Abdoulaye, AM, Meli, VN, Kongni, SJ, Njougouo, T & Louodop, P 2025, 'Chimera state in neural network with the Proportional–Integral–Derivative coupling', Chaos, Solitons & Fractals, vol. 191, pp. 115847-115847.
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Abousnina, R, Ghaffour, N & Nghiem, LD 2025, 'Mitigating offshore oily wastewater pollution: Sustainable strategies for treatment, disposal, and reuse', Marine Pollution Bulletin, vol. 219, pp. 118240-118240.
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Acharya, M, Deo, RC, Barua, PD, Devi, A & Tao, X 2025, 'EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals', Computer Methods and Programs in Biomedicine, vol. 262, pp. 108652-108652.
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Acharya, M, Deo, RC, Tao, X, Barua, PD, Devi, A, Atmakuru, A & Tan, R-S 2025, 'Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024)', Computer Methods and Programs in Biomedicine, vol. 259, pp. 108506-108506.
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Achu, AL, Aju, CD, Raicy, MC, Bhadran, A, George, A, Surendran, U, Girishbai, D, Ajayakumar, P, Gopinath, G & Pradhan, B 2025, 'Improved flood risk assessment using multi-model ensemble machine-learning techniques in a tropical river basin of Southern India', Physical Geography, vol. 46, no. 2, pp. 127-155.
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Adak, C, Chattopadhyay, S & Saqib, M 2025, 'Deep Analysis of Visual Product Reviews', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 2033-2038.
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Adeoti, OS, Haremi, R, Kandasamy, J & Vigneswaran, S 2025, 'Evaluating the effectiveness of smart water management systems in enhancing the resilience and sustainability of water infrastructure in Nigeria', AQUA — Water Infrastructure, Ecosystems and Society, vol. 74, no. 2, pp. 253-266.
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ABSTRACT This study rigorously evaluates the effectiveness of smart water management systems in addressing prevalent water infrastructure failures, resilience, and sustainability challenges in Nigeria. Employing a transdisciplinary approach that integrates technological, social, and economic disciplines, along with industry and community insights, it analyses 1,095 days of operational data from a smart water kiosk. The data were processed employing Target 6.1 software for comprehensive comparative analysis, trend analysis, predictive modeling, and impact assessment. Initially, the kiosk achieved a 22% self-sustainability rating (SSR), which dropped to zero due to aid overlap – a novel challenge documented for the first time in the literature as a significant challenge to infrastructure sustainability. Additionally, the research highlighted infrastructure underutilization as a critical yet under-explored issue. Despite these challenges, the kiosk ultimately achieved a 100% sustainability rating (SR) with external support and maintained a high reliability rating of 97.1%. The findings of this study guide strategic research and policy recommendations, aiming to optimize the deployment of smart water management systems in Nigeria and other regions with similar socio-economic settings, thereby enriching the global discourse on sustainable water infrastructure.
Adibi, T, Razavi, SE, Ahmed, SF, Saha, SC & Shah, NA 2025, 'Role of a Hinged Single Separator in Heat Transfer Enhancement and Drag Reduction in Circular Cylinder Flow', Arabian Journal for Science and Engineering, vol. 50, no. 12, pp. 9365-9387.
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Aditiya, HB, Panuganti, SR, Hsia, ICC, Mat, TMUT, Mahlia, TMI & Huang, Z 2025, 'Hydrogen transport across oceans: A techno-economic assessment of hydrogen carriers', Applied Energy, vol. 399, pp. 126513-126513.
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Aditiya, HB, Panuganti, SR, Hsia, ICC, Mat, TMUT, Mahlia, TMI & Huang, Z 2025, 'Levelised cost of hydrogen for domestic transport and stationary applications', International Journal of Hydrogen Energy, vol. 139, pp. 291-315.
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Afrazi, M, Armaghani, DJ, Afrazi, H, Rouhanifar, S & Fattahi, H 2025, 'Geotechnical implications of sand–rubber mixture in transportation infrastructure: assessing shear strength and compressibility characteristics', Innovative Infrastructure Solutions, vol. 10, no. 9.
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Abstract The increase of discarded tires in urban environments has emerged as a pressing environmental concern. This study explores the potential of incorporating scrap tire particles into sand matrices as a sustainable solution to diminish tire stockpiles and decrease environmental pollution. The main focus of this research is to investigate the mechanical properties of loose sand–rubber mixtures (SRM) characterized by a void ratio of 0.86, with varying rubber-to-sand particle size ratios (SR) of 0.25, 1, and 4. An extensive set of 300 direct shear tests was conducted using normal stresses (NS) of 50, 100, and 150 kPa. These tests were supplemented by 110 Oedometer tests using constant NS of 60 kPa for three days, 60 kPa for 1.5 days with an additional 140 kPa for 1.5 days, and 200 kPa for three days. Analysis of shear stress and deformation characteristics reveals that mixtures with different size ratios show similar trends but different values, which means characteristics of SRM depend not only on rubber content but also on size ratio. The addition of rubber particles to the mixtures makes the material more deformable and alters its softening behaviour. Specifically, adding up to 20% rubber content increases the mixture's friction angle, while higher rubber percentages cause it to decrease. A critical transition point is identified at approximately 20% rubber content, where the sand component begins to mimic rubber behaviour. Additionally, mixtures with SR = 0.25 exhibited a lower dilation angle compared to those with higher SR values, indicating that smaller rubber particles contribute to reduced dilation. Furthermore, the compressibility tendency of SRM escalates with higher rubber proportions, with mixtures featuring an SR of 0.25 exhibiting the most pronounced compressibility under equivalent NS conditions.
Ahangama, I, Meedeniya, D & Pradhan, B 2025, 'Explainable image segmentation for spatio-temporal and multivariate image data in precipitation nowcasting', Results in Engineering, vol. 26, pp. 105595-105595.
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Ahmad, A, Xiao, X, Mo, H & Dong, D 2025, 'TFTformer: A novel transformer based model for short-term load forecasting', International Journal of Electrical Power & Energy Systems, vol. 166, pp. 110549-110549.
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Ahmad, F, Qureshi, MI, Rawat, S, Alkharisi, MK & Alturki, M 2025, 'E-waste in concrete construction: recycling, applications, and impact on mechanical, durability, and thermal properties—a review', Innovative Infrastructure Solutions, vol. 10, no. 6.
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Abstract The exponential growth in industrialization, urbanization, and population has subsequently increased the accumulation of different wastes to hazardous levels. Among these, electronic waste (E-waste) poses a serious threat to the environment with its production rising due to technological advancements worldwide. Therefore, recycling E-waste as an alternate aggregate replacement material in the construction industry can be advantageous in managing this waste stream. This study aims to present a comprehensive insight into the integrated applications of E-waste concrete composite materials in the construction industry including applications to increase the environmental sustainability of concrete structures. This review starts with an illustration of the environmental issues caused by E-waste, elucidates its sources and prevailing recycling practices, and investigates its utility as supplementary cementitious materials. This discussion is further followed by an analysis on recycled E-waste plastic aggregate concrete (RPAC) composites, covering their fresh properties, mechanical properties, thermal properties, durability, and serviceability performance. Besides, this paper also explores the evaluation of the utilization of E-waste fibers (E-fiber) as well as the application of E-waste glass (E-glass) from cathode ray tubes (CRT) in concrete. Based on the detailed review, the positive aspects and the restrictions associated with utilizing E-waste in construction have been highlighted.
Ahmad, F, Rawat, S, (Chunhui) Yang, R, Zhang, L, Fanna, DJ, Soe, K & Zhang, YX 2025, 'Effect of Metakaolin and Ground Granulated Blast Furnace Slag on the Performance of Hybrid Fibre-Reinforced Magnesium Oxychloride Cement-Based Composites', International Journal of Civil Engineering, vol. 23, no. 5, pp. 853-868.
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Abstract This study investigates the effect of ground granulated blast furnace slag (GGBFS) and metakaolin (MK) on the strength and ductility of magnesium oxychloride cement (MOC) based hybrid basalt and polyethylene fibre reinforced cementitious composite (FRMOC). MOC was chosen as the matrix due to its unique properties and environment friendliness as a green cement. MK and GGBFS were selected as primary additives to reinforce the MOC matrix owing to their outstanding performance in cementitious composites, coupled with their widespread availability and sustainable characteristics. The influence of GGBFS and MK on physical and mechanical properties of FRMOC was studied in this paper through extensive physical and mechanical testing and microscopic analysis. It was found that the hardened density of FRMOC was not significantly affected by these additives, and it ranged from 1909.3 to 1976.0 kg/m3, retaining its lightweight characteristics. Compressive strength of specimens cured for one day reached approximately 69.1–84.0% of that for specimens cured for 28 days, indicating the high early strength characteristics of the material. All FRMOC specimens exhibited tensile strain hardening properties, with tensile strength and strain capacity ranging from 6.74 to 8.58 MPa and 1.14 to 2.22%, respectively. The mix containing 30% GGBFS, 0.75% basalt fibre, and 1.25% polyethylene fibre was identified as the optimum MOC mix with enhanced compressive strength (73.9 MPa), tensile strength (8.52 MPa), and strain capacity (2.22%). Microscopic analysis further revealed that the addition of GGBFS-MK blends did not alter the primary phase composition of hydration products but essentially promoted the formation of phase 5, demonstrating their effectiveness in enhancing the performance of FRMOC.
Ahmad, F, Rawat, S, Yang, RC, Zhang, L & Zhang, YX 2025, 'Fire resistance and thermal performance of hybrid fibre-reinforced magnesium oxychloride cement-based composites', Construction and Building Materials, vol. 472, pp. 140867-140867.
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Ahmed, SF, Sharmin, S, Kuldeep, SA, Lameesa, A, Alam, MSB, Liu, G & Gandomi, AH 2025, 'Transformative impacts of the internet of medical things on modern healthcare', Results in Engineering, vol. 25, pp. 103787-103787.
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Akbar, HM, Habib, S, Akoumeh, R, Mahdi, E, Al-Ejji, M, Altaee, A & Hawari, AH 2025, 'Lithium Extraction Methodology and Recovery from Conventional Resources: A Critical Review', Water, Air, & Soil Pollution, vol. 236, no. 11.
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Abstract Lithium recovery from various primary sources, such as brine, ores, seawater, and clay, or secondary resources that include lithium-ion batteries (LIB) and lithium-ion metal oxide batteries (LIMOB) poses a challenge due to the complexity of the extraction processes. This review aims to examine recent advancements in lithium extraction and recovery from both primary and secondary sources. It provides an overview of the established recovery and separation techniques for primary sources, including precipitation, chromatography, ion exchange, and membrane technologies, alongside the chemical agents used in these processes. Additionally, lithium recovery from secondary sources through methods such as hydrometallurgy, pyrometallurgy, and bioleaching, highlighting the use of various organic and inorganic sorbents, is also addressed. Some of the advantages and disadvantages of the recovery techniques, as well as economic, environmental, and technical data analysis, are also discussed. While the recovery of lithium from primary sources has been extensively studied, secondary sources—particularly spent LIBs and LIMOBs—have received relatively less attention, mainly due to challenges such as the hazardous nature of recycling processes, stringent environmental regulations, high operational costs, and significant energy requirements. Nevertheless, the emergence of bioleaching technologies offers a promising alternative technique for lithium recovery from secondary sources, owing to their potential for environmentally sustainable operations, cost-effectiveness, and lower energy consumption, availability of materials and bio-organisms, despite the new emergence for lithium recovery from secondary resources. The major highlight of this review paper is the comparison of each recovery technique. Among the primary resources -brine, ore, clay- recovery techniques, precipitation techniques were found to recover ~ 99.5% of ...
Akbarzadeh, M, Oberst, S & Halkon, B 2025, 'Manipulation of an acoustically levitated object using externally excited standing waves', The Journal of the Acoustical Society of America, vol. 157, no. 3, pp. 1852-1861.
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Ultrasonic standing waves can be used to manipulate the position and control the movement of levitated objects through acoustic radiation forces. Within this context, the theory of the Gor'kov potential function and its acoustic contrast factor are revisited, considering the scenario of a harmonic disturbance to the standing wave and its influence on the levitated spherical object. This disturbance causes a levitated object—trapped within a standing, plane ultrasonic wave field in an ideal fluid—to undergo oscillations in sympathy with the resulting motion of the wave field. In this paper, we determine how the acoustic contrast factor depends on the properties of the object, the fluid and the external excitation, in combination. We show that positive, negative, and zero acoustic radiation forces can be achieved, causing the object to be pushed towards the nearest pressure or velocity node. We experimentally verify—through external excitation of an ultrasonic standing wave generator—that the disturbance vibration frequency and amplitude are transmitted to the object. The dependence on the external excitation amplitude and force reversal are novel features that can be employed in acoustic manipulation for non-contact dynamic characterization of small objects.
Akram, J, Hussain, W, Jhaveri, RH, Rathore, RS & Anaissi, A 2025, 'Dynamic GNN-based multimodal anomaly detection for spatial crowdsourcing drone services', Digital Communications and Networks.
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Akter, F, Krishnan, L, Mestres, G, Gustafsson, J, Ralph, PJ & Kuzhiumparambil, U 2025, 'Physicochemical characterization and evaluation of the antioxidant potential of water-soluble polysaccharides from red microalgae, Rhodomonas salina', International Journal of Biological Macromolecules, vol. 310, pp. 143417-143417.
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Akter, K, Rahman, MA, Islam, MR, Sheikh, MRI & Hossain, MJ 2025, 'Attack-resilient framework for wind power forecasting against civil and adversarial attacks', Electric Power Systems Research, vol. 238, pp. 111065-111065.
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Akter, T, Hoque, MA-A, Mukul, SA & Pradhan, B 2025, 'Coastal Flood Induced Salinity Intrusion Risk Assessment Using a Spatial Multi-criteria Approach in the South-Western Bangladesh', Earth Systems and Environment, vol. 9, no. 1, pp. 31-49.
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Abstract Bangladesh is extremely vulnerable to sea-level rise and other climate-induced extreme events, such as flooding, storm surge, and salinity intrusion. The south-western coastal region of Bangladesh is particularly vulnerable to salinity intrusion caused by cyclone induced storm surges and coastal floods. Salinity intrusion endanger land productivity by increasing both soil and surface water salinity. Detailed risk assessment using spatial mapping approach can contribute to mitigating the effects of salinity intrusion on natural capital and the environment. In this study, we established and evaluated a spatial multi-criteria approach for mapping the risk levels of areas to salinity intrusion impacts using field data and geospatial techniques at the local scale. We evaluated the viability of the proposed approach using Khulna District, a major coastal city and saline prone area in the south-western Bangladesh. We considered three risk components (i.e. vulnerability, exposure and hazard) with 16 relevant criteria for the study. For each criterion, an Analytical Hierarchy Process (AHP) was used to build and weight spatial raster map layers. Individual maps for each risk component were generated using a weighted sum technique, and lastly, a risk map was created by combining those. Our generated maps correctly identified relevant spatial dimensions as well as risk levels (i.e. very-high to very-low). The outcomes of our study suggest that the southern (east and west) parts of the study area are mostly susceptible to salinity intrusion due to higher storm surge impacts, lower elevation, and land use patterns than other parts. We validate our findings using a qualitative and quantitative approach. We believe that this novel approach would be useful to create risk maps that policymakers and relevant stakeholders could potentially use to evaluate risks posed by flood induced salinity intrusion in coastal regions ...
Akter, Z, Nag, P, Akter, H, Molla, MM & Saha, G 2025, 'Assessing thermal and hydrodynamic performance of non-Newtonian nano-coolant flow through a porous backward-facing step channel with non-Darcian effects', Results in Engineering, vol. 27, pp. 106027-106027.
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Alagesan, S, Indraratna, B, Malisetty, RS, Qi, Y & Rujikiatkamjorn, C 2025, 'Prediction of rail ballast breakage using a hybrid ML methodology', Transportation Geotechnics, vol. 52, pp. 101555-101555.
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Alam, M, Rakib, AKM, Hasan, ASMM, Siddique, MNI, Kabir, MA & Trianni, A 2025, 'Decarbonizing road transportation: Barriers and drivers in an emerging economy context', Transportation Research Part D: Transport and Environment, vol. 143, pp. 104723-104723.
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Alam, MM, Hossain, MJ, Zamee, MA & Al-Durra, A 2025, 'Design and operation of future low-voltage community microgrids: An AI-based approach with real case study', Applied Energy, vol. 377, pp. 124523-124523.
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Alandoli, EA, Fan, Y & Liu, D 2025, 'A review of extensible continuum robots: mechanical structure, actuation methods, stiffness variability, and control methods', Robotica, vol. 43, no. 2, pp. 764-791.
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AbstractExtensible continuum robots (ECRs) offer distinct advantages over conventional continuum robots due to their ability to enhance workspace adaptability through length adjustments. This makes ECRs particularly promising for applications that require variable lengths involving the manipulation of objects in challenging environments, such as risky, cluttered, or confined. The development of ECRs necessitates careful consideration of mechanical structures, actuation methods, methods of stiffness variability, and control methods. The selection of papers is based on their relevance to ECRs within the period of 2010 to 2023 in the databases of Scopus and Web of Science. Distinguishing itself from other review papers, this paper aims to deliver a comprehensive and critical discussion about the advantages and disadvantages of ECRs concerning their mechanical structures, actuation methods, stiffness variability, and control methods. It is a beneficial resource for researchers and engineers interested in ECRs, providing essential insights to guide future developments in this field. Based on the literature, existing ECRs exhibit an inherent trade-off between flexibility and structural strength due to the absence of systematic design methods. Additionally, there is a lack of intelligent and effective controllers for achieving complex control performance and autonomous stiffness variability.
Alavi, Z, Khalilpour, K, Florin, N, Hadigheh, A & Hoadley, A 2025, 'End-of-life wind turbine blade management across energy transition: A life cycle analysis', Resources, Conservation and Recycling, vol. 213, pp. 108008-108008.
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Albaba, MT, Talhami, M, Omar, A, Varghese, S, Akoumeh, R, Ayari, MA, Das, P, Altaee, A, AL-Ejji, M & Hawari, AH 2025, 'Machine learning-aided prediction of COD removal in the electrocoagulation process using a super learner model', Journal of Environmental Chemical Engineering, vol. 13, no. 5, pp. 117469-117469.
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Al‐Badri, AR, Al‐Waaly, AAY, Saha, G, Saha, T & Saha, SC 2025, 'Improving Thermal Performance in Building Heating, Ventilation, and Air Conditioning Systems: A Study of Natural Convection and Entropy in Plus‐Shaped Cavity', Heat Transfer, vol. 54, no. 3, pp. 2235-2250.
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ABSTRACTThe impact of building design on energy efficiency has been widely studied, with cavity cooling emerging as an effective solution for indoor thermal comfort, where obstacles within the cavity can enhance fluid flow and improve natural convection heat transfer (HT). This research builds on the principles of cavity cooling for indoor thermal comfort, investigating entropy generation and HT behavior in a unique plus‐shaped cavity containing a cold cylindrical element, analyzed through Computational Fluid Dynamics simulations. The Rayleigh number (Ra) ranges from 103 to 106, with a fixed Prandtl number (Pr) of 0.71, representing air as the working fluid, radius (r) of the cylinder ranges from 0 to 0.1, where r = 0 indicates no cylinder. The results indicate significant shifts in flow structure and temperature distribution across the cavity at varying Ra values, impacting the local and global entropy generation. High Rayleigh numbers lead to enhanced convective flows, intensifying entropy production near the cylinder surface due to steeper thermal gradients and vigorous recirculation zones. The increase in Ra from 103 to 106 leads to an increase in Nuavg from 24.27 to 56.40 for the model without a cold object while from 39.62 to 123.83 for the model with a cold object. Moreover, the maximum enhancement in Nuavg was 137.48% for Ra = 105. Whereas, for the same value of Ra = 105, the maximum increase in Egen and Be wa...
Albluwi, I, Hriez, R & Lister, R 2025, 'Varying Program Input to Assess Code Reading Skills', ACM Transactions on Computing Education, vol. 25, no. 3, pp. 1-40.
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Explain-in-Plain-English (EiPE) questions are used by some researchers and educators to assess code reading skills. EiPE questions require students to briefly explain (in plain English) the purpose of a given piece of code, without restating what the code does line-by-line. The premise is that novices who can explain the purpose of a piece of code have higher code reading skills than those who can trace the code but cannot see its high-level purpose. However, using natural language in EiPE questions poses challenges. Students (especially those whose first language is not English) may struggle to convey their understanding of the code unambiguously. Also, grading responses written in natural language is time-consuming, requires the design of a rubric, and is difficult to automate. We propose a new code reading question type that addresses these issues with EiPE questions. Given a piece of code involving repetition (in the form of iteration or recursion), the student is asked to provide the output for a set of inputs, where the output for some of these inputs cannot be determined using code tracing alone and requires higher-level code comprehension. In empirical evaluations, using CS1 exams, think-aloud interviews with students, and interviews with instructors, we found that assessments of code reading skills using the new question type are highly consistent with the assessments using EiPE questions, yet are more reliable. These results put forward the proposed question type as another way to assess high-level code reading skills without the issues associated with expressing in natural language or grading responses expressed in natural language.
Alempijevic, A, Vidal-Calleja, T, Falque, R, Walmsley, B & McPhee, M 2025, '3D imaging for on-farm estimation of live cattle traits and carcass weight prediction', Meat Science, vol. 225, pp. 109810-109810.
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This study presents a 3-dimensional (3D) imaging system, operating at processing speed, deployed at a commercial feedlot, that assesses hip height (cm), subcutaneous fat thickness at the P8 site (mm), and hot standard carcass weight (HSCW, kg) from the shape of individual live cattle. A two-part study was conducted: Study 1 evaluated measured hip height (cm) on 247 steers and ultrasound scanned P8 fat (mm) on 219 steers versus projections from 3D images; and Study 2 evaluated abattoir HSCW on 32 Angus steers versus predictions from 3D images. Hip height was directly estimated from the 3D images, while P8 fat and HSCW were predicted using a model based on features extracted from these images through supervised learning with Gaussian Processes. The models were evaluated using cross-validation. The measured hip height versus live estimates from 3D imaging resulted in a RMSE = 3.07 cm, and R2 = 0.69. The ultrasound scanned P8 fat versus live predictions from 3D imaging resulted in a RMSE = 2.38 mm, and R2 = 0.78; and the abattoir HSCW versus live predictions from 3D imaging resulted in a RMSE = 8.15 kg, and R2 = 0.79. The design of the 3D imaging system, with multiple cameras, was installed into a traditional race for processing cattle and effectively operates with variation in length and breeds of cattle. The 3D imaging system demonstrates the feasibility of adoption by the beef industry that creates value through the integration of 3D imaging and BeefSpecs into a technology called CattleAssess3D.
Alghanmi, NA, Alghanmi, NA, Alghanmi, SA, Zhao, M & Hussain, FK 2025, 'Data-driven approach for selection of on-chain vs off-chain carbon credits data storage methods', Knowledge-Based Systems, vol. 310, pp. 112871-112871.
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Alharbi, RS & Hussain, FK 2025, 'Smart-contracts-driven personal carbon credit management in smart cities: A review and future research directions', Internet of Things, vol. 32, pp. 101636-101636.
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Alhelou, HH, Bahrani, B & Ma, J 2025, 'An Affordable Sophisticated Frequency Control Ancillary Services for Australian National Electricity Market System Considering Industry and Infrastructure Challenges', IEEE Transactions on Energy Markets, Policy and Regulation, vol. 3, no. 1, pp. 72-82.
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Ali, A, Salah, A, Bekhit, M & Fathalla, A 2025, 'Predicting Trip Duration and Distance in Bike-Sharing Systems Using Dynamic Time Warping', Applied Artificial Intelligence, vol. 39, no. 1.
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Ali, HM, Lawag, RA, Mahlia, TMI & Fattah, IMR 2025, 'RT 42 and RT 50 phase change materials-based heat sinks for thermal management of electronics', Journal of Thermal Analysis and Calorimetry, vol. 150, no. 5, pp. 3463-3473.
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Alkadour, F, Alkhamri, KA, Kazwini, T, Altaee, A, AL-Ejji, M, Das, P & Hawari, AH 2025, 'Economic and performance optimization of hollow fiber forward osmosis using treated sewage effluent and novel sodium metasilicate sol-gel draw solution', Results in Engineering, vol. 27, pp. 106502-106502.
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Almalki, R, Khaki, M, Saco, PM & Rodriguez, JF 2025, 'Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach', Journal of Hydrology: Regional Studies, vol. 58, pp. 102221-102221.
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Almalki, R, Khaki, M, Saco, PM & Rodriguez, JF 2025, 'Understanding Environmental Factors Influencing Vegetation Cover Downstream of Dams', International Journal of Environmental Research, vol. 19, no. 1.
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Almuntashiri, A, Jiang, J, Hosseinzadeh, A, Badeti, U, Navidpour, AH, Dorji, P, Ghaffour, N, Shon, HK & Phuntsho, S 2025, 'Removal of antibiotics from a biologically nitrified human urine using granular activated carbon adsorption for a safe nutrient recovery in a circular economy', Process Safety and Environmental Protection, vol. 201, pp. 107516-107516.
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Almustafa, G, Alwan, RA, Shon, HK, Rodríguez, J & AlNashef, I 2025, 'Roadmap for integrating deep eutectic solvents into adsorption processes: A critical review & design blueprint', Progress in Materials Science, vol. 154, pp. 101501-101501.
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Alsaka, L, Altaee, A, Alsaka, L, Al-Ejji, M, Hawari, AH & Zhou, J 2025, 'kappa-Carrageenan-based hydrogel for leachate wastewater treatment and resource recovery', Desalination, vol. 614, pp. 119208-119208.
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Alsenwi, M, Abolhasan, M & Lipman, J 2025, 'RIS-UAV Integration for Enhanced Coverage and Energy-Efficient 6G Wireless Networks', IEEE Transactions on Green Communications and Networking, pp. 1-1.
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Alshawkani, K & Hussain, FK 2025, 'Intelligent chatbot dialogue breakdown solutions and challenges: A systematic literature review', Knowledge-Based Systems, vol. 326, pp. 114054-114054.
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Altukhi, ZM, Pradhan, S & Aljohani, N 2025, 'A Systematic Literature Review of the Latest Advancements in XAI', Technologies, vol. 13, no. 3, pp. 93-93.
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This systematic review details recent advancements in the field of Explainable Artificial Intelligence (XAI) from 2014 to 2024. XAI utilises a wide range of frameworks, techniques, and methods used to interpret machine learning (ML) black-box models. We aim to understand the technical advancements in the field and future directions. We followed the PRISMA methodology and selected 30 relevant publications from three main databases: IEEE Xplore, ACM, and ScienceDirect. Through comprehensive thematic analysis, we categorised the research into three main topics: ‘model developments’, ‘evaluation metrics and methods’, and ‘user-centred and XAI system design’. Our results uncover ‘What’, ‘How’, and ‘Why’ these advancements were developed. We found that 13 papers focused on model developments, 8 studies focused on the XAI evaluation metrics, and 12 papers focused on user-centred and XAI system design. Moreover, it was found that these advancements aimed to bridge the gap between technical model outputs and user understanding.
Al‐Waaly, AAY, Paul, AR, Saha, G & Saha, SC 2025, 'Exploring Heat Transfer and Entropy Generation in a Dual Cavity System', Heat Transfer, vol. 54, no. 3, pp. 2279-2292.
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ABSTRACTThis study investigates heat transfer and entropy generation in a dual‐cavity system filled with air, focusing on the effects of uniform and nonuniform heating conditions on natural convection. The system features heated left walls, cooled right walls, and insulated remaining walls, presenting a novel approach to thermal management. This research employs COMSOL Multiphysics and finite element method to study the interplay between Rayleigh numbers () and heat transfer efficiency, focusing on thermal patterns and irreversibility. The findings indicate that as Ra increases, convective heat transfer improves significantly, with the average Nusselt number rising from 15.23 at Ra = 103 to 74.61 at Ra = 106 under uniform heating conditions. However, this improvement comes at the cost of increased entropy generation, which escalates from 2.91 to 307.74, highlighting a trade‐off between enhanced heat transfer and greater irreversibility. These results underscore the need to optimize Ra values to achieve a balance between thermal efficiency and entropy generation. The insights gained from this study have practical implications for designing energy‐efficient cooling systems in electronics and microfluidic devices, as well as for architectural designs targeting improved thermal management.
Al-zqebah, R, Guertler, M & Clemon, L 2025, 'Powder bed fusion factory productivity increases using discrete event simulation and genetic algorithm', Production Engineering, vol. 19, no. 1, pp. 29-45.
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Abstract Powder bed fusion is importance is growing with uses across industries in both polymer and metallic components, particularly in mass individualization. However, due to the relatively slow mass deposition speed compared to conventional methods, scheduling and production planning play a crucial role in scaling up additive manufacturing productivity to higher volumes. This paper introduces a framework combining discrete event simulation and a genetic algorithm showing makespan improvement opportunities for multiple powder bed fusion factories varying workers, jobs and available equipment. The results show that bottlenecks move among workstations based on worker and capital equipment availability, which depend on the size of the facility indicating a resource-driven constraint for makespan. A makespan reduction of 78% is achieved in the simulation. This shows the trade-off of worker and capital equipment to achieve makespan improvements. The addition of personnel or equipment increases production with further gains achieved by scheduling optimization. Two levels of job demands are analyzed showing productivity gains of 45% makespan improvement when adding the first worker and additional savings with scheduling optimization using a genetic algorithm up to 11%. Most research on additive manufacturing production has focused on the quality of produced parts and printing technology rather than factory level management. This is the first application of this methodology to varying sizes of these potential factories. The method developed here will help decision-makers to determine the appropriate number of resources to meet their customer demand on time, additionally, finding the optimal route for jobs before starting the production process.
Amin, M, Umar, H, Rizal, TA, Amir, F, Abdullah, NA, Ginting, SF & Mahlia, TMI 2025, 'Thermal performance improvement of a solar distillation system using a spiral coil condenser with a parabolic dish', Journal of Water Process Engineering, vol. 71, pp. 107353-107353.
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Amir, F, Cheng, Y-S, Tseng, S-C & Kalam, MA 2025, 'Optimized production and functionalization of water hyacinth-derived biochar for biocatalysis and dye adsorption', Process Safety and Environmental Protection, vol. 201, pp. 107519-107519.
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Amirbagheri, K, Merigó, JM & Corrons, A 2025, '30 years of Energy for Sustainable Development: A bibliometric overview', Energy for Sustainable Development, vol. 87, pp. 101741-101741.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2025, 'Development of a polarization-neutral metamaterial absorber for efficient low-power EM energy harvesting', Sensors and Actuators A: Physical, vol. 381, pp. 116055-116055.
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Amirkhani, F, Dashti, A, Abedsoltan, H, Mohammadi, AH, Zhou, JL & Altaee, A 2025, 'Modeling and estimation of CO2 capture by porous liquids through machine learning', Separation and Purification Technology, vol. 359, pp. 130445-130445.
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Anand, N, Saifulla, MA, Babu Ponnuru, R, Reddy Alavalapati, G, Patan, R & Gandomi, AH 2025, 'Securing Software Defined Networks: A Comprehensive Analysis of Approaches, Applications, and Future Strategies Against DoS Attacks', IEEE Access, vol. 13, pp. 64473-64515.
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Ang, KCS, Sankaran, S & Liu, D 2025, 'Advancing sociotechnical systems theory: New principles for human-robot team design and development', Applied Ergonomics, vol. 129, pp. 104604-104604.
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Ansari, M, Song, L, Qin, P-Y, Smith, SL & Guo, YJ 2025, 'Advances in Multibeam Flat GRIN Lens Antennas: A promising 3D-printed design', IEEE Antennas and Propagation Magazine, pp. 2-13.
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Ansari, S, Finelli, M, Papaconstantinou, EA, McGregor, C & Nonoyama, ML 2025, 'Implementation Strategies Used to Reduce Unplanned Extubations in the Neonatal ICU', Respiratory Care, vol. 70, no. 2, pp. 143-152.
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Arachchige, SM, Pradhan, B & Park, H-J 2025, 'A critical review of hurricane risk assessment models and predictive frameworks', Geoscience Frontiers, vol. 16, no. 3, pp. 102012-102012.
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Arango, E, Nogal, M, Sousa, HS, Matos, JC & Stewart, MG 2025, 'Wildfire preparedness: Optimal adaptation measures for strengthening road transport resilience', International Journal of Disaster Risk Reduction, vol. 121, pp. 105371-105371.
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Arango, E, Nogal, M, Yang, M, Sousa, HS, Matos, JC & Stewart, MG 2025, 'Resilience assessment in post-wildfire recovery of road transport networks by dynamic thresholds and characteristic curves', Reliability Engineering & System Safety, vol. 264, pp. 111365-111365.
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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, Ryall, M, Islam, MS & Bennett, NS 2025, 'Numerical and experimental analysis of triply periodic minimal surface (TPMS)-based metal lattice heat sinks integrated with different phase change materials for enhanced thermal management of electronics', Journal of Energy Storage, vol. 132, pp. 117784-117784.
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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, Triantafyllidis, L, Koutras, EI, Zygouris, N, Drosopoulos, GA, Fountas, NA, Vaxevanidis, NM, Bardhan, A, Samui, P, Hatzigeorgiou, GD, Zhou, J, Leontari, KV, Evangelidis, P, Sakellari, I & Gavriilaki, E 2025, 'Pre-transplant and transplant parameters predict long-term survival after hematopoietic cell transplantation using machine learning', Transplant Immunology, vol. 90, pp. 102211-102211.
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Atmakuru, A, Chakraborty, S, Salvi, M, Faust, O, Datta Barua, P, Kobayashi, M, San Tan, R, Molinari, F, Hafeez-Baig, A & Rajendra Acharya, U 2025, 'Fibromyalgia Detection and Diagnosis: A Systematic Review of Data-Driven Approaches and Clinical Implications', IEEE Access, vol. 13, pp. 25026-25044.
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Atmakuru, A, Shahini, A, Chakraborty, S, Seoni, S, Salvi, M, Hafeez-Baig, A, Rashid, S, Tan, RS, Barua, PD, Molinari, F & Acharya, UR 2025, 'Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023)', Information Fusion, vol. 114, pp. 102673-102673.
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Aung, HW, Jiao Li, J, An, Y & Su, SW 2025, 'A Real-Time Framework for EEG Signal Decoding With Graph Neural Networks and Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Aung, HW, Li, JJ, Shi, B, An, Y & Su, SW 2025, 'EEG_GLT-Net: Optimising EEG graphs for real-time motor imagery signals classification', Biomedical Signal Processing and Control, vol. 104, pp. 107458-107458.
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Auriemma, F, Andrisani, G, Facciorusso, A, franchellucci, G, De, LL, Calabrese, F, Fiacca, M, Citterio, N, De Deo, D, Paduano, D, Gentile, C, Hassan, C, Francesco, DM, Repici, A & Benedetto, M 2025, 'Haemocer Plus in the treatment and prevention of lower GI post-resectional bleeding: prospective multi center study', Endoscopy, vol. 57, no. S 02, pp. S440-S440.
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Azfar, M, Gao, W, Van den Haute, C, Xiao, L, Karsa, M, Pandher, R, Karsa, A, Spurling, D, Ronca, E, Bongers, A, Guo, X, Mayoh, C, Fayt, Y, Schoofs, A, Burns, MR, Verhelst, SHL, Norris, MD, Haber, M, Vangheluwe, P & Somers, K 2025, 'The polyamine transporter ATP13A3 mediates difluoromethylornithine‐induced polyamine uptake in neuroblastoma', Molecular Oncology, vol. 19, no. 3, pp. 913-936.
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High‐risk neuroblastomas, often associated with MYCN protooncogene amplification, are addicted to polyamines, small polycations vital for cellular functioning. We have previously shown that neuroblastoma cells increase polyamine uptake when exposed to the polyamine biosynthesis inhibitor difluoromethylornithine (DFMO), and this mechanism is thought to limit the efficacy of the drug in clinical trials. This finding resulted in the clinical development of polyamine transport inhibitors, including AMXT 1501, which is presently under clinical investigation in combination with DFMO. However, the mechanisms and transporters involved in DFMO‐induced polyamine uptake are unknown. Here, we report that knockdown of ATPase 13A3 (ATP13A3), a member of the P5B‐ATPase polyamine transporter family, limited basal and DFMO‐induced polyamine uptake, attenuated MYCN‐amplified and non‐MYCN‐amplified neuroblastoma cell growth, and potentiated the inhibitory effects of DFMO. Conversely, overexpression of ATP13A3 in neuroblastoma cells increased polyamine uptake, which was inhibited by AMXT 1501, highlighting ATP13A3 as a key target of the drug. An association between high ATP13A3 expression and poor survival in neuroblastoma further supports a role of this transporter in neuroblastoma progression. Thus, this study identified ATP13A3 as a critical regulator of basal and DFMO‐induced polyamine uptake and a novel therapeutic target for neuroblastoma.
Aziz, NAM, Mohamed, H, Ong, MY, Yunus, R, Law, MC, Hamid, HA, Kania, D & Mahlia, TMI 2025, 'Numerical simulation of hotspot in polyol ester production using microwave-assisted reaction', Results in Engineering, vol. 25, pp. 104577-104577.
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Baharami, H, Piovarci, M, Tarini, M, Bickel, B & Pietroni, N 2025, 'Fabricable Discretized Ruled Surfaces', ACM Transactions on Graphics, vol. 44, no. 3, pp. 1-15.
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We present a method to automatically approximate a given surface with a small set of patches, each being a developable ruled surface featuring long-ruling lines. These construction primitives are attractive for their inherent ease of fabrication by cutting and folding inextensible materials and for their favo rable structural properties. Our algorithm strikes a good tradeoff between the simplicity of produced designs (in terms of the number and shapes of the patches) and approximation quality. To this end, it is guided by a smooth curvature-aligned cross-field. Compared to traditional methods, we rely on final discretization steps to ensure the developability of the ruled surfaces and produce a fabricable layout, bypassing the need to enforce that the strips are strictly developable in continuous settings (which requires difficulty in enforcing geometric conditions). We demonstrate the effectiveness of the proposed algorithm by producing several viable designs and using them to physically fabricate various physical objects.
Bahmanpour, M, Kalhori, H & Li, B 2025, 'A data-driven hybrid recurrent neural network and model-based framework for accurate impact force estimation', Mechanical Systems and Signal Processing, vol. 229, pp. 112503-112503.
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Bai, J, Wu, D, Zeng, S, Zhao, Y, Qu, Y & Yu, S 2025, 'Non-IID Free Federated Learning with Fuzzy Optimization for Consumer Electronics Systems', IEEE Transactions on Consumer Electronics, pp. 1-1.
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Bai, K, Zhang, W, Wen, S, Jia, D & Meng, W 2025, 'An Interpretable Data-Driven Fuzzy Petri Net Method for Industrial Domain Knowledge Modeling of Energy Efficiency Management', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 16603-16615.
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Bakar, MAAA, Ker, PJ, Tang, SGH, Arman Shah, FN, Mahlia, TMI, Baharuddin, MZ & Omar, AR 2025, 'A hybrid chromaticity-morphological machine learning model to overcome the limit of detecting newcastle disease in experimentally infected chicken within 36 h', Computers and Electronics in Agriculture, vol. 234, pp. 110248-110248.
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Bakhshianlamouki, E, Augustijn, E-W, Brugnach, M, Voinov, A & Wijnberg, K 2025, 'Agent-based modelling of beach visitation patterns: insights for sustainable coastal design and management', Environmental Research Communications, vol. 7, no. 4, pp. 045004-045004.
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Abstract Coastal areas are complex socio-environmental systems, yet most models focus on biophysical processes, with few addressing social systems or their interaction with biophysical dynamics. This paper introduces an agent-based model (ABM) simulating beach visitor spatial behaviour, considering factors like landscape design and its impact on visitor distribution. We analyse how service and facility placement affects areas with high and low visitation intensity. Tailored for Dutch beaches created by mega-nourishments like the Sand Motor, the model’s modular design allows adaptation to other coastal areas. Qualitative calibration and validation against field data ensured realistic predictions of daily visitor numbers and representation of areas of high and low visitation intensity across the beach. We analysed model sensitivity by varying visitor activities and pathway preference probabilities, finding that pathway probabilities significantly influence visitor choices. The model was also applied to assess the impact of adding an entrance with access to a restaurant and car parking on visitor numbers, affected areas, and impact intensity. Scenario analysis, combined with its application to two case studies with different landscape layouts, highlights the model’s versatility as a valuable tool for designing beaches to effectively explore and manage visitation intensity and its effects on ecological systems, such as vegetation.
Bano, M, Chaudhri, ZH & Zowghi, D 2025, 'Mapping the Scholarly Landscape on AI and Diplomacy', The Hague Journal of Diplomacy, vol. 20, no. 2, pp. 171-206.
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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 & Wen, S 2025, 'Long Short-Term Financial Time Series Forecasting Based on Residual Multiscale TCN Sparse Expert Network and Informer', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-10.
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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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Bao, Y, Tang, C & Yu, Y 2025, 'Seismic Performance of Steel Tube-Reinforced Concrete Columns after Exposure to Fire on Two Adjacent Sides', International Journal of Structural Stability and Dynamics, vol. 25, no. 18.
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A nonlinear finite element model has been developed to numerically simulate the hysteretic behavior of steel tube-reinforced concrete columns after exposure to fire on two adjacent faces under reciprocating loads. This model considers various parameters, including fire duration, slenderness ratio, section size, core area ratio, external concrete strength, and reinforcement ratio. The study systematically investigates and analyzes characteristics such as the skeleton curve, ductility coefficient, stiffness degradation, and hysteretic energy dissipation of columns post-fire. Results indicate that member stiffness decreases with increasing displacement loading, and the equivalent viscous damping coefficient of the member escalates with increased horizontal displacement. Moreover, as fire duration and slenderness ratio increase, there is a corresponding decrease in member stiffness, leading to a reduction in the equivalent viscous damping coefficient. In contrast, increases in section size and core area ratio enhance member stiffness and decrease the equivalent viscous damping coefficient. Furthermore, enhancements in external concrete strength and reinforcement ratio elevate member stiffness, which subsequently increases the equivalent viscous damping coefficient.
Barcellos‐Paula, L, Merigó, JM & Gil‐Lafuente, AM 2025, 'Two Hundred Years of the Annals of the New York Academy of Sciences: A Bibliometric Overview', Natural Sciences, vol. 5, no. 3.
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ABSTRACTFounded in 1824, the Annals of the New York Academy of Sciences (ANYAS) is a distinguished international journal that embraces various scientific disciplines. In 2024, the journal marks its 200th anniversary. To honor this remarkable milestone, this article provides a thorough bibliometric analysis of the journal's publications. The aim is to identify the main trends in the journal, particularly over the past few decades. Bibliographic data have been gathered from the Web of Science Core Collection and Scopus databases. The study also uses VOSviewer software to create and visualize bibliometric maps. This analysis reveals that researchers affiliated with American institutions are the most productive authors, surpassing their peers from other countries, with notable contributions also coming from France and Israel. The United States of America emerges as the leading nation in the total number of publications and citations, followed by the United Kingdom and Germany. Additionally, an in‐depth examination of keywords and topics illustrates that ANYAS encompasses a diverse range of subjects, prominently featuring chemistry, hematology, and psychology research. This breadth of exploration underscores the journal's role as a significant platform for advancing scientific knowledge across multiple domains.
Barnet, MB, Jackson, KJL, Masle-Farquhar, E, Russell, A, Burnett, DL, Chye, A, Jara, CJ, Faulks, M, Mawson, A, Peters, TJ, Brink, R, Wright, K, Allen, I, Junankar, S, Davis, ID, Heller, G, Khan, Z, Bruce, J, Yang, C, Prokopec, S, Pugh, T, Behren, A, Hold, GL, Zhang, F, Cooper, WA, Gao, B, Nagrial, A, Joshua, AM, John, T, Peters, G, Hui, R, Boyer, M, Blinman, PL, Kao, SC, Cebon, J & Goodnow, CC 2025, 'Common inherited loss-of-function mutations in the innate sensor NOD2 contribute to exceptional immune response to cancer immunotherapy', Proceedings of the National Academy of Sciences, vol. 122, no. 28.
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Lung cancers and melanomas have many somatically mutated self-proteins that would be expected to trigger an immune rejection response, yet therapeutic responses can only be induced in a subset of patients. Here, we investigated the possibility that inherited differences in immune tolerance checkpoints contribute to variability in outcomes. Whole genome sequencing revealed biallelic germline loss-of-function (LOF) mutations in the immune tolerance checkpoint gene, NOD2 , in an exceptional immune responder to targeted radiotherapy for metastatic melanoma. In 40 exceptional immune responders to anti-PD1 monotherapy for non–small cell lung cancer (NSCLC), genome sequencing showed 30% had inherited a NOD2 LOF variant, more than twice the population frequency ( P = 0.0021). Conversely, a gain-of-function RIPK2 allele known to increase NOD2 signaling was inherited by 61% of nonresponders from the same cohort, compared to 10% of exceptional responders and much higher than the population frequency ( P < 0.0001). Within the overall recruited cohort of 144 NSCLC anti-PD1 patients, individuals with immune-related adverse events (irAE) had better overall survival, further improved in those with NOD2 LOF. In independent anti-PD1 monotherapy cohorts with a range of cancers, inherited NOD2 LOF was associated with complete or partial response ( P = 0.0107). Experimental validation in mice showed germline No...
Basack, S & Khabbaz, H 2025, 'Analytical solution for single pile in two-layered soil subjected to torsion', Geomechanics and Geoengineering, vol. 20, no. 3, pp. 618-634.
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Baum, CM, Sick, N & Bröring, S 2025, 'Drivers for the emergence of interdisciplinary knowledge areas: An actor-level perspective on building legitimacy for the case of synthetic life sciences', Technovation, vol. 141, pp. 103173-103173.
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Bayat, A, Das, PK, Saha, G & Saha, SC 2025, 'Optimizing proton exchange membrane electrolyzer cells: A comprehensive parametric analysis of flow, electrochemical, and geometrical factors', International Journal of Thermofluids, vol. 27, pp. 101177-101177.
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Belkina, M, Daniel, S, Nikolic, S, Haque, R, Lyden, S, Neal, P, Grundy, S & Hassan, GM 2025, 'Implementing generative AI (GenAI) in higher education: A systematic review of case studies', Computers and Education: Artificial Intelligence, vol. 8, pp. 100407-100407.
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Benedetto, M, Paduano, D, Ramai, D, franchellucci, G, Barbera, C, Frigo, F, Fugazza, A, De Nucci, G, Vanella, G, Ko, C, Francesco, DM, Larghi, A, Anderloni, A, Auriemma, F, Minini, F, Arcidiacono, P, Gentile, C, Federica, C, De, LL, Teoh, AY, Gallo, C, Forti, E, Mutignani, M, Santi, M, Bertani, H, Aragona, G, Troncone, E, Del Vecchio Blanco, G, Pham, KC, Mirante, VG, Crinò, SF, Aljahdli, E, Sundaram, S, Al-Lehibi, A, Alfadda, A, Fiacca, M, Manes, G, Decembrino, F, Fierro, G, Manes, G, Morris, JD, Martínez, B, Aparicio, JR, Stigliano, S, Bronswijk, M, Van der Merwe, S, Lakhtakia, S, Ventra, A, Repici, A & Facciorusso, A 2025, 'Endoscopic Ultrasound (EUS)-Guided Gallbladder Drainage vs EUS-Guided Bile Duct Drainage for First Line Therapy of Malignant Biliary Obstruction: An International Multicenter Study', Endoscopy, vol. 57, no. S 02, pp. S75-S76.
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Benedetto, M, Paduano, D, Ramai, D, franchellucci, G, Barbera, C, Fugazza, A, Vanella, G, Frigo, F, Arcidiacono, P, De Nucci, G, Anderloni, A, Larghi, A, Crinò, SF, Sundaram, S, Troncone, E, Pham, KC, Al-Lehibi, A, Forti, E, Gentile, C, Aragona, G, Manes, G, Ko, C, Francesco, DM, Auriemma, F, Minini, F, Fiacca, M, Federica, C, De, LL, Teoh, AY, Gallo, C, Mutignani, M, Santi, M, Bertani, H, Del Vecchio Blanco, G, Mirante, VG, Aljahdli, E, Alfadda, A, Decembrino, F, Antonio, F, Bronswijk, M, Van der Merwe, S, Fierro, G, Manes, G, Morris, JD, Martínez, B, Stigliano, S, Aparicio, JR, Lakhtakia, S, Ventra, A, Repici, A & Facciorusso, A 2025, 'Endoscopic Ultrasound (EUS)-Guided Gallbladder Drainage vs EUS-Guided Bile Duct Drainage After Failed ERCP for Malignant Biliary Obstruction: An International Multicenter Study', Endoscopy, vol. 57, no. S 02, pp. S198-S199.
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Berahmand, K, Zhou, X, Li, Y, Gururajan, R, Barua, PD, Acharya, UR & Chennakesavan, SK 2025, 'NEDL-GCP: A nested ensemble deep learning model for Gynecological cancer risk prediction', Array, vol. 27, pp. 100468-100468.
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Gynecological cancer remains a critical global health concern, where early detection significantly improves patient outcomes. Despite advances in deep learning for medical diagnostics, existing models often struggle with feature redundancy, lack of generalizability, and suboptimal integration of diverse feature representations, limiting their effectiveness in clinical applications. In this study, we present NEDL-GCP, a Nested Ensemble Deep Learning model for Gynecological Cancer Risk Prediction, which uses a hierarchical ensemble framework to improve the accuracy of the classification. NEDL-GCP integrates CNNs, RNNs, and SVMs as base learners, extracting diverse feature representations, while a meta-classifier combining J48 and Stochastic Gradient Descent (SGD) refines predictions. Evaluated on the Herlev and SIPaKMeD Pap Smear datasets, NEDL-GCP achieved state-of-the-art accuracy scores of 99.1% and 98.5%, outperforming existing methods. These results demonstrate the robustness and reliability of the model, making it a valuable tool for the early detection of cervical cancer. By enhancing diagnostic accuracy and optimizing clinical workflows, NEDL-GCP supports timely decision-making, ultimately improving patient care.
Berezowski, V, Taoum, K, Wang, J, Birch, P, Roux, C & Huo, H 2025, 'Investigating identity crime and misuse in Australia: the role of prevention technologies and the likelihood of victimisation', Journal of Criminological Research, Policy and Practice, vol. 11, no. 1, pp. 50-63.
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PurposeThis study examines identity theft as a significant and growing issue in Australia, not only due to its financial impact but also because of the emotional, psychological, and physical harm it causes, making it a public health concern. This study aims to analyse the results of the 2019 Australian Institute of Criminology (AIC) survey to identify factors associated with an increased likelihood of identity theft victimisation.Design/methodology/approachThis study involved a detailed analysis of the 2019 AIC survey, which had 9,968 respondents from a sample of 10,000. The research focused on whether respondents had ever been victimised by identity theft and analysed various characteristics, including demographics (gender, age, Indigenous status, education), income, computer usage, and preventive technology use, as potential indicators of future victimisation. Univariate analyses (chi-squared test and two-sample t-test) were used to assess individual associations, whereas a multivariate analysis (logistic regression) identified significant predictors of victimisation.FindingsThe univariate analyses indicated that all sub-variables were individually associated with identity theft victimisation. However, the multivariate analysis revealed that only identifying as Aboriginal and Torres Strait Islander, having an income between $18,201 and $37,000, and using multiple preventive technologies were significant predictors of victimisation. The unexpected finding that increased preventive technology use correlates with a higher risk of victimisation contradicts the survey’s suggestion that victims adopt more careful behaviour post-victimisation....
Berta, M, Lami, L & Tomamichel, M 2025, 'Continuity of Entropies via Integral Representations', IEEE Transactions on Information Theory, vol. 71, no. 3, pp. 1896-1908.
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Bhandari, S, Fatahi, B, Nguyen, LD & Karimi, R 2025, 'Cyclic and post-cyclic responses of cement-treated landfill waste under triaxial testing', Soil Dynamics and Earthquake Engineering, vol. 197, pp. 109525-109525.
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Bhowmik, A, Hasan, M, Redoy, MMH & Saha, G 2025, 'Nipah virus outbreak trends in Bangladesh during the period 2001 to 2024: a brief review', Science in One Health, vol. 4, pp. 100103-100103.
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Bi, G, Yan, H, Jiang, J, Tong, L, Li, Y, Chen, T & Wang, H 2025, 'Microstructure and Yield Plateau of an Annealed Extruded Mg-Y-Cu Alloy', Journal of Materials Engineering and Performance, vol. 34, no. 8, pp. 7244-7252.
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Bibi, H, Abolhasan, M, Lipman, J, Abdollahi, M & Ni, W 2025, 'A comprehensive survey on privacy-preserving technologies for Smart Grids', Computers and Electrical Engineering, vol. 124, pp. 110371-110371.
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Bidwai, P, Gite, S, Pradhan, B & Almari, A 2025, 'Explainable Diabetic Retinopathy Detection Using a Distributed CNN and LightGBM Framework', Computers, Materials & Continua, vol. 84, no. 2, pp. 2645-2676.
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Borazjani, Z, Azin, R, Osfouri, S, Karami, R, Kennedy, E & Stockenhuber, M 2025, 'Hydrothermal liquefaction of Caulerpa sertularioides: Optimized biocrude production and characterization with pretreatment techniques', Biomass and Bioenergy, vol. 194, pp. 107635-107635.
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Bryant, L, Stubbs, P, Bailey, B, Nguyen, V, Bluff, A & Hemsley, B 2025, 'Interacting with virtual characters, objects and environments: investigating immersive virtual reality in rehabilitation', Disability and Rehabilitation: Assistive Technology, vol. 20, no. 1, pp. 107-117.
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PURPOSE: This pilot study aimed to (a) investigate opportunities for immersive Virtual Reality (VR) technology in communication, physical, and visual rehabilitation by examining the interaction of people without disabilities in a range of structured virtual environments; and (b) validate research protocols that might be used to evaluate the physical, visual, and verbal interaction of users in virtual worlds, and their safety while using the technology. METHODS: Thirteen adults identifying as people without disability were exposed to VR via a head-mounted display. A video-review method was used to qualitatively code and analyse each participant's communication, movement, orientation, and support needs. RESULTS: All participants oriented to their virtual environments sufficiently to use applications. Their spoken language was effective for interaction, although unconventional social behaviours were also observed. Two participants reported minor adverse reactions consistent with mild cybersickness. CONCLUSION: The results provide insight into the types of environments and characters that support the greatest communicative, physical, and visual interaction in immersive VR. The tested protocols are useful to assess safety when using VR, and to observe communicative, physical, and visual interaction with virtual environments and characters. Implications for future research and use of VR with people with communication, physical and visual disability are discussed.
Cagno, E, Accordini, D, Thollander, P, Andrei, M, Hasan, ASMM, Pessina, S & Trianni, A 2025, 'Energy management and industry 4.0: Analysis of the enabling effects of digitalization on the implementation of energy management practices', Applied Energy, vol. 390, pp. 125877-125877.
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Cai, G-Q, Diao, X-F, He, X-Z, Gao, S & Liu, T 2025, 'Micromechanical analysis of contact erosion under cyclic loads using the coupled CFD–DEM method', Géotechnique, vol. 75, no. 5, pp. 649-672.
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Different layers of soil often have distinct particle sizes. When exposed to the natural environment, soil is easily affected by natural rainfall, rising groundwater levels and human activities, leading to particle contact erosion, which reduces the safety and service performance of the soil structure. In this paper, a coupled computational fluid dynamics–discrete-element method (CFD–DEM) model was employed to investigate the particle migration phenomena, mechanical response of contact interfaces, variations in flow fields and macroscopic deformation during the contact erosion process under cyclic loads at different frequencies and amplitudes. The conclusions are presented as follows. (a) Within one cycle of cyclic loading, both compression during loading and stress relaxation during unloading are the main factors triggering the migration of fine particles. (b) The migration and loss of fine particles mainly occur in the early stages of cyclic loading, where strong contact force chains are formed within the fine particle layer, leading to significant plastic deformation of the soil at the macroscopic level. (c) Under cyclic loading, changes in the soil pore structure cause an upwards hydraulic gradient in the initial quiescent water flow field. This hydraulic gradient can rupture weak contact force chains and cause particle pumping. (d) Increasing the frequency and amplitude of cyclic loading intensifies the erosion of fine particles, causing greater axial deformation of the soil. Compared to cyclic loading frequency, the amplitude of cyclic loading has a greater impact on contact erosion.
Cai, J, Deng, L, Lai, J, Li, S, Islam Oni, MA, Dey, S & Yang, Y 2025, 'Additive Manufactured Multi-Material 3D metasurfaces for broadband achromatic electromagnetic focusing', Materials & Design, vol. 255, pp. 114210-114210.
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Cai, J, Yuan, Y, Pan, L, Pei, Z, Zhang, Y, Xi, X, Ukrainczyk, N, Koenders, EAB, Zhang, L, Zhang, YX, Pan, J, Wang, Y & Xie, W 2025, 'Intelligent Thermoelectric Sensing with Sustainable Strain‐Hardening Geopolymeric Composites', Small Science, vol. 5, no. 3.
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Traditional thermoelectric (TE) building materials are limited in both performance and durability, requiring enhancements for effective energy solutions. This research investigates strain‐hardening geopolymeric composites (SHGC) for TE sensing applications. The influence of metal oxides on mechanical strength and TE characteristics is evaluated using isothermal calorimetry, computed tomography scanning, and focused ion beam (FIB)–transmission electron microscopy analysis. At ambient temperature, SHGC samples with MnO2 exhibit the highest Seebeck coefficient of 5470 μV K−1 with a measured power density of 29 μW m−2. Despite the presence of small strain cracks, the SHGC maintains about 69% of its original ZT value even after long‐term use. This discovery underlines the durability and efficiency of SHGC, demonstrating their potential for future infrastructure applications. The cost‐effectiveness, temperature‐sensing abilities, and environmental advantages of SHGC make them well suited for large‐scale smart applications.
Cai, Q, Cao, J, Xu, G & Zhu, N 2025, 'Distributed Recommendation Systems: Survey and Research Directions', ACM Transactions on Information Systems, vol. 43, no. 1, pp. 1-38.
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With the explosive growth of online information, recommendation systems have become essential tools for alleviating information overload. In recent years, researchers have increasingly focused on centralized recommendation systems, capitalizing on the powerful computing capabilities of cloud servers and the rich historical data they store. However, the rapid development of edge computing and mobile devices in recent years has provided new alternatives for building recommendation systems. These alternatives offer advantages such as privacy protection and low-latency recommendations. To leverage the advantages of different computing nodes, including cloud servers, edge servers, and terminal devices, researchers have proposed recommendation systems that involve the collaboration of these nodes, known as distributed recommendation systems. This survey provides a systematic review of distributed recommendation systems. Specifically, we design a taxonomy for these systems from four perspectives and comprehensively summarize each study by category. In particular, we conduct a detailed analysis of the collaboration mechanisms of distributed recommendation systems. Finally, we discuss potential future research directions in this field.
Cai, X, Sun, Y, Shi, K, Yan, H, Wen, S, Cheng, Q & Tian, Z 2025, 'Communication Security and Stability in NNCSs: Realistic DoS Attacks Model and ISTA-Supervised Adaptive Event-Triggered Controller Design', IEEE Transactions on Cybernetics, vol. 55, no. 2, pp. 615-624.
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Cai, Z, Cai, Y, Pan, Y, Song, Y, Liu, Y, Duan, J, Nghiem, LD & Sun, X 2025, 'Solar driven levofloxacin degradation by C-doped oxygen-rich bismuth oxyhalide with SPR intensified built-in electric field: DFT study and toxicity evaluation', Journal of Alloys and Compounds, vol. 1016, pp. 178860-178860.
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Cai, Z, Zhao, J, Song, Y, Liu, Y, Nghiem, LD, Duan, J, Che, C & Sun, X 2025, 'Chalcocite-catalyzed Fenton coupling with biodegradation for N,N-dimethylformamide treatment: insights into mechanism and cost-effectiveness', Journal of Cleaner Production, vol. 518, pp. 145920-145920.
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Calabrese, F, Auriemma, F, Paduano, D, Gentile, C, Fiacca, M, Repici, A & Benedetto, M 2025, 'Anti-Reflux Mucosal Ablation with Z-Line Involvement (ARMA-Z) for Gastroesophageal Reflux Disease (GERD): Preliminary Results from the First International Experience', Endoscopy, vol. 57, no. S 02, pp. S459-S459.
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Calderon, P, Soen, A & Rizoiu, M-A 2025, 'Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data', IEEE Transactions on Computational Social Systems, vol. 12, no. 1, pp. 25-37.
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Canda, E, Nguyen, QD, San Nicolas, R, Rasekh, H & Castel, A 2025, 'Effect of calcined clay reactivity on the risk of restrained shrinkage‐induced early‐age concrete cracking', Structural Concrete, vol. 26, no. 2, pp. 1892-1910.
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AbstractA combination of limestone and calcined clay has emerged as a promising supplementary cementitious material due to its abundant availability to replace traditional supplementary cementitious materials such as fly ash or ground‐granulated blast‐furnace slag in reducing concrete's carbon footprint. Although different properties have been considered, very limited attention was paid to the early‐age cracking behavior of limestone calcined clay cement (LC3) concretes. This study aims to investigate the influence of calcined clay reactivity on the early‐age cracking potential of LC3 concretes using the restrained ring test. Mechanical properties, time to cracking, tensile creep coefficient, and total shrinkage were measured. Results showed that the reactivity of calcined clay significantly impacted total shrinkage, creep coefficients, and time to cracking. LC3 concretes exhibited higher tensile creep coefficients than pure ordinary Portland cement concrete, which can provide beneficial tensile stress relaxation delaying concrete cracking. An apparent calcined clay reactivity coefficient (Rapp) was proposed correlating well with the time to cracking of the restrained LC3 concrete rings, thus offering practical guidance for the selection of suitable calcined clays and mix designs for specific high‐risk applications.
Cao, B, Li-Ting Tsai, C, Zhou, N, Do, T & Lin, C-T 2025, 'A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 33, pp. 1562-1573.
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Cao, HQ, Karimi, M, Williams, P & Dylejko, P 2025, 'Passive control of hydro-elastic vibrations of plates using shunted piezoelectric patches', Thin-Walled Structures, vol. 206, pp. 112493-112493.
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Cao, L 2025, 'Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science', International Journal of Data Science and Analytics, vol. 20, no. 2, pp. 1617-1629.
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Cao, M, Xie, W, Zhang, X, Zhang, J, Jiang, K, Lei, J & Li, Y 2025, 'M³amba: CLIP-Driven Mamba Model for Multi-Modal Remote Sensing Classification', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 8, pp. 7605-7617.
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Cao, Y, Zhao, S, Wen, S & Huang, T 2025, 'Input-Retention Strategies for Secure Synchronization of Piecewise Markov Neural Networks Under Hybrid Cyber-Attacks', IEEE Transactions on Circuits and Systems I: Regular Papers, pp. 1-11.
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Cao, Z, Zhao, D, Wang, Q, Yuan, H, Ma, H & Yu, S 2025, 'CrossTrace: Privacy-Aware Cross-System Trajectory Recovery via Hybrid Split and Federated Learning', IEEE Transactions on Mobile Computing, vol. 24, no. 9, pp. 9255-9272.
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Caredda, G, Makoond, N, Sagaseta, J, Chryssanthopoulos, M, Stewart, MG & Adam, JM 2025, 'Analysing the cost-effectiveness of improving building robustness through segmentation', Engineering Structures, vol. 343, pp. 121165-121165.
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Cashel, J, Yan, D, Han, R, Jeong, H, Yoon, CW, Ambay, JA, Liu, Y, Ung, AT, Yang, L & Huang, Z 2025, 'Chemical Bonds Containing Hydrogen: Choices for Hydrogen Carriers and Catalysts', Angewandte Chemie, vol. 137, no. 21.
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AbstractCompounds containing B─H, C─H, N─H, or O─H bonds with high hydrogen content have been extensively studied as potential hydrogen carriers. Their hydrogen storage performance is largely determined by the nature of these bonds, decomposition pathways, and the properties of the dehydrogenation products. Among these compounds, methanol, cyclohexane, and ammonia stand out due to their low costs and established infrastructure, making them promising hydrogen carriers for large‐scale storage and transport. They offer viable pathways for decarbonizing society by enabling hydrogen to serve as a clean energy source. However, several challenges persist, including the high temperatures required for (de)hydrogenation, slow kinetics, and the reliance on costly catalysts. To address these issues, strategies such as chemical modification and catalyst development are being pursued to improve hydrogen cycling performance. This review highlights recent progress in hydrogen carriers with B─H, C─H, N─H, or O─H bonds. It examines the fundamental characteristics of these bonds and carriers, as well as advances in catalyst development. Our objective is to offer a comprehensive understanding of current state of hydrogen carriers and identify future research directions, such as molecular modification and system optimization. Innovations in these areas are crucial to advance hydrogen storage technologies for a large‐scale hydrogen deployment.
Cashel, J, Yan, D, Han, R, Jeong, H, Yoon, CW, Ambay, JA, Liu, Y, Ung, AT, Yang, L & Huang, Z 2025, 'Chemical Bonds Containing Hydrogen: Choices for Hydrogen Carriers and Catalysts', Angewandte Chemie International Edition, vol. 64, no. 21.
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AbstractCompounds containing B─H, C─H, N─H, or O─H bonds with high hydrogen content have been extensively studied as potential hydrogen carriers. Their hydrogen storage performance is largely determined by the nature of these bonds, decomposition pathways, and the properties of the dehydrogenation products. Among these compounds, methanol, cyclohexane, and ammonia stand out due to their low costs and established infrastructure, making them promising hydrogen carriers for large‐scale storage and transport. They offer viable pathways for decarbonizing society by enabling hydrogen to serve as a clean energy source. However, several challenges persist, including the high temperatures required for (de)hydrogenation, slow kinetics, and the reliance on costly catalysts. To address these issues, strategies such as chemical modification and catalyst development are being pursued to improve hydrogen cycling performance. This review highlights recent progress in hydrogen carriers with B─H, C─H, N─H, or O─H bonds. It examines the fundamental characteristics of these bonds and carriers, as well as advances in catalyst development. Our objective is to offer a comprehensive understanding of current state of hydrogen carriers and identify future research directions, such as molecular modification and system optimization. Innovations in these areas are crucial to advance hydrogen storage technologies for a large‐scale hydrogen deployment.
Castel, A, Nguyen, QD, Law, D, Kim, T, Samson, G, Cyr, M, Provis, JL & Li, W 2025, 'Recommendation of RILEM TC 283-CAM: performance-based assessment of alkali-activated concrete durability using the 10 V rapid chloride permeability test', Materials and Structures, vol. 58, no. 6.
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Abstract The major barriers to the widespread adoption of alkali-activated materials by the construction industry include concerns about durability and their exclusion from current standards. The chemical reactions characterizing alkali-activated binder systems differ drastically from the conventional hydration process of Portland cement. Thus, the mechanisms by which concrete achieves potential durability are different between the two types of binders. RILEM Technical Committee (TC) 283-CAM (Chloride transport in Alkali-activated Materials) aimed to address key questions related to chloride transport in alkali-activated binders and concretes, with a view toward drafting recommendations for the appropriate selection and application of testing methods, and this document represents a key output of that TC. The standard ASTM C1202 Rapid Chloride Permeability Test (RCPT) method fails to measure the charge passed through most alkali-activated concretes due to samples overheating when applying the specified 60 V potential difference. A modified RCPT using a 10 V potential difference was used in the interlaboratory testing campaign of TC 283-CAM. The 10 V-RCPT method described in this Recommendation allowed the successful completion of tests for all alkali-activated concretes considered. Various precursors were investigated including fly ash, GGBS, calcined clay and ferronickel slag. 10 V-RCPT results are validated against ASTM C1556 bulk diffusion test results. Performance-based specifications are proposed.
Castro Mota, R, Williams, P, Karimi, M, Kirby, R & Jacob, S 2025, 'An approximate hybrid approach for computing low-frequency outdoor sound propagation', Engineering Analysis with Boundary Elements, vol. 179, pp. 106308-106308.
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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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Cecilio, MO, Indraratna, B, Rujikiatkamjorn, C & Malisetty, RS 2025, 'Dynamic stress analysis of rock joints under railway loading', Géotechnique, vol. 75, no. 4, pp. 529-549.
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The proper estimation of stresses generated by the passage of a train is fundamentally important to the serviceability and longevity of railways, and yet very limited knowledge is available where the track substructure is built on a jointed rock mass. The present study introduces an analytical solution for estimating the ground stresses arising from moving wheel loads, causing a change in the three-dimensional stress state in the track formation, in relation to the stress variation with depth and along the longitudinal track section – that is, the direction of train passage. Based on 21 case histories, an array of field measurements and numerical simulations covering a wide range of freight tonnage, train speeds and different formation conditions were considered to validate the proposed analytical solution. The proposed methodology (analytical solution) was then applied to a jointed rock subgrade to determine the normal and shear stresses acting along a specific discontinuity plane. The main analytical outcome demonstrates that the orthogonal vertical and shear stresses present different and phase-shifted history plots for homogeneous ground conditions with principal stresses rotation. However, conversely for a jointed subgrade, the normal and shear stresses along the discontinuity have the same history plot pattern and are in phase. As a practical guide, the results from this study would help to define which cyclic loads should be applied in laboratory tests to simulate realistic traffic patterns of trains travelling over a jointed rock subgrade.
