Xue Zhang¹, Jie Chen^2,*, Zhihui Zhang³, Dewei Zhang³, Yuejiao Ming³, Xinde Zhang³

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069487 - 27 December 2025

Abstract The integration of wind power and natural gas for hydrogen production forms a Green and Blue Hydrogen Integrated Energy System (GBH-IES), which is a promising cogeneration approach characterized by multi-energy complementarity, flexible dispatch, and efficient utilization. This system can meet the demands for electricity, heat, and hydrogen while demonstrating significant performance in energy supply, energy conversion, economy, and environment (4E). To evaluate the GBH-IES system effectively, a comprehensive performance evaluation index system was constructed from the 4E dimensions. The fuzzy DEMATEL method was used to quantify the causal relationships between indicators, establishing a scientific input-output… More > Graphic Abstract

Shaojie Min¹, Yaxiao Luo¹, Kebing Liu¹, Qingyuan Gong², Yang Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.073175 - 09 December 2025

Abstract User identity linkage (UIL) across online social networks seeks to match accounts belonging to the same real-world individual. This cross-platform mapping enables accurate user modeling but also raises serious privacy risks. Over the past decade, the research community has developed a wide range of UIL methods, from structural embeddings to multimodal fusion architectures. However, corresponding adversarial and defensive approaches remain fragmented and comparatively understudied. In this survey, we provide a unified overview of both mapping and anti-mapping methods for UIL. We categorize representative mapping models by learning paradigm and data modality, and systematically compare them… More >

Abdullah Alourani¹, Mehtab Alam^2,*, Ashraf Ali³, Ihtiram Raza Khan⁴, Chandra Kanta Samal²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-32, 2026, DOI:10.32604/cmc.2025.070161 - 10 November 2025

Abstract The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management. Earlier approaches have often advanced one dimension—such as Internet of Things (IoT)-based data acquisition, Artificial Intelligence (AI)-driven analytics, or digital twin visualization—without fully integrating these strands into a single operational loop. As a result, many existing solutions encounter bottlenecks in responsiveness, interoperability, and scalability, while also leaving concerns about data privacy unresolved. This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing, distributed intelligence, and simulation-based decision support. The… More >

Ameer Hamza, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-41, 2026, DOI:10.32604/cmc.2025.069721 - 10 November 2025

Abstract This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities, focusing on recent trends from 2022 to 2025. The primary objective is to evaluate methodological advancements, model performance, dataset usage, and existing challenges in developing clinically robust AI systems. We included peer-reviewed journal articles and high-impact conference papers published between 2022 and 2025, written in English, that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification. Excluded were non-open-access publications, books, and non-English articles. A structured search was… More >

Ravita Chahar, Ashutosh Kumar Dubey^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-65, 2026, DOI:10.32604/cmc.2025.066990 - 10 November 2025

Abstract Objective: The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods. Conditions such as anxiety, depression, stress, bipolar disorder (BD), and autism spectrum disorder (ASD) frequently arise from the complex interplay of demographic, biological, and socioeconomic factors, resulting in aggravated symptoms. This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions. Methods: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025. The potential… More >

Yang Liu¹, Biao Chen¹, Xin Liu², Chenxi Luo², Shihui Xiao^2,*

Frontiers in Heat and Mass Transfer, Vol.23, No.6, pp. 2127-2146, 2025, DOI:10.32604/fhmt.2025.070973 - 31 December 2025

Abstract This study evaluated corn kernel drying performance and quality changes using hot air drying (HAD) and infrared drying (ID) across temperatures ranging from 55°C to 80°C. Optimal drying parameters were determined by using the entropy weight method, with drying time, specific energy consumption, damage rate, fatty acids, starch, polyphenols, and flavonoids as indicators. Results demonstrated that ID significantly outperformed HAD, achieving drying times up to 20% shorter and reducing specific energy consumption and kernel damage by up to 79.3% and 66.7%, respectively, while also better preserving quality attributes. Both methods exhibited drying profiles characterized by More >

