkawi, D, Alajlouni, DA, Blyth, F, March, L, Blank, RD & Center, JR 2023, 'Antiresorptive Medication Use Is not Associated With Acute Cardiovascular Risk: An Observational Study', The Journal of Clinical Endocrinology & Metabolism, vol. 108, no. 5, pp. e110-e119.
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Abstract Context Bisphosphonates have been reported to be cardioprotective in some, but not all, studies. It is unknown whether denosumab (Dmab) use protects against cardiovascular events (CVEs). Objective To determine whether oral bisphosphonate (oBP) or Dmab use is associated with CVEs in persons with incident fracture. Methods Participants with an incident minimal trauma fracture from the Sax Institute’s 45 and Up Study, a population-based cohort from NSW, Australia, were followed between 2005/2009 and 2017. Questionnaire data were linked to hospital admissions (Admitted Patients Data Collection [APDC]) by the Centre for Health Record Linkage). Medicare Benefit Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data sets were provided by Services Australia. Data was stored in a secure computing environment (Secure Unified Research Environment). Fractures, CVEs, and comorbidities were identified from the APDC and oBP and Dmab medication from the PBS. oBP and Dmab users were matched to never users (NoRx) by propensity scores. The main outcome measures were association between oBP and Dmab with CVE (acute myocardial infarction, unstable angina, cerebrovascular accident, and transient ischemic attack) and were determined using a stratified Cox's proportional hazards model. Results There were 880 pairs of oBP and NoRx (616 women) and 770 pairs of Dmab and NoRx (615 women) followed for ∼4.3 years. CVE risk was similar for oBP and NoRx Hazard Ratios (HR) wome...

Boshir Ahmed, M, Alom, J, Hasan, MS, Asaduzzaman, M, Rahman, MS, Hossen, R, Abu Hasan Johir, M, Taufiq Alam, M, Zhou, JL, Zhu, Y & Zargar, M 2023, 'General Doping Chemistry of Carbon Materials', ChemNanoMat, vol. 9, no. 4.
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Bui, HT, Hussain, OK, Prior, D, Hussain, FK & Saberi, M 2023, 'SIAEF/PoE: Accountability of Earnestness for encoding subjective information in Blockchain', Knowledge-Based Systems, vol. 269, pp. 110501-110501.
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Cai, P, Li, H, Guo, Q & Huang, X 2023, 'UAMP-Based Equalization for Dual Pulse Shaping Transmission Systems', IEEE Wireless Communications Letters, pp. 1-1.
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Cancino, CA, Merigó, JM, Urbano, D & Amorós, JE 2023, 'Evolution of the entrepreneurship and innovation research in Ibero-America between 1986 and 2015', Journal of Small Business Management, vol. 61, no. 2, pp. 322-352.
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Cao, MX & Tomamichel, M 2023, 'Comments on “Channel Coding Rate in the Finite Blocklength Regime”: On the Quadratic Decaying Property of the Information Rate Function', IEEE Transactions on Information Theory, pp. 1-1.
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Cao, X & Tsang, IW 2023, 'Distribution Matching for Machine Teaching', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Cao, Y, Cheng, H, Gu, N, Ou, K, Wang, Z, Liu, Q, Guan, R, Fu, Q & Sun, Y 2023, 'Excellent mechanical durability of superhydrophobic coating by electrostatic spraying', Materials Chemistry and Physics, vol. 301, pp. 127658-127658.
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Cao, Y, Zhao, L, Zhong, Q, Wen, S, Shi, K, Xiao, J & Huang, T 2023, 'Adaptive fixed-time output synchronization for complex dynamical networks with multi-weights', Neural Networks, vol. 163, pp. 28-39.
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Cao, Z & Lin, C-T 2023, 'Reinforcement Learning From Hierarchical Critics', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 1066-1073.
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Chan, KY, Abu-Salih, B, Muhammad, K, Palade, V & Chai, R 2023, 'Editorial: Deep neural networks with cloud computing', Neurocomputing, vol. 521, pp. 189-190.
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Chang, X, Ren, P, Xu, P, Li, Z, Chen, X & Hauptmann, A 2023, 'A Comprehensive Survey of Scene Graphs: Generation and Application', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 1-26.
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Chen, D, Wu, C, Li, J & Liao, K 2023, 'An overpressure-time history model of methane-air explosion in tunnel-shape space', Journal of Loss Prevention in the Process Industries, vol. 82, pp. 105004-105004.
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Chen, J, Vinod, JS, Indraratna, B, Ngo, T & Liu, Y 2023, 'DEM study on the dynamic responses of a ballasted track under moving loading', Computers and Geotechnics, vol. 153, pp. 105105-105105.
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This paper presents the discrete element modeling of the dynamic response of a ballasted track under moving loads. The DEM model, consisting of sleepers, ballast, and sub-ballast, has been calibrated using field and laboratory data. This model was further used to examine the dynamic responses of the ballasted track subjected to a series of moving traffic loading representing various train axle loads and speeds. The results show that the permanent settlement of the sleeper, the breakage of ballast, and the dynamic stresses in the track substructure increase with an increase in train axle load and speed. As the train moves, the magnitudes of dynamic stresses and the orientations of principal stress axes in the track change continuously, and a more pronounced principal stress rotation is observed at sleeper edges than those underneath sleepers. The capping layer is found to play a critical role in reducing train-induced stress and further alleviating the disturbance from the trains to the subgrade. The interparticle contacts and the vibration of ballast during the movement of the train including the influences of train axle load and speed on the dynamic responses of ballasted railway tracks are captured and analyzed from a micromechanical perspective.

Chen, K, He, X, Liang, F & Sheng, D 2023, 'Contribution of capillary pressure to effective stress for unsaturated soils: Role of wet area fraction and water retention curve', Computers and Geotechnics, vol. 154, pp. 105140-105140.
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Chen, K, Wang, Q, Long, P & Ying, M 2023, 'Unitarity Estimation for Quantum Channels', IEEE Transactions on Information Theory, pp. 1-1.
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Chen, L, Chen, L, Zhu, H, Gomez-Garcia, R & Zhu, X 2023, 'A Wideband Balanced Amplifier Using Edge-Coupled Quadrature Couplers in 0.13-μm SiGe HBT Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 70, no. 2, pp. 631-641.
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Chen, L, Liu, Y, Yang, S, Hu, J & Guo, YJ 2023, 'Synthesis of Wideband Frequency-Invariant Beam Patterns for Nonuniformly Spaced Arrays by Generalized Alternating Projection Approach', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 1099-1104.
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Chen, Q, Yu, R, Gaoliang, T & Nimbalkar, S 2023, 'Microstructure, strength and durability of nano-cemented soils under different seawater conditions: laboratory study', Acta Geotechnica, vol. 18, no. 3, pp. 1607-1627.
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Chen, Q, Zhang, H, Ye, J, Tao, G & Nimbalkar, S 2023, 'Corrosion Resistance and Compressive Strength of Cemented Soil Mixed with Nano-Silica in Simulated Seawater Environment', KSCE Journal of Civil Engineering, vol. 27, no. 4, pp. 1535-1550.
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Chen, R, Pye, JS, Li, J, Little, CB & Li, JJ 2023, 'Multiphasic scaffolds for the repair of osteochondral defects: Outcomes of preclinical studies', Bioactive Materials, vol. 27, pp. 505-545.
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Chen, S, Zhang, P, Xie, G-S, Peng, Q, Cao, Z, Yuan, W & You, X 2023, 'Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 2, pp. 824-837.
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Chen, W-H, Biswas, PP, Ong, HC, Hoang, AT, Nguyen, T-B & Dong, C-D 2023, 'A critical and systematic review of sustainable hydrogen production from ethanol/bioethanol: Steam reforming, partial oxidation, and autothermal reforming', Fuel, vol. 333, pp. 126526-126526.
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Chen, W-H, Biswas, PP, Ubando, AT, Kwon, EE, Lin, K-YA & Ong, HC 2023, 'A review of hydrogen production optimization from the reforming of C1 and C2 alcohols via artificial neural networks', Fuel, vol. 345, pp. 128243-128243.
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Chen, X, Chen, X, Zeng, RJ, Nie, W-B, Yang, L, Wei, W & Ni, B-J 2023, 'Instrumental role of bioreactors in nitrate/nitrite-dependent anaerobic methane oxidation-based biotechnologies for wastewater treatment: A review', Science of The Total Environment, vol. 857, pp. 159728-159728.
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Chen, X, Feng, Z, Andrew Zhang, J, Wei, Z, Yuan, X & Zhang, P 2023, 'Sensing-Aided Uplink Channel Estimation for Joint Communication and Sensing', IEEE Wireless Communications Letters, vol. 12, no. 3, pp. 441-445.
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Chen, X, Feng, Z, Wei, Z, Yuan, X, Zhang, P, Andrew Zhang, J & Yang, H 2023, 'Multiple Signal Classification Based Joint Communication and Sensing System', IEEE Transactions on Wireless Communications, pp. 1-1.
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Chen, X, Feng, Z, Wei, Z, Zhang, JA, Yuan, X & Zhang, P 2023, 'Concurrent Downlink and Uplink Joint Communication and Sensing for 6G Networks', IEEE Transactions on Vehicular Technology, pp. 1-6.
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Chen, X, Wang, D, Nie, W-B, Yang, L, Wei, W, Ni, B-J & Chen, X 2023, 'Impacts of Biofilm Properties on the Start-Up and Performance of a Membrane Biofilm Reactor Performing Anammox and Nitrate/Nitrite-Dependent Anaerobic Methane Oxidation Integrated Processes: A Model-Based Investigation', ACS ES&T Water, vol. 3, no. 4, pp. 1141-1149.
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Chen, X, Yao, L, McAuley, J, Zhou, G & Wang, X 2023, 'Deep reinforcement learning in recommender systems: A survey and new perspectives', Knowledge-Based Systems, vol. 264, pp. 110335-110335.
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Chen, X, Zhang, Y, Tsang, IW, Pan, Y & Su, J 2023, 'Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation', ACM Transactions on Information Systems, vol. 41, no. 1, pp. 1-31.
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Cross domain recommendation (CDR) is one popular research topic in recommender systems. This article focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learning the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of choosing samples to train the mapping. In this article, we attempt to learn both features of user preferences in a more principled way. We assume that each user’s preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformation. Based on this assumption, we propose an equivalent transformation learner (ETL), which models the joint distribution of user behaviors across domains. The equivalent transformation in ETL relaxes the idea of shared-user representation and allows the learned preferences in different domains to preserve the domain-specific features as well as the overlapped features. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-the-art methods. Codes and data are available online: https://github.com/xuChenSJTU/ETL-master.

Chen, Y, Li, C, Yang, T, Ekimov, EA, Bradac, C, Ha, ST, Toth, M, Aharonovich, I & Tran, TT 2023, 'Real-Time Ratiometric Optical Nanoscale Thermometry', ACS Nano, vol. 17, no. 3, pp. 2725-2736.
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Chen, Y-N, Ding, C, Zhu, H & Liu, Y 2023, 'A ±45°-Polarized Antenna System With Four Isolated Channels for In-Band Full-Duplex (IBFD)', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3000-3010.
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Chen, Z, Shi, X, Zhang, J, Wu, L, Wei, W & Ni, B-J 2023, 'Nanoplastics are significantly different from microplastics in urban waters', Water Research X, vol. 19, pp. 100169-100169.
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Chen, Z, Wei, W, Chen, H & Ni, B-J 2023, 'Eco-designed electrocatalysts for water splitting: A path toward carbon neutrality', International Journal of Hydrogen Energy, vol. 48, no. 16, pp. 6288-6307.
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Chen, Z, Yun, S, Wu, L, Zhang, J, Shi, X, Wei, W, Liu, Y, Zheng, R, Han, N & Ni, B-J 2023, 'Waste-Derived Catalysts for Water Electrolysis: Circular Economy-Driven Sustainable Green Hydrogen Energy', Nano-Micro Letters, vol. 15, no. 1.
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AbstractThe sustainable production of green hydrogen via water electrolysis necessitates cost-effective electrocatalysts. By following the circular economy principle, the utilization of waste-derived catalysts significantly promotes the sustainable development of green hydrogen energy. Currently, diverse waste-derived catalysts have exhibited excellent catalytic performance toward hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and overall water electrolysis (OWE). Herein, we systematically examine recent achievements in waste-derived electrocatalysts for water electrolysis. The general principles of water electrolysis and design principles of efficient electrocatalysts are discussed, followed by the illustration of current strategies for transforming wastes into electrocatalysts. Then, applications of waste-derived catalysts (i.e., carbon-based catalysts, transitional metal-based catalysts, and carbon-based heterostructure catalysts) in HER, OER, and OWE are reviewed successively. An emphasis is put on correlating the catalysts’ structure–performance relationship. Also, challenges and research directions in this booming field are finally highlighted. This review would provide useful insights into the design, synthesis, and applications of waste-derived electrocatalysts, and thus accelerate the development of the circular economy-driven green hydrogen energy scheme.

Chen, Z, Zheng, R, Zou, H, Wang, R, Huang, C, Dai, W, Wei, W, Duan, L, Ni, B-J & Chen, H 2023, 'Amorphous iron-doped nickel boride with facilitated structural reconstruction and dual active sites for efficient urea electrooxidation', Chemical Engineering Journal, vol. 465, pp. 142684-142684.
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Chen, Z, Zuo, W, Zhou, K, Li, Q, Huang, Y & E, J 2023, 'Multi-factor impact mechanism on the performance of high temperature proton exchange membrane fuel cell', Energy, vol. 278, pp. 127982-127982.
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Chen, Z, Zuo, W, Zhou, K, Li, Q, Huang, Y & E, J 2023, 'Multi-objective optimization of proton exchange membrane fuel cells by RSM and NSGA-II', Energy Conversion and Management, vol. 277, pp. 116691-116691.
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Cheng, K, Zhang, T, Peng, K, Feng, Y, Liu, H & Medawela, S 2023, 'Zone of flow: A new finding on the characteristics of airflow within the zone of influence during air sparging in aquifers', Journal of Contaminant Hydrology, vol. 255, pp. 104165-104165.
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Chi, K, Li, J & Wu, C 2023, 'Numerical simulation of buried steel pipelines subjected to ground surface blast loading', Thin-Walled Structures, vol. 186, pp. 110716-110716.
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Chu, NH, Hoang, DT, Nguyen, DN, Van Huynh, N & Dutkiewicz, E 2023, 'Joint Speed Control and Energy Replenishment Optimization for UAV-Assisted IoT Data Collection With Deep Reinforcement Transfer Learning', IEEE Internet of Things Journal, vol. 10, no. 7, pp. 5778-5793.
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Chu, NH, Nguyen, DN, Hoang, DT, Pham, Q-V, Phan, KT, Hwang, W-J & Dutkiewicz, E 2023, 'AI-enabled mm-Waveform Configuration for Autonomous Vehicles with Integrated Communication and Sensing', IEEE Internet of Things Journal, pp. 1-1.
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Ci, Q, Wang, Y, Wu, B, Coy, E, Li, JJ, Jiang, D, Zhang, P & Wang, G 2023, 'Fe‐Doped Carbon Dots as NIR‐II Fluorescence Probe for In Vivo Gastric Imaging and pH Detection', Advanced Science, vol. 10, no. 7, pp. 2206271-2206271.
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Crowther, CA, Samuel, D, McCowan, LME, Edlin, R, Tran, T & McKinlay, CJ 2023, 'Lower Versus Higher Glycemic Criteria for Diagnosis of Gestational Diabetes', Obstetrical & Gynecological Survey, vol. 78, no. 2, pp. 85-87.
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Cui, L, Ma, J, Zhou, Y & Yu, S 2023, 'Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses', IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 910-920.
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Cui, Y, Miao, MZ, Wang, M, Su, QP, Qiu, K, Arbeeva, L, Chubinskaya, S, Diekman, BO & Loeser, RF 2023, 'Yes-associated protein nuclear translocation promotes anabolic activity in human articular chondrocytes', Osteoarthritis and Cartilage.
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Curtis, K, Brown, J, Sharwood, LN, Risi, D, Eager, D, Holland, AJA, Beck, B, Erskine, C, Lockhart, K, Cooke, K, Adams, S, Teague, WJ & Mitchell, R 2023, 'Playground injury prevention: the need for consistent and national implementation of Australian safety standards', Australian and New Zealand Journal of Public Health, vol. 47, no. 2, pp. 100023-100023.
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da Rocha, CG, Wijayaratna, K, Jong, K & Haller, D 2023, 'Examining Off-Site Construction from a Flow Viewpoint', Journal of Construction Engineering and Management, vol. 149, no. 3.
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Dang, B-T, Bui, X-T, Nguyen, T-T, Ngo, HH, Nghiem, LD, Huynh, K-P-H, Vo, T-K-Q, Vo, T-D-H, Lin, C & Chen, S-S 2023, 'Effect of biomass retention time on performance and fouling of a stirred membrane photobioreactor', Science of The Total Environment, vol. 864, pp. 161047-161047.
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Dang, KB, Pham, HH, Nguyen, TN, Giang, TL, Pham, TPN, Nghiem, VS, Nguyen, DH, Vu, KC, Bui, QD, Pham, HN, Nguyen, TT & Ngo, HH 2023, 'Monitoring the effects of urbanization and flood hazards on sandy ecosystem services', Science of The Total Environment, vol. 880, pp. 163271-163271.
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Dang, Z, Luo, M, Jia, C, Yan, C, Chang, X & Zheng, Q 2023, 'Counterfactual Generation Framework for Few-Shot Learning', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Dashti, A, Raji, M, Riasat Harami, H, Zhou, JL & Asghari, M 2023, 'Biochar performance evaluation for heavy metals removal from industrial wastewater based on machine learning: Application for environmental protection', Separation and Purification Technology, vol. 312, pp. 123399-123399.
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Dehkordi, AA, Etaati, B, Neshat, M & Mirjalili, S 2023, 'Adaptive Chaotic Marine Predators Hill Climbing Algorithm for Large-Scale Design Optimizations', IEEE Access, vol. 11, pp. 39269-39294.
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Deng, R, Huo, P, Chen, X, Chen, Z, Yang, L, Liu, Y, Wei, W & Ni, B-J 2023, 'Towards efficient heterotrophic recovery of N2O via Fe(II)EDTA-NO: A modeling study', Science of The Total Environment, vol. 859, pp. 160285-160285.
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Deng, S, Wang, C, Ngo, HH, Guo, W, You, N, Tang, H, Yu, H, Tang, L & Han, J 2023, 'Comparative review on microbial electrochemical technologies for resource recovery from wastewater towards circular economy and carbon neutrality', Bioresource Technology, vol. 376, pp. 128906-128906.
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Deveci, Ö, Shannon, AG & Mansımova, A 2023, 'The cyclic sequences in non-Abelian groups', Communications in Algebra, vol. 51, no. 7, pp. 2956-2962.
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Devitt, SJ 2023, 'How do we quantify the utility of quantum algorithms?', Research Directions: Quantum Technologies, vol. 1, pp. 1-4.
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Quantum computing advantage emerges not from brute force power, but from subtle differences in information processing that can occur for key bottleneck subroutines. In 2019, the Google Quantum AI team performed a landmark experiment demonstrating quantum computational supremacy (Arute et al., 2019) where they performed a quantum computation that, at the time, could not be done on a classical supercomputer. This was remarkable because it was achieved by a processor with only 53 qubits, an observation that emerged from theoretical work which identified that quantum computers could have a massive advantage for certain specially designed benchmarking tasks (Boixo et al., 2018; Bremner et al., 2016).

Ding, A, Lin, W, Shi, H, Chen, R, Ngo, HH, He, X, Nan, J, Li, G & Ma, J 2023, 'Enhanced Sludge Dewaterability by Efficient Oxidation of α-Mn₂O₃/Peroxymonosulfate: Analysis of the Mechanism and Evaluation of Engineering Application', ACS ES&T Engineering, vol. 3, no. 2, pp. 236-247.
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Ding, Z, Yu, Y & Xia, Y 2023, 'Nonlinear hysteretic parameter identification using an attention-based long short-term memory network and principal component analysis', Nonlinear Dynamics, vol. 111, no. 5, pp. 4559-4576.
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Doan, T, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Interactive Role of Rolling Friction and Cohesion on the Angle of Repose through a Microscale Assessment', International Journal of Geomechanics, vol. 23, no. 1.
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Dong, C, Weng, J, Li, M, Liu, J-N, Liu, Z, Cheng, Y & Yu, S 2023, 'Privacy-Preserving and Byzantine-Robust Federated Learning', IEEE Transactions on Dependable and Secure Computing, pp. 1-16.
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Dong, M, Yao, L, Wang, X, Xu, X & Zhu, L 2023, 'Adversarial dual autoencoders for trust-aware recommendation', Neural Computing and Applications, vol. 35, no. 18, pp. 13065-13075.
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Dong, S, Mei, F, Li, JJ & Xing, D 2023, 'Global Cluster Analysis and Network Visualization in Prosthetic Joint Infection: A Scientometric Mapping', Orthopaedic Surgery, vol. 15, no. 4, pp. 1165-1178.
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Dong, Y, Ran, Q, Chao, X, Li, C & Yu, S 2023, 'Personalized Individual Semantics Learning to Support a Large-Scale Linguistic Consensus Process', ACM Transactions on Internet Technology, vol. 23, no. 2, pp. 1-27.
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When making decisions, individuals often express their preferences linguistically. The computing with words methodology is a key basis for supporting linguistic decision making, and the words in that methodology may mean different things to different individuals. Thus, in this article, we propose a continual personalized individual semantics learning model to support a consensus-reaching process in large-scale linguistic group decision making. Specifically, we first derive personalized numerical scales from the data of linguistic preference relations. We then perform a clustering ensemble method to divide large-scale group and conduct consensus management. Finally, we present a case study of intelligent route optimization in shared mobility to illustrate the usability of our proposed model. We also demonstrate its effectiveness and feasibility through a comparative analysis.

Dou, Y, Li, Y, Yue, S, Li, Y & Zhu, J 2023, 'Measurement of alternating and rotational magnetostrictions of Non-oriented silicon steel sheets', Journal of Magnetism and Magnetic Materials, vol. 571, pp. 170566-170566.
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Du, G, Pu, T, Zhou, Q, Wang, L, Lei, G & Zhu, J 2023, 'Multiphysics Comparative Study of High Speed PM Machines for Ring PM Rotor and Solid PM Rotor', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 1421-1432.
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Du, J, Xue, Y, Sugumaran, V, Hu, M & Dong, P 2023, 'Improved biogeography-based optimization algorithm for lean production scheduling of prefabricated components', Engineering, Construction and Architectural Management, vol. 30, no. 4, pp. 1601-1635.
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PurposeFor prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively.Design/methodology/approachThis paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures.FindingsThis paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiveness of the...

Du, S, Shen, Y, Zheng, Y, Cheng, Y, Xu, X, Chen, D & Xia, D 2023, 'Systematic in vitro and in vivo study on biodegradable binary Zn-0.2 at% Rare Earth alloys (Zn-RE: Sc, Y, La–Nd, Sm–Lu)', Bioactive Materials, vol. 24, pp. 507-523.
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Du, X, Chen, Z, Li, Q, Yang, S, Jiang, L, Yang, Y, Li, Y & Gu, Z 2023, 'Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence', Bio-Design and Manufacturing, vol. 6, no. 3, pp. 319-339.
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Du, Y, Zhang, Y, Pu, X, Fu, X, Li, X, Bai, L, Chen, Y & Qian, J 2023, 'Synthesis of bifunctional NiFe layered double hydroxides (LDH)/Mo-doped g-C3N4 electrocatalyst for efficient methanol oxidation and seawater splitting', Chemosphere, vol. 312, pp. 137203-137203.
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Duan, Y, Lu, Y, Shen, S, Yu, S, Zhang, P, Zhang, W & Igorevich, KK 2023, 'NFLCS: An Service Function Chain Path Optimization Strategy Based on Network-Functional Layout Clustering', IEEE Transactions on Vehicular Technology, pp. 1-13.
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Elgharabawy, A, Prasad, M & Lin, C-T 2023, 'Preference Neural Network', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-15.
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Elmakki, T, Zavahir, S, Gulied, M, Qiblawey, H, Hammadi, B, Khraisheh, M, Shon, HK, Park, H & Han, DS 2023, 'Potential application of hybrid reverse electrodialysis (RED)-forward osmosis (FO) system to fertilizer-producing industrial plant for efficient water reuse', Desalination, vol. 550, pp. 116374-116374.
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Entezari, A, Liu, N-C, Zhang, Z, Fang, J, Wu, C, Wan, B, Swain, M & Li, Q 2023, 'Nondeterministic multiobjective optimization of 3D printed ceramic tissue scaffolds', Journal of the Mechanical Behavior of Biomedical Materials, vol. 138, pp. 105580-105580.
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Fahmideh, M, Grundy, J, Ahmad, A, Shen, J, Yan, J, Mougouei, D, Wang, P, Ghose, A, Gunawardana, A, Aickelin, U & Abedin, B 2023, 'Engineering Blockchain-based Software Systems: Foundations, Survey, and Future Directions', ACM Computing Surveys, vol. 55, no. 6, pp. 1-44.
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Many scientific and practical areas have shown increasing interest in reaping the benefits of blockchain technology to empower software systems. However, the unique characteristics and requirements associated with Blockchain-based Software (BBS) systems raise new challenges across the development lifecycle that entail an extensive improvement of conventional software engineering. This article presents a systematic literature review of the state-of-the-art in BBS engineering research from the perspective of the software engineering discipline. We characterize BBS engineering based on the key aspects of theoretical foundations, processes, models , and roles . Based on these aspects, we present a rich repertoire of development tasks, design principles, models, roles, challenges, and resolution techniques. The focus and depth of this survey not only give software engineering practitioners and researchers a consolidated body of knowledge about current BBS development but also underpin a starting point for further research in this field.

Faisal, SN, Do, T-TN, Torzo, T, Leong, D, Pradeepkumar, A, Lin, C-T & Iacopi, F 2023, 'Noninvasive Sensors for Brain–Machine Interfaces Based on Micropatterned Epitaxial Graphene', ACS Applied Nano Materials, vol. 6, no. 7, pp. 5440-5447.
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Fan, H, Yang, Y & Kankanhalli, M 2023, 'Point Spatio-Temporal Transformer Networks for Point Cloud Video Modeling', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 2181-2192.
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Fan, J, Li, J, Wang, Y, Li, Y, Hsieh, M-H & Du, J 2023, 'Partially Concatenated Calderbank-Shor-Steane Codes Achieving the Quantum Gilbert-Varshamov Bound Asymptotically', IEEE Transactions on Information Theory, vol. 69, no. 1, pp. 262-272.
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Fan, W, Xiao, F, Cai, H, Chen, X & Yu, S 2023, 'Disjoint Paths Construction and Fault-tolerant Routing in BCube of Data Center Networks', IEEE Transactions on Computers, pp. 1-14.
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Fan, X, Li, Y, Chen, L, Li, B & Sisson, SA 2023, 'Hawkes Processes With Stochastic Exogenous Effects for Continuous-Time Interaction Modelling', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 1848-1861.
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Fang, Z, Lu, J, Liu, F & Zhang, G 2023, 'Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 1087-1105.
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Fathalla, A, Salah, A, Bekhit, M, Eldesouky, E, Talha, A, Zenhom, A & Ali, A 2023, 'Real-Time and Automatic System for Performance Evaluation of Karate Skills Using Motion Capture Sensors and Continuous Wavelet Transform', International Journal of Intelligent Systems, vol. 2023, pp. 1-11.
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In sports science, the automation of performance analysis and assessment is urgently required to increase the evaluation accuracy and decrease the performance analysis time of a subject. Existing methods of performance analysis and assessment are either performed manually based on human experts’ opinions or using motion analysis software, i.e., biomechanical analysis software, to assess only one side of a subject. Therefore, we propose an automated system for performance analysis and assessment that can be used for any human movement. The performance of any skill can be described by a curve depicting the joint angle over the time required to perform a skill. In this study, we focus on only 14 body joints, and each joint comprises three angles. The proposed system comprises three main stages. In the first stage, data are obtained using motion capture inertial measurement unit sensors from top professional fighters/players while they are performing a certain skill. In the second stage, the collected sensor data obtained are input to the biomechanical software to extract the player’s joint angle curve. Finally, each joint angle curve is processed using a continuous wavelet transform to extract the main curve points (i.e., peaks and valleys). Finally, after extracting the joint curves from several top players, we summarize the players’ curves based on five statistical indicators, i.e., the minimum, maximum, mean, and mean  $\pm$  standard deviation. These five summarized curves are regarded as standard performance curves for the joint angle. When a player’s joint curve is surrounded by the five summarized curves, the performance is considered acceptable. Otherwise, the performance is considered unsatisfactory. The proposed system is evaluated based on four differen...

Fattah, IMR, Farhan, ZA, Kontoleon, KJ, kianfar, E & Hadrawi, SK 2023, 'Hollow fiber membrane contactor based carbon dioxide absorption − stripping: a review', Macromolecular Research, vol. 31, no. 4, pp. 299-325.
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Fei, Z, Wang, X, Wu, N, Huang, J & Zhang, JA 2023, 'Air-Ground Integrated Sensing and Communications: Opportunities and Challenges', IEEE Communications Magazine, vol. 61, no. 5, pp. 55-61.
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Feng, A, Mao, S, Onggowarsito, C, Naidu, G, Li, W & Fu, Q 2023, 'Tillandsia-Inspired Composite Materials for Atmospheric Water Harvesting', ACS Sustainable Chemistry & Engineering, vol. 11, no. 15, pp. 5819-5825.
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Feng, K, Ji, JC & Ni, Q 2023, 'A novel adaptive bandwidth selection method for Vold–Kalman filtering and its application in wind turbine planetary gearbox diagnostics', Structural Health Monitoring, vol. 22, no. 2, pp. 1027-1048.
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The planetary gearbox transmission system in wind turbines has complex structures and generally operates under non-stationary conditions. Thus its measured responses are of high complexity and nonlinearity, which brings a great challenge in the development of reliable condition monitoring techniques for the planetary gearbox transmission system. As a prevalent and effective tool for analyzing the non-stationary vibration signal with strong nonlinearity, the Vold–Kalman filtration technique has excellent capabilities of tracking the targeted harmonic components of vibrations, which can significantly benefit planetary gearbox fault diagnostics. However, the tracking accuracy is heavily enslaved to the selection of the rational bandwidth for the Vold–Kalman filter. An inappropriate bandwidth could impair the characteristics of the targeted harmonic responses, and as a consequence, the monitoring process becomes no longer reliable. To address this issue, a novel bandwidth selection methodology for the Vold–Kalman filter is developed in this paper. Through comprehensively depicting the targeted harmonic response using features in multiple domains, the rational bandwidth can be selected for Vold–Kalman filtering, and then, a reliable monitoring process can be ensured. Additionally, a tacho-less speed estimation procedure is utilized in this paper to acquire the instantaneous rotational speed from the vibration signal directly. With the rational bandwidth and the estimated rotational speed, the desired harmonic components of vibrations can be adaptively extracted and tracked through the Vold–Kalman filter with high accuracy, and at the same time, the irrelevant or unwanted components are excluded completely. The effectiveness and superiority of the proposed adaptive Vold–Kalman filtration for wind turbine planetary gearbox diagnostics are demonstrated and validated experimentally.

Feng, K, Ji, JC, Ni, Q, Li, Y, Mao, W & Liu, L 2023, 'A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system', Wear, vol. 522, pp. 204697-204697.
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Feng, S, Ngo, HH, Guo, W, Khan, MA, Zhang, S, Luo, G, Liu, Y, An, D & Zhang, X 2023, 'Fruit peel crude enzymes for enhancement of biohydrogen production from synthetic swine wastewater by improving biohydrogen-formation processes of dark fermentation', Bioresource Technology, vol. 372, pp. 128670-128670.
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Feng, X, Chen, Z, Wang, S, Cen, L, Ni, B-J & Liu, Q 2023, 'Insights into the weathering behavior of pyrite in alkaline soil through electrochemical characterizations: Actual hazards or potentially benefits?', Journal of Hazardous Materials, vol. 451, pp. 131145-131145.
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Feng, Y, Wang, Q, Chen, X, Wu, D & Gao, W 2023, 'Virtual modelling technique for geometric-material nonlinear dynamics of structures', Structural Safety, vol. 100, pp. 102284-102284.
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Feng, Y, Wang, Q, Yu, Y, Zhang, T, Wu, D, Chen, X, Luo, Z & Gao, W 2023, 'Experimental-numerical-virtual (ENV) modelling technique for composite structure against low velocity impacts', Engineering Structures, vol. 278, pp. 115488-115488.
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Feng, Y, Wu, D, Stewart, MG & Gao, W 2023, 'Past, current and future trends and challenges in non-deterministic fracture mechanics: A review', Computer Methods in Applied Mechanics and Engineering, vol. 412, pp. 116102-116102.
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Fraile Navarro, D, Kocaballi, AB, Dras, M & Berkovsky, S 2023, 'Collaboration, not Confrontation: Understanding General Practitioners’ Attitudes Towards Natural Language and Text Automation in Clinical Practice', ACM Transactions on Computer-Human Interaction, vol. 30, no. 2, pp. 1-34.
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General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it remains unclear what automation tasks would benefit clinicians most, what features such automation should exhibit, and how clinicians will interact with the automation. We conducted semi-structured interviews with General Practitioners uncovering their views and attitudes toward text automation. The main emerging theme was doctor-AI collaboration, addressing a reciprocal clinician-technology relationship that does not threaten to substitute clinicians, but rather establishes a constructive synergistic relationship. Other themes included: (i) desired features for clinical text automation; (ii) concerns around clinical text automation; and (iii) the consultation of the future. Our findings will inform the design of future natural language processing systems, to be implemented in general practice.

