Publications

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, Reshaping Engineering Education, Springer Nature Singapore.
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Alanazi, I, AL-Doghman, F, Alsubhi, A & Hussain, F 2024, 'Carbon Credits Price Prediction Model (CCPPM)' in Advanced Information Networking and Applications, Springer Nature Switzerland, pp. 143-150.
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Alkhalaf, A & Hussain, FK 2024, 'Towards Priority VM Placement in Fog Networks' in Advanced Information Networking and Applications, Springer Nature Switzerland, pp. 404-414.
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Aqeel, A, Zafar, J, Mohammadi, P, Tabatabaei, M, Aghbashlo, M, Mahlia, TMI & Nizami, A-S 2024, 'Chapter 1 Biodiesel: the fundamentals' in Sustainable Biodiesel, Academic Press, UK, pp. 1-20.
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Barzegarkhoo, R, Farhangi, M, Lee, SS, Siwakoti, YP & Blaabjerg, F 2024, 'Switched-boost-based multilevel inverters' in Control of Power Electronic Converters and Systems: Volume 4, Elsevier, pp. 127-154.
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Bläser, M, Chen, Z, Duong, DH, Joux, A, Nguyen, T, Plantard, T, Qiao, Y, Susilo, W & Tang, G 2024, 'On Digital Signatures Based on Group Actions: QROM Security and Ring Signatures' in Post-Quantum Cryptography, Springer Nature Switzerland, pp. 227-261.
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Boyd-Weetman, B, Thomas, P, De Silva, P, Tapas, MJ & Sirivivatnanon, V 2024, 'Assessing the Role of Standard Test Methods in Alkali Content Threshold Determination for ASR Reactive Aggregates' in RILEM Bookseries, Springer Nature Switzerland, pp. 409-417.
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Cai, Z, Yuan, G, Zhuang, X, Liu, X, Bing, R & Wang, S 2024, 'All You See Is the Tip of the Iceberg: Distilling Latent Interactions Can Help You Find Treasures' in Communications in Computer and Information Science, Springer Nature Singapore, pp. 244-257.
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Recommender systems suffer from data sparsity problem severely, which can be attributed to the combined action of various possible causes like: gradually strengthened privacy protection policies, exposure bias, etc. In these cases, the unobserved items do not always refer to the items that users are not interested in; they could also be imputed to the inaccessibility of interaction data or users’ unawareness over items. Thus, blindly fitting all unobserved interactions as negative interactions in the training stage leads to the incomplete modeling of user preferences. In this work, we propose a novel training strategy to distill latent interactions for recommender systems (shorted as DLI). Latent interactions refer to the possible links between users and items that can reflect user preferences but not happened. We first design a False-negative interaction selecting module to dynamically distill latent interactions along the training process. After that, we devise two types of loss paradigms: Truncated Loss and Reversed Loss. The former one can reduce the detrimental effect of False-negative interactions by discarding the False-negative samples in the loss computing stage, while the latter turning them into positive ones to enrich the interaction data. Meanwhile, both loss functions can be further detailed into full mode and partial mode to discriminate different confidence levels of False-negative interactions. Extensive experiments on three benchmark datasets demonstrate the effectiveness of DLI in improving the recommendation performance of backbone models.

Candra, H, Yuniati, U & Chai, R 2024, 'The Application of Virtual Reality Using Kinect Sensor in Biomedical and Healthcare Environment: A Review' in Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics, Springer Nature Singapore, pp. 15-38.
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Carmichael, MG, Tran, A, Aldini, S & Liu, D 2024, 'Methodologies in Physical Human–Robot Collaboration for Infrastructure Maintenance', Wiley, pp. 105-126.
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Chakrabarty, P, Alam, KCA, Paul, SK & Saha, SC 2024, 'Hydrogen production by electrolysis' in Hydrogen Energy Conversion and Management, Elsevier, pp. 81-102.
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Chandanshree, NS, Anitha Kumari, SD & Nimbalkar, S 2024, 'Physical and Mechanical Characterization of Recycled Concrete Aggregates (RCA) as Compacted Fill in Road Pavement' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 233-244.
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Deng, S-H, Wang, C, Ngo, HH, Guo, W, Xi, B, Wang, X & Han, J 2024, 'Treatment of landfill leachate' in Environmental Pollution and Public Health, Elsevier, pp. 107-152.
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Deuse, J, Wöstmann, R, Syberg, M, West, N, Wagstyl, D & Moreno, VH 2024, 'Establishing a Machine Learning and Internet of Things Learning Infrastructure by Operating Transnational Cyber-Physical Brewing Labs' in Lecture Notes in Networks and Systems, Springer Nature Switzerland, pp. 171-178.
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Fattah, IMR, Mofijur, M, Silitonga, AS, Kalam, MA, Mahlia, TMI & Ampah, JD 2024, 'Blending Low Carbon Fuels for Sustainable Transportation' in Reference Module in Earth Systems and Environmental Sciences, Elsevier.
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Feng, Y, Wu, D, Chen, X & Gao, W 2024, 'Machine learning aided stochastic free vibration analysis of functionally graded porous plates' in Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures, Elsevier, pp. 293-311.
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Feng, Y, Wu, D, Chen, X & Gao, W 2024, 'Machine learning-aided stochastic static analysis of functionally graded porous plates' in Machine Learning Aided Analysis, Design, and Additive Manufacturing of Functionally Graded Porous Composite Structures, Elsevier, pp. 271-292.
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Garces, K, Xuan, J & Zuo, H 2024, 'Transformed Successor Features for Transfer Reinforcement Learning' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 298-309.
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Gibson, H, Le, DDK & Dikai, L 2024, 'A Climbing Robot for Maintenance Operations in Confined Spaces' in Infrastructure Robotics: Methodologies, Robotic Systems and Applications, Wiley, pp. 263-284.
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The text also presents many case studies of robotic systems developed for real-world applications in maintaining various civil infrastructures, including steel bridges, tunnels, underground water mains, underwater structures, and sewer ...

Gill, A 2024, 'Adaptive Information Architecture for Sustainable Digital Ecosystem' in Sustainability Handbook.

Gu, X, Ud Din, F & Wu, RMX 2024, 'ICT Programs in China: A Case Study of Shanxi Universities' in Teaching and Learning in the Digital Era, WORLD SCIENTIFIC, pp. 303-325.
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Gunawardane, K 2024, 'Evolution of hydrogen energy and its potential opportunities around the globe' in Hydrogen Energy Conversion and Management, Elsevier, pp. 3-33.
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Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'A Systems Approach to Addressing Human Challenges' in Reshaping Engineering Education, Springer Nature Singapore, pp. 17-51.
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AbstractIn Chap. 2, this chapter discusses the nuances of complex systems, accentuating that human communications and technology are integral parts of our social systems. Whenever humans are positioned at the centre of any system, complexity emerges. Interventions in complex systems are very difficult, due to feedback loops and their associated time delays and the presence of many interacting components. Each element within a complex system intertwines with others, making any intervention a precarious act, where intervening in a part of the system invariably affects other parts. Engineering education is a complex system, and it is evolving under the new AI technologies. Interventions are required and the relational dynamics among students, as well as between students and educators, are critical axes around which educational experiences must revolve. The dynamic educational ecosystems, burgeoning with myriad informal and formal, verbal and non-verbal communications,shape the educational experience and outcomes in profound ways. Ensuring that interventions do not inadvertently disrupt or diminish these interactional ecosystems requires an adept understanding of the inherent complexity embedded within the educational systems.

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'Design for Complexity' in Reshaping Engineering Education, Springer Nature Singapore, pp. 53-73.
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AbstractDesign has traditionally been the process of moving from a problem statement, or need, to a solution.

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'Educational Transformation' in Reshaping Engineering Education, Springer Nature Singapore, pp. 161-177.
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AbstractWhat we have been addressing in the previous chapters is more fundamental change in engineering education from a disciplinary to an interdisciplinary learning discourse by applying more student centred learning methods which embrace generic competencies, interdisciplinary learning, and learning to work on boundaries in engineering education. But the big question is: how do we convince our colleagues of the need for change or transformation of engineering education?

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'Generic Competencies' in Reshaping Engineering Education, Springer Nature Singapore, pp. 139-160.
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AbstractWe have learned that technologies are becoming more complex both in terms of technical combinations and in terms of the societal problems that must be solved. But what is often forgotten is that behind the technologies, there are humans. As technology becomes more complex, more humans will have to collaborate and there will be many more boundaries to cross. These include academics with different disciplinary backgrounds and users with different levels of education and social position in society. In this chapter, there is focus on the understandings of competencies and learning necessary for interdisciplinary collaboration.

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'Introduction' in Reshaping Engineering Education, Springer Nature Singapore, pp. 1-12.
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AbstractThe world is facing unprecedented challenges, which its inhabitants and governments are struggling to address.

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'Invitation to Change' in Reshaping Engineering Education, Springer Nature Singapore, pp. 255-265.
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AbstractWe invite you to make changes in your engineering programs.

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'New Competencies for Systems Thinking' in Reshaping Engineering Education, Springer Nature Singapore, pp. 75-96.
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AbstractThe growing complexity of technological systems, which has been elaborated upon in previous chapters, has increased the focus on the sociotechnical dimension of engineering, which engineers must increasingly address.

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'Problem and Project-Based Learning at Aalborg' in Reshaping Engineering Education, Springer Nature Singapore, pp. 199-222.
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AbstractAalborg University has grown out of a certain educational mindset based on reform pedagogy and German critical theory back in the 1970s. Aalborg University has developed problem and project based learning as the core principles for education at a systemic level. In this chapter, the history and contemporary principles and the educational practices are presented.

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'Studios Reshape Engineering Curricula at UTS' in Reshaping Engineering Education, Springer Nature Singapore, pp. 223-251.
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AbstractAs identified earlier, engineering curricula must develop three categories of skills and knowledge—(i) design and problem solving in complex, sociotechnical situations, (ii) the technical knowledge to support the design process, and (iii) the interpersonal skills to support engineering team processes. Traditional curricula, based on teaching the technical knowledge and skills, under-deliver developing the design and interpersonal skill sets. Studios are a place where all three categories of skills can be developed.

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'Teaching and Learning Strategies' in Reshaping Engineering Education, Springer Nature Singapore, pp. 119-138.
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AbstractThere is not one type of curriculum that fits all institutions nor all students. The engineering programs are developed based on various learning principles and cultural conditions and represent different learning ecosystems. This chapter and chapter 7 address different learning principles which can be taken into considerations when developing a student centred curriculum has to be developed. We are emphasizing exemplarity, variation, and reflection as basic learning and design principles, followed by types of more active learning strategies for lectures, inquiry-based, studios, problem-based, and project-based approaches to learning, and we emphasize teamwork and diversity of thinking as core elements to address complex socio-technical systems.

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'Teaching Practices at Harvard Engineering' in Reshaping Engineering Education, Springer Nature Singapore, pp. 183-197.
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AbstractIn the previous chapters, we discussed critical components of effective learning curriculum. We pointed out that problem-solving skills are critical for engineers of this century. In particular, we pointed out the importance of systems thinking as the core of the learning, and the design process as the tool to achieve the goal of addressing large-scale human challenges.

Habbal, F, Kolmos, A, Hadgraft, RG, Holgaard, JE & Reda, K 2024, 'Toward a Comprehensive Pedagogy' in Reshaping Engineering Education, Springer Nature Singapore, pp. 99-118.
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AbstractAs we discussed in part 1, humanity is facing many critical challenges, most of which are open-ended and complex systems, and they require broad input and experiences to address. We expect students and educators to be engaged in such societal issues and form multidisciplinary teams to address them. The increased complexity of technical methodologies and know-how expected of future engineers, challenges existing curriculum strategies. The deep learning needed to dig into a discipline, to understand and apply it, must be combined with a learning strategy to increase the ability of students to relate to, and connect with, other disciplines in a meaningful way. It is not a matter of substituting students’ learning of core technical competencies; it is a matter of creating synergy in the learning process so students will experience the inevitable interaction between technical and contextual learning.

Hassan, M & Liu, D 2024, 'Coverage Planning and Motion Planning of Intelligent Robots for Civil Infrastructure Maintenance', Wiley, pp. 81-103.
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Hildenbrand, D, Saribatir, E, Piña, AM, Lopes, WB, Wegner, FV, Storey, P, Yan, Z, Wen, S & Arnold, M 2024, 'Geometric Algebra Algorithm Code Optimised by GAALOP Executing on a Simulated Memristor Crossbar Array' in Springer Proceedings in Mathematics & Statistics, Springer Nature Switzerland, pp. 245-257.
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Hu, Y, McFarlane, A & Hussain, F 2024, 'CarbonApp: Blockchain Enabled Carbon Offset Project Management' in Advanced Information Networking and Applications, Springer Nature Switzerland, pp. 13-25.
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Huang, S, Pan, K & Dissanayake, G 2024, 'Perception in Complex and Unstructured Infrastructure Environments', Wiley, pp. 33-58.
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Isaac, G, Nicholas, P, Paul, G, Pietroni, N, Vidal Calleja, T, Xie, M & Schork, T 2024, 'Automated Shotcrete: A More Sustainable Construction Technology' in Sustainable Engineering, Springer International Publishing, pp. 331-345.
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Kodagoda, S, Kumar Viswanathan, V, Thiyagarajan, K, Tran, A, Wickramanayake, S, Barclay, S & Vitanage, D 2024, 'Robotics and Sensing for Condition Assessment of Wastewater Pipes', Wiley, pp. 243-261.
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Kodagoda, S, Kumar Viswanathan, V, Thiyagarajan, K, Tran, A, Wickramanayake, S, Barclay, S & Vitanage, D 2024, 'Robotics and Sensing for Condition Assessment of Wastewater Pipes' in Liu, D, Balaguer, C, Dissanayake, G & Kovac, M (eds), Infrastructure Robotics: Methodologies, Robotic Systems and Applications, Wiley, pp. 243-261.
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The text also presents many case studies of robotic systems developed for real-world applications in maintaining various civil infrastructures, including steel bridges, tunnels, underground water mains, underwater structures, and sewer ...

Kumari, G, Adak, C & Ekbal, A 2024, 'Mu2STS: A Multitask Multimodal Sarcasm-Humor-Differential Teacher-Student Model for Sarcastic Meme Detection' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 19-37.
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Laccone, F, Pietroni, N, Froli, M, Cignoni, P & Malomo, L 2024, 'Statics and Stability of Bending-Optimized Double-Layer Grid Shell' in Lecture Notes in Civil Engineering, Springer Nature Switzerland, pp. 569-578.
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Liang, S, Guo, A, Liang, B, Li, Z, Ding, Y, Wang, Y & Chen, F 2024, 'Machine Learning and Computer Vision Applications in Civil Infrastructure Inspection and Monitoring', Wiley, pp. 59-80.
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Liu, D & Dissanayake, G 2024, 'Infrastructure Robotics: An Introduction', Wiley, pp. 3-18.
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Liu, P, Yuan, J, Siwakoti, YP & Blaabjerg, F 2024, 'Control and modulation techniques of Z-source converter' in Control of Power Electronic Converters and Systems: Volume 4, Elsevier, pp. 27-70.
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Liu, X & Cao, Z 2024, 'Enhance Reading Comprehension from EEG-Based Brain-Computer Interface' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 545-555.
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Luo, Z, Guo, Y, Zhao, C, Wang, X, Zhang, X, Wu, V & Li, W 2024, 'Nano/micro characterization on interfacial transition zones in fly ash–based geopolymer concrete' in The Path to Green Concrete, Elsevier, pp. 445-475.
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Luo, Z, Zhao, H & Li, W 2024, 'Quasi-static and Dynamic Mechanical Properties of Recycled Aggregate Concrete-Filled Steel Tube Columns Exposed to High Temperature' in Circular Economy for Buildings and Infrastructure, Springer International Publishing, pp. 67-82.
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Manthey, SI & Cetindamar Kozanoglu, D 2024, 'From Technology Idea to Value Proposition' in Munoz, M & Terzidis, O (eds), Technology Entrepreneurship, Taylor & Francis, pp. 1-12.
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Strategic use of managerial tools might simplify technology entrepreneurs’ struggle in developing value propositions for new technologies. This chapter introduces the Technology Application Selection framework and Business Model approaches that can help managers and entrepreneurs to turn their technology-based ideas into value propositions. By doing so, they speed up their process of technology commercialization.

Martin, L, Thomas, P, de Silva, P & Sirivivatnanon, V 2024, 'Assessing the Role of ASR Reactive Aggregates in Concrete Mixes Susceptible to DEF' in RILEM Bookseries, Springer Nature Switzerland, pp. 168-175.
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Mofijur, M, Hasan, MM, Fattah, IMR, Huang, Y & Shahriar, S 2024, 'Thermochemical conversion of microalgae into biofuels' in Waste Valorization for Bioenergy and Bioproducts, Elsevier, pp. 315-333.
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Narayanan, AK, Qiao, Y & Tang, G 2024, 'Algorithms for Matrix Code and Alternating Trilinear Form Equivalences via New Isomorphism Invariants' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 160-187.
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Nguyen, CT, Hoang, DT, Nguyen, DN, Nguyen, V-D & Dutkiewicz, E 2024, 'Summarizing Proof-of-Stake mechanisms and their practical deployments: applications, attacks, solutions, and future directions' in Proof-of-Stake for Blockchain Networks: Fundamentals, challenges and approaches, Institution of Engineering and Technology, pp. 189-217.
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Ou, L, Do, T, Tran, X-T, Leong, D, Chang, Y-C, Wang, Y-K & Lin, C-T 2024, 'Improving CCA Algorithms on SSVEP Classification with Reinforcement Learning Based Temporal Filtering' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 376-386.
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Pratten, DR & Mathieson, L 2024, 'Relational Expressions for Data Transformation and Computation' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 241-255.
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Sapkota, S, Huang, H, Hu, Y & Hussain, F 2024, 'A Deep Neural Network (DNN) Based Contract Policy on Hyperledger Fabric for Secure Internet of Things (IoTs)' in Advanced Information Networking and Applications, Springer Nature Switzerland, pp. 313-325.
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Saribatir, E, Zurstraßen, N, Hildenbrand, D, Stock, F, Piña, AM, Wegner, FV, Yan, Z, Wen, S & Arnold, M 2024, 'Game Physics Engine Using Optimised Geometric Algebra RISC-V Vector Extensions Code Using Fourier Series Data' in Advances in Computer Graphics, Springer Nature Switzerland, pp. 250-261.
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Sharma, R, Saqib, M, Lin, CT & Blumenstein, M 2024, 'Maritime Surveillance Using Instance Segmentation Techniques' in Studies in Autonomic, Data-driven and Industrial Computing, Springer Nature Singapore, pp. 31-47.
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Tang, W & Long, G 2024, 'WeightRelay: Efficient Heterogeneous Federated Learning on Time Series' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 129-140.
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Tapas, MJ, Sofia, L, Thomas, P, Vessalas, K, Sirivivatnanon, V & Scrivener, K 2024, 'Long Term Efficacy of Fly Ash in Mitigating Alkali-Silica Reaction Assessed by Pore Solution Method' in RILEM Bookseries, Springer Nature Switzerland, pp. 403-408.
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Thalakotuna, DN, Esselle, KP, Matekovits, L & Ranga, Y 2024, 'Making UWB Antennas Unidirectional: Phase Coherence with an Ultra-Wide Band Frequency Selective Surface Reflector' in Lakhtakia, A, Furse, CM & Mackay, TG (eds), The Advancing World of Applied Electromagnetics, Springer International Publishing, pp. 73-110.
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Thomas, P, Martin, L, De Silva, P, Sirivivatnanon, V & Šimon, P 2024, 'Modelling the Kinetic Behaviour of Delayed Ettringite Formation in Concrete Prisms' in RILEM Bookseries, Springer Nature Switzerland, pp. 201-208.
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Tian, X & Liu, F 2024, 'Take a Close Look at the Optimization of Deep Kernels for Non-parametric Two-Sample Tests' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 17-29.
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Tran, HV, McGregor, C & Kennedy, PJ 2024, 'Detecting Stress from Multivariate Time Series Data Using Topological Data Analysis' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 341-353.
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Trianni, A, Bennett, N, Cantley-Smith, R, Cheng, C-T, Dunstall, S, Hasan, ASMM, Katic, M, Leak, J, Lindsay, D, Pears, A, Wheatland, FT, White, S & Zeichner, F 2024, 'Industry 4.0 for Energy Productivity: Insights and Future Perspective for Australia' in Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems, Springer Nature Switzerland, pp. 547-554.
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Valls Miro, J 2024, 'Robots for Underground Pipe Condition Assessment', Wiley, pp. 221-242.
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Wang, K, Lu, J, Liu, A & Zhang, G 2024, 'An Augmented Learning Approach for Multiple Data Streams Under Concept Drift' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 391-402.
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Multiple data streams learning attracts more and more attention recently. Different from learning a single data stream, the uncertain and complex occurrence of concept drift in multiple data streams, bring challenges in real-time learning task. To address this issue, this paper proposed a method called time-warping-based concept drift learning method (TW-CDM) for dealing with multiple data streams. First, a time-warping-based drift identification process is given to recognize the drift region. Second, an augmented learning process is developed by crossly using the located region data. Finally, a selectively augmented learning process is given to reduce the influence of different drift severity. The proposed method is evaluated on both synthetic and real-world datasets, and compared with benchmark methods. The experiment results show the efficiency of the proposed method.

Wang, R, Zuo, H, Fang, Z & Lu, J 2024, 'Multiple Teacher Model for Continual Test-Time Domain Adaptation' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 304-314.
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Weng, H, Liu, Y & Chen, L 2024, 'Spatial Bottleneck Transformer for Cellular Traffic Prediction in the Urban City' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 265-276.
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Wing Keung To, A, Le, K & Liu, D 2024, 'Underwater Robots for Cleaning and Inspection of Underwater Structures', Wiley, pp. 161-183.
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Wu, K, Zhang, JA, Huang, X & Guo, YJ 2024, 'Frequency-Hopping MIMO Radar-based Data Communications' in Signal Processing for Joint Radar Communications, pp. 277-294.
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Abstract This chapter introduces a recently conceived dual-function radar-communications (DFRC) system that exploits a frequency-hopping (FH) multiple-input and multiple-output (MIMO) radar to achieve a reasonably high-speed and long-distance data communication link. To promote the awareness and general understanding of the novel DFRC, its essential aspects, e.g. signal model and information embedding/demodulation, as well as some recent breakthroughs in timing and channel estimations are illustrated in detail.

Wu, K, Zhang, JA, Huang, X & Guo, YJ 2024, 'Frequency‐Hopping MIMO Radar‐based Data Communications' in Signal Processing for Joint Radar Communications, Wiley, pp. 275-294.
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The goal of this book is to further contribute to the diffusion of newly developed JRC tools into the radar and communications communities and to illustrate recent successes in applying modern signal processing theories to solving core ...

Xu, X, Li, C, Chen, Y, Chang, X, Liu, J & Wang, S 2024, 'No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 28-41.
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Ye, K, Ji, JC, Li, J & Yamada, K 2024, 'Origami-Inspired Vibration Isolation with Inherent Nonlinear Inerter' in Lecture Notes in Electrical Engineering, Springer Nature Singapore, pp. 876-884.
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Ye, S & Lu, J 2024, 'Sequence Unlearning for Sequential Recommender Systems' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 403-415.
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Yin, H, Wang, K, Zhang, W, Wen, D, Wang, X & Zhang, Y 2024, 'Discovering Densest Subgraph over Heterogeneous Information Networks' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 341-355.
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Ying, M 2024, 'Acknowledgements' in Foundations of Quantum Programming, Elsevier, pp. xv-xv.
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Ying, M 2024, 'Preface to the second edition' in Foundations of Quantum Programming, Elsevier, pp. xi-xi.
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Yuan, J, Liu, P, Siwakoti, YP & Blaabjerg, F 2024, 'Z-source converters and their classifications' in Control of Power Electronic Converters and Systems: Volume 4, Elsevier, pp. 3-25.
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Zamri, MFMA, Bahru, R & Fattah, IMR 2024, 'Gas to liquids from biogas and landfill gases' in Waste Valorization for Bioenergy and Bioproducts, Elsevier, pp. 411-422.
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Abbasi, QH & Reddy, CJ 2024, 'Reports on AP-S Committees and Activities [AP-S Committees & Activities]', IEEE Antennas and Propagation Magazine, vol. 66, no. 2, pp. 86-88.
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Abbasnejad, B, Nasirian, A, Duan, S, Diro, A, Prasad Nepal, M & Song, Y 2024, 'Measuring BIM Implementation: A Mathematical Modeling and Artificial Neural Network Approach', Journal of Construction Engineering and Management, vol. 150, no. 5.
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Abdul-Kader, HA, Shakor, ZM, Sherhan, BY, Al-Humairi, ST, Aboughaly, M, Hazrat, MA & Fattah, IMR 2024, 'Biodiesel production from waste cooking oil using KOH/HY-type nano-catalyst derived from silica sand', Biofuels, vol. 15, no. 5, pp. 527-543.
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Abedini, A, Abedin, B & Zowghi, D 2024, 'A Framework of Environmental, Personal, and Behavioral Factors of Adult Learning in Online Communities of Practice', Information Systems Frontiers, vol. 26, no. 3, pp. 1201-1218.
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AbstractAdult learning is a complex phenomenon that takes place over an adult’s lifetime and is not limited to a particular age. It includes a set of activities to enhance life through improving skills, knowledge and capabilities. The foundational theories of adult learning, such as andragogy theory, place the individual adult centre stage and differentiate adult learning from formal learning. They also shift the focus from the individuals to the environment in which adult learning takes place. In line with this movement, online communities of practice (OCOPs) have evolved from being considered as online environments for learning to specialised forums that allow practitioners to collaborate around a project of mutual interest. The principles of adult learning are directly applicable to engagement in OCOPs because they include practical methods founded on the belief that adults are self-directed, autonomous learners and that learning is most effective when the environment plays the role of a facilitator, rather than being just a supportive and traditional setting for learning. However, how individual adults engage in OCOPs and benefit from them is not well understood. This paper draws on social cognitive theory to examine: how environmental, personal and behavioural factors shape engagement in OCOPs. To answer this question, twenty-one interviews were conducted with members of GitHub, a large online community of practice for IT professionals. The findings revealed that adults’ engagement in OCOPs involves project-based activities on mutual interests and willingness to help others. The findings also show that engaging in online communities does not only satisfy intrinsic, well-defined, expected outcomes and shape adults’ engagement, but also has an impact on adults’ lifelong learning achievements, such as professional experience and credit recognition. Based on these findings, a revised framework for adults’ engagement in...

Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Prior, D 2024, 'Adaptive identification of supply chain disruptions through reinforcement learning', Expert Systems with Applications, vol. 248, pp. 123477-123477.
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Abraham, MT, Satyam, N & Pradhan, B 2024, 'A novel approach for quantifying similarities between different debris flow sites using field investigations and numerical modelling', Terra Nova, vol. 36, no. 2, pp. 138-147.
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AbstractDebris flows are geomorphological processes that affect the landscape evolution process of any region. In this study, an integrated methodology is proposed to identify the chance of further debris flows and quantify the similarities between debris flow locations, materials and rheology, using field and laboratory investigations and remote sensing data. The method was tested for four failure‐triggered debris flow sites in the Western Ghats of India, using dimensionless parametric similarity values ranging from 0 to 1. The maximum parametric similarity was observed as 0.84 when comparing the flow accumulation values of Sites 3 and 4, and the maximum overall site similarity was 0.68. The calibrated rheological parameters of one site were found to be satisfactory in modelling the shape of debris flow at all other sites. The findings can be used to identify similar hotspots in the region and to simulate debris flows for quantitative hazard assessment.

Abtahi, H, Karimi, M & Maxit, L 2024, 'Identification of low-wavenumber wall pressure field beneath a turbulent boundary layer using vibration data', Journal of Fluids and Structures, vol. 127, pp. 104135-104135.
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Abtahi, H, Karimi, M & Maxit, L 2024, 'On the challenges of estimating the low-wavenumber wall pressure field beneath a turbulent boundary layer using a microphone array', Journal of Sound and Vibration, vol. 574, pp. 118230-118230.
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Adak, C, Chattopadhyay, S & Saqib, M 2024, 'Deep Analysis of Visual Product Reviews', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-6.
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Adeoti, OS, Kandasamy, J & Vigneswaran, S 2024, 'Water infrastructure sustainability challenge in Nigeria: A detailed examination of infrastructure failures and potential solutions', Water Supply, vol. 24, no. 6, pp. 2066-2076.
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ABSTRACT Achieving Sustainable Development Goal 6.1 – universal and equitable access to safe and affordable drinking water – is a critical global challenge. This study contributes to this aim by analyzing the functionality and sustainability of rural water boreholes in Nigeria. It employs GIS mapping, Spearman's rho correlation analysis, and interviews across 1,696 communities to investigate borehole failure dynamics, the impact of multidimensional poverty index (MPI) on water access, technical failure causes, and the influence of ownership on functionality. Findings show that while 49.8% of communities lack improved water sources, 25.5 benefit from functional boreholes, and 24.5 grapple with failures. This study reveals a complex relationship between MPI and water access, with community ownership associated with better functionality. Consequently, the study proposes holistic strategies, emphasizing community mapping and smart infrastructure, to enhance water system sustainability. Although the study is centered in Nigeria, its insights are applicable to regions with similar socio-economic conditions, contributing to the global pursuit of sustainable water access in alignment with SDG 6.1.

Adhiatma, A, Hakim, A, Fachrunnisa, O & Hussain, FK 2024, 'The role of social media business and organizational resources for successful digital transformation', Journal of Media Business Studies, vol. 21, no. 1, pp. 23-50.
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Adhikari, S, Alsadoon, A, Bekhit, M, Jerew, OD, Siddiqi, M & Ali, A 2024, 'Optimizing Real-Time Video Transmission for Surgical Tele-Education Through ERV Algorithm', IEEE Access, vol. 12, pp. 98707-98722.
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Aditya, L, Vu, HP, Johir, MAH, Mao, S, Ansari, A, Fu, Q & Nghiem, LD 2024, 'Synthesizing cationic polymers and tuning their properties for microalgae harvesting', Science of The Total Environment, vol. 917, pp. 170423-170423.
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Aedan, Y, Altaee, A, Zhou, JL & Shon, HK 2024, 'Perfluorooctanoic acid-contaminated wastewater treatment by forward osmosis: Performance analysis', Science of The Total Environment, vol. 934, pp. 173368-173368.
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Perfluorooctanoic acid (PFOA) is a persistent compound, raising considerable global apprehension due to its resistance to breakdown and detrimental impacts on human health and aquatic environments. Pressure-driven membrane technologies treating PFAS-contaminated water are expensive and prone to fouling. This study presented a parametric investigation of the effectiveness of cellulose triacetate membrane in the forward osmosis (FO) membrane for removing PFOA from an aqueous solution. The study examined the influence of membrane orientation modes, feed pH, draw solution composition and concentration, and PFOA concentration on the performance of FO. The experimental results demonstrated that PFOA rejection was 99 % with MgCl2 and slightly >98 % with NaCl draw solutions due to the mechanism of PFOA binding to the membrane surface through Mg2+ ions. This finding highlights the crucial role of the draw solution's composition in PFOA treatment. Laboratory results revealed that membrane rejection of PFOA was 99 % at neutral and acidic pH levels but decreased to 95 % in an alkaline solution at pH 9. The decrease in membrane rejection is attributed to the dissociation of the membrane's functional groups, consequently causing pore swelling. The results were confirmed by calculating the average pore radius of the CTA membrane, which increased from 27.94 nm at pH 5 to 30.70 nm at pH 9. Also, variations in the PFOA concentration from 5 to 100 mg/L did not significantly impact the membrane rejection, indicating the process's capability to handle a wide range of PFOA concentrations. When seawater was the draw solution, the FO membrane rejected 99 % of PFOA concentrations ranging from 5 mg/L to 100 mg/L. The CTA FO treating PFOA-contaminated wastewater from soil remediation achieved a 90 % recovery rate and water flux recovery of 96.5 % after cleaning with DI water at 40 °C, followed by osmotic backwash. The results suggest the potential of using abundant and cost-effec...

Aghajani, S, Wu, C, Li, Q & Fang, J 2024, 'Additively manufactured composite lattices: A state-of-the-art review on fabrications, architectures, constituent materials, mechanical properties, and future directions', Thin-Walled Structures, vol. 197, pp. 111539-111539.
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Aguilera, RP, Acuna, P, Rojas, CA, Pou, J, Konstantinou, G & Watanabe, EH 2024, 'An Instantaneous Power Theory Extension for the Inter-Phase Power Imbalance Problem', IEEE Transactions on Power Electronics, pp. 1-10.
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Ahamed, E, Keshavarz, R, Franklin, D & Shariati, N 2024, 'Software-Defined Programmable Metamaterial Lens System for Dynamic Wireless Power Transfer Applications', IEEE Transactions on Antennas and Propagation, vol. 72, no. 7, pp. 5840-5851.
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Ahmed, A, Al-Dweik, A, Iraqi, Y & Damiani, E 2024, 'Integrated Terrestrial-Wired and LEO Satellite With Offline Bidirectional Cooperation for 6G IoT Networks', IEEE Internet of Things Journal, vol. 11, no. 9, pp. 15767-15782.
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Ahmed, F, Afzal, MU, Esselle, KP & Thalakotuna, DN 2024, 'Novel Dual-Band Phase-Gradient Metascreen and Dual-Band Near-Field Meta-Steering Antennas', IEEE Transactions on Antennas and Propagation, vol. 72, no. 3, pp. 2202-2216.
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Ahmed, SF, Alam, MSB, Afrin, S, Rafa, SJ, Rafa, N & Gandomi, AH 2024, 'Insights into Internet of Medical Things (IoMT): Data fusion, security issues and potential solutions', Information Fusion, vol. 102, pp. 102060-102060.
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Ahmed, SF, Alam, MSB, Afrin, S, Rafa, SJ, Taher, SB, Kabir, M, Muyeen, SM & Gandomi, AH 2024, 'Toward a Secure 5G-Enabled Internet of Things: A Survey on Requirements, Privacy, Security, Challenges, and Opportunities', IEEE Access, vol. 12, pp. 13125-13145.
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Ahmed, SF, Islam, N, Tasannum, N, Mehjabin, A, Momtahin, A, Chowdhury, AA, Almomani, F & Mofijur, M 2024, 'Microplastic removal and management strategies for wastewater treatment plants', Chemosphere, vol. 347, pp. 140648-140648.
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Discharging microplastics into the environment with treated wastewater is becoming a major concern around the world. Wastewater treatment plants (WWTPs) release microplastics into terrestrial and aquatic habitats, mostly from textile, laundry, and cosmetic industries. Despite extensive research on microplastics in the environment, their removal, and WWTP management strategies, highlighting their environmental effects, little is known about microplastics' fate and behaviour during various treatment processes. Microplastics interact with treatment technologies differently due to their diverse physical and chemical characteristics, resulting in varying removal efficiency. Microplastics removed from WWTPs may accumulate in soil and harm terrestrial ecosystems. Few studies have examined the cost, energy use, and trade-offs of large-scale implementation of modern treatment methods for the removal of microplastics. To safeguard aquatic and terrestrial habitats from microplastics' contamination, focused and efficient management techniques must bridge these knowledge gaps. This review summarizes microplastic detection, collection, removal and management strategies. A compilation of treatment process studies on microplastics' removal efficiency and their destiny and transit paths shows recent improvement. Bioremediation, membrane bioreactor (MBR), electrocoagulation, sol-gel technique, flotation, enhanced filtering, and AOPs are evaluated for microplastic removal. The fate and behaviour of microplastics in WWTPs suggest they may be secondary suppliers of microplastics to receiving ecosystems. Innovative microplastic removal strategies and technologies such as nanoparticles, microorganism-based remediation, and tertiary treatment raise issues. These new WWTP technologies are examined for feasibility, limitations, and implementation issues. Pretreatment modifies microplastic size, adsorption potential, and surface morphology to remove microplastics from WWTPs. Memb...

Ahmed, SF, Kumar, PS, Ahmed, B, Mehnaz, T, Shafiullah, GM, Nguyen, VN, Duong, XQ, Mofijur, M, Badruddin, IA & Kamangar, S 2024, 'Carbon-based nanomaterials: Characteristics, dimensions, advances and challenges in enhancing photocatalytic hydrogen production', International Journal of Hydrogen Energy, vol. 52, pp. 424-442.
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Ai, J, Abdelraheem, WHM, Peng, S, Guo, W, Duan, X, Peng, S, Zhang, W, Wang, Q & Dionysiou, DD 2024, 'AsIII-enhanced oxidation by coexisting MnIII-phenolic complexes during arsenic contaminated groundwater treatment by MnO2', Separation and Purification Technology, vol. 344, pp. 127254-127254.
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Ai, J, Wang, K, Fu, Q, Dong, T, Li, L, Peng, S, Wang, D, Wang, Q & Zhang, W 2024, 'Novel insights into the biopolymers transformation under wastewater sludge drying process at different temperatures in relation to drying behavior', Chemical Engineering Journal, vol. 486, pp. 150376-150376.
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Akter, A, Zafir, EI, Dana, NH, Joysoyal, R, Sarker, SK, Li, L, Muyeen, SM, Das, SK & Kamwa, I 2024, 'A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation', Energy Strategy Reviews, vol. 51, pp. 101298-101298.
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Akter, MM, Surovy, IZ, Sultana, N, Faruk, MO, Gilroyed, BH, Tijing, L, Arman, Didar-ul-Alam, M, Shon, HK, Nam, SY & Kabir, MM 2024, 'Techno-economics and environmental sustainability of agricultural biomass-based energy potential', Applied Energy, vol. 359, pp. 122662-122662.
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Alaminos, D, Guillén-Pujadas, M, Vizuete-Luciano, E & Merigó, JM 2024, 'What is going on with studies on financial speculation? Evidence from a bibliometric analysis', International Review of Economics & Finance, vol. 89, pp. 429-445.
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Alcaide, AM, Poblete, P, Vazquez, S, Aguilera, RP, Leon, JI, Kouro, S & Franquelo, LG 2024, 'Generalized Feed-Forward Sampling Method for Multilevel Cascaded H-Bridge Converters', IEEE Transactions on Industrial Electronics, vol. 71, no. 8, pp. 8259-8267.
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Algayyim, SJM, Saleh, K, Wandel, AP, Fattah, IMR, Yusaf, T & Alrazen, HA 2024, 'Influence of natural gas and hydrogen properties on internal combustion engine performance, combustion, and emissions: A review', Fuel, vol. 362, pp. 130844-130844.
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Alhosaini, H, Alharbi, S, Wang, X & Xu, G 2024, 'API Recommendation For Mashup Creation: A Comprehensive Survey', The Computer Journal, vol. 67, no. 5, pp. 1920-1940.
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Abstract Mashups are web applications that expedite software development by reusing existing resources through integrating multiple application programming interfaces (APIs). Recommending the appropriate APIs plays a critical role in assisting developers in building such web applications easily and efficiently. The proliferation of publicly available APIs on the Internet has inspired the community to adopt various models to accomplish the recommendation task. Until present, considerable efforts have been made to recommend the optimal set of APIs, delivering fruitful results and achieving varying recommendation performance. This paper presents a timely review on the topic of API recommendations for mashup creation. Specifically, we investigate and compare not only traditional data mining approaches and recommendation techniques but also more recent approaches based on network representation learning and deep learning techniques. By analyzing the merits and pitfalls of existing approaches, we pinpoint a few promising directions to address the remaining challenges in the current research. This survey provides a timely comprehensive review of the API recommendation research and could be a useful reference for relevant researchers and practitioners.

Al-Hunaity, SA & Far, H 2024, 'Vibration performance of cold-formed steel and plywood composite floors – Experimental studies', Journal of Constructional Steel Research, vol. 219, pp. 108793-108793.
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Alkhalaf, A & Hussain, FK 2024, 'EleVMate — A data-driven approach for ‘on-the-fly’ horizontal small datacentre scalability and VM starvation', Future Generation Computer Systems, vol. 159, pp. 91-101.
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Alnahhal, MF, Hamdan, A, Hajimohammadi, A, Castel, A & Kim, T 2024, 'Hydrothermal synthesis of sodium silicate from rice husk ash: Effect of synthesis on silicate structure and transport properties of alkali-activated concrete', Cement and Concrete Research, vol. 178, pp. 107461-107461.
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Alshuaibi, EA, Hamdi, AM & Hussain, FK 2024, 'Volunteer Computing for fog scalability: A systematic literature review', Internet of Things, vol. 25, pp. 101072-101072.
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Al-Zainati, N, Ibrar, I, Altaee, A, Subbiah, S & Zhou, J 2024, 'Multiple staging of pressure retarded osmosis: Impact on the energy generation', Desalination, vol. 573, pp. 117199-117199.
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AlZainati, N, Ibrar, I, Braytee, A, Altaee, A, Chowdhury, MH, Subbiah, S, Zhou, J, Alanezi, AA & Samal, AK 2024, 'Machine learning to predict the intrinsic membrane parameters in pressure retarded osmosis for an economic salinity gradient power plant', Journal of Water Process Engineering, vol. 64, pp. 105674-105674.
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Ambrosio, L, Vadalà, G, Tavakoli, J, Scaramuzzo, L, Brodano, GB, Lewis, SJ, Kato, S, Cho, SK, Yoon, ST, Kim, H-J, Gary, MF & Denaro, V 2024, 'Surgeon Preference Regarding Wound Dressing Management in Lumbar Fusion Surgery: An AO Spine Global Cross-Sectional Study', Neurospine, vol. 21, no. 1, pp. 204-211.
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Objective: To evaluate the global practice pattern of wound dressing use after lumbar fusion for degenerative conditions.Methods: A survey issued by AO Spine Knowledge Forums Deformity and Degenerative was sent out to AO Spine members. The type of postoperative dressing employed, timing of initial dressing removal, and type of subsequent dressing applied were investigated. Differences in the type of surgery and regional distribution of surgeons’ preferences were analyzed.Results: Right following surgery, 60.6% utilized a dry dressing, 23.2% a plastic occlusive dressing, 5.7% glue, 6% a combination of glue and polyester mesh, 2.6% a wound vacuum, and 1.2% other dressings. The initial dressing was removed on postoperative day 1 (11.6%), 2 (39.2%), 3 (20.3%), 4 (1.7%), 5 (4.3%), 6 (0.4%), 7 or later (12.5%), or depending on drain removal (9.9%). Following initial dressing removal, 75.9% applied a dry dressing, 17.7% a plastic occlusive dressing, and 1.3% glue, while 12.1% used no dressing. The use of no additional coverage after initial dressing removal was significantly associated with a later dressing change (p < 0.001). Significant differences emerged after comparing dressing management among different AO Spine regions (p < 0.001).Conclusion: Most spine surgeons utilized a dry or plastic occlusive dressing initially applied after surgery. The first dressing was more frequently changed during the first 3 postoperative days and replaced with the same type of dressing. While dressing policies tended not to vary according to the type of surgery, regional differences suggest that actual practice may be based on personal experience rather than available evidence.

Amin, M, Umar, H, Ginting, SF, Amir, F, Rizal, TA, Septiadi, WN & Mahlia, TMI 2024, 'Enhancing solar distillation through beeswax-infused tubular solar still with a heat exchanger using parabolic trough collector', Journal of Energy Storage, vol. 86, pp. 111262-111262.
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An, Y, Wong, J & Ling, SH 2024, 'Development of real-time brain-computer interface control system for robot', Applied Soft Computing, vol. 159, pp. 111648-111648.
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Arafat, MY, Hossain, MJ & Alam, MM 2024, 'Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects', Renewable and Sustainable Energy Reviews, vol. 190, pp. 114088-114088.
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Arango, E, Jiménez, P, Nogal, M, Sousa, HS, Stewart, MG & Matos, JC 2024, 'Enhancing infrastructure resilience in wildfire management to face extreme events: Insights from the Iberian Peninsula', Climate Risk Management, vol. 44, pp. 100595-100595.
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Arango, E, Nogal, M, Sousa, HS, Matos, JC & Stewart, MG 2024, 'Improving societal resilience through a GIS-based approach to manage road transport networks under wildfire hazards', Transportation Engineering, vol. 15, pp. 100219-100219.
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Aref, A, Martin, A, Aboulkheyr Es, H, Krzesaj, P, Adler, V, Khardenavis, K, Sarafraz-Yazdi, E & Perricone, MA 2024, 'Abstract LB218: Advanced ex vivo platform for drug response analysis: Evaluating the potent effects of NMC-521 mAb on mouse-and patient-derived organotypic tumor spheroids', Cancer Research, vol. 84, no. 7_Supplement, pp. LB218-LB218.
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Abstract In response to the high failure rate of new drugs in clinical trials, we have developed an innovative organotypic tumor spheroids platform, designed to mitigate the risks associated with advancing drugs to clinical stages. Utilizing patient-derived organotypic tumor spheroids (PDOTS), this platform uniquely captures both the phenotype and genotype of individual patient tumor microenvironments. This approach not only enhances the precision of preclinical models but also bridges the gap between laboratory research and clinical applicability. In this study, we assess the effectiveness of the first-in-class monoclonal antibody NMC-521 (NomoCan Pharmaceuticals, NY) using our ex vivo platform. Targeting the novel cancer-specific antigen NMC-1, NMC-521 exhibited significant growth inhibition of CT-26 tumors in syngeneic BALB/c mice, reducing tumor size by approximately 55% compared to the control. Similarly, in ex vivo settings with freshly excised CT-26 tumors (n=18), NMC-521 demonstrated a dose-dependent cytotoxic effect, achieving about a 65% reduction in tumor viability with an effective dose (ED50) of 10 µg/ml. Further insights were gained into NMC-521's mechanism of action using our ex vivo platform. The drug’s ability to induce tumor cell death in CT26 ex vivo was significantly reversed (p&lt;0.01) when co-treated with α-NKG2D but not α-CD8, indicating a critical role for natural killer (NK) cells in its therapeutic effect. Complementing these findings, our analyses on freshly excised patient tumors (n=15) corroborated the cytotoxic effects of this drug on colorectal cancer but not to the same magnitude as in the CT-26 model. Furthermore, unlike the syngeneic murine tumor model, the response rate was (8 of 15 responsive patient tumors. Gene expression profiling indicated that NK cell-associated RNAs were elevated in NMC-521 treated PDOTS. Additionally, combination therapy studies using NMC-521...

Arqam, M, Jahangiri, A, Mitchell, M, Bennett, NS & Woodfield, P 2024, 'Second law efficiency of air-cooled refrigeration compressors', Progress in Engineering Science, vol. 1, no. 1, pp. 100002-100002.
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Arsad, SR, Arsad, AZ, Ker, PJ, Hannan, MA, Tang, SGH, Goh, SM & Mahlia, TMI 2024, 'Recent advancement in water electrolysis for hydrogen production: A comprehensive bibliometric analysis and technology updates', International Journal of Hydrogen Energy, vol. 60, pp. 780-801.
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Arsad, SR, Ker, PJ, Hannan, MA, Tang, SGH, R S, N, Chau, CF & Mahlia, TMI 2024, 'Patent landscape review of hydrogen production methods: Assessing technological updates and innovations', International Journal of Hydrogen Energy, vol. 50, pp. 447-472.
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Asheralieva, A, Niyato, D & Miyanaga, Y 2024, '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, vol. 23, no. 4, pp. 2614-2638.
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Askari, M, Rajabzadeh, S, Tijing, L & Shon, HK 2024, 'Advances in capacitive deionization (CDI) systems for nutrient recovery from wastewater: Paving the path towards a circular economy', Desalination, vol. 583, pp. 117695-117695.
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Asteris, PG, Gandomi, AH, Armaghani, DJ, Kokoris, S, Papandreadi, AT, Roumelioti, A, Papanikolaou, S, Tsoukalas, MZ, Triantafyllidis, L, Koutras, EI, Bardhan, A, Mohammed, AS, Naderpour, H, Paudel, S, Samui, P, Ntanasis-Stathopoulos, I, Dimopoulos, MA & Terpos, E 2024, 'Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm', European Journal of Internal Medicine, vol. 125, pp. 67-73.
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Asteris, PG, Gandomi, AH, Armaghani, DJ, Tsoukalas, MZ, Gavriilaki, E, Gerber, G, Konstantakatos, G, Skentou, AD, Triantafyllidis, L, Kotsiou, N, Braunstein, E, Chen, H, Brodsky, R, Touloumenidou, T, Sakellari, I, Alkayem, NF, Bardhan, A, Cao, M, Cavaleri, L, Formisano, A, Guney, D, Hasanipanah, M, Khandelwal, M, Mohammed, AS, Samui, P, Zhou, J, Terpos, E & Dimopoulos, MA 2024, 'Genetic justification of COVID‐19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm', Journal of Cellular and Molecular Medicine, vol. 28, no. 4.
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AbstractComplement inhibition has shown promise in various disorders, including COVID‐19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement‐related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID‐19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence‐based algorithm to predict disease outcome (ICU vs. non‐ICU admission). A recently introduced alpha‐index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha‐index in ranking a substantial number of genetic variants. This approach enables the implementation of well‐established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.

Atapattu, S, Indraratna, B & Rujikiatkamjorn, C 2024, 'Influence of periodic cyclic loading and rest period on soft clay consolidation', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 177, no. 1, pp. 30-43.
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Railways are often subjected to periodic cyclic loading and intermittent rest periods. Excessive consolidation settlements can affect the performance of railway tracks built on the soft subgrade. The consolidation behaviour under railway loading conditions with rest periods has not been evaluated thoroughly. In this study, laboratory testing was conducted to investigate the influence of periodic cyclic loading and rest periods on the consolidation of Holocene soft clay from Ballina NSW. The specimens were subjected to a loading frequency of 1 Hz for 54 h with multiple rest periods. The recorded settlements and excess pore-water pressures (EPWP) during cyclic consolidation were employed to determine the corresponding hydraulic gradient, void ratio, resilient (dynamic) modulus and damping ratio. The settlement and accumulated EPWP can be observed during cyclic loading. In contrast, settlements do not occur within a rest period, despite the rapidly dissipating EPWP at the start of a given rest period. The maximum EPWP and settlements decrease as the number of resting period increases. An analytical model capturing the effect of cyclic loading and rest period is proposed where the unique relationships between the hydraulic gradient and the flow rate are established.

Atgur, V, Manavendra, G, Rao, BN, Veza, I & Fattah, IMR 2024, 'Thermal and combustion characteristics of honge, jatropha, and honge‐jatropha mixed biodiesels', Environmental Progress & Sustainable Energy, vol. 43, no. 1.
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AbstractThermal characteristics of biodiesels are useful in system design, modeling, and operation. Such investigations are extensively being carried out in combustors, engine, and process industries. This article examines the thermal characteristics of jatropha (Jatropha curcas), honge (Pongamia pinnata), and their equal mixing from thermogravitometry and differential scanning calorimetry (TG‐DSC) curves for the specific 10°C/min heating rate under atmospheric air. Fuel properties are measured following ASTM standards to compare with diesel properties. Each experiment was repeated three times, and the properties showed insignificant scatter. The average properties of the repeated tests are presented. Two phases of decomposition were observed for diesel, whereas three (viz., devolatilization of aqueous fractions, combustion of methyl esters, and combustion of carbonaceous residues) in biodiesels. Jatropha oil methyl ester (JOME) is thermally stable compared to honge oil methyl ester (HOME). Mixed biodiesel (JOME+HOME) is prone to oxidation due to the high content of oleic and linoleic acids. Recorded onset and offset temperatures of mixed biodiesel are low compared to pure biodiesel. Mixed biodiesel exhibited high volatility resembling diesel characteristics. It exhibited an enthalpy of 240 J/g, whereas the enthalpy of diesel, jatropha, and honge exhibited enthalpies of 130, 321, and 570 J/g, respectively. The combustion index (S) of diesel, jatropha, honge, and mixed biodiesel was 41.6, 82.8, 77.74, and 64.6, respectively. Mixed biodiesel reduces the intensity of combustion (Hf), promising better combustion characteristics. Thus, mixed biodiesel shows the potential of an efficient alternative energy source.

Athuraliya, S, Indraratna, B, Medawela, S, Rowe, RK & Thamwattana, N 2024, 'Modelling biogeochemical clogging affecting piezometers in acid sulfate soil terrain', Canadian Geotechnical Journal, vol. 61, no. 1, pp. 149-164.
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This study offers an analytical solution for radial consolidation that captures the biogeochemical clogging effect in acid sulfate soils. Field sites and personal communication with industry practitioners have provided evidence of piezometers exhibiting retarded pore pressure readings that do not follow conventional soil consolidation and seepage principles when installed in coastal acidic floodplains. This retarded response together with a variation in pH, ion concentrations, and piezometric heads provided evidence of clogging at and around the piezometers. This paper uses the proposed biogeochemical clogging model, which is an analytically derived system of equations to estimate the excess pore water pressure dissipation of piezometers installed in clogging-prone acid sulfate soils. The inclusion of the total porosity reduction attributed to biological and geochemical clogging improves the predictions of the retarded dissipation of excess pore pressure, especially after about 1 year. This method is validated for two previously identified acidic field sites in coastal Australia, where piezometers measured a very slow rate of dissipation. It is concluded that this model has potential to accurately monitor the performance of critical infrastructure, such as dams and embankment foundations built on acidic terrain.

Athuraliya, S, Indraratna, B, Medawela, S, Rowe, RK & Thamwattana, N 2024, 'Modelling biogeochemical clogging affecting piezometers in acid sulfate soil terrain', CANADIAN GEOTECHNICAL JOURNAL, vol. 61, no. 1, pp. 149-164.
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Atmakuru, A, Chakraborty, S, Faust, O, Salvi, M, Datta Barua, P, Molinari, F, Acharya, UR & Homaira, N 2024, 'Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques', Expert Systems with Applications, vol. 255, pp. 124665-124665.
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Babakian, A, Huston, G, Braun, R & Lipman, J 2024, 'Internet Identifiers: A Survey of History, Challenges, and Future Perspectives', IEEE Access, vol. 12, pp. 51919-51941.
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Badeti, U, Jiang, J, Kumarasingham, S, Almuntashiri, A, Pathak, NK, Chanan, A, Freguia, S, Ang, WL, Ghaffour, N, Shon, HK & Phuntsho, S 2024, 'Source separation of urine and treatment: Impact on energy consumption, greenhouse gas emissions, and decentralised wastewater treatment process', Desalination, vol. 583, pp. 117633-117633.
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Bai, K, Zhang, W, Wen, S, Zhao, C, Meng, W, Zeng, Y & Jia, D 2024, 'A data-knowledge-driven interval type-2 fuzzy neural network with interpretability and self-adaptive structure', Information Sciences, vol. 660, pp. 120133-120133.
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Balaji, J, Seikh, AH, Kalam, MA & Venkatesh, R 2024, 'Influences of Rotational Speed on Friction Stir Welding Quality, Mechanical and Fatigue Behaviour of AA6061/SiC Composite', Silicon, vol. 16, no. 1, pp. 323-329.
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Bano, M, Hoda, R, Zowghi, D & Treude, C 2024, 'Large language models for qualitative research in software engineering: exploring opportunities and challenges', Automated Software Engineering, vol. 31, no. 1.
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Barua, PD, Vicnesh, J, Lih, OS, Palmer, EE, Yamakawa, T, Kobayashi, M & Acharya, UR 2024, 'Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review', Cognitive Neurodynamics, vol. 18, no. 1, pp. 1-22.
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Baygin, M, Barua, PD, Dogan, S, Tuncer, T, Hong, TJ, March, S, Tan, R-S, Molinari, F & Acharya, UR 2024, 'Automated anxiety detection using probabilistic binary pattern with ECG signals', Computer Methods and Programs in Biomedicine, vol. 247, pp. 108076-108076.
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Berta, M & Tomamichel, M 2024, 'Entanglement Monogamy via Multivariate Trace Inequalities', Communications in Mathematical Physics, vol. 405, no. 2.
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AbstractEntropy is a fundamental concept in quantum information theory that allows to quantify entanglement and investigate its properties, for example its monogamy over multipartite systems. Here, we derive variational formulas for relative entropies based on restricted measurements of multipartite quantum systems. By combining these with multivariate matrix trace inequalities, we recover and sometimes strengthen various existing entanglement monogamy inequalities. In particular, we give direct, matrix-analysis-based proofs for the faithfulness of squashed entanglement by relating it to the relative entropy of entanglement measured with one-way local operations and classical communication, as well as for the faithfulness of conditional entanglement of mutual information by relating it to the separably measured relative entropy of entanglement. We discuss variations of these results using the relative entropy to states with positive partial transpose, and multipartite setups. Our results simplify and generalize previous derivations in the literature that employed operational arguments about the asymptotic achievability of information-theoretic tasks.

Berta, M, Brandão, FGSL, Gour, G, Lami, L, Plenio, MB, Regula, B & Tomamichel, M 2024, 'The tangled state of quantum hypothesis testing', Nature Physics, vol. 20, no. 2, pp. 172-175.
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Bérubé, C, Nißen, M, Vinay, R, Geiger, A, Budig, T, Bhandari, A, Pe Benito, CR, Ibarcena, N, Pistolese, O, Li, P, Sawad, AB, Fleisch, E, Stettler, C, Hemsley, B, Berkovsky, S, Kowatsch, T & Kocaballi, AB 2024, 'Proactive behavior in voice assistants: A systematic review and conceptual model', Computers in Human Behavior Reports, vol. 14, pp. 100411-100411.
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Best, G, Garg, R, Keller, J, Hollinger, GA & Scherer, S 2024, 'Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy', The International Journal of Robotics Research, vol. 43, no. 4, pp. 485-512.
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We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultaneously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wide range of environments, (c) be resilient to adverse events, and (d) execute this onboard teams of physical robots. Our solution centers around a behavior tree architecture, which adaptively switches between various behaviors involving coordinated exploration and responding to adverse events. Our exploration strategy exploits the benefits of both visual and range sensors with a generalized frontier-based exploration algorithm and an OpenVDB-based map processing pipeline. Our local planner utilizes a dynamically feasible trajectory library and a GPU-based Euclidean distance transform map to allow fast and safe navigation through both tight doorways and expansive spaces. The autonomy pipeline is evaluated with an extensive set of field experiments, with teams of up to three robots that fly up to 3 m/s and distances exceeding 1 km in confined spaces. We provide a summary of various field experiments and detail resilient behaviors that arose: maneuvering narrow doorways, adapting to unexpected environment changes, and emergency landing. Experiments are also detailed from the DARPA Subterranean Challenge, where our proposed autonomy pipeline contributed to us winning the “Most Sectors Explored” award. We provide an extended discussion of lessons learned, release software as open source, and present a video that illustrates our extensive field trials.

Beydoun, G, Low, G, Gill, A, Moniruzzaman, M & Shen, J 2024, 'Tailoring ontology retrieval for supporting requirements analysis', Advanced Engineering Informatics, vol. 59, pp. 102231-102231.
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It is well accepted that domain ontologies can support requirement analysis activities, particularly in detecting inconsistencies and incompleteness of requirement models. These benefits critically depend on the provision of a suitable ontology. We observe the context of supporting requirement analysis provides both opportunities and restrictions when choosing the most appropriate ontology retrieval mechanisms. Requirement models are the basis for retrieving the most influential ontologies and are not the typical retrieval domain ontologies. For instance, a retrieval ontology derived from the requirement is not expected only to be a hierarchical taxonomy, nor is it limited to the boundaries of a single domain, nor does it cover any particular domain completely. Hence, retrieval methods cannot be based on classes only and computational constraints do not necessarily apply as the retrieval process is expected to run only once at the outset of the analysis phase. It is also important to assume that the retrieval in this context is targeting multiple ontologies describing multiple but related domains. In this paper, we deduce that avoiding structural based retrieval mechanisms in fact benefits to the requirement models. Instead, we formulate a new retrieval method based on the PageRank algorithm that takes into account the indirect influences of various concepts within plausible supporting ontologies. This paper provides an empirical analysis that evidences the strength of our retrieval algorithm in supporting the identification of ontologies to support requirement analysis.

Beyhan, B, Akcomak, IS & Cetindamar, D 2024, 'How do technology-based accelerators build their legitimacy as new organizations in an emerging entrepreneurship ecosystem?', Journal of Entrepreneurship in Emerging Economies, vol. 16, no. 4, pp. 954-976.
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PurposeThis paper aims to understand technology-based accelerators’ legitimation efforts in an emerging entrepreneurship ecosystem.Design/methodology/approachThis research is based on qualitative inductive methodology using ten Turkish technology-based accelerators.FindingsThe 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/valueThis 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.

Beyhan, B, Akçomak, S & Cetindamar, D 2024, 'The Startup Selection Process in Accelerators: Qualitative Evidence from Turkey', Entrepreneurship Research Journal, vol. 14, no. 1, pp. 27-51.
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Abstract Startup selection is an essential mechanism of how accelerators create value. Through in-depth case studies of 10 accelerators in Turkey, our research explores the selection process in accelerators. Our findings indicate that accelerators overcome their context’s extreme uncertainty by involving various actors in the selection process and reducing the information asymmetries for investors and startups. Accelerators tend to select effortlessly coachable startups, willing to collaborate with accelerators, mentors, or other actors, and passionate enough to overcome the pressure of creating a business at a fast pace. Our research also exhibits that the selection process serves startups by directing and training them to transmit the right signals to receivers, primarily investors. Accelerators prefer to work with entrepreneurial teams that are coachable, passionate, and collaborative to vibrate the right signals. Similarly, the accelerators’ selection process helps investors by decreasing signaling noise and mitigate information asymmetry. By doing so, accelerators contribute to a well-functioning and more effective entrepreneurship ecosystem.

Bi, S, Li, K, Hu, S, Ni, W, Wang, C & Wang, X 2024, 'Detection and Mitigation of Position Spoofing Attacks on Cooperative UAV Swarm Formations', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 1883-1895.
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Bone, EK, Huber, E, Gribble, L, Lys, I, Dickson-Deane, C, Campbell, C, Yu, P, Markauskaite, L, Carvalho, L & Brown, C 2024, 'A community-based practice for the co-development of women academic leaders', International Journal for Academic Development, vol. 29, no. 2, pp. 238-254.
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Booth, E, Lee, J, Rizoiu, M-A & Farid, H 2024, 'Conspiracy, Misinformation, Radicalisation: Understanding the Online Pathway to Indoctrination and Opportunities for Intervention', Journal of Sociology, vol. 60, no. 2, pp. 440-457.
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In response to the rise of various fringe movements in recent years, from anti-vaxxers to QAnon, there has been increased public and scholarly attention to misinformation and conspiracy theories and the online communities that produce them. However, efforts at understanding the radicalisation process largely focus on those who go on to commit violent crimes. This article draws on three waves of research exploring the experiences of individuals currently or formerly involved in fringe communities, including the different stages of investment they progressed through, and ultimately, what made people leave. We propose a pathway model for understanding contemporary online radicalisation, including potential interventions that could be safely made at each stage. Insight into the experience of being immersed in these communities is essential for engaging with these people empathetically, and therefore preventing both the emergence of violent terrorists and protecting vulnerable people from being drawn into these communities.

Bosco Mofatto, PM, Cosenza, A, Di Trapani, D, Wu, L, Ni, B-J & Mannina, G 2024, 'Carbon footprint reduction by coupling intermittent aeration with submerged MBR: A pilot plant study', Journal of Environmental Chemical Engineering, vol. 12, no. 4, pp. 113115-113115.
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Braytee, A & Liu, W 2024, 'Robust multi-label feature learning-based dual space', International Journal of Data Science and Analytics, vol. 17, no. 4, pp. 373-387.
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AbstractMulti-label learning handles instances associated with multiple class labels. The original label space is a logical matrix with entries from the Boolean domain $$\in \left\{ 0,1 \right\} $$ ∈ 0 , 1 . Logical labels cannot show the relative importance of each semantic label to the instances. Most existing methods map the input features to the label space using linear projections considering the label dependencies using a logical label matrix. However, the discriminative features are learned using one-way projection from the feature representation of an instance into a logical label space. There is no manifold in the learning space of logical labels, which limits the potential of learned models. We propose a novel method in multi-label learning to learn the projection matrix from the feature space to the semantic label space and project it back to the original feature space using encoder–decoder deep learning architecture. The key intuition which guides our method is that the discriminative features are identified due to mapping the features back and forth using two linear projections. To the best of our knowledge, this is one of the first attempts to study the ability to reconstruct the original features from the label manifold in multi-label learning. We show that the learned projection matrix identifies a subset of discriminative features across multiple semantic labels. Extensive experiments on real-world datasets show the superiority of the...

Brown, C, Huber, E, Bone, E, Gribble, L, Lys, I, Dickson-Deane, C, Yu, P, Markauskaite, L & Campbell, C 2024, 'Academic Women Co-designing Education Futures in a Postdigital World', Postdigital Science and Education, vol. 6, no. 1, pp. 300-320.
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AbstractThis paper draws on the collective knowledge-building of nine women from diverse disciplines, roles, cultures, and institutions in Australasian women in leadership programme. Brought together during Covid-19 through a shared interest and purpose concerning current and future developments in digital education, we offer knowledge and insight from our perspective as women leaders in academia, on co-designing futures in a postdigital world. Drawing on a duoethnographic research design, we reflected on our experiences as academic leaders and practitioners to systematically explore people, situations, and contexts through co-construction and dialogue. Our joint exploration uncovered themes of visibility, gravitas, and relationships. We provide evidence of the role co-design plays in our own practices, in our classrooms, and how our research design was strengthened through co-design. Finally, we offer an evolving model of co-design for leadership in higher education with communities of practice at its core.

Bryant, L, Sedlarevic, N, Stubbs, P, Bailey, B, Nguyen, V, Bluff, A, Barnett, D, Estela, M, Hayes, C, Jacobs, C, Kneebone, I, Lucas, C, Mehta, P, Power, E & Hemsley, B 2024, 'Collaborative co-design and evaluation of an immersive virtual reality application prototype for communication rehabilitation (DISCOVR prototype)', Disability and Rehabilitation: Assistive Technology, vol. 19, no. 1, pp. 90-99.
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PURPOSE: Virtual reality (VR) lends itself to communication rehabilitation by creating safe, replicable, and authentic simulated environments in which users learn and practice communication skills. The aim of this research was to obtain the views of health professionals and technology specialists on the design characteristics and usability of a prototype VR application for communication rehabilitation. MATERIALS AND METHODS: Nine professionals from different health and technology disciplines participated in an online focus group or individual online interview to evaluate the application and use of the VR prototype. Data sources were analysed using a content thematic analysis. RESULTS: Four main themes relating to VR design and implementation in rehabilitation were identified: (i) designing rehabilitation-focused virtual worlds; (ii) understanding and using VR hardware; (iii) making room for VR in rehabilitation and training; and (iv) implementing VR will not replace the health professional's role. DISCUSSION: Health professionals and technology specialists engaged in co-design while evaluating the VR prototype. They identified software features requiring careful consideration to ensure improved usability, client safety, and success in communication rehabilitation outcomes. Continuing inclusive co-design, engaging health professionals, clients with communication disability, and their families will be essential to creating useable VR applications and integrating these successfully into rehabilitation. Implications for rehabilitationHealth and technology professionals, along with clients, are integral to the co-design of new VR technology applications.Design of VR applications needs to consider the client's communication, physical, cognitive, sensory, psychosocial, and emotional needs for greater usability of these programs.Realism and authenticity of interactions, characters, and environments are considered important factors to allow users to be fully immersed in v...

Bryant, L, Stubbs, P, Bailey, B, Nguyen, V, Bluff, A & Hemsley, B 2024, 'Interacting with virtual characters, objects and environments: investigating immersive virtual reality in rehabilitation', Disability and Rehabilitation: Assistive Technology, pp. 1-11.
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PURPOSE: This pilot study aimed to (a) investigate opportunities for immersive Virtual Reality (VR) technology in communication, physical, and visual rehabilitation by examining the interaction of people without disabilities in a range of structured virtual environments; and (b) validate research protocols that might be used to evaluate the physical, visual, and verbal interaction of users in virtual worlds, and their safety while using the technology. METHODS: Thirteen adults identifying as people without disability were exposed to VR via a head-mounted display. A video-review method was used to qualitatively code and analyse each participant's communication, movement, orientation, and support needs. RESULTS: All participants oriented to their virtual environments sufficiently to use applications. Their spoken language was effective for interaction, although unconventional social behaviours were also observed. Two participants reported minor adverse reactions consistent with mild cybersickness. CONCLUSION: The results provide insight into the types of environments and characters that support the greatest communicative, physical, and visual interaction in immersive VR. The tested protocols are useful to assess safety when using VR, and to observe communicative, physical, and visual interaction with virtual environments and characters. Implications for future research and use of VR with people with communication, physical and visual disability are discussed.