Cervero-Martín, E & Tomamichel, M 2025, 'Device independent security of quantum key distribution from monogamy-of-entanglement games', Quantum, vol. 9, pp. 1652-1652.
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We analyse two party non-local games whose predicate requires Alice and Bob to generate matching bits, and their three party extensions where a third player receives all inputs and is required to output a bit that matches that of the original players. We propose a general device independent quantum key distribution protocol for the subset of such non-local games that satisfy a monogamy-of-entanglement property characterised by a gap in the maximum winning probability between the bipartite and tripartite versions of the game. This gap is due to the optimal strategy for two players requiring entanglement, which due to its monogamy property cannot be shared with any additional players. Based solely on the monogamy-of-entanglement property, we provide a simple proof of information theoretic security of our protocol. Lastly, we numerically optimize the finite and asymptotic secret key rates of our protocol using the magic square game as an example, for which we provide a numerical bound on the maximal tripartite quantum winning probability which closely matches the bipartite classical winning probability. Further, we show that our protocol is robust for depolarizing noise up to about 2.88%, providing the first such bound for general attacks for magic square based quantum key distribution.
Chadalavada, S, Faust, O, Salvi, M, Seoni, S, Raj, N, Raghavendra, U, Gudigar, A, Barua, PD, Molinari, F & Acharya, R 2025, 'Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review', Environmental Modelling & Software, vol. 185, pp. 106312-106312.
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Chadalavada, S, Yaman, S, Sengur, A, Hafeez-Baig, A, Tan, R-S, Datta Barua, P, Deo, RC, Kobayashi, M & Rajendra Acharya, U 2025, 'Gated-LNN: Gated Liquid Neural Networks for Accurate Water Quality Index Prediction and Classification', IEEE Access, vol. 13, pp. 69500-69512.
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Chan, SF, DeWitt, D & Loban, R 2025, 'A comparative exploration of virtual reality’s role in Mandarin intercultural communicative competence development', Information and Learning Sciences, vol. 126, no. 5/6, pp. 335-361.
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PurposeIntercultural communicative competence (ICC) is important when different cultural speakers learn Mandarin as a Foreign Language (MFL). The use of virtual reality (VR) has been shown to be effective for improving ICC. Hence, this study investigates a production-based instructional strategy where students use VR to view and create VR environments with cultural elements for learning MFL to determine if this strategy was effective with the current cohort of students (2023). In addition, it would be investigated whether there was a difference in ICC between the 2019 cohort and the 2023 cohort.Design/methodology/approachThe study employed a quasi-experimental method to assess ICC using the Survey of student’s Intercultural Competence (SSIC) and gauged the improvement within the 2023 cohort. Next, the ICC between the 2023 and 2019 cohorts was compared to determine if there was a significant difference. Data was analysed using paired-samples t-tests and thematic analysis for the open-ended responses.FindingsThere was a significant improvement in ICC after the intervention for the 2023 cohort, which was supported with the open-ended response. However, the t-test results indicated no significant difference in ICC between the 2019 and 2023 cohorts. However, the 2023 cohort seemed to be more motivated, confident and eager to continue using VR. The findings indicate that VR when combined with an appropriate pedagogy could improve students ICC.Originality/valueThe use of VR and this production-based instructional strategy could be used in other languages and could possibly be used to i...
Chandra, N, Vaidya, H, Sawant, S, Gite, S & Pradhan, B 2025, 'Attention Driven YOLOv5 Network for Enhanced Landslide Detection Using Satellite Imagery of Complex Terrain', Computer Modeling in Engineering & Sciences, vol. 143, no. 3, pp. 3351-3375.
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Chang, Y, Chen, Y, Wei, Y, Miao, N, Kang, Z, Zhang, Y, Nghiem, LD, Johir, MAH, Guo, Y, Qiao, Y, Shi, X & Li, J 2025, 'Multi-source biochar: Effects on composting humification, soil properties and plant growth', Journal of Environmental Management, vol. 392, pp. 126667-126667.
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Chang, Y, Zhang, L, Nghiem, LD & Wei, Y 2025, 'Enhanced microbial strategies to mitigate microplastic transfer via composting to agricultural ecosystems—A short review', Current Opinion in Environmental Science & Health, vol. 45, pp. 100625-100625.
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Che, H, Li, C, Leung, M-F, Ouyang, D, Dai, X & Wen, S 2025, 'Robust Hypergraph Regularized Deep Non-Negative Matrix Factorization for Multi-View Clustering', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 1817-1829.
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Chen, D, Li, J, Liu, J & Wu, C 2025, 'Damage assessment of ultra-high-performance concrete protective wall against gaseous explosion', Engineering Structures, vol. 332, pp. 120071-120071.
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Chen, D, Li, J, Shao, R & Wu, C 2025, 'Hydrogen/methane explosion loads and their effects on high-performance concrete: A comprehensive review', Structures, vol. 80, pp. 109684-109684.
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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, D, Zhang, H, Li, J & Wu, C 2025, 'Blast loading prediction from methane-air explosion in long straight tunnels', Tunnelling and Underground Space Technology, vol. 165, pp. 106834-106834.
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Chen, G, Zeng, H, Zhu, S, Wen, S & Hu, J 2025, 'Finite-time H∞ control and energy cost optimization for nonlinear delayed systems through switching analysis and interval matrix method', Science China Information Sciences, vol. 68, no. 3.
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Chen, H, Zhu, T, Liu, B, Zhou, W & Yu, PS 2025, 'Fine-Tuning a Biased Model for Improving Fairness', IEEE Transactions on Big Data, vol. 11, no. 3, pp. 1397-1410.
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Chen, H, Zhu, T, Zhang, L, Liu, B, Wang, D, Zhou, W & Xue, M 2025, 'QUEEN: Query Unlearning Against Model Extraction', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 2143-2156.
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Chen, J, Indraratna, B & Ngo, T 2025, 'Assessing the Macro to Micro Properties of Recycled Ballast Mixtures by DEM Analyses for Enhanced Railroad Engineering', Journal of Geotechnical and Geoenvironmental Engineering, vol. 151, no. 6.
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Chen, J, Indraratna, B, Vinod, JS, Ngo, T & Liu, Y 2025, 'Effects of Particle Shape on the Shear Behavior and Breakage of Ballast: A DEM Approach', International Journal of Geomechanics, vol. 25, no. 1.
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Chen, J, Wu, K, Niu, J, Li, Y, Xu, P & Andrew Zhang, J 2025, 'Spectral and Energy Efficient Waveform Design for RIS-Assisted ISAC', IEEE Transactions on Communications, vol. 73, no. 1, pp. 158-172.
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Chen, K, Dong, W, Huang, Y, Wang, F, Zhou, JL & Li, W 2025, 'Photocatalysis for sustainable energy and environmental protection in construction: A review on surface engineering and emerging synthesis', Journal of Environmental Chemical Engineering, vol. 13, no. 5, pp. 117529-117529.
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Chen, K, He, X, Liang, F & Sheng, D 2025, 'Strength and dilatancy of an unsaturated expansive soil at high suction levels', Journal of Rock Mechanics and Geotechnical Engineering, vol. 17, no. 8, pp. 5079-5088.
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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, vol. 22, no. 4, pp. 3708-3722.
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Chen, N, Zhang, X, Qi, L, Gao, F, Wu, G, Li, H, Guo, W & Ngo, HH 2025, 'Enhancement of volatile fatty acids degradation and rapid methanogenesis in a biochar-assisted anaerobic membrane bioreactor via enhancing direct interspecies electron transfer', Journal of Environmental Management, vol. 380, pp. 125045-125045.
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Chen, Q, Xiong, Z, Tao, G, Tian, Z & Nimbalkar, S 2025, 'Strength, Durability, Corrosion Resistance, and Microstructure of Cemented Soil Incorporating Nano-MgO under Static and Cyclic Loading: A Laboratory Study', Journal of Materials in Civil Engineering, vol. 37, no. 4.
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Chen, W-H, Wu, D-R, Chang, M-H, Rajendran, S, Ong, HC & Lin, K-YA 2025, 'Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods', International Journal of Hydrogen Energy, vol. 140, pp. 1004-1016.
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Chen, X, Ansari, AJ, Han, S, Peng, Y & Song, X 2025, 'Performance and microbial response of anaerobic membrane bioreactor to oil and salt co-occurrence in food wastewater treatment', Environmental Technology & Innovation, vol. 38, pp. 104208-104208.
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Chen, X, Han, Y, Li, C, Chang, X, Sun, Y & Yang, Y 2025, 'A Static-Dynamic Composition Framework for Efficient Action Recognition', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 8, pp. 14664-14677.
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Chen, Y, Guo, W, Ngo, HH, Chen, Z, Wei, C, Bui, XT, Van Tung, T & Zhang, H 2025, 'Ways to assess hydrogen production via life cycle analysis', Science of The Total Environment, vol. 977, pp. 179355-179355.
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Chen, Y, Li, Z, Yang, C, Wang, X & Xu, G 2025, 'Large language models are few-shot multivariate time series classifiers', Data Mining and Knowledge Discovery, vol. 39, no. 5.
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Abstract Large Language Models (LLMs) are widely applied in time series analysis. Yet, their utility in few-shot classification—a scenario with limited training data—remains unexplored. We aim to leverage the pre-trained knowledge in LLMs to overcome the data scarcity problem within multivariate time series. To this end, we propose LLMFew, an LLM-enhanced framework, to investigate the feasibility and capacity of LLMs for few-shot multivariate time series classification (MTSC). We first introduce a Patch-wise Temporal Convolution Encoder (PTCEnc) to align time series data with the textual embedding input of LLMs. Then, we fine-tune the pre-trained LLM decoder with Low-rank Adaptations (LoRA) to enable effective representation learning from time series data. Experimental results show our model consistently outperforms state-of-the-art baselines by a large margin, achieving 125.2% and 50.2% improvement in classification accuracy on Handwriting and EthanolConcentration datasets, respectively. Our results also show LLM-based methods achieve comparable performance to traditional models across various datasets in few-shot MTSC, paving the way for applying LLMs in practical scenarios where labeled data are limited. Our code is available at https://github.com/junekchen/llm-fewshot-mtsc.
Chen, Y, Shi, K, WU, Z, Chen, J, Wang, X, Mcauley, J, Xu, G & Yu, S 2025, 'A temporally disentangled contrastive diffusion model for spatiotemporal imputation', CAAI Transactions on Intelligence Technology.
Chen, Y, Tuan, HD, Fang, Y, Tan, G, Yu, H & Poor, HV 2025, 'Multiobjective Joint Design of Finite-Resolution RISs and Downlink Beamforming for Double-RIS-Assisted IoT Networks', IEEE Internet of Things Journal, vol. 12, no. 15, pp. 31154-31167.
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Chen, Y, Zhu, S, Li, Y, Liu, X & Wen, S 2025, 'Finite-Time Synchronization for Delayed NNs via Generalized Halanay Inequalities', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 7, pp. 4742-4751.
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Chen, Y, Zhu, S, Shen, M, Liu, X & Wen, S 2025, 'Finite-time synchronization control for a class of delayed neural networks: an improved two-step control method', Science China Information Sciences, vol. 68, no. 9.
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Chen, Y-N, Ding, C, Zhu, H, Huang, X, Jia, Y, Liu, Y & Guo, YJ 2025, 'A Four-Port Shared-Aperture In-Band Full-Duplex Antenna Array Based on A Novel Common-Mode and Differential-Mode Combination Method', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Chen, Z, Ji, JC, Ni, Q, Ye, B, Ding, X & Yu, W 2025, 'Bi-structural spatial–temporal network for few-shot fault diagnosis of rotating machinery', Mechanical Systems and Signal Processing, vol. 227, pp. 112378-112378.
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Chen, Z, Khalilpour, K & Zhao, R 2025, 'Coordination of Risk‐Averse Behaviors in a Green Supply Chain‐to‐Chain Competition', Managerial and Decision Economics, vol. 46, no. 5, pp. 3142-3156.
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ABSTRACTThis research investigates the coordination of risk‐averse behaviors among members in green supply chain‐to‐chain competition. We establish models encompassing member rationality, manufacturer risk aversion, cost‐sharing contracts, and revenue‐sharing contracts, providing the optimal equilibrium results for each scenario. This paper compares the effectiveness of cost‐sharing versus revenue‐sharing contracts, finding that risk‐averse behavior diminishes profits, with total profits falling below those achieved under member rationality. Both contract types can facilitate coordination, but the revenue‐sharing contract proves more effective. Under the revenue‐sharing contract, wholesale and retail prices decrease, product greenness enhances, and profits for manufacturers, retailers, and the entire supply chain increase.
Chen, Z, Li, J, Zong, Z, Li, J, Li, M & Wu, C 2025, 'Damage analysis of RC panels subjected to shock wave and bubble pulse in underwater explosion', Engineering Structures, vol. 335, pp. 120331-120331.
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Chen, Z, Li, J, Zong, Z, Li, J, Xia, M & Wu, C 2025, 'Underwater explosion response of steel-concrete-steel composite panels considering bubble pulse', Engineering Structures, vol. 343, pp. 121227-121227.
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Chen, Z, Liang, J, Yu, Z, Cheng, H, Min, G & Li, J 2025, 'Resilient Collaborative Caching for Multi-Edge Systems With Robust Federated Deep Learning', IEEE Transactions on Networking, vol. 33, no. 2, pp. 654-669.
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Chen, Z, Zuo, W, Zhou, K, Li, Q, Huang, Y & E, J 2025, 'Corrigendum to “Multi-objective optimization of proton exchange membrane fuel cells by RSM and NSGA-II” [Energy Convers. Manag. 277 (2023) 116691]', Energy Conversion and Management, vol. 326, pp. 119448-119448.
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Cheng, K, Duan, M, Indraratna, B & Nguyen, TT 2025, 'Critical Examination of Internal Stability Criteria for Granular Soils and Development of a Coupled PSD-CSD Approach', Journal of Geotechnical and Geoenvironmental Engineering, vol. 151, no. 9.
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Chi, K, Li, J, Shao, R & Wu, C 2025, 'Dynamic behaviour of geopolymer-based ultra-high-performance concrete at low and cryogenic temperature', Cold Regions Science and Technology, vol. 239, pp. 104600-104600.
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Chi, K, Li, J, Shao, R & Wu, C 2025, 'Experimental study on dynamic characterisation of ultra-high performance concrete (UHPC) after cryogenic freeze-thaw cycles', Cement and Concrete Composites, vol. 160, pp. 106011-106011.
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Choden, Y, Askari, M, Kabir, MM, Choo, Y, Sabur, GM, Mamun, MFK, Choi, J-S, Woo, YC, Kim, Y, Hong, S, Shon, HK & Phuntsho, S 2025, 'Recovery of bromide for bromine extraction: a review of technologies and circular economy implications', Desalination, vol. 613, pp. 118968-118968.
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Chu, L, Shi, J & Braun, R 2025, 'Mechanical Reliability of Compressible Microinterconnects in Replaceable Integrated Chiplet Assembly', IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 15, no. 4, pp. 757-765.
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Chu, NH, Hieu, NQ, Nguyen, DN, Hoang, DT, Phan, KT, Dutkiewicz, E, Niyato, D & Shu, T 2025, 'Dynamic Multi-Tier Resource Allocation Framework for Metaverse', IEEE Network, vol. 39, no. 1, pp. 197-204.
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Clemon, L 2025, 'Constitutive Relation for Prolate Pin–Reinforced Transversely Isotropic Media for Additive Manufacturing', Journal of Engineering Mechanics, vol. 151, no. 2.
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Coward-Smith, M, Zhang, Y, Donovan, C, Kim, RY, Wang, B, Zakarya, R, Chen, H, Li, JJ & Oliver, BG 2025, 'Beyond conventional biomarkers: Emerging importance of extracellular vesicles in osteoarthritis, metabolic disorders and cardiovascular disease', Extracellular Vesicle, vol. 5, pp. 100079-100079.
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Cox, TR, Lesmana, D, O'Keeffe, CJ, Lam, ATL, Zou, W, Lin, Z, Lin, X, Roberts, TH, Lim, KS, Oh, SKW, Radfar, P, Ebrahimi Warkiani, M & Ding, L 2025, 'Maximising adherent cell production via customisable and dissolvable bio-polymer microcarriers', Biomedical Materials.
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Abstract Large-scale cellular production systems offer a significant and diverse benefit impacting the therapeutic (stem cell and vaccine production) and cellular agriculture (lab-grown meat) sectors. Producing desired cells at mass can improve production yield whilst reducing the environmental and ethical burden associated with industrialised agriculture and production of therapeutic goods. Many existing large-scale cultivation strategies of adherent cells leverage the use of microcarriers (MCs) within bioreactors. However, currently commercial MCs are not dissolvable and lack specificity for different cell types and bioprocessing contexts.
In this work, we validate the effectiveness of customisable, polymeric MCs engineered to enhance cell growth and productivity. These MCs, which can be adjusted in terms of stiffness, surface charge, and size, maintain their structural integrity while offering precise property modifications. Under specific bioprocessing conditions, the custom MCs demonstrated significant improvements in cell productivity and sustainability compared to other commercial options. Our study (1) highlights how tailored substrate properties, particularly stiffness, can significantly impact cell yield and outcomes, and (2) suggests additional optimisations in surface charge and size that could further enhance MC technology. These advancements have the potential to improve large-scale cell and virus production efficiency, ultimately reducing the cost of production.
Cui, L, Wu, F, Qu, Y, Gu, B, Gao, L & Yu, S 2025, 'RE4ETD: A Relative Entropy Optimization-Based Method for Efficient Electricity Theft Detection With Dual-Privacy Preservation', IEEE Transactions on Smart Grid, vol. 16, no. 5, pp. 4073-4086.
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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, vol. 72, no. 9, pp. 9696-9706.
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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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Dagdanov, R, Andrejević, M, Liu, D & Lin, C-T 2025, 'Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-Grained Timescales', IEEE Robotics and Automation Letters, vol. 10, no. 8, pp. 8562-8569.
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Dai, C, Zuo, W, Li, Q, Zhou, K, Huang, Y, Zhang, G & E, J 2025, 'Numerical investigations on the performance of a hydrogen-fueled micro planar combustor with V-shaped baffle for thermophotovoltaic applications', Energy, vol. 326, pp. 136270-136270.
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Dai, S, Gao, F, Niu, G, He, X, Zhang, S & Sheng, D 2025, 'Dynamics of particle segregation and its impact on mechanical properties', Acta Geotechnica, vol. 20, no. 5, pp. 1991-2007.
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Abstract Particle segregation is a widespread phenomenon in nature. Vertical vibration systems have been a focal point in studying particle segregation, providing valuable insights into the mechanisms and patterns that influence this process. However, despite extensive research on the mechanisms and patterns of particle separation, the consequences, particularly the mechanical properties of samples resulting from particle segregation, remain less understood. This study aims to investigate the segregation process of a binary mixture under vertical vibration and examine the consequences through monotonic and cyclic triaxial drained tests. The results reveal that large and small particles segregate nearly simultaneously, with more thorough separation observed for large particles. The segregation index, D s , effectively describes this evolution process, offering a quantitative metric for both mixing and segregation. Granular temperature analysis unveils three distinct states during segregation: solid-like, fluid-like, and solid–liquid transitional phase, corresponding to varying activity levels of particle segregation. Drained triaxial shear tests demonstrate the sensitivity of stress–strain relationships to the degree of segregation. Interestingly, ultimate strength is found to be essentially unrelated to the degree of segregation. When the segregation index approaches zero, signifying particles approaching a uniform distribution, the granular system reaches a harmonic state. This state exhibits optimal mechanical performance characterised by maximum peak stress, friction angle, and the highest elastic modulus. These findings underscore the potential impact of segregation on the mechanical response of granular mixtures and emphasise the necessity of a comprehensive understanding of particle segregatio...
Dai, S, He, X, Gao, F, Zhong, W, Zheng, Y & Zhang, S 2025, 'Laboratory model test of contact erosion in railway substructure', Transportation Geotechnics, vol. 51, pp. 101499-101499.
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Dai, S, Shan, F, Xiong, H, Zhang, S, He, X & Sheng, D 2025, 'Evolution of pore structure and flow properties in particle segregation', Journal of Hydrology, vol. 652, pp. 132651-132651.
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Dang, Z, Luo, M, Wang, J, Jia, C, Han, H, Wan, H, Dai, G, Chang, X & Wang, J 2025, 'Disentangled Noisy Correspondence Learning', IEEE Transactions on Image Processing, vol. 34, pp. 2602-2615.
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Das, S, Thiyagarajan, K, Kodagoda, S, Krishnan, A & Bhattacharjee, M 2025, 'A Novel Tactile Sensing Skin for Surface Perception', IEEE Sensors Journal, vol. 25, no. 13, pp. 23155-23162.
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Datta Barua, P, Kobayashi, M, Dogan, S, Baygin, M, Tuncer, T, Kunnel Paul, J, Iype, T & Acharya, UR 2025, 'Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals', IEEE Access, vol. 13, pp. 99032-99047.
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Datta, B, Manasur, B, Sreelekha, G, Verma, P, Adak, C, Shukla, RP & Dutta, G 2025, 'Quantification of L-lactic acid in human plasma samples using Ni-based electrodes and machine learning approach', Talanta, vol. 286, pp. 127493-127493.
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Davis Weaver, N, Bertolacci, GJ, Rosenblad, E, Ghoba, S, Cunningham, M, Ikuta, KS, Moberg, ME, Mougin, V, Han, C, Wool, EE, Abate, YH, Adewuyi, HO, Adnani, QES, Adzigbli, LA, Afolabi, AA, Agampodi, SB, Ahinkorah, BO, Ahmad, A, Ahmad, D, Ahmad, S, Ahmed, A, Ahmed, H, Al Hamad, H, Al-Ajlouni, Y, Al-amer, RM, Albashtawy, M, Aldhaleei, WA, Ali, SS, Ali, W, Alomari, MA, Alsabri, MA, Alvis-Guzman, N, Al-Worafi, YM, Amindarolzarbi, A, Amiri, S, Andrei, T, Anvari, S, Arabloo, J, Areda, D, Artamonov, AA, Ashraf, T, Athari, SS, Atout, MMW, Azzam, AY, Badiye, AD, Baghcheghi, N, Bahramian, S, Banach, M, Barker-Collo, SL, Bärnighausen, TW, Barrow, A, Bashiri, A, Bashiru, HA, Bastan, M-M, Batra, K, Batra, R, Bayati, M, Benjet, C, Benzian, H, Bertuccio, P, Bhagavathula, AS, Bhattacharjee, P, Bills, CB, Boppana, SH, Borges, G, Borhany, H, Bustanji, Y, Caetano dos Santos, FL, Castelpietra, G, Caye, A, Cenderadewi, M, Chandika, RM, Chandrasekar, EK, Charalampous, P, Chen, Y, Chimoriya, R, Chopra, H, Choudhari, SG, Chu, D-T, Chukwu, IS, Chutiyami, M, Cowden, RG, Dachew, BA, Dadras, O, Dai, X, Dalal, K, Dandona, L, Dandona, R, Darcho, SD, Darvishi Cheshmeh Soltani, R, Dávila-Cervantes, CA, de la Torre-Luque, A, Debopadhaya, S, Degenhardt, L, Delgado-Enciso, I, Dervišević, E, Diaz, MJ, Dongarwar, D, Doshi, OP, Dsouza, HL, Dumith, SC, Duraisamy, S, Eboreime, E, Efendi, F, Ekholuenetale, M, El Arab, RA, Elhadi, M, ELNahas, G, Eltaha, C, Emdadul Haque, SE, Eskandarieh, S, Fahim, A, Faro, A, Fatehizadeh, A, Fazeli, P, Feizkhah, A, Fekadu, G, Ferreira, N, Fischer, F, Franklin, RC, Fridayani, NKY, Gajdács, M, Gandhi, AP, Ganesan, B, Gebregergis, MW, Gebrehiwot, M, Gebremeskel, TG, Getie, M, Ghadimi, DJ, Ghailan, KY, Ghashghaee, A, Gholamrezanezhad, A, Goleij, P, Grada, A, Grivna, M, Guan, S-Y, Gulati, S, Gupta, S, Gutiérrez, RA, Gutiérrez-Murillo, RS, Hamilton, EB, Hanifi, N, Hasan, I, Hassan Zadeh Tabatabaei, MS, Hay, SI, Heidari, M, Hemmati, M, Hoan, NQ, Hosseinzadeh, M, Hostiuc, S, Huang, J, Huynh, H-H, Ibitoye, SE, Ilesanmi, OS, Ilic, IM, Ilic, MD, Immurana, M, Inok, A, Iwu, CD, Jahrami, H, Jaka, S, Jalilzadeh Yengejeh, R, Ji, Z, Jin, S, Joseph, N, Joshua, CE, Jozwiak, JJ, Kabir, Z, Kadashetti, V, Kanmodi, KK, Kantar, RS, Kapoor, N, Karaye, IM, Karmakar, S, Kaur, H, Kerr, JA, Khajuria, H, Khan, A, Khatab, K, Kheirallah, KA & et al. 2025, 'Global, regional, and national burden of suicide, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021', The Lancet Public Health, vol. 10, no. 3, pp. e189-e202.
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Deng, D, Shen, W, Deng, Z, Li, T & Liu, A 2025, 'An Ensemble Learning Model Based on Three-Way Decision for Concept Drift Adaptation', Tsinghua Science and Technology, vol. 30, no. 5, pp. 2029-2047.
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The ensemble learning model can effectively detect drift and utilize diversity to improve the performance of adapting to drift. However, local concept drift can occur in different types at different time points, causing basic learners are difficult to distinguish the drift of local boundaries, and the drift range is difficult to determine. Thus, the ensemble learning model to adapt local concept drifts is still challenging problem. Moreover, there are often differences in decision boundaries after drift adaptation, and employing overall diversity measurement is inappropriate. To address these two issues, this paper proposes a novel ensemble learning model called instance-weighted ensemble learning based on the three-way decision (IWE-TWD). In IWE-TWD, a divide-and-conquer strategy is employed to handle uncertain drift and to select base learners; Density clustering dynamically constructs density regions to lock drift range; Three-way decision is adopted to estimate whether the region distribution changes, and the instance is weighted with the probability of region distribution change; The diversities between base learners are determined with three-way decision also. Experimental results show that IWE-TWD has better performance than the state-of-the-art models in data stream classification on ten synthetic data sets and seven real-world data sets.
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, Liu, T, Li, F, He, X & Yang, R 2025, 'Microscopic analysis of erosion in gap-graded soil using a coupled VOF-DEM method', International Journal of Heat and Mass Transfer, vol. 251, pp. 127319-127319.
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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, H, Lin, F, Yin, Z, Yang, Y & Sun, H 2025, 'Additively Manufactured Broadband Miniaturized Quadrature Coupler and Its Application to 2-D Scanning Broadband Butler Matrix', IEEE Transactions on Microwave Theory and Techniques, pp. 1-10.
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Ding, J, Wang, J, Shi, K, Cai, X, Kwon, O-M & Wen, S 2025, 'Resilient Cyber-Physical Co-Design for Fuzzy Renewable-Integrated Power System Under Frequency-Driven Cyberattacks', IEEE Transactions on Fuzzy Systems, pp. 1-11.
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Ding, L, Cox, T, Ghobadi, S, Lim, KS, Roberts, T, Warkiani, ME, Radfar, P & OH, S 2025, 'Development of Xeno-Free, Fast-Dissolving Microcarriers for Scalable Stem Cell Therapy Applications', Cytotherapy, vol. 27, no. 5, pp. S152-S152.
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Ding, L, Zhong, Z, Chen, C, Liu, B, Chen, Z, Zhang, L, Mao, J, Zhang, M, Su, QP & Cheng, F 2025, 'Advances in multiplexed photoelectrochemical sensors for multiple components', Chemical Engineering Journal, vol. 505, pp. 159319-159319.
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Ding, W, Geng, Y, Huang, J, Ju, H, Wang, H & Lin, C-T 2025, 'MGRW-Transformer: Multigranularity Random Walk Transformer Model for Interpretable Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 1104-1118.
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Ding, Z, Kuok, S-C, Lei, Y, Yu, Y, Zhang, G, Hu, S & Yuen, K-V 2025, 'A Novel Bayesian Empowered Piecewise Multi-Objective Sparse Evolution for Structural Condition Assessment', International Journal of Structural Stability and Dynamics, vol. 25, no. 10.
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In this study, a novel Bayesian empowered piecewise multi-objective function is developed, in which a traditional objective function is applied to realize the rough optimization in the first stage to determine the approximate results. Then, a sparse Bayesian learning-based objective function is applied to realize refined optimization with the obtained approximate results in the second stage. On the other hand, considering the sparsity of the structural damage identification, two simple but effective calculation frameworks, the colony initial sparsification and elite clustering framework, are integrated into the evolution, making the algorithm adaptable to handle the defined sparse optimization problem. Therefore, the proposed calculation framework is more efficient and robust while no initial conditions are needed. We will carry out a numerical example on a truss and an experimental validation on a fixed-end beam with a single-sensor measurement system to verify the method.
Dinh, TQ, Dau, SH, Lagunas, E, Chatzinotas, S, Nguyen, DN & Hoang, DT 2025, 'Quantum Annealing for Complex Optimization in Satellite Communication Systems', IEEE Internet of Things Journal, vol. 12, no. 4, pp. 3771-3784.
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Do, KT, Hoang, DK, Luong, QN, Nguyen, HG, Do, AT, Ho‐Pham, LT & Nguyen, TV 2025, 'Reference Values of Handgrip and Lower Extremity Strength for Vietnamese Men and Women: The Vietnam Osteoporosis Study', Journal of Cachexia, Sarcopenia and Muscle, vol. 16, no. 1.
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ABSTRACTBackgroundFalls and sarcopenia are significant public health issues in Vietnam. Despite muscle strength being a critical predictor for these conditions, reference data on muscle strength within the Vietnamese population are lacking.PurposeTo establish the reference ranges for muscle strength among Vietnamese individuals.MethodsThe study involved 4096 individuals, including 1419 men and 2677 women aged 18 years and above, from the Vietnam Osteoporosis Study. Muscle strength was assessed using a Baseline hand dynamometer for handgrip strength and a Back‐Leg‐Chest dynamometer for leg strength. We calculated mean values, standard deviations, interquartile ranges, and peak muscle strength (pMS) for both handgrip and leg strength across various ages. Reference curves were created with the Generalised Additive Model for Location Scale and Shape, and polynomial regression models were employed to analyse the relationship between muscle strength and age.ResultsAdvancing age was significantly associated with lower muscle strength. Peak muscle strength typically occurred between ages 30 and 40, with earlier peaks in women, especially in leg strength. Men consistently showed higher muscle strength than women, with variations depending on the measurement site. Specifically, average handgrip strength was 36.4 kg ± 8.4 (mean ± SD) for men and 23.2 kg ± 6.0 for women (p < 0.001). Leg strength averaged 63.9 kg ± 27.2 for men and 29.5 kg ± 13.9 for women (p < 0.001). Additionally, we produced a percentile chart illustrating muscle weakness ranges based on the 25th percentile of muscle strength and the appendicular skeletal mus...
Doan, T, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2025, 'Coupled CFD-DEM modelling of clogging of granular columns by cohesive fines', Computers and Geotechnics, vol. 177, pp. 106902-106902.
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Dong, B, Yu, Y, Gao, W, Gunasekara, C, Zhao, G, Castel, A & Setunge, S 2025, 'Electro-chemo-physical analysis for long-term reinforcement corrosion within the reactive system of concrete', Cement and Concrete Composites, vol. 155, pp. 105846-105846.
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Dong, D, Li, J-S & Jonckheere, E 2025, 'Technical Committee on Quantum Computing, Systems and Control [Technical Activities]', IEEE Control Systems, vol. 45, no. 3, pp. 22-23.
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Dong, L, Wu, C, Liu, Z, Wu, P, Shao, R, Ren, Q & Liu, J 2025, 'Chloride transport anisotropy and interfacial degradation in 3D-printed ultra-high-performance concrete: Multi-scale evaluation and engineering implications', Construction and Building Materials, vol. 491, pp. 142722-142722.
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Dong, Q, Wu, X, Song, Y, Du, Y, Qi, J, Huang, L, Li, W, Huang, Y & Shi, L 2025, 'Temperature behaviors of transparent solar PV panels under various operating modes: an experimental and numerical study', Renewable Energy, vol. 250, pp. 123279-123279.
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Dong, S, Xie, W, Yang, D, Li, Y, Zhang, J, Tian, J & Lei, J 2025, 'SeaDATE: Remedy Dual-Attention Transformer With Semantic Alignment via Contrast Learning for Multimodal Object Detection', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 5, pp. 4713-4726.
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Dong, W, Ahmed, AH, Liebscher, M, Li, H, Guo, Y, Pang, B, Adresi, M, Li, W & Mechtcherine, V 2025, 'Electrical resistivity and self-sensing properties of low-cement limestone calcined clay cement (LC3) mortar', Materials & Design, vol. 252, pp. 113790-113790.
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Dong, W, Duan, Z, Peng, S, Chen, Y, Chu, D, Tai, H & Li, W 2025, 'Triboelectric nanogenerator-powering piezoresistive cement-based sensors for energy harvesting and structural health monitoring', Nano Energy, vol. 137, pp. 110823-110823.
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Dong, W, Gao, Z, Duan, Z, Zhang, Q, Xi, X, Peng, S, Tai, H, Wang, K, Chen, Y & Li, W 2025, 'Sliding-mode cement-based triboelectric nanogenerators in intelligent infrastructure for a new energy harvesting paradigm', Materials Today Energy, vol. 52, pp. 101943-101943.
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Dong, W, Peng, S, Wang, K, Huang, Y, Shi, L, Wu, F & Li, W 2025, 'Integrated triboelectric self-powering and piezoresistive self-sensing cementitious composites for intelligent civil infrastructure', Nano Energy, vol. 135, pp. 110656-110656.
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Dong, W, Tang, J, Wang, K, Huang, Y, Shah, SP & Li, W 2025, 'Cement-based batteries for renewable and sustainable energy storage toward an energy-efficient future', Energy, vol. 315, pp. 134382-134382.
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Dong, W, Zhao, C, Peng, S, Wu, C, Kim, T, Wang, K & Li, W 2025, 'Recycled carbon fibre/cement-based triboelectric nanogenerators toward energy-efficient and smart civil infrastructure', Composites Part B: Engineering, vol. 303, pp. 112603-112603.
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Dong, Z-L, Tong, C-X, Zhang, S, Cheng, YP & Sheng, D 2025, 'Strength and dilatancy of crushable soils with different gradings', Canadian Geotechnical Journal, vol. 62, pp. 1-21.
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Peak strength and dilatancy of granular materials generally decrease with increasing mean effective stress, and such a decrease will be enhanced due to the occurrence of particle breakage. This paper presents a simple empirical approach to modify Bolton’s original strength and dilatancy equation for crushable soils with different crushability. The proposed approach is based on data of a series of drained triaxial tests on carbonate soils with five different particle size distributions and three initial relative densities. It is also validated against other published experimental data on various crushable soils, including carbonate soils, limestones, coarse aggregates, and silica sands. The modified relation retains a similar form to Bolton’s equation with only one additional parameter introduced. As a result, the crushing strength-related parameter in the original relation is modified to incorporate the impacts of particle shape, gradings, and mineralogy on particle breakage. This modified parameter tends to increase as soil crushability decreases, which keeps a similar physical meaning to Bolton’s crushing strength-related parameter, and is suitable for a wider range of crushable soils with different gradings. The proposed strength and dilatancy equation for crushable soils yields to Bolton’s equation for strong soil particles where particle breakage is negligible.
Donovan, ML, Sadeghirad, H, Berrell, N, Monkman, J, Naei, VY, Tan, CW, Yunis, J, West, Z, Warkiani, ME, Ladwa, R, Hughes, BG, Da Gama Duarte, J & Kulasinghe, A 2025, 'Abstract 5252: 2D and 3D spatial characterisation of tertiary lymphoid structures in head and neck cancer patients receiving immune checkpoint blockade therapy', Cancer Research, vol. 85, no. 8_Supplement_1, pp. 5252-5252.
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Tertiary lymphoid structures (TLS) play an important role in the tumor microenvironment (TME) of Head and Neck Cancer (HNC) and have been linked to improved response to immunotherapy. Moreover, HNC patients with human papillomavirus (HPV)+ tumors have better survival compared to those with HPV- tumors, although both groups have better clinical outcomes when the tumors are TLS-rich. How TLS composition, characteristics, and signatures differ between response to treatment in relation to HPV status is still unclear. To investigate this, whole-tissue samples from 55 HNC patients were collected across two separate sites in Brisbane, Australia (The Princess Alexandra Hospital and The Royal Brisbane and Women’s Hospital) prior to immune checkpoint therapy (ICI). Using a multi-pathway panel, covering the ‘hallmarks of cancer’ and ‘TLS-associated proteins,’ we profiled 53 spatially resolved biomarkers using both a PhenoCycler-Fusion panel and 7-plex PSP panel (Akoya Biosciences). Using spatial proteomics, the TLS were assessed for their composition, activity, maturity, and distance metrics from the tumor. Using spatial transcriptomics, we profiled 96 TLS using whole transcriptome profiling (Bruker Spatial Biology). In addition, in patient samples abundant with TLS, we profiled 50 serial sections to obtain and profile the volumetric 3D data. Our study identified various degrees of TLS maturity, proteomic, and transcriptomic profiles associated with TLS localisation within the TME and proximity to the tumor. In addition, we identified TLS signatures associated with clinical endpoints such as response and resistance to ICI therapy. Taken together, this research highlights the multifaceted role of TLS in modulating the immunogenic landscape of HNC, influencing immunotherapy efficacy, and potentially serving as a biomarker for patient stratification and therapeutic strategies. Citation Format:
Du, G, Cui, C, Li, L, Li, N, Lei, G & Zhu, J 2025, 'Comprehensive Performance Analysis of High-Speed PM Motors With Layered Rotor Structures', IEEE Transactions on Industrial Electronics, vol. 72, no. 4, pp. 3460-3470.
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Du, G, Fan, Z, Fan, K, Liu, H, Zhang, J, Li, D, Yan, L, Jiu, J, Li, R, Li, X, Li, S, Jia, L, Liu, H, Ren, Y, Liu, X, Li, JJ & Wang, B 2025, 'Risk-stratified lifetime risk and incidence of hip fracture and falls in middle-aged and elderly Chinese population: The China health and retirement longitudinal study', Journal of Orthopaedic Translation, vol. 50, pp. 174-184.
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Du, L, Chen, Z, Hu, H, Liu, X & Guo, Y 2025, 'An improved uncooperative space target de-tumbling method using electromagnetic de-tumbling devices with AC excitation', Advances in Space Research, vol. 75, no. 1, pp. 1264-1276.
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Du, T-K, Lin, Y, Ji, J-C & Ding, H 2025, 'Series gravity-based track nonlinear energy Sinks: Design and experiment', Mechanical Systems and Signal Processing, vol. 229, pp. 112559-112559.
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Du, X, Chang, J, Lin, L, Peng, X, Han, H & Bai, Y 2025, 'Performance differences and mechanism of carbonation-hardened compacts produced with different C2S polymorphs: A new perspective of carbonatable binder', Cement and Concrete Composites, vol. 163, pp. 106216-106216.
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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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Duan, C, Liu, Z, Xia, J, Zhang, M, Liao, J & Cao, L 2025, 'Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier and Dynamic Gaussian Smoothing Supervision', IEEE Transactions on Intelligent Vehicles, vol. 10, no. 1, pp. 282-295.
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Duan, W, Lu, J & Xuan, J 2025, 'Inferring Latent Temporal Sparse Coordination Graph for Multiagent Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 8, pp. 14358-14370.
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Duc Manh, B, Nguyen, C-H, Thai Hoang, D, Nguyen, DN, Zeng, M & Pham, Q-V 2025, 'Privacy-Preserving Cyberattack Detection in Blockchain-Based IoT Systems Using AI and Homomorphic Encryption', IEEE Internet of Things Journal, vol. 12, no. 11, pp. 16478-16492.
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Dumortier, F, Kha, J, Karimi, M, Meyer, V & Maxit, L 2025, 'A subtractive modelling approach for predicting the radiation of a cylindrical shell in a waveguide', Acta Acustica, vol. 9, pp. 29-29.
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Modeling the sound radiated from underwater structures immersed in various environments is necessary in ocean acoustics and naval engineering. Typically, an underwater vibroacoustic system is composed of an elastic cylindrical shell that is radiated into an unbounded fluid domain. However, in contrast to deep oceans, for a shallow water environment, the influence of the sea surface and seabed can no longer by ignored. The significant fluid-structure interaction arising from the coupling at the boundary of the structure and surrounding fluid complicates the prediction of vibroacoustic behaviour. A sub-structuring technique based on the condensed transfer function (CTF) approach and reverse condensed transfer function (rCTF) approach has been proposed recently to tackle complex vibroacoustic problems by coupling/decoupling the necessary subsystems. Its potential is demonstrated in the present study through a two-dimensional case study to predict the sound radiation from an elastic structure of a cylindrical shell excited by a harmonic line force and immersed in a fluid domain of a perfect underwater acoustic waveguide, that is composed of an upper free surface and a lower rigid floor. The targeted model is obtained from a perfect underwater waveguide in which a water disk is subtracted from, and an excited shell is added in place of the water disk. The predictions of the proposed CTF-rCTF process are verified against analytical solutions for two different partitions of the global system and two types of condensation functions.
Eklund, M, Voinov, A, Hossain, MJ & Khalilpour, K 2025, 'Evaluating the interplay of community behaviour and microgrid design through optimisation modelling in local energy markets', Renewable and Sustainable Energy Reviews, vol. 210, pp. 115271-115271.
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El‐Helou, AJ, Liu, Y, Chen, C, Wang, F, Altug, H, Reece, PJ & Zhu, Y 2025, 'Optical Metasurfaces for the Next‐Generation Biosensing and Bioimaging', Laser & Photonics Reviews, vol. 19, no. 10.
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AbstractRecent advances in this understanding of light‐matter interactions, combined with innovations in the design and fabrication of large‐scale nanostructured metasurfaces, have enabled transformative approaches to biosensing and bioimaging. This review delves into the profound impact of optical metasurfaces, highlighting innovations that leverage their tunable properties and adaptability. It begins with an overview of key sensing mechanisms across various metasurface modalities, comparing their effects on metrics such as sensitivity and limits of detection. The discussion then shifts to recent advancements in refractometric biosensing, focusing on novel transduction methods that exploit the intensity, phase, and colorimetric responses of these metasurfaces. The latest developments in surface‐enhanced spectroscopic sensing are also examined, exploring how metasurfaces contribute to enhanced molecular fingerprinting capabilities in these applications. Additionally, the role of optical metasurfaces in advancing bioimaging are assessed, emphasizing label‐free elastic scattering, spectroscopic/chemical contrast imaging, and metasurface‐assisted super‐resolution microscopy. Finally, the review addresses current challenges and future directions for optical metasurfaces in biosensing and imaging, including material limitations, difficulties in large‐scale fabrication, and the complexity of data analysis and readout methods. It also discusses the integration of novel detector hardware to improve spatiotemporal resolution of sensing and imaging techniques.
Ellis, JT & Kennedy, PJ 2025, 'Multi-criteria decision making and its application to in silico discovery of vaccine candidates for Toxoplasma gondii', Vaccine, vol. 58, pp. 127242-127242.
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Elmakki, T, Zavahir, S, Shon, HK, Gago, GH, Park, H & Han, DS 2025, 'Capacitive lithium capture system using a mixed LiMn2O4 and LiAlO2 material', Desalination, vol. 593, pp. 118195-118195.
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Elsamadony, M, Pileggi, SF, Liu, J & Fujii, M 2025, 'Crafting a resilient and sustainable future amid major crises', Journal of Cleaner Production, vol. 511, pp. 145583-145583.
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Ene, A, Lee, T, Micek, P & Sachdeva, S 2025, 'Introduction: ACM-SIAM Symposium on Discrete Algorithms (SODA) 2021 Special Issue', ACM Transactions on Algorithms, vol. 21, no. 3, pp. 1-2.
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Errey, N, Zowghi, D, Leong, TW, Wu, Y, Yuan, X, Huang, W & Liang, CJ 2025, 'Heuristics for evaluating narrative visualization: a validation study', Journal of Visualization, vol. 28, no. 3, pp. 645-659.
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Abstract Narrative visualization integrates data visualization and narrative techniques to convey a compelling story. Narrative visualization is notoriously difficult to evaluate. One solution is heuristic evaluation, using a domain-specific set of heuristics. This paper validates a set of heuristics proposed specifically for evaluating narrative visualization. We conducted studies with experienced narrative visualization practitioners in both summative and formative settings. We found that the set of heuristics showed promise in a summative setting, where similar responses evidenced that the set of heuristics could provide reliable evaluation metrics. Furthermore, in a formative setting, implementing the set of heuristics was reported to be useful in the design process; however, due to their limited focus, we recommend that it be implemented in conjunction with other evaluation guidelines. Graphical abstract
Esfandiari, M, Lv, X, Chamani, S & Yang, Y 2025, 'Graphene metasurfaces: Advances in lens applications, design strategies, and fabrication techniques', Materials Today Electronics, vol. 11, pp. 100140-100140.
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Esfandiari, M, Zhu, J & Yang, Y 2025, 'Additively manufactured metasurfaces and metamaterials: Designs, fabrications, and applications from microwave to photonics', APL Photonics, vol. 10, no. 4.
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Additive manufacturing (AM) or 3D printing wholly reinvents the making of metasurfaces/metamaterials and opens new opportunities from microwave to photonics. Conventionally, metasurfaces/metamaterials (engineered to control electromagnetic waves strongly) have been challenging and expensive to fabricate using traditional manufacturing techniques. However, AM provides an innovative turn in transforming the design and production of such complicated surfaces. It renders flexibility, efficiency, and economic viability to the fabrication process, enabling rapid prototyping and customization. This will, therefore, enable production times to be shorter, reduce material waste, and allow the creation of more complicated and much smaller metasurface structures than hitherto unattainable. Thus, AM drives strong advancements in microwave and photonic, enabling new applications and improved performance in telecommunications, wearable sensors, and imaging systems.
Faddoul, Y & Sirivivatnanon, V 2025, 'Sustainable concrete structures by optimising structural and concrete mix design', Australian Journal of Structural Engineering, vol. 26, no. 2, pp. 86-94.
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Fairley, N, Fatahi, B & Hokmabadi, AS 2025, 'Impacts of Transition Piece Designs on the Resilience of Large Offshore Wind Turbines Subject to Combined Earthquake, Wind and Wave Loads and Soil‐Structure Interaction', Earthquake Engineering & Structural Dynamics, vol. 54, no. 3, pp. 773-798.
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ABSTRACTThe urgent global drive to mitigate greenhouse gas emissions has significantly boosted renewable energy production, notably expanding offshore wind energy across the globe. With the technological evolution enabling higher‐capacity turbines on larger foundations, these installations are increasingly situated in earthquake‐prone areas, underscoring the critical need to ensure their seismic resilience as they become a pivotal component of the global energy infrastructure. This study scrutinises the dynamic behaviour of a 15 MW offshore wind turbine (OWT) under concurrent earthquake, wind and wave loads, focusing on the performance of the ultra‐high‐strength cementitious grout that bonds the monopile to the transition piece. Employing LS DYNA for numerical simulations, we explored the seismic responses of four OWT designs with diverse transition piece cone angles, incorporating nonlinear soil springs to model soil‐structure interactions (SSIs) and conducting a site response analysis (SRA) to account for local site effects on ground motion amplification. Our findings reveal that transition pieces with larger cone angles exhibit substantially enhanced stress distribution and resistance to grout damage, evidenced by decreased ovalisation in the coned sections of the transition piece and monopile, and improved bending flexibility. The observed disparities in damage across different cone angles highlight shortcomings in current design guidelines pertaining to the prediction of grout stresses in conical transition piece designs, with the current code‐specified calculations predicting higher stresses for transition piece designs with larger cone angles. This study also highlights the code's limitations when accounting for grout damage induced by stress concentrations in the grouted connections under seismic dynamic loading conditions. The results of the study demonstrate the need for refinement of these guidelines to improv...
Fan, W, Xiao, F, Pan, Y, Chen, X, Han, L & Yu, S 2025, 'Latency-Aware Joint Task Offloading and Energy Control for Cooperative Mobile Edge Computing', IEEE Transactions on Services Computing, vol. 18, no. 3, pp. 1515-1528.
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Fan, W, Xiao, F, Zhang, P, Cai, H, Han, L & Yu, S 2025, 'Topology-Awareness Fault-Tolerant Migration for Node Cascading Failures in Data Center Networks', IEEE Transactions on Networking, pp. 1-16.
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Fang, G, Tseng, P-H, Liao, J, Zhu, S, Zhou, T, Liu, H, Zhu, H, Jin, D, Yang, L & Chen, Y-C 2025, 'Laser-emitting aqueous bioreactors for ultrasensitive bioactivity analysis', Proceedings of the National Academy of Sciences, vol. 122, no. 34, p. e2425829122.
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Water droplets, acting as natural bioreactors and optical whispering-gallery-mode (WGM) resonators, hold the potential for laser-assisted analysis. However, water/aqueous droplet lasers can only survive in air with a limited lifespan (<100 s) due to rapid evaporation, restricting their applications in bioreactions. To address this challenge, we introduce laser-emitting aqueous bioreactors (LEABs) in fluorocarbon oils. These LEABs enable stable laser emission and extend a droplet lifespan over 1,000-fold. LEABs enable the encapsulation of bioactive materials for long-term analysis with unique lasing characteristic fingerprints. The reactions within LEAB can interact with the most resonating light, enhancing detection sensitivity by over 100-fold compared to conventional WGM sensors. By integrating LEABs with microfluidic droplet technology, we demonstrated their application in monitoring enzyme activity and cellular metabolism at single-cell and multicellular levels. Furthermore, we showed the laser threshold-gated screening of single yeast. This platform can bridge the gap between laser technology and biochemical applications, broadening the scope of laser-based analysis.
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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Abstract The 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, W, Chen, Y, Zhang, A, Nghiem, LD, Wei, Y, Johir, MAH, Ding, G, Xu, T & Li, J 2025, 'Reactive oxygen species (ROS)-mediated conversion of organic matter into humus: A meta-analysis on mechanisms and environmental implications', Journal of Environmental Sciences.
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Fang, Z, Lu, J & Zhang, G 2025, 'Out-of-distribution detection with non-semantic exploration', Information Sciences, vol. 705, pp. 121989-121989.
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Farah, N, Lei, G, Zhu, J & Guo, Y 2025, 'A Robustness Evaluation Method for the Robust Control of Electrical Drive Systems Based on Six-Sigma Methodology', CES Transactions on Electrical Machines and Systems, vol. 9, no. 2, pp. 131-145.
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Farah, N, Lei, G, Zhu, J & Guo, Y 2025, 'Robust Model-Free Reinforcement Learning Based Current Control of PMSM Drives', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 1061-1076.
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Farooq, H & Nimbalkar, S 2025, 'Viscoelastoplastic model for an integrated tunnel-track system in weak rock formations', Computers and Geotechnics, vol. 186, pp. 107433-107433.
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Farooq, U, Riaz, HH, Munir, A, Tariq, A, Chan, TC, Zhao, M & Islam, MS 2025, 'Heliox: An advanced method for targeted drug delivery in respiratory airways', Journal of the Taiwan Institute of Chemical Engineers, vol. 176, pp. 106323-106323.
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Fattah, IMR, Alom, J, Zaman, JU, Ban, S, Veza, I, Kalam, MA, Hessel, V & Ahmed, MB 2025, 'Hydrogel-derived materials for microbial fuel cell', Journal of Power Sources, vol. 625, pp. 235688-235688.
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Fawns, T, Bearman, M, Dawson, P, Nieminen, JH, Ashford-Rowe, K, Willey, K, Jensen, LX, Damşa, C & Press, N 2025, 'Authentic assessment: from panacea to criticality', Assessment & Evaluation in Higher Education, vol. 50, no. 3, pp. 396-408.
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Feng, K, Ansari, AJ, Zhang, N, Peng, Y & Song, X 2025, 'Biochar addition to mitigate oil inhibition in anaerobic digestion of food wastewater: Microbial insights from biochemical methane potential tests', Environmental Technology & Innovation, vol. 37, pp. 104000-104000.
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Feng, S, Guo, W, Ding, A, Parsa, SM, Pan, J, Cheng, D, Tung, TV & Ngo, HH 2025, 'Enzyme sources in wastewater treatment: Their influence on enzymatic bioremediation and large-scale applications', Chemical Engineering Journal, vol. 510, pp. 161891-161891.
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Feng, S, Guo, W, Zhang, S, Luo, G, Nguyen, HT, Nguyen, NC, Cheng, D, Ye, Y & Ngo, HH 2025, 'Optimization of hydraulic retention time in continuous orange peel crude enzyme - mediated dark fermentation for sustainable biohydrogen production from synthetic swine wastewater', Journal of Water Process Engineering, vol. 73, pp. 107714-107714.
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Feng, Z, Huang, J, Zhang, W, Wen, S, Liu, Y & Huang, T 2025, 'YOLOv8-G2F: A portable gesture recognition optimization algorithm', Neural Networks, vol. 188, pp. 107469-107469.
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Flores-Sosa, M, Merigó, JM & Sanchez-Valenzuela, K 2025, '30 years of the Journal of Heuristics: a bibliometric analysis', Journal of Heuristics, vol. 31, no. 1.
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Fu, N, Cheng, G, Teng, Y, Dai, G, Yu, S & Chen, Z 2025, 'Intelligent Root Cause Localization in MicroService Systems: A Survey and New Perspectives', ACM Computing Surveys, vol. 57, no. 12, pp. 1-37.
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Root cause localization is the process of monitoring system behavior and analyzing fault patterns from behavioral data. It is applicable in software development, network operations, and cloud computing. However, with the advent of microservice architectures and cloud-native technologies, root cause localization becomes an arduous task. Frequent updates in systems result in large-scale data and complex dependencies. Traditional analysis methods relying on manual experience and predefined rules have limited data processing and cannot learn new fault patterns from historical knowledge. Artificial Intelligence techniques have emerged as powerful tools to leverage historical knowledge and are now widely used in root cause localization. In this article, we provide a structured overview and a qualitative analysis of root cause localization in microservice systems. To begin with, we review the literature in this area and abstract a workflow of root cause localization, including multimodal data collection, intelligent root cause analysis, and performance evaluation. In particular, we highlight the role played by Artificial Intelligence techniques. Finally, we discuss some open challenges and research directions and propose an end-to-end framework from a new perspective, providing insights for future works.
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, vol. 72, no. 8, pp. 8397-8407.
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Gajjar, T, Yang, R, Ye, L & Zhang, YX 2025, 'Effects of key process parameters on tensile properties and interlayer bonding behavior of 3D printed PLA using fused filament fabrication', Progress in Additive Manufacturing, vol. 10, no. 2, pp. 1261-1280.
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Abstract Fused Filament Fabrication (FFF), also known as Fused Deposition Modelling (FDM), is one of the innovative 3D printing technologies for fabricating complex components and products. Mechanical properties of 3D-printed components mostly depend on intricate process parameters of 3D printing. This study experimentally investigates the effects of four key process parameters, including layer thickness, raster angle, feed rate, and nozzle temperature, on the tensile properties and interfacial bonding behaviours of FFF printed Polylactic Acid (PLA), and their failure mechanisms. The effect of the key parameters on surface roughness is also evaluated, which is critical for enhancing manufacturing and material performance, expecting to provide a potential guide for optimisation of the FFF printing process for improving product quality. The experimental results demonstrate that tensile strength improves up to 10 and 7% with increasing nozzle temperature (200 °C to 220 °C) and low feed rate (60 mm/sec to 40 mm/sec) during the 3D printing process. The tensile strength increases up to 12% with decreasing layer thickness (0.4 mm to 0.2 mm) and 40% with decreasing raster angle (90° to 0°). The experimental findings on surface roughness indicate that FFF-printed PLA samples were significantly influenced by the layer thickness and raster angle, and an improvement in surface roughness is observed with the increase of nozzle temperature and reduction in feed rate. Microstructural SEM analysis was conducted to investigate the ruptured surfaces of the FFF printed PLA samples, focusing on the interlayer bonding quality and morphological characteristics including the effect of void formation, poor adhesion, and insufficient fusion between adjacent surface contact area with the effect of printing parameters. The feed rate and nozzle temperature were found to substantially influence the interlayer bonding between two adjacent s...
Ganko, R, Madhavan, A, Hamouda, W, Muthu, S, Jain, A, Yoon, ST, El-Rozz, H, Cyril, D, Pabbruwe, M, Tipper, JL & Tavakoli, J 2025, 'Spinal implant wear particles: Generation, characterization, biological impacts, and future considerations', iScience, vol. 28, no. 4, pp. 112193-112193.
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Gao, H, Hussain, W, Durán Barroso, RJ & Obaidat, MS 2025, 'Guest Editorial Special Section on “User Behavior Modeling for Trustworthy Recommendation over Consumer Electronics Products”', IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 7211-7212.
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Gao, L & Yu, N 2025, 'Optimal Tomography of Quantum Markov Chains via Continuity of Petz Recovery States', IEEE Transactions on Information Theory, pp. 1-1.
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Gao, S, Wang, C, Gao, C, Luo, W, Han, P, Liao, Q & Xu, G 2025, 'Improving long‐tail classification via decoupling and regularisation', CAAI Transactions on Intelligence Technology, vol. 10, no. 1, pp. 62-71.
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AbstractReal‐world data always exhibit an imbalanced and long‐tailed distribution, which leads to poor performance for neural network‐based classification. Existing methods mainly tackle this problem by reweighting the loss function or rebalancing the classifier. However, one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle distribution. In this paper, the authors shed light on the significance of the angle distribution in achieving a balanced feature space, which is essential for improving model performance under long‐tailed distributions. Nevertheless, it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature norm. To tackle these challenges, the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components: classifier norm (i.e. the magnitude of the classifier vector), feature norm (i.e. the magnitude of the feature vector), and cosine similarity between the classifier vector and feature vector. In this way, the authors analyse the change of each component in the training process and reveal three critical problems that should be solved, that is, the imbalanced angle distribution, the lack of feature discrimination, and the low feature norm. Drawing from this analysis, the authors propose a novel loss function that incorporates hyperspherical uniformity, additive angular margin, and feature norm regularisation. Each component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature space. The authors conduct extensive experiments on three popular benchmark datasets including CIFAR‐10/100‐LT, ImageNet‐LT, and iNaturalist 2018. The experimental results demonstrate that the authors’ loss function outperforms several previous state‐of‐th...
Gao, W, Karsa, M, Xiao, L, Spurling, D, Karsa, A, Ronca, E, Bongers, A, Guo, X, Mayoh, C, Azfar, M, Verhelst, SHL, Tanaka, K, Cheung, LC, Kotecha, RS, Lock, RB, Burns, MR, Vangheluwe, P, Norris, MD, Haber, M & Somers, K 2025, 'Polyamine depletion limits progression of acute leukaemia', International Journal of Cancer, vol. 156, no. 12, pp. 2360-2376.
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AbstractCancer cells are addicted to polyamines, polycations essential for cellular function. While dual targeting of cellular polyamine biosynthesis and polyamine uptake is under clinical investigation in solid cancers, preclinical and clinical studies into its potential in haematological malignancies are lacking. Here we investigated the preclinical efficacy of polyamine depletion in acute leukaemia. The polyamine biosynthesis inhibitor difluoromethylornithine (DFMO) inhibited growth of a molecularly diverse panel of acute leukaemia cell lines, while non‐malignant cells were unaffected. Responsiveness to DFMO was linked to decreased levels of its molecular target, the rate‐limiting polyamine biosynthesis enzyme ODC1, and of the polyamine transporters ATP13A2 and ATP13A3. DFMO increased polyamine uptake and upregulated expression of polyamine transporters in acute leukaemia cells, a compensatory effect abolished by treatment with the polyamine transport inhibitor AMXT 1501. This drug, currently in a phase 1 clinical trial in solid tumours in combination with DFMO, potentiated the inhibitory effects of DFMO, and their combination synergistically inhibited the growth of acute leukaemia cell lines by inducing apoptosis. DFMO and AMXT 1501 limited disease progression in highly aggressive xenograft models of infant KMT2A‐rearranged leukaemia, even when treatment was initiated at high disease burden. Increased expression of c‐MYC was associated with enhanced sensitivity to the combination of DFMO and AMXT 1501, suggesting this oncoprotein as a potential predictive marker of response to the drug combination. In conclusion, targeting polyamine biosynthesis and polyamine uptake limits disease progression in models of acute leukaemia, supporting further preclinical and clinical investigation into this approach for acute leukaemia.
Gao, X, Zhu, Q, Liao, X, Wu, M, Han, L & Yang, J 2025, 'Virtual quiet zone method for sound zone reproduction in coupled rooms', Applied Acoustics, vol. 228, pp. 110341-110341.
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Gao, Y, Chen, L, Lai, J, Wang, T, Wu, X & Yu, S 2025, 'IoT-Dedup: Device Relationship-Based IoT Data Deduplication Scheme', IEEE Transactions on Parallel and Distributed Systems, vol. 36, no. 5, pp. 847-860.
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Ge, M, Pineda, JA, Burton, GJ, Sheng, D & Li, N 2025, 'Stress-path-dependent behaviour of compacted loess', Canadian Geotechnical Journal, vol. 62, pp. 1-19.
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This paper describes an experimental study aimed at evaluating the effects of mechanical and hydraulic paths on the compressibility and water retention behaviour of compacted loess from Xi'an, China. Suction-controlled oedometer tests, performed on statically compacted samples, are combined with mercury intrusion porosimetry (MIP) tests to evaluate the volumetric behaviour as well as the evolution of soil fabric for the compacted material. Oedometer test results are analyzed in the degree of saturation versus suction plane, using the water retention curve (WRC) for as-compacted material as reference. Negligible volume change, and hence no movement of the as-compacted WRC, is observed upon drying paths under low stresses. Wetting paths cause plastic deformations and hence the movement of the water retention curve. Soil densification shifts the WRC rightwards (in the degree of saturation versus suction plane), enlarging the air entry/air occlusion values and promoting steeper wetting and drying branches. MIP tests show that specimens subjected to drying and loading paths retain their as-compacted bi-modal pore size distribution (PSD). However, re-arrangement in soil fabric towards predominant mono-modal PSDs is observed in specimens wetted and loaded. Soil suction increases the compressibility and the yield stress of compacted loess. These experimental features are properly captured using an elasto-plastic constitutive model for nonactive soils that accounts for the evolution of the loading-collapse (LC) yield locus with plastic deformation.
Ge, M, Zhu, C, Sheng, D, Pineda, J & Li, N 2025, 'Study on the gas permeability of unsaturated compacted loess and its underlying micro-mechanism', Yanshilixue Yu Gongcheng Xuebao Chinese Journal of Rock Mechanics and Engineering, vol. 44, no. 1, pp. 221-235.
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Aiming to investigate the gas permeability of unsaturated soil and its underlying micro-mechanism，the gas permeability coefficients of compacted loess under various initial states，along wetting/drying，and constant stress ratio compression paths were examined in this study by utilizing a modified gas permeation device. The Mercury Intrusion Porosimetry(MIP) technique was employed to further examine the microstructure changes of compacted losses，thereby analyzing the micro-mechanism of air permeation. The test results indicate that the gas permeability coefficient(keff) of compacted loess varies within the range of 10－12 to10－15 m2 in response to the increasing compaction saturation degree(Sr0). At low dry densities，keff exhibits an initial rise followed by a decline as Sr0 increases，whereas at high dry densities，a nonlinear decrement is observed. Wetting significantly reduces keff of compacted loess by up to three orders of magnitude，with a rapid decrease after the wetting saturation degree reaches 0.65. Conversely，drying improves gas permeability，but its impact is much less significant than that of wetting. Under constant stress ratio compression，keff decreases exponentially with increasing stress，and the decrease is more pronounced at lower stress ratios. The MIP test results reveal that the macro porosity first increases and then decreases with the increase of Sr0. Wetting has a minor effect on the pore size distribution curve(PSD)，while drying can increase macro porosity. Under similar state，the as-compacted soil exhibits more macropore structures compared to the after-wetting soil，while less macropore structures compared to the after-drying soil. The constant stress ratio compression results in a reduction of macropores，with a greater reduction at lower stress ratio. According to the variation of keff，the gas permeability of unsat...
Gharoun, H, Yazdanjue, N, Khorshidi, MS, Chen, F & Gandomi, AH 2025, 'Leveraging Neural Networks and Calibration Measures for Confident Feature Selection', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 3, pp. 2179-2193.
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Ghimire, S, Deo, RC, Hopf, K, Liu, H, Casillas-Pérez, D, Helwig, A, Prasad, SS, Pérez-Aracil, J, Barua, PD & Salcedo-Sanz, S 2025, 'Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach', Energy and AI, vol. 20, pp. 100492-100492.
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Ghosh, S, Bhattacharya, S, Roy, P, Pal, U & Blumenstein, M 2025, 'MMC: Multi-modal colorization of images using textual description', Signal, Image and Video Processing, vol. 19, no. 2.
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Gill, AQ 2025, 'Trimodal Thinking for Architecting Human-Centric AI Systems: Fast, Slow, and Control', IEEE Transactions on Technology and Society, pp. 1-11.
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Golnary, F, Kalhori, H, Liu, W & Li, B 2025, 'Vehicle-based autonomous modal analysis for enhanced bridge health monitoring', International Journal of Mechanical Sciences, vol. 287, pp. 109910-109910.
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Gomes, SDC, Nguyen, QD, Li, W & Castel, A 2025, 'Shrinkage and carbonation of alkali-activated calcined clay-ground granulated blast furnace slag (GGBFS) concrete', Cement and Concrete Research, vol. 194, pp. 107899-107899.
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Gomes, SDC, Pang, Y, Nguyen, QD, Li, W, Vessalas, K & Castel, A 2025, 'The effect of calcined clay reactivity on the mechanical properties and chloride diffusion resistance of alkali-activated calcined clay-GGBFS concrete', Journal of Building Engineering, vol. 102, pp. 111996-111996.
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Gong, Z, Fang, B & Wei, D 2025, 'A hybrid springback compensation method for geometry complexity in stamping', The International Journal of Advanced Manufacturing Technology, vol. 136, no. 11-12, pp. 4815-4828.
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Abstract Springback compensation is a crucial approach to maintaining the accuracy of stamping parts with complex features. It has been challenging to determine the position of a characteristic point at the geometrical features during springback compensation. The currently available numerical approaches are not always sufficiently accurate and reliable, particularly when high-strength steels are increasingly used for lightweight structures. An enhanced hybrid method named springback path–displacement adjustment (SP-DA) method has been developed based on the well-known conventional displacement adjustment (DA) method to resolve the issue. A finite element method (FEM) model of stamping owning geometry complexity was established, and ST14F, BH300 and DP500, representing low, medium and high-strength steels, respectively, were selected for the study. Springback analyses were conducted, and the springback paths were acquired in the FEM simulation, based on which the spatial position of a node on the mesh of the compensation model was obtained using the SP-DA method. Its effectiveness was first verified numerically, and then, experiments were conducted to validate that the new SP-DA method could significantly increase the accuracy of springback compensation. Stamping of high-strength steels can benefit most from the proposed SP-DA method.
Gooch, LJ, Masia, MJ, Stewart, MG & Hossain, MA 2025, 'Experimental Testing of Unreinforced Masonry Shear Walls and Comparison with Nominal Capacity Predictions', Journal of Structural Engineering, vol. 151, no. 3.
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Gooch, LJ, Masia, MJ, Stewart, MG & Spadari, M 2025, 'Experimental characterisation of the friction coefficient of mortar bed joints in clay-brick masonry', Construction and Building Materials, vol. 489, pp. 142348-142348.
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Gooch, LJ, Stewart, MG & Masia, MJ 2025, 'Accuracy of stochastic finite element analyses for the safety assessment of unreinforced masonry shear walls', Civil Engineering and Environmental Systems, vol. 42, no. 2, pp. 95-124.
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Goodarzimehr, V, Fazli, M, Fanaie, N & Gandomi, AH 2025, 'Enhanced Special Relativity Search Algorithm-Based Lorentz Force for Structural Optimization', International Journal of Computational Methods, vol. 22, no. 04.
