Publications

Li, W, Zhao, H & Wang, K 2025, Nanocharacterization of Cementitious Composites, Springer Nature Singapore.
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Rujikiatkamjorn, C, Xue, J & Indraratna, B 2025, Preface.

Wang, Y, Li, Z, Liang, B, Tian, H, Guo, T & Chen, F 2025, Data Analytics for Smart Infrastructure Asset Management and Network Performance, CRC Press.
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This book presents, for the first time, data analytics for smart infrastructures.

Abeywickrama, A, Indraratna, B & Rujikiatkamjorn, C 2025, 'Experimental and Numerical Study on Cyclic Loading of Railway Subgrade' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 219-228.
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Aboughaly, M & Fattah, IMR 2025, 'Biochar, hydrochar, and their derivative biocomposites for dye adsorption' in Engineered Biocomposites for Dye Adsorption, Elsevier, pp. 73-85.
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Aghdam, R & Ahmad, N 2025, 'Circular dynamics of energy' in Elgar Encyclopedia of Energy Economics, Edward Elgar Publishing, pp. 53-55.
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Ahmad, N & Aghdam, R 2025, 'Energy flows diagram' in Elgar Encyclopedia of Energy Economics, Edward Elgar Publishing, pp. 157-158.
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Ahmadi, S, Cheraghian, A, Saberi, M, Abir, MT, Dastmalchi, H, Hussain, F & Rahman, S 2025, 'Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-Free Adaptor' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 178-195.
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Alagesan, S, Indraratna, B, Malisetty, RS, Qi, Y & Rujikiatkamjorn, C 2025, 'An Overview of Single-Objective Optimization Models for Assessing the Performance of Railway Ballast Under Cyclic Loading' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 275-282.
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Alharbi, A, Sharma, N & Hussain, F 2025, 'Arabic Sentiment Analysis Leveraging Hybrid Word Embeddings with Deep Learning Techniques' in Lecture Notes on Data Engineering and Communications Technologies, Springer Nature Switzerland, pp. 129-140.
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Aljaafreh, R, Al-Doghman, F & Hussain, F 2025, 'Toward a Hybrid Cybersecurity Framework of Machine Learning and Business Intelligence into Supply Chain Risk Management' in Studies in Systems, Decision and Control, Springer Nature Switzerland, pp. 1021-1029.
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Alqaisi, R, Le, M-T, Khabbaz, H & Fatahi, B 2025, 'Eggshell Powder as an Ameliorating Agent for Cement-Stabilised Expansive Soil in Road Construction' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 55-68.
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Alsuhaibani, A, Razzak, I, Jameel, S, Wang, X & Xu, G 2025, 'CLIMB: Imbalanced Data Modelling Using Contrastive Learning with Limited Labels' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 60-75.
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Alzahrani, A, Gay, V, Alturki, R & Alanazi, F 2025, 'Conducting Usability Testing for Artificial Intelligence-Enabled Mobile Wireless Applications for Self-care Management in Saudi Arabia, the Sehhaty Wa Daghty App' in Communications in Computer and Information Science, Springer Nature Switzerland, pp. 44-67.
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Arachchige, C, Indraratna, B, Rujikiatkamjorn, C & Qi, Y 2025, 'Utilizing Waste Rubber Derivatives in Ballasted Tracks: A Field Study' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 31-37.
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Arivalagan, J, Rujikiatkamjorn, C, Indraratna, B & Warwick, A 2025, 'Subgrade Fluidization Under Cyclic Loading and Preventive Measure by Geosynthetics' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 129-138.
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Atapattu, S, Indraratna, B & Rujikiatkamjorn, C 2025, 'Effect of Intermittent Rest Periods on the Accumulative Excess Pore Pressure in Railway Subgrades' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 221-229.
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Bąk, A, Klempous, R, Segen, J, Nikodem, J, Rozenblit, J, Chaczko, Z, Kulbacki, M, Gruszecka, K, Skoczyńska, M, Atsushi, I, Bożejko, W, Jagielski, D, Panejko, A, Kowalczyk, H, Kluwak, K, Wojciechowski, K & Kulbacki, M 2025, 'A Survey of Machine Learning Methods for Analyzing Synovitis Arthritis in Human Joints' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 103-112.
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Booth, E 2025, '“Follow the White Rabbit”' in Digital (Dis)Information Operations, Routledge, pp. 13-29.
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Buradagunta, RS, Sundara Kumar, P, Kamala Devi, K & Fattah, IMR 2025, 'Predicting and Monitoring the Failure of Steel Structures Using Artificial Neural Networks' in Advances in Computational Intelligence and Robotics, IGI Global, pp. 453-468.
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Artificial neural networks (ANN) have emerged as powerful tool to compute, assess, predict and understand the structural system behavior subjected to combination of complex input variables such as mechanical loads, extreme environmental conditions, seismic conditions etc. The existing experimental and simulation methods may not completely predict the structure response under combination of different input variables. In this context, assessing the failure of the steel structures by using ANN techniques ease the efforts of engineers in structure health monitoring. Compared with the available traditional methods, using ANN enables to assess the failure of the structures with more accuracy and reliability. The present chapter provides a summary of using ANNs in predicting different failures usually observed in steel structures. Additionally, the challenges and future perspectives are also discussed.

Candra, H, Chai, R, Nguyen, HT & Su, S 2025, 'Optimizing electroencephalography-emotion classification through strategic window selection methodology' in Digital Technology in Public Health and Rehabilitation Care, Elsevier, pp. 261-280.
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Cetindamar, D & Burdon, S 2025, 'Digital competitiveness and policy making: lessons from Indonesia, Malaysia, and the Republic of South Korea' in Digital Leadership, Edward Elgar Publishing, pp. 42-55.
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Chen, J, Indraratna, B, Vinod, JS, Ngo, NT & Liu, Y 2025, 'Dem Study on the Dynamic Performance of a Fouled Ballasted Track Under Repeated Traffic Loading' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 21-33.
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Cloutier, T, McGregor, C & Andersen, JP 2025, 'Challenges and Opportunities in a Big Data Pipeline for Resilience and Wellness Development: A Case Study for Police Training' in Communications in Computer and Information Science, Springer Nature Switzerland, pp. 340-350.
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Dai, S, He, X, Zhang, S & Sheng, D 2025, 'Experimental Study on Particle Migration in Saturated Sandy-Silt Mixtures Under Cyclic Loading' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 9-18.
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Dhanya, P, Geethalakshmi, V, Ramanathan, S, Senthilraja, K, Dhasarathan, M, Sreeraj, P, Dheebakaran, G, Pradipa, C, Bhuvaneshwari, K, Vidhya Priya, NS, Sivasubramaniam, S, Arul, P & Vigneswaran, S 2025, 'Accuracy of climate and weather early warnings for sustainable crop water and river basin management' in Hydrosystem Restoration Handbook, Elsevier, pp. 121-133.
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Dissanayake, S, Indraratna, B, Rujikiatkamjorn, C & Abeywickrama, A 2025, 'Behaviour of Soft Soil Improved with Prefabricated Vertical Drains in Railways Under Cyclic Loading' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 77-84.
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Doan, T, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2025, 'A Coupled CFD-DEM Approach to Examine Clogging Behaviour of Granular Medium by Fines' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 1-9.
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Fattah, IMR, Huang, Y, Wei, W, Silitonga, AS & Kalam, MA 2025, 'Microwave-Assisted Production of Sustainable Fuels' in Encyclopedia of Green Chemistry, Elsevier, pp. 381-388.
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Fattah, IMR, Rahman, MM, Kusumo, F, Ogunkunle, O, Ahmed, A, Kalam, MA & Mahlia, TMI 2025, 'Biodiesel production from algal bioreactors' in Algal Bioreactors, Elsevier, pp. 615-637.
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Gerrard, L, Peng, X, Clarke, A & Long, G 2025, 'Claimsformer: Pretrained Transformer for Administrative Claims Data to Predict Chronic Conditions' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 348-362.
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Ghosh, S, Bhattacharya, S, Roy, P, Pal, U & Blumenstein, M 2025, '$$\lambda $$-Color: Amplifying Long-Range Dependencies for Image Colorization' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 172-187.
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Gooch, LJ, Stewart, MG & Masia, MJ 2025, 'FEA Model Errors for the Load Capacity Prediction of Cyclically Loaded Unreinforced Masonry Shear Walls' in Lecture Notes in Civil Engineering, Springer Nature Switzerland, pp. 3-14.
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Guo, T, Du, H, Huo, H, Liu, B & Yu, X 2025, 'Who is Being Impersonated? Deepfake Audio Detection and Impersonated Identification via Extraction of Id-Specific Features' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 301-320.
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Hadgraft, R 2025, 'Engineering systems thinking in education' in The Routledge Handbook of Global Sustainability Education and Thinking for the 21st Century, Routledge India, pp. 355-371.
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Halder, A, Palaiahnakote, S, Pal, U, Blumenstein, M & Gornale, SS 2025, 'A New HourGlass Network for Detecting Text in Shaky and Non-shaky Video Frames' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 118-134.
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Hasan, ASMM & Trianni, A 2025, 'Productivity Benefits Beyond Energy Savings: An Analysis of Industrial Energy Efficiency Measures and Industry 4.0 Technologies' in Lecture Notes in Mechanical Engineering, Springer Nature Singapore, pp. 15-25.
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Hettiyahandi, S, Indraratna, B, Ngo, T, Qi, Y & Arachchige, C 2025, 'The Effect of Recycled-Rubber Energy Absorbing Grids on the Performance of Railway Ballast: A Review' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 23-30.
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Hu, Z, Zhang, Y, Liu, C, Li, L, Peng, S, Zhou, X, Fan, C & Yu, X 2025, 'CPT-VR: Improving Surface Rendering via Closest Point Transform with View-Reflection Appearance' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 223-239.
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Hunt, H, Indraratna, B & Qi, Y 2025, 'Predicting the Shear Strength of Granular Waste Materials Using Machine Learning' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 155-163.
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Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2025, 'A Synthetic Energy- Absorbing Layer for Rail Tracks (Solution 2: CW–RC Mixtures)' in Waste Materials Utilisation for Transport Infrastructure, CRC Press, pp. 55-70.
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Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2025, 'A Synthetic Energy-Absorbing Layer for Rail Tracks (Solution 1: SFS + CW + RC Mixtures)' in Waste Materials Utilisation for Transport Infrastructure, CRC Press, pp. 27-54.
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Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2025, 'CW-based Waste Granular Materials for Roads and Port Reclamation' in Waste Materials Utilisation for Transport Infrastructure, CRC Press, pp. 4-26.
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Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2025, 'Introduction' in Waste Materials Utilisation for Transport Infrastructure, CRC Press, pp. 1-3.
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Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2025, 'Modelling the Behaviour of Industry Wastes' in Waste Materials Utilisation for Transport Infrastructure, CRC Press, pp. 187-196.
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Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2025, 'Rubber Intermixed Ballast System (RIBS)' in Waste Materials Utilisation for Transport Infrastructure, CRC Press, pp. 87-99.
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Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2025, 'Under Sleeper Pad (USP) and Under Ballast Mat (UBM)' in Waste Materials Utilisation for Transport Infrastructure, CRC Press, pp. 100-116.
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Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2025, 'Use of Recycled Rubber Elements in Tracks (Rubber Geogrid, Tyre Cells, and Truck Tyre Segments)' in Waste Materials Utilisation for Transport Infrastructure, CRC Press, pp. 71-86.
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Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2025, 'Utilisation of Waste Materials for Transport Infrastructure' in Waste Materials Utilisation for Transport Infrastructure, CRC Press, pp. 117-133.
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Iqbal, A, Medawela, S & Indraratna, B 2025, 'Impacts of Acidic Soil on Infrastructure Development' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 321-331.
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Jansing, S, Vakhnina, E, Weßkamp, V & Deuse, J 2025, 'Exploratory Pilot Study Investigating Effects of Exoskeletons on Movement Patterns' in Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2023, Springer Nature Switzerland, pp. 27-38.
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Abstract The ergonomic configuration of processes is becoming increasingly important, especially considering the changing demographics and increasing shortage of skilled workers. Exoskeletons are widely discussed as a means of protecting employees from overstraining at the level of personal protective measures. The field of industrial exoskeletons research is still relatively new and has many unanswered questions. For example, there have not yet been sufficient studies on the influence of exoskeletons on the movements of employees. This publication discusses the effects of exoskeletons in manual processes. For this purpose, exemplary physical activities are carried out in a pilot study by a subject collective, whereby the tasks are executed with and without an exoskeleton. During the execution, a motion capturing system is used to record the movement data. Different back-supporting exoskeletons are taken into account in the study. The evaluation is based on the joint angles of the participants while performing tasks with and without exoskeletons. It is shown that the use of exoskeletons has a significant effect on the movement patterns, with a distinction made between rigid and soft support structures.

Jayasuriya, C, Arachchige, C & Indraratna, B 2025, 'Application of Elastic Inclusions to Improve Ballasted Track Performances' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 109-116.
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Jazebi, M, Indraratna, B, Malisetty, RS & Rujikiatkamjorn, C 2025, 'Numerical Modelling of Jointed Rock Foundation Under Heavy Haul Train Loading' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 53-60.
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Jiang, K, Huang, J, Xie, W, Lei, J, Li, Y, Shao, L & Lu, S 2025, 'DA-BEV: Unsupervised Domain Adaptation for Bird’s Eye View Perception' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 322-341.
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Khuat, TT, Bassett, R, Otte, E & Gabrys, B 2025, 'Online Machine Learning for Real-Time Cell Culture Process Monitoring' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 363-375.
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Kulbacki, M, Segen, J, Chaczko, Z, Kulbacki, M, Nikodem, J, Klempous, R, Bąk, A, Rozenblit, J, Zyśko, D, Hrymniak, B & Jagielski, D 2025, 'Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 88-102.
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Kulbacki, M, Segen, J, Kulbacki, M, Chaczko, Z, Bożejko, W, Paz Suárez Araujo, C, Klempous, R & Bonikowski, M 2025, 'Advancements and Applications of Medical Human Digital Twin Technology in Cerebral Palsy Diagnosis, Therapy, and Rehabilitation' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 125-139.
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Kwon, I, Li, J, Shah, RR & Prasad, M 2025, 'Lightweight Motion-Aware Video Super-Resolution for Compressed Videos' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 254-267.
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Li, M, Lin, H, Qiu, L, Liang, X, Chen, L, Elsaddik, A & Chang, X 2025, 'Contrastive Learning with Counterfactual Explanations for Radiology Report Generation' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 162-180.
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Li, X, Wu, Y, Wang, X, Yang, Z, Zhang, W & Zhang, Y 2025, 'Keyword-Based Betweenness Centrality Maximization in Attributed Graphs' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 209-223.
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Li, X, Zhang, Z, Li, J, Gao, L, Liu, H & Wang, Q 2025, 'Detection of antibiotic resistance genes in wastewater and sludge' in Sample Handling and Trace Analysis of Pollutants, Elsevier, pp. 651-662.
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Lin, Y, Guan, J, Fang, W, Ying, M & Su, Z 2025, 'A obustness fication Tool for  uantum Machine Learning Models' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 403-421.
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AbstractAdversarial noise attacks present a significant threat to quantum machine learning (QML) models, similar to their classical counterparts. This is especially true in the current Noisy Intermediate-Scale Quantum era, where noise is unavoidable. Therefore, it is essential to ensure the robustness of QML models before their deployment. To address this challenge, we introduce VeriQR, the first tool designed specifically for formally verifying and improving the robustness of QML models, to the best of our knowledge. This tool mimics real-world quantum hardware’s noisy impacts by incorporating random noise to formally validate a QML model’s robustness. VeriQR supports exact (sound and complete) algorithms for both local and global robustness verification. For enhanced efficiency, it implements an under-approximate (complete) algorithm and a tensor network-based algorithm to verify local and global robustness, respectively. As a formal verification tool, VeriQR can detect adversarial examples and utilize them for further analysis and to enhance the local robustness through adversarial training, as demonstrated by experiments on real-world quantum machine learning models. Moreover, it permits users to incorporate customized noise. Based on this feature, we assess VeriQR using various real-world examples, and experimental outcomes confirm that the addition of specific quantum noise can enhance the global robustness of QML models. These processes are made accessible through a user-friendly graphical interface provided by VeriQR, catering to general users without requiring a deep understanding of the counter-intuitive probabilistic nature of quantum computing.

Liu, B, Liu, B & Ding, M 2025, 'Outliers are Real: Detecting VLM-Generated Images via One-Class Classification' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 291-308.
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Liu, B, Zhu, T & Yu, PS 2025, 'Privacy in Location-Based Data Mining' in Encyclopedia of Cryptography, Security and Privacy, Springer Nature Switzerland, pp. 1887-1892.
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Malisetty, RS, Indraratna, B, Qi, Y & Rujikiatkamjorn, C 2025, 'Estimating the Shakedown Limit for Granular Materials Under Cyclic Loading' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 183-191.
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Manthey, S & Konazoglu, DC 2025, 'From Technology Idea to Value Proposition' in Technology Entrepreneurship in Theory and Practice, Routledge, pp. 46-56.
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Medawela, S & Indraratna, B 2025, 'Improving Low-Lying Acidic Floodplains for Infrastructure Development' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 135-143.
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Mi, Y, Li, Y, Zhang, Y, Chen, C & Zhang, Y 2025, 'Maximum Weight Relative Fair Clique Computation in Attributed Graphs' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 492-505.
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Nazari, H, Asbagh, RA, Abasipanah, P, Nazari, Z & Warkiani, ME 2025, 'Microfluidics in the diagnosis, treatment, and drug delivery of chronic respiratory disorders: Advancements and potential applications' in Technological Advances and Innovations in the Treatment of Chronic Respiratory Disorders, Elsevier, pp. 209-265.
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Nejad, MZ, Naderpour, M, Behbood, V, Ramezani, F & Lu, J 2025, 'Residential Real Estate Image Classification for Property Valuation' in Communications in Computer and Information Science, Springer Nature Switzerland, pp. 111-125.
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Ngo, T & Indraratna, B 2025, 'Numerical Analysis for Ballasted Rail Tracks: Coupled DEM-FEM Approach' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 35-41.
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Nguyen, TT, Indraratna, B & Gedela, R 2025, 'Enhanced Cyclic Resistance of Low Plasticity Soil Using Biopolymer' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 197-204.
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Ni, Z & Zhang, JA 2025, 'Communication-centric multi-metric ISAC waveform optimization' in Integrated Sensing and Communications for Future Wireless Networks, Elsevier, pp. 151-170.
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Nimbalkar, S, Farooq, MA, Punetha, P & Meena, NK 2025, 'Computational Models and Applications in Railway Geotechnics' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 1-11.
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Nimbalkar, S, Punetha, P, Meena, NK & Farooq, MA 2025, 'Recent Advances in Computational Methods, Predictions, and Applications to Critical Zones in Railways' in Recent Advances and Innovative Developments in Transportation Geotechnics, Springer Nature Singapore, pp. 191-201.
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Ou, J, Zhou, J, Dong, Y & Chen, F 2025, 'Chain of Thought Prompting in Vision-Language Model for Vision Reasoning Tasks' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 298-311.
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Priya, K, Adak, C, Chaudhuri, BB & Blumenstein, M 2025, 'Handwriting Intra-Variability Across Surface Transitions: Implications for Writer Identification' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 196-208.
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Punetha, P & Nimbalkar, S 2025, 'Three-Dimensional Finite Element Modelling of Sealed and Unsealed Roads Considering Effects of Moving Wheel Loads' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 113-120.
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Riyad, ASM, Qi, Y, Arachchige, CK, Indraratna, B & Khabbaz, H 2025, 'Predicting the Behavior of a Synthetic Energy Absorbing Layer for Subballast Using Nor-Sand Model' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 71-76.
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Roy, A, Palaiahnakote, S, Pal, U, Antonacopoulos, A & Blumenstein, M 2025, 'XLSI: A New Xception and Log Polar Transform Based Approach for Scene Text Script Identification' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 183-198.
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Roy, P, Bhattacharya, S, Ghosh, S, Pal, U & Blumenstein, M 2025, 'd-Sketch: Improving Visual Fidelity of Sketch-to-Image Translation with Pretrained Latent Diffusion Models without Retraining' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 277-292.
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Roy, P, Bhattacharya, S, Ghosh, S, Pal, U & Blumenstein, M 2025, 'Semantically Consistent Person Image Generation' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 293-309.
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Sali, S, Chai, R & Ganesan, B 2025, 'Recent trends and digital technology applications in lower limb injury rehabilitation' in Digital Technology in Public Health and Rehabilitation Care, Elsevier, pp. 99-124.
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Siddiqui, AR, Indraratna, B, Ngo, T & Rujikiatkamjorn, C 2025, 'Recycled Rubber Grids for Improved Performance of Ballasted Tracks' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 311-320.
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Silva, IN, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2025, 'Role of Fabric on the Shear Behaviour of Subgrade Soil under Cyclic Loading' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 211-220.
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Singh, M, Indraratna, B, Rujikiatkamjorn, C & Nguyen, TT 2025, 'Subgrade Instability and Stiffness Degradation of Subgrade Soils Prone to Fluidisation' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 263-272.
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Soo, Z, Guo, K, Wu, M, Huang, C, Lin, H, Grosser, M, Zhang, Y & Lu, J 2025, 'A Deep Transfer Learning Approach for Predicting Diabetes Complications Using Genomic Data' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 140-151.
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Soundararajan, S, Raychowdhury, P & Nimbalkar, S 2025, 'Performance Assessment of Existing Railway Track Subjected to Varying Train Speed' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 129-136.
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Tang, Y, Wang, J, Chen, C, Wang, X, Zhang, W & Zhang, Y 2025, 'Mitigate the Damage of Rumor on Susceptible Group' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 389-402.
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Tsigklifis, K, Wong, M, Dylejko, P, Karimi, M, Croaker, P & Skvortsov, A 2025, 'Reduced-Order Modelling of Flow-Induced Vibration from Turbulence Impingement' in Flinovia—Flow Induced Noise and Vibration Issues and Aspects—IV, Springer Nature Switzerland, pp. 331-354.
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Vaseghi, S, Sheng, D, Kodikara, J & Khabbaz, H 2025, 'Effect of Principal Stress Rotation on Plastic Strain Accumulation in Granular Materials' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 11-20.
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Wang, X, Ma, B, Liu, RP & Oppermann, I 2025, 'DPAC: A New Data-Centric Privacy-Preserving Access Control Model' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 267-276.
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Wang, ZZ, Al-Tabbaa, A, Hakim, B & Indraratna, B 2025, 'Data-Driven Pavement Performance Modelling: A Short Review' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 231-239.
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Xu, B-H, Indraratna, B, Rujikiatkamjorn, C, He, N & Nguyen, TT 2025, 'Spectral-Based Solutions for Consolidation Analysis of Multilayered Soil under Various Drainage Boundary Conditions' in Lecture Notes in Civil Engineering, Springer Nature Singapore, pp. 17-28.
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Xu, W, Wu, Y, Jiang, H, Wang, Y, Wu, Q & Zhang, J 2025, 'Task Consistent Prototype Learning for Incremental Few-Shot Semantic Segmentation' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 147-162.
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Xue, H, Yuan, X, Liu, B & Ding, M 2025, 'DBFIA: Diffusion-Based Face Image Anonymization' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 40-59.
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Ying, J, Shen, X & Yu, X 2025, 'Vision-Based Abnormal Action Dataset for Recognising Body Motion Disorders' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 443-455.
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Zhang, H, Xu, J, Tang, T, Sun, H, Yu, X, Huang, Z & Yu, K 2025, 'OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detection' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 1-19.
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Zhang, Z, Yin, H, Wang, Y, Yu, G, Wang, X, Ni, W & Liu, RP 2025, 'Enabling Efficient Cross-Shard Smart Contract Calling via Overlapping' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 164-178.
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Zhao, T, Zuo, H & Zhang, G 2025, 'Bidirectional Dependency Representation Disentanglement for Time Series Classification' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 98-110.
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Zhou, J, Guo, D, Mao, Y, Zhong, Y, Chang, X & Wang, M 2025, 'Label-Anticipated Event Disentanglement for Audio-Visual Video Parsing' in Lecture Notes in Computer Science, Springer Nature Switzerland, pp. 35-51.
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Zi, X, Shi, Y, Zhu, T, Jin, K, Tao, X, Li, J, Thiyagarajan, K & Prasad, M 2025, 'BDC Dataset: A Comprehensive Dataset for Automated Build Damage Classification' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 91-104.
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Zuo, X, Wang, M, Ye, D & Yu, S 2025, 'A Scheme of Dynamic Location Privacy-Preserving with Blockchain in Intelligent Transportation System' in Lecture Notes in Computer Science, Springer Nature Singapore, pp. 21-39.
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Acharya, M, Deo, RC, Barua, PD, Devi, A & Tao, X 2025, 'EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals', Computer Methods and Programs in Biomedicine, vol. 262, pp. 108652-108652.
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Acharya, M, Deo, RC, Tao, X, Barua, PD, Devi, A, Atmakuru, A & Tan, R-S 2025, 'Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024)', Computer Methods and Programs in Biomedicine, vol. 259, pp. 108506-108506.
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Achu, AL, Aju, CD, Raicy, MC, Bhadran, A, George, A, Surendran, U, Girishbai, D, Ajayakumar, P, Gopinath, G & Pradhan, B 2025, 'Improved flood risk assessment using multi-model ensemble machine-learning techniques in a tropical river basin of Southern India', Physical Geography, vol. 46, no. 2, pp. 127-155.
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Adak, C, Chattopadhyay, S & Saqib, M 2025, 'Deep Analysis of Visual Product Reviews', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 2033-2038.
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Adeoti, OS, Haremi, R, Kandasamy, J & Vigneswaran, S 2025, 'Evaluating the effectiveness of smart water management systems in enhancing the resilience and sustainability of water infrastructure in Nigeria', AQUA — Water Infrastructure, Ecosystems and Society, vol. 74, no. 2, pp. 253-266.
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ABSTRACT This study rigorously evaluates the effectiveness of smart water management systems in addressing prevalent water infrastructure failures, resilience, and sustainability challenges in Nigeria. Employing a transdisciplinary approach that integrates technological, social, and economic disciplines, along with industry and community insights, it analyses 1,095 days of operational data from a smart water kiosk. The data were processed employing Target 6.1 software for comprehensive comparative analysis, trend analysis, predictive modeling, and impact assessment. Initially, the kiosk achieved a 22% self-sustainability rating (SSR), which dropped to zero due to aid overlap – a novel challenge documented for the first time in the literature as a significant challenge to infrastructure sustainability. Additionally, the research highlighted infrastructure underutilization as a critical yet under-explored issue. Despite these challenges, the kiosk ultimately achieved a 100% sustainability rating (SR) with external support and maintained a high reliability rating of 97.1%. The findings of this study guide strategic research and policy recommendations, aiming to optimize the deployment of smart water management systems in Nigeria and other regions with similar socio-economic settings, thereby enriching the global discourse on sustainable water infrastructure.

Aditiya, HB, Panuganti, SR, Hsia, ICC, Mat, TMUT, Mahlia, TMI & Huang, Z 2025, 'Levelised cost of hydrogen for domestic transport and stationary applications', International Journal of Hydrogen Energy, vol. 139, pp. 291-315.
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Aedan, Y, Altaee, A & Shon, HK 2025, 'Performance of pressure stimuli-responsive nanofiltration and cellulose acetate forward osmosis membranes for PFOA contaminated wastewater treatment', Separation and Purification Technology, vol. 364, pp. 132458-132458.
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Ahmad, A, Xiao, X, Mo, H & Dong, D 2025, 'TFTformer: A novel transformer based model for short-term load forecasting', International Journal of Electrical Power & Energy Systems, vol. 166, pp. 110549-110549.
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Ahmad, F, Rawat, S, (Chunhui) Yang, R, Zhang, L, Fanna, DJ, Soe, K & Zhang, YX 2025, 'Effect of Metakaolin and Ground Granulated Blast Furnace Slag on the Performance of Hybrid Fibre-Reinforced Magnesium Oxychloride Cement-Based Composites', International Journal of Civil Engineering, vol. 23, no. 5, pp. 853-868.
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Abstract This study investigates the effect of ground granulated blast furnace slag (GGBFS) and metakaolin (MK) on the strength and ductility of magnesium oxychloride cement (MOC) based hybrid basalt and polyethylene fibre reinforced cementitious composite (FRMOC). MOC was chosen as the matrix due to its unique properties and environment friendliness as a green cement. MK and GGBFS were selected as primary additives to reinforce the MOC matrix owing to their outstanding performance in cementitious composites, coupled with their widespread availability and sustainable characteristics. The influence of GGBFS and MK on physical and mechanical properties of FRMOC was studied in this paper through extensive physical and mechanical testing and microscopic analysis. It was found that the hardened density of FRMOC was not significantly affected by these additives, and it ranged from 1909.3 to 1976.0 kg/m3, retaining its lightweight characteristics. Compressive strength of specimens cured for one day reached approximately 69.1–84.0% of that for specimens cured for 28 days, indicating the high early strength characteristics of the material. All FRMOC specimens exhibited tensile strain hardening properties, with tensile strength and strain capacity ranging from 6.74 to 8.58 MPa and 1.14 to 2.22%, respectively. The mix containing 30% GGBFS, 0.75% basalt fibre, and 1.25% polyethylene fibre was identified as the optimum MOC mix with enhanced compressive strength (73.9 MPa), tensile strength (8.52 MPa), and strain capacity (2.22%). Microscopic analysis further revealed that the addition of GGBFS-MK blends did not alter the primary phase composition of hydration products but essentially promoted the formation of phase 5, demonstrating their effectiveness in enhancing the performance of FRMOC.

Ahmad, F, Rawat, S, Yang, RC, Zhang, L & Zhang, YX 2025, 'Fire resistance and thermal performance of hybrid fibre-reinforced magnesium oxychloride cement-based composites', Construction and Building Materials, vol. 472, pp. 140867-140867.
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Ahmed, SF, Sharmin, S, Kuldeep, SA, Lameesa, A, Alam, MSB, Liu, G & Gandomi, AH 2025, 'Transformative impacts of the internet of medical things on modern healthcare', Results in Engineering, vol. 25, pp. 103787-103787.
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Ajmal, Z, Hayat, A, Qadeer, A, Zhao, Y, Ibrahim, EH, Haq, MU, Iqbal, K, Imran, M, Kuku, M, Hussain, I, Ali, H, Orooji, Y, Zhou, JL & Ben, T 2025, 'Advancements in MXene-based frameworks towards photocatalytic hydrogen production, carbon dioxide reduction and pollutant degradation: Current challenges and future prospects', Coordination Chemistry Reviews, vol. 523, pp. 216226-216226.
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Akbarzadeh, M, Oberst, S & Halkon, B 2025, 'Manipulation of an acoustically levitated object using externally excited standing waves', The Journal of the Acoustical Society of America, vol. 157, no. 3, pp. 1852-1861.
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Ultrasonic standing waves can be used to manipulate the position and control the movement of levitated objects through acoustic radiation forces. Within this context, the theory of the Gor'kov potential function and its acoustic contrast factor are revisited, considering the scenario of a harmonic disturbance to the standing wave and its influence on the levitated spherical object. This disturbance causes a levitated object—trapped within a standing, plane ultrasonic wave field in an ideal fluid—to undergo oscillations in sympathy with the resulting motion of the wave field. In this paper, we determine how the acoustic contrast factor depends on the properties of the object, the fluid and the external excitation, in combination. We show that positive, negative, and zero acoustic radiation forces can be achieved, causing the object to be pushed towards the nearest pressure or velocity node. We experimentally verify—through external excitation of an ultrasonic standing wave generator—that the disturbance vibration frequency and amplitude are transmitted to the object. The dependence on the external excitation amplitude and force reversal are novel features that can be employed in acoustic manipulation for non-contact dynamic characterization of small objects.

Akram, J, Hussain, W, Jhaveri, RH, Rathore, RS & Anaissi, A 2025, 'Dynamic GNN-based multimodal anomaly detection for spatial crowdsourcing drone services', Digital Communications and Networks.
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Akter, F, Krishnan, L, Mestres, G, Gustafsson, J, Ralph, PJ & Kuzhiumparambil, U 2025, 'Physicochemical characterization and evaluation of the antioxidant potential of water-soluble polysaccharides from red microalgae, Rhodomonas salina', International Journal of Biological Macromolecules, vol. 310, pp. 143417-143417.
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Akter, K, Rahman, MA, Islam, MR, Sheikh, MRI & Hossain, MJ 2025, 'Attack-resilient framework for wind power forecasting against civil and adversarial attacks', Electric Power Systems Research, vol. 238, pp. 111065-111065.
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Akter, T, Hoque, MA-A, Mukul, SA & Pradhan, B 2025, 'Coastal Flood Induced Salinity Intrusion Risk Assessment Using a Spatial Multi-criteria Approach in the South-Western Bangladesh', Earth Systems and Environment, vol. 9, no. 1, pp. 31-49.
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Abstract Bangladesh is extremely vulnerable to sea-level rise and other climate-induced extreme events, such as flooding, storm surge, and salinity intrusion. The south-western coastal region of Bangladesh is particularly vulnerable to salinity intrusion caused by cyclone induced storm surges and coastal floods. Salinity intrusion endanger land productivity by increasing both soil and surface water salinity. Detailed risk assessment using spatial mapping approach can contribute to mitigating the effects of salinity intrusion on natural capital and the environment. In this study, we established and evaluated a spatial multi-criteria approach for mapping the risk levels of areas to salinity intrusion impacts using field data and geospatial techniques at the local scale. We evaluated the viability of the proposed approach using Khulna District, a major coastal city and saline prone area in the south-western Bangladesh. We considered three risk components (i.e. vulnerability, exposure and hazard) with 16 relevant criteria for the study. For each criterion, an Analytical Hierarchy Process (AHP) was used to build and weight spatial raster map layers. Individual maps for each risk component were generated using a weighted sum technique, and lastly, a risk map was created by combining those. Our generated maps correctly identified relevant spatial dimensions as well as risk levels (i.e. very-high to very-low). The outcomes of our study suggest that the southern (east and west) parts of the study area are mostly susceptible to salinity intrusion due to higher storm surge impacts, lower elevation, and land use patterns than other parts. We validate our findings using a qualitative and quantitative approach. We believe that this novel approach would be useful to create risk maps that policymakers and relevant stakeholders could potentially use to evaluate risks posed by flood induced salinity intrusion in coastal regions ...

Alagesan, S, Indraratna, B, Malisetty, RS, Qi, Y & Rujikiatkamjorn, C 2025, 'Prediction of rail ballast breakage using a hybrid ML methodology', Transportation Geotechnics, vol. 52, pp. 101555-101555.
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Alam, M, Rakib, AKM, Hasan, ASMM, Siddique, MNI, Kabir, MA & Trianni, A 2025, 'Decarbonizing road transportation: Barriers and drivers in an emerging economy context', Transportation Research Part D: Transport and Environment, vol. 143, pp. 104723-104723.
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Alam, MM, Hossain, MJ, Zamee, MA & Al-Durra, A 2025, 'Design and operation of future low-voltage community microgrids: An AI-based approach with real case study', Applied Energy, vol. 377, pp. 124523-124523.
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Alandoli, EA, Fan, Y & Liu, D 2025, 'A review of extensible continuum robots: mechanical structure, actuation methods, stiffness variability, and control methods', Robotica, vol. 43, no. 2, pp. 764-791.
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AbstractExtensible continuum robots (ECRs) offer distinct advantages over conventional continuum robots due to their ability to enhance workspace adaptability through length adjustments. This makes ECRs particularly promising for applications that require variable lengths involving the manipulation of objects in challenging environments, such as risky, cluttered, or confined. The development of ECRs necessitates careful consideration of mechanical structures, actuation methods, methods of stiffness variability, and control methods. The selection of papers is based on their relevance to ECRs within the period of 2010 to 2023 in the databases of Scopus and Web of Science. Distinguishing itself from other review papers, this paper aims to deliver a comprehensive and critical discussion about the advantages and disadvantages of ECRs concerning their mechanical structures, actuation methods, stiffness variability, and control methods. It is a beneficial resource for researchers and engineers interested in ECRs, providing essential insights to guide future developments in this field. Based on the literature, existing ECRs exhibit an inherent trade-off between flexibility and structural strength due to the absence of systematic design methods. Additionally, there is a lack of intelligent and effective controllers for achieving complex control performance and autonomous stiffness variability.

Alavi, Z, Khalilpour, K, Florin, N, Hadigheh, A & Hoadley, A 2025, 'End-of-life wind turbine blade management across energy transition: A life cycle analysis', Resources, Conservation and Recycling, vol. 213, pp. 108008-108008.
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Albaba, MT, Talhami, M, Omar, A, Varghese, S, Akoumeh, R, Ayari, MA, Das, P, Altaee, A, AL-Ejji, M & Hawari, AH 2025, 'Machine learning-aided prediction of COD removal in the electrocoagulation process using a super learner model', Journal of Environmental Chemical Engineering, pp. 117469-117469.
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Al‐Badri, AR, Al‐Waaly, AAY, Saha, G, Saha, T & Saha, SC 2025, 'Improving Thermal Performance in Building Heating, Ventilation, and Air Conditioning Systems: A Study of Natural Convection and Entropy in Plus‐Shaped Cavity', Heat Transfer, vol. 54, no. 3, pp. 2235-2250.
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ABSTRACTThe impact of building design on energy efficiency has been widely studied, with cavity cooling emerging as an effective solution for indoor thermal comfort, where obstacles within the cavity can enhance fluid flow and improve natural convection heat transfer (HT). This research builds on the principles of cavity cooling for indoor thermal comfort, investigating entropy generation and HT behavior in a unique plus‐shaped cavity containing a cold cylindrical element, analyzed through Computational Fluid Dynamics simulations. The Rayleigh number (Ra) ranges from 103 to 106, with a fixed Prandtl number (Pr) of 0.71, representing air as the working fluid, radius (r) of the cylinder ranges from 0 to 0.1, where r = 0 indicates no cylinder. The results indicate significant shifts in flow structure and temperature distribution across the cavity at varying Ra values, impacting the local and global entropy generation. High Rayleigh numbers lead to enhanced convective flows, intensifying entropy production near the cylinder surface due to steeper thermal gradients and vigorous recirculation zones. The increase in Ra from 103 to 106 leads to an increase in Nuavg from 24.27 to 56.40 for the model without a cold object while from 39.62 to 123.83 for the model with a cold object. Moreover, the maximum enhancement in Nuavg was 137.48% for Ra = 105. Whereas, for the same value of Ra = 105, the maximum increase in Egen and Be wa...

Alempijevic, A, Vidal-Calleja, T, Falque, R, Walmsley, B & McPhee, M 2025, '3D imaging for on-farm estimation of live cattle traits and carcass weight prediction', Meat Science, vol. 225, pp. 109810-109810.
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This study presents a 3-dimensional (3D) imaging system, operating at processing speed, deployed at a commercial feedlot, that assesses hip height (cm), subcutaneous fat thickness at the P8 site (mm), and hot standard carcass weight (HSCW, kg) from the shape of individual live cattle. A two-part study was conducted: Study 1 evaluated measured hip height (cm) on 247 steers and ultrasound scanned P8 fat (mm) on 219 steers versus projections from 3D images; and Study 2 evaluated abattoir HSCW on 32 Angus steers versus predictions from 3D images. Hip height was directly estimated from the 3D images, while P8 fat and HSCW were predicted using a model based on features extracted from these images through supervised learning with Gaussian Processes. The models were evaluated using cross-validation. The measured hip height versus live estimates from 3D imaging resulted in a RMSE = 3.07 cm, and R2 = 0.69. The ultrasound scanned P8 fat versus live predictions from 3D imaging resulted in a RMSE = 2.38 mm, and R2 = 0.78; and the abattoir HSCW versus live predictions from 3D imaging resulted in a RMSE = 8.15 kg, and R2 = 0.79. The design of the 3D imaging system, with multiple cameras, was installed into a traditional race for processing cattle and effectively operates with variation in length and breeds of cattle. The 3D imaging system demonstrates the feasibility of adoption by the beef industry that creates value through the integration of 3D imaging and BeefSpecs into a technology called CattleAssess3D.

Alghanmi, NA, Alghanmi, NA, Alghanmi, SA, Zhao, M & Hussain, FK 2025, 'Data-driven approach for selection of on-chain vs off-chain carbon credits data storage methods', Knowledge-Based Systems, vol. 310, pp. 112871-112871.
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Alharbi, RS & Hussain, FK 2025, 'Smart-contracts-driven personal carbon credit management in smart cities: A review and future research directions', Internet of Things, vol. 32, pp. 101636-101636.
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Alhelou, HH, Bahrani, B & Ma, J 2025, 'An Affordable Sophisticated Frequency Control Ancillary Services for Australian National Electricity Market System Considering Industry and Infrastructure Challenges', IEEE Transactions on Energy Markets, Policy and Regulation, vol. 3, no. 1, pp. 72-82.
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Ali, A, Salah, A, Bekhit, M & Fathalla, A 2025, 'Predicting Trip Duration and Distance in Bike-Sharing Systems Using Dynamic Time Warping', Applied Artificial Intelligence, vol. 39, no. 1.
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Almalki, R, Khaki, M, Saco, PM & Rodriguez, JF 2025, 'Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach', Journal of Hydrology: Regional Studies, vol. 58, pp. 102221-102221.
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Almalki, R, Khaki, M, Saco, PM & Rodriguez, JF 2025, 'Understanding Environmental Factors Influencing Vegetation Cover Downstream of Dams', International Journal of Environmental Research, vol. 19, no. 1.
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Altukhi, ZM, Pradhan, S & Aljohani, N 2025, 'A Systematic Literature Review of the Latest Advancements in XAI', Technologies, vol. 13, no. 3, pp. 93-93.
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This systematic review details recent advancements in the field of Explainable Artificial Intelligence (XAI) from 2014 to 2024. XAI utilises a wide range of frameworks, techniques, and methods used to interpret machine learning (ML) black-box models. We aim to understand the technical advancements in the field and future directions. We followed the PRISMA methodology and selected 30 relevant publications from three main databases: IEEE Xplore, ACM, and ScienceDirect. Through comprehensive thematic analysis, we categorised the research into three main topics: ‘model developments’, ‘evaluation metrics and methods’, and ‘user-centred and XAI system design’. Our results uncover ‘What’, ‘How’, and ‘Why’ these advancements were developed. We found that 13 papers focused on model developments, 8 studies focused on the XAI evaluation metrics, and 12 papers focused on user-centred and XAI system design. Moreover, it was found that these advancements aimed to bridge the gap between technical model outputs and user understanding.

Al‐Waaly, AAY, Paul, AR, Saha, G & Saha, SC 2025, 'Exploring Heat Transfer and Entropy Generation in a Dual Cavity System', Heat Transfer, vol. 54, no. 3, pp. 2279-2292.
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ABSTRACTThis study investigates heat transfer and entropy generation in a dual‐cavity system filled with air, focusing on the effects of uniform and nonuniform heating conditions on natural convection. The system features heated left walls, cooled right walls, and insulated remaining walls, presenting a novel approach to thermal management. This research employs COMSOL Multiphysics and finite element method to study the interplay between Rayleigh numbers () and heat transfer efficiency, focusing on thermal patterns and irreversibility. The findings indicate that as Ra increases, convective heat transfer improves significantly, with the average Nusselt number rising from 15.23 at Ra = 103 to 74.61 at Ra = 106 under uniform heating conditions. However, this improvement comes at the cost of increased entropy generation, which escalates from 2.91 to 307.74, highlighting a trade‐off between enhanced heat transfer and greater irreversibility. These results underscore the need to optimize Ra values to achieve a balance between thermal efficiency and entropy generation. The insights gained from this study have practical implications for designing energy‐efficient cooling systems in electronics and microfluidic devices, as well as for architectural designs targeting improved thermal management.

Al-zqebah, R, Guertler, M & Clemon, L 2025, 'Powder bed fusion factory productivity increases using discrete event simulation and genetic algorithm', Production Engineering, vol. 19, no. 1, pp. 29-45.
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Abstract Powder bed fusion is importance is growing with uses across industries in both polymer and metallic components, particularly in mass individualization. However, due to the relatively slow mass deposition speed compared to conventional methods, scheduling and production planning play a crucial role in scaling up additive manufacturing productivity to higher volumes. This paper introduces a framework combining discrete event simulation and a genetic algorithm showing makespan improvement opportunities for multiple powder bed fusion factories varying workers, jobs and available equipment. The results show that bottlenecks move among workstations based on worker and capital equipment availability, which depend on the size of the facility indicating a resource-driven constraint for makespan. A makespan reduction of 78% is achieved in the simulation. This shows the trade-off of worker and capital equipment to achieve makespan improvements. The addition of personnel or equipment increases production with further gains achieved by scheduling optimization. Two levels of job demands are analyzed showing productivity gains of 45% makespan improvement when adding the first worker and additional savings with scheduling optimization using a genetic algorithm up to 11%. Most research on additive manufacturing production has focused on the quality of produced parts and printing technology rather than factory level management. This is the first application of this methodology to varying sizes of these potential factories. The method developed here will help decision-makers to determine the appropriate number of resources to meet their customer demand on time, additionally, finding the optimal route for jobs before starting the production process.

Amin, M, Umar, H, Rizal, TA, Amir, F, Abdullah, NA, Ginting, SF & Mahlia, TMI 2025, 'Thermal performance improvement of a solar distillation system using a spiral coil condenser with a parabolic dish', Journal of Water Process Engineering, vol. 71, pp. 107353-107353.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2025, 'Development of a polarization-neutral metamaterial absorber for efficient low-power EM energy harvesting', Sensors and Actuators A: Physical, vol. 381, pp. 116055-116055.
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Amirkhani, F, Dashti, A, Abedsoltan, H, Mohammadi, AH, Zhou, JL & Altaee, A 2025, 'Modeling and estimation of CO2 capture by porous liquids through machine learning', Separation and Purification Technology, vol. 359, pp. 130445-130445.
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Anand, N, Saifulla, MA, Babu Ponnuru, R, Reddy Alavalapati, G, Patan, R & Gandomi, AH 2025, 'Securing Software Defined Networks: A Comprehensive Analysis of Approaches, Applications, and Future Strategies Against DoS Attacks', IEEE Access, vol. 13, pp. 64473-64515.
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Ansari, S, Finelli, M, Papaconstantinou, EA, McGregor, C & Nonoyama, ML 2025, 'Implementation Strategies Used to Reduce Unplanned Extubations in the Neonatal ICU', Respiratory Care, vol. 70, no. 2, pp. 143-152.
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Arachchige, SM, Pradhan, B & Park, H-J 2025, 'A critical review of hurricane risk assessment models and predictive frameworks', Geoscience Frontiers, vol. 16, no. 3, pp. 102012-102012.
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Arango, E, Nogal, M, Sousa, HS, Matos, JC & Stewart, MG 2025, 'Wildfire preparedness: Optimal adaptation measures for strengthening road transport resilience', International Journal of Disaster Risk Reduction, vol. 121, pp. 105371-105371.
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Aravind, S, Barik, D, Pullagura, G, Chandran, SSR, PV, E, Paramasivam, P, Balasubramanian, D, Fouad, Y, Soudagar, MEM, Kalam, MA & Kit, CC 2025, 'Exposure the role of hydrogen with algae spirogyra biodiesel and fuel-borne additive on a diesel engine: An experimental assessment on dual fuel combustion mode', Case Studies in Thermal Engineering, vol. 65, pp. 105566-105566.
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Arqam, M, Raffa, LS, Spisiak, S, Clemon, L, Luo, Z, Ryall, M, Islam, MS & Bennett, NS 2025, 'Computational and experimental analysis of a novel triply periodic minimal surface heat sink with phase change material', Journal of Energy Storage, vol. 117, pp. 116121-116121.
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Asheghi Mehmandari, T, Mohammadifar, M, Halvaei Jalali, H, Zare, P, Fahimifar, A & Armaghani, DJ 2025, 'Micro and macrostructural investigations on the fracture mechanism of the brittle rocks under compressive loading', Multiscale and Multidisciplinary Modeling, Experiments and Design, vol. 8, no. 1.
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Asif Raja, MJA, Masood, Z, Hussain, I, Zameer, A, Mehmood, A & Raja, MAZ 2025, 'Design of Nonlinear Delay Differential System for Analyzing Vulnerabilities in Nanoscale Hardware Implants: A Deep Dive into Intelligent Computing Networks', Nano, vol. 20, no. 03.
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In the annals of contemporary innovation, the study of miniature marvels nanoscale hardware implants emerged as a pivotal instrument, scarcely perceptible to the naked eye, embodies a prowess of cutting-edge technologies and clandestine intrigue. The objective of this research is to introduce a time delay nonlinear system for nanoscale hardware implants vulnerabilities that portray the exploitation of a system by utilizing the stupendous knacks of advanced deep bidirectional long short-term memory (LSTM) networks for time series predictions. Firmware-level bugs present the potential for escalating privileges and executing of code remotely beneath the operating system, allowing for infiltration or complete intervention within a computer system. The designed deep bidirectional LSTM is configured to precisely predict and forecast the dynamic states of time delay differential system, offering a robust framework for mimicking delays that commonly manifest in the real cyber-physical systems. To orchestrate the system compromises in real scenarios through the activation of bugged hardware, time delay factors [Formula: see text] and [Formula: see text] are introduced to account the time delays necessary for exploiting the bugged and patched nodes, respectively. Synthetic data is generated to train the LSTM network for all scenarios of the model for the dynamics of bugged, compromised, patched nodes and these acquired information is used for training, testing and validation purposes regarding exploitation of the bugged hardware. Comparative analysis on exhaustive simulations revealed a minimal difference between the LSTM’s predictions and those from the numerical outcomes with MSE in the range of [Formula: see text], underscoring the network’s effectiveness, robustness and stability in modeling complex system dynamics of hardware vulnerabilities.

Asteris, PG, Gandomi, AH, Armaghani, DJ, Mohammed, AS, Bousiou, Z, Batsis, I, Spyridis, N, Karavalakis, G, Vardi, A, Triantafyllidis, L, Koutras, EI, Zygouris, N, Drosopoulos, GA, Fountas, NA, Vaxevanidis, NM, Bardhan, A, Samui, P, Hatzigeorgiou, GD, Zhou, J, Leontari, KV, Evangelidis, P, Sakellari, I & Gavriilaki, E 2025, 'Pre-transplant and transplant parameters predict long-term survival after hematopoietic cell transplantation using machine learning', Transplant Immunology, vol. 90, pp. 102211-102211.
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Atmakuru, A, Chakraborty, S, Salvi, M, Faust, O, Datta Barua, P, Kobayashi, M, San Tan, R, Molinari, F, Hafeez-Baig, A & Rajendra Acharya, U 2025, 'Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications', IEEE Access, vol. 13, pp. 25026-25044.
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Atmakuru, A, Shahini, A, Chakraborty, S, Seoni, S, Salvi, M, Hafeez-Baig, A, Rashid, S, Tan, RS, Barua, PD, Molinari, F & Acharya, UR 2025, 'Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023)', Information Fusion, vol. 114, pp. 102673-102673.
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Aung, HW, Jiao Li, J, An, Y & Su, SW 2025, 'A Real-Time Framework for EEG Signal Decoding With Graph Neural Networks and Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Aung, HW, Li, JJ, Shi, B, An, Y & Su, SW 2025, 'EEG_GLT-Net: Optimising EEG graphs for real-time motor imagery signals classification', Biomedical Signal Processing and Control, vol. 104, pp. 107458-107458.
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Auriemma, F, Andrisani, G, Facciorusso, A, franchellucci, G, De, LL, Calabrese, F, Fiacca, M, Citterio, N, De Deo, D, Paduano, D, Gentile, C, Hassan, C, Francesco, DM, Repici, A & Benedetto, M 2025, 'Haemocer Plus in the treatment and prevention of lower GI post-resectional bleeding: prospective multi center study', Endoscopy, vol. 57, no. S 02, pp. S440-S440.
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Azfar, M, Gao, W, Van den Haute, C, Xiao, L, Karsa, M, Pandher, R, Karsa, A, Spurling, D, Ronca, E, Bongers, A, Guo, X, Mayoh, C, Fayt, Y, Schoofs, A, Burns, MR, Verhelst, SHL, Norris, MD, Haber, M, Vangheluwe, P & Somers, K 2025, 'The polyamine transporter ATP13A3 mediates difluoromethylornithine‐induced polyamine uptake in neuroblastoma', Molecular Oncology, vol. 19, no. 3, pp. 913-936.
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High‐risk neuroblastomas, often associated with MYCN protooncogene amplification, are addicted to polyamines, small polycations vital for cellular functioning. We have previously shown that neuroblastoma cells increase polyamine uptake when exposed to the polyamine biosynthesis inhibitor difluoromethylornithine (DFMO), and this mechanism is thought to limit the efficacy of the drug in clinical trials. This finding resulted in the clinical development of polyamine transport inhibitors, including AMXT 1501, which is presently under clinical investigation in combination with DFMO. However, the mechanisms and transporters involved in DFMO‐induced polyamine uptake are unknown. Here, we report that knockdown of ATPase 13A3 (ATP13A3), a member of the P5B‐ATPase polyamine transporter family, limited basal and DFMO‐induced polyamine uptake, attenuated MYCN‐amplified and non‐MYCN‐amplified neuroblastoma cell growth, and potentiated the inhibitory effects of DFMO. Conversely, overexpression of ATP13A3 in neuroblastoma cells increased polyamine uptake, which was inhibited by AMXT 1501, highlighting ATP13A3 as a key target of the drug. An association between high ATP13A3 expression and poor survival in neuroblastoma further supports a role of this transporter in neuroblastoma progression. Thus, this study identified ATP13A3 as a critical regulator of basal and DFMO‐induced polyamine uptake and a novel therapeutic target for neuroblastoma.

Aziz, NAM, Mohamed, H, Ong, MY, Yunus, R, Law, MC, Hamid, HA, Kania, D & Mahlia, TMI 2025, 'Numerical simulation of hotspot in polyol ester production using microwave-assisted reaction', Results in Engineering, vol. 25, pp. 104577-104577.
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Bahmanpour, M, Kalhori, H & Li, B 2025, 'A data-driven hybrid recurrent neural network and model-based framework for accurate impact force estimation', Mechanical Systems and Signal Processing, vol. 229, pp. 112503-112503.
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Bai, J, Wu, D, Zeng, S, Zhao, Y, Qu, Y & Yu, S 2025, 'Non-IID Free Federated Learning with Fuzzy Optimization for Consumer Electronics Systems', IEEE Transactions on Consumer Electronics, pp. 1-1.
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Bakar, MAAA, Ker, PJ, Tang, SGH, Arman Shah, FN, Mahlia, TMI, Baharuddin, MZ & Omar, AR 2025, 'A hybrid chromaticity-morphological machine learning model to overcome the limit of detecting newcastle disease in experimentally infected chicken within 36 h', Computers and Electronics in Agriculture, vol. 234, pp. 110248-110248.
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Bakhshianlamouki, E, Augustijn, E-W, Brugnach, M, Voinov, A & Wijnberg, K 2025, 'Agent-based modelling of beach visitation patterns: insights for sustainable coastal design and management', Environmental Research Communications, vol. 7, no. 4, pp. 045004-045004.
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Abstract Coastal areas are complex socio-environmental systems, yet most models focus on biophysical processes, with few addressing social systems or their interaction with biophysical dynamics. This paper introduces an agent-based model (ABM) simulating beach visitor spatial behaviour, considering factors like landscape design and its impact on visitor distribution. We analyse how service and facility placement affects areas with high and low visitation intensity. Tailored for Dutch beaches created by mega-nourishments like the Sand Motor, the model’s modular design allows adaptation to other coastal areas. Qualitative calibration and validation against field data ensured realistic predictions of daily visitor numbers and representation of areas of high and low visitation intensity across the beach. We analysed model sensitivity by varying visitor activities and pathway preference probabilities, finding that pathway probabilities significantly influence visitor choices. The model was also applied to assess the impact of adding an entrance with access to a restaurant and car parking on visitor numbers, affected areas, and impact intensity. Scenario analysis, combined with its application to two case studies with different landscape layouts, highlights the model’s versatility as a valuable tool for designing beaches to effectively explore and manage visitation intensity and its effects on ecological systems, such as vegetation.

Bano, M, Chaudhri, ZH & Zowghi, D 2025, 'Mapping the Scholarly Landscape on AI and Diplomacy', The Hague Journal of Diplomacy, pp. 1-36.
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Abstract This study examines how artificial intelligence (AI) has been discussed in the context of diplomacy in the 21st century, highlighting the emerging challenges and opportunities identified in academic literature. While recent developments such as OpenAI’s ChatGPT and other generative AI (GenAI) tools have revolutionised various sectors, their specific applications to diplomacy remain underexplored. This research maps the scholarly landscape by systematically reviewing 231 academic articles on AI and diplomacy, identifying key themes, topics and geographical focuses in the literature. The insights from this review are analysed to explore potential future directions for AI, including GenAI, in diplomacy, offering insights from the academic discourse and identifying gaps for future research.