Galpathage, SG, Indraratna, B, Heitor, A & Rujikiatkamjorn, C 2023, 'Pull-out behaviour of simulated tree roots embedded in compacted soil', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 176, no. 1, pp. 54-64.
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Vegetated ground is strengthened by the suction generated during evapotranspiration as well as physically and mechanically by the reinforcement effect induced by tree roots. When suction suddenly decreases during flooding or intense rainfall, the shear strength of the soil–plant system relies mainly on the physical root reinforcement. In this paper, the pull-out behaviour of simulated roots embedded in a compacted soil is investigated to assess how the soil–plant system is mechanically strengthened. An analytical framework is developed to estimate the pull-out force and it is validated using a series of pull-out tests. The tests are carried out on simulated roots embedded in a box of compacted soil having different water contents and equivalent dry unit weights. The results show that the pull-out capacity of this root system is mainly influenced by the initial (as-compacted) suction, and the length and diameter of the roots. The model predictions agree reasonably well with the experimental observations.

Ganguly, D, Schmidt, MO, Coleman, M, Ngo, T-VC, Sorrelle, N, Dominguez, ATA, Murimwa, GZ, Toombs, JE, Lewis, C, Fang, YV, Valdes-Mora, F, Gallego-Ortega, D, Wellstein, A & Brekken, RA 2023, 'Pleiotrophin drives a prometastatic immune niche in breast cancer', Journal of Experimental Medicine, vol. 220, no. 5.
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Metastatic cancer cells adapt to thrive in secondary organs. To investigate metastatic adaptation, we performed transcriptomic analysis of metastatic and non-metastatic murine breast cancer cells. We found that pleiotrophin (PTN), a neurotrophic cytokine, is a metastasis-associated factor that is expressed highly by aggressive breast cancers. Moreover, elevated PTN in plasma correlated significantly with metastasis and reduced survival of breast cancer patients. Mechanistically, we find that PTN activates NF-κB in cancer cells leading to altered cytokine production, subsequent neutrophil recruitment, and an immune suppressive microenvironment. Consequently, inhibition of PTN, pharmacologically or genetically, reduces the accumulation of tumor-associated neutrophils and reverts local immune suppression, resulting in increased T cell activation and attenuated metastasis. Furthermore, inhibition of PTN significantly enhanced the efficacy of immune checkpoint blockade and chemotherapy in reducing metastatic burden in mice. These findings establish PTN as a previously unrecognized driver of a prometastatic immune niche and thus represents a promising therapeutic target for the treatment of metastatic breast cancer.

Gao, H, Dai, B, Miao, H, Yang, X, Barroso, RJD & Walayat, H 2023, 'A Novel GAPG Approach to Automatic Property Generation for Formal Verification: The GAN Perspective', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 19, no. 1, pp. 1-22.
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Formal methods have been widely used to support software testing to guarantee correctness and reliability. For example, model checking technology attempts to ensure that the verification property of a specific formal model is satisfactory for discovering bugs or abnormal behavior from the perspective of temporal logic. However, because automatic approaches are lacking, a software developer/tester must manually specify verification properties. A generative adversarial network (GAN) learns features from input training data and outputs new data with similar or coincident features. GANs have been successfully used in the image processing and text processing fields and achieved interesting and automatic results. Inspired by the power of GANs, in this article, we propose a GAN-based automatic property generation (GAPG) approach to generate verification properties supporting model checking. First, the verification properties in the form of computational tree logic (CTL) are encoded and used as input to the GAN. Second, we introduce regular expressions as grammar rules to check the correctness of the generated properties. These rules work to detect and filter meaningless properties that occur because the GAN learning process is uncontrollable and may generate unsuitable properties in real applications. Third, the learning network is further trained by using labeled information associated with the input properties. These are intended to guide the training process to generate additional new properties, particularly those that map to corresponding formal models. Finally, a series of comprehensive experiments demonstrate that the proposed GAPG method can obtain new verification properties from two aspects: (1) using only CTL formulas and (2) using CTL formulas combined with Kripke structures.

Gao, H, Luo, B, Barroso, RJD & Hussain, W 2023, 'Guest Editorial Special Issue on Computational Intelligence to Edge AI for Ubiquitous IoT Systems', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 1, pp. 36-38.
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Gao, L, Li, X, Li, M, Zamyadi, A & Wang, Q 2023, 'Recent research advances in aqueous pollutants and treatment approaches', Process Safety and Environmental Protection, vol. 171, pp. 132-135.
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Gao, S, Wang, R, Wang, X, Yu, S, Dong, Y, Yao, S & Zhou, W 2023, 'Detecting Adversarial Examples on Deep Neural Networks with Mutual Information Neural Estimation', IEEE Transactions on Dependable and Secure Computing, pp. 1-14.
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Gautam, K, Sharma, P, Dwivedi, S, Singh, A, Gaur, VK, Varjani, S, Srivastava, JK, Pandey, A, Chang, J-S & Ngo, HH 2023, 'A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil', Environmental Research, vol. 225, pp. 115592-115592.
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Gedela, R, Indraratna, B, Medawela, S & Nguyen, TT 2023, 'Effects Of Fines Content On The Strength And Stiffness Of Biopolymer Treated Low-Plasticity Soils', Australian Geomechanics Journal, pp. 33-41.
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The use of biopolymers to enhance the engineering properties of soil has received increasing attention in recent years, however, the interactive role that biopolymers and the fines content of the soil play in governing the geotechnical parameters still requires insightful investigation, in relation to chemical soil treatment that can be ecologically detrimental. This paper examines the combined effects of Xanthan Gum (XG) derived from specific bacterial strains and the presence of clay fines content (kaolin) on the strength and stiffness of low plasticity soils, with special reference of cyclic traffic (road and rail) loading. In this study, fine sand is mixed with different contents of kaolin, whereby laboratory compression and tensile tests were conducted on natural (untreated) and XG-treated soil specimens. The results indicate that soil strength can be enhanced significantly when XG is added, however the effectiveness is a function of the kaolin content (KC). At an optimum XG content of 2% and a fines content increasing from 5% to 30%, split tensile strength increases from 230 to 750 kPa,while the unconfined compressive strength rises from 1.4 to 7.9 MPa, respectively. For XG content between 0.5% and 2%, the small strain stiffness of treated soil increases fourfold from 206 to 854 MPa.

Germon, ZP, Sillar, JR, Mannan, A, Duchatel, RJ, Staudt, D, Murray, HC, Findlay, IJ, Jackson, ER, McEwen, HP, Douglas, AM, McLachlan, T, Schjenken, JE, Skerrett-Byrne, DA, Huang, H, Melo-Braga, MN, Plank, MW, Alvaro, F, Chamberlain, J, De Iuliis, G, Aitken, RJ, Nixon, B, Wei, AH, Enjeti, AK, Huang, Y, Lock, RB, Larsen, MR, Lee, H, Vaghjiani, V, Cain, JE, de Bock, CE, Verrills, NM & Dun, MD 2023, 'Blockade of ROS production inhibits oncogenic signaling in acute myeloid leukemia and amplifies response to precision therapies', Science Signaling, vol. 16, no. 778.
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Mutations in the type III receptor tyrosine kinase FLT3 are frequent in patients with acute myeloid leukemia (AML) and are associated with a poor prognosis. AML is characterized by the overproduction of reactive oxygen species (ROS), which can induce cysteine oxidation in redox-sensitive signaling proteins. Here, we sought to characterize the specific pathways affected by ROS in AML by assessing oncogenic signaling in primary AML samples. The oxidation or phosphorylation of signaling proteins that mediate growth and proliferation was increased in samples from patient subtypes with FLT3 mutations. These samples also showed increases in the oxidation of proteins in the ROS-producing Rac/NADPH oxidase-2 (NOX2) complex. Inhibition of NOX2 increased the apoptosis of FLT3-mutant AML cells in response to FLT3 inhibitors. NOX2 inhibition also reduced the phosphorylation and cysteine oxidation of FLT3 in patient-derived xenograft mouse models, suggesting that decreased oxidative stress reduces the oncogenic signaling of FLT3. In mice grafted with FLT3 mutant AML cells, treatment with a NOX2 inhibitor reduced the number of circulating cancer cells, and combining FLT3 and NOX2 inhibitors increased survival to a greater extent than either treatment alone. Together, these data raise the possibility that combining NOX2 and FLT3 inhibitors could improve the treatment of FLT3 mutant AML.

Ghabussi, A, Mortazavi, M & Betha, R 2023, 'Seismic performance of a cold-formed and hot-rolled steel wall system equipped with curved steel dampers', Structures, vol. 53, pp. 296-316.
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Ghalambaz, M, Mehryan, SAM, Ramezani, SR, Hajjar, A, El Kadri, M, Islam, MS, Younis, O & Ghodrat, M 2023, 'Phase change heat transfer in a vertical metal foam-phase change material thermal energy storage heat dissipator', Journal of Energy Storage, vol. 66, pp. 107370-107370.
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Ghezelbash, R, Maghsoudi, A, Shamekhi, M, Pradhan, B & Daviran, M 2023, 'Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity', Neural Computing and Applications, vol. 35, no. 1, pp. 719-733.
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Gholami, K, Azizivahed, A, Arefi, A & Li, L 2023, 'Risk-averse Volt-VAr management scheme to coordinate distributed energy resources with demand response program', International Journal of Electrical Power & Energy Systems, vol. 146, pp. 108761-108761.
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Gong, S, Ball, J & Surawski, N 2023, 'A method of estimating imperviousness for the catchment modelling of urban environments', Journal of Hydroinformatics, vol. 25, no. 2, pp. 451-468.
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Abstract Urban impervious surfaces, a symbol of urbanisation, have permanently changed urban hydrology behaviour and play a critical role in modelling rainfall-runoff process. The distribution pattern of impervious surfaces is intrinsically connected with functional land zoning schemes. However, estimating impervious fractions for catchment modelling is becoming increasingly difficult due to intricate land zoning categories and heterogeneous land use land cover (LULC) during urbanisation. This study demonstrates an integrated approach of deep learning (DL) and grid sampling method to overcome the challenges of LULC classification, sample standardisation and statistical sample extraction. The classified impervious features were extracted within the land zoning scope and translated into polynomial functions using a probability-fitting approach to measure the occurrence likelihood distribution of samples' impervious fraction. Then, we use the information entropy (IE) to evaluate prediction stability by quantifying the condition entropy and information gain (IG) from each functional land zones to the occurrence likelihood of different impervious fraction intervals. The DL model shows robust LULC prediction, while probability-fitting study of impervious samples reflects the distribution differential of impervious fractions under the land zoning categories. The IE stability test shows a robust approach that clarifies different confident ranges of imperviousness estimation based on land zoning information.

Gong, S, Ball, JE & Surawski, N 2023, 'An initial parameter estimation approach for urban catchment modelling', Urban Water Journal, vol. 20, no. 2, pp. 171-183.
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Gong, Y, Li, Z, Liu, W, Lu, X, Liu, X, Tsang, IW & Yin, Y 2023, 'Missingness-Pattern-Adaptive Learning With Incomplete Data', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-14.
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Gooch, LJ, Masia, MJ, Stewart, MG & Lam, CY 2023, 'Statistical assessment of tensile and shear properties of unreinforced clay brick masonry', Construction and Building Materials, vol. 386, pp. 131578-131578.
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Goodswen, SJ, Kennedy, PJ & Ellis, JT 2023, 'A guide to current methodology and usage of reverse vaccinology towardsin silicovaccine discovery', FEMS Microbiology Reviews, vol. 47, no. 2.
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AbstractReverse vaccinology (RV) was described at its inception in 2000 as an in silico process that starts from the genomic sequence of the pathogen and ends with a list of potential protein and/or peptide candidates to be experimentally validated for vaccine development. Twenty-two years later, this process has evolved from a few steps entailing a handful of bioinformatics tools to a multitude of steps with a plethora of tools. Other in silico related processes with overlapping workflow steps have also emerged with terms such as subtractive proteomics, computational vaccinology, and immunoinformatics. From the perspective of a new RV practitioner, determining the appropriate workflow steps and bioinformatics tools can be a time consuming and overwhelming task, given the number of choices. This review presents the current understanding of RV and its usage in the research community as determined by a comprehensive survey of scientific papers published in the last seven years. We believe the current mainstream workflow steps and tools presented here will be a valuable guideline for all researchers wanting to apply an up-to-date in silico vaccine discovery process.

Goss, DM, Vasilescu, SA, Sacks, G, Gardner, DK & Warkiani, ME 2023, 'Microfluidics facilitating the use of small extracellular vesicles in innovative approaches to male infertility', Nature Reviews Urology, vol. 20, no. 2, pp. 66-95.
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Sperm are transcriptionally and translationally quiescent and, therefore, rely on the seminal plasma microenvironment for function, survival and fertilization of the oocyte in the oviduct. The male reproductive system influences sperm function via the binding and fusion of secreted epididymal (epididymosomes) and prostatic (prostasomes) small extracellular vesicles (S-EVs) that facilitate the transfer of proteins, lipids and nucleic acids to sperm. Seminal plasma S-EVs have important roles in sperm maturation, immune and oxidative stress protection, capacitation, fertilization and endometrial implantation and receptivity. Supplementing asthenozoospermic samples with normospermic-derived S-EVs can improve sperm motility and S-EV microRNAs can be used to predict non-obstructive azoospermia. Thus, S-EV influence on sperm physiology might have both therapeutic and diagnostic potential; however, the isolation of pure populations of S-EVs from bodily fluids with current conventional methods presents a substantial hurdle. Many conventional techniques lack accuracy, effectiveness, and practicality; yet microfluidic technology has the potential to simplify and improve S-EV isolation and detection.

Grelewicz, P, Khuat, TT, Czeczot, J, Nowak, P, Klopot, T & Gabrys, B 2023, 'Application of Machine Learning to Performance Assessment for a Class of PID-Based Control Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-13.
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Grochow, J & Qiao, Y 2023, 'On the Complexity of Isomorphism Problems for Tensors, Groups, and Polynomials I: Tensor Isomorphism-Completeness', SIAM Journal on Computing, vol. 52, no. 2, pp. 568-617.
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Guan, W, Song, X, Wang, K, Wen, H, Ni, H, Wang, Y & Chang, X 2023, 'Egocentric Early Action Prediction via Multimodal Transformer-based Dual Action Prediction', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Gul, M, Zulkifli, NWM, Masjuki, HH, Kalam, MA, Mujtaba, MA, Harith, MH, Syahir, AZ, Ahmed, W & Farooq, AB 2023, 'Corrigendum to “Effect of TMP-based-cottonseed oil-biolubricant blends on tribological behavior of cylinder liner-piston ring combinations” [Fuel 278 (2020) 118242]', Fuel, vol. 331, pp. 125742-125742.
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Guo, B, Zhang, X, Su, M, Ma, H, Yang, Y & Siwakoti, YP 2023, 'A Single-Phase Common-Ground Five-Level Transformerless Inverter With Low Component Count for PV Applications', IEEE Transactions on Industrial Electronics, vol. 70, no. 3, pp. 2662-2674.
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Guo, CA, Guo, YJ, Zhu, H, Ni, W & Yuan, J 2023, 'Optimization of Multibeam Antennas Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 215-224.
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Guo, J, Lou, H, Yu, J, Li, R, Fang, W, Liu, J, Long, P, Ying, S & Ying, M 2023, 'isQ: An Integrated Software Stack for Quantum Programming', IEEE Transactions on Quantum Engineering, pp. 1-18.
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Guo, P, Zhong, W, Chen, S-L, Chen, D & Liu, Y 2023, 'A Novel Programmable Stacked Patch Antenna With the Diversity of Sixteen Linear Polarizations and Four Frequency Bands', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 1035-1040.
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Guo, Y, Yu, H, Ma, L, Zeng, L & Luo, X 2023, 'THFE: A Triple-hierarchy Feature Enhancement method for tiny boat detection', Engineering Applications of Artificial Intelligence, vol. 123, pp. 106271-106271.
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Gupta, BB, Prajapati, V, Nedjah, N, Vijayakumar, P, El-Latif, AAA & Chang, X 2023, 'Machine learning and smart card based two-factor authentication scheme for preserving anonymity in telecare medical information system (TMIS)', Neural Computing and Applications, vol. 35, no. 7, pp. 5055-5080.
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Hafiz, M, Alfahel, R, Altaee, A & Hawari, AH 2023, 'Techno-economic assessment of forward osmosis as a pretreatment process for mitigation of scaling in multi-stage flash seawater desalination process', Separation and Purification Technology, vol. 309, pp. 123007-123007.
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Hakim, WL, Fadhillah, MF, Park, S, Pradhan, B, Won, J-S & Lee, C-W 2023, 'InSAR time-series analysis and susceptibility mapping for land subsidence in Semarang, Indonesia using convolutional neural network and support vector regression', Remote Sensing of Environment, vol. 287, pp. 113453-113453.
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Han, C, Zheng, Z, Su, K, Yu, D, Yuan, Z, Gao, C, Sang, N & Yang, Y 2023, 'DMRNet++: Learning Discriminative Features With Decoupled Networks and Enriched Pairs for One-Step Person Search', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 7319-7337.
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Han, Z, Xu, C, Ma, S, Hu, Y, Zhao, G & Yu, S 2023, 'DTE-RR: Dynamic Topology Evolution-Based Reliable Routing in VANET', IEEE Wireless Communications Letters, pp. 1-1.
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Han, Z, Xu, C, Zhao, G, Wang, S, Cheng, K & Yu, S 2023, 'Time-Varying Topology Model for Dynamic Routing in LEO Satellite Constellation Networks', IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3440-3454.
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Haq, S, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Hydromechanical state of soil fluidisation: a microscale perspective', Acta Geotechnica, vol. 18, no. 3, pp. 1149-1167.
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AbstractThis paper investigates soil fluidisation at the microscale using the discrete element method (DEM) in combination with the lattice Boltzmann method (LBM). Numerical simulations were carried out at varying hydraulic gradients across the granular assembly of soil. The development of local hydraulic gradients, the contact distribution, and the associated fabric changes were investigated. Microscale findings suggest that a critical hydromechanical state inducing fluid-like instability of a granular assembly can be defined by a substantial increase in grain slip associated with a rapid reduction in interparticle contacts. Based on these results, a new micromechanical criterion is proposed to characterise the transformation of granular soil from a hydromechanically stable to an unstable state. The constraint ratio (ratio of the number of constraints to the number of degrees of freedom) is introduced to portray the relative slippage between particles and the loss of interparticle contacts within the granular fabric. Its magnitude of unity corresponds to the condition of zero effective stress, representing the critical hydromechanical state. In practical terms, the results of this study reflect the phenomenon of subgrade mud pumping that occurs in railways when heavy-haul trains pass through at certain axle loads and speeds.

Haq, S, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2023, 'Micromechanical Analysis of Internal Instability during Shearing', International Journal of Geomechanics, vol. 23, no. 6.
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Hasanpour, S, Siwakoti, YP & Blaabjerg, F 2023, 'A New High Efficiency High Step-Up DC/DC Converter for Renewable Energy Applications', IEEE Transactions on Industrial Electronics, vol. 70, no. 2, pp. 1489-1500.
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Hasanpour, S, Siwakoti, YP & Blaabjerg, F 2023, 'A New Soft-Switched High Step-Up Trans-Inverse DC/DC Converter Based on Built-In Transformer', IEEE Open Journal of Power Electronics, vol. 4, pp. 381-394.
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Hassan, M, Kennard, M, Yoshitake, S, Ishac, K, Takahashi, S, Kim, S, Matsui, T & Hirokawa, M 2023, 'Augmenting the Sense of Social Presence in Online Video Games Through the Sharing of Biosignals'.
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Hassani, S, Mousavi, M & Dackermann, U 2023, 'Johansen cointegration of frequency response functions contaminated with nonstationary colored noise for structural damage detection', Journal of Sound and Vibration, vol. 552, pp. 117641-117641.
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He, T, Wu, M, Aguilera, RP, Lu, DD-C, Liu, Q & Vazquez, S 2023, 'Low Computational Burden Model Predictive Control for Single-Phase Cascaded H-Bridge Converters Without Weighting Factor', IEEE Transactions on Industrial Electronics, vol. 70, no. 3, pp. 2396-2406.
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He, X, Xu, H & Sheng, D 2023, 'Ready-to-use deep-learning surrogate models for problems with spatially variable inputs and outputs', Acta Geotechnica, vol. 18, no. 4, pp. 1681-1698.
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AbstractData-driven intelligent surrogate models gain popularity recently. Particularly in Monte-Carlo-style stochastic analysis, the influencing factors are considered as inputs, the quantities of interest are considered as outputs, and cheaper-to-evaluate surrogates models are built from a small amount of sample data and are used for the full Monte-Carlo analysis. This paper presents a framework with three innovations: (1) we build surrogate models for a particular problem that covers any possible material properties or boundary conditions commonly encountered in practice, so the models are ready to use, and do not require new data or training anymore. (2) The inputs and outputs to the problem are both spatially variable. Even after discretization, the input and output sizes are in the order of tens of thousands, which is challenging for traditional machine-learning algorithms. We take the footing failure mechanism as an example. Two types of neural networks are examined, fully connected networks and deep neural networks with complicated non-sequential structures (a modified U-Net). (3) This study is also the first attempt to use U-Nets as surrogate models for geotechnical problems. Results show that fully connected networks can fit well simple problems with a small input and output size, but fail for complex problems. Deep neural networks that account for the data structure give better results.

He, Z, Zhou, X, Fan, X, Jin, W, Chen, C, Yan, B, Yin, S, Zhou, T, Li, X & Jiang, G 2023, 'Advanced oxidation-based combined conditioning technologies to improve sludge dewaterability: A mini review', Journal of Water Process Engineering, vol. 53, pp. 103773-103773.
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Hieu, NQ, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2023, 'When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach', IEEE Journal on Selected Areas in Communications, vol. 41, no. 5, pp. 1536-1548.
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Hong, Z, Tao, M, Cui, X, Wu, C & Zhao, M 2023, 'Experimental and numerical studies of the blast-induced overbreak and underbreak in underground roadways', Underground Space, vol. 8, pp. 61-79.
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Hong, Z, Tao, M, Wu, C, Zhou, J & Wang, D 2023, 'The spatial distribution of excavation damaged zone around underground roadways during blasting excavation', Bulletin of Engineering Geology and the Environment, vol. 82, no. 4.
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Horry, MJ, Chakraborty, S, Pradhan, B, Shulka, N & Almazroui, M 2023, 'Two-Speed Deep-Learning Ensemble for Classification of Incremental Land-Cover Satellite Image Patches', Earth Systems and Environment, vol. 7, no. 2, pp. 525-540.
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AbstractHigh-velocity data streams present a challenge to deep learning-based computer vision models due to the resources needed to retrain for new incremental data. This study presents a novel staggered training approach using an ensemble model comprising the following: (i) a resource-intensive high-accuracy vision transformer; and (ii) a fast training, but less accurate, low parameter-count convolutional neural network. The vision transformer provides a scalable and accurate base model. A convolutional neural network (CNN) quickly incorporates new data into the ensemble model. Incremental data are simulated by dividing the very large So2Sat LCZ42 satellite image dataset into four intervals. The CNN is trained every interval and the vision transformer trained every half interval. We call this combination of a complementary ensemble with staggered training a “two-speed” network. The novelty of this approach is in the use of a staggered training schedule that allows the ensemble model to efficiently incorporate new data by retraining the high-speed CNN in advance of the resource-intensive vision transformer, thereby allowing for stable continuous improvement of the ensemble. Additionally, the ensemble models for each data increment out-perform each of the component models, with best accuracy of 65% against a holdout test partition of the RGB version of the So2Sat dataset.

Hossain, SM, Yu, H, Choo, Y, Naidu, G, Han, DS & Shon, HK 2023, 'ZiF-8 induced carbon electrodes for selective lithium recovery from aqueous feed water by employing capacitive deionization system', Desalination, vol. 546, pp. 116201-116201.
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Hu, X, Zhu, T, Zhai, X, Zhou, W & Zhao, W 2023, 'Privacy Data Propagation and Preservation in Social Media: A Real-World Case Study', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 4137-4150.
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Huang, C-W, Huang, W-Y, Lin, C, Li, Y-L, Huang, T-P, Bui, X-T & Ngo, HH 2023, 'Ecological risk assessment and corrective actions for dioxin-polluted sediment in a chemical plant's brine water storage pond', Science of The Total Environment, vol. 859, pp. 160239-160239.
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Huang, C-W, Li, Y-L, Lin, C, Bui, X-T, Vo, T-D-H & Ngo, HH 2023, 'Seasonal influence on pollution index and risk of multiple compositions of microplastics in an urban river', Science of The Total Environment, vol. 859, pp. 160021-160021.
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Emerging contaminant microplastics (MPs) are getting worldwide attention for their ubiquitous occurrence and potential risk to the environment. However, the seasonal influence on freshwater MP pollution remains poorly understood. To better understand and evaluate the riverine MPs in different seasons, this study conducted the risk assessment of MPs in an urban river, Houjin River, during the different seasons. The present study found that the MPs (0.1-5 mm, mostly 0.1-2 mm) were more abundant in the dry season (183.33 ± 128.95 items/m3) compared with the wet season (102.08 ± 45.80 items/m3). Similarly, the mixture of different MPs polymers was more diverse in the dry season. The related pollution indices such as the contamination factor (CF) and pollution load index (PLI) showed that average CF and PLI were 5.15 and 2.10 in the dry season, which significantly decreased to 1.58 and 1.25, respectively, in the wet season (p < 0.05). Additionally, significant difference of the average risk quotient (RQ) was observed, which was 0.037 in the dry season and 0.021 in the wet season (p < 0.05). To sum up, the results of this study indicate the seasonal effects on the pollution and risk of multiple compositions of MPs in the urban river, suggesting higher impacts of riverine MPs pollution in the dry season, as well as the potential increase of MPs, may lead to environmental risk in the future.

Huang, H, Zhao, G, Bo, Y, Yu, J, Liang, L, Yang, Y & Ou, K 2023, 'Railway intrusion detection based on refined spatial and temporal features for UAV surveillance scene', Measurement, vol. 211, pp. 112602-112602.
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Huang, J, Song, X, Xiao, F, Cao, Z & Lin, C-T 2023, 'Belief f-divergence for EEG complexity evaluation', Information Sciences, pp. 119189-119189.
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Huang, M, Cao, C, Liu, L, Wei, W, Zhu, Q-L & Huang, Z 2023, 'Controlled synthesis of MOF-derived hollow and yolk–shell nanocages for improved water oxidation and selective ethylene glycol reformation', eScience, pp. 100118-100118.
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Huang, M, Li, G, Liu, Z, Wu, Y, Gong, C, Zhu, L & Yang, Y 2023, 'Exploring viewport features for semi-supervised saliency prediction in omnidirectional images', Image and Vision Computing, vol. 129, pp. 104590-104590.
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Huang, W, Zhuo, M, Zhu, T, Zhou, S & Liao, Y 2023, 'Differential privacy: Review of improving utility through cryptography‐based technologies', Concurrency and Computation: Practice and Experience, vol. 35, no. 5.
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Huang, W-Y, Huang, C-W, Li, Y-L, Huang, T-P, Lin, C, Ngo, HH & Bui, X-T 2023, 'Reduced pollution level and ecological risk of mercury-polluted sediment in a alkali-chlorine factory’s brine water storage pond after corrective actions: A case study in Southern Taiwan', Environmental Technology & Innovation, vol. 29, pp. 103003-103003.
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Huang, X, Tuyen Le, A & Guo, YJ 2023, 'Joint Analog and Digital Self-Interference Cancellation for Full Duplex Transceiver With Frequency-Dependent I/Q Imbalance', IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2364-2378.
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Huang, Z, Shivakumara, P, Kaljahi, MA, Kumar, A, Pal, U, Lu, T & Blumenstein, M 2023, 'Writer age estimation through handwriting', Multimedia Tools and Applications, vol. 82, no. 11, pp. 16033-16055.
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Huo, P, Deng, R, Chen, X, Liu, Y, Yang, L, Wu, L, Wei, W & Ni, B-J 2023, 'Model-Based Evaluation of N2O Recovery as an Energy Source in Sulfur-Driven NO-Based Autotrophic Denitrification', Chemical Engineering Journal, vol. 453, pp. 139732-139732.
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Hussain, W, Merigó, JM, Gil-Lafuente, J & Gao, H 2023, 'Complex nonlinear neural network prediction with IOWA layer', Soft Computing, vol. 27, no. 8, pp. 4853-4863.
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AbstractNeural network methods are widely used in business problems for prediction, clustering, and risk management to improving customer satisfaction and business outcome. The ability of a neural network to learn complex nonlinear relationship is due to its architecture that uses weight parameters to transform input data within the hidden layers. Such methods perform well in many situations where the ordering of inputs is simple. However, for a complex reordering of a decision-maker, the process is not enough to get an optimal prediction result. Moreover, existing machine learning algorithms cannot reduce computational complexity by reducing data size without losing any information. This paper proposes an induced ordered weighted averaging (IOWA) operator for the artificial neural network IOWA-ANN. The operator reorders the data according to the order-inducing variable. The proposed sorting mechanism in the neural network can handle a complex nonlinear relationship of a dataset, which results in reduced computational complexities. The proposed approach deals with the complexity of the neuron, collects the data and allows a degree of customisation of the structure. The application further extended to IGOWA and Quasi-IOWA operators. We present a numerical example in a financial decision-making process to demonstrate the approach's effectiveness in handling complex situations. This paper opens a new research area for various complex nonlinear predictions where the dataset is big enough, such as cloud QoS and IoT sensors data. The approach can be used with different machine learning, neural networks or hybrid fuzzy neural methods with other extensions of the OWA operator.

Ikram, MM, Saha, G & Saha, SC 2023, 'Unsteady conjugate heat transfer characteristics in hexagonal cavity equipped with a multi-blade dynamic modulator', International Journal of Heat and Mass Transfer, vol. 200, pp. 123527-123527.
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Inayat, A, Jamil, F, Ahmed, SF, Ayoub, M, Abdul, PM, Aslam, M, Mofijur, M, Khan, Z & Mustafa, A 2023, 'Thermal degradation characteristics, kinetic and thermodynamic analyses of date palm surface fibers at different heating rates', Fuel, vol. 335, pp. 127076-127076.
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Inbanaathan, PV, Balasubramanian, D, Nguyen, VN, Le, VV, Wae-Hayee, M, R, R, Veza, I, Yukesh, N, Kalam, MA, Sonthalia, A & Varuvel, EG 2023, 'Comprehensive study on using hydrogen-gasoline-ethanol blends as flexible fuels in an existing variable speed SI engine', International Journal of Hydrogen Energy.
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Indraratna, B, Armaghani, DJ, Gomes Correia, A, Hunt, H & Ngo, T 2023, 'Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques', Transportation Geotechnics, vol. 38, pp. 100895-100895.
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Islam Rony, Z, Mofijur, M, Hasan, MM, Rasul, MG, Jahirul, MI, Forruque Ahmed, S, Kalam, MA, Anjum Badruddin, I, Yunus Khan, TM & Show, P-L 2023, 'Alternative fuels to reduce greenhouse gas emissions from marine transport and promote UN sustainable development goals', Fuel, vol. 338, pp. 127220-127220.
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Islam, MM, Ramezani, F, Lu, HY & Naderpour, M 2023, 'Optimal placement of applications in the fog environment: A systematic literature review', Journal of Parallel and Distributed Computing, vol. 174, pp. 46-69.
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Izadi, R, Assarian, D, Altaee, A & Mahinroosta, M 2023, 'Investigation of methods for fuel desulfurization wastewater treatment', Chemical Engineering Research and Design, vol. 190, pp. 198-219.
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Jafarizadeh, S & Veitch, D 2023, 'Robust Weighted-Average Continuous-Time Consensus With Communication Time Delay', IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2074-2086.
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Jain, K, Pradhan, B & Mishra, V 2023, 'Preface', Lecture Notes in Civil Engineering, vol. 304, pp. v-vi.

Jathar, LD, Ganesan, S, Awasarmol, U, Nikam, K, Shahapurkar, K, Soudagar, MEM, Fayaz, H, El-Shafay, AS, Kalam, MA, Bouadila, S, Baddadi, S, Tirth, V, Nizami, AS, Lam, SS & Rehan, M 2023, 'Comprehensive review of environmental factors influencing the performance of photovoltaic panels: Concern over emissions at various phases throughout the lifecycle', Environmental Pollution, vol. 326, pp. 121474-121474.
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Javaherian, C & Ferrie, C 2023, 'Energy transport and optimal design of noisy Platonic quantum networks', Physica Scripta, vol. 98, no. 3, pp. 035105-035105.
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Abstract Optimal energy transport is one of the primary goals for designing efficient quantum networks. In this work, the maximum energy transport is investigated for three-dimensional quantum networks with Platonic geometries affected by dephasing and dissipative Markovian noise. The network and the environmental characteristics corresponding the optimal design are obtained and investigated for five Platonic networks with 4, 6, 8, 12, and 20 number of sites that one of the sites is connected to a sink site through a Markovian dissipative process. Such optimal designs could have various applications like switching and multiplexing in quantum circuits.

Jena, R, Pradhan, B, Almazroui, M, Assiri, M & Park, H-J 2023, 'Earthquake-induced liquefaction hazard mapping at national-scale in Australia using deep learning techniques', Geoscience Frontiers, vol. 14, no. 1, pp. 101460-101460.
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Jia, M, Gabrys, B & Musial, K 2023, 'A Network Science Perspective of Graph Convolutional Networks: A Survey', IEEE Access, vol. 11, pp. 39083-39122.
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Jia, Z, Xu, X, Zhu, D & Zheng, Y 2023, 'Design, printing, and engineering of regenerative biomaterials for personalized bone healthcare', Progress in Materials Science, vol. 134, pp. 101072-101072.
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Jiang, L, Li, F, Chen, Z, Zhu, B, Yi, C, Li, Y, Zhang, T, Peng, Y, Si, Y, Cao, Z, Chen, A, Yao, D, Chen, X & Xu, P 2023, 'Information transmission velocity-based dynamic hierarchical brain networks', NeuroImage, vol. 270, pp. 119997-119997.
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Jiang, Y, Li, C, Wu, C, Rabczuk, T & Fang, J 2023, 'A double-phase field method for mixed mode crack modelling in 3D elasto-plastic solids with crack-direction-based strain energy decomposition', Computer Methods in Applied Mechanics and Engineering, vol. 405, pp. 115886-115886.
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Jiang, Z, Li, C, Chang, X, Chen, L, Zhu, J & Yang, Y 2023, 'Dynamic Slimmable Denoising Network', IEEE Transactions on Image Processing, vol. 32, pp. 1583-1598.
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Jin, P, Chang, L, Liu, Y, Guo, Y, Lei, G & Zhu, J 2023, 'Design and Implementation of Novel Rotor Side Brushless Controller with Bidirectional Wireless Power Transmission for Doubly-fed Machine', IEEE Transactions on Industrial Electronics, pp. 1-10.
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Jin, P, Zhang, J, Lu, Y, Guo, Y, Lei, G & Zhu, J 2023, 'Variable Frequency Isolated Bidirectional CLLC Resonant Converter With Voltage Controlled Variable Capacitors', IEEE Transactions on Industrial Electronics, vol. 70, no. 9, pp. 8907-8917.
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Jin, W, Zhao, B, Yu, H, Tao, X, Yin, R & Liu, G 2023, 'Improving embedded knowledge graph multi-hop question answering by introducing relational chain reasoning', Data Mining and Knowledge Discovery, vol. 37, no. 1, pp. 255-288.
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Jin, W, Zhao, B, Zhang, L, Liu, C & Yu, H 2023, 'Back to common sense: Oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis', Information Processing & Management, vol. 60, no. 3, pp. 103260-103260.
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Jupp, JR & Parkes, M 2023, 'Integrated Construction Enterprise Systems: A Strategic Approach to Model-based Data and Process Management', Automation in Construction, vol. (to appear).