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Optimization of real-world problems is an important challenge that researchers face due to the discrete nature of the search space, multiple local optimum, the large dimension of the problem, and computation costs. The Special Relativity Search (SRS) algorithm is one of the newest metaheuristic methods that has recently been developed. In this work, the Enhanced SRS (ESRS) algorithm has been developed based on the Lorentz Force coefficient to solve this class of problems. The SRS is a single-objective algorithm based on swarm intelligence inspired by the physics of special relativity. The cause of movement between particles in magnetic space is the Lorentz Force. Due to the type of particle charge, this force repels or attracts particles. SRS performance is sensitive to the Lorentz Force. Developing an empirical equation can enhance the performance of the algorithm. An empirical equation for simulating the Lorentz Force Coefficient is proposed as LC. LC decreases proportionally to the number of different iterations. This equation has a constant coefficient to control the movement step. This constant is more accurate for small values than for large values. Several structural problems with discrete and continuous variables have been investigated to evaluate the performance of the proposed algorithm. The objective function is the weight of structural elements that are under loading and must be reduced to the minimum value while observing the constraints of the problem. ESRS results have been compared with the original SRS and several state-of-the-art algorithms. The results show that ESRS has obtained the best optimal weight with the least computational effort and with high accuracy.
Goss, D, Vasilescu, SA, Vasilescu, P, Susetio, D, Benny, D, Cooke, S, Hua, VK, Sacks, GP, Kim, SH, Warkiani, ME & Gardner, DK 2025, 'O-226 Clinical evaluation of an artificial intelligence (AI) model for rare sperm detection in testis biopsies and azoospermic semen for ICSI', Human Reproduction, vol. 40, no. Supplement_1.
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Abstract Study question Does an artificial intelligence (AI)-based image detection model improve the speed and accuracy of identifying rare sperm in testis biopsies and azoospermic semen for ICSI? Summary answer AI sperm detection aids embryologists in finding more sperm in a shorter period. What is known already Non-obstructive azoospermia (NOA) is a form of severe male-factor infertility, affecting nearly 5% of infertile couples seeking treatment. Isolating sperm from macerated testicular tissue for intracytoplasmic sperm injection (ICSI) has changed marginally in the last two decades and requires embryologists to manually search through a background of obstructing collateral cells including red blood cells (RBC’s), white blood cells (WBC’s), leydig, sertoli and epithelial cells, causing fatigue and reducing sample coverage. Image analysis using AI presents itself as a candidate to dramatically reduce processing times in both surgical and non-surgical sperm cases. Study design, size, duration This multi-site, pilot clinical study consists of side-by-side testing of sperm searching with and without the aid of AI for rare sperm detection from a live-camera feed beside an inverted ICSI microscope over 12 months. The AI model was trained on a wide spectrum of sample types, and testing was performed to observe the effect of a reduction in search time on the clinical outcomes of severe male-factor cases. ...
Grigorev, A, Saleh, K, Ou, Y & Mihăiţă, A-S 2025, 'Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification', International Journal of Intelligent Transportation Systems Research, vol. 23, no. 1, pp. 259-280.
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Grigorev, A, Shafiei, S, Grzybowska, H & Mihăiţă, A-S 2025, 'Predicting the Duration of Traffic Incidents for Sydney Greater Metropolitan Area using Machine Learning Methods', International Journal of Intelligent Transportation Systems Research, vol. 23, no. 1, pp. 104-125.
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Gu, Z, Jia, W, Piccardi, M & Yu, P 2025, 'Empowering large language models for automated clinical assessment with generation-augmented retrieval and hierarchical chain-of-thought', Artificial Intelligence in Medicine, vol. 162, pp. 103078-103078.
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Guan, L, Abbasi, A, Ryan, MJ & Merigó, JM 2025, 'A dynamic risk interdependency network-based model for project risk assessment and treatment throughout a project life cycle', Computers & Industrial Engineering, vol. 201, pp. 110921-110921.
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Guan, L, Laporte, G, Merigó, JM, Nickel, S, Rahimi, I & Saldanha-da-Gama, F 2025, '50 years of Computers & Operations Research: A bibliometric analysis', Computers & Operations Research, vol. 175, pp. 106910-106910.
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Guan, L, Merigó, JM & Beydoun, G 2025, '40 years of Decision Support Systems: A bibliometric analysis', Decision Support Systems, vol. 194, pp. 114469-114469.
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Guan, W, Teng, J, Shan, F, Liu, J & Wu, H 2025, 'A novel transformer-based approach for predicting frost heave of high-speed railway in cold regions', Journal of Rock Mechanics and Geotechnical Engineering.
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Guillen-Pujadas, M, Alaminos, D, Vizuete-Luciano, E & Merigo, JM 2025, 'Half a Century of The Journal of Portfolio Management: A Bibliometric Overview', JOURNAL OF PORTFOLIO MANAGEMENT, vol. 51, no. 6, pp. 175-215.
Gul, OM, Hamidoglu, A, Khan, MK & Yu, S 2025, 'Novel Game Theoretical Approach in Blockchain Aided Communication at Internet of Drones', IEEE Consumer Electronics Magazine, pp. 1-11.
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Guo, C, Xu, Y, Ni, C, Pan, X, Tijing, LD, Shon, HK, Deng, N & Huang, X 2025, 'Tailoring pore size to enhance dissolution of layered double oxides for efficient nitrogen and phosphorus recovery via crystallization of struvite from wastewater', Journal of Colloid and Interface Science, vol. 692, pp. 137546-137546.
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Guo, K, Guo, Y, Fang, S, Wu, W & Gao, X 2025, 'Design Optimization and Performance Evaluation of a Novel Linear-Rotary Motor with Moving Coils', IEEE Transactions on Energy Conversion, pp. 1-16.
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Guo, L, Song, C, Guo, F, Han, X, Chang, X & Zhu, L 2025, 'Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-domain Recommendation', ACM Transactions on Information Systems, vol. 43, no. 5, pp. 1-27.
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Non-overlapping Cross-domain Sequential Recommendation (NCSR) is the task that focuses on domain knowledge transfer without overlapping entities. Compared with traditional Cross-domain Sequential Recommendation (CSR), NCSR poses several challenges: (1) NCSR methods often rely on explicit item IDs, overlooking semantic information among entities. (2) Existing CSR mainly relies on domain alignment for knowledge transfer, risking semantic loss during alignment. (3) Most previous studies do not consider the many-to-one characteristic, which is challenging because of the utilization of multiple source domains. Given the above challenges, we introduce the prompt learning technique for Many-to-one Non-overlapping Cross-domain Sequential Recommendation (MNCSR) and propose a Text-enhanced Co-attention Prompt Learning Paradigm (TCPLP). Specifically, we capture semantic meanings by representing items through text rather than IDs, leveraging natural language universality to facilitate cross-domain knowledge transfer. Unlike prior works that need to conduct domain alignment, we directly learn transferable domain information, where two types of prompts, i.e., domain-shared and domain-specific prompts, are devised, with a co-attention-based network for prompt encoding. Then, we develop a two-stage learning strategy, i.e., pre-train and prompt-tuning paradigm, for domain knowledge pre-learning and transferring, respectively. We conduct extensive experiments on three datasets and the experimental results demonstrate the superiority of our TCPLP. Our source codes have been publicly released ( https://github.com/songchenlong/TCPLP ).
Guo, W, Che, H, Leung, M-F, Jin, L & Wen, S 2025, 'Robust Mixed-order Graph Learning for incomplete multi-view clustering', Information Fusion, vol. 115, pp. 102776-102776.
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Guo, W, Liu, D, Xie, W, Li, Y, Ning, X, Meng, Z, Zeng, S, Lei, J, Fang, Z & Wang, Y 2025, 'ShiftQuant: Towards Accurate and Efficient Sub-8-Bit Integer Training', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Guo, X & Yang, Y 2025, 'Low-Profile Full-Space Transmission-Reflection- Integrated Multiple Focused and OAM Beams Metasurface for Indoor Communication', IEEE Transactions on Microwave Theory and Techniques, vol. 73, no. 6, pp. 3644-3654.
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Guo, X, Guo, Y, Luo, Y & Yang, Y 2025, 'Full-Space Transmission–Reflection-Integrated Metasurface for Multibeam Generation on Orthogonal Planes', IEEE Transactions on Microwave Theory and Techniques, pp. 1-11.
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Guo, X-Y, Fang, S-E, Zhu, X & Li, J 2025, 'A semi-Markov process based digital twin for safety evaluation of cable-stayed bridges with cable corrosion', Advanced Engineering Informatics, vol. 65, pp. 103270-103270.
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The cables of a cable-stayed bridge are susceptible to structural degradation due to environmental corrosion and fatigue, which directly affects the safety and operational performance of the bridge. As the process of the degradation in practice is very slow, it is difficult to be monitored during the bridge service life. Hence, this study aims to develop a novel semi-Markov process based digital twin (DT) framework for safety evaluation of cable-stayed bridges considering cable corrosion. The framework encompasses a physical twin layer, a DT layer and the information interaction medium. The physical twin layer mainly comprises the bridge physical entity and its associated monitoring system that provides a variety of perceptual data for DT modeling. In the DT layer, the DT model acts as a virtual counterpart of the physical bridge for mirroring and forecasting the bridge's mechanical behaviors. The information interaction medium plays a crucial role in the bidirectional information communication between the physical and digital twin layers. Two types of information interaction media have been utilized including a cable force influence matrix and a semi-Markov process. The former enables updating the DT model to precisely match the data measured from the physical bridge. Meanwhile, the semi-Markov process depicts the probability of the bridge's condition considering the cable corrosion during the different service periods. The proposed procedure can predict the bridge state and evaluate the safety by comparing the predicted state with the monitored values. The proposed framework has been successfully validated on a real-world cable-stayed bridge. The results showed the proposed DT framework was reliable and effective for evaluating the bridge condition.
Guo, Y, Qu, F, Dong, W, Wang, Y, Yoo, D-Y, Maruyama, I & Li, W 2025, 'Self-sensing performance of nanoengineered one-part alkali-activated materials-based sensors after exposure to elevated temperature', Cement and Concrete Composites, vol. 164, pp. 106257-106257.
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Guo, Y, Qu, F, Tiwari, R, Yoo, D-Y, Wang, K, Wang, Y & Li, W 2025, 'Development of self-sensing asphalt cementitious composites using conductive carbon fibre and recycled copper tailing', Construction and Building Materials, vol. 474, pp. 140965-140965.
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Guo, Y, Xu, X, Jin, P, Zhou, T, Lei, G & Zhu, J 2025, 'Design and Analysis of a Novel Simultaneous Wireless Power and Data Transfer System With a High-Frequency Tank-Type Ferrite Core Loosely-Coupled Transformer', IEEE Transactions on Energy Conversion, vol. 40, no. 3, pp. 2280-2292.
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Habiba, U, Ahmed Siddique, T, Fattah, IMR, Mohammed, S, Attenborough, E, Lai, Q & Wang, Z 2025, 'Porous alumina-supported lithium aluminum titanium phosphate membrane for lithium extraction using the electrodialysis process', Separation and Purification Technology, vol. 354, pp. 128657-128657.
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Haeri, H, Fu, J, Sarfarazi, V, Abharian, S, rasekh, H, Rezaei, M & Khandelwal, M 2025, 'Surgical face masks as reinforcement to improve the tensile mode fracture toughness of reinforced concrete under three-point bending tests', Engineering Fracture Mechanics, vol. 314, pp. 110741-110741.
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Hamdi, FM, Altaee, A, Aedan, Y, Zhou, J, AL-Ejji, M, Hawari, AH, Zaidi, SJ, Mohsen, M & Kardani, R 2025, 'Decontamination of Heavy Metals from Soil by Electrokinetic Combined with Reactive Filter Media from Industrial Wastes', Water, Air, & Soil Pollution, vol. 236, no. 9.
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Abstract The electrokinetic (EK) is an in-situ method for soil remediation, aiming to reduce extensive excavation and mitigate risks associated with exposure to hazardous substances. However, heavy metal precipitation near the cathode under alkaline pH remains challenging. This study employed recyclable waste materials of sawdust crosslinked by glutaraldehyde with iron slag as a reactive filter media (RFM) for single and mixed heavy metals from kaolinite and natural soils. Experiments were conducted over two and three weeks, employing 20 to 30 mA electric currents. Incorporating iron slag RFM into the EK process resulted in a notable enhancement in copper removal from 3.21% to 23.76%. Mixing sawdust with iron slag in the RFM further improved the efficiency of copper extraction from the soil, reaching 71.80%. Also, copper removal improved as the electric current increased from 20 to 25 mA, reaching 88.10% in a three-week experiment. A slight improvement in copper removal was recorded due to the electric current increasing to 30 mA, indicating that copper removal is not linear with the applied electric current. However, sawdust treatment with HCl lowered the RFM pH, resulting in a total copper removal of 90.30% at electrical currents of 25 mA. Crosslinking sawdust with iron slag by 2% glutaraldehyde achieved a remarkable 97.92% copper removal at 0.18 kWh/kg specific energy from kaolinite soil, while in natural soil, the removal rates for copper, nickel, and zinc reached 26.72%, 54.36%, and 56.44%, respectively after 5 weeks. The discrepancy in heavy metals removal between kaolinite and natural soils reflects the complicated environmental conditions in natural soils on the efficiency of the electrokinetic process when laboratory tests are applied to the field.
Hamdi, FM, Ganbat, N, Altaee, A, Samal, AK, Ibrar, I, Zhou, JL & Sharif, AO 2025, 'Hybrid and enhanced electrokinetic system for soil remediation from heavy metals and organic matter', Journal of Environmental Sciences, vol. 147, pp. 424-450.
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Han, M, Zhu, T, Zhang, L, Huo, H & Zhou, W 2025, 'Vertical Federated Unlearning via Backdoor Certification', IEEE Transactions on Services Computing, vol. 18, no. 2, pp. 1110-1123.
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Han, X, Jiang, F, Wen, S & Tian, T 2025, 'Kolmogorov-Arnold network-based enhanced fusion transformer for hyperspectral image classification', Information Sciences, vol. 717, pp. 122323-122323.
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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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Haque, MR, Moniruzzaman, M, Arman, Hasan, MR, Lat, T, Kabir, N, Sarker, MJ, Tijing, L, Shon, HK, Ayejoto, DA, Khan, MYA & Kabir, MM 2025, 'Land use Transition and Ecological Consequences: A Spatiotemporal Analysis in South-Eastern Bangladesh', Earth Systems and Environment, vol. 9, no. 2, pp. 1135-1148.
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Hasan, M, Hoque, MA-A & Pradhan, B 2025, 'Mapping seismic risk in Rangpur Division, Bangladesh: an integrated geospatial approach', Spatial Information Research, vol. 33, no. 5.
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Hasan, MM, Haque, R, Jahirul, MI, Rasul, MG, Fattah, IMR, Hassan, NMS & Mofijur, M 2025, 'Advancing energy storage: The future trajectory of lithium-ion battery technologies', Journal of Energy Storage, vol. 120, pp. 116511-116511.
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Hashmi, A, Sidhu, S & Hutvagner, G 2025, 'Prognostic relevance of AGO2 expression in adrenocortical carcinoma (ACC): associations with clinicopathological features', Pathology, vol. 57, pp. S36-S36.
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Hassan, MA, Jamshidi, MB, Manh, BD, Chu, NH, Nguyen, C-H, Hieu, NQ, Nguyen, CT, Hoang, DT, Nguyen, DN, Van Huynh, N, Alsheikh, MA & Dutkiewicz, E 2025, 'Enabling technologies for Web 3.0: A comprehensive survey', Computer Networks, vol. 264, pp. 111242-111242.
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Hassani, S, Mustapha, S, Li, J, Mousavi, M & Dackermann, U 2025, 'Next-generation coupled structure-human sensing technology: Enhanced pedestrian-bridge interaction analysis using data fusion and machine learning', Information Fusion, vol. 118, pp. 102983-102983.
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Hayatdavoodi, A & Li, Y 2025, 'An analytical method to determine the ultimate shear strength of continuous steel plate girders', Asian Journal of Civil Engineering, vol. 26, no. 2, pp. 565-575.
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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, vol. 18, no. 2, pp. 513-526.
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He, B, Li, J, Armaghani, DJ, Hashim, H, He, X, Pradhan, B & Sheng, D 2025, 'The deep continual learning framework for prediction of blast-induced overbreak in tunnel construction', Expert Systems with Applications, vol. 264, pp. 125909-125909.
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He, H, Zhang, Q, Yi, K, Shi, K, Niu, Z & Cao, L 2025, 'Distributional Drift Adaptation With Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 4, pp. 7287-7301.
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He, J, Zhang, Y, Yuan, S, Han, R & Gao, P 2025, 'Magnesium salt-synergistic roasting of mixed rare earth concentrate: killing two birds with one stone for enhanced rare earth leaching and fluorine fixation', Process Safety and Environmental Protection, vol. 201, pp. 107598-107598.
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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, K, Zhang, W, Lin, X, Zhang, Y & Ni, W 2025, 'Robust Privacy-Preserving Triangle Counting under Edge Local Differential Privacy', Proceedings of the ACM on Management of Data, vol. 3, no. 3, pp. 1-26.
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Counting the number of triangles in a graph is a fundamental task and has been extensively studied recently. In real-world applications, continuously releasing the triangle count of a graph poses a significant privacy risk for users. To protect sensitive edge information from a central server, we study the problem of estimating the number of triangles under edge local differential privacy (edge LDP). Existing approaches adopt a multi-round computing scheme, allowing the vertices to perform local triangle counting using the noisy graph constructed in the previous round. However, these algorithms not only restrict the noisy graph that can be downloaded to each vertex, but also have coarse upper bounds for the scale of noise added to the estimates. In this paper, we propose a vertex-centric triangle counting algorithm under edge LDP, which improves data utility by leveraging a larger part of the noisy adjacency matrix. Our approach fully exploits the local graph structure to obtain refined estimates of per-vertex triangle counts. We also devise tight bounds for global sensitivities to not only comply with privacy requirements but also control the scale of added noise. Furthermore, we perform a rigorous analysis of the L2 loss of our unbiased estimators and design optimizations for allocating the privacy budget to minimize L2 loss based on the input graph. Extensive experiments on 12 datasets validate the effectiveness and efficiency of our proposed algorithms.
He, Y, Wang, S, Wei, G, Ding, C & Jay Guo, Y 2025, 'A New Buffering Scheme for Shared-Aperture Dual-Band Base Station Antenna Array Utilizing Tight-Coupling Concept', IEEE Transactions on Antennas and Propagation, vol. 73, no. 6, pp. 3492-3502.
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He, Y, Yu, G, Wang, X, Wang, Q, Niu, Z, Ni, W & Liu, RP 2025, 'Accountability and Reliability in 6G O-RAN: Who is Responsible When it Fails?', IEEE Wireless Communications, vol. 32, no. 2, pp. 52-59.
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Hedman, J, Gleasure, R & Bandara, M 2025, 'Preface', Lecture Notes in Business Information Processing, vol. 541 LNBIP, pp. v-viii.
Hesam-Shariati, N, Alexander, L, Stapleton, F, Newton-John, T, Lin, C-T, Zahara, P, Chen, KY, Restrepo, S, Skinner, IW, McAuley, JH, Moseley, GL, Jensen, MP & Gustin, SM 2025, 'The effect of an EEG neurofeedback intervention for corneal neuropathic pain: A single-case experimental design with multiple baselines', The Journal of Pain, vol. 32, pp. 105394-105394.
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Hettiyahandi, S, Indraratna, B, Ngo, T, Qi, Y & Arachchige, C 2025, 'Enhancing Rail Track Performance Using Recycled Rubber Energy-Absorbing Grids: Laboratory and Field Evidence', Journal of Geotechnical and Geoenvironmental Engineering, vol. 151, no. 7.
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Hiep Dinh, T, Kumar Singh, A, Manh Doan, Q, Linh Trung, N, Nguyen, DN & Lin, C-T 2025, 'An EEG signal smoothing algorithm using upscale and downscale representation*', Journal of Neural Engineering, vol. 22, no. 3, pp. 036012-036012.
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Abstract Objective. Effective smoothing of electroencephalogram (EEG) signals while maintaining the original signal’s features is important in EEG signal analysis and brain–computer interface. This paper proposes a novel EEG signal-smoothing algorithm and its potential application in cognitive conflict (CC) processing. Approach. Instead of being processed in the time domain, the input signal is visualized in increasing line width, the representation frame of which is converted into a binary image. An effective thinning algorithm is employed to obtain a unit-width skeleton as the smoothed signal. Main results. Experimental results on data fitting have verified the effectiveness of the proposed approach on different levels of signal-to-noise (SNR) ratio, especially on high noise levels (SNR ⩽ 5 dB), where our fitting error is only 86.4%–90.4% compared to that of its best counterpart. The potential application of the proposed algorithm in EEG-based CC processing is comprehensively evaluated in a classification and a visual inspection task. The employment of the proposed approach in pre-processing the input data has significantly boosted the F 1 score of state-of-the-art models by more than 1%. The robustness of our algorithm is also evaluated via a visual inspection task, where specific CC peaks, i.e. the prediction error negativity and error-related positive potential (Pe), can be easily observed at ...
Hill, M, Stapleton, S, Nguyen, PT, Sais, D, Deutsch, F, Gay, VC, Marsh, DJ & Tran, N 2025, 'The potential regulation of the miR-17–92a cluster by miR-21', The International Journal of Biochemistry & Cell Biology, vol. 178, pp. 106705-106705.
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Hjorth, M, Egan, CL, Telles, GD, Pal, M, Gallego-Ortega, D, Fuller, OK, McLennan, ED, Gillis, RD, Oh, TG, Muscat, GEO, Tegegne, S, Mah, MSM, Skhinas, J, Estevez, E, Adams, TE, McKay, MJ, Molloy, M, Watt, KI, Qian, H, Gregorevic, P, Cox, TR, Hojman, P, Midtgaard, J, Christensen, JF, Friedrichsen, M, Iozzo, RV, Sloan, EK, Drew, BG, Wojtaszewski, JFP, Whitham, M & Febbraio, MA 2025, 'Decorin, an exercise-induced secretory protein, is associated with improved prognosis in breast cancer patients but does not mediate anti-tumorigenic tissue crosstalk in mice', Journal of Sport and Health Science, vol. 14, pp. 100991-100991.
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Hoang, LM, Nguyen, DN, Zhang, JA & Hoang, DT 2025, 'Adaptive Nullification of Multiple Correlated Jammers Using Deep Reinforcement Learning', IEEE Transactions on Vehicular Technology, pp. 1-14.
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Hopwood, N, Palmer, T-A, Koh, GA, Lai, MY, Dong, Y, Loch, S & Yu, K 2025, 'Understanding student emotions when completing assessment: technological, teacher and student perspectives', International Journal of Research & Method in Education, vol. 48, no. 2, pp. 194-209.
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Hoque, MA-A, Sardar, ML, Mukul, SA & Pradhan, B 2025, 'Mapping dengue susceptibility in Dhaka city: a geospatial multi-criteria approach integrating environmental and demographic factors', Spatial Information Research, vol. 33, no. 4.
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Abstract Dengue, a rapidly spreading mosquito-borne disease, poses a serious public health threat in tropical cities like Dhaka, Bangladesh—one of the world’s most densely populated megacities. In 2023 alone, Dhaka experienced its worst outbreak, recording 321,179 cases and 1,705 deaths. This study aims to assess dengue susceptibility across Dhaka using a geospatial Multi-Criteria Decision-Making (MCDM) approach. Fourteen environmental and demographic factors were selected, and thematic raster layers were developed and weighted using the Analytical Hierarchy Process (AHP). These layers were integrated to generate spatial dengue susceptibility maps, highlighting risk zones across the city. Findings reveal that southern and southeastern Dhaka, particularly under the South City Corporation, are highly susceptible based on environmental factors. Demographic analysis shows moderate to very high susceptibility in central and southern wards, with population density and proximity to waterlogged areas identified as key drivers. The model was validated through field surveys with 80 stakeholders, with 67.5% agreeing with the susceptibility classifications. This study provides a scalable and transferable framework for dengue risk assessment and can inform targeted interventions in other endemic regions. The results offer critical guidance for urban health planning, vector control, and resource allocation to mitigate dengue and similar vector-borne diseases.
Hoque, MA-A, Sardar, ML, Sami, MS, Roy, S, Mukul, SA & Pradhan, B 2025, 'Mapping Tropical Cyclone Risks in Coastal Bangladesh: An Integrated Geospatial Approach', Earth Systems and Environment, vol. 9, no. 2, pp. 1353-1370.
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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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Hosseinzadeh, A, Amirkhani, F, Azizi, N, Dashti, A, Zhou, JL & Altaee, A 2025, 'Machine learning modeling of microplastics removal by coagulation in water and wastewater treatment', Journal of Water Process Engineering, vol. 76, pp. 108108-108108.
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Hu, C, Wang, Z, Yuan, B, Liu, J, Zhang, C & Yao, X 2025, 'Robust Dynamic Material Handling via Adaptive Constrained Evolutionary Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Hu, J, Guo, D, Li, K, Si, Z, Yang, X, Chang, X & Wang, M 2025, 'Unified Static and Dynamic Network: Efficient Temporal Filtering for Video Grounding', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 8, pp. 6445-6462.
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Hu, X, Li, X-K, Wen, S, Li, X, Zeng, T-S, Zhang, J-Y, Wang, W, Bi, Y, Zhang, Q, Tian, S-H, Min, J, Wang, Y, Liu, G, Huang, H, Peng, M, Zhang, J, Wu, C, Li, Y-M, Sun, H, Ning, G & Chen, L-L 2025, 'Retraction notice to 'Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study' [Heliyon 8 (2022) e12343]', Heliyon, vol. 11, no. 9, pp. e43252-e43252.
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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, Y, Wu, K, Andrew Zhang, J, Deng, W & Jay Guo, Y 2025, 'Cross-Frequency Sensing in Bistatic ISAC Systems', IEEE Transactions on Wireless Communications, pp. 1-1.
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Hu, Z, Chen, Q, Xu, C, Nimbalkar, S & Huang, N 2025, 'Effectiveness and Optimization Analysis of Vibration Isolation Performance of Circular and Rectangular Hollow Pipes: Numerical Modeling', International Journal of Geomechanics, vol. 25, no. 4.
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Huang, H, Sun, Y, Du, Q, Gao, F, Song, Z, Wang, Z, Chang, S, Zhang, X, Guo, W & Ngo, HH 2025, 'Impact of in–situ bioelectric field on biogas production, membrane fouling and microbial community in an anaerobic membrane bioreactor under sulfadiazine stress', Chemical Engineering Journal, vol. 506, pp. 160225-160225.
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Huang, J, Li, J, Shao, R & Wu, C 2025, 'Effect of high temperature and cooling method on compression and fracture properties of geopolymer-based ultra-high performance concrete', Journal of Building Engineering, vol. 105, pp. 112433-112433.
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Huang, J, Liu, Y, Tu, M & Sohaib, O 2025, 'Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network.', PLoS One, vol. 20, no. 7, p. e0327199.
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Accurate risk assessment in international trade settlement has become increasingly critical as global financial transactions grow in scale and complexity. This study proposes a hybrid model-Genetic Algorithm-optimized Fuzzy Neural Network (GA-FNN)-to enhance bank risk identification within this context. The objective is to improve the classification of bank-related risks by integrating the adaptability of fuzzy logic with the global optimization capability of genetic algorithms. The GA is used to fine-tune the structure, membership functions, and parameters of the FNN to improve predictive performance. Experiments were conducted on three public datasets: Bank Marketing, Lending Club, and German Credit. Results show that GA-FNN achieves an average classification accuracy of approximately 90% across high, medium, and low risk levels, outperforming traditional methods such as logistic regression, SVM (Support Vector Machine), and other metaheuristics like PSO (Particle Swarm Optimization) and SA (Simulated Algorithm). These findings demonstrate the model's effectiveness and practical value in dynamic international trade scenarios, offering a reliable approach for enhanced bank credit risk evaluation.
Huang, J, Wu, B, Duan, Q, Dong, L & Yu, S 2025, 'A Fast UAV Trajectory Planning Framework in RIS-Assisted Communication Systems With Accelerated Learning via Multithreading and Federating', IEEE Transactions on Mobile Computing, vol. 24, no. 8, pp. 6870-6885.
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Huang, L, Ge, C, Mao, X & Yu, S 2025, 'DARB: Decentralized, Accountable and Redactable Blockchain for Data Management', IEEE Transactions on Network and Service Management, vol. 22, no. 2, pp. 1608-1617.
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Huang, Q, Zhou, L, Fang, W, Zhao, M & Ying, M 2025, 'Efficient Formal Verification of Quantum Error Correcting Programs', Proceedings of the ACM on Programming Languages, vol. 9, no. PLDI, pp. 1068-1093.
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Quantum error correction (QEC) is fundamental for suppressing noise in quantum hardware and enabling fault-tolerant quantum computation. In this paper, we propose an efficient verification framework for QEC programs. We define an assertion logic and a program logic specifically crafted for QEC programs and establish a sound proof system. We then develop an efficient method for handling verification conditions (VCs) of QEC programs: for Pauli errors, the VCs are reduced to classical assertions that can be solved by SMT solvers, and for non-Pauli errors, we provide a heuristic algorithm. We formalize the proposed program logic in Coq proof assistant, making it a verified QEC verifier. Additionally, we implement an automated QEC verifier, Veri-QEC, for verifying various fault-tolerant scenarios. We demonstrate the efficiency and broad functionality of the framework by performing different verification tasks across various scenarios. Finally, we present a benchmark of 14 verified stabilizer codes.
Huang, S, Tegg, L, Aminorroaya Yamini, S, Tuli, V, Burr, P, McCarroll, I, Yang, L, Moore, KL & Cairney, JM 2025, 'Atom probe study of second-phase particles in Zircaloy-4', Journal of Nuclear Materials, vol. 616, pp. 156049-156049.
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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, W, Xu, G, Jia, W, Perry, S & Gao, G 2025, 'ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions', IEEE Transactions on Image Processing, pp. 1-1.
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Huang, X, Li, J, Yang, H, Zhou, T, Luo, Y, Yu, S, Liu, J, Shen, PK, Chen, L & Tian, ZQ 2025, 'Rich {1 0 0} faceted PtIrW nanocubes with high-filling bonding orbitals of NH2 dimerization for enhancing electrochemical ammonia oxidation', Journal of Energy Chemistry, vol. 103, pp. 361-370.
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Huang, X, Saha, G, Paul, AR, Tahan, A & Saha, SC 2025, 'A computational fluid dynamics analysis of BiPAP pressure settings on airway biomechanics using a CT-based respiratory tract model', Respiratory Physiology & Neurobiology, vol. 333, pp. 104397-104397.
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Huang, X, Yin, Y, Saha, G, Francis, I & Saha, SC 2025, 'A Comprehensive Numerical Study on the Transport and Deposition of Nasal Sprayed Pharmaceutical Aerosols in a Nasal‐To‐Lung Respiratory Tract Model', Particle & Particle Systems Characterization, vol. 42, no. 2.
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AbstractUtilizing a computed tomography (CT)‐based realistic nasal‐to‐lung respiratory tract model allows for a comprehensive investigation of the transport and deposition of nasal sprayed aerosols. This study has three main objectives: first, to determine the optimal mesh that achieves the quickest convergence for computational fluid‐particle dynamics (CFPD) simulations of a nasal‐to‐lung nasal respiratory tract by assessing the performance of different element types, sizes, and prism boundary layers; second, to design and validate a numerical method to compare grid data with different mesh structures and densities for simulation result validation; and finally, to observe and analyze fluid‐particle dynamics in the respiratory tract to aid in the development of nasal sprayed medications. This study involves reverse‐engineering a realistic and anatomically accurate respiratory tract model from CT scans. Results reveal that the optimal numerical approach for minimum calculation time is the polyhedral hybrid mesh with four boundary prism layers and the SIMPLE pressure‐velocity coupling scheme. Furthermore, observations of particle dynamics reveal that the vocal cords' location contains a concentration site of deposited small aerosols due to the turbulent airflow in the region. The optimal diameters of nasal sprayed aerosols to target each region are concluded in the end.
Huang, Y, Gao, D, Ying, S & Li, S 2025, 'DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 44, no. 8, pp. 2966-2978.
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Huang, Y, Huang, Y, Zhang, Z, Wu, Q, Zhong, Y & Wang, L 2025, 'CSFRNet: Integrating Clothing Status Awareness for Long-Term Person Re-identification', International Journal of Computer Vision, vol. 133, no. 6, pp. 3180-3202.
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Huang, Y, Wang, HB, Mak, HMW, Chu, M, Ning, Z, Organ, B, Chan, EFC, Liu, C-H, Mok, W-C, Gromke, C, Shon, HK, Lei, C & Zhou, JL 2025, 'Suitability of using carbon dioxide as a tracer gas for studying vehicle emission dispersion in a real street canyon', Journal of Environmental Sciences, vol. 155, pp. 832-845.
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Huang, Y, Yang, G, Yuan, D & Yu, S 2025, 'DBSSL: A Scheme to Detect Backdoor Attacks in Self-Supervised Learning Models', IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 4, pp. 3371-3382.
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Husna Shabrina, N, Gunawan, D, Rizkinia, M, Stephanie Harahap, A, Ikhsan, M, Chai, R & Francisca Ham, M 2025, 'Deep Residual Network With Integrated StarDist Nuclei Segmentation for Papillary Thyroid Cancer Identification: A Pathologist-Inspired Approach', IEEE Access, vol. 13, pp. 121139-121157.
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Hussain, M, Keshavarz, R & Shariati, N 2025, 'Advancements in Agricultural Microwave Remote Sensing: An Overview From Indoor to Space-Borne Platforms', IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-22.
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Hussain, T, Li, Y, Ren, M & Li, J 2025, 'Pixel-level crack segmentation and quantification enabled by multi-modality cross-fusion of RGB and depth images', Construction and Building Materials, vol. 487, pp. 141961-141961.
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Hussain, W, Merigó, JM, Rahimi, I & Lev, B 2025, 'Half a century of Omega – The International Journal of Management Science: A bibliometric analysis', Omega, vol. 133, pp. 103226-103226.
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Huynh, N, De Dios, K, Tran, TS, Center, JR & Nguyen, TV 2025, 'Association between the Sp1-binding-site polymorphism in the collagen type I alpha 1 (COLIA1) gene and bone phenotypes: the Dubbo Osteoporosis Epidemiology Study', Journal of Bone and Mineral Metabolism, vol. 43, no. 2, pp. 114-122.
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Huynh, TQ, Nguyen, TT & Indraratna, B 2025, 'Evaluating cohesive models in discrete element simulation through drawdown test with new assessment perspectives', Powder Technology, vol. 452, pp. 120542-120542.
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Indraratna, B, Arachchige, CMK, Rujikiatkamjorn, C, Ngo, T, Qi, Y & Tucho, A 2025, 'Effects of rubber-intermixed ballast on train loading response through field monitoring in Western Sydney', Canadian Geotechnical Journal, vol. 62, pp. 1-20.
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The use of waste tyres in transportation infrastructure promotes environmentally sustainable engineering practices and reduces the cost of transportation and disposal. Laboratory testing has shown that a Rubber Intermixed Ballast System (RIBS) has significant advantages over conventional ballast. This study extends the evaluation of RIBS via a fully instrumented field trial near Western Sydney to assess its compatibility and efficiency under real-life conditions. The trial revealed a beneficial redistribution of stress along the depth of the track, despite an initial increase in deformation during construction and stabilizing the track in the initial loading phases. The measured acceleration response indicated a reduction of ground vibrations in the RIBS track, while the reduced particle breakage suggested its lifespan would exceed that of conventional ballast. Finite element modelling (FEM) of the RIBS track, calibrated with large-scale triaxial tests and field data, was carried out under moving wheel loads to compare its performance with a conventional ballasted track. The FEM simulations highlighted the differences in vertical stress and track settlement at varying train speeds. These findings indicate that RIBS support a circular economy and also offer a sustainable approach to stabilizing ballasted tracks by enhancing their longevity and reducing their maintenance costs.
Indraratna, B, Hunt, H, Malisetty, RS, Alagesan, S, Qi, Y & Rujikiatkamjorn, C 2025, 'Optimization of inputs for the application of ANN to rail track granular materials', Canadian Geotechnical Journal, vol. 62, pp. 1-22.
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Machine learning (ML) models such as artificial neural networks (ANNs) have gained increasing popularity in geotechnical engineering applications as an alternative to conventional empirical and computational models. At present, very few ML models exist for predicting the mechanical responses of track granular materials such as ballast and subballast which may even comprise of composite mixtures of blended granular materials. Moreover, the performance of any ML model depends not only on the quality and quantity of available data but also on the selection process for input parameters, which often lacks adequate justification in the past literature. In this context, the current study introduces ANN models for track granular materials based on published laboratory data with special emphasis on the selection of an optimal set of input parameters. Two applications of ANN are considered to (i) predict the peak friction angle ([Formula: see text]) of a variety of granular mixtures under static loading and (ii) predict ballast breakage under cyclic loading. The selection process involves prudent analysis of key influential parameters in a geotechnical perspective, while also ensuring that they are conveniently measurable. Performance evaluation of these models with various input combinations is carried out, while proposing optimal input parameters for both applications.
Indraratna, B, Mehmood, F, Ngo, T, Rujikiatkamjorn, C & Grant, J 2025, 'Performance of Tire Cell Foundation as a Subballast Capping Layer under Cyclic Train Loading', Journal of Geotechnical and Geoenvironmental Engineering, vol. 151, no. 1.
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Iqra, NA, Li, J, Wang, XH & Yang, G 2025, 'Wave prediction using Graph Neural Network at Darwin Harbour, Australia', Regional Studies in Marine Science, vol. 84, pp. 104088-104088.
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Ismail, M, Liu, J, Wang, N, Zhang, D, Qin, C, Shi, B & Zheng, M 2025, 'Advanced nanoparticle engineering for precision therapeutics of brain diseases', Biomaterials, vol. 318, pp. 123138-123138.
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Iyer, KS, Bao, L, Zhai, J, Jayachandran, A, Luwor, R, Li, JJ & Li, H 2025, 'Microgel-based bioink for extrusion-based 3D bioprinting and its applications in tissue engineering', Bioactive Materials, vol. 48, pp. 273-293.
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Jacob Kedziora, D, Musiał, A, Rudno-Rudziński, W & Gabrys, B 2025, 'Transfer learning and the early estimation of single-photon source quality using machine learning methods', Machine Learning: Science and Technology, vol. 6, no. 2, pp. 025014-025014.
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Abstract The use of single-photon sources (SPSs) is central to numerous systems and devices proposed amidst a modern surge in quantum technology. However, manufacturing schemes remain imperfect, and single-photon emission purity must often be experimentally verified via interferometry. Such a process is typically slow and costly, which has motivated growing research into whether SPS quality can be more rapidly inferred from incomplete emission statistics. Hence, this study is a sequel to previous work that demonstrated significant uncertainty in the standard method of quality estimation, i.e. the least-squares fitting of a physically motivated function, and asks: can machine learning (ML) do better? The study leverages eight datasets obtained from measurements involving an exemplary quantum emitter, i.e. a single InGaAs/GaAs epitaxial quantum dot; these eight contexts predominantly vary in the intensity of the exciting laser. Specifically, via a form of ‘transfer learning’, five ML models, three linear and two ensemble-based, are trained on data from seven of the contexts and tested on the eighth. Validation metrics quickly reveal that even a linear regressor can outperform standard fitting when it is tested on the same contexts it was trained on, but the success of transfer learning is less assured, even though statistical analysis, made possible by data augmentation, suggests its superiority as an early estimator. Accordingly, the study concludes by discussing future strategies for grappling with the problem of SPS context dissimilarity, e.g. feature engineering and model adaptation.
Jacob, PE, Choudhary, N, Dikshit, A, Evans, JP, Pradhan, B & Huete, AR 2025, 'Flash drought prediction using deep learning', Environmental Research Letters, vol. 20, no. 7, pp. 074006-074006.
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Abstract Flash droughts are rapid, short-term drought events that develop within weeks, driven by factors such as low rainfall, high temperatures, and strong winds, which deplete soil moisture and stress vegetation. These events have profound agricultural, economic, and ecological impacts, yet the use of machine learning to predict flash droughts remains underexplored, hindered by challenges like imbalanced datasets and limited data. This study addresses these issues by applying Convolutional neural networks (CNNs) to predict flash droughts in Eastern Australia, a region prone to such events. We identified flash droughts from 2001 to 2022, training the model with data from 2001–2015, validating it on 2016–2017 data, and testing it on 2018–2022 data. The model’s performance was evaluated across drought duration, spatial distribution, and seasonal variability. Achieving a balanced accuracy of 80% and an Area under the curve of 93%, the CNN demonstrated strong predictive capability. However, it tended to overestimate the spatial extent of droughts, indicating areas for future improvement. These results highlight the potential of deep learning in flash drought prediction, offering valuable insights for early warning systems and drought management strategies.
Jadhav, P, Sairam, VA, Bhojane, N, Singh, A, Gite, S, Pradhan, B, Bachute, M & Alamri, A 2025, 'Multimodal Gas Detection Using E-Nose and Thermal Images: An Approach Utilizing SRGAN and Sparse Autoencoder', Computers, Materials & Continua, vol. 83, no. 2, pp. 3493-3517.
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Jafari, M, Tao, X, Barua, P, Tan, R-S & Acharya, UR 2025, 'Application of transfer learning for biomedical signals: A comprehensive review of the last decade (2014–2024)', Information Fusion, vol. 118, pp. 102982-102982.
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Jafari, P, Rasekh, E, Asheghi Mehmandari, T, Mohammadifar, M, Fahimifar, A & Jahed Armaghani, D 2025, 'Upper-Bound Solutions for Active Face Failure in Shallow Rectangular Tunnels in Anisotropic and Non-homogeneous Undrained Clays', Geotechnical and Geological Engineering, vol. 43, no. 3.
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Abstract As urbanization accelerates, the demand for efficient underground infrastructure has grown, with rectangular tunnels gaining prominence due to their enhanced space utilization and construction efficiency. However, ensuring the stability of shallow rectangular tunnel faces in undrained clays presents significant challenges due to complex soil behaviors, including anisotropy and non-homogeneity. This study addresses these challenges by developing a novel failure mechanism within the kinematic approach of limit analysis, integrating soil arching effects alongside anisotropic and non-homogeneous undrained shear strength. The mechanism's analytical solutions are rigorously validated against finite element simulations using PLAXIS 3D and existing models, demonstrating superior accuracy. Key findings show that the proposed model improves predictive performance for critical support pressure, with relative differences as low as 5% for wide rectangular tunnels compared to numerical simulations. Results reveal that limit support pressure decreases with increasing non-homogeneity ratios and rises with higher anisotropy factors. However, both effects diminish in wider tunnels, where increasing width in soils with high non-homogeneity and low anisotropy factors significantly enhances stability. Practical implications of this study are substantial, offering design formulas and dimensionless coefficients for estimating critical face pressures in shallow rectangular tunnels. These tools enable engineers to account for soil anisotropy and non-homogeneity, optimizing design and ensuring safety in urban environments. Furthermore, the proposed model’s applicability extends to circular tunnels, where it offers comparable accuracy. This study bridges a critical gap in understanding the stability of rectangular tunnels, providing a robust framework for tackling the challenges of modern urban construction.
Jafaryahya, J, Keshavarz, R, Abolhasan, M, Lipman, J & Shariati, N 2025, 'Integrating Electrical Features for Simultaneous Prediction of Soil Moisture and Potassium Levels Based on Neural Network Prediction Model', IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-12.
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Jafaryahya, J, Keshavarz, R, Nikkhah, N, Nagatani, A & Shariati, N 2025, 'Soil Salinity Frequency-Dependent Prediction Model Using Permittivity Spectroscopy', IEEE Transactions on AgriFood Electronics, pp. 1-12.
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Jahed Armaghani, D, Hayati, M, Momeni, E, Dowlatshahi, MB & Asteris, PG 2025, 'Estimation of powder factor in mine blasting: feasibility of tree-based predictive models', Multiscale and Multidisciplinary Modeling, Experiments and Design, vol. 8, no. 2.
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Abstract Drilling and blasting is a process frequently used in rock-surface and deep excavation. For a proper drilling plan, accurate prediction of the amount of explosive material is essential to reduce the environmental effects associated with blasting operations. This study introduces a series of tree-based models, namely extreme gradient boosting machine (XGBoost), gradient boosting machine (GBM), adaptive boosting machine (AdaBoost), and random forest (RF), for predicting powder factor (PF) values obtained from blasting operations. The predictive models were constructed based on geomechanical characteristics at the blasting site, blasting pattern parameters, and rock material properties. These tree-based models were designed and tuned to minimize system error or maximize accuracy in predicting PF. Subsequently, the best model from each category was evaluated using various statistical metrics. It was found that the XGBoost model outperformed the other implemented techniques and exhibited outstanding potential in establishing the relationship between PF and input variables in the training set. Among the input parameters, hole diameter received the highest significance rating for predicting the system output, while the point load index had the least impact on the PF values.
Jaiswal, A, Jha, K, Bugalia, N & Ha, QP 2025, 'Vision-based volumetric estimation of localized construction and demolition waste', Waste Management, vol. 206, pp. 115046-115046.
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Jamshidi, MB, Hoang, DT, Nguyen, DN, Niyato, D & Warkiani, ME 2025, 'Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning', Computers in Biology and Medicine, vol. 189, pp. 109970-109970.
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Javed, F, Tariq, MF, Ikhlaq, A, Munir, HMS & Altaee, A 2025, 'Remediation of textile wastewater by hybrid technique using ZIF-67 catalyzed ozonation coupled with electrocoagulation', Journal of Water Process Engineering, vol. 69, pp. 106604-106604.
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Jay Guo, Y, Guo, CA, Li, M & Latva-aho, M 2025, 'Antenna Technologies for 6G – Advances and Challenges', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Jayanthakumaran, M, Shukla, N, Pradhan, B & Beydoun, G 2025, 'A systematic review of sentiment analytics in banking headlines', Decision Analytics Journal, vol. 15, pp. 100584-100584.
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Jeon, G, Rodrigues, J, Wen, S, Chen, J, Ji, N & Chehri, A 2025, 'Guest Editorial AI-Generated Content Empowered Healthcare Electronics', IEEE Transactions on Consumer Electronics, vol. 71, no. 1, pp. 1319-1321.
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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, vol. 35, no. 6, pp. 5153-5165.
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Jia, H, Wei, H, Li, J, Cui, S, Xu, L & Zheng, S 2025, 'Blast resistance of steel jacket reinforced double-column bridge pier', Engineering Structures, vol. 329, pp. 119882-119882.
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Jia, T, Xia, J, Zhang, C, Sun, B, Yuan, K, Liu, T, Xu, X & Liu, J 2025, 'Comparing analgesic effects of temporal interference stimulation on ventral posterolateral thalamus and high-definition transcranial alternating current stimulation on sensorimotor cortex during sustained experimental pain', Brain Stimulation, vol. 18, no. 3, pp. 701-703.
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Jiang, 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, F, Zhang, Y, Fang, G, Wang, Y, Dupuy, A, Jin, J, Shen, Y, Lim, KS, Wang, Y, Zhang, YS, Cho, A, Lu, H & Ju, LA 2025, 'Intravasation‐On‐µDevice (INVADE): Engineering Dynamic Vascular Interfaces to Study Cancer Cell Intravasation', Advanced Materials, vol. 37, no. 26.
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AbstractCancer metastasis begins with intravasation, where cancer cells enter blood vessels through complex interactions with the endothelial barrier. Understanding this process remains challenging due to the lack of physiologically relevant models. Here, INVADE (Intravasation‐on‐µDevice), a biomimetic microfluidic platform, is presented, enabling high‐throughput analysis of cancer cell intravasation under controlled conditions. This engineered platform integrates 23 parallel niche chambers with an endothelialized channel, providing both precise microenvironmental control and optical accessibility for real‐time visualization. Using this platform, distinct intravasation mechanisms are uncovered: MCF‐7 cells exhibit collective invasion, while MDA‐MB‐231 cells demonstrate an interactive mode with three functionally distinct subpopulations. A previously unknown epithelial‐mesenchymal transition (EMT) and mesenchymal‐epithelial transition (MET) switch is We discovered during intravasation, where MDA‐MB‐231 cells initially increase Vimentin expression before undergoing a 2.3 fold decrease over 96 h alongside a 1.5 fold increase in epithelial cell adhesion molecule (EpCAM). Remarkably, endothelial cells directly suppress cancer cell mesenchymal properties, as evidenced by a 4.6 fold reduction in Vimentin expression compared to mono‐cultures. Additionally, bilateral cancer‐endothelial interactions are revealed, aggressive cancer cells induce significant intercellular adhesion molecule‐1 (ICAM‐1) upregulation in endothelium. The INVADE platform represents an engineering advancement for studying complex cell–cell interactions with implications for understanding metastatic mechanisms.
Jiang, W, Li, H, Xu, G, Ren, H, Yang, H, Zhang, T & Yu, S 2025, 'Rethinking the Design of Backdoor Triggers and Adversarial Perturbations: A Color Space Perspective', IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 3, pp. 2823-2840.
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Jiang, W, Wei, H, Xu, Z, Kang, J, Wang, S, Liu, D, Ren, Y, Ngo, HH, Guo, W & Ye, Y 2025, 'Lighting promotes sulfate removal and improves microbial community stability in upflow anaerobic sludge bed reactors under low ratio of chemical oxygen demand to sulfate', Bioresource Technology, vol. 428, pp. 132473-132473.
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Jiang, X, Ou, L, Chen, Y, Ao, N, Chang, Y-C, Do, T & Lin, C-T 2025, 'A Fuzzy Logic-Based Approach to Predict Human Interaction by Functional Near-Infrared Spectroscopy', IEEE Transactions on Fuzzy Systems, vol. 33, no. 5, pp. 1664-1677.
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Jiang, Y, Zhu, S, Shen, M, Wen, S & Mu, C 2025, 'Aperiodically Intermittent Control Approach to Finite-Time Synchronization of Delayed Inertial Memristive Neural Networks', IEEE Transactions on Artificial Intelligence, vol. 6, no. 4, pp. 1014-1023.
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Jiang, Y, Zhu, S, Shen, M, Wen, S & Mu, C 2025, 'Finite time dynamic analysis of memristor-based fuzzy NNs with inertial term: Nonreduced-order approach', Neural Networks, vol. 190, pp. 107672-107672.
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Jiang, Y, Zhu, S, Wen, S & Mu, C 2025, 'Reachable Set Estimation of Memristive Inertial Neural Networks', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 72, no. 7, pp. 903-907.
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Jiang, Z, Fang, G, Han, J, Lu, G, Xu, H, Liao, S, Chang, X & Liang, X 2025, 'RealignDiff: Boosting Text-to-Image Diffusion Model With Coarse-to-Fine Semantic Realignment', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Jin, P, Cao, C, Guo, Y, Lei, G & Zhu, J 2025, 'A Novel SVM Strategy to Reduce Current Stress of 3-3 AC/AC HFLMC', IEEE Transactions on Industrial Electronics, pp. 1-10.
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Jin, P, Hu, Y, Liu, J, Guo, Y, Lei, G & Zhu, J 2025, 'A Flexible Compensation Control Strategy for High-Frequency Link Matrix Converters', IEEE Transactions on Industrial Electronics, vol. 72, no. 9, pp. 9666-9674.
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Jin, P, Wang, H, Xu, H, Cao, C, Guo, Y, Lei, G & Zhu, J 2025, 'Stability Analysis for Three-Stage Serial PMSM Drive System Based on Bidirectional WPT', IEEE Transactions on Industrial Electronics, vol. 72, no. 5, pp. 4526-4534.
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Jin, X, Rao, P, Feng, W, Cui, J, Nimbalkar, S & Chen, Q 2025, 'Coupled Electric‐Thermal Damage Model for Lightning Strikes on Buried Pipeline', Contributions to Plasma Physics, vol. 65, no. 4.
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ABSTRACTA coupled electrothermal damage theory model for pipelines is proposed to assess the failure behavior of buried pipelines under lightning strikes. This article considers local thermal nonequilibrium (LTNE) conditions in the soil–water porous medium and the nonlinear characteristics of lightning functions. The calculation results show that the proposed theoretical model has better applicability and accuracy compared with previous models. Parametric analysis shows that under lightning conditions of Im = 20 kA and T1/T2 = 1.2/50 μs, the maximum local temperature of the soil around the pipeline can reach 2160 K, leading to pipeline breakdown. Metal pipelines are shown to be more effective in conducting charges, which alters the electric field distribution in the soil and impacts the formation of plasma channels. The half‐peak value of the lightning waveform has a significant impact on pipeline breakdown, and its increase will increase the risk of pipeline breakdown gradually. When considering LTNE conditions, the change in the pipeline surface temperature becomes more pronounced. Under 8/30 and 8/40 μs lightning waveforms, the pipeline surface temperature is approximately 150 and 550 K higher, respectively, compared with the thermal equilibrium conditions. The thermal conductivity and porosity of backfill soil can also affect the thermal damage of lightning‐struck pipelines. With clay filling, the highest pipeline surface temperature can reach 2590 K, while with fine sand and coarse sand, it is 1980 and 1510 K, respectively. The pipeline lightning disaster model proposed in this article has engineering significance for the investigation of pipeline lightning failure and disaster prevention mechanisms.
Jiu, J, Liu, H, Li, D, Li, X, Zhang, J, Yan, L, Fan, Z, Li, S, Du, G, Li, JJ, Wu, A, Liu, W, Du, Y, Zhao, B & Wang, B 2025, '3D Mechanical Response Stem Cell Complex Repairs Spinal Cord Injury by Promoting Neurogenesis and Regulating Tissue Homeostasis', Advanced Healthcare Materials, vol. 14, no. 7.
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AbstractSpinal cord injury (SCI) leads to acute tissue damage that disrupts the microenvironmental homeostasis of the spinal cord, inhibiting cell survival and function, and thereby undermining treatment efficacy. Traditional stem cell therapies have limited success in SCI, due to the difficulties in maintaining cell survival and inducing sustained differentiation into neural lineages. A new solution may arise from controlling the fate of stem cells by creating an appropriate mechanical microenvironment. In this study, mechanical response stem cell complex (MRSCC) is created as an innovative therapeutic strategy for SCI, utilizing 3D bioprinting technology and gelatin microcarriers (GM) loaded with mesenchymal stem cells (MSCs). GM creates an optimal microenvironment for MSCs growth and paracrine activity. Meanwhile, 3D bioprinting allows accurate control of spatial pore architecture and mechanical characteristics of the cell construct to encourage neuroregeneration. The mechanical microenvironment created by MRSCC is found to activate the Piezo1 channel and prevent excessive nuclear translocation of YAP, thereby increasing neural‐related gene expression in MSCs. Transplanting MRSCC in rats with spinal cord injuries boosts sensory and motor recovery, reduces inflammation, and stimulates the regeneration of neurons and glial cells. The MRSCC offers a new tissue engineering solution that can promote spinal cord repair.
Ju, M, He, C, Ding, C, Ren, W, Zhang, L & Ma, K-K 2025, 'All-Inclusive Image Enhancement for Degraded Images Exhibiting Low-Frequency Corruption', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 1, pp. 838-856.
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Junaid, M, Saha, G, Shahrear, P & Saha, SC 2025, 'Numerical evaluation of a dual phase change material-integrated cap for prolonged thermal protection in extreme heat', Results in Engineering, vol. 27, pp. 105870-105870.
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Kabir, MM, Choden, Y, Phuntsho, S, Tijing, L & Shon, HK 2025, 'Predictive machine learning optimization of anion exchange membrane water electrolysis systems', Desalination, pp. 119198-119198.
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Kabir, MM, Im, KS, Tijing, L, Choden, Y, Phuntsho, S, Mamun, MFK, Sabur, GM, Nam, SY & Shon, HK 2025, 'Integrated membrane distillation-solid electrolyte-based alkaline water electrolysis for enhancing green hydrogen production', Desalination, vol. 601, pp. 118580-118580.
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Kabir, MM, Sabur, GM, Mamun, MFK, Arman, Tijing, L, Choden, Y, Phuntsho, S & Shon, HK 2025, 'Hydrogels in next-generation energy solutions', Desalination, vol. 603, pp. 118639-118639.
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Kadiri, M, Tanji, A, Fan, X, Liaw, PK, Mahlia, TMI & Hermawan, H 2025, 'Corrosion of TiHfZrNbx high-entropy alloys in a simulated condition of proton exchange membrane water electrolyser', Electrochimica Acta, vol. 521, pp. 145925-145925.
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Kalhori, H, Tashakori, S, Halkon, B, Makki Alamdari, M, Li, B & Saberi, M 2025, 'Advances in impact force identification: A comprehensive review of techniques and mathematical innovations', Results in Engineering, vol. 26, pp. 105568-105568.
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Kamal, MS, Nimmy, SF & Dey, N 2025, 'Interpretable Code Summarization', IEEE Transactions on Reliability, vol. 74, no. 1, pp. 2280-2289.
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Kamat, P, Chandekar, H, Dlima, L, Gite, S, Pradhan, B & Alamri, A 2025, 'Comprehensive dataset on ripening stages of strawberries and avocados: From unripe to rotten', Data in Brief, vol. 60, pp. 111663-111663.
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Kanis, JA, Johansson, H, McCloskey, EV, Liu, E, Schini, M, Vandenput, L, Åkesson, KE, Anderson, FA, Azagra, R, Bager, CL, Beaudart, C, Bischoff-Ferrari, HA, Biver, E, Bruyère, O, Cauley, JA, Center, JR, Chapurlat, R, Christiansen, C, Cooper, C, Crandall, CJ, Cummings, SR, da Silva, JAP, Dawson-Hughes, B, Diez-Perez, A, Dufour, AB, Eisman, JA, Elders, PJM, Ferrari, S, Fujita, Y, Fujiwara, S, Glüer, C-C, Goldshtein, I, Goltzman, D, Gudnason, V, Hall, J, Hans, D, Hoff, M, Hollick, RJ, Huisman, M, Iki, M, Ish-Shalom, S, Jones, G, Karlsson, MK, Khosla, S, Kiel, DP, Koh, W-P, Koromani, F, Kotowicz, MA, Kröger, H, Kwok, T, Lamy, O, Langhammer, A, Larijani, B, Lippuner, K, McGuigan, FEA, Mellström, D, Merlijn, T, Nguyen, TV, Nordström, A, Nordström, P, O´Neill, TW, Obermayer-Pietsch, B, Ohlsson, C, Orwoll, ES, Pasco, JA, Rivadeneira, F, Schott, A-M, Shiroma, EJ, Siggeirsdottir, K, Simonsick, EM, Sornay-Rendu, E, Sund, R, Swart, K, Szulc, P, Tamaki, J, Torgerson, DJ, van Schoor, NM, van Staa, TP, Vila, J, Wright, NC, Yoshimura, N, Zillikens, MC, Zwart, M, Harvey, NC, Lorentzon, M & Leslie, WD 2025, 'Rheumatoid arthritis and subsequent fracture risk: an individual person meta-analysis to update FRAX', Osteoporosis International, vol. 36, no. 4, pp. 653-671.
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Karami, H, Thurn, B, de Boer, NK, Ramos, J, Covington, JA, Lozano, J, Liu, T, Zhang, W, Su, S & Ueland, M 2025, 'Application of gas sensor technology to locate victims in mass disasters – a review', Natural Hazards, vol. 121, no. 1, pp. 31-60.
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Abstract The occurrence of mass disasters are increasing as a result of changing climates and the growing threat of terrorist activities/conflicts. When these tragedies strike, it is critical to locate victims. While search and rescue dogs are trained to locate the living, cadaver detection dogs are trained to locate the deceased. These dogs rely on the volatile organic compounds (VOCs) emitted from the victims to do so. Knowing which dog unit to deploy can be challenging, and the victims’ makeup is likely to change following disasters in densely inhabited places, where commingling is likely to occur. The use of electronic nose technologies in forensic science is a recent breakthrough. Due to their ability to detect differing VOCs, this technology can be used to assist in the recovery of victims in disaster events. The most popular types of accessible gas sensor technologies are briefly introduced and compared in this article for their potential use to locate missing persons, both living and deceased. The current and future market needs are articulated, and the lack of enrichment of these needs is examined in relation to the capabilities of existing gas sensors. This will inform further research areas of preference to increase victim detection capabilities.
Kardani, R, Yadav, S, Altaee, A, Alsaka, L & Zhou, J 2025, 'Eco-friendly kappa-carrageenan-nano zerovalent iron hydrogel water and wastewater purification', Journal of Hazardous Materials, vol. 492, pp. 138123-138123.
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Karthikeyan, N, M, K, Prabagaran, S, Selvam, L, Venkatesh, R, Priya, CB, Al Obaid, S, Ali Alharbi, S & Kalam, MA 2025, 'Blending action of pyrolysis oil/diesel fuel featured with hydrogen on compression ignition engine performance and emission analysis', Petroleum Science and Technology, vol. 43, no. 13, pp. 1506-1521.
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Kathiresan, AC, Jeyaraj, PR, Albert, BK, Thanikanti, SB, Nallapaneni, MK & Alhelou, HH 2025, 'A versatile control of solar DVR for enhanced utilization and power quality improvement in a series‐connected wind‐solar farm', IET Renewable Power Generation, vol. 19, no. 1.
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AbstractThis paper presents a solar dynamic voltage restorer for hybrid series‐connected solar‐wind farms to mitigate the power quality problems. The operating region of the proposed hybrid system is derived and studied through graphical analysis. Upon examination of the system's operation under various grid conditions, the feasibility of solar PV power injected into the grid is verified. It is further proved that the series injection of voltage is also capable of mitigating the effects of voltage sag/swell, and unbalance, which have adverse effects on wind‐connected induction generators. Further, the effectiveness of Solar DVR to mitigate the fault ride‐through capability of the wind farm is analyzed. The non‐requirement for an energy storage device such as a battery is also validated. A control system for the series PV inverter is proposed and the computer simulations are performed to confirm the efficacy and ride‐through capability of the system.
Kazwini, T, Altaee, A, Ibrar, I, Alkadour, F, Hawari, AH, Zhou, J & Alsaka, L 2025, 'Sodium metasilicate sol-gel draw solution for seawater desalination and supplementing nutrients to soil', Desalination, vol. 600, pp. 118517-118517.
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Ke, Y, Xu, W, Ni, W, Yuan, X & Niyato, D 2025, 'Combating Beam Misalignment in mmWave UAV Networks: An Attitude Compensation-Based Method', IEEE Transactions on Vehicular Technology, vol. 74, no. 2, pp. 3521-3526.
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Keshavarz, R, Jafaryahya, J, Raikhman, M, Kikuchi, T & Shariati, N 2025, 'Multi-Band Accurate Soil Sensor for Simultaneous Moisture and Salinity Measurement: A Machine Learning-Enhanced Approach', IEEE Transactions on Instrumentation and Measurement, pp. 1-1.
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Khairnar, S, Gite, S, Pradhan, B, Thepade, SD & Alamri, A 2025, 'Optimizing CNN Architectures for Face Liveness Detection: Performance, Efficiency, and Generalization across Datasets', Computer Modeling in Engineering & Sciences, vol. 143, no. 3, pp. 3677-3707.
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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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Khanafer, D, Altaee, A, Hawari, AH, Aedan, Y, Zhou, J & Samal, AK 2025, 'Seawater pretreatment for thermal plant by pressure stimuli-responsive forward osmosis membrane', Energy Nexus, pp. 100488-100488.
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Khari, M, Dehghanbanadaki, A, Armaghani, DJ & Khandelwal, M 2025, 'Optimisation of Ensemble Learning Algorithms for Geotechnical Applications: A Mathematical Approach to Relative Density Prediction', Advances in Civil Engineering, vol. 2025, no. 1.
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The challenge of predicting relative dry density (Dr) in granular materials is addressed through advanced mathematical modelling and machine learning (ML) techniques. A novel approach to optimise ensemble learning algorithms is presented, with a focus placed on the mathematical foundations of these methods. An experimental dataset obtained from a mobile pluviator was utilised to develop and analyse various ML models. The mathematical analysis was centred on the optimisation and comparative performance of ensemble methods, with particular emphasis given to gradient boosting regression (GBR), AdaBoost regression, and extreme gradient boosting (XGBoost). The mathematical formulation of the GBR model was rigorously examined and optimised using advanced tuning functions, achieving exceptional performance metrics (mean squared error [MSE] = 11.91, mean absolute error [MAE] = 1.93, R2 = 0.997). Through sensitivity analysis, it was revealed that the distance between the shutter plate and the top sieve is the most significant factor affecting Dr prediction. A computational platform was developed within the Google Colab environment, demonstrating the practical application of the mathematical models. This research contributes to applied mathematics by showcasing advanced algorithmic approaches to solving complex geotechnical engineering problems while providing a rigorous mathematical foundation for future developments.
Khlaifat, N, Altaee, A, Zhou, J & Huang, Y 2025, 'Evaluation of wind resource potential using statistical analysis of probability density functions in New South Wales, Australia', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 47, no. 1, pp. 194-211.
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Wind energy is a vital part of Australia's energy mix. The first step in a wind power project at a particular site is to assess the wind resource potential and feasibility for wind energy production. Research on wind potential and statistical analysis has been done throughout the world. Currently, recent potential wind studies are lacking, especially in New South Wales (NSW), Australia. This study highlighted the feasibility of wind potential at four sites in NSW, namely Ballina, Merriwa, Deniliquin, and the Bega region. The type of wind speed distribution function dramatically affects the output of the available wind energy and wind turbine performance at a particular site. Therefore, the accuracy of four probability density functions was evaluated, namely Rayleigh, Weibull, Gamma, and Lognormal distributions. The outcomes showed Weibull provided the most accurate distribution. The annual average scale and shape parameters of Weibull distribution varied between 2.935-5.042 m/s and 1.137-2.096, respectively. The maximum shape and scale factors were at Deniliquin, while the minimum shape and scale factors were at Bega area. Assessment of power density indicated that Deniliquin had a marginal wind speed resource, while Ballina, Bega, and Merriwa had poor wind resources.
Khorshidi, MS, Merigó, JM & Beydoun, G 2025, 'Half a Century of Information Processing & Management: A bibliometric retrospective', Information Processing & Management, vol. 62, no. 6, pp. 104238-104238.
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Khorshidi, MS, Yazdanjue, N, Gharoun, H, Yazdani, D, Nikoo, MR, Chen, F & Gandomi, AH 2025, 'Semantic-Preserving Feature Partitioning for multi-view ensemble learning', Information Fusion, vol. 122, pp. 103152-103152.
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Khuat, TT, Bassett, R, Otte, E & Gabrys, B 2025, 'Uncertainty Quantification Using Ensemble Learning and Monte Carlo Sampling for Performance Prediction and Monitoring in Cell Culture Processes', Journal of Raman Spectroscopy, vol. 56, no. 7, pp. 623-636.
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ABSTRACTBiopharmaceutical products, particularly monoclonal antibodies (mAbs), have gained prominence in the pharmaceutical market due to their high specificity and efficacy. As these products are projected to constitute a substantial portion of global pharmaceutical sales, the application of machine learning models in mAb development and manufacturing is gaining momentum. This paper addresses the critical need for uncertainty quantification in machine learning predictions, particularly in scenarios with limited training data. Leveraging ensemble learning and Monte Carlo simulations, our proposed method generates additional input samples to enhance the robustness of the model in small training datasets. We evaluate the efficacy of our approach through two case studies: predicting antibody concentrations in advance and real‐time monitoring of glucose concentrations during bioreactor runs using Raman spectra data. Our findings demonstrate the effectiveness of the proposed method in estimating the uncertainty levels associated with process performance predictions and facilitating real‐time decision‐making in biopharmaceutical manufacturing. This contribution not only introduces a novel approach for uncertainty quantification but also provides insights into overcoming challenges posed by small training datasets in bioprocess development. The evaluation demonstrates the effectiveness of our method in addressing key challenges related to uncertainty estimation within upstream cell cultivation, illustrating its potential impact on enhancing process control and product quality in the dynamic field of biopharmaceuticals.
Kielly, M, Chacon, A, Caracciolo, A, Bolst, D, Rosenfeld, A, Carminati, M, Fiorini, C, Franklin, DR, Guatelli, S & Safavi-Naeini, M 2025, 'An exploratory study of shielding strategies for boron neutron capture discrimination in 10B Neutron Capture Enhanced Particle Therapy', Physica Medica, vol. 129, pp. 104866-104866.
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Kim, J, He, T & Shon, HK 2025, 'Editorial of special issue: Application of desalination technology', Desalination, vol. 608, pp. 118952-118952.
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Kim, J, Xuan, J, Liang, C & Hussain, FK 2025, 'Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model'.
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Kirsch, C, Baki Kocaballi, A, Johnston, A, Naweed, A & Stevenson, I 2025, 'Co‐Designing the Sound of Safety: Embracing Complexity in the Acoustic Vehicle Alerting System Sound for Zero Emission Buses', Human Factors and Ergonomics in Manufacturing & Service Industries, vol. 35, no. 3.
