Wei-Chiang Hong^1,*, Yi Liang²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.080415 - 30 March 2026

Abstract This article has no abstract. More >

Ilsun You^1,*, Gaurav Choudhary², Gökhan Kul³, Francesco Falmieri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.080244 - 30 March 2026

Abstract This article has no abstract. More >

Chih-Yu Liu^1,2, Cheng-Yu Ku^1,2,*, Ting-Yuan Wu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079302 - 30 March 2026

Abstract Accurate soil classification is essential for pavement design; however, the traditional American Association of State Highway and Transportation Officials (AASHTO) classification system relies on extensive laboratory testing and subjective judgment. This study presents an artificial intelligence (AI) enhanced framework for AASHTO soil classification. A synthetic dataset of 349,015 samples was generated using parameter ranges for five AASHTO input variables to support model development. Four machine learning models were trained, analyzed, and compared where the random forest (RF) consistently achieved the highest accuracy of 100% among the four models in predicting AASHTO soil groups. Feature importance More >

Sojin Shin¹, Guk Heon Kim², Seung Hwan Kim³, Jaemin Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079127 - 30 March 2026

Abstract This study develops a surrogate super-resolution (SR) framework that accelerates finite element method (FEM)-based computational fluid dynamics (CFD) using deep learning. High-resolution (HR) FEM-based CFD remains computationally prohibitive for time-sensitive applications, including patient-specific aneurysm hemodynamics where rapid turnaround is valuable. The proposed pipeline learns to reconstruct HR velocity-magnitude fields from low-resolution (LR) FEM solutions generated under the same governing equations and boundary conditions. It consists of three modules: (i) offline pre-training of a residual network on representative vascular geometries; (ii) lightweight fine-tuning to adapt the pretrained model to geometric variability, including patient-specific aneurysm morphologies; and… More >

Saira Batool^*, Muhammad Imran^*, Brett McKinney^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079067 - 30 March 2026

Abstract Bacterial growth requires strategic allocation of limited intracellular resources, especially under cold stress, where stabilized messenger ribonucleic acid (mRNA) secondary structures slow translation by impairing ribosome binding. Escherichia coli (E. coli) counters this bottleneck by inducing the cold-shock protein A (CspA), an RNA chaperone that remodels inhibitory structures. However, synthesizing CspA diverts biosynthetic capacity from ribosome production and metabolism, creating a fundamental resource-allocation trade-off. In this work, we develop a dynamical model capturing the interplay between metabolic precursors, ribosomes, and CspA, and use it to examine how growth and allocation patterns shift with temperature. Steady-state analysis shows… More >

Chengang Dong^1,2, Yongkang Ding², Jianwei Hu^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.079062 - 30 March 2026

Abstract Real-time multi-person pose estimation (MPE) built upon neural network architectures aims to simultaneously detect multiple human instances and regress joint coordinates in dynamic scenes. However, due to factors such as high model complexity and limited expression of keypoint information, both the efficiency and accuracy of real-time MPE remain to be improved. To mitigate the adverse impacts caused by the aforementioned issues, this work develops FSEM-Pose, a real-time MPE model rooted in the YOLOv10 framework. In detail, first, FSEM-Pose upgrades the backbone module of the baseline network by introducing the Feature Shuffling-Convolution (FS-Conv), which effectively reduces More >

Mojgan Mohammadi¹, Naser Khaji^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078945 - 30 March 2026

Abstract The failure of liquid storage tanks, one of the most critical infrastructure systems widely used, during severe earthquakes can have direct or indirect impacts on public safety. The significance of their safe performance even after destructive earthquakes and their potential for operational use underscores the necessity of appropriate seismic design. Hence, seismic isolation, specifically base isolation, has gained attention as a seismic control method to reduce damage to these infrastructures by increasing their vibration period. One prevalent type of seismic isolator used for tanks and other structures is the friction pendulum system (FPS) isolator. However,… More >

Jaeho Hwang¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078940 - 30 March 2026

Abstract Illicit web ecosystems, encompassing phishing, illegal online gambling, scam platforms, and malicious advertising, have rapidly expanded in scale and complexity, creating severe social, financial, and cybersecurity risks. Traditional rule-based and blacklist-driven detection approaches struggle to cope with polymorphic, multilingual, and adversarially manipulated threats, resulting in increasing demand for Artificial Intelligence (AI)-based solutions. This review provides a comprehensive synthesis of research on AI-driven threat detection for illicit web environments. It surveys detection models across multiple modalities, including text-based analysis of Uniform Resource Locator (URL) and HyperText Markup Language (HTML), vision-based recognition of webpage layouts and logos,… More >

Yuxin Tian¹, Wangxin Yu¹, Wanqing Yuan¹, Qingquan Liu^1,*, Xiaoliang Wang^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078776 - 30 March 2026

Abstract Granular flow, such as hopper discharge and debris flows, involves complex multi-scale, multi-phase, and multi-physics coupling, posing significant challenges for numerical simulation. Over the past two decades, methods like the Discrete Element Method (DEM), Smoothed Particle Hydrodynamics (SPH), and Depth-Averaging Method (DAM), have been developed to address these problems. However, their applicability across different scales remains unclear due to differences in physical assumptions and numerical algorithms. Therefore, a comprehensive evaluation is critically needed. This study selects three typical methods (DEM, SPH, and DAM) to examine their convergence behavior, boundary condition implementation, and limitations in physical More >

Muhammad Adnan Tariq¹, Sunawar Khan², Tehseen Mazhar^2,3, Tariq Shahzad⁴, Sahar Arooj⁵, Khmaies Ouahada⁶, Muhammad Adnan Khan^7,*, Habib Hamam^8,9,10,11

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078672 - 30 March 2026

Abstract The contemporary smart cities, smart homes, smart buildings, and smart health care systems are the results of the explosive growth of Internet of Things (IoT) devices and deep learning. Yet the centralized training paradigms have fundamental issues in data privacy, regulatory compliance, and ownership silo alongside the scaled limitations of the real-life application. The concept of Federated Deep Learning (FDL) is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings. It is an overview of the privacy-preserving… More >