Kabir, MM, Akter, MM, Huang, Z, Tijing, L & Shon, HK 2023, 'Hydrogen production from water industries for a circular economy', Desalination, vol. 554, pp. 116448-116448.
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Kabir, MM, Nahar, N, Akter, MM, Alam, F, Gilroyed, BH, Misu, MM, Didar-ul-Alam, M, Hakim, M, Tijing, L & Shon, HK 2023, 'Agro-waste-based functionalized and economic adsorbents for the effective treatment of toxic contaminants from tannery effluent', Journal of Water Process Engineering, vol. 52, pp. 103578-103578.
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Kaim, V, Singh, N, Kanaujia, BK, Matekovits, L, Esselle, KP & Rambabu, K 2023, 'Multi-Channel Implantable Cubic Rectenna MIMO System With CP Diversity in Orthogonal Space for Enhanced Wireless Power Transfer in Biotelemetry', IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 200-214.
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Kang, K, Li, L & Sohaib, O 2023, 'Graduates’ intention to develop live commerce: The educational background perspective using multi-group analysis', Entrepreneurial Business and Economics Review, vol. 11, no. 1, pp. 113-126.
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Kang, X, Li, C, Ding, W, Ma, Y, Zhou, X, Gao, S, Chen, C, Liu, W, He, Z, Li, X & Jiang, G 2023, 'Optimization of biological enzymes combined with Fe2+-activated advanced oxidation process for waste activated sludge conditioning using the response surface method', Journal of Water Process Engineering, vol. 53, pp. 103634-103634.
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Karbassiyazdi, E, Kasula, M, Modak, S, Pala, J, Kalantari, M, Altaee, A, Esfahani, MR & Razmjou, A 2023, 'A juxtaposed review on adsorptive removal of PFAS by metal-organic frameworks (MOFs) with carbon-based materials, ion exchange resins, and polymer adsorbents', Chemosphere, vol. 311, pp. 136933-136933.
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Katzmarek, DA, Yang, Y, Ghasemian, MB, Kalantar-Zadeh, K, Ziolkowski, RW & Iacopi, F 2023, 'Characteristics of Epitaxial Graphene on SiC/Si Substrates in the Radio Frequency Spectrum', IEEE Electron Device Letters, vol. 44, no. 2, pp. 297-300.
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Keshavarz-Fathi, M, Yazdanpanah, N, Kolahchi, S, Ziaei, H, Darmstadt, GL, Dorigo, T, Dochy, F, Levin, L, Thongboonkerd, V, Ogino, S, Chen, W-H, Perc, M, Tremblay, MS, Olusanya, BO, Rao, IM, Hatziargyriou, N, Moradi-Lakeh, M, Bella, F, Rosivall, L, Gandomi, AH, Sorooshian, A, Gupta, M, Gal, C, Lozano, AM, Weaver, C, Tanzer, M, Poggi, A, Sepanlou, SG, Weiskirchen, R, Jambrak, AR, Torres, PJ, Capanoglu, E, Barba, FJ, Ernest, CKJ, Sigman, M, Pluchino, S, Gharehpetian, GB, Fereshtehnejad, S-M, Yang, M-H, Thomas, S, Cai, W, Comini, E, Scolding, NJ, Myles, PS, Nieto, JJ, Perry, G, Sedikides, C & Rezaei, N 2023, 'Universal research index: An inclusive metric to quantify scientific research output', The Journal of Academic Librarianship, vol. 49, no. 3, pp. 102714-102714.
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Kha, J, Karimi, M, Maxit, L, Skvortsov, A & Kirby, R 2023, 'Forced vibroacoustic response of a cylindrical shell in an underwater acoustic waveguide', Ocean Engineering, vol. 273, pp. 113899-113899.
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Khade, S, Gite, S, D. Thepade, S, Pradhan, B & Alamri, A 2023, 'Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers', Computer Modeling in Engineering & Sciences, vol. 136, no. 1, pp. 323-345.
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Khan, A, Ibrar, I, Mirdad, A, Al-Juboori, RA, Deka, P, Subbiah, S & Altaee, A 2023, 'Novel Approach to Landfill Wastewater Treatment Fouling Mitigation: Air Gap Membrane Distillation with Tin Sulfide-Coated PTFE Membrane', Membranes, vol. 13, no. 5, pp. 483-483.
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This study addressed the fouling issue in membrane distillation (M.D.) technology, a promising method for water purification and wastewater reclamation. To enhance the anti-fouling properties of the M.D. membrane, a tin sulfide (TS) coating onto polytetrafluoroethylene (PTFE) was proposed and evaluated with air gap membrane distillation (AGMD) using landfill leachate wastewater at high recovery rates (80% and 90%). The presence of TS on the membrane surface was confirmed using various techniques, such as Field Emission Scanning Electron Microscopy (FE-SEM), Fourier Transform Infrared Spectroscopy (FT-IR), Energy Dispersive Spectroscopy (EDS), contact angle measurement, and porosity analysis. The results indicated the TS-PTFE membrane exhibited better anti-fouling properties than the pristine PTFE membrane, and its fouling factors (FFs) were 10.4–13.1% compared to 14.4–16.5% for the PTFE membrane. The fouling was attributed to pore blockage and cake formation of carbonous and nitrogenous compounds. The study also found that physical cleaning with deionized (DI) water effectively restored the water flux, with more than 97% recovered for the TS-PTFE membrane. Additionally, the TS-PTFE membrane showed better water flux and product quality at 55 °C and excellent stability in maintaining the contact angle over time compared to the PTFE membrane.

Khan, AUH, Naidu, R, Dharmarajan, R, Fang, C, Shon, H, Dong, Z & Liu, Y 2023, 'The interaction mechanisms of co-existing polybrominated diphenyl ethers and engineered nanoparticles in environmental waters: A critical review', Journal of Environmental Sciences, vol. 124, pp. 227-252.
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Khavari Kashani, MR, Wang, Q, Khatebasreh, M, Li, X, Sheikh Asadi, AM, Boczkaj, G & Ghanbari, F 2023, 'Sequential treatment of landfill leachate by electrocoagulation/aeration, PMS/ZVI/UV and electro-Fenton: Performance, biodegradability and toxicity studies', Journal of Environmental Management, vol. 338, pp. 117781-117781.
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Khoa, TV, Hoang, DT, Trung, NL, Nguyen, CT, Quynh, TTT, Nguyen, DN, Ha, NV & Dutkiewicz, E 2023, 'Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks', IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8578-8589.
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Khodasevych, I, Wesemann, L, Roberts, A & Iacopi, F 2023, 'Tunable nonlocal metasurfaces based on graphene for analogue optical computation', Optical Materials Express, vol. 13, no. 5, pp. 1475-1475.
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Meta-optical devices have recently emerged as ultra-compact candidates for real-time computation in the spatial domain. The use of meta-optics for applications in image processing and wavefront sensing could enable an order of magnitude increase in processing speed and data throughput, while simultaneously drastically reducing the footprint of currently available solutions to enable miniaturisation. Most research to date has focused on static devices that can perform a single operation. Dynamically tunable devices, however, offer increased versatility. Here we propose graphene covered subwavelength silicon carbide gratings as electrically tunable optical computation and image processing devices at mid-infrared wavelengths.

Khuat, TT & Gabrys, B 2023, 'An online learning algorithm for a neuro-fuzzy classifier with mixed-attribute data', Applied Soft Computing, vol. 137, pp. 110152-110152.
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Khuat, TT & Gabrys, B 2023, 'Random Hyperboxes', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 1008-1022.
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Kotha, M, Pradhan, S & Cetindamar, D 2023, 'Relevance of Engineering Management Courses to Managerial Skills in the Industry', IEEE Transactions on Engineering Management, pp. 1-14.
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Digital technologies have radically influenced the modern workplace, which is exacerbated by the Covid-19 pandemic making it possible to work from home or remotely. Furthermore, these changes have been assimilated, so it is now necessary and difficult to separate ourselves from the flexible working environment. This study examines the various engineering management courses offered by Australian institutions to compare the skillsets taught and those deemed significant by engineering managers. The goal is to determine this connection in the current digital age, where re-skilling, digital intelligence, and empathy are some of the themes identified in the literature review. The study is based on a review of 20 relevant research articles, interviews with ten Australian engineering and technical managers transitioning from engineering to managerial and team leadership roles, and an analysis of six Australian university curriculums. The findings highlight the importance of digital and emotional intelligence for managers. The study show that most of the skillsets offered by the Australian university curricula did not include key skills.

Krunz, M, Aykin, I, Sarkar, S & Akgun, B 2023, 'Online Reinforcement Learning for Beam Tracking and Rate Adaptation in Millimeter-wave Systems', IEEE Transactions on Mobile Computing, pp. 1-16.
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Kurdkandi, NV, Marangalu, MG, Husev, O, Aghaie, A, Islam, MR, Siwakoti, YP, Muttaqi, KM & Hosseini, SH 2023, 'A New Seven-Level Transformer-Less Grid-Tied Inverter With Leakage Current Limitation and Voltage Boosting Feature', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 1, pp. 228-241.
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Kurdkandi, NV, Marangalu, MG, Naderi, Y, Husev, O, Hosseini, SH, Siwakoti, Y & Mehrizi‐Sani, A 2023, 'An improved nine‐level switched capacitor‐based inverter with voltage boosting capability and limitation of capacitor current spikes for PV applications', IET Renewable Power Generation, vol. 17, no. 3, pp. 725-749.
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Lalsangi, S, Yaliwal, VS, Banapurmath, NR, Soudagar, MEM, Ağbulut, Ü & Kalam, MA 2023, 'Analysis of CRDI diesel engine characteristics operated on dual fuel mode fueled with biodiesel-hydrogen enriched producer gas under the single and multi-injection scheme', International Journal of Hydrogen Energy.
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Le, T, Desa, S & Khabbaz, H 2023, 'The Influence Of Bagasse Fly Ash Particle Size In Controlling Expansive Soils In Combination With Hydrated Lime', Australian Geomechanics Journal, pp. 47-57.
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Sugarcane is the second largest export crop in Australia. Industrial production of sugar, from sugarcane, results in bagasse fly ash (BFA), a by-product from the cogeneration in sugar milling operations that generate electricity by steam. The chemical and physical properties of BFA highlight its potential as a promising pozzolan for the stabilization of expansive soils, due primarily to a high content and surface area of the amorphous silicate found in BFA. Silicate in bagasse fly ash reacts extensively with calcium hydrate in lime to produce hydrated products via pozzolanic reactions, this results in a hardening of the material to which BFA and lime have been added. This reaction has been studied to be a function of the size of BFA particles and conditions of the curing process. This study explored the variables that influence the reaction and evaluated shrinkage and compressive strength of the mixtures to which bagasse fly ash, in the form of different particle size distributions, and hydrated lime are added. The maximum BFA particles sizes considered within this study include 75, 150 and 425 μm; curing times of 7 and 28 days are also explored. A suite of testing, including Atterberg limits, linear shrinkage (LS), and unconfined compressive strength (UCS) tests were completed on the prepared mixtures. The findings indicate that bagasse fly ash with a maximum size of 425 μm yields a higher UCS and lower LS, compared to finer BFA particle mixtures. The ash with a maximum particle size of 425-μm also improves the ductility of treated soils and accelerates their strength gain, compared to soil- lime stabilized samples. The results of the study build towards a better understanding of BFA, and the ways in which such a material maybe engineered to replace concrete in road work projects and other applications involving expansive soils.

Lee, SS, Cao, S, Barzegarkhoo, R, Farhangi, M & Siwakoti, YP 2023, 'Single-Phase 5-Level Split-Midpoint Cross-Clamped (5L-SMCC) Inverter: An Alternative to the Two-Stage ANPC Topology', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 2, pp. 1995-2003.
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Lee, SS, Lim, RJS, Barzegarkhoo, R, Lim, CS, Grigoletto, FB & Siwakoti, YP 2023, 'A Family of Single-Phase Single-Stage Boost Inverters', IEEE Transactions on Industrial Electronics, vol. 70, no. 8, pp. 7955-7964.
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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 & Lee, K-B 2023, 'Five-Level Unity-Gain Active Neutral-Point-Clamped Inverters Designed Using Half-Bridges', IEEE Transactions on Industry Applications, vol. 59, no. 3, pp. 3520-3529.
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Lei, Y, Liu, H, Yang, Z, Zhao, L, Lai, W, Chen, M, Liu, H, Dou, S & Wang, Y 2023, 'A Review on the Status and Challenges of Cathodes in Room‐Temperature Na‐S Batteries', Advanced Functional Materials, vol. 33, no. 11, pp. 2212600-2212600.
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Lei, Y, Ye, D, Shen, S, Sui, Y, Zhu, T & Zhou, W 2023, 'New challenges in reinforcement learning: a survey of security and privacy', Artificial Intelligence Review, vol. 56, no. 7, pp. 7195-7236.
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Li, B, Guo, T, Li, R, Wang, Y, Gandomi, AH & Chen, F 2023, 'Self-adaptive Predictive Passenger Flow Modelling for Large-scale Railway Systems', IEEE Internet of Things Journal, pp. 1-1.
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Li, B, Wen, S, Yan, Z, Wen, G & Huang, T 2023, 'A Survey on the Control Lyapunov Function and Control Barrier Function for Nonlinear-Affine Control Systems', IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 3, pp. 584-602.
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Li, C, Fang, J, Wan, Y, Qiu, N, Steven, G & Li, Q 2023, 'Phase field fracture model for additively manufactured metallic materials', International Journal of Mechanical Sciences, vol. 251, pp. 108324-108324.
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Li, C, Li, P, Zhang, Y, Li, N, Si, Y, Li, F, Cao, Z, Chen, H, Chen, B, Yao, D & Xu, P 2023, 'Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Li, D, Ou, T, Fu, Q, Li, D-S, Liu, Z & Sun, Y 2023, 'A Novel Thin Film Composite Membrane for Osmotic Energy Generation', Industrial & Engineering Chemistry Research, vol. 62, no. 14, pp. 5889-5897.
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Li, F, Zheng, J, Zhang, Y-F, Jia, W, Wei, Q & He, X 2023, 'Cross-domain learning for underwater image enhancement', Signal Processing: Image Communication, vol. 110, pp. 116890-116890.
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Li, H, Cai, Z, Wang, J, Tang, J, Ding, W, Lin, C-T & Shi, Y 2023, 'FedTP: Federated Learning by Transformer Personalization', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Li, J, Jiang, M, Qin, Y, Zhang, R & Ling, SH 2023, 'Intelligent depression detection with asynchronous federated optimization', Complex & Intelligent Systems, vol. 9, no. 1, pp. 115-131.
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AbstractThe growth of population and the various intensive life pressures everyday deepen the competitions among people. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. The development of social networks such as Facebook, Twitter, Weibo, and QQ provides more convenient communication and provides a new emotional vent window. People communicate with their friends, sharing their opinions, and shooting videos to reflect their feelings. It provides an opportunity to detect depression in social networks. Although depression detection using social networks has reflected the established connectivity across users, fewer researchers consider the data security and privacy-preserving schemes. Therefore, we advocate the federated learning technique as an efficient and scalable method, where it enables the handling of a massive number of edge devices in parallel. In this study, we conduct the depression analysis on the basis of an online microblog called Weibo. A novel algorithm termed as CNN Asynchronous Federated optimization (CAFed) is proposed based on federated learning to improve the communication cost and convergence rate. It is shown that our proposed method can effectively protect users' privacy under the premise of ensuring the accuracy of prediction. The proposed method converges faster than the Federated Averaging (FedAvg) for non-convex problems. Federated learning techniques can identify quality solutions of mental health problems among Weibo users.

Li, J, Pan, Y & Tsang, IW 2023, 'Taming Overconfident Prediction on Unlabeled Data From Hindsight', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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Li, J, Pan, Y, Lyu, Y, Yao, Y, Sui, Y & Tsang, IW 2023, 'Earning Extra Performance from Restrictive Feedbacks', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-13.
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Li, K, Lu, J, Zuo, H & Zhang, G 2023, 'Multidomain Adaptation With Sample and Source Distillation', IEEE Transactions on Cybernetics, pp. 1-13.
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Li, K, Lu, J, Zuo, H & Zhang, G 2023, 'Source-Free Multi-Domain Adaptation with Fuzzy Rule-based Deep Neural Networks', IEEE Transactions on Fuzzy Systems, pp. 1-15.
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Li, L & Kang, K 2023, 'Why ethnic minority groups’ online-startups are booming in China’s tight cultural ecosystem?', Journal of Entrepreneurship in Emerging Economies, vol. 15, no. 2, pp. 278-300.
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Purpose Although most Chinese ethnic minority groups (EMGs) hold conservative thinking to online-startups, the new entrepreneurial model is booming on live streaming platforms. In China’s tight cultural ecosystem, the tight cultural control would lead EMG entrepreneurs to keep conservative thinking and avoid challenging careers. Still, it would be helpful for Chinese Governments to issue systematical entrepreneurial policies and improve online-startup environment for EMGs. To discover the relationships among influencing factors and EMGs’ online-startup motivation, this paper aims to draw on the tight and loose cultural theory and the capability-opportunity-motivation-behaviour (COM-B) behaviour changing theory and establishes the research model based on China’s tight cultural ecosystem. Design/methodology/approach Through analysing 617 questionnaires from 37 EMGs based on the partial least squares path modelling and variance-based structural equation modelling method, the study proves that environmental opportunity factors and personal capability factors have positive impacts on EMGs’ online-startup motivation and EMGs’ conservative thinking negatively moderates the relationship between their online-startup motivation and entrepreneurial development behaviour. In addition to testing the hypotheses, the paper also measures the importance-performance map analysis to explore additional findings of influencing factors and provide suitable suggestions for EMG entrepreneurs and related departments. Findings Regarding the environmental opportunity unit, both policy support and platform support significantly impact Chinese EMGs’ motivation to promote online-startups. For the personal capability un...

Li, L, Guo, R, You, P, Bai, J, Qin, P-Y & Liu, Y 2023, 'Pattern-Reconfigurable Sparse Linear Array Synthesis Under Minimum Element Spacing Control by Alternating Sequential Quadratic Programming', IEEE Antennas and Wireless Propagation Letters, vol. 22, no. 6, pp. 1271-1275.
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Li, L, Kang, K & Sohaib, O 2023, 'Investigating factors affecting Chinese tertiary students’ online-startup motivation based on the COM-B behaviour changing theory', Journal of Entrepreneurship in Emerging Economies, vol. 15, no. 3, pp. 566-588.
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Purpose This study aims to present the Chinese entrepreneurial environment and explore Chinese tertiary students’ online-startup motivation on live streaming platforms. Based on the COM-B behaviour changing theory, this paper discovers various influencing factors from environmental opportunity and personal capability aspects. It analyses their effects under the cooperative system established among official departments, industries and universities. Meanwhile, considering social and cultural control, it also refers to the uncertainty-avoidance dimension from the Hofstede cultural theory and re-evaluates its influence on Chinese tertiary students’ online-startup motivation. Design/methodology/approach The authors analyse 474 responses from online questionnaires through partial least squares path modelling and variance-based structural equation modelling. The paper claims that environmental opportunity and personal capability factors positively affect students’ online-startup motivation, but uncertainty-avoidance thinking plays a negative role. The study also measures the importance-performance map analysis to explore additional findings and discuss managerial implications. Findings Both platform support and official department support positively impact Chinese tertiary students’ online-startup motivation and entrepreneurial skills learned from universities are beneficial for them to build online-startup confidence. Meanwhile, influenced by the cooperative system implemented among official departments, industries and universities, official department support positively affects platform support and entrepreneurial skills. Conversely, influenced by Chinese traditional Confucian culture, uncertai...

Li, M & Yang, Y 2023, 'Single- and Multiple-Material Additively Manufactured Electronics: A Further Step From the Microwave-to-Terahertz Regimes', IEEE Microwave Magazine, vol. 24, no. 1, pp. 30-45.
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Li, M, Liu, R, Wang, F, Chang, X & Liang, X 2023, 'Auxiliary signal-guided knowledge encoder-decoder for medical report generation', World Wide Web, vol. 26, no. 1, pp. 253-270.
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AbstractMedical reports have significant clinical value to radiologists and specialists, especially during a pandemic like COVID. However, beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure. Secondly, there are many similar sentences used in each medical report to describe the normal regions of the image, which causes serious data bias. This deviation is likely to teach models to generate these inessential sentences on a regular basis. To address these problems, we propose an Auxiliary Signal-Guided Knowledge Encoder-Decoder (ASGK) to mimic radiologists’ working patterns. Specifically, the auxiliary patches are explored to expand the widely used visual patch features before fed to the Transformer encoder, while the external linguistic signals help the decoder better master prior knowledge during the pre-training process. Our approach performs well on common benchmarks, including CX-CHR, IU X-Ray, and COVID-19 CT Report dataset (COV-CTR), demonstrating combining auxiliary signals with transformer architecture can bring a significant improvement in terms of medical report generation. The experimental results confirm that auxiliary signals driven Transformer-based models are with solid capabilities to outperform previous approaches on both medical terminology classification and paragraph generation metrics.

Li, M, Liu, Y, Bao, Z, Chen, L, Hu, J & Guo, YJ 2023, 'Efficient Phase-Only Dual- and Multi-Beam Pattern Synthesis With Accurate Beam Direction and Power Control Employing Partitioned Iterative FFT', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3719-3724.
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Li, P, Li, W, Wang, K, Zhou, JL, Castel, A, Zhang, S & Shah, SP 2023, 'Hydration of Portland cement with seawater toward concrete sustainability: Phase evolution and thermodynamic modelling', Cement and Concrete Composites, vol. 138, pp. 105007-105007.
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Li, P, Yu, H, Luo, X & Wu, J 2023, 'LGM-GNN: A Local and Global Aware Memory-Based Graph Neural Network for Fraud Detection', IEEE Transactions on Big Data, pp. 1-13.
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Li, Q, Wang, X, Wang, Z & Xu, G 2023, 'Be Causal: De-Biasing Social Network Confounding in Recommendation', ACM Transactions on Knowledge Discovery from Data, vol. 17, no. 1, pp. 1-23.
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In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called “exposure” perspective, i.e., modeling how an item is exposed (provided) to a user. Most of the existing approaches use heuristic models or re-weighting strategy on observed ratings to mimic the missing-at-random setting. However, little research has been done to reveal how the ratings are missing from a causal perspective. To bridge the gap, we propose an unbiased and robust method called DENC ( De-Bias Network Confounding in Recommendation ), inspired by confounder analysis in causal inference. In general, DENC provides a causal analysis on MNAR from both the inherent factors (e.g., latent user or item factors) and auxiliary network’s perspective. Particularly, the proposed exposure model in DENC can control the social network confounder meanwhile preserve the observed exposure information. We also develop a deconfounding model through the balanced representation learning to retain the primary user and item features, which enables DENC generalize well on the rating prediction. Extensive experiments on three datasets validate that our proposed model outperforms the state-of-the-art baselines.

Li, Q, Wu, D, Gao, W & Hui, D 2023, 'Nonlinear dynamic stability analysis of axial impact loaded structures via the nonlocal strain gradient theory', Applied Mathematical Modelling, vol. 115, pp. 259-278.
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Li, S, Ji, JC, Xu, Y, Sun, X, Feng, K, Sun, B, Wang, Y, Gu, F, Zhang, K & Ni, Q 2023, 'IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions', Reliability Engineering & System Safety, vol. 237, pp. 109387-109387.
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Li, T, Lian, S, Zhao, S, Lu, J & Burnett, IS 2023, 'Distributed Active Noise Control Based on an Augmented Diffusion FxLMS Algorithm', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 1449-1463.
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Li, W, Ji, J, Huang, L & Cai, Z 2023, 'Periodic orbit analysis for a delayed model of malicious signal transmission in wireless sensor networks with discontinuous control', Mathematical Methods in the Applied Sciences, vol. 46, no. 5, pp. 5267-5285.
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Li, W, Ji, J, Huang, L & Zhang, L 2023, 'Global dynamics and control of malicious signal transmission in wireless sensor networks', Nonlinear Analysis: Hybrid Systems, vol. 48, pp. 101324-101324.
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Li, W, Ji, J, Huang, L & Zhang, Y 2023, 'Complex dynamics and impulsive control of a chemostat model under the ratio threshold policy', Chaos, Solitons & Fractals, vol. 167, pp. 113077-113077.
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Li, W, Li, X, Han, C, Gao, L, Wu, H & Li, M 2023, 'A new view into three-dimensional excitation-emission matrix fluorescence spectroscopy for dissolved organic matter', Science of The Total Environment, vol. 855, pp. 158963-158963.
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Li, W, Wang, Y, Yu, C, He, Z, Zuo, C & Yu, Y 2023, 'Nano-scale study on molecular structure, thermal stability, and mechanical properties of geopolymer', Journal of the Korean Ceramic Society, vol. 60, no. 2, pp. 413-423.
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Li, X, Cui, Y, Zhang, JA, Liu, F, Zhang, D & Hanzo, L 2023, 'Integrated Human Activity Sensing and Communications', IEEE Communications Magazine, vol. 61, no. 5, pp. 90-96.
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Li, X, Liu, H, Zhang, Z, Zhou, T & Wang, Q 2023, 'Sulfite pretreatment enhances the medium-chain fatty acids production from waste activated sludge anaerobic fermentation', Science of The Total Environment, vol. 871, pp. 162080-162080.
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Li, X, Liu, Q, Wu, S, Cao, Z & Bai, Q 2023, 'Game theory based compatible incentive mechanism design for non-cryptocurrency blockchain systems', Journal of Industrial Information Integration, vol. 31, pp. 100426-100426.
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Li, X, Peng, Y & Xu, M 2023, 'Patch-shuffle-based semi-supervised segmentation of bone computed tomography via consistent learning', Biomedical Signal Processing and Control, vol. 80, pp. 104239-104239.
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Li, X, Yan, L, Li, D, Fan, Z, Liu, H, Wang, G, Jiu, J, Yang, Z, Li, JJ & Wang, B 2023, 'Failure modes after anterior cruciate ligament reconstruction: a systematic review and meta-analysis', International Orthopaedics, vol. 47, no. 3, pp. 719-734.
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Li, X, Zhang, S, Sherchan, S, Orive, G, Lertxundi, U, Haramoto, E, Honda, R, Kumar, M, Arora, S, Kitajima, M & Jiang, G 2023, 'Correlation between SARS-CoV-2 RNA concentration in wastewater and COVID-19 cases in community: A systematic review and meta-analysis', Journal of Hazardous Materials, vol. 441, pp. 129848-129848.
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Li, Y, Chen, H, Li, Y, Li, L, Yu, PS & Xu, G 2023, 'Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation', IEEE Transactions on Knowledge and Data Engineering, pp. 1-16.
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Li, Y, Chen, Z, Sun, X, Gao, C, Liu, X & Guo, Y 2023, 'Back propagation neural network‐based torque ripple reduction strategy for high frequency square‐wave voltage injection‐based interior permanent magnet synchronous motor sensorless control', IET Electric Power Applications, vol. 17, no. 2, pp. 195-205.
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Li, Y, Liu, Z, Chang, X, McAuley, J & Yao, L 2023, 'Diversity-Boosted Generalization-Specialization Balancing for Zero-Shot Learning', IEEE Transactions on Multimedia, pp. 1-11.
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Li, Y, Ma, B, Zheng, J, Zhu, J & Lei, G 2023, 'Electromagnetic and Mechanical Topology Optimization for SynRM Rotors Considering High Dimensional Constraints', IEEE Transactions on Industrial Electronics, pp. 1-12.
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Li, Y, Shen, J & Cetindamar, D 2023, 'Think Tank Innovation-Driven Knowledge Service Ecosystems: A Conceptual Framework and Case Study Application', Sustainability, vol. 15, no. 10, pp. 8355-8355.
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By drawing on ecosystem and innovation-driven development theories, the aim of this paper is to increase our understanding of their application to think tanks. The composition, structure, and features of the knowledge service ecosystem of think tanks are conceptualized via a literature review. The model developed from this was validated by analyzing the data collected from 25 think tanks in the United States (US). The model constructed provides a reference for the sustainable and healthy development of knowledge services in think tanks and an innovation-driven development perspective for researchers interested in their innovation ecosystem dynamics. The intake of talent forms a necessary part of think tank construction, but, more importantly, this continuous intake is a crucial driving force for their sustainable development. This paper suggests that an increasing focus on talents in knowledge service ecosystems can lead to and assist in establishing innovative think tanks in many countries.

Li, Y, Yin, J & Chen, L 2023, 'Informative pseudo-labeling for graph neural networks with few labels', Data Mining and Knowledge Discovery, vol. 37, no. 1, pp. 228-254.
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AbstractGraph neural networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs with very few labels is still under-explored. As one of the prevalent semi-supervised methods, pseudo-labeling has been proposed to explicitly address the label scarcity problem. It is the process of augmenting the training set with pseudo-labeled unlabeled nodes to retrain a model in a self-training cycle. However, the existing pseudo-labeling approaches often suffer from two major drawbacks. First, these methods conservatively expand the label set by selecting only high-confidence unlabeled nodes without assessing their informativeness. Second, these methods incorporate pseudo-labels to the same loss function with genuine labels, ignoring their distinct contributions to the classification task. In this paper, we propose a novel informative pseudo-labeling framework (InfoGNN) to facilitate learning of GNNs with very few labels. Our key idea is to pseudo-label the most informative nodes that can maximally represent the local neighborhoods via mutual information maximization. To mitigate the potential label noise and class-imbalance problem arising from pseudo-labeling, we also carefully devise a generalized cross entropy with a class-balanced regularization to incorporate pseudo-labels into model retraining. Extensive experiments on six real-world graph datasets validate that our proposed approach significantly outperforms state-of-the-art baselines and competitive self-supervised methods on graphs.

Li, Y, Zhang, X, Ngo, HH, Guo, W, Long, T, Wen, H & Zhang, D 2023, 'Combination of magnetic biochar beads and peroxymonosulfate pretreatment process for mitigating ultrafiltration membrane fouling caused by typical natural organic matters in water', Journal of Membrane Science, vol. 670, pp. 121383-121383.
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Li, Z, Xu, P, Chang, X, Yang, L, Zhang, Y, Yao, L & Chen, X 2023, 'When Object Detection Meets Knowledge Distillation: A Survey', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-25.
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Lian, J-W, Ansari, M, Hu, P, Guo, YJ & Ding, D 2023, 'Wideband and High-Efficiency Parallel-Plate Luneburg Lens Employing All-Metal Metamaterial for Multibeam Antenna Applications', IEEE Transactions on Antennas and Propagation, vol. 71, no. 4, pp. 3193-3203.
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Lian, M, Guo, Z, Wang, X, Wen, S & Huang, T 2023, 'Adaptive Exact Penalty Design for Optimal Resource Allocation', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 3, pp. 1430-1438.
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Liang, C, Wang, W, Zhou, T, Miao, J, Luo, Y & Yang, Y 2023, 'Local-Global Context Aware Transformer for Language-Guided Video Segmentation', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-15.
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Liang, C, Yang, Z, Zhu, L & Yang, Y 2023, 'Co-Learning Meets Stitch-Up for Noisy Multi-Label Visual Recognition', IEEE Transactions on Image Processing, vol. 32, pp. 2508-2519.
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Lin, C-T 2023, '2023 IEEE CIS Awards [Society Briefs]', IEEE Computational Intelligence Magazine, vol. 18, no. 1, pp. 5-9.
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Lin, C-T 2023, 'Human-centirc artificial intelligence: Frontier research and building industry capability', Open Access Government, vol. 38, no. 1, pp. 310-311.
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Human-centirc artificial intelligence: Frontier research and building industry capability Chin-Teng Lin, Distinguished Professor from the University of Technology Sydney, Human-centric AI Centre, AAII, explores human-centric AI, focusing on frontier research & building industry capability. Distinguished Professor Chin-Teng Lin is a pioneer, inventor and world leader in computational intelligence and co-founder of the GrapheneX-UTS Human-centric AI Centre (HAI) at the University of Technology Sydney, Australia. For the past three decades, he has significantly advanced artificial intelligence (AI), brain-computer interfaces and human-AI teaming across theory, methodology and applications. Here, he provides an overview of his frontier research in combining human intelligence with AI to enable humans to make better decisions in complex, stressful situations and what it takes to translate research into tangible products and services. He also highlights the importance of ethical considerations when teaming humans with robots.