Cai, X, Shi, K, Sun, Y, Cao, J, Wen, S & Tian, Z 2024, 'Intelligent Event-Triggered Control Supervised by Mini-Batch Machine Learning and Data Compression Mechanism for T-S Fuzzy NCSs Under DoS Attacks', IEEE Transactions on Fuzzy Systems, vol. 32, no. 3, pp. 804-815.
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Cai, X, Shi, K, Sun, Y, Cao, J, Wen, S, Chen, P & Tian, Z 2024, 'Dual-channel NCSs performance error estimation under DoS attacks and intelligent control supervised by machine learning to AGV application', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Cai, X, Shi, K, Sun, Y, Cao, J, Wen, S, Qiao, C & Tian, Z 2024, 'Stability Analysis of Networked Control Systems Under DoS Attacks and Security Controller Design With Mini-Batch Machine Learning Supervision', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 3857-3865.
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Cai, X, Shi, K, Sun, Y, Wen, S, Yan, H & Xie, Y 2024, 'Fuzzy Memory Controller Design Based-Machine Learning Algorithm and Stability Analysis for Nonlinear NCSs Under Asynchronous Cyber Attacks', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 2, pp. 1082-1093.
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Cai, Y, Che, H, Pan, B, Leung, M-F, Liu, C & Wen, S 2024, 'Projected cross-view learning for unbalanced incomplete multi-view clustering', Information Fusion, vol. 105, pp. 102245-102245.
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Camaya, I, Hill, M, Sais, D, Tran, N, O’Brien, B & Donnelly, S 2024, 'The Parasite‐Derived Peptide, FhHDM‐1, Selectively Modulates miRNA Expression in β‐Cells to Prevent Apoptotic Pathways Induced by Proinflammatory Cytokines', Journal of Diabetes Research, vol. 2024, no. 1.
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We have previously identified a parasite‐derived peptide, FhHDM‐1, that prevented the progression of diabetes in nonobese diabetic (NOD) mice. Disease prevention was mediated by the activation of the PI3K/Akt pathway to promote β‐cell survival and metabolism without inducing proliferation. To determine the molecular mechanisms driving the antidiabetogenic effects of FhHDM‐1, miRNA:mRNA interactions and in silico predictions of the gene networks were characterised in β‐cells, which were exposed to the proinflammatory cytokines that mediate β‐cell destruction in Type 1 diabetes (T1D), in the presence and absence of FhHDM‐1. The predicted gene targets of miRNAs differentially regulated by FhHDM‐1 mapped to the biological pathways that regulate β‐cell biology. Six miRNAs were identified as important nodes in the regulation of PI3K/Akt signaling. Additionally, IGF‐2 was identified as a miRNA gene target that mediated the beneficial effects of FhHDM‐1 on β‐cells. The findings provide a putative mechanism by which FhHDM‐1 positively impacts β‐cells to permanently prevent diabetes. As β‐cell death/dysfunction underlies diabetes development, FhHDM‐1 opens new therapeutic avenues.

Canales, M, Castilla-Rho, J, Rojas, R, Vicuña, S & Ball, J 2024, 'Agent-based models of groundwater systems: A review of an emerging approach to simulate the interactions between groundwater and society', Environmental Modelling & Software, vol. 175, pp. 105980-105980.
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Cang, M, Zhang, L, Wang, Y, Fu, J, Luo, Z, Kang, Z, Fu, MW & Wang, MY 2024, 'An efficient method for design of lattice core sandwich structures with superior buckling strength under compression', Engineering Optimization, vol. 56, no. 4, pp. 506-524.
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Cao, X & Tsang, IW 2024, 'Distribution Matching for Machine Teaching', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Cao, Y, Ni, Q, Jia, M, Zhao, X & Yan, X 2024, 'Online Knowledge Distillation for Machine Health Prognosis Considering Edge Deployment', IEEE Internet of Things Journal, pp. 1-1.
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Cao, Y, Yao, L, Pan, L, Sheng, QZ & Chang, X 2024, 'Guided Image-to-Image Translation by Discriminator-Generator Communication', IEEE Transactions on Multimedia, vol. 26, pp. 1528-1538.
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Cao, Z, Shi, Y, Chang, Y-C, Yao, X & Lin, C-T 2024, 'Twin Fuzzy Networks With Interpolation Consistency Regularization for Weakly-Supervised Anomaly Detection', IEEE Transactions on Fuzzy Systems, pp. 1-12.
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Carbajal Piña, CA, Acur, N & Cetindamar, D 2024, 'An activity theory analysis of digital innovation orchestration in Industry 4.0', Journal of Manufacturing Technology Management, vol. 35, pp. 1-26.
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PurposeThis paper explores the orchestration of digital innovation in Industry 4.0 organisations.Design/methodology/approachThe study applies the activity theory to explorative multiple case studies. Observations of innovation activities in five business cases take place at two large international organisations.FindingsThe results underline five logics of action that drive digital innovation: (1) digital transformation, (2) technology translation, (3) catalyst agents, (4) digital thread and (5) empowerment. Further, the case study organisations highlight the importance of developing a sustainable culture capable of continuously adopting new technologies, processes and infrastructure that will allow the management of digital innovations.Originality/valueThe study empirically shows the motivations and challenges in orchestrating digital innovation in Industry 4.0 organisations.

Cedieu, S, Grigoletto, FB, Lee, SS, Barzegarkhoo, R & Siwakoti, YP 2024, 'A Five-Level Common-Ground Inverter With Reduced Switch Count for Transformerless Grid-Tied PV Applications', IEEE Transactions on Industry Applications, pp. 1-15.
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Cetindamar, D, Abedin, B & Shirahada, K 2024, 'The Role of Employees in Digital Transformation: A Preliminary Study on How Employees’ Digital Literacy Impacts Use of Digital Technologies', IEEE Transactions on Engineering Management, vol. 71, pp. 7837-7848.
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Even though digital technologies such as cloud technologies are prevalent in transforming businesses, the role of employees and their digital skills in the process is, to a large extent, neglected. This study brings forward the novel concept of digital literacy to explore the role of employees in understanding the wide variety of opportunities of digital technologies and their actualization. By treating digital literacy as the antecedent of cognitive behavior of employees in utilizing cloud technology at companies, we apply the Theory of Planned Behavior (TPB) for analyzing preliminary empirical data collected from 124 Australian employees’ technology use intentionality and behavior. The quantitative analysis shows that the TPB holds for the utilization of cloud technology and there is a positive relationship between employees' digital literacy and the utilization of cloud technology at companies. Overall, the study contributes to the technology management literature by offering a workable construct to measure the digital skills of employees in the form of digital literacy. Further, it expands the TPB framework by introducing digital literacy as a perceived behavior control variable that helps to examine the role of employees in digital transformation. The paper ends with implications and limitations of our preliminary study, followed with suggestions for future studies.

Cetindamar, D, Abedin, B, Gerdsri, N & Shirahada, K 2024, 'Editorial Overview of Digital Literacy of Employees and Organizational Transformation and Innovation', IEEE Transactions on Engineering Management, vol. 71, pp. 7832-7836.
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This Special Issue (SI) aims to provide organizations with a theoretical, conceptual, and applied grounded discussion of the Digital Literacy of Employees and organizational Transformation and Innovation to aid in innovative, sustainable development and effective decision-making. By doing so, our SI hopes to expand the technology management discipline in understanding the human side of technological innovations.

Cetindamar, D, Kitto, K, Wu, M, Zhang, Y, Abedin, B & Knight, S 2024, 'Explicating AI Literacy of Employees at Digital Workplaces', IEEE Transactions on Engineering Management, vol. 71, pp. 810-823.
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This paper aims to understand the definition and dimensions of artificial intelligence (AI) literacy. Digital technologies, including AI, trigger organizational affordances in workplaces, yet few studies have investigated employees’ AI literacy. This paper uses a bibliometrics analysis of 270 articles to explore the meaning of AI literacy of employees in the extant literature. Descriptive statistics, keyword co-occurrence analysis, and a hierarchical topic tree are employed to profile the research landscape and identify the core research themes and relevant papers related to AI literacy’s definition, dimensions, challenges, and future directions. Findings highlight four sets of capabilities associated with AI literacy, namely technology-related, work-related, human-machine-related, and learning-related capabilities, pointing also to the importance of operationalizing AI literacy for non AI professionals. This result contributes to the literature associated with technology management studies by offering a novel conceptualization of AI literacy and link it to the employee’s role in digital workplaces. We conclude by inviting researchers to examine the effect of employee-technology interactions on employees’ AI literacy, which might improve the design and use of AI.

Cetindamar, D, Renando, C, Bliemel, M & Klerk, SD 2024, 'The Evolution of the Australian Start-up and Innovation Ecosystem: Mapping Policy Developments, Key Actors, Activities, and Artefacts', Science, Technology and Society, vol. 29, no. 1, pp. 13-33.
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This study maps the evolution of the Australian start-up and innovation ecosystem by exploring policy developments and mapping the key actors, activities, and artefacts. This study unpacks policy developments over the past two decades to show the government’s role in shaping the innovation ecosystem and the implications for start-ups. We outline the ecosystem’s key actors, including start-ups, scale-ups, support organisations, investors, research institutions, and their growth over time. We examine the artefacts of the ecosystem to understand start-up and innovation performance in a global context. We also explore the activities of the ecosystem in terms of collaboration, research, and development. The study concludes with a discussion of policy gaps.

Ceylan, O, Neshat, M & Mirjalili, S 2024, 'Cascaded H-bridge multilevel inverters optimization using adaptive grey wolf optimizer with local search', Electrical Engineering, vol. 106, no. 2, pp. 1765-1779.
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Chacon, A, Rutherford, H, Hamato, A, Nitta, M, Nishikido, F, Iwao, Y, Tashima, H, Yoshida, E, Akamatsu, G, Takyu, S, Kang, HG, Franklin, DR, Parodi, K, Yamaya, T, Rosenfeld, A, Guatelli, S & Safavi-Naeini, M 2024, 'A quantitative assessment of Geant4 for predicting the yield and distribution of positron-emitting fragments in ion beam therapy', Physics in Medicine & Biology, vol. 69, no. 12, pp. 125015-125015.
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Abstract Objective. To compare the accuracy with which different hadronic inelastic physics models across ten Geant4 Monte Carlo simulation toolkit versions can predict positron-emitting fragments produced along the beam path during carbon and oxygen ion therapy. Approach. Phantoms of polyethylene, gelatin, or poly(methyl methacrylate) were irradiated with monoenergetic carbon and oxygen ion beams. Post-irradiation, 4D PET images were acquired and parent 11C, 10C and 15O radionuclides contributions in each voxel were determined from the extracted time activity curves. Next, the experimental configurations were simulated in Geant4 Monte Carlo versions 10.0 to 11.1, with three different fragmentation models—binary ion cascade (BIC), quantum molecular dynamics (QMD) and the Liege intranuclear cascade (INCL++) - 30 model-version combinations. Total positron annihilation and parent isotope production yields predicted by each simulation were compared between simulations and experiments using normalised mean squared error and Pearson cross-correlation coefficient. Finally, we compared the depth of the maximum positron annihilation yield and the distal point at which the positron yield decreases to 50% of peak between each model and the experimental results. Main results. Performance varied considerably across versions and models, with no one version/model combination providing the best prediction of all positron-emitting fragments in all evaluated target materials and irradiation conditions. BIC in Geant4 10.2 provided the best overall agreement with experimental results in the largest number of test cases. QMD consistently provided the best estimates of both the depth of peak positron yield (10.4 and 10.6) and the distal 50%-of-peak point (10.2), while...

Chandan, B, Pal, PK, Jana, KC & Siwakoti, YP 2024, 'Performance Evaluation of a New Transformerless Grid-Connected Six-Level Inverter with Integrated Voltage Boosting', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Chang, X, Sato, Y & Zhang, C 2024, 'Multi-peak Solutions of a Class of Fractional p-Laplacian Equations', The Journal of Geometric Analysis, vol. 34, no. 1.
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Chen, B, Cao, Z & Bai, Q 2024, 'SATF: A Scalable Attentive Transfer Framework for Efficient Multiagent Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Chen, D, Li, J, Wang, L & Wu, C 2024, 'A generic approach for modelling hydrogen-methane-air detonation in hydrocode', Journal of Cleaner Production, vol. 465, pp. 142840-142840.
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Chen, F & Long, G 2024, 'FedGE: Break the scalability limitation of Graph Neural Network with Federated Graph Embedding', IEEE Transactions on Big Data, vol. PP, no. 99, pp. 1-11.
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Neighborhood aggregation algorithms, represented as graph convolutional networks, have attained non-negligible success in numerous topological structure-based scenarios with the assumption that the topological structure of the given graph is pre-defined and relatively small. However, a real-world graph generally is a super large graph consisting of many small graphs that are interconnected and overlapping. Which makes graph embedding in real-life industries, by nature, fall into the federated learning scheme. While current graph-based algorithms are only able to capture the individual topology of each natural graph, learning the complete structural information of the merged large graph remains challenging due to the unsustainable computational cost of graph convolutional operations. We propose a tailored federated graph embedding framework to learn the intact structural information of the numerous inherently linked small-scale graphs and the embedding of each node. We leverage graphs with around two and a half million nodes to validate the effectiveness and the correctness of the proposed framework.

Chen, H, Zhu, T, Liu, C, Yu, S & Zhou, W 2024, 'High-Frequency Matters: Attack and Defense for Image-Processing Model Watermarking', IEEE Transactions on Services Computing, pp. 1-15.
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Chen, J, Zhu, K, Wu, K, Niu, J & Zhang, JA 2024, 'Introducing User Grouping to Counteract Channel Correlation in IRS-Assisted ISAC', IEEE Communications Letters, vol. 28, no. 4, pp. 808-812.
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Chen, K, He, X, Liang, F & Sheng, D 2024, 'Influences of ink-bottle effect evolution on water retention hysteresis of unsaturated soils: An experimental investigation', Engineering Geology, vol. 330, pp. 107409-107409.
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Chen, K, Liu, Q, Chen, B, Zhang, S, Ferrara, L & Li, W 2024, 'Effect of raw materials on the performance of 3D printing geopolymer: A review', Journal of Building Engineering, vol. 84, pp. 108501-108501.
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Chen, K, Qu, F, Huang, Y, Cai, J, Wu, F & Li, W 2024, 'Advancing photocatalytic concrete technologies in design, performance and application for a sustainable future', Advanced Nanocomposites, vol. 1, no. 1, pp. 180-200.
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Chen, L, Li, H, Su, Y, Yang, Z, He, Z, Wang, D, Li, JJ & Xing, D 2024, 'Using A Google Web Search Analysis to Assess the Utility of ChatGPT in Stem Cell Therapy', Stem Cells Translational Medicine, vol. 13, no. 1, pp. 60-68.
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Abstract Objective Since its introduction, the use of ChatGPT has increased significantly for medically related purposes. However, current research has not captured its applications in providing information on stem cell therapy. To address this gap, the present study compared the effectiveness of ChatGPT to Google in answering medical questions related to stem cell therapy. Methods The search term “stem cell therapy” was used to perform a Google web search, and the top 20 frequently asked questions along with answers were recorded together with relevant website sources. Of these questions, the top 10 questions were separately entered into ChatGPT, and the answers and the sources were recorded. Then, the following statement was entered into ChatGPT: “Do a Google search with the search term ‘stem cell therapy’ and record 20 common questions related to the search term.” After obtaining these questions, each question was separately entered into ChatGPT for an answer and source. Results A majority of the top 20 questions provided by Google were related to fact, whereas a majority of the questions provided by ChatGPT were related to policy. The answer sources used by Google were mostly drawn from medical practice, while those used by ChatGPT were mostly drawn from academic information. Conclusion Compared to Google, ChatGPT exhibits stronger capabilities in promoting awareness of stem cell therapy. ChatGPT has the ability to eliminate misleading informatio...

Chen, L, Liu, Y, Ban, Y-L, Yang, S & Guo, YJ 2024, 'Synthesis of Large-Scale Planar Isophoric Sparse Arrays Using Iterative Least Squares With Nonredundant Constraints (ILS-NRC)', IEEE Transactions on Antennas and Propagation, vol. 72, no. 5, pp. 4232-4245.
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Chen, N, Zhang, X, Du, Q, Wang, H, Wang, Z, Ren, J, Li, H, Guo, W & Ngo, HH 2024, 'An in-situ biochar-enhanced anaerobic membrane bioreactor for swine wastewater treatment under various organic loading rates', Journal of Environmental Sciences, vol. 146, pp. 304-317.
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Chen, Q, Choo, Y, Akther, N, Shon, HK & Naidu, G 2024, 'Generating sustainable cement material from seawater with low-cost ultrafiltration (UF) membrane electrolysis', Chemical Engineering Journal, vol. 494, pp. 153007-153007.
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Chen, Q, Xiong, Z, Tao, G, Nimbalkar, S & Wang, C 2024, 'Laboratory investigation on static and dynamic properties, durability, and microscopic structure of Nano-SiO2 and polypropylene fiber cemented soil under coupled seawater corrosion and cyclic loading', Construction and Building Materials, vol. 440, pp. 137449-137449.
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Chen, S-L, Liu, Y, Li, M, Jones, B & Guo, YJ 2024, 'Analysis, Design, and Measurement of Continuous Frequency-Scanning Polarization-Rotating Antenna', IEEE Transactions on Antennas and Propagation, vol. 72, no. 2, pp. 1911-1916.
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Chen, W, Hussain, W, Cauteruccio, F & Zhang, X 2024, 'Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models', Computer Modeling in Engineering & Sciences, vol. 139, no. 1, pp. 187-224.
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Chen, X, Feng, Z, Zhang, JA, Gao, F, Yuan, X, Yang, Z & Zhang, P 2024, 'Complex CNN CSI Enhancer for Integrated Sensing and Communications', IEEE Journal of Selected Topics in Signal Processing, pp. 1-15.
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Chen, X, Feng, Z, Zhang, JA, Wei, Z, Yuan, X, Zhang, P & Peng, J 2024, 'Downlink and Uplink Cooperative Joint Communication and Sensing', IEEE Transactions on Vehicular Technology, pp. 1-15.
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Chen, X, Feng, Z, Zhang, JA, Yuan, X & Zhang, P 2024, 'Kalman Filter-Based Sensing in Communication Systems With Clock Asynchronism', IEEE Transactions on Communications, vol. 72, no. 1, pp. 403-417.
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Chen, X, Liang, Q, Chen, Y, Wang, P, Yu, H & Luo, X 2024, 'Cognitive-based knowledge learning framework for recommendation', Knowledge-Based Systems, vol. 287, pp. 111446-111446.
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Chen, X, Liu, T, Fournier-Viger, P, Zhang, B, Long, G & Zhang, Q 2024, 'A fine-grained self-adapting prompt learning approach for few-shot learning with pre-trained language models', Knowledge-Based Systems, vol. 299, pp. 111968-111968.
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Chen, X, Pan, Y, Tsang, I & Zhang, Y 2024, 'Learning node representations against perturbations', Pattern Recognition, vol. 145, pp. 109976-109976.
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Chen, Y, Hu, R, Li, Z, Yang, C, Wang, X, Long, G & Xu, G 2024, 'Exploring explicit and implicit graph learning for multivariate time series imputation', Engineering Applications of Artificial Intelligence, vol. 127, pp. 107217-107217.
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Chen, Y, Li, G, An, P, Liu, Z, Huang, X & Wu, Q 2024, 'Light Field Salient Object Detection With Sparse Views via Complementary and Discriminative Interaction Network', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 2, pp. 1070-1085.
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Chen, Y, Ren, L, Li, X & Zhou, JL 2024, 'Competitive adsorption and bioaccumulation of sulfamethoxazole and roxithromycin by sediment and zebrafish (Danio rerio) during individual and combined exposure in water', Journal of Hazardous Materials, vol. 464, pp. 132894-132894.
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Chen, Y, Tuan, HD, Fang, Y, Yu, H, Poor, HV & Hanzo, L 2024, 'Enhancing the Downlink Rate Fairness of Low-Resolution Active RIS-Aided Signaling by Closed-Form Expression-Based Iterative Optimization', IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 8013-8029.
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Chen, Y, Tuan, HD, Yu, H, Poor, HV & Hanzo, L 2024, 'Active RIS-Assisted Multi-User Multi-Stream Transmit Precoding Relying on Scalable-Complexity Iterations', IEEE Transactions on Communications, pp. 1-1.
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This is the first investigation focused on delivering multi-stream information to multiple multi-antenna users employing an active reconfigurable intelligent surface (aRIS)-assisted system. We conceive the joint design of the transmit precoders and of the aRIS’s power-amplified reconfigurable elements (APRES) to enhance the log-det rate objective functions for all users, which poses large-scale mixed discrete continuous problems. We develop a max-min log-det solver, which iterates quadratic-solvers of cubic complexity to maximize the nonsmooth function representing the minimum of the users’ log-det rate functions. To mitigate the computational burden associated with cubically escalating complexity in large-scale scenarios, we introduce a pair of alternative problems aimed at maximizing the smooth functions representing the sum of the users’ log-det rate function (sum log-det) and the soft minimum of the users’ log-det rate function (soft min log-det). We develop sum log-det and soft max-min solvers, leveraging closed-form expressions of scalable (linear) complexity for efficient computation. This approach ensures practicality in addressing large-scale scenarios. Furthermore, the soft min log-det enables us to enhance the log-det rates for all users and their sum, ultimately improving the quality of delivering multi-user multi-stream information.

Chen, Y, Zhu, S, Shen, M, Liu, X & Wen, S 2024, 'Event-Based Output Quantized Synchronization Control for Multiple Delayed Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 1, pp. 428-438.
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This article concentrates on the global exponential synchronization problem of multiple neural networks with time delay by the event-based output quantized coupling control method. In order to reduce the signal transmission cost and avoid the difficulty of obtaining the systems' full states, this article adopts the event-triggered control and output quantized control. A new dynamic event-triggered mechanism is designed, in which the control parameters are time-varying functions. Under weakened coupling matrix conditions, by using a Halanay-type inequality, some simple and easily verified sufficient conditions to ensure the exponential synchronization of multiple neural networks are presented. Moreover, the Zeno behaviors of the system are excluded. Some numerical examples are given to verify the effectiveness of the theoretical analysis in this article.

Chen, Y, Zhu, S, Yan, H, Shen, M, Liu, X & Wen, S 2024, 'Event-Based Global Exponential Synchronization for Quaternion-Valued Fuzzy Memristor Neural Networks With Time-Varying Delays', IEEE Transactions on Fuzzy Systems, vol. 32, no. 3, pp. 989-999.
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Chen, Z, Ji, J, Yu, W, Ni, Q, Lu, G & Chang, X 2024, 'A multi-scale graph convolutional network with contrastive-learning enhanced self-attention pooling for intelligent fault diagnosis of gearbox', Measurement, vol. 230, pp. 114497-114497.
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Chen, Z, Ji, JC, Chen, K, Ni, Q, Ding, X & Yu, W 2024, 'Adaptive Topology-Aware Siamese Network for Cross-domain Fault Diagnosis with Small Samples', IEEE Sensors Journal, pp. 1-1.
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Chen, Z, Wei, W, Chen, X, Liu, Y, Shen, Y & Ni, B-J 2024, 'Upcycling of plastic wastes for hydrogen production: Advances and perspectives', Renewable and Sustainable Energy Reviews, vol. 195, pp. 114333-114333.
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Chen, Z, Yan, R, Ma, Y, Sui, Y & Xue, J 2024, 'A Smart Status Based Monitoring Algorithm for the Dynamic Analysis of Memory Safety', ACM Transactions on Software Engineering and Methodology, vol. 33, no. 4, pp. 1-47.
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C is a dominant programming language for implementing system and low-level embedded software. Unfortunately, the unsafe nature of its low-level control of memory often leads to memory errors. Dynamic analysis has been widely used to detect memory errors at runtime. However, existing monitoring algorithms for dynamic analysis are not yet satisfactory, as they cannot deterministically and completely detect some types of errors, such as segment confusion errors, sub-object overflows, use-after-frees and memory leaks. We propose a new monitoring algorithm, namely Smatus , short for smart status , that improves memory safety by performing comprehensive dynamic analysis. The key innovation is to maintain at runtime a small status node for each memory object. A status node records the status value and reference count of an object, where the status value denotes the liveness and segment type of this object, and the reference count tracks the number of pointer variables pointing to this object. Smatus maintains at runtime a pointer metadata for each pointer variable, to record not only the base and bound of a pointer’s referent but also the address of the referent’s status node. All the pointers pointing to the same referent share the same status node in their pointer metadata. A status node is smart in the sense that it is automatically deleted when it becomes useless (indicated by its reference count reaching zero). To the best of our knowledge, Smatus represents the most comprehensive approach of its kind. ...

Chen, Z, Zuo, W, Zhou, K, Li, Q, Yi, Z & Huang, Y 2024, 'Numerical investigation on the performance enhancement of PEMFC with gradient sinusoidal-wave fins in cathode channel', Energy, vol. 288, pp. 129894-129894.
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Cheng, H, Guo, Y, Wang, T, Li, Q, Chang, X & Nie, L 2024, 'Voice-Face Homogeneity Tells Deepfake', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 3, pp. 1-22.
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Detecting forgery videos is highly desirable due to the abuse of deepfake. Existing detection approaches contribute to exploring the specific artifacts in deepfake videos and fit well on certain data. However, the growing technique on these artifacts keeps challenging the robustness of traditional deepfake detectors. As a result, the development of these approaches has reached a blockage. In this article, we propose to perform deepfake detection from an unexplored voice-face matching view. Our approach is founded on two supporting points: first, there is a high degree of homogeneity between the voice and face of an individual (i.e., they are highly correlated), and second, deepfake videos often involve mismatched identities between the voice and face due to face-swapping techniques. To this end, we develop a voice-face matching method that measures the matching degree between these two modalities to identify deepfake videos. Nevertheless, training on specific deepfake datasets makes the model overfit certain traits of deepfake algorithms. We instead advocate a method that quickly adapts to untapped forgery, with a pre-training then fine-tuning paradigm. Specifically, we first pre-train the model on a generic audio-visual dataset, followed by the fine-tuning on downstream deepfake data. We conduct extensive experiments over three widely exploited deepfake datasets: DFDC, FakeAVCeleb, and DeepfakeTIMIT. Our method obtains significant performance gains as compared to other state-of-the-art competitors. For instance, our method outperforms the baselines by nearly 2%, achieving an AUC of 86.11% on FakeAVCeleb. It is also worth noting that our method already achieves competitive results when fine-tuned on limited deepfake data.

Cheng, X, Nie, X, Li, N, Wang, H, Zheng, Z & Sui, Y 2024, 'How About Bug-Triggering Paths? - Understanding and Characterizing Learning-Based Vulnerability Detectors', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 2, pp. 542-558.
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Machine learning and its promising branch deep learning have proven to be effective in a wide range of application domains. Recently, several efforts have shown success in applying deep learning techniques for automatic vulnerability discovery, as alternatives to traditional static bug detection. In principle, these learning-based approaches are built on top of classification models using supervised learning. Depending on the different granularities to detect vulnerabilities, these approaches rely on learning models which are typically trained with well-labeled source code to predict whether a program method, a program slice, or a particular code line contains a vulnerability or not. The effectiveness of these models is normally evaluated against conventional metrics including precision, recall and F1 score. In this paper, we show that despite yielding promising numbers, the above evaluation strategy can be insufficient and even misleading when evaluating the effectiveness of current learning-based approaches. This is because the underlying learning models only produce the classification results or report individual/isolated program statements, but are unable to pinpoint bug-triggering paths, which is an effective way for bug fixing and the main aim of static bug detection. Our key insight is that a program method or statement can only be stated as vulnerable in the context of a bug-triggering path. In this work, we systematically study the gap between recent learning-based approaches and conventional static bug detectors in terms of fine-grained metrics called BTP metrics using bug-triggering paths. We then characterize and compare the quality of the prediction results of existing learning-based detectors under different granularities. Finally, our comprehensive empirical study reveals several key issues and challenges in developing classification models to pinpoint bug-triggering paths and calls for more advanced learning-based bug detection techniques.

Cheng, X-Y, Ding, C & Ziolkowski, RW 2024, 'Dual-Band Shared-Aperture Dielectric Resonator Antenna (DRA) with Suppressed Cross-Band Interactions', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Cheniti, M, Akhtar, Z, Adak, C & Siddique, K 2024, 'An Approach for Full Reinforcement-Based Biometric Score Fusion', IEEE Access, vol. 12, pp. 49779-49790.
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Chenrayan, V, Kanaginahal, G, Shahapurkar, K, Soudagar, MEM, Fouad, Y & Kalam, MA 2024, 'Analytical modeling and experimental estimation of the dynamic mechanical characteristics of green composite: Caesalpinia decapetala seed reinforcement', Polymer Engineering & Science, vol. 64, no. 3, pp. 1096-1109.
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AbstractThe emerging need for a sustainable environment prompts the research community to develop functional materials with bio‐ and organic waste. This research advocates biodegradable waste management and its performance evaluation. The involvement of Caesalpinia decapetala (CD) as a potential reinforcement in the epoxy matrix and its analytical evaluation of thermal stability are novel ideas for disposing of bio and organic waste. Three different variants (10, 20, and 30 wt%) of CD seed particles are used to develop the epoxy composite, and further, their influence on dynamic mechanical characteristics such as damping type, loss modulus, and storage modulus has been investigated. The results corroborate that the higher CD seed content (30 wt%) in the epoxy matrix enhances the storage modulus, loss modulus, and damping on a scale of 1.14, 1.25, and 1.07 times that of the neat epoxy matrix. The reason behind the improved dynamic properties has been validated through theoretical modeling. A substantial increment in the degree of entanglement and activation energy in the band of 8.33 × 10−3 moles/m3 and 20.201 kJ/mol, respectively, in comparison with neat epoxy, is considered to be good authentication for the thermal stability of the CD 30 specimen. The analytical prediction of storage modulus is executed with five different models, whereas damping behavior is executed with two different models. The analytically estimated results are matched with the experimental ones, and we conclude that they are in fair agreement with the experimental findings.

Chenrayan, V, Palanisamy, D, Mani, K, Shahapurkar, K, Elahi M. Soudagar, M, Fouad, Y, Kalam, MA, Ali, MM & Nasir Bashir, M 2024, 'Mitigation of bio-corrosion characteristics of coronary artery stent by optimising fs-laser micromachining parameters', Heliyon, vol. 10, no. 6, pp. e28057-e28057.
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Chi, H, Yang, W, Liu, F, Lan, L, Qin, T & Han, B 2024, 'Does Confusion Really Hurt Novel Class Discovery?', International Journal of Computer Vision, vol. 132, no. 8, pp. 3191-3207.
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Chi, K, Li, J & Wu, C 2024, 'Behaviour of reinforced concrete panels under impact loading after cryogenic freeze-thaw cycles', Construction and Building Materials, vol. 414, pp. 135058-135058.
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Chowdhury, S, Sais, D, Donnelly, S & Tran, N 2024, 'The knowns and unknowns of helminth–host miRNA cross-kingdom communication', Trends in Parasitology, vol. 40, no. 2, pp. 176-191.
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Chu, NH, Hoang, DT, Nguyen, DN, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 2024, 'MetaSlicing: A Novel Resource Allocation Framework for Metaverse', IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 4145-4162.
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Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically. To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively.

Chu, NH, Van Huynh, N, Nguyen, DN, Hoang, DT, Gong, S, Shu, T, Dutkiewicz, E & Phan, KT 2024, 'Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications', IEEE Transactions on Wireless Communications, pp. 1-1.
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Chu, Y, Zhao, S, Niu, F, Dong, Y & Zhao, Y 2024, 'A New Diffusion Filtered-X Affine Projection Algorithm: Performance Analysis and Application in Windy Environment', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 1596-1608.
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Cong, J, He, M, Jang, JS, Huang, J, Privat, K, Chen, Y, Li, J, Yang, L, Green, MA, Kim, JH, Cairney, JM & Hao, X 2024, 'Unveiling the Role of Ge in CZTSSe Solar Cells by Advanced Micro‐To‐Atom Scale Characterizations', Advanced Science, vol. 11, no. 15, p. e2305938.
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AbstractKesterite is an earth‐abundant energy material with high predicted power conversion efficiency, making it a sustainable and promising option for photovoltaics. However, a large open circuit voltage Voc deficit due to non‐radiative recombination at intrinsic defects remains a major hurdle, limiting device performance. Incorporating Ge into the kesterite structure emerges as an effective approach for enhancing performance by manipulating defects and morphology. Herein, how different amounts of Ge affect the kesterite growth pathways through the combination of advanced microscopy characterization techniques are systematically investigated. The results demonstrate the significance of incorporating Ge during the selenization process of the CZTSSe thin film. At high temperature, the Ge incorporation effectively delays the selenization process due to the formation of a ZnSe layer on top of the metal alloys through decomposition of the Cu‐Zn alloy and formation of Cu‐Sn alloy, subsequently forming of Cu‐Sn‐Se phase. Such an effect is compounded by more Ge incorporation that further postpones kesterite formation. Furthermore, introducing Ge mitigates detrimental “horizontal” grain boundaries by increasing the grain size on upper layer. The Ge incorporation strategy discussed in this study holds great promise for improving device performance and grain quality in CZTSSe and other polycrystalline chalcogenide solar cells.

Crowther, CA, Ashwood, P, Middleton, PF, McPhee, A, Tran, T & Harding, JE 2024, 'Prenatal Intravenous Magnesium at 30–34 Weeks' Gestation and Neurodevelopmental Outcomes in Offspring: The MAGENTA Randomized Clinical Trial', Obstetrical & Gynecological Survey, vol. 79, no. 2, pp. 78-80.
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(Abstracted from JAMA 2023;330(7):603–614) Preterm birth is a focus of many studies and interventions but remains the leading cause of global neonatal morbidity and mortality. One particular risk that is elevated in preterm infants is cerebral palsy, which affects movement and/or posture, causing health problems and high health care costs for children and their families.

Cui, H, Han, M, Xu, F, Liu, Q & Saha, SC 2024, 'Scaling analysis of intrusion flow and thermal plume for Pr > 1 in the triangular cavity', International Journal of Thermal Sciences, vol. 195, pp. 108616-108616.
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Cui, Z, Sun, X, Chen, H, Pan, L, Cui, L, Liu, S & Xu, G 2024, 'Dynamic Recommendation Based on Graph Diffusion and Ebbinghaus Curve', IEEE Transactions on Computational Social Systems, vol. 11, no. 2, pp. 2755-2764.
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Cuzmar, R, Montenegro, A, Mora, A, Pereda, J & Aguilera, RP 2024, 'Constrained MPC for Intercluster Energy Control of Modular Multilevel Matrix Converters', IEEE Transactions on Industrial Electronics, vol. 71, no. 7, pp. 7766-7776.
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Cuzmar, RH, Mora, A, Pereda, J & Aguilera, RP 2024, 'An Improved Reference Generator Based on MPC of Circulating Currents and Common-Mode Voltage for Modular Multilevel Matrix Converters', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Cuzmar, RH, Mora, A, Pereda, J, Poblete, P & Aguilera, RP 2024, 'Long-Horizon Sequential FCS-MPC Approaches for Modular Multilevel Matrix Converters', IEEE Transactions on Industrial Electronics, vol. 71, no. 5, pp. 5137-5147.
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Dai, J, Liu, Z, Song, Y, Liu, Y, Nghiem, LD, Wang, Q, Liu, W, Sun, X & Cai, Z 2024, 'Heterocyclic polymerization modified g-C3N4 nanotube with advanced charge separation for solar light driven degradation of ciprofloxacin', Separation and Purification Technology, vol. 348, pp. 127692-127692.
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Dai, S, He, X, Tong, C, Gao, F, Zhang, S & Sheng, D 2024, 'Stability of sandy soils against internal erosion under cyclic loading and quantitatively examination of the composition and origin of eroded particles', Canadian Geotechnical Journal, vol. 61, no. 4, pp. 732-747.
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Internal erosion refers to the movement of fine particles within soil framework due to subsurface water seepage. Existing criteria for assessing internal erosion usually are based on static loading, and the effect of cyclic load is not considered. Additionally, there are limited studies to examine the particle-size distribution and origin of eroded fine particles. This study presents an experimental investigation that examines the impact of cyclic loading on internal stability through a series of seepage tests. The composition and origin of lost particles are quantitatively studied using particle staining and image recognition techniques. With increasing hydraulic gradient, particle erosion progresses from top layer to bottom layer, with a gradual increase in the maximum particle size of eroded particles from each layer. After significant loss of particles, the specimens reach a state of transient equilibrium, resulting in a gradual slowdown of both particle loss rate and average flow velocity. The results indicate that cyclic loading promotes massive particle loss and causes erosion failure of specimens that are considered stable according to existing criteria. The reason is that under cyclic loading, local hydraulic gradients is oscillating, and a larger than average hydraulic gradient may occur, which is responsible for the internal instability. The analysis suggests that existing criteria can provide a reasonable assessment of the relative stabilities of specimens under static loads but fail to capture the stabilities under cyclic loading conditions.

Dai, S, Zhang, S, Gao, F, He, X & Sheng, D 2024, 'Investigation of particle segregation in a vertically vibrated binary mixture: Segregation process and mechanism', Computers and Geotechnics, vol. 169, pp. 106236-106236.
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Dalai, SK, Panda, KP, Siwakoti, YP & Panda, G 2024, 'Three-Phase Switched-Capacitor Boost Self-Balanced Multilevel Inverter for Photovoltaic Applications', IEEE Transactions on Energy Conversion, pp. 1-10.
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Dang, KB, Nguyen, CQ, Tran, QC, Nguyen, H, Nguyen, TT, Nguyen, DA, Tran, TH, Bui, PT, Giang, TL, Nguyen, DA, Lenh, TA, Ngo, VL, Yasir, M, Nguyen, TT & Ngo, HH 2024, 'Comparison between U-shaped structural deep learning models to detect landslide traces', Science of The Total Environment, vol. 912, pp. 169113-169113.
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Landslides endanger lives and public infrastructure in mountainous areas. Monitoring landslide traces in real-time is difficult for scientists, sometimes costly and risky because of the harsh terrain and instability. Nowadays, modern technology may be able to identify landslide-prone locations and inform locals for hours or days when the weather worsens. This study aims to propose indicators to detect landslide traces on the fields and remote sensing images; build deep learning (DL) models to identify landslides from Sentinel-2 images automatically; and apply DL-trained models to detect this natural hazard in some particular areas of Vietnam. Nine DL models were trained based on three U-shaped architectures, including U-Net, U2-Net, and U-Net3+, and three options of input sizes. The multi-temporal Sentinel-2 images were chosen as input data for training all models. As a result, the U-Net, using an input image size of 32 × 32 and a performance of 97 % with a loss function of 0.01, can detect typical landslide traces in Vietnam. Meanwhile, the U-Net (64 × 64) can detect more considerable landslide traces. Based on multi-temporal remote sensing data, a different case study in Vietnam was chosen to see landslide traces over time based on the trained U-Net (32 × 32) model. The trained model allows mountain managers to track landslide occurrences during wet seasons. Thus, landslide incidents distant from residential areas may be discovered early to warn of flash floods.

Dang, Z, Luo, M, Wang, J, Jia, C, Yan, C, Dai, G, Chang, X & Zheng, Q 2024, 'Disentangled Generation With Information Bottleneck for Enhanced Few-Shot Learning', IEEE Transactions on Image Processing, vol. 33, pp. 3520-3535.
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Das, A, Im, KS, Kabir, MM, Shon, HK & Nam, SY 2024, 'Polybenzimidazole (PBI)-based membranes for fuel cell, water electrolysis and desalination', Desalination, vol. 579, pp. 117500-117500.
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Dashti, A, Navidpour, AH, Amirkhani, F, Zhou, JL & Altaee, A 2024, 'Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts', Chemosphere, vol. 362, pp. 142792-142792.
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Dayioglu, M, Küskü, F & Cetindamar, D 2024, 'The Impact of Business Environmental Factors on Performance Through Strategic Agility and Business Model Innovation: An Analysis Based on Dynamic Capabilities Theory', IEEE Transactions on Engineering Management, vol. 71, pp. 3656-3670.
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Deng, Z, Bao, D, Jiang, L, Zheng, W & Xu, X 2024, 'One-step prepared of e-Gelatin-CNTs nano-composite fiber modified carbon fiber microelectrode for continuous dopamine monitoring: Characterization, biocompatibility evaluation and in vivo experiment', Microchemical Journal, vol. 197, pp. 109876-109876.
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Deng, Z-H, Jiang, J, Long, G & Zhang, C 2024, 'Causal Reinforcement Learning: A Survey', Transactions on Machine Learning Research.

Deshpande, NM, Gite, S, Pradhan, B, Alamri, A & Lee, C-W 2024, 'A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database', Computer Modeling in Engineering & Sciences, vol. 139, no. 1, pp. 593-631.
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Devi, A, Palmer, EE, Ganguly, R & Barua, PD 2024, 'Teachers’ Educational Experiences and Preparedness in Teaching Students with Autism', The Asia-Pacific Education Researcher, vol. 33, no. 1, pp. 71-81.
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Dhruva, S, Krishankumar, R, Zavadskas, EK, Ravichandran, KS & Gandomi, AH 2024, 'Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo', Informatica, pp. 65-98.
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Cloud computing has emerged as a transformative technology in the healthcare industry, but selecting the most suitable CV (“cloud vendor”) remains a complex task. This research presents a decision framework for CV selection in the healthcare industry, addressing the challenges of uncertainty, expert hesitation, and conflicting criteria. The proposed framework incorporates FFS (“Fermatean fuzzy set”) to handle uncertainty and data representation effectively. The importance of experts is attained via the variance approach, which considers hesitation and variability. Furthermore, the framework addresses the issue of extreme value hesitancy in criteria through the LOPCOW (“logarithmic percentage change-driven objective weighting”) method, which ensures a balanced and accurate assessment of criterion importance. Personalized grading of CVs is done via the ranking algorithm that considers the formulation of CoCoSo (“combined compromise solution”) with rank fusion, providing a compromise solution that balances conflicting criteria. By integrating these techniques, the proposed framework aims to enhance the rationale and reduce human intervention in CV selection for the healthcare industry. Also, valuable insights are gained from the framework for making informed decisions when selecting CVs for efficient data management and process implementation. A case example from Tamil Nadu is presented to testify to the applicability, while sensitivity and comparison analyses reveal the pros and cons of the framework.

Dhull, P, Schreurs, D, Paolini, G, Costanzo, A, Abolhasan, M & Shariati, N 2024, 'Multitone PSK Modulation Design for Simultaneous Wireless Information and Power Transfer', IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 1, pp. 446-460.
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Dikshit, A, Pradhan, B, Matin, SS, Beydoun, G, Santosh, M, Park, H-J & Maulud, KNA 2024, 'Artificial Intelligence: A new era for spatial modelling and interpreting climate-induced hazard assessment', Geoscience Frontiers, vol. 15, no. 4, pp. 101815-101815.
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Ding, H, Zhang, H, Fu, G, Jiang, C, Luo, F, Xiao, C & Xu, M 2024, 'Towards High-Quality Photorealistic Image Style Transfer', IEEE Transactions on Multimedia, pp. 1-15.
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Ding, L, Chen, C, Shan, X, Liu, B, Wang, D, Du, Z, Zhao, G, Su, QP, Yang, Y, Halkon, B, Tran, TT, Liao, J, Aharonovich, I, Zhang, M, Cheng, F, Fu, L, Xu, X & Wang, F 2024, 'Optical Nonlinearity Enabled Super‐Resolved Multiplexing Microscopy', Advanced Materials, vol. 36, no. 2.
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AbstractOptical multiplexing for nanoscale object recognition is of great significance within the intricate domains of biology, medicine, anti‐counterfeiting, and microscopic imaging. Traditionally, the multiplexing dimensions of nanoscopy are limited to emission intensity, color, lifetime, and polarization. Here, a novel dimension, optical nonlinearity, is proposed for super‐resolved multiplexing microscopy. This optical nonlinearity is attributable to the energy transitions between multiple energy levels of the doped lanthanide ions in upconversion nanoparticles (UCNPs), resulting in unique optical fingerprints for UCNPs with different compositions. A vortex beam is applied to transport the optical nonlinearity onto the imaging point‐spread function (PSF), creating a robust super‐resolved multiplexing imaging strategy for differentiating UCNPs with distinctive optical nonlinearities. The composition information of the nanoparticles can be retrieved with variations of the corresponding PSF in the obtained image. Four channels multiplexing super‐resolved imaging with a single scanning, applying emission color and nonlinearity of two orthogonal imaging dimensions with a spatial resolution higher than 150 nm (1/6.5λ), are demonstrated. This work provides a new and orthogonal dimension – optical nonlinearity – to existing multiplexing dimensions, which shows great potential in bioimaging, anti‐counterfeiting, microarray assays, deep tissue multiplexing detection, and high‐density data storage.

Ding, L, Chen, C, Shan, X, Liu, B, Wang, D, Du, Z, Zhao, G, Su, QP, Yang, Y, Halkon, B, Tran, TT, Liao, J, Aharonovich, I, Zhang, M, Cheng, F, Fu, L, Xu, X & Wang, F 2024, 'Optical Nonlinearity Enabled Super‐Resolved Multiplexing Microscopy (Adv. Mater. 2/2024)', Advanced Materials, vol. 36, no. 2.
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Ding, W, Fan, X, Zhou, X, Liu, R, Chen, C, Jin, W, Sun, J, Li, X, Jiang, G & Liu, H 2024, 'Performance and mechanisms of zero valent iron enhancing short-chain fatty acids production during thermophilic anaerobic fermentation of waste activated sludge', Science of The Total Environment, vol. 912, pp. 169025-169025.
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Dinh, PV, Nguyen, QU, Hoang, DT, Nguyen, DN, Bao, SP & Dutkiewicz, E 2024, 'Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems', IEEE Internet of Things Journal, vol. 11, no. 8, pp. 14789-14803.
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Dios, KD, Huynh, N, Tran, TS, Center, JR & Nguyen, TV 2024, 'Association between Fat Mass and Obesity-Related Transcript Polymorphisms and Osteoporosis Phenotypes', Journal of Bone Metabolism, vol. 31, no. 1, pp. 48-55.
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Background: Common variants in the fat mass and obesity-related transcript (<i>FTO</i>) gene are related to body mass index and obesity, suggesting its potential association with bone mineral density (BMD) and fracture risk. This study sought to define the association between <i>FTO</i> gene variants and the following phenotypes: (1) BMD; (2) bone loss; and (3) fracture risk.Methods: This analysis was based on the Dubbo Osteoporosis Epidemiology Study that included 1,277 postmenopausal women aged ≥60 years living in Dubbo, Australia. BMD at the femoral neck and lumbar spine was measured biennially by dual energy X-ray absorptiometry (GE Lunar). Fractures were radiologically ascertained. Six single nucleotide polymorphisms (SNPs; rs1421085, rs1558902, rs1121980, rs17817449, rs9939609, and rs9930506) of the <i>FTO</i> gene were genotyped using TaqMan assay.Results: Women homozygous for the minor allele (GG) of rs9930506 had a significantly higher risk of hip fracture (adjusted hazard ratio, 1.93; 95% confidence interval, 1.15–3.23) than those homozygous for the major allele (AA) after adjusting for potential confounding effects. Similar associations were also observed for the minor allele of rs1121980. However, there was no significant association between the <i>FTO</i> SNPs and BMD or the rate of bone loss.Conclusions: Common variations in the <i>FTO</i> gene are associated with a hip fracture risk in women, and the association is not mediated through BMD or bone loss.

Doan, S & Fatahi, B 2024, 'Simplified analytical solution for time dependent deformation of soft soil improved with pervious column considering load transfer between column and soil', Computers and Geotechnics, vol. 166, pp. 105988-105988.
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Dogan, A, Barua, PD, Baygin, M, Tuncer, T, Dogan, S, Yaman, O, Dogru, AH & Acharya, RU 2024, 'Automated accurate emotion classification using Clefia pattern-based features with EEG signals', International Journal of Healthcare Management, vol. 17, no. 1, pp. 32-45.
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Dogan, S, Barua, PD, Tuncer, T & Acharya, UR 2024, 'An accurate hypertension detection model based on a new odd-even pattern using ballistocardiograph signals', Engineering Applications of Artificial Intelligence, vol. 133, pp. 108306-108306.
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Dong, C, Weng, J, Li, M, Liu, J-N, Liu, Z, Cheng, Y & Yu, S 2024, 'Privacy-Preserving and Byzantine-Robust Federated Learning', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 2, pp. 889-904.
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Dong, J, Cong, Y, Sun, G, Fang, Z & Ding, Z 2024, 'Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 3, pp. 1664-1681.
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Dong, L, Yang, Y, Liu, Z, Yang, T, Xue, C, Shao, R & Wu, C 2024, 'Effect of chloride ion migration behaviour on the microstructure and mechanical properties of ultra-high performance concrete: A review', Journal of Building Engineering, vol. 82, pp. 108233-108233.
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Dong, L, Yang, Y, Liu, Z, Zhang, Y & Wu, C 2024, 'Interface bonding characteristics of 3D printed ultra-high performance concrete after elevated temperatures', Journal of Building Engineering, vol. 93, pp. 109801-109801.
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Dong, T, Ai, J, Zong, Y, Zhang, Y, Li, L, Zhou, H, Peng, S, He, H, Zhang, Z & Wang, Q 2024, 'Novel multiplexed alkali enzyme lysis coupled with EDTA pretreatment for RNA virus extraction from wastewater sludge: Optimization, recovery, and detection', Journal of Environmental Management, vol. 352, pp. 120102-120102.
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Dong, X, Kedziora, DJ, Musial, K & Gabrys, B 2024, 'Automated Deep Learning: Neural Architecture Search Is Not the End', Foundations and Trends® in Machine Learning, vol. 17, no. 5, pp. 767-920.
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Dong, Y, Liang, CJ, Chen, Y & Hua, J 2024, 'A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis: Applied COVID-19 aggregated datasets', Computational Visual Media, vol. 10, no. 1, pp. 161-186.
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AbstractThe visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, combining COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from local government area-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological...

Dong, Z-L, Tong, C-X, Zhang, S, Teng, J-D & Sheng, D 2024, 'A Comparative Study on Shear Behavior of Uniform-, Gap-, and Fractal-Graded Carbonate Soils', Journal of Geotechnical and Geoenvironmental Engineering, vol. 150, no. 1.
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Dou, G, Guo, W, Kong, L, Sun, J, Guo, M & Wen, S 2024, 'Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning', IEEE Transactions on Biomedical Circuits and Systems, pp. 1-11.
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Dou, J, Xie, G, Tian, Z, Cui, L & Yu, S 2024, 'Modeling and Analyzing the Spatial–Temporal Propagation of Malware in Mobile Wearable IoT Networks', IEEE Internet of Things Journal, vol. 11, no. 2, pp. 2438-2452.
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Douville, C, Lahouel, K, Kuo, A, Grant, H, Avigdor, BE, Curtis, SD, Summers, M, Cohen, JD, Wang, Y, Mattox, A, Dudley, J, Dobbyn, L, Popoli, M, Ptak, J, Nehme, N, Silliman, N, Blair, C, Romans, K, Thoburn, C, Gizzi, J, Schoen, RE, Tie, J, Gibbs, P, Ho-Pham, LT, Tran, BNH, Tran, TS, Nguyen, TV, Goggins, M, Wolfgang, CL, Wang, T-L, Shih, I-M, Lennon, AM, Hruban, RH, Bettegowda, C, Kinzler, KW, Papadopoulos, N, Vogelstein, B & Tomasetti, C 2024, 'Machine learning to detect the SINEs of cancer', Science Translational Medicine, vol. 16, no. 731.
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We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the validation cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and eight common protein biomarkers detected 51% of the cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature—the global reduction of AluS subfamily elements in the circulating DNA of patients with solid cancer. We confirmed this reduction through the analysis of another independent dataset obtained with a different approach (whole-genome sequencing). The evaluation of Alu elements may therefore have the potential to enhance the performance of several methods designed for the earlier detection of cancer.

Du, A, Zhou, T, Pang, S, Wu, Q & Zhang, J 2024, 'PCL: Point Contrast and Labeling for Weakly Supervised Point Cloud Semantic Segmentation', IEEE Transactions on Multimedia, pp. 1-12.
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Du, G, Yan, H, Li, N, Li, L, Chen, Y, Lei, G & Zhu, J 2024, 'Four Rotor Structures for High Speed Interior Permanent Magnet Motor Considering Mechanical, Electromagnetic and Thermal Performance', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Du, X, Yu, X, Liu, J, Dai, B & Xu, F 2024, 'Ethics-aware face recognition aided by synthetic face images', Neurocomputing, vol. 600, pp. 128129-128129.
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Duan, W, Xuan, J, Qiao, M & Lu, J 2024, 'Graph Convolutional Neural Networks With Diverse Negative Samples via Decomposed Determinant Point Processes', IEEE Transactions on Neural Networks and Learning Systems, vol. PP, no. 99, pp. 1-12.
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Graph convolutional neural networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs generally follow a message-passing mechanism, each node aggregates information from its first-order neighbor to update its representation. As a result, the representations of nodes with edges between them should be positively correlated and thus can be considered positive samples. However, there are more non-neighbor nodes in the whole graph, which provide diverse and useful information for the representation update. Two non-adjacent nodes usually have different representations, which can be seen as negative samples. Besides the node representations, the structural information of the graph is also crucial for learning. In this article, we used quality-diversity decomposition in determinant point processes (DPPs) to obtain diverse negative samples. When defining a distribution on diverse subsets of all non-neighboring nodes, we incorporate both graph structure information and node representations. Since the DPP sampling process requires matrix eigenvalue decomposition, we propose a new shortest-path-base method to improve computational efficiency. Finally, we incorporate the obtained negative samples into the graph convolution operation. The ideas are evaluated empirically in experiments on node classification tasks. These experiments show that the newly proposed methods not only improve the overall performance of standard representation learning but also significantly alleviate over-smoothing problems.

Entezari, A, Wu, Q, Mirkhalaf, M, Lu, Z, Roohani, I, Li, Q, Dunstan, CR, Jiang, X & Zreiqat, H 2024, 'Unraveling the influence of channel size and shape in 3D printed ceramic scaffolds on osteogenesis', Acta Biomaterialia, vol. 180, pp. 115-127.
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Errey, N, Liang, J, Leong, TW & Zowghi, D 2024, 'Evaluating narrative visualization: a survey of practitioners', International Journal of Data Science and Analytics, vol. 18, no. 1, pp. 19-34.
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Erten, M, Aydemir, E, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Tan, R-S, Hafeez-Baig, A & Rajendra Acharya, U 2024, 'Novel tiny textural motif pattern-based RNA virus protein sequence classification model', Expert Systems with Applications, vol. 242, pp. 122781-122781.
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Etaati, B, Neshat, M, Dehkordi, AA, Pargoo, NS, El-Abd, M, Sadollah, A & Gandomi, AH 2024, 'Shape and sizing optimisation of space truss structures using a new cooperative coevolutionary-based algorithm', Results in Engineering, vol. 21, pp. 101859-101859.
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Fallahpoor, M, Chakraborty, S, Pradhan, B, Faust, O, Barua, PD, Chegeni, H & Acharya, R 2024, 'Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space', Computer Methods and Programs in Biomedicine, vol. 243, pp. 107880-107880.
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Fan, L, Zhang, X, Zhao, Y, Sood, K & Yu, S 2024, 'Online Training Flow Scheduling for Geo-Distributed Machine Learning Jobs Over Heterogeneous and Dynamic Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 1, pp. 277-291.
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Fan, W, Li, H, Jiang, W, Hao, M, Yu, S & Zhang, X 2024, 'Stealthy Targeted Backdoor Attacks Against Image Captioning', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 5655-5667.
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Fan, Y, Xu, B, Zhang, L, Tan, G, Yu, S, Li, K-C & Zomaya, A 2024, 'psvCNN: A Zero-Knowledge CNN Prediction Integrity Verification Strategy', IEEE Transactions on Cloud Computing, vol. 12, no. 2, pp. 359-369.
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Fan, Y, Yi, B & Liu, D 2024, 'An overview of stiffening approaches for continuum robots', Robotics and Computer-Integrated Manufacturing, vol. 90, pp. 102811-102811.
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Fan, Z, Yan, Z, Cao, Y, Yang, Y & Wen, S 2024, 'Enhancing skeleton-based human motion recognition with Lie algebra and memristor-augmented LSTM and CNN', AIMS Mathematics, vol. 9, no. 7, pp. 17901-17916.
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<abstract><p>Lately, as a subset of human-centric studies, vision-oriented human action recognition has emerged as a pivotal research area, given its broad applicability in fields like healthcare, video surveillance, autonomous driving, sports, and education. This brief applies Lie algebra and standard bone length data to represent human skeleton data. A multi-layer long short-term memory (LSTM) recurrent neural network and convolutional neural network (CNN) are applied for human motion recognition. Finally, the trained network weights are converted into the crossbar-based memristor circuit, which can accelerate the network inference, reduce energy consumption, and obtain an excellent computing performance.</p></abstract>

Fang, W & Ying, M 2024, 'Symbolic Execution for Quantum Error Correction Programs', Proceedings of the ACM on Programming Languages, vol. 8, no. PLDI, pp. 1040-1065.
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We define QSE, a symbolic execution framework for quantum programs by integrating symbolic variables into quantum states and the outcomes of quantum measurements. The soundness of QSE is established through a theorem that ensures the correctness of symbolic execution within operational semantics. We further introduce symbolic stabilizer states, which symbolize the phases of stabilizer generators, for the efficient analysis of quantum error correction (QEC) programs. Within the QSE framework, we can use symbolic expressions to characterize the possible discrete Pauli errors in QEC, providing a significant improvement over existing methods that rely on sampling with simulators. We implement QSE with the support of symbolic stabilizer states in a prototype tool named QuantumSE.jl. Our experiments on representative QEC codes, including quantum repetition codes, Kitaev's toric codes, and quantum Tanner codes, demonstrate the efficiency of QuantumSE.jl for debugging QEC programs with over 1000 qubits. In addition, by substituting concrete values in symbolic expressions of measurement results, QuantumSE.jl is also equipped with a sampling feature for stabilizer circuits. Despite a longer initialization time than the state-of-the-art stabilizer simulator, Google's Stim, QuantumSE.jl offers a quicker sampling rate in the experiments.

Fang, W, Ying, M & Wu, X 2024, 'Differentiable Quantum Programming with Unbounded Loops.', ACM Trans. Softw. Eng. Methodol., vol. 33, pp. 19:1-19:1.

Fang, XS, Wang, X, Sheng, QZ & Yao, L 2024, 'Generalizing truth discovery by incorporating multi-truth features', Computing, vol. 106, no. 5, pp. 1557-1583.
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Fang, Z, Li, Y, Liu, F, Han, B & Lu, J 2024, 'On the Learnability of Out-of-distribution Detection', JOURNAL OF MACHINE LEARNING RESEARCH, vol. 25.

Fang, Z, Lu, J & Zhang, G 2024, 'An Extremely Simple Algorithm for Source Domain Reconstruction', IEEE Transactions on Cybernetics, vol. 54, no. 3, pp. 1921-1933.
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Farah, N, Lei, G, Zhu, J & Guo, Y 2024, 'Robust Model-Free Reinforcement Learning Based Current Control of PMSM Drives', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Farhangi, M, Barzegarkhoo, R, Lee, SS, Aguilera, RP, Lu, D & Siwakoti, YP 2024, 'An Interleaved Single-Stage Switched-Boost Common-Ground Multilevel Inverter: Design, Control, and Experimental Validation', IEEE Transactions on Industry Applications, vol. 60, no. 3, pp. 4168-4182.
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Faro, B, Abedin, B, Cetindamar, D & Daneshgar, F 2024, 'Dynamic capabilities for nimbleness and resilience in a continuous digital transformation: action design research in an Australian financial services organisation', Journal of Enterprise Information Management, vol. 37, no. 4, pp. 1206-1226.
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PurposeThe research aims to understand the co-existence of nimbleness and resilience in a continuous digital transformation, along with the dynamic capabilities needed to balance the challenges of their co-existence.Design/methodology/approachThe current study draws on dialogical action design research (D-ADR) to investigate interactions among practitioners and executives. Data are collected from a major Australian financial services organisation (FSO) and many international experts.FindingsThe study presents a framework, the continuous transformation model (CTM), to describe digital transformation within an FSO context, emphasising nimbleness and resilience as its foundational pillars. This framework facilitates the identification of the critical role of organisational capabilities in managing continuous digital transformation, supported by dynamic IT capabilities. More importantly, the findings underscore how these capabilities enable managers to effectively balance the coexistence of nimbleness and resilience.Research limitations/implicationsThe CTM contributes to the enterprise information systems literature by offering a coherent understanding of balancing resilience and nimbleness to succeed in digital transformation. In particular, the research model elucidates the relationship between dynamic capabilities and continuous digital transformations.Practical implicationsDigital transformations are not a one-off exercise. Managers in the FSO context must cultivate their organisational capab...

Farooq, MA & Nimbalkar, S 2024, 'Monotonic and cyclic triaxial testing of untreated and polyurethane-treated soil and soil–rubber mixtures', Acta Geotechnica, vol. 19, no. 2, pp. 605-630.
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AbstractThe present research focuses on developing alternate sustainable base materials for a high-speed slab track. In this study, a series of monotonic triaxial, cyclic triaxial and permeability tests were conducted on four types of materials, viz. mix-A (gravel soil), mix-B (soil mixed with rubber), mix-C (polyurethane foam adhesive (PFA)-treated soil), and mix-D (PFA-treated soil–rubber mixture). The influence of cyclic loading frequency, effective confining pressure, drainage condition and relative density on the deformation, excess pore water pressure, resilient modulus and damping ratio of these different mixes is evaluated. The monotonic triaxial test results indicate that the PFA treatment of mix-A and mix-B increased their shear strength and critical state strength. In contrast, incorporating rubber into mix-A and mix-C helped enhance their ductility. The cyclic triaxial test results show that the PFA treatment of mix-A and mix-B significantly reduced the magnitude of deformation and generation of excess pore water pressure, which caused these untreated mixes to fail prematurely under lower confinement to which a typical base layer is subjected. The influence of cyclic loading frequency and effective confining pressure on the material's response differed for untreated and treated soil. The permeability test results indicate good drainage for mix-D comparable to mix-A.

Farooq, MA & Nimbalkar, S 2024, 'Static and cyclic performance of polyurethane foam adhesive bound soil–rubber mixtures under drained conditions', Acta Geotechnica, vol. 19, no. 2, pp. 561-589.
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AbstractThe major drawbacks of a railway track include noise, vibration, and aggravated track degradation. Resilient mats and asphalt have been increasingly used in recent years to mitigate this noise and vibration. However, these materials are quite expensive. Conventional asphalt is very stiff and brittle, making it more prone to cracking. The present work aims to develop a novel material that can be used as a base layer in ballasted and slab tracks. The current research proposes a sustainable and resilient base course layer comprising ground rubber (GR) and polyurethane foam adhesive (PFA). In this study, the performance of GR embedded in the sand is investigated. The use of PFA-treated sand with and without GR is then explored. The optimum dosage of PFA for soil and GR for treated and untreated soil is recommended based on static direct simple shear (SDSS) and cyclic direct simple shear (CDSS) tests. SDSS tests were performed to evaluate the monotonic performance of all mixtures. CDSS tests were performed to assess the long-term performance of these different mixes under repeated cyclic loading (50,000 load cycles) and varying cyclic shear stress amplitude. It is shown that PFA helps reduce the settlement and enhance soil shear strength, while GR increases the damping ratio of the soil. The optimum dosage of PFA is recommended 10%. The optimum GR content for untreated and PFA-treated soil is recommended 5 and 10%, respectively.

Farooq, MU, Fritz, T, Haapasalo, E & Tomamichel, M 2024, 'Matrix Majorization in Large Samples', IEEE Transactions on Information Theory, vol. 70, no. 5, pp. 3118-3144.
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Farooq, MU, Sadiq, K, Anis, M, Hussain, G, Usman, M, Fouad, Y, Mujtaba, MA, Fayaz, H & Silitonga, AS 2024, 'Turning trash into treasure: Torrefaction of mixed waste for improved fuel properties. A case study of metropolitan city', Heliyon, vol. 10, no. 7, pp. e28980-e28980.
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Fatahi, B 2024, 'Uncertainty, modeling, and decision making in geotechnics Uncertainty, modeling, and decision making in geotechnics , edited by Kok-Kwang Phoon, Takayuki Shuku, and Jianye Ching, Boca Raton, CRC Press, 2024, 502 pp., ISBN: 978-1-032-36749-1 (hbk), ISBN: 978-1-032-36750-7 (pbk), ISBN: 978-1-003-33358-6 (ebk), $315 hardback, doi:10.1201/9781003333586', Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, vol. 18, no. 1, pp. 314-316.
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Feng, A, Shi, Y, Onggowarsito, C, Zhang, XS, Mao, S, Johir, MAH, Fu, Q & Nghiem, LD 2024, 'Structure‐Property Relationships of Hydrogel‐based Atmospheric Water Harvesting Systems', ChemSusChem, vol. 17, no. 11.
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AbstractAtmospheric water harvesting (AWH) is considered one of the promising technologies to alleviate the uneven‐distribution of water resources and water scarcity in arid regions of the world. Hydrogel‐based AWH materials are currently attracting increasing attention due to their low cost, high energy efficiency and simple preparation. However, there is a knowledge gap in the screening of hydrogel‐based AWH materials in terms of structure‐property relationships, which may increase the cost of trial and error in research and fabrication. In this study, we synthesised a variety of hydrogel‐based AWH materials, characterized their physochemcial properties visualized the electrostatic potential of polymer chains, and ultimately established the structure‐property‐application relationships of polymeric AWH materials. Poly(2‐acrylamido‐2‐methyl‐1‐propanesulfonic acid) (PAMPS) hydrogel is able to achieve an excellent water adsorption capacity of 0.62 g g−1 and a high water desorption efficiency of more than 90 % in relatively low‐moderate humidity environments, which is regarded as one of the polymer materials with potential for future AWH applications.

Feng, S, Liu, F, Zhu, S, Xu, Z, Qin, L, Feng, P, Wang, Z, Chen, H, Guo, W & Hao Ngo, H 2024, 'Role of hydraulic retention time in integration of microalgae and activated sludge process for nutrient recycle from diluted dairy liquid digestate', Chemical Engineering Journal, vol. 484, pp. 149538-149538.
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Feng, S, Zhang, G, Yang, Y, Zhou, X & Hong, J 2024, '3-D Printed Multiband Filtering Crossovers Based on Mixed Spherical Cavity Resonators', IEEE Microwave and Wireless Technology Letters, pp. 1-4.
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Feng, W, Rao, P, Cui, J, Ouyang, P, Chen, Q & Nimbalkar, S 2024, 'Multiphysics Multicoupled Modeling of Rock Fragmentation under High-Voltage Electrical Pulse', International Journal of Geomechanics, vol. 24, no. 9.
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Feng, Y, Alamdari, MM, Wu, D, Luo, Z, Ruan, D, Egbelakin, T, Chen, X & Gao, W 2024, 'Virtual modelling aided safety assessment for ductile structures against high-velocity impact', Engineering Structures, vol. 301, pp. 117373-117373.
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Feng, Y, Li, L & Zhao, A 2024, 'A Cognitive-Emotional Model From Mobile Short-Form Video Addiction to Intermittent Discontinuance: The Moderating Role of Neutralization', International Journal of Human–Computer Interaction, vol. 40, no. 7, pp. 1505-1517.
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Discontinuance is considered very effective in the treatment of IT addiction. However, prior literature on the mechanism from IT addiction to discontinuance has reached inconsistent conclusions. Also, scholars cannot explain why some individuals do not make any changes after becoming addicted, even when they realize that their behavior is not in line with the norms. To fill the gaps, our research developed a cognitive-emotional model, with neutralization as a moderator, to understand the internal mechanism from mobile short-form video addiction to intermittent discontinuance. Based on the online survey of 493 Chinese mobile short-form video users, this study used partial least squares structural equation modeling (PLS-SEM) to empirically verify our model and hypothesis. The results show that mobile short-form video addiction positively affects cognitive dissonance and emotional fluctuation. And cognitive dissonance and emotional fluctuation are found to have a positive effect on intermittent discontinuance through attitudinal ambivalence. Furthermore, neutralization negatively moderates the relationship between cognitive dissonance and attitudinal ambivalence, emotional fluctuation and attitudinal ambivalence. Theoretical and practical implications of these findings are discussed.

Ferreira, FB & Nimbalkar, S 2024, 'Guest Editorial for the Special Issue on “Geosynthetic-Reinforced Sustainable Transport Infrastructures”', International Journal of Geosynthetics and Ground Engineering, vol. 10, no. 3.
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Fu, B, Boutros, F, Lin, C-T & Damer, N 2024, 'A Survey on Drowsiness Detection – Modern Applications and Methods', IEEE Transactions on Intelligent Vehicles, pp. 1-23.
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Fu, H, Yu, X, Li, L & Zhang, L 2024, 'CBARF: Cascaded Bundle-Adjusting Neural Radiance Fields From Imperfect Camera Poses', IEEE Transactions on Multimedia, pp. 1-12.
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Gao, F, Zhang, J, Zhang, S, Zheng, J & Sheng, D 2024, 'Experimental study on migration and deposition of particles under alternating dynamic and static loads', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 46, no. 5, pp. 1057-1066.
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The study on the migration and deposition of suspended particles in subgrade is important to reveal the generation and disaster-causing mechanism of mud pumping. The effects of magnitude of dynamic loads, intermittent duration of static loads and repetition number of alternating dynamic and static loads on the migration and deposition of particles are studied by carrying out the mud pumping tests on the layered gravel-sandy silt column. According to the hydrodynamic response characteristics of specimens, the driving mechanism of particle migration is analyzed. The test results show that the suspended particles migrate upward under hydrodynamic force and settle under gravity during the interval of static loads, which results in the fluctuating growth of slurry turbidity in the gravel layer. The particle suspension increment tends to decrease with the increasing repetition of alternating dynamic and static loads, and these particles gradually clog gravel pores and inhibit mud pumping to a certain extent. Increasing the dynamic frequency and stress can further reduce the internal stability of the sandy silt surface layer. However, compared with increasing the dynamic stress, increasing the loading frequency is more beneficial to increase the particle migration mass and vertical migration distance.

