Tae-hyeon Yun¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2707-2731, 2025, DOI:10.32604/cmes.2025.072357 - 26 November 2025

Abstract The dynamic, heterogeneous nature of Edge computing in the Internet of Things (Edge-IoT) and Industrial IoT (IIoT) networks brings unique and evolving cybersecurity challenges. This study maps cyber threats in Edge-IoT/IIoT environments to the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) framework by MITRE and introduces a lightweight, data-driven scoring model that enables rapid identification and prioritization of attacks. Inspired by the Factor Analysis of Information Risk model, our proposed scoring model integrates four key metrics: Common Vulnerability Scoring System (CVSS)-based severity scoring, Cyber Kill Chain–based difficulty estimation, Deep Neural Networks-driven detection scoring, and frequency… More >

Si-Kai Wang¹, Ti-Cai Wang¹, Di-Fei Yi², Jia Rui³, Peng-Fei Cao⁴, Hua-Ping Wang^1,5,*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1411-1432, 2025, DOI:10.32604/sdhm.2025.070034 - 17 November 2025

Abstract As a key storage facility, the structural safety of large oil tanks is directly related to the stable operation of the energy system. The static pressure caused by the change of liquid level is one of the main loads in the service process of storage tanks, which determines the structural deformation and damage risk. To explore the structural deformation properties under the change of liquid levels and provide a theoretical basis for the prevention and control of damage risk, this paper systematically analyzes the mechanical response of storage tanks under the pressures induced by different… More > Graphic Abstract

Abdulbari Bener^1,2,*, Ünsal Veli Üstündağ³, Emir Barışık⁴, Cem Cahit Barışık⁵

Canadian Journal of Urology, Vol.32, No.5, pp. 401-409, 2025, DOI:10.32604/cju.2025.067184 - 30 October 2025

Abstract Objectives: The evidence remains insufficient and controversial for evaluating modifiable parameters—such as vitamin D, calcium, ferritin, and uric acid—as preclinical biomarkers to contribute to the prevention and early diagnosis of prostate cancer, a disease with a prevalence of up to 10%–20% in men over 50 and strongly associated with environmental factors including diet (high in fat and red meat), obesity, physical inactivity, and carcinogen exposure. This study aims to investigate the potential biomarker role of vitamin D, calcium, ferritin, and uric acids in reducing the risk of prostate cancer (PCa). Methods: The case-control design was… More >

Hui Qi¹, Jingyi Lian², Congjun Rao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 995-1028, 2025, DOI:10.32604/cmes.2025.069722 - 30 October 2025

Abstract For the past few years, the prevalence of cardiovascular disease has been showing a year-on-year increase, with a death rate of 2/5. Coronary heart disease (CHD) rates have increased 41% since 1990, which is the number one disease endangering human health in the world today. The risk indicators of CHD are complicated, so selecting effective methods to screen the risk characteristics can make the risk prediction more efficient. In this paper, we present a comprehensive analysis of CHD risk indicators from both data and algorithmic levels, propose a method for CHD risk indicator identification based… More >

Muhammad Farman^1,2,3,*, Noreen Asghar⁴, Muhammad Umer Saleem⁴, Kottakkaran Sooppy Nisar^5,6, Kamyar Hosseini^1,2,7, Mohamed Hafez^8,9

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 721-751, 2025, DOI:10.32604/cmes.2025.069541 - 30 October 2025

Abstract Smoking is harmful to the lungs and has numerous effects on our bodies. This leads to decreased lung function, which increases the lungs’ susceptibility to asthma triggers. In this paper, we develop a new fractional-order model and investigate the impact of smoking on the progression of asthma by using the Caputo operator to analyze different factors. Using the Banach contraction principle, the existence and uniqueness of solutions are established, and the positivity and boundedness of the model are proved. The model further incorporates different stages of smoking to account for incubation periods and other latent… More >

Mingyu Cai^1,2, Xingchun Li^1,2, Kunfeng Zhang^1,2,*, Shugang Yang^1,2, Shuangxing Liu^1,2, Ming Xue^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-2, 2025, DOI:10.32604/icces.2025.010490