Lin, C-T, Liu, J, Fang, C-N, Hsiao, S-Y, Chang, Y-C & Wang, Y-K 2023, 'Multistream 3-D Convolution Neural Network With Parameter Sharing for Human State Estimation', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 1, pp. 261-271.
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Lin, D, Ji, J, Yu, C, Wang, X & Xu, N 2023, 'A non-linear model of screen panel for dynamics analysis of a flip-flow vibrating screen', Powder Technology, vol. 418, pp. 118312-118312.
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Lin, J-Y, Yang, Y, Wong, S-W, Li, X, Wang, L & Dutkiewicz, E 2023, 'Two-Way Waveguide Diplexer and Its Application to Diplexing In-Band Full-Duplex Antenna', IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 3, pp. 1171-1179.
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Lin, S, Liu, A, Wang, J & Kong, X 2023, 'An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse', Journal of Computational Science, vol. 67, pp. 101938-101938.
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Lin, W, Guo, J, Zeng, J, Chen, R, Ngo, HH, Nan, J, Li, G, Ma, J & Ding, A 2023, 'Enhanced sludge dewaterability by ferrate/ferric chloride: The key role of Fe(IV) on the changes of EPS properties', Science of The Total Environment, vol. 858, pp. 159562-159562.
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Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2023, 'Decentralized, Privacy-Preserving Routing of Cellular-Connected Unmanned Aerial Vehicles for Joint Goods Delivery and Sensing', IEEE Transactions on Intelligent Transportation Systems, pp. 1-15.
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Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2023, 'Optimal Routing of Unmanned Aerial Vehicle for Joint Goods Delivery and in-Situ Sensing', IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 3, pp. 3594-3599.
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Liu, C, Chen, H, Zhu, T, Zhang, J & Zhou, W 2023, 'Making DeepFakes More Spurious: Evading Deep Face Forgery Detection via Trace Removal Attack', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Liu, H, Li, X, Zhang, Z, Nghiem, LD, Gao, L, Batstone, DJ & Wang, Q 2023, 'Achieving expanded sludge treatment capacity with additional benefits for an anaerobic digester using free ammonia pretreatment', Chemical Engineering Journal, vol. 465, pp. 142846-142846.
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Liu, H, Li, X, Zhou, T, Zhang, Z, Nghiem, LD, Gao, L & Wang, Q 2023, 'Long-term effect of free ammonia pretreatment on the semi-continuous anaerobic primary sludge digester for enhancing performance: Towards sustainable sludge treatment', Chemical Engineering Journal, vol. 465, pp. 142780-142780.
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Liu, H, Yan, X, Jiu, J, Li, JJ, Zhang, Y, Wang, G, Li, D, Yan, L, Du, Y, Zhao, B & Wang, B 2023, 'Self-assembly of gelatin microcarrier-based MSC microtissues for spinal cord injury repair', Chemical Engineering Journal, vol. 451, pp. 138806-138806.
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Liu, H, Zhou, Q, Xia, Q, Lei, Y, Long Huang, X, Tebyetekerwa, M & Song Zhao, X 2023, 'Interface challenges and optimization strategies for aqueous zinc-ion batteries', Journal of Energy Chemistry, vol. 77, pp. 642-659.
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Liu, J, He, Z, Liu, P, Wei, J, Li, J & Wu, C 2023, 'High-velocity projectile impact resistance of reinforced concrete slabs with ultra-high performance concrete strengthening - A numerical study', Structures, vol. 52, pp. 422-436.
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Liu, K, Liu, J, Li, J, Tao, M & Wu, C 2023, 'Experimental investigation of heating–cooling effects on the mechanical properties of geopolymer-based high performance concrete heated to elevated temperatures', Structures, vol. 47, pp. 735-747.
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Liu, L, Ba, X, Guo, Y, Lei, G, Sun, X & Zhu, J 2023, 'Improved Iron Loss Prediction Models for Interior PMSMs Considering Coupling Effects of Multiphysics Factors', IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 416-427.
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Liu, P, Fei, Z, Wang, X, Zhang, JA, Zheng, Z & Zhang, Q 2023, 'Securing Multi-user Uplink Communications Against Mobile Aerial Eavesdropper via Sensing', IEEE Transactions on Vehicular Technology, pp. 1-6.
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Liu, Q, Geng, X, Huang, H, Qin, T, Lu, J & Jiang, D 2023, 'MGRC: An End-to-End Multigranularity Reading Comprehension Model for Question Answering', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 5, pp. 2594-2605.
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Liu, Q, Li, X, Yuan, D, Yang, C, Chang, X & He, Z 2023, 'LSOTB-TIR: A Large-Scale High-Diversity Thermal Infrared Single Object Tracking Benchmark', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Liu, T, Xia, J, Ling, Z, Fu, X, Yu, S & Chen, M 2023, 'Efficient Federated Learning for AIoT Applications Using Knowledge Distillation', IEEE Internet of Things Journal, vol. 10, no. 8, pp. 7229-7243.
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Liu, X, Shi, K, Zhou, K, Wen, S, Tang, Y & Tang, L 2023, 'Event-triggering-based H∞ load frequency control for multi-area cyber–physical power system under DoS attacks', Franklin Open, vol. 3, pp. 100012-100012.
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Liu, X, Tian, K, Chen, Z, Wei, W, Xu, B & Ni, B-J 2023, 'Online TG-FTIR-MS analysis of the catalytic pyrolysis of polyethylene and polyvinyl chloride microplastics', Journal of Hazardous Materials, vol. 441, pp. 129881-129881.
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Liu, Y & Piccardi, M 2023, 'Topic-Based Unsupervised and Supervised Dictionary Induction', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 22, no. 3, pp. 1-21.
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Word translation is a natural language processing task that provides translation between the words of a source and a target language. As a task, it reduces to the induction of a bilingual dictionary, which is typically performed by aligning word embeddings of the source language to word embeddings of the target language. To date, all the existing approaches have focused on performing a single, global alignment in word embedding space. However, semantic differences between the various languages, in addition to differences in the content of the corpora used for training the word embeddings, can hinder the effectiveness of such a global alignment. For this reason, in this article we propose conducting the alignment between the source and target embedding spaces by multiple mappings at topic level. The experimental results show that our approach has been able to achieve an average accuracy improvement of +3.30 percentage points over a state-of-the-art approach in unsupervised dictionary induction from languages as diverse as German, French, Italian, Spanish, Finnish, Turkish, and Chinese to English, and +3.95 points average improvement in supervised dictionary induction.

Liu, Y, Lee, T-U, Koronaki, A, Pietroni, N & Xie, YM 2023, 'Reducing the number of different nodes in space frame structures through clustering and optimization', Engineering Structures, vol. 284, pp. 116016-116016.
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Liu, Y, Zhang, X, Xu, Y, Liu, Q, Ngo, HH & Cao, W 2023, 'Transport behaviors of biochar particles in saturated porous media under DC electric field', Science of The Total Environment, vol. 856, pp. 159084-159084.
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Liu, Y, Zhang, X, Zhao, Y, He, Y, Yu, S & Zhu, K 2023, 'Chronos: Accelerating Federated Learning With Resource Aware Training Volume Tuning at Network Edges', IEEE Transactions on Vehicular Technology, vol. 72, no. 3, pp. 3889-3903.
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Liu, Y, Zhu, L, Wang, X, Yamada, M & Yang, Y 2023, 'Bilaterally Normalized Scale-Consistent Sinkhorn Distance for Few-Shot Image Classification', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-11.
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Liu, Z, Sun, G, Chen, Z, Ma, Y, Qiu, K, Li, M & Ni, B-J 2023, 'Anchoring Cu-N active sites on functionalized polyacrylonitrile fibers for highly selective H2S/CO2 separation', Journal of Hazardous Materials, vol. 450, pp. 131084-131084.
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Liu, Z, Wei, J, Li, R & Zhou, J 2023, 'Learning multi-modal brain tumor segmentation from privileged semi-paired MRI images with curriculum disentanglement learning', Computers in Biology and Medicine, vol. 159, pp. 106927-106927.
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Lo, Y-C, Blamires, SJ, Liao, C-P & Tso, I-M 2023, 'Nocturnal and diurnal predator and prey interactions with crab spider color polymorphs', Behavioral Ecology and Sociobiology, vol. 77, no. 2.
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Loengbudnark, W, Khalilpour, K, Bharathy, G, Voinov, A & Thomas, L 2023, 'Impact of occupant autonomy on satisfaction and building energy efficiency', Energy and Built Environment, vol. 4, no. 4, pp. 377-385.
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Loganathan, P, Vigneswaran, S, Kandasamy, J, Nguyen, TV, Katarzyna Cuprys, A & Ratnaweera, H 2023, 'Bisphenols in water: Occurrence, effects, and mitigation strategies', Chemosphere, vol. 328, pp. 138560-138560.
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Long, G, Xie, M, Shen, T, Zhou, T, Wang, X & Jiang, J 2023, 'Multi-center federated learning: clients clustering for better personalization', World Wide Web, vol. 26, no. 1, pp. 481-500.
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AbstractPersonalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy risk of collaborative training since it merely collects local gradients from users without access to their data. However, FL is fragile in the presence of statistical heterogeneity that is commonly encountered in personalized decision making, e.g., non-IID data over different clients. Existing FL approaches usually update a single global model to capture the shared knowledge of all users by aggregating their gradients, regardless of the discrepancy between their data distributions. By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i.e., centers) in FL. To this end, we propose a novel multi-center aggregation mechanism to cluster clients using their models’ parameters. It learns multiple global models from data as the cluster centers, and simultaneously derives the optimal matching between users and centers. We then formulate it as an optimization problem that can be efficiently solved by a stochastic expectation maximization (EM) algorithm. Experiments on multiple benchmark datasets of FL show that our method outperforms several popular baseline methods. The experimental source codes are publicly available on the Github repository (GitHub repository: https://github.com/mingxuts/multi-center-fed-learning).

Lu, X, Qiu, J, Lei, G & Zhu, J 2023, 'Degradation Mode Knowledge Transfer Method for LFP Batteries', IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 1142-1152.
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Lu, X, Qiu, J, Lei, G & Zhu, J 2023, 'State of Health Estimation of Lithium Iron Phosphate Batteries based on Degradation Knowledge Transfer Learning', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Lu, X, Xiao, L, Li, P, Ji, X, Xu, C, Yu, S & Zhuang, W 2023, 'Reinforcement Learning-Based Physical Cross-Layer Security and Privacy in 6G', IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 425-466.
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Lu, Y, Xiao, W & Lu, DD-C 2023, 'Enhanced Voltage Regulation for PV Power Conversion Using Quasi-Proportional Resonant Extended State Observer', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 1, pp. 70-77.
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Lu, Y, Xiao, W & Lu, DD-C 2023, 'Improved Voltage Regulation of PV System With Current-Sensorless Active Damping Technique', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 4, no. 1, pp. 60-69.
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Luo, H, Tao, M, Wu, C & Cao, W 2023, 'Dynamic response of an elliptic cylinder inclusion with imperfect interfaces subjected to plane SH wave', Geomechanics and Geophysics for Geo-Energy and Geo-Resources, vol. 9, no. 1.
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AbstractUnderground chambers or tunnels often contain inclusions, the interface between the inclusion and the surrounding rock is not always perfect, which influences stress wave propagation. In this study, the imperfect interface and transient seismic wave were represented using the spring model and Ricker wavelet. Based on the wave function expansion method and Fourier transform, an analytical formula for the dynamic stress concentration factor (DSCF) for an elliptical inclusion with imperfect interfaces subjected to a plane SH-wave was determined. The theoretical solution was verified via numerical simulations using the LS-DYNA software, and the results were analyzed. The effects of the wave number (k), radial coordinate (ξ), stiffness parameter (β), and differences in material properties on the dynamic response were evaluated. The numerical results revealed that the maximum DSCF always occurred at both ends of the elliptical minor axis, and the transient DSCF was generally a factor of 2–3 greater than the steady-state DSCF. Changes in k and ξ led to variations in the DSCF value and spatial distribution, changes in β resulted only in variations in the DSCF value, and lower values of ωp and β led to a greater DSCF under the same parameter conditions. In addition, the differences in material properties between the medium and inclusion significantly affected the variation characteristics of the DSCF with k and ξ.

Luo, L, Li, B, Fan, X, Wang, Y, Koprinska, I & Chen, F 2023, 'Dynamic customer segmentation via hierarchical fragmentation-coagulation processes', Machine Learning, vol. 112, no. 1, pp. 281-310.
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Luo, T, Dai, X, Chen, Z, Wu, L, Wei, W, Xu, Q & Ni, B-J 2023, 'Different microplastics distinctively enriched the antibiotic resistance genes in anaerobic sludge digestion through shifting specific hosts and promoting horizontal gene flow', Water Research, vol. 228, pp. 119356-119356.
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Luo, T, Wei, W & Ni, B-J 2023, 'Reply for comment on “Different microplastics distinctively enriched the antibiotic resistance genes in anaerobic sludge digestion through shifting specific hosts and promoting horizontal gene flow [Water Research 228 (2023), 119356]”', Water Research, vol. 236, pp. 119928-119928.
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Luu, MH, Mai, HS, Pham, XL, Le, QA, Le, QK, Walsum, TV, Le, NH, Franklin, D, Le, VH, Moelker, A, Chu, DT & Trung, NL 2023, 'Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration', Computer Methods and Programs in Biomedicine, vol. 233, pp. 107453-107453.
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Lv, X, Xiao, Z, Fang, J, Li, Q, Lei, F & Sun, G 2023, 'On safety design of vehicle for protection of vulnerable road users: A review', Thin-Walled Structures, vol. 182, pp. 109990-109990.
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Ly, QV, Cui, L, Asif, MB, Khan, W, Nghiem, LD, Hwang, Y & Zhang, Z 2023, 'Membrane-based nanoconfined heterogeneous catalysis for water purification: A critical review✰', Water Research, vol. 230, pp. 119577-119577.
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Lyu, B, Hamdi, M, Yang, Y, Cao, Y, Yan, Z, Li, K, Wen, S & Huang, T 2023, 'Efficient Spectral Graph Convolutional Network Deployment on Memristive Crossbars', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 2, pp. 415-425.
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Lyu, B, Wang, S, Wen, S, Shi, K, Yang, Y, Zeng, L & Huang, T 2023, 'AutoGMap: Learning to Map Large-Scale Sparse Graphs on Memristive Crossbars', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-11.
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Lyu, B, Wen, S, Yang, Y, Chang, X, Sun, J, Chen, Y & Huang, T 2023, 'Designing Efficient Bit-Level Sparsity-Tolerant Memristive Networks', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-10.
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Lyu, B, Yang, Y, Wen, S, Huang, T & Li, K 2023, 'Neural Architecture Search for Portrait Parsing', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 3, pp. 1112-1121.
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Ma, C, Li, J, Ding, M, Liu, B, Wei, K, Weng, J & Poor, HV 2023, 'RDP-GAN: A Rényi-Differential Privacy Based Generative Adversarial Network', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Ma, M, Tam, VW, Le, KN, Butera, A, Li, W & Wang, X 2023, 'COMPARATIVE ANALYSIS ON INTERNATIONAL CONSTRUCTION AND DEMOLITION WASTE MANAGEMENT POLICIES AND LAWS FOR POLICY MAKERS IN CHINA', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 29, no. 2, pp. 107-130.
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In the current age of enhanced environmental awareness, transformation to sustainable management in the construction sector is needed. China currently produces the largest amount of construction and demolition (C&D) waste around the world, but the average recovery rate of the waste was only about 5% in 2017. In order to investigate problems in current C&D waste management in China, a cross-national comparative analysis is conducted among China and seven selected countries (Japan, South Korea, Germany, Italy, Austria, the Netherlands and the United Kingdom), to compare legal texts of national policies and laws which relate to C&D waste management and are currently being used. Through the comparison, problems in management of C&D waste in China are investigated. The problems could be concluded to: (a) inadequate guidance on recycling, (b) lack of market incentives in utilising recycled materials, (c) incomplete knowledge of stakeholders’ responsibilities, (d) lack of penalty for other stakeholders, and (e) inefficient supervision system. By understanding these problems, this paper further provides recommendations to enhance the performance of C&D waste management in China.

Ma, X, Xu, H, Gao, H, Bian, M & Hussain, W 2023, 'Real-Time Virtual Machine Scheduling in Industry IoT Network: A Reinforcement Learning Method', IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 2129-2139.
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Ma, Y, Wu, N, Wu, K & Zhang, JA 2023, 'VAMP-Based Iterative Equalization for Index-Modulated Multicarrier FTN Signaling', IEEE Transactions on Communications, vol. 71, no. 4, pp. 2304-2316.
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Mahmoudi, A, Khezri, R, Bidram, A, Khooban, M, Aki, H, Khalilpour, K, Abdeltawab, H & Muyeen, SM 2023, 'Guest editorial: Application of cloud energy storage systems in power systems', IET Generation, Transmission & Distribution, vol. 17, no. 8, pp. 1687-1689.
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Maidi, AM, Kalam, MA & Begum, F 2023, 'Unsafe food additive sensing through octagonal-core photonic crystal fibre sensor', Physica Scripta, vol. 98, no. 6, pp. 065528-065528.
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Abstract To detect food additives, a simple photonic crystal fibre design based on an octagonal hole and hollow circular cladding holes in two layers has been introduced. The numerical study of the design is conducted by simulation in the COMSOL Multiphysics software with the infiltrated test analytes: saccharin, sorbitol, and butyl acetate, operating in the wavelength variation from 1.6 to 4.0 μm. The performance of the proposed sensor is determined by analysing the principal optical parameters: effective refractive index, power fraction, relative sensitivity, confinement loss, chromatic dispersion, propagation constant, V-parameter, spot size, and beam divergence. At the optimal wavelength of 2.0 μm, the sensor design depicts high relative sensitivities of 98.06% for saccharin, 97.05% for sorbitol, 95.81% for butyl acetate, and 3.82 × 10−23 dBm−1 for saccharin, 3.44 × 10−22 dBm−1 for sorbitol, and 1.81 × 10−21 dBm−1 for butyl acetate for confinement loss, which is extremely low. Hence, the proposed food additive sensor is suitable for actual sensing applications based on these obtained results.

Mao, Y, Wan, Z, Dai, Y & Yu, X 2023, 'Deep Idempotent Network for Efficient Single Image Blind Deblurring', IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 1, pp. 172-185.
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Mao, Z, Zhao, L, Huang, S, Jin, T, Fan, Y & Lee, AP 2023, 'Complete region of interest reconstruction by fusing multiview deformable three‐dimensional transesophageal echocardiography images', Medical Physics, vol. 50, no. 1, pp. 61-73.
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Mas-Tur, A, Roig-Tierno, N, Sarin, S, Haon, C, Sego, T, Belkhouja, M, Porter, A & Merigó, JM 2023, 'Corrigendum to ‘Co-citation, bibliographic coupling and leading authors, institutions and countries in the 50 years of technological forecasting and social change’ [Technol. Forecast. Soc. Change 165 (2021) 120487]', Technological Forecasting and Social Change, vol. 186, pp. 122157-122157.
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Matheson, S, Fleck, R, Irga, PJ & Torpy, FR 2023, 'Phytoremediation for the indoor environment: a state-of-the-art review', Reviews in Environmental Science and Bio/Technology, vol. 22, no. 1, pp. 249-280.
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AbstractPoor indoor air quality has become of particular concern within the built environment due to the time people spend indoors, and the associated health burden. Volatile organic compounds (VOCs) off-gassing from synthetic materials, nitrogen dioxide and harmful outdoor VOCs such benzene, toluene, ethyl-benzene and xylene penetrate into the indoor environment through ventilation and are the main contributors to poor indoor air quality with health effects. A considerable body of literature over the last four decades has demonstrate the removal of gaseous contaminants through phytoremediation, a technology that relies on plant material and technologies to remediate contaminated air streams. In this review we present a state-of-the-art on indoor phytoremediation over the last decade. Here we present a review of 38 research articles on both active and passive phytoremediation, and describe the specific chemical removal efficiency of different systems. The literature clearly indicates the efficacy of these systems for the removal of gaseous contaminants in the indoor environment, however it is evident that the application of phytoremediation technologies for research purposes in-situ is currently significantly under studied. In addition, it is common for research studies to assess the removal of single chemical species under controlled conditions, with little relevancy to real-world settings easily concluded. The authors therefore recommend that future phytoremediation research be conducted both in-situ and on chemical sources of a mixed nature, such as those experienced in the urban environment like petroleum vapour, vehicle emissions, and mixed synthetic furnishings off-gassing. The assessment of these systems both in static chambers for their theoretical performance, and in-situ for these mixed chemical sources is essential for the progression of th...

Medawela, S, Indraratna, B & Rowe, RK 2023, 'The reduction in porosity of permeable reactive barriers due to bio-geochemical clogging caused by acidic groundwater flow', Canadian Geotechnical Journal, vol. 60, no. 2, pp. 151-165.
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This study demonstrates the change in porosity of permeable reactive barrier (PRB) material when it reacts with acidic flow. The laboratory column test data obtained over 9 months prove that the porosity of a granular limestone assembly decreases significantly due to bio-geochemical clogging caused by a continuous flow of acidic groundwater. The variations in pH, the pressure measurements, ion concentrations, and the results from X-ray diffraction suggest that clogging at the outlet of the column is much less than at the inlet. About 57% of the total reduction in porosity of the column is attributed to chemical clogging, while the remainder is mainly due to biological clogging. In this paper, a mathematical approach is proposed to estimate the reduction of reactive surface area based on changes in the pore volume. These proposed equations suggest that at the end of experimentation, the dissolution of calcite and bio-geochemical clogging can reduce the total surface area of limestone aggregates by more than 70%. The rigorous approach presented in this paper to determine the dominant clogging component within a granular filter at a given time is vital in estimating the longevity of a PRB and for planning its maintenance.

Mei, F, Li, JJ, Li, J, Dong, S, Li, Z & Xing, D 2023, 'Global Cluster Analysis and Network Visualization in Musculoskeletal Pain Management: A Scientometric Mapping', Orthopaedic Surgery, vol. 15, no. 1, pp. 301-314.
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Mei, F, Li, JJ, Lin, J, Zhou, D & Xing, D 2023, 'Constrained Condylar Prostheses for the Treatment of Charcot Arthropathy: A Case Report and Literature Review', Orthopaedic Surgery, vol. 15, no. 5, pp. 1423-1430.
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Meng, L, Jiang, X, Huang, J, Zeng, Z, Yu, S, Jung, T-P, Lin, C-T, Chavarriaga, R & Wu, D 2023, 'EEG-Based Brain–Computer Interfaces are Vulnerable to Backdoor Attacks', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 2224-2234.
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Meng, X, Li, X, Charteris, A, Wang, Z, Naushad, M, Nghiem, LD, Liu, H & Wang, Q 2023, 'Impacts of site real-time adaptive control of water-sensitive urban designs on the stormwater trunk drainage system', Journal of Water Process Engineering, vol. 53, pp. 103656-103656.
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Mirzaeipoueinak, M, Mordechai, HS, Bangar, SS, Sharabi, M, Tipper, JL & Tavakoli, J 2023, 'Structure-function characterization of the transition zone in the intervertebral disc', Acta Biomaterialia, vol. 160, pp. 164-175.
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Mishra, AK, Singh, O, Kumar, A, Puthal, D, Sharma, PK & Pradhan, B 2023, 'Hybrid Mode of Operation Schemes for P2P Communication to Analyze End-Point Individual Behaviour in IoT', ACM Transactions on Sensor Networks, vol. 19, no. 2, pp. 1-23.
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The Internet of Behavior is the recent trend in the Internet of Things (IoT), which analyzes the behaviour of individuals using huge amounts of data collected from their activities. The behavioural data collection process from an individual to a data center in the network layer of the IoT is addressed by the Routing Protocol for Low-powered Lossy Networks (RPL) downward routing policy. A hybrid mode of operation in RPL is designed to minimize the limitations of standard modes of operations in the downward routing of RPL. The existing hybrid modes use the common parameters, such as routing table capacity, energy level, and hop-count for making storing mode decisions at each node. However, none of these works have utilized the deciding parameters, such as number of Destination-Oriented Directed Acyclic Graph (DODAG) children, rank, and transmission traffic density for this purpose. In this article, we propose two hybrid MOPs for RPL focusing on the aspect of efficient downward communication for the Internet of Behaviors. The first version decides the mode of each node based on the rank and number of DODAG children of the node. In addition, the proposed Mode of Operation (MOP) has the provision to balance the task of a storing node that is currently running on low power and computational resources by a handover mechanism among the ancestors. The second version of the hybrid MOP utilizes the upward and downward transmission traffic probabilities together with 170 rule or 1D cellular automata to decide the operating mode of a node. The analysis on the upper bound on communication shows that both proposed works have communication overhead nearly equal to the storing mode. The experimental results also infer that the proposed adaptive MOP have lower communication overhead compared with standard storing modes and existing schemes ARPL, MERPL, and HIMOPD.

Mishra, R, Ong, HC & Lin, C-W 2023, 'Progress on co-processing of biomass and plastic waste for hydrogen production', Energy Conversion and Management, vol. 284, pp. 116983-116983.
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Mistry, G, Popat, K, Patel, J, Panchal, K, Ngo, HH, Bilal, M & Varjani, S 2023, 'Corrigendum to “New outlook on hazardous pollutants in the wastewater environment: Occurrence, risk assessment and elimination by electrodeionization technologies” [Environ. Res. 219 (2023) 115112]', Environmental Research, vol. 227, pp. 115693-115693.
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Mistry, G, Popat, K, Patel, J, Panchal, K, Ngo, HH, Bilal, M & Varjani, S 2023, 'New outlook on hazardous pollutants in the wastewater environment: Occurrence, risk assessment and elimination by electrodeionization technologies', Environmental Research, vol. 219, pp. 115112-115112.
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Mofijur, M, Ahmed, SF, Rony, ZI, Khoo, KS, Chowdhury, AA, Kalam, MA, Le, VG, Badruddin, IA & Khan, TMY 2023, 'Screening of non-edible (second-generation) feedstocks for the production of sustainable aviation fuel', Fuel, vol. 331, pp. 125879-125879.
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Mojiri, A, Zhou, JL, Ozaki, N, KarimiDermani, B, Razmi, E & Kasmuri, N 2023, 'Occurrence of per- and polyfluoroalkyl substances in aquatic environments and their removal by advanced oxidation processes', Chemosphere, vol. 330, pp. 138666-138666.
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Moles, E, Howard, CB, Huda, P, Karsa, M, McCalmont, H, Kimpton, K, Duly, A, Chen, Y, Huang, Y, Tursky, ML, Ma, D, Bustamante, S, Pickford, R, Connerty, P, Omari, S, Jolly, CJ, Joshi, S, Shen, S, Pimanda, JE, Dolnikov, A, Cheung, LC, Kotecha, RS, Norris, MD, Haber, M, de Bock, CE, Somers, K, Lock, RB, Thurecht, KJ & Kavallaris, M 2023, 'Delivery of PEGylated liposomal doxorubicin by bispecific antibodies improves treatment in models of high-risk childhood leukemia', Science Translational Medicine, vol. 15, no. 696.
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High-risk childhood leukemia has a poor prognosis because of treatment failure and toxic side effects of therapy. Drug encapsulation into liposomal nanocarriers has shown clinical success at improving biodistribution and tolerability of chemotherapy. However, enhancements in drug efficacy have been limited because of a lack of selectivity of the liposomal formulations for the cancer cells. Here, we report on the generation of bispecific antibodies (BsAbs) with dual binding to a leukemic cell receptor, such as CD19, CD20, CD22, or CD38, and methoxy polyethylene glycol (PEG) for the targeted delivery of PEGylated liposomal drugs to leukemia cells. This liposome targeting system follows a “mix-and-match” principle where BsAbs were selected on the specific receptors expressed on leukemia cells. BsAbs improved the targeting and cytotoxic activity of a clinically approved and low-toxic PEGylated liposomal formulation of doxorubicin (Caelyx) toward leukemia cell lines and patient-derived samples that are immunophenotypically heterogeneous and representative of high-risk subtypes of childhood leukemia. BsAb-assisted improvements in leukemia cell targeting and cytotoxic potency of Caelyx correlated with receptor expression and were minimally detrimental in vitro and in vivo toward expansion and functionality of normal peripheral blood mononuclear cells and hematopoietic progenitors. Targeted delivery of Caelyx using BsAbs further enhanced leukemia suppression while reducing drug accumulation in the heart and kidneys and extended overall survival in patient-derived xenograft models of high-risk childhood leukemia. Our methodology using BsAbs therefore represents an attractive targeting platform to potentiate the therapeutic efficacy and safety of liposomal drugs for improved treatment of high-risk leukemia.

Mora, A, Leiva, F, Cardenas, R, Rojas, F, Pereda, J, Aguilera, RP & Travieso, JC 2023, 'Optimal Switching Sequence MPC for Four-Leg Two-Level Grid-Connected Converters', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Motahari, R, Alavifar, Z, Zareh Andaryan, A, Chipulu, M & Saberi, M 2023, 'A multi-objective linear programming model for scheduling part families and designing a group layout in cellular manufacturing systems', Computers & Operations Research, vol. 151, pp. 106090-106090.
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Mousavi, M, Taskhiri, MS & Gandomi, AH 2023, 'Standing tree health assessment using contact–ultrasonic testing and machine learning', Computers and Electronics in Agriculture, vol. 209, pp. 107816-107816.
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MS, K, Johnson, I, Ngo, H-H, Guo, W & Kumar, M 2023, 'Application of Chlorella vulgaris for nutrient removal from synthetic wastewater and MBR-treated bio-park secondary effluent: growth kinetics, effects of carbon and phosphate concentrations', Environmental Monitoring and Assessment, vol. 195, no. 3.
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Munasinghe, N, Romeijn, T & Paul, G 2023, 'Voxel-based sensor placement for additive manufacturing applications', Journal of Intelligent Manufacturing, vol. 34, no. 2, pp. 739-751.
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Namisango, F, Kang, K & Rehman, J 2023, 'Examining the relationship between sociomaterial practices enacted in the organizational use of social media and the emerging role of organizational generativity', International Journal of Information Management, vol. 71, pp. 102643-102643.
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Nazari, H, Shrestha, J, Naei, VY, Bazaz, SR, Sabbagh, M, Thiery, JP & Warkiani, ME 2023, 'Advances in TEER measurements of biological barriers in microphysiological systems', Biosensors and Bioelectronics, vol. 234, pp. 115355-115355.
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Neshat, M, Nezhad, MM, Mirjalili, S, Garcia, DA, Dahlquist, E & Gandomi, AH 2023, 'Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy', Energy, vol. 278, pp. 127701-127701.
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Newsom, ET, Sadeghpour, A, Entezari, A, Vinzons, JLU, Stanford, RE, Mirkhalaf, M, Chon, D, Dunstan, CR & Zreiqat, H 2023, 'Design and evaluation of 3D-printed Sr-HT-Gahnite bioceramic for FDA regulatory submission: A Good Laboratory Practice sheep study', Acta Biomaterialia, vol. 156, pp. 214-221.
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Ngo, HH, Guo, W, Pandey, A, Varjani, S & Tsang, DCW 2023, 'Preface', Current Developments in Biotechnology and Bioengineering: Biochar Towards Sustainable Environment, pp. xix-xx.
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Ngo, QT, Phan, KT, Mahmood, A & Xiang, W 2023, 'Physical Layer Security in IRS-Assisted Cache-Enabled Satellite Communication Networks', IEEE Transactions on Green Communications and Networking, pp. 1-1.
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Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Pham, H-A, Dutkiewicz, E & Tuong, NH 2023, 'FedChain: Secure Proof-of-Stake-based Framework for Federated-blockchain Systems', IEEE Transactions on Services Computing, pp. 1-14.
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Nguyen, CT, Nguyen, DN, Hoang, DT, Phan, KT, Niyato, D, Pham, H-A & Dutkiewicz, E 2023, 'Elastic Resource Allocation for Coded Distributed Computing Over Heterogeneous Wireless Edge Networks', IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2636-2649.
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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, DDN, Sood, K, Xiang, Y, Gao, L, Chi, L & Yu, S 2023, 'Towards IoT Node Authentication Mechanism in Next Generation Networks', IEEE Internet of Things Journal, pp. 1-1.
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Nguyen, LN, Vu, MT, Vu, HP, Johir, MAH, Labeeuw, L, Ralph, PJ, Mahlia, TMI, Pandey, A, Sirohi, R & Nghiem, LD 2023, 'Microalgae-based carbon capture and utilization: A critical review on current system developments and biomass utilization', Critical Reviews in Environmental Science and Technology, vol. 53, no. 2, pp. 216-238.
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Nguyen, MK, Lin, C, Hoang, HG, Bui, XT, Ngo, HH, Le, VG & Tran, H-T 2023, 'Investigation of biochar amendments on odor reduction and their characteristics during food waste co-composting', Science of The Total Environment, vol. 865, pp. 161128-161128.
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Nguyen, TT & Indraratna, B 2023, 'Influence of varying water content on permanent deformation of mud-fouled ballast', Transportation Geotechnics, vol. 38, pp. 100919-100919.
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Nguyen, TTH, Nguyen, XC, Nguyen, DLT, Nguyen, DD, Vo, TYB, Vo, QN, Nguyen, TD, Ly, QV, Ngo, HH, Vo, D-VN, Nguyen, TP, Kim, IT & Van Le, Q 2023, 'Converting biomass of agrowastes and invasive plant into alternative materials for water remediation', Biomass Conversion and Biorefinery, vol. 13, no. 6, pp. 5391-5406.
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Three types of biomass of invasive plants and agrowastes, namely, the wattle bark of Acacia auriculiformis (BA), mimosa (BM), and coffee husks (BC), were converted into biochars through slow pyrolysis and investigated for their ability to remove dyes in water. The properties of the materials were characterized using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and Brunauer–Emmett–Teller (BET) analysis. The BET surface area (total pore volume) of BC was 2.62 m2/g (0.007 cm3/g), far below those of BA and BM with 393.15 cm2/g (0.195 m3/g) and 285.53 cm2/g (0.153 m3/g), respectively. The optimal adsorption doses for the removal of methylene blue (MB) were found to be 2, 5, and 5 g/L for BC, BA, and BM, respectively. The suitable pH ranges for MB removal were 6–12 for BA, 7–12 for BC, and 2–10 for BM. The majority of MB (over 83%) was removed in the initial 30 min, followed by a more quasisteady state condition after the removal rate exceeded 90%. The experimental data were fitted with the kinetic models (PFO, PSO, Bangham, IDP), indicating that physicochemical adsorption, pore diffusion process, and multiple stages are the dominant mechanisms for the MB adsorption onto biochars. Finally, BA and BM showed similar adsorption efficiencies, while BC may not be favorable for use as an adsorbent due to its low surface area and low pore volume.