Gao, L, Qin, Y, Zhou, X, Jin, W, He, Z, Li, X & Wang, Q 2024, 'Microalgae as future food: Rich nutrients, safety, production costs and environmental effects', Science of The Total Environment, vol. 927, pp. 172167-172167.
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Gao, S, Wang, X, Song, B, Liu, R, Yao, S, Zhou, W & Yu, S 2024, 'Exploiting Type I Adversarial Examples to Hide Data Information: A New Privacy-Preserving Approach', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 3, pp. 2518-2528.
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Gao, X, Xu, Z, Shi, T, Qi, C, Nghiem, LD, Li, G & Luo, W 2024, 'Role of Lignocellulosic Biomass Composition to Regulate Microbial Mutualism for Organic Mineralization and Humification during Digestate Composting', ACS ES&T Engineering, vol. 4, no. 4, pp. 771-782.
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Gao, Y, Chen, L, Han, J, Yu, S & Fang, H 2024, 'Similarity-Based Secure Deduplication for IIoT Cloud Management System', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 4, pp. 2242-2256.
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Gao, Y, Yu, J, Hu, C, Wen, S & Kong, F 2024, 'Fixed/preassigned-time output synchronization for T–S fuzzy complex networks via quantized control', Nonlinear Analysis: Hybrid Systems, vol. 51, pp. 101434-101434.
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García-Díaz, JA, Beydoun, G & Valencia-García, R 2024, 'Evaluating Transformers and Linguistic Features integration for Author Profiling tasks in Spanish', Data & Knowledge Engineering, vol. 151, pp. 102307-102307.
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Ge, H, Dai, G, Wang, F, Yu, Y & Liu, W 2024, 'Theoretical solution for bond-slip behavior of composite structures consisting of H-shape beam and concrete based on experiment, numerical simulation, and theoretical derivation', Engineering Structures, vol. 302, pp. 117456-117456.
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Ge, Y, Gao, Y, Li, X, Cai, B, Xi, J & Yu, S 2024, 'EMTD-SSC: An Enhanced Malicious Traffic Detection Model Using Transfer Learning Under Small Sample Conditions in IoT', IEEE Internet of Things Journal, pp. 1-1.
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Geng, C, Lian, J-W, Guo, YJ & Ding, D 2024, 'Millimeter-Wave Three-Layer Substrate-Integrated 9 × 9 Butler Matrix and Its Application to Wide-Angle Endfire Multibeam Metasurface Antenna', IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 4, pp. 2253-2266.
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Geng, X-F, Ding, H, Ji, J-C, Wei, K-X, Jing, X-J & Chen, L-Q 2024, 'A state-of-the-art review on the dynamic design of nonlinear energy sinks', Engineering Structures, vol. 313, pp. 118228-118228.
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Ghalambaz, M, Ayoubi Ayoubloo, K, Mozaffari, M, Yusaf, T, Islam, MS, Shah, NA & Baro, M 2024, 'Mixed convection of nano-encapsulated phase change suspensions in a wavy wall lid-driven trapezoid cavity', Journal of Thermal Analysis and Calorimetry, vol. 149, no. 9, pp. 4195-4207.
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Gholami, K, Azizivahed, A, Arefi, A, Rahman, MM, Islam, MR, Li, L, Arif, MT & Haque, ME 2024, 'Hybrid uncertainty approach for management of energy storage-embedded soft open points in distribution grids', Journal of Energy Storage, vol. 87, pp. 111394-111394.
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Gong, C, Lin, W, Ding, X, Liu, X, He, X, Nan, J, Li, G, Ma, J, Hao Ngo, H & Ding, A 2024, 'A three-stage process of Mn(VII)-Fe(III)/PDS system for enhancing sludge dewaterability: Effective driving of Fe(II)/Fe(III) cycle and adequate assurance of ROS', Separation and Purification Technology, vol. 330, pp. 125377-125377.
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Gong, S, Guo, Z, Ou, S, Wen, S & Huang, T 2024, 'Synchronization Control for T-S Fuzzy Neural Networks With Time Delay: A Novel Event-Triggered Mechanism', IEEE Transactions on Fuzzy Systems, vol. 32, no. 2, pp. 586-594.
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Gong, Z, Shen, L, Zhang, Y, Zhang, LY, Wang, J, Bai, G & Xiang, Y 2024, 'AgrAmplifier: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 1241-1250.
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Goodarzimehr, V, Talatahari, S, Shojaee, S & Gandomi, AH 2024, 'Computer-aided dynamic structural optimization using an advanced swarm algorithm', Engineering Structures, vol. 300, pp. 117174-117174.
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Goss, DM, Vasilescu, SA, Vasilescu, PA, Cooke, S, Kim, SHK, Sacks, GP, Gardner, DK & Warkiani, ME 2024, 'Evaluation of an artificial intelligence-facilitated sperm detection tool in azoospermic samples for use in ICSI', Reproductive BioMedicine Online, vol. 49, no. 1, pp. 103910-103910.
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Grigoletto, FB, Cedieu, S, Chaves, DB, Sing Lee, S & Siwakoti, YP 2024, 'A five‐level common‐ground inverter with step‐up/step‐down dual‐mode operation for transformerless grid‐connected PV application', International Journal of Circuit Theory and Applications, vol. 52, no. 3, pp. 1210-1230.
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AbstractStep‐up multilevel inverters with common‐ground feature are attractive for transformerless photovoltaic systems. However, their performance deteriorates at step‐down voltage range. Considering a five‐level inverter with double voltage gain, the number of output voltage levels decreases from 5 to 3 for a modulation index smaller than 0.5, declining the quality of the output currents. This paper proposes a new dual‐mode five‐level common‐grounded inverter with a reduced number of switches. The proposed topology has the ability to operate as step‐up or step‐down making it well suited for application with wide input voltage range. The proposed inverter consists of five unidirectional switches, one bidirectional switch, two diodes, and two capacitors. In addition, the description of the topology, the design guidelines, and a comparison among the main topologies are given in detail. Experimental results are obtained to attest the practicability of the proposed solution.

Grigorev, A, Mihăiţă, A-S, Saleh, K & Chen, F 2024, 'Automatic Accident Detection, Segmentation and Duration Prediction Using Machine Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 2, pp. 1547-1568.
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Grochow, JA & Qiao, Y 2024, 'On p -Group Isomorphism: Search-to-Decision, Counting-to-Decision, and Nilpotency Class Reductions via Tensors', ACM Transactions on Computation Theory, vol. 16, no. 1, pp. 1-39.
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In this article, we study some classical complexity-theoretic questions regarding Group Isomorphism ( GpI ). We focus on p -groups (groups of prime power order) with odd p , which are believed to be a bottleneckcase for GpI , and work in the model of matrix groups over finite fields. Our main results are as follows: • Although search-to-decision and counting-to-decision reductions have been known for more than four decades for Graph Isomorphism , they had remained open for GpI , explicitly asked by Arvind and Torán ( EATCS Bull. , 2005). Extending methods from Tensor Isomorphism (TI) (Grochow and Qiao, ITCS 2021), we show moderately exponential-time such reductions within p -groups of class 2 and exponent p . • Despite the widely held belief that p -groups of class 2 and exponent p are the hardest cases of GpI , there was no reduction to these groups from ...

Gu, P, Hu, H & Xu, G 2024, 'Modeling multi-behavior sequence via HyperGRU contrastive network for micro-video recommendation', Knowledge-Based Systems, vol. 295, pp. 111841-111841.
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Gu, Z, He, X, Yu, P, Jia, W, Yang, X, Peng, G, Hu, P, Chen, S, Chen, H & Lin, Y 2024, 'Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model', Artificial Intelligence in Medicine, vol. 150, pp. 102822-102822.
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Guan, S, Yu, X, Huang, W, Fang, G & Lu, H 2024, 'DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition', IEEE Transactions on Image Processing, vol. 33, pp. 395-407.
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Guan, Y, Zou, S, Peng, H, Ni, W, Sun, Y & Gao, H 2024, 'Cooperative UAV Trajectory Design for Disaster Area Emergency Communications: A Multiagent PPO Method', IEEE Internet of Things Journal, vol. 11, no. 5, pp. 8848-8859.
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Gudigar, A, Kadri, NA, Raghavendra, U, Samanth, J, Maithri, M, Inamdar, MA, Prabhu, MA, Hegde, A, Salvi, M, Yeong, CH, Barua, PD, Molinari, F & Acharya, UR 2024, 'Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023)', Computers in Biology and Medicine, vol. 172, pp. 108207-108207.
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Guertler, M & Sick, N 2024, 'Action research: Combining research and problem solving for socio-technical engineering and innovation management research', CERN IdeaSquare Journal of Experimental Innovation, vol. 8, no. 1, pp. 5-8.
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Guertler, MR, Schneider, D, Heitfeld, J & Sick, N 2024, 'Analysing Industry 4.0 technology-solution dependencies: a support framework for successful Industry 4.0 adoption in the product generation process', Research in Engineering Design, vol. 35, no. 2, pp. 115-136.
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AbstractIndustry 4.0 (i4.0) is central to advanced manufacturing. Building on novel digital technologies, it enables smart and flexible manufacturing with systems connected across company boundaries and product lifecycle phases. Despite its benefits, the adoption of i4.0 has been limited, especially in small and medium-sized enterprises. A key challenge is the technological complexity of i4.0. While advanced functionality requires technological complexity, it complicates an understanding of which enabling technologies are particularly useful and required. This article presents a framework to support successful i4.0 adoption across the entire product generation process through a systematic matrix-based dependency analysis of i4.0 solutions and underlying i4.0 technologies. Through increasing transparency around technological complexity of i4.0 solutions, this research contributes to a better understanding of which technologies are required for specific i4.0 solutions and which technologies could be strategic enablers for a broad variety of i4.0 applications. Knowing these technological dependencies supports both, the systematic adoption of existing i4.0 solutions and the development of new i4.0 solutions. This also sets the basis for a future socio-technical investigation.

Guleria, S, Chawla, P, Relhan, A, Kumar, A, Bhasin, A & Zhou, JL 2024, 'Antibacterial and photocatalytic potential of bioactive compounds extracted from freshwater microalgae species (Spirogyra and Ocillatoria): A comparative analysis', Science of The Total Environment, vol. 912, pp. 169224-169224.
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Gulied, M, Zavahir, S, Elmakki, T, Park, H, Gago, GH, Shon, HK & Han, DS 2024, 'Efficient lithium recovery from simulated brine using a hybrid system: Direct contact membrane distillation (DCMD) and electrically switched ion exchange (ESIX)', Desalination, vol. 572, pp. 117127-117127.
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Seawater reverse osmosis (SWRO) brine is a readily available resource hub in many countries, fulfilling the country's freshwater need by SWRO, yet lower in a concentration of high-demand elements like Li. This study outlines developing a novel hybrid system that combines direct contact membrane distillation (DCMD) and electrically switched ion exchange (ESIX) to facilitate simultaneous SWRO brine enrichment followed by selective Li recovery. The DCMD process concentrates the SWRO brine utilizing electrospun nanofiber membranes (ENMs) composed of polyvinylidene fluoride (PVDF). Incorporating reduced graphene oxide (rGO) nanoparticles augments the morphological, thermal, and mechanical stability of the PVDF ENMs. The water contact angle (WCA) of the 1-rGO/PVDF ENM stands at 142.08°, a testament to an enhanced hydrophobic property which resulted in a 12 % freshwater recovery from simulated SWRO brine and a 2.4-fold increase in Li+ concentration. The durability of the 1-rGO/PVDF ENM is evident in its minimal 11 % reduction in WCA after 15 h of brine concentration. In the context of hybrid operation, a Li-selective LiAlO2 electrode, coupled with an activated carbon counter electrode, demonstrated remarkable Li recovery for Li capture solutions enriched by the rGO-PVDF membrane in the DCMD phase. Compared to the Li concentration in the DCMD feed, sequential Li capture and release cycles recovered 91.8 % of Li, thereby underlining the critical role of the hybrid mode operation in concentrating Li from simulated brine solutions.

Gunatilake, A & Miro, JV 2024, 'Multimodel Neural Network for Live Classification of Water Pipe Leaks From Vibro-Acoustic Signals', IEEE Sensors Journal, vol. 24, no. 9, pp. 14825-14832.
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Gunawan, R, Tran, Y, Zheng, J, Nguyen, H, Carrigan, A, Mills, MK & Chai, R 2024, 'Combining Multistaged Filters and Modified Segmentation Network for Improving Lung Nodules Classification', IEEE Journal of Biomedical and Health Informatics, pp. 1-9.
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Guo, B, Li, H, Dong, H, Han, T, Sun, Y, Hou, J, Jiang, Z & Ni, Q 2024, 'A novel cross-domain adaption network based on Se-Sk-DenseNet for remaining useful life prediction of rolling bearings under different working conditions', Measurement Science and Technology, vol. 35, no. 7, pp. 076114-076114.
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Abstract Effectively predicting the remaining useful life (RUL) of rolling bearings can ensure reliability and safety, minimize machine downtime, and reduce the operation and maintenance costs of enterprises. To solve the problems of data distribution discrepancy caused by different working conditions and the collected signals containing a lot of useless information and noise, a novel cross-domain adaption network (CDAN) is proposed in this study. Firstly, a novel feature extractor, squeeze-and-excitation (Se)-selective kernel (Sk)-DenseNet, is developed to extract useful critical features from the input data and remove the ineffective features by embedding Se and Sk attention blocks; besides, a new objective loss function consist of the RUL loss, the multi-kernel maximum mean discrepancy loss, the contrastive loss, and the Kullback–Leibler divergence loss, is proposed to solve the problem of data distribution shift; finally, the effectiveness and superiority of CDAN are proved on the PHM2012 bearings dataset. The results demonstrate that CDAN can extract deep critical features and achieve the high cross-domain RUL prediction accuracy under different working conditions.

Guo, B, Qiao, Z, Dong, H, Wang, Z, Huang, S, Xu, Z, Wu, F, Huang, C & Ni, Q 2024, 'Temporal convolutional approach with residual multi-head attention mechanism for remaining useful life of manufacturing tools', Engineering Applications of Artificial Intelligence, vol. 128, pp. 107538-107538.
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Guo, CA, Guo, YJ & Yuan, J 2024, 'Multibeam Receiving Antennas Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 424-432.
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Owing to the characteristics of directional couplers, series fed multibeam receiving antenna arrays require different treatment from their transmitting counterparts. In this paper, the theory and strategies for the feed network synthesis of multibeam receiving antennas employing the generalized joined coupler matrix (GJC) are presented. Given M incident waves in any set of directions, we first derive the output signal matrix as a function of the incident signal matrix. This serves as an important tool for both synthesizing and analysing GJC receiving matrices. Then, we present three different synthesis strategies and show how they are related to the array patterns. We also reveal how the receiving power efficiency of the GJC matrix changes with the antenna beam pattern. We further demonstrate that, despite the employment of matched loads, high receiving power efficiencies can be achieved using the GJC matrix.

Guo, H, Miao, Z, Ji, JC & Pan, Q 2024, 'An effective collaboration evolutionary algorithm for multi-robot task allocation and scheduling in a smart farm', Knowledge-Based Systems, vol. 289, pp. 111474-111474.
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Guo, K & Guo, Y 2024, 'Design and Optimization of Linear Rotary Drilling Motor', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 11195-11205.
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Guo, K, Cheng, A, Li, Y, Li, J, Duffield, R & Su, SW 2024, 'Cooperative Markov Decision Process model for human–machine co-adaptation in robot-assisted rehabilitation', Knowledge-Based Systems, vol. 291, pp. 111572-111572.
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Guo, K, Guo, Y, Fang, S, Li, C & Xue, W 2024, 'Design and Analysis of a Permanent Magnet Frameless Motor', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 12, no. 3, pp. 3124-3134.
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Guo, M, Sun, Y, Zhu, Y, Han, M, Dou, G & Wen, S 2024, 'Pruning and quantization algorithm with applications in memristor-based convolutional neural network', Cognitive Neurodynamics, vol. 18, no. 1, pp. 233-245.
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The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks.

Guo, Y, Yu, H, Ma, L, Luo, X & Xie, S 2024, 'DIE-CDK: A Discriminative Information Enhancement Method with Cross-modal Domain Knowledge for Fine-grained Ship Detection', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Guo, Y, Yu, H, Xie, S, Ma, L, Cao, X & Luo, X 2024, 'DSCA: A Dual Semantic Correlation Alignment Method for domain adaptation object detection', Pattern Recognition, vol. 150, pp. 110329-110329.
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Habaraduwa Peellage, W, Fatahi, B & Rasekh, H 2024, 'Stiffness and damping characteristics of jointed rocks under cyclic triaxial loading subjected to prolonged cyclic loading', International Journal of Fatigue, vol. 181, pp. 108121-108121.
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Haddad, H, Fatahi, B, Khabbaz, H, Hsi, J & Li, I 2024, 'Effects of stress history on compressibility characteristics of undisturbed landfill waste material', Construction and Building Materials, vol. 422, pp. 135725-135725.
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Halder, A, Shivakumara, P, Pal, U, Blumenstein, M & Ghosal, P 2024, 'A Locally Weighted Linear Regression-Based Approach for Arbitrary Moving Shaky and Nonshaky Video Classification', International Journal of Pattern Recognition and Artificial Intelligence, vol. 38, no. 01.
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Classification and identification of objects are complex and challenging in pattern recognition and artificial intelligence if a shaky and nonshaky camera captures the videos at different distances during the day and nighttime. This work presents a model for classifying a given video as a static, uniform, or arbitrarily moving videos so that the complexity of the problem can be reduced. To avoid the threat of different distances between the objects and the camera, the proposed work introduces new steps for estimating the depth of the objects in the video frames. We explore locally weighted linear regression for feature extraction from depth information based on the notion that the regression line fits almost all the points for uniformity and does not fit for arbitrary moving. The extracted features are fed to a random forest classifier to classify static, uniform, or arbitrary moving video. The results on a large dataset, which includes videos captured day and night, show that the proposed method successfully classifies static, uniform and arbitrary videos with 0.86, 1.00 and 0.67 F-measures, respectively. Overall, our method obtains 87% accuracy for classification of static, uniform and arbitrary video, which is superior to the state-of-the-art methods.

Hamdi, FM, Altaee, A, Alsaka, L, Ibrar, I, AL-Ejji, M, Zhou, J, Samal, AK & Hawari, AH 2024, 'Iron slag/activated carbon-electrokinetic system with anolyte recycling for single and mixture heavy metals remediation', Science of The Total Environment, vol. 930, pp. 172516-172516.
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Han, M, Wang, Y, Li, M, Chang, X, Yang, Y & Qiao, Y 2024, 'Progressive Frame-Proposal Mining for Weakly Supervised Video Object Detection', IEEE Transactions on Image Processing, vol. 33, pp. 1560-1573.
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Han, S, Ansari, AJ, Zhang, N, Wu, C, Chen, X, Peng, Y & Song, X 2024, 'Role of biochar addition to improve anaerobic membrane bioreactors to resist oil stress in synthetic food wastewater treatment', Environmental Technology & Innovation, vol. 35, pp. 103665-103665.
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Han, Z, Zhao, G, Hu, Y, Xu, C, Cheng, K & Yu, S 2024, 'Dynamic Bond Percolation-Based Reliable Topology Evolution Model for Dynamic Networks', IEEE Transactions on Network and Service Management, pp. 1-1.
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Hannigan, IP, Rosengren, SM, Bharathy, GK, Prasad, M, Welgampola, MS & Watson, SRD 2024, 'Subjective and objective responses to caloric stimulation help separate vestibular migraine from other vestibular disorders', Journal of Neurology, vol. 271, no. 2, pp. 887-898.
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Abstract Background Nystagmus generated during bithermal caloric test assesses the horizontal vestibulo-ocular-reflex. Any induced symptoms are considered unwanted side effects rather than diagnostic information. Aim We hypothesized that nystagmus slow-phase-velocity (SPV) and subjective symptoms during caloric testing would be higher in vestibular migraine (VM) patients compared with peripheral disorders such as Meniere’s disease (MD) and non-vestibular dizziness (NVD). Methods Consecutive patients (n = 1373, 60% female) referred for caloric testing were recruited. During caloric irrigations, patients scored their subjective sensations. We assessed objective-measures, subjective vertigo (SVS), subjective nausea (SNS), and test completion status. Results Nystagmus SPV for VM, MD (unaffected side), and NVD were 29 ± 12.8, 30 ± 15.4, and 28 ± 14.2 for warm irrigation and 24 ± 8.9, 22 ± 10.0, and 25 ± 12.8 for cold-irrigation. The mean SVS were 2.5 ± 1.1, 1.5 ± 1.33, and 1.5 ± 1.42 for warm irrigation and 2.2 ± 1.1, 1.1 ± 1.19, and 1.1 ± 1.16 for cold-irrigation. Age was significantly correlated with SVS and SNS, (p < 0.001) for both. The SVS and SNS were significantly higher in VM compared with non-VM groups (p < 0.001), and there was no difference in nystagmus SPV. VM patients SVS was significantly different to the SVS of migraineurs in the other diagnostic groups (p < 0.001). Testing was incomplete for 34.4% of VM an...

Hasan, MM, Rasul, MG, Jahirul, MI & Mofijur, M 2024, 'Fuelling the future: Unleashing energy and exergy efficiency from municipal green waste pyrolysis', Fuel, vol. 357, pp. 129815-129815.
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Hasanpour, S, Siwakoti, YP & Blaabjerg, F 2024, 'A new soft‐switching high gain DC/DC converter with bipolar outputs', IET Power Electronics, vol. 17, no. 1, pp. 144-156.
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AbstractThis paper introduces a new single‐input multi‐output step‐up DC/DC converter with soft‐switching performance and low input current for renewable energy applications. The proposed topology uses a three‐winding coupled‐inductor (TWCI) and voltage multiplier circuits to achieve high voltage gains. The bipolar output voltages of the proposed converter can be varied independently by tuning the turns ratios of the TWCI. Due to the semi‐trans‐inverse specification of the suggested topology, high voltage gains can be obtained under a lower number of turns ratio in the magnetic device. Furthermore, a regenerative passive clamp technique mitigates the voltage stress on the single power switch. Additionally, the power dissipations are further reduced by considering a resonant tank in the circuit. In the converter, the parasitic leakage inductances of the TWCI windings help to provide the soft‐switching conditions for the switch and also to eliminate the reverse‐recovery loss for all converter diodes. The operating mode of the presented converter has been introduced and the steady state, along with the main operating equations have also been derived. Finally, the theoretical analysis is verified by a sample prototype 235 W at the input voltage 25 V and outputs of 200 V and −200 V.

Hassani, S, Dackermann, U, Mousavi, M & Li, J 2024, 'Enhanced damage detection for noisy input signals using improved reptile search algorithm and data analytics techniques', Computers & Structures, vol. 296, pp. 107293-107293.
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Hastings, C 2024, 'Homelessness: A Critical Introduction Homelessness: A Critical Introduction , by Cameron Parsell, 2023, Polity Press, ISBN 978-1-5095-5450-8(pb). pp. 245', International Journal of Housing Policy, vol. 24, no. 2, pp. 391-394.
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He, B, Armaghani, DJ, Lai, SH, He, X, Asteris, PG & Sheng, D 2024, 'A deep dive into tunnel blasting studies between 2000 and 2023—A systematic review', Tunnelling and Underground Space Technology, vol. 147, pp. 105727-105727.
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He, B, Jahed Armaghani, D, Hin Lai, S, Samui, P & Tonnizam Mohamad, E 2024, 'Applying data augmentation technique on blast-induced overbreak prediction: Resolving the problem of data shortage and data imbalance', Expert Systems with Applications, vol. 237, pp. 121616-121616.
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He, H, Zhang, Q, Wang, S, Yi, K, Niu, Z & Cao, L 2024, 'Learning Informative Representation for Fairness-Aware Multivariate Time-Series Forecasting: A Group-Based Perspective', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 6, pp. 2504-2516.
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He, H, Zhang, Q, Yi, K, Shi, K, Niu, Z & Cao, L 2024, 'Distributional Drift Adaptation With Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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He, J, Zhu, Y, Hua, B, Xu, Z, Zhang, Y, Chu, L, Shi, Q, Braun, R & Shi, J 2024, 'Non-Motorized Lane Target Behavior Classification Based on Millimeter Wave Radar With P-Mrca Convolutional Neural Network', IEEE Transactions on Biometrics, Behavior, and Identity Science, pp. 1-1.
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He, L, Shi, K, Wang, D, Wang, X & Xu, G 2024, 'A topic‐controllable keywords‐to‐text generator with knowledge base network', CAAI Transactions on Intelligence Technology, vol. 9, no. 3, pp. 585-594.
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AbstractWith the introduction of more recent deep learning models such as encoder‐decoder, text generation frameworks have gained a lot of popularity. In Natural Language Generation (NLG), controlling the information and style of the output produced is a crucial and challenging task. The purpose of this paper is to develop informative and controllable text using social media language by incorporating topic knowledge into a keyword‐to‐text framework. A novel Topic‐Controllable Key‐to‐Text (TC‐K2T) generator that focuses on the issues of ignoring unordered keywords and utilising subject‐controlled information from previous research is presented. TC‐K2T is built on the framework of conditional language encoders. In order to guide the model to produce an informative and controllable language, the generator first inputs unordered keywords and uses subjects to simulate prior human knowledge. Using an additional probability term, the model increases the likelihood of topic words appearing in the generated text to bias the overall distribution. The proposed TC‐K2T can produce more informative and controllable senescence, outperforming state‐of‐the‐art models, according to empirical research on automatic evaluation metrics and human annotations.

He, W, Di, P, Ming, M, Zhang, C, Su, T, Li, S & Sui, Y 2024, 'Finding and Understanding Defects in Static Analyzers by Constructing Automated Oracles', Proceedings of the ACM on Software Engineering, vol. 1, no. FSE, pp. 1656-1678.
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Static analyzers are playing crucial roles in helping find programming mistakes and security vulnerabilities. The correctness of their analysis results is crucial for the usability in practice. Otherwise, the potential defects in these analyzers (, implementation errors, improper design choices) could affect the soundness (leading to false negatives) and precision (leading to false positives). However, finding the defects in off-the-shelf static analyzers is challenging because these analyzers usually lack clear and complete specifications, and the results of different analyzers may differ. To this end, this paper designs two novel types of automated oracles to find defects in static analyzers with randomly generated programs. The first oracle is constructed by using dynamic program executions and the second one leverages the inferred static analysis results. We applied these two oracles on three state-of-the-art static analyzers: Clang Static Analyzer (CSA), GCC Static Analyzer (GSA), and Pinpoint. We found 38 unique defects in these analyzers, 28 of which have been confirmed or fixed by the developers. We conducted a case study on these found defects followed by several insights and lessons learned for improving and better understanding static analyzers. We have made all the artifacts publicly available at https://github.com/Geoffrey1014/SA_Bugs for replication and benefit the community.

He, W-N, Huang, X-L, Xu, Z, Hu, F & Yu, S 2024, 'Robust Localization for Mobile Targets Along a Narrow Path With LoS/NLoS Interference', IEEE Internet of Things Journal, vol. 11, no. 11, pp. 20853-20866.
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He, Y, Ding, C, Chang, C, Wei, G & Guo, YJ 2024, 'A Bowl-Shaped Filtering Antenna with Wideband Cross-Band Scattering Mitigation for Dual-Band Base Stations', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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He, Z, Zheng, G, Luo, Q, Li, Q & Sun, G 2024, 'Fatigue life improvement mechanisms of CFRP/Al hybrid joints – Load sharing study using a digital image correlation technique', Composite Structures, vol. 327, pp. 117625-117625.
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Hemsley, B, Dann, S, Reddacliff, C, Smith, R, Given, F, Gay, V, Leong, TW, Josserand, E, Skellern, K, Bull, C, Palmer, S & Balandin, S 2024, 'Views on the usability, design, and future possibilities of a 3D food printer for people with dysphagia: outcomes of an immersive experience', Disability and Rehabilitation: Assistive Technology, vol. 19, no. 3, pp. 527-536.
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PURPOSE: Although 3D food printing is expected to enable the creation of visually appealing pureed food for people with disability and dysphagia, little is known about the user experience in engaging with 3D food printing or the feasibility of use with populations who need texture-modified foods. The aim of this study was to explore the feasibility and usability of using domestic-scale 3D food printer as an assistive technology to print pureed food into attractive food shapes for people with dysphagia. MATERIALS AND METHODS: In total, 16 participants engaged in the unfamiliar, novel process of using a domestic-scale 3D food printer (choosing, printing, tasting), designed for printing pureed food, and discussed their impressions in focus group or individual interviews. RESULTS AND CONCLUSIONS: Overall, results demonstrated that informed experts who were novice users perceived the 3D food printing process to be fun but time consuming, and that 3D food printers might not yet be suitable for people with dysphagia or their supporters. Slow response time, lack of user feedback, scant detail on the appropriate recipes for the pureed food to create a successful print, and small font on the user panel interface were perceived as barriers to accessibility for people with disability and older people. Participants expected more interactive elements and feedback from the device, particularly in relation to resolving printer or user errors. This study will inform future usability trials and food safety research into 3D printed foods for people with disability and dysphagia. IMPLICATIONS FOR REHABILITATION3D food printers potentially have a role as an assistive technology in the preparation of texture-modified foods for people with disability and dysphagia.To increase feasibility, 3D food printers should be co-designed with people with disability and their supporters and health professionals working in the field of dysphagia and rehabilitation.Experts struggled to be ...

Hernandez Moreno, V, Jansing, S, Polikarpov, M, Carmichael, MG & Deuse, J 2024, 'Obstacles and opportunities for learning from demonstration in practical industrial assembly: A systematic literature review', Robotics and Computer-Integrated Manufacturing, vol. 86, pp. 102658-102658.
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Hertrampf, T & Oberst, S 2024, 'Recurrence Rate spectrograms for the classification of nonlinear and noisy signals', Physica Scripta, vol. 99, no. 3, pp. 035223-035223.
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Abstract Time series analysis of real-world measurements is fundamental in natural sciences and engineering, and machine learning has been recently of great assistance especially for classification of signals and their understanding. Yet, the underlying system’s nonlinear response behaviour is often neglected. Recurrence Plot (RP) based Fourier-spectra constructed through τ-Recurrence Rate (RR τ ) have shown the potential to reveal nonlinear traits otherwise hidden from conventional data processing. We report a so far disregarded eligibility for signal classification of nonlinear time series by training RESnet-50 on spectrogram images, which allows recurrence-spectra to outcompete conventional Fourier analysis. To exemplify its functioning, we employ a simple nonlinear physical flow of a continuous stirred tank reactor, able to exhibit exothermic, first order, irreversible, cubic autocatalytic chemical reactions, and a plethora of fast-slow dynamics. For dynamics with noise being ten times stronger than the signal, the classification accuracy was up to ≈ 75% compared to ≈ 17% for the periodogram. We show that an increase in entropy only detected by the RR τ allows differentiation. This shows that RP power spectra, combined with off-the-shelf machine learning techniques, have the potential to significantly improve the detection of nonlinear and noise contaminated signals.

Heusdens, R & Zhang, G 2024, 'Distributed Optimisation With Linear Equality and Inequality Constraints Using PDMM', IEEE Transactions on Signal and Information Processing over Networks, vol. 10, pp. 294-306.
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Hoang, TM, Vahid, A, Tuan, HD & Hanzo, L 2024, 'Physical Layer Authentication and Security Design in the Machine Learning Era', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Security at the physical layer (PHY) is a salient research topic in wireless systems, and machine learning (ML) is emerging as a powerful tool for providing new data-driven security solutions. Therefore, the application of ML techniques to the PHY security is of crucial importance in the landscape of more and more data-driven wireless services. In this context, we first summarize the family of bespoke ML algorithms that are eminently suitable for wireless security. Then, we review the recent progress in ML-aided PHY security, where the term “PHY security” is classified into two different types: i) PHY authentication and ii) secure PHY transmission. Moreover, we treat NNs as special types of ML and present how to deal with PHY security optimization problems using NNs. Finally, we identify some major challenges and opportunities in tackling PHY security challenges by applying carefully tailored ML tools.

Hossain, MAM, Tiong, SK, Hannan, MA, Ker, PJ, Fattah, IMR & Mahlia, TMI 2024, 'Recent advances in silicon nanomaterials for lithium-ion batteries: Synthesis approaches, emerging trends, challenges, and opportunities', Sustainable Materials and Technologies, vol. 40, pp. e00964-e00964.
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Hossain, MS, Bacaoco, M, Mai, TNA, Ponchon, G, Chen, C, Ding, L, Chen, Y, Ekimov, E, Xu, X, Solntsev, AS & Tran, TT 2024, 'Fiber-Based Ratiometric Optical Thermometry with Silicon Vacancy in Microdiamonds', ACS Applied Optical Materials, vol. 2, no. 1, pp. 97-107.
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Hou, J, Zhao, Z, Wang, Z, Lu, W, Jin, G, Wen, D & Du, X 2024, 'AeonG: An Efficient Built-in Temporal Support in Graph Databases', Proceedings of the VLDB Endowment, vol. 17, no. 6, pp. 1515-1527.
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Real-world graphs are often dynamic and evolve over time. It is crucial for storing and querying a graph's evolution in graph databases. However, existing works either suffer from high storage overhead or lack efficient temporal query support, or both. In this paper, we propose AeonG, a new graph database with built-in temporal support. AeonG is based on a novel temporal graph model. To fit this model, we design a storage engine and a query engine. Our storage engine is hybrid, with one current storage to manage the most recent versions of graph objects, and another historical storage to manage the previous versions of graph objects. This separation makes the performance degradation of querying the most recent graph object versions as slight as possible. To reduce the historical storage overhead, we propose a novel anchor+delta strategy, in which we periodically create a complete version (namely anchor) of a graph object, and maintain every change (namely delta) between two adjacent anchors of the same object. To boost temporal query processing, we propose an anchor-based version retrieval technique in the query engine to skip unnecessary historical version traversals. Extensive experiments are conducted on both real and synthetic datasets. The results show that AeonG achieves up to 5.73× lower storage consumption and 2.57× lower temporal query latency against state-of-the-art approaches, while introducing only 9.74% performance degradation for supporting temporal features.

Hou, J, Zhu, Y, Liu, J, Lin, L, Zheng, M, Yang, L, Wei, W, Ni, B-J & Chen, X 2024, 'Competitive enrichment of comammox Nitrospira in floccular sludge', Water Research, vol. 251, pp. 121151-121151.
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Hou, S, Zheng, J, Pan, H, Feng, K, Liu, Q & Ni, Q 2024, 'Multivariate multi-scale cross-fuzzy entropy and SSA-SVM-based fault diagnosis method of gearbox', Measurement Science and Technology, vol. 35, no. 5, pp. 056102-056102.
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Abstract Fuzzy entropy (FuzzyEn) is widely recognized as a powerful tool for analyzing nonlinear dynamics and measuring the complexity of time series data. It has been utilized as an effective indicator to capture nonlinear fault features in gearbox vibration signals. However, FuzzyEn only measures complexity at a single scale, ignoring the valuable information contained in large-scale features of the time series. Furthermore, FuzzyEn does not account for coupling characteristics between related or synchronized time series. To address these limitations, a novel entropy-based approach called multivariate multi-scale cross-fuzzy entropy (MvMCFE) is proposed in this paper for measuring the complexity and mutual predictability of two multivariate time series. Relying on the advantages of MvMCFE in nonlinear feature extraction, a new fault diagnosis method for gearboxes is proposed based on MvMCFE and an optimized support vector machine (SVM) using the salp swarm algorithm (SSA-SVM). Ultimately, the proposed gearbox diagnostic method is employed to analyze the gearbox experimental data and a comparison with existing fault diagnosis approaches is conducted. The comparison results indicate that the proposed method can effectively extract nonlinear fault features of gearboxes and achieve the highest recognition rate compared to the other methods.

Howell, N, Middleton, RJ, Sierro, F, Fraser, BH, Wyatt, NA, Chacon, A, Bambery, KR, Livio, E, Dobie, C, Bevitt, JJ, Davies, J, Dosseto, A, Franklin, DR, Garbe, U, Guatelli, S, Hirayama, R, Matsufuji, N, Mohammadi, A, Mutimer, K, Rendina, LM, Rosenfeld, AB & Safavi-Naeini, M 2024, 'Neutron Capture Enhances Dose and Reduces Cancer Cell Viability in and out of Beam During Helium and Carbon Ion Therapy', International Journal of Radiation Oncology*Biology*Physics.
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Hu, K, Li, L, Xie, Q, Liu, J, Tao, X & Xu, G 2024, 'Decoupled Progressive Distillation for Sequential Prediction with Interaction Dynamics', ACM Transactions on Information Systems, vol. 42, no. 3, pp. 1-35.
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Sequential prediction has great value for resource allocation due to its capability in analyzing intents for next prediction. A fundamental challenge arises from real-world interaction dynamics where similar sequences involving multiple intents may exhibit different next items. More importantly, the character of volume candidate items in sequential prediction may amplify such dynamics, making deep networks hard to capture comprehensive intents. This article presents a sequential prediction framework with Decoupled Progressive Distillation (DePoD), drawing on the progressive nature of human cognition. We redefine target and non-target item distillation according to their different effects in the decoupled formulation. This can be achieved through two aspects: (1) Regarding how to learn, our target item distillation with progressive difficulty increases the contribution of low-confidence samples in the later training phase while keeping high-confidence samples in the earlier phase. And, the non-target item distillation starts from a small subset of non-target items from which size increases according to the item frequency. (2) Regarding whom to learn from, a difference evaluator is utilized to progressively select an expert that provides informative knowledge among items from the cohort of peers. Extensive experiments on four public datasets show DePoD outperforms state-of-the-art methods in terms of accuracy-based metrics.

Hu, R, Wang, X, Chang, X, Zhang, Y, Hu, Y, Liu, X & Yu, S 2024, 'CStrCRL: Cross-view Contrastive Learning through Gated GCN with Strong Augmentations for Skeleton Recognition', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Hu, S, Yuan, X, Ni, W, Wang, X, Hossain, E & Vincent Poor, H 2024, 'OFDMA-F²L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface', IEEE Transactions on Wireless Communications, vol. 23, no. 7, pp. 6793-6807.
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Hu, X, Liu, T, Shu, T & Nguyen, D 2024, 'Spoofing Detection for LiDAR in Autonomous Vehicles: A Physical-Layer Approach', IEEE Internet of Things Journal, pp. 1-1.
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Hu, X, Wang, L, Chen, G, Zhu, S & Wen, S 2024, 'Intermittent boundary control for robust fixed‐time stabilization of uncertain reaction‐diffusion systems', International Journal of Robust and Nonlinear Control, vol. 34, no. 4, pp. 2457-2471.
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AbstractAchieving the robust fixed‐time stabilization (FxTS) of uncertain reaction‐diffusion systems (RDSs) utilizing intermittent boundary control is a valuable and unresolved problem. This paper simultaneously considers the influence of the uncertainty of non‐diffusion and diffusion coefficients on RDSs and provides an effective solution for the treatment of the uncertainty of diffusion coefficient in the investigation of FxTS. Then, a fixed‐time control scheme is presented to stabilize uncertain RDSs within a fixed time, which incorporates the merits of intermittent and boundary control and reduces control costs in both the spatial and temporal domains. Besides, some sufficient conditions in the form of linear matrix inequalities are deduced to guarantee the robust FxTS of uncertain RDSs with Neumann and mixed boundary conditions, respectively, which contain and generalize some existing results. Finally, the validity of the intermittent boundary control scheme and the FxTS criteria developed in this paper are verified by numerical simulations.

Hu, Y, Deng, W, Zhang, JA & Guo, YJ 2024, 'Resource Optimization for Delay Estimation in Perceptive Mobile Networks', IEEE Wireless Communications Letters, vol. 13, no. 1, pp. 223-227.
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Hu, Y, Jin, P, Guo, Y, Lei, G & Zhu, J 2024, 'A New SVM Strategy to Suppress Total Harmonic Distortion and Current Stress in HFLMCs', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 10512-10522.
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Hu, Y, Jung, C, Qin, Q, Han, J, Liu, Y & Li, M 2024, 'HDVC: Deep Video Compression With Hyperprior-Based Entropy Coding', IEEE Access, vol. 12, pp. 17541-17551.
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HU, Y, LI, H, ZHANG, JA, HUANG, X & CHENG, Z 2024, 'Optimal Design of Wideband mmWave LoS MIMO Systems Using Hybrid Arrays with Beam Squint', IEICE Transactions on Communications, vol. E107.B, no. 1, pp. 244-252.
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Huang, J, Gong, Y, Shi, Y, Zhang, X, Zhang, J & Yin, Y 2024, 'Focusing on Subtle Differences: A Feature Disentanglement Model for Series Photo Selection', IEEE Transactions on Multimedia, pp. 1-14.
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Huang, J, Gong, Y, Zhang, L, Zhang, J, Nie, L & Yin, Y 2024, 'Modeling Multiple Aesthetic Views for Series Photo Selection', IEEE Transactions on Multimedia, vol. 26, pp. 1983-1995.
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Huang, J, Ma, B, Wang, M, Zhou, X, Yao, L, Wang, S, Qi, L & Chen, Y 2024, 'Incentive Mechanism Design of Federated Learning for Recommendation Systems in MEC', IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 2596-2607.
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Huang, K-C, Tseng, C-Y & Lin, C-T 2024, 'EEG Information Transfer Changes in Different Daily Fatigue Levels During Drowsy Driving', IEEE Open Journal of Engineering in Medicine and Biology, vol. 5, pp. 180-190.
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Huang, S, Hauser, K & Shell, DA 2024, 'Selected papers from RSS2022', The International Journal of Robotics Research, vol. 43, no. 4, pp. 387-388.
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Huang, S, Tsang, IW, Xu, Z & Lv, J 2024, 'CGDD: Multiview Graph Clustering via Cross-Graph Diversity Detection', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 3, pp. 4206-4219.
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Huang, S, Zheng, J, Qin, P, Zhan, Q & Chen, X 2024, 'Improved planar near-field measurement based on data assimilation', Measurement, vol. 227, pp. 114265-114265.
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Huang, T, Ben, X, Gong, C, Xu, W, Wu, Q & Zhou, H 2024, 'GaitDAN: Cross-view Gait Recognition via Adversarial Domain Adaptation', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Huang, W, Liao, X, Chen, H, Hu, Y, Jia, W & Wang, Q 2024, 'Deep local-to-global feature learning for medical image super-resolution', Computerized Medical Imaging and Graphics, vol. 115, pp. 102374-102374.
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Huang, X, Francis, I, Saha, G, Rahman, MM & Saha, SC 2024, 'Large eddy simulation-based modeling of cold-air inhalation from nasal cavities to the distal lung: Insights for athlete health and performance', Results in Engineering, vol. 23, pp. 102475-102475.
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Huang, X, Le, AT, Zhang, H, Zhang, JA & Guo, YJ 2024, 'Orthogonal Expanded Memory Polynomial Model for Circular Complex Gaussian Processes', IEEE Transactions on Signal Processing, pp. 1-15.
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Huang, X, Saha, SC, Saha, G, Francis, I & Luo, Z 2024, 'Transport and deposition of microplastics and nanoplastics in the human respiratory tract', Environmental Advances, vol. 16, pp. 100525-100525.
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Huang, X, Zhang, H, Le, AT, Andrew Zhang, J & Jay Guo, Y 2024, 'Received Signal Modelling for Millimeter Wave and Terahertz Systems with Practical Impairments', IEEE Transactions on Communications, pp. 1-1.
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Huang, Y, Feng, B, Tian, A, Dong, P, Yu, S & Zhang, H 2024, 'An Efficient Differentiated Routing Scheme for MEO/LEO-Based Multi-Layer Satellite Networks', IEEE Transactions on Network Science and Engineering, vol. 11, no. 1, pp. 1026-1041.
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Huang, Y, Huang, Y, Zhang, Z, Wu, Q, Zhong, Y & Wang, L 2024, 'Enhancing Person Re-Identification Performance Through In Vivo Learning', IEEE Transactions on Image Processing, vol. 33, pp. 639-654.
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Huang, Y, Kang, D, Chen, L, Jia, W, He, X, Duan, L, Zhe, X & Bao, L 2024, 'CARD: Semantic Segmentation with Efficient Class-Aware Regularized Decoder', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Huang, Y, Wang, HB, Mak, HMW, Chu, M, Ning, Z, Organ, B, Chan, EFC, Liu, C-H, Mok, W-C, Gromke, C, Shon, HK, Lei, C & Zhou, JL 2024, 'Suitability of using carbon dioxide as a tracer gas for studying vehicle emission dispersion in a real street canyon', Journal of Environmental Sciences.
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Huang, Y, Wu, Q, Zhang, Z, Shan, C, Huang, Y, Zhong, Y & Wang, L 2024, 'Meta Clothing Status Calibration for Long-Term Person Re-Identification', IEEE Transactions on Image Processing, vol. 33, pp. 2334-2346.
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Huang, Y, Yang, D, Feng, B, Tian, A, Dong, P, Yu, S & Zhang, H 2024, 'A GNN-Enabled Multipath Routing Algorithm for Spatial-Temporal Varying LEO Satellite Networks', IEEE Transactions on Vehicular Technology, vol. 73, no. 4, pp. 5454-5468.
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Huang, Y, Yang, G, Zhou, H, Dai, H, Yuan, D & Yu, S 2024, 'VPPFL: A verifiable privacy-preserving federated learning scheme against poisoning attacks', Computers & Security, vol. 136, pp. 103562-103562.
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Huang, Y, Zhang, Z, Huang, Y, Wu, Q, Huang, H, Zhong, Y & Wang, L 2024, 'Customized meta-dataset for automatic classifier accuracy evaluation', Pattern Recognition, vol. 146, pp. 110026-110026.
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Huang, Y, Zhao, S & Lu, J 2024, 'Acoustic contrast control with a sound intensity constraint for personal sound systems', The Journal of the Acoustical Society of America, vol. 155, no. 2, pp. 879-890.
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Personal sound systems have received significant research interest in the past two decades due to their promising applications in a variety of scenarios. Various methods have been proposed to generate personal sound zones, most of which are based on sound pressure manipulation in both the acoustically bright and dark zones. Since sound intensity is closely related to human perception of sound localization, this paper proposes an acoustic contrast control method with a sound intensity constraint to increase the spatial planarity in the bright zone. In the proposed method, the sound intensity in the bright zone is projected to a specific direction while the sound pressure level in the dark zone is minimized. Simulations and experiments are carried out to compare the proposed method with the existing planarity control and pressure matching methods in terms of acoustic contrast, array effort, and planarity. The results demonstrate that the proposed method improves the planarity in the bright zone compared to existing planarity control method, while exhibitig a higher acoustic contrast and a lower array effort than the pressure matching method.

Huang, Z, Su, Q, Huang, J, He, X, Pei, Y & Yang, C 2024, 'Field assessment of a subgrade-culvert transition zone constructed with foamed concrete in the ballasted railway', International Journal of Rail Transportation, vol. 12, no. 3, pp. 391-413.
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Huang, Z, Zhao, R, Leung, FHF, Banerjee, S, Lam, K-M, Zheng, Y-P & Ling, SH 2024, 'Landmark Localization From Medical Images With Generative Distribution Prior', IEEE Transactions on Medical Imaging, vol. 43, no. 7, pp. 2679-2692.
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Huo, L, Xia, J, Zhang, L, Zhang, H & Xu, M 2024, 'Center-bridged Interaction Fusion for hyperspectral and LiDAR classification', Neurocomputing, vol. 590, pp. 127757-127757.
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Huo, X, Jiang, Z, Gu, X, Luo, Q, Li, Q & Sun, G 2024, 'On thermal and strain-rate dependences of polymethacrylimide (PMI) foam materials', Thin-Walled Structures, vol. 202, pp. 111986-111986.
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Hussain, F, Khan, A & Mirdad, A 2024, 'Smart Contracts and Marketplace for Just-in-Time Management of Pharmaceutical Drugs', International Journal of Web and Grid Services, vol. 20, no. 1.
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Hussain, I, Yaqub, M, Mortazavi, M, Ehsan, MA & Hussain, M 2024, 'Study of fire-damaged circular RC columns repaired using composite confinement techniques', Proceedings of the Institution of Civil Engineers - Structures and Buildings, vol. 177, no. 3, pp. 222-234.
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Numerical and regression modelling of 21 undamaged, fire-damaged and repaired fire-damaged circular reinforced concrete (RC) columns was undertaken. The columns were exposed to temperatures of 300°C, 500°C and 900°C and tested for axial residual capacity. It was found that the concrete lost strength after exposure to a temperature of 300°C or above. Fire-damaged columns were then repaired using various composite confinement techniques. Strength was regained when carbon-fibre-reinforced polymer (CFRP) confinement was applied to the fire-damaged columns but it also increased the deformation and thus reduced the stiffness, which is undesirable. As an alternative, steel wire mesh, filled with cement–sand mortar and wrapped with CFRP was employed. A numerical model to predict the residual capacity of these columns was developed. The development of numerical techniques, including material properties, geometry, elements, loading, boundary conditions and contact algorithms for undamaged, fire-damaged and repaired fire-damaged columns is reviewed and summarised. Analytical equations were developed using linear, multiple and quadratic regression modelling. The results obtained using the proposed model and regression equations showed that these models offer a better alternative to experimental testing for the prediction of the post-fire performance of damaged and repaired RC columns.

Hussain, W, Mabrok, M, Gao, H, Rabhi, FA & Rashed, EA 2024, 'Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems', DIGITAL HEALTH, vol. 10.
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The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include ‘ML’, ‘Deep Learning’, and ‘Artificial Intelligence’.

Hwang, Y-S, Um, J-S, Pradhan, B, Choudhury, T & Schlueter, S 2024, 'How does ChatGPT evaluate the value of spatial information in the 4th industrial revolution?', Spatial Information Research, vol. 32, no. 2, pp. 187-194.
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AbstractChat Generative Pre-trained Transformer (ChatGPT), developed by OpenAI, is a prominent AI model capable of understanding and generating human-like text based on input. Since terms and concepts of spatial information are contextual, the applications of ChatGPT on spatial information disciplines can be biased by the perceptions and perspectives of ChatGPT towards spatial information. Therefore, a thorough understanding of the real magnitude and level of comprehension of spatial information by ChatGPT is essential before exploring its potential applications in spatial information disciplines. This article aims to investigate how ChatGPT evaluates spatial information and its potential contributions to 4th Industrial Revolution (Industry 4.0). ChatGPT has summarized a notable perspective on evaluating and utilizing spatial information in the context of the Industry 4.0. The result of this study shows that ChatGPT has a good understanding on contextual concepts related to spatial information. However, it exhibits potential biases and challenges, as its responses lean towards the technological and analytical aspects. The results provide a crucial understanding on how to leverage ChatGPT’s benefits to the fullest while recognizing its constraints, with the aim to enhance the efficacy from the perspective of applications linked to spatial information.

Iacopi, F & Ferrari, AC 2024, 'Tailoring graphene for electronics beyond silicon', Nature, vol. 625, no. 7993, pp. 34-35.
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Ibanez-Hidalgo, I, Sanchez-Ruiz, A, Perez-Basante, A, Zubizarreta, A, Ceballos, S, Gil-Lopez, S & Aguilera, RP 2024, 'Real Time Selective Harmonic Control—PWM Based on Artificial Neural Networks', IEEE Transactions on Power Electronics, vol. 39, no. 1, pp. 768-783.
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Ibrahim, IA, Choudhury, T, Sargeant, J, Shah, R, Hossain, MJ & Islam, S 2024, 'CEREI: An open-source tool for Cost-Effective Renewable Energy Investments', SoftwareX, vol. 26, pp. 101708-101708.
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Ijaz Malik, MA, Mujtaba, MA, Kalam, MA, Silitonga, AS & Ikram, A 2024, 'Recent advances in hydrogen supplementation to promote biomass fuels for reducing greenhouse gases', International Journal of Hydrogen Energy, vol. 49, pp. 463-487.
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Energy security is the foremost concern for a sustainable environment. To make a sustainable environment, biomass waste products like biomass oil and biofuels must be efficiently burned. As millions of tons of waste biomass are dumped daily in major cities worldwide, it must be brought into energy products utilization. The quest for a sustainable ecosystem has pushed scientists to explore alternative fuels that are not only compatible with the engine but also eco-friendly. Hydrogen exhibits excellent combustion characteristics during dual fuel mode in a compression ignition (CI) engine. Carbon dioxide and NOx emissions are the two significant pollutants alternative fuels produce. This review study has tried to mitigate these two pollutants by combining biodiesel and hydrogen. It has been investigated that hydrogen possesses zero carbon content and can reduce CO2 emission, and biodiesel made from algae resources can help reduce NOx emission. Therefore, it is highlighted through the current review study to use the blend of hydrogen and algae-based biodiesel fuels to achieve benefits from their combined physicochemical properties and mitigate greenhouse gas emissions. The carbon-free nature of hydrogen and the oxygenated nature of biodiesel can be an excellent combination for combustion in diesel engines. Adopting third-generation fuels such as algae appears to be a viable solution to meet future energy demands. Biodiesel has a lower calorific value and viscous nature, negatively impacting fuel spray characteristics and creating abrupt fuel consumption. The purpose of this study is to promote biomass oil burning using hydrogen as a promoter supplement blend. Hydrogen has a higher heating value that can help overcome the less heating value of biodiesel fuels. Therefore, hydrogen as a blend with biodiesel makes the mixture lean and positively impacts engine performance, emissions, and combustion parameters.

Ikram, MM, Saha, G & Saha, SC 2024, 'Second law analysis of a transient hexagonal cavity with a rotating modulator', International Journal of Heat and Mass Transfer, vol. 221, pp. 125039-125039.
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Im, K, Park, M, Kabir, MM, Sohn, W, Choo, Y, Shon, HK & Nam, SY 2024, 'Human urine electrolysis for simultaneous green hydrogen and liquid fertilizer production for a circular economy: A proof of concept', Desalination, vol. 570, pp. 117059-117059.
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This study explores a novel process for hydrogen production and urine concentration using water electrolysis, employing a hydrophobic membrane and hydrogel electrolyte. The process utilizes a hydrophobic membrane to provide pure water from human urine, while simultaneously producing hydrogen through electrolysis, and concentrating urine for liquid fertilizer production. A suitable hydrogel electrolyte was developed, with polyvinyl alcohol (PVA)-based hydrogels and varying potassium hydroxide (KOH) concentration, showing efficient ion conductivity. The PVA-KOH 30 wt % hydrogel incorporating melamine exhibited promising performance in cell testing, achieving a current density of 204.35 mA/cm2 at 2 V. Long-term electrolysis tests indicated sustained efficiency, although a decline in current density during 96 h was attributed to hydrophobic membrane fouling. Nonetheless, the hydrogel electrolyte demonstrated minimal fouling, successfully concentrating the urine about 5 times. This concentrated urine serves as liquid fertilizer, while the produced hydrogen acts as an energy source, and the oxygen can be recycled for use in a membrane bioreactor (MBR), establishing a sustainable energy cycle system.

Indraratna, B, Arachchige, CMK, Rujikiatkamjorn, C, Heitor, A & Qi, Y 2024, 'Utilization of Granular Wastes in Transportation Infrastructure', Geotechnical Testing Journal, vol. 47, no. 1, pp. 20220233-20220233.
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Abstract Attributed to environmental preservation in urban infrastructure development, the recycling of waste materials produced in the coal and steel industry as well as the reusing of waste tires is a high priority in Australia. In this article, the practical applications of (i) coal wash (CW) and steel furnace slag mixtures, (ii) CW and fly ash mixtures, and (iii) rubber elements derived from recycled tires are discussed. In this regard, some examples of real-life applications are elucidated in relation to coastal reclamation as well as road and rail construction (e.g., Port Kembla, Kangaroo Valley highway, and Chullora Rail Precinct). The article outlines various aspects of site investigation, construction techniques, and the installation of instrumentation to evaluate the field performance of these waste materials in contrast to traditional (natural) quarried materials. The results from these case studies demonstrate that properly engineered granular waste mixtures can exhibit promising characteristics even to exceed the current technical standards, implying reduced intensity of maintenance. The research outcomes strongly support sustainable solutions to be embraced in the future development of transportation infrastructure, capable of withstanding increased freight loading and enhanced longevity.

Indraratna, B, Atapattu, S, Rujikiatkamjorn, C, Arivalagan, J & Jing, N 2024, 'Soft Soil Improvement by Geosynthetics for Enhanced Performance of Transport Infrastructure', Geotechnical Engineering, vol. 55, no. 1, pp. 1-10.
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Increasing demand for transportation has forced new infrastructure to be built on weak subgrade soils such as estuarine or marine clays. The application of heavy and high-frequency cyclic loads due to vehicular movement during the operational (post-construction) stage of tracks can cause (i) cyclic undrained failure, (ii) mud pumping or subgrade fluidisation, and (iii) differential and excessive settlement. This keynote paper presents the use of prefabricated vertical drains (PVDs) to enhance the performance of tracks. A series of laboratory experiments were carried out to investigate the cyclic response of remoulded soil specimens collected from a railway site near Wollongong, NSW, Australia. The results of the laboratory tests showed that beyond the critical cyclic stress ratio (CSRc), there is an internal redistribution of moisture within the specimen which causes the top portion of the specimen to soften and fluidise. The role that geosynthetics play in controlling and preventing mud pumping was analysed by assessing the development of excess pore water pressure (EPWP), the change in particle size distribution, and the water content of subgrade soil. The experimental data showed that PVDs can prevent the EPWP from building up to critical levels. PVDs provide shorter-radial drainage for EPWP to dissipate during cyclic loading, resulting in less accumulation of EPWP. Moreover, PVDs cause soil to behave in a partially drained rather than an undrained condition, while geotextiles can provide adequate surficial drainage and effective confinement at the ballast/subgrade interface. Partially drained cyclic models were developed by adopting the modified Cam clay theory to predict the behaviour of soil under cyclic loadings. The Sandgate Rail Grade Separation project case study presents a design of short PVDs to minimise the settlement and associated lateral displacement due to heavy-haul train loadings.

Indraratna, B, Malisetty, RS, Nair, L & Rujikiatkamjorn, C 2024, 'Instrumentation and Data Interpretation in Transportation Geotechnics', Indian Geotechnical Journal, vol. 54, no. 1, pp. 40-62.
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AbstractTransportation networks on the eastern coast of Australia are often built on or traverse coastal flood plains and marine clays with unfavourable soil conditions. In the past two decades, a significant number of laboratory investigations were carried out in soft soil improvement using Prefabricated Vertical Drains (PVDs) combined with vacuum-assisted surcharge preloading. In addition, significant research efforts were made to reduce the maintenance costs of railway tracks and increase their longevity by using synthetic inclusions such as geocomposites, geogrids and shock mats. These research outcomes were implemented and validated in practice through several field investigations along the eastern coast of Australia. This paper demonstrates the significance of instrumentation and data interpretation in geotechnical field investigations through 6 case histories. Field trials including Port of Brisbane Land Reclamation, Ballina Bypass Upgrade and Sandgate Rail Separation Projects were presented highlighting innovative ways of monitoring the performance of PVDs with vacuum and non-vacuum surcharge preloading. Also, railway track design improvements using geosynthetic and shock mats were discussed through Bulli and Singleton trial track case studies. Further, the heavy haul track testing facility at Russell Vale, New South Wales, was discussed as an alternative for expensive and time-consuming field trials.

Indraratna, B, Nguyen, TT, Atapattu, S, Ngo, T & Rujikiatkamjorn, C 2024, 'Subgrade soil response to rail loading: Instability mechanisms, causative factors, and preventive measures', Transportation Geotechnics, vol. 46, pp. 101267-101267.
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Iqbal, H, Zheng, J, Chai, R & Chandrasekaran, S 2024, 'Electric powered wheelchair control using user-independent classification methods based on surface electromyography signals', Medical & Biological Engineering & Computing, vol. 62, no. 1, pp. 167-182.
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AbstractWheelchairs are one of the most popular assistive technology (AT) among individuals with motor impairments due to their comfort and mobility. People with finger problems may find it difficult to operate wheelchairs using the conventional joystick control method. Therefore, in this research study, a hand gesture-based control method is developed for operating an electric-powered wheelchair (EPW). This study selected a comfort-based hand position to determine the stop maneuver. An additional exploration was undertaken to investigate four gesture recognition methods: linear regression (LR), regularized linear regression (RLR), decision tree (DT), and multi-class support vector machine (MC-SVM). The first two methods, LR and RLR, have promising accuracy values of 94.85% and 95.88%, respectively, but each new user must be trained. To overcome this limitation, this study explored two user-independent classification methods: MC-SVM and DT. These methods effectively addressed the finger dependency issue and demonstrated remarkable success in recognizing gestures across different users. MC-SVM has about 99.05% of both precision and accuracy, and the DT has about 97.77% accuracy and precision. All six participants were successful in controlling the EPW without any collisions. According to the experimental results, the proposed approach has high accuracy and can address finger dependency issues.

Irham, A, Roslan, MF, Jern, KP, Hannan, MA & Mahlia, TMI 2024, 'Hydrogen energy storage integrated grid: A bibliometric analysis for sustainable energy production', International Journal of Hydrogen Energy, vol. 63, pp. 1044-1087.
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Islam Rony, Z, Rasul, MG, Jahirul, MI & Mofijur, M 2024, 'Harnessing marine biomass for sustainable fuel production through pyrolysis to support United Nations' Sustainable Development Goals', Fuel, vol. 358, pp. 130099-130099.
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Islam, MR, Akter, S, Islam, L, Razzak, I, Wang, X & Xu, G 2024, 'Strategies for evaluating visual analytics systems: A systematic review and new perspectives', Information Visualization, vol. 23, no. 1, pp. 84-101.
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In recent times, visual analytics systems (VAS) have been used to solve various complex issues in diverse application domains. Nonetheless, an inherent drawback arises from the insufficient evaluation of VAS, resulting in occasional inaccuracies when it comes to analytical reasoning, information synthesis, and deriving insights from vast, ever-changing, ambiguous, and frequently contradictory data. Hence, the significance of implementing an appropriate evaluation methodology cannot be overstated, as it plays a pivotal role in enhancing the design and development of visualization systems. This paper assesses visualization systems by providing a systematic exploration of various evaluation strategies (ES). While several existing studies have examined some ES, the extent of comprehensive and systematic review for visualization research remains limited. In this work, we introduce seven state-of-the-art and widely recognized ES namely (1) dashboard comparison; (2) insight-based evaluation; (3) log data analysis; (4) Likert scales; (5) qualitative and quantitative analysis; (6) Nielsen’s heuristics; and (7) eye trackers. Moreover, it delves into their historical context and explores numerous applications where these ES have been employed, shedding light on the associated evaluation practices. Through our comprehensive review, we overview and analyze the predominant evaluation goals within the visualization community, elucidating their evolution and the inherent contrasts. Additionally, we identify the open challenges that arise with the emergence of new ES, while also highlighting the key themes gleaned from the existing literature that hold potential for further exploration in future studies.

Ismaeel, HK, Albayati, TM, Al-Sudani, FT, Salih, IK, Dhahad, HA, Saady, NMC, Zendehboudi, S & Fattah, IMR 2024, 'The role of catalysts in biodiesel production as green energy applications: A review of developments and prospects', Chemical Engineering Research and Design, vol. 204, pp. 636-653.
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Jahangoshai Rezaee, M, Abbaspour Onari, M & Saberi, M 2024, 'A data-driven decision support framework for DEA target setting: an explainable AI approach', Engineering Applications of Artificial Intelligence, vol. 127, pp. 107222-107222.
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Jaiswal, AK, Srivastava, R, Jayakumar, A, Ahmad, A, Naidu, G & Swaminathan, J 2024, 'Evaporative cooling and sensible heat recovery enable practical waste-heat driven water purification', Desalination, vol. 586, pp. 117839-117839.
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Jakubowski, K, Vohradsky, J, Chacon, A, Franklin, DR, Tran, LT, Guatelli, S, Safavi-Naeini, M & Rosenfeld, A 2024, 'Computational design and evaluation of a quad-MOSFET device for quality control of therapeutic accelerator-based neutron beams', Radiation Measurements, pp. 107253-107253.
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Jamshir M, M, Vijayakumar, S, Das, S, Thiyagarajan, K & Kodagoda, S 2024, 'A Novel Direct Digitizer for Leaky Differential Capacitive Sensors Using Phase Sensitive Integration', IEEE Sensors Letters, vol. 8, no. 2, pp. 1-4.
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Jathar, LD, Nikam, K, Awasarmol, UV, Gurav, R, Patil, JD, Shahapurkar, K, Soudagar, MEM, Khan, TMY, Kalam, MA, Hnydiuk-Stefan, A, Gürel, AE, Hoang, AT & Ağbulut, Ü 2024, 'A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning', Heliyon, vol. 10, no. 3, pp. e25407-e25407.
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Javan, K, Altaee, A, BaniHashemi, S, Darestani, M, Zhou, J & Pignatta, G 2024, 'A review of interconnected challenges in the water–energy–food nexus: Urban pollution perspective towards sustainable development', Science of The Total Environment, vol. 912, pp. 169319-169319.
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Jazebi, M, Indraratna, B, Malisetty, RS & Rujikiatkamjorn, C 2024, 'Stability assessment of jointed rock capturing roughness degradation under cyclic loading with special reference to railway formation', Transportation Geotechnics, vol. 48, pp. 101310-101310.
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Ji, X, Li, Y, Wen, P, Barua, P & Acharya, UR 2024, 'MixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model', Computer Methods and Programs in Biomedicine, vol. 244, pp. 107992-107992.
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Jiang, C, Li, W, Ng, C-T & Deng, M 2024, 'Numerical and experimental investigations on quasistatic pulse generation of ultrasonic guided waves in fiber reinforced composite pipes', Journal of Sound and Vibration, vol. 574, pp. 118238-118238.
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Jiang, C, Li, W, Ng, C-T & Deng, M 2024, 'Quasistatic component generation of group velocity mismatched guided waves in tubular structures for microdamage localization', Applied Acoustics, vol. 217, pp. 109813-109813.
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Jiang, J, Dorji, P, Badeti, U, Sohn, W, Freguia, S, Phuntsho, S, El Saliby, I & Shon, HK 2024, 'Corrigendum to “Potential nutrient recovery from source-separated urine through hybrid membrane bioreactor and membrane capacitive deionisation” [Desalination 566 (2023) 116924]', Desalination, vol. 573, pp. 117236-117236.
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Jiang, J, Sohn, W, Almuntashiri, A, Phuntsho, S, Wang, Q, Freguia, S, El-Saliby, I & Shon, HK 2024, 'Feasibility study of powdered activated carbon membrane bioreactor (PAC-MBR) for source-separated urine treatment: A comparison with MBR', Desalination, vol. 580, pp. 117544-117544.
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Jiang, M, Sui, Y, Lei, Y, Xie, X, Li, C, Liu, Y & Tsang, IW 2024, 'Adversarial Learning for Coordinate Regression through k-layer Penetrating Representation', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Jiang, Y, Ma, B, Wang, X, Yu, G, Yu, P, Wang, Z, Ni, W & Liu, RP 2024, 'Blockchained Federated Learning for Internet of Things: A Comprehensive Survey', ACM Computing Surveys, vol. 56, no. 10, pp. 1-37.
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The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data diversity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.

Jin, P, Chang, L, Liu, Y, Guo, Y, Lei, G & Zhu, J 2024, 'Design and Implementation of Novel Rotor Side Brushless Controller With Bidirectional Wireless Power Transmission for Doubly-Fed Machine', IEEE Transactions on Industrial Electronics, vol. 71, no. 1, pp. 183-193.
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Jin, P, Hu, Y, Lei, G, Guo, Y & Zhu, J 2024, 'A Novel SVM Strategy to Reduce Current Stress of High-Frequency Link Matrix Converter', IEEE Transactions on Industrial Electronics, vol. 71, no. 5, pp. 4652-4662.
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Jin, Q, Chen, H, Zhang, Y, Wang, X & Zhu, D 2024, 'Unraveling Scientific Evolutionary Paths: An Embedding-Based Topic Analysis', IEEE Transactions on Engineering Management, vol. 71, pp. 8964-8978.
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Jin, W, Zhao, B, Zhang, Y, Huang, J & Yu, H 2024, 'WordTransABSA: Enhancing Aspect-based Sentiment Analysis with masked language modeling for affective token prediction', Expert Systems with Applications, vol. 238, pp. 122289-122289.
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Jing, H, Ge, H, Tang, H, Farnoud, A, Saidul Islam, M, Wang, L, Wang, C & Cui, X 2024, 'Assessing airflow unsteadiness in the human respiratory tract under different expiration conditions', Journal of Biomechanics, vol. 162, pp. 111910-111910.
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Jiu, J, Liu, H, Li, D, Li, J, Liu, L, Yang, W, Yan, L, Li, S, Zhang, J, Li, X, Li, JJ & Wang, B 2024, '3D bioprinting approaches for spinal cord injury repair', Biofabrication, vol. 16, no. 3, pp. 032003-032003.
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Abstract Regenerative healing of spinal cord injury (SCI) poses an ongoing medical challenge by causing persistent neurological impairment and a significant socioeconomic burden. The complexity of spinal cord tissue presents hurdles to successful regeneration following injury, due to the difficulty of forming a biomimetic structure that faithfully replicates native tissue using conventional tissue engineering scaffolds. 3D bioprinting is a rapidly evolving technology with unmatched potential to create 3D biological tissues with complicated and hierarchical structure and composition. With the addition of biological additives such as cells and biomolecules, 3D bioprinting can fabricate preclinical implants, tissue or organ-like constructs, and in vitro models through precise control over the deposition of biomaterials and other building blocks. This review highlights the characteristics and advantages of 3D bioprinting for scaffold fabrication to enable SCI repair, including bottom–up manufacturing, mechanical customization, and spatial heterogeneity. This review also critically discusses the impact of various fabrication parameters on the efficacy of spinal cord repair using 3D bioprinted scaffolds, including the choice of printing method, scaffold shape, biomaterials, and biological supplements such as cells and growth factors. High-quality preclinical studies are required to accelerate the translation of 3D bioprinting into clinical practice for spinal cord repair. Meanwhile, other technological advances will continue to improve the regenerative capability of bioprinted scaffolds, such as the incorporation of nanoscale biological particles and the development of 4D printing.