Bao, W, Cao, Y, Yang, Y, Che, H, Huang, J & Wen, S 2025, 'Data-driven stock forecasting models based on neural networks: A review', Information Fusion, vol. 113, pp. 102616-102616.
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Barua, PD, Kobayashi, M, Dogan, S, Baygin, M, Tuncer, T, Paul, JK, Iype, T & Acharya, UR 2025, 'Flower automata pattern-based discrimination of fibromyalgia from control subjects using fusion of sleep EEG and ECG signals', IEEE Access, pp. 1-1.
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Basack, S & Khabbaz, H 2025, 'Analytical solution for single pile in two-layered soil subjected to torsion', Geomechanics and Geoengineering, vol. 20, no. 3, pp. 618-634.
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Baum, CM, Sick, N & Bröring, S 2025, 'Drivers for the emergence of interdisciplinary knowledge areas: An actor-level perspective on building legitimacy for the case of synthetic life sciences', Technovation, vol. 141, pp. 103173-103173.
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Bayat, A, Das, PK, Saha, G & Saha, SC 2025, 'Optimizing proton exchange membrane electrolyzer cells: A comprehensive parametric analysis of flow, electrochemical, and geometrical factors', International Journal of Thermofluids, vol. 27, pp. 101177-101177.
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Belkina, M, Daniel, S, Nikolic, S, Haque, R, Lyden, S, Neal, P, Grundy, S & Hassan, GM 2025, 'Implementing generative AI (GenAI) in higher education: A systematic review of case studies', Computers and Education: Artificial Intelligence, vol. 8, pp. 100407-100407.
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Benedetto, M, Paduano, D, Ramai, D, franchellucci, G, Barbera, C, Frigo, F, Fugazza, A, De Nucci, G, Vanella, G, Ko, C, Francesco, DM, Larghi, A, Anderloni, A, Auriemma, F, Minini, F, Arcidiacono, P, Gentile, C, Federica, C, De, LL, Teoh, AY, Gallo, C, Forti, E, Mutignani, M, Santi, M, Bertani, H, Aragona, G, Troncone, E, Del Vecchio Blanco, G, Pham, KC, Mirante, VG, Crinò, SF, Aljahdli, E, Sundaram, S, Al-Lehibi, A, Alfadda, A, Fiacca, M, Manes, G, Decembrino, F, Fierro, G, Manes, G, Morris, JD, Martínez, B, Aparicio, JR, Stigliano, S, Bronswijk, M, Van der Merwe, S, Lakhtakia, S, Ventra, A, Repici, A & Facciorusso, A 2025, 'Endoscopic Ultrasound (EUS)-Guided Gallbladder Drainage vs EUS-Guided Bile Duct Drainage for First Line Therapy of Malignant Biliary Obstruction: An International Multicenter Study', Endoscopy, vol. 57, no. S 02, pp. S75-S76.
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Benedetto, M, Paduano, D, Ramai, D, franchellucci, G, Barbera, C, Fugazza, A, Vanella, G, Frigo, F, Arcidiacono, P, De Nucci, G, Anderloni, A, Larghi, A, Crinò, SF, Sundaram, S, Troncone, E, Pham, KC, Al-Lehibi, A, Forti, E, Gentile, C, Aragona, G, Manes, G, Ko, C, Francesco, DM, Auriemma, F, Minini, F, Fiacca, M, Federica, C, De, LL, Teoh, AY, Gallo, C, Mutignani, M, Santi, M, Bertani, H, Del Vecchio Blanco, G, Mirante, VG, Aljahdli, E, Alfadda, A, Decembrino, F, Antonio, F, Bronswijk, M, Van der Merwe, S, Fierro, G, Manes, G, Morris, JD, Martínez, B, Stigliano, S, Aparicio, JR, Lakhtakia, S, Ventra, A, Repici, A & Facciorusso, A 2025, 'Endoscopic Ultrasound (EUS)-Guided Gallbladder Drainage vs EUS-Guided Bile Duct Drainage After Failed ERCP for Malignant Biliary Obstruction: An International Multicenter Study', Endoscopy, vol. 57, no. S 02, pp. S198-S199.
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Berezowski, V, Taoum, K, Wang, J, Birch, P, Roux, C & Huo, H 2025, 'Investigating identity crime and misuse in Australia: the role of prevention technologies and the likelihood of victimisation', Journal of Criminological Research, Policy and Practice, vol. 11, no. 1, pp. 50-63.
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PurposeThis study examines identity theft as a significant and growing issue in Australia, not only due to its financial impact but also because of the emotional, psychological, and physical harm it causes, making it a public health concern. This study aims to analyse the results of the 2019 Australian Institute of Criminology (AIC) survey to identify factors associated with an increased likelihood of identity theft victimisation.Design/methodology/approachThis study involved a detailed analysis of the 2019 AIC survey, which had 9,968 respondents from a sample of 10,000. The research focused on whether respondents had ever been victimised by identity theft and analysed various characteristics, including demographics (gender, age, Indigenous status, education), income, computer usage, and preventive technology use, as potential indicators of future victimisation. Univariate analyses (chi-squared test and two-sample t-test) were used to assess individual associations, whereas a multivariate analysis (logistic regression) identified significant predictors of victimisation.FindingsThe univariate analyses indicated that all sub-variables were individually associated with identity theft victimisation. However, the multivariate analysis revealed that only identifying as Aboriginal and Torres Strait Islander, having an income between $18,201 and $37,000, and using multiple preventive technologies were significant predictors of victimisation. The unexpected finding that increased preventive technology use correlates with a higher risk of victimisation contradicts the survey’s suggestion that victims adopt more careful behaviour post-victimisation....

Berta, M, Lami, L & Tomamichel, M 2025, 'Continuity of Entropies via Integral Representations', IEEE Transactions on Information Theory, vol. 71, no. 3, pp. 1896-1908.
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Bhandari, S, Fatahi, B, Nguyen, LD & Karimi, R 2025, 'Cyclic and post-cyclic responses of cement-treated landfill waste under triaxial testing', Soil Dynamics and Earthquake Engineering, vol. 197, pp. 109525-109525.
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Bhowmik, A, Hasan, M, Redoy, MMH & Saha, G 2025, 'Nipah virus outbreak trends in Bangladesh during the period 2001 to 2024: a brief review', Science in One Health, vol. 4, pp. 100103-100103.
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Bibi, H, Abolhasan, M, Lipman, J, Abdollahi, M & Ni, W 2025, 'A comprehensive survey on privacy-preserving technologies for Smart Grids', Computers and Electrical Engineering, vol. 124, pp. 110371-110371.
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Bryant, L, Stubbs, P, Bailey, B, Nguyen, V, Bluff, A & Hemsley, B 2025, 'Interacting with virtual characters, objects and environments: investigating immersive virtual reality in rehabilitation', Disability and Rehabilitation: Assistive Technology, vol. 20, no. 1, pp. 107-117.
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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.

Cagno, E, Accordini, D, Thollander, P, Andrei, M, Hasan, ASMM, Pessina, S & Trianni, A 2025, 'Energy management and industry 4.0: Analysis of the enabling effects of digitalization on the implementation of energy management practices', Applied Energy, vol. 390, pp. 125877-125877.
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Cai, G-Q, Diao, X-F, He, X-Z, Gao, S & Liu, T 2025, 'Micromechanical analysis of contact erosion under cyclic loads using the coupled CFD–DEM method', Géotechnique, vol. 75, no. 5, pp. 649-672.
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Different layers of soil often have distinct particle sizes. When exposed to the natural environment, soil is easily affected by natural rainfall, rising groundwater levels and human activities, leading to particle contact erosion, which reduces the safety and service performance of the soil structure. In this paper, a coupled computational fluid dynamics–discrete-element method (CFD–DEM) model was employed to investigate the particle migration phenomena, mechanical response of contact interfaces, variations in flow fields and macroscopic deformation during the contact erosion process under cyclic loads at different frequencies and amplitudes. The conclusions are presented as follows. (a) Within one cycle of cyclic loading, both compression during loading and stress relaxation during unloading are the main factors triggering the migration of fine particles. (b) The migration and loss of fine particles mainly occur in the early stages of cyclic loading, where strong contact force chains are formed within the fine particle layer, leading to significant plastic deformation of the soil at the macroscopic level. (c) Under cyclic loading, changes in the soil pore structure cause an upwards hydraulic gradient in the initial quiescent water flow field. This hydraulic gradient can rupture weak contact force chains and cause particle pumping. (d) Increasing the frequency and amplitude of cyclic loading intensifies the erosion of fine particles, causing greater axial deformation of the soil. Compared to cyclic loading frequency, the amplitude of cyclic loading has a greater impact on contact erosion.

Cai, J, Yuan, Y, Pan, L, Pei, Z, Zhang, Y, Xi, X, Ukrainczyk, N, Koenders, EAB, Zhang, L, Zhang, YX, Pan, J, Wang, Y & Xie, W 2025, 'Intelligent Thermoelectric Sensing with Sustainable Strain‐Hardening Geopolymeric Composites', Small Science, vol. 5, no. 3.
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Traditional thermoelectric (TE) building materials are limited in both performance and durability, requiring enhancements for effective energy solutions. This research investigates strain‐hardening geopolymeric composites (SHGC) for TE sensing applications. The influence of metal oxides on mechanical strength and TE characteristics is evaluated using isothermal calorimetry, computed tomography scanning, and focused ion beam (FIB)–transmission electron microscopy analysis. At ambient temperature, SHGC samples with MnO2 exhibit the highest Seebeck coefficient of 5470 μV K−1 with a measured power density of 29 μW m−2. Despite the presence of small strain cracks, the SHGC maintains about 69% of its original ZT value even after long‐term use. This discovery underlines the durability and efficiency of SHGC, demonstrating their potential for future infrastructure applications. The cost‐effectiveness, temperature‐sensing abilities, and environmental advantages of SHGC make them well suited for large‐scale smart applications.

Cai, Q, Cao, J, Xu, G & Zhu, N 2025, 'Distributed Recommendation Systems: Survey and Research Directions', ACM Transactions on Information Systems, vol. 43, no. 1, pp. 1-38.
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With the explosive growth of online information, recommendation systems have become essential tools for alleviating information overload. In recent years, researchers have increasingly focused on centralized recommendation systems, capitalizing on the powerful computing capabilities of cloud servers and the rich historical data they store. However, the rapid development of edge computing and mobile devices in recent years has provided new alternatives for building recommendation systems. These alternatives offer advantages such as privacy protection and low-latency recommendations. To leverage the advantages of different computing nodes, including cloud servers, edge servers, and terminal devices, researchers have proposed recommendation systems that involve the collaboration of these nodes, known as distributed recommendation systems. This survey provides a systematic review of distributed recommendation systems. Specifically, we design a taxonomy for these systems from four perspectives and comprehensively summarize each study by category. In particular, we conduct a detailed analysis of the collaboration mechanisms of distributed recommendation systems. Finally, we discuss potential future research directions in this field.

Cai, X, Sun, Y, Shi, K, Yan, H, Wen, S, Cheng, Q & Tian, Z 2025, 'Communication Security and Stability in NNCSs: Realistic DoS Attacks Model and ISTA-Supervised Adaptive Event-Triggered Controller Design', IEEE Transactions on Cybernetics, vol. 55, no. 2, pp. 615-624.
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Calabrese, F, Auriemma, F, Paduano, D, Gentile, C, Fiacca, M, Repici, A & Benedetto, M 2025, 'Anti-Reflux Mucosal Ablation with Z-Line Involvement (ARMA-Z) for Gastroesophageal Reflux Disease (GERD): Preliminary Results from the First International Experience', Endoscopy, vol. 57, no. S 02, pp. S459-S459.
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Calderon, P, Soen, A & Rizoiu, M-A 2025, 'Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data', IEEE Transactions on Computational Social Systems, vol. 12, no. 1, pp. 25-37.
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Canda, E, Nguyen, QD, San Nicolas, R, Rasekh, H & Castel, A 2025, 'Effect of calcined clay reactivity on the risk of restrained shrinkage‐induced early‐age concrete cracking', Structural Concrete, vol. 26, no. 2, pp. 1892-1910.
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AbstractA combination of limestone and calcined clay has emerged as a promising supplementary cementitious material due to its abundant availability to replace traditional supplementary cementitious materials such as fly ash or ground‐granulated blast‐furnace slag in reducing concrete's carbon footprint. Although different properties have been considered, very limited attention was paid to the early‐age cracking behavior of limestone calcined clay cement (LC3) concretes. This study aims to investigate the influence of calcined clay reactivity on the early‐age cracking potential of LC3 concretes using the restrained ring test. Mechanical properties, time to cracking, tensile creep coefficient, and total shrinkage were measured. Results showed that the reactivity of calcined clay significantly impacted total shrinkage, creep coefficients, and time to cracking. LC3 concretes exhibited higher tensile creep coefficients than pure ordinary Portland cement concrete, which can provide beneficial tensile stress relaxation delaying concrete cracking. An apparent calcined clay reactivity coefficient (Rapp) was proposed correlating well with the time to cracking of the restrained LC3 concrete rings, thus offering practical guidance for the selection of suitable calcined clays and mix designs for specific high‐risk applications.

Cao, B, Li-Ting Tsai, C, Zhou, N, Do, T & Lin, C-T 2025, 'A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 1562-1573.
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Cao, HQ, Karimi, M, Williams, P & Dylejko, P 2025, 'Passive control of hydro-elastic vibrations of plates using shunted piezoelectric patches', Thin-Walled Structures, vol. 206, pp. 112493-112493.
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Cao, Y, Zhao, S, Wen, S & Huang, T 2025, 'Input-Retention Strategies for Secure Synchronization of Piecewise Markov Neural Networks Under Hybrid Cyber-Attacks', IEEE Transactions on Circuits and Systems I: Regular Papers, pp. 1-11.
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Cashel, J, Yan, D, Han, R, Jeong, H, Yoon, CW, Ambay, JA, Liu, Y, Ung, AT, Yang, L & Huang, Z 2025, 'Chemical Bonds Containing Hydrogen: Choices for Hydrogen Carriers and Catalysts', Angewandte Chemie, vol. 137, no. 21.
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AbstractCompounds containing B─H, C─H, N─H, or O─H bonds with high hydrogen content have been extensively studied as potential hydrogen carriers. Their hydrogen storage performance is largely determined by the nature of these bonds, decomposition pathways, and the properties of the dehydrogenation products. Among these compounds, methanol, cyclohexane, and ammonia stand out due to their low costs and established infrastructure, making them promising hydrogen carriers for large‐scale storage and transport. They offer viable pathways for decarbonizing society by enabling hydrogen to serve as a clean energy source. However, several challenges persist, including the high temperatures required for (de)hydrogenation, slow kinetics, and the reliance on costly catalysts. To address these issues, strategies such as chemical modification and catalyst development are being pursued to improve hydrogen cycling performance. This review highlights recent progress in hydrogen carriers with B─H, C─H, N─H, or O─H bonds. It examines the fundamental characteristics of these bonds and carriers, as well as advances in catalyst development. Our objective is to offer a comprehensive understanding of current state of hydrogen carriers and identify future research directions, such as molecular modification and system optimization. Innovations in these areas are crucial to advance hydrogen storage technologies for a large‐scale hydrogen deployment.

Cashel, J, Yan, D, Han, R, Jeong, H, Yoon, CW, Ambay, JA, Liu, Y, Ung, AT, Yang, L & Huang, Z 2025, 'Chemical Bonds Containing Hydrogen: Choices for Hydrogen Carriers and Catalysts', Angewandte Chemie International Edition, vol. 64, no. 21.
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AbstractCompounds containing B─H, C─H, N─H, or O─H bonds with high hydrogen content have been extensively studied as potential hydrogen carriers. Their hydrogen storage performance is largely determined by the nature of these bonds, decomposition pathways, and the properties of the dehydrogenation products. Among these compounds, methanol, cyclohexane, and ammonia stand out due to their low costs and established infrastructure, making them promising hydrogen carriers for large‐scale storage and transport. They offer viable pathways for decarbonizing society by enabling hydrogen to serve as a clean energy source. However, several challenges persist, including the high temperatures required for (de)hydrogenation, slow kinetics, and the reliance on costly catalysts. To address these issues, strategies such as chemical modification and catalyst development are being pursued to improve hydrogen cycling performance. This review highlights recent progress in hydrogen carriers with B─H, C─H, N─H, or O─H bonds. It examines the fundamental characteristics of these bonds and carriers, as well as advances in catalyst development. Our objective is to offer a comprehensive understanding of current state of hydrogen carriers and identify future research directions, such as molecular modification and system optimization. Innovations in these areas are crucial to advance hydrogen storage technologies for a large‐scale hydrogen deployment.

Catchpoole, D, Hettiaratchi, A & Fonseca Tumilasci, V 2025, 'ISBER 2025 Annual Meeting and Exhibits: Celebrating the Impact of Biobanking Worldwide!', Biopreservation and Biobanking, vol. 23, no. 1, pp. 65-66.
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Cecilio, MO, Indraratna, B, Rujikiatkamjorn, C & Malisetty, RS 2025, 'Dynamic stress analysis of rock joints under railway loading', Géotechnique, vol. 75, no. 4, pp. 529-549.
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The proper estimation of stresses generated by the passage of a train is fundamentally important to the serviceability and longevity of railways, and yet very limited knowledge is available where the track substructure is built on a jointed rock mass. The present study introduces an analytical solution for estimating the ground stresses arising from moving wheel loads, causing a change in the three-dimensional stress state in the track formation, in relation to the stress variation with depth and along the longitudinal track section – that is, the direction of train passage. Based on 21 case histories, an array of field measurements and numerical simulations covering a wide range of freight tonnage, train speeds and different formation conditions were considered to validate the proposed analytical solution. The proposed methodology (analytical solution) was then applied to a jointed rock subgrade to determine the normal and shear stresses acting along a specific discontinuity plane. The main analytical outcome demonstrates that the orthogonal vertical and shear stresses present different and phase-shifted history plots for homogeneous ground conditions with principal stresses rotation. However, conversely for a jointed subgrade, the normal and shear stresses along the discontinuity have the same history plot pattern and are in phase. As a practical guide, the results from this study would help to define which cyclic loads should be applied in laboratory tests to simulate realistic traffic patterns of trains travelling over a jointed rock subgrade.

Cervero-Martí­n, E & Tomamichel, M 2025, 'Device independent security of quantum key distribution from monogamy-of-entanglement games', Quantum, vol. 9, pp. 1652-1652.
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We analyse two party non-local games whose predicate requires Alice and Bob to generate matching bits, and their three party extensions where a third player receives all inputs and is required to output a bit that matches that of the original players. We propose a general device independent quantum key distribution protocol for the subset of such non-local games that satisfy a monogamy-of-entanglement property characterised by a gap in the maximum winning probability between the bipartite and tripartite versions of the game. This gap is due to the optimal strategy for two players requiring entanglement, which due to its monogamy property cannot be shared with any additional players. Based solely on the monogamy-of-entanglement property, we provide a simple proof of information theoretic security of our protocol. Lastly, we numerically optimize the finite and asymptotic secret key rates of our protocol using the magic square game as an example, for which we provide a numerical bound on the maximal tripartite quantum winning probability which closely matches the bipartite classical winning probability. Further, we show that our protocol is robust for depolarizing noise up to about 2.88%, providing the first such bound for general attacks for magic square based quantum key distribution.

Chadalavada, S, Faust, O, Salvi, M, Seoni, S, Raj, N, Raghavendra, U, Gudigar, A, Barua, PD, Molinari, F & Acharya, R 2025, 'Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review', Environmental Modelling & Software, vol. 185, pp. 106312-106312.
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Chadalavada, S, Yaman, S, Sengur, A, Hafeez-Baig, A, Tan, R-S, Datta Barua, P, Deo, RC, Kobayashi, M & Rajendra Acharya, U 2025, 'Gated-LNN: Gated Liquid Neural Networks for Accurate Water Quality Index Prediction and Classification', IEEE Access, vol. 13, pp. 69500-69512.
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Chan, SF, DeWitt, D & Loban, R 2025, 'A comparative exploration of virtual reality’s role in Mandarin intercultural communicative competence development', Information and Learning Sciences, vol. 126, no. 5/6, pp. 335-361.
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PurposeIntercultural communicative competence (ICC) is important when different cultural speakers learn Mandarin as a Foreign Language (MFL). The use of virtual reality (VR) has been shown to be effective for improving ICC. Hence, this study investigates a production-based instructional strategy where students use VR to view and create VR environments with cultural elements for learning MFL to determine if this strategy was effective with the current cohort of students (2023). In addition, it would be investigated whether there was a difference in ICC between the 2019 cohort and the 2023 cohort.Design/methodology/approachThe study employed a quasi-experimental method to assess ICC using the Survey of student’s Intercultural Competence (SSIC) and gauged the improvement within the 2023 cohort. Next, the ICC between the 2023 and 2019 cohorts was compared to determine if there was a significant difference. Data was analysed using paired-samples t-tests and thematic analysis for the open-ended responses.FindingsThere was a significant improvement in ICC after the intervention for the 2023 cohort, which was supported with the open-ended response. However, the t-test results indicated no significant difference in ICC between the 2019 and 2023 cohorts. However, the 2023 cohort seemed to be more motivated, confident and eager to continue using VR. The findings indicate that VR when combined with an appropriate pedagogy could improve students ICC.Originality/valueThe use of VR and this production-based instructional strategy could be used in other languages and could possibly be used to i...

Chang, Y, Zhang, L, Nghiem, LD & Wei, Y 2025, 'Enhanced microbial strategies to mitigate microplastic transfer via composting to agricultural ecosystems—A short review', Current Opinion in Environmental Science & Health, vol. 45, pp. 100625-100625.
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Che, H, Li, C, Leung, M-F, Ouyang, D, Dai, X & Wen, S 2025, 'Robust Hypergraph Regularized Deep Non-Negative Matrix Factorization for Multi-View Clustering', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 1817-1829.
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Chen, D, Li, J, Liu, J & Wu, C 2025, 'Damage assessment of ultra-high-performance concrete protective wall against gaseous explosion', Engineering Structures, vol. 332, pp. 120071-120071.
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Chen, D, Wu, C & Li, J 2025, 'A fast and reliable model for predicting hydrogen-methane-air blast loading in unconfined spaces for blast-resistant design', International Journal of Hydrogen Energy, vol. 97, pp. 1316-1326.
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Chen, G, Zeng, H, Zhu, S, Wen, S & Hu, J 2025, 'Finite-time H∞ control and energy cost optimization for nonlinear delayed systems through switching analysis and interval matrix method', Science China Information Sciences, vol. 68, no. 3.
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Chen, H, Zhu, T, Liu, B, Zhou, W & Yu, PS 2025, 'Fine-Tuning a Biased Model for Improving Fairness', IEEE Transactions on Big Data, vol. 11, no. 3, pp. 1397-1410.
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Chen, H, Zhu, T, Zhang, L, Liu, B, Wang, D, Zhou, W & Xue, M 2025, 'QUEEN: Query Unlearning Against Model Extraction', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 2143-2156.
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Chen, J, Indraratna, B & Ngo, T 2025, 'Assessing the Macro to Micro Properties of Recycled Ballast Mixtures by DEM Analyses for Enhanced Railroad Engineering', Journal of Geotechnical and Geoenvironmental Engineering, vol. 151, no. 6.
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Chen, J, Indraratna, B, Vinod, JS, Ngo, T & Liu, Y 2025, 'Effects of Particle Shape on the Shear Behavior and Breakage of Ballast: A DEM Approach', International Journal of Geomechanics, vol. 25, no. 1.
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Chen, J, Wu, K, Niu, J, Li, Y, Xu, P & Andrew Zhang, J 2025, 'Spectral and Energy Efficient Waveform Design for RIS-Assisted ISAC', IEEE Transactions on Communications, vol. 73, no. 1, pp. 158-172.
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Chen, L, Wang, W, Tian, Z, Zhang, C & Yu, S 2025, 'Backdoored Sample Cleansing for Unlabeled Datasets Via Bootstrapped Dual Set Purification', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Chen, N, Zhang, X, Qi, L, Gao, F, Wu, G, Li, H, Guo, W & Ngo, HH 2025, 'Enhancement of volatile fatty acids degradation and rapid methanogenesis in a biochar-assisted anaerobic membrane bioreactor via enhancing direct interspecies electron transfer', Journal of Environmental Management, vol. 380, pp. 125045-125045.
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Chen, Q, Xiong, Z, Tao, G, Tian, Z & Nimbalkar, S 2025, 'Strength, Durability, Corrosion Resistance, and Microstructure of Cemented Soil Incorporating Nano-MgO under Static and Cyclic Loading: A Laboratory Study', Journal of Materials in Civil Engineering, vol. 37, no. 4.
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Chen, W, March, LM, Blyth, FM, Alarkawi, D, Blank, RD, Bliuc, D, Tran, T & Center, JR 2025, 'Excess subsequent fracture and mortality risk after ankle fractures: a relative survival analysis of the 45 and Up Study', Osteoporosis International, vol. 36, no. 6, pp. 1031-1038.
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Chen, W-H, Wu, D-R, Chang, M-H, Rajendran, S, Ong, HC & Lin, K-YA 2025, 'Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods', International Journal of Hydrogen Energy, vol. 140, pp. 1004-1016.
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Chen, X, Ansari, AJ, Han, S, Peng, Y & Song, X 2025, 'Performance and microbial response of anaerobic membrane bioreactor to oil and salt co-occurrence in food wastewater treatment', Environmental Technology & Innovation, vol. 38, pp. 104208-104208.
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Chen, X, Han, Y, Li, C, Chang, X, Sun, Y & Yang, Y 2025, 'A Static–Dynamic Composition Framework for Efficient Action Recognition', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Chen, Y, Guo, W, Ngo, HH, Chen, Z, Wei, C, Bui, XT, Van Tung, T & Zhang, H 2025, 'Ways to assess hydrogen production via life cycle analysis', Science of The Total Environment, vol. 977, pp. 179355-179355.
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Chen, Y, Zhu, S, Li, Y, Liu, X & Wen, S 2025, 'Finite-Time Synchronization for Delayed NNs via Generalized Halanay Inequalities', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-10.
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Chen, Y, Zhu, S, Shen, M, Liu, X & Wen, S 2025, 'Finite-time synchronization control for a class of delayed neural networks: an improved two-step control method', Science China Information Sciences, vol. 68, no. 9.
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Chen, Y-N, Ding, C, Zhu, H, Huang, X, Jia, Y, Liu, Y & Guo, YJ 2025, 'A Four-Port Shared-Aperture In-Band Full-Duplex Antenna Array Based on A Novel Common-Mode and Differential-Mode Combination Method', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Chen, Z, Ji, JC, Ni, Q, Ye, B, Ding, X & Yu, W 2025, 'Bi-structural spatial–temporal network for few-shot fault diagnosis of rotating machinery', Mechanical Systems and Signal Processing, vol. 227, pp. 112378-112378.
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Chen, Z, Khalilpour, K & Zhao, R 2025, 'Coordination of Risk‐Averse Behaviors in a Green Supply Chain‐to‐Chain Competition', Managerial and Decision Economics, vol. 46, no. 5, pp. 3142-3156.
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ABSTRACTThis research investigates the coordination of risk‐averse behaviors among members in green supply chain‐to‐chain competition. We establish models encompassing member rationality, manufacturer risk aversion, cost‐sharing contracts, and revenue‐sharing contracts, providing the optimal equilibrium results for each scenario. This paper compares the effectiveness of cost‐sharing versus revenue‐sharing contracts, finding that risk‐averse behavior diminishes profits, with total profits falling below those achieved under member rationality. Both contract types can facilitate coordination, but the revenue‐sharing contract proves more effective. Under the revenue‐sharing contract, wholesale and retail prices decrease, product greenness enhances, and profits for manufacturers, retailers, and the entire supply chain increase.

Chen, Z, Li, J, Zong, Z, Li, J, Li, M & Wu, C 2025, 'Damage analysis of RC panels subjected to shock wave and bubble pulse in underwater explosion', Engineering Structures, vol. 335, pp. 120331-120331.
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Chen, Z, Liang, J, Yu, Z, Cheng, H, Min, G & Li, J 2025, 'Resilient Collaborative Caching for Multi-Edge Systems With Robust Federated Deep Learning', IEEE Transactions on Networking, vol. 33, no. 2, pp. 654-669.
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Chen, Z, Zuo, W, Zhou, K, Li, Q, Huang, Y & E, J 2025, 'Corrigendum to “Multi-objective optimization of proton exchange membrane fuel cells by RSM and NSGA-II” [Energy Convers. Manag. 277 (2023) 116691]', Energy Conversion and Management, vol. 326, pp. 119448-119448.
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Cheng, D, Zou, Y, Xiang, S & Jiang, C 2025, 'Graph neural networks for financial fraud detection: a review', Frontiers of Computer Science, vol. 19, no. 9.
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Chi, K, Li, J, Shao, R & Wu, C 2025, 'Experimental study on dynamic characterisation of ultra-high performance concrete (UHPC) after cryogenic freeze-thaw cycles', Cement and Concrete Composites, vol. 160, pp. 106011-106011.
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Choden, Y, Askari, M, Kabir, MM, Choo, Y, Sabur, GM, Mamun, MFK, Choi, J-S, Woo, YC, Kim, Y, Hong, S, Shon, HK & Phuntsho, S 2025, 'Recovery of bromide for bromine extraction: a review of technologies and circular economy implications', Desalination, vol. 613, pp. 118968-118968.
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Chu, L, Shi, J & Braun, R 2025, 'Mechanical Reliability of Compressible Microinterconnects in Replaceable Integrated Chiplet Assembly', IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 15, no. 4, pp. 757-765.
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Chu, NH, Hieu, NQ, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 2025, 'Dynamic Multi-Tier Resource Allocation Framework for Metaverse', IEEE Network, vol. 39, no. 1, pp. 197-204.
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Chu, R, Chik, L, Song, Y, Chan, J & Li, X 2025, 'An effective approach for early fuel leakage detection with enhanced explainability', Intelligent Systems with Applications, vol. 26, pp. 200504-200504.
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Clemon, L 2025, 'Constitutive Relation for Prolate Pin–Reinforced Transversely Isotropic Media for Additive Manufacturing', Journal of Engineering Mechanics, vol. 151, no. 2.
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Coward-Smith, M, Zhang, Y, Donovan, C, Kim, RY, Wang, B, Zakarya, R, Chen, H, Li, JJ & Oliver, BG 2025, 'Beyond conventional biomarkers: Emerging importance of extracellular vesicles in osteoarthritis, metabolic disorders and cardiovascular disease', Extracellular Vesicle, vol. 5, pp. 100079-100079.
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Cui, L, Wu, F, Qu, Y, Gu, B, Gao, L & Yu, S 2025, 'RE4ETD: A Relative Entropy Optimization-Based Method for Efficient Electricity Theft Detection With Dual-Privacy-Preservation', IEEE Transactions on Smart Grid, pp. 1-1.
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Cullen, M & Ji, JC 2025, 'Online defect detection and penetration estimation system for gas metal arc welding', The International Journal of Advanced Manufacturing Technology, vol. 136, no. 5-6, pp. 2143-2164.
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Cuzmar, RH, Mora, A, Montenegro, A, Pereda, J, Gajardo, J & Aguilera, RP 2025, 'Optimal Fault-Tolerant Reference Generator for Circulating Currents in Modular Multilevel Matrix Converters', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Cuzmar, RH, Mora, A, Pereda, J & Aguilera, RP 2025, 'An Improved Reference Generator Based on MPC of Circulating Currents and Common-Mode Voltage for Modular Multilevel Matrix Converters', IEEE Transactions on Industrial Electronics, vol. 72, no. 2, pp. 1958-1968.
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Dai, C, Zuo, W, Li, Q, Zhou, K, Huang, Y, Zhang, G & E, J 2025, 'Numerical investigations on the performance of a hydrogen-fueled micro planar combustor with V-shaped baffle for thermophotovoltaic applications', Energy, vol. 326, pp. 136270-136270.
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Dai, S, Gao, F, Niu, G, He, X, Zhang, S & Sheng, D 2025, 'Dynamics of particle segregation and its impact on mechanical properties', Acta Geotechnica, vol. 20, no. 5, pp. 1991-2007.
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Abstract Particle segregation is a widespread phenomenon in nature. Vertical vibration systems have been a focal point in studying particle segregation, providing valuable insights into the mechanisms and patterns that influence this process. However, despite extensive research on the mechanisms and patterns of particle separation, the consequences, particularly the mechanical properties of samples resulting from particle segregation, remain less understood. This study aims to investigate the segregation process of a binary mixture under vertical vibration and examine the consequences through monotonic and cyclic triaxial drained tests. The results reveal that large and small particles segregate nearly simultaneously, with more thorough separation observed for large particles. The segregation index, D s , effectively describes this evolution process, offering a quantitative metric for both mixing and segregation. Granular temperature analysis unveils three distinct states during segregation: solid-like, fluid-like, and solid–liquid transitional phase, corresponding to varying activity levels of particle segregation. Drained triaxial shear tests demonstrate the sensitivity of stress–strain relationships to the degree of segregation. Interestingly, ultimate strength is found to be essentially unrelated to the degree of segregation. When the segregation index approaches zero, signifying particles approaching a uniform distribution, the granular system reaches a harmonic state. This state exhibits optimal mechanical performance characterised by maximum peak stress, friction angle, and the highest elastic modulus. These findings underscore the potential impact of segregation on the mechanical response of granular mixtures and emphasise the necessity of a comprehensive understanding of particle segregatio...

Dai, S, He, X, Gao, F, Zhong, W, Zheng, Y & Zhang, S 2025, 'Laboratory model test of contact erosion in railway substructure', Transportation Geotechnics, vol. 51, pp. 101499-101499.
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Dai, S, Shan, F, Xiong, H, Zhang, S, He, X & Sheng, D 2025, 'Evolution of pore structure and flow properties in particle segregation', Journal of Hydrology, vol. 652, pp. 132651-132651.
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Dang, Z, Luo, M, Wang, J, Jia, C, Han, H, Wan, H, Dai, G, Chang, X & Wang, J 2025, 'Disentangled Noisy Correspondence Learning', IEEE Transactions on Image Processing, vol. 34, pp. 2602-2615.
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Datta, B, Manasur, B, Sreelekha, G, Verma, P, Adak, C, Shukla, RP & Dutta, G 2025, 'Quantification of L-lactic acid in human plasma samples using Ni-based electrodes and machine learning approach', Talanta, vol. 286, pp. 127493-127493.
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Davis Weaver, N, Bertolacci, GJ, Rosenblad, E, Ghoba, S, Cunningham, M, Ikuta, KS, Moberg, ME, Mougin, V, Han, C, Wool, EE, Abate, YH, Adewuyi, HO, Adnani, QES, Adzigbli, LA, Afolabi, AA, Agampodi, SB, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, S, Ahmed, A, Ahmed, H, Al Hamad, H, Al-Ajlouni, Y, Al-amer, RM, Albashtawy, M, Aldhaleei, WA, Ali, SS, Ali, W, Alomari, MA, Alsabri, MA, Alvis-Guzman, N, Al-Worafi, YM, Amindarolzarbi, A, Amiri, S, Andrei, T, Anvari, S, Arabloo, J, Areda, D, Artamonov, AA, Ashraf, T, Athari, SS, Atout, MMW, Azzam, AY, Badiye, AD, Baghcheghi, N, Bahramian, S, Banach, M, Barker-Collo, SL, Bärnighausen, TW, Barrow, A, Bashiri, A, Bashiru, HA, Bastan, M-M, Batra, K, Batra, R, Bayati, M, Benjet, C, Benzian, H, Bertuccio, P, Bhagavathula, AS, Bhattacharjee, P, Bills, CB, Boppana, SH, Borges, G, Borhany, H, Bustanji, Y, Caetano dos Santos, FL, Castelpietra, G, Caye, A, Cenderadewi, M, Chandika, RM, Chandrasekar, EK, Charalampous, P, Chen, Y, Chimoriya, R, Chopra, H, Choudhari, SG, Chu, D-T, Chukwu, IS, Chutiyami, M, Cowden, RG, Dachew, BA, Dadras, O, Dai, X, Dalal, K, Dandona, L, Dandona, R, Darcho, SD, Darvishi Cheshmeh Soltani, R, Dávila-Cervantes, CA, de la Torre-Luque, A, Debopadhaya, S, Degenhardt, L, Delgado-Enciso, I, Dervišević, E, Diaz, MJ, Dongarwar, D, Doshi, OP, Dsouza, HL, Dumith, SC, Duraisamy, S, Eboreime, E, Efendi, F, Ekholuenetale, M, El Arab, RA, Elhadi, M, ELNahas, G, Eltaha, C, Emdadul Haque, SE, Eskandarieh, S, Fahim, A, Faro, A, Fatehizadeh, A, Fazeli, P, Feizkhah, A, Fekadu, G, Ferreira, N, Fischer, F, Franklin, RC, Fridayani, NKY, Gajdács, M, Gandhi, AP, Ganesan, B, Gebregergis, MW, Gebrehiwot, M, Gebremeskel, TG, Getie, M, Ghadimi, DJ, Ghailan, KY, Ghashghaee, A, Gholamrezanezhad, A, Goleij, P, Grada, A, Grivna, M, Guan, S-Y, Gulati, S, Gupta, S, Gutiérrez, RA, Gutiérrez-Murillo, RS, Hamilton, EB, Hanifi, N, Hasan, I, Hassan Zadeh Tabatabaei, MS, Hay, SI, Heidari, M, Hemmati, M, Hoan, NQ, Hosseinzadeh, M, Hostiuc, S, Huang, J, Huynh, H-H, Ibitoye, SE, Ilesanmi, OS, Ilic, IM, Ilic, MD, Immurana, M, Inok, A, Iwu, CD, Jahrami, H, Jaka, S, Jalilzadeh Yengejeh, R, Ji, Z, Jin, S, Joseph, N, Joshua, CE, Jozwiak, JJ, Kabir, Z, Kadashetti, V, Kanmodi, KK, Kantar, RS, Kapoor, N, Karaye, IM, Karmakar, S, Kaur, H, Kerr, JA, Khajuria, H, Khan, A, Khatab, K, Kheirallah, KA & et al. 2025, 'Global, regional, and national burden of suicide, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021', The Lancet Public Health, vol. 10, no. 3, pp. e189-e202.
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Deng, Z, Nguyen, QD, Mahmood, AH, Pang, Y, Shi, T & Sheng, D 2025, 'Piezoresistivity assessment of self-sensing asphalt-based pavements with machine learning algorithm', Construction and Building Materials, vol. 468, pp. 140291-140291.
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Diao, X, Cai, G, Yang, R & He, X 2025, 'Contact erosion of soil layers with different water table levels under cyclic loading using VOF-DEM coupled method', Computers and Geotechnics, vol. 180, pp. 107090-107090.
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Ding, L, Cox, T, Ghobadi, S, Lim, KS, Roberts, T, Warkiani, ME, Radfar, P & OH, S 2025, 'Development of Xeno-Free, Fast-Dissolving Microcarriers for Scalable Stem Cell Therapy Applications', Cytotherapy, vol. 27, no. 5, pp. S152-S152.
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Ding, L, Zhong, Z, Chen, C, Liu, B, Chen, Z, Zhang, L, Mao, J, Zhang, M, Su, QP & Cheng, F 2025, 'Advances in multiplexed photoelectrochemical sensors for multiple components', Chemical Engineering Journal, vol. 505, pp. 159319-159319.
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Ding, W, Geng, Y, Huang, J, Ju, H, Wang, H & Lin, C-T 2025, 'MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 1104-1118.
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Ding, Z, Kuok, S-C, Lei, Y, Yu, Y, Zhang, G, Hu, S & Yuen, K-V 2025, 'A Novel Bayesian Empowered Piecewise Multi-Objective Sparse Evolution for Structural Condition Assessment', International Journal of Structural Stability and Dynamics, vol. 25, no. 10.
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In this study, a novel Bayesian empowered piecewise multi-objective function is developed, in which a traditional objective function is applied to realize the rough optimization in the first stage to determine the approximate results. Then, a sparse Bayesian learning-based objective function is applied to realize refined optimization with the obtained approximate results in the second stage. On the other hand, considering the sparsity of the structural damage identification, two simple but effective calculation frameworks, the colony initial sparsification and elite clustering framework, are integrated into the evolution, making the algorithm adaptable to handle the defined sparse optimization problem. Therefore, the proposed calculation framework is more efficient and robust while no initial conditions are needed. We will carry out a numerical example on a truss and an experimental validation on a fixed-end beam with a single-sensor measurement system to verify the method.

Dinh, TQ, Dau, SH, Lagunas, E, Chatzinotas, S, Nguyen, DN & Hoang, DT 2025, 'Quantum Annealing for Complex Optimization in Satellite Communication Systems', IEEE Internet of Things Journal, vol. 12, no. 4, pp. 3771-3784.
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Do, KT, Hoang, DK, Luong, QN, Nguyen, HG, Do, AT, Ho‐Pham, LT & Nguyen, TV 2025, 'Reference Values of Handgrip and Lower Extremity Strength for Vietnamese Men and Women: The Vietnam Osteoporosis Study', Journal of Cachexia, Sarcopenia and Muscle, vol. 16, no. 1.
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ABSTRACTBackgroundFalls and sarcopenia are significant public health issues in Vietnam. Despite muscle strength being a critical predictor for these conditions, reference data on muscle strength within the Vietnamese population are lacking.PurposeTo establish the reference ranges for muscle strength among Vietnamese individuals.MethodsThe study involved 4096 individuals, including 1419 men and 2677 women aged 18 years and above, from the Vietnam Osteoporosis Study. Muscle strength was assessed using a Baseline hand dynamometer for handgrip strength and a Back‐Leg‐Chest dynamometer for leg strength. We calculated mean values, standard deviations, interquartile ranges, and peak muscle strength (pMS) for both handgrip and leg strength across various ages. Reference curves were created with the Generalised Additive Model for Location Scale and Shape, and polynomial regression models were employed to analyse the relationship between muscle strength and age.ResultsAdvancing age was significantly associated with lower muscle strength. Peak muscle strength typically occurred between ages 30 and 40, with earlier peaks in women, especially in leg strength. Men consistently showed higher muscle strength than women, with variations depending on the measurement site. Specifically, average handgrip strength was 36.4 kg ± 8.4 (mean ± SD) for men and 23.2 kg ± 6.0 for women (p < 0.001). Leg strength averaged 63.9 kg ± 27.2 for men and 29.5 kg ± 13.9 for women (p < 0.001). Additionally, we produced a percentile chart illustrating muscle weakness ranges based on the 25th percentile of muscle strength and the appendicular skeletal mus...

Doan, T, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2025, 'Coupled CFD-DEM modelling of clogging of granular columns by cohesive fines', Computers and Geotechnics, vol. 177, pp. 106902-106902.
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Dong, B, Yu, Y, Gao, W, Gunasekara, C, Zhao, G, Castel, A & Setunge, S 2025, 'Electro-chemo-physical analysis for long-term reinforcement corrosion within the reactive system of concrete', Cement and Concrete Composites, vol. 155, pp. 105846-105846.
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Dong, Q, Wu, X, Song, Y, Du, Y, Qi, J, Huang, L, Li, W, Huang, Y & Shi, L 2025, 'Temperature behaviors of transparent solar PV panels under various operating modes: an experimental and numerical study', Renewable Energy, vol. 250, pp. 123279-123279.
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Dong, S, Xie, W, Yang, D, Li, Y, Zhang, J, Tian, J & Lei, J 2025, 'SeaDATE: Remedy Dual-Attention Transformer With Semantic Alignment via Contrast Learning for Multimodal Object Detection', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 5, pp. 4713-4726.
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Dong, W, Ahmed, AH, Liebscher, M, Li, H, Guo, Y, Pang, B, Adresi, M, Li, W & Mechtcherine, V 2025, 'Electrical resistivity and self-sensing properties of low-cement limestone calcined clay cement (LC3) mortar', Materials & Design, vol. 252, pp. 113790-113790.
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Dong, W, Duan, Z, Peng, S, Chen, Y, Chu, D, Tai, H & Li, W 2025, 'Triboelectric nanogenerator-powering piezoresistive cement-based sensors for energy harvesting and structural health monitoring', Nano Energy, vol. 137, pp. 110823-110823.
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Dong, W, Peng, S, Wang, K, Huang, Y, Shi, L, Wu, F & Li, W 2025, 'Integrated triboelectric self-powering and piezoresistive self-sensing cementitious composites for intelligent civil infrastructure', Nano Energy, vol. 135, pp. 110656-110656.
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Dong, W, Tang, J, Wang, K, Huang, Y, Shah, SP & Li, W 2025, 'Cement-based batteries for renewable and sustainable energy storage toward an energy-efficient future', Energy, vol. 315, pp. 134382-134382.
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Dong, W, Zhao, C, Peng, S, Wu, C, Kim, T, Wang, K & Li, W 2025, 'Recycled carbon fibre/cement-based triboelectric nanogenerators toward energy-efficient and smart civil infrastructure', Composites Part B: Engineering, vol. 303, pp. 112603-112603.
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Du, G, Cui, C, Li, L, Li, N, Lei, G & Zhu, J 2025, 'Comprehensive Performance Analysis of High-Speed PM Motors With Layered Rotor Structures', IEEE Transactions on Industrial Electronics, vol. 72, no. 4, pp. 3460-3470.
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Du, G, Fan, Z, Fan, K, Liu, H, Zhang, J, Li, D, Yan, L, Jiu, J, Li, R, Li, X, Li, S, Jia, L, Liu, H, Ren, Y, Liu, X, Li, JJ & Wang, B 2025, 'Risk-stratified lifetime risk and incidence of hip fracture and falls in middle-aged and elderly Chinese population: The China health and retirement longitudinal study', Journal of Orthopaedic Translation, vol. 50, pp. 174-184.
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Du, L, Chen, Z, Hu, H, Liu, X & Guo, Y 2025, 'An improved uncooperative space target de-tumbling method using electromagnetic de-tumbling devices with AC excitation', Advances in Space Research, vol. 75, no. 1, pp. 1264-1276.
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Du, T-K, Lin, Y, Ji, J-C & Ding, H 2025, 'Series gravity-based track nonlinear energy Sinks: Design and experiment', Mechanical Systems and Signal Processing, vol. 229, pp. 112559-112559.
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Duc Manh, B, Nguyen, C-H, Thai Hoang, D, Nguyen, DN, Zeng, M & Pham, Q-V 2025, 'Privacy-Preserving Cyberattack Detection in Blockchain-Based IoT Systems Using AI and Homomorphic Encryption', IEEE Internet of Things Journal, vol. 12, no. 11, pp. 16478-16492.
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Eklund, M, Voinov, A, Hossain, MJ & Khalilpour, K 2025, 'Evaluating the interplay of community behaviour and microgrid design through optimisation modelling in local energy markets', Renewable and Sustainable Energy Reviews, vol. 210, pp. 115271-115271.
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El‐Helou, AJ, Liu, Y, Chen, C, Wang, F, Altug, H, Reece, PJ & Zhu, Y 2025, 'Optical Metasurfaces for the Next‐Generation Biosensing and Bioimaging', Laser & Photonics Reviews, vol. 19, no. 10.
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AbstractRecent advances in this understanding of light‐matter interactions, combined with innovations in the design and fabrication of large‐scale nanostructured metasurfaces, have enabled transformative approaches to biosensing and bioimaging. This review delves into the profound impact of optical metasurfaces, highlighting innovations that leverage their tunable properties and adaptability. It begins with an overview of key sensing mechanisms across various metasurface modalities, comparing their effects on metrics such as sensitivity and limits of detection. The discussion then shifts to recent advancements in refractometric biosensing, focusing on novel transduction methods that exploit the intensity, phase, and colorimetric responses of these metasurfaces. The latest developments in surface‐enhanced spectroscopic sensing are also examined, exploring how metasurfaces contribute to enhanced molecular fingerprinting capabilities in these applications. Additionally, the role of optical metasurfaces in advancing bioimaging are assessed, emphasizing label‐free elastic scattering, spectroscopic/chemical contrast imaging, and metasurface‐assisted super‐resolution microscopy. Finally, the review addresses current challenges and future directions for optical metasurfaces in biosensing and imaging, including material limitations, difficulties in large‐scale fabrication, and the complexity of data analysis and readout methods. It also discusses the integration of novel detector hardware to improve spatiotemporal resolution of sensing and imaging techniques.

Ellis, JT & Kennedy, PJ 2025, 'Multi-criteria decision making and its application to in silico discovery of vaccine candidates for Toxoplasma gondii', Vaccine, vol. 58, pp. 127242-127242.
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Elmakki, T, Zavahir, S, Shon, HK, Gago, GH, Park, H & Han, DS 2025, 'Capacitive lithium capture system using a mixed LiMn2O4 and LiAlO2 material', Desalination, vol. 593, pp. 118195-118195.
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Elsamadony, M, Pileggi, SF, Liu, J & Fujii, M 2025, 'Crafting a resilient and sustainable future amid major crises', Journal of Cleaner Production, vol. 511, pp. 145583-145583.
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Errey, N, Zowghi, D, Leong, TW, Wu, Y, Yuan, X, Huang, W & Liang, CJ 2025, 'Heuristics for evaluating narrative visualization: a validation study', Journal of Visualization, vol. 28, no. 3, pp. 645-659.
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Abstract Narrative visualization integrates data visualization and narrative techniques to convey a compelling story. Narrative visualization is notoriously difficult to evaluate. One solution is heuristic evaluation, using a domain-specific set of heuristics. This paper validates a set of heuristics proposed specifically for evaluating narrative visualization. We conducted studies with experienced narrative visualization practitioners in both summative and formative settings. We found that the set of heuristics showed promise in a summative setting, where similar responses evidenced that the set of heuristics could provide reliable evaluation metrics. Furthermore, in a formative setting, implementing the set of heuristics was reported to be useful in the design process; however, due to their limited focus, we recommend that it be implemented in conjunction with other evaluation guidelines. Graphical abstract

Esfandiari, M, Lv, X, Chamani, S & Yang, Y 2025, 'Graphene metasurfaces: Advances in lens applications, design strategies, and fabrication techniques', Materials Today Electronics, vol. 11, pp. 100140-100140.
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Faddoul, Y & Sirivivatnanon, V 2025, 'Sustainable concrete structures by optimising structural and concrete mix design', Australian Journal of Structural Engineering, vol. 26, no. 2, pp. 86-94.
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Fairley, N, Fatahi, B & Hokmabadi, AS 2025, 'Impacts of Transition Piece Designs on the Resilience of Large Offshore Wind Turbines Subject to Combined Earthquake, Wind and Wave Loads and Soil‐Structure Interaction', Earthquake Engineering & Structural Dynamics, vol. 54, no. 3, pp. 773-798.
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ABSTRACTThe urgent global drive to mitigate greenhouse gas emissions has significantly boosted renewable energy production, notably expanding offshore wind energy across the globe. With the technological evolution enabling higher‐capacity turbines on larger foundations, these installations are increasingly situated in earthquake‐prone areas, underscoring the critical need to ensure their seismic resilience as they become a pivotal component of the global energy infrastructure. This study scrutinises the dynamic behaviour of a 15 MW offshore wind turbine (OWT) under concurrent earthquake, wind and wave loads, focusing on the performance of the ultra‐high‐strength cementitious grout that bonds the monopile to the transition piece. Employing LS DYNA for numerical simulations, we explored the seismic responses of four OWT designs with diverse transition piece cone angles, incorporating nonlinear soil springs to model soil‐structure interactions (SSIs) and conducting a site response analysis (SRA) to account for local site effects on ground motion amplification. Our findings reveal that transition pieces with larger cone angles exhibit substantially enhanced stress distribution and resistance to grout damage, evidenced by decreased ovalisation in the coned sections of the transition piece and monopile, and improved bending flexibility. The observed disparities in damage across different cone angles highlight shortcomings in current design guidelines pertaining to the prediction of grout stresses in conical transition piece designs, with the current code‐specified calculations predicting higher stresses for transition piece designs with larger cone angles. This study also highlights the code's limitations when accounting for grout damage induced by stress concentrations in the grouted connections under seismic dynamic loading conditions. The results of the study demonstrate the need for refinement of these guidelines to improv...

Fan, Y, Feng, C, Hang, Z, Shen, L & Li, W 2025, 'Optimal design of electrical conductivity of hybrid multi-dimensional carbon fillers reinforced porous cement-based Composites: Experiment and modelling', Composite Structures, vol. 352, pp. 118714-118714.
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Fang, L, Lin, X, Xu, R, Liu, L, Zhang, Y, Tian, F, Li, JJ & Xue, J 2025, 'Advances in the Development of Gradient Scaffolds Made of Nano-Micromaterials for Musculoskeletal Tissue Regeneration', Nano-Micro Letters, vol. 17, no. 1.
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AbstractThe intricate hierarchical structure of musculoskeletal tissues, including bone and interface tissues, necessitates the use of complex scaffold designs and material structures to serve as tissue-engineered substitutes. This has led to growing interest in the development of gradient bone scaffolds with hierarchical structures mimicking the extracellular matrix of native tissues to achieve improved therapeutic outcomes. Building on the anatomical characteristics of bone and interfacial tissues, this review provides a summary of current strategies used to design and fabricate biomimetic gradient scaffolds for repairing musculoskeletal tissues, specifically focusing on methods used to construct compositional and structural gradients within the scaffolds. The latest applications of gradient scaffolds for the regeneration of bone, osteochondral, and tendon-to-bone interfaces are presented. Furthermore, the current progress of testing gradient scaffolds in physiologically relevant animal models of skeletal repair is discussed, as well as the challenges and prospects of moving these scaffolds into clinical application for treating musculoskeletal injuries.

Fang, Z, Lu, J & Zhang, G 2025, 'Out-of-distribution detection with non-semantic exploration', Information Sciences, vol. 705, pp. 121989-121989.
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Farah, N, Lei, G, Zhu, J & Guo, Y 2025, 'Robust Model-Free Reinforcement Learning Based Current Control of PMSM Drives', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 1061-1076.
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Fattah, IMR, Alom, J, Zaman, JU, Ban, S, Veza, I, Kalam, MA, Hessel, V & Ahmed, MB 2025, 'Hydrogel-derived materials for microbial fuel cell', Journal of Power Sources, vol. 625, pp. 235688-235688.
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Fawns, T, Bearman, M, Dawson, P, Nieminen, JH, Ashford-Rowe, K, Willey, K, Jensen, LX, Damşa, C & Press, N 2025, 'Authentic assessment: from panacea to criticality', Assessment & Evaluation in Higher Education, vol. 50, no. 3, pp. 396-408.
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Fei, Z, Ma, Y, Zhao, J, Wang, B & Yang, J 2025, 'KNEG-CL: Unveiling data patterns using a k-nearest neighbor evolutionary graph for efficient clustering', Information Sciences, vol. 690, pp. 121602-121602.
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Fei, Z, Zhai, H, Yang, J, Wang, B & Ma, Y 2025, 'Discovering generalized clusters with adaptive mixture density-based clustering', Knowledge-Based Systems, vol. 314, pp. 113250-113250.
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Feng, K, Ansari, AJ, Zhang, N, Peng, Y & Song, X 2025, 'Biochar addition to mitigate oil inhibition in anaerobic digestion of food wastewater: Microbial insights from biochemical methane potential tests', Environmental Technology & Innovation, vol. 37, pp. 104000-104000.
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Feng, S, Guo, W, Ding, A, Parsa, SM, Pan, J, Cheng, D, Tung, TV & Ngo, HH 2025, 'Enzyme sources in wastewater treatment: Their influence on enzymatic bioremediation and large-scale applications', Chemical Engineering Journal, vol. 510, pp. 161891-161891.
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Feng, S, Guo, W, Zhang, S, Luo, G, Nguyen, HT, Nguyen, NC, Cheng, D, Ye, Y & Ngo, HH 2025, 'Optimization of hydraulic retention time in continuous orange peel crude enzyme - mediated dark fermentation for sustainable biohydrogen production from synthetic swine wastewater', Journal of Water Process Engineering, vol. 73, pp. 107714-107714.
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Feng, Z, Huang, J, Zhang, W, Wen, S, Liu, Y & Huang, T 2025, 'YOLOv8-G2F: A portable gesture recognition optimization algorithm', Neural Networks, vol. 188, pp. 107469-107469.
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Flores-Sosa, M, Merigó, JM & Sanchez-Valenzuela, K 2025, '30 years of the Journal of Heuristics: a bibliometric analysis', Journal of Heuristics, vol. 31, no. 1.
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Fu, Z, Zuo, W, Li, Q, Zhou, K, Huang, Y & Li, Y 2025, 'Multi-objective optimization of liquid cooling plate partially filled with porous medium for thermal management of lithium-ion battery pack by RSM, NSGA-II and TOPSIS', Energy, vol. 318, pp. 134853-134853.
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Fu, Z, Zuo, W, Li, Q, Zhou, K, Huang, Y & Li, Y 2025, 'Performance enhancement studies on the liquid cooling plate fully filled with porous medium for thermal management of lithium-ion battery pack', Journal of Energy Storage, vol. 116, pp. 116072-116072.
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Gajardo, J, Pereda, J, Aguilera, RP, Mora, A, Cuzmar, RH & Poblete, P 2025, 'Optimization-Based Control of Modular Multilevel Matrix Converters With Integrated Energy Storage for Simultaneous Variable-Speed Drive and Grid-Feeding', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Gajjar, T, Yang, R, Ye, L & Zhang, YX 2025, 'Effects of key process parameters on tensile properties and interlayer bonding behavior of 3D printed PLA using fused filament fabrication', Progress in Additive Manufacturing, vol. 10, no. 2, pp. 1261-1280.
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Abstract Fused Filament Fabrication (FFF), also known as Fused Deposition Modelling (FDM), is one of the innovative 3D printing technologies for fabricating complex components and products. Mechanical properties of 3D-printed components mostly depend on intricate process parameters of 3D printing. This study experimentally investigates the effects of four key process parameters, including layer thickness, raster angle, feed rate, and nozzle temperature, on the tensile properties and interfacial bonding behaviours of FFF printed Polylactic Acid (PLA), and their failure mechanisms. The effect of the key parameters on surface roughness is also evaluated, which is critical for enhancing manufacturing and material performance, expecting to provide a potential guide for optimisation of the FFF printing process for improving product quality. The experimental results demonstrate that tensile strength improves up to 10 and 7% with increasing nozzle temperature (200 °C to 220 °C) and low feed rate (60 mm/sec to 40 mm/sec) during the 3D printing process. The tensile strength increases up to 12% with decreasing layer thickness (0.4 mm to 0.2 mm) and 40% with decreasing raster angle (90° to 0°). The experimental findings on surface roughness indicate that FFF-printed PLA samples were significantly influenced by the layer thickness and raster angle, and an improvement in surface roughness is observed with the increase of nozzle temperature and reduction in feed rate. Microstructural SEM analysis was conducted to investigate the ruptured surfaces of the FFF printed PLA samples, focusing on the interlayer bonding quality and morphological characteristics including the effect of void formation, poor adhesion, and insufficient fusion between adjacent surface contact area with the effect of printing parameters. The feed rate and nozzle temperature were found to substantially influence the interlayer bonding between two adjacent s...