Nguyen, TV, Nguyen, DN, Renzo, MD & Zhang, R 2023, 'Leveraging Secondary Reflections and Mitigating Interference in Multi-IRS/RIS Aided Wireless Networks', IEEE Transactions on Wireless Communications, vol. 22, no. 1, pp. 502-517.
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Ni, B-J, Thomas, KV & Kim, E-J 2023, 'Microplastics and nanoplastics in urban waters', Water Research, vol. 229, pp. 119473-119473.
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Ni, Q, Ji, JC & Feng, K 2023, 'Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network', IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1301-1311.
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Ni, Z, Andrew Zhang, J, Wu, K, Yang, K & Liu, RP 2023, 'Receiver Design in Full-Duplex Joint Radar-Communication Systems', IEEE Transactions on Communications, pp. 1-1.
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Ni, Z, Zhang, JA & Liu, RP 2023, 'Waveform Optimizations Using Virtual Arrays in Broadband Radar Communications', IEEE Wireless Communications Letters, vol. 12, no. 5, pp. 912-916.
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Nie, X, Liu, L, He, L, Zhao, L, Lu, H, Lou, S, Xiong, R & Wang, Y 2023, 'Weakly-Interactive-Mixed Learning: Less Labelling Cost for Better Medical Image Segmentation', IEEE Journal of Biomedical and Health Informatics, pp. 1-12.
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Nimbalkar, S & Basack, S 2023, 'Pile group in clay under cyclic lateral loading with emphasis on bending moment: Numerical modelling', Marine Georesources & Geotechnology, vol. 41, no. 3, pp. 269-284.
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Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2023, 'An optimized Belief-Rule-Based (BRB) approach to ensure the trustworthiness of interpreted time-series decisions', Knowledge-Based Systems, vol. 271, pp. 110552-110552.
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Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2023, 'Interpreting the antecedents of a predicted output by capturing the interdependencies among the system features and their evolution over time', Engineering Applications of Artificial Intelligence, vol. 117, pp. 105596-105596.
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Nirbhav, Malik, A, Maheshwar, Jan, T & Prasad, M 2023, 'Landslide Susceptibility Prediction based on Decision Tree and Feature Selection Methods', Journal of the Indian Society of Remote Sensing, vol. 51, no. 4, pp. 771-786.
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Niwa, K, Ueda, N, Sawada, H, Fujino, A, Takeda, S, Zhang, G & Kleijn, WB 2023, 'CoordiNet: Constrained Dynamics Learning for State Coordination Over Graph', IEEE Transactions on Signal and Information Processing over Networks, vol. 9, pp. 242-257.
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Nosouhi, MR, Yu, S, Sood, K, Grobler, M, Jurdak, R, Dorri, A & Shen, S 2023, 'UCoin: An Efficient Privacy Preserving Scheme for Cryptocurrencies', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 242-255.
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Nurek, M, Michalski, R, Lizardo, O & Rizoiu, M-A 2023, 'Predicting Relationship Labels and Individual Personality Traits From Telecommunication History in Social Networks Using Hawkes Processes', IEEE Access, vol. 11, pp. 8492-8503.
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Ou, Y, Zhou, JL, Jia, Y, Liang, M, Hu, H & Ren, L 2023, 'Complete genome of Mycolicibacterium phocaicum RL-HY01, a PAEs-degrading marine bacterial strain isolated from Zhanjiang Bay, China', Marine Genomics, vol. 69, pp. 101019-101019.
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Patil, G, Shivakumara, P, Gornale, SS, Pal, U & Blumenstein, M 2023, 'A new robust approach for altered handwritten text detection', Multimedia Tools and Applications, vol. 82, no. 14, pp. 20925-20949.
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Patmore, G, Balnave, N & Marjanovic, O 2023, 'Business Co-operatives in Australia: “Unlikely Soil for a Co-operative Movement”', Enterprise & Society, vol. 24, no. 1, pp. 149-173.
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While co-operatives are traditionally associated with workers, consumers, and farmers, the business model, with its emphasis on democracy and community, has also been adopted by small business owners, the self-employed, and professionals. These business co-operatives are distinct phenomenon, because they primarily consist of independent organizational entities that are not co-operatives and are generally in direct competition with one another. They are unique in that they bring together separate organizations that seek to combat market threats while adopting a philosophy based on co-operative principles. This article begins with an overview of the Australian co-operative landscape. It then defines the concept of business co-operatives and then draws upon the Visual Atlas of Australian Co-operatives History Project, which has developed a large database of Australian co-operatives over time and space, to examine the development of business co-operatives in Australia. It looks at where business co-operatives formed in the economy, the motivation underlying their formation, their average life spans, and their relationships with the broader co-operative movement. The article highlights the value of business co-operatives in introducing the values of participatory democracy and working for the common good into unanticipated markets and reinforcing the co-operative movement.

Paul, A, Shukla, N & Trianni, A 2023, 'Modelling supply chain sustainability challenges in the food processing sector amid the COVID-19 outbreak', Socio-Economic Planning Sciences, vol. 87, pp. 101535-101535.
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Peellage, WH, Fatahi, B & Rasekh, H 2023, 'Assessment of cyclic deformation and critical stress amplitude of jointed rocks via cyclic triaxial testing', Journal of Rock Mechanics and Geotechnical Engineering, vol. 15, no. 6, pp. 1370-1390.
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Pei, C, Qiu, Y, Li, F, Huang, X, Si, Y, Li, Y, Zhang, X, Chen, C, Liu, Q, Cao, Z, Ding, N, Gao, S, Alho, K, Yao, D & Xu, P 2023, 'The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS', Cerebral Cortex, vol. 33, no. 8, pp. 4740-4751.
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Abstract Human language units are hierarchical, and reading acquisition involves integrating multisensory information (typically from auditory and visual modalities) to access meaning. However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in auditory and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory, visual, or combined audio-visual modalities while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e. characters/monosyllabic words) and higher-level linguistic structures (i.e. phrases and sentences) across the 3 modalities separately. We found that audio-visual integration occurs in all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.

Peng, L, Qiu, H, Li, S, Xu, Y, Liang, C, Wang, N, Liu, Y & Ni, B-J 2023, 'The mitigation effect of free ammonia and free nitrous acid on nitrous oxide production from the full-nitrification and partial-nitritation systems', Bioresource Technology, vol. 372, pp. 128564-128564.
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Peng, Y, Song, A, Ciesielski, V, Fayek, HM & Chang, X 2023, 'PRE-NAS: Evolutionary Neural Architecture Search With Predictor', IEEE Transactions on Evolutionary Computation, vol. 27, no. 1, pp. 26-36.
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Pérez-Romero, ME, Alfaro-García, VG, Merigó, JM & Flores-Romero, MB 2023, 'Covariance Logarithmic Aggregation Operators in Decision-Making Processes', Cybernetics and Systems, vol. 54, no. 2, pp. 220-238.
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Pietroni, N, Campen, M, Sheffer, A, Cherchi, G, Bommes, D, Gao, X, Scateni, R, Ledoux, F, Remacle, J & Livesu, M 2023, 'Hex-Mesh Generation and Processing: A Survey', ACM Transactions on Graphics, vol. 42, no. 2, pp. 1-44.
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In this article, we provide a detailed survey of techniques for hexahedral mesh generation. We cover the whole spectrum of alternative approaches to mesh generation, as well as post-processing algorithms for connectivity editing and mesh optimization. For each technique, we highlight capabilities and limitations, also pointing out the associated unsolved challenges. Recent relaxed approaches, aiming to generate not pure-hex but hex-dominant meshes, are also discussed. The required background, pertaining to geometrical as well as combinatorial aspects, is introduced along the way.

Power, T, Kennedy, P, Chen, H, Martinez-Maldonado, R, McGregor, C, Johnson, A, Townsend, L & Hayes, C 2023, 'Learning to Manage De-escalation Through Simulation: An Exploratory Study', Clinical Simulation in Nursing, vol. 77, pp. 23-29.
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Pradhan, B, Dikshit, A, Lee, S & Kim, H 2023, 'An explainable AI (XAI) model for landslide susceptibility modeling', Applied Soft Computing, vol. 142, pp. 110324-110324.
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Pradhan, B, Lee, S, Dikshit, A & Kim, H 2023, 'Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model', Geoscience Frontiers, vol. 14, no. 6, pp. 101625-101625.
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Pradhan, S, Zhang, G, Zhao, S, Niwa, K & Bastiaan Kleijn, W 2023, 'On eigenvalue shaping for two-channel decentralized active noise control systems', Applied Acoustics, vol. 205, pp. 109260-109260.
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Punetha, P & Nimbalkar, S 2023, 'An innovative rheological approach for predicting the behaviour of critical zones in a railway track', Acta Geotechnica.
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AbstractThe poor performance of critical zones along a railway line has long been a subject of concern for rail infrastructure managers. The rapid deterioration of track geometry in these zones is primarily ascribed to limited understanding of the underlying mechanism and scarcity of adequate tools to assess the severity of the potential issue. Therefore, a comprehensive evaluation of their behaviour is paramount to improve the design and ensure adequate service quality. With this objective, a novel methodology is introduced, which can predict the differential plastic deformations at the critical zones and assess the suitability of different countermeasures in improving the track performance. The proposed technique employs a three-dimensional geotechnical rheological track model that considers varied support conditions of the critical zone. The approach is successfully validated with published field data and predictions from finite element analysis. This methodology is then applied to a bridge-open track transition zone, where it is observed that an increase in axle load exacerbates the track geometry degradation problem. The results show that the performance of critical zones with weak subgrade can be improved by increasing the granular layer thickness. Interpretation of the predicted differential settlement for different countermeasures exemplifies the practical significance of the proposed methodology.

Punetha, P & Nimbalkar, S 2023, 'Numerical investigation on dynamic behaviour of critical zones in railway tracks under moving train loads', Transportation Geotechnics, vol. 41, pp. 101009-101009.
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Puthal, D, Yeun, CY, Damiani, E, Mishra, AK, Yelamarthi, K & Pradhan, B 2023, 'Blockchain Data Structures and Integrated Adaptive Learning: Features and Futures', IEEE Consumer Electronics Magazine, pp. 1-8.
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Qamar, A, Shaukat, R, Imran, S, Farooq, M, Amjad, M, Anwar, Z, Ali, H, Farhan, M, Mujtaba, MA, Korakianitis, T, Kalam, MA & Almomani, F 2023, 'Effect of surfactants on the convective heat transfer and pressure drop characteristics of ZnO/DIW nanofluids: An experimental study', Case Studies in Thermal Engineering, vol. 42, pp. 102716-102716.
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Qi, Y & Indraratna, B 2023, 'Closure to “Influence of Rubber Inclusion on the Dynamic Response of Rail Track”', Journal of Materials in Civil Engineering, vol. 35, no. 8.
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Qian, J, Mi, X, Chen, Z, Xu, W, Liu, W, Ma, R, Zhang, Y, Du, Y & Ni, B-J 2023, 'Efficient emerging contaminants (EM) decomposition via peroxymonosulfate (PMS) activation by Co3O4/carbonized polyaniline (CPANI) composite: Characterization of tetracycline (TC) degradation property and application for the remediation of EM-polluted water body', Journal of Cleaner Production, vol. 405, pp. 137023-137023.
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Qin, H, Mason, M & Stewart, MG 2023, 'Fragility assessment for new and deteriorated portal framed industrial buildings subjected to tropical cyclone winds', Structural Safety, vol. 100, pp. 102287-102287.
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Qin, Y, Jia, H, Liu, W, Lu, N, Ngo, HH & Wang, J 2023, 'Application of in-situ micro laser transmission on real-time monitoring of flocculation process', Journal of Water Process Engineering, vol. 51, pp. 103364-103364.
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Qiu, N, Zhang, J, Li, C, Shen, Y & Fang, J 2023, 'Mechanical properties of three-dimensional functionally graded triply periodic minimum surface structures', International Journal of Mechanical Sciences, vol. 246, pp. 108118-108118.
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Qiu, Y-X, Wen, D, Li, R-H, Qin, L, Yu, M & Lin, X 2023, 'Computing Significant Cliques in Large Labeled Networks', IEEE Transactions on Big Data, vol. 9, no. 3, pp. 904-917.
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Qu, J, Zhao, X, Xiao, Y, Chang, X, Li, Z & Wang, X 2023, 'Adaptive Manifold Graph representation for Two-Dimensional Discriminant Projection', Knowledge-Based Systems, vol. 266, pp. 110411-110411.
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Quyet Truong, D, Choo, Y, Akther, N, Roobavannan, S, Norouzi, A, Gupta, V, Blumenstein, M, Vinh Nguyen, T & Naidu, G 2023, 'Selective rubidium recovery from seawater with metal-organic framework incorporated potassium cobalt hexacyanoferrate nanomaterial', Chemical Engineering Journal, vol. 454, pp. 140107-140107.
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Rabie, M, Ali, AYM, Abo-Zahhad, EM, Elkady, MF, El-Shazly, AH, Salem, MS, Radwan, A, Rajabzadeh, S, Matsuyama, H & Shon, HK 2023, 'New hybrid concentrated photovoltaic/membrane distillation unit for simultaneous freshwater and electricity production', Desalination, vol. 559, pp. 116630-116630.
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Radfar, P, Ding, L, de la Fuente, LR, Aboulkheyr, H, Gallego-Ortega, D & Warkiani, ME 2023, 'Rapid metabolomic screening of cancer cells via high-throughput static droplet microfluidics', Biosensors and Bioelectronics, vol. 223, pp. 114966-114966.
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Raggam, S, Mohammad, M, Choo, Y, Naidu, G, Zargar, M, Shon, HK & Razmjou, A 2023, 'Advances in metal organic framework (MOF) – Based membranes and adsorbents for lithium-ion extraction', Separation and Purification Technology, vol. 307, pp. 122628-122628.
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Rahimi, I, Gandomi, AH, Chen, F & Mezura-Montes, E 2023, 'A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization', Archives of Computational Methods in Engineering, vol. 30, no. 3, pp. 2181-2209.
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AbstractMost real-world problems involve some type of optimization problems that are often constrained. Numerous researchers have investigated several techniques to deal with constrained single-objective and multi-objective evolutionary optimization in many fields, including theory and application. This presented study provides a novel analysis of scholarly literature on constraint-handling techniques for single-objective and multi-objective population-based algorithms according to the most relevant journals and articles. As a contribution to this study, the paper reviews the main ideas of the most state-of-the-art constraint handling techniques in population-based optimization, and then the study addresses the bibliometric analysis, with a focus on multi-objective, in the field. The extracted papers include research articles, reviews, book/book chapters, and conference papers published between 2000 and 2021 for analysis. The results indicate that the constraint-handling techniques for multi-objective optimization have received much less attention compared with single-objective optimization. The most promising algorithms for such optimization were determined to be genetic algorithms, differential evolutionary algorithms, and particle swarm intelligence. Additionally, “Engineering,” “Computer Science,” and “ Mathematics” were identified as the top three research fields in which future research work is anticipated to increase.

Rajabipour, A, Kutay, C, Guenther, J & Bazli, M 2023, 'Factors to be considered in the design of indigenous communities' houses, with a focus on Australian first nation housing in the Northern Territory', Development Engineering, vol. 8, pp. 100109-100109.
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Ramakrishnan, N, Tomamichel, M & Berta, M 2023, 'Moderate deviation expansion for fully quantum tasks', IEEE Transactions on Information Theory, pp. 1-1.
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Ramu, YK, Thomas, PS, Sirivivatnanon, V & Vessalas, K 2023, 'Non-expansive delayed ettringite formation in low sulphate and low alkali cement mortars', Australian Journal of Civil Engineering, vol. 21, no. 1, pp. 68-79.
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Ran, H, Wen, S, Li, Q, Cao, Y, Shi, K & Huang, T 2023, 'Compact and Stable Memristive Visual Geometry Group Neural Network', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 987-998.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2023, 'Statistical Learning-based Adaptive Network Access for the Industrial Internet-of-Things', IEEE Internet of Things Journal, pp. 1-1.
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Razavi Bazaz, S, Zhand, S, Salomon, R, Beheshti, EH, Jin, D & Warkiani, ME 2023, 'ImmunoInertial microfluidics: A novel strategy for isolation of small EV subpopulations', Applied Materials Today, vol. 30, pp. 101730-101730.
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Rehman, A, Razzak, I & Xu, G 2023, 'Federated Learning for Privacy Preservation of Healthcare Data From Smartphone-Based Side-Channel Attacks', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 2, pp. 684-690.
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Rehman, J, Hawryszkiewycz, I, Sohaib, O, Namisango, F & Dahri, AS 2023, 'How Professional Service Firms Derive Triple Value Bottomline: An IC Perspective', Journal of Information & Knowledge Management, vol. 22, no. 01.
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The ever-increasing market turbulence has turned today’s corporate landscape more competitive and complex. Particularly during the last two decades, the increased utilization of Information & Communication Technologies (ICTs) globally transformed the services sector in terms of ease of business processes and improved client service delivery. However, in the current knowledge-based era, these tools & technologies would only be meaningful if these are appropriately utilized by a knowledgeable workforce. In other words, this knowledge age has changed the success mantra of business competitiveness by re-shifting the focus from ICT-based transformations to knowledge-based transformations, though the availability of ICT systems has further augmented the organizational capabilities. Moreover, truly capitalizing on these warrants a knowledge-enabled work culture and recognizing as such the strategic significance of in-house Intellectual Capital (IC) that serves as a prime mover of achieving a sustainable competitive advantage. However, the maximum potential of IC for deriving multi-stakeholder value has not been fully achieved. Therefore, by administering 12 face-to-face semi-structured interviews at Australian Professional Service Firms (PSFs), this research offers a novel perspective on IC valuation by presenting the concept of ‘Triple Value Bottomline’ coupled with ‘IC Best Practices for PSFs’. These collectively offer IC evaluation, measurement and management mechanisms. Overall, the findings reveal immense potential of IC for achieving diverse value outcomes for multi-stakeholders in PSFs.

Reinhartz-Berger, I, Zdravkovic, J & Gill, A 2023, 'Guest editorial for EMMSAD’2021 special section', Software and Systems Modeling, vol. 22, no. 1, pp. 9-11.
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Ren, L, Guo, Z, Zhang, L, Hu, H, Li, C, Lin, Z, Zhen, Z & Zhou, JL 2023, 'A novel aerobic denitrifying phosphate-accumulating bacterium efficiently removes phthalic acid ester, total nitrogen and phosphate from municipal wastewater', Journal of Water Process Engineering, vol. 52, pp. 103532-103532.
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Ren, L, Weng, L, Chen, D, Hu, H, Jia, Y & Zhou, JL 2023, 'Bioremediation of PAEs-contaminated saline soil: The application of a marine bacterial strain isolated from mangrove sediment', Marine Pollution Bulletin, vol. 192, pp. 115071-115071.
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Ren, Z, Cao, H, Desmond, P, Liu, B, Ngo, HH, He, X, Li, G, Ma, J & Ding, A 2023, 'Ions play different roles in virus removal caused by different NOMs in UF process: Removal efficiency and mechanism analysis', Chemosphere, vol. 313, pp. 137644-137644.
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Rennie, C, Huang, Y, Siwakoti, P, Du, Z, Padula, M, Bao, G, Tuch, BE, Xu, X & McClements, L 2023, 'In vitro evaluation of a hybrid drug-delivery nanosystem for fibrosis prevention in cell therapy for Type 1 diabetes', Nanomedicine, vol. 18, no. 1, pp. 53-66.
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Background: Implantation of insulin-secreting cells has been trialed as a treatment for Type 1 diabetes mellitus; however, the host immunogenic response limits their effectiveness. Methodology: The authors developed a core-shell nanostructure of upconversion nanoparticle-mesoporous silica for controlled local delivery of an immunomodulatory agent, MCC950, using near-infrared light and validated it in in vitro models of fibrosis. Results: The individual components of the nanosystem did not affect the proliferation of insulin-secreting cells, unlike fibroblast proliferation (p < 0.01). The nanosystem is effective at releasing MCC950 and preventing fibroblast differentiation (p < 0.01), inflammation (IL-6 expression; p < 0.05) and monocyte adhesion (p < 0.01). Conclusion: This MCC950-loaded nanomedicine system could be used in the future together with insulin-secreting cell implants to increase their longevity as a curative treatment for Type 1 diabetes mellitus.

Roche, CD, Lin, H, Huang, Y, de Bock, CE, Beck, D, Xue, M & Gentile, C 2023, '3D bioprinted alginate-gelatin hydrogel patches containing cardiac spheroids recover heart function in a mouse model of myocardial infarction', Bioprinting, vol. 30, pp. e00263-e00263.
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Rony, ZI, Mofijur, M, Hasan, MM, Ahmed, SF, Almomani, F, Rasul, MG, Jahirul, MI, Loke Show, P, Kalam, MA & Mahlia, TMI 2023, 'Unanswered issues on decarbonizing the aviation industry through the development of sustainable aviation fuel from microalgae', Fuel, vol. 334, pp. 126553-126553.
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Ruan, J, Cui, H, Huang, Y, Li, T, Wu, C & Zhang, K 2023, 'A review of occluded objects detection in real complex scenarios for autonomous driving', Green Energy and Intelligent Transportation, vol. 2, no. 3, pp. 100092-100092.
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Ruan, Z, Song, W, Zhao, L, Zhang, Y & Guo, Y 2023, 'A Variable Switching Frequency Space Vector Pulse Width Modulation Control Strategy of Induction Motor Drive System With Torque Ripple Prediction', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 993-1003.
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Rybarczyk, A, Smułek, W, Grzywaczyk, A, Kaczorek, E, Jesionowski, T, Nghiem, LD & Zdarta, J 2023, '3D printed polylactide scaffolding for laccase immobilization to improve enzyme stability and estrogen removal from wastewater', Bioresource Technology, vol. 381, pp. 129144-129144.
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Sadeghi, F, Mousavi, M, Zhu, X, Rashidi, M, Samali, B & Gandomi, AH 2023, 'Damage Detection of Composite Beams via Variational Mode Decomposition of Shear-Slip Data', Journal of Structural Engineering, vol. 149, no. 1.
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Sadeghirad, H, Bahrami, T, Layeghi, SM, Yousefi, H, Rezaei, M, Hosseini‐Fard, SR, Radfar, P, Warkiani, ME, O'Byrne, K & Kulasinghe, A 2023, 'Immunotherapeutic targets in non‐small cell lung cancer', Immunology, vol. 168, no. 2, pp. 256-272.
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Saha, S, Kundu, B, Saha, A, Mukherjee, K & Pradhan, B 2023, 'Manifesting deep learning algorithms for developing drought vulnerability index in monsoon climate dominant region of West Bengal, India', Theoretical and Applied Climatology, vol. 151, no. 1-2, pp. 891-913.
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Sakhare, A, Punetha, P, Meena, NK, Nimbalkar, S & Dodagoudar, G-R 2023, 'Dynamic behaviour of integral abutment bridge transition under moving train loads', Transportation Geotechnics, vol. 40, pp. 100989-100989.
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Transition zones, such as bridge approaches, are discontinuities along a railway line that are highly prone to differential movement due to a rapid variation of support conditions along the track. The concrete approach slabs are often provided before and after the bridges to reduce this differential movement and provide a gradual variation in track stiffness. This paper provides insights into the dynamic behaviour of an integral abutment railway bridge (IARB) transition zone consisting of approach slab under moving train loads using finite element (FE) analyses. Firstly, the FE model is successfully validated against the published field data. Subsequently, the validated model is employed to investigate the influence of parameters such as approach slab geometry (length, thickness, inclination, and shape), backfill soil type, direction of train movement and train speed. Results show that the behaviour of IARB is sensitive to the length of the approach slab, backfill soil type and train speed. The findings of this study enhance the current understanding of the behaviour of IARBs subjected to moving train loading and identify the important parameters that influence their performance.

Sanderson, B, Field, JD, Kocaballi, AB, Estcourt, LJ, Magrabi, F, Wood, EM & Coiera, EW 2023, 'Multicenter, multidisciplinary user‐centered design of a clinical decision‐support and simulation system for massive transfusion', Transfusion, vol. 63, no. 5, pp. 993-1004.
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Santra, SB, Chatterjee, D & Siwakoti, YP 2023, 'Coupled Inductor Based Soft Switched High Gain Bidirectional DC-DC Converter With Reduced Input Current Ripple', IEEE Transactions on Industrial Electronics, vol. 70, no. 2, pp. 1431-1443.
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Saputra, YM, Hoang, DT, Nguyen, DN, Tran, L-N, Gong, S & Dutkiewicz, E 2023, 'Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles', IEEE Transactions on Mobile Computing, vol. 22, no. 4, pp. 2100-2115.
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Sateesh, KA, Yaliwal, VS, Banapurmath, NR, Soudagar, MEM, Yunus Khan, TM, Harari, PA, El-Shafay, AS, Mujtaba, MA, Elfaskhany, A & Kalam, MA 2023, 'Effect of MWCNTs nano-additive on a dual-fuel engine characteristics utilizing dairy scum oil methyl ester and producer gas', Case Studies in Thermal Engineering, vol. 42, pp. 102661-102661.
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Sayem, ASM, Lalbakhsh, A, Esselle, KP, Moloudian, G, Buckley, JL & Simorangkir, RBVB 2023, 'Advancements, Challenges, and Prospects of Water-Filled Antennas', IEEE Access, vol. 11, pp. 8301-8323.
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Shahabuddin, M, Uddin, MN, Chowdhury, JI, Ahmed, SF, Uddin, MN, Mofijur, M & Uddin, MA 2023, 'A review of the recent development, challenges, and opportunities of electronic waste (e-waste)', International Journal of Environmental Science and Technology, vol. 20, no. 4, pp. 4513-4520.
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AbstractThis study reviews recent developments, challenges, and the prospect of electronic waste (e-waste). Various aspects of e-waste, including collection, pre-treatment, and recycling, are discussed briefly. It is found that Europe is the leading collector of e-waste, followed by Asia, America, Oceania, and Africa. The monetary worth of e-waste raw materials is estimated to be $57.0 billion. However, only $10.0 billion worth of e-waste is recycled and recovered sustainably, offsetting 15.0 million tonnes (Mt) of CO2. The major challenges of e-waste treatment include collection, sorting and inhomogeneity of waste, low energy density, prevention of further waste, emission, and cost-effective recycling. Only 78 countries in the world now have e-waste related legislation. Such legislation is not effectively implemented in most regions. Developing countries like south-eastern Asia and Northern Africa have limited or no e-waste legislation. Therefore, country-specific standards and legislation, public awareness, effective implementation, and government incentives for developing cost-effective technologies are sought to manage e-waste, which will play an important role in the circular economy.

Shaharuddin, S, Abdul Maulud, KN, Syed Abdul Rahman, SAF, Che Ani, AI & Pradhan, B 2023, 'The role of IoT sensor in smart building context for indoor fire hazard scenario: A systematic review of interdisciplinary articles', Internet of Things, vol. 22, pp. 100803-100803.
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Shakor, P, Nejadi, S, Paul, G & Gowripalan, N 2023, 'Effects of Different Orientation Angle, Size, Surface Roughness, and Heat Curing on Mechanical Behavior of 3D Printed Cement Mortar With/Without Glass Fiber in Powder-Based 3DP', 3D Printing and Additive Manufacturing, vol. 10, no. 2, pp. 330-355.
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Shan, F, He, X, Armaghani, DJ, Zhang, P & Sheng, D 2023, 'Response to Discussion on “Success and challenges in predicting TBM penetration rate using recurrent neural networks” by Georg H. Erharter, Thomas Marcher', Tunnelling and Underground Space Technology, pp. 105064-105064.
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Shanmugam, S, Mathimani, T, Rajendran, K, Sekar, M, Rene, ER, Chi, NTL, Ngo, HH & Pugazhendhi, A 2023, 'Perspective on the strategies and challenges in hydrogen production from food and food processing wastes', Fuel, vol. 338, pp. 127376-127376.
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Shao, R, Wu, C, Li, J, Liu, Z, Wu, P & Yang, Y 2023, 'Mechanical behaviour and environmental benefit of eco-friendly steel fibre-reinforced dry UHPC incorporating high-volume fly ash and crumb rubber', Journal of Building Engineering, vol. 65, pp. 105747-105747.
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Sharma, M & Joshi, S 2023, 'Digital supplier selection reinforcing supply chain quality management systems to enhance firm's performance', The TQM Journal, vol. 35, no. 1, pp. 102-130.
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PurposeThe geographical scattering of physical facilities in conventional supply chains enforces firms to shift toward digital supply chains (DSCs). While switching to DSCs, the decision-making becomes more complex with an upsurge in the size of the manufacturing firms. The manufacturing firms need to develop supply chain quality management (SCQM) systems to improvise their processes for delivering advance products and services. For developing SCQM, the role of the digital supplier is significant, as they may recuperate the quality management systems (QMS) for enhancing the firm's performance. The purpose of this paper is to explore the factors that affect the selection of digital suppliers. The other purpose is to evaluate the alternatives for identifying the best supplier that enhances the QMS for DSCs.Design/methodology/approachThe decision-making is complex for digital supplier selection (DSS) and thus, the study has utilized integrated SWARA-WASPAS methods for their critical evaluation. The stepwise weight assessment ratio analysis (SWARA) method has been utilized for identifying the weightage of factors and weighted aggregated sum product assessment (WASPAS) for assessing the digital suppliers to explore the best alternative. The integrated SWARA-WASPAS method is the most advance approach in multi-criteria decision-making (MCDM) for the evaluation of the factors.FindingsThe study reveals that supplier competency is the most significant factor in selecting digital supplier in DSC that may improve the product and service quality. The study also explores that manufacturing firms needs an efficient system for developing value for the internal and external partners that help them to cope up wi...

Sharma, M, Joshi, S, Prasad, M & Bartwal, S 2023, 'Overcoming barriers to circular economy implementation in the oil & gas industry: Environmental and social implications', Journal of Cleaner Production, vol. 391, pp. 136133-136133.
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Sharwood, LN, Martiniuk, A, Sarrami Foroushani, P, Seggie, J, Wilson, S, Hsu, J, Burns, B & Logan, DB 2023, 'Intentions and willingness to engage in risky driving behaviour among high school adolescents: evaluating thebstreetsmart road safety programme', Injury Prevention, vol. 29, no. 1, pp. 1-7.
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ObjectiveTo investigate the impact of a road safety programme on adolescents’ willingness to engage in risky behaviour as probationary drivers, adjusted for covariates of interest.MethodThebstreetsmart is a road safety programme delivered to around 25 000 adolescent students annually in New South Wales. Using a smartphone-based app, student and teacher participation incentives, students were surveyed before and after programme attendance. Mixed-methods linear regression analysed pre/post-modified Behaviour of Young Novice Driver (BYNDS_M) scores.Results2360 and 1260 students completed pre-event and post-event surveys, respectively. Post-event BYNDS_M scores were around three points lower than pre-event scores (−2.99, 95% CI −3.418 to −2.466), indicating reduced intention to engage in risky driving behaviours. Covariates associated with higher stated intentions of risky driving were exposure to risky driving as a passenger (1.21, 95% CI 0.622 to 2.011) and identifying as non-binary gender (2.48, 95% CI 1.879 to 4.085), adjusting for other predictors.ConclusionsTrauma-informed, reality-based injury prevention programmes can be effective in changing short-term stated intentions to engage in risky driving, among a pre-independent driving student population. The adolescent novice driver age group is historically challenging to engage, and injury prevention action must be multipronged to address the many factors influencing their behaviour.

Shen, M, Ye, K, Liu, X, Zhu, L, Kang, J, Yu, S, Li, Q & Xu, K 2023, 'Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey', IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 791-824.
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Shen, S, Wu, X, Sun, P, Zhou, H, Wu, Z & Yu, S 2023, 'Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems', Expert Systems with Applications, vol. 225, pp. 120192-120192.
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Sheng, Z, Wen, S, Feng, Z-K, Gong, J, Shi, K, Guo, Z, Yang, Y & Huang, T 2023, 'A Survey on Data-Driven Runoff Forecasting Models Based on Neural Networks', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-15.
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Sheng, Z, Wen, S, Feng, Z-K, Shi, K & Huang, T 2023, 'A Novel Residual Gated Recurrent Unit Framework for Runoff Forecasting', IEEE Internet of Things Journal, pp. 1-1.
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Shi, D, Zhu, L, Li, J, Zhang, Z & Chang, X 2023, 'Unsupervised Adaptive Feature Selection With Binary Hashing', IEEE Transactions on Image Processing, vol. 32, pp. 838-853.
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Shi, K, Cai, X, She, K, Wen, S, Zhong, S, Park, P & Kwon, O-M 2023, 'Stability analysis and security-based event-triggered mechanism design for T-S fuzzy NCS with traffic congestion via DoS attack and its application', IEEE Transactions on Fuzzy Systems, pp. 1-11.
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Shi, X, Chen, Z, Wei, W, Chen, J & Ni, B-J 2023, 'Toxicity of micro/nanoplastics in the environment: Roles of plastisphere and eco-corona', Soil & Environmental Health, vol. 1, no. 1, pp. 100002-100002.
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Shi, X, Chen, Z, Wu, L, Wei, W & Ni, B-J 2023, 'Microplastics in municipal solid waste landfills: Detection, formation and potential environmental risks', Current Opinion in Environmental Science & Health, vol. 31, pp. 100433-100433.
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Shi, Y, Han, Y, Hu, Q, Yang, Y & Tian, Q 2023, 'Query-Efficient Black-Box Adversarial Attack With Customized Iteration and Sampling', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 2226-2245.
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Shu, X, Yang, Y, Liu, J, Chang, X & Wu, B 2023, 'ALVLS: Adaptive local variances-Based levelset framework for medical images segmentation', Pattern Recognition, vol. 136, pp. 109257-109257.
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Siddiqui, AR, Indraratna, B, Ngo, T & Rujikiatkamjorn, C 2023, 'Laboratory assessment of rubber grid-reinforced ballast under impact testing', Géotechnique Letters, vol. 13, no. 2, pp. 1-11.
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This study presents the use of rubber grids (RGs) fabricated from end-of-life conveyor belts (i.e. discarded from the mining industry) to improve the performance of ballast tracks. The square apertures of these recycled rubber sheets were cast using a waterjet cutting process. A series of large-scale impact tests were performed on ballast specimens stabilised with three different grids of varied effective area ratios (KA.eff) to evaluate their effectiveness in mitigating the applied impact forces, in relation to both displacement and breakage of the ballast aggregates. Smart Ballast particles with motion-sensing capabilities were adopted to monitor the interaction between the grid and ballast assembly. The impact test results indicate that the inclusion of a RG decreases the deformation and breakage of ballast as well as reduces its vibrations. This study demonstrates that these recycled RGs with optimum effective area ratios can be more effective than conventional polymer geogrids, apart from the obvious environmental benefits.

