Monalisa Jena¹, Noman Khan^2,*, Mi Young Lee^3,*, Seungmin Rho³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075119 - 29 January 2026

Abstract Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises. When such risks go undetected, consequences can escalate to self-harm, long-term disability, reduced productivity, and significant societal and economic burden. Despite recent advances, detecting risk from online text remains challenging due to heterogeneous language, evolving semantics, and the sequential emergence of new datasets. Effective solutions must encode clinically meaningful cues, reason about causal relations, and adapt to new domains without forgetting prior knowledge. To address these challenges, this paper presents a Continual Neuro-Symbolic Graph… More >

Boda Zhang^1,*, Fuhua Luo¹, Yunhao Yu¹, Chameiling Di¹, Ruibin Wen¹, Fei Chen²

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.069323 - 27 January 2026

Abstract The increasing intelligence of power systems is transforming distribution networks into Cyber-Physical Distribution Systems (CPDS). While enabling advanced functionalities, the tight interdependence between cyber and physical layers introduces significant security challenges and amplifies operational risks. To address these critical issues, this paper proposes a comprehensive risk assessment framework that explicitly incorporates the physical dependence of information systems. A Bayesian attack graph is employed to quantitatively evaluate the likelihood of successful cyber attacks. By analyzing the critical scenario of fault current path misjudgment, we define novel system-level and node-level risk coupling indices to precisely measure the… More >

Vaskar Chakma^1,*, Md Jaheid Hasan Nerab¹, Abdur Rouf¹, Abu Sayed², Hossem Md Saim³, Md. Nournabi Khan³

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 1-35, 2026, DOI:10.32604/jimh.2026.074347 - 23 January 2026

Abstract Smoking continues to be a major preventable cause of death worldwide, affecting millions through damage to the heart, metabolism, liver, and kidneys. However, current medical screening methods often miss the early warning signs of smoking-related health problems, leading to late-stage diagnoses when treatment options become limited. This study presents a systematic comparative evaluation of machine learning approaches for smoking-related health risk assessment, emphasizing clinical interpretability and practical deployment over algorithmic innovation. We analyzed health screening data from 55,691 individuals, examining various health indicators including body measurements, blood tests, and demographic information. We tested three advanced… More >

Cheng-Hsun Chuang^1,2,3,#, Ping-Kun Tsai^3,4,5,6,#, Shih-Wen Kao^7,8, Yu-Hsun Wang^8,9,*, Chao-Bin Yeh^1,2,3,*

Oncology Research, Vol.34, No.1, 2026, DOI:10.32604/or.2025.072875 - 30 December 2025

Abstract Background: To determine whether initiating a glucagon-like peptide-1 receptor agonist (GLP-1 RA) within 3 months of type 2 diabetes (T2DM) diagnosis alters the subsequent risk of overall and site-specific cancer and whether this association differs by baseline body-mass index (BMI). Methods: This retrospective cohort study used electronic health records from the TriNetX U.S. research network. Adults aged 20 years or older diagnosed with T2DM between 2016 and 2024 were included if they received any hypoglycemic agents within 3 months before and after diagnosis. Following 1:1 propensity score matching, both the GLP-1 RA user and non-user… More > Graphic Abstract

Heping Qi, Wenyao Sun^*, Yi Zhao, Xiaoyi Qian, Xingyu Jiang

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069603 - 27 December 2025

Abstract Virtual power plant (VPP) integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions, promote the consumption of renewable energy, and improve economic efficiency. In this paper, aiming at the uncertainty of distributed wind power and photovoltaic output, considering the coupling relationship between power, carbon trading, and green card market, the optimal operation model and bidding scheme of VPP in spot market, carbon trading market, and green card market are established. On this basis, through the Shapley value and independent risk contribution theory in cooperative game theory, the quantitative… More > Graphic Abstract

Kyung-Hyun Suh^1,*, Sung-Jin Chung¹, Goo-Churl Jeong¹, Kunho Lee¹, Ji-Hyun Ryu²

International Journal of Mental Health Promotion, Vol.27, No.12, pp. 1829-1845, 2025, DOI:10.32604/ijmhp.2025.071879 - 31 December 2025

