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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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.
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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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 ...
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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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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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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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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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-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.
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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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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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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Asteris, PG, Gandomi, AH, Armaghani, DJ, Mohammed, AS, Bousiou, Z, Batsis, I, Spyridis, N, Karavalakis, G, Vardi, A, Yannaki, E, 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, 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, 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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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.
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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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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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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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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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.
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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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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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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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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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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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, 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, 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, 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, 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, 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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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, pp. 106011-106011.
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Chu, L, Shi, J & Braun, R 2025, 'Mechanical reliability of compressible micro-interconnects in replaceable integrated chiplet assembly', IEEE Transactions on Components, Packaging and Manufacturing Technology, pp. 1-1.
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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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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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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, 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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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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Deng, F, Sang, R, Li, Y, Yang, B, Zhai, X, Xue, R, Zhang, C, Deng, W & Goldys, EM 2025, 'Hairpin-locker mediated CRISPR/Cas tandem system for ultrasensitive detection of DNA without pre-amplification', Microchemical Journal, vol. 210, pp. 113025-113025.
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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, 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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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, S, Xie, W, Yang, D, Tian, J, Li, Y, Zhang, 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, pp. 1-1.
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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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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, X, Sun, H, Lu, M, Zhu, T & Yu, X 2025, 'DreamCar: Leveraging Car-Specific Prior for In-the-Wild 3D Car Reconstruction', IEEE Robotics and Automation Letters, vol. 10, no. 2, pp. 1840-1847.
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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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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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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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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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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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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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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, pp. 1-14.
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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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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, 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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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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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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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, 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, pp. 1-14.
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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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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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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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Hayawi, K, Makhdoom, I, Khalid, S, Ikuesan, RA, Kaosar, M & Ahmad, I 2025, 'A False Positive Resilient Distributed Trust Management Framework for Collaborative Intrusion Detection Systems', IEEE Transactions on Services Computing, pp. 1-15.
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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, 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, pp. 1-1.
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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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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, 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, 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, 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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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, pp. 1-1.
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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, 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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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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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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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, pp. 1-1.
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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.
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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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, 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, pp. 1-15.
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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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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.
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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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.
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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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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Lan, D, Sun, C, Dong, X, Qiu, P, Gong, Y, Liu, X, Fournier-Viger, P & Zhang, C 2025, 'TK-RNSP: Efficient Top-K Repetitive Negative Sequential Pattern mining', Information Processing & Management, vol. 62, no. 3, pp. 104077-104077.
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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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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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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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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, 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, Li, Z & Chang, X 2025, 'BossNAS Family: Block-wisely Self-supervised Neural Architecture Search', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-15.
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Li, C, Liu, J, Dong, L, Wu, C, Steven, G, Li, Q & Fang, J 2025, 'Phase field fracture in elastoplastic solids: a stress-state, strain-rate, and orientation dependent model in explicit dynamics and its applications to additively manufactured metals', Journal of the Mechanics and Physics of Solids, vol. 197, pp. 105978-105978.
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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, pp. 1-1.
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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, pp. 1-13.
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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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, pp. 1-9.
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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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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, 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, D, Balaguer, C, Dissanayake, G & Kovac, M 2025, 'Preface', Infrastructure Robotics: Methodologies, Robotic Systems and Applications.
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, pp. 1-10.
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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, 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, 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, Gong, S, Zhang, M, Zhang, X, Wang, H, Wen, H, Chang, S, Guo, W & Hao Ngo, H 2025, 'Enhanced anti-fouling performance through integrated coagulation and membrane co-deposition modification for sustainable water treatment', Separation and Purification Technology, vol. 361, pp. 131407-131407.
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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, 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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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, 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, pp. 1-17.
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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, 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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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, 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, pp. 1-13.
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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, 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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Manh, BD, Nguyen, C-H, Hoang, DT, 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, pp. 1-1.
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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, J, Zou, J, Xiang, Z, Wang, C, Wang, S, Li, Y & Kim, J 2025, 'Visible and thermal image fusion network with diffusion models for high-level visual tasks', Applied Intelligence, vol. 55, no. 4.
