Zhengliang Zhang¹, Shijian Wu², Xinna Tang³, Haowen Wu^3,*

International Journal of Mental Health Promotion, Vol.28, No.4, 2026, DOI:10.32604/ijmhp.2025.071999 - 28 April 2026

Abstract Objectives: Amid accelerating urbanization, digitalization, and population aging, mental health issues have become increasingly salient among urban community residents. This study aims to examine how community environmental governance influences mental health (MH) by conceptualizing the community environment as comprising social capital (SC) and environmental perception (EP). Aging anxiety (AA) and digital usage tendency (DUT) are introduced as psychosocial background variables to analyze MH pathways under multifactor influences. Methods: Using data from the 2021 Chinese General Social Survey (CGSS), this study constructed a structural equation model (SEM) based on 362 urban residents from the Yangtze River Delta.… More >

Huan Song^1,2, Yuan Zhao³, Chenglin He^2,4,*

Journal of Psychology in Africa, Vol.36, No.2, pp. 249-255, 2026, DOI:10.32604/jpa.2026.069347 - 29 April 2026

Abstract This study aimed to explore the mediating roles of body dissatisfaction and self-esteem on the relationship between body mass index (BMI) and among female college students. Using a cross-sectional study design, 669 Chinese female college students completed the Body Mass Index, the Body Image States Scale, the Self-Esteem Scale, and the Social Avoidance and Distress Scale. The results showed that body dissatisfaction partially mediated the relationship between BMI and social avoidance. Additionally, body dissatisfaction and self-esteem together formed a serial mediation pathway between BMI and social avoidance. In other words, BMI shows a direct association More >

Qiaobo Wei^1,2, Hui Zhou^1,3,*

Journal of Psychology in Africa, Vol.36, No.2, pp. 277-284, 2026, DOI:10.32604/jpa.2026.068776 - 29 April 2026

Abstract We investigated the relationship between social anxiety on adolescent students’ internet fiction reading and mediation by self-esteem. A total of 774 adolescent students (female = 48.9%; mean age 13.39 ± 1.46) completed surveys on internet fiction addiction, social anxiety and self-esteem. Mediation analysis results indicated a significant school grade placement effect in internet fiction reading to be lower . The self-esteem of adolescent students plays a mediating role between social anxiety and internet fiction reading for higher internet fiction reading with higher self-esteem. This mediating effect accounts for about two-thirds of the total effect. This More >

Higinio Ramos^1,2,*, M Chandru^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.083154 - 27 April 2026

Abstract This article has no abstract. More >

Nishu Gupta^1,*, Manuel J. C. S. Reis²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.082568 - 27 April 2026

Abstract This article has no abstract. More >

Mamoona Jabbar, Uzma Jamil^*, Muhammad Younas, Bushra Zafar

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080241 - 27 April 2026

Abstract Magnetic Resonance Imaging is one of the most commonly used neuro-oncology imaging modalities, which is a non-invasive mode of imaging and helps in detecting brain abnormalities in an effective way. Earlier researchers have demonstrated that brain tumor segmentation and classification can be effectively performed using deep learning techniques. Existing studies are primarily aimed at increasing prediction accuracy and provide insignificant consideration to model interpretability, limiting their practical application in clinical practice. To address this limitation, this research presents a two-stage explainable deep learning model, which combines transformer-based segmentation with an ensemble classification model that is… More >

Yousef Lafi A. Alshammari^1,2, Muhammad Khan^1,*, Hilal Doganay Kati^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079258 - 27 April 2026

Abstract Cracks can severely degrade the integrity and service performance of plate structures. Although most existing studies focus on identifying straight crack patterns using dynamic response data, curved crack paths have received far less attention, despite being more realistic in practice and having a stronger influence on structural behaviour. This study presents a computational and experimental framework for analyzing and identifying curved crack paths in cantilever plate structures based on dynamic response characteristics. Curved crack paths are modelled using second-order polynomial equations. Finite Element Analysis (FEA) is employed to evaluate the effects of polynomial coefficients and… More >

Ghada Atteia¹, Abdulaziz Altamimi², Nihal Abuzinadah³, Khaled Alnowaiser⁴, Muhammad Umer^5,*, Yunyoung Nam⁶, Yongwon Cho^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078510 - 27 April 2026

Abstract Cardiovascular diseases (CVD) remain the leading cause of global mortality, making early and accurate diagnosis essential for improving patient outcomes. However, most existing deep learning approaches address cardiac image segmentation or disease classification independently, limiting their effectiveness in complex clinical decision-making scenarios. In this study, we propose an explainable spatio-temporal deep learning framework that integrates segmentation-guided representation learning with efficient temporal modeling for automated CVD detection. The proposed architecture incorporates the Segment Anything Model for Medical Imaging in 2D (SAM-Med2D) to achieve accurate cardiac structure segmentation, followed by Mamba-based temporal feature extraction and Transformer-driven spatial… More >

Chun-I Fan^1,2, Sheng-Feng Lu¹, Cheng-Han Shie¹, Ming-Feng Tsai¹, Tomohiro Morikawa^3,*, Takeshi Takahashi⁴, Tao Ban⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077799 - 27 April 2026

Abstract Due to its partly open-source architecture, which allows for application analysis and repackaging, along with its large market share, the Android operating system is a main target for malware. In recent years, researchers have widely adopted neural network-based methods for detecting Android malware, achieving impressive results but without interpretability. Interpretability is crucial for showing how models behave and identifying biases in their predictions, which helps in validating and improving them. Additionally, in urgent malware analysis situations, interpretability lets analysts quickly assess harmful behaviors and aids in future malware development and investigation. Therefore, interpretability is vital… More >

Ovidijus Grigas^*, Rytis Maskeliūnas

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077324 - 27 April 2026

Abstract Early detection of Mild Cognitive Impairment (MCI) with FDG-PET is essential for timely Alzheimer’s disease intervention. However, PET image quality is limited by low spatial resolution, partial volume effects, and Poisson noise. Standard enhancement methods, such as Bilateral filtering or Contrast Limited Adaptive Histogram Equalization (CLAHE), can increase contrast but often introduce heavy noise or distort image texture, while deep learning methods may produce hallucinated structures. We propose a fully data-adaptive, non-learned 3D enhancement framework whose output is deterministic for a given input volume, that combines Laplacian-based local contrast modulation with a gradient-gated Difference-of-Gaussians (DoG)… More >