Abstract Background: Adolescents increasingly engage with social media for connection, self-expression, and identity exploration. This growing digital engagement has raised concerns about its potential risks and mental health implications. Methods: This narrative review examines literature on adolescent social media use by exploring underlying motivations, risk and protective factors across personal, environmental, and digital domains, with a focus on mental health outcomes. Results: Individual vulnerabilities—such as low self-esteem, impulsivity, and poor sleep—interact with contextual factors like peer pressure and family conflict to elevate risks. Digital environments shaped by algorithmic feeds, feedback mechanisms, and curated content promote social comparison and More >

Hongrong Wang^1,2, Hang Geng^1,*, Jing Yuan¹, Wen Zhang², Hanmin Sheng¹, Qiuhua Wang³, Xinjian Li^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3701-3721, 2025, DOI:10.32604/cmes.2025.073513 - 23 December 2025

Abstract Machine learning has emerged as a key approach in wildfire risk prediction research. However, in practical applications, the scarcity of data for specific regions often hinders model performance, with models trained on region-specific data struggling to generalize due to differences in data distributions. While traditional methods based on expert knowledge tend to generalize better across regions, they are limited in leveraging multi-source data effectively, resulting in suboptimal predictive accuracy. This paper addresses this challenge by exploring how accumulated domain expertise in wildfire prediction can reduce model reliance on large volumes of high-quality data. An active More >

Edward Danso Ansong^*, Simon Bonsu Osei^*, Raphael Adjetey Adjei

Journal of Cyber Security, Vol.7, pp. 533-564, 2025, DOI:10.32604/jcs.2025.061821 - 11 December 2025

Abstract The surge in identity fraud, driven by the rapid adoption of mobile money, internet banking, and e-services during the COVID-19 pandemic, underscores the need for robust cybersecurity solutions. Zero-Knowledge Proofs (ZKPs) enable secure identity verification by allowing individuals to prove possession of a National ID card without revealing sensitive information. This study implements a ZKP-based identity verification system using Camenisch-Lysyanskaya (CL) signatures, reducing reliance on complex trusted setup ceremonies. While a trusted issuer is still required, as assumed in this work, our approach eliminates the need for broader system-wide trusted parameters. We evaluate the system’s More >

Chih-Yin Chien^1,2, Tsae-Jyy Wang¹, Pei-Hung Liao¹, Ying-Hsiang Lee^3,4,5,*, Wei-Sho Ho^6,7,*

Congenital Heart Disease, Vol.20, No.5, pp. 601-612, 2025, DOI:10.32604/chd.2025.072431 - 30 November 2025

Abstract Background: Cardiac implantable electronic devices (CIEDs) are essential for preventing sudden cardiac death in patients with cardiovascular diseases, but implantation procedures carry risks of complications such as infection, hematoma, and bleeding, with incidence rates of 3–4%. Previous studies have examined individual risk factors separately, but integrated predictive models are lacking. We compared the predictive performance and interpretability of artificial neural network (ANN) and logistic regression models to evaluate their respective strengths in clinical risk assessment. Methods: This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device (CIED) implantation in Taiwan between 2017… More >

Zhe He¹, Yaning Zhang^2,*

International Journal of Mental Health Promotion, Vol.27, No.11, pp. 1747-1768, 2025, DOI:10.32604/ijmhp.2025.070491 - 28 November 2025

Abstract Background: Depression represents a significant global mental health burden, particularly among middle-aged and older Chinese with chronic diseases in high-altitude regions, where harsh environmental conditions and limited social support exacerbate mental health disparities. This paper aims to develop an interpretable machine learning prediction framework to identify the key factors of depression in this vulnerable population, thereby proposing targeted intervention measures. Methods: Utilizing data from the China Health and Retirement Longitudinal Study in 2020, this paper screened out and analyzed 2431 samples. Subsequently, Recursive Feature Elimination and Least Absolute Shrinkage and Selection Operator were applied to screen… More >