Sirivivatnanon, V, Thomas, P, Joshua Tapas, M & Nhu Nguyen, T 2023, 'Reliability of AMBT and CPT in testing the effectiveness of SCM to mitigate alkali–silica reaction of field concrete', Construction and Building Materials, vol. 369, pp. 130510-130510.
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Siva, V, Raghuram, M, Singh, A, Singh, SK & Siwakoti, YP 2023, 'Switching Strategy to Reduce Inductor Current Ripple and Common Mode Voltage in Quasi Z-Source Ultra Sparse Matrix Converter', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, pp. 1-11.
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Siwakoti, P, Rennie, C, Huang, Y, Li, JJ, Tuch, BE, McClements, L & Xu, X 2023, 'Challenges with Cell-based Therapies for Type 1 Diabetes Mellitus', Stem Cell Reviews and Reports, vol. 19, no. 3, pp. 601-624.
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Sohn, W, Jiang, J, Phuntsho, S, Choden, Y, Tran, VH & Shon, HK 2023, 'Nutrients in a circular economy: Role of urine separation and treatment', Desalination, vol. 560, pp. 116663-116663.
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Song, F, Wang, L, Wang, Q & Wen, S 2023, 'Finite/fixed-time practical sliding mode: An event-triggered approach', Information Sciences, vol. 631, pp. 241-255.
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Soo, A, Wang, L, Wang, C & Shon, HK 2023, 'MachIne learning for nutrient recovery in the smart city circular economy – A review', Process Safety and Environmental Protection, vol. 173, pp. 529-557.
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Sood, K, Nosouhi, MR, Kumar, N, Gaddam, A, Feng, B & Yu, S 2023, 'Accurate Detection of IoT Sensor Behaviors in Legitimate, Faulty and Compromised Scenarios', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 1, pp. 288-300.
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Sood, S & Pattinson, H 2023, 'Marketing Education Renaissance Through Big Data Curriculum: Developing Marketing Expertise Using AI Large Language Models', International Journal of Innovation and Economic Development, vol. 8, no. 6, pp. 23-40.
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Utilising big data sources and artificial intelligence (AI) tools with marketing activities and analysis contrast with questionnaires and small n observations, essentially creating a renaissance in marketing education. As a result, marketing education keeps pace with AI developments and ensures learners (or students) prepare for the demands of the modern marketing landscape 2025-30. The authors advocate a central focus on a big data-driven marketing curriculum for marketing education. Such a curriculum places AI and machine learning center stage to help understand, analyze and utilize large and complex marketing datasets for predictive marketing. In doing so, the potential exists for practitioners to link marketing strategy directly with marketing execution, allowing learners to use big data and AI for upstream strategy design and marketing plan development while downstream predicting the results of marketing campaigns, programs, and initiatives But necessary changes in pedagogy are creating adaptive learning experiences breaking free from traditional assessments In our model of learning educators enable the development of practical marketing expertise using the techniques and tools of micro-testing to nudge learners using Python data science notebooks. Overall, a renaissance in marketing education is made possible with a focus on a big data AI tools-driven curriculum. Such attention ensures learners prepare for the demands of the modern marketing landscape, moving well beyond marketing analytics using the AI technologies of Large Language Models, further expanding the use of big data Learners use role play, witnessing firsthand experiences fulfilling new hitherto emerging marketing roles By 2025, Educators fostering a big data AI-focused marketing education curriculum ensure the next generation of AI marketers will eagerly shape the future of marketing practice and behavior with new roles combining human work with AI.

Spina, I, Rogers, DJ, Brando, G, Chatzinikolaou, E & Siwakoti, YP 2023, 'Maximum Power per Ampere Modulation for Cascaded H-Bridge Converters', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 11, no. 1, pp. 264-275.
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Stewart, MG 2023, 'Climate Adaptation Engineering: An Optimist’s View', ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, vol. 9, no. 1.
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Stewart, MG 2023, 'Spatial variability of explosive blast loading and its effect on damage risks to reinforced concrete buildings', Engineering Structures, vol. 285, pp. 115650-115650.
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Stratton-Powell, AA, Williams, S, Tipper, JL, Redmond, AC & Brockett, CL 2023, 'Isolation and characterisation of wear debris surrounding failed total ankle replacements', Acta Biomaterialia, vol. 159, pp. 410-422.
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Su, G, Zulkifli, NWM, Ong, HC, Ibrahim, S, Cheah, MY, Zhu, R & Bu, Q 2023, 'Co-pyrolysis of medical protective clothing and oil palm wastes for biofuel: Experimental, techno-economic, and environmental analyses', Energy, vol. 273, pp. 127221-127221.
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Sulaiman, M, Rabbani, FA, Iqbal, T, Kazmi, MA, Yasin, S, Mujtaba, MA, Kalam, MA & Almomani, F 2023, 'Impact of eco-friendly chemical pretreatment on physicochemical and surface mechanical properties of sustainable lignocellulosic agricultural waste', Algal Research, vol. 71, pp. 103051-103051.
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Sun, J, Wang, Y, Liu, P, Wen, S & Wang, Y 2023, 'Memristor-Based Neural Network Circuit With Multimode Generalization and Differentiation on Pavlov Associative Memory', IEEE Transactions on Cybernetics, vol. 53, no. 5, pp. 3351-3362.
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Sun, X, Cheng, H, Liu, B, Li, J, Chen, H, Xu, G & Yin, H 2023, 'Self-Supervised Hypergraph Representation Learning for Sociological Analysis', IEEE Transactions on Knowledge and Data Engineering, pp. 1-12.
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Sun, X, Pan, Y, Song, Y, Liu, W, Nghiem, LD, Wang, Q & Cai, Z 2023, 'Ceftriaxone sodium degradation by carbon quantum dots (CQDs)-decorated C-doped α-Bi2O3 nanorods', Environmental Science and Ecotechnology, vol. 13, pp. 100219-100219.
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Sun, Y, Zhang, JA, Wu, K & Liu, RP 2023, 'Frequency-Domain Sensing in Time-Varying Channels', IEEE Wireless Communications Letters, vol. 12, no. 1, pp. 16-20.
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Swaminathan, GV, Periasamy, S & Lu, DD-C 2023, 'Capacitor Current Control Based Virtual Inertia Control of Autonomous DC Microgrid', IEEE Transactions on Industrial Electronics, vol. 70, no. 7, pp. 6908-6918.
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Syasegov, YY, Farhangi, M, Barzegarkhoo, R, Li, L, Lu, DD-C, Aguilera, RP & Siwakoti, YP 2023, 'HERIC-Clamped and PN-NPC Inverters With Five-Level Output Voltage and Reduced Grid-Interfaced Filter Size', IEEE Open Journal of Power Electronics, vol. 4, pp. 306-318.
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Syasegov, YY, Farhangi, M, Barzegarkhoo, R, Siwakoti, YP, Li, L, Lu, DD-C, Aguilera, RP & Pou, J 2023, 'A 5-Level HERIC Active-Clamped Inverter With Full Reactive Power Capability for Grid-Connected Applications', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 135-148.
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Tang, R, Yu, Z & Li, J 2023, 'KINN: An alignment-free accurate phylogeny reconstruction method based on inner distance distributions of k-mer pairs in biological sequences', Molecular Phylogenetics and Evolution, vol. 179, pp. 107662-107662.
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Tang, X-W, Huang, Y, Shi, Y, Huang, X-L & Yu, S 2023, 'UAV Placement for VR Reconstruction: A Tradeoff Between Resolution and Delay', IEEE Communications Letters, vol. 27, no. 5, pp. 1382-1386.
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Tanveer, M, Ganaie, MA, Beheshti, I, Goel, T, Ahmad, N, Lai, K-T, Huang, K, Zhang, Y-D, Del Ser, J & Lin, C-T 2023, 'Deep learning for brain age estimation: A systematic review', Information Fusion, vol. 96, pp. 130-143.
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Tanveer, M, Lin, C-T & Singh, AK 2023, 'Guest Editorial Advanced Machine Learning Algorithms for Biomedical Data and Imaging—Part II', IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 1, pp. 188-189.
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Tanveer, M, Lin, C-T, Ting, C-K & Andreu-Perez, J 2023, 'Guest Editorial: Special Issue on Emerging Computational Intelligence Techniques to Address Challenges in Biomedical Data and Imaging', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 2, pp. 292-294.
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Tao, M, Luo, H, Wu, C, Cao, W & Zhao, R 2023, 'Dynamic analysis of the different types of elliptic cylindrical inclusions subjected to plane SH‐wave scattering', Mathematical Methods in the Applied Sciences, vol. 46, no. 2, pp. 2773-2800.
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Tao, X, Adak, C, Chun, P-J, Yan, S & Liu, H 2023, 'ViTALnet: Anomaly on Industrial Textured Surfaces With Hybrid Transformer', IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-13.
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Tao, X, Yan, S, Gong, X & Adak, C 2023, 'Learning Multi-Resolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces', IEEE Transactions on Artificial Intelligence, pp. 1-13.
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Tapas, MJ, Thomas, P, Vessalas, K, Nsiah-Baafi, E, Martin, L & Sirivivatnanon, V 2023, 'Comparative study of the efficacy of fly ash and reactive aggregate powders in mitigating alkali-silica reaction', Journal of Building Engineering, vol. 63, pp. 105571-105571.
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Tavakoli, J, Diwan, AD & Tipper, JL 2023, 'Intervertebral disc-on-a-chip: a precision engineered toolbox for low back pain studies', Trends in Biotechnology.
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Tawalbeh, M, Mohammed, S, Al-Othman, A, Yusuf, M, Mofijur, M & Kamyab, H 2023, 'MXenes and MXene-based materials for removal of pharmaceutical compounds from wastewater: Critical review', Environmental Research, vol. 228, pp. 115919-115919.
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Thiri Zun, M, Shakeel Ahmad, M, Fayaz, H, Selvaraj, J, Ahmed, W, Wang, Y, Ben Khedher, N, Silitonga, AS, Elfasakhany, A, Kalam, MA & Rashid, B 2023, 'Towards techno-economics of green hydrogen as a primary combustion fuel for recreational vehicle vapor absorption refrigeration system', Sustainable Energy Technologies and Assessments, vol. 56, pp. 103007-103007.
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Tian, A, Feng, B, Zhou, H, Huang, Y, Sood, K, Yu, S & Zhang, H 2023, 'Efficient Federated DRL-Based Cooperative Caching for Mobile Edge Networks', IEEE Transactions on Network and Service Management, vol. 20, no. 1, pp. 246-260.
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Tian, H, Liu, B, Zhu, T, Zhou, W & Yu, PS 2023, 'CIFair: Constructing continuous domains of invariant features for image fair classifications', Knowledge-Based Systems, vol. 268, pp. 110417-110417.
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Tian, Y, Li, Q, Feng, Y, Yu, Y, Wu, D, Chen, X & Gao, W 2023, 'Nonlinear dynamic analysis of the functionally graded graphene platelets reinforced porous plate under moving mass', Thin-Walled Structures, vol. 183, pp. 110363-110363.
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Tian, Z, Cui, L, Liang, J & Yu, S 2023, 'A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning', ACM Computing Surveys, vol. 55, no. 8, pp. 1-35.
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The prosperity of machine learning has been accompanied by increasing attacks on the training process. Among them, poisoning attacks have become an emerging threat during model training. Poisoning attacks have profound impacts on the target models, e.g., making them unable to converge or manipulating their prediction results. Moreover, the rapid development of recent distributed learning frameworks, especially federated learning, has further stimulated the development of poisoning attacks. Defending against poisoning attacks is challenging and urgent. However, the systematic review from a unified perspective remains blank. This survey provides an in-depth and up-to-date overview of poisoning attacks and corresponding countermeasures in both centralized and federated learning. We firstly categorize attack methods based on their goals. Secondly, we offer detailed analysis of the differences and connections among the attack techniques. Furthermore, we present countermeasures in different learning framework and highlight their advantages and disadvantages. Finally, we discuss the reasons for the feasibility of poisoning attacks and address the potential research directions from attacks and defenses perspectives, separately.

Tong, M, Huang, X & Andrew Zhang, J 2023, 'Faster-Than-Nyquist Transmission with Frame-by-Frame Decision-Directed Successive Interference Cancellation', IEEE Transactions on Communications, pp. 1-1.
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Tonini de Araújo, M, Tonatto Ferrazzo, S, Mansur Chaves, H, Gravina da Rocha, C & Cesar Consoli, N 2023, 'Mechanical behavior, mineralogy, and microstructure of alkali-activated wastes-based binder for a clayey soil stabilization', Construction and Building Materials, vol. 362, pp. 129757-129757.
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Tran, MV, Vu, TH & Nguyen, THY 2023, 'Simplified assessment for one-part 3D-printable geopolymer concrete based on slump and slump flow measurements', Case Studies in Construction Materials, vol. 18, pp. e01889-e01889.
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Uddin, A, Cetindamar, D, Hawryszkiewycz, I & Sohaib, O 2023, 'The Role of Dynamic Cloud Capability in Improving SME’s Strategic Agility and Resource Flexibility: An Empirical Study', Sustainability, vol. 15, no. 11, pp. 8467-8467.
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This research explores how the cloud’s technological capability helps small and medium enterprises (SMEs) adapt to challenging business environments, providing long-term sustainability and strategic agility. The article uses a theoretical and quantitative empirical approach, known as the positivist research paradigm, in offering a unique capability called dynamic cloud capability that leverages the cloud’s technological capabilities. Based on the quantitative analysis of 222 Australian Information and Communication Technology (ICT) SMEs, dynamic cloud capability favourably improves the flexible allocation of resources (resource fluidity) and the ability to adapt business models (strategic agility). Additionally, because of the successful mediating effect of resource fluidity, it is inferred that dynamic cloud capability allows for the flexible allocation of resources leading to improved strategic agility. Hence, adopting dynamic cloud capability in an organisation’s strategy would be particularly appealing to ICT SMEs as it has been verified to enhance adaptability to a challenging business environment and flexible allocation of resources.

Ullah, MA, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2023, 'Multi-Service Compact Pixelated Stacked Antenna with Different Pixel Shapes for IoT Applications', IEEE Internet of Things Journal, pp. 1-1.
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Unanue, IJ, Borzeshi, EZ & Piccardi, M 2023, 'Regressing Word and Sentence Embeddings for Low-Resource Neural Machine Translation', IEEE Transactions on Artificial Intelligence, vol. 4, no. 3, pp. 450-463.
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Uzair, M, Li, L, Eskandari, M, Hossain, J & Zhu, JG 2023, 'Challenges, advances and future trends in AC microgrid protection: With a focus on intelligent learning methods', Renewable and Sustainable Energy Reviews, vol. 178, pp. 113228-113228.
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Vo, TPT, Ngo, HH, Guo, W, Turney, C, Liu, Y, Nguyen, DD, Bui, XT & Varjani, S 2023, 'Influence of the COVID-19 pandemic on climate change summit negotiations from the climate governance perspective', Science of The Total Environment, vol. 878, pp. 162936-162936.
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Volpe, G, Maragò, OM, Rubinsztein-Dunlop, H, Pesce, G, Stilgoe, AB, Volpe, G, Tkachenko, G, Truong, VG, Chormaic, SN, Kalantarifard, F, Elahi, P, Käll, M, Callegari, A, Marqués, MI, Neves, AAR, Moreira, WL, Fontes, A, Cesar, CL, Saija, R, Saidi, A, Beck, P, Eismann, JS, Banzer, P, Fernandes, TFD, Pedaci, F, Bowen, WP, Vaippully, R, Lokesh, M, Roy, B, Thalhammer-Thurner, G, Ritsch-Marte, M, García, LP, Arzola, AV, Castillo, IP, Argun, A, Muenker, TM, Vos, BE, Betz, T, Cristiani, I, Minzioni, P, Reece, PJ, Wang, F, McGloin, D, Ndukaife, JC, Quidant, R, Roberts, RP, Laplane, C, Volz, T, Gordon, R, Hanstorp, D, Marmolejo, JT, Bruce, GD, Dholakia, K, Li, T, Brzobohatý, O, Simpson, SH, Zemánek, P, Ritort, F, Roichman, Y, Bobkova, V, Wittkowski, R, Denz, C, Kumar, GVP, Foti, A, Donato, MG, Gucciardi, PG, Gardini, L, Bianchi, G, Kashchuk, AV, Capitanio, M, Paterson, L, Jones, PH, Berg-Sørensen, K, Barooji, YF, Oddershede, LB, Pouladian, P, Preece, D, Adiels, CB, De Luca, AC, Magazzù, A, Bronte Ciriza, D, Iatì, MA & Swartzlander, GA 2023, 'Roadmap for optical tweezers', Journal of Physics: Photonics, vol. 5, no. 2, pp. 022501-022501.
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Abstract Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects, ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in the life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nano-particle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration.

Vu, L, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2023, 'Deep Generative Learning Models for Cloud Intrusion Detection Systems', IEEE Transactions on Cybernetics, vol. 53, no. 1, pp. 565-577.
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Wallace, K & Michopoulou, E 2023, 'Building Resilience and Understanding Complexitites of Event Project Stakeholder Management'.
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Wallace, K & Michopoulou, E 2023, 'Stakeholder Requirements and Value Cocreation in Events', Event Management, vol. 27, no. 2, pp. 281-299.
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The festival and events sector comprises a wide range of stakeholders across the private, public, and not-for-profit sectors. In order to achieve stakeholder satisfaction, it is necessary to understand what is important to stakeholders, what they consider constitutes project success, and what the factors and measures of that success may be. Once identified and effectively managed, meaningful evaluation can then be undertaken to assess success on stakeholder's terms. This approach also provides an opportunity to consider value creation for stakeholders in relation to their measures of success. The purpose of this research is to develop a robust framework that enables success factors and measures to be identified and effectively measured as part of a holistic evaluation process that contributes to the identification of stakeholder value. Although research is regularly undertaken to assess impacts of festivals and their benefits to stakeholders, there can be competing agendas, project success can be interpreted in different ways with tensions and disagreements in relation to expected outcomes. Therefore, it is necessary to clearly understand stakeholder expectations, community dynamics, and visitors and residents' perceptions of impacts of festivals. A multimethod inductive approach was used to capture the motivations and influences of the stakeholders as social actors during the Tour de Yorkshire (TdY) event. Using this event as a longitudinal case study over an 18-month period, the methodology comprised of qualitative questionnaires and interviews to engage a wide range of stakeholders and used the conceptual Stakeholder Sandwich as the core model to produce a framework and methodology to generate richer data. Results indicated that this model, framework, and methodology proved to be effective for the understanding of stakeholder success factors and contributes towards the understanding of value cocreation for stakeholders in events and festivals. Wi...

Wang, C, Park, MJ, Gonzales, RR, Matsuyama, H, Drioli, E & Shon, HK 2023, 'Graphene oxide-based layer-by-layer nanofiltration membrane using inkjet printing for desalination', Desalination, vol. 549, pp. 116357-116357.
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Wang, C, Wang, L, Soo, A, Bansidhar Pathak, N & Kyong Shon, H 2023, 'Machine learning based prediction and optimization of thin film nanocomposite membranes for organic solvent nanofiltration', Separation and Purification Technology, vol. 304, pp. 122328-122328.
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Wang, C, Wang, Y, Chen, Z, Wei, W, Chen, X, Mannina, G & Ni, B-J 2023, 'A novel strategy for efficiently transforming waste activated sludge into medium-chain fatty acid using free nitrous acid', Science of The Total Environment, vol. 862, pp. 160826-160826.
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Wang, C, Wei, W, Zhang, Y-T, Chen, X & Ni, B-J 2023, 'Hydrochar alleviated the inhibitory effects of polyvinyl chloride microplastics and nanoplastics on anaerobic granular sludge for wastewater treatment', Chemical Engineering Journal, vol. 452, pp. 139302-139302.
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Wang, E, Chen, C, Zhang, G, Luo, Q, Li, Q & Sun, G 2023, 'Multiaxial mechanical characterization of additively manufactured open-cell Kelvin foams', Composite Structures, vol. 305, pp. 116505-116505.
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Wang, F, Long, G, Bai, M, Shi, Y & Zhou, JL 2023, 'Feasibility of low-carbon electrolytic manganese residue-based supplementary cementitious materials', Science of The Total Environment, vol. 883, pp. 163672-163672.
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Wang, F, Long, G, Bai, M, Wang, J, Shi, Y, Zhou, X & Zhou, JL 2023, 'A new perspective on Belite-ye'elimite-ferrite cement manufactured from electrolytic manganese residue: Production, properties, and environmental analysis', Cement and Concrete Research, vol. 163, pp. 107019-107019.
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Wang, H, Ye, Y, Zhang, J & Xu, B 2023, 'A comparative study of 13 deep reinforcement learning based energy management methods for a hybrid electric vehicle', Energy, vol. 266, pp. 126497-126497.
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Wang, J, Li, L & Zhang, J 2023, 'Deep reinforcement learning for energy trading and load scheduling in residential peer-to-peer energy trading market', International Journal of Electrical Power & Energy Systems, vol. 147, pp. 108885-108885.
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Wang, J, Mishra, DK, Li, L & Zhang, J 2023, 'Demand Side Management and Peer-to-Peer Energy Trading for Industrial Users Using Two-Level Multi-Agent Reinforcement Learning', IEEE Transactions on Energy Markets, Policy and Regulation, vol. 1, no. 1, pp. 23-36.
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Wang, J, Wang, K, Li, Z, Lu, H, Jiang, H & Xing, Q 2023, 'A Multitask integrated deep-learning probabilistic prediction for load forecasting', IEEE Transactions on Power Systems, pp. 1-11.
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Wang, J, Zhang, J, Li, L & Lin, Y 2023, 'Peer-to-Peer Energy Trading for Residential Prosumers With Photovoltaic and Battery Storage Systems', IEEE Systems Journal, vol. 17, no. 1, pp. 154-163.
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Wang, J, Zhu, S, Liu, X & Wen, S 2023, 'Mittag-Leffler stability of fractional-order quaternion-valued memristive neural networks with generalized piecewise constant argument', Neural Networks, vol. 162, pp. 175-185.
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Wang, K, Lin, X, Qin, L, Zhang, W & Zhang, Y 2023, 'Accelerated butterfly counting with vertex priority on bipartite graphs', The VLDB Journal, vol. 32, no. 2, pp. 257-281.
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AbstractBipartite graphs are of great importance in many real-world applications. Butterfly, which is a complete $$2 \times 2$$ 2 × 2 biclique, plays a key role in bipartite graphs. In this paper, we investigate the problem of efficient counting the number of butterflies. The most advanced techniques are based on enumerating wedges which is the dominant cost of counting butterflies. Nevertheless, the existing algorithms cannot efficiently handle large-scale bipartite graphs. This becomes a bottleneck in large-scale applications. In this paper, instead of the existing layer-priority-based techniques, we propose a vertex-priority-based paradigm $${\mathsf {BFC}}$$ BFC -$${\mathsf {VP}}$$ VP to enumerate much fewer wedges; this leads to a significant improvement of the time complexity of the state-of-the-art algorithms. In addition, we present cache-aware strategies to further improve the time efficiency while theoretically retaining the time complexity of $${\mathsf {BFC}}$$ ...

Wang, K, Lu, J, Liu, A, Zhang, G & Xiong, L 2023, 'Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept Drift', IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2110-2123.
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Wang, K, Zhao, G, Zhang, W, Lin, X, Zhang, Y, He, Y & Li, C 2023, 'Cohesive Subgraph Discovery over Uncertain Bipartite Graphs', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Wang, L, Yi, B & Su, H 2023, 'An I & I Adaptive Redesign Approach for Asymptotic Stability without PE', IFAC-PapersOnLine, vol. 56, no. 1, pp. 264-269.
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Wang, LJ, Wu, CQ, Fan, LF & Wang, M 2023, 'Investigation of wave reflection at the joint with different wave impedances on two sides', Waves in Random and Complex Media, vol. 33, no. 2, pp. 237-253.
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When the stress wave propagates through rock mass with different wave impedances on two sides. It shows different reflection tendencies compared with the traditional study of wave propagation. A characteristic line method combined with the displacement discontinuity model was proposed to study the wave reflection through such rock mass. The effects of incident frequency, joint stiffness and wave impedance ratio on the reflection coefficient were discussed. The results show that the reflection coefficient increases as the wave impedance ratio increases. The traditional study of wave reflection can be regarded as a special case of the present study when the wave impedance ratio equals 1.0. Moreover, the difference of reflection coefficients decreases as the incident frequency increases, while increases as the joint stiffness and wave impedance ratio increase when the wave impedance ratio is larger than 1.0. When the wave impedance ratio is smaller than 1.0, the difference of reflection coefficients increases firstly and then decreases with the increase of incident frequency and joint stiffness, while decreases as the wave impedance ratio increases. For the cases of large incident frequency or small joint stiffness, the effects of wave impedance ratio can be neglected for the approximated prediction of wave reflection.

Wang, M, Zhu, T, Zuo, X, Yang, M, Yu, S & Zhou, W 2023, 'Differentially Private Crowdsourcing With the Public and Private Blockchain', IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8918-8930.
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Wang, M-M, Liu, L-J, Xi, J-R, Ding, Y, Liu, P-X, Mao, L, Ni, B-J, Wang, W-K & Xu, J 2023, 'Lattice doping of Zn boosts oxygen vacancies in Co3O4 Nanocages: Improving persulfate activation via forming Surface-Activated complex', Chemical Engineering Journal, vol. 451, pp. 138605-138605.
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Wang, Q & Ying, M 2023, 'Quantum random access stored-program machines', Journal of Computer and System Sciences, vol. 131, pp. 13-63.
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Random access machines (RAMs) and random access stored-program machines (RASPs) are models of computing that are closer to the architecture of real-world computers than Turing machines (TMs). They are also convenient in complexity analysis of algorithms. The relationships between RAMs, RASPs and TMs are well-studied. However, clear relationships between their quantum counterparts are still missing in the literature. We fill in this gap by formally defining the models of quantum random access machines (QRAMs) and quantum random access stored-program machines (QRASPs) and clarifying the relationships between QRAMs, QRASPs and quantum Turing machines (QTMs). In particular, we show that P⊆EQRAMP⊆EQP⊆BQP=BQRAMP, where EQRAMP and BQRAMP stand for the sets of problems that can be solved by polynomial-time QRAMs with certainty and bounded-error, respectively. At the heart of our proof, is a standardisation of QTM with an extended halting scheme, which is of independent interest.

Wang, Q, Han, N, Shen, Z, Li, X, Chen, Z, Cao, Y, Si, W, Wang, F, Ni, B-J & Thakur, VK 2023, 'MXene-based electrochemical (bio) sensors for sustainable applications: Roadmap for future advanced materials', Nano Materials Science, vol. 5, no. 1, pp. 39-52.
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Wang, Q, Zhang, Z, Chen, K, Guan, J, Fang, W, Liu, J & Ying, M 2023, 'Quantum Algorithm for Fidelity Estimation', IEEE Transactions on Information Theory, vol. 69, no. 1, pp. 273-282.
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Wang, QY, Teng, JD, Zhong, Y, Zhang, S & Sheng, DC 2023, 'Mesoscale simulation of pore ice formation in saturated frozen soil by using lattice Boltzmann method', Yantu Lixue/Rock and Soil Mechanics, vol. 44, no. 1, pp. 317-326.
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The frost heave of subgrade has an important effect on the operation of high-speed railway in cold regions, while the ice-water phase transition is the key to understanding the mechanism of frost heave. The lattice Boltzmann method is applied in this study, which is a mesoscale numerical method. The modified freezing temperature algorithm of pore water is combined with the enthalpy-based lattice Boltzmann phase transition model. Two freezing processes including the freezing of suspended droplets and the formation of pore water into ice in frozen soil are investigated, which aim to reveal the mesoscopic mechanism of the ice-water phase transition in free state and pore-bound state, respectively. The numerical results show that the process of ice crystals growing from the inside to the outside in the pores is completely opposite to the freezing process of droplets suspended in the air, and the pore water has a lower freezing temperature when it is closer to the surface of the soil particles. The soil freezing characteristic curves (SFCCs) differ obviously for the particles with the same size but in different particle arrangements. Meanwhile, the morphology of SFCC becomes steeper with increasing soil particle size, and the residual water content gradually decreases. The numerical results of the ice-water phase transition process are validated by measured data in the literature, which indicate that the lattice Boltzmann method can provide a new tool to study the water-gas migration and phase transformation process in porous media in mesoscale.

Wang, R, Luo, S, Zheng, R, Shangguan, Y, Feng, X, Zeng, Q, Liang, J, Chen, Z, Li, J, Yang, D & Chen, H 2023, 'Interfacial Coordination Bonding-Assisted Redox Mechanism-Driven Highly Selective Precious Metal Recovery on Covalent-Functionalized Ultrathin 1T-MoS₂', ACS Applied Materials & Interfaces, vol. 15, no. 7, pp. 9331-9340.
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Wang, S, Wang, Y, Liu, C, Lei, G, Guo, Y & Zhu, J 2023, 'Performance Comparison of Tubular Flux-Switching Permanent Magnet Machines Using Soft Magnetic Composite Material and Hybrid Material Magnetic Cores', IEEE Transactions on Energy Conversion, vol. 38, no. 2, pp. 1118-1129.
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Wang, S, Wen, S, Yang, Y, Cao, Y, Shi, K & Huang, T 2023, 'Suboptimal Safety-Critical Control for Continuous Systems Using Prediction–Correction Online Optimization', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-11.
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Wang, S, Yang, Z, Tao, J, Qiu, X & Burnett, IS 2023, 'Transmission loss and directivity of sound transmitted through a slit on ground', The Journal of the Acoustical Society of America, vol. 153, no. 1, pp. 224-235.
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An analytical model is proposed for sound transmission through a slit on a rigid ground based on the modal superposition method to investigate the transmission loss (TL). A simple formula is derived for estimation of the TL for plane waves with and without the ground, which gives a more precise prediction than existing approaches. It is found that a larger slit height generally decreases the TL, except at the resonant frequencies of the slit. The slit width has little effect on the TL at high frequencies, and the slit depth affects the resonant frequencies significantly even though it has little effect on the overall TL. Compared with the same size slit in the free field, the rigid ground reduces the TL at most frequencies, and that reduction is a constant between 3 and 9 dB in the low frequency range. It is also found that the sound transmitted through the slit is almost omnidirectional at low frequencies, while most of the sound energy at high frequencies falls within the range where the long side of the slit is located. The experimental results demonstrate the validity of the analytical model and the findings in numerical simulations.

Wang, S-N, Cao, J-S, Zhang, J-L, Luo, J-Y, Ni, B-J & Fang, F 2023, 'Recovery of phosphorus from wastewater containing humic substances through vivianite crystallization: Interaction and mechanism analysis', Journal of Environmental Management, vol. 331, pp. 117324-117324.
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Wang, T, Li, B, Chen, M & Yu, S 2023, 'Preface', SpringerBriefs in Computer Science, p. v.

Wang, T, Lu, W, Yu, H & Liu, D 2023, 'Modular transfer learning with transition mismatch compensation for excessive disturbance rejection', International Journal of Machine Learning and Cybernetics, vol. 14, no. 1, pp. 295-311.
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Wang, W, Karimi, F, Khalilpour, K, Green, D & Varvarigos, M 2023, 'Robustness analysis of electricity networks against failure or attack: The case of the Australian National Electricity Market (NEM)', International Journal of Critical Infrastructure Protection, vol. 41, pp. 100600-100600.
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Wang, X, Chen, H-T, Wang, Y-K & Lin, C-T 2023, 'Implicit Robot Control Using Error-Related Potential-Based Brain–Computer Interface', IEEE Transactions on Cognitive and Developmental Systems, vol. 15, no. 1, pp. 198-209.
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Wang, X, Chen, W-H, Huang, Y, Wang, L, Zhao, Y & Gao, J 2023, 'Advances in soot particles from gasoline direct injection engines: A focus on physical and chemical characterisation', Chemosphere, vol. 311, pp. 137181-137181.
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Wang, X, Han, C, Li, H, Su, P, Ta, N, Ma, Y, Huang, Z & Liu, J 2023, 'Fabrication of monodispersed B, N co-doped hierarchical porous carbon nanocages through confined etching to boost electrocatalytic oxygen reduction', Nano Research, vol. 16, no. 1, pp. 290-298.
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Wang, X, Qin, P-Y, Song, L-Z, Jin, R & Guo, YJ 2023, 'Tightly Coupled Huygens Element-Based Conformal Transmitarray for E-Band Airborne Communication Systems', IEEE Transactions on Antennas and Propagation, vol. 71, no. 3, pp. 2467-2475.
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Wang, X, Thiyagarajan, K, Kodagoda, S & Zhang, M 2023, 'PIPE-CovNet: Automatic In-Pipe Wastewater Infrastructure Surface Abnormality Detection Using Convolutional Neural Network', IEEE Sensors Letters, vol. 7, no. 4, pp. 1-4.
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Wang, X, Zheng, Z, He, Y, Yan, F, Zeng, Z & Yang, Y 2023, 'Progressive Local Filter Pruning for Image Retrieval Acceleration', IEEE Transactions on Multimedia, pp. 1-11.
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Wang, X, Zhu, L, Wu, Y & Yang, Y 2023, 'Symbiotic Attention for Egocentric Action Recognition With Object-Centric Alignment', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 6605-6617.
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In this paper, we propose to tackle egocentric action recognition by suppressing background distractors and enhancing action-relevant interactions. The existing approaches usually utilize two independent branches to recognize egocentric actions, i.e., a verb branch and a noun branch. However, the mechanism to suppress distracting objects and exploit local human-object correlations is missing. To this end, we introduce two extra sources of information, i.e., the candidate objects' spatial location and their discriminative features, to enable concentration on the occurring interactions. We design a Symbiotic Attention withObject-centric featureAlignmentframework (SAOA) to provide meticulous reasoning between the actor and the environment. First, we introduce an object-centric feature alignment method to inject the local object features to the verb branch and noun branch. Second, we propose a symbiotic attention mechanism to encourage the mutual interaction between the two branches and select the most action-relevant candidates for classification. The framework benefits from the communication among the verb branch, the noun branch, and the local object information. Experiments based on different backbones and modalities demonstrate the effectiveness of our method. Notably, our framework achieves the state-of-the-art on the largest egocentric video dataset.