John, AR, Singh, AK, Gramann, K, Liu, D & Lin, C-T 2024, 'Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human–robot collaboration', Journal of Neural Engineering, vol. 21, no. 2, pp. 026010-026010.
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Abstract Objective. Brain–computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC. Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials. Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed a...

Jorquera, E, Saco, PM, Verdon-Kidd, D, Rodríguez, JF, Timmermans, H & Nelson, F 2024, 'Effects of tropical cyclones on catchment sediment delivery to coastal ecosystems', CATENA, vol. 238, pp. 107805-107805.
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Jozmaleki, M, Jahangoshai Rezaee, M & Saberi, M 2024, 'A Hybrid Semi-Supervised Approach for Estimating the Efficient and Optimal Level of Hospitals Outputs', Cybernetics and Systems, vol. 55, no. 2, pp. 585-613.
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Ju, X, Li, S, Zhang, Y, Wu, P & Li, Y 2024, 'Design of multi-stable metamaterial cell with improved and programmable energy trapping ability based on frame reinforced curved beams', Thin-Walled Structures, vol. 202, pp. 112120-112120.
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Jupp, JR & Parkes, M 2024, 'Integrated Construction Enterprise Systems: A Strategic Approach to Model-based Data and Process Management', Automation in Construction, vol. (to appear).

Kabir, MM, Maleha, SM, Hossain, MS, Sultana, N, Islam, R, Islam, S, Ahmed, F, Bahadur, NM, Choudhury, TR, Didar-ul-Alam, M, Kabir, N, Tijing, L & Shon, HK 2024, 'Molecular characterization and human health risk assessment of multi-drug and heavy metals tolerant bacteria from urban river water', Desalination and Water Treatment, vol. 317, pp. 100298-100298.
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Kabir, MM, Sabur, GM, Akter, MM, Nam, SY, Im, KS, Tijing, L & Shon, HK 2024, 'Electrodialysis desalination, resource and energy recovery from water industries for a circular economy', Desalination, vol. 569, pp. 117041-117041.
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The water industries (WIN) are now approaching towards sustainability of resource use, recovery process, and water and energy management based on the circular economy (CRE) framework. Thus, the integration of electrodialysis (ED) technology in the WIN with a CRE paradigm should be recommended for ensuring the sustainability of ED desalination, resource, and energy recovery (EDDRER). According to the literature review, and to the best of our knowledge, there is no systematic study devoted to the sustainable EDDRER; hence a comprehensive and critical knowledge generation of EDDRER is essential for further technological advancements of ED. Consequently, this review paper investigated the plausible incorporation of ED in the WIN for a CRE of EDDRER. The recent progress of EDDRER has been described comprehensively and critically. Moreover, an all-inclusive techno-economics and environmental sustainability analysis of EDDRER from WIN for a CRE has been carried out. This paper marks the first instance in which energy recovery techniques employing ED have been reported and critically discussed. In addition, the latest case studies of EDDRER in the WIN have been discussed critically, and the significant scaling-up issues of EDDRER have been assessed based on the state-of-the-art recent scientific findings. Furthermore, the potential mitigation measures for the scaling-up issues have also been addressed. This study is the first comprehensive assessment of EDDRER from WIN for a closed-loop economy. The novel insights of this study could be essential for the development of a sustainable CRE-based EDDRER process for WIN to attain sustainable development goals (SDGs).

Kaliyannan, P, Seikh, AH, Kalam, MA & Venkatesh, R 2024, 'Fabrication and Characteristics Study of Aluminium Alloy Hybrid Nanocomposite Synthesized with SiC and Waste Metal Powder', Silicon, vol. 16, no. 2, pp. 843-851.
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Kan, S, Gao, Y, Zhong, Z & Sui, Y 2024, 'Cross-Language Taint Analysis: Generating Caller-Sensitive Native Code Specification for Java', IEEE Transactions on Software Engineering, vol. 50, no. 6, pp. 1518-1533.
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Karabey Aksalli, I, Baygin, N, Hagiwara, Y, Paul, JK, Iype, T, Barua, PD, Koh, JEW, Baygin, M, Dogan, S, Tuncer, T & Acharya, UR 2024, 'Automated characterization and detection of fibromyalgia using slow wave sleep EEG signals with glucose pattern and D’hondt pooling technique', Cognitive Neurodynamics, vol. 18, no. 2, pp. 383-404.
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Karabulut, E, Pileggi, SF, Groth, P & Degeler, V 2024, 'Ontologies in digital twins: A systematic literature review', Future Generation Computer Systems, vol. 153, pp. 442-456.
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Karkera, T, Adak, C, Chattopadhyay, S & Saqib, M 2024, 'Detecting severity of Diabetic Retinopathy from fundus images: A transformer network-based review', Neurocomputing, vol. 597, pp. 127991-127991.
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Karki, D, Far, H & Nejadi, S 2024, 'Structural Behavior of Prefabricated Composite Cold-Formed Steel and Timber Flooring Systems', Journal of Structural Engineering (United States), vol. 150, no. 7.
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In this study, the structural performance of a new type of lightweight composite cold-formed steel and timber (CFST) flooring system has been investigated by conducting four-point bending tests on 13 specimens. A bare cold-formed steel system without timber sheathing was also tested to provide a benchmark response to which the strength and stiffness of the composite system were compared. This paper presents key findings on the flooring system's structural behavior and performance parameters, such as ultimate bending capacity, load-deflection response, load-slip response, and failure modes, by categorizing 13 specimens into four subgroups based on shear connector types and spacings. In the proposed composite CFST flooring system, 45-mm thick structural plywood panels were connected to the 2.4-mm thick cold-formed steel C-section joist using self-drilling screws, coach screws, and nuts and bolts. The performance of different types of shear connectors on the composite action is experimentally investigated and compared with the theoretical plastic section. Furthermore, the load-carrying capacity, effective bending stiffness, and deflection of composite CFST beams were computed theoretically using elastic theory and compared to experimental results, which showed good agreement.

Karsa, M, Xiao, L, Ronca, E, Bongers, A, Spurling, D, Karsa, A, Cantilena, S, Mariana, A, Failes, TW, Arndt, GM, Cheung, LC, Kotecha, RS, Sutton, R, Lock, RB, Williams, O, de Boer, J, Haber, M, Norris, MD, Henderson, MJ & Somers, K 2024, 'FDA-approved disulfiram as a novel treatment for aggressive leukemia', Journal of Molecular Medicine, vol. 102, no. 4, pp. 507-519.
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Abstract Acute leukemia continues to be a major cause of death from disease worldwide and current chemotherapeutic agents are associated with significant morbidity in survivors. While better and safer treatments for acute leukemia are urgently needed, standard drug development pipelines are lengthy and drug repurposing therefore provides a promising approach. Our previous evaluation of FDA-approved drugs for their antileukemic activity identified disulfiram, used for the treatment of alcoholism, as a candidate hit compound. This study assessed the biological effects of disulfiram on leukemia cells and evaluated its potential as a treatment strategy. We found that disulfiram inhibits the viability of a diverse panel of acute lymphoblastic and myeloid leukemia cell lines (n = 16) and patient-derived xenograft cells from patients with poor outcome and treatment-resistant disease (n = 15). The drug induced oxidative stress and apoptosis in leukemia cells within hours of treatment and was able to potentiate the effects of daunorubicin, etoposide, topotecan, cytarabine, and mitoxantrone chemotherapy. Upon combining disulfiram with auranofin, a drug approved for the treatment of rheumatoid arthritis that was previously shown to exert antileukemic effects, strong and consistent synergy was observed across a diverse panel of acute leukemia cell lines, the mechanism of which was based on enhanced ROS induction. Acute leukemia cells were more sensitive to the cytotoxic activity of disulfiram than solid cancer cell lines and non-malignant cells. While disulfiram is currently under investigation in clinical trials for solid cancers, this study provides evidence for the potential of disulfiram for acute leukemia treatment. Key messages

Katubi, KM, Shiong, NS, Pakhuruddin, MZ, Alkhalayfeh, MA, Abubaker, SA & Al-Soeidat, MR 2024, 'Over 35% efficiency of three absorber layers of perovskite solar cells using SCAPS 1-D', Optik, vol. 297, pp. 171579-171579.
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Kazemi Shariat Panahi, H, Hosseinzadeh-Bandbafha, H, Dehhaghi, M, Orooji, Y, Mahian, O, Shahbeik, H, Kiehbadroudinezhad, M, Kalam, MA, Karimi-Maleh, H, Salehi Jouzani, G, Mei, C, Guillemin, GG, Nizami, A-S, Wang, Y, Gupta, VK, Lam, SS, Pan, J, Kim, K-H, Peng, W, Aghbashlo, M & Tabatabaei, M 2024, 'Nanotechnology applications in biodiesel processing and production: A comprehensive review', Renewable and Sustainable Energy Reviews, vol. 192, pp. 114219-114219.
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Ke, L, Xiao, P, Chen, X, Yu, S, Chen, X & Wang, H 2024, 'A novel cross-domain adaptation framework for unsupervised criminal jargon detection via pre-trained contextual embedding of darknet corpus', Expert Systems with Applications, vol. 242, pp. 122715-122715.
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Ke, W, Li, Y, Chen, Q & Nimbalkar, S 2024, 'Kinematic response of a pile within a soil slope to SH wave excitation', Soil Dynamics and Earthquake Engineering, vol. 183, pp. 108730-108730.
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Ke, Y, Zhang, SS, Jedrzejko, MJ, Lin, G, Li, WG & Nie, XF 2024, 'Strength models of near-surface mounted (NSM) fibre-reinforced polymer (FRP) shear-strengthened RC beams based on machine learning approaches', Composite Structures, vol. 337, pp. 118045-118045.
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Kedziora, DJ, Musial, K & Gabrys, B 2024, 'AutonoML: Towards an Integrated Framework for Autonomous Machine Learning', Foundations and Trends® in Machine Learning, vol. 17, no. 4, pp. 590-766.
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Kedziora, DJ, Nguyen, T-D, Musial, K & Gabrys, B 2024, 'On taking advantage of opportunistic meta-knowledge to reduce configuration spaces for automated machine learning', Expert Systems with Applications, vol. 239, pp. 122359-122359.
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Keshavarz, R, Majidi, E, Raza, A & Shariati, N 2024, 'Ultra-Fast and Efficient Design Method Using Deep Learning for Capacitive Coupling WPT System', IEEE Transactions on Power Electronics, vol. 39, no. 1, pp. 1738-1748.
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Keshavarz, R, Nikkhah, N & Shariati, N 2024, 'In Situ Wide-Range Permittivity Measurement: Compact, Cost-Effective, and Highly Sensitive Sensor Using Reconfigurable Phase Shifter', IEEE Transactions on Circuits and Systems I: Regular Papers, pp. 1-10.
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Keshavarz, R, Sounas, DL, Keshavarz, S & Shariati, N 2024, 'Enabling Wireless Communications, Energy Harvesting, and Energy Saving by Using a Multimode Smart Nonlinear Circuit (MSNC)', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-11.
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Kha, J, Croaker, P, Karimi, M & Skvortsov, A 2024, 'Uncertainty Analysis in Airfoil–Turbulence Interaction Noise Using Polynomial Chaos Expansion', AIAA Journal, vol. 62, no. 2, pp. 657-667.
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Airfoil–turbulence interaction noise is a known source of environmental disturbance and acoustic performance loss in aeroacoustics and hydroacoustics. This noise can be predicted using semi-analytical models that require input measurements of the incoming turbulent flow parameters. However, the turbulence parameters are inherently difficult to measure accurately. These parameters, which include the turbulence kinetic energy and its dissipation rate, have a stochastic nature. This study aims to investigate how small variations in the measurements of turbulence parameters affect the uncertainty of the predicted airfoil–turbulence interaction noise. This is achieved by applying polynomial chaos expansion (PCE) to the semi-analytical model of Amiet’s theory for airfoil-interaction noise. The validity of the deterministic and stochastic simulations is ensured by comparisons against available experimental data from the literature, and Monte Carlo simulations, respectively. Uncertainty quantification is then performed using a stochastic collocation technique, where the aerodynamic noise is evaluated at specific collocation points to estimate the coefficients required for PCE. Both the individual and combined effects of varying the uncertain input turbulence parameters are simulated to quantify the uncertainty of the output aerodynamic noise. The insights gained from the results suggest it is important to incorporate the stochastic behavior of the incoming turbulent flow in operational models for airfoil–turbulence interaction noise predictions.

Kha, J, Karimi, M, Maxit, L & Kirby, R 2024, 'Near- and far-field radiated acoustic pressures from a vibrating three-dimensional cylindrical shell in an underwater acoustic waveguide', Journal of Sound and Vibration, pp. 118534-118534.
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Khademi, P, Mousavi, M, Dackermann, U & Gandomi, AH 2024, 'Enhancing load prediction for structures with concrete overlay using transfer learning of time–frequency feature-based deep models', Engineering Structures, vol. 305, pp. 117734-117734.
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Khanafer, D, Altaee, A, Hawari, AH, Zhou, J & Alsaka, L 2024, 'Innovative stimuli-responsive membrane MSF brine rejection dilution by tertiary treated sewage effluent', Journal of Environmental Management, vol. 365, pp. 121517-121517.
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Khoa, TV, Son, DH, Hoang, DT, Trung, NL, Quynh, TTT, Nguyen, DN, Ha, NV & Dutkiewicz, E 2024, 'Collaborative Learning for Cyberattack Detection in Blockchain Networks', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 7, pp. 3920-3933.
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Khodadadi, N, Talatahari, S & Gandomi, AH 2024, 'ANNA: advanced neural network algorithm for optimisation of structures', Proceedings of the Institution of Civil Engineers - Structures and Buildings, vol. 177, no. 6, pp. 529-551.
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The purpose of this study is to develop an advanced neural network algorithm as a new optimisation for the optimal design of truss structures. The central concept of the algorithm is based on biological nerve structures and artificial neural networks. The performance of the proposed method is explored in engineering design problems. Two efficient methods for improving the standard neural network algorithm are considered here. The first is an enhanced initialisation mechanism based on opposite-based learning. The second relies on using a few tunable parameters to provide proper exploration and exploitation abilities for the algorithm, enabling better solutions to be found while the required structural analyses are reduced. The new algorithm's performance is investigated by using five well-known restricted benchmarks to assess its efficiency in relation to the latest optimisation techniques. The outcome of the examples demonstrates that the upgraded version of the algorithm has increased efficacy and robustness in comparison with the original version of the algorithm and some other methods.

Khorshidi, MS, Izady, A, Nikoo, MR, Al-Maktoumi, A, Chen, M & Gandomi, AH 2024, 'An Agent-based Framework for Transition from Traditional to Advanced Water Supply Systems in Arid Regions', Water Resources Management, vol. 38, no. 7, pp. 2565-2579.
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Khounani, Z, Abdul Razak, NN, Hosseinzadeh-Bandbafha, H, Madadi, M, Sun, F, Mohammadi, P, Mahlia, TMI, Aghbashlo, M & Tabatabaei, M 2024, 'Biphasic pretreatment excels over conventional sulfuric acid in pinewood biorefinery: An environmental analysis', Environmental Research, vol. 248, pp. 118286-118286.
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Khuat, TT, Bassett, R, Otte, E, Grevis-James, A & Gabrys, B 2024, 'Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities', Computers & Chemical Engineering, vol. 182, pp. 108585-108585.
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Kilic, M, Barua, PD, Keles, T, Yildiz, AM, Tuncer, I, Dogan, S, Baygin, M, Tuncer, T, Kuluozturk, M, Tan, R-S & Acharya, UR 2024, 'GCLP: An automated asthma detection model based on global chaotic logistic pattern using cough sounds', Engineering Applications of Artificial Intelligence, vol. 127, pp. 107184-107184.
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Kim, HT, Philip, L, McDonagh, A, Johir, M, Ren, J, Shon, HK & Tijing, LD 2024, 'Recent Advances in High‐Rate Solar‐Driven Interfacial Evaporation', Advanced Science, vol. 11, no. 26.
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AbstractRecent advances in solar‐driven interfacial evaporation (SDIE) have led to high evaporation rates that open promising avenues for practical utilization in freshwater production and industrial application for pollutant and nutrient concentration, and resource recovery. Breakthroughs in overcoming the theoretical limitation of 2D interfacial evaporation have allowed for developing systems with high evaporation rates. This study presents a comprehensive review of various evaporator designs that have achieved pure evaporation rates beyond 4 kg m−2 h−1, including structural and material designs allowing for rapid evaporation, passive 3D designs, and systems coupled with alternative energy sources of wind and joule heating. The operational mechanisms for each design are outlined together with discussion on the current benefits and areas for improvement. The overarching challenges encountered by SDIE concerning the feasibility of direct integration into contemporary practical settings are assessed, and issues relating to sustaining elevated evaporation rates under diverse environmental conditions are addressed.

Kim, J, Dong, L, Shon, HK & Park, K 2024, 'Current progress in semi-batch reverse osmosis for brackish water desalination', Desalination, vol. 578, pp. 117434-117434.
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Kim, J, Lee, J, Lee, S, Tijing, L, Shon, HK & Hong, S 2024, 'Electrically conductive membrane for fouling control: Its mechanisms and applications', Desalination, vol. 578, pp. 117445-117445.
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Kim, S, Corah, M, Keller, J, Best, G & Scherer, S 2024, 'Multi-Robot Multi-Room Exploration With Geometric Cue Extraction and Circular Decomposition', IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1190-1197.
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Kirik, S, Tasci, I, Barua, PD, Yildiz, AM, Keles, T, Baygin, M, Tuncer, I, Dogan, S, Tuncer, T, Devi, A, Tan, R-S & Acharya, UR 2024, 'DSWIN: Automated hunger detection model based on hand-crafted decomposed shifted windows architecture using EEG signals', Knowledge-Based Systems, vol. 300, pp. 112150-112150.
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Kong, M, Hou, M, Zhao, S, Liu, F, Su, R & Chen, Y 2024, 'DADIN: Domain Adversarial Deep Interest Network for cross domain recommender systems', Expert Systems with Applications, vol. 243, pp. 122880-122880.
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Kong, X, Lu, Z, Guo, X, Zhang, J & Li, H 2024, 'Resilience Evaluation of Cyber-Physical Power System Considering Cyber Attacks', IEEE Transactions on Reliability, vol. 73, no. 1, pp. 245-256.
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Kotha, M, Pradhan, S & Cetindamar, D 2024, 'Relevance of Engineering Management Courses to Managerial Skills in the Industry', IEEE Transactions on Engineering Management, vol. 71, pp. 7849-7862.
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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.

Krishankumar, R, Ecer, F, Mishra, AR, Ravichandran, KS, Gandomi, AH & Kar, S 2024, 'A SWOT-Based Framework for Personalized Ranking of IoT Service Providers With Generalized Fuzzy Data for Sustainable Transport in Urban Regions', IEEE Transactions on Engineering Management, vol. 71, no. 99, pp. 2937-2950.
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Sustainable transport in cities has been gaining a lot of attraction recently and a core focus of engineering management. Internet of Things (IoT) is seen as a widely accepted technology that promotes sustainability through the interconnection of diverse computing sources for solving environmental problems. Previous studies on IoT have discussed interesting factors toward its adoption, but selecting a suitable IoT service provider (IoTSP) is an open challenge due to a diverse set of factors in practice. Driven by the challenge, in this article, a generalized fuzzy-based decision model is put forward for IoTSP selection, which is the prime objective of the study. Initially, a strength, weakness, opportunity, threat (SWOT) analysis is adopted to identify the crucial challenges in IoT adoption. Later, the relative significance of these challenges is calculated by adopting the regret/rejoice approach. Due to uncertainty, certain rating information of IoTSPs is missing that are rationally imputed by proposing an analytical approach. Rating matrices from experts are transformed into opinion vectors, and a prioritization algorithm is developed with query vector for rational personalized ordering of IoTSPs. Data for the study are acquired via questionnaire, which is filled by experts. The efficacy of the developed model is exemplified by using a real case study of IoTSP selection for pollution management in Chennai. Concerning the findings, mobility, security, and connectivity are the most vital factors for IoTSP selection. Results show that the proposed model is a viable tool for IoTSP selection and it is robust, unique, and stable compared to its counterparts.

Krishankumar, R, Ramanujam, N, Zavadskas, EK, Ravichandran, KS & Gandomi, AH 2024, 'Ranking Barriers Impeding Sustainability Adoption in Clean Energy Supply Chains: A Hybrid Framework With Fermatean Fuzzy Data', IEEE Transactions on Engineering Management, vol. 71, pp. 5506-5522.
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Kumar, A, Huang, Y, Lin, J, Hui, D & Fohrer, N 2024, 'Heavily modified freshwater: Potential ecological indicators', Ecological Indicators, vol. 159, pp. 111620-111620.
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Kumar, A, Singh, UK & Pradhan, B 2024, 'Enhancing Interpretability in Deep Learning-Based Inversion of 2-D Ground Penetrating Radar Data: An Explainable AI (XAI) Strategy', IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5.
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Kumar, A, Singh, UK & Pradhan, B 2024, 'Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study', Journal of Environmental Management, vol. 351, pp. 119943-119943.
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Kumar, S, Chattopadhyay, S & Adak, C 2024, 'TPMCF: Temporal QoS Prediction Using Multi-Source Collaborative Features', IEEE Transactions on Network and Service Management, pp. 1-1.
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Kumari, A, Tanveer, M & Lin, CT 2024, 'Class Probability and Generalized Bell Fuzzy Twin SVM for Imbalanced Data', IEEE Transactions on Fuzzy Systems, vol. 32, no. 5, pp. 3037-3048.
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Kurunathan, H, Huang, H, Li, K, Ni, W & Hossain, E 2024, 'Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey', IEEE Communications Surveys & Tutorials, vol. 26, no. 1, pp. 496-533.
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Laccone, F, Pietroni, N, Cignoni, P & Malomo, L 2024, 'Bending-Reinforced Grid Shells for Free-form Architectural Surfaces.', Comput. Aided Des., vol. 168, pp. 103670-103670.
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Laghari, SUA, Li, W, Manickam, S, Nanda, P, Al-Ani, AK & Karuppayah, S 2024, 'Securing MQTT Ecosystem: Exploring Vulnerabilities, Mitigations, and Future Trajectories', IEEE Access, pp. 1-1.
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Lai, J & Yang, Y 2024, '3D Terahertz Antenna Measurement: A comparison between solid-state electronics and photomixing approaches', IEEE Antennas and Propagation Magazine, pp. 2-15.
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Lai, J, Lv, X & Yang, Y 2024, 'A 3-D Printed 140 GHz Multifocal Dielectric Transmitarray Antenna for 2-D Mechanical Beam Scanning', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 4, pp. 1366-1370.
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Lai, N, Chang, G, Yang, Y, He, M, Tang, W, Huang, Q, Zhang, Q, Su, QP, Liao, J, Yang, Y, Wang, C & Wang, R 2024, 'CsPbX3 quantum Dots@ZIF-8 composites with enhanced luminescence emission and stability', Journal of Luminescence, vol. 266, pp. 120280-120280.
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Larpruenrudee, P, Bennett, NS, Fitch, R, Sauret, E, Gu, Y & Islam, MS 2024, 'Investigation of metal hydride hydrogen storage performance using phase change materials', International Journal of Hydrogen Energy, vol. 60, pp. 996-1019.
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Laskar, JI, Sen, MK, Dutta, S, Gandomi, AH & Tewari, S 2024, 'An approach for data-driven time-varying flood resilience quantification of housing infrastructure system', Sustainable and Resilient Infrastructure, vol. 9, no. 2, pp. 124-144.
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Le Gentil, C, Ouabi, O-L, Wu, L, Pradalier, C & Vidal-Calleja, T 2024, 'Accurate Gaussian-Process-Based Distance Fields With Applications to Echolocation and Mapping', IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1365-1372.
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Le, AT, Huang, X, Ding, C, Zhang, H & Guo, YJ 2024, 'An In-Band Full-Duplex Prototype With Joint Self-Interference Cancellation in Antenna, Analog, and Digital Domains', IEEE Transactions on Microwave Theory and Techniques, pp. 1-10.
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Ledic Neto, J, Andrade, DF, Lu, H-YH, Petrassi, ACMA & Moro, ARP 2024, 'The development and evaluation of a scale to assess job satisfaction in public universities with item response theory: a Brazilian study', International Journal of Public Sector Management, vol. 37, no. 4, pp. 486-503.
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PurposeThis study aimed to develop a psychometrically reliable job satisfaction (JS) measure for university employees, guiding administrative decisions and monitoring satisfaction over time in public universities.Design/methodology/approachA JS survey developed by a Brazilian federal university’s sustainability committee containing 58 items across physical, cognitive and organizational domains was longitudinally tested with 1,214 responses collected. The data were analyzed using Item Response Theory (IRT) analysis, employing the Graded Response Model, with tools such as frequency analysis, item characteristic curve, and full-information factor analysis in RStudio. The scale’s criterion validity was also established via expert qualitative interpretation.FindingsThe instrument’s internal consistency was confirmed as the results demonstrated its high reliability with a marginal reliability coefficient of 0.95. Significant findings revealed that recognition and supervisor relationships were key discriminators of JS and that workers began to perceive satisfaction when basic environmental conditions were met.Research limitations/implicationsIt is important to mention that the application of this scale is specifically limited to higher education institutions and may not be directly applicable to other educational settings or industry sectors without modifications.Originality/valueAlthough numerous measures and scales have been developed to assess JS, one elaborated by using IRT in a public universit...

Lee, SS, Ho, A-V, Barzegarkhoo, R, Grigoletto, FB, Siwakoti, YP & Cao, S 2024, 'Single-Phase Boost Inverters Designed Using Half-Bridges', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 11690-11695.
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Lei, B, Li, X, Guo, Y, Qu, F, Zhao, C, Tam, VWY, Wu, V & Li, W 2024, 'Recycling of copper tailing as filler material in asphalt paving mastic: A sustainable solution for mining waste recovery', Case Studies in Construction Materials, vol. 20, pp. e03237-e03237.
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Lei, B, Yang, W, Guo, Y, Wang, X, Xiong, Q, Wang, K & Li, W 2024, 'Interfacial adhesion between recycled aggregate and asphalt mastic filled with recycled concrete powder', Case Studies in Construction Materials, vol. 20, pp. e02721-e02721.
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Recycled aggregate (RA) and recycled concrete powder (RCP) hold significant potential as environmentally sustainable raw materials for asphalt mixtures. In this study, a comprehensive investigation was conducted on the bonding properties between RA and RCP-filled asphalt mastic (RCPAM). This investigation utilized an image processing-assisted modified water boiling test, binder bond strength (BBS) tests, and the surface free energy (SFE) method. The results indicate that the boiling water test method, even with the assistance of 2D image processing analysis, cannot adequately evaluate the adhesive characteristics of the RA-RCPAM interface. This limitation could be attributed to the relatively small number of samples tested and the significant variation in surface properties of RA. Increasing both the filler-to-asphalt (F/A) ratio and RCP replacement ratio adversely affected the interfacial bond strength of the RA-RCPAM interface. On the other hand, an increase in RA surface roughness contributed to a higher bond strength. Based on the experimental results, a best-fit multivariate mixed model was proposed to predict the interfacial bond strength between RCP-filled asphalt mastic and recycled aggregate within a given range of RCP replacement ratio, surface roughness, and filler-to-asphalt (F/A) ratios. The analysis of SFE suggested that moisture damage to RCPAM was caused by both cohesive and adhesive failure. Additionally, the minimal impact of adhesion work in wet condition with increasing RCP content suggested that adhesion failure energy was only marginally affected by the inclusion of RCP, even in the presence of moisture. These findings are expected to enhance the understanding of interfacial adhesion characteristics and moisture susceptibility of the RA-RCPAM interface.

Lei, B, Yu, L, Guo, Y, Xue, H, Wang, X, Zhang, Y, Dong, W, Dehn, F & Li, W 2024, 'Triaxial mechanical behaviours and life cycle assessment of sustainable multi-recycled aggregate concrete', Science of The Total Environment, vol. 923, pp. 171381-171381.
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Lei, Y, Bossut, C, Sui, Y & Zhang, Q 2024, 'Context-Free Language Reachability via Skewed Tabulation', Proceedings of the ACM on Programming Languages, vol. 8, no. PLDI, pp. 1830-1853.
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Context-free language reachability (CFL-reachability) is a prominent model for formulating program analysis problems. Almost all CFL-reachability algorithms are based on the Reps-Horwitz-Sagiv (RHS) tabulation. In essence, the RHS tabulation, based on normalized context-free grammars, is similar to the CYK algorithm for CFL-parsing. Consider a normalized rule S ::= A B and a CFL-reachability problem instance of computing S-edges in the input graph. The RHS tabulation obtains all summary edges (i.e., S-, A-, and B-edges) based on the grammar rules. However, many A- and B-edges are wasted because only a subset of those edges eventually contributes to generating S-edges in the input graph. This paper proposes a new tabulation strategy for speeding up CFL-reachability by eliminating wasted and unnecessary summary edges. We particularly focus on recursive nonterminals. Our key technical insight is that the wasted edge generations and insertions caused by recursive nonterminals can be avoided by modifying the parse trees either statically (by transforming the grammar) or dynamically (using a specialized online CFL-reachability solver). For example, if a recursive nonterminal B, generated by a rule B ::= B X, appears on the right-hand side of a rule S ::= A B, we can make S recursive (by introducing a new rule S ::= S X) and eliminate the original recursive rule (B ::= B X). Due to the rule S ::= S X, the shapes of the parse trees associated with the left-hand-side nonterminal S become more 'skewed'. Thus, we name our approach skewed tabulation for CFL-reachability. Skewed tabulation can significantly improve the scalability of CFL-reachability by reducing wasted and unnecessary summary edges. We have implemented skewed tabulation and applied the corresponding CFL-reachability algorithm to an alias analysis, a value-flow analysis, and a taint analysis. Our extensive evaluation based on SPEC 2017 benchmarks yields promising results. For th...

Lei, Y, Ye, D, Zhu, C, Shen, S, Zhou, W & Zhu, T 2024, 'A GNN-based teacher–student framework with multi-advice', Expert Systems with Applications, vol. 250, pp. 123887-123887.
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Leng, D, Lv, P, Zhu, Z, Li, Y & Liu, G 2024, 'Experimental study on semi-active magnetorheological elastomer based isolation system for offshore platform using wave tank', Ocean Engineering, vol. 292, pp. 116467-116467.
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Leng, D, You, S, Wang, R, Li, Y & Liu, G 2024, 'Fatigue damage mitigation of monopile offshore wind turbines utilizing a rotational inertia double tuned mass damper (RIDTMD) under realistic wind-wave conditions', Ocean Engineering, vol. 305, pp. 117756-117756.
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Leng, J, Wang, J, Shi, K, Cheng, J, Wen, S & Tang, Y 2024, 'Enhanced cubic function negative-determination Lemma on stability analysis for delayed neural networks via new analytical techniques', Journal of the Franklin Institute, vol. 361, no. 3, pp. 1155-1166.
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Leon-Castro, E, Blanco-Mesa, F, Hussain, W, Flores-Sosa, M & Perez-Arellano, LA 2024, 'Tax Revenue Measurement Using OWA Operators', Cybernetics and Systems, vol. 55, no. 1, pp. 230-244.
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Li, C, Fang, J, Qiu, N, Wu, C, Steven, G & Li, Q 2024, 'On fracture mechanism of additively manufactured triply periodic minimal surface structures using an explicit phase field model', Additive Manufacturing, vol. 86, pp. 104192-104192.
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Li, C, Fang, J, Qiu, N, Wu, C, Steven, G & Li, Q 2024, 'Phase field fracture in elasto-plastic solids: Considering complex loading history for crushing simulations', International Journal of Mechanical Sciences, vol. 268, pp. 108994-108994.
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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 2024, '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, C, Ni, W, Ding, M, Qu, Y, Chen, J, Smith, D, Zhang, W & Rakotoarivelo, T 2024, 'Decentralized Privacy Preservation for Critical Connections in Graphs', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Li, C, Tang, T, Pan, Y, Yang, L, Zhang, S, Chen, Z, Li, P, Gao, D, Chen, H, Li, F, Yao, D, Cao, Z & Xu, P 2024, 'An Efficient Graph Learning System for Emotion Recognition Inspired by the Cognitive Prior Graph of EEG Brain Network', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Li, C, Zhang, Y, Wu, J, Luo, Y & Yu, S 2024, 'Smart Contract-Based Decentralized Data Sharing and Content Delivery for Intelligent Connected Vehicles in Edge Computing', IEEE Transactions on Intelligent Transportation Systems, pp. 1-11.
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Li, D, Jiang, W, Ye, Y, Luo, J, Zhou, X, Yang, L, Guo, G, Wang, S, Liu, Z, Guo, W & Ngo, HH 2024, 'A change in substance and microbial community structure during the co-composting of kitchen waste anaerobic digestion effluent, sewage sludge and Chinese medicine residue', Science of The Total Environment, vol. 907, pp. 167679-167679.
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Li, D, Jiu, J, Liu, H, Yan, X, Li, X, Yan, L, Zhang, J, Fan, Z, Li, S, Du, G, Li, JJ, Du, Y, Liu, W & Wang, B 2024, 'Tissue-engineered mesenchymal stem cell constructs alleviate tendinopathy by suppressing vascularization', Bioactive Materials, vol. 36, pp. 474-489.
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Li, F, Wang, B, Zhu, L, Li, J, Zhang, Z & Chang, X 2024, 'Cross-Domain Transfer Hashing for Efficient Cross-modal Retrieval', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Li, F, Zuo, W, Zhou, K, Li, Q & Huang, Y 2024, 'State of charge estimation of lithium-ion batteries based on PSO-TCN-Attention neural network', Journal of Energy Storage, vol. 84, pp. 110806-110806.
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Li, F, Zuo, W, Zhou, K, Li, Q, Huang, Y & Zhang, G 2024, 'State-of-charge estimation of lithium-ion battery based on second order resistor-capacitance circuit-PSO-TCN model', Energy, vol. 289, pp. 130025-130025.
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Li, G, Peng, F, Wu, Z, Wang, S & Xu, RYD 2024, 'ODCL: An Object Disentanglement and Contrastive Learning Model for Few-Shot Industrial Defect Detection', IEEE Sensors Journal, vol. 24, no. 11, pp. 18568-18577.
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Li, G, Xu, P, Peng, S, Wang, C, Cai, Y & Yu, S 2024, 'TTSR: Tensor-Train Subspace Representation Method for Visual Domain Adaptation', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Li, H, Bi, K, Hao, H, Yu, Y & Xu, L 2024, 'Experimental study of a novel quasi-active negative stiffness damper system for achieving optimal active control performance', Engineering Structures, vol. 299, pp. 117082-117082.
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Li, H, Zhao, J, Huo, H, Fang, S, Chen, J, Yao, L & Hua, Y 2024, 'T3srs: Tensor Train Transformer for Compressing Sequential Recommender Systems', Expert Systems with Applications, vol. 238, pp. 122260-122260.
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Li, H, Zheng, T, Qin, W, Tian, R, Ding, H, Ji, JC & Chen, L 2024, 'Theoretical and experimental study of a bi-stable piezoelectric energy harvester under hybrid galloping and band-limited random excitations', Applied Mathematics and Mechanics, vol. 45, no. 3, pp. 461-478.
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AbstractIn the practical environment, it is very common for the simultaneous occurrence of base excitation and crosswind. Scavenging the combined energy of vibration and wind with a single energy harvesting structure is fascinating. For this purpose, the effects of the wind speed and random excitation level are investigated with the stochastic averaging method (SAM) based on the energy envelope. The results of the analytical prediction are verified with the Monte-Carlo method (MCM). The numerical simulation shows that the introduction of wind can reduce the critical excitation level for triggering an inter-well jump and make a bi-stable energy harvester (BEH) realize the performance enhancement for a weak base excitation. However, as the strength of the wind increases to a particular level, the influence of the random base excitation on the dynamic responses is weakened, and the system exhibits a periodic galloping response. A comparison between a BEH and a linear energy harvester (LEH) indicates that the BEH demonstrates inferior performance for high-speed wind. Relevant experiments are conducted to investigate the validity of the theoretical prediction and numerical simulation. The experimental findings also show that strong random excitation is favorable for the BEH in the range of low wind speeds. However, as the speed of the incoming wind is up to a particular level, the disadvantage of the BEH becomes clear and evident.

Li, J, Pan, Y & Tsang, IW 2024, '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, Yu, H, Zhang, Z, Luo, X & Xie, S 2024, 'Concept Drift Adaptation by Exploiting Drift Type', ACM Transactions on Knowledge Discovery from Data, vol. 18, no. 4, pp. 1-22.
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Concept drift is a phenomenon where the distribution of data streams changes over time. When this happens, model predictions become less accurate. Hence, models built in the past need to be re-learned for the current data. Two design questions need to be addressed in designing a strategy to re-learn models: which type of concept drift has occurred, and how to utilize the drift type to improve re-learning performance. Existing drift detection methods are often good at determining when drift has occurred. However, few retrieve information about how the drift came to be present in the stream. Hence, determining the impact of the type of drift on adaptation is difficult. Filling this gap, we designed a framework based on a lazy strategy called Type-Driven Lazy Drift Adaptor (Type-LDA). Type-LDA first retrieves information about both how and when a drift has occurred, then it uses this information to re-learn the new model. To identify the type of drift, a drift type identifier is pre-trained on synthetic data of known drift types. Furthermore, a drift point locator locates the optimal point of drift via a sharing loss. Hence, Type-LDA can select the optimal point, according to the drift type, to re-learn the new model. Experiments validate Type-LDA on both synthetic data and real-world data, and the results show that accurately identifying drift type can improve adaptation accuracy.

Li, J, Zhu, X, Chen, S & Ruan, W 2024, 'Contact-point response reconstruction for indirect bridge monitoring via Bayesian expectation-maximization based augmented Kalman filter', Engineering Structures, vol. 309, pp. 118066-118066.
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The drive-by bridge dynamic measurement using an instrumented vehicle during its passage over the bridge can be an indirect bridge structural health monitoring system. The identification of the contact-point (CP) responses between vehicle wheels and bridge structure from vehicle responses offers an efficient and economical way for the modal identification and condition assessment of short- to mid-span road bridges. This paper proposes a novel method for the contact-point displacement response reconstruction of the vehicle-bridge interaction (VBI) system in a joint input-state estimation manner based on Bayesian Expectation-Maximization (BEM). An analytical model of VBI including the road approach in front of a simply-supported bridge is presented firstly. Then the augmented state-space model of the vehicle with the contact-point responses included as variables along with vehicle states is established. A new method by integrating the augmented Kalman filter (AKF) with a BEM strategy is proposed to solve the state estimation problem without knowing the vehicle axle responses. The vehicle states and CP displacement responses are identified simultaneously. The effects of the measurement noise, road surface roughness and vehicle speed on the identified results are investigated using numerical simulations. The experimental tests are used to further verify the feasibility and accuracy of the proposed method. The results show that the proposed method has potential for drive-by bridge condition assessment using on-board vehicle response measurements for a large population of short- to mid-span bridges.

Li, K, Liu, Y, Sun, X, Tian, X & Lei, G 2024, 'Applications of Wireless Power Transfer System in Motors: A Review', IEEE Access, vol. 12, pp. 80590-80606.
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Li, K, Lu, J, Zuo, H & Zhang, G 2024, 'Multidomain Adaptation With Sample and Source Distillation', IEEE Transactions on Cybernetics, vol. 54, no. 4, pp. 2193-2205.
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Li, K, Lu, J, Zuo, H & Zhang, G 2024, 'Multi-source domain adaptation handling inaccurate label spaces', Neurocomputing, vol. 594, pp. 127824-127824.
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Li, K, Zha, X, Sun, X & Lei, G 2024, 'Model Predictive Control with Series Structure for Five-phase PMSHM Based on Discrete Space Virtual Voltage Modulation', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Li, K, Zheng, J, Yuan, X, Ni, W, Akan, OB & Poor, HV 2024, 'Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 3465-3480.
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Li, L 2024, 'Impact of the propensity to cross-cultural adaptation on online viewers' attitudes toward ethnic minority group cultural streaming: An extension of the theory of planned behaviour', Computers in Human Behavior, vol. 160, pp. 108365-108365.
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Li, L 2024, 'Online consumers build trust with online merchants through real-time interaction function', Journal of Information Economics, vol. 1, no. 4, pp. 37-48.
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<p class='MsoNormal' style='text-align: justify; text-justify: inter-ideograph; line-height: normal; mso-pagination: none; margin: 12.0pt 0cm 0cm 0cm;'><span lang='EN-US' style='font-size: 14pt; font-family: 'times new roman', times, serif;'>Given the rapid development of live streaming commerce in China, this study focuses on the interactivity and sociability of live streaming shopping activities and explores online consumers&rsquo; real-time interaction intentions and trust-building behaviours with online merchants. To discover the real-time interaction between online consumers and online merchants, this study builds a research model based on the Theory of Planned Behaviour (TPB). Through the data analysis based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the key findings state that, three factors, including attitude, subject norm, and perceived control, positively affect online consumers&rsquo; real-time interaction intentions and lead them to build trust with online merchants. Meanwhile, control variables, including gender, age, and educational background, demonstrate insignificant effects across the model. Unlike existing literature, the current study pays much attention to the interactive characteristics of live streaming shopping activities and can provide some valuable suggestions both for online consumers and online merchants, which can promote the co-development of the commercial and social aspects of live streaming platforms.</span></p>

Li, L & Kang, K 2024, 'Effect of the Social and Cultural Control on Young Eastern Ethnic Minority Groups’ Online-Startup Motivation', Entrepreneurship Research Journal, vol. 14, no. 2, pp. 491-514.
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Abstract This study is developed based on particular social and cultural backgrounds and discovers young Eastern ethnic minority groups’ (EMGs) online-startup motivation on live streaming platforms. Drawing on the Hofstede cultural dimensions, this paper explores various influencing factors, including peers’ support, conservative thinking and family support. It analyses young Eastern EMGs’ entrepreneurial motivation and behaviour based on the Stimulus, Organism and Response (S-O-R) model. Compared with traditional research models, the combination of the Hofstede cultural theory and the S-O-R model could be conducive to make the research model reflect influencing factors and present their specific relationships. By analysing 531 valid online questionnaires based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the paper proves that peers’ support and family support can reduce young EMGs’ conservative thinking and positively affect young people EMGs’ online-startup motivation. Based on the analysis results, some suggestions are provided for related departments, aiming to enhance young EMGs’ online-startup confidence.

Li, L & Kang, K 2024, 'Impact of opportunity and capability on e-entrepreneurial motivation: a comparison of urban and rural perspectives', Journal of Entrepreneurship in Emerging Economies, vol. 16, no. 4, pp. 932-953.
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Purpose: E-entrepreneurship is developed based on digital platforms, having specific technical opportunities, such as the interactive ecosystem, fast payment method and online store function, without strict requirements for online entrepreneurs. Considering China’s e-entrepreneurship environment and cultural background, this paper aims to analyse individuals’ e-entrepreneurship motivation based on the capability–opportunity–motivation–behaviour (COM-B) behaviour changing theory. Design/methodology/approach: Through testing 602 samples based on the partial least squares path modelling and variance-based structural equation modelling, the factors from the opportunity and capability units positively affect individuals’ e-entrepreneurship motivation. Meanwhile, because of the economic and social environmental differences between China’s urban and rural regions, this study promotes the multi-group analysis based on individuals’ regional backgrounds. Findings: First, as opportunity factors, technical and policy opportunities have significantly positive relationships with individuals’ e-entrepreneurship motivation. Second, entrepreneurial and cultural capabilities are essential for Chinese entrepreneurs while making an entrepreneurial decision. Third, because of the e-entrepreneurial environment difference and educational system gap, entrepreneurial capability exerts a greater influence on the e-entrepreneurship motivation for Chinese individuals from urban regions, and cultural capability exerts a higher impact on the e-entrepreneurship motivation for Chinese individuals from rural regions. Originality/value: Whilst the phenomenon of e-entrepreneurship is emerging as a popular entrepreneurship area of study, little research has systematically explored individuals’ e-entrepreneurial motivation and analysed influencing factors from macro and minor aspects. According to the COM-B behaviour changing theory, this paper discovers influencing factors from environmen...

Li, L & Kang, K 2024, 'Impact of opportunity and capability on e-entrepreneurial motivation: a comparison of urban and rural perspectives', Journal of Entrepreneurship in Emerging Economies, vol. 16, no. 4, pp. 932-953.
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PurposeE-entrepreneurship is developed based on digital platforms, having specific technical opportunities, such as the interactive ecosystem, fast payment method and online store function, without strict requirements for online entrepreneurs. Considering China’s e-entrepreneurship environment and cultural background, this paper aims to analyse individuals’ e-entrepreneurship motivation based on the capability–opportunity–motivation–behaviour (COM-B) behaviour changing theory.Design/methodology/approachThrough testing 602 samples based on the partial least squares path modelling and variance-based structural equation modelling, the factors from the opportunity and capability units positively affect individuals’ e-entrepreneurship motivation. Meanwhile, because of the economic and social environmental differences between China’s urban and rural regions, this study promotes the multi-group analysis based on individuals’ regional backgrounds.FindingsFirst, as opportunity factors, technical and policy opportunities have significantly positive relationships with individuals’ e-entrepreneurship motivation. Second, entrepreneurial and cultural capabilities are essential for Chinese entrepreneurs while making an entrepreneurial decision. Third, because of the e-entrepreneurial environment difference and educational system gap, entrepreneurial capability exerts a greater influence on the e-entrepreneurship motivation for Chinese individuals from urban regions, and cultural capability exerts a higher impact on the e-entrepreneurship motivation for Chinese individuals from rural regions.Originality/value

Li, L, Kang, K, Feng, Y & Zhao, A 2024, 'Factors affecting online consumers’ cultural presence and cultural immersion experiences in live streaming shopping', Journal of Marketing Analytics, vol. 12, no. 2, pp. 250-263.
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Li, L, Mortazavi, M, Far, H, El-Sherbeeny, AM & Ahmadian Fard Fini, A 2024, 'Simulation and modeling of polymer concrete panels using deep neural networks', Case Studies in Construction Materials, vol. 20, pp. e02912-e02912.
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Li, L, Wang, W, Zhou, T, Quan, R & Yang, Y 2024, 'Semantic Hierarchy-Aware Segmentation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 4, pp. 2123-2138.
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Li, L, Xiao, J, Shi, H, Wang, W, Shao, J, Liu, A-A, Yang, Y & Chen, L 2024, 'Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 1, pp. 195-206.
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Li, M, Cao, Z & Li, Z 2024, 'Augmented Mixed Vehicular Platoon Control With Dense Communication Reinforcement Learning for Traffic Oscillation Alleviation', IEEE Internet of Things Journal, pp. 1-1.
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Li, M, Chen, S, Shen, Y, Liu, G, Tsang, IW & Zhang, Y 2024, 'Online Multi-Agent Forecasting With Interpretable Collaborative Graph Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 4768-4782.
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Li, M, Li, Z & Cao, Z 2024, 'Enhancing Car-Following Performance in Traffic Oscillations Using Expert Demonstration Reinforcement Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 7, pp. 7751-7766.
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Li, M, Wang, H, Chen, Q & Zong, Z 2024, 'Event-Based Weak Oil Impurity Detection', IEEE Access, vol. 12, pp. 18868-18876.
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Li, M, Xu, Y & Fang, J 2024, 'Orthotropic mechanical properties of PLA materials fabricated by fused deposition modeling', Thin-Walled Structures, vol. 199, pp. 111800-111800.
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Li, M, Yang, Y, Zhang, Y & Iacopi, F 2024, '3-D Printed Vertically Integrated Composite Right/Left-Handed Transmission Line and Its Applications to Microwave Circuits', IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 6, pp. 3311-3321.
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Li, P, Gao, X, Li, C, Yi, C, Huang, W, Si, Y, Li, F, Cao, Z, Tian, Y & Xu, P 2024, 'Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Li, S, Feng, K, Xu, Y, Li, Y, Ni, Q, Zhang, K, Wang, Y & Ding, W 2024, 'Cross-modal zero-sample diagnosis framework utilizing non-contact sensing data fusion', Information Fusion, vol. 110, pp. 102453-102453.
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Li, S, Ji, JC, Xu, Y, Feng, K, Zhang, K, Feng, J, Beer, M, Ni, Q & Wang, Y 2024, 'Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults', Mechanical Systems and Signal Processing, vol. 210, pp. 111142-111142.
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Li, T, Walker, P, Khonasty, R, van de Graaf, VA, Yelf, E, Zhao, L & Huang, S 2024, 'Robotic‐assisted burring in total hip replacement: A new surgical technique to optimise acetabular preparation', The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 20, no. 1.
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AbstractBackgroundIn Total Hip replacement (THR) surgery, a critical step is to cut an accurate hemisphere into the acetabulum so that the component can be fitted accurately and obtain early stability. This study aims to determine whether burring rather than reaming the acetabulum can achieve greater accuracy in the creation of this hemisphere.MethodsA preliminary robotic system was developed to demonstrate the feasibility of burring the acetabulum using the Universal Robot (UR10). The study will describe mechanical design, robot trajectory optimisation, control algorithm development, and results from phantom experiments compared with both robotic reaming and conventional reaming. The system was also tested in a cadaver experiment.ResultsThe proposed robotic burring system can produce a surface in 2 min with an average error of 0.1 and 0.18 mm, when cutting polyurethane bone block #15 and #30, respectively. The performance was better than robotic reaming and conventional hand reaming.ConclusionThe proposed robotic burring system outperformed robotic and conventional reaming methods to produce an accurate acetabular cavity. The findings show the potential usage of a robotic‐assisted burring in THR for acetabular preparation.

Li, W, Guo, Y, Zhang, X, Dong, W, Li, X, Yu, T & Wang, K 2024, 'Development of self-sensing ultra-high-performance concrete using hybrid carbon black and carbon nanofibers', Cement and Concrete Composites, vol. 148, pp. 105466-105466.
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Li, X, Wang, Y, Shi, J, Zhao, Z, Wang, D, Chen, Z, Cheng, L, Lu, G-H, Liang, Y, Dong, H, Shan, X, Liu, B, Chen, C, Liu, Y, Liu, F, Sun, L-D, Zhong, X & Wang, F 2024, 'Large-Area Near-Infrared Emission Enhancement on Single Upconversion Nanoparticles by Metal Nanohole Array', Nano Letters, vol. 24, no. 19, pp. 5831-5837.
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Li, X, Wang, Z, Hu, Y, Huang, Y, Xiang, L & Cheng, X 2024, 'Experimental and numerical studies of the evaporation and combustion characteristics of large-angle impinging sprays', Applied Thermal Engineering, vol. 246, pp. 122918-122918.
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Li, Y, Meng, X, Wang, Z, Lin, X, Xu, Y, Mou, J, Zhou, R, Tang, Y, Sze Ki Lin, C & Li, X 2024, 'Utilization of tofu wastewater and Nannochloropsis oceanica for eutrophication mitigation and eicosapentaenoic acid valorization: Advancing carbon neutrality and resource recycling', Chemical Engineering Journal, vol. 493, pp. 152706-152706.
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Li, Y, Sun, X, Chen, H, Zhang, S, Yang, Y & Xu, G 2024, 'Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Li, Y, Zhang, J, Cheng, D, Guo, W, Liu, H, Guo, A, Chen, X, Wang, Y & Ngo, HH 2024, 'Magnetic biochar serves as adsorbents and catalyst supports for the removal of antibiotics from wastewater: A review', Journal of Environmental Management, vol. 366, pp. 121872-121872.
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Li, Y, Zhao, H, Hu, Y, Qu, F, Zhu, D, Wang, K & Li, W 2024, 'Effect of pore water pressure on mechanical performance of recycled aggregate concrete under triaxial compression', Cement and Concrete Composites, vol. 146, pp. 105402-105402.
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Li, Z, Chen, Y, Wang, X, Yao, L & Xu, G 2024, 'Multi-view GCN for loan default risk prediction', Neural Computing and Applications, vol. 36, no. 20, pp. 12149-12162.
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AbstractAs a significant application of machine learning in financial scenarios, loan default risk prediction aims to evaluate the client’s default probability. However, most existing deep learning solutions treat each application as an independent individual, neglecting the explicit connections among different application records. Besides, these attempts suffer from the problem of missing data and imbalanced distribution (i.e., the default records are small samples against all the applications). We believe similar records could provide some auxiliary signals, which are of critical importance to alleviate the data missing issue and facilitate data argumentation. To this end, we propose multi-view loan application graphs, dubbed MLAGs. By evaluating the similarity between the records, a loan application graph can be constructed. Furthermore, we arrange different similarity thresholds to organize various graph structures for multi-graph constructions; thus, a variety of representations can be generated via information propagation and aggregation for small sample argumentation. Consequently, the imbalanced data distribution and missing values issues can be alleviated effectively. We conduct experiments on three public datasets from real-world home credit and P2P lending platforms, which show that MGCN outperforms both conventional and deep learning models. Ablation studies also illustrated the validity of each module design.

Li, Z, Lee, T-U, Pietroni, N, Snooks, R & Xie, YM 2024, 'Design and construction of catenary-ruled surfaces', Structures, vol. 59, pp. 105755-105755.
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Li, Z-R, Lv, T-R, Yang, Z, Zhang, W-H, Yin, M-J, Yong, K-T & An, Q-F 2024, '3D microprinting of QR-code integrated hydrogel tactile sensor for real-time E-healthcare', Chemical Engineering Journal, vol. 484, pp. 149375-149375.
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Lian, M, Guo, Z, Wen, S & Huang, T 2024, 'Distributed Adaptive Algorithm for Resource Allocation Problem Over Weight-Unbalanced Graphs', IEEE Transactions on Network Science and Engineering, vol. 11, no. 1, pp. 416-426.
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Lian, M, Guo, Z, Wen, S & Huang, T 2024, 'Distributed Predefined-Time Algorithm for System of Linear Equations Over Directed Networks', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 4, pp. 2139-2143.
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Liang, H, Xu, W, Chiclana, F, Yu, S, Dong, Y & Herrera-Viedma, EE 2024, 'Evolution of Credit Scores of Enterprises in a Social Network: A Perspective Based on Opinion Dynamics', IEEE Transactions on Computational Social Systems, vol. 11, no. 3, pp. 3637-3651.
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Liang, M, Huang, S & Liu, W 2024, 'Dynamic semantic structure distillation for low-resolution fine-grained recognition', Pattern Recognition, vol. 148, pp. 110216-110216.
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Liang, R, Zhang, Q, Wang, J & Lu, J 2024, 'A Hierarchical Attention Network for Cross-Domain Group Recommendation', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 3, pp. 3859-3873.
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Many online services allow users to participate in various group activities such as online meeting or group buying, and thus need to provide user groups with services that they are interested. The group recommender systems (GRSs) emerge as required and provide personalized services for various online user groups. Data sparsity is an important issue in GRSs, since even fewer group-item interactions are observed. Moreover, the group and the group members have complex and mutual relationships with each other, which exacerbates the difficulty in modeling the preferences of both a group and its members for recommendation. The cross-domain recommender system (CDRS) is a solution to alleviate data sparsity and assist preference modeling by transferring knowledge from a source domain which has relatively dense data to another. The existing CDRSs are usually developed for individual users and cannot be directly applied for group recommendation. To alleviate the data sparsity issue in GRSs, we first study the cross-domain group recommendation problem and propose a hierarchical attention network-based cross-domain group recommendation method, called HAN-CDGR. HAN-CDGR takes the advantage of data from a source domain to benefit recommendation generation for both the individual users and groups in the target domain which has data sparsity and cannot generate accurate recommendation. In HAN-CDGR, a hierarchical attention network is constructed to learn and model individual and group preferences, with consideration of both group members' interactions and dynamic weights and the complex relationships between individuals and groups. Adversarial learning is used to effectively transfer knowledge from a source domain to the target domain. Extensive experiments, which demonstrate the effectiveness and superiority of our proposal, providing accurate recommendation for both individual users and groups, are conducted on three tasks.

Liang, Y, Wu, W, Li, H, Chang, X, Chen, X, Peng, J & Xu, P 2024, 'DCS-Gait: A Class-Level Domain Adaptation Approach for Cross-Scene and Cross-State Gait Recognition Using Wi-Fi CSI', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 2997-3007.
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Liang, Y, Zhang, C, An, S, Wang, Z, Shi, K, Peng, T, Ma, Y, Xie, X, He, J & Zheng, K 2024, 'FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification', Journal of Neural Engineering, vol. 21, no. 3, pp. 036011-036011.
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Abstract Objective. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting. Approach. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets. Main results. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios. Significance. FetchEEG is a novel hybrid method based on EEG for emotion cla...

Liang, Y, Zhu, L, Wang, X & Yang, Y 2024, 'IcoCap: Improving Video Captioning by Compounding Images', IEEE Transactions on Multimedia, vol. 26, pp. 4389-4400.
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Liang, Y, Zhu, L, Wang, X & Yang, Y 2024, 'Penalizing the Hard Example But Not Too Much: A Strong Baseline for Fine-Grained Visual Classification', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 5, pp. 7048-7059.
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Though significant progress has been achieved on fine-grained visual classification (FGVC), severe overfitting still hinders model generalization. A recent study shows that hard samples in the training set can be easily fit, but most existing FGVC methods fail to classify some hard examples in the test set. The reason is that the model overfits those hard examples in the training set, but does not learn to generalize to unseen examples in the test set. In this article, we propose a moderate hard example modulation (MHEM) strategy to properly modulate the hard examples. MHEM encourages the model to not overfit hard examples and offers better generalization and discrimination. First, we introduce three conditions and formulate a general form of a modulated loss function. Second, we instantiate the loss function and provide a strong baseline for FGVC, where the performance of a naive backbone can be boosted and be comparable with recent methods. Moreover, we demonstrate that our baseline can be readily incorporated into the existing methods and empower these methods to be more discriminative. Equipped with our strong baseline, we achieve consistent improvements on three typical FGVC datasets, i.e., CUB-200-2011, Stanford Cars, and FGVC-Aircraft. We hope the idea of moderate hard example modulation will inspire future research work toward more effective fine-grained visual recognition.

Lin, A, Li, J, Xiang, Y, Bian, W & Prasad, M 2024, 'Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics', IEEE Transactions on Intelligent Vehicles, pp. 1-11.
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Lin, C-T, Zhang, H, Ou, L, Chang, Y-C & Wang, Y-K 2024, 'Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 1, pp. 229-239.
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Lin, J-Y, Wong, S-W, Qian, L, Wang, Y, Yang, Y & Liu, Q-H 2024, 'Single and Multiple-Band Bandpass Filters Using Bandstop Resonator Sections', IEEE Journal of Microwaves, vol. 4, no. 2, pp. 293-302.
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Lin, S, Liu, A, Wang, J & Kong, X 2024, 'An improved fault-tolerant cultural-PSO with probability for multi-AGV path planning', Expert Systems with Applications, vol. 237, pp. 121510-121510.
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This paper presents a hybrid evolutionary algorithm, cultural-particle swarm optimization (C-PSO), which is inspired by the cultural algorithm and the particle swarm optimization algorithm. It is aimed to balance the performance of exploration and exploitation and avoid trapping in the local optima. It introduces a probabilistic approach to update the inertia weight based on the improved metropolis rule. Generating the optimal path without collisions is challenging to ensure vehicles operate safely in real-time implementation. The contributions of C-PSO are to solve the path planning problem of multiple vehicles in modern industrial warehouses, achieving task allocation, fault tolerance and collision avoidance by a dual-layer framework. It was compared with the other algorithms, including PSO, PSO-GA, CA, HS, ABC, HPSGWO, TS and MA, by CEC benchmark functions and statistical tests to demonstrate its great performance with fewer iterations and runtime and the best solutions. It is validated through computational experiments, which involve 15 AGVs and 20 tasks for demonstration.

Lin, W, Yin, X, Wang, S & Khosravi, MR 2024, 'A Blockchain-enabled decentralized settlement model for IoT data exchange services', Wireless Networks, vol. 30, no. 5, pp. 3453-3467.
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IoT data exchange services are emerging to connect various and distributed IoT data sources, which facilitate data owners exchange their IoT data flexibly. Traditional IoT data exchange services rely on a centralized third-party to negotiate the settlement of trades between data consumers and providers. Such settlement model suffers from issues including single point of failure, extra settlement fee, un-transparency of settlement details, to name a few. New settlement model is imperative to overcome such limitations. Blockchain is an innovative technology that is competent in governing the decentralized network, which poses great opportunity to implement fair settlement in decentralized manner. In this paper, we propose a Blockchain-enabled settlement model for decentralized IoT data exchange services. Firstly, Bitcoin-based time commitment scheme is adopted to build fair and autonomous settlement model. Furthermore, to enhance the accountability of all transactions, an optimized practical Byzantine fault tolerant consensus protocol named ReBFT, is proposed to enable all members involved in the IoT data exchange application achieve identical shared ledger recording all transactions. Finally, experiments are conducted to verify the feasibility of our proposal.

Lin, X, Castel, A, Deng, Z, Dong, B, Zhang, X, Zhang, S & Li, W 2024, 'Effect of crystalline admixtures on shrinkage and alkali-silica reaction of biochar-cementitious composites', Developments in the Built Environment, vol. 18, pp. 100456-100456.
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Lin, Y, Li, L, Zhang, J & Wang, J 2024, 'A scenario-based stochastic model predictive control approach for microgrid operation at an Australian cotton farm under uncertainties', International Journal of Electrical Power & Energy Systems, vol. 159, pp. 110025-110025.
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Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2024, 'Optimal Electric Vehicle Charging Strategies for Long-Distance Driving', IEEE Transactions on Vehicular Technology, vol. 73, no. 4, pp. 4949-4960.
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Electric vehicles (EVs) provide sustainable and eco-friendly transportation. However, long-range driving is challenging due to limited battery capacity. While charging stations can replenish the battery, they are not as widely deployed as gas stations, causing the battery depletion of EVs. This paper presents a novel optimal charging strategy for EV long-range driving. The strategy offers EV drivers the optimal charging instructions for a given trip. A new finite-horizon Markov decision process (FH-MDP) problem is formulated to minimize the travel time of an EV while preventing its battery depletion, by optimally selecting the charging stations and charging times along the trip. By confirming the monotonicity and subadditivity of the FH-MDP, we prove the existence of a monotone deterministic Markovian policy for the optimal charging decision and reveal the optimal charging time is monotone regarding the remaining battery level and driven distance. We also reveal that the optimal charging time only changes when either of two thresholds regarding the remaining battery level or driving distance is met. By comparing its state with the thresholds, the EV can make optimal decisions with linear complexity. Simulations corroborate that our algorithm can save travel time by at least 12.6% under our simulation settings, compared to alternative methods.

Liu, C, Zhang, H, Wang, S, Wang, Y, Lei, G & Zhu, J 2024, 'Multiphysical Design and Optimization of High-Speed Permanent Magnet Synchronous Motor with Sinusoidal Segmented Permanent Magnet Structure', Journal of Electrical Engineering & Technology, vol. 19, no. 3, pp. 1459-1473.
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Liu, D, Balaguer, C, Dissanayake, G & Kovac, M 2024, 'Preface', Infrastructure Robotics: Methodologies, Robotic Systems and Applications, p. xix.

Liu, D, Tsang, IW & Yang, G 2024, 'A Convergence Path to Deep Learning on Noisy Labels', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5170-5182.
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Liu, G, Zhang, W, Wang, X, King, S & Yu, S 2024, 'A Membership Inference and Adversarial Attack Defense Framework for Network Traffic Classifiers', IEEE Transactions on Artificial Intelligence, pp. 1-16.
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Liu, G-Y, Li, J-P, Indraratna, B & Zhou, P 2024, 'A hydraulic-mechanical (HM) coupling constitutive model for unsaturated soil-continuum interfaces considering bonding effect', Computers and Geotechnics, vol. 166, pp. 105989-105989.
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Liu, H, Li, X, Zhang, Z, Li, J, Zhou, T, Wang, Z & Wang, Q 2024, 'Urine pretreatment enhances energy recovery by boosting medium-chain fatty acids production from waste activate sludge through anaerobic fermentation', Chemical Engineering Journal, vol. 482, pp. 148842-148842.
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Liu, H, Zhang, Z, Li, X, Zhou, T, Wang, Z, Li, J, Li, Y & Wang, Q 2024, 'Temperature-phased anaerobic sludge digestion effectively removes antibiotic resistance genes in a full-scale wastewater treatment plant', Science of The Total Environment, vol. 924, pp. 171555-171555.
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Liu, J, Chen, G, Wen, S & Zhu, S 2024, 'Finite-time piecewise control for discrete-time stochastic nonlinear time-varying systems with time-varying delays', Chaos, Solitons & Fractals, vol. 184, pp. 114982-114982.
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Liu, J, Chen, H & Peng, X 2024, 'Efficacy and Safety of Thermocoagulation vs. Cryotherapy for Cervical Precancerous Lesions: A Systematic Review and Meta-Analysis', Clinical and Experimental Obstetrics & Gynecology, vol. 51, no. 3, pp. 72-72.
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Liu, J, Dong, L, Li, C, Fang, J, Chen, Y & Cui, J 2024, 'Quasi-static and dynamic tensile behaviour of 316L stainless steels: Rolled versus laser-powder bed fusion (LPBF) fabricated samples', International Journal of Impact Engineering, vol. 190, pp. 104972-104972.
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Liu, J, Wei, J, Li, J, Su, Y & Wu, C 2024, 'A comprehensive review of ultra-high performance concrete (UHPC) behaviour under blast loads', Cement and Concrete Composites, vol. 148, pp. 105449-105449.
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Liu, J, Wu, K, Su, T & Chen, S-L 2024, 'A Versatile Digital Antenna Array Transceiving System With Online Self-Calibration', IEEE Antennas and Wireless Propagation Letters, pp. 1-5.
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Liu, J, Wu, K, Su, T & Zhang, JA 2024, 'Practical frequency-hopping MIMO joint radar communications: design and experiment', Digital Communications and Networks.
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Liu, J, Wu, K, Su, T, Pang, G & Chen, S-L 2024, 'Near-Field Calibration of Millimeter-Wave Massive MIMO Antenna Array Using Sphere Reflectors', IEEE Antennas and Wireless Propagation Letters, pp. 1-5.
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Liu, L, He, Y, Li, Q, Cao, C, Huang, M, Ma, D, Wu, X & Zhu, Q 2024, 'Self‐supported bimetallic array superstructures for high‐performance coupling electrosynthesis of formate and adipate', Exploration, vol. 4, no. 3.
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AbstractThe coupling electrosynthesis involving CO2 upgrade conversion is of great significance for the sustainable development of the environment and energy but is challenging. Herein, we exquisitely constructed the self‐supported bimetallic array superstructures from the Cu(OH)2 array architecture precursor, which can enable high‐performance coupling electrosynthesis of formate and adipate at the anode and the cathode, respectively. Concretely, the faradaic efficiencies (FEs) of CO2‐to‐formate and cyclohexanone‐to‐adipate conversion simultaneously exceed 90% at both electrodes with excellent stabilities. Such high‐performance coupling electrosynthesis is highly correlated with the porous nanosheet array superstructure of CuBi alloy as the cathode and the nanosheet‐on‐nanowire array superstructure of CuNi hydroxide as the anode. Moreover, compared to the conventional electrolysis process, the cell voltage is substantially reduced while maintaining the electrocatalytic performance for coupling electrosynthesis in the two‐electrode electrolyzer with the maximal FEformate and FEadipate up to 94.2% and 93.1%, respectively. The experimental results further demonstrate that the bimetal composition modulates the local electronic structures, promoting the reactions toward the target products. Prospectively, our work proposes an instructive strategy for constructing adaptive self‐supported superstructures to achieve efficient coupling electrosynthesis.

Liu, M & Chang, X 2024, 'Normalized ground state solutions for nonlinear Schrödinger equations with general Sobolev critical nonlinearities', Discrete and Continuous Dynamical Systems - S, vol. 0, no. 0, pp. 0-0.
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Liu, M, Tu, Z, Su, T, Wang, X, Xu, X & Wang, Z 2024, 'BehaviorNet: A Fine-grained Behavior-aware Network for Dynamic Link Prediction', ACM Transactions on the Web, vol. 18, no. 2, pp. 1-26.
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Dynamic link prediction has become a trending research subject because of its wide applications in the web, sociology, transportation, and bioinformatics. Currently, the prevailing approach for dynamic link prediction is based on graph neural networks, in which graph representation learning is the key to perform dynamic link prediction tasks. However, there are still great challenges because the structure of graphs evolves over time. A common approach is to represent a dynamic graph as a collection of discrete snapshots, in which information over a period is aggregated through summation or averaging. This way results in some fine-grained time-related information loss, which further leads to a certain degree of performance degradation. We conjecture that such fine-grained information is vital because it implies specific behavior patterns of nodes and edges in a snapshot. To verify this conjecture, we propose a novel fine-grained behavior-aware network (BehaviorNet) for dynamic network link prediction. Specifically, BehaviorNet adapts a transformer-based graph convolution network to capture the latent structural representations of nodes by adding edge behaviors as an additional attribute of edges. GRU is applied to learn the temporal features of given snapshots of a dynamic network by utilizing node behaviors as auxiliary information. Extensive experiments are conducted on several real-world dynamic graph datasets, and the results show significant performance gains for BehaviorNet over several state-of-the-art (SOTA) discrete dynamic link prediction baselines. Ablation study validates the effectiveness of modeling fine-grained edge and node behaviors.