Ganko, R, Madhavan, A, Hamouda, W, Muthu, S, Jain, A, Yoon, ST, El-Rozz, H, Cyril, D, Pabbruwe, M, Tipper, JL & Tavakoli, J 2025, 'Spinal implant wear particles: Generation, characterization, biological impacts, and future considerations', iScience, vol. 28, no. 4, pp. 112193-112193.
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Gao, S, Wang, C, Gao, C, Luo, W, Han, P, Liao, Q & Xu, G 2025, 'Improving long‐tail classification via decoupling and regularisation', CAAI Transactions on Intelligence Technology, vol. 10, no. 1, pp. 62-71.
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AbstractReal‐world data always exhibit an imbalanced and long‐tailed distribution, which leads to poor performance for neural network‐based classification. Existing methods mainly tackle this problem by reweighting the loss function or rebalancing the classifier. However, one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle distribution. In this paper, the authors shed light on the significance of the angle distribution in achieving a balanced feature space, which is essential for improving model performance under long‐tailed distributions. Nevertheless, it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature norm. To tackle these challenges, the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components: classifier norm (i.e. the magnitude of the classifier vector), feature norm (i.e. the magnitude of the feature vector), and cosine similarity between the classifier vector and feature vector. In this way, the authors analyse the change of each component in the training process and reveal three critical problems that should be solved, that is, the imbalanced angle distribution, the lack of feature discrimination, and the low feature norm. Drawing from this analysis, the authors propose a novel loss function that incorporates hyperspherical uniformity, additive angular margin, and feature norm regularisation. Each component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature space. The authors conduct extensive experiments on three popular benchmark datasets including CIFAR‐10/100‐LT, ImageNet‐LT, and iNaturalist 2018. The experimental results demonstrate that the authors’ loss function outperforms several previous state‐of‐th...

Gao, W, Karsa, M, Xiao, L, Spurling, D, Karsa, A, Ronca, E, Bongers, A, Guo, X, Mayoh, C, Azfar, M, Verhelst, SHL, Tanaka, K, Cheung, LC, Kotecha, RS, Lock, RB, Burns, MR, Vangheluwe, P, Norris, MD, Haber, M & Somers, K 2025, 'Polyamine depletion limits progression of acute leukaemia', International Journal of Cancer, vol. 156, no. 12, pp. 2360-2376.
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AbstractCancer cells are addicted to polyamines, polycations essential for cellular function. While dual targeting of cellular polyamine biosynthesis and polyamine uptake is under clinical investigation in solid cancers, preclinical and clinical studies into its potential in haematological malignancies are lacking. Here we investigated the preclinical efficacy of polyamine depletion in acute leukaemia. The polyamine biosynthesis inhibitor difluoromethylornithine (DFMO) inhibited growth of a molecularly diverse panel of acute leukaemia cell lines, while non‐malignant cells were unaffected. Responsiveness to DFMO was linked to decreased levels of its molecular target, the rate‐limiting polyamine biosynthesis enzyme ODC1, and of the polyamine transporters ATP13A2 and ATP13A3. DFMO increased polyamine uptake and upregulated expression of polyamine transporters in acute leukaemia cells, a compensatory effect abolished by treatment with the polyamine transport inhibitor AMXT 1501. This drug, currently in a phase 1 clinical trial in solid tumours in combination with DFMO, potentiated the inhibitory effects of DFMO, and their combination synergistically inhibited the growth of acute leukaemia cell lines by inducing apoptosis. DFMO and AMXT 1501 limited disease progression in highly aggressive xenograft models of infant KMT2A‐rearranged leukaemia, even when treatment was initiated at high disease burden. Increased expression of c‐MYC was associated with enhanced sensitivity to the combination of DFMO and AMXT 1501, suggesting this oncoprotein as a potential predictive marker of response to the drug combination. In conclusion, targeting polyamine biosynthesis and polyamine uptake limits disease progression in models of acute leukaemia, supporting further preclinical and clinical investigation into this approach for acute leukaemia.

Gao, X, Zhu, Q, Liao, X, Wu, M, Han, L & Yang, J 2025, 'Virtual quiet zone method for sound zone reproduction in coupled rooms', Applied Acoustics, vol. 228, pp. 110341-110341.
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Gao, Y, Chen, L, Lai, J, Wang, T, Wu, X & Yu, S 2025, 'IoT-Dedup: Device Relationship-Based IoT Data Deduplication Scheme', IEEE Transactions on Parallel and Distributed Systems, vol. 36, no. 5, pp. 847-860.
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Ge, M, Zhu, C, Sheng, D, Pineda, J & Li, N 2025, 'Study on the gas permeability of unsaturated compacted loess and its underlying micro-mechanism', Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering, vol. 44, no. 1, pp. 221-235.
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Aiming to investigate the gas permeability of unsaturated soil and its underlying micro-mechanism，the gas permeability coefficients of compacted loess under various initial states，along wetting/drying，and constant stress ratio compression paths were examined in this study by utilizing a modified gas permeation device. The Mercury Intrusion Porosimetry(MIP) technique was employed to further examine the microstructure changes of compacted losses，thereby analyzing the micro-mechanism of air permeation. The test results indicate that the gas permeability coefficient(keff) of compacted loess varies within the range of 10－12 to10－15 m2 in response to the increasing compaction saturation degree(Sr0). At low dry densities，keff exhibits an initial rise followed by a decline as Sr0 increases，whereas at high dry densities，a nonlinear decrement is observed. Wetting significantly reduces keff of compacted loess by up to three orders of magnitude，with a rapid decrease after the wetting saturation degree reaches 0.65. Conversely，drying improves gas permeability，but its impact is much less significant than that of wetting. Under constant stress ratio compression，keff decreases exponentially with increasing stress，and the decrease is more pronounced at lower stress ratios. The MIP test results reveal that the macro porosity first increases and then decreases with the increase of Sr0. Wetting has a minor effect on the pore size distribution curve(PSD)，while drying can increase macro porosity. Under similar state，the as-compacted soil exhibits more macropore structures compared to the after-wetting soil，while less macropore structures compared to the after-drying soil. The constant stress ratio compression results in a reduction of macropores，with a greater reduction at lower stress ratio. According to the variation of keff，the gas permeability of unsaturated compacted loess depends on the amount of the interconnected macropores，the more macropore structures，the better po...

Geethukrishnan, Bagde, PP, KH, S, Adak, C, Shukla, RP & Tadi, KK 2025, 'Smart sensing of creatinine in urine samples: Leveraging Cu-nanowires/MoS2 quantum dots and machine learning', Sensing and Bio-Sensing Research, vol. 47, pp. 100727-100727.
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Ghanbari Motlagh, S, Oladigbolu, J & Li, L 2025, 'A review on electric vehicle charging station operation considering market dynamics and grid interaction', Applied Energy, vol. 392, pp. 126058-126058.
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Gharoun, H, Yazdanjue, N, Khorshidi, MS, Chen, F & Gandomi, AH 2025, 'Leveraging Neural Networks and Calibration Measures for Confident Feature Selection', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 3, pp. 2179-2193.
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Ghimire, S, Deo, RC, Hopf, K, Liu, H, Casillas-Pérez, D, Helwig, A, Prasad, SS, Pérez-Aracil, J, Barua, PD & Salcedo-Sanz, S 2025, 'Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach', Energy and AI, vol. 20, pp. 100492-100492.
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Ghosh, S, Bhattacharya, S, Roy, P, Pal, U & Blumenstein, M 2025, 'MMC: Multi-modal colorization of images using textual description', Signal, Image and Video Processing, vol. 19, no. 2.
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Gill, AQ 2025, 'Trimodal Thinking for Architecting Human-Centric AI Systems: Fast, Slow, and Control', IEEE Transactions on Technology and Society, pp. 1-11.
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Golnary, F, Kalhori, H, Liu, W & Li, B 2025, 'Vehicle-based autonomous modal analysis for enhanced bridge health monitoring', International Journal of Mechanical Sciences, vol. 287, pp. 109910-109910.
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Gomes, SDC, Nguyen, QD, Li, W & Castel, A 2025, 'Shrinkage and carbonation of alkali-activated calcined clay-ground granulated blast furnace slag (GGBFS) concrete', Cement and Concrete Research, vol. 194, pp. 107899-107899.
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Gomes, SDC, Pang, Y, Nguyen, QD, Li, W, Vessalas, K & Castel, A 2025, 'The effect of calcined clay reactivity on the mechanical properties and chloride diffusion resistance of alkali-activated calcined clay-GGBFS concrete', Journal of Building Engineering, vol. 102, pp. 111996-111996.
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Gong, Z, Fang, B & Wei, D 2025, 'A hybrid springback compensation method for geometry complexity in stamping', The International Journal of Advanced Manufacturing Technology, vol. 136, no. 11-12, pp. 4815-4828.
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Abstract Springback compensation is a crucial approach to maintaining the accuracy of stamping parts with complex features. It has been challenging to determine the position of a characteristic point at the geometrical features during springback compensation. The currently available numerical approaches are not always sufficiently accurate and reliable, particularly when high-strength steels are increasingly used for lightweight structures. An enhanced hybrid method named springback path–displacement adjustment (SP-DA) method has been developed based on the well-known conventional displacement adjustment (DA) method to resolve the issue. A finite element method (FEM) model of stamping owning geometry complexity was established, and ST14F, BH300 and DP500, representing low, medium and high-strength steels, respectively, were selected for the study. Springback analyses were conducted, and the springback paths were acquired in the FEM simulation, based on which the spatial position of a node on the mesh of the compensation model was obtained using the SP-DA method. Its effectiveness was first verified numerically, and then, experiments were conducted to validate that the new SP-DA method could significantly increase the accuracy of springback compensation. Stamping of high-strength steels can benefit most from the proposed SP-DA method.

Gooch, LJ, Masia, MJ, Stewart, MG & Hossain, MA 2025, 'Experimental Testing of Unreinforced Masonry Shear Walls and Comparison with Nominal Capacity Predictions', Journal of Structural Engineering, vol. 151, no. 3.
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Goodarzimehr, V, Fazli, M, Fanaie, N & Gandomi, AH 2025, 'Enhanced Special Relativity Search Algorithm-Based Lorentz Force for Structural Optimization', International Journal of Computational Methods, vol. 22, no. 04.
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Optimization of real-world problems is an important challenge that researchers face due to the discrete nature of the search space, multiple local optimum, the large dimension of the problem, and computation costs. The Special Relativity Search (SRS) algorithm is one of the newest metaheuristic methods that has recently been developed. In this work, the Enhanced SRS (ESRS) algorithm has been developed based on the Lorentz Force coefficient to solve this class of problems. The SRS is a single-objective algorithm based on swarm intelligence inspired by the physics of special relativity. The cause of movement between particles in magnetic space is the Lorentz Force. Due to the type of particle charge, this force repels or attracts particles. SRS performance is sensitive to the Lorentz Force. Developing an empirical equation can enhance the performance of the algorithm. An empirical equation for simulating the Lorentz Force Coefficient is proposed as LC. LC decreases proportionally to the number of different iterations. This equation has a constant coefficient to control the movement step. This constant is more accurate for small values than for large values. Several structural problems with discrete and continuous variables have been investigated to evaluate the performance of the proposed algorithm. The objective function is the weight of structural elements that are under loading and must be reduced to the minimum value while observing the constraints of the problem. ESRS results have been compared with the original SRS and several state-of-the-art algorithms. The results show that ESRS has obtained the best optimal weight with the least computational effort and with high accuracy.

Grigorev, A, Saleh, K, Ou, Y & Mihăiţă, A-S 2025, 'Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification', International Journal of Intelligent Transportation Systems Research, vol. 23, no. 1, pp. 259-280.
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Grigorev, A, Shafiei, S, Grzybowska, H & Mihăiţă, A-S 2025, 'Predicting the Duration of Traffic Incidents for Sydney Greater Metropolitan Area using Machine Learning Methods', International Journal of Intelligent Transportation Systems Research, vol. 23, no. 1, pp. 104-125.
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Gu, Z, Jia, W, Piccardi, M & Yu, P 2025, 'Empowering large language models for automated clinical assessment with generation-augmented retrieval and hierarchical chain-of-thought', Artificial Intelligence in Medicine, vol. 162, pp. 103078-103078.
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Guan, L, Abbasi, A, Ryan, MJ & Merigó, JM 2025, 'A dynamic risk interdependency network-based model for project risk assessment and treatment throughout a project life cycle', Computers & Industrial Engineering, vol. 201, pp. 110921-110921.
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Guan, L, Laporte, G, Merigó, JM, Nickel, S, Rahimi, I & Saldanha-da-Gama, F 2025, '50 years of Computers & Operations Research: A bibliometric analysis', Computers & Operations Research, vol. 175, pp. 106910-106910.
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Guan, L, Merigó, JM & Beydoun, G 2025, '40 years of Decision Support Systems: A bibliometric analysis', Decision Support Systems, vol. 194, pp. 114469-114469.
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Guan, W, Teng, J, Shan, F, Liu, J & Wu, H 2025, 'A novel transformer-based approach for predicting frost heave of high-speed railway in cold regions', Journal of Rock Mechanics and Geotechnical Engineering.
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Guillen-Pujadas, M, Alaminos, D, Vizuete-Luciano, E & Merigo, JM 2025, 'Half a Century of The Journal of Portfolio Management: A Bibliometric Overview', JOURNAL OF PORTFOLIO MANAGEMENT, vol. 51, no. 6, pp. 175-215.

Gul, OM, Hamidoglu, A, Khan, MK & Yu, S 2025, 'Novel Game Theoretical Approach in Blockchain Aided Communication at Internet of Drones', IEEE Consumer Electronics Magazine, pp. 1-11.
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Guo, C, Xu, Y, Ni, C, Pan, X, Tijing, LD, Shon, HK, Deng, N & Huang, X 2025, 'Tailoring pore size to enhance dissolution of layered double oxides for efficient nitrogen and phosphorus recovery via crystallization of struvite from wastewater', Journal of Colloid and Interface Science, vol. 692, pp. 137546-137546.
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Guo, K, Guo, Y, Fang, S, Wu, W & Gao, X 2025, 'Design Optimization and Performance Evaluation of a Novel Linear-Rotary Motor with Moving Coils', IEEE Transactions on Energy Conversion, pp. 1-16.
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Guo, W, Che, H, Leung, M-F, Jin, L & Wen, S 2025, 'Robust Mixed-order Graph Learning for incomplete multi-view clustering', Information Fusion, vol. 115, pp. 102776-102776.
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Guo, W, Liu, D, Xie, W, Li, Y, Ning, X, Meng, Z, Zeng, S, Lei, J, Fang, Z & Wang, Y 2025, 'ShiftQuant: Towards Accurate and Efficient Sub-8-Bit Integer Training', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Guo, X, Guo, Y, Luo, Y & Yang, Y 2025, 'Full-Space Transmission–Reflection-Integrated Metasurface for Multibeam Generation on Orthogonal Planes', IEEE Transactions on Microwave Theory and Techniques, pp. 1-11.
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Guo, X-Y, Fang, S-E, Zhu, X & Li, J 2025, 'A semi-Markov process based digital twin for safety evaluation of cable-stayed bridges with cable corrosion', Advanced Engineering Informatics, vol. 65, pp. 103270-103270.
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The cables of a cable-stayed bridge are susceptible to structural degradation due to environmental corrosion and fatigue, which directly affects the safety and operational performance of the bridge. As the process of the degradation in practice is very slow, it is difficult to be monitored during the bridge service life. Hence, this study aims to develop a novel semi-Markov process based digital twin (DT) framework for safety evaluation of cable-stayed bridges considering cable corrosion. The framework encompasses a physical twin layer, a DT layer and the information interaction medium. The physical twin layer mainly comprises the bridge physical entity and its associated monitoring system that provides a variety of perceptual data for DT modeling. In the DT layer, the DT model acts as a virtual counterpart of the physical bridge for mirroring and forecasting the bridge's mechanical behaviors. The information interaction medium plays a crucial role in the bidirectional information communication between the physical and digital twin layers. Two types of information interaction media have been utilized including a cable force influence matrix and a semi-Markov process. The former enables updating the DT model to precisely match the data measured from the physical bridge. Meanwhile, the semi-Markov process depicts the probability of the bridge's condition considering the cable corrosion during the different service periods. The proposed procedure can predict the bridge state and evaluate the safety by comparing the predicted state with the monitored values. The proposed framework has been successfully validated on a real-world cable-stayed bridge. The results showed the proposed DT framework was reliable and effective for evaluating the bridge condition.

Guo, Y, Canning, J, Chaczko, Z & Peng, G-D 2025, 'Magneto-optic fiber-coupled tuneable optical attenuation', Optics Letters, vol. 50, no. 2, pp. 277-277.
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Tuneable, variable, optical attenuation through an optical circulator with a broad, linear attenuation range of Δα ∼ (30–40) dB is demonstrated using non-reciprocal Faraday rotation in a double-pass configuration with a combination of permanent magnets and an electromagnet. A fiber-coupled magneto-optical variable optical attenuator (MVOA) operates over the near IR with an attenuation tuning range of Δα > 30 dB, a resolution of Δα ∼ 0.02 dB, a response time of t < 2 ms, and a temperature dependence over T = 25–70°C of Δα / ΔT = −8 × 10−4 dB/°C. At λ = 1550 nm, the calculated Faraday material effective Verdet constant is (1/A)Veff = 109 × 103 dB/(T.m).

Guo, Y, Qu, F, Tiwari, R, Yoo, D-Y, Wang, K, Wang, Y & Li, W 2025, 'Development of self-sensing asphalt cementitious composites using conductive carbon fibre and recycled copper tailing', Construction and Building Materials, vol. 474, pp. 140965-140965.
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Guo, Y, Xu, X, Jin, P, Zhou, T, Lei, G & Zhu, J 2025, 'Design and Analysis of a Novel Simultaneous Wireless Power and Data Transfer System with a High-Frequency Tank-Type Ferrite Core Loosely-Coupled Transformer', IEEE Transactions on Energy Conversion, pp. 1-12.
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Habiba, U, Ahmed Siddique, T, Fattah, IMR, Mohammed, S, Attenborough, E, Lai, Q & Wang, Z 2025, 'Porous alumina-supported lithium aluminum titanium phosphate membrane for lithium extraction using the electrodialysis process', Separation and Purification Technology, vol. 354, pp. 128657-128657.
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Haeri, H, Fu, J, Sarfarazi, V, Abharian, S, rasekh, H, Rezaei, M & Khandelwal, M 2025, 'Surgical face masks as reinforcement to improve the tensile mode fracture toughness of reinforced concrete under three-point bending tests', Engineering Fracture Mechanics, vol. 314, pp. 110741-110741.
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Hamdi, FM, Altaee, A, Aedan, Y, Zhou, J, AL-Ejji, M, Hawari, AH, Zaidi, SJ, Mohsen, M & Kardani, R 2025, 'Decontamination of Heavy Metals from Soil by Electrokinetic Combined with Reactive Filter Media from Industrial Wastes', Water, Air, & Soil Pollution, vol. 236, no. 9.
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Abstract The electrokinetic (EK) is an in-situ method for soil remediation, aiming to reduce extensive excavation and mitigate risks associated with exposure to hazardous substances. However, heavy metal precipitation near the cathode under alkaline pH remains challenging. This study employed recyclable waste materials of sawdust crosslinked by glutaraldehyde with iron slag as a reactive filter media (RFM) for single and mixed heavy metals from kaolinite and natural soils. Experiments were conducted over two and three weeks, employing 20 to 30 mA electric currents. Incorporating iron slag RFM into the EK process resulted in a notable enhancement in copper removal from 3.21% to 23.76%. Mixing sawdust with iron slag in the RFM further improved the efficiency of copper extraction from the soil, reaching 71.80%. Also, copper removal improved as the electric current increased from 20 to 25 mA, reaching 88.10% in a three-week experiment. A slight improvement in copper removal was recorded due to the electric current increasing to 30 mA, indicating that copper removal is not linear with the applied electric current. However, sawdust treatment with HCl lowered the RFM pH, resulting in a total copper removal of 90.30% at electrical currents of 25 mA. Crosslinking sawdust with iron slag by 2% glutaraldehyde achieved a remarkable 97.92% copper removal at 0.18 kWh/kg specific energy from kaolinite soil, while in natural soil, the removal rates for copper, nickel, and zinc reached 26.72%, 54.36%, and 56.44%, respectively after 5 weeks. The discrepancy in heavy metals removal between kaolinite and natural soils reflects the complicated environmental conditions in natural soils on the efficiency of the electrokinetic process when laboratory tests are applied to the field.

Hamdi, FM, Altaee, A, Aedan, Y, Zhou, J, Zaidi, SJ, Alsaka, L, Almalki, R, Al-Askar, A & Samal, AK 2025, 'Black tea waste/iron slag reactive filter media-electrokinetic for mixed heavy metals treatment from contaminated site', Journal of Contaminant Hydrology, vol. 270, pp. 104517-104517.
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Hamdi, FM, Ganbat, N, Altaee, A, Samal, AK, Ibrar, I, Zhou, JL & Sharif, AO 2025, 'Hybrid and enhanced electrokinetic system for soil remediation from heavy metals and organic matter', Journal of Environmental Sciences, vol. 147, pp. 424-450.
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Han, M, Zhu, T, Zhang, L, Huo, H & Zhou, W 2025, 'Vertical Federated Unlearning via Backdoor Certification', IEEE Transactions on Services Computing, vol. 18, no. 2, pp. 1110-1123.
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Hannan, MA, Nair, MS, Ahmed, PK, Vaithilingam, S, Wali, SB, Reza, MS, Abu, SM, Ker, PJ, Begum, RA, Ong, HC, Ng, DKS & Jang, G 2025, 'Return on values of hydrogen energy transitions: A perspective on the conceptual framework', Technology in Society, vol. 81, pp. 102821-102821.
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Hasan, MM, Haque, R, Jahirul, MI, Rasul, MG, Fattah, IMR, Hassan, NMS & Mofijur, M 2025, 'Advancing energy storage: The future trajectory of lithium-ion battery technologies', Journal of Energy Storage, vol. 120, pp. 116511-116511.
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Hashmi, A, Sidhu, S & Hutvagner, G 2025, 'Prognostic relevance of AGO2 expression in adrenocortical carcinoma (ACC): associations with clinicopathological features', Pathology, vol. 57, pp. S36-S36.
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Hassan, MA, Jamshidi, MB, Manh, BD, Chu, NH, Nguyen, C-H, Hieu, NQ, Nguyen, CT, Hoang, DT, Nguyen, DN, Van Huynh, N, Alsheikh, MA & Dutkiewicz, E 2025, 'Enabling technologies for Web 3.0: A comprehensive survey', Computer Networks, vol. 264, pp. 111242-111242.
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Hassani, S, Mustapha, S, Li, J, Mousavi, M & Dackermann, U 2025, 'Next-generation coupled structure-human sensing technology: Enhanced pedestrian-bridge interaction analysis using data fusion and machine learning', Information Fusion, vol. 118, pp. 102983-102983.
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Hayatdavoodi, A & Li, Y 2025, 'An analytical method to determine the ultimate shear strength of continuous steel plate girders', Asian Journal of Civil Engineering, vol. 26, no. 2, pp. 565-575.
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He, B, Li, J, Armaghani, DJ, Hashim, H, He, X, Pradhan, B & Sheng, D 2025, 'The deep continual learning framework for prediction of blast-induced overbreak in tunnel construction', Expert Systems with Applications, vol. 264, pp. 125909-125909.
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He, H, Zhang, Q, Yi, K, Shi, K, Niu, Z & Cao, L 2025, 'Distributional Drift Adaptation With Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 4, pp. 7287-7301.
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He, J, Zhu, Y, Hua, B, Xu, Z, Zhang, Y, Chu, L, Shi, Q, Braun, R & Shi, J 2025, '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, vol. 7, no. 1, pp. 71-81.
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He, X, Cai, G & Sheng, D 2025, 'Indirect models for SWCC parameters: reducing prediction uncertainty with machine learning', Computers and Geotechnics, vol. 177, pp. 106823-106823.
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He, Y, Wang, S, Wei, G, Ding, C & Jay Guo, Y 2025, 'A New Buffering Scheme for Shared-Aperture Dual-Band Base Station Antenna Array Utilizing Tight-Coupling Concept', IEEE Transactions on Antennas and Propagation, vol. 73, no. 6, pp. 3492-3502.
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He, Y, Yu, G, Wang, X, Wang, Q, Niu, Z, Ni, W & Liu, RP 2025, 'Accountability and Reliability in 6G O-RAN: Who is Responsible When it Fails?', IEEE Wireless Communications, vol. 32, no. 2, pp. 52-59.
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He, Z, Xu, Y, Wang, B, Tian, G & Zhang, H 2025, 'Fabrication and mechanical properties of the NiAl sheet with a 3-dimensional network structure prepared by Ni nets and Al foils', Journal of Materials Processing Technology, vol. 336, pp. 118680-118680.
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Hedman, J, Gleasure, R & Bandara, M 2025, 'Preface', Lecture Notes in Business Information Processing, vol. 541 LNBIP, pp. v-viii.

Hettiyahandi, S, Indraratna, B, Ngo, T, Qi, Y & Arachchige, C 2025, 'Enhancing Rail Track Performance Using Recycled Rubber Energy-Absorbing Grids: Laboratory and Field Evidence', Journal of Geotechnical and Geoenvironmental Engineering, vol. 151, no. 7.
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Hiep Dinh, T, Kumar Singh, A, Manh Doan, Q, Linh Trung, N, Nguyen, DN & Lin, C-T 2025, 'An EEG signal smoothing algorithm using upscale and downscale representation^*', Journal of Neural Engineering, vol. 22, no. 3, pp. 036012-036012.
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Abstract Objective. Effective smoothing of electroencephalogram (EEG) signals while maintaining the original signal’s features is important in EEG signal analysis and brain–computer interface. This paper proposes a novel EEG signal-smoothing algorithm and its potential application in cognitive conflict (CC) processing. Approach. Instead of being processed in the time domain, the input signal is visualized in increasing line width, the representation frame of which is converted into a binary image. An effective thinning algorithm is employed to obtain a unit-width skeleton as the smoothed signal. Main results. Experimental results on data fitting have verified the effectiveness of the proposed approach on different levels of signal-to-noise (SNR) ratio, especially on high noise levels (SNR ⩽ 5 dB), where our fitting error is only 86.4%–90.4% compared to that of its best counterpart. The potential application of the proposed algorithm in EEG-based CC processing is comprehensively evaluated in a classification and a visual inspection task. The employment of the proposed approach in pre-processing the input data has significantly boosted the F 1 score of state-of-the-art models by more than 1%. The robustness of our algorithm is also evaluated via a visual inspection task, where specific CC peaks, i.e. the prediction error negativity and error-related positive potential (Pe), can be easily observed at ...

Hill, M, Stapleton, S, Nguyen, PT, Sais, D, Deutsch, F, Gay, VC, Marsh, DJ & Tran, N 2025, 'The potential regulation of the miR-17–92a cluster by miR-21', The International Journal of Biochemistry & Cell Biology, vol. 178, pp. 106705-106705.
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Hjorth, M, Egan, CL, Telles, GD, Pal, M, Gallego-Ortega, D, Fuller, OK, McLennan, ED, Gillis, RD, Oh, TG, Muscat, GEO, Tegegne, S, Mah, MSM, Skhinas, J, Estevez, E, Adams, TE, McKay, MJ, Molloy, M, Watt, KI, Qian, H, Gregorevic, P, Cox, TR, Hojman, P, Midtgaard, J, Christensen, JF, Friedrichsen, M, Iozzo, RV, Sloan, EK, Drew, BG, Wojtaszewski, JFP, Whitham, M & Febbraio, MA 2025, 'Decorin, an exercise-induced secretory protein, is associated with improved prognosis in breast cancer patients but does not mediate anti-tumorigenic tissue crosstalk in mice', Journal of Sport and Health Science, vol. 14, pp. 100991-100991.
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Hoang, LM, Nguyen, DN, Zhang, JA & Hoang, DT 2025, 'Adaptive Nullification of Multiple Correlated Jammers Using Deep Reinforcement Learning', IEEE Transactions on Vehicular Technology, pp. 1-14.
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Hopwood, N, Palmer, T-A, Koh, GA, Lai, MY, Dong, Y, Loch, S & Yu, K 2025, 'Understanding student emotions when completing assessment: technological, teacher and student perspectives', International Journal of Research & Method in Education, vol. 48, no. 2, pp. 194-209.
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Hosseinzadeh, A, Altaee, A, Ibrar, I & Zhou, JL 2025, 'Modeling and optimization of reverse salt diffusion and water flux in forward osmosis by response surface methodology and artificial neural network', Chemical Engineering and Processing - Process Intensification, vol. 208, pp. 110140-110140.
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Hu, Y, Andrew Zhang, J, Wu, K, Deng, W & Jay Guo, Y 2025, 'Anchor Points Assisted Uplink Sensing in Perceptive Mobile Networks', IEEE Transactions on Communications, vol. 73, no. 2, pp. 904-920.
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Hu, Z, Chen, Q, Xu, C, Nimbalkar, S & Huang, N 2025, 'Effectiveness and Optimization Analysis of Vibration Isolation Performance of Circular and Rectangular Hollow Pipes: Numerical Modeling', International Journal of Geomechanics, vol. 25, no. 4.
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Huang, H, Sun, Y, Du, Q, Gao, F, Song, Z, Wang, Z, Chang, S, Zhang, X, Guo, W & Ngo, HH 2025, 'Impact of in–situ bioelectric field on biogas production, membrane fouling and microbial community in an anaerobic membrane bioreactor under sulfadiazine stress', Chemical Engineering Journal, vol. 506, pp. 160225-160225.
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Huang, J, Li, J, Shao, R & Wu, C 2025, 'Effect of high temperature and cooling method on compression and fracture properties of geopolymer-based ultra-high performance concrete', Journal of Building Engineering, vol. 105, pp. 112433-112433.
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Huang, L, Ge, C, Mao, X & Yu, S 2025, 'DARB: Decentralized, Accountable and Redactable Blockchain for Data Management', IEEE Transactions on Network and Service Management, vol. 22, no. 2, pp. 1608-1617.
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Huang, S, Zheng, J, Qin, P, Zhan, Q & Chen, X 2025, 'Corrigendum to “Improved planar near-field measurement based on data assimilation” [Measurement 227 (2024) 114265]', Measurement, vol. 242, pp. 116168-116168.
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Huang, X, Li, J, Yang, H, Zhou, T, Luo, Y, Yu, S, Liu, J, Shen, PK, Chen, L & Tian, ZQ 2025, 'Rich {1 0 0} faceted PtIrW nanocubes with high-filling bonding orbitals of NH2 dimerization for enhancing electrochemical ammonia oxidation', Journal of Energy Chemistry, vol. 103, pp. 361-370.
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Huang, X, Saha, G, Paul, AR, Tahan, A & Saha, SC 2025, 'A computational fluid dynamics analysis of BiPAP pressure settings on airway biomechanics using a CT-based respiratory tract model', Respiratory Physiology & Neurobiology, vol. 333, pp. 104397-104397.
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Huang, X, Yin, Y, Saha, G, Francis, I & Saha, SC 2025, 'A Comprehensive Numerical Study on the Transport and Deposition of Nasal Sprayed Pharmaceutical Aerosols in a Nasal‐To‐Lung Respiratory Tract Model', Particle & Particle Systems Characterization, vol. 42, no. 2.
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AbstractUtilizing a computed tomography (CT)‐based realistic nasal‐to‐lung respiratory tract model allows for a comprehensive investigation of the transport and deposition of nasal sprayed aerosols. This study has three main objectives: first, to determine the optimal mesh that achieves the quickest convergence for computational fluid‐particle dynamics (CFPD) simulations of a nasal‐to‐lung nasal respiratory tract by assessing the performance of different element types, sizes, and prism boundary layers; second, to design and validate a numerical method to compare grid data with different mesh structures and densities for simulation result validation; and finally, to observe and analyze fluid‐particle dynamics in the respiratory tract to aid in the development of nasal sprayed medications. This study involves reverse‐engineering a realistic and anatomically accurate respiratory tract model from CT scans. Results reveal that the optimal numerical approach for minimum calculation time is the polyhedral hybrid mesh with four boundary prism layers and the SIMPLE pressure‐velocity coupling scheme. Furthermore, observations of particle dynamics reveal that the vocal cords' location contains a concentration site of deposited small aerosols due to the turbulent airflow in the region. The optimal diameters of nasal sprayed aerosols to target each region are concluded in the end.

Huang, Y, Gao, D, Ying, S & Li, S 2025, 'DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-1.
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Huang, Y, Huang, Y, Zhang, Z, Wu, Q, Zhong, Y & Wang, L 2025, 'CSFRNet: Integrating Clothing Status Awareness for Long-Term Person Re-identification', International Journal of Computer Vision, vol. 133, no. 6, pp. 3180-3202.
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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 2025, 'Suitability of using carbon dioxide as a tracer gas for studying vehicle emission dispersion in a real street canyon', Journal of Environmental Sciences, vol. 155, pp. 832-845.
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Huang, Y, Yang, G, Yuan, D & Yu, S 2025, 'DBSSL: A Scheme to Detect Backdoor Attacks in Self-Supervised Learning Models', IEEE Transactions on Dependable and Secure Computing, pp. 1-12.
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Hussain, M, Keshavarz, R & Shariati, N 2025, 'Advancements in Agricultural Microwave Remote Sensing: An Overview From Indoor to Space-Borne Platforms', IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-22.
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Hussain, T, Li, Y, Ren, M & Li, J 2025, 'Pixel-level crack segmentation and quantification enabled by multi-modality cross-fusion of RGB and depth images', Construction and Building Materials, vol. 487, pp. 141961-141961.
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Hussain, W, Merigó, JM, Rahimi, I & Lev, B 2025, 'Half a century of Omega – The International Journal of Management Science: A bibliometric analysis', Omega, vol. 133, pp. 103226-103226.
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Huynh, N, De Dios, K, Tran, TS, Center, JR & Nguyen, TV 2025, 'Association between the Sp1-binding-site polymorphism in the collagen type I alpha 1 (COLIA1) gene and bone phenotypes: the Dubbo Osteoporosis Epidemiology Study', Journal of Bone and Mineral Metabolism, vol. 43, no. 2, pp. 114-122.
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Huynh, TQ, Nguyen, TT & Indraratna, B 2025, 'Evaluating cohesive models in discrete element simulation through drawdown test with new assessment perspectives', Powder Technology, vol. 452, pp. 120542-120542.
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Indraratna, B, Hunt, H, Malisetty, RS, Alagesan, S, Qi, Y & Rujikiatkamjorn, C 2025, 'Optimization of inputs for the application of ANN to rail track granular materials', Canadian Geotechnical Journal, vol. 62, pp. 1-22.
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Machine learning (ML) models such as artificial neural networks (ANNs) have gained increasing popularity in geotechnical engineering applications as an alternative to conventional empirical and computational models. At present, very few ML models exist for predicting the mechanical responses of track granular materials such as ballast and subballast which may even comprise of composite mixtures of blended granular materials. Moreover, the performance of any ML model depends not only on the quality and quantity of available data but also on the selection process for input parameters, which often lacks adequate justification in the past literature. In this context, the current study introduces ANN models for track granular materials based on published laboratory data with special emphasis on the selection of an optimal set of input parameters. Two applications of ANN are considered to (i) predict the peak friction angle ([Formula: see text]) of a variety of granular mixtures under static loading and (ii) predict ballast breakage under cyclic loading. The selection process involves prudent analysis of key influential parameters in a geotechnical perspective, while also ensuring that they are conveniently measurable. Performance evaluation of these models with various input combinations is carried out, while proposing optimal input parameters for both applications.

Indraratna, B, Mehmood, F, Ngo, T, Rujikiatkamjorn, C & Grant, J 2025, 'Performance of Tire Cell Foundation as a Subballast Capping Layer under Cyclic Train Loading', Journal of Geotechnical and Geoenvironmental Engineering, vol. 151, no. 1.
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Iqra, NA, Li, J, Wang, XH & Yang, G 2025, 'Wave prediction using Graph Neural Network at Darwin Harbour, Australia', Regional Studies in Marine Science, vol. 84, pp. 104088-104088.
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Ismail, M, Liu, J, Wang, N, Zhang, D, Qin, C, Shi, B & Zheng, M 2025, 'Advanced nanoparticle engineering for precision therapeutics of brain diseases', Biomaterials, vol. 318, pp. 123138-123138.
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Iyer, KS, Bao, L, Zhai, J, Jayachandran, A, Luwor, R, Li, JJ & Li, H 2025, 'Microgel-based bioink for extrusion-based 3D bioprinting and its applications in tissue engineering', Bioactive Materials, vol. 48, pp. 273-293.
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Jacob Kedziora, D, Musiał, A, Rudno-Rudziński, W & Gabrys, B 2025, 'Transfer learning and the early estimation of single-photon source quality using machine learning methods', Machine Learning: Science and Technology, vol. 6, no. 2, pp. 025014-025014.
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Abstract The use of single-photon sources (SPSs) is central to numerous systems and devices proposed amidst a modern surge in quantum technology. However, manufacturing schemes remain imperfect, and single-photon emission purity must often be experimentally verified via interferometry. Such a process is typically slow and costly, which has motivated growing research into whether SPS quality can be more rapidly inferred from incomplete emission statistics. Hence, this study is a sequel to previous work that demonstrated significant uncertainty in the standard method of quality estimation, i.e. the least-squares fitting of a physically motivated function, and asks: can machine learning (ML) do better? The study leverages eight datasets obtained from measurements involving an exemplary quantum emitter, i.e. a single InGaAs/GaAs epitaxial quantum dot; these eight contexts predominantly vary in the intensity of the exciting laser. Specifically, via a form of ‘transfer learning’, five ML models, three linear and two ensemble-based, are trained on data from seven of the contexts and tested on the eighth. Validation metrics quickly reveal that even a linear regressor can outperform standard fitting when it is tested on the same contexts it was trained on, but the success of transfer learning is less assured, even though statistical analysis, made possible by data augmentation, suggests its superiority as an early estimator. Accordingly, the study concludes by discussing future strategies for grappling with the problem of SPS context dissimilarity, e.g. feature engineering and model adaptation.

Jacob, PE, Choudhary, N, Dikshit, A, Evans, JP, Pradhan, B & Huete, AR 2025, 'Flash drought prediction using deep learning', Environmental Research Letters, vol. 20, no. 7, pp. 074006-074006.
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Abstract Flash droughts are rapid, short-term drought events that develop within weeks, driven by factors such as low rainfall, high temperatures, and strong winds, which deplete soil moisture and stress vegetation. These events have profound agricultural, economic, and ecological impacts, yet the use of machine learning to predict flash droughts remains underexplored, hindered by challenges like imbalanced datasets and limited data. This study addresses these issues by applying Convolutional neural networks (CNNs) to predict flash droughts in Eastern Australia, a region prone to such events. We identified flash droughts from 2001 to 2022, training the model with data from 2001–2015, validating it on 2016–2017 data, and testing it on 2018–2022 data. The model’s performance was evaluated across drought duration, spatial distribution, and seasonal variability. Achieving a balanced accuracy of 80% and an Area under the curve of 93%, the CNN demonstrated strong predictive capability. However, it tended to overestimate the spatial extent of droughts, indicating areas for future improvement. These results highlight the potential of deep learning in flash drought prediction, offering valuable insights for early warning systems and drought management strategies.

Jadhav, P, Sairam, VA, Bhojane, N, Singh, A, Gite, S, Pradhan, B, Bachute, M & Alamri, A 2025, 'Multimodal Gas Detection Using E-Nose and Thermal Images: An Approach Utilizing SRGAN and Sparse Autoencoder', Computers, Materials & Continua, vol. 83, no. 2, pp. 3493-3517.
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Jafari, M, Tao, X, Barua, P, Tan, R-S & Acharya, UR 2025, 'Application of transfer learning for biomedical signals: A comprehensive review of the last decade (2014–2024)', Information Fusion, vol. 118, pp. 102982-102982.
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Jafari, P, Rasekh, E, Asheghi Mehmandari, T, Mohammadifar, M, Fahimifar, A & Jahed Armaghani, D 2025, 'Upper-Bound Solutions for Active Face Failure in Shallow Rectangular Tunnels in Anisotropic and Non-homogeneous Undrained Clays', Geotechnical and Geological Engineering, vol. 43, no. 3.
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Abstract As urbanization accelerates, the demand for efficient underground infrastructure has grown, with rectangular tunnels gaining prominence due to their enhanced space utilization and construction efficiency. However, ensuring the stability of shallow rectangular tunnel faces in undrained clays presents significant challenges due to complex soil behaviors, including anisotropy and non-homogeneity. This study addresses these challenges by developing a novel failure mechanism within the kinematic approach of limit analysis, integrating soil arching effects alongside anisotropic and non-homogeneous undrained shear strength. The mechanism's analytical solutions are rigorously validated against finite element simulations using PLAXIS 3D and existing models, demonstrating superior accuracy. Key findings show that the proposed model improves predictive performance for critical support pressure, with relative differences as low as 5% for wide rectangular tunnels compared to numerical simulations. Results reveal that limit support pressure decreases with increasing non-homogeneity ratios and rises with higher anisotropy factors. However, both effects diminish in wider tunnels, where increasing width in soils with high non-homogeneity and low anisotropy factors significantly enhances stability. Practical implications of this study are substantial, offering design formulas and dimensionless coefficients for estimating critical face pressures in shallow rectangular tunnels. These tools enable engineers to account for soil anisotropy and non-homogeneity, optimizing design and ensuring safety in urban environments. Furthermore, the proposed model’s applicability extends to circular tunnels, where it offers comparable accuracy. This study bridges a critical gap in understanding the stability of rectangular tunnels, providing a robust framework for tackling the challenges of modern urban construction.

Jafaryahya, J, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2025, 'Integrating Electrical Features for Simultaneous Prediction of Soil Moisture and Potassium Levels Based on Neural Network Prediction Model', IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-12.
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Jafaryahya, J, Keshavarz, R, Nikkhah, N, Nagatani, A & Shariati, N 2025, 'Soil Salinity Frequency-Dependent Prediction Model Using Permittivity Spectroscopy', IEEE Transactions on AgriFood Electronics, pp. 1-12.
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Jahed Armaghani, D, Hayati, M, Momeni, E, Dowlatshahi, MB & Asteris, PG 2025, 'Estimation of powder factor in mine blasting: feasibility of tree-based predictive models', Multiscale and Multidisciplinary Modeling, Experiments and Design, vol. 8, no. 2.
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Abstract Drilling and blasting is a process frequently used in rock-surface and deep excavation. For a proper drilling plan, accurate prediction of the amount of explosive material is essential to reduce the environmental effects associated with blasting operations. This study introduces a series of tree-based models, namely extreme gradient boosting machine (XGBoost), gradient boosting machine (GBM), adaptive boosting machine (AdaBoost), and random forest (RF), for predicting powder factor (PF) values obtained from blasting operations. The predictive models were constructed based on geomechanical characteristics at the blasting site, blasting pattern parameters, and rock material properties. These tree-based models were designed and tuned to minimize system error or maximize accuracy in predicting PF. Subsequently, the best model from each category was evaluated using various statistical metrics. It was found that the XGBoost model outperformed the other implemented techniques and exhibited outstanding potential in establishing the relationship between PF and input variables in the training set. Among the input parameters, hole diameter received the highest significance rating for predicting the system output, while the point load index had the least impact on the PF values.

Jamshidi, MB, Hoang, DT, Nguyen, DN, Niyato, D & Warkiani, ME 2025, 'Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning', Computers in Biology and Medicine, vol. 189, pp. 109970-109970.
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Javed, F, Tariq, MF, Ikhlaq, A, Munir, HMS & Altaee, A 2025, 'Remediation of textile wastewater by hybrid technique using ZIF-67 catalyzed ozonation coupled with electrocoagulation', Journal of Water Process Engineering, vol. 69, pp. 106604-106604.
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Jay Guo, Y, Guo, CA, Li, M & Latva-aho, M 2025, 'Antenna Technologies for 6G – Advances and Challenges', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Jia, C, Luo, M, Dang, Z, Dai, G, Chang, X & Wang, J 2025, 'PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Jia, H, Wei, H, Li, J, Cui, S, Xu, L & Zheng, S 2025, 'Blast resistance of steel jacket reinforced double-column bridge pier', Engineering Structures, vol. 329, pp. 119882-119882.
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Jia, T, Xia, J, Zhang, C, Sun, B, Yuan, K, Liu, T, Xu, X & Liu, J 2025, 'Comparing analgesic effects of temporal interference stimulation on ventral posterolateral thalamus and high-definition transcranial alternating current stimulation on sensorimotor cortex during sustained experimental pain', Brain Stimulation, vol. 18, no. 3, pp. 701-703.
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Jiang, B, Liu, J, Wang, Z, Zhang, C, Yang, J, Wang, Y, Sheng, W & Ding, W 2025, 'Semi-supervised multi-view feature selection with adaptive similarity fusion and learning', Pattern Recognition, vol. 159, pp. 111159-111159.
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Jiang, F, Han, X, Wen, S & Tian, T 2025, 'Spatiotemporal interactive learning dynamic adaptive graph convolutional network for traffic forecasting', Knowledge-Based Systems, vol. 311, pp. 113115-113115.
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Jiang, W, Wei, H, Xu, Z, Kang, J, Wang, S, Liu, D, Ren, Y, Ngo, HH, Guo, W & Ye, Y 2025, 'Lighting promotes sulfate removal and improves microbial community stability in upflow anaerobic sludge bed reactors under low ratio of chemical oxygen demand to sulfate', Bioresource Technology, vol. 428, pp. 132473-132473.
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Jiang, X, Ou, L, Chen, Y, Ao, N, Chang, Y-C, Do, T & Lin, C-T 2025, 'A Fuzzy Logic-Based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy', IEEE Transactions on Fuzzy Systems, vol. 33, no. 5, pp. 1664-1677.
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Jiang, Y, Xiang, S, Dai, Y & Cheng, D 2025, 'Generative Artificial Intelligence Model for Multi-Granularity Power Data Simulation', International Journal of Pattern Recognition and Artificial Intelligence, vol. 39, no. 02.
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Electric power resources are essential for the efficient and orderly development of society. Accurate power load forecasting is a key driver for the low-carbon upgrade of power systems. Traditional forecasting methods often struggle to capture long-term dependencies. Additionally, extracting complex nonlinear features from data remains a significant challenge, making it challenging to meet the accuracy demands of modern power systems. Besides, current deep learning-based forecasting methods cannot simulate multi-granularity power load data. To address these challenges, this paper presents a Generative Pre-trained Transformer model, GPT4PLTS, designed for power data simulation and fine-grained power load forecasting. The model leverages the Transformer architecture, incorporating the first six layers of the GPT decoder structure. It utilizes a multi-head attention mechanism to extract temporal features and includes a time alignment layer to maintain the sequence of time-series data, addressing both short-term and long-term dependencies. Extensive experiments are conducted on load observations from 2000 enterprises. The results demonstrate that GPT4PLTS achieves high accuracy in data simulation and forecasts across different time granularities, particularly excelling in short and medium-term predictions. Future research could focus on optimizing the model structure to enhance the model’s generalization ability.

Jiang, Y, Zhu, S, Shen, M, Wen, S & Mu, C 2025, 'Aperiodically Intermittent Control Approach to Finite-Time Synchronization of Delayed Inertial Memristive Neural Networks', IEEE Transactions on Artificial Intelligence, vol. 6, no. 4, pp. 1014-1023.
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Jin, P, Hu, Y, Liu, J, Guo, Y, Lei, G & Zhu, J 2025, 'A Flexible Compensation Control Strategy for High-Frequency Link Matrix Converters', IEEE Transactions on Industrial Electronics, pp. 1-9.
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Jin, P, Wang, H, Xu, H, Cao, C, Guo, Y, Lei, G & Zhu, J 2025, 'Stability Analysis for Three-Stage Serial PMSM Drive System Based on Bidirectional WPT', IEEE Transactions on Industrial Electronics, vol. 72, no. 5, pp. 4526-4534.
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Jin, X, Rao, P, Feng, W, Cui, J, Nimbalkar, S & Chen, Q 2025, 'Coupled Electric‐Thermal Damage Model for Lightning Strikes on Buried Pipeline', Contributions to Plasma Physics, vol. 65, no. 4.
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ABSTRACTA coupled electrothermal damage theory model for pipelines is proposed to assess the failure behavior of buried pipelines under lightning strikes. This article considers local thermal nonequilibrium (LTNE) conditions in the soil–water porous medium and the nonlinear characteristics of lightning functions. The calculation results show that the proposed theoretical model has better applicability and accuracy compared with previous models. Parametric analysis shows that under lightning conditions of Im = 20 kA and T1/T2 = 1.2/50 μs, the maximum local temperature of the soil around the pipeline can reach 2160 K, leading to pipeline breakdown. Metal pipelines are shown to be more effective in conducting charges, which alters the electric field distribution in the soil and impacts the formation of plasma channels. The half‐peak value of the lightning waveform has a significant impact on pipeline breakdown, and its increase will increase the risk of pipeline breakdown gradually. When considering LTNE conditions, the change in the pipeline surface temperature becomes more pronounced. Under 8/30 and 8/40 μs lightning waveforms, the pipeline surface temperature is approximately 150 and 550 K higher, respectively, compared with the thermal equilibrium conditions. The thermal conductivity and porosity of backfill soil can also affect the thermal damage of lightning‐struck pipelines. With clay filling, the highest pipeline surface temperature can reach 2590 K, while with fine sand and coarse sand, it is 1980 and 1510 K, respectively. The pipeline lightning disaster model proposed in this article has engineering significance for the investigation of pipeline lightning failure and disaster prevention mechanisms.

Jiu, J, Liu, H, Li, D, Li, X, Zhang, J, Yan, L, Fan, Z, Li, S, Du, G, Li, JJ, Wu, A, Liu, W, Du, Y, Zhao, B & Wang, B 2025, '3D Mechanical Response Stem Cell Complex Repairs Spinal Cord Injury by Promoting Neurogenesis and Regulating Tissue Homeostasis', Advanced Healthcare Materials, vol. 14, no. 7.
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AbstractSpinal cord injury (SCI) leads to acute tissue damage that disrupts the microenvironmental homeostasis of the spinal cord, inhibiting cell survival and function, and thereby undermining treatment efficacy. Traditional stem cell therapies have limited success in SCI, due to the difficulties in maintaining cell survival and inducing sustained differentiation into neural lineages. A new solution may arise from controlling the fate of stem cells by creating an appropriate mechanical microenvironment. In this study, mechanical response stem cell complex (MRSCC) is created as an innovative therapeutic strategy for SCI, utilizing 3D bioprinting technology and gelatin microcarriers (GM) loaded with mesenchymal stem cells (MSCs). GM creates an optimal microenvironment for MSCs growth and paracrine activity. Meanwhile, 3D bioprinting allows accurate control of spatial pore architecture and mechanical characteristics of the cell construct to encourage neuroregeneration. The mechanical microenvironment created by MRSCC is found to activate the Piezo1 channel and prevent excessive nuclear translocation of YAP, thereby increasing neural‐related gene expression in MSCs. Transplanting MRSCC in rats with spinal cord injuries boosts sensory and motor recovery, reduces inflammation, and stimulates the regeneration of neurons and glial cells. The MRSCC offers a new tissue engineering solution that can promote spinal cord repair.

Ju, M, He, C, Ding, C, Ren, W, Zhang, L & Ma, K-K 2025, 'All-Inclusive Image Enhancement for Degraded Images Exhibiting Low-Frequency Corruption', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 1, pp. 838-856.
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Kabir, MM, Im, KS, Tijing, L, Choden, Y, Phuntsho, S, Mamun, MFK, Sabur, GM, Nam, SY & Shon, HK 2025, 'Integrated membrane distillation-solid electrolyte-based alkaline water electrolysis for enhancing green hydrogen production', Desalination, vol. 601, pp. 118580-118580.
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Kabir, MM, Sabur, GM, Mamun, MFK, Arman, Tijing, L, Choden, Y, Phuntsho, S & Shon, HK 2025, 'Hydrogels in next-generation energy solutions', Desalination, vol. 603, pp. 118639-118639.
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Kadiri, M, Tanji, A, Fan, X, Liaw, PK, Mahlia, TMI & Hermawan, H 2025, 'Corrosion of TiHfZrNbx high-entropy alloys in a simulated condition of proton exchange membrane water electrolyser', Electrochimica Acta, vol. 521, pp. 145925-145925.
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Kamal, MS, Nimmy, SF & Dey, N 2025, 'Interpretable Code Summarization', IEEE Transactions on Reliability, vol. 74, no. 1, pp. 2280-2289.
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Kamat, P, Chandekar, H, Dlima, L, Gite, S, Pradhan, B & Alamri, A 2025, 'Comprehensive dataset on ripening stages of strawberries and avocados: From unripe to rotten', Data in Brief, vol. 60, pp. 111663-111663.
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Kamlade, PK, Panta, J, Mammone, M, Yang, RC, Mildren, RP, Wang, J, Ibrahim, M, Thomson, R & Zhang, YX 2025, 'Degradation behaviour and damage mechanisms of carbon fibre reinforced polymer composite laminates subjected to laser irradiation', Composites Part C: Open Access, vol. 17, pp. 100605-100605.
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Kanis, JA, Johansson, H, McCloskey, EV, Liu, E, Schini, M, Vandenput, L, Åkesson, KE, Anderson, FA, Azagra, R, Bager, CL, Beaudart, C, Bischoff-Ferrari, HA, Biver, E, Bruyère, O, Cauley, JA, Center, JR, Chapurlat, R, Christiansen, C, Cooper, C, Crandall, CJ, Cummings, SR, da Silva, JAP, Dawson-Hughes, B, Diez-Perez, A, Dufour, AB, Eisman, JA, Elders, PJM, Ferrari, S, Fujita, Y, Fujiwara, S, Glüer, C-C, Goldshtein, I, Goltzman, D, Gudnason, V, Hall, J, Hans, D, Hoff, M, Hollick, RJ, Huisman, M, Iki, M, Ish-Shalom, S, Jones, G, Karlsson, MK, Khosla, S, Kiel, DP, Koh, W-P, Koromani, F, Kotowicz, MA, Kröger, H, Kwok, T, Lamy, O, Langhammer, A, Larijani, B, Lippuner, K, McGuigan, FEA, Mellström, D, Merlijn, T, Nguyen, TV, Nordström, A, Nordström, P, O´Neill, TW, Obermayer-Pietsch, B, Ohlsson, C, Orwoll, ES, Pasco, JA, Rivadeneira, F, Schott, A-M, Shiroma, EJ, Siggeirsdottir, K, Simonsick, EM, Sornay-Rendu, E, Sund, R, Swart, K, Szulc, P, Tamaki, J, Torgerson, DJ, van Schoor, NM, van Staa, TP, Vila, J, Wright, NC, Yoshimura, N, Zillikens, MC, Zwart, M, Harvey, NC, Lorentzon, M & Leslie, WD 2025, 'Rheumatoid arthritis and subsequent fracture risk: an individual person meta-analysis to update FRAX', Osteoporosis International, vol. 36, no. 4, pp. 653-671.
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Karami, H, Thurn, B, de Boer, NK, Ramos, J, Covington, JA, Lozano, J, Liu, T, Zhang, W, Su, S & Ueland, M 2025, 'Application of gas sensor technology to locate victims in mass disasters – a review', Natural Hazards, vol. 121, no. 1, pp. 31-60.
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Abstract The occurrence of mass disasters are increasing as a result of changing climates and the growing threat of terrorist activities/conflicts. When these tragedies strike, it is critical to locate victims. While search and rescue dogs are trained to locate the living, cadaver detection dogs are trained to locate the deceased. These dogs rely on the volatile organic compounds (VOCs) emitted from the victims to do so. Knowing which dog unit to deploy can be challenging, and the victims’ makeup is likely to change following disasters in densely inhabited places, where commingling is likely to occur. The use of electronic nose technologies in forensic science is a recent breakthrough. Due to their ability to detect differing VOCs, this technology can be used to assist in the recovery of victims in disaster events. The most popular types of accessible gas sensor technologies are briefly introduced and compared in this article for their potential use to locate missing persons, both living and deceased. The current and future market needs are articulated, and the lack of enrichment of these needs is examined in relation to the capabilities of existing gas sensors. This will inform further research areas of preference to increase victim detection capabilities.

