Mubashir Ali^1,2, Jingzhen Li¹, Zedong Nie^1,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073048

Abstract Diabetes imposes a substantial burden on global healthcare systems. Worldwide, nearly half of individuals with diabetes remain undiagnosed, while conventional diagnostic techniques are often invasive, painful, and expensive. In this study, we propose a noninvasive approach for diabetes detection using photoplethysmography (PPG), which is widely integrated into modern wearable devices. First, we derived velocity plethysmography (VPG) and acceleration plethysmography (APG) signals from PPG to construct multi-channel waveform representations. Second, we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal, statistical, and nonlinear features with recursive feature elimination and deep feature extraction using… More >

Changyu Jeon, Younghoon Kim^*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073030

Abstract Accurate State-of-Health (SOH) prediction is critical for the safe and efficient operation of lithium-ion batteries (LiBs). However, conventional methods struggle with the highly nonlinear electrochemical dynamics and declining accuracy over long-horizon forecasting. To address these limitations, this study proposes CIPF-Informer, a novel digital twin framework that integrates the Informer architecture with Channel Independence (CI) and a Particle Filter (PF). The CI mechanism enhances robustness by decoupling multivariate state dependencies, while the PF captures the complex stochastic variations missed by purely deterministic models. The proposed framework was evaluated using the Massachusetts Institute of Technology (MIT) battery More >

Khulud Salem Alshudukhi^1,*, Sijjad Ali², Mamoona Humayun^3,*, Omar Alruwaili⁴

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073705

Abstract Problem: The integration of Artificial Intelligence (AI) into cybersecurity, while enhancing threat detection, is hampered by the “black box” nature of complex models, eroding trust, accountability, and regulatory compliance. Explainable AI (XAI) aims to resolve this opacity but introduces a critical new vulnerability: the adversarial exploitation of model explanations themselves. Gap: Current research lacks a comprehensive synthesis of this dual role of XAI in cybersecurity—as both a tool for transparency and a potential attack vector. There is a pressing need to systematically analyze the trade-offs between interpretability and security, evaluate defense mechanisms, and outline a… More >

Yongwei Li¹, Kaituo Jiao^2,*, Dongxu Han³, Bo Yu², Xiaoze Du¹

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073239

Abstract The permeability contrast between the Hot Dry Rock (HDR) reservoir and the surrounding formations is a key factor governing fluid loss in Enhanced Geothermal Systems (EGS). This study thus aims to investigate its impact on system performance under varying operating conditions, and a three-dimensional thermo–hydro–mechanical (THM) coupled EGS model is developed based on the geological parameters of the GR1 well in the Qiabuqia region. The coupled processes of fluid flow, heat transfer, and geomechanics within the reservoir under varying reservoir–surrounding rock permeability contrasts, as well as the flow and heat exchange along the wellbores from… More >

Michał Guminiak¹, Marcin Kamiński^2,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.071887

Abstract The analysis of the dynamics of surface girders is of great importance in the design of engineering structures such as steel welded bridge plane girders or concrete plate-column structures. This work is an extension of the classical deterministic problem of free vibrations of thin (Kirchhoff) plates. The main aim of this work is the study of stochastic eigenvibrations of thin (Kirchhoff) elastic plates resting on internal continuous and column supports by the Boundary Element Method (BEM). This work is a continuation of previous research related to the random approach in plate analysis using the BEM.… More >

Quang-Thai Nguyen^1,2,#, Duong Ngoc Hai^3,4,#, The-Duc Nguyen^1,3,4,*, Van-Tu Nguyen^2,*, Jinyul Hwang², Warn-Gyu Park²

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.070570

Abstract This study presents a numerical analysis of the effects of a rigid flat wall with oscillating motion on the pressure wave propagation during a single spherical cavitation bubble collapse at different initial bubble positions. Different nondimensional distances S = 0.8, 0.9, 1.0, 1.1, 1.2 and 1.3 were considered to investigate the effects of initial in-phase and out-of-phase oscillations of the flat wall. Numerical simulations of cavitation bubble collapse near an oscillating wall were conducted using a compressible two-phase flow model. This model incorporated the Volume of Fluid (VOF) interface-sharpening technique on a general curvilinear moving… More > Graphic Abstract

Manuel J. C. S. Reis^1,*, Nishu Gupta²

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073366

Abstract The forthcoming sixth generation (6G) of mobile communication networks is envisioned to be AI-native, supporting intelligent services and pervasive computing at unprecedented scale. Among the key paradigms enabling this vision, Federated Learning (FL) has gained prominence as a distributed machine learning framework that allows multiple devices to collaboratively train models without sharing raw data, thereby preserving privacy and reducing the need for centralized storage. This capability is particularly attractive for vision-based applications, where image and video data are both sensitive and bandwidth-intensive. However, the integration of FL with 6G networks presents unique challenges, including communication… More >

Yi-Ting Peng^1,*, Chin-Laung Lei²

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073258

Abstract The rapid advancement of Large Language Models (LLMs) has enabled their application in diverse professional domains, including law. However, research on automatic judicial document generation remains limited, particularly for Taiwanese courts. This study proposes a keyword-guided training framework that enhances LLMs’ ability to generate structured and semantically coherent judicial decisions in Chinese. The proposed method first employs LLMs to extract representative legal keywords from absolute court judgments. Then it integrates these keywords into Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback using Proximal Policy Optimization (RLHF-PPO). Experimental evaluations using models such as Chinese Alpaca More >

Rytis Maskeliūnas¹, Robertas Damaševičius^1,*, Audrius Kulikajevas¹, Kipras Pribuišis², Nora Ulozaitė-Stanienė², Virgilijus Uloza²

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072790

Abstract This study introduces a novel voice cloning framework driven by Mordukhovich Subdifferential Optimization (MSO) to address the complex multi-objective challenges of pathological speech synthesis in under-resourced Lithuanian language with unique phonemes not present in most pre-trained models. Unlike existing voice synthesis models that often optimize for a single objective or are restricted to major languages, our approach explicitly balances four competing criteria: speech naturalness, speaker similarity, computational efficiency, and adaptability to pathological voice patterns. We evaluate four model configurations combining Lithuanian and English encoders, synthesizers, and vocoders. The hybrid model (English encoder, Lithuanian synthesizer, English More >

Rıdvan Yayla, Hakan Üçgün^*, Onur Ali Korkmaz

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072703

Abstract Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems. Artificial intelligence enables real-time sensing, decision-making, and control on embedded platforms with improved efficiency. This study presents the design and implementation of an autonomous radio-controlled (RC) vehicle prototype capable of lane line detection, obstacle avoidance, and navigation through dynamic path planning. The system integrates image processing and ultrasonic sensing, utilizing Raspberry Pi for vision-based tasks and Arduino Nano for real-time control. Lane line detection is achieved through conventional image processing techniques, providing the basis for local path generation, while traffic sign classification employs a… More > Graphic Abstract