Liu, Q, Cheng, S, Sun, C, Chen, K, Li, W & Tam, VWY 2024, 'Steel cable bonding in fresh mortar and 3D printed beam flexural behavior', Automation in Construction, vol. 158, pp. 105165-105165.
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Liu, Q, Li, X, Yuan, D, Yang, C, Chang, X & He, Z 2024, 'LSOTB-TIR: A Large-Scale High-Diversity Thermal Infrared Single Object Tracking Benchmark', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 7, pp. 9844-9857.
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Unlike visual object tracking, thermal infrared (TIR) object tracking methods can track the target of interest in poor visibility such as rain, snow, and fog, or even in total darkness. This feature brings a wide range of application prospects for TIR object-tracking methods. However, this field lacks a unified and large-scale training and evaluation benchmark, which has severely hindered its development. To this end, we present a large-scale and high-diversity unified TIR single object tracking benchmark, called LSOTB-TIR, which consists of a tracking evaluation dataset and a general training dataset with a total of 1416 TIR sequences and more than 643 K frames. We annotate the bounding box of objects in every frame of all sequences and generate over 770 K bounding boxes in total. To the best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object tracking benchmark to date. We spilt the evaluation dataset into a short-term tracking subset and a long-term tracking subset to evaluate trackers using different paradigms. What’s more, to evaluate a tracker on different attributes, we also define four scenario attributes and 12 challenge attributes in the short-term tracking evaluation subset. By releasing LSOTB-TIR, we encourage the community to develop deep learning-based TIR trackers and evaluate them fairly and comprehensively. We evaluate and analyze 40 trackers on LSOTB-TIR to provide a series of baselines and give some insights and future research directions in TIR object tracking. Furthermore, we retrain several representative deep trackers on LSOTB-TIR, and their results demonstrate that the proposed training dataset significantly improves the performance of deep TIR trackers. Codes and dataset are available at https://github.com/QiaoLiuHit/LSOTB-TIR.

Liu, S, Huang, X, Mu, H, Zheng, M, Kuang, S, Chen, H, Xu, Y, Wang, D, Liu, H & Li, X 2024, 'Biogeography and diversity patterns of functional genes associated with C, N, P, S cycling processes across China classical sea sediments', Science of The Total Environment, vol. 906, pp. 167678-167678.
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Liu, T, Lu, J, Yan, Z & Zhang, G 2024, 'Robust Gaussian Process Regression With Input Uncertainty: A PAC-Bayes Perspective', IEEE Transactions on Cybernetics, vol. 54, no. 2, pp. 962-973.
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The Gaussian process (GP) algorithm is considered as a powerful nonparametric-learning approach, which can provide uncertainty measurements on the predictions. The standard GP requires clearly observed data, unexpected perturbations in the input may lead to learned regression model mismatching. Besides, GP also suffers from the lack of good generalization performance guarantees. To deal with data uncertainty and provide a numerical generalization performance guarantee on the unknown data distribution, this article proposes a novel robust noisy input GP (NIGP) algorithm based on the probably approximately correct (PAC) Bayes theory. Furthermore, to reduce the computational complexity, we develop a sparse NIGP algorithm, and then develop a sparse PAC-Bayes NIGP approach. Compared with NIGP algorithms, instead of maximizing the marginal log likelihood, one can optimize the PAC-Bayes bound to pursue a tighter generalization error upper bound. Experiments verify that the NIGP algorithms can attain greater accuracy. Besides, the PAC-NIGP algorithms proposed herein can achieve both robust performance and improved generalization error upper bound in the face of both uncertain input and output data.

Liu, T, Zhang, W, Wang, L, Ueland, M, Forbes, SL, Zheng, WX & Su, SW 2024, 'Numerical Differentiation From Noisy Signals: A Kernel Regularization Method to Improve Transient-State Features for the Electronic Nose', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 6, pp. 3497-3511.
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Liu, W, Xia, R, Lin, X, Wang, Z, Ansari, AJ, Li, G & Luo, W 2024, 'Tracking fouling layer formation in membrane distillation of landfill leachate concentrate: Insights from periodic membrane autopsies', Journal of Membrane Science, vol. 693, pp. 122331-122331.
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Liu, X, Chen, Z, Lu, S, Shi, X, Qu, F, Cheng, D, Wei, W, Shon, HK & Ni, B-J 2024, 'Persistent free radicals on biochar for its catalytic capability: A review', Water Research, vol. 250, pp. 120999-120999.
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Liu, X, Jiao, Q, Qiao, S, Yan, Z, Wen, S & Wang, P 2024, 'A Hybrid Monotonic Neural Network Approach for Multi-Area Power System Load Frequency Control Against FGSM Attack', IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1-1.
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Liu, X, Shen, H, Yu, J, Luo, F, Li, T, Li, Q, Yuan, X, Sun, Y & Zhou, Z 2024, 'Resolving the heterogeneous tumour microenvironment in cardiac myxoma through single‐cell and spatial transcriptomics', Clinical and Translational Medicine, vol. 14, no. 2.
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AbstractBackgroundCardiac myxoma (CM) is the most common (58%–80%) type of primary cardiac tumours. Currently, there is a need to develop medical therapies, especially for patients not physically suitable for surgeries. However, the mechanisms that shape the tumour microenvironment (TME) in CM remain largely unknown, which impedes the development of targeted therapies. Here, we aimed to dissect the TME in CM at single‐cell and spatial resolution.MethodsWe performed single‐cell transcriptomic sequencing and Visium CytAssist spatial transcriptomic (ST) assays on tumour samples from patients with CM. A comprehensive analysis was performed, including unsupervised clustering, RNA velocity, clonal substructure inference of tumour cells and cell–cell communication.ResultsUnsupervised clustering of 34 759 cells identified 12 clusters, which were assigned to endothelial cells (ECs), mesenchymal stroma cells (MSCs), and tumour‐infiltrating immune cells. Myxoma tumour cells were found to encompass two closely related phenotypic states, namely, EC‐like tumour cells (ETCs) and MSC‐like tumour cells (MTCs). According to RNA velocity, our findings suggest that ETCs may be directly differentiated from MTCs. The immune microenvironment of CM was found to contain multiple factors that promote immune suppression and evasion, underscoring the potential of using immunotherapies as a treatment option. Hyperactive signals sent primarily by tumour cells were identified, such as MDK, HGF, chemerin, and GDF15 signalling. Finally, the ST assay uncovered spatial features of the subclusters, proximal cell–cell communication, and clonal evolution of myxoma tumour cells.ConclusionsOur study presents the first comprehensive characte...

Liu, X, Zhao, Y, Wen, S, Chen, B & Ge, SS 2024, 'Motion segmentation with event camera: N-patches optical flow estimation and Pairwise Markov Random Fields', Expert Systems with Applications, vol. 254, pp. 124342-124342.
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Liu, Y, Cui, G, Luo, J, Chang, X & Yao, L 2024, 'Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 5, pp. 1-22.
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As a fundamental aspect of human life, two-person interactions contain meaningful information about people’s activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased body occlusion and overlap compared to single-person actions. In this article, we propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. To achieve this, we introduce a designed frame selection method named Interval Frame Sampling (IFS), which efficiently samples frames from videos, capturing more discriminative information in a relatively short processing time. Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions. Finally, we apply a transformer to perform self-attention on the learned features for the final classification. Extensive experiments are conducted on two large-scale datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The results show that our network outperforms state-of-the-art approaches in most standard evaluation settings.

Liu, Y, Cui, X, Zhang, X, Ren, J, Li, H, Wang, Z, Guo, W & Ngo, HH 2024, 'Recent advances and trends of carbon-based biocarriers for performance enhancement of anaerobic membrane bioreactor system', Journal of Water Process Engineering, vol. 59, pp. 104949-104949.
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Liu, Y, Feng, Y, Wu, Z, Alamdari, MM, Wu, D, Luo, Z, Chen, X & Gao, W 2024, 'Dynamic crack propagation in elasto-plastic materials using phase-field virtual modelling method', Computer Methods in Applied Mechanics and Engineering, vol. 429, pp. 117160-117160.
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Liu, Y, Lee, T-U, Javan, AR, Pietroni, N & Xie, YM 2024, 'Reducing the Number of Different Faces in Free-Form Surface Approximations Through Clustering and Optimization.', Comput. Aided Des., vol. 166, pp. 103633-103633.
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Liu, Y, Li, B, Wang, X, Sammut, C & Yao, L 2024, 'Attention-Aware Social Graph Transformer Networks for Stochastic Trajectory Prediction', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Liu, Y, Wang, S, Huo, J, Zhang, X, Wen, H, Zhang, D, Zhao, Y, Kang, D, Guo, W & Ngo, HH 2024, 'Adsorption recovery of phosphorus in contaminated water by calcium modified biochar derived from spent coffee grounds', Science of The Total Environment, vol. 909, pp. 168426-168426.
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Liu, Y, Yang, Y, He, Y, Xin, C, Ren, F & Yu, Y 2024, 'Experimental investigation on effects of ultrasonic process parameters on the degree of impregnation of BF/PP composites', Materials Research Express, vol. 11, no. 4, pp. 045303-045303.
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Abstract The properties of basalt fiber reinforced polypropylene composites (BF/PP) were improved by ultrasonic treatment of resin building pressure to assist melt impregnation. Combined with the study of ultrasonic pressure building theory, the mechanical properties of the modified composites were analyzed using the characterization of tensile, flexural and impact strengths in response to porosity and fracture rate. The effects of ultrasonic power, frequency and distance of action on resin building pressure and composite properties were investigated. The results showed that the best effect was achieved when the ultrasonic frequency was 25 kHz, the ultrasonic power was 300 W, and the action distance was 4 mm, at which time the porosity of the prepreg was reduced to 2.99%, the fracture rate was 3.36%, and the tensile, flexural, and impact strengths were 108.73 MPa, 116.81 MPa, and 51.59 KJ.m−2.

Liu, Y, Yuan, Y, Wang, Y, Ngo, HH & Wang, J 2024, 'Research and application of active species based on high-valent iron for the degradation of pollutants: A critical review', Science of The Total Environment, vol. 924, pp. 171430-171430.
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Liu, Y, Zhang, J, Cheng, D, Guo, W, Liu, X, Chen, Z, Zhang, Z & Ngo, HH 2024, 'Fate and mitigation of antibiotics and antibiotic resistance genes in microbial fuel cell and coupled systems', Science of The Total Environment, vol. 938, pp. 173530-173530.
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Liu, Y, Zhang, X, Zeng, Z & Yu, S 2024, 'FedEco: Achieving Energy-Efficient Federated Learning by Hyperparameter Adaptive Tuning', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Liu, Y, Zhu, L, Wang, X, Yamada, M & Yang, Y 2024, '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, Li, Y, Yao, L, Chang, X, Fang, W, Wu, X & Saddik, AE 2024, 'Simple Primitives With Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-Shot Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 1, pp. 543-560.
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Liu, Z, Wang, Q, Fatahi, B, Khabbaz, H, Sheng, D & Wu, D 2024, 'Hybrid uncertain buckling analysis for engineering structures through machine learning method', Engineering Structures, vol. 310, pp. 118083-118083.
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Liu, Z, Xiao, F, Lin, C-T & Cao, Z 2024, 'A Robust Evidential Multisource Data Fusion Approach Based on Cooperative Game Theory and Its Application in EEG', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 2, pp. 729-740.
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Liu, Z, Zhang, G & Lu, J 2024, 'Semi-supervised heterogeneous domain adaptation for few-sample credit risk classification', Neurocomputing, vol. 596, pp. 127948-127948.
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Lu, D, Qu, F, Punetha, P, Zeng, X, Luo, Z & Li, W 2024, 'Graphene oxide nano-engineered recycled aggregate concrete for sustainable construction: A critical review', Developments in the Built Environment, vol. 18, pp. 100444-100444.
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Lu, J, Ma, G & Zhang, G 2024, 'Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review', IEEE Transactions on Fuzzy Systems, vol. 32, no. 7, pp. 3861-3878.
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Lu, Q, Zhu, L, Xu, X, Whittle, J, Zowghi, D & Jacquet, A 2024, 'Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering', ACM Computing Surveys, vol. 56, no. 7, pp. 1-35.
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Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific challenges of our time and is key to increase the adoption of Artificial Intelligence (AI). Recently, a number of AI ethics principles frameworks have been published. However, without further guidance on best practices, practitioners are left with nothing much beyond truisms. In addition, significant efforts have been placed at algorithm level rather than system level, mainly focusing on a subset of mathematics-amenable ethical principles, such as fairness. Nevertheless, ethical issues can arise at any step of the development lifecycle, cutting across many AI and non-AI components of systems beyond AI algorithms and models. To operationalize RAI from a system perspective, in this article, we present an RAI Pattern Catalogue based on the results of a multivocal literature review. Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The RAI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and RAI-by-design product patterns. These patterns provide systematic and actionable guidance for stakeholders to implement RAI.

Lu, W, Wei, S, Peng, X, Wang, Y-F, Naseem, U & Wang, S 2024, 'Medical Question Summarization with Entity-driven Contrastive Learning', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 4, pp. 1-19.
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By summarizing longer consumer health questions into shorter and essential ones, medical question-answering systems can more accurately understand consumer intentions and retrieve suitable answers. However, medical question summarization is very challenging due to obvious distinctions in health trouble descriptions from patients and doctors. Although deep learning has been applied to successfully address the medical question summarization (MQS) task, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance. To address these challenges, this article proposes a novel medical question summarization framework based on e ntity-driven c ontrastive l earning (ECL). ECL employs medical entities present in frequently asked questions (FAQs) as focuses and devises an effective mechanism to generate hard negative samples. This approach compels models to focus on essential information and consequently generate more accurate question summaries. Furthermore, we have discovered that some MQS datasets, such as the iCliniq dataset with a 33% duplicate rate, have significant data leakage issues. To ensure an impartial evaluation of the related methods, this article carefully examines leaked samples to reorganize more reasonable datasets. Extensive experiments demonstrate that our ECL method outperforms the existing methods and achieves new state-of-the-art performance, i.e., 52.85, 43.16, 41.31, 43.52 in terms of ROUGE-1 metric on MeQSum, CHQ-Summ, iCliniq, HealthCareMagic dataset, respectively. The code and datasets are available at https://git...

Lu, X, Qiu, J, Lei, G & Zhu, J 2024, 'An Interval Prediction Method for Day-Ahead Electricity Price in Wholesale Market Considering Weather Factors', IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 2558-2569.
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Lu, X, Qiu, J, Zhang, C, Lei, G & Zhu, J 2024, 'Assembly and Competition for Virtual Power Plants With Multiple ESPs Through a “Recruitment–Participation” Approach', IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 4382-4396.
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Lu, X, Qiu, J, Zhang, C, Lei, G & Zhu, J 2024, 'Seizing unconventional arbitrage opportunities in virtual power plants: A profitable and flexible recruitment approach', Applied Energy, vol. 358, pp. 122628-122628.
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Lu, Y, Cai, B, Tang, X, Liu, L, Du, J, Yu, S, Atiquzzaman, M & Dustdar, S 2024, 'Tree-ORAP: A Tree-Based Oblivious Random-Access Protocol for Privacy-Protected Blockchain', IEEE Transactions on Services Computing, vol. 17, no. 3, pp. 1252-1264.
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Lu, Y, Ni, F, Wang, H, Guo, X, Zhu, L, Yang, Z, Song, R, Cheng, L & Yang, Y 2024, 'Show Me a Video: A Large-Scale Narrated Video Dataset for Coherent Story Illustration', IEEE Transactions on Multimedia, vol. 26, pp. 2456-2466.
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Lu, Y, Zhao, G, Xu, C & Yu, S 2024, 'An Efficient Hypergraph-Based Routing Algorithm in Time-Sensitive Networks', IEEE Signal Processing Letters, vol. 31, pp. 835-839.
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Luo, J, Zou, K, Luo, Q, Li, Q & Sun, G 2024, 'On asymmetric failure in additively manufactured continuous carbon fiber reinforced composites', Composites Part B: Engineering, vol. 284, pp. 111605-111605.
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Luo, Y, Li, M, Wen, G, Tan, Y & Shi, C 2024, 'SHIP-YOLO: A Lightweight Synthetic Aperture Radar Ship Detection Model Based on YOLOv8n Algorithm', IEEE Access, vol. 12, pp. 37030-37041.
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Lv, L, Wei, Z, Li, W, Chen, J, Tian, Y, Gao, W, Wang, P, Sun, L, Ren, Z, Zhang, G, Liu, X & Ngo, HH 2024, 'Regulation of extracellular polymers based on quorum sensing in wastewater biological treatment from mechanisms to applications: A critical review', Water Research, vol. 250, pp. 121057-121057.
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Lv, Y, Liu, Z, Li, G & Chang, X 2024, 'Noise-Aware Intermediary Fusion Network for Off-Road Freespace Detection', IEEE Transactions on Intelligent Vehicles, pp. 1-11.
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Lyu, B, Wang, S, Wen, S, Shi, K, Yang, Y, Zeng, L & Huang, T 2024, '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 2024, 'Designing Efficient Bit-Level Sparsity-Tolerant Memristive Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. PP, no. 99, pp. 1-10.
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Lyu, B, Yang, Y, Cao, Y, Shi, T, Chen, Y, Huang, T & Wen, S 2024, 'A memristive all-inclusive hypernetwork for parallel analog deployment of full search space architectures', Neural Networks, vol. 175, pp. 106312-106312.
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Lyu, B, Yang, Y, Cao, Y, Wang, P, Zhu, J, Chang, J & Wen, S 2024, 'Efficient multi-objective neural architecture search framework via policy gradient algorithm', Information Sciences, vol. 661, pp. 120186-120186.
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Lyu, X, Wang, W, Li, H, Li, J & Yu, Y 2024, 'Numerical and experimental analysis on the axial compression performance of T-shaped concrete-filled thin-walled steel', Steel and Composite Structures, vol. 50, no. 4, pp. 383-401.
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The research comprehensively studies the axial compression performance of T-shaped concrete-filled thin-walled steel tubular (CTST) long columns after fire exposure. Initially, a series of tests investigate the effects of heating time, load eccentricity, and stiffeners on the column's performance. Furthermore, Finite Element (FE) analysis is employed to establish temperature and mechanical field models for the T-shaped CTST long column with stiffeners after fire exposure, using carefully determined key parameters such as thermal parameters, constitutive relations, and contact models. In addition, a parametric analysis based on the numerical models is conducted to explore the effects of heating time, section diameter, material strength, and steel ratio on the axial compressive bearing capacity, bending bearing capacity under normal temperature, as well as residual bearing capacity after fire exposure. The results reveal that the maximum lateral deformation occurs near the middle of the span, with bending increasing as heating time and eccentricity rise. Despite a decrease in axial compressive load and bending capacity after fire exposure, the columns still exhibit desirable bearing capacity and deformability. Moreover, the obtained FE results align closely with experimental findings, validating the reliability of the developed numerical models. Additionally, this study proposes a simplified design method to calculate these mechanical property parameters, satisfying the ISO-834 standard. The relative errors between the proposed simplified formulas and FE models remain within 10%, indicating their capability to provide a theoretical reference for practical engineering applications.

M.S., S, Elmakki, T, Schipper, K, Ihm, S, Yoo, Y, Park, B, Park, H, Shon, HK & Han, DS 2024, 'Integrated seawater hub: A nexus of sustainable water, energy, and resource generation', Desalination, vol. 571, pp. 117065-117065.
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Ma, B, Li, Y, Zheng, J, Zhang, J, Huang, S, Zhu, J & Lei, G 2024, 'Multiphysics Topology Optimization of SynRMs Considering Control Performance and Machinability', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Ma, C, Shi, Q, Hua, B, Zhang, Y, Xu, Z, Chu, L, Braun, R & Shi, J 2024, 'Noncontact Heartbeat and Respiratory Signal Separation Using a Sub 6 GHz SDR Micro-Doppler Radar', IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, vol. 8, no. 2, pp. 122-134.
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Ma, G, Lu, J & Zhang, G 2024, 'Multisource Domain Adaptation With Interval-Valued Target Data via Fuzzy Neural Networks', IEEE Transactions on Fuzzy Systems, vol. 32, no. 5, pp. 3094-3106.
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Ma, G, Lu, J, Fang, Z, Liu, F & Zhang, G 2024, 'Multiview Classification Through Learning From Interval-Valued Data', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Ma, G, Lu, J, Liu, F, Fang, Z & Zhang, G 2024, 'Domain Adaptation With Interval-Valued Observations: Theory and Algorithms', IEEE Transactions on Fuzzy Systems, vol. 32, no. 5, pp. 3107-3120.
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Ma, G, Lu, J, Liu, F, Fang, Z & Zhang, G 2024, 'Multiclass Classification With Fuzzy-Feature Observations: Theory and Algorithms', IEEE Transactions on Cybernetics, vol. 54, no. 2, pp. 1048-1061.
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The theoretical analysis of multiclass classification has proved that the existing multiclass classification methods can train a classifier with high classification accuracy on the test set, when the instances are precise in the training and test sets with same distribution and enough instances can be collected in the training set. However, one limitation with multiclass classification has not been solved: how to improve the classification accuracy of multiclass classification problems when only imprecise observations are available. Hence, in this article, we propose a novel framework to address a new realistic problem called multiclass classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations. First, we give the theoretical analysis of the MCIMO problem based on fuzzy Rademacher complexity. Then, two practical algorithms based on support vector machine and neural networks are constructed to solve the proposed new problem. The experiments on both synthetic and real-world datasets verify the rationality of our theoretical analysis and the efficacy of the proposed algorithms.

Ma, H, Li, L, Fan, K, Guo, Y, Jin, Z & Luo, J 2024, 'A novel programmed hybrid modulation for electromagnetic interference mitigation and current ripple control in miniaturised synchronous machine drives', IET Electric Power Applications, vol. 18, no. 5, pp. 527-542.
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AbstractA novel programmed hybrid spreading spectrum modulation method to mitigate electromagnetic interference (EMI) emission and bearing current in the context of miniaturised and lightweight synchronous machine drives is proposed. The proposed method adopts the combination of conventional pulsewidth modulation (PWM) and low common mode voltage PWM. The offline look‐up table for switching frequency is acquired by analysing the root mean square voltage values at different modulation indices and angles. By adjusting the switching frequency according to this table, the spectrum can be spread into the sidebands of the switching frequency, and the harmonic spike can be suppressed. To address the challenge of increased current ripple and harmonic distortion in high torque density motors under overload conditions, a motor model that considers the cross‐saturation and slotting effect is presented. This motor model is used to derive the switching frequency correction factors, aiming to mitigate the influence of saturation and slotting effect on harmonic amplification. The proposed method is well‐suited for controlling synchronous motors using three‐phase two‐level voltage source inverters. The simulation and experimental results validate the effectiveness of the proposed method, showcasing its advantages in terms of computational complexity reduction and significant attenuation of peak EMI currents.

Ma, X, Huang, M, Ni, W, Yin, M, Min, J & Jamalipour, A 2024, 'Balancing Time and Energy Efficiency by Sizing Clusters: A New Data Collection Scheme in UAV-Aided Large-Scale Internet of Things', IEEE Internet of Things Journal, vol. 11, no. 6, pp. 9355-9367.
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Ma, X, Li, Y, Yang, Z, Li, S & Li, Y 2024, 'Lightweight network for millimeter-level concrete crack detection with dense feature connection and dual attention', Journal of Building Engineering, vol. 94, pp. 109821-109821.
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Mahafujur Rahaman, M, Bhowmick, S, Pada Ghosh, B, Xu, F, Nath Mondal, R & Saha, SC 2024, 'Transient natural convection flows and heat transfer in a thermally stratified air-filled trapezoidal cavity', Thermal Science and Engineering Progress, vol. 47, pp. 102377-102377.
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Mahmud, T, Nag, P, Molla, MM & Saha, SC 2024, 'Convective heat transfer efficacy of an experimentally observed non-Newtonian MWCNT-Fe3O4-EG hybrid nanofluid in a driven enclosure with a heated cylinder', International Journal of Thermal Sciences, vol. 204, pp. 109203-109203.
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Mai, TNA, Ali, S, Hossain, MS, Chen, C, Ding, L, Chen, Y, Solntsev, AS, Mou, H, Xu, X, Medhekar, N & Tran, TT 2024, 'Cryogenic Thermal Shock Effects on Optical Properties of Quantum Emitters in Hexagonal Boron Nitride', ACS Applied Materials & Interfaces, vol. 16, no. 15, pp. 19340-19349.
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Solid-state quantum emitters are vital building blocks for quantum information science and quantum technology. Among various types of solid-state emitters discovered to date, color centers in hexagonal boron nitride have garnered tremendous traction in recent years, thanks to their environmental robustness, high brightness, and room-temperature operation. Most recently, these quantum emitters have been employed for satellite-based quantum key distribution. One of the most important requirements to qualify these emitters for space-based applications is their optical stability against cryogenic thermal shock. Such an understanding has, however, remained elusive to date. Here, we report on the effects caused by such thermal shock that induces random, irreversible changes in the spectral characteristics of the quantum emitters. By employing a combination of structural characterizations and density functional calculations, we attribute the observed changes to lattice strain caused by cryogenic temperature shock. Our study sheds light on the stability of the quantum emitters under extreme conditions─similar to those countered in outer space.

Maidi, AM, Kalam, MA & Begum, F 2024, 'Detection of different drinkable milk using photonic crystal fibre biosensor in IR regime', Physica Scripta, vol. 99, no. 3, pp. 035516-035516.
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Abstract A simplified PCF sensor has been designed to detect the different drinkable milk that includes camel, cow and buffalo milk, and can also assess its quality. The sensor features a singular circular core design and two layers octagonal cladding air holes that was analysed using the Finite Element Method technique in COMSOL Multiphysics software and determine the sensing and optical performance parameters: power fraction, relative sensitivity, confinement loss, effective area, numerical aperture, V-Parameter, spot size, and beam divergence. At the optimum wavelength of 6.0 μm, the relative sensitivities are 96.58%, 96.78%, and 96.84%, and confinement losses of 3.51 × 10−8 dB/m, 1.47 × 10−8 dB m−1, and 8.59 × 10−9 dB/m, for camel, cow, and buffalo milk, respectively. The efficacy of the proposed PCF structure for sensing applications in the dairy industry in distinguishing between different types of milk is evidenced by these findings. Moreover, the results of confinement loss and chromatic dispersion suggest potential applications of this design in optical communication.

Maidi, AM, Salam, R, Kalam, MA & Begum, F 2024, 'Design and simulation of photonic crystal fibre sensor for harmful chemicals detection in polycarbonate plastics', Optical and Quantum Electronics, vol. 56, no. 1.
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Majnooni, S, Fooladi, M, Nikoo, MR, Al-Rawas, G, Haghighi, AT, Nazari, R, Al-Wardy, M & Gandomi, AH 2024, 'Smarter water quality monitoring in reservoirs using interpretable deep learning models and feature importance analysis', Journal of Water Process Engineering, vol. 60, pp. 105187-105187.
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Makepeace, RW, Tabandeh, A, Hossain, MJ & Asaduz-Zaman, M 2024, 'Techno-economic analysis of green hydrogen export', International Journal of Hydrogen Energy, vol. 56, pp. 1183-1192.
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Makhdoom, I, Abolhasan, M, Lipman, J, Piccardi, M & Franklin, D 2024, 'PrivySeC: A secure and privacy-compliant distributed framework for personal data sharing in IoT ecosystems', Blockchain: Research and Applications, pp. 100220-100220.
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Malisetty, RS & Indraratna, B 2024, 'Critical speed of ballasted railway tracks: Influence of ballast and subgrade degradation', Transportation Geotechnics, vol. 46, pp. 101246-101246.
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Manandhar, B, Paudel, KR, Clarence, DD, De Rubis, G, Madheswaran, T, Panneerselvam, J, Zacconi, FC, Williams, KA, Pont, LG, Warkiani, ME, MacLoughlin, R, Oliver, BG, Gupta, G, Singh, SK, Chellappan, DK, Hansbro, PM & Dua, K 2024, 'Zerumbone-incorporated liquid crystalline nanoparticles inhibit proliferation and migration of non-small-cell lung cancer in vitro', Naunyn-Schmiedeberg's Archives of Pharmacology, vol. 397, no. 1, pp. 343-356.
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AbstractLung cancer is the second most prevalent type of cancer and is responsible for the highest number of cancer-related deaths worldwide. Non-small-cell lung cancer (NSCLC) makes up the majority of lung cancer cases. Zerumbone (ZER) is natural compound commonly found in the roots of Zingiber zerumbet which has recently demonstrated anti-cancer activity in both in vitro and in vivo studies. Despite their medical benefits, ZER has low aqueous solubility, poor GI absorption and oral bioavailability that hinders its effectiveness. Liquid crystalline nanoparticles (LCNs) are novel drug delivery carrier that have tuneable characteristics to enhance and ease the delivery of bioactive compounds. This study aimed to formulate ZER-loaded LCNs and investigate their effectiveness against NSCLC in vitro using A549 lung cancer cells. ZER-LCNs, prepared in the study, inhibited the proliferation and migration of A549 cells. These inhibitory effects were superior to the effects of ZER alone at a concentration 10 times lower than that of free ZER, demonstrating a potent anti-cancer activity of ZER-LCNs. The underlying mechanisms of the anti-cancer effects by ZER-LCNs were associated with the transcriptional regulation of tumor suppressor genes P53 and PTEN, and metastasis-associated gene KRT18. The protein array data showed downregulation of several proliferation associated proteins such as AXL, HER1, PGRN, and BIRC5 and metastasis-associated proteins such as DKK1, CAPG, CTSS, CTSB, CTSD, and PLAU. This study provides evidence of potential for increasing the potency and effectiveness of ZER with LCN formulation and developing ZER-LCNs as a treatment strategy for mitigation and treatment of NSCLC.

Manasa, P, Ananth, P, Natarajan, P, Somasundaram, K, Rajkumar, ER, Ravichandran, KS, Balasubramanian, V & Gandomi, AH 2024, 'An analysis of causative factors for road accidents using partition around medoids and hierarchical clustering techniques', Engineering Reports, vol. 6, no. 6.
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AbstractInsufficient progress in the development of national highways and state highways, coupled with a lack of public awareness regarding road safety, has resulted in prevalent traffic congestion and a high rate of accidents. Understanding the dominant and contributing factors that may influence road traffic accident severity is essential. This study identified the primary causes and the most significant target‐specific causative factors for road accident severity. A modified partitioning around medoids model determined the dominant road accident features. These clustering algorithms will extract hidden information from the road accident data and generate new features for our implementation. Then, the proposed method is compared with the other state‐of‐the‐art clustering techniques with three performance metrics: the silhouette coefficient, the Davies–Bouldin index, and the Calinski–Harabasz index. This article's main contribution is analyzing six different scenarios (different angles of the problem) concerning grievous and non‐injury accidents. This analysis provides deeper insights into the problem and can assist transport authorities in Tamil Nadu, India, in deriving new rules for road traffic. The output of different scenarios is compared with hierarchical clustering, and the overall clustering of the proposed method is compared with other clustering algorithms. Finally, it is proven that the proposed method outperforms other recently developed techniques.

Manikandan, G, Pragadeesh, B, Manojkumar, V, Karthikeyan, AL, Manikandan, R & Gandomi, AH 2024, 'Classification models combined with Boruta feature selection for heart disease prediction', Informatics in Medicine Unlocked, vol. 44, pp. 101442-101442.
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Marghade, D, Shelare, S, Prakash, C, Soudagar, MEM, Yunus Khan, TM & Kalam, MA 2024, 'Innovations in metal-organic frameworks (MOFs): Pioneering adsorption approaches for persistent organic pollutant (POP) removal', Environmental Research, vol. 258, pp. 119404-119404.
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Martin, A, Hill, AJ, Seiler, KM & Balamurali, M 2024, 'Automatic excavator action recognition and localisation for untrimmed video using hybrid LSTM-Transformer networks', International Journal of Mining, Reclamation and Environment, vol. 38, no. 5, pp. 353-372.
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Mastio, EA, Clegg, SR, Pina e Cunha, M & Dovey, K 2024, 'Leadership Ignoring Paradox to Maintain Inertial Order', Journal of Change Management, vol. 24, no. 2, pp. 83-101.
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Matin, A, Islam, MR, Zhu, Y, Wang, X, Huo, H & Xu, G 2024, 'Hybrid Deep Learning for Assembly Action Recognition in Smart Manufacturing', International Journal of Computer Vision & Signal Processing, vol. 14, no. 1, pp. 9-17.

Matos, PH, Muggleton, JM, Brennan, MJ, Almeida, FCL, Campos, BC, Paupitz, PJ, Iwanaga, MK, Karimi, M & Rustighi, E 2024, 'Enhancing Leak Location in Buried Water Pipes using Array Signal Processing Techniques: the Effect of Wave Velocity Variation', Journal of Physics: Conference Series, vol. 2647, no. 8, pp. 082011-082011.
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Abstract Leakage in buried pipelines is a significant cause of water wastage in distribution systems, resulting in water losses ranging from 30% to 50% in many countries. To address this issue, techniques have been developed to detect leaks in buried pipes over the last few decades. The leak detection procedure typically involves three steps: (1) leak detection, which involves analysis of water pressure/flow measurements along the pipelines; (2) estimation of the approximate region where the leak occurred through local pressure variations; and (3) pinpointing the estimated location of the leak to perform maintenance procedures. Acoustic pinpointing techniques are among the most effective ones to deal with the latter step. These techniques exploit the delays in time of arrival of acoustic waves, caused by the leak, between different sensors placed around the suspected leak. By calculating the cross-spectral densities (CSDs) between sensors and analysing their phase difference over frequency, it is possible to infer the estimated location of the radiating source. Existing methods rely on access points to the pipeline through correlators. However, the buried pipe acts as a radiating source, and its location could also be estimated through ground vibration signals. Although array signal processing techniques applied to source localization are well-established in the acoustics field, their adaptation to the vibroacoustic field is less well developed. Among the many challenges, the identification of wave velocity is one of the most troublesome. In this paper, the effect of the wave velocity variation on the leak pinpointing is investigated and tested against numerical data. Results show that the estimation of the leak position is sensitive to the wave velocity variabilities. The pinpointing error is found to be more significant in terms of the depth of the pipe, compared to the error on the ground surface.

McCourt, LR, Routley, BS, Ruppert, MG & Fleming, AJ 2024, 'Feasibility of gold nanocones for collocated tip‐enhanced Raman spectroscopy and atomic force microscope imaging', Journal of Raman Spectroscopy, vol. 55, no. 3, pp. 336-346.
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AbstractMicrocantilever probes for tip‐enhanced Raman spectroscopy (TERS) have a grainy metal coating that may exhibit multiple plasmon hotspots near the tip apex, which may compromise spatial resolution and introduce imaging artefacts. It is also possible that the optical hotspot may not occur at the mechanical apex, which introduces an offset between TERS and atomic force microscope maps. In this article, a gold nanocone TERS probe is designed and fabricated for 638 nm excitation. The imaging performance is compared to grainy probes by analysing high‐resolution TERS cross‐sections of single‐walled carbon nanotubes. Compared to the tested conventional TERS probes, the nanocone probe exhibited a narrow spot diameter, comparable optical contrast, artefact‐free images, and collocation of TERS and atomic force microscope topographic maps. The 1/ spot diameter was 12.5 nm and 19 nm with 638 nm and 785 nm excitation, respectively. These results were acquired using a single gold nanocone probe to experimentally confirm feasibility. Future work will include automating the fabrication process and statistical analysis of many probes.

Medawela, S & Indraratna, B 2024, 'PRB technology incorporating acidic ground conditions and bio-geochemical clogging – A critical review', Australian Geomechanics Journal, vol. 59, no. 2, pp. 33-56.
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Groundwater acidity resulting from pyrite oxidation in acid sulphate soil terrain presents a severe threat to the environment. The exposure of low-lying acidic coastal belts to the atmospheric oxygen, exacerbated by phreatic surface lowering in dry seasons and activities like infrastructure development and agriculture, leads to pyrite oxidation and sulfuric acid production in soil. This paper reviews the challenges posed by acid sulphate soils by emphasising the environmental and infrastructure damage caused by acidic soil leaching into water bodies. Permeable reactive barriers (PRBs) have emerged as a promising method of passive treatment for mitigating groundwater acidity in pyritic terrain. This review mainly focuses on the effectiveness of PRBs in low-lying floodplains by addressing the bio-geochemical clogging that diminishes the reactivity and porosity of PRBs over time. This paper also summarises the numerical methods needed to design PRBs in acidic terrains by identifying gaps in current research that could enhance the accuracy of future PRB designs. This comprehensive review contains valuable insights into the ongoing efforts of addressing the challenges associated with groundwater contamination in regions containing acid sulphate soil.

Mehdipour, H, Amini, E, Naeeni, STO, Neshat, M & Gandomi, AH 2024, 'Optimization of power take-off system settings and regional site selection procedure for a wave energy converter', Energy Conversion and Management: X, vol. 22, pp. 100559-100559.
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Mehmood, A, Raja, MAZ, Jalili, M & Ling, SH 2024, 'Identification of fractional Hammerstein model for electrical stimulated muscle: An application of fuzzy-weighted differential evolution.', Biomed. Signal Process. Control., vol. 87, pp. 105545-105545.

Mehta, M, Bui, TA, Care, A & Deng, W 2024, 'Targeted polymer lipid hybrid nanoparticles for in-vitro siRNA therapy in triple-negative breast cancer', Journal of Drug Delivery Science and Technology, vol. 98, pp. 105911-105911.
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Mekala, MS, Srivastava, G, Gandomi, AH, Park, JH & Jung, H-Y 2024, 'A Quantum-Inspired Sensor Consolidation Measurement Approach for Cyber-Physical Systems', IEEE Transactions on Network Science and Engineering, vol. 11, no. 1, pp. 511-524.
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Meng, Y, Qiu, J, Zhang, C, Lei, G & Zhu, J 2024, 'A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints', Applied Energy, vol. 356, pp. 122396-122396.
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Meraj, T, Sharif, MI, Raza, M, Alabrah, A, Kadry, S & Gandomi, AH 2024, 'Computer vision-based plants phenotyping: A comprehensive survey', iScience, vol. 27, no. 1, pp. 108709-108709.
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Milano, J, Ong, HC, Ong, ZC, Ghadyani, G, Ismail, ZB, Veza, I, Masudi, A, Tiong, SK & Silitonga, AS 2024, 'Strategies in the application of nanoadditives to achieve high-performance diesel, biodiesels, and their blends', Fuel Communications, vol. 19, pp. 100111-100111.
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Min, C, Akther, N, Lee, T, Choo, Y, Naidu, G, Han, D-S, Kim, S-H & Shon, HK 2024, 'Atmospheric water harvesting by osmotic distillation and direct contact membrane distillation using hydrophobic hollow fiber membranes', Process Safety and Environmental Protection, vol. 182, pp. 527-534.
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Mirdad, AR, Khan, AM & Hussain, FK 2024, 'Smart contracts and marketplace for just-in-time management of pharmaceutical drugs', International Journal of Web and Grid Services, vol. 20, no. 1, pp. 25-53.
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Mishra, DK, Eskandari, M, Abbasi, MH, Sanjeevikumar, P, Zhang, J & Li, L 2024, 'A detailed review of power system resilience enhancement pillars', Electric Power Systems Research, vol. 230, pp. 110223-110223.
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Mishra, DK, Wang, J, Li, L, Zhang, J & Hossain, MJ 2024, 'Resilience-Driven Scheme in Multiple Microgrids With Secure Transactive Energy System Framework', IEEE Transactions on Industry Applications, vol. 60, no. 2, pp. 2277-2289.
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Mishra, S, Indraratna, B, Rujikiatkamjorn, C & Ngo, T 2024, 'Use of recycled tyre segments to enhance the stability of ballasted track by increased confinement', Canadian Geotechnical Journal, vol. 61, no. 7, pp. 1385-1398.
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The most common railway ballast is produced by quarrying, and its mechanical characteristics are crucial for both stability and drainage for safer and faster rail operations. Ballasted tracks have certain drawbacks, primarily because ballast starts to degrade over time. In this regard, reducing the rate of ballast degradation is vital to enhance track longevity and minimise maintenance costs. This study demonstrates how segments of huge waste rubber tyres (e.g., 3 m in diameter) from the mining industry can be used to improve the stability of tracks, while contributing to reduced ballast deformation and degradation. By placing arched segments cut from these tyres along the track shoulders beyond the edge of sleepers (i.e., the plan view gives a schematic impression of a caterpillar), the in-situ lateral confining pressure can be increased from 20–25 (standard track) to 40–50 kPa. This novel idea of confined-caterpillar track (CCT) was tested at a prototype physical model (1:1 scale) at the National Facility for the Heavy-haul Railroad Testing, and the experimental outcomes were compared with the performance of a conventional track. Apart from constributing to at least 25% saving of quarried aggregates, the test results prove that the CCT concept can curtail the lateral displacement and settlement of the ballast layer, while reducing particle breakage and affecting significant stress reduction in the underlying substructure layers.

Moazzen, F, Alikhani, M, Aghaei, J & Hossain, MJ 2024, 'Social welfare evaluation during demand response programs execution considering machine learning-based load profile clustering', Applied Energy, vol. 357, pp. 122518-122518.
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Mofijur, M, Ahmed, SF, Ahmed, B, Mehnaz, T, Mehejabin, F, Shome, S, Almomani, F, Chowdhury, AA, Kalam, MA, Badruddin, IA & Kamangar, S 2024, 'Impact of nanoparticle-based fuel additives on biodiesel combustion: An analysis of fuel properties, engine performance, emissions, and combustion characteristics', Energy Conversion and Management: X, vol. 21, pp. 100515-100515.
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Mofijur, M, Hasan, MM, Ahmed, SF, Djavanroodi, F, Fattah, IMR, Silitonga, AS, Kalam, MA, Zhou, JL & Khan, TMY 2024, 'Advances in identifying and managing emerging contaminants in aquatic ecosystems: Analytical approaches, toxicity assessment, transformation pathways, environmental fate, and remediation strategies', Environmental Pollution, vol. 341, pp. 122889-122889.
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Mohamad Aziz, NA, Mohamed, H, Kania, D, Ong, HC, Zainal, BS, Junoh, H, Ker, PJ & Silitonga, AS 2024, 'Bioenergy production by integrated microwave-assisted torrefaction and pyrolysis', Renewable and Sustainable Energy Reviews, vol. 191, pp. 114097-114097.
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Mohammadi, M, Oberst, S & Halkon, BJ 2024, 'Application of Time Synchronous Averaging in Mitigating UAV Noise and Signal Loss for Continuous Scanning Laser Doppler Vibrometry', Journal of Physics: Conference Series, vol. 2698, no. 1, pp. 012005-012005.
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Abstract The laser Doppler vibrometer (LDV) has been shown to be effective for a wide application of vibration assessments that are well accepted. One of the new avenues for exploring alternative measurement scenarios, mounting LDVs on unmanned aerial vehicles (UAVs) is emerging as a potential avenue for remote and harsh environment measurements. Such configurations grapple with the challenge of the LDV sensor head being sensitive to UAV vibration during flight and signal loss due to tracking error. This study investigates the effectiveness of several Time Synchronous Averaging (TSA) techniques to circumvent these obstacles. Through comprehensive evaluations, all three TSA techniques under investigation demonstrated significant potential in suppressing UAV-induced noise and minimising the effects of signal dropout. Traditional TSA showcased a remarkable sixfold enhancement in signal quality when analysed via the mean square error. However, the study also highlighted that while TSA and Multi-Cycle Time Synchronous Average (MCTSA) elevated signal clarity, there is a trade-off between noise suppression and signal duration. Additionally, the findings emphasise the importance of synchronisation between scanning and target vibration. To achieve optimal results in Continuous Scanning Laser Doppler Vibrometer measurements, there is a need for advanced algorithms capable of estimating target vibration and synchronising scanning in real-time. As the study was rooted in steady-state vibrations, future research should explore transient vibration scenarios, thereby broadening the application scope of TSA techniques in UAV-mounted LDV systems.

Mohanty, N, Behera, BK & Ferrie, C 2024, 'Solving the vehicle routing problem via quantum support vector machines', Quantum Machine Intelligence, vol. 6, no. 1.
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AbstractThe vehicle routing problem (VRP) is an example of a combinatorial optimization problem that has attracted academic attention due to its potential use in various contexts. VRP aims to arrange vehicle deliveries to several sites in the most efficient and economical manner possible. Quantum machine learning offers a new way to obtain solutions by harnessing the natural speedups of quantum effects, although many solutions and methodologies are modified using classical tools to provide excellent approximations of the VRP. In this paper, we employ 6 and 12 qubit circuits, respectively, to build and evaluate a hybrid quantum machine learning approach for solving VRP of 3- and 4-city scenarios. The approach employs quantum support vector machines (QSVMs) trained using a variational quantum eigensolver on a static or dynamic ansatz. Different encoding strategies are used in the experiment to transform the VRP formulation into a QSVM and solve it. Multiple optimizers from the IBM Qiskit framework are also evaluated and compared

Mojiri, A, Vishkaei, MN, Zhou, JL, Trzcinski, AP, Lou, Z, Kasmuri, N, Rezania, S, Gholami, A, Vakili, M & Kazeroon, RA 2024, 'Impact of polystyrene microplastics on the growth and photosynthetic efficiency of diatom Chaetoceros neogracile', Marine Environmental Research, vol. 194, pp. 106343-106343.
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Mondal, MIH, Chandra Chakraborty, S, Rahman, MS, Marjuban, SMH, Ahmed, F, Zhou, JL, Ahmed, MB & Zargar, M 2024, 'Adsorbents from rice husk and shrimp shell for effective removal of heavy metals and reactive dyes in water', Environmental Pollution, vol. 346, pp. 123637-123637.
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Mora, A, Leiva, F, Cardenas, R, Rojas, F, Pereda, J, Aguilera, RP & Travieso-Torres, JC 2024, 'Optimal Switching Sequence MPC for Four-Leg Two-Level Grid-Connected Converters', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 12, no. 2, pp. 1271-1281.
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Mortazavi, H, Beni, HM, Nadooshan, AA, Islam, MS & Ghalambaz, M 2024, '4E analysis and triple objective NSGA-II optimization of a novel solar-driven combined ejector-enhanced power and two-stage cooling (EORC-TCRC) system', Energy, vol. 294, pp. 130803-130803.
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Muhammad, G, Butler, TO, Chen, B, Lv, Y, Xiong, W, Zhao, X, Solovchenko, AE, Zhao, A, Mofijur, M, Xu, J & Alam, MA 2024, 'Sustainable production of lutein—an underexplored commercially relevant pigment from microalgae', Biomass Conversion and Biorefinery, vol. 14, no. 6, pp. 7255-7276.
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Currently, microalgae-derived lutein is gaining attention for its potential applications in cosmeceutical, nutraceutical, pharmaceutical, and food industries. Lutein is of commercial interest for a broad variety of health benefits: antioxidant activity, skin health improvement, reducing age-related macular degeneration, and the treatment of cancer. Microalgae are the fastest-growing lutein source, have the highest content in nature, and are a promising sustainable alternative to the current commercial source, marigold flowers. Microalgal cultivation has added environmental benefits over plants with higher carbon sequestration, reduced water footprint, and no pesticide use. To date, no industrial facility exists for the production of lutein from microalgae. This review outlines the existing technologies for bioprocessing of lutein at pilot scale (cultivation, harvesting, extraction, and purification). In addition, lutein encapsulation, a seldom discussed area, is explored in depth. In view of this knowledge, lutein could be anticipated as the next successful sustainable product from microalgae obtained at industrial scale for the circular economy. Graphical abstract: [Figure not available: see fulltext.]

Musale, A, Hunashyal, AM, Patil, AY, Kumar, R, Ahamad, T, Kalam, MA & Patel, M 2024, 'Study on Nanomaterials Coated Natural Coir Fibers as Crack Arrestor in Cement Composite', Advances in Civil Engineering, vol. 2024, pp. 1-16.
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The process of inclusion of carbon nanotubes as fibers in cement paste has been proved to have optimistic effect as it enhances the tensile property of cement paste composite. Coir fibers have exceptional mechanical qualities and are thus employed as reinforcement in cement composites. Epoxy resin, which has a high Young’s modulus, is an ideal component for bonding carbon nanotubes (CNTs) to coir fiber. This paper describes a novel kind of nanocomposite made of L-12 epoxy resin and CNTs at the nanolevel, along with coir fibers at the microlevel which operate as crack arrestors. To remove surface contaminants, coir fibers are first treated with sodium hydroxide (NaOH). Epoxy/CNTs polymer coatings were developed at varying CNTs fractions (0.05, 0.1, 0.15, and 0.2 wt.% of cement). Multiwalled CNTs were combined in distilled water, followed by epoxy resin and hardener (9 : 1 v/v) polymer in an ultrasonic sonicator for 90 min to ensure full dispersion of CNTs within the epoxy polymer. This blend is now coated on the treated clustered coir fiber (length 10 cm, 10 strands) and reinforced along the length of a cement composite beam 20 mm × 20 mm × 80 mm in size. Tensile and three-point tests were performed to evaluate the mechanical characteristics of the hybrid composite. The linear elastic finite element analysis is employed to distinguish their failure phenomena via fatigue or fracture behavior. The microstructure behavior and the effect of coating material on the coir fibers were investigated using scanning electron microscope and EDX analysis. The reinforcing impact of nanopolymer coated coir fiber in cement composite diminished the tensile and flexural strength after 0.1 wt.% of CNT fraction but increased the composite’s durability and Young’s modulus. Fourier transform infrared spectroscopy analysis was carried out to assess the chemical interaction between the epoxy/CNTs and the coir fibers. The simulation was performed using ANSYS workbench, an...

Mustafizur Rahman, M, Al-Amin, M & Hossain, J 2024, 'Machine learning models for chronic kidney disease diagnosis and prediction', Biomedical Signal Processing and Control, vol. 87, pp. 105368-105368.
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Mysore, THM, Patil, AY, Hegde, C, Sudeept, MA, Kumar, R, Soudagar, MEM & Fattah, IMR 2024, 'Apatite insights: From synthesis to biomedical applications', European Polymer Journal, vol. 209, pp. 112842-112842.
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Nabeel, MI, Afzal, MU, Singh, K, Thalakotuna, DN & Esselle, KP 2024, 'Dual-Band Printed Near-Field Metasurface with Independent Phase Transformation for Enhanced Antenna Gain', IEEE Antennas and Wireless Propagation Letters, pp. 1-5.
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Nagaprasad, KS, Banapurmath, NR, Madhu, D, Soudagar, MEM, Mujtaba, MA, Kalam, MA, Vadlamudi, C & Krishnappa, S 2024, 'Emission characteristics of diesel engines fuelled with B20 with different DPF, DOC and EGR arrangements', International Journal of Ambient Energy, vol. 45, no. 1.
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Namadchian, Z, Shoeibi, A, Zare, A, Gorriz, JM, Lam, H-K & Ling, SH 2024, 'Stability Analysis of Dynamic General Type-2 Fuzzy Control System With Uncertainty', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 3, pp. 1755-1767.
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Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 819, p. v.

Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 821, p. v.

Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 818, p. v.

Nanda, SJ, Yadav, RP, Gandomi, AH & Saraswat, M 2024, 'Preface', Lecture Notes in Networks and Systems, vol. 820, p. v.

Nandanwar, L, Shivakumara, P, Jalab, HA, Ibrahim, RW, Raghavendra, R, Pal, U, Lu, T & Blumenstein, M 2024, 'A Conformable Moments-Based Deep Learning System for Forged Handwriting Detection', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5407-5420.
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Naseem, U, Thapa, S, Zhang, Q, Wang, S, Rashid, J, Hu, L & Hussain, A 2024, 'Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare', Neurocomputing, vol. 592, pp. 127736-127736.
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Naseri, H, Golroo, A, Shokoohi, M & Gandomi, AH 2024, 'Sustainable pavement maintenance and rehabilitation planning using the marine predator optimization algorithm', Structure and Infrastructure Engineering, vol. 20, no. 3, pp. 340-352.
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The sustainability of pavement, especially in Maintenance and Rehabilitation (M&R) scheduling, has become an immense concern and has received limited attention in previous studies. Therefore, this study aimed to develop the M&R scheduling optimization based on sustainability. To this end, a novel sustainability index was introduced, in which all the sustainable development aspects were considered, including highway agency cost, environmental impacts, and social effects. A conventional model was used to assess the sustainable model’s effectiveness. Two new constraints are introduced to reduce the budget fluctuation and not to apply the M&R treatments for two consecutive years to make the model practical. On the other hand, highway agencies face large-scale networks, in which the optimization of M&R scheduling has computational complexities. Thus, the novel and powerful metaheuristic algorithm, named Marine Predator Algorithm (MPA), was applied to solve the pavement M&R scheduling problem. A large-scale pavement network, including 110 sections, was analyzed over a 5-year plan as the case study. The results indicated that using the sustainable model rather than the conventional one leads to a 6.5% reduction in CO2 emission. Besides, utilizing the sustainable approach enhances the equity and safety indices by 40.7% and 2.5% compared to the conventional treatment. However, the highway agency cost is increased by 1.1% using the sustainable model.

Navidpour, AH, Hosseinzadeh, A, Huang, Z, Li, D & Zhou, JL 2024, 'Application of machine learning algorithms in predicting the photocatalytic degradation of perfluorooctanoic acid', Catalysis Reviews, vol. 66, no. 2, pp. 687-712.
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Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2  = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2  = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2  = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order.

Navidpour, AH, Safaei, J, Johir, MAH, Ni, B-J, Dashti, A, Li, X & Zhou, JL 2024, 'Zinc oxide@citric acid-modified graphitic carbon nitride nanocomposites for adsorption and photocatalytic degradation of perfluorooctanoic acid', Advanced Composites and Hybrid Materials, vol. 7, no. 2.
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AbstractPerfluorooctanoic acid (PFOA) is a highly persistent organic pollutant of global concern. A novel nanocomposite composed of ZnO nanoparticles and citric acid-modified g-C3N4 was synthesized by ball milling process. The synthesized nanocomposite was more efficient than pure ball-milled ZnO nanoparticles for PFOA elimination under visible light irradiation. The optimal hybrid photocatalyst, produced by the addition of 5 wt% of citric acid-modified g-C3N4, demonstrated significantly better performance for PFOA removal than pure ZnO nanoparticles under UV irradiation, with the apparent rate constants of 0.468 h−1 and 0.097 h−1, respectively. The addition of peroxymonosulfate (0.53 g L−1) significantly increased PFOA removal, clarifying the crucial effect of sulfate radicals on PFOA photodegradation. In comparison, citric acid-modified g-C3N4 was not effective for PFOA elimination under visible light irradiation, even with the addition of peroxymonosulfate. Further experiments under dark conditions identified surface adsorption on hybrid photocatalyst as a key process in total PFOA removal. In summary, PFOA removal by ZnO@citric acid-modified graphitic carbon nitride nanocomposites is due to the combined action from adsorption and photodegradation, with adsorption as the dominating mechanism.

Navidpour, AH, Xu, B, Ahmed, MB & Zhou, JL 2024, 'Immobilization of TiO2 and ZnO by facile surface engineering methods to improve semiconductor performance in photocatalytic wastewater treatment: A review', Materials Science in Semiconductor Processing, vol. 179, pp. 108518-108518.
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Neshat, M, Sergiienko, NY, Nezhad, MM, da Silva, LSP, Amini, E, Marsooli, R, Astiaso Garcia, D & Mirjalili, S 2024, 'Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method', Applied Energy, vol. 362, pp. 122955-122955.
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Neshat, M, Sergiienko, NY, Rafiee, A, Mirjalili, S, Gandomi, AH & Boland, J 2024, 'Meta Wave Learner: Predicting wave farms power output using effective meta-learner deep gradient boosting model: A case study from Australian coasts', Energy, vol. 304, pp. 132122-132122.
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Ngo, QT, Phan, KT, Mahmood, A & Xiang, W 2024, 'Hybrid IRS-Assisted Secure Satellite Downlink Communications: A Fast Deep Reinforcement Learning Approach', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-12.
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Ngo, QT, Tang, Z, Jayawickrama, B, He, Y, Dutkiewicz, E & Senanayake, B 2024, 'Timeliness of Information in 5G Non-Terrestrial Networks: A Survey', IEEE Internet of Things Journal, pp. 1-1.
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Ngo, T & Indraratna, B 2024, 'Use of Geogrid for Improved Performance of Ballasted Tracks: Experimental and DEM Approaches', International Journal of Geosynthetics and Ground Engineering, vol. 10, no. 3.
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AbstractThis paper presents a study on the enhanced performance of ballasted tracks through the implementation of geogrids. A series of large-scale direct shear tests and impact tests was conducted with three distinct types of geogrids. The behavior of ballast was evaluated in terms of shear stress–strain responses and stress concentration using stress sensing sheets. Additionally, a micromechanical analysis utilizing the discrete element method was simulated on ballast assemblies with different geogrid reinforcements. The shear stress–strain responses of ballast simulated from DEM are comparable with those measured from large-scale direct shear tests, indicating that the inclusion of geogrid can enhance the performance of ballast by increasing its shear strength, as well as reducing the vertical displacement and the load distribution with depth. Micromechanical analysis was performed to investigate the influences of geogrids on contact force distribution, coordination number and orientation of contact which could not be captured in a laboratory environment. The use of geogrids in ballasted tracks certainly shows promise for sustainable and efficient railway infrastructure, as evidenced by the experimental and DEM-based findings, offering valuable insights into optimizing track stability and longevity.

Nguyen, BM, Nguyen, T, Vu, Q-H, Tran, HH, Vo, H, Bao Son, D, Thanh Binh, HT, Yu, S & Wu, Z 2024, 'A Novel Nature-Inspired Algorithm for Optimal Task Scheduling in Fog–Cloud Blockchain System', IEEE Internet of Things Journal, vol. 11, no. 2, pp. 2043-2057.
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Nguyen, C-H, Saputra, YM, Hoang, DT, Nguyen, DN, Nguyen, V-D, Xiao, Y & Dutkiewicz, E 2024, 'Encrypted Data Caching and Learning Framework for Robust Federated Learning-Based Mobile Edge Computing', IEEE/ACM Transactions on Networking, vol. 32, no. 3, pp. 2705-2720.
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Nguyen, CT, Hoang, DT, Nguyen, DN, Xiao, Y, Niyato, D & Dutkiewicz, E 2024, 'MetaShard: A Novel Sharding Blockchain Platform for Metaverse Applications', IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 4348-4361.
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Due to its security, transparency, and flexibility in verifying virtual assets, blockchain has been identified as one of the key technologies for Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges such as massive resource demands, scalability, and security/privacy concerns. To address these issues, this paper proposes a novel sharding-based blockchain framework, namely MetaShard, for Metaverse applications. Particularly, we first develop an effective consensus mechanism, namely Proof-of-Engagement, that can incentivize MUs' data and computing resource contribution. Moreover, to improve the scalability of MetaShard, we propose an innovative sharding management scheme to maximize the network's throughput while protecting the shards from 51% attacks. Since the optimization problem is NP-complete, we develop a hybrid approach that decomposes the problem (using the binary search method) into sub-problems that can be solved effectively by the Lagrangian method. As a result, the proposed approach can obtain solutions in polynomial time, thereby enabling flexible shard reconfiguration and reducing the risk of corruption from the adversary. Extensive numerical experiments show that, compared to the state-of-the-art commercial solvers, our proposed approach can achieve up to 66.6% higher throughput in less than 1/30 running time. Moreover, the proposed approach can achieve global optimal solutions in most experiments.

Nguyen, CT, Saputra, YM, Huynh, NV, Nguyen, TN, Hoang, DT, Nguyen, DN, Pham, V-Q, Voznak, M, Chatzinotas, S & Tran, D-H 2024, 'Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications', IEEE Open Journal of the Communications Society, vol. 5, pp. 3852-3885.
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Nguyen, DDN, Sood, K, Xiang, Y, Gao, L, Chi, L, Singh, G & Yu, S 2024, 'Design and Robust Evaluation of Next Generation Node Authentication Approach', IEEE Transactions on Dependable and Secure Computing, pp. 1-12.
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Nguyen, HAD, Le, TH, Ha, QP, Duc, H & Azzi, M 2024, 'Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations', E3S Web of Conferences, vol. 496, pp. 04001-04001.
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The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydney’s suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure.

Nguyen, HD, Leys, J, Riley, M, White, S, Azzi, M, Trieu, T, Salter, D, Ji, F, Nguyen, H, Chang, LT-C, Monk, K, Firth, J, Fuchs, D & Barthelemy, X 2024, 'Effects of dust storm and wildfire events on phytoplankton growth in the Southern Ocean and Tasman Sea, southeast Australia', E3S Web of Conferences, vol. 496, pp. 04003-04003.
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Dust storms and wildfires occur frequently in southeastern Australia. Their effects on ecology, environment and population exposure have been the focus of many studies recently. Dust storms do not emit ground-sequestered carbon but wildfires emit significant quantities of carbon into the atmosphere. However, both natural events promote phytoplankton growth in water bodies because carbon, and other trace elements such as iron, deposit on the surface water of oceans and promote phytoplankton growth. Carbon di-oxide is reabsorbed by phytoplankton via photosynthesis. The carbon balance of dust storms and wildfires are not well known.This study focusses on the association of dust storms and wildfires in southeastern Australia with phytoplankton growth in the Southern Ocean and Tasman Sea due to the February 2019 dust storm event and the 2019-2020 black summer wildfires. The results show the similarities and differences in phytoplankton growth patterns and carbon reabsorption amount from these events.

Nguyen, HG, Nguyen, HT, Nguyen, LTT, Tran, TS, Ho-Pham, LT, Ling, SH & Nguyen, TV 2024, 'Development of a shape-based algorithm for identification of asymptomatic vertebral compression fractures: A proof-of-principle study', Osteoporosis and Sarcopenia, vol. 10, no. 1, pp. 22-27.
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Nguyen, LV, Le, TH, Nguyen, TD, Kwok, NM & Ha, QP 2024, 'Monorail bridge inspection using digitally-twinned UAVs', E3S Web of Conferences, vol. 496, pp. 04004-04004.
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This paper introduces a comprehensive approach to monorail bridge inspection utilizing unmanned aerial vehicles (UAVs) and digital twin technology. The autonomous UAV-based inspection design encompasses UAV dynamics, tracking control, path planning, and task execution. A dedicated digital twin platform is developed to facilitate rigorous testing and verification of UAV control, mitigating the necessity for extensive physical testing. Methodology validation is achieved through a combination of simulations and real-world experiments, affirming its efficacy in authentic scenarios and demonstrating the potential for advancing infrastructure inspection practices.

Nguyen, NHT & Nguyen, TT 2024, 'Numerical investigation of the instability of dry granular bed induced by water leakage', Acta Geotechnica, vol. 19, no. 5, pp. 3229-3239.
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AbstractUnderground pipe defects or cracks under transport infrastructure can cause water leakage to upper soil layers (e.g. subgrade and capping), inducing local cavities or even failure of overlying road/railway formation. Although numerous studies on the instability of granular beds induced by injected water have been conducted, most of them focused on the behaviour of saturated granular beds, while research on dry granular beds is still limited. This paper aims to address this gap using a numerical model coupling volume of fluid method with discrete element method. We observed that dry granular beds go through three distinct regimes as water jet velocity increases including stationary, stable deformation with heave and fluidisation. However, the flow velocities required to deform and fluidise dry granular beds are significantly higher than those required for saturated beds. Increasing granular bed thickness can alter its failure mechanism from full depth to localised erosion, leading to cavity formation around pipe cracks prior to the bed fluidisation. The gravitational and frictional components of granular mass are identified as two main resisting forces of dry granular beds against water jet force, evidenced by the increase of critical jet velocities as particle density and friction coefficient increase. Nevertheless, the moblised zone of granular mass is practically independent of both the buried depth of dry granular beds.

Nguyen, N-K-Q, Bui, X-T, Dao, T-S, Pham, M-D-T, Ngo, HH, Lin, C, Lin, K-YA, Nguyen, P-D, Huynh, K-P-H, Vo, T-K-Q, Tra, V-T & Le, T-S 2024, 'Influence of hydrodynamic shear stress on activated algae granulation process for wastewater treatment', Environmental Technology & Innovation, vol. 33, pp. 103494-103494.
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Nguyen, V-D, Vu, TX, Nguyen, NT, Nguyen, DC, Juntti, M, Luong, NC, Hoang, DT, Nguyen, DN & Chatzinotas, S 2024, 'Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework', IEEE Journal on Selected Areas in Communications, vol. 42, no. 2, pp. 389-405.
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Ni, M, Sun, Z & Liu, W 2024, 'Fraud's Bargain Attack: Generating Adversarial Text Samples via Word Manipulation Process', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 7, pp. 3062-3075.
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Ni, Q, Ji, JC, Feng, K, Zhang, Y, Lin, D & Zheng, J 2024, 'Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit', Reliability Engineering & System Safety, vol. 242, pp. 109753-109753.
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Nikkhah, N, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2024, 'Highly Sensitive Differential Microwave Sensor Using Enhanced Spiral Resonators for Precision Permittivity Measurement', IEEE Sensors Journal, vol. 24, no. 9, pp. 14177-14188.
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Nikolic, S, Suesse, TF, Grundy, S, Haque, R, Lyden, S, Hassan, GM, Daniel, S, Belkina, M & Lal, S 2024, 'Laboratory learning objectives: ranking objectives across the cognitive, psychomotor and affective domains within engineering', European Journal of Engineering Education, vol. 49, no. 3, pp. 454-473.
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Nikoo, MR, Izady, A, Bakhtiari, PH, Al-Maktoumi, A, Chen, M & Gandomi, AH 2024, 'A Water Resources Management Simulation–Optimization Model: Application of Graph-Based Hypergame Model in Water Supply Conflicts Resolution', Group Decision and Negotiation, vol. 33, no. 2, pp. 291-326.
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Nimbalkar, S & Basack, S 2024, 'Pile group in clay subjected to cyclic lateral load: Numerical modelling and design recommendation', Marine Georesources & Geotechnology, vol. 42, no. 1, pp. 67-87.
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Nirbhav, NA, Malik, A, Maheshwar, NA & Prasad, M 2024, 'Landslide susceptibility assessment along the major transport corridor using decision tree model: a case study of Kullu-Rohtang Pass', International Journal of Business Intelligence and Data Mining, vol. 24, no. 1, pp. 1-24.
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Nirmala, M, Gandomi, AH, Babu, MR, Babu, LDD & Patan, R 2024, 'An Emoticon-Based Novel Sarcasm Pattern Detection Strategy to Identify Sarcasm in Microblogging Social Networks', IEEE Transactions on Computational Social Systems, pp. 1-8.
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Niu, C, Pang, G & Chen, L 2024, 'Affinity Uncertainty-Based Hard Negative Mining in Graph Contrastive Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-11.
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Niu, G, He, X, Xu, H & Dai, S 2024, 'Tunnelling-induced ground surface settlement: A comprehensive review with particular attention to artificial intelligence technologies', Natural Hazards Research, vol. 4, no. 1, pp. 148-168.
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Niu, S, Yin, Q, Ma, J, Song, Y, Xu, Y, Bai, L, Pan, W & Yang, X 2024, 'Enhancing healthcare decision support through explainable AI models for risk prediction', Decision Support Systems, vol. 181, pp. 114228-114228.
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Oberst, S & Martin, R 2024, 'Feature-preserving synthesis of termite-mimetic spinodal nest morphology', iScience, vol. 27, no. 1, pp. 108674-108674.
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Oh, K-S, Qin, P-Y & Seo, D-W 2024, 'Near-Field Radar Equation Based on Fresnel Diffraction Formula to Detect Short-Ranged Targets for Automotive Radar', Journal of Electromagnetic Engineering and Science, vol. 24, no. 2, pp. 161-169.
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Although the classic radar equation has been widely used to analyze the link budget of an automotive radar, it is valid only when the targets are located in the far-field region of the transmitted field and the receiving antenna is located in the far-field region of the scattered field. This paper confirms this hypothesis using measured and simulated data for short-range radars. Furthermore, a novel radar equation based on the Fresnel diffraction formula is proposed for application in situations where the receiving antenna is located in the radiative near-field region (or Fresnel region) of a target, but the target is situated in the far-field region of the transmitting antenna. In addition, the proposed radar equation is assessed by comparing the measured and simulated data.