Wang, Y, He, Y, Zheng, K, Wei, W, Ngo, HH, Guo, W, Ni, B-J, Zhu, T, Horn, H & Liu, Y 2023, 'Ferric oxide stimulates medium-chain carboxylic acids synthesis from waste activated sludge via ethanol-driven chain elongation: Mechanisms and implications', Journal of Cleaner Production, vol. 389, pp. 136044-136044.
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Wang, Z, Li, X, Xiang, L, Huang, Y, Lang, B, Cheng, X & Zhang, J 2023, 'Potential of kerosene-diesel blends as alternative fuels for diesel engines: Perspectives from spray combustion characteristics', Fuel, vol. 335, pp. 127112-127112.
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Wang, Z, Zhang, JA, Xu, M & Guo, YJ 2023, 'Single-Target Real-Time Passive WiFi Tracking', IEEE Transactions on Mobile Computing, vol. 22, no. 6, pp. 3724-3742.
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Wei, J, Li, J & Wu, C 2023, 'Study on hybrid fibre reinforced UHPC beams under single and repeated lateral impact loading', Construction and Building Materials, vol. 368, pp. 130403-130403.
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Wei, J, Li, J, Wu, C, Hao, H & Liu, J 2023, 'Experimental and numerical study on the impact resistance of ultra-high performance concrete strengthened RC beams', Engineering Structures, vol. 277, pp. 115474-115474.
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Wen, L, Yang, F, Li, X, Liu, S, Lin, Y, Hu, E, Gao, L & Li, M 2023, 'Composition of dissolved organic matter (DOM) in wastewater treatment plants influent affects the efficiency of carbon and nitrogen removal', Science of The Total Environment, vol. 857, pp. 159541-159541.
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White, T, Opremcak, A, Sterling, G, Korotkov, A, Sank, D, Acharya, R, Ansmann, M, Arute, F, Arya, K, Bardin, JC, Bengtsson, A, Bourassa, A, Bovaird, J, Brill, L, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Cogan, J, Collins, R, Crook, AL, Curtin, B, Demura, S, Dunsworth, A, Erickson, C, Fatemi, R, Burgos, LF, Forati, E, Foxen, B, Giang, W, Giustina, M, Grajales Dau, A, Hamilton, MC, Harrington, SD, Hilton, J, Hoffmann, M, Hong, S, Huang, T, Huff, A, Iveland, J, Jeffrey, E, Kieferová, M, Kim, S, Klimov, PV, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Laws, L, Lee, K, Lester, BJ, Lill, A, Liu, W, Locharla, A, Lucero, E, McCourt, T, McEwen, M, Mi, X, Miao, KC, Montazeri, S, Morvan, A, Neeley, M, Neill, C, Nersisyan, A, Ng, JH, Nguyen, A, Nguyen, M, Potter, R, Quintana, C, Roushan, P, Sankaragomathi, K, Satzinger, KJ, Schuster, C, Shearn, MJ, Shorter, A, Shvarts, V, Skruzny, J, Smith, WC, Szalay, M, Torres, A, Woo, BWK, Yao, ZJ, Yeh, P, Yoo, J, Young, G, Zhu, N, Zobrist, N, Chen, Y, Megrant, A, Kelly, J & Naaman, O 2023, 'Readout of a quantum processor with high dynamic range Josephson parametric amplifiers', Applied Physics Letters, vol. 122, no. 1, pp. 014001-014001.
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We demonstrate a high dynamic range Josephson parametric amplifier (JPA) in which the active nonlinear element is implemented using an array of rf-SQUIDs. The device is matched to the 50 Ω environment with a Klopfenstein-taper impedance transformer and achieves a bandwidth of 250–300 MHz with input saturation powers up to −95 dBm at 20 dB gain. A 54-qubit Sycamore processor was used to benchmark these devices, providing a calibration for readout power, an estimation of amplifier added noise, and a platform for comparison against standard impedance matched parametric amplifiers with a single dc-SQUID. We find that the high power rf-SQUID array design has no adverse effect on system noise, readout fidelity, or qubit dephasing, and we estimate an upper bound on amplifier added noise at 1.6 times the quantum limit. Finally, amplifiers with this design show no degradation in readout fidelity due to gain compression, which can occur in multi-tone multiplexed readout with traditional JPAs.

Wijayaratna, KP, Hossein Rashidi, T & Gardner, L 2023, 'Adapting to the Emergence of Generation Z in Tertiary Education: Application of Blended Learning Initiatives in Transport Engineering', Journal of Civil Engineering Education, vol. 149, no. 3.
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Williams, P, Kirby, R & Karimi, M 2023, 'The effect of axial boundary conditions on breakout noise from finite cylindrical ducts', International Journal of Mechanical Sciences, vol. 242, pp. 107951-107951.
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Win, HM, Tan, SH & Sui, Y 2023, 'Event-aware precise dynamic slicing for automatic debugging of Android applications', Journal of Systems and Software, vol. 198, pp. 111606-111606.
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Wu, G, Wang, H, Zhang, H, Zhao, Y, Yu, S & Shen, S 2023, 'Computation Offloading Method Using Stochastic Games for Software Defined Network-based Multi-Agent Mobile Edge Computing', IEEE Internet of Things Journal, pp. 1-1.
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Wu, G, Xie, L, Zhang, H, Wang, J, Shen, S & Yu, S 2023, 'STSIR: An individual-group game-based model for disclosing virus spread in Social Internet of Things', Journal of Network and Computer Applications, vol. 214, pp. 103608-103608.
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Wu, G, Xu, Z, Zhang, H, Shen, S & Yu, S 2023, 'Multi-agent DRL for joint completion delay and energy consumption with queuing theory in MEC-based IIoT', Journal of Parallel and Distributed Computing, vol. 176, pp. 80-94.
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Wu, J, Li, L & Zhang, J 2023, 'Maximum demand flexibility from the demand response of a big group of residential homes', International Journal of Electrical Power & Energy Systems, vol. 147, pp. 108800-108800.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 2023, 'OTFS-Based Joint Communication and Sensing for Future Industrial IoT', IEEE Internet of Things Journal, vol. 10, no. 3, pp. 1973-1989.
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Wu, K, Zhang, JA, Huang, X, Guo, YJ & Hanzo, L 2023, 'Simultaneous Beam and User Selection for the Beamspace mmWave/THz Massive MIMO Downlink', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1785-1797.
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Wu, K, Zhang, JA, Huang, X, Heath, RW & Guo, YJ 2023, 'Green Joint Communications and Sensing Employing Analog Multi-Beam Antenna Arrays', IEEE Communications Magazine, pp. 1-7.
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Wu, L, Wei, W, Chen, Z, Chen, X & Ni, B-J 2023, 'Long-chain alcohol production in open culture anaerobic fermentation', Chemical Engineering Journal, vol. 452, pp. 139225-139225.
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Wu, Y, Chen, M, Li, Y, Liu, J, Li, Z, Li, J & Wu, X 2023, 'ONP-Miner: One-off Negative Sequential Pattern Mining', ACM Transactions on Knowledge Discovery from Data, vol. 17, no. 3, pp. 1-24.
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Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike positive SPM, negative SPM can discover events that should have occurred but have not occurred, and it can be used for financial risk management and fraud detection. However, existing methods generally ignore the repetitions of the pattern and do not consider gap constraints, which can lead to mining results containing a large number of patterns that users are not interested in. To solve this problem, this article discovers frequent one-off negative sequential patterns (ONPs). This problem has the following two characteristics. First, the support is calculated under the one-off condition, which means that any character in the sequence can only be used once at most. Second, the gap constraint can be given by the user. To efficiently mine patterns, this article proposes the ONP-Miner algorithm, which employs depth-first and backtracking strategies to calculate the support. Therefore, ONP-Miner can effectively avoid creating redundant nodes and parent-child relationships. Moreover, to effectively reduce the number of candidate patterns, ONP-Miner uses pattern join and pruning strategies to generate and further prune the candidate patterns, respectively. Experimental results show that ONP-Miner not only improves the mining efficiency but also has better mining performance than the state-of-the-art algorithms. More importantly, ONP mining can find more interesting patterns in traffic volume data to predict future traffic.

Wu, Y, Fang, J, Wu, C, Li, C, Sun, G & Li, Q 2023, 'Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption', International Journal of Mechanical Sciences, vol. 246, pp. 108102-108102.
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Wu, Y, Jiang, L & Yang, Y 2023, 'Switchable Novel Object Captioner', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 1162-1173.
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Wu, Z, Xie, J, Shen, S, Lin, C, Xu, G & Chen, E 2023, 'A Confusion Method for the Protection of User Topic Privacy in Chinese Keyword-based Book Retrieval', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 22, no. 5, pp. 1-19.
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In this article, aiming at a Chinese keyword-based book search service, from a technological perspective, we propose to modify a user query sequence carefully to confuse the user query topics and thus protect the user topic privacy on the untrusted server, without compromising the accuracy of each book search service. First, we propose a client-based framework for the privacy protection of book search, and then a privacy model to formulate the constraints in terms of accuracy, efficiency, and security, which the cover queries generated based on a user query sequence should meet. Second, we present a modification algorithm for a user query sequence, based on some heuristic strategies, which can quickly generate a cover query sequence meeting the privacy model by replacing, deleting, and adding keywords for each user query. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed approach, i.e., which can improve the security of users’ topic privacy on the untrusted server without compromising the efficiency, accuracy, and usability of an existing Chinese keyword book search service, so it has a positive impact for the construction of a privacy-preserving text retrieval platform under an untrusted network environment.

Xia, J, Xu, M, Zhang, H, Zhang, J, Huang, W, Cao, H & Wen, S 2023, 'Robust Face Alignment via Inherent Relation Learning and Uncertainty Estimation', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-18.
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Xia, N, Yu, H, Wang, Y, Xuan, J & Luo, X 2023, 'DAFS: a domain aware few shot generative model for event detection', Machine Learning, vol. 112, no. 3, pp. 1011-1031.
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Xiao, F, Guan, J, Zhu, Q & Wang, W 2023, 'Graph Attention for Automated Audio Captioning', IEEE Signal Processing Letters, vol. 30, pp. 413-417.
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Xiao, Y, Xia, R, Li, Y, Shi, G, Nguyen, DN, Hoang, DT, Niyato, D & Krunz, M 2023, 'Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach', IEEE Transactions on Mobile Computing, pp. 1-15.
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Xiao, Y, Zhang, X, Li, Y, Shi, G, Krunz, M, Nguyen, DN & Hoang, DT 2023, 'Time-sensitive Learning for Heterogeneous Federated Edge Intelligence', IEEE Transactions on Mobile Computing, pp. 1-18.
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Xie, X, Cui, Y, Yu, Y & Chen, P 2023, 'Modeling and identification of nonlinear hysteresis behavior of piezoelectric actuators using a computationally efficient phenomenological model and modified cuckoo search algorithm', Smart Materials and Structures, vol. 32, no. 1, pp. 015013-015013.
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Abstract Hysteresis, an intrinsic characteristic of piezoelectric (PZT) actuators, has been demonstrated to dramatically reduce the capability and stability of the system. This paper proposes a novel computationally efficient model to describe nonlinear and hysteresis behaviors of PZT actuators. First of all, the model parameters are analyzed to investigate their effects on the output response. Then, a modified cuckoo search algorithm is used to identify the model parameters, without falling into the local optimum problems through introducing adaptive egg discovery probability and step length control factor. Further, the performance of the proposed model is validated using experimental data, via the comparison with classical Bouc-Wen and Prandtl-Ishlinskii hysteresis models. Finally, the rate-dependence of the parameters of proposed model is analyzed, which contributes to a generalized hysteresis model for the compensation control application of PZT actuators.

Xiong, J, Guo, L, Shan, M, Liu, B, Yu, P & Guo, L 2023, 'Wireless Resources Cooperation of Assembled Small UAVs for Data Collections of IoT', IEEE Internet of Things Journal, vol. 10, no. 11, pp. 9411-9422.
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Xu, B-H, Indraratna, B, Rujikiatkamjorn, C, Nguyen, TT & He, N 2023, 'Nonlinear consolidation analysis of multilayered soil with coupled vertical-radial drainage using the spectral method', Acta Geotechnica, vol. 18, no. 4, pp. 1841-1861.
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AbstractThe nonlinear variation of soil compressibility and permeability with void ratio (i.e., e-log σ′ and e-log k) has been included in the consolidation theory to accurately predict the behavior of soft soil stabilized by vertical drains. However, most current nonlinear consolidation models incorporating the coupled radial-vertical flow are based on some simplified assumptions, while including some features such as the complex implementation of multilayered computations, time-dependent loading and stress distribution with depth. This study hence introduces a novel approach where the spectral method is used to analyze the nonlinear consolidation behavior of multilayered soil associated with coupled vertical-radial drainage. In addition, time- and depth-dependent stress and soil properties at each soil layer are incorporated into the proposed model. Subsequently, the solution is verified against experimental and field data with comparison to previous analytical solutions. The results show greater accuracy of the proposed method in predicting in-situ soil behavior. A parametric study based on the proposed solution indicates that the ratio between the compression and permeability indices (ω = Cc/Ck) has a great impact on the consolidation rate, i.e., the greater the ω, the smaller the consolidation rate. Increasing the load increment ratio and the absolute difference between unity and ω (i.e., |ω − 1|) can exacerbate prediction error if the conventional simplified methods are used.

Xu, C, Jia, W, Wang, R, He, X, Zhao, B & Zhang, Y 2023, 'Semantic Navigation of PowerPoint-Based Lecture Video for AutoNote Generation', IEEE Transactions on Learning Technologies, vol. 16, no. 1, pp. 1-17.
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Xu, H, Yan, Z, Xuan, J, Zhang, G & Lu, J 2023, 'Improving proximal policy optimization with alpha divergence', Neurocomputing, vol. 534, pp. 94-105.
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Xu, S, Yang, T, Wu, P, Yang, Y, Liu, Z, Liu, J & Wu, C 2023, 'Effects of bond-slip on flexural behavior of reinforced nano-material modified UHPC beams: Experimental and numerical investigation', Composite Structures, vol. 311, pp. 116793-116793.
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Xu, T, Li, Y & Leng, D 2023, 'Mitigating jacket offshore platform vibration under earthquake and ocean waves utilizing tuned inerter damper', Bulletin of Earthquake Engineering, vol. 21, no. 3, pp. 1627-1650.
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AbstractThe unwanted vibrations of offshore structures induced by wave or earthquake loads can lead to the reduction of the service life and fatigue failure of the offshore platforms. This paper introduces tuned inerter damper (TID) to a jacket offshore platform as passive control device for mitigating the excessive vibrations of platform structure induced by wave and earthquake loads. An analytical design method is proposed for jacket platforms and the influence of installation location on the modal response is investigated. The proposed design method can determine the optimal installation position and obtain the optimal design parameters by transform the original multi-degree of freedom (MDOF) system to a single DOF (SDOF) modal system. Two sets of closed-form solutions of which corresponding to wave and earthquake excitations are derived based on the $${\mathrm{H}}_{2}$$ H 2 optimization criterion. Further, a practical 90 (m) high and 80 (m) deep in-water jacket offshore platform is used in numerical simulation and the wave forces are modeled using Morison’s equation. The case study finds that the optimal installation location of TID is deck level for both wave and earthquake loads. The proposed design method is validated by the numerical example and the results demonstrate that TID system can effectively mitigate the maximum, minimum, and RMS responses of jacket platforms. Besides, the TID is more effective when the jacket platform is under the action of waves and the tuning of TID according to earthquake load is more reliable when the jacket platform subjected to both wave and seismic loads.

Xu, Y, Chen, L, Duan, L, Tsang, IW & Luo, J 2023, 'Open Set Domain Adaptation With Soft Unknown-Class Rejection', IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 3, pp. 1601-1612.
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The goal of domain adaptation (DA) is to train a good model for a target domain, with a large amount of labeled data in a source domain but only limited labeled data in the target domain. Conventional closed set domain adaptation (CSDA) assumes source and target label spaces are the same. However, this is not quite practical in real-world applications. In this work, we study the problem of open set domain adaptation (OSDA), which only requires the target label space to partially overlap with the source label space. Consequently, the solution to OSDA requires unknown classes detection and separation, which is normally achieved by introducing a threshold for the prediction of target unknown classes; however, the performance can be quite sensitive to that threshold. In this article, we tackle the above issues by proposing a novel OSDA method to perform soft rejection of unknown target classes and simultaneously match the source and target domains. Extensive experiments on three standard datasets validate the effectiveness of the proposed method over the state-of-the-art competitors.

Xu, Y, Gu, Y, Peng, L, Wang, N, Chen, S, Liang, C, Liu, Y & Ni, B-J 2023, 'Unravelling ciprofloxacin removal in a nitrifying moving bed biofilm reactor: Biodegradation mechanisms and pathways', Chemosphere, vol. 320, pp. 138099-138099.
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Xu, Y, Zhou, C, Yu, X & Yang, Y 2023, 'Cyclic Self-Training With Proposal Weight Modulation for Cross-Supervised Object Detection', IEEE Transactions on Image Processing, vol. 32, pp. 1992-2002.
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Xu, Z, Khabbaz, H, Fatahi, B & Wu, D 2023, 'Double-layered granular soil modulus extraction for intelligent compaction using extended support vector machine learning considering soil-structure interaction', Engineering Structures, vol. 274, pp. 115180-115180.
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Intelligent Compaction (IC) has been acquiring a growing interest in real-time quality control of compacted soil layers because of its high efficiency and full-area coverage. The current intelligent compaction technology allows the determination of the uniformity level of compaction over large areas according to the dynamic response of the roller. However, accurate real-time determination of the soil modulus during compaction based on roller acceleration has been challenging due to the multi-layered composite nature of the soil and the nonlinearities of the governing dynamic equations of motion and soil response. This study adopts a double-layered soil profile, and a three-dimensional finite element model, accounting for soil-drum interaction, is utilised for the analysis. The isotropic hardening elastoplastic hysteretic model was implemented to simulate the soil behaviour subjected to cyclic loading ranging from small to large strain amplitudes and account for stiffness degradation. The comprehensive dataset composed of the roller acceleration response and ground characteristics is then used to correlate the predicted soil modulus via an advanced machine learning approach. The adopted machine learning method incorporating Gaussian Kernel and Generalised Gegenbauer Kernel functions can reasonably predict the double-layered soil modulus during roller compaction. Additional analyses were conducted to observe the proper training size and number of iterations to achieve real-time quality control to be used by site engineers. Furthermore, the influences of the relative modulus ratio, drum length and top layer modulus on the soil surface dynamic displacement are discussed.

Xu, Z, Li, J & Wu, C 2023, 'A numerical study of blast resistance of fire damaged ultra-high performance concrete columns', Engineering Structures, vol. 279, pp. 115613-115613.
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Xue, C, Sirivivatnanon, V, Nezhad, A & Zhao, Q 2023, 'Comparisons of alkali-activated binder concrete (ABC) with OPC concrete - A review', Cement and Concrete Composites, vol. 135, pp. 104851-104851.
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Xue, C, Tapas, MJ & Sirivivatnanon, V 2023, 'Cracking and stimulated autogenous self-healing on the sustainability of cement-based materials: a review', Journal of Sustainable Cement-Based Materials, vol. 12, no. 2, pp. 184-206.
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Xue, H, Liu, B, Yuan, X, Ding, M & Zhu, T 2023, 'Face image de‐identification by feature space adversarial perturbation', Concurrency and Computation: Practice and Experience, vol. 35, no. 5.
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Yam, AO, Bailey, J, Lin, F, Jakovija, A, Youlten, SE, Counoupas, C, Gunzer, M, Bald, T, Woodruff, TM, Triccas, JA, Goldstein, LD, Gallego-Ortega, D, Grey, ST & Chtanova, T 2023, 'Neutrophil Conversion to a Tumor-Killing Phenotype Underpins Effective Microbial Therapy', Cancer Research, vol. 83, no. 8, pp. 1315-1328.
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Abstract The inflammatory microenvironment of solid tumors creates a protumorigenic milieu that resembles chronic inflammation akin to a subverted wound healing response. Here, we investigated the effect of converting the tumor microenvironment from a chronically inflamed state to one of acute microbial inflammation by injecting microbial bioparticles directly into tumors. Intratumoral microbial bioparticle injection led to rapid and dramatic changes in the tumor immune composition, the most striking of which was a substantial increase in the presence of activated neutrophils. In situ photoconversion and intravital microscopy indicated that tumor neutrophils transiently switched from sessile producers of VEGF to highly motile neutrophils that clustered to make neutrophil-rich domains in the tumor. The neutrophil clusters remodeled tumor tissue and repressed tumor growth. Single-cell transcriptional analysis of microbe-stimulated neutrophils showed a profound shift in gene expression towards heightened activation and antimicrobial effector function. Microbe-activated neutrophils also upregulated chemokines known to regulate neutrophil and CD8+ T-cell recruitment. Microbial therapy also boosted CD8+ T-cell function and enhanced the therapeutic benefit of checkpoint inhibitor therapy in tumor-bearing mice and provided protection in a model of tumor recurrence. These data indicate that one of the major effector mechanisms of microbial therapy is the conversion of tumor neutrophils from a wound healing to an acutely activated cytotoxic phenotype, highlighting a rationale for broader deployment of microbial therapy in the treatment of solid cancers. Significance: Intratumoral injection of microbial bioparticles stimulates neutrophil...

Yang, A, Lin, S, Yeh, C-H, Shu, M, Yang, Y & Chang, X 2023, 'Context Matters: Distilling Knowledge Graph for Enhanced Object Detection', IEEE Transactions on Multimedia, pp. 1-14.
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Yang, C, Wang, X, Yao, L, Long, G, Jiang, J & Xu, G 2023, 'Attentional Gated Res2Net for Multivariate Time Series Classification', Neural Processing Letters, vol. 55, no. 2, pp. 1371-1395.
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AbstractMultivariate time series classification is a critical problem in data mining with broad applications. It requires harnessing the inter-relationship of multiple variables and various ranges of temporal dependencies to assign the correct classification label of the time series. Multivariate time series may come from a wide range of sources and be used in various scenarios, bringing the classifier challenge of temporal representation learning. We propose a novel convolutional neural network architecture called Attentional Gated Res2Net for multivariate time series classification. Our model uses hierarchical residual-like connections to achieve multi-scale receptive fields and capture multi-granular temporal information. The gating mechanism enables the model to consider the relations between the feature maps extracted by receptive fields of multiple sizes for information fusion. Further, we propose two types of attention modules, channel-wise attention and block-wise attention, to better leverage the multi-granular temporal patterns. Our experimental results on 14 benchmark multivariate time-series datasets show that our model outperforms several baselines and state-of-the-art methods by a large margin. Our model outperforms the SOTA by a large margin, the classification accuracy of our model is 10.16% better than the SOTA model. Besides, we demonstrate that our model improves the performance of existing models when used as a plugin. Further, based on our experiments and analysis, we provide practical advice on applying our model to a new problem.

Yang, M, Du, Z, Bao, H, Zhang, X, Liu, Q, Guo, W, Ngo, H-H & Nghiem, LD 2023, 'Experimental and Theoretical Insight of Perfluorooctanoic Acid Destruction by Alkaline Hydrothermal Treatment Enhanced with Zero-Valent Iron in Biochar', ACS ES&T Water, vol. 3, no. 5, pp. 1286-1293.
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Yang, M, Lam, K, Zhu, T & Tang, C 2023, 'SPoFC: A framework for stream data aggregation with local differential privacy', Concurrency and Computation: Practice and Experience, vol. 35, no. 5.
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Yang, S, Verma, S, Cai, B, Jiang, J, Yu, K, Chen, F & Yu, S 2023, 'Variational co-embedding learning for attributed network clustering', Knowledge-Based Systems, vol. 270, pp. 110530-110530.
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Yang, W, Huang, J, Wen, S & He, X 2023, 'Fixed‐time synchronization of neural networks with time delay via quantized intermittent control', Asian Journal of Control, vol. 25, no. 3, pp. 1823-1833.
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Yang, Y, Wu, C & Liu, Z 2023, 'Rate dependent behaviour of 3D printed ultra-high performance fibre-reinforced concrete under dynamic splitting tensile', Composite Structures, vol. 309, pp. 116727-116727.
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Yang, Z, Wang, B, Liu, W, Li, X, Liang, K, Fan, Z, Li, JJ, Niu, Y, He, Z, Li, H, Wang, D, Lin, J, Du, Y, Lin, J & Xing, D 2023, 'In situ self-assembled organoid for osteochondral tissue regeneration with dual functional units', Bioactive Materials, vol. 27, pp. 200-215.
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Yao, J, Han, B, Zhou, Z, Zhang, Y & Tsang, IW 2023, 'Latent Class-Conditional Noise Model', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-17.
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Yao, Q, Lu, DD-C & Lei, G 2023, 'A Surface Temperature Estimation Method for Lithium-Ion Battery Using Enhanced GRU-RNN', IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 1103-1112.
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Yazdani, D, Yazdani, D, Branke, J, Omidvar, MN, Gandomi, AH & Yao, X 2023, 'Robust Optimization Over Time by Estimating Robustness of Promising Regions', IEEE Transactions on Evolutionary Computation, pp. 1-1.
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Ye, K & Ji, JC 2023, 'A Novel Morphing Propeller System Inspired by Origami-Based Structure', Journal of Mechanisms and Robotics, vol. 15, no. 1.
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Abstract For a standard propeller system, the thrust output and the energy dissipation are proportionally dependent on its rotating speed, as its physical characteristics and working conditions are normally fixed during its operation. In order to improve the system performance and meet special application requirements, this paper presents the design of a novel two-stage propeller system with a morphing blade structure for higher thrust output and energy efficiency in operations. Based on the stacked Miura-ori (SMO) pattern, an origami-based structure is designed to enable a change in blade length for a propeller system and thus improve the system performance. The unique snap-through feature of the proposed origami structure is utilized to provide a two-stage working condition according to its rotating speed. The geometric parameter analysis of the SMO structure is first investigated, specifically focusing on the operating mechanism due to the snap-through behavior. Then, the implementation of the SMO structure into a rotating system is studied. The effects of design parameters on the critical transition points, which correspond to two operating states of the proposed propeller system, are numerically discussed. The simulation results confirm the performance improvement in the thrust output and energy-saving. The feasibility of using origami-based structures provides valuable insights into more applications in similar domains, such as fan system and wind turbine blades.

Ye, K & Ji, JC 2023, 'Dynamic analysis of the effects of self-weight induced structural and damping nonlinearity on the performance of an origami-inspired vibration isolator', Journal of Sound and Vibration, vol. 547, pp. 117538-117538.
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Ye, Y, Zhang, J, Pilla, S, Rao, AM & Xu, B 2023, 'Application of a new type of lithium‑sulfur battery and reinforcement learning in plug-in hybrid electric vehicle energy management', Journal of Energy Storage, vol. 59, pp. 106546-106546.
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Yi, B & Ortega, R 2023, 'Conditions for Convergence of Dynamic Regressor Extension and Mixing Parameter Estimators Using LTI Filters', IEEE Transactions on Automatic Control, vol. 68, no. 2, pp. 1253-1258.
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Yi, B, Wang, L & Manchester, IR 2023, 'Attitude Estimation from Vector Measurements: Necessary and Sufficient Conditions and Convergent Observer Design', IFAC-PapersOnLine, vol. 56, no. 1, pp. 343-348.
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Yi, B, Wang, L & Manchester, IR 2023, 'Attitude Estimation From Vector Measurements: Necessary and Sufficient Conditions and Convergent Observer Design', IEEE Transactions on Automatic Control, pp. 1-8.
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Yi, B, Wang, L & Zhang, W 2023, 'Online Gyro Bias Estimation from Single Vector Measurements Using Regression Models', IFAC-PapersOnLine, vol. 56, no. 1, pp. 252-257.
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Yin, H, Sun, Y, Xu, G & Kanoulas, E 2023, 'Trustworthy Recommendation and Search: Introduction to the Special Issue - Part 1', ACM Transactions on Information Systems, vol. 41, no. 3, pp. 1-5.
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Yin, S, Liu, P, Kong, L, Zhang, X, Qi, Y & Huang, J 2023, 'Accumulated plastic strain behavior of granite residual soil under traffic loading', Soil Dynamics and Earthquake Engineering, vol. 164, pp. 107617-107617.
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Yu, D, Li, Q, Wang, X & Xu, G 2023, 'Deconfounded recommendation via causal intervention', Neurocomputing, vol. 529, pp. 128-139.
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Yu, H, Hossain, SM, Wang, C, Choo, Y, Naidu, G, Han, DS & Shon, HK 2023, 'Selective lithium extraction from diluted binary solutions using metal-organic frameworks (MOF)-based membrane capacitive deionization (MCDI)', Desalination, vol. 556, pp. 116569-116569.
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Yu, H, Liu, W, Lu, J, Wen, Y, Luo, X & Zhang, G 2023, 'Detecting group concept drift from multiple data streams', Pattern Recognition, vol. 134, pp. 109113-109113.
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Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2023, 'Low-Resolution Hybrid Beamforming in Millimeter-wave Multi-user Systems', IEEE Transactions on Vehicular Technology, pp. 1-15.
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Yu, H, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2023, 'Regularized Zero-Forcing Aided Hybrid Beamforming for Millimeter-Wave Multiuser MIMO Systems', IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3280-3295.
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Yu, X, Wang, F, Luo, Z, Kang, Z & Wang, Y 2023, 'Design of hierarchical microstructures with isotropic elastic stiffness', Materials & Design, vol. 229, pp. 111895-111895.
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Yu, Y, Li, J, Li, J, Xia, Y, Ding, Z & Samali, B 2023, 'Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion', Developments in the Built Environment, vol. 14, pp. 100128-100128.
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Yuan, B, Wang, Z, Cao, J, Huang, Y, Shen, Y, Song, Z, Zhao, H & Cheng, X 2023, 'Mixture formation characteristics and feasibility of methanol as an alternative fuel for gasoline in port fuel injection engines: Droplet evaporation and spray visualization', Energy Conversion and Management, vol. 283, pp. 116956-116956.
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Yuan, D, Chang, X, Liu, Q, Yang, Y, Wang, D, Shu, M, He, Z & Shi, G 2023, 'Active Learning for Deep Visual Tracking', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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Yuan, P, Xiang, H, Xu, S, Liu, J, Su, Y, Qu, K & Wu, C 2023, 'Experimental and numerical study of steel wire mesh reinforced G-HPC slab protected by UHMWPE FRC under multiple blast loadings', Engineering Structures, vol. 275, pp. 115224-115224.
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Yuan, P, Xu, S, Liu, J, Su, Y & Wu, C 2023, 'Experimental and numerical study of blast resistance of geopolymer based high performance concrete sandwich walls incorporated with metallic tube core', Engineering Structures, vol. 278, pp. 115505-115505.
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Zabed, HM, Akter, S, Yun, J, Zhang, G, Zhao, M, Mofijur, M, Awasthi, MK, Kalam, MA, Ragauskas, A & Qi, X 2023, 'Towards the sustainable conversion of corn stover into bioenergy and bioproducts through biochemical route: Technical, economic and strategic perspectives', Journal of Cleaner Production, vol. 400, pp. 136699-136699.
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Zafra, E, Vazquez, S, Geyer, T, Aguilera, RP & Franquelo, LG 2023, 'Long Prediction Horizon FCS-MPC for Power Converters and Drives', IEEE Open Journal of the Industrial Electronics Society, vol. 4, pp. 159-175.
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Zareei, SM, Sepehrirahnama, S, Jamshidian, M & Ziaei-Rad, S 2023, 'Three-dimensional numerical simulation of particle acoustophoresis: COMSOL implementation and case studies', Engineering with Computers, vol. 39, no. 1, pp. 735-750.
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Zeng, J, Wu, T, Feng, W, Ni, W, Lv, T, Zhou, S, Wang, X & Guo, YJ 2023, 'Analysis of Massive Ultra-Reliable and Low-Latency Communications Over the κ-μ Shadowed Fading Channel', IEEE Transactions on Communications, vol. 71, no. 3, pp. 1798-1813.
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Zeng, J, Xiao, C, Wu, T, Ni, W, Liu, RP & Guo, YJ 2023, 'Uplink Non-Orthogonal Multiple Access With Statistical Delay Requirement: Effective Capacity, Power Allocation, and α Fairness', IEEE Transactions on Wireless Communications, vol. 22, no. 2, pp. 1298-1313.
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Zeng, W, Huang, J, Wen, S & Fu, Z 2023, 'A masked-face detection algorithm based on M-EIOU loss and improved ConvNeXt', Expert Systems with Applications, vol. 225, pp. 120037-120037.
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Zhai, C, Zhao, Y, Wen, W, Qin, H & Xie, L 2023, 'A novel urban seismic resilience assessment method considering the weighting of post-earthquake loss and recovery time', International Journal of Disaster Risk Reduction, vol. 84, pp. 103453-103453.
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Zhand, S, Zhu, Y, Nazari, H, Sadraeian, M, Warkiani, ME & Jin, D 2023, 'Thiolate DNAzymes on Gold Nanoparticles for Isothermal Amplification and Detection of Mesothelioma-derived Exosomal PD-L1 mRNA', Analytical Chemistry, vol. 95, no. 6, pp. 3228-3237.
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Zhang, B, Pu, L, Zhao, T, Wang, L, Shu, C, Xu, S, Sun, J, Zhang, R & Han, L 2023, 'Global Burden of Cardiovascular Disease from 1990 to 2019 Attributable to Dietary Factors', The Journal of Nutrition, vol. 153, no. 6, pp. 1730-1741.
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Zhang, C, Zhang, S, Zou, X, Yu, S & Yu, JJQ 2023, 'Toward Large-Scale Graph-Based Traffic Forecasting: A Data-Driven Network Partitioning Approach', IEEE Internet of Things Journal, vol. 10, no. 5, pp. 4506-4519.
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Zhang, D, Ouyang, D, Shu, L, Hu, C, Shi, K & Wen, S 2023, 'Sum-based event-triggered dynamic output feedback control for synchronization of fuzzy neural networks with deception attacks', Neural Computing and Applications, vol. 35, no. 14, pp. 10221-10237.
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Zhang, D, Peng, C, Chang, X & Xia, F 2023, 'The Effect of Facial Perception and Academic Performance on Social Centrality', IEEE Transactions on Computational Social Systems, vol. 10, no. 3, pp. 970-981.
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Zhang, G, Chen, J, Zhang, J, Zhang, Z, Sun, M, Yu, Y & Wang, J 2023, 'An Experimental Study and Modeling on the Thermo‐Rheological Properties of Polyurethane‐Based Magnetorheological Gel', Advanced Engineering Materials, vol. 25, no. 8, pp. 2201345-2201345.
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Zhang, G, Chen, J, Zhang, Z, Sun, M, Yu, Y, Wang, J & Cai, S 2023, 'A novel parametric model for nonlinear hysteretic behaviours with strain-stiffening of magnetorheological gel composite', Composite Structures, vol. 318, pp. 117082-117082.
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Zhang, G, Liu, B, Zhu, T, Ding, M & Zhou, W 2023, 'Label-Only Membership Inference Attacks and Defenses in Semantic Segmentation Models', IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 2, pp. 1435-1449.
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Zhang, H & Xu, M 2023, 'Recognition of Emotions in User-Generated Videos through Frame-Level Adaptation and Emotion Intensity Learning', IEEE Transactions on Multimedia, vol. 25, pp. 881-891.
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Zhang, H, Li, B, Karimi, M, Saydam, S & Hassan, M 2023, 'Recent Advancements in IoT Implementation for Environmental, Safety, and Production Monitoring in Underground Mines', IEEE Internet of Things Journal, pp. 1-1.
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Zhang, H, Xia, J, Zhang, G & Xu, M 2023, 'Learning Graph Representations Through Learning and Propagating Edge Features', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Zhang, H, Zhang, L, Dong, S, Duan, X, Zhu, D, Ni, B-J & Lyu, C 2023, 'Regulating energy band structures of triazine covalent organic frameworks with electron-donating/withdrawing substituents for visible-light-responsive photocatalytic tetracycline degradation and Cr(VI) reduction', Journal of Hazardous Materials, vol. 446, pp. 130756-130756.
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Zhang, J, Zhang, N, Wang, D, Gao, B, Shon, HK, Yang, X, Zhao, H & Wang, Z 2023, 'Polyamidoamine and carboxylated cellulose nanocrystal grafted antifouling forward osmosis membranes for efficient leachate treatment via integrated forward osmosis and membrane distillation process', Journal of Membrane Science, vol. 668, pp. 121241-121241.
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Zhang, JA, Wu, K, Huang, X & Guo, YJ 2023, 'Beam Alignment for Analog Arrays Based on Gaussian Approximation', IEEE Transactions on Vehicular Technology, pp. 1-6.
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Zhang, K, Fan, Z, Song, X & Yu, S 2023, 'Enhancing Trajectory Recovery From Gradients via Mobility Prior Knowledge', IEEE Internet of Things Journal, vol. 10, no. 6, pp. 5583-5594.
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Zhang, L, Shi, Y, Chang, Y-C & Lin, C-T 2023, 'Federated Fuzzy Neural Network With Evolutionary Rule Learning', IEEE Transactions on Fuzzy Systems, vol. 31, no. 5, pp. 1653-1664.
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Zhang, L, Shi, Y, Chang, Y-C & Lin, C-T 2023, 'Robust Fuzzy Neural Network With an Adaptive Inference Engine', IEEE Transactions on Cybernetics, pp. 1-11.
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Zhang, L, Szmalenberg, A, Cook, K, Liu, B, Ding, L, Wang, F & McGloin, D 2023, 'Trapped aerosol sizes under fiber-based counterpropagation optical trapping', Journal of the Optical Society of America B, vol. 40, no. 2, pp. 460-460.
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Quantifying the size range of aerosols that can be trapped in a counterpropagation dual-fiber trapping configuration is important in understanding how these particles can be manipulated and characterized in such traps. Here, we present simulations and experiments investigating the trapped aerosol size range variations in the intermediate position of two fibers under different fiber separations, aerosol particle sizes, fiber powers, and radial offset. By doing so, we establish a parametric space plot of stable aerosol trapping, and the parametric analysis provides insight into the tolerance of such traps to trapping fluctuations.