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ABSTRACTAround the world, transport organizations are transitioning their bus fleets from internal combustion engines to electrified zero emission buses (ZEBs). The quiet nature of these buses raises safety concerns for vulnerable road users. To address these concerns, new standards mandate that electric vehicles, including ZEBs, be equipped with an acoustic vehicle alerting system (AVAS) emitting sound at low speeds, to make vehicles more detectable. However, developing an effective AVAS sound requires balancing safety and technical constraints with diverse stakeholder needs. Using codesign, this study conducted a series of risk‐focused subject matter expertize workshops (n = 15), and a large user‐experience focused participatory design workshop (n = 41) to inform the AVAS sound design. The latter included empathy and user journey mapping techniques, facilitating the collection of insights from various perspectives. Results revealed stakeholder preferences for an AVAS sound that was both alerting and positive, and embodied qualities like calmness, politeness, and vibrancy. The workshops allowed for refinement of sound design requirements, although challenges emerged in balancing conflicting preferences and managing technical limitations to create a sound that could be both perceptible and nonintrusive. This study provides a framework for the development of an AVAS sound that could capture a range of stakeholder needs and preferences and lays a foundation for AVAS sounds that enhance safety while being positively received. It highlights the importance of inclusive, iterative design in advancing public transport safety and sustainability, with outcomes supporting the future sound design, testing, and implementation.
Klettner, A, Cetindamar, D & Sainty, R 2025, 'Stakeholder Governance and Corporate Purpose in Certified B Corps: Minimizing Conflict and Fostering Collaboration', Business & Society.
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Although a stakeholder-inclusive approach to corporate governance is becoming accepted more widely, we have little idea on how it can be achieved in practice or understood theoretically. B Lab Australia and Aotearoa New Zealand requires certified B Corporations (B Corps) to include in their corporate constitution formal commitments to pursuing a pro-social purpose and taking their stakeholders’ interests into account. Through interviews with 20 B Corp leaders, we explore how this model of stakeholder governance is implemented. We find that an organization-specific corporate purpose helps leaders to attract, select, and retain supportive stakeholders and minimize stakeholder conflicts. We identify three decision-making processes (Establishing, Applying, Developing) and three signaling processes (Announcing, Demonstrating, Sharing) through which stakeholder governance is achieved. Our findings extend emerging theories of stakeholder governance by moving away from a focus on conflict resolution to demonstrate its potential to foster collaborative systemic change. B Corps display a helpful model for purpose-led stakeholder governance with the potential to be applied more widely.
Krishankumar, R, Mishra, AR, Rani, P, Ecer, F, Zavadskas, EK, Ravichandran, KS & Gandomi, AH 2025, 'Two-Stage EDAS Decision Approach with Probabilistic Hesitant Fuzzy Information', Informatica, pp. 65-97.
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This paper develops a two-stage decision approach with probabilistic hesitant fuzzy data. Research challenges in earlier models are: (i) the calculation of occurrence probability; (ii) imputation of missing elements; (iii) consideration of attitude and hesitation of experts during weight calculation; (iv) capturing of interdependencies among experts during aggregation; and (v) ranking of alternatives with resemblance to human cognition. Driven by these challenges, a new group decision-making model is proposed with integrate methods for data curation and decision-making. The usefulness and superiority of the model is realized via an illustrative example of a logistic service provider selection.
Krishna, RS, Mishra, S, Sethy, N, Mustakim, SM, Boopathy, R, Rawat, S & Qureshi, TS 2025, 'Impact of high-temperature exposure on the thermal and physio-mechanical performance of graphene-reinforced geopolymer composites', Construction and Building Materials, vol. 489, pp. 142285-142285.
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Geopolymer composites are emerging as sustainable materials with significant potential in the construction industry. While geopolymers are known for their inherent thermal resistance properties, their mechanical stability at elevated temperatures remains a key challenge due to microstructural degradation and moisture-induced damage. This study investigates the reinforcing effect of graphene oxide (GO) on fly ash-based geopolymer composites under ambient and high-temperature (900°C) conditions. Low-cost, industrial-grade GO with iron impurities was combined into the geopolymer matrix at varying dosages (0.1–0.4 wt% of binder). The mechanical properties and microstructural characteristics of graphene-reinforced geopolymer composites (GRGC) were then compared with plain geopolymer composites (without GO)- control. Results indicated that the addition of 0.2 wt. (%) GO in GRGC composites enhanced the compressive strength by 16.59 (%) and 18.48 (%) at 7 and 28 days of curing, respectively, compared to the control specimens. The strength enhancement in GRGC was more significant at a high-temperature exposure, as reflected by a 104 (%) increase in compressive strength compared with the control specimens. The physio-mechanical behaviour was analysed through microstructural investigations, such as Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and Thermogravimetric Analysis (TGA). Microstructural analyses revealed that GO did not contribute to any new phase formation, acting as a nanofiller, refining the pore structure and enhancing matrix densification without altering the primary amorphous gel phase, while also improving thermal stability and reducing mass loss at elevated temperatures. The results suggest that GO enhanced thermal stability by reducing dehydration rates and transforming the amorphous gel matrix into a uniform crystalline structure after high-temperature exposure. These findings demon...
Krishna, RS, Prasad, VD, Sethy, N, Panda, B, Boopathy, R, Mustakim, SM & Rawat, S 2025, 'Investigation on in‐situ geopolymer coatings: A comparative analysis of lycopodium powder and polydimethylsiloxane as functional additives', Structural Concrete, vol. 26, no. 3, pp. 2704-2725.
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AbstractGeopolymer‐based inorganic coatings provide an effective alternative to conventional chemical‐based solutions. However, the coatings are not universally applicable, especially in critical applications such as underwater. This research aims to investigate and enhance geopolymer‐based coating through specific utilization of functional additives such as reduced graphene oxide (rGO), lycopodium powder (LCP), and polydimethylsiloxane (PDMS) for underwater applications. These additives were incorporated in limited dosages (<3 wt.% of binder), and their potential is further assessed in improving the coating material in terms of the resulting water resistance, mechanical properties, and durability against NaCl, MgSO4, HNO3, and H2SO4 attacks. The introduction of rGO facilitated a 35% increase in the 28‐day compressive strength and 33.83% in the scratch hardness, whereas (rGO + LCP) improved the water contact angle by 125%. Additionally, the developed coatings were further analyzed using an optical microscope and x‐ray diffractometer to understand their bonding potential with steel or cementitious substrates and phase formation. Life cycle cost analysis was also conducted to evaluate the economic viability of the geopolymer coatings, revealing a potential cost reduction of approximately 16%–42% with the sourcing of locally available materials.
Kumar Dalai, S, Prasad Panda, K, Siwakoti, YP & Panda, G 2025, 'Three‐Phase Neutral‐Point‐Clamped Multilevel Inverter Interfacing Grid Interactive Renewable Energy System', International Journal of Circuit Theory and Applications, vol. 53, no. 6, pp. 3450-3461.
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ABSTRACTThe potentiality of DC microgrid to incorporate renewable energy sources and improve power distribution efficiency has garnered considerable interest, especially in light of the ever‐increasing demand for efficient and clean energy systems. Multilevel inverters (MLIs) play a crucial role in interfacing these grid‐tied microgrids with various loads and energy sources. To improve the efficiency and dependability of such systems, this paper presents a three‐phase MLI interfacing solar photovoltaic (PV)‐battery‐based grid‐tied system. The system comprises of MLI with features such as (a) neutral point clamped input, (b) reduced number of switches to produce 7‐level output per phase, (c) self‐balanced capacitors, and (d) inherent voltage boosting ability. A novel power management control algorithm is designed to enhance the dynamic response and efficiency of the developed system. The effectiveness of the proposed system and its workability is validated through extensive experiments demonstrating enhanced power quality and efficient energy utilization.
Kurian, JC & John, BM 2025, 'Designing a Digital Health Intervention Using Protection Motivation Theory: A Design Science Research', Journal of Technology in Behavioral Science.
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Abstract Digital health plays a vital role in addressing the needs of communities through health interventions. In recent years, one of the ways to promote positive health outcomes was through mobile health applications (i.e., mHealth apps). The development of mHealth apps was further intensified by the recent pandemic, and the digital health support services offered by these apps to chronic patients are immense even after the pandemic. However, little research provides theoretical guidance on how to design and develop the functionalities of mHealth apps based on existing theories and their constructs. Theory-driven design and development of applications are necessary to guide researchers and practitioners on the process of building health behavior interventions which will also help to understand research challenges across fields. To address this research gap, we designed and developed an mHealth app through the lens of Protection Motivation Theory (PMT) and tested it with general users and an industry partner in Australia. We followed the design science methodology, and our findings suggest five functionalities (i.e., secure login, awareness and updates, learning resources, recording test results, and social networking and analytics) supported by four user themes (i.e., Mobile App Characteristics, Design and Privacy Issues, Information Archiving, and Information Authentication). From a theoretical perspective, this study extends the PMT by introducing positive and negative aspects to the theoretical constructs of Response Efficacy and Response Cost which is a coping appraisal construct. The study also recommends three design principles to follow for future mHealth apps and present the practical implications of this study using the National Digital Health Strategy.
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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Lai, W, Xie, H, Xu, G & Li, Q 2025, 'RVISA: Reasoning and Verification for Implicit Sentiment Analysis', IEEE Transactions on Affective Computing, pp. 1-12.
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Larpruenrudee, P, Bennett, NS, Fitch, R, Sauret, E, Gu, Y, Hossain, MJ & Islam, MS 2025, 'The enhancement of metal hydride hydrogen storage performance using novel triple-branched fin', Journal of Energy Storage, vol. 123, pp. 116659-116659.
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Le, DT, Sutjipto, S, Nguyen, DDK & Paul, G 2025, 'Design, Integration, and Field Testing of a Digital Twin-Based Teleoperated Rock Scaling Robot', IEEE Transactions on Field Robotics, vol. 2, pp. 188-207.
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This paper presents the design, integration, and field testing of a digital twin-based teleoperated rock scaling robot aimed at improving safety in mining operations. Traditional rock scaling, which involves the removal loose rocks to prevent rockfall, poses significant risks to mine site workers. The proposed solution is a teleoperated custom mobile manipulator capable of rope-based abseiling locomotion, equipped with an air chipper end-effector. Teleoperation is facilitated by live digital twins of the robot and environment, with a virtual reality (VR) interface that allows operators to perform rock scaling tasks within an immersive virtual reconstruction of the remote scene. The robot's hardware design and sensing capabilities are detailed, along with the system's teleoperation architecture. Key components include the integration of an optimised, hardware accelerated, image-based point cloud streaming implementation; a markerless depth-camera extrinsic calibration process suitable for field settings; and the system’s teleoperation interfaces featuring a cyber-physical VR interface with affordance feedback. Field tests at a sandstone quarry and an open-pit mine demonstrate significant improvements in operator safety, and highlight the system’s ability to withstand harsh mining environments while performing teleoperated rock scaling at its current scaled-down size and power. We collected and analysed user data from rope access technicians with no prior experience in robot teleoperation or VR. The results suggest the system's intuitiveness with learning effects over time. Lessons from these site trials, including hardware and software limitations, are discussed, providing directions for further robot design improvements and enhancements to the digital twin teleoperation architecture.
Le, NT, Keenan, M, Nguyen, A, Ghazvineh, S, Yu, Y, Li, J & Manalo, A 2025, 'A supervised machine learning approach for structural overload classification in railway bridges using weigh-in-motion data', Structures, vol. 71, pp. 108005-108005.
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Le, TD, Nguyen, HPT, Nguyen, MT, Le, BNM, Dang, KK, Ha, QP, Nguyen, TVT & Nguyen, HQ 2025, 'Exploring new frontiers: Current status and future research directions for AIoT application in shrimp farming in the Vietnamese Mekong delta', Aquacultural Engineering, vol. 111, pp. 102559-102559.
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Le, TH, Nguyen, HAD, Ha, QP, Ahmed, M, Barthelemy, X, Jiang, N, Duc, H, Azzi, M & Riley, M 2025, 'Dependable Dempster-Shafer Inference Framework for Urban Air Quality Monitoring', IEEE Sensors Journal, vol. 25, no. 14, pp. 27662-27672.
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Le, TH, Nguyen, HAD, Ha, QP, Tran, MQ, Ahmed, M, Kong, J, Barthelemy, X, Duc, H, Jiang, N, Azzi, M & Riley, M 2025, 'Dempster-Shafer ensemble learning framework for air pollution nowcasting', E3S Web of Conferences, vol. 626, pp. 01003-01003.
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Deep-learning has emerged as a powerful approach to significantly improve forecast accuracy for air quality estimation. Several models have been developed, demonstrating their own merits in some scenarios and for certain pollutants. In nowcasting, the prediction of air pollution over a small time period essentially demands accurate and reliable estimates, especially in the event cases. From these, selecting the most suitable model to achieve the required forecast performance remains challenging. This paper presents an ensemble framework based on the Dempster-Shafer theory for data fusion to identify the most accurate and reliable forecasts of air pollution obtained from multiple deep neural network models. Our framework is evaluated against three popular machine learning methods, namely, LightGBM, Random Forest, and XGBoost. Experiments are conducted on two horizons: 6-hour and 12-hour predictions using real-world air quality data collected from state-run monitoring stations and low-cost wireless sensor networks.
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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Lee, Y, Kim, S, Shon, HK & Jeong, S 2025, 'Effective removal of Micro- and nanoplastics from water using Iron oxide nanoparticles: Mechanisms and optimization', Chemical Engineering Journal, vol. 519, pp. 165739-165739.
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Legg, R, Prior, J, McIntyre, E, Liu, E, Tracy, M, Tan, L, Dawson, A, Richmond, J & Perry, C 2025, 'Health system adaptation to extreme weather events in Australia: A scoping review', The Journal of Climate Change and Health, vol. 22, pp. 100443-100443.
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Lenin Kumar, AR, Nimbalkar, S & Dodagoudar, GR 2025, 'Performance of Railway Embankment on Soft Ground Reinforced with Granular Columns', Transportation Infrastructure Geotechnology, vol. 12, no. 6.
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Leong, D, Do, T & Lin, C-T 2025, 'The Distinction Between Object Recognition and Object Identification in Brain Connectivity for Brain–Computer Interface Applications', IEEE Transactions on Cognitive and Developmental Systems, vol. 17, no. 1, pp. 89-101.
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Lewis, E, Wetzler, Z, Li, G, Saw, W, Nathan, G, Kennedy, E, Stockenhuber, M, Oliver, T & Chinnici, A 2025, 'Thermal treatment of lizardite for mineral carbonation using high flux radiation', Fuel, vol. 386, pp. 134187-134187.
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Li, A, Yang, B, Huo, H, Hussain, FK & Xu, G 2025, 'Self-supervised dual graph learning for recommendation', Knowledge-Based Systems, vol. 310, pp. 112967-112967.
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Li, B, Guo, Z, Hu, C, Zhu, S & Wen, S 2025, 'Safe Formation Control of Uncertain Multiagent Systems From a Bayesian Perspective', IEEE Transactions on Automatic Control, vol. 70, no. 3, pp. 1929-1934.
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Li, B, Wang, S, Guo, Z, Zhu, S, Huang, J, Sun, J, Wen, G & Wen, S 2025, 'Safe Control Framework of Multi-Agent Systems From a Performance Enhancement Perspective', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 7622-7631.
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Li, C, Lai, JCS & Oberst, S 2025, 'Optimizing self-organized topology of recurrence-based complex networks', Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 35, no. 3.
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Networks and graphs have emerged as powerful tools to model and analyze nonlinear dynamical systems. By constructing an adjacency matrix from recurrence networks, it is possible to capture critical structural and geometric information about the underlying dynamics of a time series. However, randomization of data often raises concerns about the potential loss of deterministic relationships. Here, in using the spring-electrical-force model, we demonstrate that by optimizing the distances between randomized points through minimizing an entropy-related energy measure, the deterministic structure of the original system is not destroyed. This process allows us to approximate the time series shape and correct the phase, effectively reconstructing the initial invariant set and attracting dynamics of the system. Our approach highlights the importance of adjacency matrices derived from recurrence plots, which preserve crucial information about the nonlinear dynamics. By using recurrence plots and the entropy of diagonal line lengths and leveraging the Kullback–Leibler divergence as a relative entropic measure, we fine-tune the parameters and initial conditions for recurrence plots, ensuring an optimal representation of the system’s dynamics. Through the integration of network geometry and energy minimization, we show that data-driven graphs can self-organize to retain and regenerate the fundamental features of the time series, including its phase space structures. This study underscores the robustness of recurrence networks as a tool for analyzing nonlinear systems and demonstrates that randomization, when guided by informed optimization, does not erase deterministic relationships, opening new avenues for reconstructing dynamical systems from observational data.
Li, C, Li, J & Zhu, X 2025, 'Flexural Behavior of Steel–Concrete Composite Beams Utilizing Innovative Long-Nut Shear Connectors: A Numerical and Experimental Study', Journal of Structural Engineering, vol. 151, no. 4.
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Li, C, Lin, S, Tang, T, Wang, G, Li, M, Liang, X & Chang, X 2025, 'BossNAS Family: Block-Wisely Self-Supervised Neural Architecture Search', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 5, pp. 3500-3514.
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Li, 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, C, Qi, X, Chen, B, Huang, S, Valls Miro, J, Huang, H, Ni, W & Ma, H 2025, 'Marden-Based Homotopic Enclosed Safe Motion Corridor Generation for UAV Navigation in Complex Environments', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 17486-17500.
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Li, C, Wang, Q, Zhang, M, Gao, W & Luo, Z 2025, 'Two-scale concurrent topology optimization of multiple lattice materials with non-uniform thickness interfaces', Computer Methods in Applied Mechanics and Engineering, vol. 444, pp. 118108-118108.
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Li, F, Sun, Y, Wang, T, Zhu, L & Chang, X 2025, 'Fast Partial-Modal Online Cross-Modal Hashing', IEEE Transactions on Image Processing, vol. 34, pp. 4440-4455.
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Li, F, Wang, Z, Wang, T, Zhu, L & Chang, X 2025, 'Generative Augmentation Hashing for Few-shot Cross-Modal Retrieval', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Li, G, Luan, TH, Zheng, J, Lai, C, Zhang, K & Yu, S 2025, 'SECR: A Secure and Efficient Charging Reservation Scheme Based on Digital Twin in Vehicular Network', IEEE Internet of Things Journal, vol. 12, no. 8, pp. 10434-10452.
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Li, G, Wang, J, Shi, K, Cai, X, Dong, S & Wen, S 2025, 'Synchronization of Markovian reaction–diffusion neural networks in complex noise settings: A Time-Space control approach', Communications in Nonlinear Science and Numerical Simulation, vol. 147, pp. 108800-108800.
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Li, H, Teng, F, Guo, Q, Zhang, JA, Huang, X & Cheng, Z 2025, 'Efficient Asynchronous Uplink Sensing in Perceptive Mobile Networks via Accurate Delay Estimation', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Li, H, Xiang, Y, Guo, Q, Liu, L, Huang, X, Cheng, Z & Pang, Y 2025, 'An Efficient Direct Downlink Sensing Method Using 5G NR SSB Signals in Perceptive Mobile Networks', IEEE Internet of Things Journal, vol. 12, no. 11, pp. 15360-15369.
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Li, J, Li, X, Li, Y, Liu, H & Wang, Q 2025, 'Artificial sweeteners in wastewater treatment plants: A systematic review of global occurrence, distribution, removal, and degradation pathways', Journal of Hazardous Materials, vol. 494, pp. 138644-138644.
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Li, J, Ni, M, Dong, Y, Zhu, T, Gong, Y & Liu, W 2025, 'AICAttack: Adversarial Image Captioning Attack with Attention-based Optimization', Machine Intelligence Research, vol. 22, no. 4, pp. 769-782.
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Abstract Recent advances in deep learning research have shown remarkable achievements across many tasks in computer vision (CV) and natural language processing (NLP). At the intersection of CV and NLP is the problem of image captioning, where the related models’ robustness against adversarial attacks has not been well studied. This paper presents a novel adversarial attack strategy, attention-based image captioning attack (AICAttack), designed to attack image captioning models through subtle perturbations to images. Operating within a black-box attack scenario, our algorithm requires no access to the target model’s architecture, parameters, or gradient information. We introduce an attention-based candidate selection mechanism that identifies the optimal pixels for attack, followed by a customized differential evolution method to optimize the perturbations of the pixels’ RGB values. We demonstrate AICAttack’s effectiveness through extensive experiments on benchmark datasets against multiple victim models. The experimental results demonstrate that our method outperforms current leading-edge techniques by achieving consistently higher attack success rates.
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, vol. 13, no. 4, pp. 5084-5096.
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Li, K, Lu, J, Zuo, H & Zhang, G 2025, 'Fuzzy Domain Adaptation From Heterogeneous Source Teacher Models', IEEE Transactions on Fuzzy Systems, vol. 33, no. 6, pp. 1840-1852.
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Li, K, Zha, X, Sun, X & Lei, G 2025, 'Model Predictive Control With Series Structure for Five-Phase PMSHM Based on Discrete Space Virtual Voltage Modulation', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 1313-1323.
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Li, L & Kang, K 2025, 'How do family support factors influence college students’ online-startup thinking?', Journal of Entrepreneurship in Emerging Economies, vol. 17, no. 2, pp. 215-238.
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PurposeThe purpose of this study is to present the relationship between family support factors and Chinese college students’ online-startup thinking on live streaming platforms. Considering China's specific online entrepreneurial environment, this paper divides Chinese college students’ online-startup thinking according to the liberal–conservative thinking theory. This study classifies family support factors based on the tangible–intangible resource division theory. Different tangible and intangible factors have different impacts on their online-startup thinking.Design/methodology/approachThis study tests 588 samples based on the partial least squares path modelling and variance-based structural equation modelling. This study promotes importance-performance map analysis to explore additional findings of influencing factors and provide suitable suggestions for Chinese college students and related departments.FindingsTangible family support factors, such as labour resources support, and intangible family support factors, such as verbal encouragement, can positively enhance Chinese college students’ liberal thinking to online-startup and decrease their conservative thinking. Meanwhile, according to importance-performance map analysis results, verbal encouragement from the intangible unit instead of financial resource support from the tangible unit has a higher total effect and performance on Chinese college students’ liberal thinking and conservative thinking.Originality/valueThis study draws on psychology research based on Chinese college students’ unique entrepreneurial menta...
Li, L, Deng, M, Su, S, Hall, RM & Tipper, JL 2025, 'An enhanced UHMWPE wear particle detection approach based on YOLOv9', Medical Engineering & Physics, vol. 142, pp. 104377-104377.
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Li, L, Kang, K & Feng, Y 2025, 'Parents’ effects on Chinese students’ digital entrepreneurship motivation on live streaming platforms: regional perspective using multi-group analysis', Journal of Entrepreneurship in Emerging Economies, vol. 17, no. 3, pp. 650-671.
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PurposeThis paper aims to explore the effects of parents’ support factors on Chinese university students’ digital entrepreneurship motivation on live streaming platforms. Based on the Social support theory, this study divides influencing factors into emotional, instrumental, informational and appraisal aspects. Meanwhile, considering the impact of China’s regional differences, the paper refers to the Regional difference theory and performs a multi-group analysis to assess the differences based on Chinese university students’ regional backgrounds.Design/methodology/approachBy testing 556 samples based on the partial least squares path modelling and variance-based structural equation modelling, all support factors parents provide can stimulate Chinese university students’ digital entrepreneurship motivation.FindingsBased on the multi-group comparison, parents’ informational support exerts a more substantial influence on the digital entrepreneurship motivation for university students from central and east regions rather than those from the western region, and parents’ instrumental support exerts a lower influence on digital entrepreneurship motivation for east university students than for west university students.Originality/valueThis paper applies the Social support theory as a theoretical framework to divide the impact factors, and it uses the Regional difference theory as a guide for the multi-group analysis of correlations, which is significant for online entrepreneurial motivation research and a better understanding of student groups. In addition to testing the hypotheses, the...
Li, M, Beck, B, Rathnayake, T, Meng, L, Chen, Z, Cosgun, A, Chang, X & Kulić, D 2025, 'A benchmark for cycling close pass detection from video streams', Transportation Research Part C: Emerging Technologies, vol. 174, pp. 105112-105112.
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Li, M, Cai, J, Deng, L, Li, X, Iacopi, F & Yang, Y 2025, 'Additively manufactured conductive and dielectric 3D metasurfaces for independent manipulation of broadband orbital angular momentum', Materials & Design, vol. 249, pp. 113500-113500.
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Li, M, Guo, YJ & Chen, S-L 2025, 'Enable Continuous Beam Scanning for Joint Communication and Sensing Employing the Generalized Joined Coupler Matrix', IEEE Transactions on Vehicular Technology, pp. 1-13.
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Li, N, Zhu, X, Miao, Y, Wang, Z, Lin, CSK & Li, C 2025, 'Meta-analysis and empirical research on the effectiveness of biochar in remediating tetracyclines pollution in water bodies', Bioresource Technology, vol. 435, pp. 132917-132917.
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Li, Q, Li, H, Li, X, Li, J, Liu, K, Wei, X, Peng, K & Wu, C 2025, 'Radial Pressure Characteristics on Borehole Walls at Decoupled Charge Blasting', Rock Mechanics and Rock Engineering, vol. 58, no. 2, pp. 2403-2418.
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Li, Q, Wang, Z, Xia, H, Li, G, Cao, Y, Yao, L & Xu, G 2025, 'HOT-GAN: Hilbert Optimal Transport for Generative Adversarial Network', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, pp. 4371-4384.
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Li, S, Ji, J, Feng, K, Zhang, K, Ni, Q & Xu, Y 2025, 'Composite Neuro-Fuzzy System-Guided Cross-Modal Zero-Sample Diagnostic Framework Using Multisource Heterogeneous Noncontact Sensing Data', IEEE Transactions on Fuzzy Systems, vol. 33, no. 1, pp. 302-313.
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Li, S, Zhao, H, Huang, Y, Ding, H, Hua, S & Wang, Z 2025, 'Optimization of material-energy Co-management in a proton exchange membrane fuel cell', International Journal of Hydrogen Energy, vol. 101, pp. 391-402.
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Li, S, Zhou, X & Feng, Y 2025, 'Benchmarking Quantum Circuit Transformation With QKNOB Circuits', IEEE Transactions on Quantum Engineering, vol. 6, pp. 1-15.
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Li, T, Li, G, Deng, X, Zhu, T, Li, JJ, Chen, C, Jia, J, Zhang, S & Zhang, K 2025, 'α‐Asarone Promotes Tendon–Bone Healing Through Regulating Dmp1 Transcription via Targeting Transcription Factor PPARG in BMSCs', The FASEB Journal, vol. 39, no. 11.
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ABSTRACTThe tendon–bone interface (TBI) is challenging to restore following injury, frequently resulting in unsatisfactory healing even after surgical reconstruction. α‐Asarone (αASA), a bioactive ingredient derived from the Chinese medicinal plant Calamus, has shown benefits in the treatment of inflammatory conditions. However, its applications in musculoskeletal repair are rarely investigated. This was the first study to examine the therapeutic effects of αASA on TBI healing and elucidate the associated healing mechanisms. In a mouse model of TBI healing, αASA treatment significantly improved the biomechanical properties and osseointegration of tendon–bone samples over 10 weeks. The addition of αASA to in vitro cultures of bone marrow mesenchymal stem cells (BMSCs) greatly enhanced osteogenic differentiation. Using network pharmacology, 114 co‐targeting genes were identified between αASA targets and TBI‐related genes. RNA‐seq analysis revealed that the top 20 differentially expressed genes (DEGs) were involved in tissue mineralization and ossification processes. A total of 207 transcription factors (TFs) were predicted for these DEGs, with 9 identified as core co‐target genes. Surface plasmon resonance (SPR) confirmed the strong affinity of αASA for the PPARG TF, while luciferase assays demonstrated PPARG binding to the Dmp1 promoter to regulate transcription. Thus, αASA promotes osteogenic differentiation and improves TBI healing by selectively downregulating PPARG, hence reducing PPARG binding to the Dmp1 promoter. This enhances Dmp1 transcription, a critical factor in osteoblast maturation and mineralization, leading to improved tendon–bone integration. These findings provide new insights into the potential to apply αASA for enhancing TBI healing in the management of tendon–bone injuries.
Li, T, Sun, X, Yang, Z & Lei, G 2025, 'Simplified Two-Step Model Predictive Control With Fast Voltage Vector Search', IEEE Transactions on Industrial Electronics, vol. 72, no. 4, pp. 3303-3312.
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Li, T, Zhao, S, Huang, Y, Lu, J & Burnett, IS 2025, 'A distributed adaptive wave field synthesis system', The Journal of the Acoustical Society of America, vol. 157, no. 3, pp. 2221-2235.
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The conventional wave field synthesis (WFS) theory is based on the free field assumption and the performance of systems based on it deteriorates significantly in reverberant environments. By introducing an error microphone array to monitor reproduction errors, the adaptive WFS (AWFS) system adjusts the loudspeaker signals to correct the sound field in reverberant environments. The AWFS system utilizes a centralized control strategy with a single processor, which imposes a high computational burden on the processor due to global error estimation, limiting the application scale. To address this issue, this paper proposes a distributed AWFS (DAWFS) system for an acoustic sensor and actuator network using a distributed signal processing strategy. Simulation results in a rectangular room demonstrate that the proposed DAWFS system can achieve comparable sound reproduction performance to the conventional AWFS system, both at the near-field error microphone array and in the target listening area. A global computational complexity analysis shows that the proposed DAWFS system exhibits significantly lower computational complexity than existing AWFS systems in various application scenarios, especially for massive channel systems. The results further demonstrate the potential applicability of the proposed DAWFS system in realistic reverberant environments.
Li, W, Xu, Z, Chu, L, Shi, Q, Braun, R & Shi, J 2025, 'FMCW Radar-Based Drowsiness Detection With a Convolutional Adaptive Pooling Attention Gated-Recurrent-Unit Network', IEEE Transactions on Radar Systems, vol. 3, pp. 71-87.
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Li, W, Zhao, B, Zhu, L, Wang, Y, Zhong, Q & Yu, S 2025, 'TCEC: Integrity Protection for Containers by Trusted Chip on IoT Edge Computing Nodes', IEEE Sensors Journal, vol. 25, no. 9, pp. 16269-16280.
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Li, X, Fang, Z, Zhang, Y, Ma, N, Bu, J, Han, B & Wang, H 2025, 'Characterizing Submanifold Region for Out-of-Distribution Detection', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 1, pp. 130-147.
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Li, X, Fu, Q & Wang, Q 2025, 'Wildfires jeopardize drinking water safety', Science, vol. 388, no. 6743, pp. 159-159.
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Li, X, Li, J, Liu, H, Mínguez-Alarcón, L, van Loosdrecht, MCM & Wang, Q 2025, 'Lifting of travel restrictions brings additional noise in COVID-19 surveillance through wastewater-based epidemiology in post-pandemic period', Water Research, vol. 274, pp. 123114-123114.
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Li, X, Zhang, Y, Long, G, Hu, Y, Lu, W, Chen, M, Zhang, C & Gong, Y 2025, 'Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 8, pp. 4446-4459.
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Li, X, Zhang, Z, Liu, H, Wen, H & Wang, Q 2025, 'The fate of intracellular and extracellular antibiotic resistance genes during ultrafiltration-ultraviolet-chlorination in a full-scale wastewater treatment plant', Journal of Hazardous Materials, vol. 486, pp. 137088-137088.
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Li, Y, Feng, K, Noman, K, Ji, J & Li, Z 2025, 'Editorial: Application of digital twin technology in prognostic and health management of complex machineries', Measurement, vol. 239, pp. 115629-115629.
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Li, Y, Sun, R, Chen, C, Wang, X, Zhang, Y & Zhang, W 2025, 'Discovering Cliques in Attribute Graphs Based on Proportional Fairness', IEEE Transactions on Knowledge and Data Engineering, pp. 1-6.
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Li, Y, Wang, H, Duan, Y, Zhang, J & Li, X 2025, 'A closer look at the explainability of Contrastive language-image pre-training', Pattern Recognition, vol. 162, pp. 111409-111409.
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Li, Y, Wang, H, Xu, W, Xiao, T, Liu, H, Tu, M, Wang, Y, Yang, X, Zhang, R, Yu, S, Guo, S & Li, R 2025, 'Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Li, Y, Zhang, J, Liu, X, Liu, H, Wang, L, Cheng, D, Wang, Y, Guo, W & Ngo, HH 2025, 'Efficient antibiotics removal by pig manure-based magnetic biochar-driven catalytic degradation', Journal of Water Process Engineering, vol. 70, pp. 107013-107013.
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Li, Z, Liu, S, Yu, X, Bhavya, K, Cao, J, Diffenderfer, JD, Bremer, P-T & Pascucci, V 2025, '“Understanding Robustness Lottery”: A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches', IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 9, pp. 6337-6352.
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Li, Z, Yang, C, Chen, Y, Wang, X, Chen, H, Xu, G, Yao, L & Sheng, M 2025, 'Graph and Sequential Neural Networks in Session-based Recommendation: A Survey', ACM Computing Surveys, vol. 57, no. 2, pp. 1-37.
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Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users’ short-term preferences and aims at providing a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.
Lian, M, Guo, Z, Wang, X, Wen, S & Huang, T 2025, 'Distributed Algorithms for Linear Equations Over General Directed Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 9127-9135.
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Lian, X, Zhang, Y, Wu, M & Guo, Y 2025, 'Do scientific knowledge flows inspire exploratory innovation? Evidence from US biomedical and life sciences firms', Technovation, vol. 140, pp. 103153-103153.
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Liang, H, Qin, L, Feng, R, Shim, J, Huang, X, Xu, X, Zhao, D, Yu, Z, Boczek, T, Li, M, Tong, Y, Huang, J, Gao, Q, Wang, L, Cao, X, Liu, D, Du, K, Xu, J, Zhao, Y, Wang, W, Seehus, CR, Zhao, W & Guo, F 2025, 'Increased NaV1.2 expression and its interaction with CaM contribute to the hyperexcitability induced by prolonged inhibition of CaMKII', Epilepsia, vol. 66, no. 7, pp. 2521-2537.
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AbstractObjectiveDysfunction of calcium/calmodulin (CaM)–dependent kinase II (CaMKII) has been involved in hyperexcitability‐related disorders including epilepsy. However, the relationship between CaMKII and neuronal excitability remains unclear.MethodsNeuronal excitability was detected in vivo and in vitro by electroencephalography (EEG), patch clamp and multi‐electrode array (MEA), respectively. Next, we assessed the currents of voltage‐gated sodium channels (VGSCs) by patch clamp, and mRNA and protein expressions of VGSCs were determined by real‐time quantitative reverse transcription‐polymerase chain reaction (qRT‐PCR) and western blot, respectively. Meanwhile, the association between the nuclear receptor subfamily 4 group A member 2 (NR4A2) and promoters of Scn2a, was determined by chromatin immunoprecipitation (ChIP)‐qPCR. In addition, we utilized co‐immunoprecipitation (Co‐IP), immunofluorescence labeling, and pull‐down to determine the interaction between VGSCs and CaM.ResultsProlonged CaMKII inhibition by KN93, an inhibitor of CaMKII, for 24 h and CaMKII knockdown induced more seizure‐like events in Wistar rats, TRM rats and C57BL/6 mice, and led to hyperexcitability in primary hippocampal neurons and human induced‐pluripotent stem cell (hiPSC)–derived cortical neurons. In addition, prolonged CaMKII inhibition resulted in elevated persistent sodium current (INaP)/transient sodium current (INaT) and increased mRNA and protein expressions of NaV1.2. Meanwhile, prolonged CaMKII inhibition by KN93 decreased NR4A2 expression and contributed to a transcriptional repression role of NR4A2 in <...
Liao, D, Shu, X, Li, Z, Liu, Q, Yuan, D, Chang, X & He, Z 2025, 'Fine-grained Feature and Template Reconstruction for TIR Object Tracking', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Lim, JY, Goh, CYM, Kang, KW, Li, J & Wu, C 2025, 'Structural response of steel-concrete composite panels to near field simultaneous blast and fragmentation loading', International Journal of Impact Engineering, vol. 195, pp. 105142-105142.
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Lin, A, Xiang, Y, Li, J & Prasad, M 2025, 'Dynamic Appearance Particle Neural Radiance Field', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 7, pp. 6853-6866.
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Lin, B, Nie, Y, Wei, Z, Chen, J, Ma, S, Han, J, Xu, H, Chang, X & Liang, X 2025, 'NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 7, pp. 5945-5957.
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Lin, K, Chen, E, Taylor, I, Hunter, S & Sun, J 2025, 'National improvements in quality of prenatal healthcare across states and territories in India from 2006 to 2021: a systematic analysis', Journal of Public Health, vol. 33, no. 3, pp. 565-576.
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Lin, K, Jia, J, Zhu, X, Zhang, B, Zhu, Z, Li, L & Sun, J 2025, 'Drug addiction and impact of urbanization: a systematic review', Current Opinion in Psychiatry, vol. 38, no. 3, pp. 235-251.
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Purpose of review Using the ecological public health framework, this study aims to systematically review the risk of illicit drug use and its associated negative health outcomes relating to urbanization. Recent findings Previous studies have indicated that urbanization associated with increased population density drives segregation of vulnerable communities of low socioeconomic status (SES). Marginalized individuals in segregated communities have increased risk of poor mental health and illicit drug use. Summary The results indicated that urban-specific environmental risk factors, individual characteristics and level of social support all influenced risk of drug use, substance use disorder (SUD), overdose, and drug-use associated death. Urban environmental risk factors of economic disparity, marginalization and barriers in accessing healthcare and negative individual characteristics of low education, low income and comorbid diagnosis of mental illness significantly increased risk of drug use. In contrast, better social support reduced the risk of drug use.
Lin, X, Liu, R, Cao, Y, Zou, L, Li, Q, Wu, Y, Liu, Y, Yin, D & Xu, G 2025, 'Contrastive Modality-Disentangled Learning for Multimodal Recommendation', ACM Transactions on Information Systems, vol. 43, no. 3, pp. 1-31.
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Multimodal recommendation, which utilizes rich multimodal information to learn user preferences, has attracted significant attention. Most works focus on designing powerful encoders for extracting multimodal features, and simply aggregate the learned features together to make prediction. Consequently, they have a limited capacity to learn the inter-modality knowledge including the modality-shared and modality-unique knowledge. In fact, learning the modality-shared knowledge enables us to align cross-modality data for fusing heterogeneous modality features. Learning the modality-unique knowledge is equally important when recommendation tasks only involve a small amount of shared features and the necessary information is contained within specific modality. In this article, we propose Contrastive Modality-Disentangled Learning (CMDL) to overcome this critical limitation. CMDL exactly captures the inter-modality knowledge by achieving modality disentanglement. Specifically, CMDL first disentangles the initial representation into the modality-invariant and modality-specific representations. Afterwards, CMDL introduces a novel manner of contrastive learning to approximate the MI upper bounds for achieving disentanglement regularization. Building upon the proposed regularization, CMDL encourages the modality-invariant and modality-specific representations to capture the modality-shared and modality-unique knowledge respectively and to be statistically independent to each other. Empirically, extensive experiments are conducted on benchmark datasets, demonstrating the superior performance of CMDL compared with strong multimodal recommenders.
Lin, X, Ma, B, Wang, X, Yu, G, He, Y, Ni, W & Liu, RP 2025, 'CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories', IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 3, pp. 3223-3236.
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Lin, X, Nguyen, QD, Castel, A, Deng, Z, Li, W & Tam, VWY 2025, 'Self-healing of biochar-cement composites with crystalline admixture exposed to sulphate solution and simulated seawater', Journal of Building Engineering, vol. 99, pp. 111564-111564.
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Lin, X, Nguyen, QD, Castel, A, Li, P, Tam, VWY & Li, W 2025, 'Self-healing efficiency of sustainable biochar-cement composites incorporating crystalline admixtures', Construction and Building Materials, vol. 458, pp. 139542-139542.
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Lin, X, Nguyen, QD, Castel, A, Pang, Y, Deng, Z, Shi, T, Li, W & Tam, VWY 2025, 'Durability of biochar-cementitious composites incorporating crystalline admixture in chloride and sulphate environments', Construction and Building Materials, vol. 458, pp. 139554-139554.
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Lin, X, Zhang, Y, Li, J, Oliver, BG, Wang, B, Li, H, Yong, K-T & Li, JJ 2025, 'Biomimetic multizonal scaffolds for the reconstruction of zonal articular cartilage in chondral and osteochondral defects', Bioactive Materials, vol. 43, pp. 510-549.
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Liu Chung Ming, C, Patil, R, Refaat, A, Lal, S, Wang, X & Gentile, C 2025, 'Acetylcholine-loaded nanoparticles protect against doxorubicin-induced toxicity in in vitro cardiac spheroids', Biofabrication, vol. 17, no. 2, pp. 025023-025023.
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Abstract Doxorubicin (DOX) is widely used in chemotherapy, yet it significantly contributes to heart failure-associated death. Acetylcholine (ACh) is cardioprotective by enhancing heart rate variability and reducing mitochondrial dysfunction and inflammation. Nonetheless, the protective role of ACh in countering DOX-induced cardiotoxicity (DIC) remains underexplored as current approaches to increasing ACh levels are invasive and unsafe for patients. In this study, we explore the protective effects of ACh against DIC through three distinct ACh administration strategies: (i) freely-suspended 100 µM ACh; (ii) ACh-producing cholinergic neurons (CNs); or (iii) ACh-loaded nanoparticles (ACh-NPs). These are tested in in vitro cardiac spheroids (CSs), which have previously been shown to approximate the complex DIC. We assess ACh’s protective effects by measuring the toxicity ratio (cell death/viability), contractile activity, gene expression changes via qPCR and nitric oxide (NO) signaling. Our findings show that ACh effectively attenuates DOX-induced cell death and contractile dysfunction. ACh also counteracts the DOX-induced downregulation of genes controlling myocardial fibrosis, endothelial and cardiomyocyte dysfunction, and autonomic dysregulation. ACh cardioprotection against DOX is dependent on NO signaling in endothelial cells but not in cardiac myocytes or fibroblasts. Altogether, this study shows for the first time that elevating ACh levels showed a promising therapeutic approach for preventing DIC.
Liu, B, Liu, B, Zhu, T & Ding, M 2025, 'A Review of Deepfake and Its Detection: From Generative Adversarial Networks to Diffusion Models', International Journal of Intelligent Systems, vol. 2025, no. 1.
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Deepfake technology, leveraging advanced artificial intelligence (AI) algorithms, has emerged as a powerful tool for generating hyper‐realistic synthetic human faces, presenting both innovative opportunities and significant challenges. Meanwhile, the development of Deepfake detectors represents another branch of models striving to recognize AI‐generated fake faces and protect people from the misinformation of Deepfake. This ongoing cat‐and‐mouse game between generation and detection has spurred a dynamic evolution in the landscape of Deepfake. This survey comprehensively studies recent advancements in Deepfake generation and detection techniques, focusing particularly on the utilization of generative adversarial networks (GANs) and diffusion models (DMs). For both GAN‐based and DM‐based Deepfake generators, we categorize them based on whether they synthesize new content or manipulate existing content. Correspondingly, we examine various strategies employed to identify synthetic and manipulated Deepfake, respectively. Finally, we summarize our findings by discussing the unique capabilities and limitations of GANs and DM in the context of Deepfake. We also identify promising future directions for research, including the development of hybrid approaches that leverage the strengths of both GANs and DM, the exploration of novel detection strategies utilizing advanced AI techniques, and the ethical considerations surrounding the development of Deepfake. This survey paper serves as a valuable resource for researchers, practitioners, and policymakers seeking to understand the state‐of‐the‐art in Deepfake technology, its implications, and potential avenues for future research and development.
Liu, B, Ni, W, Liu, RP, Guo, YJ & Zhu, H 2025, 'Delay-Sensitive Goods Delivery and In-Situ Sensing Using A Multi-Task Drone', IEEE Transactions on Mobile Computing, pp. 1-14.
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Liu, C, Du, H, Lei, G, Wang, Y & Zhu, J 2025, 'Design and Analysis of Modular Permanent Magnet Claw Pole Machines With Hybrid Cores for Electric Vehicles', IEEE Transactions on Energy Conversion, vol. 40, no. 2, pp. 1047-1061.
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Liu, C, Du, H, Lei, G, Wang, Y & Zhu, J 2025, 'Multi-Physics Coupling Analysis of Permanent Magnet Claw Pole Machine with Hybrid Cores by Inverter Power Supply', Diangong Jishu Xuebao Transactions of China Electrotechnical Society, vol. 40, no. 6, pp. 1758-1770.
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The stator core of the permanent magnet claw pole machine (PMCPM) is typically made of soft magnetic composite material (SMC). SMC has defects such as high eddy current loss at low frequency, low magnetic permeability, and poor mechanical strength. In addition, because of the high cost of manufacturing large-sized SMC cores, previous designs for PMCPM were often based on low-power motors. If SMC is combined with other materials, using hybrid cores can improve PMCPM Performance and reduce manufacturing costs. However, research on PMCPM with hybrid cores is limited to electromagnetic fields. Various physical fields inside the motor are coupled, and a multi-physics coupling analysis should be used to comprehensively study the impact of hybrid cores.Compared with traditional PMCPM with SMC cores, the paper studies the impact of inverter power supply on the Performance of PMCPM with hybrid cores and summarizes the advantages of hybrid cores. Firstly, the electromagnetic Performance of PMCPM with hybrid cores is analyzed using the field-circuit coupling method. The temperature distribution of PMCPM with hybrid cores under different power supply modes is obtained, and the influence of the power supply mode is studied. Then, a comparison is made between PMCPM with hybrid cores and PMCPM with SMC cores from the perspectives of electromagnetic field, temperature field, and stress field. Finally, the Simulation results are validated through prototype experiments.The multi-physics coupling Simulation analysis shows that under rated conditions, the torque ripple of the motor is 22.5% when powered by the inverter and only 10% when powered by the current source. The inverter power supply greatly increases the torque ripple, increasing the iron and permanent magnet eddy current losses by 22.2% and 44%, respectively. Compared with the PMCPM with SMC cores, hybrid cores reduce the maximum compressive stress on the SMC part of the stator core by 34.1% and the material cos...
Liu, C, Yang, G, Wang, Y & Lei, G 2025, 'Shape Optimization Design of Claw Pole Permanent Magnet Wind Generator with Hybrid Core', Journal of Electrical Engineering & Technology, vol. 20, no. 5, pp. 3135-3146.
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Liu, C, Yang, G, Wang, Y, Lei, G & Zhu, J 2025, 'Comparative Study of Radial, Axial and Transverse Flux Generators with Hybrid Cores for 5 kW Wind Generator', Journal of Electrical Engineering & Technology, vol. 20, no. 5, pp. 3123-3134.
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Liu, C, Zowghi, D, Peng, G & Kong, S 2025, 'Information quality of conversational agents in healthcare', Information Development, vol. 41, no. 3, pp. 1080-1102.
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Artificial Intelligence has found applications in a wide range of fields, including conversational agents designed for healthcare services. The quality of healthcare services greatly depends on the quality of the information provided by the agents. Achieving quality-assured information from conversational agents to support effective decision-making remains as a significant challenge in healthcare. Although prior review studies have shown an interest in investigating the information quality (IQ) of conversational agents in healthcare, no systematic review has been performed to present IQ definitions, factors influencing IQ, and IQ impacts. We conducted a systematic review of 45 articles published up to 2021 to investigate IQ definitions, factors influencing IQ, and IQ impacts in the context of conversational agents applied in healthcare. The findings of this review are integrated into a conceptual framework for the IQ research program in the context of conversational agents in healthcare, which has not been received attention in the literature, guiding future research directions. The present study also discusses implications for both researchers and practitioners to enhance the agents’ IQ and improve the quality of health-related services.
Liu, CZ, Zhang, Y, Qin, L & Hussain, F 2025, 'Kolmogrov Anorld carbon informed network orchestration', Applied Intelligence, vol. 55, no. 11.
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Liu, D, Balaguer, C, Dissanayake, G & Kovac, M 2025, 'Preface', Infrastructure Robotics: Methodologies, Robotic Systems and Applications.
Liu, F, Shi, P, Shi, G, Yu, D, Sun, X, Li, L, Huang, Z & Wu, Y 2025, 'High-performance microwave absorber by boron carbon nitride', Surfaces and Interfaces, vol. 66, pp. 106549-106549.
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Liu, F, Wang, M, Liu, Y, Ni, W & Jamalipour, A 2025, 'Hybrid NOMA Offloading for Delay-Sensitive Applications in MEC-Based NB-IoT Networks', IEEE Internet of Things Journal, vol. 12, no. 5, pp. 5245-5259.
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Liu, F, Yuan, Z, Guo, Q, Zhang, Y, Wang, Z & Zhang, JA 2025, 'Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning', IEEE Transactions on Vehicular Technology, vol. 74, no. 5, pp. 7666-7675.
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Liu, G, Zhang, W, Wang, X, King, S & Yu, S 2025, 'A Membership Inference and Adversarial Attack Defense Framework for Network Traffic Classifiers', IEEE Transactions on Artificial Intelligence, vol. 6, no. 2, pp. 317-332.
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Liu, J, Huang, X-L, Hu, F & Yu, S 2025, 'Higher-Order Cumulant-Assisted Constant Wideband Compressive Spectrum Sensing Using Semantic Correlation Mining', IEEE Transactions on Wireless Communications, vol. 24, no. 2, pp. 893-908.
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Liu, L, Guo, Y, Yin, W, Lei, G, Sun, X & Zhu, J 2025, 'Efficient Design Optimization of PMSM Drive Systems Using Improved Equivalent-Circuit-Based Loss Minimization Control', IEEE Transactions on Industrial Electronics, vol. 72, no. 4, pp. 3280-3291.
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Liu, P, Fei, Z, Wang, X, Huang, J, Hu, J & Andrew Zhang, J 2025, 'Joint Offloading and Beamforming Design in Integrating Sensing, Communication, and Computing Systems: A Distributed Approach', IEEE Transactions on Communications, vol. 73, no. 7, pp. 4697-4712.
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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, vol. 35, no. 6, pp. 5776-5790.
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Liu, W, Xia, R, Li, G, Luo, W & Nghiem, LD 2025, 'Three-dimensional analysis of foulant distribution in ultrafiltration membrane treating biogas slurry', Journal of Membrane Science, vol. 735, pp. 124571-124571.
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Liu, W, Xia, R, Qi, C, Ansari, AJ, Nghiem, LD, Duan, X, Li, Y, Li, G & Luo, W 2025, 'Micro-nanobubbles to alleviate ultrafiltration membrane fouling for biogas slurry concentration and nutrient enrichment: Performance and molecular mechanisms', Journal of Membrane Science, vol. 731, pp. 124221-124221.
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Liu, X, Li, M, Yu, G, Wang, X, Ni, W, Li, L, Peng, H & Ping Liu, R 2025, 'BlockFUL: Enabling Unlearning in Blockchained Federated Learning', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 6635-6650.
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Liu, X, Liu, J, Gu, L, Li, Y, Chang, X & Nie, F 2025, 'Mining the Salient Spatio-Temporal Feature with S2TF-Net for action recognition', Signal Processing: Image Communication, vol. 138, pp. 117381-117381.
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Recently, 3D Convolutional Neural Networks (3D ConvNets) have been widely exploited for action recognition and achieved satisfying performance. However, the superior action features are often drowned in numerous irrelevant information, which immensely enhances the difficulty of video representation. To find a generic cost-efficient approach to balance the parameters and performance, we present a novel network to mine the Salient Spatio-Temporal Feature based on 3D ConvNets backbone for action recognition, termed as S2TF-Net. Firstly, we extract the salient features of each 3D residual block by constructing a multi-scale module for Salient Semantic Feature mining (SSF-Module). Then, with the aim of preserving the salient features in pooling operations, we establish a Two-branch Salient Feature Preserving Module (TSFP-Module). Besides, these above two modules with proper loss function can collaborate in an “easy-to-concat” fashion for most 3D ResNet backbones to classify more accurately albeit in the shallower network. Finally, we conduct experiments over three popular action recognition datasets, where our S2TF-Net is competitive compared with the deeper 3D backbones or current state-of-the-art results. Treating the P3D, 3D ResNet, Non-local I3D and X3D as baseline, the proposed method improves them to varying degrees. Particularly, for Non-local I3D ResNet, the proposed S2TF-Net enhances 4.1%, 3.0% and 4.6% in Kinetics-400, UCF101 and HMDB51 datasets, achieving the accuracy of 74.8%, 95.1% and 80.9%. We hope this study will provide useful inspiration and experience for future research about more cost-effective methods. Code is released here: https://github.com/xiaoxiAries/S2TFNet.
Liu, X, Song, W, Musial, K, Li, Y, Zhao, X & Yang, B 2025, 'Stochastic Block Models for Complex Network Analysis: A Survey', ACM Transactions on Knowledge Discovery from Data, vol. 19, no. 3, pp. 1-35.
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Complex networks enable to represent and characterize the interactions between entities in various complex systems which widely exist in the real world and usually generate vast amounts of data about all the elements, their behaviors and interactions over time. The studies concentrating on new network analysis approaches and methodologies are vital because of the diversity and ubiquity of complex networks. The stochastic block model (SBM), based on Bayesian theory, is a statistical network model. SBMs are essential tools for analyzing complex networks since SBMs have the advantages of interpretability, expressiveness, flexibility and generalization. Thus, designing diverse SBMs and their learning algorithms for various networks has become an intensively researched topic in network analysis and data mining. In this article, we review, in a comprehensive and in-depth manner, SBMs for different types of networks (i.e., model extensions), existing methods (including parameter estimation and model selection) for learning optimal SBMs for given networks and SBMs combined with deep learning. Finally, we provide an outlook on the future research directions of SBMs.
Liu, X, Wang, X, Fan, B, Xiao, G, Wen, S, Chen, B & Wang, P 2025, 'Multiagent Primal-Dual DDPG-Based Reactive Power Optimization of Active Distribution Networks via Graph Reinforcement Learning', IEEE Internet of Things Journal, vol. 12, no. 15, pp. 32058-32071.
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Liu, X, Zuo, W, Li, Q, Zhou, K, Huang, Y & Li, Y 2025, 'NO emission and thermal performance studies on ammonia/oxygen premixed combustion in micro planar combustor with a secondary oxygen injection', Energy, vol. 328, pp. 136460-136460.
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Liu, Y, Feng, Y, Wu, D, Chen, X, Yang, C & Gao, W 2025, 'Stochastic fracture and fatigue analysis in elasto-plastic materials via virtual modelling techniques', Computer Methods in Applied Mechanics and Engineering, vol. 441, pp. 117997-117997.
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Liu, Y, Forster, L, Mavridis, A, Merenda, A, Ahmed, M, D'Agostino, C, Konarova, M, Seeber, A, Della Gaspera, E, Lee, AF & Wilson, K 2025, 'Phase Effects in Zirconia Catalysed Glucose Conversion to 5‐(Hydroxymethyl)furfural', ChemSusChem, vol. 18, no. 4.
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Abstract5‐(hydroxymethyl)furfural (HMF) is a key biomass derived platform chemical used to produce fuel precursors or additives and value‐added chemicals, synthesised by the cascade isomerisation of glucose and subsequent dehydration of reactively formed fructose to HMF over Lewis and Bronsted acid catalysts, respectively. Zirconia is a promising catalyst for such reactions; however, the impact of acid properties of different zirconia phases is poorly understood. In this work, we unravel the role of the zirconia crystalline phase in glucose isomerisation and fructose dehydration to HMF. The Lewis acidic monoclinic phase of zirconia is revealed to preferentially facilitate glucose isomerisation, while the nanoparticulate tetragonal phase possesses Brønsted acid sites which favour fructose dehydration. Synergy between both zirconia phases facilitates cascade HMF production, with both catalysts investigated as physical mixtures in batch and flow reactor configurations. Using a physical mixture of only 15 wt % m‐ZrO2 with 85 wt % t‐ZrO2 in either batch or packed bed reactor configuration is sufficient to reach equilibrium conversion of glucose for subsequent dehydration by the t‐ZrO2 component. Under continuous flow, a six‐fold increase in HMF production was obtained when operating with a physical mixture of m‐ and t‐ZrO2 compared to that from a single bed of t‐ZrO2.
Liu, Y, 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, Huang, J, Wen, S, He, X, Zhang, W & Feng, Z 2025, 'CTIGEN-CDM: Controlled Text-to-Image Generation Using Cropped Diffusion Models', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Liu, Y, Xie, YM, Lee, T-U, Wang, Z & Pietroni, N 2025, 'Free-form Surface Approximation Using Rotational Patches', ACM Transactions on Graphics, vol. 44, no. 5, pp. 1-14.
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We present a method to approximate free-form surfaces using assemblies of rotational patches for architectural rationalization. Rotational surface patches inherently allow for the simultaneous repetition of multiple building elements along the arc direction. By assembling multiple patches, we can create diverse free-form-like geometries to satisfy broad design intents, while preserving local symmetry to enable cost-effective element fabrication. The main challenge lies in the strict constraint of maintaining local rotational symmetry, while ensuring the final tessellated form is seamless, smooth, and closely resembles the target surface. To address this, we propose a patch layout creation approach that segments the input surface into patches, resembling untrimmed rotational patches within a prescribed error threshold. Additionally, we develop a B-spline-based optimization framework to refine the fitted rotational patches for smooth connections and faithful surface approximation. To facilitate practical architectural applications, we provide a post-processing tool that converts the discrete patch assembly into a seamless, smooth quad mesh composed of locally repeated elements. We demonstrate that our approach is applicable to a variety of free-form surfaces, including those that mimic iconic architectural designs, and can address various practical requirements for a wide range of application scenarios.
Liu, Y, Zhang, Y, Zhang, M, Xu, W, Shao, S & Guo, Y 2025, 'Minimum Torque Ripple Control for Brushless Doubly-Fed Induction Generator-DC System Under Power Winding Open-Phase Fault', IEEE Transactions on Power Electronics, vol. 40, no. 4, pp. 5743-5755.
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Liu, Z, Lu, J, Xuan, J & Zhang, G 2025, 'Learning Latent and Changing Dynamics in Real Non-Stationary Environments', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 4, pp. 1930-1942.
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Loengbudnark, W, Khalilpour, K, Bharathy, G, Thornton, N, Voinov, A & Marshall, JP 2025, 'How do we energize community energy? An Australian perspective', Energy Research & Social Science, vol. 127, pp. 104272-104272.
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Long, G, Blumenstein, M & Chang, Y 2025, 'Foreword from the General Chairs', Www 2025 Proceedings of the ACM Web Conference, pp. iii-iv.
Long, G, Blumenstein, M & Chang, Y 2025, 'Foreword from the General Chairs', Www Companion 2025 Companion Proceedings of the ACM Web Conference 2025, pp. III-IV.
Lowe, D, Tilley, E, Willey, K & Roach, K 2025, 'Student reactions to the development of professional engineering competencies', European Journal of Engineering Education, vol. 50, no. 2, pp. 281-297.
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Lu, J, Shang, D & Zhang, G 2025, 'Novelty-Aware Concept Drift Detection for Neural Networks', Neurocomputing, vol. 617, pp. 128933-128933.
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Lu, J, Zhang, C, Li, B, Zhao, Y, Choudhary, R & Langtry, M 2025, 'Self-attention variational autoencoder-based method for incomplete model parameter imputation of digital twin building energy systems', Energy and Buildings, vol. 328, pp. 115162-115162.
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Lu, Y, Luo, Q & Tong, L 2025, 'Multi-objective and multi-constraint topology optimization of nonlinear compliant mechanisms', Thin-Walled Structures, vol. 208, pp. 112761-112761.
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Lu, Y, Luo, Q & Tong, L 2025, 'Topology optimization for metastructures with quasi-zero stiffness and snap-through features', Computer Methods in Applied Mechanics and Engineering, vol. 434, pp. 117587-117587.
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Lu, Y, Luo, Q & Tong, L 2025, 'Topology optimization for pressurized nonlinear structures using substructure and experimental studies', Structural and Multidisciplinary Optimization, vol. 68, no. 2.
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Abstract A compliant structure under fluidic pressure can undergo relatively large shape change, but the design of such type of structure is challenging as the pressure distribution depends on detailed structural geometry. In this study, a novel mixed substructure-density (MSD) model is proposed for topology representation and update in the optimal design of nonlinear compliant structures under quasi-static fluidic pressure. An optimization algorithm is developed via implementing the present model by using super-elements in commercial finite element analysis (FEA) software. Numerical examples are presented to validate the present model, algorithm, and designs numerically via full linear and nonlinear FEAs. A planar cellular network with five cells arranged in parallel is then designed for representing a pressurized wing rib structure capable of modulating airfoil thickness variation. The test results of the single-cell and five-cell PCS specimens prototyped using polyurethane material show that the respective cell thickness can be reduced by 11.9 and 6.4% respectively under a cell pressure of 250 kPa.
Lu, Z, Liu, C, Chang, X, Zhang, Y & Xie, H 2025, 'DHVT: Dynamic Hybrid Vision Transformer for Small Dataset Recognition', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 4, pp. 2615-2631.
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Lu, Z, Xu, Y, Liang, C, Guo, W, Ngo, HH & Peng, L 2025, 'Biogenic sulfide by sulfur disproportionation enhances nitrate removal and reduces N2O production during sulfur autotrophic denitrification', Chemosphere, vol. 370, pp. 143915-143915.
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Luo, 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, Jia, X, Luo, Q, Li, Q & Sun, G 2025, 'Multiscale Failure Behavior of Plain-Woven CFRP with Yarn Rotation', International Journal of Mechanical Sciences, pp. 110623-110623.
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Luo, J, Luo, Q, Li, Q & Sun, G 2025, 'Effects of tension-compression asymmetry on mixed-mode interlaminar fracture', International Journal of Mechanical Sciences, vol. 288, pp. 109948-109948.
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Luo, S, Peng, H, Shi, Y, Cai, J, Zhang, S, Shao, N & Li, J 2025, 'Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey', Briefings in Bioinformatics, vol. 26, no. 2.
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Abstract Cancer neoantigens are peptides that originate from alterations in the genome, transcriptome, or proteome. These peptides can elicit cancer-specific T-cell recognition, making them potential candidates for cancer vaccines. The rapid advancement of proteomics technology holds tremendous potential for identifying these neoantigens. Here, we provided an up-to-date survey about database-based search methods and de novo peptide sequencing approaches in proteomics, and we also compared these methods to recommend reliable analytical tools for neoantigen identification. Unlike previous surveys on mass spectrometry-based neoantigen discovery, this survey summarizes the key advancements in de novo peptide sequencing approaches that utilize artificial intelligence. From a comparative study on a dataset of the HepG2 cell line and nine mixed hepatocellular carcinoma proteomics samples, we demonstrated the potential of proteomics for the identification of cancer neoantigens and conducted comparisons of the existing methods to illustrate their limits. Understanding these limits, we suggested a novel workflow for neoantigen discovery as perspectives.
Luo, X, Yuan, D, Chang, X & He, Z 2025, 'Underwater Target Tracking Based on Uncertainty-Inspired Image Enhancement', Jisuanji Gongcheng Computer Engineering, vol. 51, no. 1, pp. 11-19.
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The task of Underwater Visual Object Tracking (UVOT) not only requires dealing with the common challenges in outdoor tracking but also faces many unique difficulties specific to the underwater environment, including but are not limited to, optical degradation and scattering, uneven illumination, low visibility, and hydrodynamics. In these scenarios, directly applying a large number of traditional outdoor scene object tracking methods directly to underwater scenes inevitably leads to performance degradation. To address the above issues, first, an Underwater Image Enhancement (UIE) module inspired by uncertainty is introduced, aimed at specifically improving the quality of underwater images. This method decomposes UIE into distribution estimation and consensus processes and introduces a new probability network to learn the enhancement distribution of underwater images, thereby addressing the bias problem in reference images. These are subsequently applied to an attention-based feature fusion network to propose a target tracking algorithm, called UTransT. The feature fusion network combines self- and cross-attention mechanisms to effectively fuse template and search region features. The experimental results show that on the UTB180 dataset, the success rate of UTransT is 0.8 percentage points higher than that of MixFormer, with the best performance in the comparison algorithm, and normalization accuracy is nearly 1.9 percentage points higher. On the VMAT dataset, the success rate is 1.2 percentage points higher than that of the best-performing Masked Appearance Transfer (MAT) algorithm, with 1.5 percentage points higher normalization accuracy. Moreover, UTransT facilitates real-time tracking at 65 frames per second. These experimental results validate the effectiveness and feasibility of the proposed algorithm in underwater object tracking tasks.
Luo, X, Yuan, D, Shu, X, Liu, Q, Chang, X & He, Z 2025, 'Adaptive Trajectory Correction for Underwater Object Tracking', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 8, pp. 8187-8196.
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Luo, Y, Zhao, S, Fan, Z, Li, Y, Peng, Z, Zhang, Y, Feng, S, Mou, J, Wang, Z, Ki Lin, CS & Li, X 2025, 'Sucrose non-fermenting 1-related protein kinase 2–14 participating in lipid elevating efficacy and biodiesel upgrade by Coccomyxa subllipsoidea', Chemical Engineering Journal, vol. 505, pp. 159607-159607.
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Luo, Z, Yang, L, Su, T, Zhu, X & Gómez-García, R 2025, 'Single-Ended and Balanced Flat-Group-Delay RF Low-Pass Filters With Input-Quasi-Reflectionless Behavior for Digital-Communication Systems', IEEE Transactions on Microwave Theory and Techniques, vol. 73, no. 1, pp. 321-334.
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Lv, X, Yang, Y, Luo, Z & Tyo, JS 2025, 'Multimaterial 3-D-Printed FSSs for Ultrawide and Dual Passbands in the K-Ka Spectra', IEEE Transactions on Microwave Theory and Techniques, vol. 73, no. 1, pp. 75-86.
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Lv, Y, Liu, Z & Chang, X 2025, 'Consistency-Queried Transformer for Audio-Visual Segmentation', IEEE Transactions on Image Processing, vol. 34, pp. 2616-2627.
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Lyu, L, Fleck, R, Matheson, S, King, WL, Bauerle, TL, Torpy, FR & Irga, PJ 2025, 'Phytoremediation of indoor air: Mechanisms of pollutant translocation and biodegradation', Critical Reviews in Environmental Science and Technology, vol. 55, no. 10, pp. 676-707.
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Lyu, X, Ayough, P, Nawaz, W & Elchalakani, M 2025, 'Development and characterization of printable rubberised ultra-high-performance concrete', Journal of Building Engineering, vol. 111, pp. 113192-113192.
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M.S., S, Elmakki, T, Park, H, Solim, S, Shon, HK, Shetty, D, Park, J-U & Han, DS 2025, 'Application of bipolar membrane (BPM)-based technology to green energy and environmental sustainability', Desalination, vol. 613, pp. 119101-119101.
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Ma, B, Li, Y, Zheng, J, Zhang, J, Huang, S, Zhu, J & Lei, G 2025, 'Multiphysics Topology Optimization of SynRMs Considering Control Performance and Machinability', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 1287-1297.
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Ma, B, Teng, J, Zhang, S & Sheng, D 2025, 'A new strength criterion for frozen soils considering temperature and pressure-melting effect', Zhongnan Daxue Xuebao Ziran Kexue Ban Journal of Central South University Science and Technology, vol. 56, no. 4, pp. 1417-1425.
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Artificial ground freezing (AGF) is widely employed in shaft construction through unstable soil layers, particularly in deep underground engineering. Ensuring the safety and feasibility of AGF relies heavily on accurately predicting the strength of frozen soils. Frozen soil strength responds to increasing confining stress in three distinct stages: it initially increases, then decreases, and finally increases again at higher stress levels. To capture this complex behavior, a series of triaxial compression tests were conducted under varying confining pressures. Based on the framework of critical state soil mechanics, temperature-dependent strength criteria were developed to reflect this non-linear strength evolution. To further explain the strength reduction observed at intermediate stress levels, the mechanism of pressure-induced ice melting was examined. A physically meaningful pressure melting coefficient was introduced to quantify the relationship between cryogenic suction and stress. This resulted in the formulation of freezing characteristic curves corresponding to different stress levels. Building on these insights, a new strength criterion was established to account for the effects of both low and high stress conditions. Model predictions were validated against experimental data, demonstrating the ability to reproduce the observed strength evolution of frozen soils. The results show that compared with the existing strength criteria, the proposed model is physically interpretable, involves fewer and more easily measurable parameters, and thus offers greater potential for practical application in AGF-related engineering design.
Ma, B, Xing, J, Huang, S, Wang, K, Zhang, J, Lei, G & Zhu, J 2025, 'Cooling System Design for High-Power-Density Permanent Magnet Synchronous Motor Based on Micro Heat Pipe Array', IEEE Transactions on Transportation Electrification, vol. 11, no. 2, pp. 6719-6730.
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Ma, G, Lu, J, Fang, Z, Liu, F & Zhang, G 2025, 'Multiview Classification Through Learning From Interval-Valued Data', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 9606-9620.
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Ma, H, Sun, Z, Dong, D & Gong, D 2025, 'Learning Informative Latent Representation for Quantum State Tomography', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-11.