Oloyede, CT, Itabiyi, OE, Popoola, OA, Jekayinfa, SO, Olaniyan, MA, Adebayo, AO, Ogunkunle, O, Zamri, MFMA & Fattah, IMR 2024, 'Navigating prospects and challenges for green fuels for achieving economical, environmental and ecological resilience: a scientific review', Biofuels, vol. 15, no. 7, pp. 929-941.
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Oloyede, CT, Jekayinfa, SO, Adebonojo, SA, Uduaghan, AA, Adebayo, JM & Islam Md Rizwanul, F 2024, 'Moisture‐modulated thermo‐physical analysis of sweetsop seed (Annona squamosa L.): A potential biofuel feedstock plant', Journal of Food Process Engineering, vol. 47, no. 7.
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AbstractSweetsop seed holds significant economic value as an oil seed, with ca. 25% oil content that finds applications as a feedstock for energy generation. The moisture‐modulated thermophysical properties of the seed were determined at varying moisture contents (8.0%–32.5%). Physical properties (length [L], width [W], thickness [T], arithmetic [Amd], and geometric mean diameters [Gmd], sphericity [Sty], surface area [SA], and bulk density [ρd]) were determined using standard methods while the thermal properties (specific heat capacity [SHC], thermal conductivity [Tcd], and thermal diffusivity [Tdf]) were analyzed using a TEMPOS thermal analyzer. The results showed that the seed L, W, T, Amd, and Gmd, Sty, SA, and ρd ranged from 13.22–14.95 mm, 7.32–7.95 mm, 5.25–5.35 mm, 8.60–8.75 mm, and 7.96–8.11 mm, 0.61–0.60, 196.72–205.28 mm2, and 210.00–270.00 kg m−3, respectively. The SHC, Tcd, and Tdf ranged from 0.14–0.52 J kg−1 K−1, 0.17–0.35 W m−1

Onari, MA, Rezaee, MJ, Saberi, M & Nobile, MS 2024, 'An explainable data-driven decision support framework for strategic customer development', Knowledge-Based Systems, vol. 295, pp. 111761-111761.
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Onggowarsito, C, Shao, Z, Mao, S, Zhang, S, Feng, A, Li, X, Wong, EHH & Fu, Q 2024, 'Versatile cationic dual-layer hydrogel filtration system for sustainable solar steam generator', Materials Today Sustainability, vol. 26, pp. 100753-100753.
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Ostermeier, FF & Deuse, J 2024, 'A review and classification of scheduling objectives in unpaced flow shops for discrete manufacturing', Journal of Scheduling, vol. 27, no. 1, pp. 29-49.
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Ostermeier, FF & Deuse, J 2024, 'Modelling forgetting due to intermittent production in mixed-model line scheduling', Flexible Services and Manufacturing Journal, vol. 36, no. 2, pp. 503-532.
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Ou, L, Chang, Y-C, Wang, Y-K & Lin, C-T 2024, 'Fuzzy Centered Explainable Network for Reinforcement Learning', IEEE Transactions on Fuzzy Systems, vol. 32, no. 1, pp. 203-213.
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Ozkaya, SG, Baygin, M, Barua, PD, Tuncer, T, Dogan, S, Chakraborty, S & Acharya, UR 2024, 'An automated earthquake classification model based on a new butterfly pattern using seismic signals', Expert Systems with Applications, vol. 238, pp. 122079-122079.
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Paeedeh, N, Pratama, M, Ma’sum, MA, Mayer, W, Cao, Z & Kowlczyk, R 2024, 'Cross-domain few-shot learning via adaptive transformer networks', Knowledge-Based Systems, vol. 288, pp. 111458-111458.
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Paeedeh, N, Pratama, M, Wibirama, S, Mayer, W, Cao, Z & Kowalczyk, R 2024, 'Few-shot class incremental learning via robust transformer approach', Information Sciences, vol. 675, pp. 120751-120751.
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Pal, PK, Jana, KC, Siwakoti, YP, Ali, JSM & Blaabjerg, F 2024, 'A Switched-Capacitor Multilevel Inverter With Modified Pulsewidth Modulation and Active DC-Link Capacitor Voltage Balancing', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 12, no. 2, pp. 1215-1229.
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Palsberg, J & Yu, N 2024, 'Optimal implementation of quantum gates with two controls', Linear Algebra and its Applications, vol. 694, pp. 206-261.
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Pan, Y, Zong, Z, Li, J, Qian, H & Wu, C 2024, 'Investigating the dynamic response of a double-box utility tunnel buried in calcareous sand against ground surface explosion', Tunnelling and Underground Space Technology, vol. 146, pp. 105636-105636.
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PAN, Z, LI, H & HUANG, X 2024, 'Optimal Design of Multiuser mmWave LOS MIMO Systems Using Hybrid Arrays of Subarrays', IEICE Transactions on Communications, vol. E107.B, no. 1, pp. 262-271.
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Pang, C, Zhu, S, Shen, M, Liu, X & Wen, S 2024, 'Novel Exponential Stability Criteria for Switched Neutral-Type Neural Networks With Mixed Delays', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 3, pp. 1306-1310.
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Parsa, K, Hassall, M, Naderpour, M, Pourreza, M & Ramezani, F 2024, 'A New Alarm System Developing Approach Through Graph Modeling', IEEE Access, vol. 12, pp. 1608-1620.
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Pavan Kumar, TVV, Taranath, NL, Rahul, R, Chandra Shekara, G, Sapra, P, Thandaiah Prabu, R, Metwally, ASM & Kalam, MA 2024, 'Photovoltaic fuzzy based modelling on defining energy efficient solar devices in industry 4.0', Optical and Quantum Electronics, vol. 56, no. 1.
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Peng, R, Guo, R, Liu, L, Ji, J, Miao, Z & Zhou, J 2024, 'Practical consensus tracking control for networked Euler–Lagrange systems based on UDE integrated with RBF neural network', Neurocomputing, vol. 583, pp. 127554-127554.
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Peng, T, Zhong, W, Wang, G, Luo, E, Yu, S, Liu, Y, Yang, Y & Zhang, X 2024, 'Privacy-Preserving Truth Discovery Based on Secure Multi-Party Computation in Vehicle-Based Mobile Crowdsensing', IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 7, pp. 7767-7779.
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Pfeffer, MA, Ling, SSH & Wong, JKW 2024, 'Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces', Computers in Biology and Medicine, vol. 178, pp. 108705-108705.
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Ping, J, Zhu, S, Mu, C, Liu, X & Wen, S 2024, 'Generalized Halanay-Type Inequalities for Finite-Time Stabilization of Delayed Systems', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 4, pp. 2194-2198.
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Piyathilaka, L, Kodagoda, S, Thiyagarajan, K, Piccardi, M, Preethichandra, DMG & Izhar, U 2024, 'Learning Spatial Affordances From 3D Point Clouds for Mapping Unseen Human Actions in Indoor Environments', IEEE Access, vol. 12, pp. 868-877.
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Poblete, P, Gajardo, J, Cuzmar, RH, Aguilera, RP, Pereda, J, Lu, D & Alcaide, AM 2024, 'Predictive Optimal Variable-Angle PS-PWM Strategy for Cascaded H-Bridge Converters', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Pradeepkumar, A, Cortie, D, Smyth, E, Le Brun, AP & Iacopi, F 2024, 'A High Temperature Operando Study of Epitaxial Graphene Growth on Cubic Silicon Carbide Using Neutron Reflectometry', Neutron News, vol. 35, no. 2, pp. 9-11.
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Pu, X, Che, H, Pan, B, Leung, M-F & Wen, S 2024, 'Robust Weighted Low-Rank Tensor Approximation for Multiview Clustering With Mixed Noise', IEEE Transactions on Computational Social Systems, vol. 11, no. 3, pp. 3268-3285.
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Puthal, D, Yeun, CY, Damiani, E, Mishra, AK, Yelamarthi, K & Pradhan, B 2024, 'Blockchain Data Structures and Integrated Adaptive Learning: Features and Futures', IEEE Consumer Electronics Magazine, vol. 13, no. 2, pp. 72-80.
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Qashlan, A, Nanda, P & Mohanty, M 2024, 'Differential privacy model for blockchain based smart home architecture', Future Generation Computer Systems, vol. 150, pp. 49-63.
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Qi, L, Wu, B, Chen, X, Zhou, L, Ni, W & Jamalipour, A 2024, 'Joint Optimization of Internet of Things and Smart Grid for Energy Generation, Battery (Dis)charging, and Information Delivery', IEEE Internet of Things Journal, vol. 11, no. 12, pp. 21647-21658.
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Qi, Y, Indraratna, B, Ngo, T, Arachchige, CMK & Hettiyahandi, S 2024, 'Sustainable solutions for railway using recycled rubber', Transportation Geotechnics, vol. 46, pp. 101256-101256.
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Qian, J, Zhang, J, Li, X, Huang, L, Hu, X, Lee, JE-Y & Zhang, W 2024, 'Dual-Function Multichannel Acoustofluidic Particle Manipulation Enabled by a PMUT Array', IEEE Transactions on Electron Devices, vol. 71, no. 8, pp. 4932-4938.
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Qin, F, Zheng, Z, Sui, Y, Gong, S, Shi, Z & Trivedi, KS 2024, 'Cross-project concurrency bug prediction using domain-adversarial neural network', Journal of Systems and Software, vol. 214, pp. 112077-112077.
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Qiu, N, Wan, Y, Shen, Y & Fang, J 2024, 'Experimental and numerical studies on mechanical properties of TPMS structures', International Journal of Mechanical Sciences, vol. 261, pp. 108657-108657.
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Qiu, N, Yu, Z, Wang, D, Xiao, M, Zhang, Y, Kim, NH & Fang, J 2024, 'Bayesian optimization of origami multi-cell tubes for energy absorption considering mixed categorical-continuous variables', Thin-Walled Structures, vol. 199, pp. 111799-111799.
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Qiu, Y, Zhou, J, He, B, Armaghani, DJ, Huang, S & He, X 2024, 'Evaluation and Interpretation of Blasting-Induced Tunnel Overbreak: Using Heuristic-Based Ensemble Learning and Gene Expression Programming Techniques', Rock Mechanics and Rock Engineering.
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Overbreak is a prevalent and detrimental phenomenon in hard rock tunnel excavation that escalates construction costs and compromises tunnel structural stability. Therefore, accurate prediction of overbreak during excavation is significant for cost reduction and risk mitigation. This study introduces an efficient metaheuristic method, namely the honey badger algorithm (HBA), for optimizing the light gradient boosting machine (LGBM) model, and proposes an explicit equation for the prediction of overbreak based on gene expression programming (GEP) technology. Utilizing a dataset comprising 523 overbreak cases collected from the Huxitai (HXT) tunnel in China, this study conducts the modeling of overbreak prediction and assesses the model's performance through various metrics and non-parametric statistical tests. The results indicate that the HBA-LGBM hybrid model developed herein achieves the highest coefficient of determination (R2) of 0.9472 among the tested models, while the GEP model reaches a R2 of 0.9275. Clearly, the overbreak prediction model constructed in this paper shows superior overall performance, and the comparative analysis of multiple models also highlights HBA's significant advantage in mitigating overfitting. Lastly, various interpretive techniques were applied to analyze the impact of input variables on overbreak, providing insights into the decision-making principles of the predictive model from both global and individual case levels. The analysis revealed that the total charge (TC) and powder factor (PF) are the most influential blasting parameters on overbreak occurrence. Furthermore, a graphical user interface for testing purposes was developed and showcased. In summary, the overbreak models established in this study effectively predict tunnel overbreak caused by blasting, demonstrating superior predictive performance and interpretability compared to previous efforts.

Qu, F, Su, Y, Lu, D, Li, N, Zeng, X & Li, W 2024, 'Anti-cracking and shrinkage performance of sustainable concrete incorporating high-volume natural pozzolans: A case design for high-speed railway concrete slab tracks', Case Studies in Construction Materials, vol. 20, pp. e03207-e03207.
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Qu, K, Liu, J, Zhang, J & Wu, C 2024, 'Three-dimensional meso-scale modelling of geopolymer-based ultra-high performance concrete (G-UHPC) with ceramic ball coarse aggregates under projectile impact', Structures, vol. 61, pp. 105935-105935.
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Qu, Y, Yu, S, Gao, L, Sood, K & Xiang, Y 2024, 'Blockchained Dual-Asynchronous Federated Learning Services for Digital Twin Empowered Edge-Cloud Continuum', IEEE Transactions on Services Computing, vol. 17, no. 3, pp. 836-849.
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Qu, Y, Yuan, X, Ding, M, Ni, W, Rakotoarivelo, T & Smith, D 2024, 'Learn to Unlearn: Insights Into Machine Unlearning', Computer, vol. 57, no. 3, pp. 79-90.
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Rafa, N, Ahmed, B, Zohora, F, Bakya, J, Ahmed, S, Ahmed, SF, Mofijur, M, Chowdhury, AA & Almomani, F 2024, 'Microplastics as carriers of toxic pollutants: Source, transport, and toxicological effects', Environmental Pollution, vol. 343, pp. 123190-123190.
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Rahman, MM, Zhao, M, Islam, MS, Dong, K & Saha, SC 2024, 'A numerical study on sedimentation effect of dust, smoke and traffic particle deposition in a realistic human lung', International Journal of Multiphase Flow, vol. 171, pp. 104685-104685.
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Raman, R, Gor, M, Meenakshi, R, Jayaseelan, GM, Chaturvedi, A, Taqui, SN, Ganeshan, P, Ouladsmane, M & Kalam, MA 2024, 'Solar Energy Measurement and Monitoring Model by Using Internet of Things', Electric Power Components and Systems, vol. 52, no. 10, pp. 1796-1807.
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Ramia, G, Mitchell, E, Morris, A, Wilson, S, Hastings, C & Davies, J 2024, 'Explaining Government Policy Inaction on International Student Housing in Australia: The Perspectives of Stakeholders', Higher Education Policy, vol. 37, no. 1, pp. 21-39.
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Housing is a major concern for many international students. This is especially so in those countries where students are mostly dependent on the private market for their accommodation. Australia is one such country, and is one of the world's major destinations for international students. This article analyses governmental failure to address problems relating to international student housing affordability and conditions. Using theory on 'policy inaction' to frame the analysis, we draw on 20 interviews with policy stakeholders to explain the Australian government's reliance on: (1) market-based housing provision for international students, and (2) a longstanding policy preference not to provide support. Interviewees were widely critical of the lack of action to address international student housing problems and understood inaction in relation, rather than in opposition, to the dominance of market-based action in housing and higher education. However, analysis of stakeholder perspectives also illuminates how policy-making action benefiting some emerges as inaction for others left behind or overlooked by the status quo. The interview data points to the need for government to overhaul its policy framework, and in doing so, to collaborate with higher education providers in revising the market-based regulatory approach. The main implications for theory and policy are discussed.

Rao, P, Feng, W, Ouyang, P, Cui, J, Nimbalkar, S & Chen, Q 2024, 'Formation of plasma channel under high-voltage electric pulse and simulation of rock-breaking process', Physica Scripta, vol. 99, no. 1, pp. 015604-015604.
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Abstract In the context of rock fragmentation, the application of high voltage electric pulses results in the transfer of electrical energy onto the surface of the rock material, leading to a rapid electrical breakdown and the formation of a plasma channel. The ionized plasma expands at a fast velocity, generating a shock wave that causes significant damage to the rock’s integrity. In this study, we develope a numerical model that couples electrical, thermal, and mechanical forces to simulate the formation of plasma channels within rocks due to high-voltage electric pulses. The model’s accuracy is verified through field tests, and the results indicate that the configuration of the high-voltage pulse waveform, electrode spacing, and conductor particles within the rock impact the pathway of plasma channel formation. Prior to the formation of the plasma channel, minimal changes are observed in temperature and stress levels, with the majority of electric pulse energy dedicated to the creation of the plasma channel. Following the establishment of the plasma channel, the application of the electric pulse continues, resulting in notable alterations in temperature and stress levels. When the duration of the action reaches 105 ns, the temperature and stress levels surpass 104 K and 50 MPa, respectively, leading to fracture and extensive damage to the rock. The outcomes derived from the numerical model’s calculations can help to facilitate the cross-integration between physics and civil engineering and contribute to a deeper understanding of the rock fragmentation process under high voltage electric pulses.

Rao, P, Meng, J, Cui, J & Nimbalkar, S 2024, 'Field Study on Rectangular Inclined Deep Foundation Excavation in Soft Soils', Geotechnical and Geological Engineering, vol. 42, no. 3, pp. 2151-2168.
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Rao, Q, Yu, X, Li, G & Zhu, L 2024, 'CMGNet: Collaborative multi-modal graph network for video captioning', Computer Vision and Image Understanding, vol. 238, pp. 103864-103864.
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Rashid, MI, Yaqoob, Z, Mujtaba, MA, Kalam, MA, Fayaz, H & Qazi, A 2024, 'Carbon capture, utilization and storage opportunities to mitigate greenhouse gases', Heliyon, vol. 10, no. 3, pp. e25419-e25419.
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Raza, A, Keshavarz, R & Shariati, N 2024, 'Precision Agriculture: Ultra-Compact Sensor and Reconfigurable Antenna for Joint Sensing and Communication', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-13.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2024, 'Multi-Agent Multi-Armed Bandit Learning for Grant-Free Access in Ultra-Dense IoT Networks', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Razavi Bazaz, S, Sayyah, A, Hazeri, AH, Salomon, R, Abouei Mehrizi, A & Ebrahimi Warkiani, M 2024, 'Micromixer research trend of active and passive designs', Chemical Engineering Science, vol. 293, pp. 120028-120028.
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Razavi, SE, Adibi, T, Ahmed, SF & Saha, SC 2024, 'Semi-analytical solution of nanofluid flow with convective and radiative heat transfer', International Journal of Modern Physics B, vol. 38, no. 25.
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The application of nanofluids has exploded in recent decades to improve the local number, mean Nusselt number, and rate of heat transfer. However, boundary layer equations of nanofluid across a flat plate with radiation have not been studied, and therefore this paper studies them mathematically for the first time. For water-based copper and aluminum oxide nanofluids, a similarity solution is presented in this study, and the subsequent system of the ordinary differential equation (ODE) is numerically solved by the Runge–Kutta method in MATLAB. Two different hydraulic boundary conditions are used in the simulations. In the first, the flow across a moving plate and the direction of the flow are analyzed, while in the second, the flow over a nonlinearly moving plate in a still fluid is investigated. The nanoparticle’s boundary layer thickness is found less than the thermal and hydraulics boundary layers. The local Nusselt number and friction factor of both the nanofluids are calculated and compared with the base fluid. The results demonstrate that the friction coefficient is high and the Nusselt number is low for nanoparticles with a high volume fraction. It also revealed that the friction factor for water–aluminum oxide is 16% greater than that for the water–CuO whereas the local Nusselt number for water–aluminum oxide is only 5% more than that for the water–CuO.

Rehman, UU, Munir, A, Bahadur Khan, N, Zhao, M, Kashif, M, Islam, MS, Saeed, Z & Ali, MA 2024, 'Numerical investigation of vortex-induced vibrations (VIV) of a rotating cylinder in in-line and cross-flow directions subjected to oscillatory flow', Ocean Engineering, vol. 304, pp. 117917-117917.
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Reja, VK, Goyal, S, Varghese, K, Ravindran, B & Ha, QP 2024, 'Hybrid self-supervised learning-based architecture for construction progress monitoring', Automation in Construction, vol. 158, pp. 105225-105225.
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Ren, J, Sui, Y, Cheng, X, Feng, Y & Zhao, J 2024, 'Dynamic Transitive Closure-based Static Analysis through the Lens of Quantum Search', ACM Transactions on Software Engineering and Methodology, vol. 33, no. 5, pp. 1-29.
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Many existing static analysis algorithms suffer from cubic bottlenecks because of the need to compute a dynamic transitive closure (DTC). For the first time, this article studies the quantum speedups on searching subtasks in DTC-based static analysis algorithms using quantum search (e.g., Grover’s algorithm). We first introduce our oracle implementation in Grover’s algorithm for DTC-based static analysis and illustrate our quantum search subroutine. Then, we take two typical DTC-based analysis algorithms: context-free-language reachability and set constraint-based analysis, and show that our quantum approach can reduce the time complexity of these two algorithms to truly subcubic ( \(O(N^2\sqrt {N}{\it polylog}(N))\) ), yielding better results than the upper bound ( O ( N 3 /log N )) of existing classical algorithms. Finally, we conducted a classical simulation of Grover’s search to validate our theoretical approach, due to the current quantum hardware limitation of lacking a practical, large-scale, noise-free quantum machine. We evaluated the correctness and efficiency of our approach using IBM Qiskit on nine open-source projects and randomly generated edge-labeled graphs/constraints. The results demonstrate the effectiveness of our approach and shed light on the promising direction of applying quantum algorithms to address the general challenges in static analysis.

Ren, L, Zhang, Y, Zhou, JL, Wang, G, Mo, Y, Ling, Y, Huang, Y, Zhang, Y, Hu, H & Wang, Y 2024, 'RL-WG26 mediated salt stress tolerance in rice seedlings: A new insight into molecular mechanisms', Plant Stress, vol. 11, pp. 100306-100306.
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Ren, Z, Ji, J, Zhu, Y & Feng, K 2024, 'An Investigation Into the Behavior of Intelligent Fault Diagnostic Models Under Imbalanced Data', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-20.
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Reza, MS, Mannan, M, Mansor, M, Ker, PJ, Mahlia, TMI & Hannan, MA 2024, 'Recent advancement of remaining useful life prediction of lithium-ion battery in electric vehicle applications: A review of modelling mechanisms, network configurations, factors, and outstanding issues', Energy Reports, vol. 11, pp. 4824-4848.
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Rezaee Jordehi, A, Mansouri, SA, Tostado-Véliz, M, Carrión, M, Hossain, MJ & Jurado, F 2024, 'A risk-averse two-stage stochastic model for optimal participation of hydrogen fuel stations in electricity markets', International Journal of Hydrogen Energy, vol. 49, pp. 188-201.
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Rezaee Jordehi, A, Mansouri, SA, Tostado-Véliz, M, Hossain, MJ, Nasir, M & Jurado, F 2024, 'Optimal placement of hydrogen fuel stations in power systems with high photovoltaic penetration and responsive electric demands in presence of local hydrogen markets', International Journal of Hydrogen Energy, vol. 50, pp. 62-76.
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Rhakho, N, Yadav, S, Jinagi, M, Altaee, A, Saxena, M, Jadhav, AH & Samal, AK 2024, 'Mitigating PFAS contaminants in water: A comprehensive survey of remediation strategies', Journal of Environmental Chemical Engineering, vol. 12, no. 5, pp. 113425-113425.
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Riaz, HH, Lodhi, AH, Munir, A, Zhao, M, Farooq, U, Qadri, MNM & Islam, MS 2024, 'Breathing in danger: Mapping microplastic migration in the human respiratory system', Physics of Fluids, vol. 36, no. 4.
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The abundance of air pollutants over the last few years, including the concentration of microplastics, has become an alarming concern across the world. Initially discovered in marine life, these toxic and inflammatory particles have recently been found in human lung tissues. When inhaled, these harmful particles settle down in the lung airways and, over time, lead to respiratory failures. A recent study analyzed the microplastic transport behavior in the mouth–throat airways. However, the knowledge of the microplastic migration in bifurcating tracheobronchial airways is missing in the literature. Therefore, this first-ever study analyzes in detail the transport behavior and settling patterns of microplastic particles of different sizes and shapes at different respiratory intensities in the tracheobronchial lung airways. A numerical technique based on discrete phase modeling is employed to simulate the flow of microplastic particles in a three-dimensional realistic lung geometry. The numerical model results indicate low velocity and turbulence intensity magnitudes with smooth flow in the trachea compared to the airways of left and right lobes, which experience higher velocities and generate secondary vortices. Lower lung lobes are the deposition hotspots for the harmful microplastic particles at a lower flow rate. These hotspots shift to upper lung lobes at a higher flow rate for the same particle size. Moreover, microplastic particle size and shape influence the overall deposition rate in the tracheobronchial lung airways. The results of the current study, including microplastic accumulation regions at different breathing intensities, will contribute to the updated knowledge of pollutant inhalation and facilitate relevant treatment measures.

Riaz, HH, Munir, A, Farooq, U, Arshad, A, Chan, T-C, Zhao, M, Khan, NB & Islam, MS 2024, 'Optimal Treatment of Tumor in Upper Human Respiratory Tract Using Microaerosols', ACS Omega, vol. 9, no. 23, pp. 25106-25123.
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Lung cancer is a frequently diagnosed respiratory disease caused by particulate matter in the environment, especially among older individuals. For its effective treatment, a promising approach involves administering drug particles through the inhalation route. Multiple studies have investigated the flow behavior of inhaled particles in the respiratory airways of healthy patients. However, the existing literature lacks studies on the precise understanding of the transportation and deposition (TD) of inhaled particles through age-specific, unhealthy respiratory tracts containing a tumor, which can potentially optimize lung cancer treatment. This study aims to investigate the TD of inhaled drug particles within a tumorous, age-specific human respiratory tract. The computational model reports that drug particles within the size range of 5-10 μm are inclined to deposit more on the tumor located in the upper airways of a 70-year-old lung. Conversely, for individuals aged 50 and 60 years, an optimal particle size range for achieving the highest degree of particle deposition onto upper airway tumor falls within the 11-20 μm range. Flow disturbances are found to be at a maximum in the airway downstream of the tumor. Additionally, the impact of varying inhalation flow rates on particle TD is examined. The obtained patterns of airflow distribution and deposition efficiency on the tumor wall for different ages and tumor locations in the upper tracheobronchial airways would be beneficial for developing an efficient and targeted drug delivery system.

Roobavannan, S, Choo, Y, Truong, DQ, Shon, HK & Naidu, G 2024, 'Selective lithium extraction using capacitive deionization with fabricated zeolitic imidazolate framework encapsulated manganese oxide carbon electrode', Chemical Engineering Journal, vol. 483, pp. 149242-149242.
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Rooholahi, B, Siwakoti, YP, Eckel, H-G, Blaabjerg, F & Bahman, AS 2024, 'Enhanced Single-Inductor Single-Input Dual-Output DC–DC Converter With Voltage Balancing Capability', IEEE Transactions on Industrial Electronics, vol. 71, no. 7, pp. 7241-7251.
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Roy, SS, Roy, A, Samui, P, Gandomi, M & Gandomi, AH 2024, 'Hateful Sentiment Detection in Real-Time Tweets: An LSTM-Based Comparative Approach', IEEE Transactions on Computational Social Systems, pp. 1-10.
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Saboj, JH, Nag, P, Saha, G & Saha, SC 2024, 'Heat transfer assessment incorporated with entropy generation within a curved corner structure enclosing a cold domain', Heat Transfer, vol. 53, no. 5, pp. 2460-2479.
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AbstractIn cold climates or winter countries, maintaining optimal room temperatures is essential for comfort and energy efficiency. Conventional square or rectangle‐shaped rooms often face challenges in achieving efficient heat transfer (HT) and uniform temperature distribution. To address these limitations, this study has been explored using curved corner cavities, varying their aspect ratio (AR = 1.0 and 0.5), and incorporating a circular shape cooler to enhance HT within the room. The curved corners promote better airflow circulation, creating a more efficient HT environment. The dimensionless governing equations and corresponding boundary conditions are solved numerically using the finite element method. This research aims to assess the optimized HT and entropy production within a curved corner cavity, varying their AR and enclosing a circular shape cooler to determine the most effective configuration for maximizing HT and energy efficiency in winter conditions. This study reveals that in the case without a cooler (WOC), the average Nusselt number () is higher in the curved rectangle cavity compared with the curved square cavity for all values. Using the curved square (AR = 1.0), increases by 191.86%, while with the curved rectangle (AR = 0.5), increases by 302.63% at . Additionally, in the case with a cooler (WC), is higher than the case WOC and , and an average total entropy increases for both the WOC and WC cases for all values. Transitioning from a square to a curved rectangle (AR = 0.5) WC, increases by 329.34% at . Furthermore, in the WOC case, the curved square cavity and, in the WC case, the curved rectangle show better energy efficiency and reduce environmental impact.

Sadeghirad, H, Yaghoubi Naei, V, O’Byrne, K, Warkiani, ME & Kulasinghe, A 2024, 'In situ characterization of the tumor microenvironment', Current Opinion in Biotechnology, vol. 86, pp. 103083-103083.
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Safarkhani, M, Ahmadi, S, Ipakchi, H, Saeb, MR, Makvandi, P, Ebrahimi Warkiani, M, Rabiee, N & Huh, Y 2024, 'Advancements in Aptamer‐Driven DNA Nanostructures for Precision Drug Delivery', Advanced Science, vol. 11, no. 26.
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AbstractDNA nanostructures exhibit versatile geometries and possess sophisticated capabilities not found in other nanomaterials. They serve as customizable nanoplatforms for orchestrating the spatial arrangement of molecular components, such as biomolecules, antibodies, or synthetic nanomaterials. This is achieved by incorporating oligonucleotides into the design of the nanostructure. In the realm of drug delivery to cancer cells, there is a growing interest in active targeting assays to enhance efficacy and selectivity. The active targeting approach involves a “key‐lock” mechanism where the carrier, through its ligand, recognizes specific receptors on tumor cells, facilitating the release of drugs. Various DNA nanostructures, including DNA origami, Tetrahedral, nanoflower, cruciform, nanostar, nanocentipede, and nanococklebur, can traverse the lipid layer of the cell membrane, allowing cargo delivery to the nucleus. Aptamers, easily formed in vitro, are recognized for their targeted delivery capabilities due to their high selectivity for specific targets and low immunogenicity. This review provides a comprehensive overview of recent advancements in the formation and modification of aptamer‐modified DNA nanostructures within drug delivery systems.

Safarkhani, M, Farasati Far, B, Lima, EC, Jafarzadeh, S, Makvandi, P, Varma, RS, Huh, Y, Ebrahimi Warkiani, M & Rabiee, N 2024, 'Integration of MXene and Microfluidics: A Perspective', ACS Biomaterials Science & Engineering, vol. 10, no. 2, pp. 657-676.
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Saha, BK, Jihan, JI, Ahammad, MZ, Saha, G & Saha, SC 2024, 'Enhanced thermal performance and entropy generation analysis in a novel cavity design with circular cylinder', Heat Transfer, vol. 53, no. 3, pp. 1446-1473.
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AbstractAnalyzing fluid dynamics and heat transfer holds significant importance in the design and enhancement of engineering systems. The current investigation utilizes the finite element method to explore natural convection and heat transfer intricacies within a novel cavity containing an inner circular cylinder under steady and laminar flow conditions. The principal aim of this study is to assess the impact of Rayleigh number (Ra), Bejan number (Be), and the presence of adiabatic, hot, and cold cylinders on heat transfer, entropy generation, and fluid flow. The range of Ra considered in this investigation spans from 103 to 106, while the Prandtl number for the air is fixed at 0.71. The findings illustrate that the presence of a cylinder leads to higher Be as Ra increase, compared to scenarios where no cylinder is present. This observation suggests that buoyancy forces dominate in the absence of a cylinder, resulting in significantly enhanced convective heat transfer efficiency. However, the presence of a heated cylinder within the tooth‐shaped cavity exerts a substantial influence on the overall thermal performance of the system. Notably, the average Nusselt Number (Nu) experiences a remarkable increase of 41.97% under the influence of a heated cylinder, when compared to situations where a cold cylinder is present. This elevated average Nu signifies improved heat transfer characteristics, ultimately resulting in an overall improvement in the thermal system's efficiency.

Saha, SC & Saha, G 2024, 'Effect of microplastics deposition on human lung airways: A review with computational benefits and challenges', Heliyon, vol. 10, no. 2, pp. e24355-e24355.
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Salgotra, R, Sharma, P, Raju, S & gandomi, AH 2024, 'A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference', Archives of Computational Methods in Engineering, vol. 31, no. 3, pp. 1749-1822.
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AbstractOptimization is a method which is used in every field, such as engineering, space, finance, fashion market, mass communication, travelling, and also in our daily activities. In every field, everyone always wants to minimize or maximize something called the objective function. Traditional and modern optimization techniques or Meta-Heuristic (MH) optimization techniques are used to solve the objective functions. But the traditional optimization techniques fail to solve the complex and real-world optimization problem consisting of non-linear objective functions. So many modern optimization techniques have been proposed exponentially over the last few decades to overcome these challenges. This paper discusses a brief review of the different benchmark test functions (BTFs) related to existing MH optimization algorithms (OA). It discusses the classification of MH algorithms reported in the literature regarding swarm-based, human-based, physics-based, and evolutionary-based methods. Based on the last half-century literature, MH-OAs are tabulated in terms of the proposed year, author, and inspiration agent. Furthermore, this paper presents the MATLAB and python code web-link of MH-OA. After reading this review article, readers will be able to use MH-OA to solve challenges in their field.

Salib, A, Jayatilleke, N, Seneviratne, JA, Mayoh, C, De Preter, K, Speleman, F, Cheung, BB, Carter, DR & Marshall, GM 2024, 'MYCN and SNRPD3 cooperate to maintain a balance of alternative splicing events that drives neuroblastoma progression', Oncogene, vol. 43, no. 5, pp. 363-377.
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AbstractMany of the pro-tumorigenic functions of the oncogene MYCN are attributed to its regulation of global gene expression programs. Alternative splicing is another important regulator of gene expression and has been implicated in neuroblastoma development, however, the molecular mechanisms remain unknown. We found that MYCN up-regulated the expression of the core spliceosomal protein, SNRPD3, in models of neuroblastoma initiation and progression. High mRNA expression of SNRPD3 in human neuroblastoma tissues was a strong, independent prognostic factor for poor patient outcome. Repression of SNRPD3 expression correlated with loss of colony formation in vitro and reduced tumorigenicity in vivo. The effect of SNRPD3 on cell viability was in part dependent on MYCN as an oncogenic co-factor. RNA-sequencing revealed a global increase in the number of genes being differentially spliced when MYCN was overexpressed. Surprisingly, depletion of SNRPD3 in the presence of overexpressed MYCN further increased differential splicing, particularly of cell cycle regulators, such as BIRC5 and CDK10. MYCN directly bound SNRPD3, and the protein arginine methyltransferase, PRMT5, consequently increasing SNRPD3 methylation. Indeed, the PRMT5 inhibitor, JNJ-64619178, reduced cell viability and SNRPD3 methylation in neuroblastoma cells with high SNRPD3 and MYCN expression. Our findings demonstrate a functional relationship between MYCN and SNRPD3, which maintains the fidelity of MYCN-driven alternative splicing in the narrow range required for neuroblastoma cell growth. SNRPD3 methylation and its protein-protein interface with MYCN represent novel therapeutic targets.

Salih, AK, Aditya, L, Matar, F, Nghiem, LD & Ton-That, C 2024, 'Improved flux and anti-fouling performance of a photocatalytic ZnO membrane on porous stainless steel substrate for microalgae harvesting', Journal of Membrane Science, vol. 694, pp. 122405-122405.
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Salvi, M, Acharya, MR, Seoni, S, Faust, O, Tan, R, Barua, PD, García, S, Molinari, F & Acharya, UR 2024, 'Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)', WIREs Data Mining and Knowledge Discovery, vol. 14, no. 3.
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AbstractAtrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand‐crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF‐related tasks. However, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population‐based clinical trials to facilitate translation into practice.This article is categorized under:Application Areas > Health CareApplication Areas > Science and TechnologyTechnologies > Artificial Intelligence

Salvi, M, Loh, HW, Seoni, S, Barua, PD, García, S, Molinari, F & Acharya, UR 2024, 'Multi-modality approaches for medical support systems: A systematic review of the last decade', Information Fusion, vol. 103, pp. 102134-102134.
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Samal, PB, Chen, SJ & Fumeaux, C 2024, '3-D-Corrugated Ground Structure: A Microstrip Antenna Miniaturization Technique', IEEE Transactions on Antennas and Propagation, vol. 72, no. 5, pp. 4010-4022.
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Samal, PB, Chen, SJ & Fumeaux, C 2024, 'Flexible Hybrid-Substrate Dual-Band Dual-Mode Wearable Antenna', IEEE Transactions on Antennas and Propagation, vol. 72, no. 2, pp. 1286-1296.
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Sardar, A, Umer, S, Rout, RK, Sahoo, KS & Gandomi, AH 2024, 'Enhanced Biometric Template Protection Schemes for Securing Face Recognition in IoT Environment', IEEE Internet of Things Journal, vol. 11, no. 13, pp. 23196-23206.
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Sarifudin, A, Yaningsih, I, Kristiawan, B, Wibawa, A, Miyazaki, T, Thu, K, Silitonga, A & Ong, H 2024, 'Investigation of granular natural stone materials as photothermal absorbers for sustainable and environmentally friendly energy harvesting', Journal of Applied Engineering Science, vol. 22, no. 2, pp. 147-162.
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The development of cost-effective and environmentally friendly solar thermal technologies that deliver high performance poses several challenges, where the collector and absorber components play a pivotal role. This research addresses these issues by investigating enhanced temperature generation using a 30 cm × 30 cm Fresnel lens collector under solar illumination from a xenon lamp. Natural stone materials (andesite, coal, and pumice), characterized by granular structures with an average diameter of 1.68-2.00 mm, were selected because of their abundance and eco-friendliness. This research is focused on evaluating the effect of Fresnel lens on temperature generation performance. Two types of temperature generation tests were carried out: wet tests (where the natural stone materials were immersed in distilled water) and dry tests (where the natural stone materials were used in dry conditions). The morphologies of the natural stone materials were examined using an optical microscope and scanning electron microscope. Furthermore, the optical properties of the natural stone materials were analyzed using an ultraviolet-visible (UV-VIS) spectrophotometer. The findings revealed that there were significant improvements in the photothermal absorber performance with the use of a Fresnel lens in dry tests, where the highest temperature was achieved for coal (103.25 °C), followed by andesite (89.00 °C) and pumice (73.00 °C). The impurities varied between the materials, where the impurities were most dominant for pumice while coal was more uniform. Further examination using scanning electron microscope showed that all materials had light-trapping structures in the form of rough surfaces, pores, and crack gaps. Andesite was dominated by rough surfaces, while coal and pumice were dominated by crack gaps and pores, respectively. However, based on the UV-VIS spectrophotometric results, there were no correlations between the optical properties (absorbance, reflect...

Sarker, IH, Janicke, H, Mohsin, A, Gill, A & Maglaras, L 2024, 'Explainable AI for cybersecurity automation, intelligence and trustworthiness in digital twin: Methods, taxonomy, challenges and prospects', ICT Express.
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Seiler, KM, Kong, FH & Fitch, R 2024, 'Multi-Horizon Multi-Agent Planning Using Decentralised Monte Carlo Tree Search', IEEE Robotics and Automation Letters, pp. 1-8.
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Senanayake, S, Pradhan, B, Wedathanthirige, H, Alamri, A & Park, H-J 2024, 'Monitoring soil erosion in support of achieving SDGs: A special focus on rainfall variation and farming systems vulnerability', CATENA, vol. 234, pp. 107537-107537.
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Senapati, A, Tripathy, HK, Sharma, V & Gandomi, AH 2024, 'Artificial intelligence for diabetic retinopathy detection: A systematic review', Informatics in Medicine Unlocked, vol. 45, pp. 101445-101445.
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Seoni, S, Molinari, F, Rajendra Acharya, U, Lih, OS, Barua, PD, García, S & Salvi, M 2024, 'Application of spatial uncertainty predictor in CNN-BiLSTM model using coronary artery disease ECG signals', Information Sciences, vol. 665, pp. 120383-120383.
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Shadmani, A, Nikoo, MR & Gandomi, AH 2024, 'Adaptive systematic optimization of a multi-axis ocean wave energy converter', Renewable and Sustainable Energy Reviews, vol. 189, pp. 113920-113920.
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Shadmani, A, Nikoo, MR, Gandomi, AH, Chen, M & Nazari, R 2024, 'Advancements in optimizing wave energy converter geometry utilizing metaheuristic algorithms', Renewable and Sustainable Energy Reviews, vol. 197, pp. 114398-114398.
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Shahapurkar, K, Zelalem, YM, Chenrayan, V, Soudagar, MEM, Fouad, Y, Kalam, MA & Kiran, MC 2024, 'Investigation on the mechanical and fracture properties of lightweight pumice epoxy composites', Polymer Engineering & Science, vol. 64, no. 3, pp. 1071-1082.
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AbstractPumice, which is prevalent in Ethiopia, is formed naturally during the quick cooling and solidifying of molten lava. Pumice is a naturally occurring mineral that, due to its high thermal resistance and lightweightness, can be an excellent candidate for reinforcing material for polymers. The present study investigates epoxy‐based composites reinforced with pumice particles by varying the pumice content (0, 10, 20, and 30 vol%). The densities of all composites reduce in comparison with neat epoxy as the volume proportion of pumice increases credited to the low density pumice particles. Tensile stress–strain curves depict neat epoxy with higher deformation than other pumice particulate‐filled composites in the linear elastic area followed by rapid brittle failure. Tensile modulus of all the composites increases in the range of 13%–67% in comparison with neat epoxy. The compressive characteristics of composites are greatly improved by the addition of pumice. Compressive moduli and specific compressive moduli of all composites increase with increasing volume fraction of pumice by 54%–58% and 65%–93%, respectively, in comparison with neat epoxy. The fracture toughness of P‐10, P‐20, and P‐30 composites improved by 18%, 54%, and 59%, respectively, as compared with neat epoxy mainly attributed to the foam‐like structure of pumice particles. SEM micrographs are used to analyze the morphology of compression‐tested specimens. Property mapping highlights the advantages of utilizing composites from present work over numerous syntactic foams.

Shams, KA, Reaz, MR, Rafi, MRU, Islam, S, Rahman, MS, Rahman, R, Reza, MT, Parvez, MZ, Chakraborty, S, Pradhan, B & Alamri, A 2024, 'MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition', IEEE Access, vol. 12, pp. 83638-83657.
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Shan, F, He, X, Armaghani, DJ & Sheng, D 2024, 'Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance', Journal of Rock Mechanics and Geotechnical Engineering, vol. 16, no. 5, pp. 1538-1551.
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Shan, F, He, X, Jahed Armaghani, D, Xu, H, Liu, X & Sheng, D 2024, 'Real-time forecasting of TBM cutterhead torque and thrust force using aware-context recurrent neural networks', Tunnelling and Underground Space Technology, vol. 152, pp. 105906-105906.
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Sharma, R, Saqib, M, Lin, CT & Blumenstein, M 2024, 'MASSNet: Multiscale Attention for Single-Stage Ship Instance Segmentation', Neurocomputing, vol. 594, pp. 127830-127830.
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Shen, H, Wang, H, Ma, Y, Li, L, Duan, S & Wen, S 2024, 'Multi-LRA: Multi logical residual architecture for spiking neural networks', Information Sciences, vol. 660, pp. 120136-120136.
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Shen, J, Li, H, Wang, L, Chen, G & Wen, S 2024, 'Fixed-Time Synergetic Control Based on SEAIQR Model for COVID-19 Epidemic', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 2, pp. 767-771.
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Shen, M, Meng, J, Xu, K, Yu, S & Zhu, L 2024, 'MemDefense: Defending against Membership Inference Attacks in IoT-based Federated Learning via Pruning Perturbations', IEEE Transactions on Big Data, pp. 1-13.
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Shen, S, Cai, C, Li, Z, Shen, Y, Wu, G & Yu, S 2024, 'Deep Q-network-based heuristic intrusion detection against edge-based SIoT zero-day attacks', Applied Soft Computing, vol. 150, pp. 111080-111080.
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Shen, S, Cai, C, Shen, Y, Wu, X, Ke, W & Yu, S 2024, 'MFGD3QN: Enhancing Edge Intelligence Defense Against DDoS With Mean-Field Games and Dueling Double Deep Q-Network', IEEE Internet of Things Journal, vol. 11, no. 13, pp. 23931-23945.
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Shen, S, Xue, S, Zhang, H, Cai, W, Huang, C, Guo, J, Wang, A-J, Ren, N, Wei, W, Ni, B-J & Hou, Y-N 2024, 'Vulnerable Methanogenic Community in Microbial Electrolysis Cells Alters Electron Allocation in Response to Community Coalescence', ACS ES&T Engineering, vol. 4, no. 6, pp. 1378-1390.
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Shen, Y, Shepherd, C, Ahmed, CM, Shen, S, Wu, X, Ke, W & Yu, S 2024, 'Game-theoretic analytics for privacy preservation in Internet of Things networks: A survey', Engineering Applications of Artificial Intelligence, vol. 133, pp. 108449-108449.
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Shen, Y, Shepherd, C, Ahmed, CM, Yu, S & Li, T 2024, 'Comparative DQN-Improved Algorithms for Stochastic Games-Based Automated Edge Intelligence-Enabled IoT Malware Spread-Suppression Strategies', IEEE Internet of Things Journal, vol. 11, no. 12, pp. 22550-22561.
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Shen, Y, Shepherd, C, Ahmed, M, Shen, S & Yu, S 2024, 'SGD3QN: Joint Stochastic Games and Dueling Double Deep Q-networks for Defending Malware Propagation in Edge Intelligence-Enabled Internet of Things', IEEE Transactions on Information Forensics and Security, pp. 1-1.
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Sheng, Z, Cao, Y, Yang, Y, Feng, Z-K, Shi, K, Huang, T & Wen, S 2024, 'Residual Temporal Convolutional Network With Dual Attention Mechanism for Multilead-Time Interpretable Runoff Forecasting', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Sheng, Z, Nie, L, Zhang, M, Chang, X & Yan, Y 2024, 'Stochastic Latent Talking Face Generation Toward Emotional Expressions and Head Poses', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 4, pp. 2734-2748.
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Shi, K, Lu, J, Fang, Z & Zhang, G 2024, 'Unsupervised Domain Adaptation Enhanced by Fuzzy Prompt Learning', IEEE Transactions on Fuzzy Systems, vol. 32, no. 7, pp. 4038-4048.
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Shi, K, Peng, X, Lu, H, Zhu, Y & Niu, Z 2024, 'Multiple Knowledge-Enhanced Meteorological Social Briefing Generation', IEEE Transactions on Computational Social Systems, vol. 11, no. 2, pp. 2002-2013.
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Shi, T, Xiang, X, Lei, J, Liu, B, Wang, F, Li, L, Aguilera, R & Li, W 2024, 'Parameter Boundary Characterization for DC Microgrid Islanding Detection Based on Time-Domain Voltage Oscillation Trajectory Analysis', IEEE Transactions on Smart Grid, pp. 1-1.
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Shi, X, Wei, W, Wu, L, Huang, Y & Ni, B-J 2024, 'Biosynthesis mechanisms of medium-chain carboxylic acids and alcohols in anaerobic microalgae fermentation regulated by pH conditions', Applied and Environmental Microbiology, vol. 90, no. 1.
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ABSTRACT Valorization of microalgae into high-value products and drop-in chemicals can reduce our dependence on non-renewable fossil fuels in an environmentally sustainable way. Among the valuable products, medium-chain carboxylic acids (MCCAs) and alcohols are attractive building blocks as fuel precursors. However, the biosynthetic mechanisms of MCCAs and alcohols in anaerobic microalgae fermentation and the regulating role of pH on the microbial structure and metabolism interaction among different functional groups have never been documented. In this work, we systematically investigated the roles of pH (5, 7, and 10) on the production of MCCAs and alcohols in anaerobic microalgae fermentation. The gene-centric and genome-centric metagenomes were employed to uncover the dynamics and metabolic network of the key players in the microbial communities. The results indicated that the pH significantly changed the product spectrum. The maximum production rate of alcohol was obtained at pH 5, while pH 7 was more beneficial for MCCA production. Metagenomic analysis reveals that this differential performance under different pH is attributed to the transformation of microbial guild and metabolism regulated by pH. The composition of various functional groups for MCCA and alcohol production also varies at different pH levels. Finally, a metabolic network was proposed to reveal the microbial interactions at different pH levels and thus provide insights into bioconversion of microalgae to high-value biofuels. IMPORTANCE Carboxylate platforms encompass a biosynthesis process involving a mixed and undefined culture, enabling the conversion of microalgae, rich in carbohydrates and protein, into valuable fuels and mitigating the risks associated with algae blooms. However, there is little known about the effects of pH on the metabolic pathways...

Shi, Y, Feng, A, Mao, S, Onggowarsito, C, Stella Zhang, X, Guo, W & Fu, Q 2024, 'Hydrogels in solar-driven water and energy production: Recent advances and future perspectives', Chemical Engineering Journal, vol. 492, pp. 152303-152303.
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Shi, Z, Feng, Y, Stewart, MG & Gao, W 2024, 'Virtual modelling based fragility assessment of structures under bushfire propagation', Reliability Engineering & System Safety, vol. 245, pp. 110000-110000.
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Shi, Z, Sun, X, Yang, Z, Cai, Y, Lei, G, Zhu, J & Lee, CHT 2024, 'Design Optimization of a Spoke-Type Axial-Flux PM Machine for In-Wheel Drive Operation', IEEE Transactions on Transportation Electrification, vol. 10, no. 2, pp. 3770-3781.
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Shivakumara, P, Banerjee, A, Nandanwar, L, Pal, U, Antonacopoulos, A, Lu, T & Blumenstein, M 2024, 'A new deep CNN for 3D text localization in the wild through shadow removal', Computer Vision and Image Understanding, vol. 238, pp. 103863-103863.
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Shu, X, Yang, Y, Liu, J, Chang, X & Wu, B 2024, 'BDAL: Balanced Distribution Active Learning for MRI Cardiac Multistructures Segmentation', IEEE Transactions on Industrial Informatics, vol. 20, no. 4, pp. 6099-6108.
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Shu, Y, Li, Q, Liu, L & Xu, G 2024, 'Semi-Supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment', IEEE Transactions on Multimedia, vol. 26, no. 99, pp. 4086-4096.
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Aesthetic attributes are crucial for aesthetics because they explicitly present some photo quality cues that a human expert might use to evaluate a photo's aesthetic quality. However, annotating aesthetic attributes is a time-consuming, costly, and error-prone task, which leads to the issue that photos available are partially annotated with attributes. To alleviate this issue, we propose a novel semi-supervised adversarial learning method for photo aesthetic assessment from partially attribute-annotated photos, which can greatly reduce the reliance on manual attribute annotation. Specifically, the proposed method consists of a score-attributes generator R, a photo generator G, and a discriminator D. The score-attributes generator learns the aesthetic score and attributes simultaneously to capture their dependencies and construct better feature representations. The photo generator reconstructs the photo by feeding aesthetic attributes, score, and informative feature representation. A discriminator is used to force the convergence of the features-attributes-score tuples generated from the score-attributes generator, the photo generator, and the ground-truth distribution in labeled data for training data. The proposed method significantly outperforms the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) from the existing best reported of 0.726 to 0.761 on Aesthetic and attributes database and 0.756 to 0.774 on Aesthetic visual analysis database, respectively.

Siami, M, Naderpour, M, Ramezani, F & Lu, J 2024, 'Risk Assessment Through Big Data: An Autonomous Fuzzy Decision Support System', IEEE Transactions on Intelligent Transportation Systems, pp. 1-12.
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Silva, IN, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2024, 'The influence of soil fabric on the monotonic and cyclic shear behaviour of consolidated and compacted specimens', Canadian Geotechnical Journal, vol. 61, no. 6, pp. 1159-1176.
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While the fabric of soil can significantly influence its behaviour, the effect of varying fabric parameters on the subgrade shear response is still not well understood. This study creates soil specimens with different fabrics which are then captured through X-ray microscopic-computed tomography scanning and quantified by image processing techniques. A comprehensive laboratory investigation is conducted to understand how the soil fabric affects its monotonic and cyclic shear behaviour. The results indicate that the consolidation method creates a more homogeneous fabric with mainly small-to-medium interconnected pores, whereas the compaction technique creates significantly large and mostly inter-aggregate pores with lower connectivity. In this regard, the consolidated specimens exhibit an elastic-perfectly plastic behaviour, while the compacted specimens show strain-hardening transformation during isotropic monotonic shearing. Under anisotropic conditions, the compacted specimens exhibit a greater strain softening response and excess pore pressure than the consolidated specimens because they have a weaker fabric. Furthermore, the compacted specimens show a smaller threshold strain at a lower critical number of cycles due to the collapse of large pores. These current findings prove the decisive role that soil fabric plays in determining the shear response and failure of subgrade soils.

Singh, M, Sahoo, KS & Gandomi, AH 2024, 'An Intelligent-IoT-Based Data Analytics for Freshwater Recirculating Aquaculture System', IEEE Internet of Things Journal, vol. 11, no. 3, pp. 4206-4217.
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Singh, RB, Patra, KC, Pradhan, B & Samantra, A 2024, 'HDTO-DeepAR: A novel hybrid approach to forecast surface water quality indicators', Journal of Environmental Management, vol. 352, pp. 120091-120091.
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Singh, S, S, S, Varma, P, Sreelekha, G, Adak, C, Shukla, RP & Kamble, VB 2024, 'Metal oxide-based gas sensor array for VOCs determination in complex mixtures using machine learning', Microchimica Acta, vol. 191, no. 4.
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AbstractDetection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO–Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80–100 nm. The NiO and CuO films have ultrafine particle sizes of < 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R2 value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring. Graphical Abstract

Singha, C, Sahoo, S, Govind, A, Pradhan, B, Alrawashdeh, S, Hamdi Aljohani, T, Almohamad, H, Md Towfiqul Islam, AR & Abdo, HG 2024, 'Impacts of hydroclimate change on climate-resilient agriculture at the river basin management', Journal of Water and Climate Change, vol. 15, no. 1, pp. 209-232.
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Abstract This paper focuses on exploring the potential of Climate resilient agriculture (CRA) for river basin-scale management. Our analysis is based on long-term historical and future climate and hydrological datasets within a GIS environment, focusing on the Ajoy River basin in West Bengal, Eastern India. The standardized anomaly index (SAI) and slope of the linear regression (SLR) methods were employed to analyse the spatial pattern of the climate variables (precipitation, Tmax and Tmin) and hydrological variables (actual evapotranspiration (AET), runoff (Q), vapor pressure deficit (VPD), potential evapotranspiration (PET), and climate water deficit (DEF)) using the TerraClimate dataset spanning from 1958 to 2020. Future climate trend analysis spanning 2021 to 2050 was conducted using the CMIP6 based GCMs (MIROC6 and EC-Earth3) dataset under shared socio-economic pathway (SSP2-4.5, SSP5-8.5 and historical). For spatiotemporal water storage analysis, we relied on Gravity Recovery and Climate Experiment (GRACE) from the Center for Space Research (CSR) and the Jet Propulsion Laboratory (JPL) data, covering the period from 2002 to 2021. Validation was performed using regional groundwater level data, employing various machine learning classification models. Our findings revealed a negative precipitation trend (approximately −0.04 mm/year) in the southern part, whereas the northern part exhibited a positive trend (approximately 0.10 mm/year).

Skarding, J, Gabrys, B & Musial, K 2024, 'On the Effectiveness of Heterogeneous Ensembles Combining Graph Neural Networks and Heuristics for Dynamic Link Prediction', IEEE Transactions on Network Science and Engineering, vol. 11, no. 4, pp. 3250-3259.
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Skarding, J, Hellmich, M, Gabrys, B & Musial, K 2024, 'Corrections to “A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction”', IEEE Access, vol. 12, pp. 6912-6913.
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Sohn, W, Jiang, J, Phuntsho, S & Shon, HK 2024, 'Membrane bioreactor incorporated with biofilm carriers and activated carbon for enhanced biological nitrification of urine', Desalination, vol. 570, pp. 117061-117061.
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Long hydraulic retention time (HRT) of a membrane bioreactor (MBR) for the nitrification of source-separated urine remains one of the major challenges of which reduces energy efficiency and increases footprint. In this study, a powdered activated carbon (PAC) incorporated MBR with biofilm carrier addition was operated to investigate their effects on the enhancement of nitrification rates and their HRT. The presence of biofilm carrier in the MBR reduced restabilisation time and the start-up period by 26 % and 18 %, respectively. The combination of biofilm carriers and PAC showed a significantly higher nitrification rate of 304 ± 73 mgN/L·d compared to 194 ± 60 mgN/L·d for the conventional MBR (control), which significantly helped reduced HRT during urine treatment. This study therefore shows that PAC and biofilm carrier incorporated MBR is more compact and high-performing contributing to commercial applications and helping achieve circular economy of nutrients.

Sohn, W, Jiang, J, Su, Z, Zheng, M, Wang, Q, Phuntsho, S & Kyong Shon, H 2024, 'Microbial community analysis of membrane bioreactor incorporated with biofilm carriers and activated carbon for nitrification of urine', Bioresource Technology, vol. 397, pp. 130462-130462.
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Song, F, Wang, L, Hu, X, Zong, X & Wen, S 2024, 'Novel distributed event/self-triggered sliding-mode control: Application to practical fixed-time consensus of second-order multi-agent systems', Information Sciences, vol. 677, pp. 120808-120808.
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Song, J, Wen, D, Xu, L, Qin, L, Zhang, W & Lin, X 2024, 'On Querying Historical Connectivity in Temporal Graphs', Proceedings of the ACM on Management of Data, vol. 2, no. 3, pp. 1-25.
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We study the historical connectivity query in temporal graphs where edges continuously arrive. Given an arbitrary time window, and two query vertices, the problem aims to identify if two vertices are connected by a path in the snapshot of the window. The state-of-the-art method designs an index based on the two-hop cover, and updating the index is costly when new edges arrive. In this paper, we propose a new framework and design a novel forest-based index for historical connectivity queries. The index enables us to answer queries by searching if two vertices are connected in the forest. We update the index by modifying a forest structure. Our techniques also work for connectivity query processing in a sliding window of temporal graphs. Extensive experiments have been conducted to show the considerable advantages of our approach compared with the state-of-the-art methods in both historical connectivity queries and sliding-window connectivity queries.

Song, L, Zhang, T, Qin, P-Y, Du, J & Guo, YJ 2024, 'Sub-THz Broadband Transmitting Metasurfaces With Enhanced Frequency-Scanning Capability', IEEE Transactions on Terahertz Science and Technology, vol. 14, no. 1, pp. 82-90.
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In this manuscript, broadband transmitting metasurfaces are developed to enhance frequency-dependent beam scanning. A triple-gold-layer unit cell is designed for wideband transmissions with low losses and quasi-linear phase variations. Comprehensive analyses of phase-gradient metasurfaces are provided to enable high-efficiency and wide-angle frequency scanning. For verifications, two metasurfaces with different phase gradients are simulated, manufactured, and measured. Continuous beam scanning performance has been demonstrated successfully from 80 GHz to 220 GHz, showing beam scanning ranges of 25° and 31.5° from two prototypes, respectively. Peak transmission efficiencies of 84% and 75% have been obtained from experiments. The results from simulation and measurement agree very well. The developed metasurfaces have many potential applications such as frequency-scanning terahertz (THz) imaging.

Song, L-Z, Squires, A, van der Laan, T & Du, J 2024, 'THz graphene-integrated metasurface for electrically reconfigurable polarization conversion', Nanophotonics, vol. 13, no. 13, pp. 2349-2359.
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Abstract Terahertz (THz) waves have been widely hailed as a key enabling technology for future sixth generation (6G) wireless networks. Dynamic modulation of their polarization states is of great attraction for high-capacity communications and anisotropic sensing. The development of such technology is, however, still in very early stage owing to the difficulties of realizing electrical reconfigurability for THz devices. Artificially constructed metasurfaces and new nanomaterials, such as graphene, have been shown to provide revolutionary platforms for manipulating and controlling the wave properties, especially at THz frequencies. This work leverages the light–matter interaction in a graphene-integrated metasurface functioning as an electrically reconfigurable THz polarization converter. A novel graphene-gold bilayer topology is applied to construct such a metasurface which enables wide-range electrical tunability of the polarization conversion. Under a y-polarized illumination, the reflected components of x- and y-polarizations are tuned dynamically through an external bias voltage across the metasurface, thereby producing an elliptically polarized wave with tuneable ellipticity and angle. By changing the voltage from 0 V to 12 V, the reflected polarization ellipticity has been tuned from −0.94 to −0.5 at around 240 GHz, featuring linear-to-circular and linear-to-elliptical polarization conversions. Meanwhile, the polarization angle has been modulated from 12° to −23° at around 236 GHz. This work provides an experimentally validated THz graphene-integrated metasurface with wide polarization modulation depths, low biasing voltages and simple configuration. It promises great potential for applications in future THz communications and sensing.

Song, L-Z, Zhang, T, Lai, J-X, Yang, Y & Du, J 2024, 'A 180-GHz to 220-GHz Wideband Transmitarray With Wide-Angle Beam Steering for Intersatellite Communications', IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 950-955.
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Song, R, Liu, K, Liu, C, Yang, J & Li, J 2024, 'The Dynamic Compressive Behavior of Waved Fiber-Reinforced Ultrahigh-Performance Cementitious Composites Containing Fly Ash and Ground Granulated Blast-Furnace Slag', Journal of Materials in Civil Engineering, vol. 36, no. 1.
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Song, X, Liu, C, Zheng, Y, Feng, Z, Li, L, Zhou, K & Yu, X 2024, 'HairStyle Editing via Parametric Controllable Strokes', IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 7, pp. 3857-3870.
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Soudagar, MEM, Shelare, S, Marghade, D, Belkhode, P, Nur-E-Alam, M, Kiong, TS, Ramesh, S, Rajabi, A, Venu, H, Yunus Khan, TM, Mujtaba, MA, Shahapurkar, K, Kalam, MA & Fattah, IMR 2024, 'Optimizing IC engine efficiency: A comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning', Energy Conversion and Management, vol. 307, pp. 118337-118337.
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Staplevan, MJ, Ansari, AJ, Ahmed, A & Hai, FI 2024, 'Impact of bioplastic contamination on the mechanical recycling of conventional plastics', Waste Management, vol. 185, pp. 1-9.
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Stuart, B, Guan, J, Collins, S, Thomas, P & Ueland, M 2024, 'A preliminary study of non-woven fabrics for forensic identification purposes', Australian Journal of Forensic Sciences, vol. 56, no. 2, pp. 144-153.
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While traditional woven textiles have been the subject of many forensic investigations, non-woven fabrics have received minimal attention thus far. Given the expansion of commercial applications of non-woven fabrics, a preliminary investigation of household wipes has been carried out to characterize the compositions of these widely available non-woven fabrics. Infrared spectroscopy and thermogravimetric analysis were employed to identify the fibre type and additives of three types of commercial wipes. Polyester and/or viscose fibres were found to be the main components and, along with the identification of binders, enable source types to be differentiated. The predicted different sensitivities of the fibre types to biodeterioration highlights the importance of future environmental studies for the correct characterization of non-woven fabrics in evidence.

Sturnieks, DL, Hicks, C, Smith, N, Ratanapongleka, M, Menant, J, Turner, J, Lo, J, Chaplin, C, Garcia, J, Valenzuela, MJ, Delbaere, K, Herbert, RD, Sherrington, C, Toson, B & Lord, SR 2024, 'Exergame and cognitive training for preventing falls in community-dwelling older people: a randomized controlled trial', Nature Medicine, vol. 30, no. 1, pp. 98-105.
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Su, G, Wang, H, Zhang, Y, Zhang, W & Lin, X 2024, 'Simple and deep graph attention networks', Knowledge-Based Systems, vol. 293, pp. 111649-111649.
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Su, Y, Qian, J, Wang, J, Mi, X, Huang, Q, Zhang, Y, Jiang, Q & Wang, Q 2024, 'Unraveling the mechanism of norfloxacin removal and fate of antibiotics resistance genes (ARGs) in the sulfur-mediated autotrophic denitrification via metagenomic and metatranscriptomic analyses', Science of The Total Environment, vol. 922, pp. 171328-171328.
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Su, Z, Sun, X, Lei, G & Yao, M 2024, 'Model-Free Predictive Current Control for Dual Three-Phase PMSM Drives With an Optimal Modulation Pattern', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 10140-10149.
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Sultan, HS, Ali, MH, Shafi, J, Fteiti, M, Baro, M, Almutairi, K, Islam, MS, Harb, K, Alharbi, FS & Ghalambaz, M 2024, 'Design improvement of latent heat thermal energy storage in wavy channel enclosures using neural networks', Journal of Energy Storage, vol. 79, pp. 110061-110061.
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Sultan, HS, Ali, MH, Shafi, J, Fteiti, M, Baro, M, Alresheedi, F, Islam, MS, Yusaf, T & Ghalambaz, M 2024, 'Improving phase change heat transfer in an enclosure filled by uniform and heterogenous metal foam layers: A neural network design approach', Journal of Energy Storage, vol. 85, pp. 110954-110954.
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Sun, J, Gao, P, Wen, S, Liu, P & Wang, Y 2024, 'Memristor-Based Conditioned Inhibition Neural Network Circuit With Blocking Generalization and Differentiation', IEEE Internet of Things Journal, vol. 11, no. 7, pp. 11259-11270.
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Sun, J, Liu, Y, Luo, Q, Ren, Y & Guo, YJ 2024, 'Efficient and Accurate Pattern Synthesis of Circular Antenna Array Employing Iterative Fast Segmented Cyclic Convolution', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 1, pp. 269-273.
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Sun, K, Wu, J, Pan, Q, Zheng, X, Li, J & Yu, S 2024, 'Leveraging Digital Twin and DRL for Collaborative Context Offloading in C-V2X Autonomous Driving', IEEE Transactions on Vehicular Technology, vol. 73, no. 4, pp. 5020-5035.
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Sun, M, Xu, K, Yang, Y, Chen, S, Wang, T, Yu, D, Yu, X & Wang, G 2024, 'Folded Transmitarray Antenna via Independent Amplitude/Phase Control With Low Side-Lobe for Millimeter-Wave Communication', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 4, pp. 2004-2008.
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Sun, W, Guo, W, Li, B, Wen, S, Cao, J & Abdel-Aty, M 2024, 'Finite/Fixed-Time Controls of Neural Networks in a Signed Graph', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 2, pp. 1049-1058.
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Sun, X, Dong, Z, Jin, Z, Lei, G & Tian, X 2024, 'System-Level Energy Management Optimization of Power-Split Hybrid Electric Vehicle Based on Nested Design', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 10987-10997.
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Sun, X, Lin, X, Guo, D, Lei, G & Yao, M 2024, 'Improved Deadbeat Predictive Current Control With Extended State Observer for Dual Three-Phase PMSMs', IEEE Transactions on Power Electronics, vol. 39, no. 6, pp. 6769-6782.
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Sun, X, Su, Z, Lei, G & Yao, M 2024, 'Robust Predictive Cascaded Speed and Current Control for PMSM Drives Considering Parameter Variations', IEEE Transactions on Industrial Electronics, vol. 71, no. 9, pp. 10235-10245.
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Sun, X, Su, Z, Lei, G, Cai, Y & Yao, M 2024, 'Adaptive Model-Free Predictive Current Control for SPMSM Drives With Optimal Virtual Vector Modulation', IEEE/ASME Transactions on Mechatronics, pp. 1-10.
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Sun, X, Wang, N, Yao, M & Lei, G 2024, 'Position Sensorless Control of SRMs Based on Improved Sliding Mode Speed Controller and Position Observer', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Sun, X, Wang, N, Yao, M & Lei, G 2024, 'Position Sensorless Control of Switched Reluctance Motors Based on Angle Adjustment Using Nonlinear Inductance and Flux Model', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Sun, X, Wen, Y, Zhang, L, Yao, M & Lei, G 2024, 'Fault Detection and Fault-Tolerant Control of Switched Reluctance Motor Based on Dual-Sensor Current Detection Scheme', IEEE Transactions on Transportation Electrification, pp. 1-1.
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Sun, X, Zhu, Y, Cai, Y, Yao, M, Sun, Y & Lei, G 2024, 'Optimized-Sector-Based Model Predictive Torque Control With Sliding Mode Controller for Switched Reluctance Motor', IEEE Transactions on Energy Conversion, vol. 39, no. 1, pp. 379-388.
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Sun, Y-L, Wang, H-L, Ngo, HH, Guo, W, Ni, B-J, Zhang, X-N & Wei, W 2024, 'Adapting to seasonal temperature variations: A dynamic multi-subunit strategy for sulfur autotrophic denitrification bioreactors', Environmental Research, vol. 240, pp. 117493-117493.
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Sun, Y-L, Wang, J-Y, Ngo, HH, Wei, W, Guo, W, Zhang, X-N, Cheng, H-Y, Yang, J-X & Wang, A-J 2024, 'Inducement mechanism and control of self-acidification in elemental sulfur fluidizing bioreactor', Bioresource Technology, vol. 393, pp. 130081-130081.
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Swain, S, Mishra, PK, Nandi, S, Pradhan, B, Sahoo, S & Al-Ansari, N 2024, 'A simplistic approach for monitoring meteorological drought over arid regions: a case study of Rajasthan, India', Applied Water Science, vol. 14, no. 2.
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AbstractThe commonly used precipitation-based drought indices typically rely on probability distribution functions that can be suitable when the data exhibit minimal discrepancies. However, in arid and semi-arid regions, the precipitation data often display significant discrepancies due to highly irregular rainfall patterns. Consequently, imposing any probability distributions on the data for drought analysis in such regions may not be effective. To address this issue, this study employs a novel drought index called the Discrepancy Precipitation Index (DPI), specifically designed for arid regions. Unlike traditional methods, the DPI does not impose a probability distribution on the precipitation data; instead, it relies on the discrepancy between the data and the mean value. Drought severity classifications (i.e., Drought-I, Drought-II, and Drought-III) are proposed based on the DPI values. The DPI is used to characterize and assess the meteorological drought years based on annual and monsoonal precipitation over nineteen districts in Western Rajasthan, India, during 1901–2019. Additionally, a novel statistic called Discrepancy Measure (DM) is employed to assess the degree of discrepancy in the precipitation climatology of the districts for annual and monsoon precipitation time series. Based on annual precipitation, Jaisalmer district exhibited the highest number of historical drought years (35), whereas three districts, i.e., Jhunjhunu, Dausa, and Bhilwara exhibited the lowest number of drought years (11). Similarly, based on monsoon precipitation, Jaisalmer and Bhilwara encountered the highest (34) and the lowest (11) number of drought years, respectively. The return period of Drought-II is lower for monsoon precipitation-based DPI as compared to that of the annual precipitation-based DPI for all the districts. The DM and DPI-based total number of droughts are found to be strongly correlated for both annual and monsoon...