Kardani, R, Yadav, S, Altaee, A, Alsaka, L & Zhou, J 2025, 'Eco-friendly kappa-carrageenan-nano zerovalent iron hydrogel water and wastewater purification', Journal of Hazardous Materials, vol. 492, pp. 138123-138123.
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Karthikeyan, N, M, K, Prabagaran, S, Selvam, L, Venkatesh, R, Priya, CB, Al Obaid, S, Ali Alharbi, S & Kalam, MA 2025, 'Blending action of pyrolysis oil/diesel fuel featured with hydrogen on compression ignition engine performance and emission analysis', Petroleum Science and Technology, vol. 43, no. 13, pp. 1506-1521.
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Kathiresan, AC, Jeyaraj, PR, Albert, BK, Thanikanti, SB, Nallapaneni, MK & Alhelou, HH 2025, 'A versatile control of solar DVR for enhanced utilization and power quality improvement in a series‐connected wind‐solar farm', IET Renewable Power Generation, vol. 19, no. 1.
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AbstractThis paper presents a solar dynamic voltage restorer for hybrid series‐connected solar‐wind farms to mitigate the power quality problems. The operating region of the proposed hybrid system is derived and studied through graphical analysis. Upon examination of the system's operation under various grid conditions, the feasibility of solar PV power injected into the grid is verified. It is further proved that the series injection of voltage is also capable of mitigating the effects of voltage sag/swell, and unbalance, which have adverse effects on wind‐connected induction generators. Further, the effectiveness of Solar DVR to mitigate the fault ride‐through capability of the wind farm is analyzed. The non‐requirement for an energy storage device such as a battery is also validated. A control system for the series PV inverter is proposed and the computer simulations are performed to confirm the efficacy and ride‐through capability of the system.

Kazwini, T, Altaee, A, Ibrar, I, Alkadour, F, Hawari, AH, Zhou, J & Alsaka, L 2025, 'Sodium metasilicate sol-gel draw solution for seawater desalination and supplementing nutrients to soil', Desalination, vol. 600, pp. 118517-118517.
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Ke, Y, Xu, W, Ni, W, Yuan, X & Niyato, D 2025, 'Combating Beam Misalignment in mmWave UAV Networks: An Attitude Compensation-Based Method', IEEE Transactions on Vehicular Technology, vol. 74, no. 2, pp. 3521-3526.
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Khairnar, S, Thepade, SD, Kolekar, S, Gite, S, Pradhan, B, Alamri, A, Patil, B, Dahake, S, Gaikwad, R & Chaudhari, A 2025, 'Enhancing semantic segmentation for autonomous vehicle scene understanding in indian context using modified CANet model', MethodsX, vol. 14, pp. 103131-103131.
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Khlaifat, N, Altaee, A, Zhou, J & Huang, Y 2025, 'Evaluation of wind resource potential using statistical analysis of probability density functions in New South Wales, Australia', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 47, no. 1, pp. 194-211.
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Wind energy is a vital part of Australia's energy mix. The first step in a wind power project at a particular site is to assess the wind resource potential and feasibility for wind energy production. Research on wind potential and statistical analysis has been done throughout the world. Currently, recent potential wind studies are lacking, especially in New South Wales (NSW), Australia. This study highlighted the feasibility of wind potential at four sites in NSW, namely Ballina, Merriwa, Deniliquin, and the Bega region. The type of wind speed distribution function dramatically affects the output of the available wind energy and wind turbine performance at a particular site. Therefore, the accuracy of four probability density functions was evaluated, namely Rayleigh, Weibull, Gamma, and Lognormal distributions. The outcomes showed Weibull provided the most accurate distribution. The annual average scale and shape parameters of Weibull distribution varied between 2.935-5.042 m/s and 1.137-2.096, respectively. The maximum shape and scale factors were at Deniliquin, while the minimum shape and scale factors were at Bega area. Assessment of power density indicated that Deniliquin had a marginal wind speed resource, while Ballina, Bega, and Merriwa had poor wind resources.

Khorshidi, MS, Yazdanjue, N, Gharoun, H, Yazdani, D, Nikoo, MR, Chen, F & Gandomi, AH 2025, 'Semantic-Preserving Feature Partitioning for multi-view ensemble learning', Information Fusion, vol. 122, pp. 103152-103152.
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Kielly, M, Chacon, A, Caracciolo, A, Bolst, D, Rosenfeld, A, Carminati, M, Fiorini, C, Franklin, DR, Guatelli, S & Safavi-Naeini, M 2025, 'An exploratory study of shielding strategies for boron neutron capture discrimination in 10B Neutron Capture Enhanced Particle Therapy', Physica Medica, vol. 129, pp. 104866-104866.
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Kim, J, Xuan, J, Liang, C & Hussain, FK 2025, 'Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model'.
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Kirsch, C, Baki Kocaballi, A, Johnston, A, Naweed, A & Stevenson, I 2025, 'Co‐Designing the Sound of Safety: Embracing Complexity in the Acoustic Vehicle Alerting System Sound for Zero Emission Buses', Human Factors and Ergonomics in Manufacturing & Service Industries, vol. 35, no. 3.
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ABSTRACTAround the world, transport organizations are transitioning their bus fleets from internal combustion engines to electrified zero emission buses (ZEBs). The quiet nature of these buses raises safety concerns for vulnerable road users. To address these concerns, new standards mandate that electric vehicles, including ZEBs, be equipped with an acoustic vehicle alerting system (AVAS) emitting sound at low speeds, to make vehicles more detectable. However, developing an effective AVAS sound requires balancing safety and technical constraints with diverse stakeholder needs. Using codesign, this study conducted a series of risk‐focused subject matter expertize workshops (n = 15), and a large user‐experience focused participatory design workshop (n = 41) to inform the AVAS sound design. The latter included empathy and user journey mapping techniques, facilitating the collection of insights from various perspectives. Results revealed stakeholder preferences for an AVAS sound that was both alerting and positive, and embodied qualities like calmness, politeness, and vibrancy. The workshops allowed for refinement of sound design requirements, although challenges emerged in balancing conflicting preferences and managing technical limitations to create a sound that could be both perceptible and nonintrusive. This study provides a framework for the development of an AVAS sound that could capture a range of stakeholder needs and preferences and lays a foundation for AVAS sounds that enhance safety while being positively received. It highlights the importance of inclusive, iterative design in advancing public transport safety and sustainability, with outcomes supporting the future sound design, testing, and implementation.

Klettner, A, Cetindamar, D & Sainty, R 2025, 'Stakeholder Governance and Corporate Purpose in Certified B Corps: Minimizing Conflict and Fostering Collaboration', Business & Society.
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Although a stakeholder-inclusive approach to corporate governance is becoming accepted more widely, we have little idea on how it can be achieved in practice or understood theoretically. B Lab Australia and Aotearoa New Zealand requires certified B Corporations (B Corps) to include in their corporate constitution formal commitments to pursuing a pro-social purpose and taking their stakeholders’ interests into account. Through interviews with 20 B Corp leaders, we explore how this model of stakeholder governance is implemented. We find that an organization-specific corporate purpose helps leaders to attract, select, and retain supportive stakeholders and minimize stakeholder conflicts. We identify three decision-making processes (Establishing, Applying, Developing) and three signaling processes (Announcing, Demonstrating, Sharing) through which stakeholder governance is achieved. Our findings extend emerging theories of stakeholder governance by moving away from a focus on conflict resolution to demonstrate its potential to foster collaborative systemic change. B Corps display a helpful model for purpose-led stakeholder governance with the potential to be applied more widely.

Krishankumar, R, Mishra, AR, Rani, P, Ecer, F, Zavadskas, EK, Ravichandran, KS & Gandomi, AH 2025, 'Two-Stage EDAS Decision Approach with Probabilistic Hesitant Fuzzy Information', Informatica, pp. 65-97.
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This paper develops a two-stage decision approach with probabilistic hesitant fuzzy data. Research challenges in earlier models are: (i) the calculation of occurrence probability; (ii) imputation of missing elements; (iii) consideration of attitude and hesitation of experts during weight calculation; (iv) capturing of interdependencies among experts during aggregation; and (v) ranking of alternatives with resemblance to human cognition. Driven by these challenges, a new group decision-making model is proposed with integrate methods for data curation and decision-making. The usefulness and superiority of the model is realized via an illustrative example of a logistic service provider selection.

Kumar Dalai, S, Prasad Panda, K, Siwakoti, YP & Panda, G 2025, 'Three‐Phase Neutral‐Point‐Clamped Multilevel Inverter Interfacing Grid Interactive Renewable Energy System', International Journal of Circuit Theory and Applications, vol. 53, no. 6, pp. 3450-3461.
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ABSTRACTThe potentiality of DC microgrid to incorporate renewable energy sources and improve power distribution efficiency has garnered considerable interest, especially in light of the ever‐increasing demand for efficient and clean energy systems. Multilevel inverters (MLIs) play a crucial role in interfacing these grid‐tied microgrids with various loads and energy sources. To improve the efficiency and dependability of such systems, this paper presents a three‐phase MLI interfacing solar photovoltaic (PV)‐battery‐based grid‐tied system. The system comprises of MLI with features such as (a) neutral point clamped input, (b) reduced number of switches to produce 7‐level output per phase, (c) self‐balanced capacitors, and (d) inherent voltage boosting ability. A novel power management control algorithm is designed to enhance the dynamic response and efficiency of the developed system. The effectiveness of the proposed system and its workability is validated through extensive experiments demonstrating enhanced power quality and efficient energy utilization.

Lai, J, Lv, X, Rahman, MM, Oni, MAI, Dey, S & Yang, Y 2025, 'An Angle-Multiplexed Multifocal Method for D-Band 2-D Beam-Scanning Transmitarray Leveraging 3-D Transmission Line Component', IEEE Antennas and Wireless Propagation Letters, vol. 24, no. 3, pp. 731-735.
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Larpruenrudee, P, Bennett, NS, Fitch, R, Sauret, E, Gu, Y, Hossain, MJ & Islam, MS 2025, 'The enhancement of metal hydride hydrogen storage performance using novel triple-branched fin', Journal of Energy Storage, vol. 123, pp. 116659-116659.
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Le, DT, Sutjipto, S, Nguyen, DDK & Paul, G 2025, 'Design, Integration, and Field Testing of a Digital Twin-Based Teleoperated Rock Scaling Robot', IEEE Transactions on Field Robotics, vol. 2, pp. 188-207.
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This paper presents the design, integration, and field testing of a digital twin-based teleoperated rock scaling robot aimed at improving safety in mining operations. Traditional rock scaling, which involves the removal loose rocks to prevent rockfall, poses significant risks to mine site workers. The proposed solution is a teleoperated custom mobile manipulator capable of rope-based abseiling locomotion, equipped with an air chipper end-effector. Teleoperation is facilitated by live digital twins of the robot and environment, with a virtual reality (VR) interface that allows operators to perform rock scaling tasks within an immersive virtual reconstruction of the remote scene. The robot's hardware design and sensing capabilities are detailed, along with the system's teleoperation architecture. Key components include the integration of an optimised, hardware accelerated, image-based point cloud streaming implementation; a markerless depth-camera extrinsic calibration process suitable for field settings; and the system’s teleoperation interfaces featuring a cyber-physical VR interface with affordance feedback. Field tests at a sandstone quarry and an open-pit mine demonstrate significant improvements in operator safety, and highlight the system’s ability to withstand harsh mining environments while performing teleoperated rock scaling at its current scaled-down size and power. We collected and analysed user data from rope access technicians with no prior experience in robot teleoperation or VR. The results suggest the system's intuitiveness with learning effects over time. Lessons from these site trials, including hardware and software limitations, are discussed, providing directions for further robot design improvements and enhancements to the digital twin teleoperation architecture.

Le, NT, Keenan, M, Nguyen, A, Ghazvineh, S, Yu, Y, Li, J & Manalo, A 2025, 'A supervised machine learning approach for structural overload classification in railway bridges using weigh-in-motion data', Structures, vol. 71, pp. 108005-108005.
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Le, TP 2025, 'The limits of quantum correlations', Nature Physics, vol. 21, no. 4, pp. 501-502.
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Lee, S, Chen, T, Sze, NN, Mao, T, Ou, Y, Mihaita, A-S & Chen, F 2025, 'Analysing driver behaviour and crash frequency at railway level crossings using connected vehicle and GIS data', Travel Behaviour and Society, vol. 39, pp. 100957-100957.
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Legg, R, Prior, J, McIntyre, E, Liu, E, Tracy, M, Tan, L, Dawson, A, Richmond, J & Perry, C 2025, 'Health system adaptation to extreme weather events in Australia: A scoping review', The Journal of Climate Change and Health, vol. 22, pp. 100443-100443.
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Leong, D, Do, T & Lin, C-T 2025, 'The Distinction Between Object Recognition and Object Identification in Brain Connectivity for Brain–Computer Interface Applications', IEEE Transactions on Cognitive and Developmental Systems, vol. 17, no. 1, pp. 89-101.
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Lewis, E, Wetzler, Z, Li, G, Saw, W, Nathan, G, Kennedy, E, Stockenhuber, M, Oliver, T & Chinnici, A 2025, 'Thermal treatment of lizardite for mineral carbonation using high flux radiation', Fuel, vol. 386, pp. 134187-134187.
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Li, A, Yang, B, Huo, H, Hussain, FK & Xu, G 2025, 'Self-supervised dual graph learning for recommendation', Knowledge-Based Systems, vol. 310, pp. 112967-112967.
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Li, B, Guo, Z, Hu, C, Zhu, S & Wen, S 2025, 'Safe Formation Control of Uncertain Multiagent Systems From a Bayesian Perspective', IEEE Transactions on Automatic Control, vol. 70, no. 3, pp. 1929-1934.
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Li, B, Wang, S, Guo, Z, Zhu, S, Huang, J, Sun, J, Wen, G & Wen, S 2025, 'Safe Control Framework of Multi-Agent Systems From a Performance Enhancement Perspective', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 7622-7631.
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Li, C, Lai, JCS & Oberst, S 2025, 'Optimizing self-organized topology of recurrence-based complex networks', Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 35, no. 3.
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Networks and graphs have emerged as powerful tools to model and analyze nonlinear dynamical systems. By constructing an adjacency matrix from recurrence networks, it is possible to capture critical structural and geometric information about the underlying dynamics of a time series. However, randomization of data often raises concerns about the potential loss of deterministic relationships. Here, in using the spring-electrical-force model, we demonstrate that by optimizing the distances between randomized points through minimizing an entropy-related energy measure, the deterministic structure of the original system is not destroyed. This process allows us to approximate the time series shape and correct the phase, effectively reconstructing the initial invariant set and attracting dynamics of the system. Our approach highlights the importance of adjacency matrices derived from recurrence plots, which preserve crucial information about the nonlinear dynamics. By using recurrence plots and the entropy of diagonal line lengths and leveraging the Kullback–Leibler divergence as a relative entropic measure, we fine-tune the parameters and initial conditions for recurrence plots, ensuring an optimal representation of the system’s dynamics. Through the integration of network geometry and energy minimization, we show that data-driven graphs can self-organize to retain and regenerate the fundamental features of the time series, including its phase space structures. This study underscores the robustness of recurrence networks as a tool for analyzing nonlinear systems and demonstrates that randomization, when guided by informed optimization, does not erase deterministic relationships, opening new avenues for reconstructing dynamical systems from observational data.

Li, C, Li, J & Zhu, X 2025, 'Flexural Behavior of Steel–Concrete Composite Beams Utilizing Innovative Long-Nut Shear Connectors: A Numerical and Experimental Study', Journal of Structural Engineering, vol. 151, no. 4.
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Li, C, Lin, S, Tang, T, Wang, G, Li, M, Liang, X & Chang, X 2025, 'BossNAS Family: Block-Wisely Self-Supervised Neural Architecture Search', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 5, pp. 3500-3514.
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Li, G, Luan, TH, Zheng, J, Lai, C, Zhang, K & Yu, S 2025, 'SECR: A Secure and Efficient Charging Reservation Scheme Based on Digital Twin in Vehicular Network', IEEE Internet of Things Journal, vol. 12, no. 8, pp. 10434-10452.
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Li, G, Wang, J, Shi, K, Cai, X, Dong, S & Wen, S 2025, 'Synchronization of Markovian reaction–diffusion neural networks in complex noise settings: A Time-Space control approach', Communications in Nonlinear Science and Numerical Simulation, vol. 147, pp. 108800-108800.
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Li, H, Teng, F, Guo, Q, Zhang, JA, Huang, X & Cheng, Z 2025, 'Efficient Asynchronous Uplink Sensing in Perceptive Mobile Networks via Accurate Delay Estimation', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Li, H, Xiang, Y, Guo, Q, Liu, L, Huang, X, Cheng, Z & Pang, Y 2025, 'An Efficient Direct Downlink Sensing Method Using 5G NR SSB Signals in Perceptive Mobile Networks', IEEE Internet of Things Journal, vol. 12, no. 11, pp. 15360-15369.
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Li, H, Yu, B, Wei, Y, Wang, K, Xu, RYD & Wang, B 2025, 'KERMIT: Knowledge graph completion of enhanced relation modeling with inverse transformation', Knowledge-Based Systems, pp. 113500-113500.
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Li, J, Li, X, Li, Y, Liu, H & Wang, Q 2025, 'Artificial sweeteners in wastewater treatment plants: A systematic review of global occurrence, distribution, removal, and degradation pathways', Journal of Hazardous Materials, vol. 494, pp. 138644-138644.
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Li, K, Ding, J, Sun, X, Lei, G, Dianov, A, Demidova, G & Prakht, V 2025, 'Compensation Control of PMSMs Based on a High-order Sliding Mode Observer with Inertia Identification', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Li, K, Lu, J, Zuo, H & Zhang, G 2025, 'Fuzzy Domain Adaptation From Heterogeneous Source Teacher Models', IEEE Transactions on Fuzzy Systems, vol. 33, no. 6, pp. 1840-1852.
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Li, K, Zha, X, Sun, X & Lei, G 2025, 'Model Predictive Control With Series Structure for Five-Phase PMSHM Based on Discrete Space Virtual Voltage Modulation', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 1313-1323.
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Li, L & Kang, K 2025, 'How do family support factors influence college students’ online-startup thinking?', Journal of Entrepreneurship in Emerging Economies, vol. 17, no. 2, pp. 215-238.
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PurposeThe purpose of this study is to present the relationship between family support factors and Chinese college students’ online-startup thinking on live streaming platforms. Considering China's specific online entrepreneurial environment, this paper divides Chinese college students’ online-startup thinking according to the liberal–conservative thinking theory. This study classifies family support factors based on the tangible–intangible resource division theory. Different tangible and intangible factors have different impacts on their online-startup thinking.Design/methodology/approachThis study tests 588 samples based on the partial least squares path modelling and variance-based structural equation modelling. This study promotes importance-performance map analysis to explore additional findings of influencing factors and provide suitable suggestions for Chinese college students and related departments.FindingsTangible family support factors, such as labour resources support, and intangible family support factors, such as verbal encouragement, can positively enhance Chinese college students’ liberal thinking to online-startup and decrease their conservative thinking. Meanwhile, according to importance-performance map analysis results, verbal encouragement from the intangible unit instead of financial resource support from the tangible unit has a higher total effect and performance on Chinese college students’ liberal thinking and conservative thinking.Originality/valueThis study draws on psychology research based on Chinese college students’ unique entrepreneurial menta...

Li, L, Kang, K & Feng, Y 2025, 'Parents’ effects on Chinese students’ digital entrepreneurship motivation on live streaming platforms: regional perspective using multi-group analysis', Journal of Entrepreneurship in Emerging Economies, vol. 17, no. 3, pp. 650-671.
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PurposeThis paper aims to explore the effects of parents’ support factors on Chinese university students’ digital entrepreneurship motivation on live streaming platforms. Based on the Social support theory, this study divides influencing factors into emotional, instrumental, informational and appraisal aspects. Meanwhile, considering the impact of China’s regional differences, the paper refers to the Regional difference theory and performs a multi-group analysis to assess the differences based on Chinese university students’ regional backgrounds.Design/methodology/approachBy testing 556 samples based on the partial least squares path modelling and variance-based structural equation modelling, all support factors parents provide can stimulate Chinese university students’ digital entrepreneurship motivation.FindingsBased on the multi-group comparison, parents’ informational support exerts a more substantial influence on the digital entrepreneurship motivation for university students from central and east regions rather than those from the western region, and parents’ instrumental support exerts a lower influence on digital entrepreneurship motivation for east university students than for west university students.Originality/valueThis paper applies the Social support theory as a theoretical framework to divide the impact factors, and it uses the Regional difference theory as a guide for the multi-group analysis of correlations, which is significant for online entrepreneurial motivation research and a better understanding of student groups. In addition to testing the hypotheses, the...

Li, M, Beck, B, Rathnayake, T, Meng, L, Chen, Z, Cosgun, A, Chang, X & Kulić, D 2025, 'A benchmark for cycling close pass detection from video streams', Transportation Research Part C: Emerging Technologies, vol. 174, pp. 105112-105112.
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Li, M, Cai, J, Deng, L, Li, X, Iacopi, F & Yang, Y 2025, 'Additively manufactured conductive and dielectric 3D metasurfaces for independent manipulation of broadband orbital angular momentum', Materials & Design, vol. 249, pp. 113500-113500.
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Li, M, Guo, YJ & Chen, S-L 2025, 'Enable Continuous Beam Scanning for Joint Communication and Sensing Employing the Generalized Joined Coupler Matrix', IEEE Transactions on Vehicular Technology, pp. 1-13.
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Li, Q, Li, H, Li, X, Li, J, Liu, K, Wei, X, Peng, K & Wu, C 2025, 'Radial Pressure Characteristics on Borehole Walls at Decoupled Charge Blasting', Rock Mechanics and Rock Engineering, vol. 58, no. 2, pp. 2403-2418.
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Li, Q, Wang, Z, Xia, H, Li, G, Cao, Y, Yao, L & Xu, G 2025, 'HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, pp. 4371-4384.
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Li, S, Ji, J, Feng, K, Zhang, K, Ni, Q & Xu, Y 2025, 'Composite Neuro-Fuzzy System-Guided Cross-Modal Zero-Sample Diagnostic Framework Using Multisource Heterogeneous Noncontact Sensing Data', IEEE Transactions on Fuzzy Systems, vol. 33, no. 1, pp. 302-313.
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Li, S, Zhao, H, Huang, Y, Ding, H, Hua, S & Wang, Z 2025, 'Optimization of material-energy Co-management in a proton exchange membrane fuel cell', International Journal of Hydrogen Energy, vol. 101, pp. 391-402.
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Li, S, Zhou, X & Feng, Y 2025, 'Benchmarking Quantum Circuit Transformation With QKNOB Circuits', IEEE Transactions on Quantum Engineering, vol. 6, pp. 1-15.
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Li, T, Sun, X, Yang, Z & Lei, G 2025, 'Simplified Two-Step Model Predictive Control With Fast Voltage Vector Search', IEEE Transactions on Industrial Electronics, vol. 72, no. 4, pp. 3303-3312.
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Li, T, Zhao, S, Huang, Y, Lu, J & Burnett, IS 2025, 'A distributed adaptive wave field synthesis system', The Journal of the Acoustical Society of America, vol. 157, no. 3, pp. 2221-2235.
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The conventional wave field synthesis (WFS) theory is based on the free field assumption and the performance of systems based on it deteriorates significantly in reverberant environments. By introducing an error microphone array to monitor reproduction errors, the adaptive WFS (AWFS) system adjusts the loudspeaker signals to correct the sound field in reverberant environments. The AWFS system utilizes a centralized control strategy with a single processor, which imposes a high computational burden on the processor due to global error estimation, limiting the application scale. To address this issue, this paper proposes a distributed AWFS (DAWFS) system for an acoustic sensor and actuator network using a distributed signal processing strategy. Simulation results in a rectangular room demonstrate that the proposed DAWFS system can achieve comparable sound reproduction performance to the conventional AWFS system, both at the near-field error microphone array and in the target listening area. A global computational complexity analysis shows that the proposed DAWFS system exhibits significantly lower computational complexity than existing AWFS systems in various application scenarios, especially for massive channel systems. The results further demonstrate the potential applicability of the proposed DAWFS system in realistic reverberant environments.

Li, W, Xu, Z, Chu, L, Shi, Q, Braun, R & Shi, J 2025, 'FMCW Radar-Based Drowsiness Detection With a Convolutional Adaptive Pooling Attention Gated-Recurrent-Unit Network', IEEE Transactions on Radar Systems, vol. 3, pp. 71-87.
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Li, W, Zhao, B, Zhu, L, Wang, Y, Zhong, Q & Yu, S 2025, 'TCEC: Integrity Protection for Containers by Trusted Chip on IoT Edge Computing Nodes', IEEE Sensors Journal, vol. 25, no. 9, pp. 16269-16280.
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Li, X, Fang, Z, Zhang, Y, Ma, N, Bu, J, Han, B & Wang, H 2025, 'Characterizing Submanifold Region for Out-of-Distribution Detection', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 1, pp. 130-147.
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Li, X, Fu, Q & Wang, Q 2025, 'Wildfires jeopardize drinking water safety', Science, vol. 388, no. 6743, pp. 159-159.
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Li, X, Li, J, Liu, H, Mínguez-Alarcón, L, van Loosdrecht, MCM & Wang, Q 2025, 'Lifting of travel restrictions brings additional noise in COVID-19 surveillance through wastewater-based epidemiology in post-pandemic period', Water Research, vol. 274, pp. 123114-123114.
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Li, X, Zhang, Y, Long, G, Hu, Y, Lu, W, Chen, M, Zhang, C & Gong, Y 2025, 'Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Li, X, Zhang, Z, Liu, H, Wen, H & Wang, Q 2025, 'The fate of intracellular and extracellular antibiotic resistance genes during ultrafiltration-ultraviolet-chlorination in a full-scale wastewater treatment plant', Journal of Hazardous Materials, vol. 486, pp. 137088-137088.
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Li, Y, Feng, K, Noman, K, Ji, J & Li, Z 2025, 'Editorial: Application of digital twin technology in prognostic and health management of complex machineries', Measurement, vol. 239, pp. 115629-115629.
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Li, Y, Sun, R, Chen, C, Wang, X, Zhang, Y & Zhang, W 2025, 'Discovering Cliques in Attribute Graphs Based on Proportional Fairness', IEEE Transactions on Knowledge and Data Engineering, pp. 1-6.
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Li, Y, Wang, H, Duan, Y, Zhang, J & Li, X 2025, 'A closer look at the explainability of Contrastive language-image pre-training', Pattern Recognition, vol. 162, pp. 111409-111409.
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Li, Y, Wang, H, Xu, W, Xiao, T, Liu, H, Tu, M, Wang, Y, Yang, X, Zhang, R, Yu, S, Guo, S & Li, R 2025, 'Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Li, Y, Zhang, J, Liu, X, Liu, H, Wang, L, Cheng, D, Wang, Y, Guo, W & Ngo, HH 2025, 'Efficient antibiotics removal by pig manure-based magnetic biochar-driven catalytic degradation', Journal of Water Process Engineering, vol. 70, pp. 107013-107013.
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Li, Z, Liu, S, Yu, X, Bhavya, K, Cao, J, Diffenderfer, JD, Bremer, P-T & Pascucci, V 2025, '“Understanding Robustness Lottery”: A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches', IEEE Transactions on Visualization and Computer Graphics, pp. 1-16.
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Li, Z, Yang, C, Chen, Y, Wang, X, Chen, H, Xu, G, Yao, L & Sheng, M 2025, 'Graph and Sequential Neural Networks in Session-based Recommendation: A Survey', ACM Computing Surveys, vol. 57, no. 2, pp. 1-37.
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Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users’ short-term preferences and aims at providing a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.

Lian, M, Guo, Z, Wang, X, Wen, S & Huang, T 2025, 'Distributed Algorithms for Linear Equations Over General Directed Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 9127-9135.
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Lian, X, Zhang, Y, Wu, M & Guo, Y 2025, 'Do scientific knowledge flows inspire exploratory innovation? Evidence from US biomedical and life sciences firms', Technovation, vol. 140, pp. 103153-103153.
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Liao, D, Shu, X, Li, Z, Liu, Q, Yuan, D, Chang, X & He, Z 2025, 'Fine-grained Feature and Template Reconstruction for TIR Object Tracking', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Lim, JY, Goh, CYM, Kang, KW, Li, J & Wu, C 2025, 'Structural response of steel-concrete composite panels to near field simultaneous blast and fragmentation loading', International Journal of Impact Engineering, vol. 195, pp. 105142-105142.
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Lin, A, Xiang, Y, Li, J & Prasad, M 2025, 'Dynamic Appearance Particle Neural Radiance Field', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Lin, B, Nie, Y, Wei, Z, Chen, J, Ma, S, Han, J, Xu, H, Chang, X & Liang, X 2025, 'NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 7, pp. 5945-5957.
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Lin, K, Buys, N, Jiang, Y & Sun, J 2025, 'Urbanization and substance use in left-behind children and adolescents: a systematic review and meta-analysis', Current Opinion in Psychiatry, vol. 38, no. 1, pp. 48-58.
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Purpose of review Inequalities in job opportunities between urban and rural regions have driven many parents to move from rural to urban regions with aims to improve household income. Financial and political barriers prevent children from moving with their parents, meaning that their children become left-behind in rural regions. This study aims to meta-analyse the risk of drinking, smoking, illicit drug use and drunk experiences in left behind children (LBC) and non-LBC. Recent findings Previous studies have indicated the negative effects of prolonged parental separation on children and adolescent mental health. Parental separation and poor mental health have both been identified as proximal risk factors for substance use in adolescence. Summary The results indicated that LBC were at significantly greater risk of drinking, smoking, using illicit drugs, and having drunk experiences. Positive psychology interventions are required to help provide LBC with positive coping strategies against psychological distress. Policy changes to reduce inequalities in job opportunities between urban and rural regions are required to reduce the prevalence of LBC.

Lin, K, Chen, E, Taylor, I, Hunter, S & Sun, J 2025, 'National improvements in quality of prenatal healthcare across states and territories in India from 2006 to 2021: a systematic analysis', Journal of Public Health, vol. 33, no. 3, pp. 565-576.
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Lin, X, Ma, B, Wang, X, Yu, G, He, Y, Ni, W & Liu, RP 2025, 'CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories', IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 3, pp. 3223-3236.
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Lin, X, Nguyen, QD, Castel, A, Deng, Z, Li, W & Tam, VWY 2025, 'Self-healing of biochar-cement composites with crystalline admixture exposed to sulphate solution and simulated seawater', Journal of Building Engineering, vol. 99, pp. 111564-111564.
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Lin, X, Nguyen, QD, Castel, A, Li, P, Tam, VWY & Li, W 2025, 'Self-healing efficiency of sustainable biochar-cement composites incorporating crystalline admixtures', Construction and Building Materials, vol. 458, pp. 139542-139542.
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Lin, X, Nguyen, QD, Castel, A, Pang, Y, Deng, Z, Shi, T, Li, W & Tam, VWY 2025, 'Durability of biochar-cementitious composites incorporating crystalline admixture in chloride and sulphate environments', Construction and Building Materials, vol. 458, pp. 139554-139554.
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Lin, X, Zhang, Y, Li, J, Oliver, BG, Wang, B, Li, H, Yong, K-T & Li, JJ 2025, 'Biomimetic multizonal scaffolds for the reconstruction of zonal articular cartilage in chondral and osteochondral defects', Bioactive Materials, vol. 43, pp. 510-549.
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Liu Chung Ming, C, Patil, R, Refaat, A, Lal, S, Wang, X & Gentile, C 2025, 'Acetylcholine-loaded nanoparticles protect against doxorubicin-induced toxicity in in vitro cardiac spheroids', Biofabrication, vol. 17, no. 2, pp. 025023-025023.
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Abstract Doxorubicin (DOX) is widely used in chemotherapy, yet it significantly contributes to heart failure-associated death. Acetylcholine (ACh) is cardioprotective by enhancing heart rate variability and reducing mitochondrial dysfunction and inflammation. Nonetheless, the protective role of ACh in countering DOX-induced cardiotoxicity (DIC) remains underexplored as current approaches to increasing ACh levels are invasive and unsafe for patients. In this study, we explore the protective effects of ACh against DIC through three distinct ACh administration strategies: (i) freely-suspended 100 µM ACh; (ii) ACh-producing cholinergic neurons (CNs); or (iii) ACh-loaded nanoparticles (ACh-NPs). These are tested in in vitro cardiac spheroids (CSs), which have previously been shown to approximate the complex DIC. We assess ACh’s protective effects by measuring the toxicity ratio (cell death/viability), contractile activity, gene expression changes via qPCR and nitric oxide (NO) signaling. Our findings show that ACh effectively attenuates DOX-induced cell death and contractile dysfunction. ACh also counteracts the DOX-induced downregulation of genes controlling myocardial fibrosis, endothelial and cardiomyocyte dysfunction, and autonomic dysregulation. ACh cardioprotection against DOX is dependent on NO signaling in endothelial cells but not in cardiac myocytes or fibroblasts. Altogether, this study shows for the first time that elevating ACh levels showed a promising therapeutic approach for preventing DIC.

Liu, B, Liu, B, Zhu, T & Ding, M 2025, 'A Review of Deepfake and Its Detection: From Generative Adversarial Networks to Diffusion Models', International Journal of Intelligent Systems, vol. 2025, no. 1.
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Deepfake technology, leveraging advanced artificial intelligence (AI) algorithms, has emerged as a powerful tool for generating hyper‐realistic synthetic human faces, presenting both innovative opportunities and significant challenges. Meanwhile, the development of Deepfake detectors represents another branch of models striving to recognize AI‐generated fake faces and protect people from the misinformation of Deepfake. This ongoing cat‐and‐mouse game between generation and detection has spurred a dynamic evolution in the landscape of Deepfake. This survey comprehensively studies recent advancements in Deepfake generation and detection techniques, focusing particularly on the utilization of generative adversarial networks (GANs) and diffusion models (DMs). For both GAN‐based and DM‐based Deepfake generators, we categorize them based on whether they synthesize new content or manipulate existing content. Correspondingly, we examine various strategies employed to identify synthetic and manipulated Deepfake, respectively. Finally, we summarize our findings by discussing the unique capabilities and limitations of GANs and DM in the context of Deepfake. We also identify promising future directions for research, including the development of hybrid approaches that leverage the strengths of both GANs and DM, the exploration of novel detection strategies utilizing advanced AI techniques, and the ethical considerations surrounding the development of Deepfake. This survey paper serves as a valuable resource for researchers, practitioners, and policymakers seeking to understand the state‐of‐the‐art in Deepfake technology, its implications, and potential avenues for future research and development.

Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2025, 'Delay-Sensitive Goods Delivery and In-Situ Sensing Using A Multi-Task Drone', IEEE Transactions on Mobile Computing, pp. 1-14.
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Liu, C, Du, H, Lei, G, Wang, Y & Zhu, J 2025, 'Design and Analysis of Modular Permanent Magnet Claw Pole Machines With Hybrid Cores for Electric Vehicles', IEEE Transactions on Energy Conversion, vol. 40, no. 2, pp. 1047-1061.
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Liu, C, Du, H, Lei, G, Wang, Y & Zhu, J 2025, 'Multi-Physics Coupling Analysis of Permanent Magnet Claw Pole Machine with Hybrid Cores by Inverter Power Supply', Diangong Jishu Xuebao Transactions of China Electrotechnical Society, vol. 40, no. 6, pp. 1758-1770.
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The stator core of the permanent magnet claw pole machine (PMCPM) is typically made of soft magnetic composite material (SMC). SMC has defects such as high eddy current loss at low frequency, low magnetic permeability, and poor mechanical strength. In addition, because of the high cost of manufacturing large-sized SMC cores, previous designs for PMCPM were often based on low-power motors. If SMC is combined with other materials, using hybrid cores can improve PMCPM Performance and reduce manufacturing costs. However, research on PMCPM with hybrid cores is limited to electromagnetic fields. Various physical fields inside the motor are coupled, and a multi-physics coupling analysis should be used to comprehensively study the impact of hybrid cores.Compared with traditional PMCPM with SMC cores, the paper studies the impact of inverter power supply on the Performance of PMCPM with hybrid cores and summarizes the advantages of hybrid cores. Firstly, the electromagnetic Performance of PMCPM with hybrid cores is analyzed using the field-circuit coupling method. The temperature distribution of PMCPM with hybrid cores under different power supply modes is obtained, and the influence of the power supply mode is studied. Then, a comparison is made between PMCPM with hybrid cores and PMCPM with SMC cores from the perspectives of electromagnetic field, temperature field, and stress field. Finally, the Simulation results are validated through prototype experiments.The multi-physics coupling Simulation analysis shows that under rated conditions, the torque ripple of the motor is 22.5% when powered by the inverter and only 10% when powered by the current source. The inverter power supply greatly increases the torque ripple, increasing the iron and permanent magnet eddy current losses by 22.2% and 44%, respectively. Compared with the PMCPM with SMC cores, hybrid cores reduce the maximum compressive stress on the SMC part of the stator core by 34.1% and the material cos...

Liu, D, Balaguer, C, Dissanayake, G & Kovac, M 2025, 'Preface', Infrastructure Robotics: Methodologies, Robotic Systems and Applications.

Liu, F, Wang, M, Liu, Y, Ni, W & Jamalipour, A 2025, 'Hybrid NOMA Offloading for Delay-Sensitive Applications in MEC-Based NB-IoT Networks', IEEE Internet of Things Journal, vol. 12, no. 5, pp. 5245-5259.
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Liu, F, Yuan, Z, Guo, Q, Zhang, Y, Wang, Z & Zhang, JA 2025, 'Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning', IEEE Transactions on Vehicular Technology, vol. 74, no. 5, pp. 7666-7675.
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Liu, G, Zhang, W, Wang, X, King, S & Yu, S 2025, 'A Membership Inference and Adversarial Attack Defense Framework for Network Traffic Classifiers', IEEE Transactions on Artificial Intelligence, vol. 6, no. 2, pp. 317-332.
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Liu, J, Huang, X-L, Hu, F & Yu, S 2025, 'Higher-Order Cumulant-Assisted Constant Wideband Compressive Spectrum Sensing Using Semantic Correlation Mining', IEEE Transactions on Wireless Communications, vol. 24, no. 2, pp. 893-908.
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Liu, L, Guo, Y, Yin, W, Lei, G, Sun, X & Zhu, J 2025, 'Efficient Design Optimization of PMSM Drive Systems Using Improved Equivalent-Circuit-Based Loss Minimization Control', IEEE Transactions on Industrial Electronics, vol. 72, no. 4, pp. 3280-3291.
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Liu, P, Fei, Z, Wang, X, Huang, J, Hu, J & Andrew Zhang, J 2025, 'Joint Offloading and Beamforming Design in Integrating Sensing, Communication, and Computing Systems: A Distributed Approach', IEEE Transactions on Communications, pp. 1-1.
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Liu, T, Hu, Y, Li, M, Yi, J, Chang, X, Gao, J & Yin, B 2025, 'Tackling Real-world Complexity: Hierarchical Modeling and Dynamic Prompting for Multimodal Long Document Classification', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Liu, W, Xia, R, Qi, C, Ansari, AJ, Nghiem, LD, Duan, X, Li, Y, Li, G & Luo, W 2025, 'Micro-nanobubbles to alleviate ultrafiltration membrane fouling for biogas slurry concentration and nutrient enrichment: Performance and molecular mechanisms', Journal of Membrane Science, vol. 731, pp. 124221-124221.
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Liu, X, Song, W, Musial, K, Li, Y, Zhao, X & Yang, B 2025, 'Stochastic Block Models for Complex Network Analysis: A Survey', ACM Transactions on Knowledge Discovery from Data, vol. 19, no. 3, pp. 1-35.
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Complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs.

Liu, X, Zuo, W, Li, Q, Zhou, K, Huang, Y & Li, Y 2025, 'NO emission and thermal performance studies on ammonia/oxygen premixed combustion in micro planar combustor with a secondary oxygen injection', Energy, vol. 328, pp. 136460-136460.
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Liu, Y, Forster, L, Mavridis, A, Merenda, A, Ahmed, M, D'Agostino, C, Konarova, M, Seeber, A, Della Gaspera, E, Lee, AF & Wilson, K 2025, 'Phase Effects in Zirconia Catalysed Glucose Conversion to 5‐(Hydroxymethyl)furfural', ChemSusChem, vol. 18, no. 4.
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Abstract5‐(hydroxymethyl)furfural (HMF) is a key biomass derived platform chemical used to produce fuel precursors or additives and value‐added chemicals, synthesised by the cascade isomerisation of glucose and subsequent dehydration of reactively formed fructose to HMF over Lewis and Bronsted acid catalysts, respectively. Zirconia is a promising catalyst for such reactions; however, the impact of acid properties of different zirconia phases is poorly understood. In this work, we unravel the role of the zirconia crystalline phase in glucose isomerisation and fructose dehydration to HMF. The Lewis acidic monoclinic phase of zirconia is revealed to preferentially facilitate glucose isomerisation, while the nanoparticulate tetragonal phase possesses Brønsted acid sites which favour fructose dehydration. Synergy between both zirconia phases facilitates cascade HMF production, with both catalysts investigated as physical mixtures in batch and flow reactor configurations. Using a physical mixture of only 15 wt % m‐ZrO2 with 85 wt % t‐ZrO2 in either batch or packed bed reactor configuration is sufficient to reach equilibrium conversion of glucose for subsequent dehydration by the t‐ZrO2 component. Under continuous flow, a six‐fold increase in HMF production was obtained when operating with a physical mixture of m‐ and t‐ZrO2 compared to that from a single bed of t‐ZrO2.

Liu, Y, Liu, R, Dong, J, Xia, X, Yang, H, Wei, S, Fan, L, Fang, M, Zou, Y, Zheng, M, Leong, KW & Shi, B 2025, 'Targeted protein degradation via cellular trafficking of nanoparticles', Nature Nanotechnology, vol. 20, no. 2, pp. 296-302.
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Liu, Y, Zhang, Y, Zhang, M, Xu, W, Shao, S & Guo, Y 2025, 'Minimum Torque Ripple Control for Brushless Doubly-Fed Induction Generator-DC System Under Power Winding Open-Phase Fault', IEEE Transactions on Power Electronics, vol. 40, no. 4, pp. 5743-5755.
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Liu, Z, Lu, J, Xuan, J & Zhang, G 2025, 'Learning Latent and Changing Dynamics in Real Non-Stationary Environments', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 4, pp. 1930-1942.
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Long, G, Blumenstein, M & Chang, Y 2025, 'Foreword from the General Chairs', Www 2025 Proceedings of the ACM Web Conference, pp. iii-iv.

Lowe, D, Tilley, E, Willey, K & Roach, K 2025, 'Student reactions to the development of professional engineering competencies', European Journal of Engineering Education, vol. 50, no. 2, pp. 281-297.
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Lu, J, Zhang, C, Li, B, Zhao, Y, Choudhary, R & Langtry, M 2025, 'Self-attention variational autoencoder-based method for incomplete model parameter imputation of digital twin building energy systems', Energy and Buildings, vol. 328, pp. 115162-115162.
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Lu, Y, Luo, Q & Tong, L 2025, 'Multi-objective and multi-constraint topology optimization of nonlinear compliant mechanisms', Thin-Walled Structures, vol. 208, pp. 112761-112761.
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Lu, Y, Luo, Q & Tong, L 2025, 'Topology optimization for metastructures with quasi-zero stiffness and snap-through features', Computer Methods in Applied Mechanics and Engineering, vol. 434, pp. 117587-117587.
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Lu, Y, Luo, Q & Tong, L 2025, 'Topology optimization for pressurized nonlinear structures using substructure and experimental studies', Structural and Multidisciplinary Optimization, vol. 68, no. 2.
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Abstract A compliant structure under fluidic pressure can undergo relatively large shape change, but the design of such type of structure is challenging as the pressure distribution depends on detailed structural geometry. In this study, a novel mixed substructure-density (MSD) model is proposed for topology representation and update in the optimal design of nonlinear compliant structures under quasi-static fluidic pressure. An optimization algorithm is developed via implementing the present model by using super-elements in commercial finite element analysis (FEA) software. Numerical examples are presented to validate the present model, algorithm, and designs numerically via full linear and nonlinear FEAs. A planar cellular network with five cells arranged in parallel is then designed for representing a pressurized wing rib structure capable of modulating airfoil thickness variation. The test results of the single-cell and five-cell PCS specimens prototyped using polyurethane material show that the respective cell thickness can be reduced by 11.9 and 6.4% respectively under a cell pressure of 250 kPa.

Lu, Z, Liu, C, Chang, X, Zhang, Y & Xie, H 2025, 'DHVT: Dynamic Hybrid Vision Transformer for Small Dataset Recognition', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 4, pp. 2615-2631.
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Lu, Z, Xu, Y, Liang, C, Guo, W, Ngo, HH & Peng, L 2025, 'Biogenic sulfide by sulfur disproportionation enhances nitrate removal and reduces N2O production during sulfur autotrophic denitrification', Chemosphere, vol. 370, pp. 143915-143915.
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Luo, C, Wang, Y, Zhang, Y & Zhang, LY 2025, 'Distributed Differentially Private Matrix Factorization for Implicit Data via Secure Aggregation', IEEE Transactions on Computers, vol. 74, no. 2, pp. 705-716.
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Luo, H, Tao, M, Hong, Z, Xiang, G & Wu, C 2025, 'Analysis of the dynamic response and damage characteristic for the tunnel under near-field blasts and far-field earthquakes', Underground Space, vol. 21, pp. 331-351.
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Luo, J, Luo, Q, Li, Q & Sun, G 2025, 'Effects of tension-compression asymmetry on mixed-mode interlaminar fracture', International Journal of Mechanical Sciences, vol. 288, pp. 109948-109948.
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Luo, S, Peng, H, Shi, Y, Cai, J, Zhang, S, Shao, N & Li, J 2025, 'Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey', Briefings in Bioinformatics, vol. 26, no. 2.
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Abstract Cancer neoantigens are peptides that originate from alterations in the genome, transcriptome, or proteome. These peptides can elicit cancer-specific T-cell recognition, making them potential candidates for cancer vaccines. The rapid advancement of proteomics technology holds tremendous potential for identifying these neoantigens. Here, we provided an up-to-date survey about database-based search methods and de novo peptide sequencing approaches in proteomics, and we also compared these methods to recommend reliable analytical tools for neoantigen identification. Unlike previous surveys on mass spectrometry-based neoantigen discovery, this survey summarizes the key advancements in de novo peptide sequencing approaches that utilize artificial intelligence. From a comparative study on a dataset of the HepG2 cell line and nine mixed hepatocellular carcinoma proteomics samples, we demonstrated the potential of proteomics for the identification of cancer neoantigens and conducted comparisons of the existing methods to illustrate their limits. Understanding these limits, we suggested a novel workflow for neoantigen discovery as perspectives.

Luo, X, Yuan, D, Chang, X & He, Z 2025, 'Underwater Target Tracking Based on Uncertainty-Inspired Image Enhancement', Jisuanji Gongcheng/Computer Engineering, vol. 51, no. 1, pp. 11-19.
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The task of Underwater Visual Object Tracking (UVOT) not only requires dealing with the common challenges in outdoor tracking but also faces many unique difficulties specific to the underwater environment, including but are not limited to, optical degradation and scattering, uneven illumination, low visibility, and hydrodynamics. In these scenarios, directly applying a large number of traditional outdoor scene object tracking methods directly to underwater scenes inevitably leads to performance degradation. To address the above issues, first, an Underwater Image Enhancement (UIE) module inspired by uncertainty is introduced, aimed at specifically improving the quality of underwater images. This method decomposes UIE into distribution estimation and consensus processes and introduces a new probability network to learn the enhancement distribution of underwater images, thereby addressing the bias problem in reference images. These are subsequently applied to an attention-based feature fusion network to propose a target tracking algorithm, called UTransT. The feature fusion network combines self- and cross-attention mechanisms to effectively fuse template and search region features. The experimental results show that on the UTB180 dataset, the success rate of UTransT is 0.8 percentage points higher than that of MixFormer, with the best performance in the comparison algorithm, and normalization accuracy is nearly 1.9 percentage points higher. On the VMAT dataset, the success rate is 1.2 percentage points higher than that of the best-performing Masked Appearance Transfer (MAT) algorithm, with 1.5 percentage points higher normalization accuracy. Moreover, UTransT facilitates real-time tracking at 65 frames per second. These experimental results validate the effectiveness and feasibility of the proposed algorithm in underwater object tracking tasks.

Luo, X, Yuan, D, Shu, X, Liu, Q, Chang, X & He, Z 2025, 'Adaptive Trajectory Correction for Underwater Object Tracking', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Luo, Y, Zhao, S, Fan, Z, Li, Y, Peng, Z, Zhang, Y, Feng, S, Mou, J, Wang, Z, Ki Lin, CS & Li, X 2025, 'Sucrose non-fermenting 1-related protein kinase 2–14 participating in lipid elevating efficacy and biodiesel upgrade by Coccomyxa subllipsoidea', Chemical Engineering Journal, vol. 505, pp. 159607-159607.
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Luo, Z, Yang, L, Su, T, Zhu, X & Gómez-García, R 2025, 'Single-Ended and Balanced Flat-Group-Delay RF Low-Pass Filters With Input-Quasi-Reflectionless Behavior for Digital-Communication Systems', IEEE Transactions on Microwave Theory and Techniques, vol. 73, no. 1, pp. 321-334.
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Lv, X, Yang, Y, Luo, Z & Tyo, JS 2025, 'Multimaterial 3-D-Printed FSSs for Ultrawide and Dual Passbands in the K-Ka Spectra', IEEE Transactions on Microwave Theory and Techniques, vol. 73, no. 1, pp. 75-86.
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Lv, Y, Liu, Z & Chang, X 2025, 'Consistency-Queried Transformer for Audio-Visual Segmentation', IEEE Transactions on Image Processing, vol. 34, pp. 2616-2627.
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Lyu, L, Fleck, R, Matheson, S, King, WL, Bauerle, TL, Torpy, FR & Irga, PJ 2025, 'Phytoremediation of indoor air: Mechanisms of pollutant translocation and biodegradation', Critical Reviews in Environmental Science and Technology, vol. 55, no. 10, pp. 676-707.
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Ma, B, Li, Y, Zheng, J, Zhang, J, Huang, S, Zhu, J & Lei, G 2025, 'Multiphysics Topology Optimization of SynRMs Considering Control Performance and Machinability', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 1287-1297.
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Ma, B, Xing, J, Huang, S, Wang, K, Zhang, J, Lei, G & Zhu, J 2025, 'Cooling System Design for High-Power-Density Permanent Magnet Synchronous Motor Based on Micro Heat Pipe Array', IEEE Transactions on Transportation Electrification, vol. 11, no. 2, pp. 6719-6730.
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Ma, G, Lu, J, Fang, Z, Liu, F & Zhang, G 2025, 'Multiview Classification Through Learning From Interval-Valued Data', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 9606-9620.
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Ma, H, Sun, Z, Dong, D & Gong, D 2025, 'Learning Informative Latent Representation for Quantum State Tomography', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-11.
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Ma, H, Sun, Z, Dong, D, Chen, C & Rabitz, H 2025, 'Tomography of Quantum States From Structured Measurements via Quantum-Aware Transformer', IEEE Transactions on Cybernetics, vol. 55, no. 6, pp. 2571-2582.
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Ma, L, Hu, C, Wen, S, Yu, Z & Jiang, H 2025, 'Fixed-Time Distributed Optimization via Edge-Based Adaptive Algorithms', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 5, pp. 3436-3448.
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Ma, N, Xuan, J, Zhang, G & Lu, J 2025, 'Global–Local Decomposition of Contextual Representations in Meta-Reinforcement Learning', IEEE Transactions on Cybernetics, vol. 55, no. 3, pp. 1277-1287.
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Ma, S, Shi, K, Gu, T, Tian, S, Zhou, Z, Li, X, Wang, C, Shon, H & Ren, J 2025, 'Hydrogel-driven salt fouling free FO-SIE integrated process for seawater desalination', Desalination, vol. 607, pp. 118815-118815.
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Ma, W, Huang, S & Sun, Y 2025, 'SkyLoc: Cross-Modal Global Localization With a Sky-Looking Fish-Eye Camera and OpenStreetMap', IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 5, pp. 5832-5842.
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Ma, W, Li, Y, Li, T, Yang, H, Li, Z, Wang, L & Xuan, J 2025, 'SFSWTS: A spatial-frequency shifted windows and time self-attention network for EEG emotion recognition', Neurocomputing, vol. 640, pp. 130309-130309.
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Ma, Y, Wang, H, Shen, H, Chen, X, Duan, S & Wen, S 2025, 'NeuroMoCo: a neuromorphic momentum contrast learning method for spiking neural networks', Applied Intelligence, vol. 55, no. 2.
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Ma, Y, Wang, H, Shen, H, Duan, S & Wen, S 2025, 'Analog Spiking U-Net integrating CBAM&ViT for medical image segmentation', Neural Networks, vol. 181, pp. 106765-106765.
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Mahajan, S, Gite, S, Pradhan, B, Alamri, A, Inamdar, S, Shriyansh, D, Shah, AA & Agarwal, S 2025, 'Integrating Speech-to-Text for Image Generation Using Generative Adversarial Networks', Computer Modeling in Engineering & Sciences, vol. 143, no. 2, pp. 2001-2026.
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Mammone, M, Panta, J, Mildren, RP, Wang, J, Escobedo-Diaz, J, Mcgarva, L, Ibrahim, M, Sharp, A, Yang, R & Zhang, YX 2025, 'Advanced characterization of thermal degradation mechanisms in carbon fibre-reinforced polymer composites under continuous wave laser irradiation', Composites Part A: Applied Science and Manufacturing, vol. 192, pp. 108817-108817.
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Mangiavillano, B, Franchellucci, G, Auriemma, F, Ramai, D, Larghi, A, Paduano, D, De Deo, D, Calabrese, F, Gentile, C, Fiacca, M, Facciorusso, A & Repici, A 2025, 'Pilot study of a novel lumen‐apposing metal stent for endoscopic ultrasound‐guided procedures in porcine models', DEN Open, vol. 5, no. 1.
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AbstractLumen‐apposing metal stents have expanded the therapeutic potential of interventional endoscopic ultrasound (EUS). The Hot‐Spaxus (Taewoong Medical Co., Ltd.), the second most commonly utilized lumen‐apposing metal stent, requires two operators for its release which has been considered a limitation compared to other lumen‐apposing metal stents. We aimed to test the feasibility and the technical success of a newly available version of the Hot‐Spaxus stent equipped with an innovative handle delivery system for EUS‐guided interventional procedures. We conducted a pilot study using porcine models. The novel Hot‐Spaxus 2 was tested by performing four EUS‐guided procedures including four EUS‐guided gallbladder drainage and 12 EUS‐guided gastrojejunostomy) procedures. Technical success was reported in 100% of cases. The mean procedure time for EUS‐guided gatrojejunostomyJ and EUS‐guided gallbladder drainage was 23.85 min (standard deviation 3.41) and 16.15 min (standard deviation 2.72), respectively. The distal and proximal flanges were safely released by the endosonographer without any complications. No adverse events were reported. In conclusion, the novel Hot‐Spaxus 2 stent may represent an improvement compared to the prior Spaxus model. Unlike its predecessor, this newly designed stent eliminates the need for two endoscopists and can be deployed by a single operator. Further human studies are necessary to validate its clinical effectiveness.

Mann, RL, Elman, SJ, Wood, DR & Chapman, A 2025, 'A graph-theoretic framework for free-parafermion solvability', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 481, no. 2312.
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We present a graph-theoretic characterization of when a quantum spin model admits an exact solution via a mapping to free parafermions. Our characterization is based on the concept of a frustration graph, which represents the commutation relations between Weyl operators of a Hamiltonian. We show that a quantum spin system has an exact free-parafermion solution if its frustration graph is an oriented indifference graph. Furthermore, we show that if the frustration graph of a model can be dipath oriented via switching operations, then the model is integrable in the sense that there is a family of commuting independent set charges. Additionally, we establish an efficient algorithm for deciding whether this is possible. Our characterization extends that given for free-fermion solvability. Finally, we apply our results to solve three qudit spin models.