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Meng, L, Liang, K, Xiao, B, Zhou, S, Liu, Y, Liu, M, Yang, X, Liu, X & Li, J 2025, 'SARF: Aliasing Relation–Assisted Self-Supervised Learning for Few-Shot Relation Reasoning', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 2, pp. 3587-3597.
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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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Meng, X, Zhou, Y, Ma, J, Jiang, F, Qi, Y, Wang, C, Kim, J & Wang, S 2025, 'STFNET: Sparse Temporal Fusion for 3D Object Detection in LiDAR Point Cloud', IEEE Sensors Journal, vol. 25, no. 3, pp. 5866-5877.
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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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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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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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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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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, 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
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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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, 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, 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.
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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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>
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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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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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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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.
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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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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Phillips, K-A, Kotsopoulos, J, Domchek, SM, Terry, MB, Chamberlain, JA, Bassett, JK, Aeilts, AM, Andrulis, IL, Buys, SS, Cui, W, Daly, MB, Eisen, AF, Foulkes, WD, Friedlander, ML, Gronwald, J, Hopper, JL, John, EM, Karlan, BY, Kim, RH, Kurian, AW, Lubinski, J, Metcalfe, K, Nathanson, KL, Singer, CF, Southey, MC, Symecko, H, Tung, N, Narod, SA, Milne, RL, Amor, D, Andrews, L, Antill, Y, Balleine, R, Beesley, J, Bennett, I, Bogwitz, M, Bodek, S, Botes, L, Brennan, M, Brown, M, Buckley, M, Burke, J, Butow, P, Caldon, L, Campbell, I, Cao, M, Chakrabarti, A, Chauhan, D, Chauhan, M, Chenevix-Trench, G, Christian, A, Cohen, P, Colley, A, Crook, A, Cui, J, Courtney, E, Cummings, M, Dawson, S-J, DeFazio, A, Delatycki, M, Dickson, R, Dixon, J, Edwards, S, Farshid, G, Fellows, A, Fenton, G, Field, M, Flanagan, J, Fong, P, Forrest, L, Fox, S, French, J, Friedlander, M, Gaff, C, Gattas, M, George, P, Greening, S, Harris, M, Hart, S, Harraka, P, Hayward, N, Hopper, J, Hoskins, C, Hunt, C, James, P, Jenkins, M, Kidd, A, Kirk, J, Koehler, J, Kollias, J, Lakhani, S, Lawrence, M, Lee, J, Li, S, Lindeman, G, Lippey, J, Lipton, L, Lobb, L, Loi, S, Mann, G, Marsh, D, McLachlan, SA, Meiser, B, Milne, R, Nightingale, S, O'Connell, S, O'Sullivan, S, Ortega, DG, Pachter, N, Pang, J-M, Pathak, G, Patterson, B, Pearn, A, Phillips, K, Pieper, E, Ramus, S, Rickard, E, Ragunathan, A, Robinson, B, Saleh, M, Skandarajah, A, Salisbury, E, Saunders, C, Saunus, J, Savas, P, Scott, R, Scott, C, Sexton, A, Shaw, J, Shelling, A, Srinivasa, S, Simpson, P, Southey, M, Spurdle, A, Taylor, J, Taylor, R, Thorne, H, Trainer, A, Tucker, K, Visvader, J, Walker, L, Williams, R, Winship, I, Young, MA & Zaheed, M 2025, 'Hormonal Contraception and Breast Cancer Risk for Carriers of Germline Mutations in BRCA1 and BRCA2', Journal of Clinical Oncology, vol. 43, no. 4, pp. 422-431.
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PURPOSE It is uncertain whether, and to what extent, hormonal contraceptives increase breast cancer (BC) risk for germline BRCA1 or BRCA2 mutation carriers. METHODS Using pooled observational data from four prospective cohort studies, associations between hormonal contraceptive use and BC risk for unaffected female BRCA1 and BRCA2 mutation carriers were assessed using Cox regression. RESULTS Of 3,882 BRCA1 and 1,509 BRCA2 mutation carriers, 53% and 71%, respectively, had ever used hormonal contraceptives for at least 1 year (median cumulative duration of use, 4.8 and 5.7 years, respectively). Overall, 488 BRCA1 and 191 BRCA2 mutation carriers developed BC during median follow-up of 5.9 and 5.6 years, respectively. Although for BRCA1 mutation carriers, neither current nor past use of hormonal contraceptives for at least 1 year was statistically significantly associated with BC risk (hazard ratio [HR], 1.40 [95% CI, 0.94 to 2.08], P = .10 for current use; 1.16 [0.80 to 1.69], P = .4, 1.40 [0.99 to 1.97], ...