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Ma, H, Sun, Z, Dong, D, Chen, C & Rabitz, H 2025, 'Tomography of Quantum States From Structured Measurements via Quantum-Aware Transformer', IEEE Transactions on Cybernetics, vol. 55, no. 6, pp. 2571-2582.
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Ma, L, Hu, C, Wen, S, Yu, Z & Jiang, H 2025, 'Fixed-Time Distributed Optimization via Edge-Based Adaptive Algorithms', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 5, pp. 3436-3448.
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Ma, N, Xuan, J, Zhang, G & Lu, J 2025, 'Global–Local Decomposition of Contextual Representations in Meta-Reinforcement Learning', IEEE Transactions on Cybernetics, vol. 55, no. 3, pp. 1277-1287.
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Ma, S, Shi, K, Gu, T, Tian, S, Zhou, Z, Li, X, Wang, C, Shon, H & Ren, J 2025, 'Hydrogel-driven salt fouling free FO-SIE integrated process for seawater desalination', Desalination, vol. 607, pp. 118815-118815.
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Ma, W, Chang, Y-C, Yang, J, Wang, Y-K & Lin, C-T 2025, 'Contrastive Learning-Based Agent Modeling for Deep Reinforcement Learning', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-8.
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Ma, W, Huang, S & Sun, Y 2025, 'SkyLoc: Cross-Modal Global Localization With a Sky-Looking Fish-Eye Camera and OpenStreetMap', IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 5, pp. 5832-5842.
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Ma, W, Li, Y, Li, T, Yang, H, Li, Z, Wang, L & Xuan, J 2025, 'SFSWTS: A spatial-frequency shifted windows and time self-attention network for EEG emotion recognition', Neurocomputing, vol. 640, pp. 130309-130309.
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Ma, Y, Shi, K, Peng, X, He, H, Zhang, P, Liu, J, Lei, Z & Niu, Z 2025, 'Deep Graph Clustering With Triple Fusion Mechanism for Community Detection', IEEE Transactions on Computational Social Systems, vol. 12, no. 4, pp. 1743-1758.
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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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Maha Arachchige, S & Pradhan, B 2025, 'AI Meets the Eye of the Storm: Machine Learning-Driven Insights for Hurricane Damage Risk Assessment in Florida', Earth Systems and Environment, vol. 9, no. 3, pp. 2143-2163.
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Abstract Due to Florida’s exposure to hurricanes originating from both the Atlantic Ocean and the Gulf of Mexico, hurricane risk assessments serve as a critical tool for mitigating potential impacts. This is the first novel study to develop a machine learning based risk assessment for hurricane induced flood damage, which demonstrates the potential of granular building level insurance data from 1985 to 2024, enriched with remote sensing derived variables. The stacked ensemble machine learning model predicted hurricane flood damage with an MAE of 11.3% at a granular ZIP Code Tabulation Area level (ZCTA). The model’s explainability tools determined that building property value was a significant predictor of hurricane damage, as it correlated with property size, complex architectural design, and proximity to waterfront locations, all of which affect potential repair costs. Other predictive factors include construction year, occupancy type, and flood zone designation. Partial dependency plots (PDPs) identified that northwest Florida is particularly susceptible to hurricane damage, attributed to the Gulf of Mexico’s warm and shallow waters than eastern Florida’s cooler Atlantic conditions and steep ocean floor. Miami’s significant coastal urbanisation, rendered it a hotspot despite southeast Florida’s overall low hurricane risk. Similarly Jacksonville in north-eastern Florida was identified as a hotspot due to compounded flooding from storm surge and nearby river systems. Partial dependency plots also quantified the significant positive impact of 1970s building code regulation. Future studies should examine coastal morphology, landfall angle, and proximity to barrier islands. A study limitation is that insurance data may be an imperfect representation of Florida, due to underinsurance and inability to afford insurance.
Mahajan, S, Gite, S, Pradhan, B, Alamri, A, Inamdar, S, Shriyansh, D, Shah, AA & Agarwal, S 2025, 'Integrating Speech-to-Text for Image Generation Using Generative Adversarial Networks', Computer Modeling in Engineering & Sciences, vol. 143, no. 2, pp. 2001-2026.
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Mahmood, AH, Hampton, I, Bescher, E, Pang, L & Castel, A 2025, 'On-site strain and temperature monitoring of belitic calcium sulfoaluminate cement concrete airport slabs: feasibility of maturity method', International Journal of Pavement Engineering, vol. 26, no. 1.
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Mai, C, Wang, Z, Chen, L, Huang, Y, Li, M, Shirazi, A, Altaee, A & Zhou, JL 2025, 'Field-based Calibration and Operation of Low-Cost Sensors for Particulate Matter by Linear and Nonlinear Methods', Atmospheric Pollution Research, pp. 102676-102676.
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Mammone, M, Panta, J, Mildren, RP, Wang, J, Escobedo-Diaz, J, Mcgarva, L, Ibrahim, M, Sharp, A, Yang, R & Zhang, YX 2025, 'Advanced characterization of thermal degradation mechanisms in carbon fibre-reinforced polymer composites under continuous wave laser irradiation', Composites Part A: Applied Science and Manufacturing, vol. 192, pp. 108817-108817.
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Mangiavillano, B, Franchellucci, G, Auriemma, F, Ramai, D, Larghi, A, Paduano, D, De Deo, D, Calabrese, F, Gentile, C, Fiacca, M, Facciorusso, A & Repici, A 2025, 'Pilot study of a novel lumen‐apposing metal stent for endoscopic ultrasound‐guided procedures in porcine models', DEN Open, vol. 5, no. 1.
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AbstractLumen‐apposing metal stents have expanded the therapeutic potential of interventional endoscopic ultrasound (EUS). The Hot‐Spaxus (Taewoong Medical Co., Ltd.), the second most commonly utilized lumen‐apposing metal stent, requires two operators for its release which has been considered a limitation compared to other lumen‐apposing metal stents. We aimed to test the feasibility and the technical success of a newly available version of the Hot‐Spaxus stent equipped with an innovative handle delivery system for EUS‐guided interventional procedures. We conducted a pilot study using porcine models. The novel Hot‐Spaxus 2 was tested by performing four EUS‐guided procedures including four EUS‐guided gallbladder drainage and 12 EUS‐guided gastrojejunostomy) procedures. Technical success was reported in 100% of cases. The mean procedure time for EUS‐guided gatrojejunostomyJ and EUS‐guided gallbladder drainage was 23.85 min (standard deviation 3.41) and 16.15 min (standard deviation 2.72), respectively. The distal and proximal flanges were safely released by the endosonographer without any complications. No adverse events were reported. In conclusion, the novel Hot‐Spaxus 2 stent may represent an improvement compared to the prior Spaxus model. Unlike its predecessor, this newly designed stent eliminates the need for two endoscopists and can be deployed by a single operator. Further human studies are necessary to validate its clinical effectiveness.
Mann, RL, Elman, SJ, Wood, DR & Chapman, A 2025, 'A graph-theoretic framework for free-parafermion solvability', Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 481, no. 2312.
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We present a graph-theoretic characterization of when a quantum spin model admits an exact solution via a mapping to free parafermions. Our characterization is based on the concept of a frustration graph, which represents the commutation relations between Weyl operators of a Hamiltonian. We show that a quantum spin system has an exact free-parafermion solution if its frustration graph is an oriented indifference graph. Furthermore, we show that if the frustration graph of a model can be dipath oriented via switching operations, then the model is integrable in the sense that there is a family of commuting independent set charges. Additionally, we establish an efficient algorithm for deciding whether this is possible. Our characterization extends that given for free-fermion solvability. Finally, we apply our results to solve three qudit spin models.
Mao, Y, Dong, X, Xu, W, Gao, Y, Wei, B & Zhang, Y 2025, 'FIT-RAG: Black-Box RAG with Factual Information and Token Reduction', ACM Transactions on Information Systems, vol. 43, no. 2, pp. 1-27.
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Due to the extraordinarily large number of parameters, fine-tuning large language models (LLMs) to update long-tail or out-of-date knowledge is impractical in lots of applications. To avoid fine-tuning, we can alternatively treat a LLM as a black-box (i.e., freeze the parameters of the LLM) and augment it with a retrieval-augmented generation (RAG) system, namely black-box RAG. Recently, black-box RAG has achieved success in knowledge-intensive tasks and has gained much attention. Existing black-box RAG methods typically fine-tune the retriever to cater to LLMs’ preferences and concatenate all the retrieved documents as the input, which suffers from two issues: (1) Ignorance of Factual Information. The LLM preferred documents may not contain the factual information for the given question, which can mislead the retriever and hurt the effectiveness of black-box RAG; (2) Waste of Tokens. Simply concatenating all the retrieved documents brings large amounts of unnecessary tokens for LLMs, which degenerates the efficiency of black-box RAG. To address these issues, this article proposes a novel black-box RAG framework which utilizes the factual information in the retrieval and reduces the number of tokens for augmentation, dubbed FIT-RAG. FIT-RAG utilizes the factual information by constructing a bi-label document scorer which takes the factual information and LLMs’ preferences as labels respectively. Besides, it reduces the tokens by introducing a self-knowledge recognizer and a sub-document-level token reducer, which enables FIT-RAG to avoid unnecessary augmentation and reduce augmentation tokens as much as possible. FIT-RAG achieves both superior effectiveness and efficiency, which is validated by extensive experiments across three open-domain question-answering datasets: TriviaQA, NQ, and PopQA. FIT-RAG can improve the answering accuracy of Llama2-13B-Chat by 14.3% on TriviaQA, 19.9% on NQ and 27.5% on PopQA, respectively. Furthermore, it can save...
Mardy, A, Nikoo, MR, Zamani, MG, Al-Rawas, G, Nazari, R, Simunek, J, Sana, A & Gandomi, AH 2025, 'Cluster-based downscaling of precipitation using Kolmogorov-Arnold Neural Networks and CMIP6 models: Insights from Oman', Journal of Environmental Management, vol. 380, pp. 124971-124971.
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Martins, D, Karimi, M & Maxit, L 2025, 'Vibration analysis of beams coupled with evenly spaced acoustic black hole pillars: Experimental and numerical insights', Mechanical Systems and Signal Processing, vol. 235, pp. 112855-112855.
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Matheson, S, Fleck, R, Pettit, T, Irga, PJ & Torpy, FR 2025, 'Active botanical biofilters for nitrogen dioxide and ozone removal using granular activated carbon', International Journal of Phytoremediation, vol. 27, no. 11, pp. 1589-1601.
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Matthews, S, Nicholas, M, Kervin, L, Paatsch, L & Wyeth, P 2025, 'Computational thinking tools for early years education: a design study', Education and Information Technologies, vol. 30, no. 12, pp. 17225-17262.
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Abstract Computational Thinking (CT) is recognised as an essential foundational skill that enhances problem-solving abilities and is a crucial learning area for effective engagement in an increasingly digital society. This paper highlights the significance of screen-less tangible tools in promoting young children’s exploration and open-ended play with technology and their exposure to CT, which adults can further support. It presents a design-led investigation involving 16 children (approximately 18 to 36 months old) and their caregivers, examining their interactions with a novel digital technology probe, ‘Embeddables.’ We aimed to explore how new types of interactions in CT tools can be developed to embody CT experiences in diverse ways. The Embeddable probes are multi-modal plush tools that respond when proximally to each other. In our study, we introduced Embeddables at an Australian children’s museum to observe how young children engaged with them. Our analysis highlights the features of the CT technology probes that foster new opportunities for social and open-ended play, paving the way for digitally enhanced experiences that embody Computational Thinking and related skills. Our discussion revolves around the potential for CT with young children in playful environments, focusing on how the design features of tools facilitate this process.
Matthews, S, Nicholas, M, Paatsch, L, Kervin, L & Wyeth, P 2025, 'Social and curious: Lessons in designing digital manipulatives for young children', International Journal of Child-Computer Interaction, vol. 44, pp. 100725-100725.
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Mattos Batista de Moraes, F, Kulay, L & Trianni, A 2025, 'Integrating life cycle assessment and ecodesign to improve product effectiveness and environmental performance: A novel approach', Sustainable Production and Consumption, vol. 55, pp. 76-89.
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Mehrabi, P, Mortazavi, M & Far, H 2025, 'Axisymmetric thermal post-buckling of the eccentric annular sector plate made of Gori-metamaterials: Introducing DNN-RF algorithm for solving the post-buckling problems', Thin-Walled Structures, vol. 208, pp. 112795-112795.
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Meng, L, 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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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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Merenda, A, Shafaghat, AH, Sohn, W, Seccombe, D, Phuntsho, S & Shon, HK 2025, 'Valorisation of liquid anaerobic digestate into liquid fertilisers via membrane bioreactors: a proof-of-concept study', Water Research, vol. 285, pp. 124026-124026.
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Miao, L, Cheng, Z, Song, J, Zhang, Q, Cui, C & Li, J 2025, 'Numerical study on the effects of the mechanical degradation of UHPC layer and stud connectors on the fatigue performance of steel-UHPC composite bridge decks', Engineering Structures, vol. 332, pp. 120069-120069.
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Mikolajczyk, K, Har, AO, Wachsmann, L, Bown, O & Ferguson, S 2025, 'Mimetic Possibilities: Collaboration through Movement in Multimedia Opera', Leonardo, vol. 58, no. 2, pp. 149-156.
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Abstract In the opera The Ghost, artists with backgrounds in music composition, Greek tragedy, creative coding, and opera performance collaborated to create an artwork that engages with philosophy and technology. A light sculpture with a novel Internet of Things (IoT) architecture was adapted for the opera’s stage, where both the sculpture and its controlling interface were programmed to visually represent musical processes within the composer’s approach. During filming, the light sculpture contributed to the dramatic context through movement and gestural interaction between the sculpture and the soprano. Drawing upon human-computer interaction (HCI) research and its inquiry into movement for engaging with new technology expressively, movement and gesture transpiring from mimetic techniques in the opera were central in this interdisciplinary collaboration.
Milano, J, Ong, MY, Tiong, SK, Ideris, F, Silitonga, AS, Sebayang, AH, Tan, CH, Fattah, IMR, Fona, Z & Hossain, N 2025, 'A comparative study of the production of methyl esters from non-edible oils as potential feedstocks: Process optimization and two-step biodiesel characterization', Results in Engineering, vol. 25, pp. 104285-104285.
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Milledge, TJ, Pradhan, B & Shukla, N 2025, 'Integrating Systems Methodologies for Australian Undersea Surveillance: A Systematic Literature Review', Systems Engineering, vol. 28, no. 4, pp. 471-497.
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ABSTRACTAustralia is a maritime nation, relying on access to its surrounding oceans for its economy and security. The Royal Australian Navy conducts undersea surveillance to monitor this vital maritime environment and gain insight into activities beneath the ocean's surface. Australia's maritime jurisdiction is vast, with the world's third‐largest Economic Exclusion Zone, but its resources are constrained, with only 5% of the United States’ defense budget and navy personnel. Fortunately, advancements in unmanned vehicles and networked sensors can address this disparity by increasing the capacity of Australia's naval fleet at a fraction of the cost of manned platforms. However, current applications of unmanned systems only address simplified scenarios without defining how the plethora of unmanned systems should be applied across the full spectrum of surveillance operations. A new naval fleet architecture must be designed using systems methodologies to integrate unmanned systems into this holistic “system of systems”. Systems engineering, systems architecture, system modeling, system dynamics, operations research, and design for Six Sigma are complementary methodologies with unique techniques to solve this complex problem. This systematic literature review critically analyzed current unmanned systems and the utility of systems methodologies to design their application in undersea surveillance. It contributes insights into the benefits and challenges of unmanned systems in Australia's maritime context, whilst demonstrating shortcomings with the currently disparate application of methodologies to design unmanned fleet architectures. Further research is justified to design an integrated framework of systems methodologies that designs fleet architectures for Australian undersea surveillance.
Minassian, R, Mihăiţă, A-S & Shirazi, A 2025, 'Optimizing indoor environmental prediction in smart buildings: A comparative analysis of deep learning models', Energy and Buildings, vol. 327, pp. 115086-115086.
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Minh Tuan, B, Nguyen, DN, Linh Trung, N, Nguyen, V-D, Van Huynh, N, Thai Hoang, D, Krunz, M & Dutkiewicz, E 2025, 'Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI', IEEE Internet of Things Journal, vol. 12, no. 11, pp. 16009-16022.
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Mirazur Rahman, M, Yang, Y & Dey, S 2025, 'Application of Metamaterials in Antennas for Gain Improvement: A Study on Integration Techniques and Performance', IEEE Access, vol. 13, pp. 49489-49503.
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Mirkhalafi, S, Mohammadnezhad, B, Mousazadehgavan, M, Kiehbadroudinezhad, M, Altaee, A, Hosseinzadeh-Bandbafha, H & Hashim, K 2025, 'Technical and Environmental Sustainability of Pharmaceutical Wastewater Treatment Using Ce-NaY Zeolite-Modified Polyethersulfone (PES) Membranes: A Life Cycle Assessment Approach', ACS ES&T Water, vol. 5, no. 7, pp. 3818-3830.
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Mishra, R, Fattah, IMR, Ong, HC, Shu, C-M, Gollakota, ARK & Kong, ZY 2025, 'Sustainable green technology integrated hydrogen production system for low carbon future', International Journal of Hydrogen Energy, vol. 134, pp. 139-163.
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Moazzen, F & Hossain, MJ 2025, 'A two-layer strategy for sustainable energy management of microgrid clusters with embedded energy storage system and demand-side flexibility provision', Applied Energy, vol. 377, pp. 124659-124659.
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Modak, NM, Beydoun, G, Merigó, JM, Rahimi, I & Susilo, W 2025, '40 years of Computer Standards & Interfaces: A bibliometric retrospective', Computer Standards & Interfaces, pp. 104046-104046.
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Mohanty, N, Behera, BK, Ferrie, C & Dash, P 2025, 'A quantum approach to synthetic minority oversampling technique (SMOTE)', Quantum Machine Intelligence, vol. 7, no. 1.
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Abstract The paper proposes the Quantum-SMOTE method, a novel solution that uses quantum computing techniques to solve the prevalent problem of class imbalance in machine learning datasets. Quantum-SMOTE, inspired by the Synthetic Minority Oversampling Technique (SMOTE), generates synthetic data points using quantum processes such as swap tests and quantum rotation. The process varies from the conventional SMOTE algorithm’s usage of K-Nearest Neighbors (KNN) and Euclidean distances, enabling synthetic instances to be generated from minority class data points without relying on neighbor proximity. The algorithm asserts greater control over the synthetic data generation process by introducing hyperparameters such as rotation angle, minority percentage, and splitting factor, which allow for customization to specific dataset requirements. Due to the use of a compact swap test, the algorithm can accommodate a large number of features. Furthermore, the approach is tested on a public dataset of TelecomChurn and evaluated alongside two prominent classification algorithms, Random Forest and Logistic Regression, to determine its impact along with varying proportions of synthetic data.
Mohiuddin, ASM, Wang, Y-C & Vigneswaran, S 2025, 'A practical method to assess the ‘flocs condition’ and ‘floc strength’ in real-time in water treatment using surrogate parameters of media filter performance', Journal of Water Process Engineering, vol. 77, pp. 108345-108345.
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Increased frequency and intensity of weather events have greatly affected the surface raw water quality in Australia. It resulted in a 3- to 5-fold increase in True colour and Dissolved Organic Carbon (DOC), representing an increase of Natural Organic Matter (NOM), in Nepean Dam, south of Sydney, New South Wales, Australia. The increased NOM caused the formation of ‘weak flocs’ in the coagulation process that broke up in dual media gravity filters, resulting in a premature backwash and short filter run time. There is no practical method to measure ‘floc strength’ in an operating water treatment facility. This research identified a novel method to assess the ‘flocs condition’ during filtration, either ‘weak’ or ‘strong’, by calculating the rate of turbidity change and reporting it as a ‘turbidity breakthrough slope’ in NTU/day (NTU/d). The end-of-run head loss of the filter represents the energy at which the flocs break. Consequently, this research used the end-of-run head loss of a filter to measure floc strength. The turbidity breakthrough slope and filter end-of-run head loss are linearly correlated, and by using the correlation, a novel Floc Strength Model (FSM) has been developed. Applying the FSM at Nepean Water Filtration Plant (WFP), for ‘very strong flocs’ and ‘very weak flocs’ conditions, the calculated ‘floc strengths’ were 2.2 m and 0.57 m (measured in ‘meter (m)’ of water column), respectively. The FSM is a practical and in-situ method to monitor ‘floc strength’ in real time during a filter operation. It enables dynamic optimisation of water treatment. As soon as the filtered water turbidity starts to increase (start of turbidity breakthrough) in a filter, the FSM calculates and predicts the low ‘floc strength’ (end-of-run head loss) of the ‘very weak flocs’ and accordingly predicts the run time of the filter. Plant operators can then proactively optimise the chemical doses to change the flocs condition to ‘very strong flocs’ and improve ‘fl...
Monteiro, LC, Zheng, Y & Ling, SH 2025, 'A Improved Hybrid Attention Recurrent Residual U-Net approach for the segmentation of ultrasound spine images', Biomedical Signal Processing and Control, vol. 108, pp. 107925-107925.
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Moradian, M, Lie, TT & Gunawardane, K 2025, 'Design and performance evaluation of low-voltage solid-state DCCB using capacitor-based surge mitigation techniques', Power Electronic Devices and Components, vol. 11, pp. 100095-100095.
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Moradian, M, Lie, TT & Gunawardane, K 2025, 'Enhanced LV solid-state DC circuit breaker design with divided surge absorption technique', Electric Power Systems Research, vol. 241, pp. 111280-111280.
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Moradian, M, Peykarporsan, R, Lie, TT & Gunawardane, K 2025, 'Bidirectional Solid State Circuit Breaker with Passive Surge Absorber for LV Applications', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Moradian, M, Peykarporsan, R, Lie, TT & Gunawardane, K 2025, 'Low-Voltage Solid State DCCB Design Based on Bypassed Bidirectional Thyristor-Capacitor Suppressor', IEEE Transactions on Power Electronics, vol. 40, no. 1, pp. 516-525.
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Moraes, NV, Costa Fernandes, SD, Gularte, AC, da Rocha, CG & Echeveste, ME 2025, 'A Prioritization Method for Sustainable Food Waste Reduction Practices', Sustainable Development, vol. 33, no. 3, pp. 4227-4247.
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ABSTRACTAmid growing concerns over food waste, effective waste management practices in the food service sector have become increasingly needed. Limited research exists on prioritizing and implementing sustainable food waste reduction practices within this sector. This study develops a method to guide decision‐makers in identifying and prioritizing impactful practices to prevent and minimize food waste. Using Design Science Research, a pilot application case was conducted in southern Brazil. This study introduces the Food Waste Reduction Action Guide, a structured and evidence‐based approach comprising two phases: a food waste audit and practices prioritization. The method quantifies food waste, assesses the efficacy and ease of implementation of reduction practices, and visually sequences them. The method aligns with Target 12.3 of the Ssustainable Ddevelopment Goal (SDG) 12 by unveiling empirical evidence, supporting efforts to scale up food waste reduction across preparation and consumption phases through an actionable guide.
Morales, MES, Costa, PCS, Pantaleoni, G, Burgarth, DK, Sanders, YR & Berry, DW 2025, 'Selection and Improvement of Product Formulae for Best Performance of Quantum Simulation', Quantum Information & Computation, vol. 25, no. 1, pp. 1-35.
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Abstract Quantum algorithms for simulation of Hamiltonian evolution are often based on product formulae. The fractal methods give a systematic way to find arbitrarily high-order product formulae, but result in a large number of exponentials. On the other hand, product formulae with fewer exponentials can be found by numerical solution of simultaneous non-linear equations. It is also possible to reduce the cost of long-time simulations by processing, where a kernel is repeated and a processor need only be applied at the beginning and end of the simulation. In this work, we found thousands of new product formulae, and numerically tested these formulae, together with many formulae from prior literature. We provide methods to fairly compare product formulae of different lengths and different orders. For the case of 8th order, we have found new product formulae with exceptional performance, about two orders of magnitude better accuracy than prior work, both in the processed and non-processed cases. The processed product formula provides the best performance due to being shorter than the non-processed product formula. It outperforms all other tested product formulae over a range of many orders of magnitude in system parameters T (time) and ε (allowable error). That includes reasonable combinations of parameters to be used in quantum algorithms, where the size of the simulation is large enough to be classically intractable, but not so large it takes an impractically long time on a quantum computer.
Moreira, C, Chou, Y-L, Hsieh, C, Ouyang, C, Pereira, J & Jorge, J 2025, 'Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box', ACM Computing Surveys, vol. 57, no. 6, pp. 1-37.
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This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: a decision tree (fully transparent, interpretable, white-box model), a random forest (semi-interpretable, grey-box model), and a neural network (fully opaque, black-box model). We tested the counterfactual generation process using four algorithms (DiCE, WatcherCF, prototype, and GrowingSpheresCF) in the literature in 25 different datasets. Our findings indicate that: (1) Different machine learning models have little impact on the generation of counterfactual explanations; (2) Counterfactual algorithms based uniquely on proximity loss functions are not actionable and will not provide meaningful explanations; (3) One cannot have meaningful evaluation results without guaranteeing plausibility in the counterfactual generation. Algorithms that do not consider plausibility in their internal mechanisms will lead to biased and unreliable conclusions if evaluated with the current state-of-the-art metrics; (4) A counterfactual inspection analysis is strongly recommended to ensure a robust examination of counterfactual explanations and the potential identification of biases.
Mostafaei, H, Kelishadi, M, Bahmani, H, Wu, C & Ghiassi, B 2025, 'Development of sustainable HPC using rubber powder and waste wire: carbon footprint analysis, mechanical and microstructural properties', European Journal of Environmental and Civil Engineering, vol. 29, no. 2, pp. 399-420.
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Mousavi, M, Dackermann, U, Hassani, S, Subhani, M & Gandomi, AH 2025, 'Raw sensor data fusion using Johansen cointegration for condition assessment of concrete poles', Journal of Sound and Vibration, vol. 599, pp. 118909-118909.
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Munot, S, Redfern, J, Bray, JE, Angell, B, Coggins, A, Denniss, AR, Jennings, G, Khanlari, S, Kovoor, P, Kumar, S, Lai, K, Marschner, S, Middleton, PM, Oppermann, I, Rock, Z, Semsarian, C, Vukasovic, M, Bauman, A & Chow, CK 2025, 'FirstCPR: A pragmatic community organisation-based cluster randomised trial to increase community training and preparedness to respond to out-of-hospital cardiac arrest', Resuscitation Plus, vol. 23, pp. 100949-100949.
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Murray, J, Meylan, MH, Ngo, T, Thamwattana, N & Indraratna, B 2025, 'Analytical Solution for Railway Transition Zones With Abrupt Changes in Elastic Stiffness', Engineering Reports, vol. 7, no. 5.
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ABSTRACTTransition zones in railway systems, where properties of the track foundation change abruptly, are known to increase dynamic loads, track deterioration, and passenger discomfort. As such, it is of particular importance to study railway transition zones with abrupt changes in foundation properties to minimize these railway problems. This paper presents a closed‐form solution for the long‐term deformation of an Euler‐Bernoulli beam on an elastic foundation with multiple abrupt changes in foundation stiffness and under multiple applied stationary point loads. The solutions are obtained by dividing the beam into segments and applying the method of undetermined coefficients. This exact analytical solution constitutes an improvement upon an approximate solution, which is presented in the literature as a recent method for modeling rail infrastructure at transition zones. A limitation of the approximate solution is its inability to account for the changed behavior of the beam close to a transition zone. The closed‐form solution overcomes this limitation and can be used to assess the suitability of the approximate solution.
Nabeel, MI, Afzal, MU, Singh, K, Thalakotuna, DN & Esselle, KP 2025, 'Waveguide-Based All-Metal Near-Field Metasurfaces for Linearly and Circularly Polarized Beam Steering Antennas', IEEE Access, vol. 13, pp. 10857-10869.
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Naei, VY, Tubelleza, R, Monkman, J, Sadeghirad, H, Donovan, ML, Blick, T, Wicher, A, Bodbin, S, Basu, S, Stad, R, Barnett, C, O’Byrne, K, Ladwa, R, Cooper, C, Warkiani, ME, Hughes, BGM & Kulasinghe, A 2025, 'Abstract 750: Exploring PD1/PDL1 interactions and macrophage-tumor barriers in head and neck cancer: A spatial approach to immunotherapy response', Cancer Research, vol. 85, no. 8_Supplement_1, pp. 750-750.
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Abstract With nearly a million new cases expected, the global death rate from mucosal head and neck cancer is projected to reach around 50% in 2024. In the U.S., patients with localized tumors have a five-year survival rate of 50-60%. While immune checkpoint inhibitors (ICIs) have shown promise in extending survival, significant challenges remain, particularly the limited effectiveness of PD-1/PD-L1 blockade therapies. Since many studies have shown that PD-L1 protein expression alone may not be a reliable predictor of response to ICI therapy, understanding the spatial context of PD-1/PD-L1 interactions might be crucial for identifying immune evasion mechanisms and predicting responses to ICIs in head and neck cancer. To address this, we aimed to take a more comprehensive approach by mapping PD-1/PD-L1 interactions across n=35 mucosal head and neck squamous cell carcinoma (HNSCC) tissue samples collected before ICI treatment, using a combination of high-plex spatial proteomics and the in situ Navinci Proximity Ligation Assay (isPLA). Formalin-fixed, paraffin-embedded (FFPE) tissue samples were stained using multiplex immunofluorescence and the Navinci Diagnostics isPLA assay to detect PD-1/PD-L1 interactions. After incubation with primary anti-PD1 and anti-PD-L1 antibodies, secondary probes tagged with oligonucleotides were applied, enabling PLA to highlight the interactions through a ligation step and an amplification process to enhance the visibility of the protein interactions. Finally, a detection solution was applied to visualize the amplification product at the sites of PD1/PD-L1 interaction.We identified distinct spatial patterns of PD-1/PD-L1 interactions at the tumor-stroma interface, particularly in progressive disease (PD) patients, who exhibited dense layers of isPLA+ macrophages, CD3e T cells, and tumor cells, suggesting an immunosuppressive barrier in this region. In contrast, complete respons...
Nair, L, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2025, 'The interactive role of cyclic vertical and torsional stresses on the instability of low-plasticity soils based on hollow cylinder testing', Canadian Geotechnical Journal, vol. 62, pp. 1-21.
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Soil instability and potential failure under principal stress rotation require greater attention than ever before due to increased operation of heavier and longer high-speed trains. This study focuses on the interplay between cyclic vertical stress and torsional shear stress on the failure condition of a low-plasticity subgrade soil, facilitated by a hollow cylinder apparatus. Combined vertical and torsional loading significantly influences strain response, with increasing torsional stress leading to higher strain accumulation. Moreover, the data indicate that an increase in torsional shear stress is generally accompanied by a swift rise in the EPWP and a corresponding decrease in the soil stiffness. In view of this, a novel parameter, the overall stiffness degradation index ( δo) that simultaneously captures both the vertical and torsional shear effects under principal stress rotation is proposed as an early indicator of instability. In addition, a normalised torsional stress ratio (NTSR), which is the ratio of the amplitude of torsional shear stress to the confining pressure, is introduced to assess the impact of torsional shear stress. Whereby, higher NTSR values correlate with premature inception of failure. These experimental results provide new insights for a better understanding of soil instability under simulated railway loading.
Nair, SG, Nguyen, QD, Zhu, Q, Karimi, M, Gan, Y, Wang, X, Castel, A, Irga, P, Rocha, CGD, Torpy, F, Wilkinson, S, Moreau, D & Delhomme, F 2025, 'Suitability of calcined clay and ground granulated blast furnace slag geopolymer binder for hempcrete applications', Built Environment Project and Asset Management.
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PurposeHempcrete has the potential to reduce both CO2 emissions and energy usage in buildings. Hempcrete has a high sound absorption capacity, excellent moisture regulator and outstanding thermal insulation properties. However, hempcrete traditionally uses lime-based binders, which are carbon-intensive materials. The low-carbon binders to increase the sustainability of hempcrete are the current research gap. Geopolymer binders are low-carbon binders composed of aluminosilicate precursors dissolved in a high alkalinity solution. This study investigated the suitability of calcined clay and ground granulated blast furnace slag geopolymer binder as a low-carbon binder for hempcrete applications.Design/methodology/approachTwo types of hemp hurds with different water absorption capacity and particle size distributions were used. Hempcrete properties tested were compressive strength, bulk density, sound absorption coefficient by a two-microphone impedance tube and thermal conductivity by a Hot Disk system.FindingsThe particle size distribution and water absorption capacity of hemp hurds did not affect the compressive strength of hempcrete when following a mixing procedure, ensuring the hurds in a saturated surface dry condition. The geopolymer hempcrete achieved a compressive strength about four times higher than the reference hydrated lime hempcrete. All hempcrete specimens achieved outstanding acoustic performance. The increase in bulk density led to the decrease in the maximum sound absorption coefficient. The geopolymer hempcrete achieved the lowest thermal conductivity.Originality/value
Nair, SG, Nguyen, QD, Zhu, Q, Karimi, M, Gan, Y, Zhong, H, Castel, A, Irga, PJ, da Rocha, CG, Torpy, FR, Wilkinson, S, Moreau, D & Delhomme, F 2025, 'Low-carbon calcined clay-based binders for sustainable hempcrete', Materials and Structures, vol. 58, no. 6.
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Abstract The building sector is responsible for approximately 40% of total anthropogenic greenhouse gas emissions and 37% of global energy consumption. Hempcrete, fabricated from industrial hemp, can offer a tremendous potential to alleviate the carbon emissions and energy usage from buildings and construction based on its carbon capture and storage capability and low thermal conductivity. However, conventional lime-based binders for hempcrete are carbon intensive. This study investigates three low carbon binder alternatives for hempcrete: HL-Ref (100% hydrated lime), HL–CC (50% hydrated lime, 50% calcined clay), HL–CC–LS (50% hydrated lime, 50% calcined clay and limestone), Geo-CC [geopolymer binder with 70% calcined clay and 30% granulated ground blast furnace slag (GGBFS)]. Compressive strength, bulk density, sound absorption coefficient, thermal conductivity, surface bond strength and crystalline phases of hempcrete were assessed and a multicriteria analysis was carried out to compare the hempcrete performance between different mix designs. Results showed that the Geo-CC hempcrete using the calcined clay/GGBFS geopolymer binder achieved the best performance in terms of compressive strength, surface bonding capacity and thermal conductivity. The performance of HL–CC–LS hempcrete also achieved outstanding properties which could not be achieved by using only calcined clay (HL–CC), highlighting the beneficial synergy between limestone and calcined clay in a lime-based system. The HL–CC–LS hempcrete achieved the best acoustic performance with the highest sound absorption coefficient.
Nanda, P, Srivastava, S, Verma, VK & Vyas, P 2025, 'Preface', Smart Innovation Systems and Technologies, vol. 398, pp. v-vi.
Naseem, U, Zhang, Q, Hu, L, Hussain, S & Wang, S 2025, 'Knowledge Enhanced Language Model for Biomedical Natural Language Processing: Introducing a New Language Model for BioNLP', IEEE Systems, Man, and Cybernetics Magazine, vol. 11, no. 1, pp. 89-94.
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Nasir, AA, Tuan, HD, Poor, HV & Hanzo, L 2025, 'Widely Linear Processing Improves the Throughput of Nonorthogonal User Access', IEEE Open Journal of the Communications Society, vol. 6, pp. 5395-5413.
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Nasir, MU, Zubair, M, Naseem, MT, Shahzad, T, Saeed, A, Adnan, KM & Gandomi, AH 2025, 'Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning', Scientific Reports, vol. 15, no. 1, p. 26379.
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Abstract Mild to severe anemia is caused by thalassemia, a common genetic disorder affecting over 100 countries worldwide, that results from the abnormality of one or several of the four globin genes. This leads to chronic hemolytic anemia and disrupted synthesis of hemoglobin chains, iron overload, and poor erythropoiesis. Although the diagnosis of thalassemia has improved globally along with the treatment and transfusion support, it is still a major problem in diagnosing in high-prevalence areas like Pakistan. This work aims to assess the performance of numerous combinations of machine learning methods to detect alpha and beta-thalassemia in their minor and major types. These results are obtained from CBC and HPLC analysis. The analyzed models are K-nearest Neighbor (KNN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). The study aims to examine the effectiveness of the developed models in discriminating thalassemia variants, especially in the light of Pakistani patients’ data. The study found that XGBoost achieved the highest performance on both the CBC and HPLC datasets, with training accuracies of roughly 99.5% for CBC and 99.3% for HPLC. The test accuracy across both datasets was consistently high and thus the best model for detecting thalassemia in this research study. The imported SVM model, slightly less accurate than XGBoost, still has strong performance, particularly on the HPLC data where the cumulative testing accuracy of the model stood at 99.4%. As can be seen from the results, XGBoost specifically shows a very high accuracy of above 99% in the detection of thalassemia types using CBC and HPLC data for Pakistani patients. To the author’s knowledge, this research is the first to predict alpha and beta-thalassemia in its major and minor forms using these diagnostic reports. These models indicate that they can offer significant support in detecting thalassemia in resource-cons...
Neshat, M, Sergiienko, NY, da Silva, LSP, Mirjalili, S, Gandomi, AH, Abdelkhalik, O & Boland, J 2025, 'Hybrid wave–wind energy site power output augmentation using effective ensemble covariance matrix adaptation evolutionary algorithm', Renewable and Sustainable Energy Reviews, vol. 222, pp. 115896-115896.
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Neshat, M, Thilakaratne, M, El-Abd, M, Mirjalili, S, Gandomi, AH & Boland, J 2025, 'Smart buildings energy consumption forecasting using adaptive evolutionary bagging extra tree learning models', Energy, vol. 333, pp. 137130-137130.
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Newman, PLH, Mirkhalaf, M, Gauci, SC, Roohani, I, Biro, M, Barner‐Kowollik, C & Zreiqat, H 2025, '3D Printed Materials with Nanovoxelated Elastic Moduli', Advanced Materials, vol. 37, no. 15.
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AbstractFabrication methods that synthesize materials with higher precision and complexity at ever smaller scales are rapidly developing. Despite such advances, generating complex 3D materials with controlled mechanical properties at the nanoscale remains challenging. Exerting precise control over mechanical properties at the nanoscale would enable material strengths near theoretical maxima, and the replication of natural structures with hitherto unattainable strength‐to‐weight ratios. Here, a method for fabricating materials with nanovoxelated elastic moduli by employing a volume‐conserving photoresist composed of a copolymer hydrogel, along with OpenScribe, an open‐source software that enables the precise programming of material mechanics, is presented. Combining these, a material composed of periodic unit cells featuring heteromechanically tessellated soft‐stiff structures, achieving a mechanical transition over an order‐of‐magnitude change in elastic modulus within 770 nm, a 130‐fold improvement on previous reports, is demonstrated. This work critically advances material design and opens new avenues for fabricating materials with specifically tailored properties and functionalities through unparalleled control over nanoscale mechanics.
Ngo, QT, He, Y, Jayawickrama, B & Dutkiewicz, E 2025, 'A Fast Fuzzy DRL-Based Joint Beam Design and Power Allocation for Multi-Beam GEO-LEO Coexisting Satellite Networks', IEEE Transactions on Wireless Communications, pp. 1-1.
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Ngo, QT, Jayawickrama, B, He, Y & Dutkiewicz, E 2025, 'A Novel Satellite-Based REM Construction in Cognitive GEO-LEO Satellite IoT Networks', IEEE Internet of Things Journal, vol. 12, no. 6, pp. 7532-7548.
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Ngo, QT, Jayawickrama, BA, He, Y, Dutkiewicz, E, Weththasinghe, K, Clark, N, Arbon, E & Bowyer, M 2025, 'Optimizing Spectrum Sensing in Cognitive GEO-LEO Satellite Networks: Overcoming Challenges for Effective Spectrum Utilization', IEEE Vehicular Technology Magazine, pp. 2-11.
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Ngo, T, Indraratna, B, Coop, M & Qi, Y 2025, 'Behaviour of ballast stabilised with recycled rubber mat under impact loading', Géotechnique, vol. 75, no. 2, pp. 192-212.
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During the passage of trains, dynamic impact loads caused by wheel imperfections or rail abnormalities cause significant ballast degradation. In this study, the use of rubber mats manufactured from recycled tyres placed underneath a ballast layer is investigated to mitigate the adverse effects of impact loads. Based on a series of tests conducted using a high-capacity drop-weight facility to evaluate the dynamic impact responses, the experimental results show that the inclusion of a rubber mat beneath the ballast assembly significantly reduces particle breakage. This study also describes a numerical analysis following a coupled discrete–continuum modelling approach to examine the complex interaction of discrete ballast grains with the recycled rubber mat. In particular, a mathematical framework coupling the discrete and continuum domains is developed to facilitate the exchange of forces and displacements at the ballast–mat interface. Laboratory data measured from large-scale impact tests are used to calibrate and validate this coupled model. Subsequently, the model is used to predict the deformation and breakage of ballast, contact force distributions, impact forces, coordination numbers and the evolution of energy components during impact testing. The energy-absorbing properties of the rubber mat are captured in terms of reducing particle breakage from a micromechanical perspective.
Nguefoue Meli, V, Njougouo, T, Kongni, SJ, Louodop, P, Bertrand Fotsin, H & Cerdeira, HA 2025, 'Mobile oscillators in a mobile multi-cluster network', Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 35, no. 5.
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Different collective behaviors emerging from the unknown have been examined in networks of mobile agents in recent years. Mobile systems, far from being limited to modeling and studying various natural and artificial systems in motion and interaction, offer versatile solutions across various domains, facilitating tasks ranging from navigation and communication to data collection and environmental monitoring. We examine the relative mobility between clusters, each composed of different elements in a multi-cluster network—a system composed of clusters interconnected to form a larger network of mobile oscillators. Each mobile oscillator exhibits both external (i.e., position in a 2D space) and internal dynamics (i.e., phase oscillations). Studying the mutual influence between external and internal dynamics often leads the system toward a state of synchronization within and between clusters. We show that synchronization between clusters is affected by their spatial closeness. The stability of complete synchronization observed within the clusters is demonstrated through analytical and numerical methods.
Nguyen, CT, Liu, Y, Du, H, Hoang, DT, Niyato, D, Nguyen, DN & Mao, S 2025, 'Generative AI-Enabled Blockchain Networks: Fundamentals, Applications, and Case Study', IEEE Network, vol. 39, no. 2, pp. 232-241.
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Nguyen, DV, Tran, TL, Nguyen, H, Chen, G, Lai, TK, Song, P, Tran, TT, Tran, C-D, Bell, J & Dinh, T 2025, 'The Concept of Pressure-Induced Conduction Band Mismatch in Soft–Hard Semiconductors for Self-Powered Phototronic Pressure Sensing', ACS Applied Materials & Interfaces, vol. 17, no. 22, pp. 32827-32837.
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Nguyen, HAD, Le, HT, Barthelemy, X, Azzi, M, Duc, H, Jiang, N, Riley, M & Ha, QP 2025, 'A Deep-Learning-Based Visualization Tool for Air Pollution Forecasting', IEEE Software, vol. 42, no. 2, pp. 47-56.
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Nguyen, HN, Roohani, I, Hayles, A, Lu, Z, Vongsvivut, J, Vasilev, K, Truong, VK & Zreiqat, H 2025, 'Antibacterial Activity and Mechanisms of Magnesium‐Doped Baghdadite Bioceramics for Orthopedic Implants', Advanced NanoBiomed Research, vol. 5, no. 2.
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Baghdadite (BAG, Ca3ZrSi2O9), a calcium silicate compound with zirconium incorporation, shows significant potential in medical implants. However, its susceptibility to infections poses a considerable challenge. To tackle this problem, doping biocompatible magnesium (Mg) into BAG to create Mg‐BAG enhances antibacterial activity and prevents infection in orthopedic implants. Mg‐BAG demonstrates effectiveness against Gram‐positive Staphylococcus aureus and Gram‐negative Pseudomonas aeruginosa. This study finds that the antibacterial activity of Mg‐BAG is multifaced including causing the generation of reactive oxygen species (ROS) within cells and disrupting membrane potential, resulting in leakage of intracellular contents. The synchrotron macro attenuated total reflectance Fourier‐transform infrared microspectroscopy shows the impact of Mg‐BAG on bacteria, resulting in modifications to biomolecules such as lipids, protein structures, and the stability of nucleic acids. The combined effect of Mg ions (Mg2+) and intracellular ROS formation contributes to the disruption of biomolecules and bacterial cell death. Mg‐BAG is a promising next‐generation bioceramic offering innovative nonantibiotic solutions for preventing infection.
Nguyen, HT, Nguyen, BT, Tran, AV, Nguyen, TT, Ngo, LH, Vo, T, Nhung Thai, TH, Mai, LD, Tran, TS, Nguyen, TV & Ho-Pham, LT 2025, 'A predictive nomogram for selective screening of asymptomatic vertebral fractures: The Vietnam Osteoporosis Study', Osteoporosis and Sarcopenia, vol. 11, no. 1, pp. 9-14.
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Nguyen, HT, Nguyen, NC, Duong, HC & Chen, S-S 2025, 'A simple-effective forward osmosis filter water bag designed for producing drinking water during an emergency', Ministry of Science and Technology, Vietnam, vol. 67, no. 1.
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Nguyen, PQK, Zohdi, N, Zhang, YX, Zhang, Z & Yang, R 2025, 'Study on material behaviours of additively manufactured high-impact polystyrene using artificial neural networks', Progress in Additive Manufacturing, vol. 10, no. 2, pp. 1461-1478.
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Abstract Fused Filament Fabrication (FFF), a process parameters-dependent manufacturing method, currently dominates the additive manufacturing (AM) sector because of its prominent ability to produce parts with intricate profiles, customise products, and minimise waste. Though the effects of FFF process parameters were investigated experimentally, recent research highlighted the importance of developing numerical modelling and computational methods on optimising the FFF printing process and FFF-printed materials. This study aims to investigate the tensile strength (TS) of FFF-printed high-impact polystyrene (HIPS) via devising a systematic testing and analysis framework, which combines experimental testing, representative volume element (RVE)-finite element method (FEM), rule of mixture (ROM), and artificial neural networks (ANN). HIPS samples are fabricated using FFF considering the variations of infill density, layer thickness, nozzle temperature, raster angle, and build orientation, and tested with standard tensile testing. The rule of mixtures (ROM) and its modified version (MROM) are employed to calculate the TS of longitudinally and transversely built samples at various infill densities, respectively, while an ANN model is constructed to investigate the effect of material anisotropy precisely. The optimal ANN architecture is built with five hidden layers with the number of neurons in each layer as 44, 82, 169, 362, and 50. Although both MROM and ANN perform well on the validation set, ANN exhibits superior accuracy with only a maximum error of 0.13% for training set and 11% for validation set. The combination of the RVE-FEM, MROM, and ANN approaches can significantly improve the FFF printing process of polymers for optimisation.
Nguyen, TD, Nguyen, CK & Nguyen, TT 2025, 'Radial Consolidation of Soft Soils in Vietnam's Red River Delta: Effect of Drain Diameter on Undisturbed and Remolded Samples', Geotechnical and Geological Engineering, vol. 43, no. 6.
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Abstract Given the increasing demand for soft soil improvement in Red River Delta of Vietnam in the past decade, this study is dedicated to characterizing radial consolidation behaviour of clayey soils in the delta, while varying drain size in the radial consolidation test. Undisturbed soil samples were collected from 3 different sites and subjected to a series of radial and vertical consolidation tests. The central drain diameter in radial consolidation test is varied from 12 to 28 mm, corresponding to the drain spacing ratio n changing from 5.17 to 2.21, to investigate the influence of drainage length on the interpreted outcomes. The alteration of consolidation parameters induced by soil remolding is also studied by comparing the test results of undisturbed and remolded soils. The test results indicate that smaller drain diameter (i.e., larger drainage length) results in larger coefficient of radial consolidation (c r ), for example, c r increases by a factor of 2 when the drain size varies from 28 mm (n = 2.21) to 20 mm (n = 3.1). However, when n > 3.1, the influence of drainage length on the value of c r decreases apparently, despite the applied pressure rising from 25 to 800 kPa. New correlations describing the relationship between the value of c r and n are proposed. On the other hand, soil remolding can cause permeabil...
Nguyen, TT, Nguyen, KL, Huynh, TQ & Tran, Q 2025, 'Influence of feature selection on machine learning prediction of pile foundation – The role of soil-pile interaction knowledge and application to base resistance', Geodata and AI, vol. 3, pp. 100019-100019.
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Nguyen, X-B, Phan, X-H & Piccardi, M 2025, 'Fine-tuning text-to-SQL models with reinforcement-learning training objectives', Natural Language Processing Journal, vol. 10, pp. 100135-100135.
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Ni, T, Wang, J, Zi, X, Thiyagarajan, K, Kodagoda, S & Prasad, M 2025, 'CLR-DLR: A Semi-Supervised Framework for High-Fidelity Remote Sensing Segmentation', IEEE Transactions on Geoscience and Remote Sensing, pp. 1-1.
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Ni, Z, Zhang, JA & Liu, RP 2025, 'Deep Learning Based Water Level Sensing With Interference Suppression for ISAC Systems', IEEE Wireless Communications Letters, pp. 1-1.
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Nie, J, Luo, Y, Ye, S, Zhang, Y, Tian, X & Fang, Z 2025, 'Out-of-Distribution Detection with Virtual Outlier Smoothing', International Journal of Computer Vision, vol. 133, no. 2, pp. 724-741.
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Abstract Detecting out-of-distribution (OOD) inputs plays a crucial role in guaranteeing the reliability of deep neural networks (DNNs) when deployed in real-world scenarios. However, DNNs typically exhibit overconfidence in OOD samples, which is attributed to the similarity in patterns between OOD and in-distribution (ID) samples. To mitigate this overconfidence, advanced approaches suggest the incorporation of auxiliary OOD samples during model training, where the outliers are assigned with an equal likelihood of belonging to any category. However, identifying outliers that share patterns with ID samples poses a significant challenge. To address the challenge, we propose a novel method, Virtual Outlier Smoothing (VOSo), which constructs auxiliary outliers using ID samples, thereby eliminating the need to search for OOD samples. Specifically, VOSo creates these virtual outliers by perturbing the semantic regions of ID samples and infusing patterns from other ID samples. For instance, a virtual outlier might consist of a cat’s face with a dog’s nose, where the cat’s face serves as the semantic feature for model prediction. Meanwhile, VOSo adjusts the labels of virtual OOD samples based on the extent of semantic region perturbation, aligning with the notion that virtual outliers may contain ID patterns. Extensive experiments are conducted on diverse OOD detection benchmarks, demonstrating the effectiveness of the proposed VOSo. Our code will be available at https://github.com/junz-debug/VOSo.
Nikkhah, N, Keshavarz, R & Shariati, N 2025, 'Highly Sensitive Differential 3-D Oblique-Gap Rectangular Sensor for Noninvasive Glucose-Level Monitoring', IEEE Sensors Journal, vol. 25, no. 13, pp. 25420-25428.
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Nik-Khorasani, A, Khuat, TT & Gabrys, B 2025, 'Hyperbox Mixture Regression for process performance prediction in antibody production', Digital Chemical Engineering, vol. 14, pp. 100221-100221.
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Nikolic, S, Quince, Z, Lindqvist, AL, Neal, P, Grundy, S, Lim, M, Tahmasebinia, F, Rios, S, Burridge, J, Petkoff, K, Chowdhury, AA, Lee, WSL, Prestigiacomo, R, Fernando, H, Lok, P & Symes, M 2025, 'Project-work Artificial Intelligence Integration Framework (PAIIF): Developing a CDIO-based framework for educational integration', STEM Education, vol. 5, no. 2, pp. 310-332.
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Nikolic, S, Suesse, TF, Grundy, S, Haque, R, Lyden, S, Lal, S, Hassan, GM, Daniel, S & Belkina, M 2025, 'Assessment integrity and validity in the teaching laboratory: adapting to GenAI by developing an understanding of the verifiable learning objectives behind laboratory assessment selection', European Journal of Engineering Education, vol. 50, no. 4, pp. 673-701.
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Niu, K, Tai, W, Peng, X, Guo, Z, Zhang, C & Li, H 2025, 'PedFed: A performance evaluation-driven federated learning framework for efficient communication', International Journal of Modeling, Simulation, and Scientific Computing, vol. 16, no. 03.
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Protecting healthcare data privacy and security is crucial in advanced manufacturing, which involves medical devices. It encompasses patient records and clinical trial data. Federated learning emerges as a solution that enables model training across different institutions without compromising data privacy and security. However, existing frameworks often exhibit a bias towards clients with larger data volumes, neglecting the connection between global and local model performance. This can result in suboptimal aggregation of the global model, thereby affecting the effectiveness and efficiency of the overall process. To address these limitations, we propose a performance evaluation-driven federated learning framework (PedFed). The primary objective of PedFed is to enhance global model aggregation and improve communication efficiency. Our approach involves a client selection strategy based on performance evaluation of local and global models. Specifically, we introduce the concept of local model improvement (LMI) using Intersection over Union (IoU) for client selection in medical image segmentation scenarios. Moreover, we introduce a dynamic aggregation framework incorporating validation IoU as a weighting factor to mitigate model divergence caused by not independent and identically distributed (non-IID) data. We focus on performing image segmentation tasks to simulate the analysis of sensitive data in the healthcare domain. Experimental results conducted on brain tumor and heart segmentation datasets demonstrate the superiority of the PedFed framework over the baseline framework, confirming its benefits in communication efficiency.
Niu, Y, Shi, K, Cai, X & Wen, S 2025, 'Adaptive smooth sampled-data control for synchronization of T–S fuzzy reaction-diffusion neural networks with actuator saturation', AIMS Mathematics, vol. 10, no. 1, pp. 1142-1161.
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<p>This paper addresses the synchronization issue in T–S fuzzy reaction–diffusion neural networks (TFRNNs) with time-varying delays and actuator saturation. First, an adaptive smooth sampled-data (ASSD) controller is proposed to optimize communication resources. In the ASSD controller, the dynamic forgetting factor is employed to process historical data smoothly, thereby preventing data distortion due to unexpected events. Second, the Lyapunov–Krasovskii functional (LKF), which captures more information about the system, is introduced, and it can provide greater flexibility than the fixed-matrix LKF. Meanwhile, by employing the semi-looped-functional method, the constraint for negative determination of the sum of its derivatives is removed, which enhances the feasibility of expanding the solution. Consequently, a novel criterion and the corresponding algorithm are established to obtain the larger maximum allowable sampling interval (MASI). Finally, simulations demonstrate the effectiveness and superiority of the proposed theoretical results.</p>
Noh, M-S, Kim, D-H, Guo, Y & Lee, H-J 2025, 'A Study on the Performance of Bearingless PMSM Based on Rotor Permanent Magnet Arrangement', The transactions of The Korean Institute of Electrical Engineers, vol. 74, no. 4, pp. 739-746.
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Nurhayati, M, Jeong, K, Kim, S, Park, J, Cho, KH, Shon, HK & Lee, S 2025, 'From comparison to integration: Enhancing forward osmosis performance prediction with mathematical and RBF neural network models', Desalination, vol. 597, pp. 118322-118322.
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Oberst, S, Tofigh, F, Lai, J, Mankowski, M, Arango, R & Kirker, G 2025, 'Development of a micro-exciter to mimic insect vibration signals and the effect of the substrate on the playback response', The Journal of the Acoustical Society of America, vol. 157, no. 4_Supplement, pp. A186-A186.
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In bioassays that involve the playback of recorded insect vibration signals using conventional commercial shakers, two issues need to be addressed. First, common shaker systems do not have the capability of producing insect excitations that are in the micro or milli Newton range. Second, recorded insect signals are the response of the substrate excited by the vibrations produced by the insect, but the influence of the substrate is rarely considered by the biotremology community. Here, we, therefore, present the development of a micro-excitation system across three stages: (1) an initial Arduino-based with a piezoelectric beam element; (2) a Raspberry Pi-controlled piezoelectric buzzer with fast-acting amplification circuit, compared to commercial stacked piezo actuators; and (3) an in-house developed microactuator consisting of a microcontroller and a piezoelectric actuator (MiAC-S). The evolution of the amplification circuit and its integration into MiAC-S will be described. The MiAC-S will be validated. Finally, we demonstrate the effect of the substrate on the playback of an impulse response signal compared to using a direct impulse as an excitation signal. The results indicate that the vibration response should not be used for playback of insect signals on substrates as it produces results inconsistent with theoretical expectations, thereby increasing the uncertainty and reducing the control quality of bioassays.
Oladigbolu, JO, Bilal, M, Gupta, S, Mujeeb, A & Li, L 2025, 'A novel approach for optimal deployment of plug-in electric vehicles with integrated renewable energy sources', Electrical Engineering, vol. 107, no. 4, pp. 3847-3881.
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Ometto, AR, Koskue, V, Cerdán, JMA, de Melo Duarte Borges, T, Roiko, A, Iftekhar, S, Cordell, D, Roods, J, Shon, HK, Freguia, S, Oliveira, MG, de Vasconcelos Gomes, LA, Evans, S & Beal, C 2025, 'Pathways to circular nutrient ecosystems: Strategic roadmaps addressing sustainability drivers and barriers in Australia', Sustainable Production and Consumption, vol. 56, pp. 593-617.
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Ong, MY, Milano, J, Nomanbhay, S, Palanisamy, K, Tan, YH & Ong, HC 2025, 'Insights into algae-plastic pyrolysis: Thermogravimetric and kinetic approaches for renewable energy', Energy, vol. 314, pp. 134322-134322.
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Onggowarsito, C, Zhang, S, Shi, Y, Mao, S, Feng, A, Martins, G, Shao, Z, Wong, EHH, Guo, W & Fu, Q 2025, 'Hydrogel-Encapsulated phase change materials for Enhanced heat storage and water evaporation efficiency', Chemical Engineering Journal, vol. 513, pp. 162838-162838.
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Ou, S, Guo, Z, Nie, X, Wen, S & Huang, T 2025, 'Fixed-Time Multi-Almost-Periodicity in Switched Fuzzy Neural Networks with Multi-Controller Strategies', IEEE Transactions on Fuzzy Systems, pp. 1-15.
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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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Owen, R, Bryant, A, Finch, L, Franklin, D, Abdollahi, M & Abolhasan, M 2025, 'Failures and Resilience in the IP Era: Navigating the Fragility of Modern Telecommunications Networks: The Sovereign Functions', IEEE Access, pp. 1-1.
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Pan, Y, Fan, W, Lv, M, Sun, X & Yu, S 2025, 'An Incremental Scalable Network Architecture With Fault-Tolerant Communication', IEEE Transactions on Reliability, pp. 1-15.
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Pang, Y, Nguyen, QD & Castel, A 2025, 'Implementing the effect of geopolymer concrete pore solution pH in the standard rapid migration test NT Build 492 protocol', Materials and Structures, vol. 58, no. 2.
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Abstract This research investigates the impact of the pore solution pH values on chloride content at the colour change boundary determined according to the standard rapid migration test (NT Build 492), with a focus on alkali-activated materials, so-called geopolymer. The study investigates a range of geopolymer formulations using various proportions of ground granulated blast furnace slag (GGBFS), fly ash, and calcined clay, alongside different activator concentrations, to examine their influence on the pH value of the pore solution. Findings from this study suggest that the pH value of the pore solution greatly influence in the chloride ion concentration at the colour change boundary, which should be accounted for in the calculation of the non-steady-state migration coefficients (Dnssm). It is noted that mixtures with higher GGBFS content exhibit higher pH values than mixtures containing fly ash or calcined clay, impacting the Dnssm. The results advocate for modifications to the standard NT Build 492 protocol to enhance its applicability and accuracy for geopolymer materials, emphasizing the importance of using revised Dnssm values calculated considering the unique properties of geopolymer concrete for more durability assessment.
Park, H-M, Lee, H-W, Park, C-B, Guo, Y & Lee, J-B 2025, 'Solution to Mitigate the Disadvantages in the Design of LLC Resonant Converters for Electric Vehicle Fast Chargers with High Output Power and Wide Output Voltage Range', The transactions of The Korean Institute of Electrical Engineers, vol. 74, no. 2, pp. 316-325.
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Parnell, J, Unanue, IJ, Matthews, S & Piccardi, M 2025, 'APG: Automatic Prompt Generation for Improved Document Summarization', IEEE Transactions on Audio, Speech and Language Processing, vol. 33, pp. 2388-2401.
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Parsa, SM, Chen, Z, Feng, S, Yang, Y, Luo, L, Ngo, HH, Wei, W, Ni, B-J & Guo, W 2025, 'Metal-free nitrogen-doped carbon-based electrocatalysts for oxygen reduction reaction in microbial fuel cells: Advances, challenges, and future directions', Nano Energy, vol. 134, pp. 110537-110537.
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Parsa, SM, Chen, Z, Ngo, HH, Wei, W, Zhang, X, Liu, Y, Ni, B-J & Guo, W 2025, '15 Years of Progress on Transition Metal-Based Electrocatalysts for Microbial Electrochemical Hydrogen Production: From Nanoscale Design to Macroscale Application', Nano-Micro Letters, vol. 17, no. 1.
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Abstract Designing high-performance electrocatalysts is one of the key challenges in the development of microbial electrochemical hydrogen production. Transition metal-based (TM-based) electrocatalysts are introduced as an astonishing alternative for future catalysts by addressing several disadvantages, like the high cost and low performance of noble metal and metal-free electrocatalysts, respectively. In this critical review, a comprehensive analysis of the major development of all families of TM-based catalysts from the beginning development of microbial electrolysis cells in the last 15 years is presented. Importantly, pivotal design parameters such as selecting efficient synthesis methods based on the type of material, main criteria during each synthesizing method, and the pros and cons of various procedures are highlighted and compared. Moreover, procedures for tuning and tailoring the structures, advanced strategies to promote active sites, and the potential for implementing novel unexplored TM-based hybrid structures suggested. Furthermore, consideration for large-scale application of TM-based catalysts for future mass production, including life cycle assessment, cost assessment, economic analysis, and recently pilot-scale studies were highlighted. Of great importance, the potential of utilizing artificial intelligence and advanced computational methods such as active learning, microkinetic modeling, and physics-informed machine learning in designing high-performance electrodes in successful practices was elucidated. Finally, a conceptual framework for future studies and remaining challenges on different aspects of TM-based electrocatalysts in microbial electrolysis cells is proposed. Graphical Abstract
Parvin, K, Tabandeh, A, Hossain, MJ & Hannan, MA 2025, 'Optimized hybrid energy systems for sustainable net-zero communities: Modelling, framework design and performance analysis', International Journal of Hydrogen Energy, vol. 160, pp. 150559-150559.
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Patel, S, Torgal, S, Purohit, T, Kumar, R, Singh, DV, Kanchan, S, Soudagar, MEM, Ahamad, T, Kalam, MA & Patel, M 2025, 'Impact of variable exhaust valve timing on diesel engine characteristics fueled with waste cooking oil biofuel blends: A numerical analysis', Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, vol. 239, no. 3, pp. 1329-1352.
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The quantity of the conventional sources of energy such as petroleum, coal, and the deposits of hydrocarbon are limited in nature. The rapid growth is observed in the industrial and transportation section due to increase in population that has compelled the mankind to look for an alternative to conventional fuels. Biodiesel can be a potential substitute due to its ease of availability, accessibility, lower cost, and nontoxic characteristics. In this study, waste cooking oil (WCO) as the feedstock for biodiesel due to its abundance and low prices is chosen. It has been observed that the high prices of cooking oil have compelled people to reuse it multiple times, which can have negative health consequences such as inflammation, cholesterol issues, diabetes, and cancer. However, if people can obtain WCO at a reasonable cost, they will refrain from reusing it repeatedly. This article presents a numerical method that investigates the effects of variable exhaust valve timing on the performance, emissions, and combustion parameters of WCO and its different blends in a conventional mechanical fuel injection system diesel. The study was conducted using a single-cylinder, water-cooled, in-line diesel engine running at a constant speed. As, it is not possible to determine the values of engine performance, emissions, and combustion parameters for different valve timings experimentally, a numerical method has been adopted. The valve timing range was taken as 46–66° before bottom dead center (BDC) for exhaust valve opening and 6–20° after top dead center (TDC) for exhaust valve closing and the inlet valve opening and closing are constant at 16° b TDC and 33° a BDC, respectively.
Paul, A & Saha, SC 2025, 'A Systematic Literature Review on Flexible Strategies and Performance Indicators for Supply Chain Resilience', Global Journal of Flexible Systems Management, vol. 26, no. S1, pp. 207-231.
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Abstract Supply chain resilience is a widely useful concept for managing risk and disruption. Designing strategies for preparedness, response, and recovery can help businesses to mitigate risks and disruptions. Among them, flexible strategies can effectively improve supply chain resilience. In the literature, several studies have considered different types of flexible strategies and investigated their impacts on supply chain resilience. However, a systematic literature review (SLR) paper on this topic can further help to understand the scientific progress, research gaps, and avenues for future research. Hence, this study aims to explore how the literature has contributed to the area of flexible strategies and the impact on supply chain resilience performance. To achieve our objective, we apply an SLR methodology to identify themes such as research areas and key findings, contexts and industry sectors, methodologies, and key strategies and performance indicators in the connection between flexible strategies and supply chain resilience. The findings show that many studies connect flexible strategies to supply chain resilience. However, research gaps exist in analysing relationships between flexible strategies and performance, conducting comparative studies, developing dynamic resilience plans, applying flexible strategies, conducting theoretically grounded empirical studies, and applying multiple analytical tools to develop decision-making models for supply chain resilience. Finally, this study suggests several future research opportunities to advance the research on the topic. The findings can be a benchmark for researchers who are interested in conducting research in the area of flexible strategies and supply chain resilience.
Payan, M, Asadi, P, Jamaldar, A, Salimi, M, Zanganeh Ranjbar, P, Jahed Armaghani, D, He, X & Sheng, D 2025, 'Artificial intelligence-based predictive models for shear wave velocity of soils: A comprehensive review', Engineering Applications of Artificial Intelligence, vol. 155, pp. 111095-111095.
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Pei, J, Jiang, Z, Men, A, Wang, H, Luo, H & Wen, S 2025, 'Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Reidentification', IEEE Internet of Things Journal, vol. 12, no. 12, pp. 22381-22392.
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Peng, J, Khuat, TT, Otte, E, Bassett, R, Grevis-James, A, Musial, K & Gabrys, B 2025, 'Adaptive Ensemble-Based Hyperparameter-Free Just-In-Time Learning for Robust Cell Culture Process Monitoring', Procedia Computer Science, vol. 264, pp. 157-166.
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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, R, Ji, J, Liu, L, Miao, Z, Li, N & Zhou, J 2025, 'Task-space fixed-time bipartite tracking control for heterogeneous networked Euler-Lagrange systems', Applied Mathematical Modelling, vol. 148, pp. 116238-116238.
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Peng, R, Ji, J, Zheng, B, Li, N, Miao, Z & Zhou, J 2025, 'Neural network-based fixed-time practical attitude synchronization control for uncertain networked spacecraft systems', ISA Transactions.
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Peng, Z, Zhong, L, Li, Y, Feng, S, Mou, J, Miao, Y, Lin, CSK, Wang, Z & Li, X 2025, 'Harnessing oleaginous protist Schizochytrium for docosahexaenoic acid: Current technologies in sustainable production and food applications', Food Research International, vol. 205, pp. 115996-115996.
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Pennington, W, Bandara, M, Peloquin, A & McMillen, C 2025, 'Where are the crystals? X-DES: Deep Eutectic Solvents based on Halogen Bonding', Structural Dynamics, vol. 12, no. 2_Supplement, pp. A134-A134.
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As a crystallographer, nothing is sadder than failed crystal growth. As an “experienced” crystallographer (a basketball ref couldn't hand signal my number of years), I’ve thrown away more than my share of oily smudges (always according to established hazardous waste procedures, of course). Fortunately, my students are more curious than I. Recently we reported the first halogen-bonding-based deep eutectic solvent, which consisted of a mixture of 1,3-dithiane and o- diiodotetrafluorobenzene – a system that simply refused to cooperate during crystal growth (Peloquin et al. Angew Chem, Int. Ed.2021, 60, 22983–22989). Based on this result and memories of many other liquid samples, we have been exploring a number of different systems, most consisting of tetraalkylammonium triiodides with a variety of organoiodines. As it turns out, many of these systems that resist crystal growth are doing exactly what they are “supposed” to do. Most of these are pseudo binary systems with two or more eutectic points and at least one cocrystalline composition (see phase diagram below). The preparation and characterization of these systems by thermal analysis and, in the case of cocrystals, structural characterization will be discussed. Efforts to correlate halogen bonding in triiodide-based cocrystals with halogen bonding in triiodide-based deep eutectic solvents are now in progress.
Pfeffer, MA, Nguyen, AHP, Kim, K, Wong, JKW & Ling, SH 2025, 'Evolving optimized transformer-hybrid systems for robust BCI signal processing using genetic algorithms', Biomedical Signal Processing and Control, vol. 108, pp. 107883-107883.