Marina Ladonkina¹, Viktoriia Podryga^1,*, Yury Poveshchenko¹, Haochen Zhang²

Frontiers in Heat and Mass Transfer, Vol.23, No.6, pp. 1789-1809, 2025, DOI:10.32604/fhmt.2025.066953 - 31 December 2025

Abstract The work presents new methods for selecting adaptive artificial viscosity (AAV) in iterative algorithms of completely conservative difference schemes (CCDS) used to solve gas dynamics equations in Euler variables. These methods allow to effectively suppress oscillations, including in velocity profiles, as well as computational instabilities in modeling gas-dynamic processes described by hyperbolic equations. The methods can be applied both in explicit and implicit (method of separate sweeps) iterative processes in numerical modeling of gas dynamics in the presence of heat and mass transfer, as well as in solving problems of magnetohydrodynamics and computational astrophysics. In… More >

Ling Li^1,#, Ninghui Xu^1,#, Wenjing Wang^2,*, Jianfen Ying^1,*

Journal of Psychology in Africa, Vol.35, No.6, pp. 833-842, 2025, DOI:10.32604/jpa.2025.071557 - 30 December 2025

Abstract This study explores the role of teachers’ professional identity (TPI) on employee learning engagement (LE), with mediation by basic needs satisfaction (BNS). Participants were 255 Chinese pre-service teachers (191 females = 74.9%, 16 freshmen = 6.2%, 135 sophomores = 52.9%, 35 juniors = 12.5%, 72 seniors = 28.2%). They completed surveys on the “QuestionStar” online survey platform and 12 of the teachers completed interviews for sharing their personal insights. The results of Structural Equation Modeling (SEM) indicated that teachers’ professional identity significantly predicted both learning engagement and basic needs satisfaction, with basic needs satisfaction partially More >

Jiling Song¹, Hua Wang^2,*, Jianbing Guo¹, Minghua Lin², Bin Zheng^2,*, Jiqiang Wu^3,*

Journal of Polymer Materials, Vol.42, No.4, pp. 1143-1157, 2025, DOI:10.32604/jpm.2025.073414 - 26 December 2025

Abstract The pursuit of safer energy storage systems is driving the development of advanced electrolytes for lithium-ion batteries. Traditional liquid electrolytes pose flammability risks, while solid-state alternatives often suffer from low ionic conductivity. Gel polymer electrolytes (GPEs) emerge as a promising compromise, combining the safety of solids with the ionic conductivity of liquids. Cellulose, an abundant and eco-friendly polymer, presents an ideal base material for sustainable GPEs due to its biocompatibility and mechanical strength. This study systematically investigates how drying methods affect cellulose-based GPEs. Cellulose hydrogels were synthesized through dissolution-crosslinking and processed using vacuum drying (VD),… More >

Guo-Feng Fan^1,2, Jia-Jing Qian¹, Li-Ling Peng¹, Xin-Hang Jia¹, Ling-Han Zuo¹, Jia-Can Yan¹, Jiang-Yan Chen¹, Anantkumar J. Umbarkar³, Wei-Chiang Hong^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3813-3837, 2025, DOI:10.32604/cmes.2025.073615 - 23 December 2025

Abstract Accurate prediction of perovskite photovoltaic materials’ optoelectronic properties is crucial for developing efficient and stable materials, advancing solar technology. To address poor interpretability, high computational complexity, and inaccurate predictions in relevant machine learning models, this paper proposes a novel methodology. The technical route of this paper mainly centers on the random forest-knowledge distillation-bidirectional gated recurrent unit with attention technology (namely RF-KD-BIGRUA), which is applied in perovskite photovoltaic materials. Primarily, it combines random forest to quantitatively assess feature importance, selecting variables with significant impacts on photoelectric conversion efficiency. Subsequently, statistical techniques analyze the weight distribution of More >