Taghikhah, FR, Taghikhah, M, Marshall, JP & Voinov, A 2024, 'Navigating the community renewable energy landscape: An analytics-driven policy formulation', Applied Energy, vol. 362, pp. 123007-123007.
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Taheri, A, RahimiZadeh, K, Beheshti, A, Baumbach, J, Rao, RV, Mirjalili, S & Gandomi, AH 2024, 'Partial reinforcement optimizer: An evolutionary optimization algorithm', Expert Systems with Applications, vol. 238, pp. 122070-122070.
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Tai, P, Indraratna, B, Rujikiatkamjorn, C, Chen, R & Li, Z 2024, 'Cyclic behaviour of stone column reinforced subgrade under partially drained condition', Transportation Geotechnics, vol. 47, pp. 101281-101281.
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Tai, T-W, Chen, H-Y, Shih, C-A, Huang, C-F, McCloskey, E, Lee, J-K, Yeap, SS, Cheung, C-L, Charatcharoenwitthaya, N, Jaisamrarn, U, Kuptniratsaikul, V, Yang, R-S, Lin, S-Y, Taguchi, A, Mori, S, Li-Yu, J, Ang, SB, Chan, D-C, Chan, WS, Ng, H, Chen, J-F, Tu, S-T, Chuang, H-H, Chang, Y-F, Chen, F-P, Tsai, K-S, Ebeling, PR, Marin, F, Nistal Rodríguez, FJ, Shi, H, Hwang, KR, Kim, K-K, Chung, Y-S, Reid, IR, Chandran, M, Ferrari, S, Lewiecki, EM, Hew, FL, Ho-Pham, LT, Nguyen, TV, Nguyen, VH, Lekamwasam, S, Pandey, D, Bhadada, S, Chen, C-H, Hwang, J-S & Wu, C-H 2024, 'Asia-Pacific consensus on long-term and sequential therapy for osteoporosis', Osteoporosis and Sarcopenia, vol. 10, no. 1, pp. 3-10.
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Tan, S, Yu, S, Liu, W, He, D & Chan, S 2024, 'You Can Glimpse but You Cannot Identify: Protect IoT Devices From Being Fingerprinted', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 3, pp. 1210-1223.
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Tang, Y, Liu, J, Zhou, Z, Yu, X & Huo, Y 2024, 'Efficient 3D Representation Learning for Medical Image Analysis', World Scientific Annual Review of Artificial Intelligence, vol. 02.
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Machine learning approaches have significantly advanced the 3D medical images analysis, such as the CT and MRI scans, which enables improved diagnosis and treatment evaluation. These image volumes provide rich spatial context for understanding the internal brain and body anatomies. Typical medical image analysis tasks, such as segmentation, reconstruction and registration, are essential for characterizing this context. Related to 3D data formats, meshes, point clouds and others are used to represent the anatomical structures, each with unique applications. To better capture the spatial information and address data scarcity, self- and semi-supervised learning methods have emerged. However, efficient 3D representation learning remains challenging. Recently, Transformers have shown promise, leveraging the self-attention mechanisms that perform well on transfer learning and self-supervised methods. These techniques are applied for medical domains without extensive manual labeling. This work explores data-efficient models, scalable deep learning, semantic context utilization and transferability in 3D medical image analysis. We also evaluated the foundational models, self-supervised pre- training, transfer learning and prompt tuning, thus advancing this critical field.

Tao, M, Yang, Z, Zhao, Y, Wu, X & Wu, C 2024, 'Failure characteristics of microwave heat-treated stressed sandstone: Implications for deep rock breakage using TBM cutting', Energy, vol. 292, pp. 130489-130489.
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Tao, X, Yan, S, Gong, X & Adak, C 2024, 'Learning Multiresolution Features for Unsupervised Anomaly Localization on Industrial Textured Surfaces', IEEE Transactions on Artificial Intelligence, vol. 5, no. 1, pp. 127-139.
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Tapas, MJ, Thomas, P & Sirivivatnanon, V 2024, 'Effect of carbonation on high-slag binders', Concrete in Australia, vol. 50, no. 2, pp. 52-58.
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Cement production results in the significant release of CO2 into the environment. Therefore, the partial substitution of cement with supplementary cementitious materials (SCMs) is a primary strategy to reduce the upfront embodied carbon of concrete. Amongst the commercially available SCMs, ground granulated blast furnace slag (GGBFS) which has a composition closest to that of cement, allows the highest level of substitution in binary and ternary blends at ≥50%. GGBFS, other than helping lower the upfront embodied carbon, also improves most durability properties of the concrete including resistance to chlorides, sulfate and alkali-silica reaction. However, despite the known benefits, the use of high substitution levels of GGBFS has also been documented to reduce the carbonation resistance of the concrete. Carbonation is a natural process that has both detrimental and beneficial effects. Whilst carbonation results in the drop of the concrete pH increasing the susceptibility of the steel to reinforcement corrosion, it also facilitates the reabsorption of CO2 into the concrete. Owing to the increasing use of GGBFS and interest in carbonation, this paper investigates the effect of carbonation on the phase development and microstructure of high GGBFS binders. This study shows that: 1) the main driver for the drop in the pH of the pore solution during carbonation is the consumption of portlandite, 2) carbonation not only reduces the pH but also results in the decalcification of other phases and modification of the microstructure of the binder and, 3) binders with lower CaO content carbonate faster but have lower capacity to absorb CO2 per gram.

Tatli, S, Macin, G, Tasci, I, Tasci, B, Barua, PD, Baygin, M, Tuncer, T, Dogan, S, Ciaccio, EJ & Acharya, UR 2024, 'Transfer-transfer model with MSNet: An automated accurate multiple sclerosis and myelitis detection system', Expert Systems with Applications, vol. 236, pp. 121314-121314.
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Teng, J, Dong, A, Zhang, S, Zhang, X & Sheng, D 2024, 'Freezing‐Thawing Hysteretic Behavior of Soils', Water Resources Research, vol. 60, no. 7.
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AbstractThe soil freezing characteristic curve (SFCC) plays a crucial role in investigating the soil freezing‐thawing process. Due to the challenges associated with measuring the SFCC, there is a shortage of high‐quality or rigorous test results with sufficient metadata to be effectively used for applications. Current researchers typically conduct freezing tests to measure the SFCC and assume a singular SFCC when studying the freezing‐thawing process of soils, although limited studies indicated that there is a hysteresis during the freezing and thawing process. In this paper, a series of freezing‐thawing tests were performed to assess the SFCC, utilizing a precise nuclear magnetic resonance apparatus. The test results reveal a hysteresis between the SFCC obtained from the freezing process and that from the thawing process. Through analyzing the test results, the hysteresis mechanism of the SFCC is attributed to supercooling. Supercooling inhibits initial pore ice formation during freezing, causing a drastic liquid water‐ice phase change once supercooling ends. Despite being considered closely related, the hysteresis of the SFCC differs from the soil water characteristic curve (SWCC), and the models used to simulate the hysteresis of SWCC cannot directly be used. To address the impact of supercooling on soil freezing‐thawing hysteresis, a novel theoretical model is proposed. Comparisons between the measured and predicted results affirm the validity of the proposed model.

Thacharodi, A, Hassan, S, Meenatchi, R, Bhat, MA, Hussain, N, Arockiaraj, J, Ngo, HH, Sharma, A, Nguyen, HT & Pugazhendhi, A 2024, 'Mitigating microplastic pollution: A critical review on the effects, remediation, and utilization strategies of microplastics', Journal of Environmental Management, vol. 351, pp. 119988-119988.
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Thacharodi, A, Meenatchi, R, Hassan, S, Hussain, N, Bhat, MA, Arockiaraj, J, Ngo, HH, Le, QH & Pugazhendhi, A 2024, 'Microplastics in the environment: A critical overview on its fate, toxicity, implications, management, and bioremediation strategies', Journal of Environmental Management, vol. 349, pp. 119433-119433.
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Thiviyanathan, VA, Ker, PJ, Hoon Tang, SG, Amin, EPP, Yee, W, Hannan, MA, Jamaludin, Z, Nghiem, LD & Indra Mahlia, TM 2024, 'Microalgae biomass and biomolecule quantification: Optical techniques, challenges and prospects', Renewable and Sustainable Energy Reviews, vol. 189, pp. 113926-113926.
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Tian, H, Liu, B, Zhu, T, Zhou, W & Yu, PS 2024, 'MultiFair: Model Fairness With Multiple Sensitive Attributes', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Tian, J, Lei, J, Zhang, J, Xie, W & Li, Y 2024, 'SwiMDiff: Scene-Wide Matching Contrastive Learning With Diffusion Constraint for Remote Sensing Image', IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-13.
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Tian, S, Li, X, Ren, J, Zhou, Z, Wang, F, Shi, K, Xu, J, Gu, T & Shon, H 2024, 'Emerging heat-localized solar distillation systems: Solar interfacial distillation VS photothermal membrane distillation', Desalination, vol. 572, pp. 117147-117147.
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Heat-localized solar distillation (HLSD) is an emerging environmentally- friendly high-efficiency distillation technology for clean water production. Solar interfacial distillation (SID) and photothermal membrane distillation (PMD) are two featured HLSD processes that have attracted a lot of attentions from researchers recently. Both SID and PMD systems produce water vapor from thin film surfaces where sunlight can be absorbed and converted as localized-heat to minimize heat loss and improve heat conversion efficiency. This article offers an overview of SID and PMD, including their respective classifications and characteristics. Subsequently, the water production capacities and purification efficiencies of the two systems are compared and the influencing factors are analyzed. Moreover, this paper compares the crystallization processes between the two systems and elucidates the methods employed to prevent salt fouling and achieve salt recovery. Finally, this study highlights the current challenges and future prospects of both systems to guide future research.

Tian, Z, Cui, L, Zhang, C, Tan, S, Yu, S & Tian, Y 2024, 'The Role of Class Information in Model Inversion Attacks Against Image Deep Learning Classifiers', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 4, pp. 2407-2420.
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Tian, Z, Vo, H, Zhang, C, Min, G & Yu, S 2024, 'An Asynchronous Multi-Task Semantic Communication Method', IEEE Network.
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Semantic communication has sparked great interest, due to the rising demands of emerging applications on high communication capacity and low latency. The majority of existing semantic communication methods are task-oriented, which transmit task-related semantic information via synchronous trained deep learning-based (DL-based) encoders and decoders. However, these methods have limitations in handling multi-task communications. Moreover, the synchronous training paradigm also leads to significant communication overhead in the establishing phase. In this article, we propose an asynchronous multi-task semantic communication method. In the proposed method, the DL-based encoder is trained independently using a contrastive learning method to extract task-independent semantic knowledge. Then, the receiver trains different DL-based decoders to perform various communication tasks based on the pre-trained encoder. Our method enables the accomplishment of multiple communication tasks in a single transmission. Moreover, the asynchronous training paradigm can reduce the communication overhead during the training phase of our system. The experimental results demonstrate that the proposed method achieves state-of-the-art performance in image classification and reconstruction tasks while requiring less than 10% of the training communication time compared to existing semantic communication systems.

Tian, Z, Vo, H, Zhang, C, Min, G & Yu, S 2024, 'An Asynchronous Multi-Task Semantic Communication Method', IEEE Network, vol. 38, no. 4, pp. 275-283.
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Tran, NH, Sais, D & Tran, N 2024, 'Advances in human papillomavirus detection and molecular understanding in head and neck cancers: Implications for clinical management', Journal of Medical Virology, vol. 96, no. 6.
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AbstractHead and neck cancers (HNCs), primarily head and neck squamous cell carcinoma (HNSCC), are associated with high‐risk human papillomavirus (HR HPV), notably HPV16 and HPV18. HPV status guides treatment and predicts outcomes, with distinct molecular pathways in HPV‐driven HNSCC influencing survival rates. HNC incidence is rising globally, with regional variations reflecting diverse risk factors, including tobacco, alcohol, and HPV infection. Oropharyngeal cancers attributed to HPV have significantly increased, particularly in regions like the United States. The HPV16 genome, characterized by oncoproteins E6 and E7, disrupts crucial cell cycle regulators, including tumor protein p53 (TP53) and retinoblastoma (Rb), contributing to HNSCC pathogenesis. P16 immunohistochemistry (IHC) is a reliable surrogate marker for HPV16 positivity, while in situ hybridization and polymerase chain reaction (PCR) techniques, notably reverse transcription‐quantitative PCR (RT‐qPCR), offer sensitive HPV detection. Liquid‐based RT‐qPCR, especially in saliva, shows promise for noninvasive HPV detection, offering simplicity, cost‐effectiveness, and patient compliance. These molecular advancements enhance diagnostic accuracy, guide treatment decisions, and improve patient outcomes in HNC management. In conclusion, advances in HPV detection and molecular understanding have significant clinical management implications. Integrating these advancements into routine practice could ultimately improve patient outcomes.

Tuan, HD, Nasir, AA, Dutkiewicz, E, Poor, HV & Hanzo, L 2024, 'Active-RIS Enhances the Multi-User Rate of Multi-Carrier Communications', IEEE Transactions on Vehicular Technology, pp. 1-15.
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Tuan, HD, Nasir, AA, Dutkiewicz, E, Poor, HV & Hanzo, L 2024, 'RIS-Aided Multiple-Input Multiple-Output Broadcast Channel Capacity', IEEE Transactions on Communications, vol. 72, no. 1, pp. 117-132.
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Tuan, LA & Ha, QP 2024, 'Adaptive fractional-order integral fast terminal sliding mode and fault-tolerant control of dual-arm robots', Robotica, vol. 42, no. 5, pp. 1476-1499.
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AbstractClosed-loop kinematics of a dual-arm robot (DAR) often induces motion conflict. Control formulation is increasingly difficult in face of actuator failures. This article presents a new approach for fault-tolerant control of DARs based on advanced sliding mode control. A comprehensive fractional-order model is proposed taking nonlinear viscous and viscoelastic friction at the joints into account. Using integral fast terminal sliding mode control and fractional calculus, we develop two robust controllers for robots subject to motor faults, parametric uncertainties, and disturbances. Their merits rest with their strong robustness, speedy finite-time convergence, shortened reaching phase, and flexible selection of derivative orders. To avoid the need for full knowledge of faults, robot parameters, and disturbances, two versions of the proposed approach, namely adaptive integral fractional-order fast terminal sliding mode control, are developed. Here, an adaptation mechanism is equipped for estimating a common representative of individual uncertainties. Simulation and experiment are provided along with an extensive comparison with existing approaches. The results demonstrate the superiority of the proposed control technique. The robot performs well the tasks with better responses (e.g., with settling time reduced by at least 16%).

Tuncer, T, Barua, PD, Tuncer, I, Dogan, S & Acharya, UR 2024, 'A lightweight deep convolutional neural network model for skin cancer image classification', Applied Soft Computing, vol. 162, pp. 111794-111794.
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Tuncer, T, Dogan, S, Baygin, M, Barua, PD, Palmer, EE, March, S, Ciaccio, EJ, Tan, R-S & Acharya, UR 2024, 'FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech', Computers in Biology and Medicine, vol. 173, pp. 108280-108280.
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Tusher, AS, Rahman, MA, Islam, MR & Hossain, MJ 2024, 'Adversarial training-based robust lifetime prediction system for power transformers', Electric Power Systems Research, vol. 231, pp. 110351-110351.
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Üstün, G, Morello, A & Devitt, S 2024, 'Single-step parity check gate set for quantum error correction', Quantum Science and Technology, vol. 9, no. 3, pp. 035037-035037.
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Abstract A key requirement for an effective quantum error correction (QEC) scheme is that the physical qubits have error rates below a certain threshold. The value of this threshold depends on the details of the specific QEC scheme, and its hardware-level implementation. This is especially important with parity-check circuits, which are the fundamental building blocks of QEC codes. The standard way of constructing the parity check circuit is using a universal set of gates, namely sequential CNOT gates, single-qubit rotations and measurements. We exploit the insight that a QEC code does not require universal logic gates, but can be simplified to perform the sole task of error detection and correction. By building gates that are fundamental to QEC, we can boost the threshold and ease the experimental demands on the physical hardware. We present a rigorous formalism for constructing and verifying the error behavior of these gates, linking the physical measurement of a process matrix to the abstract error models commonly used in QEC analysis. This allows experimentalists to directly map the gates used in their systems to thresholds derived for a broad-class of QEC codes. We give an example of these new constructions using the model system of two nuclear spins, coupled to an electron spin, showing the potential benefits of redesigning fundamental gate sets using QEC primitives, rather than traditional gate sets reliant on simple single and two-qubit gates.

Üstündağlı Erten, E, Güzeloğlu, EB, Ifaei, P, Khalilpour, K, Ifaei, P & Yoo, C 2024, 'Decoding intersectionality: A systematic review of gender and energy dynamics under the structural and situational effects of contexts', Energy Research & Social Science, vol. 110, pp. 103432-103432.
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Val, DV, Andrade, C, Sykora, M, Stewart, MG, Bastidas-Arteaga, E, Mlcoch, J, Truong, QC & El Soueidy, C-P 2024, 'TEMPORARY REMOVAL: Probabilistic modelling of deterioration of reinforced concrete structures', Structural Safety, pp. 102454-102454.
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Van Alboom, M, Baert, F, Bernardes, SF, Verhofstadt, L, Bracke, P, Jia, M, Musial, K, Gabrys, B & Goubert, L 2024, 'Examining the Role of Structural and Functional Social Network Characteristics in the Context of Chronic Pain: An Ego-centered Network Design', The Journal of Pain, pp. 104525-104525.
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Van Huynh, N, Wang, J, Du, H, Hoang, DT, Niyato, D, Nguyen, DN, Kim, DI & Letaief, KB 2024, 'Generative AI for Physical Layer Communications: A Survey', IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 3, pp. 706-728.
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Vettori, L, Tran, HA, Mahmodi, H, Filipe, EC, Wyllie, K, Liu Chung Ming, C, Cox, TR, Tipper, J, Kabakova, IV, Rnjak-Kovacina, J & Gentile, C 2024, 'Silk fibroin increases the elasticity of alginate-gelatin hydrogels and regulates cardiac cell contractile function in cardiac bioinks', Biofabrication, vol. 16, no. 3, pp. 035025-035025.
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Abstract Silk fibroin (SF) is a natural protein extracted from Bombyx mori silkworm thread. From its common use in the textile industry, it emerged as a biomaterial with promising biochemical and mechanical properties for applications in the field of tissue engineering and regenerative medicine. In this study, we evaluate for the first time the effects of SF on cardiac bioink formulations containing cardiac spheroids (CSs). First, we evaluate if the SF addition plays a role in the structural and elastic properties of hydrogels containing alginate (Alg) and gelatin (Gel). Then, we test the printability and durability of bioprinted SF-containing hydrogels. Finally, we evaluate whether the addition of SF controls cell viability and function of CSs in Alg–Gel hydrogels. Our findings show that the addition of 1% (w/v) SF to Alg–Gel hydrogels makes them more elastic without affecting cell viability. However, fractional shortening (FS%) of CSs in SF–Alg–Gel hydrogels increases without affecting their contraction frequency, suggesting an improvement in contractile function in the 3D cultures. Altogether, our findings support a promising pathway to bioengineer bioinks containing SF for cardiac applications, with the ability to control mechanical and cellular features in cardiac bioinks.

Vo, PHN, Danaee, S, Hai, HTN, Huy, LN, Nguyen, TAH, Nguyen, HTM, Kuzhiumparambil, U, Kim, M, Nghiem, LD & Ralph, PJ 2024, 'Biomining for sustainable recovery of rare earth elements from mining waste: A comprehensive review', Science of The Total Environment, vol. 908, pp. 168210-168210.
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Vu, HP, Kuzhiumparambil, U, Cai, Z, Wang, Q, Ralph, PJ & Nghiem, LD 2024, 'Enhanced biomethane production from Scenedesmus sp. using polymer harvesting and expired COVID-19 disinfectant for pretreatment', Chemosphere, vol. 356, pp. 141869-141869.
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Vu, TT, Chu, NH, Phan, KT, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2024, 'Energy-Based Proportional Fairness in Cooperative Edge Computing', IEEE Transactions on Mobile Computing, pp. 1-18.
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Waheed, S, Ahmed, A, Abid, M, Mufti, RA, Ferreira, F, Bashir, MN, Shah, AUR, Jafry, AT, Zulkifli, NW & Fattah, IMR 2024, 'Ionic liquids as lubricants: An overview of recent developments', Journal of Molecular Structure, vol. 1301, pp. 137307-137307.
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Wali, SB, Hannan, MA, Ker, PJ, Rahman, SA, Le, KN, Begum, RA, Tiong, SK & Mahlia, TMI 2024, 'Grid-connected lithium-ion battery energy storage system towards sustainable energy: A patent landscape analysis and technology updates', Journal of Energy Storage, vol. 77, pp. 109986-109986.
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Wan, S, Jin, Y, Xu, G & Nappi, M 2024, 'Editorial to Special Issue on Multimedia Cognitive Computing for Intelligent Transportation System', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 2, pp. 1-2.
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Wan, Y, Qu, Y, Ni, W, Xiang, Y, Gao, L & Hossain, E 2024, 'Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Wang, C, Ji, J, Miao, Z & Zhou, J 2024, 'Formation tracking of multi-robot systems with switching directed topologies based on Udwadia-Kalaba approach', Applied Mathematical Modelling, vol. 126, pp. 147-158.
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Wang, C, Wang, L, Yu, H, Soo, A, Wang, Z, Rajabzadeh, S, Ni, B-J & Shon, HK 2024, 'Machine learning for layer-by-layer nanofiltration membrane performance prediction and polymer candidate exploration', Chemosphere, vol. 350, pp. 140999-140999.
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Wang, C, Wei, W, Wu, L, Wang, Y, Dai, X & Ni, B-J 2024, 'A Novel Sustainable and Self-Sufficient Biotechnological Strategy for Directly Transforming Sewage Sludge into High-Value Liquid Biochemicals', Environmental Science & Technology, vol. 58, no. 28, pp. 12520-12531.
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Sewage sludge, as a carbon-rich byproduct of wastewater treatment, holds significant untapped potential as a renewable resource. Upcycling this troublesome waste stream represents great promise in addressing global escalating energy demands through its wide practice of biochemical recovery concurrently. Here, we propose a biotechnological concept to gain value-added liquid bioproducts from sewage sludge in a self-sufficient manner by directly transforming sludge into medium-chain fatty acids (MCFAs). Our findings suggest that yeast, a cheap and readily available commercial powder, would involve ethanol-type fermentation in chain elongation to achieve abundant MCFA production from sewage sludge using electron donors (i.e., ethanol) and acceptors (i.e., short-chain fatty acids) produced in situ. The enhanced abundance and transcriptional activity of genes related to key enzymes, such as butyryl-CoA dehydrogenase and alcohol dehydrogenase, affirm the robust capacity for the self-sustained production of MCFAs. This is indicative of an effective metabolic network established between yeast and anaerobic microorganisms within this innovative sludge fermentation framework. Furthermore, life cycle assessment and techno-economic analysis evidence the sustainability and economic competitiveness of this biotechnological strategy. Overall, this work provides insights into sewage sludge upgrading independent of additional carbon input, which can be applied in existing anaerobic sludge fermentation infrastructure as well as to develop new applications in a diverse range of industries.

Wang, C, Yao, T, Fan, T, Peng, S, Xu, C & Yu, S 2024, 'Modeling on Resource Allocation for Age-Sensitive Mobile-Edge Computing Using Federated Multiagent Reinforcement Learning', IEEE Internet of Things Journal, vol. 11, no. 2, pp. 3121-3131.
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Wang, C, Young, AS, Raj, C, Bradshaw, AP, Nham, B, Rosengren, SM, Calic, Z, Burke, D, Halmagyi, GM, Bharathy, GK, Prasad, M & Welgampola, MS 2024, 'Machine learning models help differentiate between causes of recurrent spontaneous vertigo', Journal of Neurology, vol. 271, no. 6, pp. 3426-3438.
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Wang, D, Zhang, X, Yin, Y, Yu, D, Xu, G & Deng, S 2024, 'Multi-View Enhanced Graph Attention Network for Session-Based Music Recommendation', ACM Transactions on Information Systems, vol. 42, no. 1, pp. 1-30.
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Traditional music recommender systems are mainly based on users’ interactions, which limit their performance. Particularly, various kinds of content information, such as metadata and description can be used to improve music recommendation. However, it remains to be addressed how to fully incorporate the rich auxiliary/side information and effectively deal with heterogeneity in it. In this paper, we propose a M ulti-view E nhanced G raph A ttention N etwork (named MEGAN ) for session-based music recommendation. MEGAN can learn informative representations (embeddings) of music pieces and users from heterogeneous information based on graph neural network and attention mechanism. Specifically, the proposed approach MEGAN firstly models users’ listening behaviors and the textual content of music pieces with a Heterogeneous Music Graph (HMG). Then, a devised Graph Attention Network is used to learn the low-dimensional embedding of music pieces and users and by integrating various kinds of information, which is enhanced by multi-view from HMG in an adaptive and unified way. Finally, users’ hybrid preferences are learned from users’ listening behaviors and music pieces that satisfy users real-time requirements are recommended. Comprehensive experiments are conducted on two real-world datasets, and the results show that MEGAN achieves better performance than baselines, including several state-of-the-art recommendation methods.

Wang, F, Long, G & Zhou, JL 2024, 'Enhanced green remediation and refinement disposal of electrolytic manganese residue using air-jet milling and horizontal-shaking leaching', Journal of Hazardous Materials, vol. 465, pp. 133419-133419.
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Wang, F, Nghiem, LD, Sarkar, D, Rene, E, Zou, L, Hu, Y, Bui, TX, Fujioka, T, Bolzonella, D, Dong, Z, Du, K & Gin, K 2024, 'Environmental Technology & Innovation Holiday Season Message Dec 2023', Environmental Technology & Innovation, vol. 33, pp. 103507-103507.
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Wang, G, Li, C, Yuan, L, Peng, J, Xian, X, Liang, X, Chang, X & Lin, L 2024, 'DNA Family: Boosting Weight-Sharing NAS With Block-Wise Supervisions', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 2722-2740.
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Wang, H, Xue, S, Qian, K, Li, Y & Wang, J 2024, 'Magnetic field-dependent dynamic behavior of magnetorheological grease composite in a wide temperature range: Experiment and modeling', Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol. 686, pp. 133468-133468.
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Wang, H, Zhang, Q-W, Chen, G, Li, X, Wang, Q-L, Gao, L, Wang, J, He, D & Li, M 2024, 'The loss of dissolved organic matter from biological soil crust at various successional stages under rainfall of different intensities: Insights into the changes of molecular components at different rainfall stages', Water Research, vol. 257, pp. 121719-121719.
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Wang, J, Duan, Y, Lyu, X, Yu, Y & Xiao, J 2024, 'Axial compression behavior of coal gangue coarse aggregate concrete-filled steel tube stub columns', Journal of Constructional Steel Research, vol. 215, pp. 108534-108534.
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Wang, J, He, Y, Su, D, Itoyama, K, Nakadai, K, Wu, J, Huang, S, Li, Y & Kong, H 2024, 'SLAM-Based Joint Calibration of Multiple Asynchronous Microphone Arrays and Sound Source Localization', IEEE Transactions on Robotics, pp. 1-20.
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Wang, J, Wang, K, Li, Z, Lu, H, Jiang, H & Xing, Q 2024, 'A Multitask Integrated Deep-Learning Probabilistic Prediction for Load Forecasting', IEEE Transactions on Power Systems, vol. 39, no. 1, pp. 1240-1250.
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Wang, J, Wei, Y, Zhang, Z, Fan, J, Zhao, Y, Yang, Y & Wang, M 2024, 'Progressive Stereo Image Dehazing Network via Cross-View Region Interaction', IEEE Transactions on Multimedia, vol. 26, pp. 7490-7502.
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Wang, J, Yu, Y, Zeng, B & Lu, H 2024, 'Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis', Energy, vol. 288, pp. 129898-129898.
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Wang, J, Zhu, S, Mu, C, Liu, X & Wen, S 2024, 'Unified analysis on multistablity of fraction-order multidimensional-valued memristive neural networks', Neural Networks, vol. 179, pp. 106498-106498.
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Wang, K, Xiong, L, Liu, A, Zhang, G & Lu, J 2024, 'A self-adaptive ensemble for user interest drift learning', Neurocomputing, vol. 577, pp. 127308-127308.
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Wang, L, Hu, Y, Hu, C, Zhou, Y & Wen, S 2024, 'Finite-time synchronization of delayed fuzzy inertial neural networks via intermittent control', Neurocomputing, vol. 574, pp. 127288-127288.
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Wang, M, Hu, Y, Wu, S, Li, W, Bai, Q, Yuan, Z & Jiang, C 2024, 'Nudging Towards Responsible Recommendations: a Graph-Based Approach to Mitigate Belief Filter Bubbles', IEEE Transactions on Artificial Intelligence, pp. 1-15.
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Wang, M, Zhu, S, Shen, M, Liu, X & Wen, S 2024, 'Fault-Tolerant Synchronization for Memristive Neural Networks With Multiple Actuator Failures', IEEE Transactions on Cybernetics, pp. 1-10.
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Wang, M, Zhu, T, Zuo, X, Ye, D, Yu, S & Zhou, W 2024, 'Blockchain-Based Gradient Inversion and Poisoning Defense for Federated Learning', IEEE Internet of Things Journal, vol. 11, no. 9, pp. 15667-15681.
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Wang, M, Zhu, T, Zuo, X, Ye, D, Yu, S & Zhou, W 2024, 'Blockchain-Empowered Multiagent Systems: Advancing IoT Security and Transaction Efficiency', IEEE Internet of Things Journal, vol. 11, no. 7, pp. 11217-11231.
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Wang, M, Zhu, T, Zuo, X, Ye, D, Yu, S & Zhou, W 2024, 'Public and Private Blockchain Infusion: A Novel Approach to Federated Learning', IEEE Internet of Things Journal, vol. 11, no. 10, pp. 17525-17537.
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Wang, Q & Ying, M 2024, 'Quantum Algorithm for Lexicographically Minimal String Rotation', Theory of Computing Systems, vol. 68, no. 1, pp. 29-74.
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AbstractLexicographically minimal string rotation (LMSR) is a problem to find the minimal one among all rotations of a string in the lexicographical order, which is widely used in equality checking of graphs, polygons, automata and chemical structures. In this paper, we propose an $$O(n^{3/4})$$ O ( n 3 / 4 ) quantum query algorithm for LMSR. In particular, the algorithm has average-case query complexity $$O(\sqrt{n} \log n)$$ O ( n log n ) , which is shown to be asymptotically optimal up to a polylogarithmic factor, compared to its $$\Omega \left( \sqrt{n/\log n}\right) $$ ...

Wang, Q & Ying, M 2024, 'Quantum Algorithm for Lexicographically Minimal String Rotation.', Theory Comput. Syst., vol. 68, pp. 29-74.

Wang, Q, Jin, W, Qin, Y, Zhou, X, Han, W, Gao, S, Li, X, Naushad, M, Jiang, G & Liu, H 2024, 'Development of an alternative low-cost culture medium for a new isolated high-production DHA strain using kitchen wastewater', Process Safety and Environmental Protection, vol. 183, pp. 698-707.
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Wang, Q, Jin, W, Zhou, X, Chen, C, Han, W, Mahlia, TMI, Li, X, Jiang, G, Liu, H & Wang, Q 2024, 'Enhancing docosahexaenoic acid production in Aurantiochytrium species using atmospheric and room temperature plasma mutagenesis and comprehensive multi-omics analysis', Science of The Total Environment, vol. 912, pp. 169217-169217.
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Wang, Q, Wu, D, Li, G, Liu, Z, Tong, J, Chen, X & Gao, W 2024, 'Machine learning aided uncertainty quantification for engineering structures involving material-geometric randomness and data imperfection', Computer Methods in Applied Mechanics and Engineering, vol. 423, pp. 116868-116868.
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Wang, S, Ko, RKL, Bai, G, Dong, N, Choi, T & Zhang, Y 2024, 'Evasion Attack and Defense on Machine Learning Models in Cyber-Physical Systems: A Survey', IEEE Communications Surveys & Tutorials, vol. 26, no. 2, pp. 930-966.
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Wang, S, Ma, Y, Ding, Y, Hu, Z, Fan, C, Lv, T, Deng, Z & Yu, X 2024, 'StyleTalk++: A Unified Framework for Controlling the Speaking Styles of Talking Heads', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 6, pp. 4331-4347.
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Wang, S, Shi, K, Cao, J & Wen, S 2024, 'Fuzzy adaptive event-triggered synchronization control mechanism for T–S fuzzy RDNNs under deception attacks', Communications in Nonlinear Science and Numerical Simulation, vol. 134, pp. 107985-107985.
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Wang, S, Shi, K, Wang, J, Yu, Y, Wen, S, Yang, J & Han, S 2024, 'Synchronization sampled-data control of uncertain neural networks under an asymmetric Lyapunov–Krasovskii functional method', Expert Systems with Applications, vol. 239, pp. 122475-122475.
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Wang, W & Cao, L 2024, 'Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 5183-5197.
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Wang, W, Luo, Y, Chen, Z, Jiang, T, Yang, Y & Xiao, J 2024, 'Taking a Closer Look At Visual Relation: Unbiased Video Scene Graph Generation With Decoupled Label Learning', IEEE Transactions on Multimedia, vol. 26, pp. 5718-5728.
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Wang, W, Zhang, C, Tian, Z & Yu, S 2024, 'Machine Unlearning via Representation Forgetting With Parameter Self-Sharing', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 1099-1111.
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Wang, X, Fei, Z, Liu, P, Zhang, JA, Wu, Q & Wu, N 2024, 'Sensing Aided Covert Communications: Turning Interference into Allies', IEEE Transactions on Wireless Communications, pp. 1-1.
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Wang, X, Li, Q, Yu, D, Li, Q & Xu, G 2024, 'Reinforced Path Reasoning for Counterfactual Explainable Recommendation', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 7, pp. 3443-3459.
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Wang, X, Li, W, Guo, Y, Kashani, A, Wang, K, Ferrara, L & Agudelo, I 2024, 'Concrete 3D printing technology for sustainable construction: A review on raw material, concrete type and performance', Developments in the Built Environment, vol. 17, pp. 100378-100378.
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Wang, X, Li, W, Guo, Y, Kashani, A, Wang, K, Ferrara, L & Agudelo, I 2024, 'Corrigendum to “Concrete 3D printing technology for sustainable construction: A review on raw material, concrete type and performance” [Dev. Built. Environ. 17 (2024) 100378]', Developments in the Built Environment, pp. 100442-100442.
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Wang, X, Thiyagarajan, K, Kodagoda, S & Sharma, C 2024, 'PIPE-CovNet+: A Hyper-Dense CNN for Improved Pipe Abnormality Detection', IEEE Sensors Letters, vol. 8, no. 4, pp. 1-4.
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Wang, X, Wang, W, Shao, J & Yang, Y 2024, 'Learning to Follow and Generate Instructions for Language-Capable Navigation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 3334-3350.
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Wang, X, Yang, S, Guo, Z, Ge, Q, Wen, S & Huang, T 2024, 'A Distributed k-Winners-Take-All Model With Binary Consensus Protocols', IEEE Transactions on Cybernetics, vol. 54, no. 5, pp. 3327-3337.
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This article concentrates on solving the k -winners-take-all (k WTA) problem with large-scale inputs in a distributed setting. We propose a multiagent system with a relatively simple structure, in which each agent is equipped with a 1-D system and interacts with others via binary consensus protocols. That is, only the signs of the relative state information between neighbors are required. By virtue of differential inclusion theory, we prove that the system converges from arbitrary initial states. In addition, we derive the convergence rate as O(1/t) . Furthermore, in comparison to the existing models, we introduce a novel comparison filter to eliminate the resolution ratio requirement on the input signal, that is, the difference between the k th and (k+1) th largest inputs must be larger than a positive threshold. As a result, the proposed distributed k WTA model is capable of solving the k WTA problem, even when more than two elements of the input signal share the same value. Finally, we validate the effectiveness of the theoretical results through two simulation examples.

Wang, Y, Chen, Q, Luo, Q, Li, Q & Sun, G 2024, 'Characterizing damage evolution in fiber reinforced composites using in-situ X-ray computed tomography, deep machine learning and digital volume correlation (DVC)', Composites Science and Technology, vol. 254, pp. 110650-110650.
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Wang, Y, Hu, L, Cao, X, Chang, Y & Tsang, IW 2024, 'Enhancing Locally Adaptive Smoothing of Graph Neural Networks Via Laplacian Node Disagreement', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 3, pp. 1099-1112.
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Wang, Y, Li, T, Li, S, Yuan, X & Ni, W 2024, 'New Adversarial Image Detection Based on Sentiment Analysis', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Wang, Y, Wang, Z, Zhang, JA, Zhang, H & Xu, M 2024, 'Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion', IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 4163-4180.
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Wang, Y, Wei, W, Dai, X, Wu, L, Chen, X & Ni, B-J 2024, 'Different Electron Donors Drive the Variation in the Performance of Medium-Chain Fatty Acid Production from Waste-Activated Sludge', ACS ES&T Engineering, vol. 4, no. 3, pp. 650-659.
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Wang, Y, Wei, Y, Qian, X, Zhu, L & Yang, Y 2024, 'ReGO: Reference-Guided Outpainting for Scenery Image', IEEE Transactions on Image Processing, vol. 33, pp. 1375-1388.
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Wang, Y, Xu, J, Li, W & Dong, W 2024, 'Improved conductive and self-sensing properties of cement concrete by PDMS/NCB-impregnated recycled fine aggregate', Construction and Building Materials, vol. 426, pp. 136229-136229.
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Wang, Y, Zhang, Q, Andrew Zhang, J, Wei, Z, Feng, Z & Peng, J 2024, 'Interference Characterization and Mitigation for Multi-beam ISAC Systems in Vehicular Networks', IEEE Transactions on Wireless Communications, pp. 1-1.
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Wang, Z, Huang, Z, Huang, Y, Wittram, C, Zhuang, Y, Wang, S & Nie, B 2024, 'Synergy of carbon capture, waste heat recovery and hydrogen production for industrial decarbonisation', Energy Conversion and Management, vol. 312, pp. 118568-118568.
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Wang, Z, Li, X, Liu, H, Li, J, Vodnar, DC, Lin, CSK & Wang, Q 2024, 'Life cycle assessment of traditional and innovative sludge management scenarios in Australia: Focusing on environmental impacts, energy balance, and economic benefits', Resources, Conservation and Recycling, vol. 204, pp. 107496-107496.
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Wang, Z, Li, X, Liu, H, Zhou, T, Li, J, Li, Y, Lin, CSK & Wang, Q 2024, 'Triclosan in sludge: Exploring its journey from the sewage treatment plants to land application and potential impacts on the environment', Critical Reviews in Environmental Science and Technology, vol. 54, no. 18, pp. 1340-1363.
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Wang, Z, Li, X, Liu, H, Zhou, T, Li, J, Siddiqui, MA, Lin, CSK, Huang, S, Cairney, JM & Wang, Q 2024, 'Enhanced short-chain fatty acids production from anaerobic fermentation of secondary sludge by lignosulfonate addition: Towards circular economy', Journal of Cleaner Production, vol. 434, pp. 140252-140252.
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Wang, Z, Lu, J, Deng, P, Li, S, Wang, K, Zhang, C, Cheng, X, Zhang, J & Huang, Y 2024, 'Mechanisms of enhanced heat transfer of piston oscillating cooling in a heavy-duty diesel engine', Applied Thermal Engineering, vol. 245, pp. 122875-122875.
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Wei, J, Li, W, Liu, J, Li, J & Wu, C 2024, 'Effect of stirrup ratio on response of ultra-high performance concrete beams subjected to low-velocity impact loadings', Journal of Building Engineering, vol. 92, pp. 109799-109799.
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Wei, J, Yang, Q, Yu, Y, Jiang, Q, Li, X, Liu, S, Li, K & Wang, Q 2024, 'Experimental study of multiscale hybrid fiber-reinforced ambient-cured LEGC under uniaxial compression', Construction and Building Materials, vol. 411, pp. 134386-134386.
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Wei, X, Huang, J, Zhao, R, Yu, H & Xu, Z 2024, 'Multi-Label Text Classification Model Based on Multi-Level Constraint Augmentation and Label Association Attention', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 1, pp. 1-20.
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In the multi-label text classification task, a text usually corresponds to multiple label categories, and the labels have correlation and hierarchical structure. However, when the label hierarchy is unknown, the number of various labels is not balanced, which makes it difficult for the model to classify low-frequency labels. In addition, labels have semantic similarities that make it difficult for the model to distinguish between them. In this article, we propose a multi-label text classification model based on multi-level constraint augmentation and label association attention. Compared with traditional methods, our method has two contributions: (1) In order to alleviate the problem of unbalanced number of different label categories and ensure the rationality of sample generation, we propose a data augmentation method based on multi-level constraints. In the process of sample generation, this method uses historical generation information, sample original text information, and sample topic to constrain the generated text. (2) In order to make the model recognize the associated labels accurately, we propose an interaction mechanism based on label association attention and filter gate. This method combines text information and label weight information. At the same time, our classification model considers the important weights of text sentences and effectively utilizes the co-occurrence relationship between labels. Experimental results on three benchmark datasets show that our model outperforms state-of-the-art methods on all main evaluation metrics, especially on low-frequency label prediction with sparse samples.

Wei, Y, Jin, X, Luo, Q, Li, Q & Sun, G 2024, 'Adhesively bonded joints – A review on design, manufacturing, experiments, modeling and challenges', Composites Part B: Engineering, vol. 276, pp. 111225-111225.
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Wei, Z, Piao, J, Yuan, X, Wu, H, Zhang, JA, Feng, Z, Wang, L & Zhang, P 2024, 'Waveform Design for MIMO-OFDM Integrated Sensing and Communication System: An Information Theoretical Approach', IEEE Transactions on Communications, vol. 72, no. 1, pp. 496-509.
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WeiKoh, JE, Rajinikanth, V, Vicnesh, J, Pham, T, Oh, SL, Yeong, CH, Sankaranarayanan, M, Kamath, A, Bairy, GM, Barua, PD & Cheong, KH 2024, 'Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals', Expert Systems, vol. 41, no. 5.
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AbstractRecently, a mix of traditional and modern approaches have been proposed to detect brain abnormalities using bio‐signal/bio‐image‐assisted methods. In hospitals, most of the initial/scheduled assessments consider the bio‐signal‐based appraisal, due to its non‐invasive nature and low cost. Further, brain bio‐signal scans can be recorded using a single/multi‐channel electrode setup, which is further evaluated by an experienced doctor, as well as computer software, to identify the nature and severity of abnormality. In this paper, we describe the development of a system for computer supported detection (CSD) of schizophrenia using the electroencephalogram (EEG) signal collected with a 19‐channel electrode array. Schizophrenia is a mental illness that interferes with the way an individual thinks and behaves. It is characterised by psychotic symptoms such as hallucinations or delusions, negative symptoms such as decreased motivation or a lack of interest in daily activities and cognitive symptoms such challenges in processing information to make informed decisions or staying focused. This research has utilized 1142 EEGs (516 normal and 626 schizophrenia) with a frame length of 25 s (6250 samples) for investigation. The work initially converts the EEG signals to images using a spectrogram. Local configuration pattern features were extracted from the images thereafter, and 10‐fold validation technique was used wherein Student's t‐test and z‐score standardization were computed per fold. The highest accuracy of 97.20% was achieved with the K‐nearest neighbour (

Welsh, JA, Goberdhan, DCI, O'Driscoll, L, Buzas, EI, Blenkiron, C, Bussolati, B, Cai, H, Di Vizio, D, Driedonks, TAP, Erdbrügger, U, Falcon‐Perez, JM, Fu, Q, Hill, AF, Lenassi, M, Lim, SK, Mahoney, MG, Mohanty, S, Möller, A, Nieuwland, R, Ochiya, T, Sahoo, S, Torrecilhas, AC, Zheng, L, Zijlstra, A, Abuelreich, S, Bagabas, R, Bergese, P, Bridges, EM, Brucale, M, Burger, D, Carney, RP, Cocucci, E, Crescitelli, R, Hanser, E, Harris, AL, Haughey, NJ, Hendrix, A, Ivanov, AR, Jovanovic‐Talisman, T, Kruh‐Garcia, NA, Ku'ulei‐Lyn Faustino, V, Kyburz, D, Lässer, C, Lennon, KM, Lötvall, J, Maddox, AL, Martens‐Uzunova, ES, Mizenko, RR, Newman, LA, Ridolfi, A, Rohde, E, Rojalin, T, Rowland, A, Saftics, A, Sandau, US, Saugstad, JA, Shekari, F, Swift, S, Ter‐Ovanesyan, D, Tosar, JP, Useckaite, Z, Valle, F, Varga, Z, van der Pol, E, van Herwijnen, MJC, Wauben, MHM, Wehman, AM, Williams, S, Zendrini, A, Zimmerman, AJ, Théry, C & Witwer, KW 2024, 'Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches', Journal of Extracellular Vesicles, vol. 13, no. 2.
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AbstractExtracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year‐on‐year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non‐vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its ‘Minimal Information for Studies of Extracellular Vesicles’, which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly.

Wen, Y, Liu, L, Li, J, Li, Y, Wang, K, Yu, S & Guizani, M 2024, 'Covert Communications Aided by Cooperative Jamming in Overlay Cognitive Radio Networks', IEEE Transactions on Mobile Computing, pp. 1-14.
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Wen, Y, Liu, X & Yu, H 2024, 'Adaptive tree-like neural network: Overcoming catastrophic forgetting to classify streaming data with concept drifts', Knowledge-Based Systems, vol. 293, pp. 111636-111636.
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Weththasinghe, K, Jayawickrama, B & He, Y 2024, 'Machine Learning-Based Channel Estimation for 5G New Radio', IEEE Wireless Communications Letters, vol. 13, no. 4, pp. 1133-1137.
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Wocker, MM, Ostermeier, FF, Wanninger, T, Zwinkau, R & Deuse, J 2024, 'Flexible job shop scheduling with preventive maintenance consideration', Journal of Intelligent Manufacturing, vol. 35, no. 4, pp. 1517-1539.
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Wu, C, Wan, B, Entezari, A, Fang, J, Xu, Y & Li, Q 2024, 'Machine learning-based design for additive manufacturing in biomedical engineering', International Journal of Mechanical Sciences, vol. 266, pp. 108828-108828.
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Wu, C, Wan, B, Xu, Y, Al Maruf, DSA, Cheng, K, Lewin, WT, Fang, J, Xin, H, Crook, JM, Clark, JR, Steven, GP & Li, Q 2024, 'Dynamic optimisation for graded tissue scaffolds using machine learning techniques', Computer Methods in Applied Mechanics and Engineering, vol. 425, pp. 116911-116911.
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Wu, G, Chen, X, Gao, Z, Zhang, H, Yu, S & Shen, S 2024, 'Privacy-preserving offloading scheme in multi-access mobile edge computing based on MADRL', Journal of Parallel and Distributed Computing, vol. 183, pp. 104775-104775.
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Wu, G, Chen, X, Shen, Y, Xu, Z, Zhang, H, Shen, S & Yu, S 2024, 'Combining Lyapunov Optimization With Actor–Critic Networks for Privacy-Aware IIoT Computation Offloading', IEEE Internet of Things Journal, vol. 11, no. 10, pp. 17437-17452.
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Wu, G, Zhang, Y, Zhang, H, Yu, S, Yu, S & Shen, S 2024, 'SIHQR model with time delay for worm spread analysis in IIoT-enabled PLC network', Ad Hoc Networks, vol. 160, pp. 103504-103504.
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Wu, J, Jiang, C, Ng, C-T & Fang, H 2024, 'Detection of edge delamination in composite laminates using edge waves', Composite Structures, vol. 340, pp. 118177-118177.
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Wu, K, Zhang, JA, Ni, Z, Huang, X, Guo, YJ & Chen, S 2024, 'Joint Communications and Sensing Employing Optimized MIMO-OFDM Signals', IEEE Internet of Things Journal, vol. 11, no. 6, pp. 10368-10383.
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Wu, L, Wei, W, Chen, Z, Shi, X, Qian, J & Ni, B-J 2024, 'Novel anaerobic fermentation paradigm of producing medium-chain fatty acids from food wastes with self-produced ethanol as electron donor', Chemical Engineering Journal, vol. 483, pp. 149236-149236.
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Wu, M, Sivertsen, G, Zhang, L, Qi, F & Zhang, Y 2024, 'Scientific Progress or Societal Progress? A Language Modelbased Classification of the Aims of the Research in Scientific Publications'.

Wu, R, Wang, M, Li, Z, Zhou, J, Chen, F, Wang, X & Sun, C 2024, 'Few-Shot Stereo Matching with High Domain Adaptability Based on Adaptive Recursive Network', International Journal of Computer Vision, vol. 132, no. 5, pp. 1484-1501.
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Wu, S-L, Wu, L, Wei, W, Shentu, J, Long, Y, Shen, D & Ni, B-J 2024, 'Challenges and enhancement technologies of medium chain carboxylates production in open culture anaerobic fermentation', Chemical Engineering Journal, vol. 494, pp. 153224-153224.
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Wu, Y, Cao, J & Xu, G 2024, 'Fairness in Recommender Systems: Evaluation Approaches and Assurance Strategies', ACM Transactions on Knowledge Discovery from Data, vol. 18, no. 1, pp. 1-37.
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With the wide application of recommender systems, the potential impacts of recommender systems on customers, item providers and other parties have attracted increasing attention. Fairness, which is the quality of treating people equally, is also becoming important in recommender system evaluation and algorithm design. Therefore, in the past years, there has been a growing interest in fairness measurement and assurance in recommender systems. Although there are several reviews on related topics, such as fairness in machine learning and debias in recommender systems, they do not present a systematic view on fairness in recommender systems, which is context aware and has a multi-sided meaning. Therefore, in this review, the concept of fairness is discussed in detail in the various contexts of recommender systems. Specifically, a comprehensive framework to classify fairness metrics is proposed from four dimensions, i.e.,Fairness for Whom,Demographic Unit,Time Frame, andQuantification Method. Then the strategies for eliminating unfairness in recommendations, fairness in different recommendation tasks and datasets are reviewed and summarized. Finally, the challenges and future work are discussed.

Wu, Y, Cao, J & Xu, G 2024, 'FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems', ACM Transactions on Information Systems, vol. 42, no. 1, pp. 1-26.
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When only users’ preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted widespread attention in recent years. For the first time, we reveal the fact that there is a more serious unfair exposure problem in session-based recommender systems (SRSs), which learn the short-term and dynamic preferences of users from anonymous sessions. Considering the fact that in SRSs, recommendations are provided multiple times and item exposures are accumulated over interactions in a session, we define new metrics both for the fairness of item exposure and recommendation quality among sessions. Moreover, we design a dynamic F airness- A ssurance ST rategy for s E ssion-based R ecommender systems ( FASTER ). FASTER is a post-processing strategy that tries to keep a balance between item exposure fairness and recommendation quality. It can also maintain the fairness of recommendation quality among sessions. The effectiveness of FASTER is verified on three real-world datasets and five original algorithms. The experiment results show that FASTER can generally reduce the unfair exposure of different session-based recommendation algorithms while still ensuring a high level of recommendation quality.

Wu, Y, Li, S, Zhang, J, Li, Y, Li, Y & Zhang, Y 2024, 'Dual attention transformer network for pixel-level concrete crack segmentation considering camera placement', Automation in Construction, vol. 157, pp. 105166-105166.
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Pixel-level crack segmentation remains a challenging task due to the trade-off between computational cost and accuracy, as well as the small size of real-world cracks, typically submillimeter in width, resulting in limited pixels for analysis. To address these challenges, this paper proposes a Pixel Crack Transformer Network (PCTNet) to investigate the impact of different camera placements on network performance. PCTNet adopts a hierarchical structure with Cross-Scale PatchEmbedding Layer and Dual Attention Transformer Block, enabling the generation of multi-scale feature maps and the fusion of global and local features. PCTNet achieves a reduction of up to 64% in computational cost compared to transformer networks while outperforming both convolutional and transformer networks, achieving 95.89% precision, 93.77% recall, 94.8% F1-score, and 90.53% mIoU. Furthermore, this work introduces Crack-R dataset, which encompasses crack images captured at varying distances, facilitating the evaluation of segmentation accuracy in real-world scenarios with different crack-to-pixel ratios.

Wu, Y, Wan, Y, Zhang, H, Sui, Y, Wei, W, Zhao, W, Xu, G & Jin, H 2024, 'Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study', Proceedings of the ACM on Management of Data, vol. 2, no. 3, pp. 1-28.
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The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, challenges persist in visualizing data sourced from unseen databases or spanning multiple tables. Taking inspiration from the remarkable generation capabilities of Large Language Models (LLMs), this paper conducts an empirical study to evaluate their potential in generating visualizations, and explore the effectiveness of in-context learning prompts for enhancing this task. In particular, we first explore the ways of transforming structured tabular data into sequential text prompts, as to feed them into LLMs and analyze which table content contributes most to the NL2Vis. Our findings suggest that transforming structured tabular data into programs is effective, and it is essential to consider the table schema when formulating prompts. Furthermore, we evaluate two types of LLMs: finetuned models (e.g., T5-Small) and inference-only models (e.g., GPT-3.5), against state-of-the-art methods, using the NL2Vis benchmarks (i.e., nvBench). The experimental results reveal that LLMs outperform baselines, with inference-only models consistently exhibiting performance improvements, at times even surpassing fine-tuned models when provided with certain few-shot demonstrations through in-context learning. Finally, we analyze when the LLMs fail in NL2Vis, and propose to iteratively update the results using strategies such as chain-of-thought, role-playing, and code-interpreter. The experimental results confirm the efficacy of iterative updates and hold great potential for future study.

Wu, Z, Guo, K, Luo, E, Wang, T, Wang, S, Yang, Y, Zhu, X & Ding, R 2024, 'Medical long-tailed learning for imbalanced data: Bibliometric analysis', Computer Methods and Programs in Biomedicine, vol. 247, pp. 108106-108106.
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Wu, Z, Rao, P, Cui, J, Chen, Q & Nimbalkar, S 2024, 'Lateral Response Evaluation of Existing Pile by Adjacent Pile Driving in Claye Slope', Geotechnical and Geological Engineering, vol. 42, no. 2, pp. 1313-1337.
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Wu, Z, Zheng, D, Pan, S, Gan, Q, Long, G & Karypis, G 2024, 'TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 2, pp. 2003-2013.
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Xi, Y, Jia, W, Miao, Q, Feng, J, Ren, J & Luo, H 2024, 'Detection-Driven Exposure-Correction Network for Nighttime Drone-View Object Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14.
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Xiang, G, Tao, M, Zhao, R, Zhao, H, Memon, MB & Wu, C 2024, 'Dynamic response of water-rich tunnel subjected to plane P wave considering excavation induced damage zone', Underground Space, vol. 15, pp. 113-130.
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Xiao, T, Halkon, B, Wang, S, Oberst, S & Qiu, X 2024, 'Refracto-vibrometry for active control of sound radiation through an opening', Journal of Sound and Vibration, vol. 577, pp. 118242-118242.
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Xiao, W, Luo, X, Xie, S & Yu, H 2024, 'An Adaptive Meta-Reinforcement Learning Algorithm for Simulation to Reality in Dynamic Scene', IEEE Transactions on Intelligent Vehicles, pp. 1-16.
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Xiao, Y, Xia, R, Li, Y, Shi, G, Nguyen, DN, Hoang, DT, Niyato, D & Krunz, M 2024, 'Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-Supervised Learning Approach', IEEE Transactions on Mobile Computing, vol. 23, no. 2, pp. 1815-1829.
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With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over $20\%$ improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples.

Xiao, Y, Zhang, X, Li, Y, Shi, G, Krunz, M, Nguyen, DN & Hoang, DT 2024, 'Time-Sensitive Learning for Heterogeneous Federated Edge Intelligence', IEEE Transactions on Mobile Computing, vol. 23, no. 2, pp. 1382-1400.
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Real-time machine learning (ML) has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles, intelligent transportation, and industry automation. In this paper, we investigate real-time ML in a federated edge intelligence (FEI) system, an edge computing system that implements federated learning (FL) solutions based on data samples collected and uploaded from decentralized data networks, e.g., Internet-of-Things (IoT) and/or wireless sensor networks. FEI systems often exhibit heterogenous communication and computational resource distribution, as well as non-i.i.d. data samples arrived at different edge servers, resulting in long model training time and inefficient resource utilization. Motivated by this fact, we propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model with desirable accuracy. Training acceleration solutions for both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous coordination (TS-FL-ASC) are investigated. To address the straggler effect in TS-FL-SC, we develop an analytical solution to characterize the impact of selecting different subsets of edge servers on the overall model training time. A server dropping-based solution is proposed to allow some slow-performance edge servers to be removed from participating in the model training if their impact on the resulting model accuracy is limited. A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, the local epoch number (the number of model training iterations per coordination), and the data batch size (the number of data samples for each model training iteration). Motivated by the fact that data samples at the slowest edge server may exhibit special characteristi...

Xie, F, Gao, Y, Meng, D, Xu, Y, Wu, C, Fang, J & Li, Q 2024, 'Topology optimization for fiber-reinforced plastic (FRP) composite for frequency responses', Computer Methods in Applied Mechanics and Engineering, vol. 428, pp. 117114-117114.
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Xie, K, Fu, Q, Chen, F, Zhu, H, Wang, X, Huang, G, Zhan, H, Liang, Q, Doherty, CM, Wang, D, Qiao, GG & Li, D 2024, 'Controlling the Supramolecular Architecture Enables High Lithium Cationic Conductivity and High Electrochemical Stability for Solid Polymer Electrolytes', Advanced Functional Materials, vol. 34, no. 17.
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AbstractSolid polymer electrolytes (SPEs) are long sought after for versatile applications due to their low cost, light weight, flexibility, ease of scale‐up, and low interfacial impedance. However, obtaining SPEs with high Li+ conductivity (σ+) and high voltage stability to avoid concentrated polarization and premature capacity loss has proven challenging. Here a stretchable dry‐SPE is reported with a semi‐interpenetrating, supermolecular architecture consisting of a cross‐linked polyethylene oxide (PEO) tetra‐network and an alternating copolymer poly(ethylene oxide‐alt‐butylene terephthalate). Such a unique supermolecular architecture suppresses the formation of Li+/PEO intermolecular complex and enhances the oxidation stability of PEO‐based electrolyte, thus maintaining high chain segmental motion even with high salt loading (up to 50 wt%) and achieving a wide electrochemical stability window of 5.3 V. These merits enable the simultaneous accomplishment of high ionic conductivity and high Li+ transference number (t+) to enhance the energy efficiency of energy storage device, and electrochemical stability.

Xie, M, Liu, J, Li, Y, Feng, K & Ni, Q 2024, 'An Ensemble Domain Adaptation Network With High-Quality Pseudo Labels for Rolling Bearing Fault Diagnosis', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-10.
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Xie, S, Li, Y, Wang, X, Zhang, H, Zhang, Z, Luo, X & Yu, H 2024, 'Hierarchical relationship modeling in multi-agent reinforcement learning for mixed cooperative–competitive environments', Information Fusion, vol. 108, pp. 102318-102318.
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Xie, X, Cui, Y & Yu, Y 2024, 'Study of nonlinear hysteretic modelling and performance evaluation for piezoelectric actuators based on activation functions', Smart Structures and Systems, vol. 33, no. 2, pp. 133-143.
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Piezoelectric (PZT) actuators have been widely used in precision positioning fields for their excellent displacement resolution. However, due to the inherent characteristics of piezoelectric actuators, hysteresis has been proven to greatly reduce positioning performance. In this paper, five mathematical hysteretic models based on activation function are proposed to characterize the nonlinear hysteresis characteristics of piezoelectric actuators. Then the performance of the proposed models is verified by particle swarm optimization (PSO) algorithm and the experiment data. Thirdly, the fitting performance of the proposed models is compared with the classical Bouc-Wen model. Finally, the performance of the five proposed models in modelling hysteresis nonlinearity of piezoelectric drivers is compared, in terms of RMSE, MAPE, SAPE and operation efficiency, and relevant suggestions are given.

Xie, Y, Far, H, Mortazavi, M & El-Sherbeeny, AM 2024, 'Improving the Mechanical Properties of Concrete Mixtures by Shape Memory Alloy Fibers and Silica Fume', Buildings, vol. 14, no. 6, pp. 1709-1709.
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Concrete, as one of the most widely applied materials in buildings, has high environmental impacts. Researchers are continually seeking solutions to mitigate these environmental issues while enhancing the mechanical strength and durability of concrete. However, there is a lack of studies on the effect of combining silica fume (SF) as pozzolanic materials and shape memory alloy (SMA) fibers on the mechanical properties of concrete. Moreover, there is very limited research on the influence of these materials on concrete mixtures after primary failure cracks using the secondary compressive strength test. In this research, 0.1, 0.2, and 0.3% SMA and 5, 7.5, and 10% SF were applied and then subjected to compressive strength, splitting tensile strength, flexural strength, secondary compressive strength, and ultrasonic pulse velocity tests. According to the results, 10% SF is more economical, which increases the compressive, splitting tensile, and flexural strength by 14%, 7%, and 10%, respectively. Also, using 0.3% SMA improves the compressive, splitting tensile, and flexural strength by 2%, 5%, and 8%, respectively. Furthermore, SMA has the ability to reduce the secondary compressive strength compared to other samples, indicating the quality of this material in controlling stress after cracking. Finally, it was indicated that the combined use of these two materials increases the strength parameters.

Xing, B & Tsang, IW 2024, 'Co-Guiding for Multi-Intent Spoken Language Understanding', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 2965-2980.
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Xing, J, Lv, T, Li, W, Ni, W & Jamalipour, A 2024, 'Joint Optimization of Beamforming and Noise Injection for Covert Downlink Transmissions in Cell-Free Internet of Things Networks', IEEE Internet of Things Journal, vol. 11, no. 6, pp. 10525-10536.
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Xiong, K, Yu, J, Hu, C, Wen, S & Kong, F 2024, 'Nonseparation analysis-based finite/fixed-time synchronization of fully complex-valued impulsive dynamical networks', Applied Mathematics and Computation, vol. 467, pp. 128500-128500.
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Xu, G, Jia, W, Wu, T, Chen, L & Gao, G 2024, 'HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation', IEEE Transactions on Image Processing, vol. 33, pp. 4202-4214.
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Xu, H, Liu, M, Bu, Y, Sun, S, Zhang, Y, Zhang, C, Acuna, DE, Gray, S, Meyer, E & Ding, Y 2024, 'The impact of heterogeneous shared leadership in scientific teams', Information Processing & Management, vol. 61, no. 1, pp. 103542-103542.
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Xu, H, Xuan, J, Zhang, G & Lu, J 2024, 'Trust region policy optimization via entropy regularization for Kullback–Leibler divergence constraint', Neurocomputing, vol. 589, pp. 127716-127716.
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Xu, J & Cao, L 2024, 'Copula Variational LSTM for High-Dimensional Cross-Market Multivariate Dependence Modeling', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Xu, J, Han, P, Jin, Y, Lu, H, Sun, B, Gao, B, He, T, Xu, X, Pinna, N & Wang, G 2024, 'Hybrid Molecular Sieve-Based Interfacial Layer with Physical Confinement and Desolvation Effect for Dendrite-free Zinc Metal Anodes', ACS Nano, vol. 18, no. 28, pp. 18592-18603.
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Xu, P, Lei, Y, Sui, Y & Xue, J 2024, 'Iterative-Epoch Online Cycle Elimination for Context-Free Language Reachability', Proceedings of the ACM on Programming Languages, vol. 8, no. OOPSLA1, pp. 1437-1462.
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Context-free language reachability (CFL-reachability) is a fundamental framework for implementing various static analyses. CFL-reachability utilizes context-free grammar (CFG) to extend the expressiveness of ordinary graph reachability from an unlabeled graph to an edge-labeled graph. Solving CFL-reachability requires a (sub)cubic time complexity with respect to the graph size, which limits its scalability in practice. Thus, an approach that can effectively reduce the graph size while maintaining the reachability result is highly desirable. Most of the existing graph simplification techniques for CFL-reachability work during the preprocessing stage, i.e., before the dynamic CFL-reachability solving process. However, in real-world CFL-reachability analyses, there is a large number of reducible nodes and edges that can only be discovered during dynamic solving, leaving significant room for on-the-fly improvements. This paper aims to reduce the graph size of CFL-reachability dynamically via online cycle elimination. We propose a simple yet effective approach to detect collapsible cycles in the graph based on the input context-free grammar. Our key insight is that symbols with particular forms of production rules in the grammar are the essence of transitivity of reachability relations in the graph. Specifically, in the graph, a reachability relation to a node v_i can be 'transited' to another node v_j if there is a transitive relation from v_i to v_j, and cycles formed by transitive relations are collapsible. In this paper, we present an approach to identify the transitive symbols in a context-free grammar and propose an iterative-epoch framework for online cycle elimination. From the perspective of non-parallelized CFL-reachability solving, our iterative-epoch framework is well compatible with both the standard (unordered) solver and the recent ordered solver, and can significantly improve their performance. Our experimen...

Xu, S, Zeng, H, Yuan, P, Liu, J, Yang, T, Shao, R, Su, Y & Wu, C 2024, 'Experimental and numerical study of G-HPC slabs rapidly repaired by G-HPC canvas and G-UHPC under contact detonations', Engineering Structures, vol. 306, pp. 117877-117877.
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Xu, T, Xu, Z, Liu, Z, Zhang, Y, Castel, A & Yang, G 2024, 'Linear and nonlinear tensile creep of steam-cured UHPC', Cement and Concrete Composites, vol. 145, pp. 105323-105323.
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Xu, Y, Feng, Z, Gao, Y, Wu, C, Fang, J, Sun, G, Qiu, N, Steven, GP & Li, Q 2024, 'Topology optimization for additive manufacturing of CFRP structures', International Journal of Mechanical Sciences, vol. 269, pp. 108967-108967.
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Xu, Y, Li, S, Yan, X, He, J, Ni, Q, Sun, Y & Wang, Y 2024, 'Multiattention-Based Feature Aggregation Convolutional Networks With Dual Focal Loss for Fault Diagnosis of Rotating Machinery Under Data Imbalance Conditions', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-11.
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Xu, Y, Liu, Y, Liang, C, Guo, W, Ngo, HH & Peng, L 2024, 'Favipiravir biotransformation by a side-stream partial nitritation sludge: Transformation mechanisms, pathways and toxicity evaluation', Chemosphere, vol. 353, pp. 141580-141580.
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Xu, Y, Lu, Y, Meng, L, Cheng, J, Ouyang, F, Duan, P, Li, W, Zhang, H, Zhu, Y & Zhang, Z 2024, 'Performance and heavy metal leaching of porous geopolymer based on solid wastes', Construction and Building Materials, vol. 427, pp. 136186-136186.
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Xu, Y, Yan, X, Sun, B, Feng, K, Kou, L, Chen, Y, Li, Y, Chen, H, Tian, E, Ni, Q & Wang, Y 2024, 'Online Knowledge Distillation-Based Multiscale Threshold Denoising Networks for Fault Diagnosis of Transmission Systems', IEEE Transactions on Transportation Electrification, vol. 10, no. 2, pp. 4421-4431.
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Xu, Y, Zhang, W, Xu, X, Li, B & Zhang, Y 2024, 'Scalable and Effective Temporal Graph Representation Learning With Hyperbolic Geometry', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Xu, Z, Gao, X, Li, G, Nghiem, LD, Luo, W & Zhang, F 2024, 'Microbial Sources and Sinks of Nitrous Oxide during Organic Waste Composting', Environmental Science & Technology, vol. 58, no. 17, pp. 7367-7379.
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Xu, Z, Zhu, S, Luo, W, Liu, X & Wen, S 2024, 'Fixed‐time synchronization and energy cost estimation of delayed coupled neural networks', International Journal of Robust and Nonlinear Control, vol. 34, no. 6, pp. 4064-4078.
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AbstractThis article concentrates on the fixed‐time synchronization (FTS) and energy cost (EC) problems of delayed coupled neural networks (NNs). A switching controller is proposed and some sufficient conditions are advanced to ensure FTS of delayed coupled NNs based on the comparison theorem. Following that, the required EC is estimated during the control process. The main results of FTS and EC for considered NNs are obtained based on the Lyapunov functions in the sense of vector 1‐norm, 2‐norm, and ‐norm, respectively. Under the same controller parameters, this article shows that the minimum estimation of settling time and EC are sometimes not in one norm sense. Finally, numerical examples are given to show the effectiveness of theoretical results.

Yan, B, Zhao, Q, Zhang, J, Zhang, JA & Yao, X 2024, 'Gridless Evolutionary Approach for Line Spectral Estimation With Unknown Model Order', IEEE Transactions on Cybernetics, vol. 54, no. 2, pp. 935-947.
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Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic l0 norm (i.e., the continuous analog of l0 norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of the atomic l0 norm, such as the nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order using the atomic l0 norm. To accomplish this idea, we build a multiobjective optimization model. The measurement error and the atomic l0 norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic l0 norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that helpfully exploring the variable and open-ended frequency search space and provide extensive potentials toward the optima. Another innovation is a model-order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model-order selection.

Yan, C, Chang, X, Luo, M, Liu, H, Zhang, X & Zheng, Q 2024, 'Semantics-Guided Contrastive Network for Zero-Shot Object Detection', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 3, pp. 1530-1544.
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Zero-shot object detection (ZSD), the task that extends conventional detection models to detecting objects from unseen categories, has emerged as a new challenge in computer vision. Most existing approaches on ZSD are based on a strict mapping-transfer strategy that learns a mapping function from visual to semantic space over seen categories, then directly generalizes the learned mapping function to unseen object detection. However, the ZSD task still remains challenging, since those works fail to consider the two key factors that hamper the ZSD performance: (a) the domain shift problem between seen and unseen classes leads to poor transferable ability of the model; (b) the original visual feature space is suboptimal for ZSD since it lacks discriminative information.To alleviate these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD (ContrastZSD), a detection framework that first brings the contrastive learning paradigm into the realm of ZSD. The pairwise contrastive tasks take advantage of class label and semantic relation as additional supervision signals. Under the guidance of those explicit semantic supervision, the model can learn more knowledge about unseen categories to avoid over-fitting to the seen concepts.