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Phosha, NN, Fuku, XG, Tijing, L & Motsa, MM 2025, 'Exploring the application of solar irradiation in driving a stand-alone membrane distillation unit', Applied Thermal Engineering, vol. 278, pp. 127169-127169.
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Phuong, J, Lam, R, Moles, R, Mason, D, White, C, Center, J & Carter, S 2025, 'The design and evaluation of a bone health teaching module for secondary school students in NSW, Australia', Health Promotion Journal of Australia, vol. 36, no. 1.
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AbstractIssue AddressedThe growing prevalence of osteoporosis requires preventative management starting from an early age as peak bone mass is typically reached by age 30. However, current Australian adolescents are not adequately addressing key osteoprotective factors. Alarmingly, around 17% have insufficient vitamin D levels, 55% consume insufficient dietary calcium, and 79% are insufficiently active. Addressing these insufficiencies via bone health education and promoting healthier lifestyle choices are crucial to mitigate the risk of osteoporosis later in life.MethodsA mixed methods study was undertaken to assess the design and effectiveness of four bone health education modules implemented in PDHPE lessons across NSW secondary schools. Pre‐ and post‐module assessments included a multiple‐choice questionnaire on osteoporosis knowledge, and a survey based on the Theory of Planned Behaviour domains to examine influences on healthy bone behaviour. Statistical analysis, qualitative interviews, and focus groups were used to evaluate changes in knowledge and behaviour resulting from the modules.ResultsParticipation in bone health teaching modules improved students' knowledge and behaviours related to bone health. Subjective norms had the largest influence regarding behaviour changes. Both students and teachers engaged positively with the bone health modules, which were designed by clinicians and delivered by teachers.So What?The modules address knowledge gaps and provide strategies from an early age, empowering students and potentially contribute to improving long term bone health. There is a need to focus on promoting positive peer influence and facilitating easy access to bone‐healthy behaviours in se...
Phuong, J, Moles, R, Mason, D, White, C, Center, J & Carter, S 2025, 'Osteoporosis screening in Australian community pharmacies: A mixed methods study', Health Promotion Journal of Australia, vol. 36, no. 1.
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AbstractIssues AddressedOsteoporosis and poor bone health impact a large proportion of the Australian population, but is drastically underdiagnosed and undertreated. Community pharmacies are a strategic location for osteoporosis screening services due to their accessibility and the demographic profile of customers. The aim of this study was to develop, implement and evaluate a community pharmacy health promotion service centred on encouraging consumers to complete an anonymous osteoporosis screening survey called Know Your Bones.MethodsThe implementation process was documented using the REAIM (reach, effectiveness, adoption, implementation, maintenance) framework. Uptake of the Know Your Bones screening tool was monitored anonymously with website traffic. Surveys and interviews were designed to capture consumer outcomes after screening. Semi‐structured interviews were conducted with Australian community pharmacy stakeholders during design and implementation phases to explore their perspectives of the barriers and facilitators.ResultsThe service was implemented in 27 community pharmacies. There were 448 visits to the screening website. Interviews were conducted with 41 stakeholders. There were a range of factors that appeared to influence implementation of the service. Perceived acceptability was critical, which depended on staff training, pharmacists' altruism, and remuneration. Staff relied heavily on their existing close relationships with consumers. No consumers completed non‐anonymous surveys or agreed to participate in interviews post‐screening.ConclusionUsing an implementation science approach, a community pharmacy osteoporosis screening service for the Australian context was designed and found ...
Piao, R, Woo, SY, Li, W & Yoo, D-Y 2025, 'Enhancement of mechanical and electrical properties of ultra-high-performance concrete through optimization of steel fiber aspect ratio and multi-walled carbon nanotube dosage', Journal of Building Engineering, vol. 111, pp. 113320-113320.
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Picano, B, Hoang, DT & Nguyen, DN 2025, 'A Matching Game for LLM Layer Deployment in Heterogeneous Edge Networks', IEEE Open Journal of the Communications Society, vol. 6, pp. 3795-3805.
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Pichot, C, Huff, GH, Yang, Y, Jiang, ZH, Tong, X, Loh, TH, Alemaryeen, A, Wagih, M, Noghanian, S, Paryani, RC, Huang, Y, Sakakibara, K, Vipiana, F, Fear, E, Bakshi, SC, Waterhouse, R, Urbina, J, Meinrath, SD, Shih, BP-J, Sharma, R, Manohar, V, Dagefu, FT & Ito, K 2025, 'New and Emerging Directions in the Fields of Antennas and Propagation', IEEE Transactions on Antennas and Propagation, vol. 73, no. 1, pp. 566-581.
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Ping, J, Zhu, S, Luo, W, Zhang, Z, Wen, S & Mu, C 2025, 'Hybrid-Dependent Event-Triggered Schemes for T–S Fuzzy Memristive NNs With Nondifferentiable Delay', IEEE Transactions on Fuzzy Systems, vol. 33, no. 4, pp. 1158-1167.
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Poblete, P, Aguilera, RP, Pereda, J, Cuzmar, RH, Alcaide, AM, Lu, DD-C, Siwakoti, YP & Acuna, P 2025, 'Offset-Free Optimal Control of Cascaded H-Bridge Converters Based on a Kalman Filter Harmonic Compensator', IEEE Transactions on Power Electronics, vol. 40, no. 1, pp. 625-637.
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Poblete, P, Cuzmar, RH, Aguilera, RP, Pereda, J, Alcaide, AM, Lu, D, Siwakoti, YP & Konstantinou, G 2025, 'Dual-Stage MPC for SoC Balancing in Second-Life Battery Energy Storage Systems Based on Delta-Connected Cascaded H-Bridge Converters', IEEE Transactions on Power Electronics, vol. 40, no. 1, pp. 500-515.
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Ponrekha A., S, Subathra, MSP, Bharatiraja, C, Manoj Kumar, N & Haes Alhelou, H 2025, 'A topology review and comparative analysis on transformerless grid‐connected photovoltaic inverters and leakage current reduction techniques', IET Renewable Power Generation, vol. 19, no. 1.
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AbstractPhotovoltaic energy source growth is significant in power generation field. Moreover, grid connected inverters strengthen this growth. Development of transformerless inverters with higher efficiency, low cost and size is competitive than the inverters with transformers. However, leakage current generation in transformerless inverters is a challenge to their growth. The research in evolution of new transformerless inverter topologies with higher efficiency, boosting capability, and reduced leakage current is interesting. This paper presents an extensive discussion of transformerless inverters under the categorization of their structures and the subcategorization with leakage current reduction techniques. The components and connections of inverters are differentiated with colours for the effortless understanding of operation. The detailed comparisons of transformerless inverters based on performance and construction are also presented with their strengths and weaknesses. To give deep intuition on characteristics of transformerless inverters, selected inverters are simulated with different operating conditions. Loss contribution of each switch in the selected inverters is analysed, to help the new researchers, designers and engineers to design efficient topologies.
Poyraz, M, Poyraz, AK, Dogan, Y, Gunes, S, Mir, HS, Paul, JK, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Molinari, F & Acharya, R 2025, 'BrainNeXt: novel lightweight CNN model for the automated detection of brain disorders using MRI images', Cognitive Neurodynamics, vol. 19, no. 1.
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Abstract The main aim of this study is to propose a novel convolutional neural network, named BrainNeXt, for the automated brain disorders detection using magnetic resonance images (MRI) images. Furthermore, we aim to investigate the performance of our proposed network on various medical applications. To achieve high/robust image classification performance, we gathered a new MRI dataset belonging to four classes: (1) Alzheimer's disease, (2) chronic ischemia, (3) multiple sclerosis, and (4) control. Inspired by ConvNeXt, we designed BrainNeXt as a lightweight classification model by incorporating the structural elements of the Swin Transformers Tiny model. By training our model on the collected dataset, a pretrained BrainNeXt model was obtained. Additionally, we have suggested a feature engineering (FE) approach based on the pretrained BrainNeXt, which extracted features from fixed-sized patches. To select the most discriminative/informative features, we employed the neighborhood component analysis selector in the feature selection phase. As the classifier for our patch-based FE approach, we utilized the support vector machine classifier. Our recommended BrainNeXt approach achieved an accuracy of 100% and 91.35% for training and validation. The recommended model obtained the test classification accuracy of 94.21%. To further improve the classification performance, we suggested a patch-based DFE approach, which achieved a test accuracy of 99.73%. The obtained results, surpassing 90% accuracy on the test dataset, demonstrate the effectiveness and high classification performance of the proposed models.
Pradeepkumar, A, Yang, Y, Castañeda, E, Angel, FA & Iacopi, F 2025, 'An ionic polymer route to a stable unpinning of the Fermi level of highly doped graphene', Journal of Applied Physics, vol. 137, no. 22.
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Epitaxial graphene on cubic silicon carbide on silicon could enable unique optical metasurface devices seamlessly integrated with CMOS technologies. However, one of the most promising methods to obtain large-scale epitaxial graphene on this challenging system typically leads to a highly p-type-doped graphene with a Fermi level pinned at ∼0.55 eV below the Dirac point. Hence, the use of conventional gate dielectric materials such as SiO2 and Si3N4 precludes the tuning of the graphene carrier concentration. We demonstrate that this limitation can be overcome with the use of polyethyleneimine (PEI) as a gate dielectric material for graphene field-effect transistors. We achieve significant tuning of the graphene's Fermi level, enabling ambipolar operation exceeding a 3 eV window. In addition, we demonstrate that excellent stability of the PEI-based devices can be achieved, thanks to the addition of a thin protective oxide film. These findings highlight the potential of ionic polymers for advancing reconfigurable graphene-based devices for photonic applications.
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.
Punetha, P & Nimbalkar, S 2025, 'Numerical investigation on the dynamic behaviour of unpaved roads under realistic moving loads', Road Materials and Pavement Design, pp. 1-46.
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Punetha, P & Nimbalkar, S 2025, 'Utilisation of construction and demolition waste and recycled glass for sustainable flexible pavements: A critical review', Transportation Geotechnics, vol. 54, pp. 101612-101612.
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Qi, J, Li, L, Hossain, J & Lei, G 2025, 'Optimizing electric vehicle parking lot profitability through vehicle-to-grid incentive decision-making in multiple energy markets', Sustainable Energy, Grids and Networks, vol. 41, pp. 101595-101595.
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Qian, H, Li, J, Pan, Y, Zong, Z & Wu, C 2025, 'Blast performance of precast segmental utility tunnel against ground surface explosion. Part 1: Experimental analysis', Structures, vol. 71, pp. 108192-108192.
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Qian, J, Wang, Y, Xue, Y, Begum, H, Fu, Y-Q & Lee, JE-Y 2025, 'Integrated functions of microfluidics and gravimetric sensing enabled by piezoelectric driven microstructures', Applied Physics Reviews, vol. 12, no. 1.
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Micro- and nano-electromechanical systems resonators have been regarded as powerful tools for precision mass detection, and their abilities to measure these in a liquid environment open various opportunities for biosensing, chemical analysis, and environmental monitoring. Apart from overcoming issues of fluidic damping and electrical interfaces, there is a great challenge of bringing microanalytes to these devices with the required precision and scaling for high throughput sensing. Herein, we address the above challenges by proposing a self-excited localized acoustic manipulation methodology based on a piezoelectric micromechanical diaphragm resonator (PMDR). Such a PMDR integrates acoustofluidics and mass sensing functions in tandem on a single device. Particle enrichment is realized within tens of seconds and the limit of detection is enhanced by mitigating common issues such as low capture rate and non-uniform distribution. The developed PMDR is versatile in its applicability to a range of particle sizes and densities for both acoustofluidic actuation and in situ mass sensing. This work addresses long-term technical challenges of inaccurate and inefficient measurement of liquid phase resonance mass sensing with great application potentials in biochemical detection and environmental monitoring.
Qiao, S, Liu, X, Wen, S, Xiao, G, Chen, B & Ge, SS 2025, 'Event-triggered memory sliding mode load frequency control of power system with BESSs against frequency-based deception attacks', Journal of the Franklin Institute, vol. 362, no. 7, pp. 107644-107644.
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Qin, JC, Jiang, R, Mo, H & Dong, D 2025, 'A data-driven mixed integer programming approach for joint chance-constrained optimal power flow under uncertainty', International Journal of Machine Learning and Cybernetics, vol. 16, no. 2, pp. 1111-1127.
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Abstract This paper introduces a novel mixed integer programming (MIP) reformulation for the joint chance-constrained optimal power flow problem under uncertain load and renewable energy generation. Unlike traditional models, our approach incorporates a comprehensive evaluation of system-wide risk without decomposing joint chance constraints into individual constraints, thus preventing overly conservative solutions and ensuring robust system security. A significant innovation in our method is the use of historical data to form a sample average approximation that directly informs the MIP model, bypassing the need for distributional assumptions to enhance solution robustness. Additionally, we implement a model improvement strategy to reduce the computational burden, making our method more scalable for large-scale power systems. Our approach is validated against benchmark systems, i.e., IEEE 14-, 57- and 118-bus systems, demonstrating superior performance in terms of cost-efficiency and robustness, with lower computational demand compared to existing methods.
Qin, Y, Zhang, X, Yu, S & Feng, G 2025, 'A survey on representation learning for multi-view data', Neural Networks, vol. 181, pp. 106842-106842.
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Qin, Y, Zhang, Y, Chen, Y, Lin, S, Shu, Y, Huang, Y, Huang, X & Zhou, M 2025, 'Impact of Snow on Underground Smoldering Wildfire in Arctic-Boreal Peatlands', Environmental Science & Technology, vol. 59, no. 8, pp. 3915-3924.
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Qing, S, Wang, H & Wen, S 2025, 'Fixed/preassigned-time non-chattering synchronization of nonlinear coupled Cohen–Grossberg neural networks via event-triggered control', Neurocomputing, vol. 617, pp. 129069-129069.
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Qiu, H, Wang, H, Li, F & Wen, S 2025, 'Intermittent dynamic event-triggered control for synchronization of Takagi–Sugeno fuzzy competitive neural networks with leakage delay and different time scales', Fuzzy Sets and Systems, vol. 498, pp. 109130-109130.
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Qiu, N, Ding, Y, Guo, J & Fang, J 2025, 'Energy dissipation of sand-filled TPMS lattices under cyclic loading', Thin-Walled Structures, vol. 209, pp. 112848-112848.
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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...
Rahmasari, K, Dewi, OC, Putra, N, Trihamdani, AR, Salsabila, ND, Nurjannah, A, Baskara, SA, Darmawiredja, MR & Mahlia, TMI 2025, 'In-situ measurement of thermal transmittance on facade components and its implications on building cooling loads in hot-humid climate', Energy and Buildings, vol. 346, pp. 116177-116177.
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Ramadan, HS, Haes Alhelou, H & Ahmed, AA 2025, 'Impartial near‐optimal control and sizing for battery hybrid energy system balance via grey wolf optimizers: Lead acid and lithium‐ion technologies', IET Renewable Power Generation, vol. 19, no. 1.
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AbstractThe balance of renewable‐energy‐based power systems has witnessed significant importance particularly with their rapid integration within these systems. The optimal sizing and control of energy storage systems (ESS) in hybrid power systems (HPSs) based on renewable energy becomes of particular interest. In this research, the HPS under study comprises PV, wind, and energy storage system. Two battery technologies, lead acid (LA) and lithium‐Ion (LI)—are conducted to reach a near‐optimal solution via metaheuristic optimization algorithms in HPS. This paper aims at reaching the equilibrium of the generation consumption for HPS through applying a novel technique, grey wolf optimization (GWO) through the optimal battery sizing of the HPS. The optimization is used for reaching the due balance between the production of power and that absorbed by the load, by minimizing the difference between the final and initial state of charge. Based on numerical simulations, the two different battery technologies are considered in the sizing of the ESS using GWO approach. From the simulation results, the proposed GWO leads to more enhanced performance with LI rather than LA by 3.1% with reduced number of parallel/series cells (Np/Ns) of 240/3450 and 270/3500. Accordingly, the GWO provides an adequate dynamic controlled performance.
Ramana, M, Santra, SB, Chatterjee, D & Siwakoti, YP 2025, 'Sector Wise Modified Droop Control to Improve Voltage Regulation and Current Sharing in Parallel Boost Converter Interfaced DC Microgrid', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 13, no. 3, pp. 2928-2943.
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Rathod, S, Sahni, M & Merigo, JM 2025, 'Development and Applications of Penalty‐Based Aggregation Operators in Multicriteria Decision Making', International Journal of Intelligent Systems, vol. 2025, no. 1.
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This article develops a new penalty‐based aggregation operator known as the penalty‐based induced ordered weighted averaging (P‐IOWA) operator which is an extension of penalty‐based ordered weighted averaging (P‐OWA) operator. Our goal is to figure out how the induced variable realigns penalties when gathering information. We extend the P‐OWA and P‐IOWA operators with the different means such as generalized mean and quasi‐arithmetic mean. This article also includes different families of P‐OWA and P‐IOWA operators. The value of these new operators is demonstrated through a case study centered on investment matters. This study evaluates the economic and governance performance of seven South Asian nations utilizing nine indicators from 2021 data. The research examines 5 economic indicators including GDP growth, exports and imports (% of GDP), inflation, and labor force metrics, alongside 4 governance indicators focusing on corruption control, government effectiveness, and political stability. We use min–max normalization to standardize the varied values, which originally ranged from 0.5% to 77.7% across various metrics. Following this, the normalized inverse penalty method is used to derive optimal weights for these indicators, tackling the task of combining multidimensional data. Subsequently, we implement and evaluate various penalty‐based aggregation methodologies on the normalized data, each offering a distinct approach to penalizing outliers and balancing indicator weights. The study compares the results obtained from these operators to assess their impact on country rankings and overall performance evaluation. This approach allows for a comprehensive comparison of countries’ performances, integrating both economic and governance dimensions into a single, quantifiable framework.
Rawat, S, Cui, H, Xie, Y, Guo, Y, Lee, CK & Zhang, Y 2025, 'An improved framework for multi-objective optimization of cementitious composites using Taguchi-TOPSIS approach', Expert Systems with Applications, vol. 272, pp. 126732-126732.
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Rawat, S, Lee, CK, Fanna, DJ, George, L & Zhang, YX 2025, 'Mechanism and effect of Re-curing on strength recovery of fire-damaged high strength engineered cementitious composite', Construction and Building Materials, vol. 461, pp. 139920-139920.
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Rawat, S, Zhang, L & Zhang, YX 2025, 'Fire-induced spalling in hybrid polyethylene fiber-reinforced engineered cementitious composite panels', Engineering Structures, vol. 338, pp. 120589-120589.
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Raza, A, Keshavarz, R & Shariati, N 2025, 'A Multiband Forward–Backward Nonlinear Metamaterial Network: An Agile Solution for Wireless Power Transfer, Sensing, and Communication', IEEE Transactions on Microwave Theory and Techniques, pp. 1-14.
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Raza, A, Keshavarz, R & Shariati, N 2025, 'In Situ Soil Moisture Monitoring Using Compact Multiband Sensing System for Smart Agriculture', IEEE Transactions on AgriFood Electronics, pp. 1-12.
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Raza, A, Keshavarz, R & Shariati, N 2025, 'Passive Nonreciprocal Signal Switching: A Compact Backward Coupler Using a Nonlinear Metamaterial Structure', IEEE Microwave and Wireless Technology Letters, pp. 1-4.
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Raza, MA, Abolhasan, M, Lipman, J & Ni, W 2025, 'Device-Level Learning-Based Distributed Power Control for Uplink NOMA in IoT Networks', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Razavi-Termeh, SV, Sadeghi-Niaraki, A, Shogrkhodaei, SZ, Pradhan, B & Choi, S-M 2025, 'Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping', Ecological Informatics, vol. 90, pp. 103326-103326.
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Rembe, C, Halkon, B & Ismail, M 2025, 'Measuring Vibrations in Large Structures with LDVy and UAS: A Review and Outlook', Advanced Devices & Instrumentation.
Ren, M, Li, Y, Hussain, T, Wu, Y & Li, J 2025, 'Pixel-level concrete crack quantification through super resolution reconstruction and multi-modality fusion', Advanced Engineering Informatics.
Ren, Z, Ding, A, He, X, Oleskowicz-Popiel, P, Li, G, Liang, H, Ngo, HH & Qiu, W 2025, 'New insight of surface water disinfection by Fe2+-SPC: Important role of carbonate radical and the influence of carbonate/bicarbonate ions on free radicals balance', Journal of Environmental Management, vol. 381, pp. 125345-125345.
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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).
Roohani, I, Wang, S, Xu, C, Newman, P, Entezari, A, Lai, Y & Zreiqat, H 2025, 'Bioinspired Nanoscale 3D Printing of Calcium Phosphates Using Bone Prenucleation Clusters', Advanced Materials, vol. 37, no. 13.
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AbstractCalcium phosphates (CaPs) are ubiquitous in biological structures, such as vertebrate bones and teeth, and have been widely used in biomedical applications. However, fabricating CaPs at the nanoscale in 3D has remained a significant challenge, particularly due to limitations in current nanofabrication techniques, such as two‐photon polymerization (2pp), which are not applicable for creating CaP nanostructures. In this study, a novel approach is presented to 3D print CaP structures with unprecedented resolution of ≈300 nm precision, achieving a level of detail three orders of magnitude finer than any existing additive manufacturing techniques for CaPs. This advancement is achieved by leveraging bioinspired chemistry, utilizing bone prenucleation nanoclusters (PNCs, average size of 5 nm), within a photosensitive resin. These nanoclusters form a highly transparent photoresist, overcoming the light‐scattering typically associated with larger calcium phosphate‐based nanoparticles. This method not only allows for nanopatterning of CaPs on diverse substrates, but also enables the precise control of microstructure down to the level of submicron grains. The method paves the way for the developing of bioinspired metamaterials, lightweight damage‐tolerant materials, cell‐modulating interfaces, and precision‐engineered coatings.
Ruan, B, Li, J, Cheng, Y, Nie, R, Shan, F & Teng, J 2025, 'Orthogonal investigation of factors influencing the splitting tensile strength of cemented aeolian sand reinforced with hybrid fibers', Case Studies in Construction Materials, vol. 22, pp. e04596-e04596.
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Rufangura, P, Cui, Y, Liu, H, Carlstrom, JD, Crozier, K, Brongersma, ML, Yang, Y & Iacopi, F 2025, 'Near unity narrowband infrared thermal emitters on silicon with silicon carbide-germanium metasurfaces', APL Photonics, vol. 10, no. 8.
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Traditional thermal emitters are characterized by an incoherent broadband emission spectrum. However, narrowband coherent thermal emission with a high-quality factor in thermally stable materials is highly desirable for applications such as sensing, thermal energy management, thermophotovoltaic systems, and other infrared technologies. Recent advances in engineered nanostructured polaritonic materials, particularly polar dielectric materials in the mid-infrared (MIR) regime, have enabled new approaches to tailoring narrowband coherent thermal emission. The use of low-loss phonon polaritons in thermally stable silicon carbide provides a promising route to MIR thermal emission. In this work, we demonstrate narrowband, near-unity MIR thermal emission by coupling coherent surface phonon polaritons in a SiC layer with a subwavelength germanium grating on a silicon substrate. The demonstrated polarization-dependent thermal emitter, compatible with silicon fabrication technologies for seamless on-chip photonic integration, exhibits narrowband high emissivity (>90%) at a wavelength of ∼11 μm. Furthermore, we show that these emitters achieve experimental quality factors well above 100 while maintaining significant emission across a wide range of incident angles for MIR radiation.
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.
Rujikiatkamjorn, C, Xue, J & Indraratna, B 2025, 'Preface', Lecture Notes in Civil Engineering, vol. 405 LNCE, pp. v-vi.
Ruppert, MG, Routley, BS, McCourt, LR, Yong, YK & Fleming, AJ 2025, 'Modulated-Illumination Intermittent-Contact Tip-Enhanced Raman Spectroscopy', Nano Letters, vol. 25, no. 14, pp. 5656-5662.
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Sabahi, N, Roohani, I, Wang, CH & Li, X 2025, 'Material extrusion 3D printing of bioactive smart scaffolds for bone tissue engineering', Additive Manufacturing, vol. 98, pp. 104636-104636.
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Saha, BK, Jihan, JI, Barai, G, Moon, NJ, Saha, G & Saha, SC 2025, 'Exploring natural convection and heat transfer dynamics of Al2O3-H2O nanofluid in a modified tooth-shaped cavity configuration', International Journal of Thermofluids, vol. 25, pp. 101005-101005.
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Saha, G, Shahrear, P, Faiyaz, A & Saha, AK 2025, 'Mathematical modeling of lumpy skin disease: New perspectives and insights', Partial Differential Equations in Applied Mathematics, vol. 14, pp. 101218-101218.
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Sais, D, Chowdhury, S, Nguyen, PT, Cwiklinski, K, Nguyen, TD, Nguyen, TA, Dalton, J, Donnelly, S & Tran, N 2025, 'Dynamic shifts in isomiR profiles during parasite maturation of Fasciola hepatica', RNA Biology, vol. 22, no. 1, pp. 1-22.
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We investigated the isomiR profiles of the parasitic worm Fasciola hepatica across three developmental stages: newly excysted juveniles (NEJ), juveniles (JUV), and adults. Our analysis revealed a distinct shift in isomiR distribution during maturation, with NEJs exhibiting a higher abundance and diversity of isomiRs compared to later stages. Notably, isomiRs were often the dominant miRNA form in NEJs, whereas a transition to canonical miRNAs occurred as the parasite matured. This temporal variation suggests that isomiR expression may be linked to the parasite's life cycle. We observed that truncated isomiRs were more prevalent, with uracil additions at the 3'end and adenosine at the 5' end being most common. At least 10% of the miRNA population consisted of 5' end isomiRs, which have the potential to redirect target interactions towards metabolic and developmental pathways. Furthermore, we show that the cleavage sites in F. hepatica primary miRNAs are similar to those found in mammalian cells, and Dicer-mediated cleavage appears to play a significant role in isomiR generation. We believe that the diversification of miRNA sequences through isomiR production is an evolutionary adaptation that enhances the parasite's ability to tune gene expression during infection and development. This regulatory plasticity may facilitate successful infection and long-term persistence within diverse mammalian hosts. Understanding the roles of isomiRs in parasitic worms could provide new insights into parasite biology and identify potential targets for controlling parasitic infections.
Sajjad, M, Chu-Van, T, Shahariar, GMH, Suara, KA, Surawski, N, Bodisco, TA, Ristovski, ZD, Brown, RJ & Zare, A 2025, 'Ship emissions and fuel economy under transient conditions: Revisiting the propeller law', Marine Pollution Bulletin, vol. 221, pp. 118535-118535.
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Saldanha, RB, Lotero, A, Jaskulski, F, Consoli, NC & da Rocha, CG 2025, 'A decision-making flowchart for tailings Stabilization: Assessing environmental and economic impacts', Journal of Cleaner Production, vol. 519, pp. 145893-145893.
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Salehmin, MNI, Kiong, TS, Mohamed, H, Mahlia, TMI, Aziz, NAM, Timmiati, SN & Zakaria, Z 2025, 'Transition pathway from blue to green ammonia production: Comparative insight into technoeconomic, environmental, and policy framework', International Journal of Hydrogen Energy, vol. 143, pp. 147-178.
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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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Sang, R, Nixdorf, S, Hung, T, Power, C, Deng, F, Bui, TA, Engel, A, Goldys, EM & Deng, W 2025, 'Unlocking the in vivo therapeutic potential of radiation-activated photodynamic therapy for locally advanced rectal cancer with lymph node involvement', eBioMedicine, vol. 116, pp. 105724-105724.
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Santra, A, Pandharipande, A, Wang, PP, Shaker, G, Mysore, BS, Dolmans, G, Chen, Y & Moghadam, NS 2025, 'Guest Editorial Special Issue on Machine Learning for Radio Frequency Sensing', IEEE Sensors Journal, vol. 25, no. 13, pp. 23163-23163.
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Santra, A, Wang, P, Shaker, G, Mysore, BS, Dolmans, G, Chen, Y, Shariati, N & Pandharipande, A 2025, 'Machine Learning-Powered Radio Frequency Sensing: A Review', IEEE Sensors Journal, vol. 25, no. 13, pp. 23164-23183.
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Saqlain, M, Merigó, JM & Kumam, P 2025, 'Neutrosophic Probabilistic Ordered Weighted Averaging (NPOWA) operator', Research in Mathematics, vol. 12, no. 1.
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Scheide, E, Best, G & Hollinger, GA 2025, 'Synthesizing compact behavior trees for probabilistic robotics domains', Autonomous Robots, vol. 49, no. 1.
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Selim, A, Mo, H, Pota, H & Dong, D 2025, 'Day ahead scheduling of battery energy storage system operation using growth optimizer within cyber–physical–social systems', Energy, vol. 331, pp. 136675-136675.
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Integrating Battery Energy Storage Systems (BESS) into Cyber–Physical–Social Systems (CPSS) is pivotal for reducing energy costs, enhancing grid stability, and extending battery lifespan. However, existing optimization methods often struggle to balance operational cost, battery degradation, and grid reliability, particularly under uncertain demand and supply conditions. This paper introduces the Growth Optimizer (GO), a novel meta-heuristic algorithm specifically designed for day-ahead BESS scheduling in CPSS environments. Unlike traditional methods, GO explicitly incorporates cyber, physical, and social dimensions, capturing the interdependent dynamics among energy consumption behavior, grid operations, and economic incentives. By leveraging adaptive scheduling under varying battery capacities, GO effectively mitigates uncertainties such as demand fluctuations and renewable intermittency. When applied to five Australian states, GO achieves up to a 15% improvement in multi-objective performance metrics, resulting in measurable financial savings, extended battery life, and reduced infrastructure costs. This approach empowers end users to optimize energy use proactively, enhancing both economic efficiency and energy autonomy.
Sercek, I, Sampathila, N, Tasci, I, Ekmekyapar, T, Tasci, B, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Tan, R-S & Acharya, UR 2025, 'A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer’s disease detection', Cognitive Neurodynamics, vol. 19, no. 1.
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Abstract Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop a computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: We retrospectively analyzed the EEG records of 134 AD and 113 non-AD patients. To generate multilevel features, a multilevel discrete wavelet transform was used to decompose the input EEG-signals. We devised a novel quantum-inspired EEG-signal feature extraction function based on 7-distinct different subgraphs of the Goldner-Harary pattern (GHPat), and selectively assigned a specific subgraph, using a forward-forward distance-based fitness function, to each input EEG signal block for textural feature extraction. We extracted statistical features using standard statistical moments, which we then merged with the extracted textural features. Other model components were iterative neighborhood component analysis feature selection, standard shallow k-nearest neighbors, as well as iterative majority voting and greedy algorithm to generate additional voted prediction vectors and select the best overall model results. With leave-one-subject-out cross-validation (LOSO CV), our model attained 88.17% accuracy. Accuracy results stratified by channel lead placement and brain regions suggested P4 and the parietal region to be the most impactful. Comparison with existing methods: The proposed model outperforms existing methods by achieving higher accuracy with a computationally efficient quantum-inspired approach, ensuring robustness and generalizability. Cortex maps were generated that allowed visual correlation of channel-wise results with various brain regions, enhancing model explainability.
Sezgin, E, Jackson, DI, Kocaballi, AB, Bibart, M, Zupanec, S, Landier, W, Audino, A, Ranalli, M & Skeens, M 2025, 'Can Large Language Models Aid Caregivers of Pediatric Cancer Patients in Information Seeking? A Cross‐Sectional Investigation', Cancer Medicine, vol. 14, no. 1.
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ABSTRACTPurposeCaregivers in pediatric oncology need accurate and understandable information about their child's condition, treatment, and side effects. This study assesses the performance of publicly accessible large language model (LLM)‐supported tools in providing valuable and reliable information to caregivers of children with cancer.MethodsIn this cross‐sectional study, we evaluated the performance of the four LLM‐supported tools—ChatGPT (GPT‐4), Google Bard (Gemini Pro), Microsoft Bing Chat, and Google SGE—against a set of frequently asked questions (FAQs) derived from the Children's Oncology Group Family Handbook and expert input (In total, 26 FAQs and 104 generated responses). Five pediatric oncology experts assessed the generated LLM responses using measures including accuracy, clarity, inclusivity, completeness, clinical utility, and overall rating. Additionally, the content quality was evaluated including readability, AI disclosure, source credibility, resource matching, and content originality. We used descriptive analysis and statistical tests including Shapiro–Wilk, Levene's, Kruskal–Wallis H‐tests, and Dunn's post hoc tests for pairwise comparisons.ResultsChatGPT shows high overall performance when evaluated by the experts. Bard also performed well, especially in accuracy and clarity of the responses, whereas Bing Chat and Google SGE had lower overall scores. Regarding the disclosure of responses being generated by AI, it was observed less frequently in ChatGPT responses, which may have affected the clarity of responses, whereas Bard maintained a balance between AI disclosure and response clarity. Google SGE generated the most readable responses whereas ChatGPT answered with the most complexity. LLM tools varied significa...
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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Shahini, A, Kamath, AP, Sharma, E, Salvi, M, Tan, R-S, Siuly, S, Seoni, S, Ganguly, R, Devi, A, Deo, R, Barua, PD & Acharya, UR 2025, 'A systematic review for artificial intelligence-driven assistive technologies to support children with neurodevelopmental disorders', Information Fusion, vol. 124, pp. 103441-103441.
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Shahsavari, M, Hussain, OK, Sharma, P & Saberi, M 2025, 'Modelling supply chain risk events by considering their contributing events: a systematic literature review', Enterprise Information Systems, vol. 19, no. 5-6.
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Proactive Supply Chain Risk Management (SCRM) helps organisations anticipate and mitigate risks, ensuring business continuity and resilience in a violet market. Existing research proposes various techniques to quantify risk occurrence, but none account for the causal relationships between contributing events and risk events. This paper addresses this gap through a systematic literature review of SCRM techniques and outlines future research directions to enhance proactive SCRM by incorporating causal dependencies in risk quantification.
Shams, RA, Zowghi, D & Bano, M 2025, 'AI and the quest for diversity and inclusion: a systematic literature review', AI and Ethics, vol. 5, no. 1, pp. 411-438.
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Abstract The pervasive presence and wide-ranging variety of artificial intelligence (AI) systems underscore the necessity for inclusivity and diversity in their design and implementation, to effectively address critical issues of fairness, trust, bias, and transparency. However, diversity and inclusion (D&I) considerations are significantly neglected in AI systems design, development, and deployment. Ignoring D&I in AI systems can cause digital redlining, discrimination, and algorithmic oppression, leading to AI systems being perceived as untrustworthy and unfair. Therefore, we conducted a systematic literature review (SLR) to identify the challenges and their corresponding solutions (guidelines/ strategies/ approaches/ practices) about D&I in AI and about the applications of AI for D&I practices. Through a rigorous search and selection, 48 relevant academic papers published from 2017 to 2022 were identified. By applying open coding on the extracted data from the selected papers, we identified 55 unique challenges and 33 unique solutions in addressing D&I in AI. We also identified 24 unique challenges and 23 unique solutions for enhancing D&I practices by AI. The result of our analysis and synthesis of the selected studies contributes to a deeper understanding of diversity and inclusion issues and considerations in the design, development and deployment of the AI ecosystem. The findings would play an important role in enhancing awareness and attracting the attention of researchers and practitioners in their quest to embed D&I principles and practices in future AI systems. This study also identifies important gaps in the research literature that will inspire future direction for researchers.
Shang, C, Thai Hoang, D, Yu, J, Hao, M & Niyato, D 2025, 'Constructing the Metaverse With a New Perspective: UAV FoV-Assisted Low-Latency Imaging', IEEE Wireless Communications Letters, vol. 14, no. 1, pp. 158-162.
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Shang, C, Yu, J & Hoang, DT 2025, 'Energy-Efficient and Intelligent ISAC in V2X Networks with Spiking Neural Networks-Driven DRL', IEEE Transactions on Wireless Communications, pp. 1-1.
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Shang, D, Zhang, G & Lu, J 2025, 'Concept drift detection based on radial distance', Neurocomputing, vol. 653, pp. 131190-131190.
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Shang, W, Wang, L, Chen, Z, Cheng, D, Liu, H, Ngo, HH, Li, J, Cao, X, Wang, Y & Zhang, J 2025, 'A novel layered porous Fe, Cu dual-loading biochar heterogeneous catalyst to guided non-free radical pathway for peroxymonosulfate activation', Journal of Colloid and Interface Science, vol. 689, pp. 137262-137262.
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Shannon, AG, Kuloğlu, B & Özkan, E 2025, 'Rhaly terraced sequences their generalizations, properties and applications', Computational and Applied Mathematics, vol. 44, no. 5.
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Abstract This paper links terraced matrices with other well-known integer sequences, such as the Hankel matrices and related Fibonacci and Lucas matrices. These, in turn, are connected with related results of Macmahon and Sloane as well as we introduce the r-Terraced matrix as a generalization of the Terraced matrix, along with its symmetric counterpart, the symmetric r-Terraced matrix. We derive key properties of these matrices, including their spectral and Euclidean norms, upper bounds for their spreads, and characteristic polynomials. To validate and exemplify the theoretical findings, we apply them to Fibonacci numbers, providing illustrative examples that strengthen the theory and confirm its accuracy. In addition to the theoretical results, we investigated how the choice of the parameter r and the matrix dimension affect the upper bounds of the spread. Our findings reveal that selecting values of $$r<1$$ r < 1 and using lower-dimensional matrices lead to tighter upper bounds while reducing computational complexity. These results highlight the practical benefits of our approach, particularly in optimization-related applications where efficiency is crucial.
Shao, Q, Sun, J, Zhu, K, Ngo, HH, Zhang, Y, Zhang, C, Zhang, S & Luo, G 2025, 'Enhanced reactor stability to shock load with hydrochar: Responses of anaerobic granular sludge', Chemical Engineering Journal, vol. 503, pp. 158274-158274.
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Shao, R, Chengqing Wu & Li, J 2025, 'Enhanced in-situ utilization of lunar simulant for fibre-reinforced high-performance concrete: Mechanical properties and cost-effectiveness for lunar applications', Journal of Materials Research and Technology, vol. 35, pp. 6849-6863.
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Shao, R, Wu, C & Li, J 2025, 'Engineering cement-free high-performance Martian concrete with enhanced in-situ utilization of soil simulant: Curing across −20 °C–40 °C and CO2-rich environments', Journal of Environmental Management, vol. 375, pp. 124426-124426.
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Shao, R, Wu, C, Li, J, Chi, K & Yu, Z 2025, 'Evaluation of high-strength concrete for lunar applications: Mechanical behaviour under static and dynamic loads at cryogenic temperatures', Construction and Building Materials, vol. 492, pp. 142966-142966.
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Shao, Y-F, Ji, J-C & Ding, H 2025, 'Dynamics and vibration reduction performance of a cross-type motion amplified nonlinear energy sink', Acta Mechanica Sinica, vol. 41, no. 7.
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Shen, L, Pu, N, Zhong, Z, Gong, M, Yu, D, Zhang, C & Han, B 2025, 'Federated Generalized Novel Category Discovery with Prompts Tuning', Transactions on Machine Learning Research, vol. 2025-July.
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Generalized category discovery (GCD) is proposed to handle categories from unseen labels during the inference stage by clustering them. Most works in GCD provide solutions for unseen classes in data-centralized settings. However, unlabeled categories possessed by clients, which are common in real-world federated learning (FL), have been largely ignored and degraded the performance of classic FL algorithms. To demonstrate and mitigate the harmful effect of unseen classes, we dive into a GCD problem setting applicable for FL named FedGCD, analyze overfitting problem in FedGCD in detail, establish a strong baseline constructed with state-of-the-art GCD algorithm simGCD, and design a learning framework with prompt tuning to tackle both the overfitting and communication burden problems in FedGCD. In our methods, clients first separately carry out prompt learning on local data. Then, we aggregate the prompts from all clients as the global prompt to help capture global knowledge and then send the global prompts to local clients to allow access to broader knowledge from other clients. By this method, we significantly reduce the parameters needed to upload in FedGCD, which is a common obstacle in the real application of most FL algorithms. We conduct experiments on both generic and fine-grained datasets like CIFAR-100 and CUB-200, and show that our method is comparable to the FL version of simGCD and surpasses other baselines with significantly fewer parameters to transmit.
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, vol. 22, no. 4, pp. 3824-3838.
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Shen, Y, Shepherd, C, Ahmed, CM, Shen, S & Yu, S 2025, 'Integrating Deep Spiking Q-Network Into Hypergame-Theoretic Deceptive Defense for Mitigating Malware Propagation in Edge Intelligence-Enabled IoT Systems', IEEE Transactions on Services Computing, vol. 18, no. 3, pp. 1487-1499.
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Shen, Y, Shepherd, C, Ahmed, CM, Shen, S & Yu, S 2025, 'Privacy Preservation Strategies for Malware-Infected Edge Intelligence Systems: A Bayesian Stochastic Game-Based Approach', IEEE Transactions on Mobile Computing, vol. 24, no. 8, pp. 7121-7135.
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Sheng, Z, Cao, Y, Yang, Y, Feng, Z-K, Shi, K, Huang, T & Wen, S 2025, 'Residual Temporal Convolutional Network With Dual Attention Mechanism for Multilead-Time Interpretable Runoff Forecasting', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 8757-8771.
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Shi, J, Zhu, Y, He, J, Xu, Z, Chu, L, Braun, R & Shi, Q 2025, 'Human Activity Recognition Based on Feature Fusion of Millimeter Wave Radar and Inertial Navigation', IEEE Journal of Microwaves, vol. 5, no. 2, pp. 409-424.
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Shi, K, Peng, X, Zhu, Y, He, H, Yi, K & Niu, Z 2025, 'Multi-KGS: Generating Social Network-Based Meteorological Decision Reports Fusing With Multiple Knowledge', IEEE Transactions on Consumer Electronics, pp. 1-1.
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Shi, T, Hu, C, Yu, J & Wen, S 2025, 'Fixed-time quantized synchronization of spatiotemporal networks with output-based quantization communication via boundary control', Journal of the Franklin Institute, vol. 362, no. 2, pp. 107460-107460.
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Shi, Y, Han, L, Wu, P, Dai, K, Liu, Z & Wu, C 2025, 'Design of 3D printing green ultra-high performance concrete based on binder system optimization', Case Studies in Construction Materials, vol. 22, pp. e04625-e04625.
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Shi, Y, You, G, Wu, P, Liu, Z & Wu, C 2025, 'Effect of water film and paste film thicknesses on printability of 3D printed low cement UHPC', Case Studies in Construction Materials, vol. 23, pp. e05008-e05008.
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Shi, Z, Feng, Y, Stewart, MG & Gao, W 2025, 'Physical-informed random field technique for virtual modelling based building probabilistic vulnerability assessment', Structural Safety, vol. 115, pp. 102595-102595.
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Shon, H, Jegatheesan, V, Phuntsho, S & Shu, L 2025, 'Challenges in Environmental Science and Engineering (CESE-2023)', Process Safety and Environmental Protection, vol. 194, pp. 1384-1386.
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Shrestha, S, Huang, X, Saleem, K, Bevinakoppa, S & Jan, T 2025, 'Quantification of Interference Constraint for Small Cells in Low SINR Regime With Steepest Ascent Method', IEEE Access, vol. 13, pp. 2328-2339.
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Shu, X, Li, Z, Chang, X & Yuan, D 2025, 'Variational methods with application to medical image segmentation: A survey', Neurocomputing, vol. 639, pp. 130260-130260.
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Shu, X, Li, Z, Tian, C, Chang, X & Yuan, D 2025, 'An active learning model based on image similarity for skin lesion segmentation', Neurocomputing, vol. 630, pp. 129690-129690.
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Shuvo, SB, Alam, SS, Ayman, SU, Chakma, A, Salvi, M, Seoni, S, Barua, PD, Molinari, F & Acharya, UR 2025, 'Application of Wavelet Transformation and Artificial Intelligence Techniques in Healthcare: A Systemic Review', WIREs Data Mining and Knowledge Discovery, vol. 15, no. 2.
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ABSTRACTThe integration of wavelet transformation and artificial intelligence techniques has demonstrated significant potential in healthcare applications. Wavelet analysis enables multi‐scale signal decomposition and feature extraction that, when combined with machine and deep learning approaches, enhance the accuracy and efficiency of medical data analysis. This systematic review synthesizes 112 relevant studies from 2013 to 2023 exploring wavelet‐based artificial intelligence in healthcare. Our analysis reveals that the discrete wavelet transform dominates (43% of studies), primarily used for feature extraction from biosignals (82%) and medical images. Major applications include cardiac abnormality detection (29%), neurological disorder diagnosis (27%), and mental health assessment (16%), with classification accuracies frequently exceeding 95%. Key findings indicate a shift from traditional machine learning to deep learning approaches after 2020, with emerging trends in hybrid architectures. The review identifies critical challenges in computational efficiency, optimal wavelet selection, and clinical validation. Future developments should focus on real‐time processing optimization, interpretable deep learning models, multi‐modal data fusion, and validation on larger clinical datasets, advancing the translation of these systems into practical clinical tools.
Silitonga, AS, Milano, J, Aziz, NAM, Nurulita, B, Sebayang, AR, Mahlia, TMI, Kalam, MA, Fattah, IMR, Sebayang, AH, Mohamed, H & Ong, MY 2025, 'ANN-GWO optimization of biolubricants from black soldier fly: A value-added approach to animal waste conversion', Results in Engineering, vol. 25, pp. 104437-104437.
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Silitonga, AS, Riayatsyah, TMI, Kalam, MA, Sarifudin, A, Fattah, IMR, Muraza, O, Djaya Putra, NS, Sebayang, AR, Sebayang, AH & Hermawan, H 2025, 'Corrigendum to ‘Status, developments, and sustainability of biowaste feedstock: A review of current progress’ [Renew Sustain Energy Rev Volume 217, July 2025, 115769]', Renewable and Sustainable Energy Reviews, vol. 219, pp. 115862-115862.
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Silitonga, AS, Riayatsyah, TMI, Kalam, MA, Sarifudin, A, Fattah, IMR, Muraza, O, Putra, NSD, Sebayang, AR, Sebayang, AH & Hermawan, H 2025, 'Status, developments, and sustainability of biowaste feedstock: A review of current progress', Renewable and Sustainable Energy Reviews, vol. 217, pp. 115769-115769.
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Sima, Q, Yu, J, Wang, X, Zhang, W, Zhang, Y & Lin, X 2025, 'Deep Overlapping Community Search via Subspace Embedding', Proceedings of the ACM on Management of Data, vol. 3, no. 1, pp. 1-26.
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Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categories: algorithm-based models and Machine Learning-based (ML) models. Despite the long-standing focus on this topic within the database domain, current solutions face two major limitations: 1) Both approaches fail to address personalized user requirements in OCS, consistently returning the same set of nodes for a given query regardless of user differences. 2) Existing ML-based CS models suffer from severe training efficiency issues. In this paper, we formally redefine the problem of OCS. By analyzing the gaps in both types of approaches, we then propose a general solution for OCS named <u>S</u> parse <u>S</u> ubspace <u>F</u> ilter (SSF), which can extend any ML-based CS model to enable personalized search in overlapping structures. To overcome the efficiency issue in the current models, we introduce <u>S</u> implified <u>M</u> ulti-hop Attention <u>N</u> etworks (SMN), a lightweight yet effective community search model with larger receptive fields. To the best of our knowledge, this is the first ML-based study of overlapping community search. Extensive experiments validate the superior performance of SMN within the SSF pipeline, achieving a 13.73% improvement in F1-Score and up to 3 orders of magnitude acceleration in model efficiency compared to state-of-the-art approaches.
Singh, VK, Prakash, S, Dixit, P & Prasad, M 2025, 'EEG Signal Based Human Emotion Recognition Brain-computer Interface using Deep Learning and High-Performance Computing', Wireless Personal Communications, vol. 140, no. 1-2, pp. 165-192.
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Sohaib, O, Alshemeili, A & Bhatti, T 2025, 'Exploring AI-enabled green marketing and green intention: An integrated PLS-SEM and NCA approach', Cleaner and Responsible Consumption, vol. 17, pp. 100269-100269.
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Song, H, Nair, SG, Kim, T, Nguyen, QD, Gan, Y, Zhong, H, Irga, PJ, da Rocha, CG, Torpy, FR, Wilkinson, S, Hajimohammadi, A & Castel, A 2025, 'Thermal and Mechanical Properties of Hempcrete with Low-Carbon Binders: Effects of 3D Distribution and Orientation of Hemp Shivs and Microstructures of Hempcrete', Journal of Building Engineering, pp. 113863-113863.
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Song, J, Jenisha, S, Teng, J, Zhang, S & Sheng, D 2025, 'Experimental study on freeze-thaw deformation mechanism of cut slope considering water migration and ice formation', Zhongnan Daxue Xuebao Ziran Kexue Ban Journal of Central South University Science and Technology, vol. 56, no. 5, pp. 1886-1899.
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Considering groundwater recharge conditions, this study employed a one-dimensional freezing method to investigate the freeze-thaw deformation mechanisms of road embankment slopes in seasonal freezing regions. A large-scale physical slope model (length×width×height is 2.0 m×0.8 m×1.1 m)was constructed to systematically examine moisture migration patterns, phase transformation, and freeze-thaw settlement evolution during cyclic freeze-thaw processes. The experimental results demonstrate that moisture redistribution serves as the primary factor inducing slope instability in seasonal frozen areas. Under the freezing condition of initial moisture content of 12% and freezing conditions at − 20 ℃, the distribution of moisture significantly influences the rate of temperature change in the slope. After 530 h of freezing, the maximum freezing temperature difference between different water contents in the same soil layer is approximately twice that of the initial state. Cyclic freeze-thaw processes drive progressive moisture accumulation toward the slope base, with some areas experiencing a maximum moisture increase of 10%. Ice lens formation predominantly occurs in moisture-enriched zones, exhibiting directional growth aligned with freeze-thaw progression. During the freeze-thaw process, moisture migration and ice formation lead to a reduction in soil consolidation that exceeds the increase caused by frost heave and thaw settlement, resulting in a maximum displacement of over 70 mm at the top of the slope. Moisture migration exacerbates ice lens growth thereby promoting the development of weak zones on slopes. The study reveals the dynamic interactions of moisture, temperature, and displacement within the slope under conditions of water recharge in a seasonally frozen region.
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, Huang, L, Wang, Y, Du, Y, Song, Z, Dong, Q, Zhao, X, Qi, J, Zhang, G, Li, W & Shi, L 2025, 'Energy performance and fire risk of solar PV panels under partial shading: An experimental study', Renewable Energy, vol. 246, pp. 122910-122910.
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Song, Y, 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, Sun, X, Nghiem, LD, Duan, J, Liu, W, Liu, Y & Cai, Z 2025, 'Novel ultrathin Bi@Fe-based metal–organic frameworks nanosheets for efficient solar-driven photocatalytic water purification: Synergistic effect of localized surface plasmon resonance and high-density exposed metal active centers', Journal of Colloid and Interface Science, vol. 689, pp. 137250-137250.
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Song, Y, Yang, H, Zhao, L & Huang, S 2025, 'Guaranteed 2D Pose Graph SLAM With Bounded Noises: An Efficient Interval Approach', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 10494-10505.
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Soo, A, Gao, L & Shon, HK 2025, 'Roadmap for Australian wastewater nutrient recovery – Towards a sustainable circular economy', Desalination and Water Treatment, vol. 323, pp. 101273-101273.
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Sood, K, Liu, S, Nguyen, DDN, Kumar, N, Feng, B & Yu, S 2025, 'Alleviating Data Sparsity to Enhance AI Models Robustness in IoT Network Security Context', IEEE Transactions on Mobile Computing, vol. 24, no. 5, pp. 3764-3778.
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Steel, N, Bauer-Staeb, CMM, Ford, JA, Abbafati, C, Abdalla, MA, Abdelkader, A, Abdi, P, Abeldaño Zuñiga, RA, Abiodun, OO, Abolhassani, H, Abu-Gharbieh, E, Abukhadijah, HJ, Abu-Zaid, A, Addo, IY, Addolorato, G, Adekanmbi, V, Adetunji, JB, Adeyeoluwa, TE, Agardh, EE, Agyemang-Duah, W, Ahmad, D, Ahmed, A, Ahmed, A, Ahmed, SA, Akinosoglou, K, Akkaif, MA, Al Awaidy, S, Al Hasan, SM, Al Zaabi, OAM, Aldridge, RW, Algammal, AM, Al-Gheethi, AAS, Ali, A, Ali, MU, Ali, SS, Ali, W, Alicandro, G, Alif, SM, Al-Jumaily, A, Allebeck, P, Alrawashdeh, A, Al-Rifai, RH, Alsabri, MA, Alshahrani, NZ, Aluh, DO, Al-Wardat, M, Al-Zyoud, WA, Amiri, S, Anderlini, D, Andrei, CL, Anil, A, Anvari, S, Anyasodor, AE, Appiah, SCY, Aquilano, M, Arabloo, J, Arafat, M, Areda, D, Aremu, A, Armani, K, Armocida, B, Ärnlöv, J, Asaduzzaman, M, Astell-Burt, T, Aujayeb, A, Ausloos, M, Azadnajafabad, S, Aziz, S, Azzam, AY, Babu, GR, Badache, AC, Badiye, AD, Bahramian, S, Baig, AA, Baker, JL, Bansal, H, Bärnighausen, TW, Barone, MTU, Barrow, A, Barteit, S, Bashir, S, Bashiru, HA, Basso, JD, Bastan, M-M, Basu, S, Batra, K, Bauckneht, M, Baune, BT, Beghi, M, Beiranvand, M, Béjot, Y, Bell, ML, Bello, OO, Belo, L, Beloukas, A, Beneke, AA, Bettencourt, PJG, Bhagavathula, AS, Bhala, N, Bhaskar, S, Bisulli, F, Bjørge, T, Bodunrin, AO, Botero Carvajal, A, Bouaoud, S, Brayne, C, Brenner, H, Briggs, ADM, Briko, NI, Bugiardini, R, Buonsenso, D, Busse, R, Bustanji, Y, Caetano dos Santos, FL, Çakmak Barsbay, M, Capodici, A, Carreras, G, Carugno, A, Carvalho, F, Carvalho, M, Castaldelli-Maia, JM, Castelpietra, G, Catapano, AL, Cattaruzza, MS, Cegolon, L, Cenko, E, Cerin, E, Cerrai, S, Chaudhary, AA, Chong, B, Choudhari, SG, Chu, D-T, Chukwu, IS, Chung, S-C, Cioffi, I, Conde, J, Cortese, S, Couto, RAS, Criqui, MH, Cruz-Martins, N, Dadras, O, Dallat, MAT, D'Amico, E, D'Anna, L, Darcho, SD, Dargan, PI, Das, S, de la Torre-Luque, A, Del Bo', C, Demetriades, AK, Dervenis, N, Devleesschauwer, B, Dhali, A, Dhama, K, Dianatinasab, M, Diaz, MJ, Dongarwar, D, D'Oria, M, Doshi, OP, Dowou, RK, Duraisamy, S, Durojaiye, OC, Dziedzic, AM, Edvardsson, D, Edvardsson, K, Eikemo, TA, Ekholuenetale, M, Ekundayo, TC, El Arab, RA, Elgar, FJ, Elhadi, M, Eltaha, C, Esposito, F, Fabin, N, Fagbamigbe, AF, Fagbule, OF, Fakhri-Demeshghieh, A, Falzone, L, Farinha, CSES, Faris, PS, Fasina, FO & et al. 2025, 'Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021', The Lancet Public Health, vol. 10, no. 3, pp. e172-e188.
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Stewart, MG, Thöns, S & Beck, AT 2025, 'Assessment of risk reduction strategies for terrorist attacks on structures', Structural Safety, vol. 113, pp. 102381-102381.
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Stone, G 2025, 'Book review [Energy storage devices for renewable energy based systems: rechargeable batteries and supercapacitors (kularatna, n. and gunawardane, k.; 2021]', IEEE Electrical Insulation Magazine, vol. 41, no. 2, pp. 39-39.
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Su, SW, Zhang, Z, Celler, B & Savkin, A 2025, 'Distributed integral controllability for non-square processes: A comprehensive study and numerical analysis', Journal of the Franklin Institute, vol. 362, no. 1, pp. 107338-107338.
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Su, X, Alatas, B & Sohaib, O 2025, 'An Express Management System With Graph Recurrent Neural Network for Estimated Time of Arrival', Journal of Organizational and End User Computing, vol. 37, no. 1, pp. 1-26.
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Estimated Time of Arrival (ETA) is a crucial task in the logistics and transportation industry, aiding businesses and individuals in optimizing time management and improving operational efficiency. This study proposes a novel Graph Recurrent Neural Network (GRNN) model that integrates external factor data. The model first employs a Multilayer Perceptron (MLP)-based external factor data embedding layer to categorize and combine influencing factors into a vector representation. A Graph Recurrent Neural Network, combining Long Short-Term Memory (LSTM) and GNN models, is then used to predict ETA based on historical data. The model undergoes both offline and online evaluation experiments. Specifically, the offline experiments demonstrate a 5.3% reduction in RMSE on the BikeNYC dataset and a 6.1% reduction on the DidiShenzhen dataset, compared to baseline models. Online evaluation using Baidu Maps data further validates the model's effectiveness in real-time scenarios. These results underscore the model's potential in improving ETA predictions for urban traffic systems.
Su, X, Liu, Y, Zhong, Y, Shangguan, P, Liu, J, Luo, Z, Qi, C, Guo, J, Li, X, Lin, D, Wang, G, Wang, D, Han, T, Wang, J, Shi, B & Tang, BZ 2025, 'A Brain-Targeting NIR-II Polymeric Phototheranostic Nanoplatform toward Orthotopic Drug-Resistant Glioblastoma', Nano Letters, vol. 25, no. 9, pp. 3445-3454.
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Su, Z, Sun, X, Lei, G & Yao, M 2025, 'Improved Model-Free Predictive Current Control for SPMSM Drives With Adaptive Prediction Horizon Strategy', IEEE Transactions on Industrial Electronics, vol. 72, no. 6, pp. 5705-5715.
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Sueza Raffa, L, Ryall, M, Bennett, NS & Clemon, L 2025, 'Experimental investigation of the performance of a phase change material thermal management module under vacuum and atmospheric pressure conditions', International Journal of Heat and Mass Transfer, vol. 236, pp. 126384-126384.
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Sueza Raffa, L, Ryall, M, Cairns, I, Bennett, NS & Clemon, L 2025, 'Investigating the performance of a heat sink for satellite avionics thermal management: From ground-level testing to space-like conditions', International Journal of Heat and Mass Transfer, vol. 248, pp. 127139-127139.
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Sukkar, F, Wakulicz, J, Lee, KMB, Zhi, W & Fitch, R 2025, 'Multiquery Robotic Manipulator Task Sequencing With Gromov-Hausdorff Approximations', IEEE Transactions on Robotics, vol. 41, pp. 2843-2860.
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Sulistyo, AB, Karningsih, PD & Alvandi, S 2025, 'Developing an Industry-Specific Lean 4.0 Readiness Assessment Tool: A Case for the Chemical Sector', Jurnal Optimasi Sistem Industri, vol. 23, no. 2, pp. 283-298.
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In an era where digital transformation is increasingly imperative, many industries struggle to navigate the complexities of technological adoption and operational efficiency. Lean principles, which emphasize waste reduction and process optimization, provide a robust foundation for digital transformation, particularly in the chemical industry, where unique operational challenges exist. This research aims to develop an integrated Lean 4.0 readiness assessment tool to bridge the gap between leanness and Industry 4.0 readiness. The study begins with a literature review on existing lean and Industry 4.0 readiness measurement tools and integrates them to create a new framework, using the Indonesia Industry 4.0 Readiness Index (INDI 4.0) as a reference, tailored specifically to the chemical industry. Expert interviews are conducted to refine the assessment tool, ensuring alignment with real-world industry conditions and practical insights. A Delphi-based expert consensus method combined with a fuzzy approach for handling imprecision in indicator ratings is employed to validate the framework, resulting in five key dimensions and 86 indicators. By gathering expert input, the tool addresses the chemical industry’s specific challenges and simplifies readiness evaluation, helping companies assess their preparedness for digital transformation and identify areas for improvement. The resulting framework enables chemical companies to bridge readiness gaps and prioritize targeted enhancements. Furthermore, this tool has the potential to serve as a scalable model for other industries, fostering more efficient and strategic digital transformation aligned with Industry 4.0 objectives globally.
Sultana, M, Hoque, MA-A & Pradhan, B 2025, 'Assessing Meghna Riverbank dynamics and morphological changes in Bangladesh using geospatial techniques', Applied Geomatics, vol. 17, no. 1, pp. 147-161.
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Sun, J, Cao, Y, Yue, Y, Wen, S & Wang, Y 2025, 'Memristor-Based Parallel Computing Circuit Optimization for LSTM Network Fault Diagnosis', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 72, no. 2, pp. 907-917.
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Sun, J, Wang, J, Wen, S, Wang, Y & Wang, Y 2025, 'Neural Network Circuits for Bionic Associative Memory and Temporal Order Memory Based on DNA Strand Displacement', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 8672-8683.
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Sun, S, Peng, C, Kong, ZY, Ong, HC, Qin, Z & Yang, A 2025, 'A different strategy to reduce energy consumption for designing heterogeneous decanter-assisted advanced pressure-swing distillation process', Separation and Purification Technology, vol. 352, pp. 128280-128280.
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Sun, X, Chen, Z, Pan, M, Cai, Y, Jin, Z, Lei, G & Tian, X 2025, 'Robust Energy Management Optimization for PHEB Considering Driving Uncertainties by Using Sequential Taguchi Method', IEEE Transactions on Transportation Electrification, vol. 11, no. 2, pp. 5191-5200.
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Sun, X, Dong, Z, Cai, Y, Jin, Z, Lei, G & Tian, X 2025, 'A comprehensive review of design optimization methods for hybrid electric vehicles', Renewable and Sustainable Energy Reviews, vol. 217, pp. 115765-115765.
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Sun, X, Huang, Z, Yang, Z, Lei, G & Li, T 2025, 'Improved Model-Free Predictive Current Control for Suppressing Inverter Nonlinearity and Parametric Time-Varying of PMSM Drive Systems', IEEE Transactions on Industrial Electronics, pp. 1-10.
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Sun, X, Shi, K, Tang, H, Wang, D, Xu, G & Li, Q 2025, 'Educating Language Models as Promoters: Multi-Aspect Instruction Alignment With Self-Augmentation', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 8, pp. 4564-4577.
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Sun, X, Wang, N, Yao, M & Lei, G 2025, 'Position Sensorless Control of SRMs Based on Improved Sliding Mode Speed Controller and Position Observer', IEEE Transactions on Industrial Electronics, vol. 72, no. 1, pp. 100-110.
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Sun, X, Wen, Y, Han, S & Lei, G 2025, 'Fault Diagnosis of Power Converter for Switched Reluctance Motors Based on Current Reconstruction Scheme', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 4169-4178.
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Sun, X, Wen, Y, Zhang, L, Yao, M & Lei, G 2025, 'Fault Detection and Fault-Tolerant Control of Switched Reluctance Motor Based on Dual-Sensor Current Detection Scheme', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 19-28.
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Sun, X, Wen, Y, Zhu, Y, Zhang, L, Yao, M & Lei, G 2025, 'FCS-MPC With Improved Prediction for Suppressing Torque and Current Pulsations of Switched Reluctance Motors', IEEE Transactions on Industrial Electronics, vol. 72, no. 7, pp. 6831-6839.
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Sun, X, Xu, Z, Pan, M, Sun, C & Lei, G 2025, 'Medium-to-High Speed Range Sensorless Control of SRM Drives Based on Unaligned Rotor Position Estimation', IEEE Transactions on Industrial Electronics, pp. 1-10.
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Sun, X, Xu, Z, Pan, M, Sun, C, Pan, W & Lei, G 2025, 'Sensorless Control Strategy for SRM Based on Flux Linkage in Medium to High-Speed Range', IEEE Transactions on Industrial Electronics, pp. 1-9.
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Sun, X, Zhang, S, Yang, Z, Lei, G & Li, T 2025, 'Improved DPCC and Parameter Identification for Permanent Magnet Synchronous Motors With Current Error Compensation', IEEE Transactions on Industrial Electronics, pp. 1-11.
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Sun, Y, Hu, C, Wen, S, Yu, J & Jiang, H 2025, 'Pinning Adaptive Passivity and Bipartite Synchronization of Leaderless Fractional Spatiotemporal Networks', IEEE Transactions on Network Science and Engineering, vol. 12, no. 1, pp. 319-331.
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Sun, Y, Hu, C, Yu, J, Li, H & Wen, S 2025, 'Bipartite Complete Synchronization of Fractional Heterogeneous Networks via Quantized Control Without Gauge Transformation', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 5, pp. 3720-3731.
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Sun, Y, Hu, C, Yu, J, Wen, S & Li, H-L 2025, 'Bipartite Output Synchronization of Fuzzy Fractional Output-Coupled Networks via Membership Function-Dependent Adaptive Control', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 14147-14157.
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Sun, Z, Liang, Z, Lv, X, Zhou, Y, Liu, S, Luo, Z & Yang, Y 2025, 'C-/Ka-Band Low-Profile Circularly Polarized Shared-Aperture Antenna for CubeSat Communications', IEEE Transactions on Antennas and Propagation, vol. 73, no. 2, pp. 1221-1226.
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Sun, Z, Lv, X & Yang, Y 2025, 'Wideband Sequential Rotation Dual-Circularly Polarized Magnetoelectric Dipole Array With Polarization Independent Control for Intelligent Vehicle Communications', IEEE Transactions on Antennas and Propagation, vol. 73, no. 4, pp. 2666-2671.
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Sun, Z, Lv, X, Zhu, X, Liang, Z & Yang, Y 2025, 'A Dual-Band Circularly Polarized Antenna With Shared Aperture for Satellite-Assisted Internet of Things Communications', IEEE Internet of Things Journal, vol. 12, no. 7, pp. 8461-8469.
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Suwal, L, Pineda, JA, Turner, B & Musso, G 2025, 'Salinity and oven-drying effects on the plasticity of a marine soft clay', Géotechnique, vol. 75, no. 7, pp. 875-885.
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Considering the extensive use of plasticity-based correlations in geotechnical practice to estimate soil parameters, this paper evaluates the influence of pore fluid salinity and soil drying on the plasticity of Ballina clay, an estuarine soft clay from northern New South Wales (Australia). A comprehensive experimental study, which includes controlled leaching/salinisation paths applied to natural (remoulded) as well as oven-dried clay, prior to the estimation of the Atterberg limits, is presented. Plasticity tests are complemented with chemical analysis of the pore fluid carried out to evaluate the processes involved in the leaching/salinisation mechanisms for remoulded and oven-dried clay. A strong dependency of liquid limit on pore fluid salinity and oven-drying is observed in Ballina clay. Leaching modifies the soil fabric from an initially saline–sodic flocculated towards a normal flocculated arrangement. The experimental results show that changes in soil plasticity upon leaching are largely reversible upon salinisation paths. Oven-drying promotes the stacking of clay minerals (aggregation), which in turn reduces the water absorption capacity of the clay. The consequences of neglecting both salinity and drying effects in practice when adopting well-established relationships between mechanical parameters and soil plasticity are also briefly discussed.
Szlachta, AI, Nowak, PR, Gajewski, T & Sielicki, PW 2025, 'Response of structural elements of temporary wooden bridges subjected to contact detonation', International Journal of Protective Structures, vol. 16, no. 1, pp. 170-183.
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The use of wood as a structural element is still commonly used in constructions, not only in civil engineering but also in military applications. The access to this material, its processing and the ability to create individual joins without advanced equipment are reasons why wood remains an important material in military operations. Another important argument is the use of wooden structures to build local crossings for the benefit of the state economy. The speed of construction and the cost of making for example a driving deck are many times lower. This is quite an important factor influencing the decision of local governments to rebuild temporary crossings. Moreover, the skilful use of this material can significantly help the army in terms of durability and effective manoeuvrability. Engineering troops can build shelters for people and equipment, as well as low-water and underwater bridges and crossings for armoured fighting vehicles from wood. Any destruction of wooden structures planned by the engineering troops must be based on calculations derived from knowledge and experience. This article presents experiments on contact explosions of wooden structural elements. Pine beams with an equal diameter of 23 cm were tested in the military field. The wooden log destruction has been inventoried and the damage to particular wooden tissues was quantified by using different amounts of explosives. Also, professional military analyses were conducted to determine the explosive mass to destroy such wooden logs. Later, the computed values were compared with the results obtained in the field experiment and a damage parameter of energy criteria for pine wood was determined for the constitutive model of wood including damage.
Takal, SU, Tahiru, A-W, Fattah, IMR, Cobbina, SJ, Asare, W & Abanyie, SK 2025, 'Enhancing resilience to climate change: a comprehensive PRISMA review of agricultural and non-agricultural adaptation strategies for Ghana', Cogent Social Sciences, vol. 11, no. 1.