Abstract Deep saline aquifers account for more than 90% of the global theoretical geological CO₂ sequestration capacity, making them the dominant choice for large-scale CO₂ storage. These aquifers offer vast storage potential, especially in comparison to oil and gas reservoirs, which are often considered for CO₂ geological sequestration. Despite their significant storage capacity, deep saline aquifers face several challenges that hinder their practical application. In particular, the lack of adequate geological infrastructure and exploration conditions for deep saline aquifers presents major obstacles to the effective monitoring of CO₂ migration and predicting leakage risks. These challenges are compounded by… More >

Jun Li¹, Kangmin Yu¹, Zhiyong Chen¹, Dan Xing², Binshan Zha¹, Wentao Xie¹, Huan Ouyang¹, Changjun Yu^3,*

Oncology Research, Vol.33, No.11, pp. 3469-3492, 2025, DOI:10.32604/or.2025.066193 - 22 October 2025

Abstract Objectives: Colorectal cancer (CRC) remains a major contributor to global cancer mortality, ranking second worldwide for cancer-related deaths in 2022, and is characterized by marked heterogeneity in prognosis and therapeutic response. We sought to construct a machine-learning prognostic model based on a complement-related risk signature (CRRS) and to situate this signature within the CRC immune microenvironment. Methods: Transcriptomic profiles with matched clinical annotations from TCGA and GEO CRC cohorts were analyzed. Prognostic CRRS genes were screened using Cox proportional hazards modeling alongside machine-learning procedures. A random survival forest (RSF) predictor was trained and externally validated.… More >

Chih-Yu Liu^1,2, Cheng-Yu Ku^1,2,*, Ming-Han Tsai¹, Jia-Yi You³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2969-2990, 2025, DOI:10.32604/cmes.2025.070663 - 30 September 2025

Abstract Amid growing typhoon risks driven by climate change with projected shifts in precipitation intensity and temperature patterns, Taiwan faces increasing challenges in flood risk. In response, this study proposes a geographic information system (GIS)-based artificial intelligence (AI) model to assess flood susceptibility in Keelung City, integrating geospatial and hydrometeorological data collected during Typhoon Krathon (2024). The model employs the random forest (RF) algorithm, using seven environmental variables excluding average elevation, slope, topographic wetness index (TWI), frequency of cumulative rainfall threshold exceedance, normalized difference vegetation index (NDVI), flow accumulation, and drainage density, with the number of… More >

Mingyang Tang^1,2,#, Shengfu He^3,#, Bao Meng^1,2, Qingyue Zhang^1,2, Chengcheng Li^1,2, Yating Sun^1,2, Weijie Sun^1,2, Cui Wang⁴, Qingxiang Kong⁵, Yanyan Liu^1,2, Lifen Hu^1,2, Yufeng Gao^1,2, Qinxiu Xie^1,2, Jiabin Li^1,2,*, Ting Wu^1,2,*

Oncology Research, Vol.33, No.10, pp. 3077-3100, 2025, DOI:10.32604/or.2025.067065 - 26 September 2025

Abstract Background: Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths. The Nod-like receptor (NLR) family is involved in innate immunity and tumor progression, but its role in HCC remains unclear. This study aimed to evaluate the prognostic value and biological function of NLR genes in HCC. Methods: Transcriptomic and clinical data from The Cancer Genome Atlas were analyzed using nonnegative matrix factorization (NMF) to classify HCC into molecular subtypes. Differentially expressed genes were used to build an NLR-based prognostic model (NLR_score) through univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox… More > Graphic Abstract

Divya Arivalagan, Vignesh Ochathevan^*, Rubankumar Dhanasekaran

Congenital Heart Disease, Vol.20, No.4, pp. 477-501, 2025, DOI:10.32604/chd.2025.070372 - 18 September 2025

Abstract Background: The accurate identification of cardiac abnormalities is essential for proper diagnosis and effective treatment of cardiovascular diseases. Method: This work introduces an advanced methodology for detecting cardiac abnormalities and estimating electrocardiographic age (ECG Age) using sophisticated signal processing and deep learning techniques. This study looks at six main heart conditions found in 12-lead electrocardiogram (ECG) data. It addresses important issues like class imbalances, missing lead scenarios, and model generalizations. A modified residual neural network (ResNet) architecture was developed to enhance the detection of cardiac abnormalities. Results: The proposed ResNet demonst rated superior performance when compared with… More > Graphic Abstract