Yan, T, Xu, S, Huang, H, Li, H, Tan, L, Chang, X & Lau, RWH 2024, 'NRGlassNet: Glass surface detection from visible and near-infrared image pairs', Knowledge-Based Systems, vol. 294, pp. 111722-111722.
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Yan, T, Zhu, X, Chen, X, He, W, Wang, C, Yang, Y, Wang, Y & Chang, X 2024, 'GLGFN: Global-Local Grafting Fusion Network for High-Resolution Image Deraining', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Yan, Z, Sun, W, Guo, W, Li, B, Wen, S & Cao, J 2024, 'Complete Stability of Delayed Recurrent Neural Networks With New Wave-Type Activation Functions', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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Yang, A, Lin, S, Yeh, C-H, Shu, M, Yang, Y & Chang, X 2024, 'Context Matters: Distilling Knowledge Graph for Enhanced Object Detection', IEEE Transactions on Multimedia, vol. 26, pp. 487-500.
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Yang, B, Sang, R, Li, Y, Goldys, E & Deng, W 2024, 'Abstract 5743: Liposome platform enables X-ray induced photodynamic therapy treatment against human triple negative breast cancer cells', Cancer Research, vol. 84, no. 6_Supplement, pp. 5743-5743.
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Abstract In this study, we employed X-ray induced photodynamic therapy (X-PDT) for the treatment on triple negative breast cancer (TNBC) cells. To do this, we rationally developed a liposome delivery system co-loaded with protoporphyrin IX (PPIX) and perfluorooctyl bromide (PFOB). Low-dose X-ray at 2Gy was employed to activate PPIX for reactive oxygen species (ROS) generation, and the co-loading of PFOB provided additional oxygen to augment ROS production. The highly toxic ROS triggered TNBC cell death. In vitro X-PDT effects including intracellular ROS generation, cytotoxicity, cell viability and apoptosis/necrosis assay in TNBC cells were studied. Our results indicate that the nanocarriers effectively induced X-PDT effect with very low dose radiation, which makes it possible to damage cancer cells. Our strategy may offer a paradigm-shifting treatment alternative for TNBC patients who need neoadjuvant radiotherapy but wish to avoid long term detrimental effect on functional outcome by undergoing X-PDT using only a fraction of the conventional radiotherapy. Citation Format: Biyao Yang, Rui Sang, Yi Li, Ewa Goldys, Wei Deng. Liposome platform enables X-ray induced photodynamic therapy treatment against human triple negative breast cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5743.

Yang, C, Wang, X, Yao, L, Long, G & Xu, G 2024, 'Dyformer: A dynamic transformer-based architecture for multivariate time series classification', Information Sciences, vol. 656, pp. 119881-119881.
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Yang, G, Lei, J, Fang, Z, Li, Y, Zhang, J & Xie, W 2024, 'HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks', ACM Transactions on Reconfigurable Technology and Systems, vol. 17, no. 2, pp. 1-24.
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Binary neural network (BNN), where both the weight and the activation values are represented with one bit, provides an attractive alternative to deploy highly efficient deep learning inference on resource-constrained edge devices. However, our investigation reveals that, to achieve satisfactory accuracy gains, state-of-the-art (SOTA) BNNs, such as FracBNN and ReActNet, usually have to incorporate various auxiliary floating-point components and increase the model size, which in turn degrades the hardware performance efficiency. In this article, we aim to quantify such hardware inefficiency in SOTA BNNs and further mitigate it with negligible accuracy loss. First, we observe that the auxiliary floating-point (AFP) components consume an average of 93% DSPs, 46% LUTs, and 62% FFs, among the entire BNN accelerator resource utilization. To mitigate such overhead, we propose a novel algorithm-hardware co-design, called FuseBNN , to fuse those AFP operators without hurting the accuracy. On average, FuseBNN reduces AFP resource utilization to 59% DSPs, 13% LUTs, and 16% FFs. Second, SOTA BNNs often use the compact MobileNetV1 as the backbone network but have to replace the lightweight 3 × 3 depth-wise convolution (DWC) with the 3 × 3 standard convolution (SC, e.g., in ReActNet and our ReActNet-adapted BaseBNN) or even more complex fractional 3 × 3 SC (e.g., in FracBNN) to bridge the accuracy gap. As a result, the model parameter size is significantly increased and becomes 2.25× larger than that of the 4-bit direct quantization with the original DWC (4-Bit-Net); the number of multiply-accumulate operations is also significantly increased so that the overall LUT resource usage of BaseBNN is almost the same as that of 4-Bit-Net. To address this issue, we propose HyBNN , where we binarize depth-wise separation convolution (DSC) blocks for the fi...

Yang, H, Feng, Z, Wei, Z, Zhang, Q, Yuan, X, Quek, TQS & Zhang, P 2024, 'Intelligent Computation Offloading for Joint Communication and Sensing-Based Vehicular Networks', IEEE Transactions on Wireless Communications, vol. 23, no. 4, pp. 3600-3616.
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Yang, H, Xiang, K, Ge, M, Li, H, Lu, R & Yu, S 2024, 'A Comprehensive Overview of Backdoor Attacks in Large Language Models within Communication Networks', IEEE Network, pp. 1-1.
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Yang, J & Lin, C-T 2024, 'Enhanced Adjacency-Constrained Hierarchical Clustering Using Fine-Grained Pseudo Labels', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 3, pp. 2481-2492.
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Yang, J & Lin, C-T 2024, 'Toward Autonomous Distributed Clustering', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-8.
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Yang, J, Huang, J, He, X, Wen, S & Wang, H 2024, 'Bipartite Synchronization of Signed Networks With Time-Vary Delays Based on T-S Fuzzy System', IEEE Transactions on Fuzzy Systems, vol. 32, no. 6, pp. 3521-3528.
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Yang, K, Tang, Z, Li, W, Wu, H, Ma, G, Xiang, Y, Xie, Y & Long, G 2024, 'A systematic review on the evaluation methods for the flexural toughness of cement-based materials: From classification analysis to case study', Journal of Building Engineering, vol. 93, pp. 109855-109855.
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Yang, W, Huang, J, He, X & Wen, S 2024, 'Fixed-time synchronization of complex-valued neural networks for image protection and 3D point cloud information protection', Neural Networks, vol. 172, pp. 106089-106089.
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Yang, W, Huang, J, He, X, Wen, S & Huang, T 2024, 'Finite-Time Synchronization of Neural Networks With Proportional Delays for RGB-D Image Protection', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 6, pp. 8149-8160.
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Since the depth information of images facilitates the analysis of the spatial distance of objects in computer vision applications, it is necessary to protect the image depth information. Thus this article proposes a novel red-green-blue-depth (RGB-D) image protection algorithm, which is implemented with the finite-time synchronization (FTS) of neural networks (NNs) with proportional delays via the quantized intermittent control to derive the system synchronization criterion based on Lyapunov stability theory. The performance of RGB-D image protection depends on the synchronization error of the system by driving the system sequence to encrypt the RGB-D image and responding to the system sequence to decrypt the encrypted image. Subsequently, the validity of the proposed criteria is verified by simulation examples, and the practical application of RGB-D image protection is verified.

Yang, X, Che, H, Leung, M-F & Wen, S 2024, 'Self-paced regularized adaptive multi-view unsupervised feature selection', Neural Networks, vol. 175, pp. 106295-106295.
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Yang, X, Li, H, Guo, Q, Zhang, JA, Huang, X & Cheng, Z 2024, 'Sensing Aided Uplink Transmission in OTFS ISAC With Joint Parameter Association, Channel Estimation and Signal Detection', IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 9109-9114.
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Yang, Y, Lu, P, Liu, Z, Dong, L, Lin, J, Yang, T, Ren, Q & Wu, C 2024, 'Effect of steel fibre with different orientations on mechanical properties of 3D-printed steel-fibre reinforced concrete: Mesoscale finite element analysis', Cement and Concrete Composites, vol. 150, pp. 105545-105545.
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Yang, Y, Lu, P, Shao, R, Zhao, Q, Yang, T & Wu, C 2024, 'A comprehensive review of multisource solid wastes in sustainable concrete: From material properties to engineering application', Construction and Building Materials, vol. 435, pp. 136775-136775.
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Yang, Y, Qi, J, Hu, J, Zhou, Y, Zheng, J, Deng, W, Inam, M, Guo, J, Xie, Y, Li, Y, Xu, C, Deng, W & Chen, W 2024, 'Lovastatin/SN38 co-loaded liposomes amplified ICB therapeutic effect via remodeling the immunologically-cold colon tumor and synergized stimulation of cGAS-STING pathway', Cancer Letters, vol. 588, pp. 216765-216765.
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Yao, K, Chang, L & Qin, L 2024, 'Identifying Large Structural Balanced Cliques in Signed Graphs', IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 3, pp. 1145-1160.
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Yao, Y, Pan, Y, Li, J, Tsang, IW & Yao, X 2024, 'Sanitized clustering against confounding bias', Machine Learning, vol. 113, no. 6, pp. 3711-3730.
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AbstractReal-world datasets inevitably contain biases that arise from different sources or conditions during data collection. Consequently, such inconsistency itself acts as a confounding factor that disturbs the cluster analysis. Existing methods eliminate the biases by projecting data onto the orthogonal complement of the subspace expanded by the confounding factor before clustering. Therein, the interested clustering factor and the confounding factor are coarsely considered in the raw feature space, where the correlation between the data and the confounding factor is ideally assumed to be linear for convenient solutions. These approaches are thus limited in scope as the data in real applications is usually complex and non-linearly correlated with the confounding factor. This paper presents a new clustering framework named Sanitized Clustering Against confounding Bias, which removes the confounding factor in the semantic latent space of complex data through a non-linear dependence measure. To be specific, we eliminate the bias information in the latent space by minimizing the mutual information between the confounding factor and the latent representation delivered by variational auto-encoder. Meanwhile, a clustering module is introduced to cluster over the purified latent representations. Extensive experiments on complex datasets demonstrate that our SCAB achieves a significant gain in clustering performance by removing the confounding bias.

Yao, Y, Pan, Y, Tsang, IW & Yao, X 2024, 'Differential-Critic GAN: Generating What You Want by a Cue of Preferences', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 3, pp. 3754-3768.
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This article proposes differential-critic generative adversarial network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property. DiCGAN generates desired data that meet the user's expectations and can assist in designing biological products with desired properties. Existing approaches select the desired samples first and train regular GANs on the selected samples to derive the user-desired data distribution. However, the selection of the desired data relies on global knowledge and supervision over the entire dataset. DiCGAN introduces a differential critic that learns from pairwise preferences, which are local knowledge and can be defined on a part of training data. The critic is built by defining an additional ranking loss over the Wasserstein GAN's critic. It endows the difference of critic values between each pair of samples with the user preference and guides the generation of the desired data instead of the whole data. For a more efficient solution to ensure data quality, we further reformulate DiCGAN as a constrained optimization problem, based on which we theoretically prove the convergence of our DiCGAN. Extensive experiments on a diverse set of datasets with various applications demonstrate that our DiCGAN achieves state-of-the-art performance in learning the user-desired data distributions, especially in the cases of insufficient desired data and limited supervision.

Yazdani, D, Omidvar, MN, Yazdani, D, Branke, J, Nguyen, TT, Gandomi, AH, Jin, Y & Yao, X 2024, 'Robust Optimization Over Time: A Critical Review', IEEE Transactions on Evolutionary Computation, pp. 1-1.
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Ye, D, Zhu, T, Gao, K & Zhou, W 2024, 'Defending Against Label-Only Attacks via Meta-Reinforcement Learning', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 3295-3308.
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Ye, Y, Guo, W, Ngo, HH, Wei, W, Cheng, D, Bui, XT, Hoang, NB & Zhang, H 2024, 'Biofuel production for circular bioeconomy: Present scenario and future scope', Science of The Total Environment, vol. 935, pp. 172863-172863.
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Yen, P, Li, Y & Liu, H 2024, 'Enhancing the profitability of pay what you want: A study of suggested prices', Psychology & Marketing, vol. 41, no. 7, pp. 1502-1513.
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AbstractA suggested price is oftentimes utilized in practice to enhance the profitability of pay what you want (PWYW). In this paper, we focus on this suggested price strategy. We conduct five experiments to explore factors that impact buyers' payments after seeing the suggested price. In Study 1, we show a relationship between the suggested price and payments. Intuitively, a higher suggested price results in increased payments. Studies 2A and 2B demonstrate that the relationship between the suggested price and payments is mediated by cost estimation, while Studies 3A and 3B display that the relationship is moderated by the presence of a charitable element. Interestingly, in the case with a charitable element, increasing the suggested price does not stimulate an increment in payments on a par with the case without a charitable element. This paper proposes that sellers who use PWYW could employ a suggested price to furnish buyers with pertinent cost information, but utilizing PWYW with a suggested price may not be suitable for products with a charitable component.

Yi, K, Zhang, Q, He, H, Shi, K, Hu, L, An, N & Niu, Z 2024, 'Deep Coupling Network for Multivariate Time Series Forecasting', ACM Transactions on Information Systems, vol. 42, no. 5, pp. 1-28.
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Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi-order interactions present within and between time series data, which can seriously degrade forecasting accuracy. In this article, we reexamine intra- and inter-series relationships from the perspective of mutual information and accordingly construct a comprehensive relationship learning mechanism tailored to simultaneously capture the intricate multi-order intra- and inter-series couplings. Based on the mechanism, we propose a novel deep coupling network for MTS forecasting, named DeepCN, which consists of a coupling mechanism dedicated to explicitly exploring the multi-order intra- and inter-series relationships among time series data concurrently, a coupled variable representation module aimed at encoding diverse variable patterns, and an inference module facilitating predictions through one forward step. Extensive experiments conducted on seven real-world datasets demonstrate that our proposed DeepCN achieves superior performance compared with the state-of-the-art baselines.

Yin, S, Gao, L, Fan, X, Gao, S, Zhou, X, Jin, W, He, Z & Wang, Q 2024, 'Performance of sewage sludge treatment for the removal of antibiotic resistance genes: Status and prospects', Science of The Total Environment, vol. 907, pp. 167862-167862.
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Yin, S, Jin, W, Xi, T, Zhou, X, He, Z, Meng, X, Naushad, M, Jiang, G & Li, X 2024, 'Factors affect the oxygen production of Chlorella pyrenoidosa in a bacterial-algal symbiotic system: Light intensity, temperature, pH and static magnetic field', Process Safety and Environmental Protection, vol. 184, pp. 492-501.
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Yin, Z, Cheng, N, Song, Y, Hui, Y, Li, Y, Luan, TH & Yu, S 2024, 'UAV-Assisted Secure Uplink Communications in Satellite-Supported IoT: Secrecy Fairness Approach', IEEE Internet of Things Journal, vol. 11, no. 4, pp. 6904-6915.
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Young, MW, Webster, C, Tanis, D, Schurr, AF, Hanna, CS, Lynch, SK, Ratkiewicz, AS, Dickinson, E, Kong, FH & Granatosky, MC 2024, 'What does climbing mean exactly? Assessing spatiotemporal gait characteristics of inclined locomotion in parrots', Journal of Comparative Physiology A, vol. 210, no. 1, pp. 19-33.
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Yu, E, Lu, J & Zhang, G 2024, 'Fuzzy Shared Representation Learning for Multistream Classification', IEEE Transactions on Fuzzy Systems, pp. 1-13.
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Yu, G, Wang, X, Sun, C, Wang, Q, Yu, P, Ni, W & Liu, RP 2024, 'IronForge: An Open, Secure, Fair, Decentralized Federated Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. PP, pp. 1-15.
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Federated learning (FL) offers an effective learning architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, overdependence on a central coordinator, and lack of an open and fair incentive mechanism collectively hinder FL's further development. We propose IronForge, a new generation of FL framework, that features a directed acyclic graph (DAG)-based structure, where nodes represent uploaded models, and referencing relationships between models form the DAG that guides the aggregation process. This design eliminates the need for central coordinators to achieve fully decentralized operations. IronForge runs in a public and open network and launches a fair incentive mechanism by enabling state consistency in the DAG. Hence, the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of IronForge. Experimental results based on a newly developed test bed FLSim highlight the superiority of IronForge to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, IronForge is the first secure and fully decentralized FL (DFL) framework that can be applied in open networks with realistic network and training settings.

Yu, G, Wang, X, Wang, Q, Bi, T, Dong, Y, Liu, RP, Georgalas, N & Reeves, A 2024, 'Toward Web3 Applications: Easing the Access and Transition', IEEE Transactions on Computational Social Systems, pp. 1-14.
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Yu, H, Guo, Y, Ye, L & Su, SW 2024, 'Optimal Experimental Design for In-Field Calibration of a Nine-Parameter Triaxial Magnetometers Model', IEEE Sensors Journal, vol. 24, no. 11, pp. 17579-17587.
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Yu, H, Guo, Y, Ye, L, Han, T & Su, SW 2024, 'Experimental Design for the Inclination-Based Linear Magnetometers Calibration Method', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-9.
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Yu, H, Tuan, HD, Nasir, AA, Dutkiewicz, E & Hanzo, L 2024, 'Rate-Fairness-Aware Low Resolution RIS-Aided Multi-User OFDM Beamforming', IEEE Transactions on Vehicular Technology, vol. 73, no. 2, pp. 2401-2415.
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Yu, H, Zhang, H, Yang, Z & Yu, S 2024, 'Edasvic: Enabling Efficient and Dynamic Storage Verification for Clouds of Industrial Internet Platforms', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 6896-6909.
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Yu, J, Wu, M, Ji, J & Yang, W 2024, 'Neural Network-Based Region Tracking Control for a Flexible-Joint Robot Manipulator', Journal of Computational and Nonlinear Dynamics, vol. 19, no. 2.
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Abstract The present paper proposes a neural network-based adaptive region-tracking control strategy for a flexible-joint robot manipulator subjected to region constraints. The developed neural network-based control strategy can globally stabilize the robot manipulator and cope with model uncertainties and the external unknown bounded disturbances. Different from the existing literature, by using the sliding mode technology and the singular perturbation theory, the developed control strategy does not require the high-order derivatives of the link states such as jerk and acceleration since the high-order derivative information is not always available in practical applications. By using Lyapunov stability theory, it is proved that the proposed neural network-based control strategy can guarantee that all the parameter variables in the closed-loop system are bounded, and the flexible-joint robot manipulator with unknown dynamics can reach inside the dynamic region and also maintain the velocity matching with the desired moving region. Since the assumption of linearization of the unknown dynamic parameters is removed, the proposed control strategy does not require the calculation of the complex regression matrix. Therefore, the proposed method has great robustness and the ability of model generalization. Simulations are given to demonstrate the validity of the proposed control strategy.

Yu, J, Wu, M, Yang, W & Ji, J 2024, 'A system decomposition method for region tracking control of a non‐holonomic mobile robot with dynamic parameter uncertainties', Asian Journal of Control, vol. 26, no. 3, pp. 1459-1471.
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AbstractThe tracking control problem of non‐holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed‐point tracking. This technical note focuses on the region tracking control for a non‐holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non‐holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non‐holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed‐point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed‐point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy.

Yu, L, Wang, Y & Pradhan, B 2024, 'Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China', Geoscience Frontiers, vol. 15, no. 4, pp. 101802-101802.
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Yu, S, Wang, X, Shen, Y, Wu, G, Yu, S & Shen, S 2024, 'Novel Intrusion Detection Strategies With Optimal Hyper Parameters for Industrial Internet of Things Based On Stochastic Games and Double Deep Q-Networks', IEEE Internet of Things Journal, pp. 1-1.
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Yu, S, Zhai, R, Shen, Y, Wu, G, Zhang, H, Yu, S & Shen, S 2024, 'Deep Q-Network-Based Open-Set Intrusion Detection Solution for Industrial Internet of Things', IEEE Internet of Things Journal, vol. 11, no. 7, pp. 12536-12550.
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Yu, X, Li, J, Yu, Y & Song, A 2024, 'Advancing service life estimation of reinforced concrete considering the coupling effects of multiple factors: Hybridized physical testing and machine learning approach', Journal of Building Engineering, vol. 84, pp. 108476-108476.
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Yu, X, Yang, Q, Tang, Y, Gao, R, Bao, S, Cai, LY, Lee, HH, Huo, Y, Moore, AZ, Ferrucci, L & Landman, BA 2024, 'Deep conditional generative model for longitudinal single-slice abdominal computed tomography harmonization', Journal of Medical Imaging, vol. 11, no. 02.
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Yuan, D, Chang, X, Liu, Q, Yang, Y, Wang, D, Shu, M, He, Z & Shi, G 2024, 'Active Learning for Deep Visual Tracking', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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Yuan, D, Geng, G, Shu, X, Liu, Q, Chang, X, He, Z & Shi, G 2024, 'Self-supervised discriminative model prediction for visual tracking', Neural Computing and Applications, vol. 36, no. 10, pp. 5153-5164.
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Yuan, D, Zhang, H, Shu, X, Liu, Q, Chang, X, He, Z & Shi, G 2024, 'An Attention Mechanism Based AVOD Network for 3D Vehicle Detection', IEEE Transactions on Intelligent Vehicles, pp. 1-13.
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Yuan, D, Zhang, H, Shu, X, Liu, Q, Chang, X, He, Z & Shi, G 2024, 'Thermal Infrared Target Tracking: A Comprehensive Review', IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-19.
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Yuan, X, Hu, S, Ni, W, Wang, X & Jamalipour, A 2024, 'Empowering Reconfigurable Intelligent Surfaces with Artificial Intelligence to Secure Air-To-Ground Internet-of-Things', IEEE Internet of Things Magazine, vol. 7, no. 2, pp. 14-21.
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Zafar, AM, Mohamed, BA, Wang, Q & Aly Hassan, A 2024, 'Life cycle analysis of seawater biodesalination using algae', Desalination, vol. 578, pp. 117433-117433.
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Zafra, E, Vazquez, S, Geyer, T, Aguilera, RP, Freire, E & Franquelo, LG 2024, 'Computational Analysis of the Long Horizon FCS-MPC Problem for Power Converters', IEEE Transactions on Power Electronics, pp. 1-11.
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Zainal, BS, Ker, PJ, Mohamed, H, Ong, HC, Fattah, IMR, Rahman, SMA, Nghiem, LD & Mahlia, TMI 2024, 'Recent advancement and assessment of green hydrogen production technologies', Renewable and Sustainable Energy Reviews, vol. 189, pp. 113941-113941.
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Zamani, MG, Nikoo, MR, Al-Rawas, G, Nazari, R, Rastad, D & Gandomi, AH 2024, 'Hybrid WT–CNN–GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features', Journal of Environmental Management, vol. 358, pp. 120756-120756.
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Zavahir, S, Riyaz, NS, Elmakki, T, Tariq, H, Ahmad, Z, Chen, Y, Park, H, Ho, Y-C, Shon, HK & Han, DS 2024, 'Ion-imprinted membranes for lithium recovery: A review', Chemosphere, vol. 354, pp. 141674-141674.
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Zeng, F, Ding, C & Guo, YJ 2024, 'A Reconfigurable Millimeter-Wave Antenna Array With Wide-Range Continuous Beamwidth Control (WCBC) Based on Polarization-Mixing', IEEE Transactions on Antennas and Propagation, vol. 72, no. 4, pp. 3092-3103.
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Zeng, J, Desmond, P, Ngo, HH, Lin, W, Liu, X, Liu, B, Li, G & Ding, A 2024, 'Membrane modification in enhancement of virus removal: A critical review', Journal of Environmental Sciences, vol. 146, pp. 198-216.
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Zhai, Z, Lin, F, Yang, Y & Sun, H 2024, 'Additively Manufactured Wideband Low-Profile Bidirectional 2-D Beam-Scanning Antenna Using Double Folded Transmitarrays With Curved Polarizers', IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 476-486.
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Zhan, P, Wang, J, Zhao, H, Li, W, Shah, SP & Xu, J 2024, 'Impact of synthetic C-S-H seeds on early hydration and pore structure evolution of cement pastes: A study by 1H low-field NMR and path analysis', Cement and Concrete Research, vol. 175, pp. 107376-107376.
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Zhang, A, Liu, K, Li, J, Song, R & Guo, T 2024, 'Static and dynamic tensile properties of ultra-high performance concrete (UHPC) reinforced with hybrid sisal fibers', Construction and Building Materials, vol. 411, pp. 134492-134492.
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Zhang, C & Wang, Z 2024, 'Data-driven distributionally robust optimization under combined ambiguity for cracking production scheduling', Computers & Chemical Engineering, vol. 181, pp. 108538-108538.
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Zhang, C, Sheng, Z, Zhang, C & Wen, S 2024, 'Multi-lead-time short-term runoff forecasting based on Ensemble Attention Temporal Convolutional Network', Expert Systems with Applications, vol. 243, pp. 122935-122935.
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Zhang, C, Wang, S, Cao, Y, Zhu, S, Guo, Z & Wen, S 2024, 'Robust model predictive control for continuous nonlinear systems with the quasi-infinite adaptive horizon algorithm', Journal of the Franklin Institute, vol. 361, no. 2, pp. 748-763.
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Zhang, C, Wang, W, Tian, Z & Yu, S 2024, 'Forgetting and Remembering Are Both You Need: Balanced Graph Structure Unlearning', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 6751-6763.
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Zhang, C, Yu, S, Tian, Z & Yu, JJQ 2024, 'Generative Adversarial Networks: A Survey on Attack and Defense Perspective', ACM Computing Surveys, vol. 56, no. 4, pp. 1-35.
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Generative Adversarial Networks (GANs) are a remarkable creation with regard to deep generative models. Thanks to their ability to learn from complex data distributions, GANs have been credited with the capacity to generate plausible data examples, which have been widely applied to various data generation tasks over image, text, and audio. However, as with any powerful technology, GANs have a flip side: their capability to generate realistic data can be exploited for malicious purposes. Many recent studies have demonstrated the security and privacy (S&P) threats brought by GANs, especially the attacks on machine learning (ML) systems. Nevertheless, so far as we know, there is no existing survey that has systematically categorized and discussed the threats and strategies of these GAN-based attack methods. In this article, we provide a comprehensive survey of GAN-based attacks and countermeasures. We summarize and articulate: (1) what S&P threats of GANs expose to ML systems; (2) why GANs are useful for certain attacks; (3) what strategies can be used for GAN-based attacks; and (4) what countermeasures can be effective to GAN-based attacks. Finally, we provide several promising research directions combining the existing limitations of GAN-based studies and the prevailing trend in the associated research fields.

Zhang, D, Li, M, Qiu, N, Yang, J, Wu, C, Steven, G, Li, Q & Fang, J 2024, '4D-printed reusable metamaterial via shape memory effect for energy dissipation', International Journal of Mechanical Sciences, vol. 275, pp. 109309-109309.
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Zhang, G & Wen, S 2024, 'New Approximate Results of Fixed-Time Stabilization for Delayed Inertial Memristive Neural Networks', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 7, pp. 3428-3432.
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Zhang, G, Liu, B, Zhu, T, Ding, M & Zhou, W 2024, 'PPFed: A Privacy-Preserving and Personalized Federated Learning Framework', IEEE Internet of Things Journal, vol. 11, no. 11, pp. 19380-19393.
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Zhang, H, Chen, S, Karimi, M, Li, B, Saydam, S & Hassan, M 2024, 'Numerical and experimental investigation of an auxetic piezoelectric energy harvester with frequency self-tuning capability', Smart Materials and Structures, vol. 33, no. 5, pp. 055022-055022.
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Abstract To deal with the limited availability of long-lasting power sources for sensor nodes in industrial environments, a novel piezoelectric energy harvester with high efficiency and a wide working bandwidth was designed to harvest broadband and random vibrations from the ambient environment. The developed energy harvester adopts a doubly clamped piezoelectric beam with a peanut-shaped auxetic structure to improve the power output. It also incorporates a sliding proof mass for frequency self-tuning, enabling a wider working bandwidth. As the doubly clamped beam exhibits geometry nonlinearity under large vibration amplitudes, the power output of the energy harvester can be further enhanced in the frequency self-tuning process. Finite element simulations are conducted to evaluate the impact of the auxetic structure and the position of the proof mass on the performance of the energy harvester. Experiments are performed to examine the energy harvesting performance of the proposed energy harvester. Under an excitation acceleration of 0.3 g, the use of the sliding proof mass widens the working bandwidth of the auxetic energy harvester (AEH) by 9 Hz, with the maximum root mean square output power of AEH reaching 18.78 μW, which is much higher than that of the plain energy harvester (PEH) or the AEH with a fixed proof mass. The developed energy harvester can successfully power a wireless temperature and humidity sensor node based on the vibration produced by a centrifuge, which demonstrates the practical feasibility of the proposed energy harvester for industrial applications.

Zhang, H, Huang, X & Zhang, JA 2024, 'Low-Overhead OTFS Transmission With Frequency or Time Domain Channel Estimation', IEEE Transactions on Vehicular Technology, vol. 73, no. 1, pp. 799-811.
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Zhang, H, Leng, S, Yin, H & Yu, S 2024, 'Intelligent Consensus Enhanced Spectrum Sharing in Heterogeneous Wireless Networks', IEEE Internet of Things Journal, pp. 1-1.
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Zhang, H, Xia, J, Zhang, G & Xu, M 2024, 'Learning Graph Representations Through Learning and Propagating Edge Features', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 6, pp. 8429-8440.
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Zhang, H, Xu, M, Zhang, G & Niwa, K 2024, 'SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 2, pp. 2223-2234.
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Zhang, H, Yin, W, Liao, G, Liu, J, Dong, G, Wang, J, Guo, W & Ngo, HH 2024, 'The identification of a correlation between lipid content in the model diatom Phaeodactylum tricornutum and pH treatment strategies', Science of The Total Environment, vol. 915, pp. 169897-169897.
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Zhang, H, Yu, H, Wang, X, Wang, M, Zhang, Z, Li, Y, Xie, S & Luo, X 2024, 'Knowledge-guided communication preference learning model for multi-agent cooperation', Information Sciences, vol. 667, pp. 120395-120395.
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Zhang, H, Zhu, L, Wang, X & Yang, Y 2024, 'Divide and Retain: A Dual-Phase Modeling for Long-Tailed Visual Recognition', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Zhang, J, Lei, J, Xie, W, Yang, G, Li, D & Li, Y 2024, 'Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Zhang, J, Pei, G, Zhu, X, Gou, X, Deng, L, Gao, L, Liu, Z, Ni, Q & Lin, J 2024, 'Diesel engine fault diagnosis for multiple industrial scenarios based on transfer learning', Measurement, vol. 228, pp. 114338-114338.
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Zhang, J, Yuan, L, Li, W, Qin, L, Zhang, Y & Zhang, W 2024, 'Label-constrained shortest path query processing on road networks', The VLDB Journal, vol. 33, no. 3, pp. 569-593.
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Zhang, J, Zhang, Q, Gong, Y, Zhang, J, Chen, L & Zeng, D 2024, 'Weakly Supervised Semantic Segmentation With Consistency-Constrained Multiclass Attention for Remote Sensing Scenes', IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-18.
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Zhang, J, Zhu, S, Liu, X, Wen, S & Mu, C 2024, 'Finite-Time Stabilization of Inertial Memristive Neural Networks via Nonreduced Order Method', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-11.
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Zhang, L, Guo, Y, Zhang, Y & Su, J 2024, 'Compete or not Evidence from global biopharmaceutical innovation and market', International Journal of Technology Management, vol. 94, no. 2, pp. 213-241.
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Zhang, L, Shi, Y, Chang, Y-C & Lin, C-T 2024, 'Robust Fuzzy Neural Network With an Adaptive Inference Engine', IEEE Transactions on Cybernetics, vol. 54, no. 5, pp. 3275-3285.
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Zhang, M, Wang, Q, Luo, Z & Gao, W 2024, 'Stochastic bandgap optimization for multiscale elastic metamaterials with manufacturing imperfections', International Journal of Mechanical Sciences, vol. 268, pp. 109035-109035.
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Zhang, M, Wang, Q, Luo, Z & Gao, W 2024, 'Virtual model-aided reliability analysis considering material and geometrical uncertainties for elastic metamaterials', Mechanical Systems and Signal Processing, vol. 211, pp. 111199-111199.
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Zhang, N, Lu, J, Li, K, Fang, Z & Zhang, G 2024, 'Source-Free Unsupervised Domain Adaptation: Current research and future directions', Neurocomputing, vol. 564, pp. 126921-126921.
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Zhang, P, Chen, N, Shen, S, Yu, S, Kumar, N & Hsu, C-H 2024, 'AI-Enabled Space-Air-Ground Integrated Networks: Management and Optimization', IEEE Network, vol. 38, no. 2, pp. 186-192.
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Zhang, P, Chen, N, Shen, S, Yu, S, Wu, S & Kumar, N 2024, 'Future Quantum Communications and Networking: A Review and Vision', IEEE Wireless Communications, vol. 31, no. 1, pp. 141-148.
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Based on quantum mechanics, quantum communications have clock synchronization properties and unbreakable security. Recently, related technologies of quantum communications have made breakthroughs one after another, and it has also realized functions and applications that are difficult to achieve in traditional networks. In this era of the rise of quantum networks similar to the eve of the birth of the classical Internet 'Arpanet', researchers are full of longing for the blueprint of the future quantum network. In this work, we summarize, analyze and look forward to quantum communications and networking compared with the traditional Internet. Specifically, the related fundamentals, related technological developments, and existing challenges of quantum networks are firstly analyzed. On this basis, we look ahead and propose a prototype of the future quantum network architectures. Finally, we sort out and analyze the paradigm, principle, and properties of quantum communications. To the best of our knowledge, this is the first review and vision work on quantum communications and networking.

Zhang, P, Zhang, L, Hao, Y, Xu, M, Pang, M, Wang, C, Yang, A & Voinov, A 2024, 'Food–energy–water nexus optimization brings substantial reduction of urban resource consumption and greenhouse gas emissions', PNAS Nexus, vol. 3, no. 2.
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Abstract Urban sustainability is a key to achieving the UN sustainable development goals (SDGs). Secure and efficient provision of food, energy, and water (FEW) resources is a critical strategy for urban sustainability. While there has been extensive discussion on the positive effects of the FEW nexus on resource efficiency and climate impacts, measuring the extent to which such synergy can benefit urban sustainability remains challenging. Here, we have developed a systematic and integrated optimization framework to explore the potential of the FEW nexus in reducing urban resource demand and greenhouse gas (GHG) emissions. Demonstrated using the Metropolis Beijing, we have identified that the optimized FEW nexus can reduce resource consumption and GHG emissions by 21.0 and 29.1%, respectively. These reductions come with increased costs compared to the siloed FEW management, but it still achieved a 16.8% reduction in economic cost compared to the business-as-usual scenario. These findings underscore the significant potential of FEW nexus management in enhancing urban resource efficiency and addressing climate impacts, while also identifying strategies to address trade-offs and increase synergies.

Zhang, Q, Hu, J, Guo, H, Yang, C, Li, J, Liu, N, Guo, W, Dai, C, Wang, L, Tian, Y & Ngo, HH 2024, 'Preparation of C=C polymerization-oriented magnetic protein molecularly imprinted polymer and the application for membrane flux improvement', Desalination, vol. 573, pp. 117206-117206.
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Zhang, Q, Lai, N, He, M, Yang, Y, Huang, Q, Quan, Y, Hou, S, Gao, X, Song, Y, Liao, J & Wang, R 2024, 'Tunable broadband luminescence of Bi‐ion‐doped glasses via Gd₂O₃ co‐doping', Journal of the American Ceramic Society, vol. 107, no. 6, pp. 3837-3844.
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AbstractThe doping of bismuth (Bi) ions in borosilicate glasses has gained attention for its potential applications in LED light sources and imaging displays. Here, gadolinium oxide (Gd2O3) was introduced into the glass matrix in varying concentrations using a high‐temperature melting method to investigate its impact on the luminescence properties of Bi ions. The resulting glass exhibited bimodal emission peaks at 465 and 750 nm when excited with 325 nm light. The luminescence intensity and fluorescence half width at half height initially increased, followed by a subsequent decrease as the Gd2O3 content in the glass increased from 10 to 43 mol%. Additionally, the color of the luminescence transformed from purple–red to green under white light irradiation. The composition and excitation wavelength of the glass can be adjusted to achieve selective tuning of the luminescence.

Zhang, Q, Yang, Y, Shi, C, Lao, A, Hu, L, Wang, S & Naseem, U 2024, 'Rumor Detection With Hierarchical Representation on Bipartite Ad Hoc Event Trees', IEEE Transactions on Neural Networks and Learning Systems, vol. PP, pp. 1-13.
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The rapid growth of social media has caused tremendous effects on information propagation, raising extreme challenges in detecting rumors. Existing rumor detection methods typically exploit the reposting propagation of a rumor candidate for detection by regarding all reposts to a rumor candidate as a temporal sequence and learning semantics representations of the repost sequence. However, extracting informative support from the topological structure of propagation and the influence of reposting authors for debunking rumors is crucial, which generally has not been well addressed by existing methods. In this article, we organize a claim post in circulation as an ad hoc event tree, extract event elements, and convert it into bipartite ad hoc event trees in terms of both posts and authors, i.e., author tree and post tree. Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite ad hoc event trees called BAET. Specifically, we introduce word embedding and feature encoder for the author and post tree, respectively, and design a root-aware attention module to perform node representation. Then we adopt the tree-like RNN model to capture the structural correlations and propose a tree-aware attention module to learn tree representation for the author tree and post tree, respectively. Extensive experimental results on two public Twitter datasets demonstrate the effectiveness of BAET in exploring and exploiting the rumor propagation structure and the superior detection performance of BAET over state-of-the-art baseline methods.

Zhang, S, Liu, C, Wang, Y, Niu, F, Lei, G & Zhu, J 2024, 'Shape Design Optimization and Comparative Analysis of a Novel Synchronous Reluctance Machine with Grain-Oriented Silicon Steel', IEEE Transactions on Magnetics, pp. 1-1.
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Zhang, S, Yin, M, Xiao, F, Cao, Z & Pelusi, D 2024, 'A Complex Gaussian Fuzzy Numbers-Based Multisource Information Fusion for Pattern Classification', IEEE Transactions on Fuzzy Systems, vol. 32, no. 5, pp. 3247-3259.
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Zhang, T, Xu, D, Tolba, A, Yu, K, Song, H & Yu, S 2024, 'Reinforcement-Learning-Based Offloading for RIS-Aided Cloud–Edge Computing in IoT Networks: Modeling, Analysis, and Optimization', IEEE Internet of Things Journal, vol. 11, no. 11, pp. 19421-19439.
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Zhang, T, Zhang, S & Jia, W 2024, 'Person Reidentification Based on Adaptive Relation Attention Network in Intelligent Monitoring System for the IoB', IEEE Transactions on Engineering Management, pp. 1-10.
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Zhang, W, Chen, J, Wen, S & Huang, T 2024, 'Event-Triggered Random Delayed Impulsive Consensus of Multi-Agent Systems With Time-Varying Delay', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-6.
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Zhang, W, Li, L, Ding, Y, Chen, W, Deng, Z & Yu, X 2024, 'Detecting Facial Action Units From Global-Local Fine-Grained Expressions', IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 2, pp. 983-994.
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Zhang, W, Peng, Y & Li, M 2024, 'Efficient Circular Flat-Top Pattern Synthesis With Circular Planar Array via Dimensionality Reduction', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 1, pp. 64-68.
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Zhang, W, Zhu, H, Wen, S & Huang, T 2024, 'Finite-Time Bipartite Tracking Consensus of Fractional-Order Multi-Layer Signed Networks by Aperiodically Intermittent Control', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 71, no. 6, pp. 3061-3065.
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Zhang, X, Fang, Y, Liu, Q & Yazdani, D 2024, 'Multi-objective Robust Optimization Over Time for Dynamic Disassembly Sequence Planning', International Journal of Precision Engineering and Manufacturing, vol. 25, no. 1, pp. 111-130.
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Zhang, X, Fang, Y, Liu, Q & Yazdani, D 2024, 'Multi-objective Robust Optimization Over Time for Dynamic Disassembly Sequence Planning', International Journal of Precision Engineering and Manufacturing, vol. 25, no. 1, pp. 111-130.
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Disassembly sequence planning aims to optimize disassembly sequences of end-of-life (EOL) products in order to minimize the cost and environmental pollutant emission. Various unpredictable factors in the disassembly environment (e.g., EOL product status and capabilities of operators) lead to significant uncertainty making the optimal disassembly sequence change over time. Considering existing multiple objectives and dynamic environment, this problem is indeed dynamic multi-objective optimization. As deploying a new solution (i.e., disassembly sequence) is costly in this problem, it is undesirable to change the deployed solution after each environmental change. In this paper, we first propose a model for disassembly sequence planning problem in which several factors including the environmental changes, deployed solution switching cost, constraints, and multiple objectives are taken into account. To solve this problem where frequently changing the deployed solution must be avoided, we propose a new multi-objective robust optimization over time (ROOT) framework to find robust solutions based on two new robustness definitions: average performance and stability. The proposed framework benefits from a novel accurate online predictor and the knee-oriented dominance which is applied to select the naturally preferred tradeoff solution to meet the application requirements of ROOT. Computational experiments demonstrate the effectiveness of the proposed ROOT framework.

Zhang, X, Peng, H, Tang, T, Liu, Y, Wang, Y & Zhang, J 2024, 'Knowledge-based Dual External Attention Network for peptide detectability prediction', Knowledge-Based Systems, vol. 286, pp. 111378-111378.
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Zhang, X, Wang, X, So, HC, Zoubir, AM, Zhang, JA & Guo, YJ 2024, 'Transmit Waveform Design for Integrated Wideband MIMO Radar and Bi-Directional Communications', IEEE Transactions on Vehicular Technology, pp. 1-16.
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Zhang, X, Xie, W, Li, Y, Lei, J, Jiang, K, Fang, L & Du, Q 2024, 'Block-Wise Partner Learning for Model Compression', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Zhang, X, Zheng, H, Wang, Z, Su, Y, Chen, H, Liu, Q, Yao, P, Mameda, N, Ngo, HH & Nghiem, LD 2024, 'Pretreatment by a novel photo-electro reactor to control organic and biofouling during reverse osmosis filtration of reclaimed water', Chemical Engineering Journal, vol. 482, pp. 148893-148893.
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Zhang, Y, Feng, B, Tian, A, Yu, S & Zhang, H 2024, 'Task Offloading Control and Customized Workload Scheduling in Multi-Layer Cloud Networks', IEEE Transactions on Network and Service Management, vol. 21, no. 1, pp. 714-728.
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Zhang, Y, Huang, Z, Chen, J & Guo, Y 2024, 'Electromagnetic Distribution of Stacked HTS Tapes Subjected to a Rotating Magnetic Field', IEEE Transactions on Applied Superconductivity, vol. 34, no. 8, pp. 1-3.
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Zhang, Y, Li, B, Liu, B & Chang, J 2024, 'Building PUF as a Service: Distributed Authentication and Recoverable Data Sharing With Multidimensional CRPs Security Protection', IEEE Internet of Things Journal, vol. 11, no. 10, pp. 17301-17316.
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Zhang, Y, Liu, Y, Hu, R, Wu, Q & Zhang, J 2024, 'Mutual Dual-Task Generator With Adaptive Attention Fusion for Image Inpainting', IEEE Transactions on Multimedia, vol. 26, pp. 1539-1550.
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Zhang, Y, Mishra, PN, Tiwari, S, Scheuermann, A & Li, L 2024, 'Suppressive effects of geotextiles on soil water evaporation', Acta Geotechnica, vol. 19, no. 4, pp. 2163-2174.
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AbstractGeotextiles find wide applications in the field for filtration and drainage. When applied on the soil surface they influence soil evaporation. The objective of this work is twofold: (a) to assess the effectiveness of four different geotextiles as cover materials on soil evaporation, (b) to study the combined effect of geotextile and perforated mechanical barriers on soil evaporation. The first set of experimental programs consisted of three soil samples i.e. kaolin, dredged mud from the port of Brisbane and a locally obtained red mud sample from Queensland, Australia tested with four types of non-woven geotextiles under four controlled climatic conditions. All the 4 geotextiles had suppression effects on soil evaporation to degrees that varied with the type of soil, ratio of pore size to thickness of geotextiles (M*), product of pore size to thickness of the geotextiles (N*) and climatic conditions. Geotextiles with a higher pore size (O95) and M* allowed water vapor to move through relatively easily leading to higher evaporation rates. Geotextile with a higher thickness and N* value provided a higher suppression effect on soil evaporation. In a recently introduced dewatering method involving perforated ventilated well method, evaporation from soil take place through geotextiles and the perforated well. Mimicking this, impacts on soil evaporation with geotextiles sandwiched between soil sample and perforated sections were also studied. Maintaining similar number and arrangement of the perforations, soil evaporation was noted to be higher with rectangular shaped perforation compared to circular shaped perforations.

Zhang, Y, Wei, X & Yu, H 2024, 'HD-LJP: A Hierarchical Dependency-based Legal Judgment Prediction Framework for Multi-task Learning', Knowledge-Based Systems, vol. 299, pp. 112033-112033.
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Zhang, Y, Zhang, C, Mayr, P, Suominen, A & Ding, Y 2024, 'An editorial of “AI + informetrics”: Robust models for large-scale analytics', Information Processing & Management, vol. 61, no. 1, pp. 103495-103495.
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Zhang, Z, Li, X, Liu, H, Zhou, T, Wang, Z, Nghiem, LD & Wang, Q 2024, 'Biofouling control of reverse osmosis membrane using free ammonia as a cleaning agent', Journal of Membrane Science, vol. 694, pp. 122414-122414.
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Zhang, Z, Veedu, PNP, Halkon, B & Chou, SM 2024, 'Application of Support Vector Machine in the Evaluation of Table Tennis Motion Profiles', Computing Open, vol. 02.
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Racket sports such as table tennis involve a wide range of three-dimensional complex spatial movements of the human body and the racket. Novice players might benefit from the evaluation of the motion profile of the racket to facilitate better adoption of more expert movement. Computer-based evaluation of such novice vs. expert play behavior characteristics includes reducing the required multiple human interactions and easy applicability for subsequent automation to accurately differentiate the motion profile of a novice player from that of an expert. This study has, for the first time, applied the widely used support vector machine (SVM) classification technique for the development of a table tennis player movement evaluation model. The model was trained using an existing dataset of displacements and velocities from various important anatomical landmarks across the body and points on the racket. These were obtained and evaluated for table tennis forehand strokes for two subgroups of expert and novice ability levels, respectively. Different combinations of variables were selected for model input from the same dataset with the outcomes being noted for each. The resulting SVM classification model exhibited good/noteworthy performance ([Formula: see text]% accuracy) in distinguishing racket motion between expert and novice players.

Zhang, Z, Yu, G, Sun, C, Wang, X, Wang, Y, Zhang, M, Ni, W, Liu, RP, Reeves, A & Georgalas, N 2024, 'TbDd: A new trust-based, DRL-driven framework for blockchain sharding in IoT', Computer Networks, vol. 244, pp. 110343-110343.
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Zhang, Z, Zheng, H, Hong, R, Fan, J, Yang, Y & Yan, S 2024, 'FRC-Net: A Simple Yet Effective Architecture for Low-Light Image Enhancement', IEEE Transactions on Consumer Electronics, pp. 1-1.
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Zhang, Z, Zhou, Y, Shi, K, Chen, H, Wen, S & Yan, H 2024, 'VSG-LFC on multi-area power system under unsecured communication channel using hybrid trigger mechanism', Expert Systems with Applications, vol. 255, pp. 124571-124571.
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Zhao, D, Mihăiţă, A-S, Ou, Y, Grzybowska, H & Li, M 2024, 'Origin–destination matrix estimation for public transport: A multi-modal weighted graph approach', Transportation Research Part C: Emerging Technologies, vol. 165, pp. 104694-104694.
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Zhao, F, Ji, JC, Cao, S, Ye, K & Luo, Q 2024, 'QZS isolators with multi-pairs of oblique bars for isolating ultralow frequency vibrations', Nonlinear Dynamics, vol. 112, no. 3, pp. 1815-1842.
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Zhao, H, Hu, Y, Li, Y, Wang, K, Dehn, F & Li, W 2024, 'Triaxial compressive performance of recycled aggregate/glass sand concrete: Experimental study and mechanism analysis', Journal of Cleaner Production, vol. 442, pp. 141006-141006.
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Zhao, J, He, W, Fu, W, Ding, Y & Guo, Y 2024, 'Comparative Studies on Performances of Slotted and Slotless High-Speed PMBLDC Motors', IEEE Access, vol. 12, pp. 13431-13441.
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Zhao, J, Lu, Z, Zhang, JA, Dong, S & Zhou, S 2024, 'Multiple-Target Doppler Frequency Estimation in ISAC With Clock Asynchronism', IEEE Transactions on Vehicular Technology, vol. 73, no. 1, pp. 1382-1387.
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Zhao, L, Zhu, H, Ding, C, Liu, G, Zhao, H, Mou, J & Guo, YJ 2024, 'An Ultrawideband Dual-Polarized Tightly Coupled Dipole Array (TCDA) With Wide Scanning Range', IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 7, pp. 1961-1965.
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Zhao, M, Qi, X, Hu, Z, Li, L, Zhang, Y, Huang, Z & Yu, X 2024, 'Calligraphy Font Generation via Explicitly Modeling Location-Aware Glyph Component Deformations', IEEE Transactions on Multimedia, vol. 26, pp. 5939-5950.
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Zhao, P, Pan, Y, Li, X, Chen, X, Tsang, IW & Liao, L 2024, 'Coarse-to-Fine Contrastive Learning on Graphs', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, pp. 4622-4634.
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Zhao, Q, Zhu, S, Mu, C, Liu, X & Wen, S 2024, 'Multistability of Complex-Valued NNs With General Periodic-Type Activation Functions and Its Application to Associative Memories', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 6, pp. 3749-3761.
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Zhao, S & Ma, F 2024, 'A circular microphone array with virtual microphones based on acoustics-informed neural networks', The Journal of the Acoustical Society of America, vol. 156, no. 1, pp. 405-415.
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Acoustic beamforming aims to focus acoustic signals to a specific direction and suppress undesirable interferences from other directions. Despite its flexibility and steerability, beamforming with circular microphone arrays suffers from significant performance degradation at frequencies corresponding to zeros of the Bessel functions. To conquer this constraint, baffled or concentric circular microphone arrays have been studied; however, the former need a bulky baffle that interferes with the original sound field, whereas the latter require more microphones that increase the complexity and cost, both of which are undesirable in practical applications. To tackle this challenge, this paper proposes a circular microphone array equipped with virtual microphones, which resolves the performance degradation commonly associated with circular microphone arrays without resorting to physical modifications. The sound pressures at the virtual microphones are predicted from those measured by the physical microphones based on an acoustics-informed neural network, and then the sound pressures measured by the physical microphones and those predicted at the virtual microphones are integrated to design the beamformer. Experimental results demonstrate that the proposed approach not only eliminates the performance degradation but also suppresses spatial aliasing at high frequencies, thereby underscoring its promising potential.

Zhao, S, Zhang, H, Xiong, L, Wen, S, Cao, J & Zhang, Y 2024, 'Resilient adaptive event‐triggered synchronization control of piecewise‐homogeneous Markov jump delayed neural networks under aperiodic DoS attacks', International Journal of Robust and Nonlinear Control, vol. 34, no. 3, pp. 1493-1521.
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SummaryUnder the aperiodic Denial‐of‐service (DoS) attacks, this paper studies the resilient adaptive event‐triggered synchronization control problem for a class of Piecewise‐Homogeneous Uncertain Markov Jump Neural Networks (PHUMJNNs) with time‐varying delays. First of all, a new way of carving DoS attacks is given from the defenders' perspective, that is, aperiodic DoS attacks based on fixed detection periods. Then, under such attacks, a new Resilient Adaptive Event‐triggered Communication (RAETC) is designed between sensor and controller, which has a threshold function based on the net change rate and is real‐time updated depending on the present sampling state. Next, a single functional is used in the construction of the Lyapunov‐Krasovskii functional, while a new looped functional is introduced that makes full use of the state information of the current instant , the trigger instant and the next trigger instant . Based on the constructed single functional, under the framework of the input delay method and the linear matrix inequality technique, the exponential mean square stabilization criterion of the error system is obtained, which makes the master system and the slave system synchronized. In the end, three simulation examples are used to illustrate the validity of the obtained results.

Zhao, S, Zhou, C, Zan, R, Suo, T & Xin, Z 2024, 'Fabrication of a Universal Composite Coating for Biliary Stents with Protein Antifouling and Antibacterial Synergies', Industrial & Engineering Chemistry Research, vol. 63, no. 22, pp. 9833-9844.
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Zhao, S, Zhu, Q, Cheng, E & Burnett, IS 2024, 'Erratum: A room impulse response database for multizone sound field reproduction (L) [J. Acoust. Soc. Am. 152(4), 2505–2512 (2022)]', The Journal of the Acoustical Society of America, vol. 155, no. 3, pp. 2170-2170.
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Zhao, Y, Liu, B, Zhu, T, Ding, M, Yu, X & Zhou, W 2024, 'Proactive image manipulation detection via deep semi-fragile watermark', Neurocomputing, vol. 585, pp. 127593-127593.
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Zhao, Z, Cao, L & Lin, K-Y 2024, 'Out-of-Distribution Detection by Cross-Class Vicinity Distribution of In-Distribution Data', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Zhao, Z, Cao, L & Wang, C-D 2024, 'Gray Learning From Non-IID Data With Out-of-Distribution Samples', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Zhao, Z, Lu, W, Peng, X, Xing, L, Zhang, W & Zheng, C 2024, 'Automated ICD Coding via Contrastive Learning With Back-Reference and Synonym Knowledge for Smart Self-Diagnosis Applications', IEEE Transactions on Consumer Electronics, pp. 1-1.
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Zheng, B, Ji, J, Peng, R, Miao, Z & Zhou, J 2024, 'Energy shaping-based consensus control in networked underactuated Euler–Lagrange systems with communication and input delays', Journal of the Franklin Institute, vol. 361, no. 6, pp. 106705-106705.
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Zheng, B, Verma, S, Zhou, J, Tsang, IW & Chen, F 2024, 'Imitation Learning: Progress, Taxonomies and Challenges', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 5, pp. 6322-6337.
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Imitation learning (IL) aims to extract knowledge from human experts' demonstrations or artificially created agents to replicate their behaviors. It promotes interdisciplinary communication and real-world automation applications. However, the process of replicating behaviors still exhibits various problems, such as the performance is highly dependent on the demonstration quality, and most trained agents are limited to perform well in task-specific environments. In this survey, we provide an insightful review on IL. We first introduce the background knowledge from development history and preliminaries, followed by presenting different taxonomies within IL and key milestones of the field. We then detail challenges in learning strategies and present research opportunities with learning policy from suboptimal demonstration, voice instructions, and other associated optimization schemes.

Zheng, B, Zhou, J, Liu, C, Li, Y & Chen, F 2024, 'Explaining Imitation Learning Through Frames', IEEE Intelligent Systems, pp. 1-9.
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Zheng, C, Hu, C, Yu, J & Wen, S 2024, 'Saturation function-based continuous control on fixed-time synchronization of competitive neural networks', Neural Networks, vol. 169, pp. 32-43.
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Zheng, Y, Ngo, HH, Luo, H, Wang, R, Li, C, Zhang, C & Wang, X 2024, 'Production of cost-competitive bioethanol and value-added co-products from distillers' grains: Techno-economic evaluation and environmental impact analysis', Bioresource Technology, vol. 397, pp. 130470-130470.
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Zheng, Y, Zhao, X, Yao, L & Cao, L 2024, 'Deep Multidilation Temporal and Spatial Dependence Modeling in Stereoscopic 3-D EEG for Visual Discomfort Assessment', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 4, pp. 2125-2136.
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Zheng, Z, Wang, X, Zheng, N & Yang, Y 2024, 'Parameter-Efficient Person Re-Identification in the 3D Space', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 6, pp. 7534-7547.
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People live in a 3D world. However, existing works on person re-identification (re-id) mostly consider the semantic representation learning in a 2D space, intrinsically limiting the understanding of people. In this work, we address this limitation by exploring the prior knowledge of the 3D body structure. Specifically, we project 2D images to a 3D space and introduce a novel parameter-efficient omni-scale graph network (OG-Net) to learn the pedestrian representation directly from 3D point clouds. OG-Net effectively exploits the local information provided by sparse 3D points and takes advantage of the structure and appearance information in a coherent manner. With the help of 3D geometry information, we can learn a new type of deep re-id feature free from noisy variants, such as scale and viewpoint. To our knowledge, we are among the first attempts to conduct person re-id in the 3D space. We demonstrate through extensive experiments that the proposed method: (1) eases the matching difficulty in the traditional 2D space; 2) exploits the complementary information of 2D appearance and 3D structure; 3) achieves competitive results with limited parameters on four large-scale person re-id datasets; and 4) has good scalability to unseen datasets. Our code, models, and generated 3D human data are publicly available at https://github.com/layumi/person-reid-3d.

Zhou, C, Lyu, B, You, C & Hoang, DT 2024, 'Cooperative Commensal and Parasitic Symbiotic Radio Communication Systems', IEEE Wireless Communications Letters, vol. 13, no. 3, pp. 676-680.
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Zhou, I, Tofigh, F, Piccardi, M, Abolhasan, M, Franklin, D & Lipman, J 2024, 'Secure Multi-Party Computation for Machine Learning: A Survey', IEEE Access, vol. 12, pp. 53881-53899.
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Zhou, J, Li, D, Chen, G & Wen, S 2024, 'Projective synchronization for distinct fractional-order neural networks consist of inconsistent orders via sliding mode control', Communications in Nonlinear Science and Numerical Simulation, vol. 133, pp. 107986-107986.
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Zhou, M, Lu, J, Lu, P & Zhang, G 2024, 'Dynamic Graph Regularization for Multi-Stream Concept Drift Self-adaptation', IEEE Transactions on Knowledge and Data Engineering, pp. 1-13.
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Zhou, M, Yang, Z, Yu, H & Yu, S 2024, 'VDFChain: Secure and verifiable decentralized federated learning via committee-based blockchain', Journal of Network and Computer Applications, vol. 223, pp. 103814-103814.
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Zhou, S, Halkon, B, Guo, Z & Eager, D 2024, 'Transient waveform replication for flexible structures in shaker testing using time-domain convolution techniques'.

Zhou, S, Zhu, T, Ye, D, Yu, X & Zhou, W 2024, 'Boosting Model Inversion Attacks With Adversarial Examples', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 3, pp. 1451-1468.
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Zhou, S, Zhu, T, Ye, D, Zhou, W & Zhao, W 2024, 'Inversion-Guided Defense: Detecting Model Stealing Attacks by Output Inverting', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 4130-4145.
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Zhou, T, Li, X, Liu, H, Dong, S, Zhang, Z, Wang, Z, Li, J, Nghiem, LD, Khan, SJ & Wang, Q 2024, 'Occurrence, fate, and remediation for per-and polyfluoroalkyl substances (PFAS) in sewage sludge: A comprehensive review', Journal of Hazardous Materials, vol. 466, pp. 133637-133637.
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Zhou, W, Zhu, T, Ye, D, Ren, W & Choo, K-KR 2024, 'A Concurrent Federated Reinforcement Learning for IoT Resources Allocation With Local Differential Privacy', IEEE Internet of Things Journal, vol. 11, no. 4, pp. 6537-6550.
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Zhou, X, Lin, Z, Gu, R, Ni, W & Jamalipour, A 2024, 'A New Meta-Learning Framework for Estimating Atmospheric Turbulence and Phase Noise in Optical Satellite Internet of Things Systems', IEEE Internet of Things Journal, vol. 11, no. 7, pp. 11190-11201.
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Zhou, Y, Cheng, G, Yu, S, Chen, Z & Hu, Y 2024, 'MTDroid: A Moving Target Defense-Based Android Malware Detector Against Evasion Attacks', IEEE Transactions on Information Forensics and Security, vol. 19, pp. 6377-6392.
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Zhou, Y, Wang, R, Liu, J, Wu, D, Yu, S & Wen, Y 2024, 'Exploring the Practicality of Differentially Private Federated Learning: A Local Iteration Tuning Approach', IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 4, pp. 3280-3294.
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Zhu, C, Ye, D, Huo, H, Zhou, W & Zhu, T 2024, 'A location-based advising method in teacher–student frameworks', Knowledge-Based Systems, vol. 285, pp. 111333-111333.
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Zhu, C, Ye, D, Zhu, T & Zhou, W 2024, 'Location-Based Real-Time Updated Advising Method for Traffic Signal Control', IEEE Internet of Things Journal, vol. 11, no. 8, pp. 14551-14562.
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Zhu, H, Wang, G, Wang, K, Liu, G, Zhou, Y, Xie, S, Di, Y, Xu, J, Zhou, H, Mou, J & Ding, C 2024, 'Grid composite meta-surface absorber with thermal isolation structure for terahertz detection', Optics Express, vol. 32, no. 1, pp. 205-205.
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This paper specifically focuses on the absorber, the critical component responsible for the detector's response performance. The meta-surface absorber combines two resonant structures and achieves over 80% absorptance around 210 GHz, resulting in a broad operating frequency range. FR-4 is selected as the dielectric layer to be compatible with standard printed circuit board (PCB) technology, which reduces the overall fabrication time and cost. The absorbing unit and array layout are symmetrically designed, providing stable absorptance performance even under incident waves of different polarization angles. The polarization-insensitive absorptance characteristic further enhances the compatibility between the absorber and the detector in the application scenario. Furthermore, the thermal insulation performance of the absorber is ensured by introducing thermal insulation gaps. After completing fabrication through PCB technology, testing revealed that the absorber maintained excellent absorptance performance within its primary operating frequency range. This performance consistency closely matched the simulation results.

Zhu, HY, Hieu, NQ, Hoang, DT, Nguyen, DN & Lin, C-T 2024, 'A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Zhu, J, Liao, S, Zhu, X, Yang, Y & Xue, Q 2024, 'C-/Ka-Band Aperture-Shared Dual Circularly Polarized Heterogeneous Reflectarray for Vehicular Communications', IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 8671-8680.
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Zhu, J, Xiao, G, Zheng, Z & Sui, Y 2024, 'Deep semi-supervised learning for recovering traceability links between issues and commits', Journal of Systems and Software, vol. 216, pp. 112109-112109.
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Zhu, J, Yang, Y, Lai, J & Li, M 2024, '3-D Printed Noninterleaved Reflective Metasurfaces Supporting Dual-Band Spin-Decoupled Quadruplex Channel Independent Beam-Shaping With Controllable Energy Distribution', IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 2, pp. 1196-1205.
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Zhu, R, Zhang, W, Wang, P, Chen, J, Wang, J & Yu, S 2024, 'Energy-Efficient Graph Reinforced vNFC Deployment in Elastic Optical Inter-DC Networks', IEEE Transactions on Network Science and Engineering, vol. 11, no. 2, pp. 1591-1604.
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Zhu, S, Guo, M, Mu, C, Liu, X & Wen, S 2024, 'Reachable Set Estimation for Delayed Memristive Neural Networks With Bounded Disturbances', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-6.
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Zhu, S, Shen, Y, Mu, C, Liu, X & Wen, S 2024, 'Generalized-Type Multistability of Almost Periodic Solutions for Memristive Cohen–Grossberg Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 7, pp. 9483-9494.
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This article investigates a generalized type of multistability about almost periodic solutions for memristive Cohen–Grossberg neural networks (MCGNNs). As the inevitable disturbances in biological neurons, almost periodic solutions are more common in nature than equilibrium points (EPs). They are also generalizations of EPs in mathematics. According to the concepts of almost periodic solutions and $\Psi$ -type stability, this article presents a generalized-type multistability definition of almost periodic solutions. The results show that $(K+1)^n$ generalized stable almost periodic solutions can coexist in a MCGNN with $n$ neurons, where $K$ is a parameter of the activation functions. The enlarged attraction basins are also estimated based on the original state space partition method. Some comparisons and convincing simulations are given to verify the theoretical results at the end of this article.

Zhu, S, Zhang, J, Liu, X, Shen, M, Wen, S & Mu, C 2024, 'Multistability and Robustness of Competitive Neural Networks With Time-Varying Delays', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Zhu, W, Tuan, HD, Dutkiewicz, E, Fang, Y, Poor, HV & Hanzo, L 2024, 'Long-Term Rate-Fairness-Aware Beamforming Based Massive MIMO Systems', IEEE Transactions on Communications, vol. 72, no. 4, pp. 2386-2398.
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Zhu, W, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2024, 'A New Class of Analog Precoding for Multi-Antenna Multi-User Communications over High-Frequency Bands', IEEE Transactions on Wireless Communications, pp. 1-1.
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A network relying on a large antenna-array-aided base station is designed for delivering multiple information streams to multi-antenna users over high-frequency bands such as the millimeter-wave and sub-Terahertz bands. The state-of-the-art analog precoder (AP) dissipates excessive circuit power due to its reliance on a large number of phase shifters. To mitigate the power consumption, we propose a novel AP relying on a controlled number of phase shifters. Within this new AP framework, we design a hybrid precoder (HP) for maximizing the users’ minimum throughput, which poses a computationally challenging problem of large-scale, nonsmooth mixed discrete-continuous log-determinant optimization. To tackle this challenge, we develop an algorithm which iterates through solving convex problems to generate a sequence of HPs that converges to the max-min solution. We also introduce a new framework of smooth optimization termed soft max-min throughput optimization. Additionally, we develop another algorithm, which iterates by evaluating closed-form expressions to generate a sequence of HPs that converges to the soft max-min solution. Simulation results reveal that the HP soft max-min solution approaches the Pareto-optimal solution constructed for simultaneously optimizing both the minimum throughput and sum-throughput. Explicitly, it achieves a minimum throughput similar to directly maximizing the users’ minimum throughput and it also attains a sum-throughput similar to directly maximizing the sum-throughput.

Zhu, W, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2024, 'Max-Min Rate Optimization of Low-Complexity Hybrid Multi-User Beamforming Maintaining Rate-Fairness', IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 5648-5662.
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Zhu, X, Gómez-García, R, Li, C-H & Schwitter, B 2024, 'Guest Editorial: Integrated Devices, Circuits, and Systems for the 6G Era', IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 14, no. 1, pp. 3-6.
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Zhu, Y, Zhang, Z & Ji, J 2024, 'Bifurcations of a Filippov ecological system with an A-type discontinuity boundary', Chaos, Solitons & Fractals, vol. 182, pp. 114834-114834.
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Zhu, Y, Zhang, Z & Ji, J 2024, 'Sliding dynamics of a Filippov ecological system with nonlinear threshold control and pest resistance', Communications in Nonlinear Science and Numerical Simulation, vol. 135, pp. 108052-108052.
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Zhuang, Z, Wang, Y-K, Chang, Y-C, Liu, J & Lin, C-T 2024, 'A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 83-93.
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Drowsy driving is one of the primary causes of driving fatalities. Electroencephalography (EEG), a method for detecting drowsiness directly from brain activity, has been widely used for detecting driver drowsiness in real-time. Recent studies have revealed the great potential of using brain connectivity graphs constructed based on EEG data for drowsy state predictions. However, traditional brain connectivity networks are irrelevant to the downstream prediction tasks. This article proposes a connectivity-aware graph neural network (CAGNN) using a self-attention mechanism that can generate task-relevant connectivity networks via end-to-end training. Our method achieved an accuracy of 72.6% and outperformed other convolutional neural networks (CNNs) and graph generation methods based on a drowsy driving dataset. In addition, we introduced a squeeze-and-excitation (SE) block to capture important features and demonstrated that the SE attention score can reveal the most important feature band. We compared our generated connectivity graphs in the drowsy and alert states and found drowsiness connectivity patterns, including significantly reduced occipital connectivity and interregional connectivity. Additionally, we performed a post hoc interpretability analysis and found that our method could identify drowsiness features such as alpha spindles. Our code is available online1.

Zhuo, M, Chen, Z, Liu, X, Wei, W, Shen, Y & Ni, B-J 2024, 'A broad horizon for sustainable catalytic oxidation of microplastics', Environmental Pollution, vol. 340, pp. 122835-122835.
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Zogan, H, Razzak, I, Jameel, S & Xu, G 2024, 'Hierarchical Convolutional Attention Network for Depression Detection on Social Media and Its Impact During Pandemic', IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 4, pp. 1815-1823.
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Zuo, W, Li, D, Li, Q, Cheng, Q & Huang, Y 2024, 'Effects of intermittent pulsating flow on the performance of multi-channel cold plate in electric vehicle lithium-ion battery pack', Energy, vol. 294, pp. 130832-130832.
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Zuo, W, Li, F, Li, Q, Chen, Z, Huang, Y & Chu, H 2024, 'Multi-objective optimization of micro planar combustor with tube outlet by RSM and NSGA-II for thermophotovoltaic applications', Energy, vol. 291, pp. 130396-130396.
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Zuo, W, Wang, Z, Li, Q, Zhou, K & Huang, Y 2024, 'Numerical investigations on the performance enhancement of a hydrogen-fueled micro planar combustor with finned bluff body for thermophotovoltaic applications', Energy, vol. 293, pp. 130752-130752.
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