Eun Hae Lee^1,2,3, Minjae Choi^4,5, Hanul Park^3,6, Joon Hee Han^3,6,7, Sujeong Yu^3,8, Joshua Kirabo Sempungu^1,2,3,6, Inbae Sohn^4,6, Yo Han Lee^3,6,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.073735 - 28 January 2026

Abstract Background: That Central and Eastern Europe and Central Asia (CEECA) experienced a major mortality crisis in the 1990s is a well-established finding, with most analyses focusing on singular causes like alcohol-related deaths. However, the utility of the integrated “deaths of despair” framework, which views alcohol, drug, and suicide deaths as a unified socio-economic phenomenon, remains under-explored in this context. Crucially, the long-term evolution of the composition of despair within the region remains a largely unexplored area of inquiry. Therefore, this study aims to analyze the long-term trends, changing composition, and regional heterogeneity of deaths from despair… 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 >

Abdullrahman A. Al-Shamma’a¹, Hassan M. Hussein Farh^1,*, Ridwan Taiwo², Al-Wesabi Ibrahim³, Abdulrhman Alshaabani¹, Saad Mekhilef ⁴, Mohamed A. Mohamed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1303-1347, 2025, DOI:10.32604/cmes.2025.071302 - 26 November 2025

Abstract Optimal sizing and allocation of distributed generators (DGs) have become essential computational challenges in improving the performance, efficiency, and reliability of electrical distribution networks. Despite extensive research, existing approaches often face algorithmic limitations such as slow convergence, premature stagnation in local minima, or suboptimal accuracy in determining optimal DG placement and capacity. This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement. It integrates both quantitative and qualitative analyses of the scholarly landscape, mapping influential research domains, co-authorship structures, the More >

Dawa Chyophel Lepcha^1,*, Bhawna Goyal^2,3, Ayush Dogra⁴, Ahmed Alkhayyat⁵, Prabhat Kumar Sahu⁶, Aaliya Ali⁷, Vinay Kukreja⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1487-1573, 2025, DOI:10.32604/cmes.2025.070964 - 26 November 2025

Abstract Medical image analysis has become a cornerstone of modern healthcare, driven by the exponential growth of data from imaging modalities such as MRI, CT, PET, ultrasound, and X-ray. Traditional machine learning methods have made early contributions; however, recent advancements in deep learning (DL) have revolutionized the field, offering state-of-the-art performance in image classification, segmentation, detection, fusion, registration, and enhancement. This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks, highlighting both foundational models and recent innovations. The article begins by introducing conventional techniques and their limitations, setting the… More >

Robyn Lesch¹, Evan David Visser¹, Ntalane Sello Seroka^1,2,*, Lindiwe Khotseng^1,*

Journal of Renewable Materials, Vol.13, No.10, pp. 1935-1977, 2025, DOI:10.32604/jrm.2025.02025-0026 - 22 October 2025

Abstract The review investigates the use of biomass-derived carbon as precursors for nanomaterials, acknowledging their sustainability and eco-friendliness. It examines various types of biomasses, such as agricultural residues and food byproducts, focussing on their transformation via environmentally friendly methods such as pyrolysis and hydrothermal carbonisation. Innovations in creating porous carbon nanostructures and heteroatom surface functionalisation are identified, enhancing catalytic performance. The study also explores the integration of biomass-derived carbon with nanomaterials for energy storage, catalysis, and other applications, noting the economic and environmental benefits. Despite these advantages, challenges persist in optimising synthesis methods and scaling production. More > Graphic Abstract

Yujie Wu¹, Xing Deng^1,*, Haijian Shao¹, Ke Cheng¹, Ming Zhang¹, Yingtao Jiang², Fei Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2709-2778, 2025, DOI:10.32604/cmes.2025.066647 - 30 September 2025

Abstract The application of generative artificial intelligence (AI) is bringing about notable changes in anime creation. This paper surveys recent advancements and applications of diffusion and language models in anime generation, focusing on their demonstrated potential to enhance production efficiency through automation and personalization. Despite these benefits, it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models. We conduct an in-depth survey of cutting-edge generative AI technologies, encompassing models such as Stable Diffusion and GPT, and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics. Review of the surveyed literature… More >

Qingsong Jiao, Xueyun Hu^*

Phyton-International Journal of Experimental Botany, Vol.94, No.9, pp. 2615-2630, 2025, DOI:10.32604/phyton.2025.069246 - 30 September 2025

Abstract Chlorophyll fluorescence, particularly the parameter F_v/F_m, has emerged as a reliable, non-invasive indicator of the maximum quantum efficiency of Photosystem II (PSII) in plants. Over the past decade, significant research has leveraged F_v/F_m to evaluate plant responses to a wide range of biotic and abiotic stresses, as well as to support crop improvement and ecological monitoring. This review synthesizes recent progress in understanding the physiological basis, measurement techniques, and applied significance of F_v/F_m across diverse plant systems. We highlight methodological advancements in fluorescence imaging and remote sensing, identify consistent patterns and contrasting findings in stress-response studies, and More >

Oshan Mudannayake^1,#, Amila Indika^2,#, Upul Jayasinghe², Gyu Myoung Lee^3,*, Janaka Alawatugoda⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2527-2578, 2025, DOI:10.32604/cmc.2025.068875 - 23 September 2025

Abstract The rapid adoption of machine learning in sensitive domains, such as healthcare, finance, and government services, has heightened the need for robust, privacy-preserving techniques. Traditional machine learning approaches lack built-in privacy mechanisms, exposing sensitive data to risks, which motivates the development of Privacy-Preserving Machine Learning (PPML) methods. Despite significant advances in PPML, a comprehensive and focused exploration of Secure Multi-Party Computing (SMPC) within this context remains underdeveloped. This review aims to bridge this knowledge gap by systematically analyzing the role of SMPC in PPML, offering a structured overview of current techniques, challenges, and future directions. More >

Jae Sung Son¹, Soo-Jin Kim^2,*

Congenital Heart Disease, Vol.20, No.4, pp. 421-440, 2025, DOI:10.32604/chd.2025.070250 - 18 September 2025

Abstract Background: Advancements in diagnostic tools, surgical techniques, and long-term management have significantly improved survival among individuals with congenital heart disease (CHD), leading to an evolving epidemiologic profile characterized by increasing procedural complexity and a growing adult CHD population. This study aimed to examine nationwide trends in CHD surgeries over a 17-year period, with a focus on temporal shifts in surgical volume, procedural complexity, and age-specific incidence. Methods: A total of 41,608 CHD surgeries and 85,417 surgical procedures performed between 2002 and 2018 were identified from a nationwide health insurance database. Temporal trends were evaluated using segmented… More >

Cecilia Castro¹, Victor Leiva^2,*, Franco Basso^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1481-1543, 2025, DOI:10.32604/cmes.2025.067992 - 31 August 2025

Abstract Metaverse technologies are increasingly promoted as game-changers in transport planning, connected-autonomous mobility, and immersive traveler services. However, the field lacks a systematic review of what has been achieved, where critical technical gaps remain, and where future deployments should be integrated. Using a transparent protocol-driven screening process, we reviewed 1589 records and retained 101 peer-reviewed journal and conference articles (2021–2025) that explicitly frame their contributions within a transport-oriented metaverse. Our review reveals a predominantly exploratory evidence base. Among the 101 studies reviewed, 17 (16.8%) apply fuzzy multi-criteria decision-making, 36 (35.6%) feature digital-twin visualizations or simulation-based testbeds,… More > Graphic Abstract