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Talaei, S, Zhu, X, Li, J, Yu, Y & Chan, THT 2025, 'A hybrid domain adaptation approach for estimation of prestressed forces in prestressed concrete bridges under moving vehicles', Engineering Structures, vol. 330, pp. 119904-119904.
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Talatahari, S, Chen, F & Gandomi, AH 2025, 'Developing a robust machine learning framework for predicting the behavior of large-scale structure', Journal of Building Engineering, vol. 105, pp. 112204-112204.
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Talatahari, S, Nouhi, B, Beheshti, A, Chen, F & Gandomi, AH 2025, 'Adaptive Strategy Management: A new framework for large-scale structural optimization design', Computer Methods in Applied Mechanics and Engineering, vol. 446, pp. 118256-118256.
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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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Tan, Y, Huang, J, Zhang, W, Wang, J, Wen, S & Huang, T 2025, 'Finite-time and fixed-time bipartite synchronization of signed networks with mixed delays', Neurocomputing, vol. 650, pp. 130942-130942.
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Tang, H, Sun, X, Wu, S, Cui, Z, Xu, G & Li, Q 2025, 'DyBooster: Leveraging large language model as booster for dynamic recommendation', Expert Systems with Applications, vol. 286, pp. 128080-128080.
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Tang, H, Wu, S, Sun, X, Zeng, J, Xu, G & Li, Q 2025, 'TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic Recommendation', ACM Transactions on Information Systems, vol. 43, no. 1, pp. 1-27.
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Dynamic recommendation systems, where users interact with items continuously over time, have been widely deployed in real-world online streaming applications. The burst of interaction stream causes a rapid evolution of both users and items. To update representations dynamically, existing studies have investigated event-level and history-level dynamics by modeling the newly arrived interactions and aggregating historical interactions, respectively. However, most of them directly learn the representation evolution as new interactions occur, without exploring the collaboration between the newly arrived and historical interactions, thus failing to scrutinize whether those new interactions would benefit the evolution learning process when generating dynamic representations. Moreover, most of them model the two levels of dynamics independently, explicitly ignoring the inherent co-evolving correlation between them. In this work, we propose the Temporal Collaboration-Aware Graph Co-Evolution Learning (TCGC) for the dynamic recommendation scenario. First, we explore the effectiveness of collaborative information and devise the collaboration-aware indicator to guide the evolution learning process. Second, we design a temporal co-evolving graph network, enabling our framework to capture the correlation between event and history dynamics. Third, we leverage the evolution task and recommendation task together for joint training. Extensive experiments on four public datasets demonstrate the superiority and effectiveness of our proposed TCGC.
Tangirala, A, Rawat, S, Tan, KH & Lahoti, M 2025, 'Enhanced Thermal Performance of Fiber-Reinforced Cementitious Composite with High-Volume Fly Ash and Steel Slag Aggregates', Journal of Materials in Civil Engineering, vol. 37, no. 7.
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Tanveer, M, Tiwari, A, Akhtar, M & Lin, C-T 2025, 'Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-10.
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Tao, G, Tang, W, Chen, Q, Zhou, H, Nimbalkar, S, Xiao, H & Huang, Z 2025, 'Fractal Shear Strength Model for Unsaturated Soils Considering the Combined Effects of Matric Suction and Surface Tension', International Journal of Geomechanics, vol. 25, no. 7.
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Tao, G, Yang, C, Chen, Q, Nimbalkar, S, Xiao, H & Wang, Q 2025, 'Laboratory assessment of tensile properties of root-soil composite of Amorpha fruticose', Journal of Mountain Science, vol. 22, no. 3, pp. 1062-1074.
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Taoyu, W, Deng, J, Liang, Y & Shi, K 2025, 'Abstractive summarization-based academic paper title drafting', CCF Transactions on Pervasive Computing and Interaction, vol. 7, no. 1, pp. 87-96.
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Tarsitano, M, Liu Chung Ming, C, Bennar, L, Mahmodi, H, Wyllie, K, Idais, D, Al Shamery, W, Paolino, D, Cox, TR, Kabakova, I, Ralph, P & Gentile, C 2025, 'Chlorella-enriched hydrogels protect against myocardial damage and reactive oxygen species production in an in vitro ischemia/reperfusion model using cardiac spheroids', Biofabrication, vol. 17, no. 1, pp. 015006-015006.
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Abstract Microalgae have emerged as promising photosynthetic microorganisms for biofabricating advanced tissue constructs, with improved oxygenation and reduced reactive oxygen species (ROS) production. However, their use in the engineering of human tissues has been limited due to their intrinsic growth requirements, which are not compatible with human cells. In this study, we first formulated alginate–gelatin (AlgGel) hydrogels with increasing densities of Chlorella vulgaris. Then, we characterised their mechanical properties and pore size. Finally, we evaluated their effects on cardiac spheroid (CS) pathophysiological response under control and ischemia/reperfusion (I/R) conditions. Our results showed that the addition of Chlorella did not affect AlgGel mechanical properties, while the mean pore size significantly decreased by 35% in the presence of the 107 cells ml−1 microalgae density. Under normoxic conditions, the addition of 107 Chlorella cells ml−1 significantly reduced CS viability starting from 14 d in. No changes in pore size nor CS viability were measured for hydrogels containing 105 and 106 Chlorella cells ml−1. In our I/R model, all Chlorella-enriched hydrogels reduced cardiac cell sensitivity to hypoxic conditions with a corresponding reduction in ROS production, as well as protected against I/R-induced reduction in cell viability. Altogether, our results support a promising use of Chlorella-enriched Alg–Gel hydrogels for cardiovascular tissue engineering.
Thi Hai, N, Ryu, S, Loganathan, P, Kandasamy, J, Vigneswaran, S & Nguyen, TV 2025, 'Theoretical adsorption modeling and simulation of toxic arsenic ions removal in batch and continuous systems using Mn/Mg/Fe layered double hydroxides', Chemical Engineering Research and Design, vol. 220, pp. 49-58.
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Thi Nguyen, H, Duong, HC, Chen, S-S, Le Quang, H, Ngo, HH, Cong, CD, Nguyen, NC, Thi Nguyen, UT & Huynh, DD 2025, 'Enhancing membrane distillation stability: Isoamyl alcohol coagulation as a novel strategy to mitigate membrane swelling at elevated temperatures', Environmental Technology & Innovation, vol. 37, pp. 104029-104029.
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Thomas, DWK, Wu, K & Guo, YJ 2025, 'A New TDU-Based Hierarchical Beamforming Framework to Suppress Grating Lobes and Save Space', IEEE Open Journal of Antennas and Propagation, pp. 1-1.
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Thomas, PS, Mansell, B, Appadoo, D, Smallwood, AS, Aldridge, L & Stuart, BH 2025, 'Characterisation of the crystallisable water in precious opal using differential scanning calorimetry and synchrotron terahertz spectroscopy', Journal of Thermal Analysis and Calorimetry, vol. 150, no. 2, pp. 1093-1103.
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Tian, H, Liu, B, Zhu, T, Zhou, W & Yu, PS 2025, 'Distilling Fair Representations From Fair Teachers', IEEE Transactions on Big Data, vol. 11, no. 3, pp. 1419-1433.
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Tian, H, Liu, B, Zhu, T, Zhou, W & Yu, PS 2025, 'MultiFair: Model Fairness With Multiple Sensitive Attributes', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, pp. 5654-5667.
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Tian, H, Zhang, G, Liu, B, Zhu, T, Ding, M & Zhou, W 2025, 'Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers', IEEE Transactions on Dependable and Secure Computing, pp. 1-16.
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Tian, P, Ma, Q, Yu, H & Lu, J 2025, 'ReCL: A Plug-and-Play Module for Enhancing Generalized Category Discovery Using Transport-Based Method to Uncover the Relationship in Samples', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Tian, Z, Wang, W, Zhang, C & Yu, S 2025, 'Model-Enabled Task-Oriented Semantic Communications Through Knowledge Synchronization', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Tian, Z, Zhang, C, Wang, W, Bogucka, H & Yu, S 2025, 'ROSE: A Receiver-Oriented Semantic Communication Framework', IEEE Network, vol. 39, no. 2, pp. 216-223.
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Tofigh, F & Lipman, J 2025, 'Human Presence Detection Sensor Using Polarization Insensitive Metamaterial Absorbers', IEEE Access, vol. 13, pp. 81725-81735.
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Tong, C, Li, J, Sun, Q, Zhang, S, Zhou, W & Sheng, D 2025, 'A noise‐based framework for randomly generating soil particle with realistic geometry', Computer-Aided Civil and Infrastructure Engineering, vol. 40, no. 13, pp. 1829-1846.
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AbstractParticle morphology influences the mechanical behavior of granular soils. Generating particles with realistic shapes for discrete element method simulations is gaining popularity. However, it is still challenging to efficiently generate very angular particles with less computational cost. Addressing this challenge, this paper introduces a novel noise‐based framework for generating realistic soil particle geometry. Noise algorithms are utilized to apply random variations with certain morphological patterns on the surface of the base geometry (e.g., a sphere), thereby generating a variety of particles with morphological patterns ranging from very angular to rounded. In addition, the base geometry can be replaced with other geometries including real particle scans, allowing rapid generation of realistic particles with morphological characteristics of the base geometry. The framework stands out for its simplicity, the wide range of particle morphologies generated, reducing the need for extensive computation and scanning, and provides a new idea for the granular soil behavior simulations.
Tong, C-X, Li, J-J, Sun, Q, He, F, Zhang, S, Zhou, W-H & Sheng, D 2025, 'Minimum mesh quality for reliable morphology characterization of 3D soil particles', Powder Technology, vol. 460, pp. 121062-121062.
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Torres, ASM, Gache, CCL, Tuazon, BJ, Martinez, DWC, Kim, HT, Tijing, LD & Dizon, JRC 2025, 'Potential of 3D printing in revolutionizing solar-driven interfacial evaporation for clean water supply – A review', Applied Materials Today, vol. 43, pp. 102639-102639.
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Torres, IJ, Aguilera, RP & Ha, QP 2025, 'Design and Performance Evaluation of Nonlinear Model-Predictive Control for 3-D Ground Target Tracking With Fixed-Wing UAVs', IEEE Open Journal of the Industrial Electronics Society, vol. 6, pp. 76-94.
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Tortorella, GL, Saurin, TA, Godinho Filho, M, Alfalla-Luque, R & Trianni, A 2025, 'Are we truly ready for what is coming? A reflection on supply chain resilience in face of megatrends', International Journal of Production Economics, vol. 283, pp. 109585-109585.
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Tra, TH, Nguyen, TT, Huynh, TQ & Ishikawa, T 2025, 'Load transfer behaviour of super long piles in multi-layer soft soil through field testing and numerical 3D FEM modelling', Soils and Foundations, vol. 65, no. 3, pp. 101627-101627.
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Tran, D-T, Nguyen, T-T-H, Le, T-H & Nghiem, LD 2025, 'Facile synthesis of ZnFe2O4/rGO magnetic adsorbent for antibiotic removal', Chemical Engineering Science, vol. 305, pp. 121132-121132.
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Tran, D-T, To, T-D, Le, T-H, Dao, Q-T & Nghiem, LD 2025, 'Synthesis of highly effective and easily recoverable MIL-100(Fe)/MgFe2O4 adsorbent for enhanced antibiotic removal from water', Journal of Industrial and Engineering Chemistry, vol. 147, pp. 149-160.
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Trianni, A, Leak, J & Hasan, ASMM 2025, 'Switching on ESCOs: Barriers, challenges and opportunities for the development of Australia's ESCO market', Energy Policy, vol. 199, pp. 114546-114546.
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Tun, NN, Lazo de la Cruz, GA, Haider, MA, Yang, Z, Wu, S, Deng, R & Agarwal, A 2025, 'Why Not for Society?', IEEE Pulse, vol. 16, no. 2, pp. 45-48.
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Tuncer, I, Baig, AH, Barua, PD, Hajiyeva, R, Massimo, S, Dogan, S, Tuncer, T & Acharya, UR 2025, 'FlexiCombFE: A flexible, combination-based feature engineering framework for brain tumor detection', Biomedical Signal Processing and Control, vol. 104, pp. 107538-107538.
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Tusher, AS, Islam, MR, Hossain, MA, Ishraque, MF, Maliha, MI, Rahman, MA & Hossain, J 2025, 'FALCON: A Semi-Supervised Framework for Addressing Physical and Cyber Anomalies in DGA-based Transformer Fault Diagnosis', IEEE Transactions on Dielectrics and Electrical Insulation, pp. 1-1.
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Tyler, G, Luo, S, Calderon Hurtado, A & Makki Alamdari, M 2025, 'Semi-supervised bridge indirect structural health monitoring using Isolation Distributional Kernels', Mechanical Systems and Signal Processing, vol. 226, pp. 112296-112296.
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Uddin, MA, Chu, NH, Rafeh, R & Barika, M 2025, 'A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles', IEEE Internet of Things Journal, pp. 1-1.
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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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Velmurugan, M, Ouyang, C, Sindhgatta, R & Moreira, C 2025, 'Through the looking glass: evaluating post hoc explanations using transparent models', International Journal of Data Science and Analytics, vol. 20, no. 2, pp. 615-635.
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Abstract Modern machine learning methods allow for complex and in-depth analytics, but the predictive models generated by these methods are often highly complex and lack transparency. Explainable Artificial Intelligence (XAI) methods are used to improve the interpretability of these complex “black box” models, thereby increasing transparency and enabling informed decision-making. However, the inherent fitness of these explainable methods, particularly the faithfulness of explanations to the decision-making processes of the model, can be hard to evaluate. In this work, we examine and evaluate the explanations provided by four XAI methods, using fully transparent “glass box” models trained on tabular data. Our results suggest that the fidelity of explanations is determined by the types of variables used, as well as the linearity of the relationship between variables and model prediction. We find that each XAI method evaluated has its own strengths and weaknesses, determined by the assumptions inherent in the explanation mechanism. Thus, though such methods are model-agnostic, we find significant differences in explanation quality across different technical setups. Given the numerous factors that determine the quality of explanations, including the specific explanation-generation procedures implemented by XAI methods, we suggest that model-agnostic XAI methods may still require expert guidance for implementation.
Verhagen, F, Tomamichel, M & Haapasalo, E 2025, 'Matrix Majorization in Large Samples with Varying Support Restrictions', IEEE Transactions on Information Theory, pp. 1-1.
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Vicnesh, J, Salvi, M, Hagiwara, Y, Yee, HY, Mir, H, Barua, PD, Chakraborty, S, Molinari, F & Rajendra Acharya, U 2025, 'Application of Infrared Thermography and Artificial Intelligence in Healthcare: A Systematic Review of Over a Decade (2013–2024)', IEEE Access, vol. 13, pp. 5949-5973.
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Vu, TH, Yang, Y, Dang, LC & Sirivivatnanon, V 2025, 'Comparative analysis of chloride and acid resistance in one-part geopolymer and low carbon concrete', Magazine of Concrete Research, vol. 77, no. 7-8, pp. 448-456.
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A comparative analysis of the chloride and acid resistance of one-part geopolymer and low carbon dioxide concretes is presented, with a focus on their potential to replace traditional systems based on ordinary Portland cement (OPC) in construction. The performance of geopolymer concretes (GPCs) was compared with concretes made with OPC and OPC blended with supplementary cementitious materials (fly ash and slag) under aggressive environmental conditions. The findings revealed that, while GPCs exhibit superior resistance to acid attack, their chloride resistance is highly dependent on the specific precursor materials used. The study also highlights the limitations of using rapid chloride permeability tests at standard voltages for GPCs, suggesting that lower voltage tests at 30 V may offer a more accurate assessment. Overall, the results underscore the need for optimised precursor selection in GPCs to enhance durability and advocate for further research into testing methodologies tailored to these innovative materials.
Wan, B, Lei, G, Guo, Y & Zhu, J 2025, 'Physics‐Informed Neural Networks Based on Unsupervised Learning for Multidomain Electromagnetic Analysis', IET Electric Power Applications, vol. 19, no. 1.
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ABSTRACTPhysics‐informed neural networks (PINNs) have attracted much attention recently due to their unique advantages, such as directly fitting the strong form of partial differential equations (PDEs) and not requiring a mesh. These advantages make them suitable for solving numerical analysis problems of complex three‐dimensional shapes. Since supervised‐learning‐based PINNs rely on the solutions obtained from traditional numerical methods, they should be regarded as performing function fitting or numerical approximation rather than truly solving a numerical computation problem. On the other hand, PINNs based on unsupervised learning can successfully solve single‐domain electromagnetic analysis problems without access to the value of the physical quantity, which can be considered the ground truth. However, they cannot solve the multidomain electromagnetic analysis problem because they cannot fit the physical quantity at the interface. If the solution at the interface is unknown, PINNs can only enforce the continuity of values at the interface. Still, they cannot express the relationship between the gradients at the interface. To address this problem, this research proposes a novel numerical analysis method that employs PINNs based on unsupervised learning to solve multidomain problems. The discretised direct boundary integral equations are utilised to solve the physical quantity at the interface, and the multidomain problem can be transformed into multiple single‐domain problems. Then, PINNs based on unsupervised learning can be utilised to solve all the subdomains. The feasibility of the proposed method is demonstrated through single‐domain and multidomain electrostatic box problems as well as the testing electromagnetic analysis methods (TEAM) problem 22. Finally, the results of finite element analysis (FEA), boundary element method (BEM) and PINN based on unsupervised learning are compared, and the accuracy of the pr...
Wan, Y, Qiu, N, Xiao, M, Xu, Y & Fang, J 2025, 'Energy dissipation of 3D-printed TPMS lattices under cyclic loading', International Journal of Mechanical Sciences, vol. 294, pp. 110245-110245.
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Wang, C, Chaturvedi, K, Nham, B, Reid, N, Bradshaw, A, Rosengren, S, Black, D, Bein, K, Halmagyi, M, Braytee, A, Prasad, M, Bharathy, G & Welgampola, M 2025, 'Separating Stroke and Vestibular Neuritis Using History, Examination and Vestibular Tests: A Machine Learning Approach (S25.009)', Neurology, vol. 104, no. 7_Supplement_1.
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Wang, C, Sreerama, J, Nham, B, Reid, N, Ozalp, N, Thomas, J, Cappelen-Smith, C, Calic, Z, Bradshaw, A, Rosengren, S, Akdal, G, Halmagyi, M, Black, D, Burke, D, Prasad, M, Bharathy, G & Welgampola, M 2025, 'Separation of Stroke from Vestibular Neuritis using the Video Head Impulse Test: Machine Learning Models versus Expert Clinicians (S2.006)', Neurology, vol. 104, no. 7_Supplement_1.
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Wang, C, Sreerama, J, Nham, B, Reid, N, Ozalp, N, Thomas, JO, Cappelen-Smith, C, Calic, Z, Bradshaw, AP, Rosengren, SM, Akdal, G, Halmagyi, GM, Black, DA, Burke, D, Prasad, M, Bharathy, GK & Welgampola, MS 2025, 'Separation of stroke from vestibular neuritis using the video head impulse test: machine learning models versus expert clinicians', Journal of Neurology, vol. 272, no. 3.
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Abstract Background Acute vestibular syndrome usually represents either vestibular neuritis (VN), an innocuous viral illness, or posterior circulation stroke (PCS), a potentially life-threatening event. The video head impulse test (VHIT) is a quantitative measure of the vestibulo-ocular reflex that can distinguish between these two diagnoses. It can be rapidly performed at the bedside by any trained healthcare professional but requires interpretation by an expert clinician. We developed machine learning models to differentiate between PCS and VN using only the VHIT. Methods We trained machine learning classification models using unedited head- and eye-velocity data from acute VHIT performed in an Emergency Room on patients presenting with acute vestibular syndrome and whose final diagnosis was VN or PCS. The models were validated using an independent test dataset collected at a second institution. We compared the performance of the models against expert clinicians as well as a widely used VHIT metric: the gain cutoff value. Results The training and test datasets comprised 252 and 49 patients, respectively. In the test dataset, the best machine learning model identified VN with 87.8% (95% CI 77.6%–95.9%) accuracy. Model performance was not significantly different (p = 0.56) from that of blinded expert clinicians who achieved 85.7% accuracy (75.5%–93.9%) and was superior (p = 0.01) to that of the optimal gain cutoff value (75.5% accuracy (63.8%–85.7%)). Conclusion Machine learning...
Wang, C, Wang, L, Dong, L, Shon, HK & Kim, J 2025, 'Specific energy consumption of seawater reverse osmosis desalination plants using machine learning', Desalination, vol. 602, pp. 118654-118654.
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Wang, C, Wei, W, Chen, X, Liu, Y, Wijayaratna, K & Ni, B-J 2025, 'Unlocking Drivers of Country-Specific Sensitivities of Atmospheric Greenhouse Gas Accumulation: Preparing for Future Pandemic Management', Environmental Science & Technology, vol. 59, no. 1, pp. 362-372.
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Wang, C, Yan, T, Huang, W, Chen, X, Xu, K & Chang, X 2025, 'APANet: Asymmetrical Parallax Attention Network for Efficient Stereo Image Deraining', IEEE Transactions on Computational Imaging, vol. 11, pp. 101-115.
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Wang, D, Yao, H, Yu, D, Song, S, Weng, H, Xu, G & Deng, S 2025, 'Graph Intention Embedding Neural Network for tag-aware recommendation', Neural Networks, vol. 184, pp. 107062-107062.
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Wang, F, Yang, Y, Gao, J, Li, X, Lu, Z, Fan, X, Cao, S, Liu, Y, Tijing, LD, Shon, HK & Ren, J 2025, 'Ultra-rapid start-up biological nitrification for nutrient recovery from source-separated urine', Water Research, vol. 287, pp. 124343-124343.
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Wang, G, Chen, Z, Xie, J, Ding, L, Zhu, J, Wei, W, Yan, Y-M, Chu, D & Ni, B-J 2025, 'Recent trends and prospects in electrochemical nitrate reduction to ammonia with an emphasis on cobalt catalysts', Coordination Chemistry Reviews, vol. 539, pp. 216751-216751.
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Wang, H, Li, D, Cai, F, Li, Y, Wang, J & Zheng, J 2025, 'Design and experiment study on a novel magnetorheological impact damper coupled with multiple parallel relief orifices for reducing higher impact peaks', Journal of Intelligent Material Systems and Structures, vol. 36, no. 4, pp. 242-258.
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Conventional magnetorheological (MR) dampers exhibit the drawbacks of excessively large and non-adjustable peak force/acceleration during the initial impact in the context of high-impact loads, limiting their applicability in the field of impact resistance. This paper introduces an innovative MR damper (MRD), which, coupled with multiple parallel relief orifices, is capable of effectively mitigating the initial impact peak and enhancing the plateau effect during the mid and late stages of impact. Firstly, the configuration and principle of the novel MRD with multiple orifices (MRDWO) are illustrated and its nonlinear model, based on Bingham-Plastic model and accounting for minor loss, is established. Secondly, the structural design and fabrication of MRDWO were conducted. Simultaneously, for comparative purposes, a traditional MRD with an identical structure to MRDWO, except for the absence of relief orifices, was also fabricated. Then, experimental investigations were conducted separately on MRDWO and MRD without relief orifices (MRDWOO) under various impact velocities and control currents to study their damping forces, accelerations, and displacement responses. Finally, the experimental results of MRDWO were utilized to validate the reliability of the aforementioned nonlinear model. Additionally, a comparative analysis was conducted between MRDWO and MRDWOO under various operating conditions in terms of impact forces, accelerations, and displacements. The results indicate that the novel MRDWO significantly reduces the impact force/acceleration peak during the initial impact and enhances the plateau effect of force/acceleration in mid to later stages, thereby reducing the required displacement for absorbing. This is crucial for mitigating the initial impact damage and improving the damping efficiency of the MR damper.
Wang, H, Liu, L, Zhang, H, Zhu, L, Chang, X & Du, H 2025, 'VisualRAG: Knowledge-Guided Retrieval Augmentation for Image-Text Matching', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Wang, J, Chen, L, Zhang, Z, He, J & Zhou, X 2025, 'Short-text topic modeling with dual reinforcement from internal and external semantics', International Journal of Machine Learning and Cybernetics, vol. 16, no. 5-6, pp. 2905-2920.
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Wang, J, Ji, J-C & Ding, H 2025, 'Multimode vibration suppression of an inclined axially preloaded beam with nonlinear energy sinks', European Journal of Mechanics - A/Solids, vol. 114, pp. 105774-105774.
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Wang, J, Wang, C, Li, Z, He, W, Zhong, Y & Huang, Y 2025, 'Overcoming Data Scarcity in Maritime Radar Target Detection via a Complex-Valued Hybrid Spatiotemporal Network', IEEE Geoscience and Remote Sensing Letters, vol. 22, pp. 1-5.
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Wang, JJ, Nie, XF, Yang, L, Li, WG & Zhang, SS 2025, 'Compressive behavior of FRP-UHPC-steel double-skin tubular columns', Engineering Structures, vol. 325, pp. 119451-119451.
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Wang, K, Huang, S, Zhang, J, Ma, B, Xing, J, Lei, G & Zhu, J 2025, 'An Effective Cooling Scheme Using Micro Heat Pipe Array for Electrical Machines With Distributed Windings', IEEE Transactions on Transportation Electrification, vol. 11, no. 2, pp. 5891-5900.
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Wang, K, Qu, C, Wang, J, Li, Z & Lu, H 2025, 'Ensemble Multitask Prediction of Air Pollutants Time Series: Based on Variational Inference, Data Projection, and Generative Adversarial Network', Journal of Forecasting, vol. 44, no. 2, pp. 646-675.
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ABSTRACTIn light of the mounting environmental pressures, especially the significant threat urban air pollution poses to public health, there arises an imperative need to develop a data‐driven model for air pollution prediction. However, contemporary deep learning techniques, such as recurrent neural networks, often struggle to effectively capture the underlying data patterns and distributions, resulting in reduced model stability. To address this gap, this study introduces an ensemble Wasserstein generative adversarial network framework (EWGF) to enhance the stability and accuracy of PM2.5 predictions by facilitating the acquisition of more informative data representations through Wasserstein generative adversarial network. The framework contains an intricate feature extraction pipeline that automatically learns features containing residual information as representations of potential features, effectively ameliorating the underutilization of feature information. We address a nonconvex multi‐objective optimization problem associated with amalgamating diverse Wasserstein generative adversarial network architectures, which enhance the inherent instability of the predictions. Furthermore, an adaptive search strategy is introduced to ascertain the optimal distribution of prediction residuals, thereby expanding the prediction interval estimation method based on residual distribution. We rigorously evaluate the proposed framework using datasets from three major Indian cities, and our experiments unequivocally show that the EWGF outperforms existing solutions in both PM2.5 point prediction and interval prediction, evidenced by an approximate 8.07% reduction in mean absolute percentage error and an approximate 19.41% improvement in prediction interval score compared to the baseline model.
Wang, L, Wang, S, Zhang, Q, Wu, Q & Xu, M 2025, 'Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation', IEEE Transactions on Multimedia, pp. 1-12.
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Wang, L, 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, L, Yi, S, Sun, X, Yu, Y & Samali, B 2025, 'Quantitative evaluation of bolt pre-load using coda wave interferometry and nonlinear coda wave interferometry: a comparative study', Journal of Civil Structural Health Monitoring, vol. 15, no. 2, pp. 563-573.
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Abstract The quantitative evaluation of bolt pre-load is crucial for the maintenance and prevention of accidents in bolt-connected structures. This study introduces the coda wave interferometry (CWI) method and the nonlinear coda wave interferometry (NCWI) method for quantitative evaluation of bolt pre-load. Experimental tests across three different scales of bolt pre-load changes were conducted on a bolt to compare the performances of CWI and NCWI in the quantitative evaluation of bolt pre-load. The results demonstrate that both CWI and NCWI can effectively characterize changes in bolt pre-load. For CWI, the relative velocity change (∆v/v) exhibits a linear relationship with the bolt pre-load. Meanwhile, for NCWI, the effective nonlinear level, denoted as $${\alpha }_{\Delta v/v}$$ α Δ v / v , demonstrates a quadratic dependence on the bolt pre-load. In CWI, the calculation of ∆v/v is dependent on the correlation coefficient between the coda waves of signals before and after bolt pre-load changes. It is prone to failure when there are significant changes in bolt pre-load. Conversely, NCWI demonstrates enhanced robustness in evaluating bolt pre-load changes across a range of magnitudes.
Wang, M, Zhu, S, Liu, X, Wen, S & Mu, C 2025, 'Finite-Time Input-to-State Stability of Neural Networks With Disturbances and Prescribed Performance', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 5, pp. 3742-3751.
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Wang, Q, Luo, Z, Zhang, M, Wu, D, Li, G & Gao, W 2025, 'Polymorphic uncertainty field quantification in structural analysis with machine learning assistance', Mechanical Systems and Signal Processing, vol. 225, pp. 112273-112273.
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Wang, Q, Yu, G & Chen, S 2025, 'Understanding BRC-20: Hope or Hype', IEEE Transactions on Computational Social Systems, pp. 1-16.
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Wang, Q, Yu, G, Sai, Y, Bandara, HMND & Chen, S 2025, 'Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage', IEEE Transactions on Services Computing, pp. 1-19.
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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, Qiu, H, Liu, R, Huo, H, Cheng, X & Liu, X 2025, 'A hybrid governance framework for adaptive and sustainable urban energy management', Sustainable Cities and Society, vol. 130, pp. 106638-106638.
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Wang, R, Xi, L, Zhang, F, Fan, H, Yu, X, Liu, L, Yu, S & Leung, VCM 2025, 'Context Correlation Discrepancy Analysis for Graph Anomaly Detection', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 1, pp. 174-187.
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Wang, R, Zuo, H & Lu, J 2025, 'Fuzzy Rule-Based Test-Time Adaptation for Class Imbalance in Dynamic Scenarios', IEEE Transactions on Fuzzy Systems, vol. 33, no. 8, pp. 2469-2480.
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Wang, R, Zuo, H, Fang, Z & Lu, J 2025, 'Integrated Image-Text Augmentation for Few-Shot Learning in Vision-Language Models', ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 2, pp. 1-19.
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Vision-language models, such as the Contrastive Language-Image Pre-Training (CLIP) model, have achieved significant success in image classification tasks. CLIP demonstrates high expressive power in few-shot learning scenarios due to its pairing of text and image encoders. However, CLIP still faces over-fitting when trained with a limited number of samples. To mitigate this, image augmentation techniques have been proposed in few-shot learning tasks to prevent over-fitting by enriching the dataset. Existing image augmentation methods, primarily designed for single-modal image models, focus solely on transformations within the image itself. However, for CLIP, merely increasing visual variety without considering textual content can reduce generalization ability and may even mislead the model. To address this issue, we introduce a novel image augmentation approach—Integrated Image-Text Augmentation (ITA)— for CLIP model in few-shot learning tasks. This method generates new and diverse augmented images to increase the diversity of the training data and reduce over-fitting. Additionally, ITA establishes an alignment between the augmented images and their textual descriptions. Through this alignment, the model not only learns to recognize visual elements in the images but also understands the semantic connections between these elements and the text descriptions. This dual-modal approach enhances the model’s flexibility and accuracy in processing few-shot learning tasks. Extensive experiments in few-shot image classification scenarios have demonstrated that ITA shows significant improvements compared to various image augmentation techniques.
Wang, S & Wen, S 2025, 'Safe Control Against Uncertainty: A Comprehensive Review of Control Barrier Function Strategies', IEEE Systems, Man, and Cybernetics Magazine, vol. 11, no. 1, pp. 34-47.
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Wang, S, Li, K, Yan, Z, Guo, Z, Zhu, S, Wen, G & Wen, S 2025, 'Optimal Parameter Adaptation for Safety-Critical Control via Safe Barrier Bayesian Optimization', IEEE Transactions on Control Systems Technology, pp. 1-7.
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Wang, S, Shi, K, Cao, J & Wen, S 2025, 'Fuzzy spatiotemporal event-triggered control for the synchronization of IT2 T–S fuzzy CVRDNNs with mini-batch machine learning supervision', Neural Networks, vol. 185, pp. 107220-107220.
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Wang, S, Wang, Q, Lu, H, Zhang, D, Xing, Q & Wang, J 2025, 'Learning about tail risk: Machine learning and combination with regularization in market risk management', Omega, vol. 133, pp. 103249-103249.
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Wang, S, Xu, C, Zhao, G, Han, Z, Hu, R & Yu, S 2025, 'APQA: An anonymous post quantum access authentication scheme based on lattice for space ground integrated network', Computer Networks, vol. 257, pp. 110979-110979.
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Wang, S, Zhao, G, Xu, C, Han, Z & Yu, S 2025, 'LPQAA: a lightweight post-quantum access authentication scheme for satellite network', The Journal of Supercomputing, vol. 81, no. 1.
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Wang, W, Mao, W, Sun, H, Hou, F, Wang, W, Liu, W, Shi, Z, Lin, G, Wang, M, Fang, G, Cheng, YY & Xu, C 2025, 'Microfluidic SERS biosensor based on Au-semicoated photonic crystals for melanoma diagnosis', Biosensors and Bioelectronics, vol. 271, pp. 116983-116983.
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Wang, W, Tian, Z, Zhang, C & Yu, S 2025, 'SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 547-558.
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Wang, W, Wang, Z, Li, M, Xiong, Z, Chen, D & Wu, C 2025, 'Close-range blast behavior of hybrid FRP-concrete-steel double-skin tubular member', Thin-Walled Structures, vol. 211, pp. 113022-113022.
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Wang, W, Zhang, C, Tian, Z & Yu, S 2025, 'FedU: Federated Unlearning via User-Side Influence Approximation Forgetting', IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 3, pp. 2550-2562.
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Wang, W, Zhang, C, Tian, Z, Liu, S & Yu, S 2025, 'CRFU: Compressive Representation Forgetting Against Privacy Leakage on Machine Unlearning', IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 4, pp. 3916-3929.
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Wang, W, Zhang, C, Tian, Z, Yu, S & Su, Z 2025, 'Evaluation of Machine Unlearning Through Model Difference', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 5211-5223.
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Wang, X, Ding, C, Zhao, G, Teng, C, Li, S & Sun, H 2025, 'Compact Dual-Polarized Self-Decoupling Shared-Aperture Antenna Array for 6G Systems', IEEE Antennas and Wireless Propagation Letters, pp. 1-5.
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Wang, X, Gong, S, Shen, W, Xing, C & Zhang, JA 2025, 'Multi-Carrier Faster-Than-Nyquist Signaling for OTFS Systems', IEEE Transactions on Vehicular Technology, pp. 1-15.
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Wang, X, Han, R, Wang, Y, He, J, Gao, P, Liu, J & Yuan, S 2025, 'Surface modification of fluorite during hydrogen-based mineral phase transformation and its effect on collector adsorption behavior', Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol. 725, pp. 137616-137616.
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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, Wu, K, Andrew Zhang, J, Gong, S & Xing, C 2025, 'Bayesian Sensing for Time-Varying Channels in ISAC Systems', IEEE Transactions on Communications, pp. 1-1.
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Wang, X, Zhang, Y, Shen, Y, Liu, J & Zhang, H 2025, 'Efficient improvement strategy for supported iridium-based catalysts in pure water electrolysis', Journal of Alloys and Compounds, vol. 1023, pp. 180071-180071.
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Wang, X-Y, Yang, S, Zhai, H-Y, Masouros, C & Andrew Zhang, J 2025, 'Windowing Optimization for Fingerprint-Spectrum-Based Passive Sensing in Perceptive Mobile Networks', IEEE Transactions on Communications, vol. 73, no. 2, pp. 1367-1382.
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Wang, Y, Bui, TA, Yang, X, Hutvagner, G & Deng, W 2025, 'Advancements in gene therapies targeting mutant KRAS in cancers', Cancer and Metastasis Reviews, vol. 44, no. 1.
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Abstract Mutations in the KRAS gene are well-known tumourigenic drivers of colorectal, pancreatic and lung cancers. Mechanistically, these mutations promote uncontrolled cell proliferation and alter the tumour microenvironment during early carcinoma stages. Given their critical carcinogenic functions, significant progress has been made in developing KRAS inhibitors for cancer treatment. However, clinical applications of these KRAS inhibitor compounds are limited to specific cancer types which carry the relevant KRAS mutations. Additionally, clinical findings have shown that these compounds can induce moderate to serious side effects. Therefore, new approaches have emerged focusing on the development of universal therapeutics capable of targeting a wider range of KRAS mutations, minimising toxicity and enhancing the therapeutic efficacy. This review aims to examine these therapeutic strategies in the context of cancer treatment. It firstly provides an overview of fundamental KRAS biology within the cell signalling landscape and how KRAS mutations are associated with cancer pathogenesis. Subsequently, it introduces the development of current KRAS inhibitors which target certain KRAS mutants in different types of cancer. It then explores the potential of gene therapy approaches, including siRNA, miRNA and CRISPR methodologies. Furthermore, it discusses the use of lipid-based nanocarriers to deliver gene cargos for targeting KRAS gene mutants. Finally, it provides the insights into the future prospects for combatting KRAS mutation-associated cancers. Graphical Abstract
Wang, Y, Qi, B, Wang, X, Liu, T & Dong, D 2025, 'Power Characterization of Noisy Quantum Kernels', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 8, pp. 13939-13952.
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Wang, Y, Wei, W, Yang, D, Wu, L, Chen, X, Dai, X & Ni, B-J 2025, 'Unraveling temperature effects on caproate and caprylate production from waste activated sludge', Bioresource Technology, vol. 417, pp. 131844-131844.
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Wang, Y, Wei, Y, Luo, Q, Li, Q & Li, S 2025, 'On tensile and bending failure behaviors of scarf repaired laminated composites', Thin-Walled Structures, vol. 215, pp. 113532-113532.
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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, Xu, M, Wang, Z, Wang, Y & Zhang, JA 2025, 'User Reidentification Through mmWave Radio Imaging', IEEE Internet of Things Journal, vol. 12, no. 16, pp. 33293-33310.
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Wang, Y, Zhao, L & Huang, S 2025, 'Occupancy-SLAM: An Efficient and Robust Algorithm for Simultaneously Optimizing Robot Poses and Occupancy Map', IEEE Transactions on Robotics, vol. 41, pp. 4057-4077.
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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, Ji, J, Xu, Y, Li, S, Sun, B & Yang, X 2025, 'Multiview Contrastive Shapelet Learning: A Novel Semi-Supervised Approach for Explainable Machine Fault Diagnosis With Insufficient Annotated Data', IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-11.
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Wang, Z, Li, X, Liu, H, Lin, CSK & Wang, Q 2025, 'Hydrothermal liquefaction of sewage sludge: A comprehensive review of biocrude oil production, byproducts valorization, and future perspectives', Renewable and Sustainable Energy Reviews, vol. 224, pp. 116086-116086.
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Wang, Z, Shi, X, Ji, Z & Yin, X 2025, 'Quantum Network Optimization: From Optimal Routing to Fair Resource Allocation', ACM SIGMETRICS Performance Evaluation Review, vol. 53, no. 1, pp. 142-144.
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Quantum networks are essential infrastructure for enabling large-scale and long-distance quantum communications but face significant challenges in routing optimization and resource allocation due to their probabilistic nature and quantum resource limitations. Existing approaches typically tackle these problems in isolation, often by simply applying classical routing algorithms, maximizing the overall profit to allocate resources without considering fairness, or improving fairness in an ad-hoc way without a rigorous model. This paper proposes a general framework to systematically address these challenges. First, we conduct a thorough analysis of quantum network metrics using routing algebra as the mathematical foundation, and design provably optimal routing algorithms to tackle the unique challenges arising from their probabilistic characteristics. Second, we formulate an optimization model that simultaneously considers fairness among concurrent requests while respecting various quantum resource constraints, and design efficient near-optimal heuristics to solve it. The proposed framework provides both theoretical insights and practical solutions for the design and management of future quantum networks.
Wang, Z, Shi, X, Ji, Z & Yin, X 2025, 'Quantum Network Optimization: From Optimal Routing to Fair Resource Allocation', Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 9, no. 2, pp. 1-26.
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Quantum networks are essential infrastructure for enabling large-scale and long-distance quantum communications but face significant challenges in routing optimization and resource allocation due to their probabilistic nature and quantum resource limitations. Existing approaches typically tackle these problems in isolation, often by simply applying classical routing algorithms, maximizing the overall profit to allocate resources without considering fairness, or improving fairness in an ad-hoc way without a rigorous model. This paper proposes a general framework to systematically address these challenges. First, we conduct a thorough analysis of quantum network metrics using routing algebra as the mathematical foundation, and design provably optimal routing algorithms to tackle the unique challenges arising from their probabilistic characteristics. Second, we formulate an optimization model that simultaneously considers fairness among concurrent requests while respecting various quantum resource constraints, and design efficient near-optimal heuristics to solve it. The proposed framework provides both theoretical insights and practical solutions for the design and management of future quantum networks.
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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Weber, NH, Mackie, JC, Redfern, H, Banks, E, Grimison, CC, Lucas, JA, Stockenhuber, M & Kennedy, EM 2025, 'Thermal mineralization of Perfluorooctanoic acid (PFOA): The synergistic role of oxygen and water vapor inhibiting products of incomplete destruction (PID) formation', Chemical Engineering Science, vol. 301, pp. 120659-120659.
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Wei, F, Zhou, X-C, Shi, Z-H, Liu, H-Y & Qin, P-Y 2025, 'A Balanced Bandpass Filter With High-Selectivity and Common-Mode Suppression Based on Inverted Microstrip Gap Waveguide', IEEE Microwave and Wireless Technology Letters, vol. 35, no. 2, pp. 181-184.
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Wei, J, Zhang, Y, Yu Zhang, L, Chen, C, Pan, S, Ong, K-L, Zhang, J & Xiang, Y 2025, 'Extracting Private Training Data in Federated Learning From Clients', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 4525-4540.
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Wei, X, Liu, Y, Tang, X, Yu, S & Chen, M 2025, 'Integrating Convolution and Sparse Coding for Learning Low-Dimensional Discriminative Image Representations', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 7, pp. 12483-12496.
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Wei, Y, Zheng, G, Luo, Q, Li, Q & Sun, G 2025, 'On impact response and damage tolerance of adhesively bonded joints–An experimental and numerical study', Thin-Walled Structures, vol. 208, pp. 112606-112606.
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Wei, Z, Yao, R, Yuan, X, Wu, H, Zhang, Q & Feng, Z 2025, 'Precoding Optimization for MIMO-OFDM Integrated Sensing and Communication Systems', IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 1, pp. 288-299.
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Wen, G, Yuan, S, Liu, J, Dong, Z, Han, R, Ding, H, Lei, S, Li, Z, Wang, W & Cao, Y 2025, 'Process optimization for recycling spent lithium iron phosphate batteries based on leaching kinetics and mechanism study', Separation and Purification Technology, vol. 370, pp. 133263-133263.
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Weththasinghe, K, He, Y, Tu Ngo, Q, Jayawickrama, B & Dutkiewicz, E 2025, 'Demonstration Platform to Emulate Cognitive GEO-LEO Dual Satellite System', IEEE Access, vol. 13, pp. 139904-139919.
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Weththasinghe, K, Ngo, QT, He, Y & Jayawickrama, B 2025, 'Optimizing Beam Size in Multibeam LEO Satellite Networks: Addressing Interbeam Interference, Doppler Shift, and Frequency Reuse', IEEE Transactions on Aerospace and Electronic Systems, vol. 61, no. 3, pp. 5871-5884.
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Whaieb, AH, Jasim, FT, Abdulrahman, AA, Khuder, IM, Gheni, SA, Fattah, IMR & Karakullukcu, NT 2025, 'Tailoring zeolites for enhanced post-combustion CO2 capture: A critical review', Current Research in Green and Sustainable Chemistry, vol. 10, pp. 100451-100451.
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Wijayaratna, K & da Rocha, C 2025, 'Leveraging Lego Serious Play ® in Examining Practitioner Perspectives of Shared Spaces', Transportation Research Record: Journal of the Transportation Research Board.
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Practitioner engagement methods are essential for understanding diverse perspectives and providing opportunities to develop a unified strategic approach to contentious transport planning issues, such as shared spaces. These are road infrastructure designs that minimize the separation between travel modes and equalize the priority across all the modes. This study investigates the design and implementation of a novel engagement methodology, Lego Serious Play ® (LSP), to discuss the implementation of shared space solutions in practice while also enhancing participant experience. A case study of practitioners in New South Wales, Australia was conducted to identify opportunities, challenges, and the future potential of applying the methodology. Outcomes from the workshops revealed that the LSP methodology enabled participants to articulate their views, debate various ideas, and compromise. Shared spaces emerged as a key strategy for achieving place-based outcomes, particularly through a zone-based implementation approach in which design elements are gradually introduced across a road network to help users adapt to the changing environment. Key aspects and metrics were identified, shaping future guidance for shared spaces design and assessment. The storytelling component of the LSP technique was particularly effective in fostering discussion and achieving consensus. The findings suggest that the LSP methodology holds potential for application in other engineering and design disciplines, offering a novel approach to engagement and collaborative problem-solving.
Wirtu, GK, Duffield, R, Garcia, J, Salisbury, J & Fox, D 2025, 'Innovative exergaming program for antenatal exercise: Pregnant women’s views and experiences', Midwifery, vol. 148, pp. 104491-104491.
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Wong, L, Wang, J, Yang, RC & Zhang, YX 2025, 'Slow-growth damage of bonded composite-metal joints subjected to fatigue loading', International Journal of Adhesion and Adhesives, vol. 139, pp. 103979-103979.
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Wong, L, Wang, J, Yang, RC & Zhang, YX 2025, 'Slow-growth disbond and delamination damage of a bonded composite-metal joint under fatigue loading', Composites Part A: Applied Science and Manufacturing, vol. 192, pp. 108816-108816.
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Woo, HJ, Rademacher, PN, Shin, HY, Lee, J, Intisar, A, Warkiani, ME, Joung, JY, Rzhevskiy, A, Coith, C, Harten, F, Hilpert, F, Schmalfeldt, B, Riethdorf, S, Pantel, K, Joosse, SA & Kim, MS 2025, 'Robust Automated Separation of Circulating Tumor Cells and Cancer-Associated Fibroblasts for Enhanced Liquid Biopsy in Breast Cancer', Analytical Chemistry, vol. 97, no. 32, pp. 17452-17461.
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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, G, Xue, S, Ma, S, Kuang, S, Dong, D & Petersen, IR 2025, 'Arbitrary state transition of open qubit system based on switching control', Automatica, vol. 179, pp. 112424-112424.
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Wu, J, Hu, C, Zhang, J, Wang, D & Jiang, J 2025, 'Gemini+: Enhancing Real-Time Video Analytics with Dual-Image FPGAs', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-1.
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Wu, J, Huang, Y, Gao, M, Niu, Y, Chen, Y & Wu, Q 2025, 'High-order diversity feature learning for pedestrian attribute recognition', Neural Networks, vol. 188, pp. 107463-107463.
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Wu, J, Huang, Y, Gao, M, Niu, Y, Chen, Y & Wu, Q 2025, 'Rethinking attention mechanism for enhanced pedestrian attribute recognition', Neurocomputing, vol. 639, pp. 130236-130236.
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Wu, J, 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 2025, 'Re-examining Two Military Reforms in the Chunqiu Zhanguo Period: the Wei Shu Phalanx and Hufu Qishe', Journal of Chinese Military History, vol. 14, no. 1, pp. 37-61.
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AbstractSome scholars believe that two important military reforms occurred in the Chunqiu Zhanguo period: the Wei Shu 魏舒 phalanx developed by Jin 晉 and the hufu qishe 胡服騎射 reform implemented by Zhao 趙. They claim that the two reforms greatly promoted military development in ancient China by replacing the chariots with infantry and cavalry respectively. However, this article argues that the so-called Wei Shu phalanx did not exist and hufu qishe was not a military reform. The former was a quanbian 權變 (temporarily adopting extraordinary means to adapt to special circumstances) applied in a particular battle and it was not a phalanx at all. The latter was about recruiting Hu mercenaries to fight for Zhao in the war against Zhongshan 中山 rather than performing a military reform inside the Zhao army.
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, L, Yang, J, Feng, S, Chen, H, Liu, H, Lin, Z & Nimbalkar, S 2025, 'Experimental assessment of freeze–thaw characteristics and microscopic mechanisms of coal-based solid waste improved loess filler', Transportation Geotechnics, vol. 54, pp. 101606-101606.
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Wu, M, Sivertsen, G, Zhang, L, Qi, F & Zhang, Y 2025, 'Scaling research aim identification: Language models for classifying scientific and societal‐oriented studies', Journal of the Association for Information Science and Technology.
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AbstractThe classification of research according to its aims has been a longstanding focus in the fields of quantitative science studies and R&D statistics. Since 1963, the Organization for Economic Co‐operation and Development (OECD) has employed a classical distinction among basic, applied, and experimental research. Building on this framework, our previous work highlighted the utility of differentiating between scientific and societal progress as two primary research objectives. This distinction enabled the quantitative analysis of scientific publication abstracts and the development of an automated method for large‐scale classification. In the current study, we systematically evaluate text classification techniques, including traditional text mining models, classification tools, BERT‐based language models, and decoder‐only large language models (LLMs) such as ChatGPT. Our findings show that the fine‐tuned GPT‐4o‐mini model performs the best among single‐model approaches. However, traditional and BERT‐based models outperform in certain fine‐grained classification tasks. Leveraging majority voting strategies to incorporate their strengths yields performance comparable to closed‐source GPT models. A case study on 10 biomedical journals further validates the method, demonstrating strong alignment between journal scopes, model predictions, and outputs generated by the fine‐tuned GPT‐4o‐mini model. These results highlight the robustness and practical effectiveness of the proposed methodology for nuanced research aim classification.
Wu, P, Li, S, Li, Z, Xia, W & Li, Y 2025, 'Modular QZS metamaterials with enhanced load adaptability for low-frequency vibration isolation', Smart Materials and Structures, vol. 34, no. 5, pp. 055017-055017.
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Abstract This study introduces a modular quasi-zero-stiffness (QZS) metamaterial for low-frequency vibration isolation with adaptable load-carrying capacity. The proposed structure integrates a double-curved beam as a negative stiffness element and two pairs of V-shaped springs as positive stiffness elements, forming an extendable modular metamaterial. These metamaterials provide scalable and tunable load-adaptive performance through multiple modular configurations. Finite element analysis is utilized to optimize the design parameters, ensuring effective and consistent QZS behavior across the modules. A dynamic model, validated using harmonic balance methods, demonstrates the isolation effectiveness under varying loads and excitation amplitudes. By arranging multiple unit modules in series, parallel, or combined configurations, the QZS characteristics scale in a linear and predictable manner, enabling versatile load adaptability and tunability. Quasi-static tests confirm the predicted QZS behavior, while dynamic tests validate the vibration isolation performance. A single unit module attenuates vibrations at 8.6 Hz with a payload of 1420 g, while the modular configuration achieves vibration suppression above 3.6 Hz with a payload of 5680 g, without requiring alterations to the design parameters. These findings underscore the potential of the proposed modular QZS metamaterial for scalable, load-adaptive, and low-frequency vibration isolation in engineering applications.
Wu, Q, Liu, Q, He, Y & Wu, Z 2025, 'Reconfigurable Intelligent Surface Assisted UAV-MCS Based on Transformer Enhanced Deep Reinforcement Learning', IEEE Transactions on Computers, vol. 74, no. 9, pp. 3143-3155.
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Wu, S, Li, Y, Wang, W, Guo, J, Fan, W, Liu, Q, Jia, W, Yu, S, Cao, J & Wang, T 2025, 'Enhancing Collaborative Inference on Heterogeneous Edge Devices via Adaptive Ensemble Knowledge Distillation', IEEE Journal on Selected Areas in Communications, pp. 1-1.
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Wu, T, Cui, C, Xian, X, Qiao, S, Wang, C, Yuan, L & Yu, S 2025, 'Understanding the robustness of graph neural networks against adversarial attacks', Knowledge-Based Systems, vol. 323, pp. 113714-113714.
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Wu, T, Dai, P, Xue, G, Guo, Y, Lei, G, Zhu, J & Wang, Y 2025, 'A Novel Thermal Analysis Method for Tubular PM Linear Motors Based on Transfer Learning', IEEE Transactions on Transportation Electrification, vol. 11, no. 3, pp. 7379-7388.
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Wu, X, Li, P, Gu, Y, Tao, J, Shen, S & Yu, S 2025, 'Improved Gale–Shapley-Based Hierarchical Federated Learning for IoT Scenarios', IEEE Internet of Things Journal, vol. 12, no. 7, pp. 9195-9205.
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Wu, X, Lin, Y, Zhong, H, Tao, J, Gu, Y, Shen, S & Yu, S 2025, 'A diversity-aware incentive mechanism for cross-silo federated learning with budget constraint', Knowledge-Based Systems, vol. 315, pp. 113212-113212.
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Wu, Y, Dang, F, Nimbalkar, S, Chen, Y & Zhang, F 2025, 'Property Evolution Law and Damage Mechanism of Pervious Concrete under Different Cycle Modes', Jianzhu Cailiao Xuebao Journal of Building Materials, vol. 28, no. 1, pp. 9-18.
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In order to investigate the effect of water on pervious concrete（PC）pavement，the performance evolution equations of PC under three cycle modes of dry-wet，freeze-thaw，dry-wet and freeze-thaw were studied，and the damage mechanism under single and double factors were revealed. The results show that the basalt fiber（BF）changes the pore structure and initial property of PC，but does not affect the freeze-thaw damage mechanism. The secondary cracks of PC pavement caused by freeze-thaw cycle increase exponentially，which is different from the performance evolution law under dry-wet cycle. Dry-wet cycle in the early stage of damage causes the dissolution，loss of pore wall surface material and edge curl and peel，while freeze-thaw in the late stage of damage causes the formation and expansion of cracks，resulting in intergranular fracture. Under the coupling effect of wet-dry cycle and freeze-thaw cycle，the PC performance index decreases more than the single factor superposition of wet-dry cycle and freeze-thaw cycle.
Wu, Y, Li, S, Li, J, Yu, Y, Li, J & Li, Y 2025, 'Deep learning in crack detection: A comprehensive scientometric review', Journal of Infrastructure Intelligence and Resilience, vol. 4, no. 3, pp. 100144-100144.
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Wu, Z, Liu, Y, Cen, J, Zheng, Z & Xu, G 2025, 'A cross-domain knowledge tracing model based on graph optimal transport', World Wide Web, vol. 28, no. 1.
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Wu, Z, Zhang, Q, Miao, D, Zhao, X & Shi, K 2025, 'Adapting GNNs for Document Understanding: A Flexible Framework With Multiview Global Graphs', IEEE Transactions on Computational Social Systems, vol. 12, no. 2, pp. 608-621.
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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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Xia, R, Liu, W, Nghiem, LD, Cao, D, Li, G & Luo, W 2025, 'Graft copolymerization synthesis of chitosan-polyferric sulfate composite coagulant to improve biogas slurry treatment toward effective irrigation', Journal of Environmental Management, vol. 377, pp. 124563-124563.
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Xia, X, Dong, J, Li, A, Wang, Y, Liu, Y, Zhu, Y, Xu, L, Jing, Z, Wang, J, Zou, Y, Sun, S, Wang, L, Lu, Y, Soeriyadi, A, Wang, X, Patrick, JW, Offler, CE, Zheng, M, Song, C-P & Shi, B 2025, 'Amino acid transporters mediate autonomous delivery of nanoparticle vehicles into living plants', Nature Communications, vol. 16, no. 1, p. 6715.
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Presence of the cell wall and the lack of streamlined pathways for cellular delivery of external agents into plants is a core challenge of plant biotechnology and crop engineering development. However, both viral and bacterial transmission have their own restrictions and the few non-heavy metal nanodelivery platforms require external forces for tissue penetration. Such dependency limits any high-throughput application considering the large plant numbers to be treated in the field or even laboratory exercises. Herein, we demonstrate Aspartic acid (Asp) decorated poly(ethylene glycol)-block-poly(2-(diisopropylamino)ethyl methacrylate) (Asp-PEG-PDPA) copolymers assembled micelles (Asp/PDPA-NP), a platform that utilises amino acid transporters (AtAAP1 and AtLHT1) as receptors for clathrin-dependent endocytosis, freely translocate to release loaded cargo into various plant tissue/cell types in a species-independent manner within ≤10 minutes through simple spray or co-culture. As proof-of-concept, abscisic acid (ABA)-loaded Asp/PDPA-NP was tested for its efficacy to confer plant drought resistance. Asp/PDPA-NP@ABA reduced the effective ABA dose down to 1 nM (one million-fold) and elicited anti-drought potency in representative eudicot (soybean) and monocot (maize) crop species. Owing to its delivery efficiency, Asp/PDPA-NP holds promise as a potent carrier for diverse chemicals and biomolecules in plant systems.
Xiang, S, Xu, C, Cheng, D & Zhang, Y 2025, 'Scalable Learning-Based Community-Preserving Graph Generation', IEEE Transactions on Big Data, pp. 1-14.
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Xiang, S, Zhang, G, Cheng, D & Zhang, Y 2025, 'Enhancing Attribute-Driven Fraud Detection With Risk-Aware Graph Representation', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 5, pp. 2501-2512.
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Xiao, P, Fang, J, Wei, Z, Dong, Y, Du, S, Wen, S & Hong, Q 2025, 'A Riccati Matrix Equation Solver Design Based Neurodynamics Method and Its Application', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 15163-15176.
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Xiao, S, Wang, Y, Zhang, J, Dong, D, Mooney, GJ, Petersen, IR & Yonezawa, H 2025, 'A Two-Stage Solution to Quantum Process Tomography: Error Analysis and Optimal Design', IEEE Transactions on Information Theory, vol. 71, no. 3, pp. 1803-1823.
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Xiao, Y, Ding, X, Liu, S, Ma, Y, Zhang, T, Xiang, Z, Zhang, R, Fukuyama, T, Zhao, J, Yu, Y, Wang, X, Lin, Q, Zhao, Y, Tian, G, Wen, S, Chen, Z & Zhou, X 2025, 'Fusion-Attention Diagnosis Network (FADNet): An end-to-end framework for optic disc segmentation and ocular disease classification', Information Fusion, vol. 124, pp. 103333-103333.
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Xiao, Y, Shao, Y, Chen, Z, Zhang, R, Ding, X, Zhao, J, Liu, S, Fukuyama, T, Zhao, Y, Peng, X, Tian, G, Wen, S & Zhou, X 2025, 'MIU-Net: Advanced multi-scale feature extraction and imbalance mitigation for optic disc segmentation', Neural Networks, vol. 182, pp. 106895-106895.
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Xiao, Z, Liu, X, Zeng, Y, Zhang, JA, Jin, S & Zhang, R 2025, 'Rethinking Waveform for 6G: Harnessing Delay-Doppler Alignment Modulation', IEEE Communications Magazine, vol. 63, no. 5, pp. 110-117.
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Xie, A, Zhang, J, Chen, C, Zhang, Y, Zhang, H, Zhao, H & Wang, G 2025, 'Enhanced selective hydrogenation of dimethyl oxalate to methyl glycolate through Cu-Ag bimetallic catalysts encapsulated in amino-functionalized mesoporous silica nanospheres', Surfaces and Interfaces, vol. 67, pp. 106612-106612.
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Xie, J, Sun, X, Zhang, D, Song, Y, Liu, Y, Nghiem, LD, Duan, J & Cai, Z 2025, 'Efficient catalytic ozonation of ciprofloxacin by Fe2O3-CoFe2O4 with dual-active sites: Degradation mechanism and environmental application', Journal of Environmental Chemical Engineering, vol. 13, no. 3, pp. 117045-117045.
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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, vol. 36, no. 8, pp. 14328-14342.
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Xie, Y, Zhou, Y, Guo, J, Zhang, Z, Zhu, Y, Goldys, EM, Deng, W & Chen, W 2025, 'Illuminating gene delivery: Insights into the light-induced gene delivery systems with emphasis on mRNA therapeutics', Materials Today.
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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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Xiong, H, Dai, S, Lan, P, He, X, Tong, C, Zhang, S & Sheng, D 2025, 'Probabilistic indirect models for undrained shear strength: addressing significant data missing and variability with advanced imputation and machine learning techniques', Computers and Geotechnics, vol. 187, pp. 107488-107488.
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Xiong, S, Song, Y, Zhang, T & Wen, S 2025, 'Distributed impulsive control for mean-square exponential synchronization of coupled stochastic delayed neural networks under intermittent couplings', Neurocomputing, vol. 653, pp. 131186-131186.
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Xiong, Z, Chen, Q, Tao, G, Nimbalkar, S, Rong, H & Xie, K 2025, 'Mechanical and micro-mechanical investigations of nano-SiO 2 and polypropylene fiber reinforced cement soil in marine environments', Marine Georesources & Geotechnology, vol. 43, no. 8, pp. 1460-1477.
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Xu, C, Zhou, W, Zhu, Z, Feng, S, Fang, F, Liu, D, Liu, X, Huang, S, Lin, Q, Peng, Y & Xie, C 2025, 'Integrated approach for cellulosic ethanol and succinic acid production: Gamma valerolactone-based pretreatment and co-fermentation of peanut shells', International Journal of Biological Macromolecules, vol. 290, pp. 138757-138757.
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Xu, G, Fan, X, Xu, S, Cao, Y, Chen, X-B, Shang, T & Yu, S 2025, 'Anonymity-Enhanced Sequential Multi-Signer Ring Signature for Secure Medical Data Sharing in IoMT', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 5647-5662.
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Xu, G, Xu, S, Cao, Y, Xiao, K, Mao, Y, Chen, X-B, Dong, M & Yu, S 2025, 'AAQ-PEKS: An Attribute-based Anti-Quantum Public Key Encryption Scheme with Keyword Search for E-healthcare Scenarios', Peer-to-Peer Networking and Applications, vol. 18, no. 2.
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Xu, H, Fang, L, Lv, H, Xu, S, Shen, S & Yu, S 2025, 'Maximizing VANETs Secrecy Data Rate Using Dueling Double Deep Q-Networks', IEEE Transactions on Vehicular Technology, vol. 74, no. 1, pp. 1267-1279.
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Xu, H, Liu, H, Huang, S & Sun, Y 2025, 'C2L-PR: Cross-Modal Camera-to-LiDAR Place Recognition via Modality Alignment and Orientation Voting', IEEE Transactions on Intelligent Vehicles, vol. 10, no. 2, pp. 1128-1144.
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Xu, H, Xuan, J, Zhang, G & Lu, J 2025, 'Twin Trust Region Policy Optimization', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 8, pp. 5422-5436.
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Xu, K, Tuyen Le, A, Huang, X & Ryu, H-G 2025, 'Generalized Analog Least Mean Square Loop for Self-Interference Cancellation in In-Band Full-Duplex Communications', IEEE Transactions on Wireless Communications, vol. 24, no. 3, pp. 2022-2035.
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Xu, K, Wu, Q, Lingyun, Z, Nguyen, R, Safri, F, Yang, W, Xu, Y, Ye, Y, Kwan, HY, Wang, Q, Liang, X, Shiddiky, MJA, Warkiani, ME, George, J, Qiao, L & Bao, J 2025, 'Extracellular vesicles as a promising platform of precision medicine in liver cancer', Pharmacological Research, vol. 217, pp. 107800-107800.
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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, M-G, Huang, C, Zhao, L, Rappé, AK, Kennedy, EM, Stockenhuber, M, Mackie, JC, Weber, NH, Lucas, JA, Ahmed, M, Blotevogel, J & Lu, W 2025, 'Direct measurement of fluorocarbon radicals in the thermal destruction of perfluorohexanoic acid using photoionization mass spectrometry', Science Advances, vol. 11, no. 9.
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Thermal destruction is a critical cornerstone of addressing the rampant contamination of natural resources with per- and polyfluoroalkyl substances (PFAS). However, grave concerns associated with stack emissions from incineration exist because mechanistic studies have thus far relied on ex situ analyses of end products and theoretical calculations. Here, we used synchrotron-based vacuum ultraviolet photoionization mass spectrometry to study the pyrolysis of a representative PFAS—perfluorohexanoic acid—and provide direct evidence of fluorocarbon radicals and intermediates. A key reaction pathway from perfluorocarboxylic acids to ketenes via acyl fluorides is proposed. We furthermore propose CF 2 /CF 3 radical–centered pyrolysis mechanisms and explain their roles in the formation of other products that may form in full-scale incinerators. These results have not only unveiled the role of radicals and intermediates in thermal PFAS decomposition and recombination mechanisms but also provide unique insight into improving the safety and viability of industrial PFAS incineration.
Xu, S, Deo, RC, Faust, O, Barua, PD, Soar, J & Acharya, R 2025, 'Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals', Diagnostics, vol. 15, no. 9, pp. 1155-1155.
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Background and objective: Chronic respiratory diseases, such as asthma and COPD, pose significant challenges to human health and global healthcare systems. This pioneering study utilises AI analysis and modelling of cough and respiratory sound signals to classify and differentiate between asthma, COPD, and healthy subjects. The aim is to develop an AI-based diagnostic system capable of accurately distinguishing these conditions, thereby enhancing early detection and clinical management. Our study, therefore, presents the first AI system that leverages dual acoustic signals to enhance the diagnostic ACC of asthma using automated, lightweight deep learning models. Methods: To build an automated, lightweight model for asthma detection, tested separately with respiratory and cough sounds to assess their suitability for detecting asthma and COPD, the proposed AI models integrate the following ML algorithms: RF, SVM, DT, NN, and KNN, with an overall aim to demonstrate the efficacy of the proposed method for future clinical use. Model training and validation were performed using 5-fold cross-validation, wherein the dataset was randomly divided into five folds and the models were trained and tested iteratively to ensure robust performance. We evaluated the model outcomes with several performance metrics: ACC, precision, recall, F1 score, and area under the AUC. Additionally, a majority voting ensemble technique was employed to aggregate the predictions of the various classifiers for improved diagnostic reliability. We applied Gabor time–frequency transformation for feature extraction and NCA) for feature selection to optimise predictive accuracy. Independent comparative experiments were conducted, where cough-sound subsets were used to evaluate asthma detection capabilities, and respiratory-sound subsets were used to evaluate COPD detection capabilities, allowing for targeted model assessment. Results: The proposed ensemble approach, facilitated by a ma...
Xu, S, Huang, Q, Yuan, P, Wu, P & Wu, C 2025, 'Experimental investigation on cyclic behaviors of exterior cast-in-place UHPC-NSC hybrid beam-column joints', Engineering Structures, vol. 343, pp. 121021-121021.
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Xu, W, Huang, H, Gong, Y, Yu, L, Wu, Q & Zhang, J 2025, 'Hierarchical Multi-Prototype Discrimination: Boosting Support-Query Matching for Few-Shot Segmentation', IEEE Transactions on Multimedia, pp. 1-14.
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Xu, W, Wu, L, Geng, M, Zhou, J, Bai, S, Nguyen, DV, Ma, R, Wu, D & Qian, J 2025, 'Biochar@MIL-88A(Fe) accelerates direct interspecies electron transfer and hydrogen transfer in waste activated sludge anaerobic digestion: Exploring electron transfer and biomolecular mechanisms', Environmental Research, vol. 268, pp. 120810-120810.
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Xu, Y & Guo, YJ 2025, 'Compact generalized joined coupler matrix with orthogonal beams', Electronics Letters, vol. 61, no. 1.
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AbstractAn analogue beamforming network, such as the Butler matrix can produce orthogonal beams, the peak of each beam being aligned with the nulls of all other beams. This is particularly important in scenarios requiring interference resilience in systems like base stations and user equipment. This paper presents a 3 × 5 Nolen‐like generalized joined coupler (GJC) matrix to produce orthogonal beams with both compact size and flexibility. The phase shifters are integrated into couplers while the coupling value range of couplers are extended to achieve the required performance of the GJC matrix. A prototype GJC matrix is designed, simulated, fabricated, and measured. The measured results agree well with the simulation ones.
Xu, Y, Ye, L, Wu, C, Fang, J, Sun, G, Chen, Y, Man, Z, Steven, GP & Li, Q 2025, 'Enhancing performance for additively manufactured optimal CFRP structures', Composites Science and Technology, vol. 269, pp. 111227-111227.
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Xu, Y, Zhang, W, Lin, X & Zhang, Y 2025, 'UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphs', IEEE Transactions on Knowledge and Data Engineering, pp. 1-16.
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Xu, Y, Zhang, W, Xu, X, Li, B & Zhang, Y 2025, 'Scalable and Effective Temporal Graph Representation Learning With Hyperbolic Geometry', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 4, pp. 6080-6094.
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Xu, 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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Xu, Z, Mittal, PS, Ahmed, MM, Adak, C & Cai, ZG 2025, 'Assessing penmanship of Chinese handwriting: a deep learning-based approach', Reading and Writing, vol. 38, no. 3, pp. 723-743.