Zhang, L, Xia, Z, He, Y, Xin, C, Yu, Y, Ren, F & Wang, R 2023, 'Fiber reinforced polypropylene composites interfacial behavior improvement fabricated by cold plasma jet SiO_x nanoparticles deposition', Journal of Composite Materials, vol. 57, no. 6, pp. 1185-1198.
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In this study, cold atmospheric pressure plasma jet was adopted to glass fibers surface modification with tetraethyl orthosilicate as a precursor. To enhance the interfacial bonding forces of glass fiber reinforced polypropylene (GFRP) composites, SiOx nanoparticles were polymerized on the fiber surfaces. The effect of two factors (the distance between nozzle and fiber (D) and the treatment time (T)) on the interfacial bonding behavior of GFRP composites was studied. The modified fibers and composites properties, including surface topography, chemical composition, wettability and interfacial mechanical properties, were studied comprehensively. The optimal parameters were obtained at D = 17 mm and T = 12 s. Our results indicated that the interlaminar shear strength of GFRP composites was increased by 30.79% compared to control group. Further studies found that plasma treatment introduced larger surface roughness, surface energy and more oxygen-containing functional groups, which was responsible for the interlaminar shear strength improvement.

Zhang, L, Xiao, F & Cao, Z 2023, 'Multi-channel EEG signals classification via CNN and multi-head self-attention on evidence theory', Information Sciences, vol. 642, pp. 119107-119107.
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Zhang, L, Zhang, X, Wang, Q, Wu, W, Chang, X & Liu, J 2023, 'RPMG-FSS: Robust Prior Mask Guided Few-Shot Semantic Segmentation', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Zhang, L, Zhu, T, Hussain, FK, Ye, D & Zhou, W 2023, 'A Game-Theoretic Method for Defending Against Advanced Persistent Threats in Cyber Systems', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 1349-1364.
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Zhang, L, Zhu, T, Xiong, P, Zhou, W & Yu, PS 2023, 'A Robust Game-Theoretical Federated Learning Framework With Joint Differential Privacy', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3333-3346.
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Zhang, L-C, Chen, L-Y, Zhou, S & Luo, Z 2023, 'Powder bed fusion manufacturing of beta-type titanium alloys for biomedical implant applications: A review', Journal of Alloys and Compounds, vol. 936, pp. 168099-168099.
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Zhang, M, Zhou, J, Zhang, G, Cui, L, Gao, T & Yu, S 2023, 'APDP: Attribute-Based Personalized Differential Privacy Data Publishing Scheme for Social Networks', IEEE Transactions on Network Science and Engineering, vol. 10, no. 2, pp. 922-933.
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Zhang, Q, Liao, W, Zhang, G, Yuan, B & Lu, J 2023, 'A Deep Dual Adversarial Network for Cross-Domain Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3266-3278.
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Zhang, Q, Yang, Y, Shi, C, Lao, A, Hu, L, Wang, S & Naseem, U 2023, 'Rumor Detection With Hierarchical Representation on Bipartite Ad Hoc Event Trees', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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Zhang, S, Chen, X, Wen, S & Li, Z 2023, 'Density-based reliable and robust explainer for counterfactual explanation', Expert Systems with Applications, vol. 226, pp. 120214-120214.
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Zhang, S, Shi, J, Sharma, E, Li, X, Gao, S, Zhou, X, O'Brien, J, Coin, L, Liu, Y, Sivakumar, M, Hai, F & Jiang, G 2023, 'In-sewer decay and partitioning of Campylobacter jejuni and Campylobacter coli and implications for their wastewater surveillance', Water Research, vol. 233, pp. 119737-119737.
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Zhang, T, Ye, D, Zhu, T, Liao, T & Zhou, W 2023, 'Evolution of cooperation in malicious social networks with differential privacy mechanisms', Neural Computing and Applications, vol. 35, no. 18, pp. 12979-12994.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Cooperation is an essential behavior in multi-agent systems. Existing mechanisms have two common drawbacks. The first drawback is that malicious agents are not taken into account. Due to the diverse roles in the evolution of cooperation, malicious agents can exist in multi-agent systems, and they can easily degrade the level of cooperation by interfering with agent’s actions. The second drawback is that most existing mechanisms have a limited ability to fit in different environments, such as different types of social networks. The performance of existing mechanisms heavily depends on some factors, such as network structures and the initial proportion of cooperators. To solve these two drawbacks, we propose a novel mechanism which adopts differential privacy mechanisms and reinforcement learning. Differential privacy mechanisms can be used to relieve the impact of malicious agents by exploiting the property of randomization. Reinforcement learning enables agents to learn how to make decisions in various social networks. In this way, the proposed mechanism can promote the evolution of cooperation in malicious social networks.

Zhang, W, Guo, S, Zhang, H, Sui, Y, Xue, Y & Xu, Y 2023, 'Challenging Machine Learning-Based Clone Detectors via Semantic-Preserving Code Transformations', IEEE Transactions on Software Engineering, vol. 49, no. 5, pp. 3052-3070.
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Zhang, X, Peng, H, Zhang, J & Wang, Y 2023, 'A cost-sensitive attention temporal convolutional network based on adaptive top-k differential evolution for imbalanced time-series classification', Expert Systems with Applications, vol. 213, pp. 119073-119073.
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Zhang, X, Zhang, X, Zhang, L, Huang, Z, Fang, F, Yang, Y, Gao, M, Pan, H & Liu, Y 2023, 'Remarkable low-temperature hydrogen cycling kinetics of Mg enabled by VH nanoparticles', Journal of Materials Science & Technology, vol. 144, pp. 168-177.
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Zhang, X, Zuo, S, Li, S, Shang, Y, Du, Q, Wang, H, Guo, W & Ngo, HH 2023, 'Responses of biofilm communities in a hybrid moving bed biofilm reactor-membrane bioreactor system to sulfadiazine antibiotic exposure', Bioresource Technology, vol. 382, pp. 129126-129126.
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Zhang, Y, Afroz, S, Nguyen, QD, Kim, T, Castel, A & Xu, T 2023, 'Modeling blended cement concrete tensile creep for standard ring test application', Structural Concrete, vol. 24, no. 2, pp. 2170-2188.
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Zhang, Y, Falque, R, Zhao, L, Chen, Y, Huang, S & Li, H 2023, 'Structure-to-Shape Aortic 3-D Deformation Reconstruction for Endovascular Interventions', IEEE Transactions on Robotics, pp. 1-19.
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Zhang, Y, Feng, K, Ji, JC, Yu, K, Ren, Z & Liu, Z 2023, 'Dynamic Model-Assisted Bearing Remaining Useful Life Prediction Using the Cross-Domain Transformer Network', IEEE/ASME Transactions on Mechatronics, vol. 28, no. 2, pp. 1070-1080.
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Zhang, Y, Liu, H, Zhao, S, Xie, C, Huang, Z & Wang, S 2023, 'Insights into the Dynamic Evolution of Defects in Electrocatalysts', Advanced Materials, vol. 35, no. 9, pp. 2209680-2209680.
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Zhang, Y, Zhang, H, Qiu, L, Wang, Z, Zhang, S, Qiu, N & Fang, J 2023, 'A stochastic framework for computationally efficient fail-safe topology optimization', Engineering Structures, vol. 283, pp. 115831-115831.
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Zhang, Z, Jiao, Y, Huang, S, Xiong, R & Wang, Y 2023, 'Map-Based Visual-Inertial Localization: Consistency and Complexity', IEEE Robotics and Automation Letters, vol. 8, no. 3, pp. 1407-1414.
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Zhang, Z, Rahbari, H & Krunz, M 2023, 'Adaptive Preamble Embedding With MIMO to Support User-Defined Functionalities in WLANs', IEEE Transactions on Mobile Computing, vol. 22, no. 2, pp. 691-707.
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Zhang, Z, Song, Y, Huang, S, Xiong, R & Wang, Y 2023, 'Toward Consistent and Efficient Map-Based Visual-Inertial Localization: Theory Framework and Filter Design', IEEE Transactions on Robotics, pp. 1-20.
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Zhang, Z, Wang, L, Wang, Y, Zhou, L, Zhang, J & Chen, F 2023, 'Dataset-Driven Unsupervised Object Discovery for Region-Based Instance Image Retrieval', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 247-263.
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Zhang, Z, Wu, L, Ma, C, Li, J, Wang, J, Wang, Q & Yu, S 2023, 'LSFL: A Lightweight and Secure Federated Learning Scheme for Edge Computing', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 365-379.
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Zhao, F, Cao, S, Luo, Q & Ji, J 2023, 'Enhanced design of the quasi-zero stiffness vibration isolator with three pairs of oblique springs: Theory and experiment', Journal of Vibration and Control, vol. 29, no. 9-10, pp. 2049-2063.
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Quasi-zero stiffness vibration isolators have been extensively studied due to superior passive vibration isolation performance. As the quasi-zero stiffness region of the isolators is generally small, the research on their responses to the excitation with high amplitude is currently quite limited. This paper presents an improved design of the quasi-zero stiffness isolator with three pairs of oblique springs to increase the amplitude of the excitation. Theoretical formulations are derived for stiffness and force, and then the influences of three independent parameters on the quasi-zero stiffness region are studied to obtain optimal design parameters. A prototype is fabricated and tested for displacement excitations with amplitudes of 5 mm, 10 mm, and 15 mm in a frequency range of 1.5–10 Hz. The absolute displacement transmissibility of the enhanced quasi-zero stiffness isolator is theoretically and experimentally compared with that of the corresponding linear isolator and that of the previous isolators with three pairs of oblique springs using the same parameter conditions of the loaded mass, the horizontal length of oblique springs, and the vertical spring. The experimental results show that the enhanced design of the quasi-zero stiffness isolator with three pairs of oblique springs can achieve lower displacement transmissibility and deal with the displacement excitation with higher amplitude.

Zhao, H, Zheng, Z, Zhou, H, Chang, L, Tsoutas, K, Yang, L, Alavi, SKH, Liu, Y, Akhavan, B, Bilek, MM & Liu, Z 2023, 'Cathodic arc deposition of high entropy alloy thin films with controllable microstructure', Surfaces and Interfaces, vol. 37, pp. 102692-102692.
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Zhao, L-H, Wen, S, Guo, Z, Shi, K, Xiao, J, Zhu, S & Huang, T 2023, 'Finite-Time Nonchattering Synchronization of Coupled Neural Networks With Multi-Weights', IEEE Transactions on Network Science and Engineering, pp. 1-14.
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Zhao, P, Pan, Y, Li, X, Chen, X, Tsang, IW & Liao, L 2023, 'Coarse-to-Fine Contrastive Learning on Graphs', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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Zhao, R, Tao, M, Wu, C, Wang, S & Zhu, J 2023, 'Spallation damage of underground openings caused by excavation disturbance of adjacent tunnels', Tunnelling and Underground Space Technology, vol. 132, pp. 104892-104892.
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Zhao, Y, Wang, S, Wang, Y & Liu, H 2023, 'MbSRS: A multi-behavior streaming recommender system', Information Sciences, vol. 631, pp. 145-163.
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Zhao, Y, Yue, H, Zhu, J & Yang, X 2023, 'A Planar Electromagnetic Actuator With Passive Adsorption for CubeSats Transport in a Weightless Environment', IEEE Transactions on Industrial Electronics, vol. 70, no. 10, pp. 10396-10408.
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Zheng, Z, Zheng, L, Yang, Y & Wu, F 2023, 'U-Turn: Crafting Adversarial Queries with Opposite-Direction Features', International Journal of Computer Vision, vol. 131, no. 4, pp. 835-854.
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Zhou, H, Long, Y, Gong, S, Zhu, K, Hoang, DT & Niyato, D 2023, 'Hierarchical Multi-Agent Deep Reinforcement Learning for Energy-Efficient Hybrid Computation Offloading', IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 986-1001.
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Zhou, J, Duan, Y, Zou, Y, Chang, Y-C, Wang, Y-K & Lin, C-T 2023, 'Speech2EEG: Leveraging Pretrained Speech Model for EEG Signal Recognition', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 2140-2153.
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Zhou, J, Peng, H, Su, S & Song, R 2023, 'Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot', IEEE Transactions on Biomedical Engineering, vol. 70, no. 6, pp. 1858-1868.
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Zhou, L, Barthe, G, Strub, P-Y, Liu, J & Ying, M 2023, 'CoqQ: Foundational Verification of Quantum Programs', Proceedings of the ACM on Programming Languages, vol. 7, no. POPL, pp. 833-865.
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CoqQ is a framework for reasoning about quantum programs in the Coq proof assistant. Its main components are: a deeply embedded quantum programming language, in which classic quantum algorithms are easily expressed, and an expressive program logic for proving properties of programs. CoqQ is foundational: the program logic is formally proved sound with respect to a denotational semantics based on state-of-art mathematical libraries (MathComp and MathComp Analysis). CoqQ is also practical: assertions can use Dirac expressions, which eases concise specifications, and proofs can exploit local and parallel reasoning, which minimizes verification effort. We illustrate the applicability of CoqQ with many examples from the literature.

Zhou, M, Lu, J, Song, Y & Zhang, G 2023, 'Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Zhou, S, Liu, C, Ye, D, Zhu, T, Zhou, W & Yu, PS 2023, 'Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity', ACM Computing Surveys, vol. 55, no. 8, pp. 1-39.
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The outstanding performance of deep neural networks has promoted deep learning applications in a broad set of domains. However, the potential risks caused by adversarial samples have hindered the large-scale deployment of deep learning. In these scenarios, adversarial perturbations, imperceptible to human eyes, significantly decrease the model’s final performance. Many papers have been published on adversarial attacks and their countermeasures in the realm of deep learning. Most focus on evasion attacks, where the adversarial examples are found at test time, as opposed to poisoning attacks where poisoned data is inserted into the training data. Further, it is difficult to evaluate the real threat of adversarial attacks or the robustness of a deep learning model, as there are no standard evaluation methods. Hence, with this article, we review the literature to date. Additionally, we attempt to offer the first analysis framework for a systematic understanding of adversarial attacks. The framework is built from the perspective of cybersecurity to provide a lifecycle for adversarial attacks and defenses.

Zhou, T, Yang, Y & Wang, W 2023, 'Differentiable Multi-Granularity Human Parsing', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-14.
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Zhou, T, Zhang, Z, Liu, H, Dong, S, Nghiem, LD, Gao, L, Chaves, AV, Zamyadi, A, Li, X & Wang, Q 2023, 'A review on microalgae-mediated biotechnology for removing pharmaceutical contaminants in aqueous environments: Occurrence, fate, and removal mechanism', Journal of Hazardous Materials, vol. 443, pp. 130213-130213.
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Zhou, X, Feng, Y & Li, S 2023, 'Supervised Learning Enhanced Quantum Circuit Transformation', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 42, no. 2, pp. 437-447.
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Zhou, Y, Liu, X, Fu, Y, Wu, D, Wang, JH & Yu, S 2023, 'Optimizing the Numbers of Queries and Replies in Convex Federated Learning with Differential Privacy', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Zhu, C, Cheng, Z, Ye, D, Hussain, FK, Zhu, T & Zhou, W 2023, 'Time-driven and Privacy-preserving Navigation Model for Vehicle-to-vehicle Communication Systems', IEEE Transactions on Vehicular Technology, pp. 1-12.
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Zhu, HY, Chen, H-T & Lin, C-T 2023, 'The Effects of Virtual and Physical Elevation on Physiological Stress During Virtual Reality Height Exposure', IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 4, pp. 1937-1950.
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Zhu, R, Wang, P, Geng, Z, Zhao, Y & Yu, S 2023, 'Double-Agent Reinforced vNFC Deployment in EONs for Cloud-Edge Computing', Journal of Lightwave Technology, pp. 1-14.
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Zhu, S, Chen, C & Wen, S 2023, 'Controller design for finite-time attractive and energy consumption of stochastic nonlinear systems', International Journal of Control, vol. 96, no. 1, pp. 74-81.
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Zhu, S, Shen, Y, Mu, C, Liu, X & Wen, S 2023, 'Generalized-Type Multistability of Almost Periodic Solutions for Memristive Cohen–Grossberg Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Zhu, T, Ye, D, Cheng, Z, Zhou, W & Yu, PS 2023, 'Learning Games for Defending Advanced Persistent Threats in Cyber Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2410-2422.
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Zhu, T, Ye, D, Zhou, S, Liu, B & Zhou, W 2023, 'Label-Only Model Inversion Attacks: Attack With the Least Information', IEEE Transactions on Information Forensics and Security, vol. 18, pp. 991-1005.
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Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y & Hanzo, L 2023, 'Low-Complexity Pareto-Optimal 3D Beamforming for the Full-Dimensional Multi-User Massive MIMO Downlink', IEEE Transactions on Vehicular Technology, pp. 1-16.
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Zhu, Y, Lin, Q, Lu, H, Shi, K, Liu, D, Chambua, J, Wan, S & Niu, Z 2023, 'Recommending Learning Objects Through Attentive Heterogeneous Graph Convolution and Operation-Aware Neural Network', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 4178-4189.
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Zhuang, T, Zhong, J, Niu, F, Karimi, M, Kirby, R & Lu, J 2023, 'A steerable non-paraxial Gaussian beam expansion for a steerable parametric array loudspeaker', The Journal of the Acoustical Society of America, vol. 153, no. 1, pp. 124-136.
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A steerable parametric array loudspeaker (PAL) aims to steer a highly directional audio beam without the need to mechanically rotate the source. The Gaussian beam expansion (GBE) method is often used to model PALs because it is a computationally efficient approach, however the method relies on a paraxial approximation that can result in significant inaccuracies at large steering angles. To address this limitation, a steerable non-paraxial GBE is proposed in this article, where the mainlobe of the steered ultrasonic beam is included in the calculation by rotating the coordinate system. A non-paraxial approximation is then used to improve the accuracy of the method when integrating the virtual audio sources. The numerical results obtained using the proposed method are compared against those using the conventional GBE, as well as an exact solution. For a typical configuration, it is shown that for a conventional GBE the prediction error can be more than 30 dB at large angles, whereas the proposed method reduces this to less than 1 dB. The advantage of the proposed method is more significant at large steering angles, low audio frequencies, and those locations outside of the paraxial region. This improvement in performance is achieved with a computational cost that remains the same as the conventional GBE.

Zogan, H, Razzak, I, Jameel, S & Xu, G 2023, 'Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic', IEEE Journal of Biomedical and Health Informatics, pp. 1-9.
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Zuo, W, Chen, Z, E, J, Li, Q, Zhang, G & Huang, Y 2023, 'Effects of structure parameters of tube outlet on the performance of a hydrogen-fueled micro planar combustor for thermophotovoltaic applications', Energy, vol. 266, pp. 126434-126434.
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Zuo, W, Luo, Q, Li, Q & Sun, G 2023, 'Effect of thermal and hydrothermal aging on the crashworthiness of carbon fiber reinforced plastic composite tubes', Composite Structures, vol. 303, pp. 116136-116136.
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Zuo, Y, Lu, W, Peng, X, Wang, S, Zhang, W & Qiao, X 2023, 'DuCL: Dual-stage contrastive learning framework for Chinese semantic textual matching', Computers and Electrical Engineering, vol. 106, pp. 108574-108574.
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Ahmed, F, Singh, K, Esselle, KP & Thalakotuna, D 1970, 'Near-Field Metallic Metasurfaces for Enhancing Antenna Capabilities', 2023 5th Australian Microwave Symposium (AMS), 2023 5th Australian Microwave Symposium (AMS), IEEE.
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Akbarzade, M, Oberst, S, Sepehrirahnama, S & Halkon, B 1970, 'Sensitivity and bifurcation analysis of an analytical model of a trapped object in an externally excited acoustic radiation force field', Proceedings of NOVEM23, Noise and Vibration Emerging Methods, Auckland, New Zealand, pp. 1-10.
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Acoustic radiation force (ARF) is a nonlinear acoustic phenomenon for which the acoustic field properties and, to an even greater extent, the explicit dynamics of the object, have received limited attention in the published literature to date. Any oscillations due to the flow field or external perturbations are thereby negligible while the particle is trapped in a stable position. By changing the viewpoint from the acoustic field to the dynamics of a levitated particle, the amplitude and frequency of external oscillation is non-negligible, we ask the question of how external excitation changes the dynamics of the object. We explicitly derive an analytical formulation of a trapped object in the form of a Duffing-like equation with its constants being defined by the object itself, the fluid, the acoustic wave, and the external vibration properties. In this case, the bifurcation behaviour is studied, and we show this together with a sensitivity analysis to represent correct dynamic behaviour in certain regimes of the bifurcation diagram.

Alalmaie, A, Nanda, P & He, X 1970, 'Zero Trust Network Intrusion Detection System (NIDS) using Auto Encoder for Attention-based CNN-BiLSTM', 2023 Australasian Computer Science Week, ACSW 2023: 2023 Australasian Computer Science Week, ACM.
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Alfredo, RD, Nie, L, Kennedy, P, Power, T, Hayes, C, Chen, H, McGregor, C, Swiecki, Z, Gašević, D & Martinez-Maldonado, R 1970, ''That Student Should be a Lion Tamer!' StressViz: Designing a Stress Analytics Dashboard for Teachers', LAK23: 13th International Learning Analytics and Knowledge Conference, LAK 2023: 13th International Learning Analytics and Knowledge Conference, ACM.
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Alsufyani, N & Gill, AQ 1970, 'A Knowledge-Graph Based Integrated Digital EA Maturity and Performance Framework', Springer International Publishing, pp. 214-229.
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Arivalagan, J, Rujikiatkamjorn, C, Indraratna, B & Warwick, A 1970, 'Effectiveness of Geosynthetics at Preventing Subgrade Instability under Cyclic Loading', Geo-Congress 2023, Geo-Congress 2023, American Society of Civil Engineers.
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Boye, T 1970, 'Accessibility of Work-Integrated Learning in Engineering, IT, and Computer Science for Students with Disabilities', The Conference on Research in Equity and Sustained Participation in Engineering, Computing, and Technology, IEEE, Philadelphia, USA, pp. 1-15.
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Universities put significant resources into supporting students with disabilities on campus. However, this support from universities can be limited off-campus. With universities and governments strongly advocating for the inclusion of Work-Integrated Learning (WIL) placements in all programs, particularly in technical areas such as engineering, IT, and computer science, students with disabilities are increasingly expected to leave campus to attain their degree. In these arrangements the expectations are unclear and greatly vary, with most employers taking full responsibility for students on a day-to-day basis. Although engineering, IT, and computer science industries have become more open to and inclusive of diverse workforces in recent years, student WIL experiences can vary dramatically between employers and this can leave students vulnerable to the culture and accessibility of the workplace they undertake WIL activities in. Additionally, many programs require students to find their own placements, which is challenging given the reportedly low employment opportunities available to people with disabilities. In this paper, we recommend that students with disabilities need to be considered more than they currently are in the design of WIL placement programs and that programs could provide greater support to these students to ensure the positive outcomes associated with WIL are equitable for all students. It is also suggested that work needs to be done to bring students with disabilities into the conversation through co-design and participatory research in order to understand what their experiences are like in WIL and how universities and employers can better support them to reach their goals.

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Chu, NH, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 1970, 'Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning', 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023 IEEE Wireless Communications and Networking Conference (WCNC), IEEE.
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Cortes, CAT, Lin, C-T, Do, T-TN & Chen, H-T 1970, 'An EEG-based Experiment on VR Sickness and Postural Instability While Walking in Virtual Environments', 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR), 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR), IEEE.
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Burdon, S, Stewart, C, John, C, Bajada, C, Kang, K & Abedin, B 2023, 'Powering National Outcomes from New Digital Technologies: An analysis of government policies to maximize the economic and social benefits', Asian Productivity Organization.
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The accelerating advancement of digital technologies is providing a myriad of opportunities for nations, both economically and socially. This study was a collaboration between the Asian Productivity Organization (APO) and the University of Technology Sydney (UTS). It considered how APO member governments might approach the advancement of digital technologies to maximize benefits for their nations. Specifically, the study considered which digital technologies, industry sectors, and regulatory and policy initiatives had the greatest potential to deliver national outcomes in areas of economic growth, productivity, and social impact. Overall, the research found that virtually all countries believed that the latest wave of digital technologies would deliver significant economic and productivity growth and that having policies and regulations in place to support a digital economy was important. It also highlighted the different opportunities, constraints, and challenges that individual APO nations faced, which meant that a one-size approach would be inappropriate. So, this report outlines a range of approaches that can be employed by nations, depending on where a country is currently positioned on the spectrum of digital economic development. It provides an initial framework that can be applied to any country, and offers some useful generic approaches, while also proposing specific recommendations for countries at different stages of digital readiness. The study led us to segment APO member economies into four ‘digital economy’ groups: the embryonic, the nascent, the emergent, and the leaders. In addition, we paid particular attention to three exemplars or ‘case study countries,’ each representing a different level of economic development and digital maturity. These were: Indonesia (nascent), Malaysia (emergent), and the Republic of Korea (leaders). Individual countries can get feedback from this report, in the first instance, by considering their relative standing, i....

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Jia, M, Gabrys, B & Musial, K 2023, 'A Network Science perspective of Graph Convolutional Networks: A survey'.

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Kumar, A, Esmaili, N & Piccardi, M 2023, 'The Contextualized Regressive Topic Model', Elsevier BV.
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McCarthy, PX, Gong, X, Stephany, F, Braesemann, F, Rizoiu, M-A & Kern, ML 2023, 'The Science of Startups: The Impact of Founder Personalities on Company Success'.

Ngo, Q, Phan, TK, Mahmood, A & Xiang, W 2023, 'Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach', Institute of Electrical and Electronics Engineers (IEEE).
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Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Niyato, D & Dutkiewicz, E 2023, 'MetaShard: A Novel Sharding Blockchain Platform for Metaverse Applications'.

Nguyen, DT, Ho-Le, TP, Pham, L, Ho-Van, VP, Hoang, TD, Tran, TS, Frost, S & Nguyen, TV 2023, 'BONEcheck: a digital tool for personalized bone health assessment', Cold Spring Harbor Laboratory.
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Nguyen, H, Saputra, YM, Hoang, D, Nguyen, D, Nguyen, V-D, Xiao, Y & Dutkiewicz, E 2023, 'Encrypted Data Caching and Learning Framework for Robust Federated Learning-based Mobile Edge Computing', Institute of Electrical and Electronics Engineers (IEEE).
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Nguyen, HD, Phung, AHT, Do, TC, Nguyen, QHN, Tran, TS, Nguyen, TV & Ho-Pham, LT 2023, 'Association of lifestyle factors and breast cancer risk in Vietnamese women: A matched case-control study', Research Square Platform LLC.
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Nguyen, V-D, Vu, TX, Nguyen, NT, Nguyen, DC, Juntti, M, Luong, NC, Hoang, DT, Nguyen, DN & Chatzinotas, S 2023, 'Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework'.

Ouyang, Y, Goswami, K, Romero, J, Sanders, BC, Hsieh, M-H & Tomamichel, M 2023, 'Approximate reconstructability of quantum states and noisy quantum secret sharing schemes'.

Owen, B, Kechagidis, K, Bazaz, SR, Enjalbert, R, Essmann, E, Mallorie, C, Mirghaderi, F, Schaaf, C, Thota, K, Vernekar, R, Zhou, Q, Warkiani, ME, Stark, H & Krüger, T 2023, 'Lattice-Boltzmann Modelling for Inertial Particle Microfluidics Applications — A Tutorial Review', Cold Spring Harbor Laboratory.
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Pileggi, SF 2023, 'Ontology in Hybrid Intelligence: a concise literature review'.

Qu, Z, Nguyen, QV, Lau, CW, Johnston, A, Kennedy, PJ, Simoff, S & Catchpoole, D 2023, 'Understanding Cancer Patient Cohorts in Virtual Reality Environment for Better Clinical Decisions: A Usability Study', Research Square Platform LLC.
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Samarawickrama, M 2023, 'Unifying Consciousness and Time to Enhance Artificial Intelligence'.

Soar, J, Lih, OS, Hui Wen, L, Ward, A, Sharma, E, Deo, R, Barua, PD, Tan, R-S, Rinen, E & Acharya, UR 2023, 'Deep Image Analysis Methods for Microalgae Identification', MDPI AG.
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Subramanian, S, Thoms, JAI, Huang, Y, Cornejo, P, Koch, FC, Jacquelin, S, Shen, S, Song, E, Joshi, S, Brownlee, C, Woll, PS, Fajardo, DC, Beck, D, Curtis, DJ, Yehson, K, Antonenas, V, Brien, TO, Trickett, A, Powell, JA, Lewis, ID, Pitson, SM, Gandhi, MK, Lane, SW, Vafaee, F, Wong, ES, Göttgens, B, Rokny, HA, Wong, JWH & Pimanda, JE 2023, 'Cell Type-Specific Regulation by a Heptad of Transcription Factors in Human Hematopoietic Stem and Progenitor Cells', Cold Spring Harbor Laboratory.
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Sukkar, F, Moreno, VH, Vidal-Calleja, T & Deuse, J 2023, 'Guided Learning from Demonstration for Robust Transferability'.

Wakulicz, J, Lee, KMB, Vidal-Calleja, T & Fitch, R 2023, 'Topological Trajectory Prediction with Homotopy Classes'.

Wills, A, Lin, T-C & Hsieh, M-H 2023, 'General Distance Balancing for Quantum Locally Testable Codes'.

Xiao, Y, Zhang, X, Shi, G, Krunz, M, Nguyen, DN & Hoang, DT 2023, 'Time-sensitive Learning for Heterogeneous Federated Edge Intelligence'.

Yu, H, Hossain, SM, Wang, C, Choo, Y, Naidu, G, Han, DS & Shon, HK 2023, 'Selective Lithium Extraction from Diluted Binary Solutions Using Metal-Organic Frameworks (Mof)-Based Membrane Capacitive Deionization (Mcdi)', Elsevier BV.
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