Mao, Y, Dong, X, Xu, W, Gao, Y, Wei, B & Zhang, Y 2025, 'FIT-RAG: Black-Box RAG with Factual Information and Token Reduction', ACM Transactions on Information Systems, vol. 43, no. 2, pp. 1-27.
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Due to the extraordinarily large number of parameters, fine-tuning large language models (LLMs) to update long-tail or out-of-date knowledge is impractical in lots of applications. To avoid fine-tuning, we can alternatively treat a LLM as a black-box (i.e., freeze the parameters of the LLM) and augment it with a retrieval-augmented generation (RAG) system, namely black-box RAG. Recently, black-box RAG has achieved success in knowledge-intensive tasks and has gained much attention. Existing black-box RAG methods typically fine-tune the retriever to cater to LLMs’ preferences and concatenate all the retrieved documents as the input, which suffers from two issues: (1) Ignorance of Factual Information. The LLM preferred documents may not contain the factual information for the given question, which can mislead the retriever and hurt the effectiveness of black-box RAG; (2) Waste of Tokens. Simply concatenating all the retrieved documents brings large amounts of unnecessary tokens for LLMs, which degenerates the efficiency of black-box RAG. To address these issues, this article proposes a novel black-box RAG framework which utilizes the factual information in the retrieval and reduces the number of tokens for augmentation, dubbed FIT-RAG. FIT-RAG utilizes the factual information by constructing a bi-label document scorer which takes the factual information and LLMs’ preferences as labels respectively. Besides, it reduces the tokens by introducing a self-knowledge recognizer and a sub-document-level token reducer, which enables FIT-RAG to avoid unnecessary augmentation and reduce augmentation tokens as much as possible. FIT-RAG achieves both superior effectiveness and efficiency, which is validated by extensive experiments across three open-domain question-answering datasets: TriviaQA, NQ, and PopQA. FIT-RAG can improve the answering accuracy of Llama2-13B-Chat by 14.3% on TriviaQA, 19.9% on NQ and 27.5% on PopQA, respectively. Furthermore, it can save...

Mardy, A, Nikoo, MR, Zamani, MG, Al-Rawas, G, Nazari, R, Simunek, J, Sana, A & Gandomi, AH 2025, 'Cluster-based downscaling of precipitation using Kolmogorov-Arnold Neural Networks and CMIP6 models: Insights from Oman', Journal of Environmental Management, vol. 380, pp. 124971-124971.
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Matthews, S, Nicholas, M, Paatsch, L, Kervin, L & Wyeth, P 2025, 'Social and curious: Lessons in designing digital manipulatives for young children', International Journal of Child-Computer Interaction, vol. 44, pp. 100725-100725.
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Mattos Batista de Moraes, F, Kulay, L & Trianni, A 2025, 'Integrating life cycle assessment and ecodesign to improve product effectiveness and environmental performance: A novel approach', Sustainable Production and Consumption, vol. 55, pp. 76-89.
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Mehrabi, P, Mortazavi, M & Far, H 2025, 'Axisymmetric thermal post-buckling of the eccentric annular sector plate made of Gori-metamaterials: Introducing DNN-RF algorithm for solving the post-buckling problems', Thin-Walled Structures, vol. 208, pp. 112795-112795.
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Meng, L, Xu, G, Dong, C & Wang, S 2025, 'Modeling information propagation for target user groups in online social networks based on guidance and incentive strategies', Information Sciences, vol. 691, pp. 121628-121628.
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Merenda, A, Gangadoo, S, Johannessen, B, Wilson, K, Chapman, J & Lee, AF 2025, 'Atomic layer deposition of antibacterial ZnO ultrathin films over SBA-15', Materials Today Chemistry, vol. 44, pp. 102566-102566.
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Miao, L, Cheng, Z, Song, J, Zhang, Q, Cui, C & Li, J 2025, 'Numerical study on the effects of the mechanical degradation of UHPC layer and stud connectors on the fatigue performance of steel-UHPC composite bridge decks', Engineering Structures, vol. 332, pp. 120069-120069.
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Mikolajczyk, K, Har, AO, Wachsmann, L, Bown, O & Ferguson, S 2025, 'Mimetic Possibilities: Collaboration through Movement in Multimedia Opera', Leonardo, vol. 58, no. 2, pp. 149-156.
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Abstract In the opera The Ghost, artists with backgrounds in music composition, Greek tragedy, creative coding, and opera performance collaborated to create an artwork that engages with philosophy and technology. A light sculpture with a novel Internet of Things (IoT) architecture was adapted for the opera’s stage, where both the sculpture and its controlling interface were programmed to visually represent musical processes within the composer’s approach. During filming, the light sculpture contributed to the dramatic context through movement and gestural interaction between the sculpture and the soprano. Drawing upon human-computer interaction (HCI) research and its inquiry into movement for engaging with new technology expressively, movement and gesture transpiring from mimetic techniques in the opera were central in this interdisciplinary collaboration.

Milano, J, Ong, MY, Tiong, SK, Ideris, F, Silitonga, AS, Sebayang, AH, Tan, CH, Fattah, IMR, Fona, Z & Hossain, N 2025, 'A comparative study of the production of methyl esters from non-edible oils as potential feedstocks: Process optimization and two-step biodiesel characterization', Results in Engineering, vol. 25, pp. 104285-104285.
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Minassian, R, Mihăiţă, A-S & Shirazi, A 2025, 'Optimizing indoor environmental prediction in smart buildings: A comparative analysis of deep learning models', Energy and Buildings, vol. 327, pp. 115086-115086.
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Minh Tuan, B, Nguyen, DN, Linh Trung, N, Nguyen, V-D, Van Huynh, N, Thai Hoang, D, Krunz, M & Dutkiewicz, E 2025, 'Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI', IEEE Internet of Things Journal, vol. 12, no. 11, pp. 16009-16022.
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Mirazur Rahman, M, Yang, Y & Dey, S 2025, 'Application of Metamaterials in Antennas for Gain Improvement: A Study on Integration Techniques and Performance', IEEE Access, vol. 13, pp. 49489-49503.
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Mishra, R, Fattah, IMR, Ong, HC, Shu, C-M, Gollakota, ARK & Kong, ZY 2025, 'Sustainable green technology integrated hydrogen production system for low carbon future', International Journal of Hydrogen Energy, vol. 134, pp. 139-163.
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Moazzen, F & Hossain, MJ 2025, 'A two-layer strategy for sustainable energy management of microgrid clusters with embedded energy storage system and demand-side flexibility provision', Applied Energy, vol. 377, pp. 124659-124659.
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Mohanty, N, Behera, BK, Ferrie, C & Dash, P 2025, 'A quantum approach to synthetic minority oversampling technique (SMOTE)', Quantum Machine Intelligence, vol. 7, no. 1.
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Abstract The paper proposes the Quantum-SMOTE method, a novel solution that uses quantum computing techniques to solve the prevalent problem of class imbalance in machine learning datasets. Quantum-SMOTE, inspired by the Synthetic Minority Oversampling Technique (SMOTE), generates synthetic data points using quantum processes such as swap tests and quantum rotation. The process varies from the conventional SMOTE algorithm’s usage of K-Nearest Neighbors (KNN) and Euclidean distances, enabling synthetic instances to be generated from minority class data points without relying on neighbor proximity. The algorithm asserts greater control over the synthetic data generation process by introducing hyperparameters such as rotation angle, minority percentage, and splitting factor, which allow for customization to specific dataset requirements. Due to the use of a compact swap test, the algorithm can accommodate a large number of features. Furthermore, the approach is tested on a public dataset of TelecomChurn and evaluated alongside two prominent classification algorithms, Random Forest and Logistic Regression, to determine its impact along with varying proportions of synthetic data.

Monteiro, LC, Zheng, Y & Ling, SH 2025, 'A Improved Hybrid Attention Recurrent Residual U-Net approach for the segmentation of ultrasound spine images', Biomedical Signal Processing and Control, vol. 108, pp. 107925-107925.
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Moradian, M, Lie, TT & Gunawardane, K 2025, 'Design and performance evaluation of low-voltage solid-state DCCB using capacitor-based surge mitigation techniques', Power Electronic Devices and Components, vol. 11, pp. 100095-100095.
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Moradian, M, Lie, TT & Gunawardane, K 2025, 'Enhanced LV solid-state DC circuit breaker design with divided surge absorption technique', Electric Power Systems Research, vol. 241, pp. 111280-111280.
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Moradian, M, Peykarporsan, R, Lie, TT & Gunawardane, K 2025, 'Bidirectional Solid State Circuit Breaker with Passive Surge Absorber for LV Applications', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Moradian, M, Peykarporsan, R, Lie, TT & Gunawardane, K 2025, 'Low-Voltage Solid State DCCB Design Based on Bypassed Bidirectional Thyristor-Capacitor Suppressor', IEEE Transactions on Power Electronics, vol. 40, no. 1, pp. 516-525.
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Moreira, C, Chou, Y-L, Hsieh, C, Ouyang, C, Pereira, J & Jorge, J 2025, 'Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box', ACM Computing Surveys, vol. 57, no. 6, pp. 1-37.
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This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: a decision tree (fully transparent, interpretable, white-box model), a random forest (semi-interpretable, grey-box model), and a neural network (fully opaque, black-box model). We tested the counterfactual generation process using four algorithms (DiCE, WatcherCF, prototype, and GrowingSpheresCF) in the literature in 25 different datasets. Our findings indicate that: (1) Different machine learning models have little impact on the generation of counterfactual explanations; (2) Counterfactual algorithms based uniquely on proximity loss functions are not actionable and will not provide meaningful explanations; (3) One cannot have meaningful evaluation results without guaranteeing plausibility in the counterfactual generation. Algorithms that do not consider plausibility in their internal mechanisms will lead to biased and unreliable conclusions if evaluated with the current state-of-the-art metrics; (4) A counterfactual inspection analysis is strongly recommended to ensure a robust examination of counterfactual explanations and the potential identification of biases.

Mostafaei, H, Kelishadi, M, Bahmani, H, Wu, C & Ghiassi, B 2025, 'Development of sustainable HPC using rubber powder and waste wire: carbon footprint analysis, mechanical and microstructural properties', European Journal of Environmental and Civil Engineering, vol. 29, no. 2, pp. 399-420.
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Mousavi, M, Dackermann, U, Hassani, S, Subhani, M & Gandomi, AH 2025, 'Raw sensor data fusion using Johansen cointegration for condition assessment of concrete poles', Journal of Sound and Vibration, vol. 599, pp. 118909-118909.
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Munot, S, Redfern, J, Bray, JE, Angell, B, Coggins, A, Denniss, AR, Jennings, G, Khanlari, S, Kovoor, P, Kumar, S, Lai, K, Marschner, S, Middleton, PM, Oppermann, I, Rock, Z, Semsarian, C, Vukasovic, M, Bauman, A & Chow, CK 2025, 'FirstCPR: A pragmatic community organisation-based cluster randomised trial to increase community training and preparedness to respond to out-of-hospital cardiac arrest', Resuscitation Plus, vol. 23, pp. 100949-100949.
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Murray, J, Meylan, MH, Ngo, T, Thamwattana, N & Indraratna, B 2025, 'Analytical Solution for Railway Transition Zones With Abrupt Changes in Elastic Stiffness', Engineering Reports, vol. 7, no. 5.
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ABSTRACTTransition zones in railway systems, where properties of the track foundation change abruptly, are known to increase dynamic loads, track deterioration, and passenger discomfort. As such, it is of particular importance to study railway transition zones with abrupt changes in foundation properties to minimize these railway problems. This paper presents a closed‐form solution for the long‐term deformation of an Euler‐Bernoulli beam on an elastic foundation with multiple abrupt changes in foundation stiffness and under multiple applied stationary point loads. The solutions are obtained by dividing the beam into segments and applying the method of undetermined coefficients. This exact analytical solution constitutes an improvement upon an approximate solution, which is presented in the literature as a recent method for modeling rail infrastructure at transition zones. A limitation of the approximate solution is its inability to account for the changed behavior of the beam close to a transition zone. The closed‐form solution overcomes this limitation and can be used to assess the suitability of the approximate solution.

Nabeel, MI, Afzal, MU, Singh, K, Thalakotuna, DN & Esselle, KP 2025, 'Waveguide-Based All-Metal Near-Field Metasurfaces for Linearly and Circularly Polarized Beam Steering Antennas', IEEE Access, vol. 13, pp. 10857-10869.
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Nair, L, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2025, 'The interactive role of cyclic vertical and torsional stresses on the instability of low-plasticity soils based on hollow cylinder testing', Canadian Geotechnical Journal, vol. 62, pp. 1-21.
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Soil instability and potential failure under principal stress rotation require greater attention than ever before due to increased operation of heavier and longer high-speed trains. This study focuses on the interplay between cyclic vertical stress and torsional shear stress on the failure condition of a low-plasticity subgrade soil, facilitated by a hollow cylinder apparatus. Combined vertical and torsional loading significantly influences strain response, with increasing torsional stress leading to higher strain accumulation. Moreover, the data indicate that an increase in torsional shear stress is generally accompanied by a swift rise in the EPWP and a corresponding decrease in the soil stiffness. In view of this, a novel parameter, the overall stiffness degradation index ( δo) that simultaneously captures both the vertical and torsional shear effects under principal stress rotation is proposed as an early indicator of instability. In addition, a normalised torsional stress ratio (NTSR), which is the ratio of the amplitude of torsional shear stress to the confining pressure, is introduced to assess the impact of torsional shear stress. Whereby, higher NTSR values correlate with premature inception of failure. These experimental results provide new insights for a better understanding of soil instability under simulated railway loading.

Nair, SG, Nguyen, QD, Zhu, Q, Karimi, M, Gan, Y, Wang, X, Castel, A, Irga, P, Rocha, CGD, Torpy, F, Wilkinson, S, Moreau, D & Delhomme, F 2025, 'Suitability of calcined clay and ground granulated blast furnace slag geopolymer binder for hempcrete applications', Built Environment Project and Asset Management.
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PurposeHempcrete has the potential to reduce both CO2 emissions and energy usage in buildings. Hempcrete has a high sound absorption capacity, excellent moisture regulator and outstanding thermal insulation properties. However, hempcrete traditionally uses lime-based binders, which are carbon-intensive materials. The low-carbon binders to increase the sustainability of hempcrete are the current research gap. Geopolymer binders are low-carbon binders composed of aluminosilicate precursors dissolved in a high alkalinity solution. This study investigated the suitability of calcined clay and ground granulated blast furnace slag geopolymer binder as a low-carbon binder for hempcrete applications.Design/methodology/approachTwo types of hemp hurds with different water absorption capacity and particle size distributions were used. Hempcrete properties tested were compressive strength, bulk density, sound absorption coefficient by a two-microphone impedance tube and thermal conductivity by a Hot Disk system.FindingsThe particle size distribution and water absorption capacity of hemp hurds did not affect the compressive strength of hempcrete when following a mixing procedure, ensuring the hurds in a saturated surface dry condition. The geopolymer hempcrete achieved a compressive strength about four times higher than the reference hydrated lime hempcrete. All hempcrete specimens achieved outstanding acoustic performance. The increase in bulk density led to the decrease in the maximum sound absorption coefficient. The geopolymer hempcrete achieved the lowest thermal conductivity.Originality/value

Nanda, P, Srivastava, S, Verma, VK & Vyas, P 2025, 'Preface', Smart Innovation Systems and Technologies, vol. 398, pp. v-vi.

Naseem, U, Zhang, Q, Hu, L, Hussain, S & Wang, S 2025, 'Knowledge Enhanced Language Model for Biomedical Natural Language Processing: Introducing a New Language Model for BioNLP', IEEE Systems, Man, and Cybernetics Magazine, vol. 11, no. 1, pp. 89-94.
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Newman, PLH, Mirkhalaf, M, Gauci, SC, Roohani, I, Biro, M, Barner‐Kowollik, C & Zreiqat, H 2025, '3D Printed Materials with Nanovoxelated Elastic Moduli', Advanced Materials, vol. 37, no. 15.
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AbstractFabrication methods that synthesize materials with higher precision and complexity at ever smaller scales are rapidly developing. Despite such advances, generating complex 3D materials with controlled mechanical properties at the nanoscale remains challenging. Exerting precise control over mechanical properties at the nanoscale would enable material strengths near theoretical maxima, and the replication of natural structures with hitherto unattainable strength‐to‐weight ratios. Here, a method for fabricating materials with nanovoxelated elastic moduli by employing a volume‐conserving photoresist composed of a copolymer hydrogel, along with OpenScribe, an open‐source software that enables the precise programming of material mechanics, is presented. Combining these, a material composed of periodic unit cells featuring heteromechanically tessellated soft‐stiff structures, achieving a mechanical transition over an order‐of‐magnitude change in elastic modulus within 770 nm, a 130‐fold improvement on previous reports, is demonstrated. This work critically advances material design and opens new avenues for fabricating materials with specifically tailored properties and functionalities through unparalleled control over nanoscale mechanics.

Ngo, QT, He, Y, Jayawickrama, B & Dutkiewicz, E 2025, 'A Fast Fuzzy DRL-Based Joint Beam Design and Power Allocation for Multi-Beam GEO-LEO Coexisting Satellite Networks', IEEE Transactions on Wireless Communications, pp. 1-1.
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Ngo, QT, Jayawickrama, B, He, Y & Dutkiewicz, E 2025, 'A Novel Satellite-Based REM Construction in Cognitive GEO-LEO Satellite IoT Networks', IEEE Internet of Things Journal, vol. 12, no. 6, pp. 7532-7548.
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Ngo, QT, Jayawickrama, BA, He, Y, Dutkiewicz, E, Weththasinghe, K, Clark, N, Arbon, E & Bowyer, M 2025, 'Optimizing Spectrum Sensing in Cognitive GEO-LEO Satellite Networks: Overcoming Challenges for Effective Spectrum Utilization', IEEE Vehicular Technology Magazine, pp. 2-11.
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Ngo, T, Indraratna, B, Coop, M & Qi, Y 2025, 'Behaviour of ballast stabilised with recycled rubber mat under impact loading', Géotechnique, vol. 75, no. 2, pp. 192-212.
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During the passage of trains, dynamic impact loads caused by wheel imperfections or rail abnormalities cause significant ballast degradation. In this study, the use of rubber mats manufactured from recycled tyres placed underneath a ballast layer is investigated to mitigate the adverse effects of impact loads. Based on a series of tests conducted using a high-capacity drop-weight facility to evaluate the dynamic impact responses, the experimental results show that the inclusion of a rubber mat beneath the ballast assembly significantly reduces particle breakage. This study also describes a numerical analysis following a coupled discrete–continuum modelling approach to examine the complex interaction of discrete ballast grains with the recycled rubber mat. In particular, a mathematical framework coupling the discrete and continuum domains is developed to facilitate the exchange of forces and displacements at the ballast–mat interface. Laboratory data measured from large-scale impact tests are used to calibrate and validate this coupled model. Subsequently, the model is used to predict the deformation and breakage of ballast, contact force distributions, impact forces, coordination numbers and the evolution of energy components during impact testing. The energy-absorbing properties of the rubber mat are captured in terms of reducing particle breakage from a micromechanical perspective.

Nguyen, CT, Liu, Y, Du, H, Hoang, DT, Niyato, D, Nguyen, DN & Mao, S 2025, 'Generative AI-Enabled Blockchain Networks: Fundamentals, Applications, and Case Study', IEEE Network, vol. 39, no. 2, pp. 232-241.
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Nguyen, HAD, Le, HT, Barthelemy, X, Azzi, M, Duc, H, Jiang, N, Riley, M & Ha, QP 2025, 'A Deep-Learning-Based Visualization Tool for Air Pollution Forecasting', IEEE Software, vol. 42, no. 2, pp. 47-56.
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Nguyen, HN, Roohani, I, Hayles, A, Lu, Z, Vongsvivut, J, Vasilev, K, Truong, VK & Zreiqat, H 2025, 'Antibacterial Activity and Mechanisms of Magnesium‐Doped Baghdadite Bioceramics for Orthopedic Implants', Advanced NanoBiomed Research, vol. 5, no. 2.
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Baghdadite (BAG, Ca3ZrSi2O9), a calcium silicate compound with zirconium incorporation, shows significant potential in medical implants. However, its susceptibility to infections poses a considerable challenge. To tackle this problem, doping biocompatible magnesium (Mg) into BAG to create Mg‐BAG enhances antibacterial activity and prevents infection in orthopedic implants. Mg‐BAG demonstrates effectiveness against Gram‐positive Staphylococcus aureus and Gram‐negative Pseudomonas aeruginosa. This study finds that the antibacterial activity of Mg‐BAG is multifaced including causing the generation of reactive oxygen species (ROS) within cells and disrupting membrane potential, resulting in leakage of intracellular contents. The synchrotron macro attenuated total reflectance Fourier‐transform infrared microspectroscopy shows the impact of Mg‐BAG on bacteria, resulting in modifications to biomolecules such as lipids, protein structures, and the stability of nucleic acids. The combined effect of Mg ions (Mg2+) and intracellular ROS formation contributes to the disruption of biomolecules and bacterial cell death. Mg‐BAG is a promising next‐generation bioceramic offering innovative nonantibiotic solutions for preventing infection.

Nguyen, HT, Nguyen, BT, Tran, AV, Nguyen, TT, Ngo, LH, Vo, T, Nhung Thai, TH, Mai, LD, Tran, TS, Nguyen, TV & Ho-Pham, LT 2025, 'A predictive nomogram for selective screening of asymptomatic vertebral fractures: The Vietnam Osteoporosis Study', Osteoporosis and Sarcopenia, vol. 11, no. 1, pp. 9-14.
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Nguyen, PQK, Zohdi, N, Zhang, YX, Zhang, Z & Yang, R 2025, 'Study on material behaviours of additively manufactured high-impact polystyrene using artificial neural networks', Progress in Additive Manufacturing, vol. 10, no. 2, pp. 1461-1478.
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Abstract Fused Filament Fabrication (FFF), a process parameters-dependent manufacturing method, currently dominates the additive manufacturing (AM) sector because of its prominent ability to produce parts with intricate profiles, customise products, and minimise waste. Though the effects of FFF process parameters were investigated experimentally, recent research highlighted the importance of developing numerical modelling and computational methods on optimising the FFF printing process and FFF-printed materials. This study aims to investigate the tensile strength (TS) of FFF-printed high-impact polystyrene (HIPS) via devising a systematic testing and analysis framework, which combines experimental testing, representative volume element (RVE)-finite element method (FEM), rule of mixture (ROM), and artificial neural networks (ANN). HIPS samples are fabricated using FFF considering the variations of infill density, layer thickness, nozzle temperature, raster angle, and build orientation, and tested with standard tensile testing. The rule of mixtures (ROM) and its modified version (MROM) are employed to calculate the TS of longitudinally and transversely built samples at various infill densities, respectively, while an ANN model is constructed to investigate the effect of material anisotropy precisely. The optimal ANN architecture is built with five hidden layers with the number of neurons in each layer as 44, 82, 169, 362, and 50. Although both MROM and ANN perform well on the validation set, ANN exhibits superior accuracy with only a maximum error of 0.13% for training set and 11% for validation set. The combination of the RVE-FEM, MROM, and ANN approaches can significantly improve the FFF printing process of polymers for optimisation.

Nguyen, TT, Nguyen, KL, Huynh, TQ & Tran, Q 2025, 'Influence of feature selection on machine learning prediction of pile foundation – The role of soil-pile interaction knowledge and application to base resistance', Geodata and AI, vol. 3, pp. 100019-100019.
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Nguyen, X-B, Phan, X-H & Piccardi, M 2025, 'Fine-tuning text-to-SQL models with reinforcement-learning training objectives', Natural Language Processing Journal, vol. 10, pp. 100135-100135.
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Nie, J, Luo, Y, Ye, S, Zhang, Y, Tian, X & Fang, Z 2025, 'Out-of-Distribution Detection with Virtual Outlier Smoothing', International Journal of Computer Vision, vol. 133, no. 2, pp. 724-741.
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Abstract Detecting out-of-distribution (OOD) inputs plays a crucial role in guaranteeing the reliability of deep neural networks (DNNs) when deployed in real-world scenarios. However, DNNs typically exhibit overconfidence in OOD samples, which is attributed to the similarity in patterns between OOD and in-distribution (ID) samples. To mitigate this overconfidence, advanced approaches suggest the incorporation of auxiliary OOD samples during model training, where the outliers are assigned with an equal likelihood of belonging to any category. However, identifying outliers that share patterns with ID samples poses a significant challenge. To address the challenge, we propose a novel method, Virtual Outlier Smoothing (VOSo), which constructs auxiliary outliers using ID samples, thereby eliminating the need to search for OOD samples. Specifically, VOSo creates these virtual outliers by perturbing the semantic regions of ID samples and infusing patterns from other ID samples. For instance, a virtual outlier might consist of a cat’s face with a dog’s nose, where the cat’s face serves as the semantic feature for model prediction. Meanwhile, VOSo adjusts the labels of virtual OOD samples based on the extent of semantic region perturbation, aligning with the notion that virtual outliers may contain ID patterns. Extensive experiments are conducted on diverse OOD detection benchmarks, demonstrating the effectiveness of the proposed VOSo. Our code will be available at https://github.com/junz-debug/VOSo.

Nikkhah, N, Keshavarz, R & Shariati, N 2025, 'Highly Sensitive Differential 3D Oblique-Gap Rectangular Sensor for Non-Invasive Glucose Level Monitoring', IEEE Sensors Journal, pp. 1-1.
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Nik-Khorasani, A, Khuat, TT & Gabrys, B 2025, 'Hyperbox Mixture Regression for process performance prediction in antibody production', Digital Chemical Engineering, vol. 14, pp. 100221-100221.
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Nikolic, S, Quince, Z, Lindqvist, AL, Neal, P, Grundy, S, Lim, M, Tahmasebinia, F, Rios, S, Burridge, J, Petkoff, K, Chowdhury, AA, Lee, WSL, Prestigiacomo, R, Fernando, H, Lok, P & Symes, M 2025, 'Project-work Artificial Intelligence Integration Framework (PAIIF): Developing a CDIO-based framework for educational integration', STEM Education, vol. 5, no. 2, pp. 310-332.
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Niu, Y, Shi, K, Cai, X & Wen, S 2025, 'Adaptive smooth sampled-data control for synchronization of T–S fuzzy reaction-diffusion neural networks with actuator saturation', AIMS Mathematics, vol. 10, no. 1, pp. 1142-1161.
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<p>This paper addresses the synchronization issue in T–S fuzzy reaction–diffusion neural networks (TFRNNs) with time-varying delays and actuator saturation. First, an adaptive smooth sampled-data (ASSD) controller is proposed to optimize communication resources. In the ASSD controller, the dynamic forgetting factor is employed to process historical data smoothly, thereby preventing data distortion due to unexpected events. Second, the Lyapunov–Krasovskii functional (LKF), which captures more information about the system, is introduced, and it can provide greater flexibility than the fixed-matrix LKF. Meanwhile, by employing the semi-looped-functional method, the constraint for negative determination of the sum of its derivatives is removed, which enhances the feasibility of expanding the solution. Consequently, a novel criterion and the corresponding algorithm are established to obtain the larger maximum allowable sampling interval (MASI). Finally, simulations demonstrate the effectiveness and superiority of the proposed theoretical results.</p>

Noh, M-S, Kim, D-H, Guo, Y & Lee, H-J 2025, 'A Study on the Performance of Bearingless PMSM Based on Rotor Permanent Magnet Arrangement', The transactions of The Korean Institute of Electrical Engineers, vol. 74, no. 4, pp. 739-746.
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Nurhayati, M, Jeong, K, Kim, S, Park, J, Cho, KH, Shon, HK & Lee, S 2025, 'From comparison to integration: Enhancing forward osmosis performance prediction with mathematical and RBF neural network models', Desalination, vol. 597, pp. 118322-118322.
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Oladigbolu, JO, Bilal, M, Gupta, S, Mujeeb, A & Li, L 2025, 'A novel approach for optimal deployment of plug-in electric vehicles with integrated renewable energy sources', Electrical Engineering, vol. 107, no. 4, pp. 3847-3881.
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Ong, MY, Milano, J, Nomanbhay, S, Palanisamy, K, Tan, YH & Ong, HC 2025, 'Insights into algae-plastic pyrolysis: Thermogravimetric and kinetic approaches for renewable energy', Energy, vol. 314, pp. 134322-134322.
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Onggowarsito, C, Zhang, S, Shi, Y, Mao, S, Feng, A, Martins, G, Shao, Z, Wong, EHH, Guo, W & Fu, Q 2025, 'Hydrogel-Encapsulated phase change materials for Enhanced heat storage and water evaporation efficiency', Chemical Engineering Journal, vol. 513, pp. 162838-162838.
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Ouyang, T, Tan, X, Zuo, K, Zhou, H, Mo, C & Huang, Y 2025, 'Transient biomass-SOFC-energy storage hybrid system for microgrids peak shaving based on optimized regulation strategy', Journal of Energy Storage, vol. 105, pp. 114668-114668.
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Pan, Y, Fan, W, Lv, M, Sun, X & Yu, S 2025, 'An Incremental Scalable Network Architecture With Fault-Tolerant Communication', IEEE Transactions on Reliability, pp. 1-15.
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Pang, Y, Nguyen, QD & Castel, A 2025, 'Implementing the effect of geopolymer concrete pore solution pH in the standard rapid migration test NT Build 492 protocol', Materials and Structures, vol. 58, no. 2.
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Abstract This research investigates the impact of the pore solution pH values on chloride content at the colour change boundary determined according to the standard rapid migration test (NT Build 492), with a focus on alkali-activated materials, so-called geopolymer. The study investigates a range of geopolymer formulations using various proportions of ground granulated blast furnace slag (GGBFS), fly ash, and calcined clay, alongside different activator concentrations, to examine their influence on the pH value of the pore solution. Findings from this study suggest that the pH value of the pore solution greatly influence in the chloride ion concentration at the colour change boundary, which should be accounted for in the calculation of the non-steady-state migration coefficients (Dnssm). It is noted that mixtures with higher GGBFS content exhibit higher pH values than mixtures containing fly ash or calcined clay, impacting the Dnssm. The results advocate for modifications to the standard NT Build 492 protocol to enhance its applicability and accuracy for geopolymer materials, emphasizing the importance of using revised Dnssm values calculated considering the unique properties of geopolymer concrete for more durability assessment.

Park, H-M, Lee, H-W, Park, C-B, Guo, Y & Lee, J-B 2025, 'Solution to Mitigate the Disadvantages in the Design of LLC Resonant Converters for Electric Vehicle Fast Chargers with High Output Power and Wide Output Voltage Range', The transactions of The Korean Institute of Electrical Engineers, vol. 74, no. 2, pp. 316-325.
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Parnell, J, Unanue, IJ, Matthews, S & Piccardi, M 2025, 'APG: Automatic Prompt Generation for Improved Document Summarization', IEEE Transactions on Audio, Speech and Language Processing, pp. 1-15.
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Parsa, SM, Chen, Z, Feng, S, Yang, Y, Luo, L, Ngo, HH, Wei, W, Ni, B-J & Guo, W 2025, 'Metal-free nitrogen-doped carbon-based electrocatalysts for oxygen reduction reaction in microbial fuel cells: Advances, challenges, and future directions', Nano Energy, vol. 134, pp. 110537-110537.
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Paul, A & Saha, SC 2025, 'A Systematic Literature Review on Flexible Strategies and Performance Indicators for Supply Chain Resilience', Global Journal of Flexible Systems Management, vol. 26, no. S1, pp. 207-231.
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Abstract Supply chain resilience is a widely useful concept for managing risk and disruption. Designing strategies for preparedness, response, and recovery can help businesses to mitigate risks and disruptions. Among them, flexible strategies can effectively improve supply chain resilience. In the literature, several studies have considered different types of flexible strategies and investigated their impacts on supply chain resilience. However, a systematic literature review (SLR) paper on this topic can further help to understand the scientific progress, research gaps, and avenues for future research. Hence, this study aims to explore how the literature has contributed to the area of flexible strategies and the impact on supply chain resilience performance. To achieve our objective, we apply an SLR methodology to identify themes such as research areas and key findings, contexts and industry sectors, methodologies, and key strategies and performance indicators in the connection between flexible strategies and supply chain resilience. The findings show that many studies connect flexible strategies to supply chain resilience. However, research gaps exist in analysing relationships between flexible strategies and performance, conducting comparative studies, developing dynamic resilience plans, applying flexible strategies, conducting theoretically grounded empirical studies, and applying multiple analytical tools to develop decision-making models for supply chain resilience. Finally, this study suggests several future research opportunities to advance the research on the topic. The findings can be a benchmark for researchers who are interested in conducting research in the area of flexible strategies and supply chain resilience.

Pei, J, Jiang, Z, Men, A, Wang, H, Luo, H & Wen, S 2025, 'Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-Identification', IEEE Internet of Things Journal, pp. 1-1.
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Peng, R, Ji, J, Guo, R, Zheng, B, Miao, Z & Zhou, J 2025, 'Fixed-time and predefined-time group-bipartite consensus for uncertain networked Euler-Lagrange systems', Information Sciences, vol. 689, pp. 121451-121451.
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Peng, Z, Zhong, L, Li, Y, Feng, S, Mou, J, Miao, Y, Lin, CSK, Wang, Z & Li, X 2025, 'Harnessing oleaginous protist Schizochytrium for docosahexaenoic acid: Current technologies in sustainable production and food applications', Food Research International, vol. 205, pp. 115996-115996.
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Pennington, W, Bandara, M, Peloquin, A & McMillen, C 2025, 'Where are the crystals? X-DES: Deep Eutectic Solvents based on Halogen Bonding', Structural Dynamics, vol. 12, no. 2_Supplement, pp. A134-A134.
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As a crystallographer, nothing is sadder than failed crystal growth. As an “experienced” crystallographer (a basketball ref couldn't hand signal my number of years), I’ve thrown away more than my share of oily smudges (always according to established hazardous waste procedures, of course). Fortunately, my students are more curious than I. Recently we reported the first halogen-bonding-based deep eutectic solvent, which consisted of a mixture of 1,3-dithiane and o- diiodotetrafluorobenzene – a system that simply refused to cooperate during crystal growth (Peloquin et al. Angew Chem, Int. Ed.2021, 60, 22983–22989). Based on this result and memories of many other liquid samples, we have been exploring a number of different systems, most consisting of tetraalkylammonium triiodides with a variety of organoiodines. As it turns out, many of these systems that resist crystal growth are doing exactly what they are “supposed” to do. Most of these are pseudo binary systems with two or more eutectic points and at least one cocrystalline composition (see phase diagram below). The preparation and characterization of these systems by thermal analysis and, in the case of cocrystals, structural characterization will be discussed. Efforts to correlate halogen bonding in triiodide-based cocrystals with halogen bonding in triiodide-based deep eutectic solvents are now in progress.

Pfeffer, MA, Nguyen, AHP, Kim, K, Wong, JKW & Ling, SH 2025, 'Evolving optimized transformer-hybrid systems for robust BCI signal processing using genetic algorithms', Biomedical Signal Processing and Control, vol. 108, pp. 107883-107883.
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Phuong, J, Lam, R, Moles, R, Mason, D, White, C, Center, J & Carter, S 2025, 'The design and evaluation of a bone health teaching module for secondary school students in NSW, Australia', Health Promotion Journal of Australia, vol. 36, no. 1.
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AbstractIssue AddressedThe growing prevalence of osteoporosis requires preventative management starting from an early age as peak bone mass is typically reached by age 30. However, current Australian adolescents are not adequately addressing key osteoprotective factors. Alarmingly, around 17% have insufficient vitamin D levels, 55% consume insufficient dietary calcium, and 79% are insufficiently active. Addressing these insufficiencies via bone health education and promoting healthier lifestyle choices are crucial to mitigate the risk of osteoporosis later in life.MethodsA mixed methods study was undertaken to assess the design and effectiveness of four bone health education modules implemented in PDHPE lessons across NSW secondary schools. Pre‐ and post‐module assessments included a multiple‐choice questionnaire on osteoporosis knowledge, and a survey based on the Theory of Planned Behaviour domains to examine influences on healthy bone behaviour. Statistical analysis, qualitative interviews, and focus groups were used to evaluate changes in knowledge and behaviour resulting from the modules.ResultsParticipation in bone health teaching modules improved students' knowledge and behaviours related to bone health. Subjective norms had the largest influence regarding behaviour changes. Both students and teachers engaged positively with the bone health modules, which were designed by clinicians and delivered by teachers.So What?The modules address knowledge gaps and provide strategies from an early age, empowering students and potentially contribute to improving long term bone health. There is a need to focus on promoting positive peer influence and facilitating easy access to bone‐healthy behaviours in se...

Phuong, J, Moles, R, Mason, D, White, C, Center, J & Carter, S 2025, 'Osteoporosis screening in Australian community pharmacies: A mixed methods study', Health Promotion Journal of Australia, vol. 36, no. 1.
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AbstractIssues AddressedOsteoporosis and poor bone health impact a large proportion of the Australian population, but is drastically underdiagnosed and undertreated. Community pharmacies are a strategic location for osteoporosis screening services due to their accessibility and the demographic profile of customers. The aim of this study was to develop, implement and evaluate a community pharmacy health promotion service centred on encouraging consumers to complete an anonymous osteoporosis screening survey called Know Your Bones.MethodsThe implementation process was documented using the REAIM (reach, effectiveness, adoption, implementation, maintenance) framework. Uptake of the Know Your Bones screening tool was monitored anonymously with website traffic. Surveys and interviews were designed to capture consumer outcomes after screening. Semi‐structured interviews were conducted with Australian community pharmacy stakeholders during design and implementation phases to explore their perspectives of the barriers and facilitators.ResultsThe service was implemented in 27 community pharmacies. There were 448 visits to the screening website. Interviews were conducted with 41 stakeholders. There were a range of factors that appeared to influence implementation of the service. Perceived acceptability was critical, which depended on staff training, pharmacists' altruism, and remuneration. Staff relied heavily on their existing close relationships with consumers. No consumers completed non‐anonymous surveys or agreed to participate in interviews post‐screening.ConclusionUsing an implementation science approach, a community pharmacy osteoporosis screening service for the Australian context was designed and found ...

Picano, B, Hoang, DT & Nguyen, DN 2025, 'A Matching Game for LLM Layer Deployment in Heterogeneous Edge Networks', IEEE Open Journal of the Communications Society, vol. 6, pp. 3795-3805.
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Pichot, C, Huff, GH, Yang, Y, Jiang, ZH, Tong, X, Loh, TH, Alemaryeen, A, Wagih, M, Noghanian, S, Paryani, RC, Huang, Y, Sakakibara, K, Vipiana, F, Fear, E, Bakshi, SC, Waterhouse, R, Urbina, J, Meinrath, SD, Shih, BP-J, Sharma, R, Manohar, V, Dagefu, FT & Ito, K 2025, 'New and Emerging Directions in the Fields of Antennas and Propagation', IEEE Transactions on Antennas and Propagation, vol. 73, no. 1, pp. 566-581.
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Ping, J, Zhu, S, Luo, W, Zhang, Z, Wen, S & Mu, C 2025, 'Hybrid-Dependent Event-Triggered Schemes for T–S Fuzzy Memristive NNs With Nondifferentiable Delay', IEEE Transactions on Fuzzy Systems, vol. 33, no. 4, pp. 1158-1167.
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Poblete, P, Aguilera, RP, Pereda, J, Cuzmar, RH, Alcaide, AM, Lu, DD-C, Siwakoti, YP & Acuna, P 2025, 'Offset-Free Optimal Control of Cascaded H-Bridge Converters Based on a Kalman Filter Harmonic Compensator', IEEE Transactions on Power Electronics, vol. 40, no. 1, pp. 625-637.
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Poblete, P, Cuzmar, RH, Aguilera, RP, Pereda, J, Alcaide, AM, Lu, D, Siwakoti, YP & Konstantinou, G 2025, 'Dual-Stage MPC for SoC Balancing in Second-Life Battery Energy Storage Systems Based on Delta-Connected Cascaded H-Bridge Converters', IEEE Transactions on Power Electronics, vol. 40, no. 1, pp. 500-515.
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Ponrekha A., S, Subathra, MSP, Bharatiraja, C, Manoj Kumar, N & Haes Alhelou, H 2025, 'A topology review and comparative analysis on transformerless grid‐connected photovoltaic inverters and leakage current reduction techniques', IET Renewable Power Generation, vol. 19, no. 1.
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AbstractPhotovoltaic energy source growth is significant in power generation field. Moreover, grid connected inverters strengthen this growth. Development of transformerless inverters with higher efficiency, low cost and size is competitive than the inverters with transformers. However, leakage current generation in transformerless inverters is a challenge to their growth. The research in evolution of new transformerless inverter topologies with higher efficiency, boosting capability, and reduced leakage current is interesting. This paper presents an extensive discussion of transformerless inverters under the categorization of their structures and the subcategorization with leakage current reduction techniques. The components and connections of inverters are differentiated with colours for the effortless understanding of operation. The detailed comparisons of transformerless inverters based on performance and construction are also presented with their strengths and weaknesses. To give deep intuition on characteristics of transformerless inverters, selected inverters are simulated with different operating conditions. Loss contribution of each switch in the selected inverters is analysed, to help the new researchers, designers and engineers to design efficient topologies.

Poyraz, M, Poyraz, AK, Dogan, Y, Gunes, S, Mir, HS, Paul, JK, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Molinari, F & Acharya, R 2025, 'BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images', Cognitive Neurodynamics, vol. 19, no. 1.
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Abstract The main aim of this study is to propose a novel convolutional neural network, named BrainNeXt, for the automated brain disorders detection using magnetic resonance images (MRI) images. Furthermore, we aim to investigate the performance of our proposed network on various medical applications. To achieve high/robust image classification performance, we gathered a new MRI dataset belonging to four classes: (1) Alzheimer's disease, (2) chronic ischemia, (3) multiple sclerosis, and (4) control. Inspired by ConvNeXt, we designed BrainNeXt as a lightweight classification model by incorporating the structural elements of the Swin Transformers Tiny model. By training our model on the collected dataset, a pretrained BrainNeXt model was obtained. Additionally, we have suggested a feature engineering (FE) approach based on the pretrained BrainNeXt, which extracted features from fixed-sized patches. To select the most discriminative/informative features, we employed the neighborhood component analysis selector in the feature selection phase. As the classifier for our patch-based FE approach, we utilized the support vector machine classifier. Our recommended BrainNeXt approach achieved an accuracy of 100% and 91.35% for training and validation. The recommended model obtained the test classification accuracy of 94.21%. To further improve the classification performance, we suggested a patch-based DFE approach, which achieved a test accuracy of 99.73%. The obtained results, surpassing 90% accuracy on the test dataset, demonstrate the effectiveness and high classification performance of the proposed models.

Pradhan, S, Pradhan, B & Joshi, A 2025, 'Leveraging geographic information systems (GIS) in water, sanitation, and hygiene (WASH) research: a systematic review of applications and challenges', Spatial Information Research, vol. 33, no. 2.
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Abstract Safe drinking water, sanitation, and hygiene (WASH) are essential for the health, well-being, and socio-economic development of communities. Despite global efforts, the challenge of providing safe access to WASH service persists, particularly in low- and middle-income countries. Geographic Information Systems (GIS) play a pivotal role in understanding and addressing these challenges by enabling the monitoring, mapping, and analysis of WASH facilities and their impacts. This systematic literature review aims to comprehensively understand how GIS is being used in WASH research. The review reveals that GIS is being used in various aspects of WASH, including mapping and monitoring of WASH facilities, spatial analysis of WASH-related health outcomes, and planning. The review also highlights the challenges of using GIS in WASH, such as data availability and quality, integration of technological advancement and adoption of a comprehensive approach. The review provides valuable insights for researchers, practitioners, and policymakers working in the field of WASH.

Qi, J, Li, L, Hossain, J & Lei, G 2025, 'Optimizing electric vehicle parking lot profitability through vehicle-to-grid incentive decision-making in multiple energy markets', Sustainable Energy, Grids and Networks, vol. 41, pp. 101595-101595.
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Qian, H, Li, J, Pan, Y, Zong, Z & Wu, C 2025, 'Blast performance of precast segmental utility tunnel against ground surface explosion. Part 1: Experimental analysis', Structures, vol. 71, pp. 108192-108192.
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Qian, J, Wang, Y, Xue, Y, Begum, H, Fu, Y-Q & Lee, JE-Y 2025, 'Integrated functions of microfluidics and gravimetric sensing enabled by piezoelectric driven microstructures', Applied Physics Reviews, vol. 12, no. 1.
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Micro- and nano-electromechanical systems resonators have been regarded as powerful tools for precision mass detection, and their abilities to measure these in a liquid environment open various opportunities for biosensing, chemical analysis, and environmental monitoring. Apart from overcoming issues of fluidic damping and electrical interfaces, there is a great challenge of bringing microanalytes to these devices with the required precision and scaling for high throughput sensing. Herein, we address the above challenges by proposing a self-excited localized acoustic manipulation methodology based on a piezoelectric micromechanical diaphragm resonator (PMDR). Such a PMDR integrates acoustofluidics and mass sensing functions in tandem on a single device. Particle enrichment is realized within tens of seconds and the limit of detection is enhanced by mitigating common issues such as low capture rate and non-uniform distribution. The developed PMDR is versatile in its applicability to a range of particle sizes and densities for both acoustofluidic actuation and in situ mass sensing. This work addresses long-term technical challenges of inaccurate and inefficient measurement of liquid phase resonance mass sensing with great application potentials in biochemical detection and environmental monitoring.

Qiao, S, Liu, X, Wen, S, Xiao, G, Chen, B & Ge, SS 2025, 'Event-triggered memory sliding mode load frequency control of power system with BESSs against frequency-based deception attacks', Journal of the Franklin Institute, vol. 362, no. 7, pp. 107644-107644.
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Qin, JC, Jiang, R, Mo, H & Dong, D 2025, 'A data-driven mixed integer programming approach for joint chance-constrained optimal power flow under uncertainty', International Journal of Machine Learning and Cybernetics, vol. 16, no. 2, pp. 1111-1127.
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Abstract This paper introduces a novel mixed integer programming (MIP) reformulation for the joint chance-constrained optimal power flow problem under uncertain load and renewable energy generation. Unlike traditional models, our approach incorporates a comprehensive evaluation of system-wide risk without decomposing joint chance constraints into individual constraints, thus preventing overly conservative solutions and ensuring robust system security. A significant innovation in our method is the use of historical data to form a sample average approximation that directly informs the MIP model, bypassing the need for distributional assumptions to enhance solution robustness. Additionally, we implement a model improvement strategy to reduce the computational burden, making our method more scalable for large-scale power systems. Our approach is validated against benchmark systems, i.e., IEEE 14-, 57- and 118-bus systems, demonstrating superior performance in terms of cost-efficiency and robustness, with lower computational demand compared to existing methods.

Qin, Y, Zhang, X, Yu, S & Feng, G 2025, 'A survey on representation learning for multi-view data', Neural Networks, vol. 181, pp. 106842-106842.
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Qin, Y, Zhang, Y, Chen, Y, Lin, S, Shu, Y, Huang, Y, Huang, X & Zhou, M 2025, 'Impact of Snow on Underground Smoldering Wildfire in Arctic-Boreal Peatlands', Environmental Science & Technology, vol. 59, no. 8, pp. 3915-3924.
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Qing, S, Wang, H & Wen, S 2025, 'Fixed/preassigned-time non-chattering synchronization of nonlinear coupled Cohen–Grossberg neural networks via event-triggered control', Neurocomputing, vol. 617, pp. 129069-129069.
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Qiu, H, Wang, H, Li, F & Wen, S 2025, 'Intermittent dynamic event-triggered control for synchronization of Takagi–Sugeno fuzzy competitive neural networks with leakage delay and different time scales', Fuzzy Sets and Systems, vol. 498, pp. 109130-109130.
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Qiu, N, Ding, Y, Guo, J & Fang, J 2025, 'Energy dissipation of sand-filled TPMS lattices under cyclic loading', Thin-Walled Structures, vol. 209, pp. 112848-112848.
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Qu, F, Zhang, Y, Li, M, Dong, W, Li, W & Tsang, DCW 2025, 'Resource recycling of industrial waste phosphogypsum in cementitious materials: Pretreatment, properties, and applications', Journal of Environmental Management, vol. 376, pp. 124291-124291.
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Rahimi, I, Li, M, Choon, J, Pamuspusan, D, Huang, Y, He, B, Cai, A, Nikoo, MR & Gandomi, AH 2025, 'Optimizing renewable energy site selection in rural Australia: Clustering algorithms and energy potential analysis', Energy Conversion and Management: X, vol. 25, pp. 100855-100855.
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Rahimi, I, Yazdanjue, N, Khorshidi, MS, Nikoo, MR, Chen, F & Gandomi, AH 2025, 'Variable interaction network analysis to enhance boundary update method for constrained optimization', Results in Engineering, vol. 25, pp. 103727-103727.
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Rahman, M, Haque, SA & Trianni, A 2025, 'Barriers to TQM implementation in SMEs in Bangladesh: an interpretive structural modeling approach', The TQM Journal, vol. 37, no. 2, pp. 319-344.
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PurposeThis study aims to recognize the significant barriers of small and medium-sized enterprises (SMEs) in Bangladesh, hindering the adoption of total quality management (TQM). Additionally, this research intends to explore the interrelations among these barriers to develop essential managerial insights for promoting TQM implementation in SMEs.Design/methodology/approachThe interpretive structural modeling (ISM) approach and Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) a cross-impact matrix multiplication applied to classification show the relationship among the barriers and classification of the barriers to TQM implementation respectively, and partial least squares structural equation modeling (PLS-SEM) is applied for ISM model validation.FindingsThis study examined previous literature and conducted interviews with professionals to identify 17 barriers. The study then develops and investigates a model that outlines the relationships and priorities among these barriers and categorizes them based on their impact and interdependence. This analysis can assist SMEs in implementing TQM during their operations successfully.Practical implicationsThis research emphasizes the crucial obstacles that greatly affect other barriers and require immediate attention. Furthermore, this study provides valuable information for SMEs to effectively prioritize their resources and efforts to overcome these obstacles.Originality/valueThis study delves into the primary obstacles impedi...

Ramadan, HS, Haes Alhelou, H & Ahmed, AA 2025, 'Impartial near‐optimal control and sizing for battery hybrid energy system balance via grey wolf optimizers: Lead acid and lithium‐ion technologies', IET Renewable Power Generation, vol. 19, no. 1.
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AbstractThe balance of renewable‐energy‐based power systems has witnessed significant importance particularly with their rapid integration within these systems. The optimal sizing and control of energy storage systems (ESS) in hybrid power systems (HPSs) based on renewable energy becomes of particular interest. In this research, the HPS under study comprises PV, wind, and energy storage system. Two battery technologies, lead acid (LA) and lithium‐Ion (LI)—are conducted to reach a near‐optimal solution via metaheuristic optimization algorithms in HPS. This paper aims at reaching the equilibrium of the generation consumption for HPS through applying a novel technique, grey wolf optimization (GWO) through the optimal battery sizing of the HPS. The optimization is used for reaching the due balance between the production of power and that absorbed by the load, by minimizing the difference between the final and initial state of charge. Based on numerical simulations, the two different battery technologies are considered in the sizing of the ESS using GWO approach. From the simulation results, the proposed GWO leads to more enhanced performance with LI rather than LA by 3.1% with reduced number of parallel/series cells (Np/Ns) of 240/3450 and 270/3500. Accordingly, the GWO provides an adequate dynamic controlled performance.

Rathod, S, Sahni, M & Merigo, JM 2025, 'Development and Applications of Penalty‐Based Aggregation Operators in Multicriteria Decision Making', International Journal of Intelligent Systems, vol. 2025, no. 1.
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This article develops a new penalty‐based aggregation operator known as the penalty‐based induced ordered weighted averaging (P‐IOWA) operator which is an extension of penalty‐based ordered weighted averaging (P‐OWA) operator. Our goal is to figure out how the induced variable realigns penalties when gathering information. We extend the P‐OWA and P‐IOWA operators with the different means such as generalized mean and quasi‐arithmetic mean. This article also includes different families of P‐OWA and P‐IOWA operators. The value of these new operators is demonstrated through a case study centered on investment matters. This study evaluates the economic and governance performance of seven South Asian nations utilizing nine indicators from 2021 data. The research examines 5 economic indicators including GDP growth, exports and imports (% of GDP), inflation, and labor force metrics, alongside 4 governance indicators focusing on corruption control, government effectiveness, and political stability. We use min–max normalization to standardize the varied values, which originally ranged from 0.5% to 77.7% across various metrics. Following this, the normalized inverse penalty method is used to derive optimal weights for these indicators, tackling the task of combining multidimensional data. Subsequently, we implement and evaluate various penalty‐based aggregation methodologies on the normalized data, each offering a distinct approach to penalizing outliers and balancing indicator weights. The study compares the results obtained from these operators to assess their impact on country rankings and overall performance evaluation. This approach allows for a comprehensive comparison of countries’ performances, integrating both economic and governance dimensions into a single, quantifiable framework.

Rawat, S, Cui, H, Xie, Y, Guo, Y, Lee, CK & Zhang, Y 2025, 'An improved framework for multi-objective optimization of cementitious composites using Taguchi-TOPSIS approach', Expert Systems with Applications, vol. 272, pp. 126732-126732.
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Rawat, S, Lee, CK, Fanna, DJ, George, L & Zhang, YX 2025, 'Mechanism and effect of Re-curing on strength recovery of fire-damaged high strength engineered cementitious composite', Construction and Building Materials, vol. 461, pp. 139920-139920.
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Rawat, S, Zhang, L & Zhang, YX 2025, 'Fire-induced spalling in hybrid polyethylene fiber-reinforced engineered cementitious composite panels', Engineering Structures, vol. 338, pp. 120589-120589.
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Raza, A, Keshavarz, R & Shariati, N 2025, 'A Multiband Forward–Backward Nonlinear Metamaterial Network: An Agile Solution for Wireless Power Transfer, Sensing, and Communication', IEEE Transactions on Microwave Theory and Techniques, pp. 1-14.
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Raza, A, Keshavarz, R & Shariati, N 2025, 'In Situ Soil Moisture Monitoring Using Compact Multiband Sensing System for Smart Agriculture', IEEE Transactions on AgriFood Electronics, pp. 1-12.
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Raza, MA, Abolhasan, M, Lipman, J & Ni, W 2025, 'Device-Level Learning-Based Distributed Power Control for Uplink NOMA in IoT Networks', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Rembe, C, Halkon, B & Ismail, M 2025, 'Measuring Vibrations in Large Structures with LDVy and UAS: A Review and Outlook', Advanced Devices & Instrumentation.

Riayatsyah, TMI, Silitonga, AS, Kalam, MA & Fattah, IMR 2025, 'Optimisation of trimethylolpropane ester synthesis from waste cooking oil methyl ester by response surface methodology, and its physicochemical properties and tribological characteristics', Results in Engineering, vol. 25, pp. 104055-104055.
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Riaz, HH, Lodhi, AH, Munir, A, Zhao, M, Ali, MH, Sauret, E, Gu, Y & Islam, MS 2025, 'Breath of pollutants: How breathing patterns influence microplastic accumulation in the human lung', International Journal of Multiphase Flow, vol. 185, pp. 105156-105156.
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Riyad, ASM, Indraratna, B, Arachchige, CK, Qi, Y & Khabbaz, H 2025, 'An Extended Perspective on the Disturbed State Concept for Rubber–Mixed Waste Material Considering Modulus Degradation under Cyclic Loading', International Journal of Geomechanics, vol. 25, no. 5.
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Riyad, ASM, Indraratna, B, Qi, Y & Tawk, M 2025, 'Constitutive behaviour of a granular matrix containing coal mine waste intermixed with rubber crumbs', Acta Geotechnica, vol. 20, no. 1, pp. 185-196.
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AbstractTraditional railway substructure materials (i.e., natural crushed rock aggregates used for ballast and capping layers) degrade under service loads, incurring higher periodic maintenance costs compared to recycled materials. Using recycled waste materials such as coal wash and rubber crumbs for infrastructure upgrades not only reduces construction and maintenance costs but also supports environmental sustainability. By exploring unconventional avenues, earlier studies have delved into the viability of blending rubber crumbs (RC) and coal wash (CW) as an innovative substitute for traditional railway substructure materials, with a specific focus on the capping layer. This study introduces a semi-empirical constitutive model to simulate the response of mixtures of coal wash and rubber crumbs (CWRC) using the bounding surface plasticity framework. The novelty of this study is that a modified volumetric strain expression is introduced to capture the compressibility of rubber, thus enabling a more accurate representation of the internal deformation of rubber within the granular matrix. The variation of rubber content in the mixture is captured by the corresponding critical state void ratio surface and the hardening modulus. The theoretical model is then calibrated and validated using static drained triaxial test data for CWRC mixtures as well as mixtures of steel furnace slag, coal wash, and rubber crumbs (SFS + CW + RC).