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 ...
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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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.
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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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...
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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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).
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.
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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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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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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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...
Sha, H, Guo, R, Zhou, J, Zhu, X, Ji, J & Miao, Z 2025, 'Reinforcement learning-based robust formation control for Multi-UAV systems with switching communication topologies', Neurocomputing, vol. 611, pp. 128591-128591.
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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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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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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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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, pp. 1-15.
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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, pp. 1-16.
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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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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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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, pp. 104437-104437.
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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.
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, 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, pp. 1-12.
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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, pp. 1-15.
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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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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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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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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, 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, 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, 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, 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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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.
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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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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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, 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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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.
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, 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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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, pp. 1-1.
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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, 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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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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Tuan, BM, Nguyen, DN, Trung, NL, Nguyen, V-D, Huynh, NV, Hoang, DT, Krunz, M & Dutkiewicz, E 2025, 'Securing MIMO Wiretap Channel With Learning Based Friendly Jamming under Imperfect CSI', IEEE Internet of Things Journal, pp. 1-1.
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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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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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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, 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, 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, Xu, M, Zhang, Q, Shi, Y & Wu, Q 2025, 'Causal disentanglement for regulating social influence bias in social recommendation', Neurocomputing, vol. 618, pp. 129133-129133.
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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, 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, 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, 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, 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, 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-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, 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, 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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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, 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, Y, Li, S, Li, J, Yu, Y, Li, J & Li, Y 2025, 'Deeep Learning in Crack Detection: A Comprehensive Scientometric Review', Journal of Infrastructure Intelligence and Resilience, 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, pp. 1-12.
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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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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, 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, 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, Zhu, H & Jay Guo, Y 2025, 'Reconfigurable True-Time-Delay Phase Shifter Using Defected Ground Structure With Loaded Stubs', IEEE Microwave and Wireless Technology Letters, vol. 35, no. 2, pp. 161-164.
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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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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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Yang, J & Lin, C-T 2025, 'Autonomous clustering by fast find of mass and distance peaks', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-14.
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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, 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, 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, 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.
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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, 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.
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.
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, 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, 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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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...
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, 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, 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, pp. 1-13.
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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, pp. 1-1.
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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, Wang, Z, Li, G, Liang, H, Liu, B, Lesage, G, Heran, M, Ding, A & Ngo, HH 2025, 'Quorum sensing on the activated performances of gravity-driven membrane (GDM) system at low temperatures: Ammonia removal and flux stabilization', Separation and Purification Technology, vol. 358, pp. 130238-130238.
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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, B.Mazomenos, E & Stoyanov, D 2025, 'Direct Camera-Only Bundle Adjustment for 3D Textured Colon Surface Reconstruction Based on Pre-Operative Model', IEEE Transactions on Medical Robotics and Bionics, pp. 1-1.
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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, 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, 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, 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, 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, pp. 1-10.
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Zhao, S, Gong, S, Gu, B, Li, L, Lyu, B, Hoang, DT & Yi, C 2025, 'Exploiting NOMA Transmissions in Multi-UAV-assisted Wireless Networks: From Aerial-RIS to Mode-switching UAVs', IEEE Transactions on Wireless Communications, 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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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, 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, 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, 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, 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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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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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, W, Zhang, M, Zhang, X, Zhang, D, Li, C, Zhong, L, Guo, W & Ngo, HH 2025, 'Optimized performance and mechanistic analysis of tetracycline hydrochloride removal using biochar-based alginate composite magnetic beads for peroxymonosulfate activation', Journal of Environmental Chemical Engineering, vol. 13, no. 2, pp. 115742-115742.
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