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Abstract The rise of the digital era has led to a decline in handwriting as the primary mode of communication, resulting in negative effects on handwriting literacy, particularly in complex writing systems such as Chinese. The marginalization of handwriting has contributed to the deterioration of penmanship, defined as the ability to write aesthetically and legibly. Despite penmanship being widely acknowledged as a crucial factor in predicting language literacy, research on its evaluation remains limited, with existing assessments primarily dependent on expert subjective ratings. Recent initiatives have started to explore the application of convolutional neural networks (CNN) for automated penmanship assessment. In this study, we adopted a similar approach, developing a CNN-based automatic assessment system for penmanship in traditional Chinese handwriting. Utilizing an existing database of 39,207 accurately handwritten characters (penscripts) from 40 handwriters, we had three human raters evaluate each penscript’s penmanship on a 10-point scale and calculated an average penmanship score. We trained a CNN on 90% of the penscripts and their corresponding penmanship scores. Upon testing the CNN model on the remaining 10% of penscripts, it achieved a remarkable performance (overall 9.82% normalized Mean Absolute Percentage Error) in predicting human penmanship scores, illustrating its potential for assessing handwriters’ penmanship. To enhance accessibility, we developed a mobile application based on the CNN model, allowing users to conveniently evaluate their penmanship.
Xu, Z, Xie, Y, Chen, W & Deng, W 2025, 'Nanocarrier‐Based Systems for Targeted Delivery: Current Challenges and Future Directions', MedComm, vol. 6, no. 9.
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ABSTRACTNanomaterials have become promising platforms in the field of drug and gene delivery, offering unique advantages over traditional therapeutic approaches. Their tunable physicochemical properties enable improved pharmacokinetics and therapeutic performance. A wide range of nanocarriers, including lipid‐based, polymer‐based, and hybrid systems, have been rapidly developed and are attracting increasing attention in both preclinical and clinical research. However, despite promising preclinical outcomes, these systems still encounter critical challenges in achieving precise delivery to specific tissues, cells, and intracellular compartments. This review provides a comprehensive assessment of recent advances in the design and application of nanocarriers for targeted delivery, with emphasis on strategies designed for nuclear targeting. In the context of nuclear targeting, it explores passive approaches involving modulation of particle size, morphology, and surface charge, alongside active targeting strategies incorporating nuclear localization signals and other ligands. In addition to highlighting progress, the review examines the limitations associated with delivery efficiency, off‐target effects, and barriers to clinical translation. By addressing both advances and ongoing challenges, this review provides valuable insights into the design and engineering of targeted nanocarriers. These developments are crucial for unlocking the full potential of precision nanomedicine.
Xuan Tung, N, Van Chien, T, Thai Hoang, D & Hwang, WJ 2025, 'Jointly Optimizing Power Allocation and Device Association for Robust IoT Networks Under Infeasible Circumstances', IEEE Transactions on Network and Service Management, vol. 22, no. 2, pp. 1372-1390.
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Xue, F, Sun, J, Xue, Y, Wu, Q, Zhu, L, Chang, X & Cheung, S-C 2025, 'Attention Guidance by Cross-Domain Supervision Signals for Scene Text Recognition', IEEE Transactions on Image Processing, vol. 34, pp. 717-728.
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Xue, R, Xiong, L & Wang, K 2025, 'An evolutionary game approach for information sharing within medical consortium based on complex network', Computers & Industrial Engineering, vol. 203, pp. 110963-110963.
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Yaman, S, Aslan, O, Güler, H, Sengur, A, Hafeez-Baig, A, Tan, R-S, Deo, RC, Barua, PD & Acharya, UR 2025, 'Deep learning techniques for automated coronary artery segmentation and coronary artery disease detection: A systematic review of the last decade (2013-2024)', Computer Methods and Programs in Biomedicine, vol. 268, pp. 108858-108858.
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Yan, B, Zhao, Q, Zhang, J & Zhang, JA 2025, 'Heuristic solution to joint deployment and beamforming design for STAR-RIS aided networks', Expert Systems with Applications, vol. 267, pp. 126144-126144.
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Yan, H, Du, G, Li, N, Li, L, Chen, Y, Lei, G & Zhu, J 2025, 'Four Rotor Structures for High-Speed Interior Permanent Magnet Motor Considering Mechanical, Electromagnetic, and Thermal Performance', IEEE Transactions on Transportation Electrification, vol. 11, no. 1, pp. 2595-2608.
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Yan, Z, Sun, W, Guo, W, Li, B, Wen, S & Cao, J 2025, 'Complete Stability of Delayed Recurrent Neural Networks With New Wave-Type Activation Functions', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 4, pp. 6584-6596.
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Yang, C, Chen, Y, Li, Z, Wang, X, Shi, K, Yao, L, Xu, G & Guo, Z 2025, 'Deep multimodal learning for time series analysis in social computing: a survey', International Journal of Multimedia Information Retrieval, vol. 14, no. 2.
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Yang, H, Jiang, F, Kumar, A, Lyu, J, Kodagoda, S & Wang, S 2025, 'NDF-SLAM: LiDAR SLAM based on neural distance field for registration and loop closure detection', Measurement, vol. 255, pp. 117904-117904.
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Yang, J & Lin, C-T 2025, 'Autonomous Clustering by Fast Find of Mass and Distance Peaks', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 7, pp. 5336-5349.
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Yang, J & Lin, C-T 2025, 'Toward Autonomous Distributed Clustering', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 2065-2072.
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Yang, M, An, S, Gao, H, Du, Z, Zhang, X, Nghiem, LD & Liu, Q 2025, 'Selective adsorption of copper by amidoxime modified low-temperature biochar: Performance and mechanism', Science of The Total Environment, vol. 958, pp. 178072-178072.
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Yang, M, Li, X, Xu, B, Nie, X, Zhao, M, Zhang, C, Zheng, Y & Gong, Y 2025, 'STDA: Spatio-Temporal Deviation Alignment Learning for Cross-City Fine-Grained Urban Flow Inference', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 8, pp. 4833-4845.
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Yang, P, Boer, G, Snow, F, Williamson, A, Cheeseman, S, Samarasinghe, RM, Rifai, A, Priyam, A, Elnathan, R, Guijt, R, Quigley, A, Kaspa, R, Nisbet, DR & Williams, RJ 2025, 'Test and tune: evaluating, adjusting and optimising the stiffness of hydrogels to influence cell fate', Chemical Engineering Journal, vol. 505, pp. 159295-159295.
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Yang, Q, Yu, H, Teo, SG, Li, B, Long, G, Jin, C, Fan, L, Liu, Y & Zhang, L 2025, 'Guest Editorial: Special Issue on Trustworthy Federated Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 5-5.
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Yang, S, Yu, S, Li, Q, Xia, K & Zhu, H 2025, 'Few-shot specific emitter identification via asymmetric dual-path masked autoencoder', Digital Signal Processing, vol. 163, pp. 105201-105201.
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Yang, T, Valls Miro, J, Wang, Y & Xiong, R 2025, 'An Improved Maximal Continuity Graph Solver for Non-Redundant Manipulator Non-Revisiting Coverage', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 3822-3834.
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Yang, T, Yang, YK, Liu, ZX, Xu, SC & Wu, CQ 2025, 'INVESTIGATION ON MECHANICAL PROPERTIES OF ULTRA-HIGH PERFORMANCE CONCRETE AFTER HIGH TEMPERATURE', Gongcheng Lixue Engineering Mechanics, vol. 42, no. 4, pp. 97-109.
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The mechanical properties of ultra-high-performance concrete (UHPC) after high temperature were studied by static and dynamic compression and splitting tests. Moreover, the microstructure of UHPC after different temperatures was observed by scanning electron microscope (SEM), which further revealed the reasons why the changes take place in mechanical properties of UHPC after different high temperatures. Five target temperatures were set, i.e., 25 ℃ (room temperature), 200 ℃, 400 ℃, 600 ℃, and 800 ℃, and a total of 120 specimens were tested. The results show that both the static and dynamic strength of UHPC increase first and then decrease with the temperature. At the same impact velocity, the compression and splitting impact toughness of UHPC also increases first and then decreases with the temperature. However, when the impact velocity is weak, the effect of temperature on the impact toughness is not significant. The formula for the variation range between strain rate and dynamic increase factor (DIF) was fitted in this study. The tensile dynamic increase factor (TDIF) of UHPC decreases first and then increases with the temperature (except 800 ℃). However, TDIF under different target temperatures (except 800 ℃) decreases with the increase of splitting strength, which shows that the strain rate enhancement effect of UHPC decreases with the splitting strength of UHPC.
Yang, X, Che, H, Leung, M-F & Wen, S 2025, 'Unbalanced Incomplete Multiview Unsupervised Feature Selection With Low-Redundancy Constraint in Low-Dimensional Space', IEEE Transactions on Industrial Informatics, vol. 21, no. 3, pp. 2679-2688.
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Yang, Y, Guo, K, Zhang, Y, Fang, Z, Lin, H, Grosser, M, Venter, D, Lu, W, Wu, M, Cordato, D, Zhang, G & Lu, J 2025, 'MetaGeno: a chromosome-wise multi-task genomic framework for ischaemic stroke risk prediction', Briefings in Bioinformatics, vol. 26, no. 4.
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Abstract Current genome-wide association studies provide valuable insights into the genetic basis of ischaemic stroke (IS) risk. However, polygenic risk scores, the most widely used method for genetic risk prediction, have notable limitations due to their linear nature and inability to capture complex, nonlinear interactions among genetic variants. While deep neural networks offer advantages in modeling these complex relationships, the multifactorial nature of IS and the influence of modifiable risk factors present additional challenges for genetic risk prediction. To address these challenges, we propose a Chromosome-wise Multi-task Genomic (MetaGeno) framework that utilizes genetic data from IS and five related diseases. The framework includes a chromosome-based embedding layer to model local and global interactions among adjacent variants, enabling a biologically informed approach. Incorporating multi-disease learning further enhances predictive accuracy by leveraging shared genetic information. Among various sequential models tested, the Transformer demonstrated superior performance, and outperformed other machine learning models and PRS baselines, achieving an AUROC of 0.809 on the UK Biobank dataset. Risk stratification identified a two-fold increased stroke risk (HR, 2.14; 95% CI: 1.81–2.46) in the top 1% risk group, with a nearly five-fold increase in those with modifiable risk factors such as atrial fibrillation and hypertension. Finally, the model was validated on the diverse All of Us dataset (AUROC = 0.764), highlighting ancestry and population differences while demonstrating effective generalization. This study introduces a predictive framework that identifies high-risk individuals and informs targeted prevention strategies, offering potential as a clinical decision-support tool.
Yang, Z, Li, X, Lin, Q, Zhou, F, Liang, K, Li, JJ, Niu, Y, Meng, Q, Zhao, T, Li, H, Wang, D, Lin, J, Li, H, Wang, B, Liu, W, Du, Y, Lin, J & Xing, D 2025, 'Cortical Actin Depolymerisation in 3D Cell Culture Enhances Extracellular Vesicle Secretion and Therapeutic Effects', Journal of Extracellular Vesicles, vol. 14, no. 6.
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ABSTRACTThree‐dimensional (3D) culture systems have been shown to enhance cellular secretion of small extracellular vesicles (sEVs) compared to two‐dimensional (2D) culture. However, the molecular mechanisms driving sEV secretion and influencing their potential for disease treatment have not been elucidated. In this study, we discovered the depolymerisation of cortical actin as a new mechanism that leads to increased sEV release, and that in 3D cultured mesenchymal stem cells (MSCs), this process was modulated by the downregulation of integrin‐α1 (ITGA1) and subsequent inhibition of the RhoA/cofilin signalling pathway. Interestingly, the knockdown of Rab27A and Rab27B significantly reduced sEV secretion by MSCs to 0.5‐ and 0.1‐fold, respectively. However, there was no difference in expression levels of Rab27A/B between MSCs cultured in 2D and 3D environments. In addition, sEVs derived from 3D cultured MSCs demonstrated enhanced therapeutic function both in vitro and in rat models of osteoarthritis (OA) and wound healing. Collectively, this study illustrates a new mechanism for enhanced secretion of sEVs, involving RhoA/cofilin pathway‐dependent cortical actin depolymerisation, which is independent of Rab27A/B. These findings provide novel insights for optimising the yield of stem cell‐derived sEVs, as well as their therapeutic efficacy for treating chronic diseases.
Yang, Z, Wang, J, Shi, K, Cai, X, Yang, J & Wen, S 2025, 'Dynamic event-triggered synchronization control for neutral-type SMJ neural networks with additive delays under synchronized attacks', ISA Transactions, vol. 158, pp. 32-49.
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Yao, L, McAuley, J, Wang, X & Jannach, D 2025, 'Special Issue on Responsible Recommender Systems Part 2', ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 1, pp. 1-3.
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Yazdanjue, N, Yazdanjouei, H, Gharoun, H, Khorshidi, MS, Rakhshaninejad, M, Amiri, B & Gandomi, AH 2025, 'A comprehensive bibliometric analysis on social network anonymization: current approaches and future directions', Knowledge and Information Systems, vol. 67, no. 1, pp. 29-108.
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Abstract In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007–2022 were collected from the Scopus Database and then preprocessed. Following this, the VOSviewer was used to visualize the network of authors’ keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.
Ye, D, Chen, H, Zhou, S, Zhu, T, Zhou, W & Ji, S 2025, 'Model Inversion Attack Against Transfer Learning: Inverting a Model Without Querying It', IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 4, pp. 4472-4487.
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Ye, D, Zhu, T, Gao, K, Zhu, C & Zhou, W 2025, 'Cooperating or Kicking Out: Defending Against Poisoning Attacks in Federated Learning via the Evolution of Cooperation', IEEE Transactions on Dependable and Secure Computing, vol. 22, no. 4, pp. 3398-3414.
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Ye, Y, Ye, J, Xu, Z, Kang, J, Liu, D, Ren, Y, Ngo, HH, Guo, W, Huang, S & Jiang, W 2025, 'Influence of ethanol supplementation on sulfate reduction and methanogenesis in UASB reactors', Journal of Water Process Engineering, vol. 74, pp. 107754-107754.
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Yi, B, Fan, Y, Liu, D & Guadalupe Romero, J 2025, 'Simultaneous Position-and-Stiffness Control of Underactuated Antagonistic Tendon-Driven Continuum Robots', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 7238-7254.
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Yi, J, Liu, T, Mao, J, Wang, Y, Zhang, H, Xie, H, Zhong, H & Chang, X 2025, 'Toward Efficient Power Scene Detection via Topology-Preserved Knowledge Distillation', IEEE Transactions on Industrial Informatics, pp. 1-12.
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Yi, J, Mao, J, Zhang, H, Li, M, Zeng, K, Feng, M, Chang, X & Wang, Y 2025, 'FMSD: Focal Multi-Scale Shape-Feature Distillation Network for Small Fasteners Detection in Electric Power Scene', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 2, pp. 1331-1346.
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Yildirim, K, Keles, T, Dogan, S, Tuncer, T, Tasci, I, Hafeez-Baig, A, Barua, PD & Acharya, UR 2025, 'DMPat-based SOXFE: investigations of the violence detection using EEG signals', Cognitive Neurodynamics, vol. 19, no. 1.
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Abstract Automatic violence detection is one of the most important research areas at the intersection of machine learning and information security. Moreover, we aimed to investigate violence detection in the context of neuroscience. Therefore, we have collected a new electroencephalography (EEG) violence detection dataset and presented a self-organized explainable feature engineering (SOXFE) approach. In the first phase of this research, we collected a new EEG violence dataset. This dataset contains two classes: (i) resting, (ii) violence. To detect violence automatically, we proposed a new SOXFE approach, which contains five main phases: (1) feature extraction with the proposed distance matrix pattern (DMPat), which generates three feature vectors, (2) feature selection with iterative neighborhood component analysis (INCA), and three selected feature vectors were created, (3) explainable results generation using Directed Lobish (DLob) and statistical analysis of the generated DLob string, (4) classification deploying t algorithm-based k-nearest neighbors (tkNN), and (5) information fusion employing mode operator and selecting the best outcome via greedy algorithm. By deploying the proposed model, classification and explainable results were generated. To obtain the classification results, tenfold cross-validation (CV), leave-one-record-out (LORO) CV were utilized, and the presented model attained 100% classification accuracy with tenfold CV and reached 98.49% classification accuracy with LORO CV. Moreover, we demonstrated the cortical connectome map related to violence. These results and findings clearly indicated that the proposed model is a good violence detection model. Moreover, this model contributes to feature engineering, neuroscience and social security.
Yin, F, Cai, G, He, X, Su, Y, Yang, X & Zheng, W 2025, 'Elucidating the strain rate-dependent mechanical properties of unsaturated red clay under controlled thermal and suction environments', Transportation Geotechnics, vol. 53, pp. 101600-101600.
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Yin, Q, Yang, Z, Chong, SW, Li, J, Liu, X, Vigolo, D, Li, JJ, Sheehy, PA & Yong, K 2025, 'Application of microfluidic technologies in veterinary science with a view toward development of animal‐on‐a‐chip models', VIEW, vol. 6, no. 1.
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AbstractThe advancement of veterinary science relies on the adoption of modern technologies, more recently including molecular diagnostics, genomic research, precision medicine approaches, and advanced diagnostic imaging. Recent advancements in microfluidics have brought tremendous attention to human disease modeling, diagnosis, and drug development. Specifically, organ‐on‐a‐chip, a subset of microfluidic technology, is characterized by its ability to mimic the human in vivo microenvironment and improve cost efficiency in drug development. Recent studies have demonstrated huge potential in translating human‐centered microfluidic technologies to veterinary science, which can help to deepen our understanding of animal diseases and disorders and develop targeted treatments for diverse animal species, including companion animals, livestock, and wildlife. Further, the ongoing impact of climate change has heightened the threat of diseases among animal populations as well as the potential impact of zoonotic pathogens. New tools for in‐depth exploration of animal physiologies and diseases are essential to mitigate the risk of species extinction and safeguard animal well‐being. Building upon the achievements in human‐based microfluidic studies, we propose the comprehensive integration of this technology into veterinary research. This review provides an overview of microfluidic technology, its current applications in veterinary science, and discusses future directions and challenges toward the development of animal‐on‐a‐chip systems.
Yin, Q, Zhong, L, Song, Y, Bai, L, Wang, Z, Li, C, Xu, Y & Yang, X 2025, 'A decision support system in precision medicine: contrastive multimodal learning for patient stratification', Annals of Operations Research, vol. 348, no. 1, pp. 579-607.
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Abstract Precision medicine aims to provide personalized healthcare for patients by stratifying them into subgroups based on their health conditions, enabling the development of tailored medical management. Various decision support systems (DSSs) are increasingly developed in this field, where the performance is limited to their capability of handling big amounts of heterogeneous and high-dimensional electronic health records (EHRs). In this paper, we focus on developing a deep learning model for patient stratification that can identify and explain patient subgroups from multimodal EHRs. The primary challenge is to effectively align and unify heterogeneous information from various modalities, which includes both unstructured and structured data. Here, we develop a Contrastive Multimodal learning model for EHR (ConMEHR) based on topic modelling. In ConMEHR, modality-level and topic-level contrastive learning (CL) mechanisms are adopted to obtain a unified representation space and diversify patient subgroups, respectively. The performance of ConMEHR will be evaluated on two real-world EHR datasets and the results show that our model outperforms other baseline methods.
Yin, W, Jia, M, Liu, L, Li, M, Guo, Y, Lei, G & Zhu, JG 2025, 'Advanced power curve modeling for wind turbines: A multivariable approach with SGBRT and grey wolf optimization', Energy Conversion and Management, vol. 332, pp. 119680-119680.
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Yin, Y, Liu, X, Cheng, X, Liu, Y, Zhu, T & Huo, H 2025, 'SpatialIE: Towards adaptive floating waste detection in unpredictable weather', Knowledge-Based Systems, vol. 323, pp. 113621-113621.
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Yin, Z, Zhu, H, Lv, X, Lai, J & Yang, Y 2025, '3-D-Printed Ultracompact Butler Matrix for Wideband Millimeter-Wave Beamforming', IEEE Transactions on Microwave Theory and Techniques, pp. 1-13.
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Yolanda, YD, Kim, S, Sohn, W, Shon, HK, Yang, E & Lee, S 2025, 'Simultaneous nutrient-abundant hydroponic wastewater treatment, direct carbon capture, and bioenergy harvesting using microalgae–microbial fuel cells', Desalination and Water Treatment, vol. 321, pp. 100941-100941.
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Yoo, Y, Arunachalam, M, Elmakki, T, Al-Ghamdi, AS, Bassi, HM, Mohammed, AM, Ryu, S, Yong, S, Shon, HK, Park, H & Han, DS 2025, 'Evaluating the economic and environmental viability of small modular reactor (SMR)-powered desalination technologies against renewable energy systems', Desalination, vol. 602, pp. 118624-118624.
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Yosun, T & Cetindamar, D 2025, 'A Typology of Competitive Strategies for Social Enterprises', Journal of Social Entrepreneurship, vol. 16, no. 1, pp. 183-209.
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This article tackles the limited theorising on social enterprises’ (SEs) decisions on the product or service mix, quality, pricing, and the targeted beneficiaries by proposing a typology of competitive strategies for them. The paper empirically observes how SEs react to the challenges faced by the marketisation of their fields. The context of this study is the supplementary education of the disabled in Turkey, a field where increased state coverage led to the entrance of many profit-focused counterparts. Based on a Grounded Theory methodology and a longitudinal dataset including ten cases, the study developed a unique typology comprising three competitive strategies, i.e., innovator, enforcer, and includer. The findings illustrate various strategic responses to heightened competition from incumbent SEs. However, deviation of these strategic responses from the typology appeared to be detrimental in the long-term. By shedding light on the intricacies of the hybrid nature of SEs and considering changes in their competitive environment over time, this study concludes with a summary of contributions to theory, practice, and policy.
You, Q, Wu, YF, Yang, Y, Chai, Y & Cheng, YJ 2025, 'Near-Field Phase-Scanning Self-Accelerating Airy–Bessel Beam for Nonline-of-Sight Wireless Communication', IEEE Transactions on Microwave Theory and Techniques, pp. 1-13.
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Yu Huat, C, Jahed Armaghani, D, Bin Hashim, H, Fattahi, H, He, X, Asteris, PG & Fakharian, P 2025, 'Development of a Practical Solution to Predict Surface Settlement Induced by Twin Tunnels', Journal of Structural Design and Construction Practice, vol. 30, no. 1.
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Yu, C, Nghiem, LD & Zou, L 2025, 'Catalytic chitosan/MXene/GO nanocomposite membrane for removing dye and heavy metals', Desalination, vol. 594, pp. 118313-118313.
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Yu, D, Guo, G, Wang, D, OuYang, T, Wan, F, Liu, J, Xu, G & Deng, S 2025, 'Dynamic Spatial-Temporal Graph Convolution Network for E-Bike Traffic Flow Forecasting', IEEE Transactions on Vehicular Technology, vol. 74, no. 4, pp. 5453-5466.
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Yu, D, Li, Q, Wang, X & Xu, G 2025, 'A Causal-Based Attribute Selection Strategy for Conversational Recommender Systems', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 5, pp. 2169-2182.
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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, Wang, C, Phuntsho, S, He, T, Naidu, G, Han, DS & Shon, HK 2025, 'Highly selective lithium recovery from seawater desalination brine using Li₂TiO₃ membrane-coated capacitive deionization', Water Research, vol. 285, pp. 124113-124113.
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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, N 2025, 'The Quantum Repeater Network Saturates the Entanglement Distribution Asymptotically', IEEE Transactions on Information Theory, vol. 71, no. 8, pp. 6155-6164.
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Yu, P, Zhang, X, Gong, Y, Zhang, J, Sun, H, Zhang, J, Zhang, X & Yin, Y 2025, 'Enhancing origin–destination flow prediction via bi-directional spatio-temporal inference and interconnected feature evolution', Expert Systems with Applications, vol. 264, pp. 125679-125679.
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Yu, Y, Wang, J, Jiang, H & Lu, H 2025, 'How to manage a multifactor-driven crude oil market more effectively? A revisit based on the multiple criteria perspective', Resources Policy, vol. 100, pp. 105446-105446.
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Yu, Y, Wen, D, Yu, M, Qin, L, Zhang, Y, Zhang, W & Lin, X 2025, 'Querying historical K-cores in large temporal graphs', The VLDB Journal, vol. 34, no. 2.
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Yuan, B, Zhao, H, Huang, Y, Zhang, M, Song, Z, Shen, Y, Cheng, X & Wang, Z 2025, 'Investigation on cold start issues of methanol engines and its improvement from the perspective of droplet evaporation', Fuel, vol. 380, pp. 133249-133249.
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Yuan, D, Zhang, H, Liu, Q, Chang, X & He, Z 2025, 'Transformer-Based RGBT Tracking With Spatio-Temporal Information Fusion', IEEE Sensors Journal, vol. 25, no. 13, pp. 25386-25396.
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Yuan, L & Rizoiu, M-A 2025, 'Generalizing Hate Speech Detection Using Multi-Task Learning: A Case Study of Political Public Figures', Computer Speech & Language, vol. 89, pp. 101690-101690.
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Yuan, P, Xu, S, Yang, T, Zhou, Y, Su, Y & Shao, R 2025, 'Multiple blast behavior of steel wire mesh reinforced geopolymer based high performance concrete (G‐HPC) slab: Experiment and numerical simulation', Structural Concrete, vol. 26, no. 2, pp. 1943-1961.
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AbstractEngineering structures face the potential of encountering repetitive or multiple blast loads stemming from accidental explosions and terrorist attacks. However, current research in this field is still relatively limited, and further investigation is needed to understand the damage mechanisms of structures under multiple explosions. Therefore, this study explores the blast resistance of G‐HPC slabs reinforced with steel wire mesh (SWM) under multiple blast loads. The failure modes of the SWM‐reinforced G‐HPC slab were experimentally studied under two consecutive explosions (with explosive equivalents of 1.6 and 3.2 kg, both at a standoff distance of 0.4 m). The results revealed that, after two consecutive explosions, the slab exhibited bulging with minimal concrete spalling, showcasing overall integrity. Subsequently, a numerical model was established, followed by a comprehensive parameter analysis. The parameter analysis investigated the effects of SWM diameters and grid size, the arrangement of SWM, and the sequence of TNT equivalents on the performance of the slab under three consecutive blast loads. The findings revealed that increasing the SWM diameter or reducing the grid size significantly enhanced the blast resistance of the slab under three consecutive explosive loads. Strategically arranging the SWM in the tensile zone reduced damage and deflection. Furthermore, the sequence of TNT equivalents had a notable impact on the damage and energy absorption of the slab.
Zeng, A, Tang, Q, O’Hagan, E, McCaffery, K, Ijaz, K, Quiroz, JC, Kocaballi, AB, Rezazadegan, D, Trivedi, R, Siette, J, Shaw, T, Makeham, M, Thiagalingam, A, Chow, CK & Laranjo, L 2025, 'Use of digital patient decision-support tools for atrial fibrillation treatments: a systematic review and meta-analysis', BMJ Evidence-Based Medicine, vol. 30, no. 1, pp. 10-21.
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ObjectivesTo assess the effects of digital patient decision-support tools for atrial fibrillation (AF) treatment decisions in adults with AF.Study designSystematic review and meta-analysis.Eligibility criteriaEligible randomised controlled trials (RCTs) evaluated digital patient decision-support tools for AF treatment decisions in adults with AF.Information sourcesWe searched MEDLINE, EMBASE and Scopus from 2005 to 2023.Risk-of-bias (RoB) assessment: We assessed RoB using the Cochrane Risk of Bias Tool 2 for RCTs and cluster RCT and the ROBINS-I tool for quasi-experimental studies.Synthesis of resultsWe used random effects meta-analysis to synthesise decisional conflict and patient knowledge outcomes reported in RCTs. We performed narrative synthesis for all outcomes. The main outcomes of interest were decisional conflict and patient knowledge.Results13 articles, reporting on 11 studies (4 RCTs, 1 cluster RCT and 6 quasi-experimental) met the inclusion criteria. There were 2714 participants across all studies (2372 in RCTs), of which 26% were women and the mean age was 71 years. Socioeconomically disadvantaged groups were poorly represented in the included studies. Seven studies (n=2508) focused on non-valvular AF and the mean CHAD2DS2-VASc across studies was 3.2 and for HAS-BLED 1.9. All tools focused on decisions regarding thromboembolic stroke prevention and most enabled calculation of individualised stroke risk. Tools were heterogeneous in features and functions; four tools were patient decision aids. The readability of content was reported in one study. Meta-analyses showed a redu...
Zeng, G, Tian, G, Zhang, G & Lu, J 2025, 'Rosilc-Rs:A Robust Similar Legal Case Recommender System Enpowered by Large Language Model and Step-Back Prompting', Neurocomputing.
Zeng, G, Zhang, Q, Zhang, G & Lu, J 2025, 'Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 6, pp. 4244-4257.
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Zeng, H, Li, Y, Niu, R, Yang, C & Wen, S 2025, 'Enhancing spatiotemporal prediction through the integration of Mamba state space models and Diffusion Transformers', Knowledge-Based Systems, vol. 316, pp. 113347-113347.
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Zeng, Y, Ding, H & Ji, JC 2025, 'An origami-inspired nonlinear energy sink: design, modeling, and analysis', Applied Mathematics and Mechanics, vol. 46, no. 4, pp. 601-616.
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Abstract Designing, modeling, and analyzing novel nonlinear elastic elements for the nonlinear energy sink (NES) have long been an attractive research topic. Since gravity is difficult to overcome, previous NES research mainly focused on horizontal vibration suppression. This study proposes an origami-inspired NES. A stacked Miura-origami (SMO) structure, consisting of two Miura-ori sheets, is adopted to construct a nonlinear elastic element. By adjusting the initial angle and the connecting crease torsional stiffness, the quasi-zero stiffness (QZS) and load-bearing capacity can be customized to match the corresponding mass, establishing the vertical SMO-NES. The dynamic model of the SMO-NES coupled with a linear oscillator (LO) is derived for vibrations in the vertical direction. The approximate analytical solutions of the dynamic equation are obtained by the harmonic balance method (HBM), and the solutions are verified numerically. The parameter design principle of the SMO-NES is provided. Finally, the vibration reduction performance of the SMO-NES is studied. The results show that the proposed SMO-NES can overcome gravity and achieve quasi-zero nonlinear restoring force. Therefore, the SMO-NES has the ability of wide-frequency vibration reduction, and can effectively suppress vertical vibrations. By adjusting the initial angle and connecting the crease torsional stiffness of the SMO, the SMO-NES can be achieved with different loading weights, effectively suppressing the vibrations with different primary system masses and excitation amplitudes. In conclusion, with the help of popular origami structures, this study proposes a novel NES, and starts the research of combining origami and NES.
Zeng, Y-C, Ding, H, Ji, J-C, Mao, X-Y & Chen, L-Q 2025, 'A tristable nonlinear energy sink with time-varying potential barriers', Communications in Nonlinear Science and Numerical Simulation, vol. 142, pp. 108559-108559.
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Zhan, P, Wang, J, Yu, W, Deng, Z, She, A, Zuo, J, Li, W & Xu, J 2025, 'Insights into the hydration kinetics, microstructure and early strength of Portland cement containing synthetic C-S-H/PCE nanocomposites', Cement and Concrete Composites, vol. 157, pp. 105886-105886.
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Zhand, S, Goss, DM, Cheng, YY & Warkiani, ME 2025, 'Recent Advances in Microfluidics for Nucleic Acid Analysis of Small Extracellular Vesicles in Cancer', Advanced Healthcare Materials, vol. 14, no. 4.
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AbstractSmall extracellular vesicles (sEVs) are membranous vesicles released from cellular structures through plasma membrane budding. These vesicles contain cellular components such as proteins, lipids, mRNAs, microRNAs, long‐noncoding RNA, circular RNA, and double‐stranded DNA, originating from the cells they are shed from. Ranging in size from ≈25 to 300 nm and play critical roles in facilitating cell‐to‐cell communication by transporting signaling molecules. The discovery of sEVs in bodily fluids and their involvement in intercellular communication has revolutionized the fields of diagnosis, prognosis, and treatment, particularly in diseases like cancer. Conventional methods for isolating and analyzing sEVs, particularly their nucleic acid content face challenges including high costs, low purity, time‐consuming processes, limited standardization, and inconsistent yield. The development of microfluidic devices, enables improved precision in sorting, isolating, and molecular‐level separation using small sample volumes, and offers significant potential for the enhanced detection and monitoring of sEVs associated with cancer. These advanced techniques hold great promise for creating next‐generation diagnostic and prognostic tools given their possibility of being cost‐effective, simple to operate, etc. This comprehensive review explores the current state of research on microfluidic devices for the detection of sEV‐derived nucleic acids as biomarkers and their translation into practical point‐of‐care and clinical applications.
Zhang, A, Lu, Z, Roohani, I, Liu, B, Jarvis, KL, Tan, R, Wise, SG, Bilek, MMM, Mirkhalaf, M, Akhavan, B & Zreiqat, H 2025, 'Bioinstructive 3D-Printed Magnesium-Baghdadite Bioceramic Scaffolds for Bone Tissue Engineering', ACS Applied Materials & Interfaces, vol. 17, no. 10, pp. 15220-15236.
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Zhang, B, Lu, J, Liu, A, Yao, X & Zhang, G 2025, 'Tracking Correlations Between Multiple Data Streams Through Evolutionary Regressor Chains', IEEE Transactions on Cybernetics, vol. 55, no. 9, pp. 4078-4088.
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Zhang, B, Van Huynh, N, Thai Hoang, D, Nguyen, DN & Pham, Q-V 2025, 'DDPG-E2E: A Novel Policy Gradient Approach for End-to-End Communication Systems', IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 3, pp. 1738-1751.
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Zhang, C, Ren, J, Zhang, S, Guo, Y, Li, N, Li, W & Qu, F 2025, 'Advanced impact resistance design through 3D-Printed concrete technology: Unleashing the potential of additive manufacturing for protective structures', Journal of Building Engineering, vol. 111, pp. 113533-113533.
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Zhang, C, Wang, W, Tian, Z & Yu, S 2025, 'Information Bottleneck-Based Subgraphs Defending Against Inference Attacks in Federated Graph Learning Systems', IEEE Transactions on Cognitive Communications and Networking, pp. 1-1.
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Zhang, H & Xu, M 2025, 'Genetic-evolutionary Graph Nerual Networks: A Paradigm for Improved Graph Representation Learning', Transactions on Machine Learning Research.
Zhang, H & Zhu, X 2025, 'Uncertainty quantification of seismic risk for an isolated large-scale dome structure', Engineering Structures, vol. 335, pp. 120410-120410.
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Zhang, H, Chen, S, Karimi, M, Li, B, Saydam, S & Hassan, M 2025, 'VIV–galloping coupled piezoelectric wind energy harvester for industrial applications', International Journal of Mechanical Sciences, vol. 302, pp. 110587-110587.
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Zhang, H, Huang, X & Zhang, JA 2025, 'Zak-OTFS With Time-Domain Offset Gradient Descent Equalization', IEEE Transactions on Vehicular Technology, pp. 1-10.
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Zhang, H, Lee, C-K & Li, Z 2025, 'Six Degrees of Freedom Tracking and Wireless Charging for Capsule Endoscopy', IEEE Transactions on Industrial Electronics, vol. 72, no. 4, pp. 3643-3652.
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Zhang, H, Liu, W, Li, Z & Lee, C-K 2025, 'Pulse Frequency Modulation of 3D Wireless Power Transfer for Capsule Endoscopy', IEEE Transactions on Industrial Electronics, vol. 72, no. 1, pp. 308-317.
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Zhang, J, Zeng, X, Gao, F, Zheng, J & Sheng, D 2025, 'Effect of replacement cushion on water-heat distribution in permafrost foundation', Zhongnan Daxue Xuebao Ziran Kexue Ban Journal of Central South University Science and Technology, vol. 56, no. 2, pp. 674-687.
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Road problems in the frozen soil areas of the Qinghai—Tibet Plateau have become increasingly severe due to climate and environmental changes and engineering thermal effects, including frequent hazard occurrences, poor prevention and maintenance measures, etc. Replacement cushion is widely used in the design and construction of frozen road engineering, playing an essential role in enhancing deformation resistance, frost damage and stability. However, the effects of replacement cushions on the water-heat distribution during the service period have not been taken seriously. Based on the permafrost foundation replacement of the Gongyu—Yu Expressway, a numerical test on the water-heat distribution of the foundation was carried out with three types of filling materials, i.e. fine sand, coarse sand and gravel in the paper. The influence mechanism of filling materials on the distribution and evolution of water-heat was studied by using silty clay without replacement as a reference. The results show that replacing the gravel has better temperature control performance and can effectively reduce the maximum melting depth. Replacement cushions change the spatial and temporal distribution of foundation moisture. The unfrozen water content in the replacement layer decreases significantly, and the unfrozen water flows from both sides of the foundation to the centre along the filling-soil interface. The replacement cushion causes the surface ponding to invade into deeper soil. Especially without active drainage measures, the magnitude and oscillation amplitude of the water content in the deep foundation increase significantly. The water permeability and thermal conductivity of filling materials are important factors affecting water-heat distribution and evolution in road foundations. The moisture migration caused by replacement is not conducive to the long-term maintenance of the mechanical properties of the foundation. Therefore, replacement should be carefully adopte...
Zhang, J, Zhang, R, Xu, L, Lu, X, Yu, Y, Xu, M & Zhao, H 2025, 'FasterSal: Robust and Real-Time Single-Stream Architecture for RGB-D Salient Object Detection', IEEE Transactions on Multimedia, vol. 27, pp. 2477-2488.
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Zhang, J, Zhu, S, Liu, X, Wen, S & Mu, C 2025, 'Finite-Time Stabilization of Inertial Memristive Neural Networks via Nonreduced Order Method', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 5, pp. 8025-8035.
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Zhang, J, Zhu, S, Wu, K-N, Shen, M & Wen, S 2025, 'Finite-Time Stabilization of Semi-Markov Reaction-Diffusion Memristive NNs With Unbounded Time-Varying Delays', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 72, no. 4, pp. 1832-1842.
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Zhang, JA, Zhang, H, Wu, K, Huang, X, Yuan, J & Guo, YJ 2025, 'Wireless Communications in Doubly Selective Channels with Domain Adaptivity', IEEE Communications Magazine, vol. 63, no. 5, pp. 102-108.
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Zhang, K, Li, R & Ying, M 2025, 'A Divide-And-Conquer Pebbling Strategy for Oracle Synthesis in Quantum Computing', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, pp. 1-1.
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Zhang, L, Li, X, Zhu, X & Zhang, C 2025, 'Design and Optimization of a Five-Phase Reverse-Salient Fault-Tolerant Permanent Magnet Motor for Electric Vehicles', IEEE Transactions on Industrial Electronics, vol. 72, no. 7, pp. 6762-6774.
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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, vol. 35, no. 6, pp. 5659-5670.
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Zhang, L, Wu, W, Liu, J, Chang, X, Hu, X, Zheng, Y, Wu, Y & Zheng, Q 2025, 'LFSRM: Few-Shot Diagram-Sentence Matching via Local-Feedback Self-Regulating Memory', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-13.
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Zhang, L, Zhong, Y, Zheng, Q, Liu, J, Wang, Q, Wang, J & Chang, X 2025, 'TDGI: Translation-Guided Double-Graph Inference for Document-Level Relation Extraction', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 4, pp. 2647-2659.
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Zhang, M, Gao, W, Zhao, S & Luo, Z 2025, 'Robust optimization of multiscale rainbow metamaterials under additive manufacturing defects', International Journal of Mechanical Sciences, vol. 304, pp. 110687-110687.
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Zhang, M, Zhang, C, Qin, P-Y, Zou, Z-Y, Lei, P, Cai, J-Y, Wang, X-C, Lu, W-Z & Lei, W 2025, 'An Ultra-wideband Linear-to-Circular Polarization Converter with Good Angular Stability Performance for Satellite Communications', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Zhang, R, Cao, Z, Huang, Y, Yang, S, Xu, L & Xu, M 2025, 'Visible-Infrared Person Re-Identification With Real-World Label Noise', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 5, pp. 4857-4869.
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Zhang, R, Li, Y, Gui, Y, Armaghani, DJ & Yari, M 2025, 'Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock Distance Prediction', Rock Mechanics and Rock Engineering, vol. 58, no. 1, pp. 679-695.
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AbstractIn the field of civil and mining engineering, blasting operations are widely and frequently used for rock excavation, However, some undesirable environmental problems induced by blasting operations cannot be ignored. Blast-induced flyrock is one important issue induced by blasting operation, which needs to be well predicted to identify the blasting zone’s safety zone. This study introduces an adaptive weighted multi-kernel learning model (AW-MKL) to provide an accurate prediction of blast-induced flyrock distance in Sungun Copper Mine site. The proposed model uses a combination of multi-kernel learning (MKL) approach and adaptive weighting strategy based on weighted Euclidean distance and modified local outlier factor (MLOF) to maximally improve the predictive ability of kernel ridge regression (KRR). To demonstrate the superiority of the proposed approach, six machine learning models were developed as comparisons, i.e., KRR, RF, GBDT, SVM, M5 Tree, MARS and AdaBoost. The outcomes of the proposed method achieved the highest accuracy in testing phase, with RMSE of 2.05, MAE of 0.98 and VAF of 99.92, which confirmed the strong predictive capability of the proposed AW-MKL in predicting blast-induced flyrock distance.
Zhang, R, 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, S, Zhao, L, Huang, S, Mazomenos, EB & Stoyanov, D 2025, 'Direct Camera-Only Bundle Adjustment for 3-D Textured Colon Surface Reconstruction Based on Pre-Operative Model', IEEE Transactions on Medical Robotics and Bionics, vol. 7, no. 1, pp. 242-253.
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Zhang, SS, Wang, S, Li, W, Cai, H & Chen, X 2025, 'Improving efflorescence resistance of metakaolin-based geopolymer via magnesium incorporation', Construction and Building Materials, vol. 489, pp. 142421-142421.
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Zhang, T, Yu, H, Yang, Z, Chen, Y & Yu, S 2025, 'LaVFL: Efficient Verifiable Federated Learning for Large Language Models', IEEE Transactions on Dependable and Secure Computing, pp. 1-16.
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Zhang, T, Zhang, H, Zhu, H, Lin, J, Huang, X, Du, J & Yang, Y 2025, 'A 50 Gbps Real-Time Wireless Communication System at 252 GHz Using FPGA Baseband Modem', IEEE Transactions on Terahertz Science and Technology, pp. 1-12.
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In this paper, we present a high-speed, real-time wireless communication system demonstration with a 50 Gigabits per second raw data rate at 252 GHz. A field programmable gate array baseband module is developed for two 5 GHz bandwidth channels with digital-to-analog converters and analog-to-digital converters sampling at 4.8 Giga-sample per second, each capable of transmitting and receiving Ethernet traffic in real-time at a 25 Gbps raw data rate with 64 quadrature amplitude modulation. Both transmitter and receiver frontends consist of two frequency-conversion stages at intermediate frequency (5-16 GHz) and THz frequency (235-270 GHz), respectively, with high-selectivity bandpass filters applied in both stages. Details of the filter's design principle and fabrication process are provided in the paper. The wireless communication link is demonstrated over a distance of 0.4 m in the laboratory environment with a coherent local oscillator setup, and an uncoded bit error rate of 1 × 10-3 was acquired. The high-speed and real-time feature makes this system a competent candidate for future wireless applications, including point-to-point communications, backhauls, and inter-satellite communications in the sixth-generation era.
Zhang, W, Chen, J, Wen, S & Huang, T 2025, 'Event-Triggered Random Delayed Impulsive Consensus of Multi-Agent Systems With Time-Varying Delay', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 9, no. 2, pp. 2059-2064.
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Zhang, W, Yang, Z, Wen, D, Li, W, Zhang, W & Lin, X 2025, 'Accelerating Core Decomposition in Billion-Scale Hypergraphs', Proceedings of the ACM on Management of Data, vol. 3, no. 1, pp. 1-27.
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Hypergraphs provide a versatile framework for modeling complex relationships beyond pairwise interactions, finding applications in various domains. k -core decomposition is a fundamental task in hypergraph analysis that decomposes hypergraphs into cohesive substructures. Existing studies capture the cohesion in hypergraphs based on the vertex neighborhood size. However, such decomposition poses unique challenges, including the efficiency of core value updates, redundant computation, and high memory consumption. We observe that the state-of-the-art algorithms do not fully address the above challenges and are unable to scale to large hypergraphs. In this paper, we propose an efficient approach for hypergraph k -core decomposition. Novel concepts and strategies are developed to compute the core value of each vertex and reduce redundant computation of vertices. Experimental results on real-world and synthetic hypergraphs demonstrate that our approach significantly outperforms the state-of-the-art algorithm by 7 times on average while reducing the average memory usage by 36 times. Moreover, while existing algorithms fail on tens of millions hyperedges, our approach efficiently handles billion-scale hypergraphs in only a single thread.
Zhang, W, Zong, Y, Zhang, J, Ai, J, He, H, Li, L, Peng, S, Zhou, H, Wang, D & Wang, Q 2025, 'Mechanistic insights into the viral microorganism inactivation during lime stabilization for wastewater sludges', Journal of Hazardous Materials, vol. 485, pp. 136884-136884.
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Zhang, X, Cao, M, Gong, Y, Wu, X, Dong, X, Guo, Y, Zhao, L & Zhang, C 2025, 'Enhancing urban flow prediction via mutual reinforcement with multi-scale regional information', Neural Networks, vol. 182, pp. 106900-106900.
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Zhang, X, Zhao, H, Tao, Z & Li, W 2025, 'Performance of sustainable cementitious mortar incorporating hybrid waste glass powder and steel slag', Construction and Building Materials, vol. 472, pp. 140928-140928.
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Zhang, X, Zhu, X, Wang, Y & Li, J 2025, 'Structural damage detection based on transmissibility functions with unsupervised domain adaptation', Engineering Structures, vol. 322, pp. 119142-119142.
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Zhang, X, Zhu, X, Yu, Y & Li, J 2025, 'Transfer Learning-Based Structural Damage Identification for Building Structures with Limited Measurement Data', International Journal of Structural Stability and Dynamics, vol. 25, no. 09.
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Structural damage detection is crucial for ensuring the safety of civil building structures in operational environments. Recently, deep learning-based methods have gained increasing attention from engineers and researchers. The performance of conventional deep learning methods for structural damage detection relies on a large number of labeled training datasets. However, it is difficult or/and impossible to obtain sufficient datasets to cover various damage scenarios for in-service structures. A little research has been conducted to identify both the damage severity and location with limited labeled measurement data. A novel transfer learning-based method for structural damage identification with limited measurements has been proposed utilizing frequency response functions (FRFs) as the input. The real structure is regarded as the target domain and its numerical model is as the source domain. The samples for various damage scenarios are generated using the numerical model, and a designed deep convolutional neural network (CNN) is pre-trained. The knowledge of the pre-trained network is transferred to identify the damage location and severity of the real structure using limited measurement data. Numerical and experimental studies have been conducted on a three-story building structure to verify the performance of the proposed method. To understand transfer learning and model interpretability, the t-SNE feature visualization is adopted to show the feature distribution changes during transfer learning. Numerical and experimental results show that the proposed approach outperforms conventional CNN models, and it is effective and accurate in identifying structural damage location and severity in real structures with limited measurement data.
Zhang, Y, Gu, C, Shi, P, Jing, Z, Li, B & Liu, B 2025, 'Bring Your Device Group (BYDG): Efficient and Privacy-Preserving User-Device Authentication Protocol in Multi-Access Edge Computing', IEEE Transactions on Information Forensics and Security, vol. 20, pp. 3346-3361.
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Zhang, Y, Li, R-H, Lin, L, Zhang, Q, Qin, L & Wang, G 2025, 'Integral Densest Subgraph Search on Directed Graphs', Proceedings of the ACM on Management of Data, vol. 3, no. 3, pp. 1-26.
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The densest subgraph (DS) search over a directed graph focuses on finding the subgraph with the highest density among all subgraphs. This problem has raised numerous applications, such as fraud detection and community detection. The state-of-the-art DS algorithms have prohibitively high costs or poor approximation ratios, making them unsuitable for practical applications. To address these dilemmas, in this paper, we propose a novel model called integral densest subgraph (IDS). We show that IDS can serve as a near-DS model that has a tight floor relationship with the density of the DS. To compute IDS, we first propose a novel flow network named (α,β)-dense network, based on which we design an exact network-flow algorithm GetIDS with O(p • log |V| • |E| 1.5 ) time complexity, where p is typically a small constant in real-world graphs. Additionally, we propose several non-trivial pruning techniques to further improve the efficiency. Subsequently, we propose a novel (2 + ε)-approximation algorithm MultiCore with near-linear time complexity, providing a good approximation guarantee with high efficiency. Finally, our extensive experiments on 10 real-world graphs demonstrate the effectiveness of the proposed IDS model, and the high efficiency and scalability of the proposed solutions.
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, vol. 6, no. 8, pp. 1977-1990.
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Zhang, Y, Xu, Y, Lyu, J, Gong, G, Chen, G & Ling, SH 2025, 'DCONet: A Dual-task Collaborative Optimization Network for Infrared Small Target Detection', IEEE Geoscience and Remote Sensing Letters, pp. 1-1.
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Zhang, YX, Leung, KY, Lachemi, M, Yang, E-H, Barros, J & Yu, K 2025, 'Structural application of high-performance fibre reinforced cementitious composites', Engineering Structures, vol. 326, pp. 119465-119465.
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Zhang, Z & Ying, M 2025, 'Quantum Register Machine: Efficient Implementation of Quantum Recursive Programs', Proceedings of the ACM on Programming Languages, vol. 9, no. PLDI, pp. 822-847.
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Quantum recursive programming has been recently introduced for describing sophisticated and complicated quantum algorithms in a compact and elegant way. However, implementation of quantum recursion involves intricate interplay between quantum control flow and recursive procedure calls. In this paper, we aim at resolving this fundamental challenge and develop a series of techniques to efficiently implement quantum recursive programs. Our main contributions include: 1. We propose a notion of quantum register machine, the first quantum architecture (including an instruction set) that provides instruction-level support for quantum control flow and recursive procedure calls at the same time. 2. Based on quantum register machine, we describe the first comprehensive implementation process of quantum recursive programs, including the compilation, the partial evaluation of quantum control flow, and the execution on the quantum register machine. 3. As a bonus, our efficient implementation of quantum recursive programs also offers automatic parallelisation of quantum algorithms. For implementing certain quantum algorithmic subroutine, like the widely used quantum multiplexor, we can even obtain exponential parallel speed-up (over the straightforward implementation) from this automatic parallelisation. This demonstrates that quantum recursive programming can be win-win for both modularity of programs and efficiency of their implementation.
Zhang, Z, Jiang, Y, Wei, X, Chen, M, Dong, H & Yu, S 2025, 'Generative-AI for XR Content Transmission in the Metaverse: Potential Approaches, Challenges, and a Generation-Driven Transmission Framework', IEEE Network, pp. 1-1.
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Zhang, Z, Li, J, Chang, X & Zhang, Y 2025, 'Guest Editorial: Domain Adaptation and Generalization for Biomedical and Health Informatics', IEEE Journal of Biomedical and Health Informatics, vol. 29, no. 8, pp. 5365-5367.
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Zhang, Z, Liu, H, Li, X, Shon, H, Vodnar, DC & Wang, Q 2025, 'Biofouling control of reverse osmosis membrane in municipal wastewater recycling plants using urine as a cleaning agent', Separation and Purification Technology, vol. 355, pp. 129629-129629.
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Zhang, Z, Zhou, Z, Tian, Z & Yu, S 2025, 'CRCGAN: Toward robust feature extraction in finger vein recognition', Pattern Recognition, vol. 158, pp. 111064-111064.
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Zhao, C, Dong, W, Indra Mahlia, TM, Shi, L, Wang, K, Shah, SP & Li, W 2025, 'Enhancing energy storage capability for renewable energy systems through advanced cement-based supercapacitors', Energy and Buildings, vol. 338, pp. 115732-115732.
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Zhao, C, Dong, W, Liu, J, Peng, S & Li, W 2025, 'Toward intelligent buildings and civil infrastructure: A review on multifunctional concrete through nanotechnology', Cement and Concrete Composites, vol. 163, pp. 106165-106165.
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Zhao, C, Dong, W, Wang, K, Tao, Z & Li, W 2025, 'Investigation on effects of LiCl, KCl and polyethylene oxide on electrochemical properties of cement-based capacitors', Construction and Building Materials, vol. 481, pp. 141612-141612.
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Zhao, F, Ji, JC, Cao, S, Zheng, J & Luo, Q 2025, 'A constant quasi-zero stiffness isolator with tension springs to isolate vibrations with ultralow frequency', International Journal of Non-Linear Mechanics, vol. 175, pp. 105129-105129.
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Zhao, H, Gan, Y, Qu, F, Tang, Z, Peng, S, Chen, Y & Li, W 2025, 'Nano- and micro-characterisation on the heterogeneity of ITZs in recycled lump concrete', Cement and Concrete Composites, vol. 161, pp. 106078-106078.
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Zhao, J, Li, D, Zhou, J, Armaghani, DJ & Zhou, A 2025, 'Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models', Deep Underground Science and Engineering, vol. 4, no. 1, pp. 3-17.
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AbstractRock fragmentation is an important indicator for assessing the quality of blasting operations. However, accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties. For this reason, optimized by the Bayesian optimization algorithm (BOA), four hybrid machine learning models, including random forest, adaptive boosting, gradient boosting, and extremely randomized trees, were developed in this study. A total of 102 data sets with seven input parameters (spacing‐to‐burden ratio, hole depth‐to‐burden ratio, burden‐to‐hole diameter ratio, stemming length‐to‐burden ratio, powder factor, in situ block size, and elastic modulus) and one output parameter (rock fragment mean size, X50) were adopted to train and validate the predictive models. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination () were used as the evaluation metrics. The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models. The hybrid model consisting of gradient boosting and BOA (GBoost‐BOA) achieved the best prediction results compared with the other hybrid models, with the highest R2 value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02, respectively. Furthermore, sensitivity analysis was carried out to study the effects of input variables on rock fragmentation. In situ block size (XB), elastic modulus (E), and stemming length‐to‐burden ratio (T/B) were set as the main influencing factors. The proposed hybrid model provided a reliable prediction result and thu...
Zhao, L, Ni, Z, Feng, Y, Li, J, Bu, X & Andrew Zhang, J 2025, 'High-Resolution Uplink Sensing in Millimeter-Wave ISAC Systems', IEEE Transactions on Communications, pp. 1-1.
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Zhao, L, Wang, L, Cao, Y, Yang, Y & Wen, S 2025, 'Learning-Based Fault-Tolerant Control With High-Order Control Barrier Functions', IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 14689-14698.
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Zhao, L, Wen, G, Guo, Z, Zhu, S, Hu, C & Wen, S 2025, 'Probabilistic Model-Based Fault-Tolerant Control for Uncertain Nonlinear Systems', IEEE Transactions on Cybernetics, vol. 55, no. 4, pp. 1838-1847.
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Zhao, Q, Zhu, S, Zhang, Z, Luo, W & Wen, S 2025, 'Multistability of Almost Periodic Solutions for Fuzzy Competitive NNs With Time-Varying Delays', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 7, pp. 11835-11846.
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Zhao, S, Gong, S, Gu, B, Li, L, Lyu, B, Thai Hoang, D & Yi, C 2025, 'Exploiting NOMA Transmissions in Multi-UAV-Assisted Wireless Networks: From Aerial-RIS to Mode-Switching UAVs', IEEE Transactions on Wireless Communications, vol. 24, no. 3, pp. 2530-2544.
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ZHAO, S, ZHAO, L, WEN, S & CHENG, L 2025, 'Secure Synchronization Control of Markovian Jump Neural Networks Under DoS Attacks with Memory-Based Adaptive Event-Triggered Mechanism', Artificial Intelligence Science and Engineering, vol. 1, no. 1, pp. 64-78.
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Zhao, S, Zhao, L, Wen, S & Cheng, L 2025, 'Secure synchronization control of Markovian jump neural networks under DoS attacks with memory-based adaptive event-triggered mechanism', Artificial Intelligence Science and Engineering, pp. 1-1.
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Zhao, S, Zhou, C & Xin, Z 2025, 'Effect of the Oxazine Structure on Antibacterial Activity of Biobased Benzoxazine and Its Application in Polyethylene Modification', ACS Applied Polymer Materials, vol. 7, no. 5, pp. 2879-2889.
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Zhao, W, Hou, Y, Wei, L, Wei, W, Zhang, K, Duan, H & Ni, B-J 2025, 'Chlorination-induced spread of antibiotic resistance genes in drinking water systems', Water Research, vol. 274, pp. 123092-123092.
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Zhao, X, Fan, J, Chang, X, Nie, F, Zhang, Q & Guo, J 2025, 'Scalable Multi-View Regression Clustering for Large-Scale Data', IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 8, pp. 7439-7454.
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Zhao, Y, Liu, B, Zhu, T, Ding, M & Zhou, W 2025, 'ROSIN: Robust Semantic Image Hiding Network', Knowledge-Based Systems, vol. 309, pp. 112885-112885.
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Zhao, Z, Cao, L & Wang, C-D 2025, 'Gray Learning From Non-IID Data With Out-of-Distribution Samples', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 1396-1409.
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Zheng, B, Ji, J, Peng, R, Miao, Z & Zhou, J 2025, 'Region-reaching control for multiple underactuated Euler-Lagrange systems based on energy-shaping framework', Communications in Nonlinear Science and Numerical Simulation, vol. 151, pp. 109020-109020.
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Zheng, C, Hu, C, Yu, J, Jiang, H & Wen, S 2025, 'Fixed-time impulsive control of multi-weighted complex networks with output coupling: an impulse-dependent lyapunov method', Nonlinear Dynamics, vol. 113, no. 17, pp. 23035-23051.
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Zheng, J, Gao, Q, Dong, D, Lü, J & Deng, Y 2025, 'A Quantum Multimodal Neural Network Model for Sentiment Analysis on Quantum Circuits', IEEE Transactions on Artificial Intelligence, vol. 6, no. 5, pp. 1128-1142.
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Zheng, Y, Zhang, Y, Peng, X, Chang, S, Wang, P, Zhu, B, Wang, G, Nghiem, LD, Johir, MAH, Wei, Y, Li, J & Wang, X 2025, 'Chemotactic response during acidogenic fermentation: The key for microorganisms to resist the stress of phthalic acid esters', Chemical Engineering Journal, vol. 520, pp. 166223-166223.
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Zhong, Y, Ying, Z, Liu, Y, Zhang, C, Yan, W & Jiang, Y 2025, 'Enhanced NO2 sensing performance of WO3 nanoparticles prepared with glycine', Sensors and Actuators A: Physical, vol. 391, pp. 116690-116690.
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Zhou, J, Li, D, Chen, G & Wen, S 2025, 'Sliding mode control for finite-time projective synchronisation in distinct Caputo fractional-order delayed neural networks with inconsistent orders', International Journal of Systems Science, vol. 56, no. 5, pp. 1095-1112.
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Zhou, K, Jiang, M, Gabrys, B & Xu, Y 2025, 'Learning Causal Representations Based on a GAE Embedded Autoencoder', IEEE Transactions on Knowledge and Data Engineering, vol. 37, no. 6, pp. 3472-3484.
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Zhou, M & Lu, J 2025, 'Continuous Graph Learning-Based Self-Adaptation for Multi-Stream Concept Drift', IEEE Transactions on Cybernetics, vol. 55, no. 8, pp. 3760-3773.
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Zhou, Q, Che, H, Guo, W, He, X, Leung, M-F & Wen, S 2025, 'Robust Low-Rank Tensor Constrained Orthogonal Symmetric Non-Negative Matrix Factorization for Multi-Layer Networks Community Detection', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-16.
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Zhou, R, Zuo, W, Xie, Y, Wang, Y, Li, C, Tang, Y, Wang, Z & Li, Y 2025, 'Sodium bisulfite boosted exopolysaccharide production by Auxenochlorella protothecoides: Potential mechanisms harnessing H2O2 signaling and carbon reallocation', Bioresource Technology, vol. 420, pp. 132121-132121.
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Zhou, S, Ma, H, Kuang, S & Dong, D 2025, 'Auxiliary Task-Based Deep Reinforcement Learning for Quantum Control', IEEE Transactions on Cybernetics, vol. 55, no. 2, pp. 712-725.
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Zhou, S, Yang, D, Zhang, Z, Zhang, J, Qu, F, Punetha, P, Li, W & Li, N 2025, 'Enhancing autonomous pavement crack detection: Optimizing YOLOv5s algorithm with advanced deep learning techniques', Measurement, vol. 240, pp. 115603-115603.
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Zhou, S, Ye, D, Zhu, T & Zhou, W 2025, 'Defending Against Neural Network Model Inversion Attacks via Data Poisoning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-16.
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Zhou, T, Fang, G, Wang, Z, Qiao, Z, Nie, N, Fu, B, Tseng, P-H, Sun, X & Chen, Y-C 2025, 'Digital Lasing Biochip for Tumor-Derived Exosome Analysis', Analytical Chemistry, vol. 97, no. 10, pp. 5605-5611.
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Zhou, T, Yu, S, Tang, Y, Huang, X, Fu, JJ, Shen, PK & Tian, ZQ 2025, 'Built-in electric field engineering of MoO2-Ni heterostructure by P3p-Mo4d-Tm4f orbitals hybridization for efficient industrial-level hydrogen production', Chemical Engineering Journal, vol. 508, pp. 160921-160921.
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Zhou, X, Li, M, Du, Y, Yang, C & Wen, S 2025, 'Interpretable Multiobjective Feature Selection via Visualization in Froth Flotation Process', IEEE Transactions on Industrial Informatics, vol. 21, no. 3, pp. 2530-2539.
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Zhou, X, Zhang, J, Yu, H, Wu, F, Huang, X & Zhang, J 2025, 'Fine-Grained Visual Tracking via Distribution-Aware Mask Modeling and Temporal Propagation', Knowledge-Based Systems, pp. 114208-114208.
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Zhu, H, Fang, G, Nie, N, Xie, J, Tseng, P-H, Xiong, Z, Jiang, D, Mao, C-J, Zhu, J-J, Chew, SY & Chen, Y-C 2025, 'Breathing Laser-Spectral Mapping of Cavity-Enhanced Redox Reactions with Subcellular Resolution', ACS Nano, vol. 19, no. 11, pp. 10955-10965.
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Zhu, H, Xu, Y, Zhang, T, Du, J & Jay Guo, Y 2025, 'Reconfigurable Generalized Joined Coupler Matrices for Independent Control of Multiple Steerable Beams', IEEE Transactions on Antennas and Propagation, vol. 73, no. 3, pp. 1573-1584.
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Zhu, J, El-Zein, A & Miao, G 2025, 'The effect of diameter and moisture content on biomechanical properties of four native Australian trees', Plant and Soil, vol. 512, no. 1-2, pp. 1117-1136.
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Abstract Background and Aims Roots of plants have been shown to be effective in reinforcing soils against slope failures. Two key mechanical properties in such reinforcement are the root’s tensile strength (TS) and elastic modulus (EM). However, knowledge on the combined effects of root moisture content (RMC) and root diameter on these properties is scarce. The study aims to quantify these relationships for root samples of four native Australian tree (A. costata, B. integrifolia, E. reticulatus, and E. racemosa). Methods A series of tensile tests were conducted and the root diameter at the fracture point and RMC were measured immediately after each test. Data were analysed using both univariate and multivariate analyses. Results Both TS and EM declined with increasing diameter. Power-law expressions were found to describe the relationship between TS and diameter moderately well, but less so the one between TS and RMC. Multivariate analyses yielded a double power-law for TS versus diameter and RMC with a stronger fit than univariate ones. A weaker power-law was found between EM and these 2 variables. Of the four trees tested, A. costata exhibited the highest tensile strength and elastic modulus at a 1 mm diameter, while B. integrifolia yielded the lowest. Conclusion Considering both diameter and RMC as explanatory variables of TS and EM yield better accounts of experimental...
Zhu, J, El-Zein, A, Hubble, TCT & Miao, G 2025, 'Effects of soil saturation and suction on root reinforcement performance: pull-out experiments on six native Australian plants', Acta Geotechnica, vol. 20, no. 5, pp. 2075-2092.
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Abstract Improving shallow slope stability with vegetation requires an understanding of root reinforcement performance, in addition to consideration of local ecological impacts. Existing root reinforcement models have not accounted for the influence of soil water content, due to insufficient experimental evidence and theoretical understanding. In this study, the root reinforcement behaviour of six Australian native plants (A. costata, B. integrifolia, E. reticulatus, P. incisa, C. citrinus and M. thymifolia) are examined through vertical pull-out tests under various levels of volumetric water content (VWC) and suction. Additionally, this study employed two root reinforcement models to illustrate the impact of VWC on comparing the performance of these models with experimental results. The study also employs an innovative approach by making an analogy to soil nails or piles and normalising pull-out force against the peripheral surface area of root-soil bundles, defining this as pull-out stress. The results show that VWC and suction have a strong influence on reinforcement, with a roughly linear inverse relationship observed between VWC and pull-out force of root bundles recorded for all species. The pull-out stress followed a nonlinear inverse relationship with VWC and suction as the pull-out force. Furthermore, discrepancies between established-model predictions and experimental data widen with increasing VWCs. It is also found that inadequate sampling can also lead to substantial errors in estimating the actual water content of the soil. The study demonstrates that VWC and suction significantly impact root reinforcement performance, with pull-out strength decreasing as VWC increases. The study also highlights the importance of accurately recording soil...
Zhu, L, Wu, R, Liu, D, Zhang, C, Wu, L, Zhang, Y & Zhang, S 2025, 'Textual semantics enhancement adversarial hashing for cross-modal retrieval', Knowledge-Based Systems, vol. 317, pp. 113303-113303.
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Zhu, Y, Xu, K, Wan, B, Lei, G & Zhu, J 2025, 'Kolmogorov–Arnold Network for Solving 2-D Magnetostatic Problems', IEEE Transactions on Magnetics, vol. 61, no. 9, pp. 1-5.
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Zhu, Y, Zuo, W, Ji, J & Zhang, Z 2025, 'Bifurcations analysis of a 3D Filippov pest-natural enemy system with stage structure for the prey', Applied Mathematics and Computation, vol. 497, pp. 129356-129356.
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Zhuang, Y, Lin, Z, Zhai, R, Huang, Y, Nie, B & Li, Y 2025, 'A study on the effect of spark plug micro-hole hydrogen injection on the spray and combustion processes of a gasoline engine with intake port water injection', Energy, vol. 315, pp. 134366-134366.
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Zhuo, L, Goay, ACY, Sangkarat, P, Xu, F, He, Y, Gao, Z, Mishra, D, He, S, Zhang, Y & Zhang, J 2025, 'Enhanced Triboelectric Outputs from PAN/MoS2 Nanofiber‐Based Nanogenerators for Powering Backscatter Communications in Sustainable 6G Networks', Advanced Energy and Sustainability Research, vol. 6, no. 3.
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This work explores the development of a triboelectric nanogenerator (TENG) based on polyacrylonitrile (PAN) and molybdenum disulfide (MoS2) nanosheets composite fibers for enhancing tribo‐positive electricity to power backscatter communication systems, contributing to the sustainable internet of things (IoT) nodes in future 6 G networks. By incorporating different concentrations of MoS2 (1, 2, 3, and 4 wt%) nanosheets into PAN nanofibers via electrospinning, the nanocomposite fiber‐based TENGs exhibit improved triboelectric properties. The TENG based on PAN/4% MoS2 nanocomposite fiber mat achieve a peak open‐circuit voltage of 296 V and a short‐circuit current of 6.16 μA, which represents an ≈95% and 77% enhancement, respectively, in comparison with the TENGs based on neat PAN nanofiber mat. The enhanced charge transfer ability at the PAN and MoS2 nanosheet interface, the increased dielectric properties, the rougher surface morphology of the composite nanofibers contribute to the enhancements in triboelectric performance. These TENGs are integrated with the backscatter communication system to power a wireless identification and sensing platform (WISP) tag, demonstrating extended transmission range and improved real‐time data acquisition. These findings suggest that TENGs can play a significant role in sustainable energy solutions for 6 G‐enabled IoT applications.
Zou, W, Zhang, M, Zhang, X, Zhang, D, Li, C, Zhong, L, Guo, W & Ngo, HH 2025, 'Biochar-based iron-doped alginate microspheres combined with Fenton-like reaction for removing oxytetracycline hydrochloride: Performance, mechanism, and degradation pathway', Journal of Water Process Engineering, vol. 69, pp. 106723-106723.
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Zou, 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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Zou, Y, Li, S, Li, Y, Zhang, D, Zheng, M & Shi, B 2025, 'Glioblastoma Cell Derived Exosomes as a Potent Vaccine Platform Targeting Primary Brain Cancers and Brain Metastases', ACS Nano, vol. 19, no. 18, pp. 17309-17322.
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Zuo, X, Wang, M, Zhu, T, Zhang, L, Yu, S & Zhou, W 2025, 'Federated Learning With Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach', IEEE Transactions on Services Computing, vol. 18, no. 3, pp. 1428-1444.
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