Roohani, I, Wang, S, Xu, C, Newman, P, Entezari, A, Lai, Y & Zreiqat, H 2025, 'Bioinspired Nanoscale 3D Printing of Calcium Phosphates Using Bone Prenucleation Clusters', Advanced Materials, vol. 37, no. 13.
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AbstractCalcium phosphates (CaPs) are ubiquitous in biological structures, such as vertebrate bones and teeth, and have been widely used in biomedical applications. However, fabricating CaPs at the nanoscale in 3D has remained a significant challenge, particularly due to limitations in current nanofabrication techniques, such as two‐photon polymerization (2pp), which are not applicable for creating CaP nanostructures. In this study, a novel approach is presented to 3D print CaP structures with unprecedented resolution of ≈300 nm precision, achieving a level of detail three orders of magnitude finer than any existing additive manufacturing techniques for CaPs. This advancement is achieved by leveraging bioinspired chemistry, utilizing bone prenucleation nanoclusters (PNCs, average size of 5 nm), within a photosensitive resin. These nanoclusters form a highly transparent photoresist, overcoming the light‐scattering typically associated with larger calcium phosphate‐based nanoparticles. This method not only allows for nanopatterning of CaPs on diverse substrates, but also enables the precise control of microstructure down to the level of submicron grains. The method paves the way for the developing of bioinspired metamaterials, lightweight damage‐tolerant materials, cell‐modulating interfaces, and precision‐engineered coatings.

Ruan, B, Li, J, Cheng, Y, Nie, R, Shan, F & Teng, J 2025, 'Orthogonal investigation of factors influencing the splitting tensile strength of cemented aeolian sand reinforced with hybrid fibers', Case Studies in Construction Materials, vol. 22, pp. e04596-e04596.
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Rujikiatkamjorn, C, Ishikawa, T, Prezzi, M & Winter, M 2025, 'Editorial for Transportation Geotechnics and Emerging Technologies', Transportation Geotechnics, vol. 50, pp. 101372-101372.
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Rujikiatkamjorn, C, Xue, J & Indraratna, B 2025, 'Preface', Lecture Notes in Civil Engineering, vol. 403 LNCE, pp. v-vi.

Rujikiatkamjorn, C, Xue, J & Indraratna, B 2025, 'Preface', Lecture Notes in Civil Engineering, vol. 402 LNCE, pp. v-vi.

Ruppert, MG, Routley, BS, McCourt, LR, Yong, YK & Fleming, AJ 2025, 'Modulated-Illumination Intermittent-Contact Tip-Enhanced Raman Spectroscopy', Nano Letters, vol. 25, no. 14, pp. 5656-5662.
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Sabahi, N, Roohani, I, Wang, CH & Li, X 2025, 'Material extrusion 3D printing of bioactive smart scaffolds for bone tissue engineering', Additive Manufacturing, vol. 98, pp. 104636-104636.
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Saha, BK, Jihan, JI, Barai, G, Moon, NJ, Saha, G & Saha, SC 2025, 'Exploring natural convection and heat transfer dynamics of Al2O3-H2O nanofluid in a modified tooth-shaped cavity configuration', International Journal of Thermofluids, vol. 25, pp. 101005-101005.
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Saha, G, Shahrear, P, Faiyaz, A & Saha, AK 2025, 'Mathematical modeling of lumpy skin disease: New perspectives and insights', Partial Differential Equations in Applied Mathematics, vol. 14, pp. 101218-101218.
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Salih, AK, Irvine, CP, Matar, F, Aditya, L, Nghiem, LD & Ton-That, C 2025, 'Photocatalytic self-cleansing ZnO-coated ceramic membranes for preconcentrating microalgae', Journal of Membrane Science, vol. 718, pp. 123700-123700.
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Salmanipour, S, Sokhansanj, A, Jafari, N, Hamishehkar, H & Saha, SC 2025, 'Engineering nanoliposomal tiotropium bromide embedded in a lactose-arginine carrier forming Trojan-particle dry powders for efficient pulmonary drug delivery: A combined approach of in vitro-3D printing and in silico-CFD modeling', International Journal of Pharmaceutics, vol. 671, pp. 125171-125171.
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Salomon, R, Razavi Bazaz, S, Mutafopulos, K, Gallego-Ortega, D, Warkiani, M, Weitz, D & Jin, D 2025, 'Challenges in blood fractionation for cancer liquid biopsy: how can microfluidics assist?', Lab on a Chip, vol. 25, no. 5, pp. 1097-1127.
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Microfluidic blood fractionation has a critical role in enhancing liquid biopsy. Liquid biopsy allows molecular and phenotypic characteristics of a patient's tumor by detecting evidence of cancerous changes in readily accessible samples like blood.

Sami, MS, Hoque, MA-A, Moniruzzaman, M & Pradhan, B 2025, 'Spatial landslide risk assessment in a highly populated Rohingya refugee settlement area of Cox’s Bazar, Bangladesh', Asian Geographer, vol. 42, no. 1, pp. 79-100.
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Santra, A, Wang, P, Shaker, G, Mysore, BS, Dolmans, G, Chen, Y, Shariati, N & Pandharipande, A 2025, 'Machine Learning-Powered Radio Frequency Sensing: A Review', IEEE Sensors Journal, pp. 1-1.
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Scheide, E, Best, G & Hollinger, GA 2025, 'Synthesizing compact behavior trees for probabilistic robotics domains', Autonomous Robots, vol. 49, no. 1.
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Sercek, I, Sampathila, N, Tasci, I, Ekmekyapar, T, Tasci, B, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Tan, R-S & Acharya, UR 2025, 'A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer’s disease detection', Cognitive Neurodynamics, vol. 19, no. 1.
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Abstract Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.

Sezgin, E, Jackson, DI, Kocaballi, AB, Bibart, M, Zupanec, S, Landier, W, Audino, A, Ranalli, M & Skeens, M 2025, 'Can Large Language Models Aid Caregivers of Pediatric Cancer Patients in Information Seeking? A Cross‐Sectional Investigation', Cancer Medicine, vol. 14, no. 1.
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ABSTRACTPurposeCaregivers in pediatric oncology need accurate and understandable information about their child's condition, treatment, and side effects. This study assesses the performance of publicly accessible large language model (LLM)‐supported tools in providing valuable and reliable information to caregivers of children with cancer.MethodsIn this cross‐sectional study, we evaluated the performance of the four LLM‐supported tools—ChatGPT (GPT‐4), Google Bard (Gemini Pro), Microsoft Bing Chat, and Google SGE—against a set of frequently asked questions (FAQs) derived from the Children's Oncology Group Family Handbook and expert input (In total, 26 FAQs and 104 generated responses). Five pediatric oncology experts assessed the generated LLM responses using measures including accuracy, clarity, inclusivity, completeness, clinical utility, and overall rating. Additionally, the content quality was evaluated including readability, AI disclosure, source credibility, resource matching, and content originality. We used descriptive analysis and statistical tests including Shapiro–Wilk, Levene's, Kruskal–Wallis H‐tests, and Dunn's post hoc tests for pairwise comparisons.ResultsChatGPT shows high overall performance when evaluated by the experts. Bard also performed well, especially in accuracy and clarity of the responses, whereas Bing Chat and Google SGE had lower overall scores. Regarding the disclosure of responses being generated by AI, it was observed less frequently in ChatGPT responses, which may have affected the clarity of responses, whereas Bard maintained a balance between AI disclosure and response clarity. Google SGE generated the most readable responses whereas ChatGPT answered with the most complexity. LLM tools varied significa...

Shafaghat, AH, Merenda, A, Seccombe, D, Phuntsho, S & Shon, HK 2025, 'From waste to high-value fertilisers: Harvesting nutrients from liquid anaerobic digestate for a circular bioeconomy', Desalination, vol. 596, pp. 118266-118266.
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Shafei, H, Farhangi, M, Li, L, Aguilera, RP & Alhelou, HH 2025, 'A Novel Cyber-Attack Detection and Mitigation for Coupled Power and Information Networks in Microgrids Using Distributed Sliding Mode Unknown Input Observer', IEEE Transactions on Smart Grid, vol. 16, no. 2, pp. 1667-1681.
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Shams, RA, Zowghi, D & Bano, M 2025, 'AI and the quest for diversity and inclusion: a systematic literature review', AI and Ethics, vol. 5, no. 1, pp. 411-438.
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Abstract The pervasive presence and wide-ranging variety of artificial intelligence (AI) systems underscore the necessity for inclusivity and diversity in their design and implementation, to effectively address critical issues of fairness, trust, bias, and transparency. However, diversity and inclusion (D&I) considerations are significantly neglected in AI systems design, development, and deployment. Ignoring D&I in AI systems can cause digital redlining, discrimination, and algorithmic oppression, leading to AI systems being perceived as untrustworthy and unfair. Therefore, we conducted a systematic literature review (SLR) to identify the challenges and their corresponding solutions (guidelines/ strategies/ approaches/ practices) about D&I in AI and about the applications of AI for D&I practices. Through a rigorous search and selection, 48 relevant academic papers published from 2017 to 2022 were identified. By applying open coding on the extracted data from the selected papers, we identified 55 unique challenges and 33 unique solutions in addressing D&I in AI. We also identified 24 unique challenges and 23 unique solutions for enhancing D&I practices by AI. The result of our analysis and synthesis of the selected studies contributes to a deeper understanding of diversity and inclusion issues and considerations in the design, development and deployment of the AI ecosystem. The findings would play an important role in enhancing awareness and attracting the attention of researchers and practitioners in their quest to embed D&I principles and practices in future AI systems. This study also identifies important gaps in the research literature that will inspire future direction for researchers.

Shang, C, Thai Hoang, D, Yu, J, Hao, M & Niyato, D 2025, 'Constructing the Metaverse With a New Perspective: UAV FoV-Assisted Low-Latency Imaging', IEEE Wireless Communications Letters, vol. 14, no. 1, pp. 158-162.
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Shang, D, Zhang, G & Lu, J 2025, 'Novelty-aware concept drift detection for neural networks', Neurocomputing, vol. 617, pp. 128933-128933.
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Shang, W, Wang, L, Chen, Z, Cheng, D, Liu, H, Ngo, HH, Li, J, Cao, X, Wang, Y & Zhang, J 2025, 'A novel layered porous Fe, Cu dual-loading biochar heterogeneous catalyst to guided non-free radical pathway for peroxymonosulfate activation', Journal of Colloid and Interface Science, vol. 689, pp. 137262-137262.
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Shannon, AG, Kuloğlu, B & Özkan, E 2025, 'Rhaly terraced sequences their generalizations, properties and applications', Computational and Applied Mathematics, vol. 44, no. 5.
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Abstract This paper links terraced matrices with other well-known integer sequences, such as the Hankel matrices and related Fibonacci and Lucas matrices. These, in turn, are connected with related results of Macmahon and Sloane as well as we introduce the r-Terraced matrix as a generalization of the Terraced matrix, along with its symmetric counterpart, the symmetric r-Terraced matrix. We derive key properties of these matrices, including their spectral and Euclidean norms, upper bounds for their spreads, and characteristic polynomials. To validate and exemplify the theoretical findings, we apply them to Fibonacci numbers, providing illustrative examples that strengthen the theory and confirm its accuracy. In addition to the theoretical results, we investigated how the choice of the parameter r and the matrix dimension affect the upper bounds of the spread. Our findings reveal that selecting values of $$r<1$$ r < 1 and using lower-dimensional matrices lead to tighter upper bounds while reducing computational complexity. These results highlight the practical benefits of our approach, particularly in optimization-related applications where efficiency is crucial.

Shao, Q, Sun, J, Zhu, K, Ngo, HH, Zhang, Y, Zhang, C, Zhang, S & Luo, G 2025, 'Enhanced reactor stability to shock load with hydrochar: Responses of anaerobic granular sludge', Chemical Engineering Journal, vol. 503, pp. 158274-158274.
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Shao, R, Chengqing Wu & Li, J 2025, 'Enhanced in-situ utilization of lunar simulant for fibre-reinforced high-performance concrete: Mechanical properties and cost-effectiveness for lunar applications', Journal of Materials Research and Technology, vol. 35, pp. 6849-6863.
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Shao, R, Wu, C & Li, J 2025, 'Engineering cement-free high-performance Martian concrete with enhanced in-situ utilization of soil simulant: Curing across −20 °C–40 °C and CO2-rich environments', Journal of Environmental Management, vol. 375, pp. 124426-124426.
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Shen, S, Cai, C, Shen, Y, Wu, X, Ke, W & Yu, S 2025, 'Joint Mean-Field Game and Multiagent Asynchronous Advantage Actor-Critic for Edge Intelligence-Based IoT Malware Propagation Defense', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Shen, Y, Chen, C, Zou, Z, Li, W, Zhang, Y, Zhang, H, Yu, Z, Zhao, H & Wang, G 2025, 'Room temperature aqueous-phase hydrogenation coupling with green hydrogen: Sustainable technologies innovating by efficient Co-CoO _x @NC catalyst derived from N-induced interfacial electron rearrangement', Nano Research, vol. 18, no. 2, pp. 94907118-94907118.
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Shen, Y, Shepherd, C, Ahmed, CM, Shen, S & Yu, S 2025, 'Privacy Preservation Strategies for Malware-Infected Edge Intelligence Systems: A Bayesian Stochastic Game-Based Approach', IEEE Transactions on Mobile Computing, pp. 1-15.
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Sheng, Z, Cao, Y, Yang, Y, Feng, Z-K, Shi, K, Huang, T & Wen, S 2025, 'Residual Temporal Convolutional Network With Dual Attention Mechanism for Multilead-Time Interpretable Runoff Forecasting', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 8757-8771.
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Shi, J, Zhu, Y, He, J, Xu, Z, Chu, L, Braun, R & Shi, Q 2025, 'Human Activity Recognition Based on Feature Fusion of Millimeter Wave Radar and Inertial Navigation', IEEE Journal of Microwaves, vol. 5, no. 2, pp. 409-424.
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Shi, T, Hu, C, Yu, J & Wen, S 2025, 'Fixed-time quantized synchronization of spatiotemporal networks with output-based quantization communication via boundary control', Journal of the Franklin Institute, vol. 362, no. 2, pp. 107460-107460.
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Shi, Y, Han, L, Wu, P, Dai, K, Liu, Z & Wu, C 2025, 'Design of 3D printing green ultra-high performance concrete based on binder system optimization', Case Studies in Construction Materials, vol. 22, pp. e04625-e04625.
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Shi, Z, Feng, Y, Stewart, MG & Gao, W 2025, 'Physical-informed random field technique for virtual modelling based building probabilistic vulnerability assessment', Structural Safety, vol. 115, pp. 102595-102595.
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Shon, H, Jegatheesan, V, Phuntsho, S & Shu, L 2025, 'Challenges in Environmental Science and Engineering (CESE-2023)', Process Safety and Environmental Protection, vol. 194, pp. 1384-1386.
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Shrestha, S, Huang, X, Saleem, K, Bevinakoppa, S & Jan, T 2025, 'Quantification of Interference Constraint for Small Cells in Low SINR Regime With Steepest Ascent Method', IEEE Access, vol. 13, pp. 2328-2339.
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Shu, X, Li, Z, Tian, C, Chang, X & Yuan, D 2025, 'An active learning model based on image similarity for skin lesion segmentation', Neurocomputing, vol. 630, pp. 129690-129690.
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Shuvo, SB, Alam, SS, Ayman, SU, Chakma, A, Salvi, M, Seoni, S, Barua, PD, Molinari, F & Acharya, UR 2025, 'Application of Wavelet Transformation and Artificial Intelligence Techniques in Healthcare: A Systemic Review', WIREs Data Mining and Knowledge Discovery, vol. 15, no. 2.
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ABSTRACTThe integration of wavelet transformation and artificial intelligence techniques has demonstrated significant potential in healthcare applications. Wavelet analysis enables multi‐scale signal decomposition and feature extraction that, when combined with machine and deep learning approaches, enhance the accuracy and efficiency of medical data analysis. This systematic review synthesizes 112 relevant studies from 2013 to 2023 exploring wavelet‐based artificial intelligence in healthcare. Our analysis reveals that the discrete wavelet transform dominates (43% of studies), primarily used for feature extraction from biosignals (82%) and medical images. Major applications include cardiac abnormality detection (29%), neurological disorder diagnosis (27%), and mental health assessment (16%), with classification accuracies frequently exceeding 95%. Key findings indicate a shift from traditional machine learning to deep learning approaches after 2020, with emerging trends in hybrid architectures. The review identifies critical challenges in computational efficiency, optimal wavelet selection, and clinical validation. Future developments should focus on real‐time processing optimization, interpretable deep learning models, multi‐modal data fusion, and validation on larger clinical datasets, advancing the translation of these systems into practical clinical tools.

Silitonga, AS, Milano, J, Aziz, NAM, Nurulita, B, Sebayang, AR, Mahlia, TMI, Kalam, MA, Fattah, IMR, Sebayang, AH, Mohamed, H & Ong, MY 2025, 'ANN-GWO optimization of biolubricants from black soldier fly: A value-added approach to animal waste conversion', Results in Engineering, vol. 25, pp. 104437-104437.
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Silitonga, AS, Riayatsyah, TMI, Kalam, MA, Sarifudin, A, Fattah, IMR, Muraza, O, Djaya Putra, NS, Sebayang, AR, Sebayang, AH & Hermawan, H 2025, 'Corrigendum to ‘Status, developments, and sustainability of biowaste feedstock: A review of current progress’ [Renew Sustain Energy Rev Volume 217, July 2025, 115769]', Renewable and Sustainable Energy Reviews, vol. 219, pp. 115862-115862.
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Silitonga, AS, Riayatsyah, TMI, Kalam, MA, Sarifudin, A, Fattah, IMR, Muraza, O, Putra, NSD, Sebayang, AR, Sebayang, AH & Hermawan, H 2025, 'Status, developments, and sustainability of biowaste feedstock: A review of current progress', Renewable and Sustainable Energy Reviews, vol. 217, pp. 115769-115769.
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Sima, Q, Yu, J, Wang, X, Zhang, W, Zhang, Y & Lin, X 2025, 'Deep Overlapping Community Search via Subspace Embedding', Proceedings of the ACM on Management of Data, vol. 3, no. 1, pp. 1-26.
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Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categories: algorithm-based models and Machine Learning-based (ML) models. Despite the long-standing focus on this topic within the database domain, current solutions face two major limitations: 1) Both approaches fail to address personalized user requirements in OCS, consistently returning the same set of nodes for a given query regardless of user differences. 2) Existing ML-based CS models suffer from severe training efficiency issues. In this paper, we formally redefine the problem of OCS. By analyzing the gaps in both types of approaches, we then propose a general solution for OCS named <u>S</u> parse <u>S</u> ubspace <u>F</u> ilter (SSF), which can extend any ML-based CS model to enable personalized search in overlapping structures. To overcome the efficiency issue in the current models, we introduce <u>S</u> implified <u>M</u> ulti-hop Attention <u>N</u> etworks (SMN), a lightweight yet effective community search model with larger receptive fields. To the best of our knowledge, this is the first ML-based study of overlapping community search. Extensive experiments validate the superior performance of SMN within the SSF pipeline, achieving a 13.73% improvement in F1-Score and up to 3 orders of magnitude acceleration in model efficiency compared to state-of-the-art approaches.

Singh, VK, Prakash, S, Dixit, P & Prasad, M 2025, 'EEG Signal Based Human Emotion Recognition Brain-computer Interface using Deep Learning and High-Performance Computing', Wireless Personal Communications, vol. 140, no. 1-2, pp. 165-192.
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Sohaib, O, Alshemeili, A & Bhatti, T 2025, 'Exploring AI-enabled green marketing and green intention: An integrated PLS-SEM and NCA approach', Cleaner and Responsible Consumption, vol. 17, pp. 100269-100269.
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Song, L-Z, Diao, Y-Z, Qin, P-Y, Ansari, M, von Loesecke, J, Maci, S & Jay Guo, Y 2025, 'A 3-D-Printed Broadband Wide-Angle Multibeam Flat GRIN Lens Aided by Multifocal Ray-Path Analyses', IEEE Transactions on Antennas and Propagation, vol. 73, no. 1, pp. 22-32.
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Song, X, Wang, Y, Huang, Y & Shan, C 2025, 'Gait recognition via View-aware Part-wise Attention and Multi-scale Dilated Temporal Extractor', Image and Vision Computing, vol. 156, pp. 105464-105464.
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Song, Y, Huang, L, Wang, Y, Du, Y, Song, Z, Dong, Q, Zhao, X, Qi, J, Zhang, G, Li, W & Shi, L 2025, 'Energy performance and fire risk of solar PV panels under partial shading: An experimental study', Renewable Energy, vol. 246, pp. 122910-122910.
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Song, Y, Liu, Z, Li, G, Xie, J, Wu, Q, Zeng, D, Xu, L, Zhang, T & Wang, J 2025, 'EMS: A Large-Scale Eye Movement Dataset, Benchmark, and New Model for Schizophrenia Recognition', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 9451-9462.
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Song, Y, Sun, X, Nghiem, LD, Duan, J, Liu, W, Liu, Y & Cai, Z 2025, 'Defective NH2-MIL-53(Fe) with highly exposed metal active centers for enhanced synergistic adsorption and photocatalysis of ibuprofen', Separation and Purification Technology, vol. 359, pp. 130754-130754.
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Song, Y, Yang, H, Zhao, L & Huang, S 2025, 'Guaranteed 2D Pose Graph SLAM With Bounded Noises: An Efficient Interval Approach', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 10494-10505.
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Sood, K, Liu, S, Nguyen, DDN, Kumar, N, Feng, B & Yu, S 2025, 'Alleviating Data Sparsity to Enhance AI Models Robustness in IoT Network Security Context', IEEE Transactions on Mobile Computing, vol. 24, no. 5, pp. 3764-3778.
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Steel, N, Bauer-Staeb, CMM, Ford, JA, Abbafati, C, Abdalla, MA, Abdelkader, A, Abdi, P, Abeldaño Zuñiga, RA, Abiodun, OO, Abolhassani, H, Abu-Gharbieh, E, Abukhadijah, HJ, Abu-Zaid, A, Addo, IY, Addolorato, G, Adekanmbi, V, Adetunji, JB, Adeyeoluwa, TE, Agardh, EE, Agyemang-Duah, W, Ahmad, D, Ahmed, A, Ahmed, A, Ahmed, SA, Akinosoglou, K, Akkaif, MA, Al Awaidy, S, Al Hasan, SM, Al Zaabi, OAM, Aldridge, RW, Algammal, AM, Al-Gheethi, AAS, Ali, A, Ali, MU, Ali, SS, Ali, W, Alicandro, G, Alif, SM, Al-Jumaily, A, Allebeck, P, Alrawashdeh, A, Al-Rifai, RH, Alsabri, MA, Alshahrani, NZ, Aluh, DO, Al-Wardat, M, Al-Zyoud, WA, Amiri, S, Anderlini, D, Andrei, CL, Anil, A, Anvari, S, Anyasodor, AE, Appiah, SCY, Aquilano, M, Arabloo, J, Arafat, M, Areda, D, Aremu, A, Armani, K, Armocida, B, Ärnlöv, J, Asaduzzaman, M, Astell-Burt, T, Aujayeb, A, Ausloos, M, Azadnajafabad, S, Aziz, S, Azzam, AY, Babu, GR, Badache, AC, Badiye, AD, Bahramian, S, Baig, AA, Baker, JL, Bansal, H, Bärnighausen, TW, Barone, MTU, Barrow, A, Barteit, S, Bashir, S, Bashiru, HA, Basso, JD, Bastan, M-M, Basu, S, Batra, K, Bauckneht, M, Baune, BT, Beghi, M, Beiranvand, M, Béjot, Y, Bell, ML, Bello, OO, Belo, L, Beloukas, A, Beneke, AA, Bettencourt, PJG, Bhagavathula, AS, Bhala, N, Bhaskar, S, Bisulli, F, Bjørge, T, Bodunrin, AO, Botero Carvajal, A, Bouaoud, S, Brayne, C, Brenner, H, Briggs, ADM, Briko, NI, Bugiardini, R, Buonsenso, D, Busse, R, Bustanji, Y, Caetano dos Santos, FL, Çakmak Barsbay, M, Capodici, A, Carreras, G, Carugno, A, Carvalho, F, Carvalho, M, Castaldelli-Maia, JM, Castelpietra, G, Catapano, AL, Cattaruzza, MS, Cegolon, L, Cenko, E, Cerin, E, Cerrai, S, Chaudhary, AA, Chong, B, Choudhari, SG, Chu, D-T, Chukwu, IS, Chung, S-C, Cioffi, I, Conde, J, Cortese, S, Couto, RAS, Criqui, MH, Cruz-Martins, N, Dadras, O, Dallat, MAT, D'Amico, E, D'Anna, L, Darcho, SD, Dargan, PI, Das, S, de la Torre-Luque, A, Del Bo', C, Demetriades, AK, Dervenis, N, Devleesschauwer, B, Dhali, A, Dhama, K, Dianatinasab, M, Diaz, MJ, Dongarwar, D, D'Oria, M, Doshi, OP, Dowou, RK, Duraisamy, S, Durojaiye, OC, Dziedzic, AM, Edvardsson, D, Edvardsson, K, Eikemo, TA, Ekholuenetale, M, Ekundayo, TC, El Arab, RA, Elgar, FJ, Elhadi, M, Eltaha, C, Esposito, F, Fabin, N, Fagbamigbe, AF, Fagbule, OF, Fakhri-Demeshghieh, A, Falzone, L, Farinha, CSES, Faris, PS, Fasina, FO & et al. 2025, 'Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021', The Lancet Public Health, vol. 10, no. 3, pp. e172-e188.
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Stewart, MG, Thöns, S & Beck, AT 2025, 'Assessment of risk reduction strategies for terrorist attacks on structures', Structural Safety, vol. 113, pp. 102381-102381.
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Stone, G 2025, 'Book Reviews', IEEE Electrical Insulation Magazine, vol. 41, no. 2, pp. 39-39.
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Su, SW, Zhang, Z, Celler, B & Savkin, A 2025, 'Distributed integral controllability for non-square processes: A comprehensive study and numerical analysis', Journal of the Franklin Institute, vol. 362, no. 1, pp. 107338-107338.
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Su, X, Alatas, B & Sohaib, O 2025, 'An Express Management System With Graph Recurrent Neural Network for Estimated Time of Arrival', Journal of Organizational and End User Computing, vol. 37, no. 1, pp. 1-26.
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Estimated Time of Arrival (ETA) is a crucial task in the logistics and transportation industry, aiding businesses and individuals in optimizing time management and improving operational efficiency. This study proposes a novel Graph Recurrent Neural Network (GRNN) model that integrates external factor data. The model first employs a Multilayer Perceptron (MLP)-based external factor data embedding layer to categorize and combine influencing factors into a vector representation. A Graph Recurrent Neural Network, combining Long Short-Term Memory (LSTM) and GNN models, is then used to predict ETA based on historical data. The model undergoes both offline and online evaluation experiments. Specifically, the offline experiments demonstrate a 5.3% reduction in RMSE on the BikeNYC dataset and a 6.1% reduction on the DidiShenzhen dataset, compared to baseline models. Online evaluation using Baidu Maps data further validates the model's effectiveness in real-time scenarios. These results underscore the model's potential in improving ETA predictions for urban traffic systems.

Su, X, Liu, Y, Zhong, Y, Shangguan, P, Liu, J, Luo, Z, Qi, C, Guo, J, Li, X, Lin, D, Wang, G, Wang, D, Han, T, Wang, J, Shi, B & Tang, BZ 2025, 'A Brain-Targeting NIR-II Polymeric Phototheranostic Nanoplatform toward Orthotopic Drug-Resistant Glioblastoma', Nano Letters, vol. 25, no. 9, pp. 3445-3454.
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Su, Z, Sun, X, Lei, G & Yao, M 2025, 'Improved Model-Free Predictive Current Control for SPMSM Drives With Adaptive Prediction Horizon Strategy', IEEE Transactions on Industrial Electronics, vol. 72, no. 6, pp. 5705-5715.
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Sueza Raffa, L, Ryall, M, Bennett, NS & Clemon, L 2025, 'Experimental investigation of the performance of a phase change material thermal management module under vacuum and atmospheric pressure conditions', International Journal of Heat and Mass Transfer, vol. 236, pp. 126384-126384.
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Sueza Raffa, L, Ryall, M, Cairns, I, Bennett, NS & Clemon, L 2025, 'Investigating the performance of a heat sink for satellite avionics thermal management: From ground-level testing to space-like conditions', International Journal of Heat and Mass Transfer, vol. 248, pp. 127139-127139.
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Sukkar, F, Wakulicz, J, Lee, KMB, Zhi, W & Fitch, R 2025, 'Multiquery Robotic Manipulator Task Sequencing With Gromov-Hausdorff Approximations', IEEE Transactions on Robotics, vol. 41, pp. 2843-2860.
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Sulistyo, AB, Karningsih, PD & Alvandi, S 2025, 'Developing an Industry-Specific Lean 4.0 Readiness Assessment Tool: A Case for the Chemical Sector', Jurnal Optimasi Sistem Industri, vol. 23, no. 2, pp. 283-298.
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In an era where digital transformation is increasingly imperative, many industries struggle to navigate the complexities of technological adoption and operational efficiency. Lean principles, which emphasize waste reduction and process optimization, provide a robust foundation for digital transformation, particularly in the chemical industry, where unique operational challenges exist. This research aims to develop an integrated Lean 4.0 readiness assessment tool to bridge the gap between leanness and Industry 4.0 readiness. The study begins with a literature review on existing lean and Industry 4.0 readiness measurement tools and integrates them to create a new framework, using the Indonesia Industry 4.0 Readiness Index (INDI 4.0) as a reference, tailored specifically to the chemical industry. Expert interviews are conducted to refine the assessment tool, ensuring alignment with real-world industry conditions and practical insights. A Delphi-based expert consensus method combined with a fuzzy approach for handling imprecision in indicator ratings is employed to validate the framework, resulting in five key dimensions and 86 indicators. By gathering expert input, the tool addresses the chemical industry’s specific challenges and simplifies readiness evaluation, helping companies assess their preparedness for digital transformation and identify areas for improvement. The resulting framework enables chemical companies to bridge readiness gaps and prioritize targeted enhancements. Furthermore, this tool has the potential to serve as a scalable model for other industries, fostering more efficient and strategic digital transformation aligned with Industry 4.0 objectives globally.

Sultana, M, Hoque, MA-A & Pradhan, B 2025, 'Assessing Meghna Riverbank dynamics and morphological changes in Bangladesh using geospatial techniques', Applied Geomatics, vol. 17, no. 1, pp. 147-161.
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Sun, J, Cao, Y, Yue, Y, Wen, S & Wang, Y 2025, 'Memristor-Based Parallel Computing Circuit Optimization for LSTM Network Fault Diagnosis', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 72, no. 2, pp. 907-917.
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Sun, J, Wang, J, Wen, S, Wang, Y & Wang, Y 2025, 'Neural Network Circuits for Bionic Associative Memory and Temporal Order Memory Based on DNA Strand Displacement', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 8672-8683.
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Sun, S, Peng, C, Kong, ZY, Ong, HC, Qin, Z & Yang, A 2025, 'A different strategy to reduce energy consumption for designing heterogeneous decanter-assisted advanced pressure-swing distillation process', Separation and Purification Technology, vol. 352, pp. 128280-128280.
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Sun, X, Chen, Z, Pan, M, Cai, Y, Jin, Z, Lei, G & Tian, X 2025, 'Robust Energy Management Optimization for PHEB Considering Driving Uncertainties by Using Sequential Taguchi Method', IEEE Transactions on Transportation Electrification, vol. 11, no. 2, pp. 5191-5200.
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Sun, X, Dong, Z, Cai, Y, Jin, Z, Lei, G & Tian, X 2025, 'A comprehensive review of design optimization methods for hybrid electric vehicles', Renewable and Sustainable Energy Reviews, vol. 217, pp. 115765-115765.
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Sun, X, Huang, Z, Yang, Z, Lei, G & Li, T 2025, 'Improved Model-Free Predictive Current Control for Suppressing Inverter Nonlinearity and Parametric Time-Varying of PMSM Drive Systems', IEEE Transactions on Industrial Electronics, pp. 1-10.
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Sun, X, Shi, K, Tang, H, Wang, D & Xu, G 2025, 'Educating Language Models as Promoters: Multi-Aspect Instruction Alignment With Self-Augmentation', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Sun, X, Wang, N, Yao, M & Lei, G 2025, 'Position Sensorless Control of SRMs Based on Improved Sliding Mode Speed Controller and Position Observer', IEEE Transactions on Industrial Electronics, vol. 72, no. 1, pp. 100-110.
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Sun, X, Wen, Y, Han, S & Lei, G 2025, 'Fault Diagnosis of Power Converter for Switched Reluctance Motors Based on Current Reconstruction Scheme', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 4169-4178.
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Sun, X, Wen, Y, Zhang, L, Yao, M & Lei, G 2025, 'Fault Detection and Fault-Tolerant Control of Switched Reluctance Motor Based on Dual-Sensor Current Detection Scheme', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 19-28.
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Sun, X, Wen, Y, Zhu, Y, Zhang, L, Yao, M & Lei, G 2025, 'FCS-MPC With Improved Prediction for Suppressing Torque and Current Pulsations of Switched Reluctance Motors', IEEE Transactions on Industrial Electronics, vol. 72, no. 7, pp. 6831-6839.
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Sun, X, Xu, Z, Pan, M, Sun, C & Lei, G 2025, 'Medium-to-High Speed Range Sensorless Control of SRM Drives Based on Unaligned Rotor Position Estimation', IEEE Transactions on Industrial Electronics, pp. 1-10.
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Sun, X, Xu, Z, Pan, M, Sun, C, Pan, W & Lei, G 2025, 'Sensorless Control Strategy for SRM Based on Flux Linkage in Medium to High-Speed Range', IEEE Transactions on Industrial Electronics, pp. 1-9.
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Sun, X, Zhang, S, Yang, Z, Lei, G & Li, T 2025, 'Improved DPCC and Parameter Identification for Permanent Magnet Synchronous Motors With Current Error Compensation', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Sun, Y, Hu, C, Wen, S, Yu, J & Jiang, H 2025, 'Pinning Adaptive Passivity and Bipartite Synchronization of Leaderless Fractional Spatiotemporal Networks', IEEE Transactions on Network Science and Engineering, vol. 12, no. 1, pp. 319-331.
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Sun, Y, Hu, C, Yu, J, Li, H & Wen, S 2025, 'Bipartite Complete Synchronization of Fractional Heterogeneous Networks via Quantized Control Without Gauge Transformation', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 5, pp. 3720-3731.
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Sun, Y, Hu, C, Yu, J, Wen, S & Li, H-L 2025, 'Bipartite Output Synchronization of Fuzzy Fractional Output-Coupled Networks via Membership Function-Dependent Adaptive Control', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 14147-14157.
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Sun, Z, Liang, Z, Lv, X, Zhou, Y, Liu, S, Luo, Z & Yang, Y 2025, 'C-/Ka-Band Low-Profile Circularly Polarized Shared-Aperture Antenna for CubeSat Communications', IEEE Transactions on Antennas and Propagation, vol. 73, no. 2, pp. 1221-1226.
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Sun, Z, Lv, X & Yang, Y 2025, 'Wideband Sequential Rotation Dual-Circularly Polarized Magnetoelectric Dipole Array With Polarization Independent Control for Intelligent Vehicle Communications', IEEE Transactions on Antennas and Propagation, vol. 73, no. 4, pp. 2666-2671.
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Sun, Z, Lv, X, Zhu, X, Liang, Z & Yang, Y 2025, 'A Dual-Band Circularly Polarized Antenna With Shared Aperture for Satellite-Assisted Internet of Things Communications', IEEE Internet of Things Journal, vol. 12, no. 7, pp. 8461-8469.
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Szlachta, AI, Nowak, PR, Gajewski, T & Sielicki, PW 2025, 'Response of structural elements of temporary wooden bridges subjected to contact detonation', International Journal of Protective Structures, vol. 16, no. 1, pp. 170-183.
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The use of wood as a structural element is still commonly used in constructions, not only in civil engineering but also in military applications. The access to this material, its processing and the ability to create individual joins without advanced equipment are reasons why wood remains an important material in military operations. Another important argument is the use of wooden structures to build local crossings for the benefit of the state economy. The speed of construction and the cost of making for example a driving deck are many times lower. This is quite an important factor influencing the decision of local governments to rebuild temporary crossings. Moreover, the skilful use of this material can significantly help the army in terms of durability and effective manoeuvrability. Engineering troops can build shelters for people and equipment, as well as low-water and underwater bridges and crossings for armoured fighting vehicles from wood. Any destruction of wooden structures planned by the engineering troops must be based on calculations derived from knowledge and experience. This article presents experiments on contact explosions of wooden structural elements. Pine beams with an equal diameter of 23 cm were tested in the military field. The wooden log destruction has been inventoried and the damage to particular wooden tissues was quantified by using different amounts of explosives. Also, professional military analyses were conducted to determine the explosive mass to destroy such wooden logs. Later, the computed values were compared with the results obtained in the field experiment and a damage parameter of energy criteria for pine wood was determined for the constitutive model of wood including damage.

Takal, SU, Tahiru, A-W, Fattah, IMR, Cobbina, SJ, Asare, W & Abanyie, SK 2025, 'Enhancing resilience to climate change: a comprehensive PRISMA review of agricultural and non-agricultural adaptation strategies for Ghana', Cogent Social Sciences, vol. 11, no. 1.
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Talaei, S, Zhu, X, Li, J, Yu, Y & Chan, THT 2025, 'A hybrid domain adaptation approach for estimation of prestressed forces in prestressed concrete bridges under moving vehicles', Engineering Structures, vol. 330, pp. 119904-119904.
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Talatahari, S, Chen, F & Gandomi, AH 2025, 'Developing a robust machine learning framework for predicting the behavior of large-scale structure', Journal of Building Engineering, vol. 105, pp. 112204-112204.
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Talhami, M, Alkhatib, A, Albaba, MT, Ayari, MA, Altaee, A, AL-Ejji, M, Das, P & Hawari, AH 2025, 'Modeling of flat sheet-based direct contact membrane distillation (DCMD) for the robust prediction of permeate flux using single and ensemble interpretable machine learning', Journal of Environmental Chemical Engineering, vol. 13, no. 2, pp. 115463-115463.
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Tan, X, Yang, R, Chen, S-L, Chan, KF, Chen, BJ, Cui, Z-Q, Qin, P-Y, Guo, YJ & Chan, CH 2025, 'Highly Integrated Full-Space Coding Metasurface for LP and CP Waves Manipulation Spanning Millimeter-Wave and Sub-THz Bands', Journal of Lightwave Technology, vol. 43, no. 1, pp. 288-298.
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Tang, H, Wu, S, Sun, X, Zeng, J, Xu, G & Li, Q 2025, 'TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic Recommendation', ACM Transactions on Information Systems, vol. 43, no. 1, pp. 1-27.
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Dynamic recommendation systems, where users interact with items continuously over time, have been widely deployed in real-world online streaming applications. The burst of interaction stream causes a rapid evolution of both users and items. To update representations dynamically, existing studies have investigated event-level and history-level dynamics by modeling the newly arrived interactions and aggregating historical interactions, respectively. However, most of them directly learn the representation evolution as new interactions occur, without exploring the collaboration between the newly arrived and historical interactions, thus failing to scrutinize whether those new interactions would benefit the evolution learning process when generating dynamic representations. Moreover, most of them model the two levels of dynamics independently, explicitly ignoring the inherent co-evolving correlation between them. In this work, we propose the Temporal Collaboration-Aware Graph Co-Evolution Learning (TCGC) for the dynamic recommendation scenario. First, we explore the effectiveness of collaborative information and devise the collaboration-aware indicator to guide the evolution learning process. Second, we design a temporal co-evolving graph network, enabling our framework to capture the correlation between event and history dynamics. Third, we leverage the evolution task and recommendation task together for joint training. Extensive experiments on four public datasets demonstrate the superiority and effectiveness of our proposed TCGC.

Tangirala, A, Rawat, S, Tan, KH & Lahoti, M 2025, 'Enhanced Thermal Performance of Fiber-Reinforced Cementitious Composite with High-Volume Fly Ash and Steel Slag Aggregates', Journal of Materials in Civil Engineering, vol. 37, no. 7.
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Tanveer, M, Tiwari, A, Akhtar, M & Lin, C-T 2025, 'Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-10.
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Tao, G, Tang, W, Chen, Q, Zhou, H, Nimbalkar, S, Xiao, H & Huang, Z 2025, 'Fractal Shear Strength Model for Unsaturated Soils Considering the Combined Effects of Matric Suction and Surface Tension', International Journal of Geomechanics, vol. 25, no. 7.
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Tao, G, Yang, C, Chen, Q, Nimbalkar, S, Xiao, H & Wang, Q 2025, 'Laboratory assessment of tensile properties of root-soil composite of Amorpha fruticose', Journal of Mountain Science, vol. 22, no. 3, pp. 1062-1074.
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Taoyu, W, Deng, J, Liang, Y & Shi, K 2025, 'Abstractive summarization-based academic paper title drafting', CCF Transactions on Pervasive Computing and Interaction, vol. 7, no. 1, pp. 87-96.
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Tarsitano, M, Liu Chung Ming, C, Bennar, L, Mahmodi, H, Wyllie, K, Idais, D, Al Shamery, W, Paolino, D, Cox, TR, Kabakova, I, Ralph, P & Gentile, C 2025, 'Chlorella-enriched hydrogels protect against myocardial damage and reactive oxygen species production in an in vitro ischemia/reperfusion model using cardiac spheroids', Biofabrication, vol. 17, no. 1, pp. 015006-015006.
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Abstract Microalgae have emerged as promising photosynthetic microorganisms for biofabricating advanced tissue constructs, with improved oxygenation and reduced reactive oxygen species (ROS) production. However, their use in the engineering of human tissues has been limited due to their intrinsic growth requirements, which are not compatible with human cells. In this study, we first formulated alginate–gelatin (AlgGel) hydrogels with increasing densities of Chlorella vulgaris. Then, we characterised their mechanical properties and pore size. Finally, we evaluated their effects on cardiac spheroid (CS) pathophysiological response under control and ischemia/reperfusion (I/R) conditions. Our results showed that the addition of Chlorella did not affect AlgGel mechanical properties, while the mean pore size significantly decreased by 35% in the presence of the 107 cells ml−1 microalgae density. Under normoxic conditions, the addition of 107 Chlorella cells ml−1 significantly reduced CS viability starting from 14 d in. No changes in pore size nor CS viability were measured for hydrogels containing 105 and 106 Chlorella cells ml−1. In our I/R model, all Chlorella-enriched hydrogels reduced cardiac cell sensitivity to hypoxic conditions with a corresponding reduction in ROS production, as well as protected against I/R-induced reduction in cell viability. Altogether, our results support a promising use of Chlorella-enriched Alg–Gel hydrogels for cardiovascular tissue engineering.

Thi Nguyen, H, Duong, HC, Chen, S-S, Le Quang, H, Ngo, HH, Cong, CD, Nguyen, NC, Thi Nguyen, UT & Huynh, DD 2025, 'Enhancing membrane distillation stability: Isoamyl alcohol coagulation as a novel strategy to mitigate membrane swelling at elevated temperatures', Environmental Technology & Innovation, vol. 37, pp. 104029-104029.
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Thomas, PS, Mansell, B, Appadoo, D, Smallwood, AS, Aldridge, L & Stuart, BH 2025, 'Characterisation of the crystallisable water in precious opal using differential scanning calorimetry and synchrotron terahertz spectroscopy', Journal of Thermal Analysis and Calorimetry, vol. 150, no. 2, pp. 1093-1103.
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Tian, H, Liu, B, Zhu, T, Zhou, W & Yu, PS 2025, 'Distilling Fair Representations From Fair Teachers', IEEE Transactions on Big Data, vol. 11, no. 3, pp. 1419-1433.
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Tian, H, Liu, B, Zhu, T, Zhou, W & Yu, PS 2025, 'MultiFair: Model Fairness With Multiple Sensitive Attributes', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, pp. 5654-5667.
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Tian, H, Zhang, G, Liu, B, Zhu, T, Ding, M & Zhou, W 2025, 'Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers', IEEE Transactions on Dependable and Secure Computing, pp. 1-16.
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Tian, Z, Wang, W, Zhang, C & Yu, S 2025, 'Model-Enabled Task-Oriented Semantic Communications Through Knowledge Synchronization', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Tian, Z, Zhang, C, Wang, W, Bogucka, H & Yu, S 2025, 'ROSE: A Receiver-Oriented Semantic Communication Framework', IEEE Network, vol. 39, no. 2, pp. 216-223.
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Tofigh, F & Lipman, J 2025, 'Human Presence Detection Sensor Using Polarization Insensitive Metamaterial Absorbers', IEEE Access, vol. 13, pp. 81725-81735.
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Tong, C, Li, J, Sun, Q, Zhang, S, Zhou, W & Sheng, D 2025, 'A noise‐based framework for randomly generating soil particle with realistic geometry', Computer-Aided Civil and Infrastructure Engineering, vol. 40, no. 13, pp. 1829-1846.
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AbstractParticle morphology influences the mechanical behavior of granular soils. Generating particles with realistic shapes for discrete element method simulations is gaining popularity. However, it is still challenging to efficiently generate very angular particles with less computational cost. Addressing this challenge, this paper introduces a novel noise‐based framework for generating realistic soil particle geometry. Noise algorithms are utilized to apply random variations with certain morphological patterns on the surface of the base geometry (e.g., a sphere), thereby generating a variety of particles with morphological patterns ranging from very angular to rounded. In addition, the base geometry can be replaced with other geometries including real particle scans, allowing rapid generation of realistic particles with morphological characteristics of the base geometry. The framework stands out for its simplicity, the wide range of particle morphologies generated, reducing the need for extensive computation and scanning, and provides a new idea for the granular soil behavior simulations.

Tong, C-X, Li, J-J, Sun, Q, He, F, Zhang, S, Zhou, W-H & Sheng, D 2025, 'Minimum mesh quality for reliable morphology characterization of 3D soil particles', Powder Technology, vol. 460, pp. 121062-121062.
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Tong, M, Huang, X & Andrew Zhang, J 2025, 'Joint Inter-Symbol Interference and I/Q Imbalance Cancellation in FTN Systems', IEEE Transactions on Wireless Communications, vol. 24, no. 4, pp. 2680-2692.
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Tong, M, Huang, X, Andrew Zhang, J & Hanzo, L 2025, 'Adaptive FTN Signaling Over Rapidly-Fading Channels', IEEE Transactions on Communications, pp. 1-1.
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Torres, ASM, Gache, CCL, Tuazon, BJ, Martinez, DWC, Kim, HT, Tijing, LD & Dizon, JRC 2025, 'Potential of 3D printing in revolutionizing solar-driven interfacial evaporation for clean water supply – A review', Applied Materials Today, vol. 43, pp. 102639-102639.
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Torres, IJ, Aguilera, RP & Ha, QP 2025, 'Design and Performance Evaluation of Nonlinear Model-Predictive Control for 3-D Ground Target Tracking With Fixed-Wing UAVs', IEEE Open Journal of the Industrial Electronics Society, vol. 6, pp. 76-94.
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Tortorella, GL, Saurin, TA, Godinho Filho, M, Alfalla-Luque, R & Trianni, A 2025, 'Are we truly ready for what is coming? A reflection on supply chain resilience in face of megatrends', International Journal of Production Economics, vol. 283, pp. 109585-109585.
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Tra, TH, Nguyen, TT, Huynh, TQ & Ishikawa, T 2025, 'Load transfer behaviour of super long piles in multi-layer soft soil through field testing and numerical 3D FEM modelling', Soils and Foundations, vol. 65, no. 3, pp. 101627-101627.
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Tran, D-T, Nguyen, T-T-H, Le, T-H & Nghiem, LD 2025, 'Facile synthesis of ZnFe2O4/rGO magnetic adsorbent for antibiotic removal', Chemical Engineering Science, vol. 305, pp. 121132-121132.
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Trianni, A, Leak, J & Hasan, ASMM 2025, 'Switching on ESCOs: Barriers, challenges and opportunities for the development of Australia's ESCO market', Energy Policy, vol. 199, pp. 114546-114546.
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Tun, NN, Lazo de la Cruz, GA, Haider, MA, Yang, Z, Wu, S, Deng, R & Agarwal, A 2025, 'Why Not for Society?', IEEE Pulse, vol. 16, no. 2, pp. 45-48.
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Tuncer, I, Baig, AH, Barua, PD, Hajiyeva, R, Massimo, S, Dogan, S, Tuncer, T & Acharya, UR 2025, 'FlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection', Biomedical Signal Processing and Control, vol. 104, pp. 107538-107538.
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Tusher, AS, Islam, MR, Hossain, MA, Ishraque, MF, Maliha, MI, Rahman, MA & Hossain, J 2025, 'FALCON: A Semi-Supervised Framework for Addressing Physical and Cyber Anomalies in DGA-based Transformer Fault Diagnosis', IEEE Transactions on Dielectrics and Electrical Insulation, pp. 1-1.
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Tyler, G, Luo, S, Calderon Hurtado, A & Makki Alamdari, M 2025, 'Semi-supervised bridge indirect structural health monitoring using Isolation Distributional Kernels', Mechanical Systems and Signal Processing, vol. 226, pp. 112296-112296.
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Usman, M, Abraz, M, Ahmad, T, Riaz, F, Rehman, F, Hayat, N, Fouad, Y, Javed, F, Mujtaba, MA & Kalam, MA 2025, 'Optimization of diesel engine characteristics using p-toluene sulfonic acid catalyst-based biodiesel from waste chicken fat oil', Alexandria Engineering Journal, vol. 116, pp. 62-72.
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Val, DV, Andrade, C, Sykora, M, Stewart, MG, Bastidas-Arteaga, E, Mlcoch, J, Truong, QC & El Soueidy, C-P 2025, 'Probabilistic modelling of deterioration of reinforced concrete structures', Structural Safety, vol. 113, pp. 102454-102454.
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Vasilescu, SA, Goss, DM, Gurner, KH, Kelley, RL, Mazi, M, De Bond, FK, Lorimer, J, Horta, F, Parast, FY, Gardner, DK, Nosrati, R & Warkiani, ME 2025, 'A biomimetic sperm selection device for routine sperm selection', Reproductive BioMedicine Online, vol. 50, no. 2, pp. 104433-104433.
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Vicnesh, J, Salvi, M, Hagiwara, Y, Yee, HY, Mir, H, Barua, PD, Chakraborty, S, Molinari, F & Rajendra Acharya, U 2025, 'Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024)', IEEE Access, vol. 13, pp. 5949-5973.
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Vu, TH, Yang, Y, Dang, LC & Sirivivatnanon, V 2025, 'Comparative analysis of chloride and acid resistance in one-part geopolymer and low carbon concrete', Magazine of Concrete Research, vol. 77, no. 7-8, pp. 448-456.
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A comparative analysis of the chloride and acid resistance of one-part geopolymer and low carbon dioxide concretes is presented, with a focus on their potential to replace traditional systems based on ordinary Portland cement (OPC) in construction. The performance of geopolymer concretes (GPCs) was compared with concretes made with OPC and OPC blended with supplementary cementitious materials (fly ash and slag) under aggressive environmental conditions. The findings revealed that, while GPCs exhibit superior resistance to acid attack, their chloride resistance is highly dependent on the specific precursor materials used. The study also highlights the limitations of using rapid chloride permeability tests at standard voltages for GPCs, suggesting that lower voltage tests at 30 V may offer a more accurate assessment. Overall, the results underscore the need for optimised precursor selection in GPCs to enhance durability and advocate for further research into testing methodologies tailored to these innovative materials.

Wang, C, Chaturvedi, K, Nham, B, Reid, N, Bradshaw, A, Rosengren, S, Black, D, Bein, K, Halmagyi, M, Braytee, A, Prasad, M, Bharathy, G & Welgampola, M 2025, 'Separating Stroke and Vestibular Neuritis Using History, Examination and Vestibular Tests: A Machine Learning Approach (S25.009)', Neurology, vol. 104, no. 7_Supplement_1.
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Wang, C, Sreerama, J, Nham, B, Reid, N, Ozalp, N, Thomas, JO, Cappelen-Smith, C, Calic, Z, Bradshaw, AP, Rosengren, SM, Akdal, G, Halmagyi, GM, Black, DA, Burke, D, Prasad, M, Bharathy, GK & Welgampola, MS 2025, 'Separation of stroke from vestibular neuritis using the video head impulse test: machine learning models versus expert clinicians', Journal of Neurology, vol. 272, no. 3.
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Abstract Background Acute vestibular syndrome usually represents either vestibular neuritis (VN), an innocuous viral illness, or posterior circulation stroke (PCS), a potentially life-threatening event. The video head impulse test (VHIT) is a quantitative measure of the vestibulo-ocular reflex that can distinguish between these two diagnoses. It can be rapidly performed at the bedside by any trained healthcare professional but requires interpretation by an expert clinician. We developed machine learning models to differentiate between PCS and VN using only the VHIT. Methods We trained machine learning classification models using unedited head- and eye-velocity data from acute VHIT performed in an Emergency Room on patients presenting with acute vestibular syndrome and whose final diagnosis was VN or PCS. The models were validated using an independent test dataset collected at a second institution. We compared the performance of the models against expert clinicians as well as a widely used VHIT metric: the gain cutoff value. Results The training and test datasets comprised 252 and 49 patients, respectively. In the test dataset, the best machine learning model identified VN with 87.8% (95% CI 77.6%–95.9%) accuracy. Model performance was not significantly different (p = 0.56) from that of blinded expert clinicians who achieved 85.7% accuracy (75.5%–93.9%) and was superior (p = 0.01) to that of the optimal gain cutoff value (75.5% accuracy (63.8%–85.7%)). Conclusion Machine learning...

Wang, C, Wang, L, Dong, L, Shon, HK & Kim, J 2025, 'Specific energy consumption of seawater reverse osmosis desalination plants using machine learning', Desalination, vol. 602, pp. 118654-118654.
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Wang, C, Wei, W, Chen, X, Liu, Y, Wijayaratna, K & Ni, B-J 2025, 'Unlocking Drivers of Country-Specific Sensitivities of Atmospheric Greenhouse Gas Accumulation: Preparing for Future Pandemic Management', Environmental Science & Technology, vol. 59, no. 1, pp. 362-372.
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Wang, C, Yan, T, Huang, W, Chen, X, Xu, K & Chang, X 2025, 'APANet: Asymmetrical Parallax Attention Network for Efficient Stereo Image Deraining', IEEE Transactions on Computational Imaging, vol. 11, pp. 101-115.
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Wang, D, Yao, H, Yu, D, Song, S, Weng, H, Xu, G & Deng, S 2025, 'Graph Intention Embedding Neural Network for tag-aware recommendation', Neural Networks, vol. 184, pp. 107062-107062.
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Wang, G, Chen, Z, Xie, J, Ding, L, Zhu, J, Wei, W, Yan, Y-M, Chu, D & Ni, B-J 2025, 'Recent trends and prospects in electrochemical nitrate reduction to ammonia with an emphasis on cobalt catalysts', Coordination Chemistry Reviews, vol. 539, pp. 216751-216751.
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Wang, H, Li, D, Cai, F, Li, Y, Wang, J & Zheng, J 2025, 'Design and experiment study on a novel magnetorheological impact damper coupled with multiple parallel relief orifices for reducing higher impact peaks', Journal of Intelligent Material Systems and Structures, vol. 36, no. 4, pp. 242-258.
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Conventional magnetorheological (MR) dampers exhibit the drawbacks of excessively large and non-adjustable peak force/acceleration during the initial impact in the context of high-impact loads, limiting their applicability in the field of impact resistance. This paper introduces an innovative MR damper (MRD), which, coupled with multiple parallel relief orifices, is capable of effectively mitigating the initial impact peak and enhancing the plateau effect during the mid and late stages of impact. Firstly, the configuration and principle of the novel MRD with multiple orifices (MRDWO) are illustrated and its nonlinear model, based on Bingham-Plastic model and accounting for minor loss, is established. Secondly, the structural design and fabrication of MRDWO were conducted. Simultaneously, for comparative purposes, a traditional MRD with an identical structure to MRDWO, except for the absence of relief orifices, was also fabricated. Then, experimental investigations were conducted separately on MRDWO and MRD without relief orifices (MRDWOO) under various impact velocities and control currents to study their damping forces, accelerations, and displacement responses. Finally, the experimental results of MRDWO were utilized to validate the reliability of the aforementioned nonlinear model. Additionally, a comparative analysis was conducted between MRDWO and MRDWOO under various operating conditions in terms of impact forces, accelerations, and displacements. The results indicate that the novel MRDWO significantly reduces the impact force/acceleration peak during the initial impact and enhances the plateau effect of force/acceleration in mid to later stages, thereby reducing the required displacement for absorbing. This is crucial for mitigating the initial impact damage and improving the damping efficiency of the MR damper.

Wang, JJ, Nie, XF, Yang, L, Li, WG & Zhang, SS 2025, 'Compressive behavior of FRP-UHPC-steel double-skin tubular columns', Engineering Structures, vol. 325, pp. 119451-119451.
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Wang, K, Huang, S, Zhang, J, Ma, B, Xing, J, Lei, G & Zhu, J 2025, 'An Effective Cooling Scheme Using Micro Heat Pipe Array for Electrical Machines With Distributed Windings', IEEE Transactions on Transportation Electrification, vol. 11, no. 2, pp. 5891-5900.
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Wang, K, Qu, C, Wang, J, Li, Z & Lu, H 2025, 'Ensemble Multitask Prediction of Air Pollutants Time Series: Based on Variational Inference, Data Projection, and Generative Adversarial Network', Journal of Forecasting, vol. 44, no. 2, pp. 646-675.
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ABSTRACTIn light of the mounting environmental pressures, especially the significant threat urban air pollution poses to public health, there arises an imperative need to develop a data‐driven model for air pollution prediction. However, contemporary deep learning techniques, such as recurrent neural networks, often struggle to effectively capture the underlying data patterns and distributions, resulting in reduced model stability. To address this gap, this study introduces an ensemble Wasserstein generative adversarial network framework (EWGF) to enhance the stability and accuracy of PM2.5 predictions by facilitating the acquisition of more informative data representations through Wasserstein generative adversarial network. The framework contains an intricate feature extraction pipeline that automatically learns features containing residual information as representations of potential features, effectively ameliorating the underutilization of feature information. We address a nonconvex multi‐objective optimization problem associated with amalgamating diverse Wasserstein generative adversarial network architectures, which enhance the inherent instability of the predictions. Furthermore, an adaptive search strategy is introduced to ascertain the optimal distribution of prediction residuals, thereby expanding the prediction interval estimation method based on residual distribution. We rigorously evaluate the proposed framework using datasets from three major Indian cities, and our experiments unequivocally show that the EWGF outperforms existing solutions in both PM2.5 point prediction and interval prediction, evidenced by an approximate 8.07% reduction in mean absolute percentage error and an approximate 19.41% improvement in prediction interval score compared to the baseline model.

Wang, L, Wang, S, Zhang, Q, Wu, Q & Xu, M 2025, 'Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation', IEEE Transactions on Multimedia, pp. 1-12.
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Wang, L, Yi, S, Sun, X, Yu, Y & Samali, B 2025, 'Quantitative evaluation of bolt pre-load using coda wave interferometry and nonlinear coda wave interferometry: a comparative study', Journal of Civil Structural Health Monitoring, vol. 15, no. 2, pp. 563-573.
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Abstract The quantitative evaluation of bolt pre-load is crucial for the maintenance and prevention of accidents in bolt-connected structures. This study introduces the coda wave interferometry (CWI) method and the nonlinear coda wave interferometry (NCWI) method for quantitative evaluation of bolt pre-load. Experimental tests across three different scales of bolt pre-load changes were conducted on a bolt to compare the performances of CWI and NCWI in the quantitative evaluation of bolt pre-load. The results demonstrate that both CWI and NCWI can effectively characterize changes in bolt pre-load. For CWI, the relative velocity change (∆v/v) exhibits a linear relationship with the bolt pre-load. Meanwhile, for NCWI, the effective nonlinear level, denoted as $${\alpha }_{\Delta v/v}$$ α Δ v / v , demonstrates a quadratic dependence on the bolt pre-load. In CWI, the calculation of ∆v/v is dependent on the correlation coefficient between the coda waves of signals before and after bolt pre-load changes. It is prone to failure when there are significant changes in bolt pre-load. Conversely, NCWI demonstrates enhanced robustness in evaluating bolt pre-load changes across a range of magnitudes.

Wang, M, Zhu, S, Liu, X, Wen, S & Mu, C 2025, 'Finite-Time Input-to-State Stability of Neural Networks With Disturbances and Prescribed Performance', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 5, pp. 3742-3751.
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Wang, Q, Luo, Z, Zhang, M, Wu, D, Li, G & Gao, W 2025, 'Polymorphic uncertainty field quantification in structural analysis with machine learning assistance', Mechanical Systems and Signal Processing, vol. 225, pp. 112273-112273.
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Wang, Q, Yu, G, Sai, Y, Sun, C, Nguyen, LD & Chen, S 2025, 'Understanding DAOs: An Empirical Study on Governance Dynamics', IEEE Transactions on Computational Social Systems, pp. 1-19.
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Wang, R, Xi, L, Zhang, F, Fan, H, Yu, X, Liu, L, Yu, S & Leung, VCM 2025, 'Context Correlation Discrepancy Analysis for Graph Anomaly Detection', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 1, pp. 174-187.
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Wang, R, Zuo, H, Fang, Z & Lu, J 2025, 'Integrated Image-Text Augmentation for Few-Shot Learning in Vision-Language Models', ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 2, pp. 1-19.
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Vision-language models, such as the Contrastive Language-Image Pre-Training (CLIP) model, have achieved significant success in image classification tasks. CLIP demonstrates high expressive power in few-shot learning scenarios due to its pairing of text and image encoders. However, CLIP still faces over-fitting when trained with a limited number of samples. To mitigate this, image augmentation techniques have been proposed in few-shot learning tasks to prevent over-fitting by enriching the dataset. Existing image augmentation methods, primarily designed for single-modal image models, focus solely on transformations within the image itself. However, for CLIP, merely increasing visual variety without considering textual content can reduce generalization ability and may even mislead the model. To address this issue, we introduce a novel image augmentation approach—Integrated Image-Text Augmentation (ITA)— for CLIP model in few-shot learning tasks. This method generates new and diverse augmented images to increase the diversity of the training data and reduce over-fitting. Additionally, ITA establishes an alignment between the augmented images and their textual descriptions. Through this alignment, the model not only learns to recognize visual elements in the images but also understands the semantic connections between these elements and the text descriptions. This dual-modal approach enhances the model’s flexibility and accuracy in processing few-shot learning tasks. Extensive experiments in few-shot image classification scenarios have demonstrated that ITA shows significant improvements compared to various image augmentation techniques.

Wang, S & Wen, S 2025, 'Safe Control Against Uncertainty: A Comprehensive Review of Control Barrier Function Strategies', IEEE Systems, Man, and Cybernetics Magazine, vol. 11, no. 1, pp. 34-47.
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Wang, S, Li, K, Yan, Z, Guo, Z, Zhu, S, Wen, G & Wen, S 2025, 'Optimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization', IEEE Transactions on Control Systems Technology, pp. 1-7.
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Wang, S, Shi, K, Cao, J & Wen, S 2025, 'Fuzzy spatiotemporal event-triggered control for the synchronization of IT2 T–S fuzzy CVRDNNs with mini-batch machine learning supervision', Neural Networks, vol. 185, pp. 107220-107220.
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Wang, S, Wang, Q, Lu, H, Zhang, D, Xing, Q & Wang, J 2025, 'Learning about tail risk: Machine learning and combination with regularization in market risk management', Omega, vol. 133, pp. 103249-103249.
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Wang, S, Xu, C, Zhao, G, Han, Z, Hu, R & Yu, S 2025, 'APQA: An anonymous post quantum access authentication scheme based on lattice for space ground integrated network', Computer Networks, vol. 257, pp. 110979-110979.
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Wang, S, Zhao, G, Xu, C, Han, Z & Yu, S 2025, 'LPQAA: a lightweight post-quantum access authentication scheme for satellite network', The Journal of Supercomputing, vol. 81, no. 1.
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Wang, W, Mao, W, Sun, H, Hou, F, Wang, W, Liu, W, Shi, Z, Lin, G, Wang, M, Fang, G, Cheng, YY & Xu, C 2025, 'Microfluidic SERS biosensor based on Au-semicoated photonic crystals for melanoma diagnosis', Biosensors and Bioelectronics, vol. 271, pp. 116983-116983.
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Wang, W, Tian, Z, Zhang, C & Yu, S 2025, 'SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 547-558.
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Wang, W, Wang, Z, Li, M, Xiong, Z, Chen, D & Wu, C 2025, 'Close-range blast behavior of hybrid FRP-concrete-steel double-skin tubular member', Thin-Walled Structures, vol. 211, pp. 113022-113022.
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Wang, W, Zhang, C, Tian, Z & Yu, S 2025, 'FedU: Federated Unlearning via User-Side Influence Approximation Forgetting', IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 3, pp. 2550-2562.
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Wang, W, Zhang, C, Tian, Z, Liu, S & Yu, S 2025, 'CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning', IEEE Transactions on Dependable and Secure Computing, pp. 1-14.
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Wang, W, Zhang, C, Tian, Z, Yu, S & Su, Z 2025, 'Evaluation of Machine Unlearning Through Model Difference', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 5211-5223.
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Wang, X, Li, W, Guo, Y, Wang, K & Huang, Y 2025, 'Performance of Cement Mortar with Inorganic Na2SO4·10H2O-Na2HPO4·12H2O Shape-Stabilization Phase Change Materials', Journal of Materials in Civil Engineering, vol. 37, no. 4.
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Wang, X, Zhang, Y, Shen, Y, Liu, J & Zhang, H 2025, 'Efficient improvement strategy for supported iridium-based catalysts in pure water electrolysis', Journal of Alloys and Compounds, vol. 1023, pp. 180071-180071.
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Wang, X-Y, Yang, S, Zhai, H-Y, Masouros, C & Andrew Zhang, J 2025, 'Windowing Optimization for Fingerprint-Spectrum-Based Passive Sensing in Perceptive Mobile Networks', IEEE Transactions on Communications, vol. 73, no. 2, pp. 1367-1382.
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Wang, Y, Bui, TA, Yang, X, Hutvagner, G & Deng, W 2025, 'Advancements in gene therapies targeting mutant KRAS in cancers', Cancer and Metastasis Reviews, vol. 44, no. 1.
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Abstract Mutations in the KRAS gene are well-known tumourigenic drivers of colorectal, pancreatic and lung cancers. Mechanistically, these mutations promote uncontrolled cell proliferation and alter the tumour microenvironment during early carcinoma stages. Given their critical carcinogenic functions, significant progress has been made in developing KRAS inhibitors for cancer treatment. However, clinical applications of these KRAS inhibitor compounds are limited to specific cancer types which carry the relevant KRAS mutations. Additionally, clinical findings have shown that these compounds can induce moderate to serious side effects. Therefore, new approaches have emerged focusing on the development of universal therapeutics capable of targeting a wider range of KRAS mutations, minimising toxicity and enhancing the therapeutic efficacy. This review aims to examine these therapeutic strategies in the context of cancer treatment. It firstly provides an overview of fundamental KRAS biology within the cell signalling landscape and how KRAS mutations are associated with cancer pathogenesis. Subsequently, it introduces the development of current KRAS inhibitors which target certain KRAS mutants in different types of cancer. It then explores the potential of gene therapy approaches, including siRNA, miRNA and CRISPR methodologies. Furthermore, it discusses the use of lipid-based nanocarriers to deliver gene cargos for targeting KRAS gene mutants. Finally, it provides the insights into the future prospects for combatting KRAS mutation-associated cancers. Graphical Abstract

Wang, Y, Qi, B, Wang, X, Liu, T & Dong, D 2025, 'Power Characterization of Noisy Quantum Kernels', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Wang, Y, Wei, W, Yang, D, Wu, L, Chen, X, Dai, X & Ni, B-J 2025, 'Unraveling temperature effects on caproate and caprylate production from waste activated sludge', Bioresource Technology, vol. 417, pp. 131844-131844.
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Wang, Y, Wu, S-L, Wei, W, Wu, L, Huang, S, Dai, X & Ni, B-J 2025, 'pH-dependent medium-chain fatty acid synthesis in waste activated sludge fermentation: Metabolic pathway regulation', Journal of Environmental Management, vol. 373, pp. 123722-123722.
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Wang, Y, Zhu, S, Shao, H, Wang, L & Wen, S 2025, 'Trade Off Analysis Between Fixed‐Time Stabilization and Energy Consumption of Nonlinear Stochastic Systems', International Journal of Robust and Nonlinear Control, vol. 35, no. 3, pp. 1244-1254.
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ABSTRACTThe trade off analysis between the fixed‐time stabilization in probability and energy consumption of nonlinear stochastic system is studied in this paper. By constructing a switching controller and using inequality techniques, sufficient conditions for fixed‐time stabilization in probability in the Lyapunov sense are given, and the upper bounds of the settling time function and energy consumption are estimated. Then, by analyzing the relationship between control parameters, control time and energy consumption, the existence of trade off between control time and energy consumption is proposed, and the corresponding optimal parameter values are given. Finally, a numerical example is used to verify the validity of the theoretical results.

Wang, Z, Zhang, JA, Zhang, H, Xu, M & Guo, J 2025, 'Passive Human Tracking With WiFi Point Clouds', IEEE Internet of Things Journal, vol. 12, no. 5, pp. 5528-5543.
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Wei, F, Zhou, X-C, Shi, Z-H, Liu, H-Y & Qin, P-Y 2025, 'A Balanced Bandpass Filter With High-Selectivity and Common-Mode Suppression Based on Inverted Microstrip Gap Waveguide', IEEE Microwave and Wireless Technology Letters, vol. 35, no. 2, pp. 181-184.
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Wei, J, Zhang, Y, Yu Zhang, L, Chen, C, Pan, S, Ong, K-L, Zhang, J & Xiang, Y 2025, 'Extracting Private Training Data in Federated Learning From Clients', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 4525-4540.
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Wei, Y, Zheng, G, Luo, Q, Li, Q & Sun, G 2025, 'On impact response and damage tolerance of adhesively bonded joints–An experimental and numerical study', Thin-Walled Structures, vol. 208, pp. 112606-112606.
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Wei, Z, Yao, R, Yuan, X, Wu, H, Zhang, Q & Feng, Z 2025, 'Precoding Optimization for MIMO-OFDM Integrated Sensing and Communication Systems', IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 1, pp. 288-299.
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Wen, G, Yuan, S, Liu, J, Dong, Z, Han, R, Ding, H, Lei, S, Li, Z, Wang, W & Cao, Y 2025, 'Process optimization for recycling spent lithium iron phosphate batteries based on leaching kinetics and mechanism study', Separation and Purification Technology, vol. 370, pp. 133263-133263.
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Whaieb, AH, Jasim, FT, Abdulrahman, AA, Khuder, IM, Gheni, SA, Fattah, IMR & Karakullukcu, NT 2025, 'Tailoring zeolites for enhanced post-combustion CO2 capture: A critical review', Current Research in Green and Sustainable Chemistry, vol. 10, pp. 100451-100451.
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Wijesena, S & Pradhan, B 2025, 'Enhancing weather index insurance through surrogate models: leveraging machine learning techniques and remote sensing data', Environmental Research Communications, vol. 7, no. 4, pp. 045009-045009.
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Abstract The agricultural industry’s crop yield production is highly vulnerable to extreme weather events, heightened by the impacts of climate change. Weather Index Insurance (WII) presents an innovative solution for insurers to protect farmers from significant yield losses. The objective of this study is to develop an index that is highly correlated with crop yield while ensuring transparency for policyholders. WII products often rely on a single weather index, which fails to encompass the complex nature of weather events. While machine learning models offer the potential to model the multifaceted nature of factors influencing crop growth, their adoption in WII products has been limited due to their lack of transparency, often perceived as ‘black box models’. This research examines soybean yield in the Corn Belt region of the USA. This study proposes a novel framework to develop a highly predictive multi-weather index based on a neural network model, and then applying a surrogate model to ensure transparency whilst maintaining predictive power. A generalised linear model (GLM) is implemented as the surrogate model in this study. The GLM achieved a mean absolute error (MAE) of 8.2%, which is comparable to the neural network model’s MAE of 7.6%. The weather index derived from the simplified approximation of the surrogate model incorporates multiple remote sensing indexes and weather variables: Potential Evapotranspiration (PET), Evapotranspiration (ET), Land Surface Temperature (LST), Vegetation Condition Index (VCI) and minimum temperature. The proposed methodology achieved a substantial hedging efficiency of 21% downside risk reduction, thereby demonstrating the effectiveness of the proposed WII product. The development of an index that closely aligns with crop yield losses is crucial for the financial viability of WII.

Wong, L, Wang, J, Yang, RC & Zhang, YX 2025, 'Slow-growth damage of bonded composite-metal joints subjected to fatigue loading', International Journal of Adhesion and Adhesives, vol. 139, pp. 103979-103979.
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Wong, L, Wang, J, Yang, RC & Zhang, YX 2025, 'Slow-growth disbond and delamination damage of a bonded composite-metal joint under fatigue loading', Composites Part A: Applied Science and Manufacturing, vol. 192, pp. 108816-108816.
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Wright, T, Crompton, M, Bishop, D, Currell, G, Suwal, L & Turner, BD 2025, 'Phytoremediation evaluation of forever chemicals using hemp (Cannabis sativa L.): Pollen bioaccumulation and the risk to bees', Chemosphere, vol. 370, pp. 143859-143859.
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Wu, J, Hu, C, Zhang, J, Wang, D & Jiang, J 2025, 'Gemini+: Enhancing Real-Time Video Analytics with Dual-Image FPGAs', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-1.
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Wu, J, Huang, Y, Gao, M, Niu, Y, Chen, Y & Wu, Q 2025, 'High-order diversity feature learning for pedestrian attribute recognition', Neural Networks, vol. 188, pp. 107463-107463.
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Wu, J, Huang, Y, Gao, M, Niu, Y, Chen, Y & Wu, Q 2025, 'Rethinking attention mechanism for enhanced pedestrian attribute recognition', Neurocomputing, vol. 639, pp. 130236-130236.
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Wu, J, Huang, Y, Gao, M, Niu, Y, Chen, Y, Wu, Q & Zhao, J 2025, 'Learning Comprehensive Representation via Selective Activation and Dual-Level Orthogonality for Pedestrian Attribute Recognition', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Wu, J, Li, L, Zhang, J & Xiao, B 2025, 'Flexibility estimation of electric vehicles and its impact on the future power grid', International Journal of Electrical Power & Energy Systems, vol. 164, pp. 110435-110435.
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Wu, L, Le Gentil, C & Vidal-Calleja, T 2025, 'VDB-GPDF: Online Gaussian Process Distance Field With VDB Structure', IEEE Robotics and Automation Letters, vol. 10, no. 1, pp. 374-381.
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Wu, P, Li, S, Li, Z, Xia, W & Li, Y 2025, 'Modular QZS metamaterials with enhanced load adaptability for low-frequency vibration isolation', Smart Materials and Structures, vol. 34, no. 5, pp. 055017-055017.
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Abstract This study introduces a modular quasi-zero-stiffness (QZS) metamaterial for low-frequency vibration isolation with adaptable load-carrying capacity. The proposed structure integrates a double-curved beam as a negative stiffness element and two pairs of V-shaped springs as positive stiffness elements, forming an extendable modular metamaterial. These metamaterials provide scalable and tunable load-adaptive performance through multiple modular configurations. Finite element analysis is utilized to optimize the design parameters, ensuring effective and consistent QZS behavior across the modules. A dynamic model, validated using harmonic balance methods, demonstrates the isolation effectiveness under varying loads and excitation amplitudes. By arranging multiple unit modules in series, parallel, or combined configurations, the QZS characteristics scale in a linear and predictable manner, enabling versatile load adaptability and tunability. Quasi-static tests confirm the predicted QZS behavior, while dynamic tests validate the vibration isolation performance. A single unit module attenuates vibrations at 8.6 Hz with a payload of 1420 g, while the modular configuration achieves vibration suppression above 3.6 Hz with a payload of 5680 g, without requiring alterations to the design parameters. These findings underscore the potential of the proposed modular QZS metamaterial for scalable, load-adaptive, and low-frequency vibration isolation in engineering applications.

Wu, S, Li, Y, Wang, W, Guo, J, Fan, W, Liu, Q, Jia, W, Yu, S, Cao, J & Wang, T 2025, 'Enhancing Collaborative Inference on Heterogeneous Edge Devices via Adaptive Ensemble Knowledge Distillation', IEEE Journal on Selected Areas in Communications, pp. 1-1.
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Wu, T, Dai, P, Xue, G, Guo, Y, Lei, G, Zhu, J & Wang, Y 2025, 'A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning', IEEE Transactions on Transportation Electrification, vol. 11, no. 3, pp. 7379-7388.
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Wu, X, Li, P, Gu, Y, Tao, J, Shen, S & Yu, S 2025, 'Improved Gale–Shapley-Based Hierarchical Federated Learning for IoT Scenarios', IEEE Internet of Things Journal, vol. 12, no. 7, pp. 9195-9205.
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Wu, Y, Dang, F, Nimbalkar, S, Chen, Y & Zhang, F 2025, 'Property Evolution Law and Damage Mechanism of Pervious Concrete under Different Cycle Modes', Jianzhu Cailiao Xuebao/Journal of Building Materials, vol. 28, no. 1, pp. 9-18.
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In order to investigate the effect of water on pervious concrete（PC）pavement，the performance evolution equations of PC under three cycle modes of dry-wet，freeze-thaw，dry-wet and freeze-thaw were studied，and the damage mechanism under single and double factors were revealed. The results show that the basalt fiber（BF）changes the pore structure and initial property of PC，but does not affect the freeze-thaw damage mechanism. The secondary cracks of PC pavement caused by freeze-thaw cycle increase exponentially，which is different from the performance evolution law under dry-wet cycle. Dry-wet cycle in the early stage of damage causes the dissolution，loss of pore wall surface material and edge curl and peel，while freeze-thaw in the late stage of damage causes the formation and expansion of cracks，resulting in intergranular fracture. Under the coupling effect of wet-dry cycle and freeze-thaw cycle，the PC performance index decreases more than the single factor superposition of wet-dry cycle and freeze-thaw cycle.

Wu, Y, Li, S, Li, J, Yu, Y, Li, J & Li, Y 2025, 'Deep learning in crack detection: A comprehensive scientometric review', Journal of Infrastructure Intelligence and Resilience, vol. 4, no. 3, pp. 100144-100144.
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Wu, Z, Liu, Y, Cen, J, Zheng, Z & Xu, G 2025, 'A cross-domain knowledge tracing model based on graph optimal transport', World Wide Web, vol. 28, no. 1.
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Xia, H, Chen, S-L, Wang, Y, Zhao, Y, Jia, H, Yang, R & Jay Guo, Y 2025, 'Deep-learning-assisted intelligent design of terahertz hybrid-functional metasurfaces with freeform patterns', Optics & Laser Technology, vol. 181, pp. 112041-112041.
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Xiang, S, Xu, C, Cheng, D & Zhang, Y 2025, 'Scalable Learning-Based Community-Preserving Graph Generation', IEEE Transactions on Big Data, pp. 1-14.
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Xiang, S, Zhang, G, Cheng, D & Zhang, Y 2025, 'Enhancing Attribute-Driven Fraud Detection With Risk-Aware Graph Representation', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 5, pp. 2501-2512.
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Xiao, P, Fang, J, Wei, Z, Dong, Y, Du, S, Wen, S & Hong, Q 2025, 'A Riccati Matrix Equation Solver Design Based Neurodynamics Method and Its Application', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 15163-15176.
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Xiao, S, Wang, Y, Zhang, J, Dong, D, Mooney, GJ, Petersen, IR & Yonezawa, H 2025, 'A Two-Stage Solution to Quantum Process Tomography: Error Analysis and Optimal Design', IEEE Transactions on Information Theory, vol. 71, no. 3, pp. 1803-1823.
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Xiao, Y, Shao, Y, Chen, Z, Zhang, R, Ding, X, Zhao, J, Liu, S, Fukuyama, T, Zhao, Y, Peng, X, Tian, G, Wen, S & Zhou, X 2025, 'MIU-Net: Advanced multi-scale feature extraction and imbalance mitigation for optic disc segmentation', Neural Networks, vol. 182, pp. 106895-106895.
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Xiao, Z, Liu, X, Zeng, Y, Zhang, JA, Jin, S & Zhang, R 2025, 'Rethinking Waveform for 6G: Harnessing Delay-Doppler Alignment Modulation', IEEE Communications Magazine, vol. 63, no. 5, pp. 110-117.
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Xie, W, Ma, J, Lu, T, Li, Y, Lei, J, Fang, L & Du, Q 2025, 'Distributed Deep Learning With Gradient Compression for Big Remote Sensing Image Interpretation', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Xing, D, Xu, M, Shuang, X, Li, JJ, Sun, D, Wang, Y, Li, M, Feng, S & Ning, G 2025, 'Immune regulated fibrous membrane loaded FK506 enhances peripheral nerve regeneration', Chemical Engineering Journal, vol. 505, pp. 159075-159075.
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Xu, G, Xu, S, Cao, Y, Xiao, K, Mao, Y, Chen, X-B, Dong, M & Yu, S 2025, 'AAQ-PEKS: An Attribute-based Anti-Quantum Public Key Encryption Scheme with Keyword Search for E-healthcare Scenarios', Peer-to-Peer Networking and Applications, vol. 18, no. 2.
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Xu, H, Fang, L, Lv, H, Xu, S, Shen, S & Yu, S 2025, 'Maximizing VANETs Secrecy Data Rate Using Dueling Double Deep Q-Networks', IEEE Transactions on Vehicular Technology, vol. 74, no. 1, pp. 1267-1279.
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Xu, K, Tuyen Le, A, Huang, X & Ryu, H-G 2025, 'Generalized Analog Least Mean Square Loop for Self-Interference Cancellation in In-Band Full-Duplex Communications', IEEE Transactions on Wireless Communications, vol. 24, no. 3, pp. 2022-2035.
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Xu, L, Li, R-H, Wen, D, Dai, Q & Wang, G 2025, 'Efficient Antagonistic $k$-plex Enumeration in Signed Graphs', IEEE Transactions on Big Data, pp. 1-14.
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Xu, L, Wen, D, Qin, L, Li, R, Zhang, Y, Lu, Y & Lin, X 2025, 'Minimum Spanning Tree Maintenance in Dynamic Graphs', Proceedings of the ACM on Management of Data, vol. 3, no. 1, pp. 1-24.
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Minimum Spanning Tree (MST) is a fundamental structure in graph analytics and can be applied in various applications. The problem of maintaining MSTs in dynamic graphs is significant, as many real-world graphs are frequently updated. Existing studies on MST maintenance primarily focus on theoretical analysis and lack practical efficiency. In this paper, we propose a novel algorithm to maintain MST in dynamic graphs, which achieves high practical efficiency. In addition to the tree structure, our main idea is to maintain a replacement edge for each tree edge. In this way, the tree structure can be immediately updated when a tree edge is deleted. We propose algorithms to maintain the replacement edge for each tree edge by sharing the computation cost in the updating process. Our performance studies on large datasets demonstrate considerable improvements over state-of-the-art solutions.

Xu, M, Jiao, J, Chen, D, Ding, Y, Chen, Q, Wu, J, Gu, P, Pan, Y, Peng, X, Xiao, N, Yang, B, Li, Q & Guo, J 2025, 'REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification', IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 3, pp. 2136-2147.
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Xu, W, Wu, L, Geng, M, Zhou, J, Bai, S, Nguyen, DV, Ma, R, Wu, D & Qian, J 2025, 'Biochar@MIL-88A(Fe) accelerates direct interspecies electron transfer and hydrogen transfer in waste activated sludge anaerobic digestion: Exploring electron transfer and biomolecular mechanisms', Environmental Research, vol. 268, pp. 120810-120810.
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Xu, Y & Guo, YJ 2025, 'Compact generalized joined coupler matrix with orthogonal beams', Electronics Letters, vol. 61, no. 1.
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AbstractAn analogue beamforming network, such as the Butler matrix can produce orthogonal beams, the peak of each beam being aligned with the nulls of all other beams. This is particularly important in scenarios requiring interference resilience in systems like base stations and user equipment. This paper presents a 3 × 5 Nolen‐like generalized joined coupler (GJC) matrix to produce orthogonal beams with both compact size and flexibility. The phase shifters are integrated into couplers while the coupling value range of couplers are extended to achieve the required performance of the GJC matrix. A prototype GJC matrix is designed, simulated, fabricated, and measured. The measured results agree well with the simulation ones.

Xu, Y, Ye, L, Wu, C, Fang, J, Sun, G, Chen, Y, Man, Z, Steven, GP & Li, Q 2025, 'Enhancing performance for additively manufactured optimal CFRP structures', Composites Science and Technology, vol. 269, pp. 111227-111227.
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Xu, Y, Zhang, W, Lin, X & Zhang, Y 2025, 'UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphs', IEEE Transactions on Knowledge and Data Engineering, pp. 1-16.
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Xu, Y, Zhang, W, Xu, X, Li, B & Zhang, Y 2025, 'Scalable and Effective Temporal Graph Representation Learning With Hyperbolic Geometry', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 4, pp. 6080-6094.
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Xu, Z, Mittal, PS, Ahmed, MM, Adak, C & Cai, ZG 2025, 'Assessing penmanship of Chinese handwriting: a deep learning-based approach', Reading and Writing, vol. 38, no. 3, pp. 723-743.
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Abstract The rise of the digital era has led to a decline in handwriting as the primary mode of communication, resulting in negative effects on handwriting literacy, particularly in complex writing systems such as Chinese. The marginalization of handwriting has contributed to the deterioration of penmanship, defined as the ability to write aesthetically and legibly. Despite penmanship being widely acknowledged as a crucial factor in predicting language literacy, research on its evaluation remains limited, with existing assessments primarily dependent on expert subjective ratings. Recent initiatives have started to explore the application of convolutional neural networks (CNN) for automated penmanship assessment. In this study, we adopted a similar approach, developing a CNN-based automatic assessment system for penmanship in traditional Chinese handwriting. Utilizing an existing database of 39,207 accurately handwritten characters (penscripts) from 40 handwriters, we had three human raters evaluate each penscript’s penmanship on a 10-point scale and calculated an average penmanship score. We trained a CNN on 90% of the penscripts and their corresponding penmanship scores. Upon testing the CNN model on the remaining 10% of penscripts, it achieved a remarkable performance (overall 9.82% normalized Mean Absolute Percentage Error) in predicting human penmanship scores, illustrating its potential for assessing handwriters’ penmanship. To enhance accessibility, we developed a mobile application based on the CNN model, allowing users to conveniently evaluate their penmanship.

Xuan Tung, N, Van Chien, T, Thai Hoang, D & Hwang, WJ 2025, 'Jointly Optimizing Power Allocation and Device Association for Robust IoT Networks Under Infeasible Circumstances', IEEE Transactions on Network and Service Management, vol. 22, no. 2, pp. 1372-1390.
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Xue, F, Sun, J, Xue, Y, Wu, Q, Zhu, L, Chang, X & Cheung, S-C 2025, 'Attention Guidance by Cross-Domain Supervision Signals for Scene Text Recognition', IEEE Transactions on Image Processing, vol. 34, pp. 717-728.
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Yaman, S, Aslan, O, Güler, H, Sengur, A, Hafeez-Baig, A, Tan, R-S, Deo, RC, Barua, PD & Acharya, UR 2025, 'Deep learning techniques for automated coronary artery segmentation and coronary artery disease detection: A systematic review of the last decade (2013-2024)', Computer Methods and Programs in Biomedicine, vol. 268, pp. 108858-108858.
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Yan, B, Zhao, Q, Zhang, J & Zhang, JA 2025, 'Heuristic solution to joint deployment and beamforming design for STAR-RIS aided networks', Expert Systems with Applications, vol. 267, pp. 126144-126144.
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Yan, C, Zhang, F, Nie, M, Luo, Y, Ding, M, Nie, S & Zhou, JL 2025, 'Influence of the source and molecular weights on sulfathiazole/sulfadiazine binding in sedimentary dissolved organic matter', Journal of Environmental Management, vol. 386, pp. 125795-125795.
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Yan, H, Du, G, Li, N, Li, L, Chen, Y, Lei, G & Zhu, J 2025, 'Four Rotor Structures for High-Speed Interior Permanent Magnet Motor Considering Mechanical, Electromagnetic, and Thermal Performance', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 2595-2608.
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Yan, Z, Sun, W, Guo, W, Li, B, Wen, S & Cao, J 2025, 'Complete Stability of Delayed Recurrent Neural Networks With New Wave-Type Activation Functions', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 4, pp. 6584-6596.
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Yang, C, Chen, Y, Li, Z, Wang, X, Shi, K, Yao, L, Xu, G & Guo, Z 2025, 'Deep multimodal learning for time series analysis in social computing: a survey', International Journal of Multimedia Information Retrieval, vol. 14, no. 2.
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Yang, J & Lin, C-T 2025, 'Autonomous Clustering by Fast Find of Mass and Distance Peaks', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 7, pp. 5336-5349.
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Yang, J & Lin, C-T 2025, 'Toward Autonomous Distributed Clustering', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 2065-2072.
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Yang, M, An, S, Gao, H, Du, Z, Zhang, X, Nghiem, LD & Liu, Q 2025, 'Selective adsorption of copper by amidoxime modified low-temperature biochar: Performance and mechanism', Science of The Total Environment, vol. 958, pp. 178072-178072.
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Yang, M, Li, X, Xu, B, Nie, X, Zhao, M, Zhang, C, Zheng, Y & Gong, Y 2025, 'STDA: Spatio-Temporal Deviation Alignment Learning for Cross-city Fine-grained Urban Flow Inference', IEEE Transactions on Knowledge and Data Engineering, pp. 1-13.
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Yang, P, Boer, G, Snow, F, Williamson, A, Cheeseman, S, Samarasinghe, RM, Rifai, A, Priyam, A, Elnathan, R, Guijt, R, Quigley, A, Kaspa, R, Nisbet, DR & Williams, RJ 2025, 'Test and tune: evaluating, adjusting and optimising the stiffness of hydrogels to influence cell fate', Chemical Engineering Journal, vol. 505, pp. 159295-159295.
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Yang, Q, Yu, H, Teo, SG, Li, B, Long, G, Jin, C, Fan, L, Liu, Y & Zhang, L 2025, 'Guest Editorial: Special Issue on Trustworthy Federated Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 5-5.
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Yang, S, Yu, S, Li, Q, Xia, K & Zhu, H 2025, 'Few-shot specific emitter identification via asymmetric dual-path masked autoencoder', Digital Signal Processing, vol. 163, pp. 105201-105201.
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Yang, T, Valls Miro, J, Wang, Y & Xiong, R 2025, 'An Improved Maximal Continuity Graph Solver for Non-Redundant Manipulator Non-Revisiting Coverage', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 3822-3834.
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Yang, T, Yang, YK, Liu, ZX, Xu, SC & Wu, CQ 2025, 'INVESTIGATION ON MECHANICAL PROPERTIES OF ULTRA-HIGH PERFORMANCE CONCRETE AFTER HIGH TEMPERATURE', Gongcheng Lixue Engineering Mechanics, vol. 42, no. 4, pp. 97-109.
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The mechanical properties of ultra-high-performance concrete (UHPC) after high temperature were studied by static and dynamic compression and splitting tests. Moreover, the microstructure of UHPC after different temperatures was observed by scanning electron microscope (SEM), which further revealed the reasons why the changes take place in mechanical properties of UHPC after different high temperatures. Five target temperatures were set, i.e., 25 ℃ (room temperature), 200 ℃, 400 ℃, 600 ℃, and 800 ℃, and a total of 120 specimens were tested. The results show that both the static and dynamic strength of UHPC increase first and then decrease with the temperature. At the same impact velocity, the compression and splitting impact toughness of UHPC also increases first and then decreases with the temperature. However, when the impact velocity is weak, the effect of temperature on the impact toughness is not significant. The formula for the variation range between strain rate and dynamic increase factor (DIF) was fitted in this study. The tensile dynamic increase factor (TDIF) of UHPC decreases first and then increases with the temperature (except 800 ℃). However, TDIF under different target temperatures (except 800 ℃) decreases with the increase of splitting strength, which shows that the strain rate enhancement effect of UHPC decreases with the splitting strength of UHPC.

Yang, X, Che, H, Leung, M-F & Wen, S 2025, 'Unbalanced Incomplete Multiview Unsupervised Feature Selection With Low-Redundancy Constraint in Low-Dimensional Space', IEEE Transactions on Industrial Informatics, vol. 21, no. 3, pp. 2679-2688.
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Yang, Z, Wang, J, Shi, K, Cai, X, Yang, J & Wen, S 2025, 'Dynamic event-triggered synchronization control for neutral-type SMJ neural networks with additive delays under synchronized attacks', ISA Transactions, vol. 158, pp. 32-49.
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Yao, J, Zhang, H, Song, D, Zhang, J & Jiang, Y-P 2025, 'Growth of monolayer 1T′-WTe2 with a nearly complete coverage', Applied Physics Letters, vol. 126, no. 7.
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Monolayer WTe2, known for its intriguing properties as a quantum spin Hall insulator, presents significant challenges for high-quality epitaxial film growth. These difficulties primarily arise from the low mobility of W on the substrate and the reduced reaction rate between W and Te. This study addresses these challenges by employing a low-temperature deposition technique combined with the introduction of a Te buffer layer to mitigate these limitations. High-quality monolayer 1T′-WTe2 with nearly complete coverage has been grown on the SrTiO3 (100) substrate. Furthermore, scanning tunneling microscopy/spectroscopy demonstrates the presence of random domain orientations and variations in gap size within the monolayer WTe2. This approach offers a solution to the primary obstacles in WTe2 epitaxial growth and may be extended to other transition metal dichalcogenides.

Yao, L, McAuley, J, Wang, X & Jannach, D 2025, 'Special Issue on Responsible Recommender Systems Part 2', ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 1, pp. 1-3.
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Yazdanjue, N, Yazdanjouei, H, Gharoun, H, Khorshidi, MS, Rakhshaninejad, M, Amiri, B & Gandomi, AH 2025, 'A comprehensive bibliometric analysis on social network anonymization: current approaches and future directions', Knowledge and Information Systems, vol. 67, no. 1, pp. 29-108.
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Abstract In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007–2022 were collected from the Scopus Database and then preprocessed. Following this, the VOSviewer was used to visualize the network of authors’ keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.

Ye, D, Chen, H, Zhou, S, Zhu, T, Zhou, W & Ji, S 2025, 'Model Inversion Attack Against Transfer Learning: Inverting a Model Without Querying It', IEEE Transactions on Dependable and Secure Computing, pp. 1-15.
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Ye, D, Zhu, T, Gao, K, Zhu, C & Zhou, W 2025, 'Cooperating or Kicking Out: Defending against Poisoning Attacks in Federated Learning via the Evolution of Cooperation', IEEE Transactions on Dependable and Secure Computing, pp. 1-16.
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Ye, S & Lu, J 2025, 'Robust Recommender Systems with Rating Flip Noise', ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 1, pp. 1-19.
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Recommender systems have become important tools in the daily life of human beings since they are powerful to address information overload, and discover relevant and useful items for users. The success of recommender systems largely relies on the interaction history between users and items, which is expected to accurately reflect the preferences of users on items. However, the expectation is easily broken in practice, due to the corruptions made in the interaction history, resulting in unreliable and untrusted recommender systems. Previous works either ignore this issue (assume that the interaction history is precise) or are limited to handling additive noise. Motivated by this, in this paper, we study rating flip noise which widely exists in the interaction history of recommender systems and combat it by modelling the noise generation process. Specifically, the rating flip noise allows a rating to be flipped to any other ratings within the given rating set, which reflects various real-world situations of rating corruption, e.g., a user may randomly click a rating from the rating set and then submit it. The noise generation process is modelled by the noise transition matrix that denotes the probabilities of a clean rating flip into a noisy rating. A statistically consistent algorithm is afterwards applied with the estimated transition matrix to learn a robust recommender system against rating flip noise. Comprehensive experiments on multiple benchmarks confirm the superiority of our method.

Ye, Y, Ye, J, Xu, Z, Kang, J, Liu, D, Ren, Y, Ngo, HH, Guo, W, Huang, S & Jiang, W 2025, 'Influence of ethanol supplementation on sulfate reduction and methanogenesis in UASB reactors', Journal of Water Process Engineering, vol. 74, pp. 107754-107754.
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Yi, B, Fan, Y, Liu, D & Guadalupe Romero, J 2025, 'Simultaneous Position-and-Stiffness Control of Underactuated Antagonistic Tendon-Driven Continuum Robots', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 7238-7254.
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Yi, J, Mao, J, Zhang, H, Li, M, Zeng, K, Feng, M, Chang, X & Wang, Y 2025, 'FMSD: Focal Multi-Scale Shape-Feature Distillation Network for Small Fasteners Detection in Electric Power Scene', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 2, pp. 1331-1346.
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Yin, Q, Yang, Z, Chong, SW, Li, J, Liu, X, Vigolo, D, Li, JJ, Sheehy, PA & Yong, K 2025, 'Application of microfluidic technologies in veterinary science with a view toward development of animal‐on‐a‐chip models', VIEW, vol. 6, no. 1.
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AbstractThe advancement of veterinary science relies on the adoption of modern technologies, more recently including molecular diagnostics, genomic research, precision medicine approaches, and advanced diagnostic imaging. Recent advancements in microfluidics have brought tremendous attention to human disease modeling, diagnosis, and drug development. Specifically, organ‐on‐a‐chip, a subset of microfluidic technology, is characterized by its ability to mimic the human in vivo microenvironment and improve cost efficiency in drug development. Recent studies have demonstrated huge potential in translating human‐centered microfluidic technologies to veterinary science, which can help to deepen our understanding of animal diseases and disorders and develop targeted treatments for diverse animal species, including companion animals, livestock, and wildlife. Further, the ongoing impact of climate change has heightened the threat of diseases among animal populations as well as the potential impact of zoonotic pathogens. New tools for in‐depth exploration of animal physiologies and diseases are essential to mitigate the risk of species extinction and safeguard animal well‐being. Building upon the achievements in human‐based microfluidic studies, we propose the comprehensive integration of this technology into veterinary research. This review provides an overview of microfluidic technology, its current applications in veterinary science, and discusses future directions and challenges toward the development of animal‐on‐a‐chip systems.

Yin, Q, Zhong, L, Song, Y, Bai, L, Wang, Z, Li, C, Xu, Y & Yang, X 2025, 'A decision support system in precision medicine: contrastive multimodal learning for patient stratification', Annals of Operations Research, vol. 348, no. 1, pp. 579-607.
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Abstract Precision medicine aims to provide personalized healthcare for patients by stratifying them into subgroups based on their health conditions, enabling the development of tailored medical management. Various decision support systems (DSSs) are increasingly developed in this field, where the performance is limited to their capability of handling big amounts of heterogeneous and high-dimensional electronic health records (EHRs). In this paper, we focus on developing a deep learning model for patient stratification that can identify and explain patient subgroups from multimodal EHRs. The primary challenge is to effectively align and unify heterogeneous information from various modalities, which includes both unstructured and structured data. Here, we develop a Contrastive Multimodal learning model for EHR (ConMEHR) based on topic modelling. In ConMEHR, modality-level and topic-level contrastive learning (CL) mechanisms are adopted to obtain a unified representation space and diversify patient subgroups, respectively. The performance of ConMEHR will be evaluated on two real-world EHR datasets and the results show that our model outperforms other baseline methods.

Yin, W, Jia, M, Liu, L, Li, M, Guo, Y, Lei, G & Zhu, JG 2025, 'Advanced power curve modeling for wind turbines: A multivariable approach with SGBRT and grey wolf optimization', Energy Conversion and Management, vol. 332, pp. 119680-119680.
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Yin, Y, Liu, X, Cheng, X, Liu, Y, Zhu, T & Huo, H 2025, 'SpatialIE: Towards adaptive floating waste detection in unpredictable weather', Knowledge-Based Systems, vol. 323, pp. 113621-113621.
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Yin, Z, Zhu, H, Lv, X, Lai, J & Yang, Y 2025, '3-D-Printed Ultracompact Butler Matrix for Wideband Millimeter-Wave Beamforming', IEEE Transactions on Microwave Theory and Techniques, pp. 1-13.
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Yolanda, YD, Kim, S, Sohn, W, Shon, HK, Yang, E & Lee, S 2025, 'Simultaneous nutrient-abundant hydroponic wastewater treatment, direct carbon capture, and bioenergy harvesting using microalgae–microbial fuel cells', Desalination and Water Treatment, vol. 321, pp. 100941-100941.
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Yoo, Y, Arunachalam, M, Elmakki, T, Al-Ghamdi, AS, Bassi, HM, Mohammed, AM, Ryu, S, Yong, S, Shon, HK, Park, H & Han, DS 2025, 'Evaluating the economic and environmental viability of small modular reactor (SMR)-powered desalination technologies against renewable energy systems', Desalination, vol. 602, pp. 118624-118624.
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Yosun, T & Cetindamar, D 2025, 'A Typology of Competitive Strategies for Social Enterprises', Journal of Social Entrepreneurship, vol. 16, no. 1, pp. 183-209.
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This article tackles the limited theorising on social enterprises’ (SEs) decisions on the product or service mix, quality, pricing, and the targeted beneficiaries by proposing a typology of competitive strategies for them. The paper empirically observes how SEs react to the challenges faced by the marketisation of their fields. The context of this study is the supplementary education of the disabled in Turkey, a field where increased state coverage led to the entrance of many profit-focused counterparts. Based on a Grounded Theory methodology and a longitudinal dataset including ten cases, the study developed a unique typology comprising three competitive strategies, i.e., innovator, enforcer, and includer. The findings illustrate various strategic responses to heightened competition from incumbent SEs. However, deviation of these strategic responses from the typology appeared to be detrimental in the long-term. By shedding light on the intricacies of the hybrid nature of SEs and considering changes in their competitive environment over time, this study concludes with a summary of contributions to theory, practice, and policy.

You, Q, Wu, YF, Yang, Y, Chai, Y & Cheng, YJ 2025, 'Near-Field Phase-Scanning Self-Accelerating Airy–Bessel Beam for Nonline-of-Sight Wireless Communication', IEEE Transactions on Microwave Theory and Techniques, pp. 1-13.
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Yu Huat, C, Jahed Armaghani, D, Bin Hashim, H, Fattahi, H, He, X, Asteris, PG & Fakharian, P 2025, 'Development of a Practical Solution to Predict Surface Settlement Induced by Twin Tunnels', Journal of Structural Design and Construction Practice, vol. 30, no. 1.
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Yu, C, Nghiem, LD & Zou, L 2025, 'Catalytic chitosan/MXene/GO nanocomposite membrane for removing dye and heavy metals', Desalination, vol. 594, pp. 118313-118313.
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Yu, D, Guo, G, Wang, D, OuYang, T, Wan, F, Liu, J, Xu, G & Deng, S 2025, 'Dynamic Spatial-Temporal Graph Convolution Network for E-Bike Traffic Flow Forecasting', IEEE Transactions on Vehicular Technology, vol. 74, no. 4, pp. 5453-5466.
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Yu, G, Wang, X, Sun, C, Wang, Q, Yu, P, Ni, W & Liu, RP 2025, 'IronForge: An Open, Secure, Fair, Decentralized Federated Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 354-368.
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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, H, Park, MJ, Wang, C, Liang, D, He, T, Naidu, G, Han, DS & Shon, HK 2025, 'Integrated sulfonated poly ether ketone membrane capacitive deionization for lithium recovery from diluted binary solutions', Separation and Purification Technology, vol. 352, pp. 128064-128064.
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Yu, H, Wen, J, Sun, Y, Wei, X & Lu, J 2025, 'CA-GNN: A Competence-Aware Graph Neural Network for Semi-Supervised Learning on Streaming Data', IEEE Transactions on Cybernetics, vol. 55, no. 2, pp. 684-697.
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Yu, H, Zhu, S, Wen, S & Mu, C 2025, 'Finite-time stability in probability of stochastic delay systems via generalized Halanay inequality', Systems & Control Letters, vol. 195, pp. 105969-105969.
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Yu, J, Wang, H, Wang, X, Li, Z, Qin, L, Zhang, W, Liao, J, Zhang, Y & Yang, B 2025, 'Temporal Insights for Group-Based Fraud Detection on e-Commerce Platforms', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 2, pp. 951-965.
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Yu, KL, Zainal, BS, Mohamed, H, Ker, PJ, Ong, HC, Zaman, HB, Abdulkareem-Alsultan, G & Taufiq-Yap, YH 2025, 'Wet torrefaction of palm oil mill effluent as an emerging technology for biohydrogen production: An optimization study', International Journal of Hydrogen Energy, vol. 104, pp. 240-251.
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Yu, L, Pradhan, B & Wang, Y 2025, 'A comparative study of various combination strategies for landslide susceptibility mapping considering landslide types', Geoscience Frontiers, vol. 16, no. 2, pp. 101999-101999.
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Yu, P, Zhang, X, Gong, Y, Zhang, J, Sun, H, Zhang, J, Zhang, X & Yin, Y 2025, 'Enhancing origin–destination flow prediction via bi-directional spatio-temporal inference and interconnected feature evolution', Expert Systems with Applications, vol. 264, pp. 125679-125679.
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Yu, Y, Wang, J, Jiang, H & Lu, H 2025, 'How to manage a multifactor-driven crude oil market more effectively? A revisit based on the multiple criteria perspective', Resources Policy, vol. 100, pp. 105446-105446.
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Yu, Y, Wen, D, Yu, M, Qin, L, Zhang, Y, Zhang, W & Lin, X 2025, 'Querying historical K-cores in large temporal graphs', The VLDB Journal, vol. 34, no. 2.
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Yuan, B, Zhao, H, Huang, Y, Zhang, M, Song, Z, Shen, Y, Cheng, X & Wang, Z 2025, 'Investigation on cold start issues of methanol engines and its improvement from the perspective of droplet evaporation', Fuel, vol. 380, pp. 133249-133249.
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Yuan, L & Rizoiu, M-A 2025, 'Generalizing Hate Speech Detection Using Multi-Task Learning: A Case Study of Political Public Figures', Computer Speech & Language, vol. 89, pp. 101690-101690.
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Yuan, P, Xu, S, Yang, T, Zhou, Y, Su, Y & Shao, R 2025, 'Multiple blast behavior of steel wire mesh reinforced geopolymer based high performance concrete (G‐HPC) slab: Experiment and numerical simulation', Structural Concrete, vol. 26, no. 2, pp. 1943-1961.
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AbstractEngineering structures face the potential of encountering repetitive or multiple blast loads stemming from accidental explosions and terrorist attacks. However, current research in this field is still relatively limited, and further investigation is needed to understand the damage mechanisms of structures under multiple explosions. Therefore, this study explores the blast resistance of G‐HPC slabs reinforced with steel wire mesh (SWM) under multiple blast loads. The failure modes of the SWM‐reinforced G‐HPC slab were experimentally studied under two consecutive explosions (with explosive equivalents of 1.6 and 3.2 kg, both at a standoff distance of 0.4 m). The results revealed that, after two consecutive explosions, the slab exhibited bulging with minimal concrete spalling, showcasing overall integrity. Subsequently, a numerical model was established, followed by a comprehensive parameter analysis. The parameter analysis investigated the effects of SWM diameters and grid size, the arrangement of SWM, and the sequence of TNT equivalents on the performance of the slab under three consecutive blast loads. The findings revealed that increasing the SWM diameter or reducing the grid size significantly enhanced the blast resistance of the slab under three consecutive explosive loads. Strategically arranging the SWM in the tensile zone reduced damage and deflection. Furthermore, the sequence of TNT equivalents had a notable impact on the damage and energy absorption of the slab.

Zamee, MA, Lee, Y & Won, D 2025, 'Corrections to “Self-Supervised Adaptive Learning Algorithm for Multi-Horizon Electricity Price Forecasting”', IEEE Access, vol. 13, pp. 55381-55381.
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Zeng, A, Tang, Q, O’Hagan, E, McCaffery, K, Ijaz, K, Quiroz, JC, Kocaballi, AB, Rezazadegan, D, Trivedi, R, Siette, J, Shaw, T, Makeham, M, Thiagalingam, A, Chow, CK & Laranjo, L 2025, 'Use of digital patient decision-support tools for atrial fibrillation treatments: a systematic review and meta-analysis', BMJ Evidence-Based Medicine, vol. 30, no. 1, pp. 10-21.
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ObjectivesTo assess the effects of digital patient decision-support tools for atrial fibrillation (AF) treatment decisions in adults with AF.Study designSystematic review and meta-analysis.Eligibility criteriaEligible randomised controlled trials (RCTs) evaluated digital patient decision-support tools for AF treatment decisions in adults with AF.Information sourcesWe searched MEDLINE, EMBASE and Scopus from 2005 to 2023.Risk-of-bias (RoB) assessment: We assessed RoB using the Cochrane Risk of Bias Tool 2 for RCTs and cluster RCT and the ROBINS-I tool for quasi-experimental studies.Synthesis of resultsWe used random effects meta-analysis to synthesise decisional conflict and patient knowledge outcomes reported in RCTs. We performed narrative synthesis for all outcomes. The main outcomes of interest were decisional conflict and patient knowledge.Results13 articles, reporting on 11 studies (4 RCTs, 1 cluster RCT and 6 quasi-experimental) met the inclusion criteria. There were 2714 participants across all studies (2372 in RCTs), of which 26% were women and the mean age was 71 years. Socioeconomically disadvantaged groups were poorly represented in the included studies. Seven studies (n=2508) focused on non-valvular AF and the mean CHAD2DS2-VASc across studies was 3.2 and for HAS-BLED 1.9. All tools focused on decisions regarding thromboembolic stroke prevention and most enabled calculation of individualised stroke risk. Tools were heterogeneous in features and functions; four tools were patient decision aids. The readability of content was reported in one study. Meta-analyses showed a redu...

Zeng, G, Zhang, Q, Zhang, G & Lu, J 2025, 'Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 6, pp. 4244-4257.
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Zeng, H, Li, Y, Niu, R, Yang, C & Wen, S 2025, 'Enhancing spatiotemporal prediction through the integration of Mamba state space models and Diffusion Transformers', Knowledge-Based Systems, vol. 316, pp. 113347-113347.
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Zeng, Y-C, Ding, H, Ji, J-C, Mao, X-Y & Chen, L-Q 2025, 'A tristable nonlinear energy sink with time-varying potential barriers', Communications in Nonlinear Science and Numerical Simulation, vol. 142, pp. 108559-108559.
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Zhan, P, Wang, J, Yu, W, Deng, Z, She, A, Zuo, J, Li, W & Xu, J 2025, 'Insights into the hydration kinetics, microstructure and early strength of Portland cement containing synthetic C-S-H/PCE nanocomposites', Cement and Concrete Composites, vol. 157, pp. 105886-105886.
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Zhand, S, Goss, DM, Cheng, YY & Warkiani, ME 2025, 'Recent Advances in Microfluidics for Nucleic Acid Analysis of Small Extracellular Vesicles in Cancer', Advanced Healthcare Materials, vol. 14, no. 4.
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AbstractSmall extracellular vesicles (sEVs) are membranous vesicles released from cellular structures through plasma membrane budding. These vesicles contain cellular components such as proteins, lipids, mRNAs, microRNAs, long‐noncoding RNA, circular RNA, and double‐stranded DNA, originating from the cells they are shed from. Ranging in size from ≈25 to 300 nm and play critical roles in facilitating cell‐to‐cell communication by transporting signaling molecules. The discovery of sEVs in bodily fluids and their involvement in intercellular communication has revolutionized the fields of diagnosis, prognosis, and treatment, particularly in diseases like cancer. Conventional methods for isolating and analyzing sEVs, particularly their nucleic acid content face challenges including high costs, low purity, time‐consuming processes, limited standardization, and inconsistent yield. The development of microfluidic devices, enables improved precision in sorting, isolating, and molecular‐level separation using small sample volumes, and offers significant potential for the enhanced detection and monitoring of sEVs associated with cancer. These advanced techniques hold great promise for creating next‐generation diagnostic and prognostic tools given their possibility of being cost‐effective, simple to operate, etc. This comprehensive review explores the current state of research on microfluidic devices for the detection of sEV‐derived nucleic acids as biomarkers and their translation into practical point‐of‐care and clinical applications.

Zhang, A, Lu, Z, Roohani, I, Liu, B, Jarvis, KL, Tan, R, Wise, SG, Bilek, MMM, Mirkhalaf, M, Akhavan, B & Zreiqat, H 2025, 'Bioinstructive 3D-Printed Magnesium-Baghdadite Bioceramic Scaffolds for Bone Tissue Engineering', ACS Applied Materials & Interfaces, vol. 17, no. 10, pp. 15220-15236.
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Zhang, H & Zhu, X 2025, 'Uncertainty quantification of seismic risk for an isolated large-scale dome structure', Engineering Structures, vol. 335, pp. 120410-120410.
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Zhang, H, Huang, X & Zhang, JA 2025, 'Zak-OTFS With Time-Domain Offset Gradient Descent Equalization', IEEE Transactions on Vehicular Technology, pp. 1-10.
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Zhang, H, Lee, C-K & Li, Z 2025, 'Six Degrees of Freedom Tracking and Wireless Charging for Capsule Endoscopy', IEEE Transactions on Industrial Electronics, vol. 72, no. 4, pp. 3643-3652.
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Zhang, H, Liu, W, Li, Z & Lee, C-K 2025, 'Pulse Frequency Modulation of 3D Wireless Power Transfer for Capsule Endoscopy', IEEE Transactions on Industrial Electronics, vol. 72, no. 1, pp. 308-317.
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Zhang, J, Shen, Y, Wu, Z, Zhang, X, Kang, J, Wu, Y, Zhang, S, Chen, S, Wang, G, Zhang, H, Yin, H & Zhao, H 2025, 'Efficient Alkaline‐Free Electrooxidation of 5‐Hydroxymethylfurfural to 2,5‐Furandicarboxylic Acid using Electrochemically‐Charged Ni_xCo_1‐x(OH)₂ as a Redox Mediator', Angewandte Chemie International Edition, vol. 64, no. 13.
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AbstractConverting biomass‐derived molecules like 5‐hydroxymethylfurfural (HMF) into value‐added products alongside hydrogen production using renewable energy offers significant opportunities for sustainable chemical and energy production. Yet, HMF electrooxidation requires strong alkaline conditions and membranes for efficient conversion. These harsh conditions destabilize HMF, leading to humin formation and reduced product purity, meanwhile membranes increase costs. Addressing these challenges, we introduce a two‐step, decoupling system that operates without strong alkaline conditions and eliminates the need of membranes. In this system, nickel‐cobalt hydroxides serve as effective redox mediators, driving HMF oxidation in pure water. The experimental results showed that Ni0.85Co0.15OOH effectively promotes the dehydrogenation of substrate and achieves a highly efficient oxidation of HMF in pure water, with the selectivity of product 2,5‐furandicarboxylic acid (FDCA) approaching 100 %. This system has been expanded to oxidize various substrates, achieving yields exceeding 92 % for the corresponding acids of functionalized compounds such as furfuryl alcohol, furfural, benzyl alcohol, and benzaldehyde. By replacing fixed electrodes with flow electrodes, the scalability of decoupling strategy was evaluated for the harvesting of pure solid FDCA, highlighting the broad prospects in practical applications.

Zhang, J, Zeng, X, Gao, F, Zheng, J & Sheng, D 2025, 'Effect of replacement cushion on water-heat distribution in permafrost foundation', Zhongnan Daxue Xuebao Ziran Kexue Ban Journal of Central South University Science and Technology, vol. 56, no. 2, pp. 674-687.
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Road problems in the frozen soil areas of the Qinghai—Tibet Plateau have become increasingly severe due to climate and environmental changes and engineering thermal effects, including frequent hazard occurrences, poor prevention and maintenance measures, etc. Replacement cushion is widely used in the design and construction of frozen road engineering, playing an essential role in enhancing deformation resistance, frost damage and stability. However, the effects of replacement cushions on the water-heat distribution during the service period have not been taken seriously. Based on the permafrost foundation replacement of the Gongyu—Yu Expressway, a numerical test on the water-heat distribution of the foundation was carried out with three types of filling materials, i.e. fine sand, coarse sand and gravel in the paper. The influence mechanism of filling materials on the distribution and evolution of water-heat was studied by using silty clay without replacement as a reference. The results show that replacing the gravel has better temperature control performance and can effectively reduce the maximum melting depth. Replacement cushions change the spatial and temporal distribution of foundation moisture. The unfrozen water content in the replacement layer decreases significantly, and the unfrozen water flows from both sides of the foundation to the centre along the filling-soil interface. The replacement cushion causes the surface ponding to invade into deeper soil. Especially without active drainage measures, the magnitude and oscillation amplitude of the water content in the deep foundation increase significantly. The water permeability and thermal conductivity of filling materials are important factors affecting water-heat distribution and evolution in road foundations. The moisture migration caused by replacement is not conducive to the long-term maintenance of the mechanical properties of the foundation. Therefore, replacement should be carefully adopte...

Zhang, J, Zhang, R, Xu, L, Lu, X, Yu, Y, Xu, M & Zhao, H 2025, 'FasterSal: Robust and Real-Time Single-Stream Architecture for RGB-D Salient Object Detection', IEEE Transactions on Multimedia, vol. 27, pp. 2477-2488.
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Zhang, J, Zhu, S, Liu, X, Wen, S & Mu, C 2025, 'Finite-Time Stabilization of Inertial Memristive Neural Networks via Nonreduced Order Method', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 8025-8035.
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Zhang, J, Zhu, S, Wu, K-N, Shen, M & Wen, S 2025, 'Finite-Time Stabilization of Semi-Markov Reaction-Diffusion Memristive NNs With Unbounded Time-Varying Delays', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 72, no. 4, pp. 1832-1842.
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Zhang, JA, Zhang, H, Wu, K, Huang, X, Yuan, J & Guo, YJ 2025, 'Wireless Communications in Doubly Selective Channels with Domain Adaptivity', IEEE Communications Magazine, vol. 63, no. 5, pp. 102-108.
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Zhang, L, Song, P, Duan, Z, Wang, S, Chang, X & Yang, X 2025, 'Video Corpus Moment Retrieval with Query-specific Context Learning and Progressive Localization', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Zhang, L, Wu, W, Liu, J, Chang, X, Hu, X, Zheng, Y, Wu, Y & Zheng, Q 2025, 'LFSRM: Few-Shot Diagram-Sentence Matching via Local-Feedback Self-Regulating Memory', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-13.
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Zhang, L, Zhong, Y, Zheng, Q, Liu, J, Wang, Q, Wang, J & Chang, X 2025, 'TDGI: Translation-Guided Double-Graph Inference for Document-Level Relation Extraction', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 4, pp. 2647-2659.
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Zhang, R, Cao, Z, Huang, Y, Yang, S, Xu, L & Xu, M 2025, 'Visible-Infrared Person Re-Identification With Real-World Label Noise', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 5, pp. 4857-4869.
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Zhang, R, Li, Y, Gui, Y, Armaghani, DJ & Yari, M 2025, 'Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock Distance Prediction', Rock Mechanics and Rock Engineering, vol. 58, no. 1, pp. 679-695.
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AbstractIn the field of civil and mining engineering, blasting operations are widely and frequently used for rock excavation, However, some undesirable environmental problems induced by blasting operations cannot be ignored. Blast-induced flyrock is one important issue induced by blasting operation, which needs to be well predicted to identify the blasting zone’s safety zone. This study introduces an adaptive weighted multi-kernel learning model (AW-MKL) to provide an accurate prediction of blast-induced flyrock distance in Sungun Copper Mine site. The proposed model uses a combination of multi-kernel learning (MKL) approach and adaptive weighting strategy based on weighted Euclidean distance and modified local outlier factor (MLOF) to maximally improve the predictive ability of kernel ridge regression (KRR). To demonstrate the superiority of the proposed approach, six machine learning models were developed as comparisons, i.e., KRR, RF, GBDT, SVM, M5 Tree, MARS and AdaBoost. The outcomes of the proposed method achieved the highest accuracy in testing phase, with RMSE of 2.05, MAE of 0.98 and VAF of 99.92, which confirmed the strong predictive capability of the proposed AW-MKL in predicting blast-induced flyrock distance.

Zhang, R, Yang, B, Xu, L, Huang, Y, Xu, X, Zhang, Q, Jiang, Z & Liu, Y 2025, 'A Benchmark and Frequency Compression Method for Infrared Few-Shot Object Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-11.
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Zhang, S, Zhao, L, Huang, S, Mazomenos, EB & Stoyanov, D 2025, 'Direct Camera-Only Bundle Adjustment for 3-D Textured Colon Surface Reconstruction Based on Pre-Operative Model', IEEE Transactions on Medical Robotics and Bionics, vol. 7, no. 1, pp. 242-253.
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Zhang, W, Chen, J, Wen, S & Huang, T 2025, 'Event-Triggered Random Delayed Impulsive Consensus of Multi-Agent Systems With Time-Varying Delay', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 2059-2064.
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Zhang, W, Yang, Z, Wen, D, Li, W, Zhang, W & Lin, X 2025, 'Accelerating Core Decomposition in Billion-Scale Hypergraphs', Proceedings of the ACM on Management of Data, vol. 3, no. 1, pp. 1-27.
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Hypergraphs provide a versatile framework for modeling complex relationships beyond pairwise interactions, finding applications in various domains. k -core decomposition is a fundamental task in hypergraph analysis that decomposes hypergraphs into cohesive substructures. Existing studies capture the cohesion in hypergraphs based on the vertex neighborhood size. However, such decomposition poses unique challenges, including the efficiency of core value updates, redundant computation, and high memory consumption. We observe that the state-of-the-art algorithms do not fully address the above challenges and are unable to scale to large hypergraphs. In this paper, we propose an efficient approach for hypergraph k -core decomposition. Novel concepts and strategies are developed to compute the core value of each vertex and reduce redundant computation of vertices. Experimental results on real-world and synthetic hypergraphs demonstrate that our approach significantly outperforms the state-of-the-art algorithm by 7 times on average while reducing the average memory usage by 36 times. Moreover, while existing algorithms fail on tens of millions hyperedges, our approach efficiently handles billion-scale hypergraphs in only a single thread.

Zhang, W, Zong, Y, Zhang, J, Ai, J, He, H, Li, L, Peng, S, Zhou, H, Wang, D & Wang, Q 2025, 'Mechanistic insights into the viral microorganism inactivation during lime stabilization for wastewater sludges', Journal of Hazardous Materials, vol. 485, pp. 136884-136884.
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Zhang, X, Cao, M, Gong, Y, Wu, X, Dong, X, Guo, Y, Zhao, L & Zhang, C 2025, 'Enhancing urban flow prediction via mutual reinforcement with multi-scale regional information', Neural Networks, vol. 182, pp. 106900-106900.
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Zhang, X, Zhao, H, Tao, Z & Li, W 2025, 'Performance of sustainable cementitious mortar incorporating hybrid waste glass powder and steel slag', Construction and Building Materials, vol. 472, pp. 140928-140928.
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Zhang, X, Zhu, X, Wang, Y & Li, J 2025, 'Structural damage detection based on transmissibility functions with unsupervised domain adaptation', Engineering Structures, vol. 322, pp. 119142-119142.
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Zhang, X, Zhu, X, Yu, Y & Li, J 2025, 'Transfer Learning-Based Structural Damage Identification for Building Structures with Limited Measurement Data', International Journal of Structural Stability and Dynamics, vol. 25, no. 09.
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Structural damage detection is crucial for ensuring the safety of civil building structures in operational environments. Recently, deep learning-based methods have gained increasing attention from engineers and researchers. The performance of conventional deep learning methods for structural damage detection relies on a large number of labeled training datasets. However, it is difficult or/and impossible to obtain sufficient datasets to cover various damage scenarios for in-service structures. A little research has been conducted to identify both the damage severity and location with limited labeled measurement data. A novel transfer learning-based method for structural damage identification with limited measurements has been proposed utilizing frequency response functions (FRFs) as the input. The real structure is regarded as the target domain and its numerical model is as the source domain. The samples for various damage scenarios are generated using the numerical model, and a designed deep convolutional neural network (CNN) is pre-trained. The knowledge of the pre-trained network is transferred to identify the damage location and severity of the real structure using limited measurement data. Numerical and experimental studies have been conducted on a three-story building structure to verify the performance of the proposed method. To understand transfer learning and model interpretability, the t-SNE feature visualization is adopted to show the feature distribution changes during transfer learning. Numerical and experimental results show that the proposed approach outperforms conventional CNN models, and it is effective and accurate in identifying structural damage location and severity in real structures with limited measurement data.

Zhang, Y, Gu, C, Shi, P, Jing, Z, Li, B & Liu, B 2025, 'Bring Your Device Group (BYDG): Efficient and Privacy-Preserving User-Device Authentication Protocol in Multi-Access Edge Computing', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 3346-3361.
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Zhang, Y, Li, R-H, Zhang, Q, Qin, H, Qin, L & Wang, G 2025, 'Density Decomposition of Bipartite Graphs', Proceedings of the ACM on Management of Data, vol. 3, no. 1, pp. 1-25.
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Mining dense subgraphs in a bipartite graph is a fundamental task in bipartite graph analysis, with numerous applications in community detection, fraud detection, and e-commerce recommendation. Existing dense subgraph models, such as biclique, k -biplex, k -bitruss, and (α,β)-core, often face challenges due to their high computational complexity or limitations in effectively capturing the density of the graph. To overcome these issues, in this paper, we propose a new dense subgraph model for bipartite graphs, namely (α,β)-dense subgraph, designed to capture the density structure inherent in bipartite graphs. We show that all (α,β)-dense subgraphs are nested within each other, forming a hierarchical density decomposition of the bipartite graph. To efficiently compute the (α,β)-dense subgraph, we develop a novel network flow algorithm with a carefully-designed core pruning technique. The time complexity of our algorithm is O(|E|+|E(R)| 1.5 ), where |E| denotes the number of edges and |E(R)| is the number of edges of the pruned graph, often significantly smaller than |E|. Armed with this algorithm, we also propose a novel and efficient divide-and-conquer algorithm to compute the entire density decomposition of the bipartite graph within O(p ⋅ log d max ⋅ |E| 1.5 ) time, where p is typically a small constant in real-world bipartite graphs and d max is the maximum degree. Extensive experiments and case studies on 11 real-world datasets demonstrate the effectiveness of our (α,β)-dense subgraph model and the high efficiency and scalability of our proposed algorithms.

Zhang, Y, Wu, M, Zhang, G & Lu, J 2025, 'Responsible AI: Portraits with Intelligent Bibliometrics', IEEE Transactions on Artificial Intelligence, pp. 1-14.
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Zhang, YX, Leung, KY, Lachemi, M, Yang, E-H, Barros, J & Yu, K 2025, 'Structural application of high-performance fibre reinforced cementitious composites', Engineering Structures, vol. 326, pp. 119465-119465.
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Zhang, Z, Jiang, Y, Wei, X, Chen, M, Dong, H & Yu, S 2025, 'Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework', IEEE Network, pp. 1-1.
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Zhang, Z, Liu, H, Li, X, Shon, H, Vodnar, DC & Wang, Q 2025, 'Biofouling control of reverse osmosis membrane in municipal wastewater recycling plants using urine as a cleaning agent', Separation and Purification Technology, vol. 355, pp. 129629-129629.
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Zhang, Z, Zhou, Z, Tian, Z & Yu, S 2025, 'CRCGAN: Toward robust feature extraction in finger vein recognition', Pattern Recognition, vol. 158, pp. 111064-111064.
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Zhao, C, Dong, W, Indra Mahlia, TM, Shi, L, Wang, K, Shah, SP & Li, W 2025, 'Enhancing energy storage capability for renewable energy systems through advanced cement-based supercapacitors', Energy and Buildings, vol. 338, pp. 115732-115732.
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Zhao, C, Dong, W, Wang, K, Tao, Z & Li, W 2025, 'Investigation on effects of LiCl, KCl and polyethylene oxide on electrochemical properties of cement-based capacitors', Construction and Building Materials, vol. 481, pp. 141612-141612.
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Zhao, F, Ji, JC, Cao, S, Zheng, J & Luo, Q 2025, 'A constant quasi-zero stiffness isolator with tension springs to isolate vibrations with ultralow frequency', International Journal of Non-Linear Mechanics, vol. 175, pp. 105129-105129.
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Zhao, H, Gan, Y, Qu, F, Tang, Z, Peng, S, Chen, Y & Li, W 2025, 'Nano- and micro-characterisation on the heterogeneity of ITZs in recycled lump concrete', Cement and Concrete Composites, vol. 161, pp. 106078-106078.
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Zhao, J, Li, D, Zhou, J, Armaghani, DJ & Zhou, A 2025, 'Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models', Deep Underground Science and Engineering, vol. 4, no. 1, pp. 3-17.
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AbstractRock fragmentation is an important indicator for assessing the quality of blasting operations. However, accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties. For this reason, optimized by the Bayesian optimization algorithm (BOA), four hybrid machine learning models, including random forest, adaptive boosting, gradient boosting, and extremely randomized trees, were developed in this study. A total of 102 data sets with seven input parameters (spacing‐to‐burden ratio, hole depth‐to‐burden ratio, burden‐to‐hole diameter ratio, stemming length‐to‐burden ratio, powder factor, in situ block size, and elastic modulus) and one output parameter (rock fragment mean size, X50) were adopted to train and validate the predictive models. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination () were used as the evaluation metrics. The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models. The hybrid model consisting of gradient boosting and BOA (GBoost‐BOA) achieved the best prediction results compared with the other hybrid models, with the highest R2 value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02, respectively. Furthermore, sensitivity analysis was carried out to study the effects of input variables on rock fragmentation. In situ block size (XB), elastic modulus (E), and stemming length‐to‐burden ratio (T/B) were set as the main influencing factors. The proposed hybrid model provided a reliable prediction result and thu...

Zhao, L, Wang, L, Cao, Y, Yang, Y & Wen, S 2025, 'Learning-Based Fault-Tolerant Control With High-Order Control Barrier Functions', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 14689-14698.
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Zhao, L, Wen, G, Guo, Z, Zhu, S, Hu, C & Wen, S 2025, 'Probabilistic Model-Based Fault-Tolerant Control for Uncertain Nonlinear Systems', IEEE Transactions on Cybernetics, vol. 55, no. 4, pp. 1838-1847.
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Zhao, S, Gong, S, Gu, B, Li, L, Lyu, B, Thai Hoang, D & Yi, C 2025, 'Exploiting NOMA Transmissions in Multi-UAV-Assisted Wireless Networks: From Aerial-RIS to Mode-Switching UAVs', IEEE Transactions on Wireless Communications, vol. 24, no. 3, pp. 2530-2544.
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Zhao, S, Zhou, C & Xin, Z 2025, 'Effect of the Oxazine Structure on Antibacterial Activity of Biobased Benzoxazine and Its Application in Polyethylene Modification', ACS Applied Polymer Materials, vol. 7, no. 5, pp. 2879-2889.
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Zhao, W, Hou, Y, Wei, L, Wei, W, Zhang, K, Duan, H & Ni, B-J 2025, 'Chlorination-induced spread of antibiotic resistance genes in drinking water systems', Water Research, vol. 274, pp. 123092-123092.
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Zhao, W, Zhang, H, Li, L, Zhang, J & Chu, L 2025, 'Spinosin enhances non-rapid eye movement sleep and alters c-Fos expression in sleep–wake regulatory brain regions in mice', Sleep and Breathing, vol. 29, no. 1.
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Zhao, X, Fan, J, Chang, X, Nie, F, Zhang, Q & Guo, J 2025, 'Scalable Multi-view Regression Clustering for Large-scale Data', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Zhao, Y, Liu, B, Zhu, T, Ding, M & Zhou, W 2025, 'ROSIN: Robust Semantic Image Hiding Network', Knowledge-Based Systems, vol. 309, pp. 112885-112885.
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Zhao, Z, Cao, L & Wang, C-D 2025, 'Gray Learning From Non-IID Data With Out-of-Distribution Samples', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 1396-1409.
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Zheng, J, Gao, Q, Dong, D, Lü, J & Deng, Y 2025, 'A Quantum Multimodal Neural Network Model for Sentiment Analysis on Quantum Circuits', IEEE Transactions on Artificial Intelligence, vol. 6, no. 5, pp. 1128-1142.
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Zhou, J, Li, D, Chen, G & Wen, S 2025, 'Sliding mode control for finite-time projective synchronisation in distinct Caputo fractional-order delayed neural networks with inconsistent orders', International Journal of Systems Science, vol. 56, no. 5, pp. 1095-1112.
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Zhou, K, Jiang, M, Gabrys, B & Xu, Y 2025, 'Learning Causal Representations Based on a GAE Embedded Autoencoder', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 6, pp. 3472-3484.
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Zhou, R, Zuo, W, Xie, Y, Wang, Y, Li, C, Tang, Y, Wang, Z & Li, Y 2025, 'Sodium bisulfite boosted exopolysaccharide production by Auxenochlorella protothecoides: Potential mechanisms harnessing H2O2 signaling and carbon reallocation', Bioresource Technology, vol. 420, pp. 132121-132121.
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Zhou, S, Ma, H, Kuang, S & Dong, D 2025, 'Auxiliary Task-Based Deep Reinforcement Learning for Quantum Control', IEEE Transactions on Cybernetics, vol. 55, no. 2, pp. 712-725.
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Zhou, S, Yang, D, Zhang, Z, Zhang, J, Qu, F, Punetha, P, Li, W & Li, N 2025, 'Enhancing autonomous pavement crack detection: Optimizing YOLOv5s algorithm with advanced deep learning techniques', Measurement, vol. 240, pp. 115603-115603.
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Zhou, S, Ye, D, Zhu, T & Zhou, W 2025, 'Defending Against Neural Network Model Inversion Attacks via Data Poisoning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-16.
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Zhou, T, Fang, G, Wang, Z, Qiao, Z, Nie, N, Fu, B, Tseng, P-H, Sun, X & Chen, Y-C 2025, 'Digital Lasing Biochip for Tumor-Derived Exosome Analysis', Analytical Chemistry, vol. 97, no. 10, pp. 5605-5611.
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Zhou, T, Yu, S, Tang, Y, Huang, X, Fu, JJ, Shen, PK & Tian, ZQ 2025, 'Built-in electric field engineering of MoO2-Ni heterostructure by P3p-Mo4d-Tm4f orbitals hybridization for efficient industrial-level hydrogen production', Chemical Engineering Journal, vol. 508, pp. 160921-160921.
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Zhou, X, Li, M, Du, Y, Yang, C & Wen, S 2025, 'Interpretable Multiobjective Feature Selection via Visualization in Froth Flotation Process', IEEE Transactions on Industrial Informatics, vol. 21, no. 3, pp. 2530-2539.
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Zhu, H, Fang, G, Nie, N, Xie, J, Tseng, P-H, Xiong, Z, Jiang, D, Mao, C-J, Zhu, J-J, Chew, SY & Chen, Y-C 2025, 'Breathing Laser-Spectral Mapping of Cavity-Enhanced Redox Reactions with Subcellular Resolution', ACS Nano, vol. 19, no. 11, pp. 10955-10965.
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Zhu, H, Xu, Y, Zhang, T, Du, J & Jay Guo, Y 2025, 'Reconfigurable Generalized Joined Coupler Matrices for Independent Control of Multiple Steerable Beams', IEEE Transactions on Antennas and Propagation, vol. 73, no. 3, pp. 1573-1584.
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Zhu, J, El-Zein, A, Hubble, TCT & Miao, G 2025, 'Effects of soil saturation and suction on root reinforcement performance: pull-out experiments on six native Australian plants', Acta Geotechnica, vol. 20, no. 5, pp. 2075-2092.
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Abstract Improving shallow slope stability with vegetation requires an understanding of root reinforcement performance, in addition to consideration of local ecological impacts. Existing root reinforcement models have not accounted for the influence of soil water content, due to insufficient experimental evidence and theoretical understanding. In this study, the root reinforcement behaviour of six Australian native plants (A. costata, B. integrifolia, E. reticulatus, P. incisa, C. citrinus and M. thymifolia) are examined through vertical pull-out tests under various levels of volumetric water content (VWC) and suction. Additionally, this study employed two root reinforcement models to illustrate the impact of VWC on comparing the performance of these models with experimental results. The study also employs an innovative approach by making an analogy to soil nails or piles and normalising pull-out force against the peripheral surface area of root-soil bundles, defining this as pull-out stress. The results show that VWC and suction have a strong influence on reinforcement, with a roughly linear inverse relationship observed between VWC and pull-out force of root bundles recorded for all species. The pull-out stress followed a nonlinear inverse relationship with VWC and suction as the pull-out force. Furthermore, discrepancies between established-model predictions and experimental data widen with increasing VWCs. It is also found that inadequate sampling can also lead to substantial errors in estimating the actual water content of the soil. The study demonstrates that VWC and suction significantly impact root reinforcement performance, with pull-out strength decreasing as VWC increases. The study also highlights the importance of accurately recording soil...

Zhu, L, Wu, R, Liu, D, Zhang, C, Wu, L, Zhang, Y & Zhang, S 2025, 'Textual semantics enhancement adversarial hashing for cross-modal retrieval', Knowledge-Based Systems, vol. 317, pp. 113303-113303.
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Zhu, P, Li, Y, Hu, Y, Xiang, S, Liu, Q, Cheng, D & Liang, Y 2025, 'MCI-GRU: Stock prediction model based on multi-head cross-attention and improved GRU', Neurocomputing, vol. 638, pp. 130168-130168.
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Zhu, Y, Xu, K, Wan, B, Lei, G & Zhu, J 2025, 'Kolmogorov-Arnold Network for Solving 2-D Magnetostatic Problems', IEEE Transactions on Magnetics, pp. 1-1.
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Zhu, Y, Zuo, W, Ji, J & Zhang, Z 2025, 'Bifurcations analysis of a 3D Filippov pest-natural enemy system with stage structure for the prey', Applied Mathematics and Computation, vol. 497, pp. 129356-129356.
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Zhuang, Y, Lin, Z, Zhai, R, Huang, Y, Nie, B & Li, Y 2025, 'A study on the effect of spark plug micro-hole hydrogen injection on the spray and combustion processes of a gasoline engine with intake port water injection', Energy, vol. 315, pp. 134366-134366.
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Zhuo, L, Goay, ACY, Sangkarat, P, Xu, F, He, Y, Gao, Z, Mishra, D, He, S, Zhang, Y & Zhang, J 2025, 'Enhanced Triboelectric Outputs from PAN/MoS₂ Nanofiber‐Based Nanogenerators for Powering Backscatter Communications in Sustainable 6G Networks', Advanced Energy and Sustainability Research, vol. 6, no. 3.
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This work explores the development of a triboelectric nanogenerator (TENG) based on polyacrylonitrile (PAN) and molybdenum disulfide (MoS2) nanosheets composite fibers for enhancing tribo‐positive electricity to power backscatter communication systems, contributing to the sustainable internet of things (IoT) nodes in future 6 G networks. By incorporating different concentrations of MoS2 (1, 2, 3, and 4 wt%) nanosheets into PAN nanofibers via electrospinning, the nanocomposite fiber‐based TENGs exhibit improved triboelectric properties. The TENG based on PAN/4% MoS2 nanocomposite fiber mat achieve a peak open‐circuit voltage of 296 V and a short‐circuit current of 6.16 μA, which represents an ≈95% and 77% enhancement, respectively, in comparison with the TENGs based on neat PAN nanofiber mat. The enhanced charge transfer ability at the PAN and MoS2 nanosheet interface, the increased dielectric properties, the rougher surface morphology of the composite nanofibers contribute to the enhancements in triboelectric performance. These TENGs are integrated with the backscatter communication system to power a wireless identification and sensing platform (WISP) tag, demonstrating extended transmission range and improved real‐time data acquisition. These findings suggest that TENGs can play a significant role in sustainable energy solutions for 6 G‐enabled IoT applications.

Zou, W, Zhang, M, Zhang, X, Zhang, D, Li, C, Zhong, L, Guo, W & Ngo, HH 2025, 'Biochar-based iron-doped alginate microspheres combined with Fenton-like reaction for removing oxytetracycline hydrochloride: Performance, mechanism, and degradation pathway', Journal of Water Process Engineering, vol. 69, pp. 106723-106723.
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Zou, Y, Li, S, Li, Y, Zhang, D, Zheng, M & Shi, B 2025, 'Glioblastoma Cell Derived Exosomes as a Potent Vaccine Platform Targeting Primary Brain Cancers and Brain Metastases', ACS Nano, vol. 19, no. 18, pp. 17309-17322.
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Zuo, Y, Hu, Y, Xu, Y, Wang, Z, Fang, Y, Yan, J, Jiang, W, Peng, Y & Huang, Y 2025, 'Learning Guided Implicit Depth Function with Scale-aware Feature Fusion', IEEE Transactions on Image Processing, pp. 1-1.
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Abdullah, A, Keshavarz, R & Shariati, N 1970, 'Chiral Metasurface for Asymmetric Transmission and Polarization Conversion in Nonreciprocal Shielding', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Ahamed, E, Keshavarz, R & Shariati, N 1970, 'Programmable 2-bit Metamaterial for Beamsteering in Smart AgTech Applications', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Ahuja, T, Gardner, A & Machet, T 1970, 'Professional skills – the balancing act!', Australasian Association for Engineering Education Annual Conference, Engineers Australia, New Zealand, pp. 1150-1159.
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Globalisation and ever-changing technology require the modern engineer to acquire a broad skill set to be able to contribute to the profession and to society at large. Engineers Australia includes a set of competencies, categorised as 'professional and personal attributes' that graduating engineers must possess for them to operate in an 'informed, responsible and sustainable fashion'. Several researchers have pointed out the inadequacy in the level of development of these attributes in recent graduates, and the gaps in higher education with respect to teaching these. PURPOSE OR GOAL: The purpose of this paper is to share preliminary findings from research in progress on academic perspectives around the development and assessment of professional skills in undergraduate engineering programs in the Australian context. The paper will report on findings from academics from GO8, ATN and RUN universities, elaborating their views on the terminology around these skills and what they consider as effective ways to develop and assess professional skills. APPROACH: A qualitative approach was adopted for the study, specifically using 'phenomenography'. Semi structured interviews were conducted with 10 academics, who were selected using a purposeful sampling technique. The data collected was analysed using NVivo and data driven, structural coding was used. A codebook was created, and the information coded was further organised and subsequently analysed, using the framework matrix within NVivo. OUTCOMES: The study brings out the current Australian academic perspective on some of the ways undergraduate engineering students can be helped to develop professional skills while also discussing some assessment strategies. The study discusses the current view on the appropriate terminology for these skills, to enable a mindset shift in academics and engineering students, helping them appreciate their importance. CONCLUSIONS: Based on the findings, academics continue to recogni...

Ahuja, T, Gardner, A & Machet, T 1970, 'World Engineering Education Forum', World Engineering Education Forum, Sydney, Australia.

Alabdali, SA & Pileggi, SF 1970, 'A Research Framework to Identify Determinants for Smart Technology Adoption in Rural Regions', Springer Nature Singapore, Jeddah, Saudi Arabia, pp. 3-15.
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Abstract Despite rural regions cover a significant portion of the world's land, they remain overlooked entities within scholarly discourse as well as in a broader socio-economic context. However, their integration into sustainable development efforts is gaining relevance and should be considered as a strategic priority. This paper presents a research framework to identify determinants for Smart Technology adoption in rural regions. The main goal is to support organizations in rural areas to better understand and analyse the challenges with a scientific focus. It is expected to contribute to unlock new opportunities for innovative solutions, as well as to improve performance and enhance business sustainability in their unique context. The framework results from the combination and in-context interpretation of three different theories to define integrated strategic solutions: Diffusion of Innovation theory (DOI), Technology Organizations-Environment (TOE) framework, and Technology Acceptance Model (TAM). This approach ideally provides support to mixed methods to enable research in fact within real organizational contexts. Such a conceptual asset is expected to contribute in practice by facilitating (i) the formulation of theories as a response to open research issues, (ii) the development of appropriate integrated strategies, and (iii) the identification of major determinants to establish a consistent road map. The combination of chosen theories contributes to better investigating the causes of the research problem through a set of factors targeting the technology adoption in the rural context, providing a clear insight into solutions for decision-makers.

Ali, A, Singh, K, Thalakotuna, DN & Esselle, KP 1970, 'A Solar Cell Integrated Antenna for on-the-Move IoT Applications', 2025 19th European Conference on Antennas and Propagation (EuCAP), 2025 19th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-5.
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Ali, A, Singh, K, Thalakotuna, DN & Esselle, KP 1970, 'Improving the Form Factor in the Slot Based Built-in Solar Cell Integrated Antennas', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Ali, MY, Singh, K, Ahmed, F & Mukhopadhyay, S 1970, 'Miniaturized 3-D Printable Composite Metasurface for 1-D Beam Steering', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Alqahtani, S, Nanda, P & Mohanty, M 1970, 'Strengthening Cybersecurity: The Influence of Student Behavior, Perceived Factors, and Mitigating Strategies on Phishing Attack Perception', Web Information Systems Engineering, Springer Nature Singapore, Doha, Qatar, pp. 313-329.
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Alshatri, N, Ghannam, S & Hussain, FK 1970, 'Multi-factor Predictive Models of Carbon Credit Prices', Springer Nature Switzerland, pp. 200-212.
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Cai, Z, Wang, S, Chu, V, Naseem, U, Wang, Y & Chen, F 1970, 'Unleashing the Potential of Diffusion Models Towards Diversified Sequential Recommendations', International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025.

Cao, H, Zhang, Z, Xia, Y, Li, X, Xia, J, Chen, G & Knoll, A 1970, 'Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection', Springer Nature Switzerland, pp. 161-177.
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Chauhan, R, Mishra, A, Jena, M, Yafi, E & Zuhairi, MF 1970, 'Mental Health Diagnostics Using Machine Learning: A Clustering and Predictive Modeling Approach', 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM), IEEE, pp. 1-8.
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Chauhan, R, Mishra, A, Mani, RJ, Yafi, E & Zuhairi, MF 1970, 'An Analytical Paradigm for Exploration of Diabetes Using Machine Learning', 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM), IEEE, pp. 1-8.
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Chen, K, Wen, D, Wang, H, Yang, Z, Zhang, W & Lin, X 1970, 'Covering K-Cliques in Billion-Scale Graphs', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2299-2308.
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Chen, W, Li, W, Gao, M, Wen, D, Cai, M & Wang, W 1970, 'Locally Balancing Signed Graphs', Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 118-129.
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Dang, X, Nanda, P, Xu, H, Mohanty, M & Deng, H 1970, 'A Novel Scheme for Recommendation Unlearning Verification (RUV) Using Non-Influential Trigger Data', 2025 IEEE 22nd Consumer Communications &amp; Networking Conference (CCNC), 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC), IEEE, Las Vegas, USA, pp. 1-6.
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Ding, D, Li, Z, Zhang, J, Liu, X, Zhang, J, Li, Y, Cai, P, Liu, J & Long, G 1970, 'eBaaS: AIoT-Enabled eBike Battery-Swap as a Service for Last-Mile Delivery', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 5045-5053.
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Ding, Y, Lin, H, Yang, Z, Wen, D, Wang, X & Zhang, W 1970, 'An Experimental Comparison of RDF Systems on Cloud', Springer Nature Singapore, pp. 30-43.
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Ding, Z, Chen, Y, Wang, H, Wang, X, Zhang, W & Zhang, Y 1970, 'TIformer: A Transformer-Based Framework for Time-Series Forecasting with Missing Data', Springer Nature Singapore, pp. 71-84.
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Dinh, PV, Nguyen, DN, Thai, HD, Nguyen, QU, Bao, SP & Dutkiewicz, E 1970, 'A Dual-Decoder Variational Auto-Encoder for Anomaly Detection', 2025 IEEE Wireless Communications and Networking Conference (WCNC), 2025 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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Fan, S, Xiao, F, Wang, W, Qi, S, Zhu, Q, Wang, W & Guan, J 1970, 'Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained Model for Acoustic Traffic Monitoring', ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Kothaguda, India, pp. 1-5.
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Gill, A 1970, 'Adaptive Information Architecture: Information Element Identification and Organisation Patterns for Digital Ecosystem', The 20th International Conference on Information and Knowledge Engineering, Springer, Las Vegas.

Guan, J, Tian, J, Zhu, Q, Xiao, F, Zhang, H & Liu, X 1970, 'Disentangling Hierarchical Features for Anomalous Sound Detection Under Domain Shift', ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Kothaguda, India, pp. 1-5.
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Haddas, M & Hussain, FK 1970, 'Human Factors Influencing Australian Manufacturing SMEs’ Adoption of Collaborative Robots: A Qualitative Study', Springer Nature Switzerland, pp. 154-164.
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Haider, W, Batool, S, Milazzo, F & Ha, QP 1970, 'Machine Learning-Based Forecasting Active Power Loss in Distribution Systems', E3S Web of Conferences, EDP Sciences, pp. 04003-04003.
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This paper presents an ensemble learning approach to predict the active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The forecast model incorporates the Gradient Boosting Machine Regression (GBMR) to estimate DG location, bus voltages, DG size, and active losses without conventional power flow calculations. The results demonstrate that the suggested estimations of power losses and DG sizing are effective, practical, and adaptable for power system management. The accuracy of the proposed model has been validated using key performance metrics and tested on the standard IEEE 33 bus system. In the case of fixed load, the GBMR outperforms other machine learning techniques with the R-squared 0.9997, with a very low mean absolute percentage error (MAPE) (0.2216%) and a root mean square error (RMSE) of 1.0673 in predicting active power losses. This approach is promising in enabling grid operators to effectively manage DG unit integration of distributed energy resources from precise and reliable estimates of the power loss.

He, M, Zhang, J & Yu, X 1970, 'Transferable Attacks for Semantic Segmentation', Springer Nature Singapore, pp. 372-388.
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He, Y, Wei, G, Ziolkowski, RW & Jay Guo, Y 1970, 'Frequency-Reconfigurable Ultra-wideband Phased Array with Enhanced Beam-scanning Performance', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Indraratna, B, Ngo, T, Qi, Y, Hettiyahandi, S & Rujikiatkamjorn, C 1970, 'Resilient Railways Using Energy-Absorbing Rubber Elements in Track Substructure', Geotechnical Frontiers 2025, Geotechnical Frontiers 2025, American Society of Civil Engineers, pp. 374-382.
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Indraratna, B, Xu, B-H & Rujikiatkamjorn, C 1970, 'Soil Response and Modeling of Soft Soil Subgrades under Cyclic Rail Loading', Geotechnical Frontiers 2025, Geotechnical Frontiers 2025, American Society of Civil Engineers, pp. 139-149.
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Irga, PJ, Torpy, FR, Fleck, R & Matheson, S 1970, 'Biosolar green roofs: harnessing the combined power of green roofs and solar energy', Acta Horticulturae, III International Symposium on Greener Cities: Improving Ecosystem Services in a Climate-Changing World (GreenCities2024), International Society for Horticultural Science (ISHS), Wisley, Woking, United Kingdom, pp. 139-146.
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The integration of greenery into building structures such as with green roofs is a promising tool in building resilient cities in a changing climate. Combining green roofs with photovoltaic solar energy generation (“Biosolar green roofs”) adds a further level of sustainability and space efficiency value. However, there is currently a lack of research that quantifies many of the services that such systems can provide. In particular, studies focusing on the quantitative performance improvements of green roofs relative to conventional building technologies are rare, as these require urban spaces where comparisons between green and conventional roofs can be made without spatial and temporal confounding. The current study investigates the benefits of a novel Biosolar green roof in Sydney, Australia, focusing on biodiversity, air pollution mitigation, urban heat reduction, stormwater management, and renewable energy production. The research compares two adjacent buildings – one with photovoltaic panels and the other integrating photovoltaic panels with a green roof. Notably, the green roof resulted in a nine-fold increase in insect species diversity and a four-fold increase in avian species diversity. Plant coverage underwent changes, with changes in shaded areas. Monitoring air pollutants revealed lower O3 levels in the green roof. We recorded a decrease in trace metals entering stormwater systems from the green roof. The green roof exhibited a significant reduction in surface temperatures, compared to the conventional roof, demonstrating an insulative capacity reducing heat transfer indoors and resulting in substantial renewable energy generation from the photovoltaic panels, with the green roof producing significantly more energy than the conventional roof. The results presented here are significant both for the generation of sustainable energy, but also to demonstrate that building owners/stakeholders should not have to choose between a green roof or a so...

Jamshid-Nejad, B & Alvandi, S 1970, 'AN INTELLIGENT SIMULATION MODEL FOR FLOW SHOP SEQUENCING', 51st International Conf. Conference on Computers and Industrial Engineering, Sydney, UNSW.

Jin, K, Zi, X, Thiyagarajan, K, Braytee, A & Prasad, M 1970, 'IP-VQA Dataset: Empowering Precision Agriculture with Autonomous Insect Pest Management through Visual Question Answering', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2000-2007.
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King, I, Long, G, Xu, Z, Zhang, Y & Yu, H 1970, 'FL@FM-TheWebConf'25: International Workshop on Federated Foundation Models for the Web 2025', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 1733-1734.
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Le, TH, Nguyen, HAD, Ha, QP, Tran, MQ, Ahmed, M, Kong, J, Barthelemy, X, Duc, H, Jiang, N, Azzi, M & Riley, M 1970, 'Dempster-Shafer ensemble learning framework for air pollution nowcasting', E3S Web of Conferences, EDP Sciences, pp. 01003-01003.
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Deep-learning has emerged as a powerful approach to significantly improve forecast accuracy for air quality estimation. Several models have been developed, demonstrating their own merits in some scenarios and for certain pollutants. In nowcasting, the prediction of air pollution over a small time period essentially demands accurate and reliable estimates, especially in the event cases. From these, selecting the most suitable model to achieve the required forecast performance remains challenging. This paper presents an ensemble framework based on the Dempster-Shafer theory for data fusion to identify the most accurate and reliable forecasts of air pollution obtained from multiple deep neural network models. Our framework is evaluated against three popular machine learning methods, namely, LightGBM, Random Forest, and XGBoost. Experiments are conducted on two horizons: 6-hour and 12-hour predictions using real-world air quality data collected from state-run monitoring stations and low-cost wireless sensor networks.

Li, A, Lv, C, Mei, G, Zuo, Y, Zhang, J & Fang, Y 1970, 'GSTran: Joint Geometric and Semantic Coherence for Point Cloud Segmentation', Springer Nature Switzerland, pp. 453-469.
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Li, J, Wang, S, Zhang, Q, Yu, S & Chen, F 1970, 'Generating with Fairness: A Modality-Diffused Counterfactual Framework for Incomplete Multimodal Recommendations', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2787-2798.
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Li, Y, Xu, Y, Lin, X, Zhang, W & Zhang, Y 1970, 'Ranking on Dynamic Graphs: An Effective and Robust Band-Pass Disentangled Approach', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 3918-3929.
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Li, Y, Zhang, Q, Lu, W, Peng, X, Zhang, W, Si, J, Gong, Y & Hu, L 1970, 'Time-aware Medication Recommendation via Intervention of Dynamic Treatment Regimes', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 5161-5172.
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Li, Z, Chen, Y, Zhang, T & Wang, X 1970, 'Reembedding and Reweighting are Needed for Tail Item Sequential Recommendation', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, Sydney, Australia, pp. 4925-4936.
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Liang, Z, Guan, H, Lu, W, Peng, X, Xu, B & Yang, M 1970, 'HiRes: Hierarchical Feature Optimization and Rescorer for Automatic ICD Coding', ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 1-5.
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Liang, Z, Sun, Z, Lv, X, Thalakotuna, DN, Esselle, KP, Li, X & Yang, Y 1970, 'Antenna Array with Memory Metal for CubeSats Applications', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Lin, Y, Guan, J, Fang, W, Ying, M & Su, Z 1970, 'VeriQR: A Robustness Verification Tool for Quantum Machine Learning Models', Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 403-421.
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Adversarial noise attacks present a significant threat to quantum machine learning (QML) models, similar to their classical counterparts. This is especially true in the current Noisy Intermediate-Scale Quantum era, where noise is unavoidable. Therefore, it is essential to ensure the robustness of QML models before their deployment. To address this challenge, we introduce VeriQR, the first tool designed specifically for formally verifying and improving the robustness of QML models, to the best of our knowledge. This tool mimics real-world quantum hardware’s noisy impacts by incorporating random noise to formally validate a QML model’s robustness. VeriQR supports exact (sound and complete) algorithms for both local and global robustness verification. For enhanced efficiency, it implements an under-approximate (complete) algorithm and a tensor network-based algorithm to verify local and global robustness, respectively. As a formal verification tool, VeriQR can detect adversarial examples and utilize them for further analysis and to enhance the local robustness through adversarial training, as demonstrated by experiments on real-world quantum machine learning models. Moreover, it permits users to incorporate customized noise. Based on this feature, we assess VeriQR using various real-world examples, and experimental outcomes confirm that the addition of specific quantum noise can enhance the global robustness of QML models. These processes are made accessible through a user-friendly graphical interface provided by VeriQR, catering to general users without requiring a deep understanding of the counter-intuitive probabilistic nature of quantum computing.

Lu, J, Dawson, A, Guo, K, Fox, D, Ramezani, F, Morton, S, Baird, K, Soo, Z, Lu, K, Long, G & Anderson, D 1970, 'AI for Women's Health: Framework and Focus Areas', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 845-848.
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Lv, X, Luo, Z, Tyo, JS & Yang, Y 1970, 'Evolutionary Paths to Systematically Unlocking the Frequency-Selective Potential of 3D Meta-Atoms', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Ma, J, Na, H, Wang, Z, Hua, Y, Liu, Y, Wang, W & Chen, L 1970, 'Detecting Conversational Mental Manipulation with Intent-Aware Prompting', Proceedings - International Conference on Computational Linguistics, COLING, pp. 9176-9183.
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Mental manipulation severely undermines mental wellness by covertly and negatively distorting decision-making. While there is an increasing interest in mental health care within the natural language processing community, progress in tackling manipulation remains limited due to the complexity of detecting subtle, covert tactics in conversations. In this paper, we propose Intent-Aware Prompting (IAP), a novel approach for detecting mental manipulations using large language models (LLMs), providing a deeper understanding of manipulative tactics by capturing the underlying intents of participants. Experimental results on the MentalManip dataset demonstrate superior effectiveness of IAP against other advanced prompting strategies. Notably, our approach substantially reduces false negatives, helping detect more instances of mental manipulation with minimal misjudgment of positive cases. The code of this paper is available at https://github.com/Anton-Jiayuan-MA/Manip-IAP.

Matthews, S, Danby, S, Westwood, S, Theobald, M & Wyeth, P 1970, 'Designing for Transactional Moments: Features of Tools for Child-centred Speech Language Teletherapy', Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-17.
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Matthews, S, Nicholas, M, Paatsch, L, Kervin, L & Wyeth, P 1970, 'Embeddables: Designing Physical-Digital Manipulatives for Young Children', Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction, TEI '25: Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction, ACM, pp. 1-7.
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Matthews, S, Nicholas, M, Paatsch, L, Kervin, L & Wyeth, P 1970, 'Wobblies: Designing Intentional Playthings with Young Children', Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction, TEI '25: Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction, ACM, pp. 1-7.
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Minton, AG, Zhu, HY, Chen, H-T, Wang, Y-K, Zhuang, Z, Notaro, G, Galvan, R, Allen, J, Ziegler, MD & Lin, C-T 1970, 'A Longitudinal Study on the Effects of Circadian Fatigue on Sound Source Identification and Localization using a Heads-Up Display', Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-12.
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Naseem, U, Hu, L, Zhang, Q, Wang, S & Jameel, S 1970, 'DiGrI: Distorted Greedy Approach for Human-Assisted Online Suicide Ideation Detection', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 5192-5201.
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Niu, G & He, X 1970, 'Appropriate Domain Size for Footing Bearing Capacity Analysis Using Random Finite Element Method', Springer Nature Singapore, pp. 283-290.
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Oraki, S, Zhuang, H & Liang, J 1970, 'LORTSAR: Low-Rank Transformer for Skeleton-Based Action Recognition', Springer Nature Switzerland, pp. 196-207.
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Ouyang, D, Wen, D, Yang, J, Li, W & Lin, X 1970, 'Weight-Constrained Simple Path Enumeration in Weighted Graph', Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 1092-1103.
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Patibanda, R, Saini, A, Li, X, Chen, Y, van den Hoven, E & Mueller, FF 1970, 'Playing with Yourself: How Electrical Muscle Stimulation Can Turn Your Body Into A Playful Interface', Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, CHI EA '25: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-3.
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Peng, B, Lu, J, Zhang, Y, Zhang, G & Fang, Z 1970, 'Distributional Prototype Learning for Out-of-distribution Detection', Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 1104-1114.
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Peng, X, Yan, P, Shen, T, Xie, M & Long, G 1970, 'MedShort: Medicine Shortage Detection on Social Media with Privacy Preservation', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2883-2886.
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Pubill-Font, M, Mesa, F, Algaba-Brazález, A, Ding, C & Quevedo-Teruel, O 1970, '2-D Ray-Tracing Approach for the Design of 3-D Dome Arrays Considering Mutual Coupling', 2025 19th European Conference on Antennas and Propagation (EuCAP), 2025 19th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-5.
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Raza, A, Keshavarz, R & Shariati, N 1970, 'Miniaturized Frequency Reconfigurable Patch Antenna for Wireless Power Transfer', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, Australia, pp. 1-2.
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Rizvi, D, Le, DT, Ahamed, M, Sutjipto, S & Paul, G 1970, 'Multi-modal Feedback for Enhanced Hydraulic Maintenance Operations', International Conference on Computers and Industrial Engineering, Sydney, Australia.

Saha, T, Saha, G, Parveen, N & Islam, T 1970, 'Unsteady MHD TiO2−Kerosene nanofluid flow in wavy octagonal cavity with rectangular vertical wall insert', AIP Conference Proceedings, 2ND INTERNATIONAL CONFERENCE ON MECHANICAL, MANUFACTURING AND PROCESS ENGINEERING (ICMMPE - 2024), AIP Publishing, pp. 020003-020003.
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Shen, S, Tan, CT, Chen, H-T, Raffe, WL & Leong, TW 1970, 'Educator Perceptions of XRAuthor: An Accessible Tool for Authoring Learning Content with Different Immersion Levels', Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-11.
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Shi, K, Sun, X, Wang, D, Fu, Y, Xu, G & Li, Q 1970, 'LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following', Proceedings International Conference on Computational Linguistics Coling, pp. 870-885.
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E-commerce authoring entails creating engaging, diverse, and targeted content to enhance preference elicitation and retrieval experience. While Large Language Models (LLMs) have revolutionized content generation, they often fall short in e-commerce applications due to their limited memorization of domain-specific features. This paper proposes LLaMA-E, the unified e-commerce authoring models that address the contextual preferences of customers, sellers, and platforms, the essential objects in e-commerce operation. We design the instruction set derived from tasks of ads generation, query-enhanced product title rewriting, product classification, purchase intent speculation, and general e-commerce Q&A. The instruction formulation ensures the interleaved cover of the presented and required object features, allowing the alignment of base models to parameterize e-commerce knowledge comprehensively. The proposed LLaMA-E models achieve state-of-the-art evaluation performance and exhibit the advantage in zero-shot practical applications. To our knowledge, this is the first LLM tailored to empower authoring applications with comprehensive scenario understanding by integrating features focused on participated objects.

Singh, A, Liu, J, Voinov, A & Lin, C-T 1970, 'Identifying Neural Biomarkers of Confirmation Bias', 2025 13th International Conference on Brain-Computer Interface (BCI), 2025 13th International Conference on Brain-Computer Interface (BCI), IEEE, pp. 1-6.
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Singh, K, Nabeel, MI, Saikia, M, Ahmed, F, Thalakotuna, DN & Esselle, KP 1970, 'Investigating Angle Sensitivity of a 1-Bit Unit cell for Reconfigurable Intelligent Surfaces', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Song, B, Qin, Y, Song, Y-R & Wang, X 1970, 'Cascading Failures Model with Noise Interference in Supply Chain Networks', Springer Nature Switzerland, pp. 165-177.
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Song, L-Z, Squires, A, Du, J & Guo, YJ 1970, 'THz Electrically Tunable Polarization Converter via Graphene-Integrated Metasurface', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Sun, Z, Yang, Y, Lv, X & Liang, Z 1970, 'A Wideband Circularly Polarized Magneto-Electric Dipole Antenna Array With Sequential Rotated Feeding For Wireless and Satellite Communication', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Tang, M, Wang, W & Yu, S 1970, 'OPMUS: A Win-Win Pricing Strategy for Machine Unlearning Service', Springer Nature Singapore, pp. 386-400.
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Tian, L, Booth, E, Bailo, F, Droogan, J & Rizoiu, M-A 1970, 'Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 5244-5254.
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Torpy, FR, Matheson, S, Duani, G, Lyu, L, Fares, R & Irga, PJ 1970, 'Rethinking urban horticulture: perspectives from applied biotechnology and environmental engineering', Acta Horticulturae, International Society for Horticultural Science (ISHS), RHS Garden Wisley, Surrey, UK, pp. 1-12.
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Van Huynh, N, Nguycn, DN, Mucchi, L, Caputo, S, Piccardi, M, Hoang, DT & Dutkiewicz, E 1970, 'Defeating Eavesdropping Attacks with Inter-Cell Interference and Deep Reinforcement Learning', 2025 IEEE Wireless Communications and Networking Conference (WCNC), 2025 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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Wang, B, Qu, Y, Gao, L, Li, C, Li, L & Smith, D 1970, 'Mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation', Springer Nature Singapore, pp. 300-314.
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Wang, J, Wu, Y, Wang, X, Chen, C, Zhang, Y & Qin, L 1970, 'Effective Influence Maximization with Priority', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 4673-4683.
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Wang, W, Tian, Z, Liu, A & Yu, S 1970, 'TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 3061-3072.
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Wang, X, Ding, C, Li, S, Zhao, G & Sun, H 1970, 'A Shared-Aperture Decoupling Network (SADN) for Dual-Band Differentially-Fed Antenna (DFA) Array', 2025 19th European Conference on Antennas and Propagation (EuCAP), 2025 19th European Conference on Antennas and Propagation (EuCAP), IEEE, pp. 1-5.
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Wu, W, Li, S, Chen, L, Li, F & Luo, C 1970, 'Sketching Very Large-scale Dynamic Attributed Networks More Practically', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 5264-5274.
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Xiang, S, Jiang, Y, Chen, Y, Cheng, D, Zhao, G & Jiang, C 1970, 'Generative Dynamic Graph Representation Learning for Conspiracy Spoofing Detection', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 5275-5284.
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Xu, C, Wang, Y, Guo, J, Liu, D, Dong, M, George, Y, Barthelemy, J, Liu, Y & Chen, L 1970, 'Workshop on Sustainable AI for the Future Web', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 2052-2055.
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Xu, Y, Zhang, W, Zhang, Y, Xu, X & Lin, X 1970, 'Fast and Accurate Temporal Hypergraph Representation for Hyperedge Prediction', Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, pp. 1727-1738.
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Xu, Y, Zhu, H & Guo, YJ 1970, 'Reconfigurable Wideband TTD Phase Shifter Based on DGS for Beam Squinting Mitigation', 2025 6th Australian Microwave Symposium (AMS), 2025 6th Australian Microwave Symposium (AMS), IEEE, pp. 1-2.
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Yan, P, Chen, S & Long, G 1970, 'Federated Intelligence in Web: A Tutorial', Companion Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 61-64.
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Yang, X, Wang, Y, Zhang, X, Wang, S, Wang, H & Lam, KY 1970, 'A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 5297-5307.
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Yang, X, Zhang, S, Guan, J, Xiao, F, Lu, W & Zhu, Q 1970, 'Spectral-Temporal Fusion Representation for Person-in-Bed Detection', ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Kothaguda, India, pp. 1-2.
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Ye, S, Lu, J & Zhang, G 1970, 'Towards Safe Machine Unlearning: A Paradigm that Mitigates Performance Degradation', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 4635-4652.
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Yu, X, Hoggenmüller, M, Tran, TTM, Wang, Y, Zhang, Q & Tomitsch, M 1970, 'Peek into the `White-Box': A Field Study on Bystander Engagement with Urban Robot Uncertainty', Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-15.
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Yuan, L, Schneider, PJ & Rizoiu, M-A 1970, 'Behavioral Homophily in Social Media via Inverse Reinforcement Learning: A Reddit Case Study', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 576-589.
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Zhang, J, Li, H, Wu, D, Sun, X, Lu, Q & Long, G 1970, 'Beyond Dataset Watermarking: Model-Level Copyright Protection for Code Summarization Models', Proceedings of the ACM on Web Conference 2025, WWW '25: The ACM Web Conference 2025, ACM, pp. 147-157.
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Zhang, S, Zhou, F, Li, Z, Wang, Y, Qi, D & Li, S 1970, 'Expert-Guided Model Cultivation: CoTeaching to Resolve Abstruseness and Enhance Learning Performance', Springer Nature Singapore, pp. 18-32.
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Zhang, T, Wu, F, Tian, Z, Zhang, C & Yu, S 1970, 'MOUSSE: A Multimodality-Oriented Unified Semantic Communication System by Contrastive Learning', 2025 IEEE Wireless Communications and Networking Conference (WCNC), 2025 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, pp. 1-6.
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Zhang, Y, Bi, Z, Xiao, F, Yang, X, Zhu, Q & Guan, J 1970, 'Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference', ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Kothaguda, India, pp. 1-2.
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Zhengyang, F, Jayan Chirayath Kurian, Mukesh, P, Nimish, B & Priya, S 1970, 'A Systematic Review of IS Literature in Dementia Care: An Application of the NASSS Framework', ANNUAL CONFERENCE OF THE UK ACADEMY FOR INFORMATION SYSTEMS 2025, Newcastle, UK.

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Zhu, Q, Qin, AK, Dia, H, Mihaita, A-S & Grzybowska, H 1970, 'An Experimental Study on Decomposition-Based Deep Ensemble Learning for Traffic Flow Forecasting', Springer Nature Singapore, pp. 394-407.
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Zhuang, Z, Lu, C-Y, Chang, Y-CF, Wang, Y-K, Do, T & Lin, C-T 1970, 'AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems', Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025: CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-19.
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