Abubakar Elsafi^1,*, Arafat Abdulgader Mohammed Elhag², Lubna A. Gabralla³, Ali Ahmed⁴, Ashraf Osman Ibrahim⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 621-647, 2025, DOI:10.32604/cmes.2025.072726 - 30 October 2025

Abstract Energy efficiency stands as an essential factor when implementing deep reinforcement learning (DRL) policies for robotic control systems. Standard algorithms, including Deep Deterministic Policy Gradient (DDPG), primarily optimize task rewards but at the cost of excessively high energy consumption, making them impractical for real-world robotic systems. To address this limitation, we propose Physics-Informed DDPG (PI-DDPG), which integrates physics-based energy penalties to develop energy-efficient yet high-performing control policies. The proposed method introduces adaptive physics-informed constraints through a dynamic weighting factor (), enabling policies that balance reward maximization with energy savings. Our motivation is to overcome the… More >

Mohamed Hammad^1,2,*, Mohammed ElAffendi¹, Souham Meshoul^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1055-1086, 2025, DOI:10.32604/cmes.2025.072650 - 30 October 2025

Abstract Skin cancer is among the most common malignancies worldwide, but its mortality burden is largely driven by aggressive subtypes such as melanoma, with outcomes varying across regions and healthcare settings. These variations emphasize the importance of reliable diagnostic technologies that support clinicians in detecting skin malignancies with higher accuracy. Traditional diagnostic methods often rely on subjective visual assessments, which can lead to misdiagnosis. This study addresses these challenges by developing HybridFusionNet, a novel model that integrates Convolutional Neural Networks (CNN) with 1D feature extraction techniques to enhance diagnostic accuracy. Utilizing two extensive datasets, BCN20000 and… More >

Hanyu Tao^1,2, Dongye Sun^1,2, Tao Fang^1,2, Wenhu Zhao^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 521-536, 2025, DOI:10.32604/cmes.2025.072089 - 30 October 2025

Abstract Structural internal flaws often weaken the performance and integral stability, while traditional nondestructive testing or inversion methods face challenges of high cost and low efficiency in quantitative flaw identification. To quickly identify internal flaws within structures, a deep learning model for flaw detection is proposed based on the image quadtree scaled boundary finite element method (SBFEM) combined with a deep neural network (DNN). The training dataset is generated from the numerical simulations using the balanced quadtree algorithm and SBFEM, where the structural domain is discretized based on recursive decomposition principles and mesh refinement is automatically… More >

Mehmet Numan Kaya^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 327-343, 2025, DOI:10.32604/cmes.2025.072000 - 30 October 2025

Abstract This study investigates the influence of mesh resolution and turbulence model selection on the accuracy of numerical simulations for transonic flow, with particular emphasis on shock-boundary layer interaction phenomena. Accurate prediction of such flows is notoriously difficult due to the sensitivity to near-wall resolution, global mesh density, and turbulence model assumptions, and this problem motivates the present work. Two solvers were employed, rhoCentralFoam (unsteady) and TSLAeroFoam (steady-state), both are compressible and density-based and implemented within the OpenFOAM framework. The investigation focuses on three different non-dimensional wall distance (y⁺) values of 1, 2.5 and 5, each implemented… More >

Abrar M. Alajlan^1,*, Abdul Razaque²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 919-946, 2025, DOI:10.32604/cmes.2025.071996 - 30 October 2025

Abstract Biometric authentication provides a reliable, user-specific approach for identity verification, significantly enhancing access control and security against unauthorized intrusions in cybersecurity. Unimodal biometric systems that rely on either face or voice recognition encounter several challenges, including inconsistent data quality, environmental noise, and susceptibility to spoofing attacks. To address these limitations, this research introduces a robust multi-modal biometric recognition framework, namely Quantum-Enhanced Biometric Fusion Network. The proposed model strengthens security and boosts recognition accuracy through the fusion of facial and voice features. Furthermore, the model employs advanced pre-processing techniques to generate high-quality facial images and voice… More >

Rui Xu¹, Chaogan Gao¹, Jiale Shi², Guorui Yu¹, Quhao Li^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1-16, 2025, DOI:10.32604/cmes.2025.071942 - 30 October 2025

Abstract To address the design challenges of helicopter hub central components under high-performance requirements, this paper conducts safe-life topology optimization design research considering fatigue performance for rotor hub central components under multi-load conditions, combined with helicopter fatigue strength engineering design theory. For dealing with the issues of derivative calculation difficulties when directly considering fatigue constraints in existing topology optimization methods, this study establishes a mathematical formulation suitable for structural topology optimization of hub central components by combining modified structural safety fatigue limits based on iso-life curves. Then the sensitivity analysis of design variables is derived, and More >

Minghai Zheng¹, Zhaoxia Cui^1,*, Jiang Liu¹, Jianjun Li²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 431-455, 2025, DOI:10.32604/cmes.2025.071913 - 30 October 2025

Abstract To accurately depict the strong nonlinear relationship between the viscosity of propellant slurry and shear rate, premix time, and temperature, and to improve the prediction accuracy, based on the sample preparation and experimental measurement of a certain type of propellant, viscosity data under multiple working conditions were obtained as the basic data for the research. By comparing typical models such as support vector regression and random forest, it was found that although the traditional BP neural network was superior to the both, its accuracy was still insufficient. Based on this, a BP model co-optimized by… More >

Adeel Munawar¹, Muhammad Nadeem Ali², Awais Qasim³, Byung-Seo Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1189-1211, 2025, DOI:10.32604/cmes.2025.071876 - 30 October 2025

Abstract Cyber-physical systems (CPS) represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing, healthcare, and autonomous infrastructure. However, their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats, potentially leading to operational failures and data breaches. Furthermore, CPS faces significant threats related to unauthorized access, improper management, and tampering of the content it generates. In this paper, we propose an intrusion detection system (IDS) optimized for CPS environments using a hybrid approach by combining a nature-inspired feature selection scheme, such as Grey Wolf Optimization (GWO),… More >

Hojun Moon, Donghyun You^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 367-390, 2025, DOI:10.32604/cmes.2025.071648 - 30 October 2025

Abstract A numerical method is presented to simulate bubble–particle interaction phenomena in particle-laden flows. The bubble surface is represented in an Eulerian framework by a volume-of-fluid (VOF) method, while particle motions are predicted in a Lagrangian framework. Different frameworks for describing bubble surfaces and particles make it difficult to predict the exact locations of collisions between bubbles and particles. An effective bubble, defined as having a larger diameter than the actual bubble represented by the VOF method, is introduced to predict the collision locations. Once the collision locations are determined, the attachment of particles to the More >

Hajer Hafaiedh^1,2, Adel Mahjoub³, Yahia Saoudi⁴, Anouar Benamor², Okba Taouali^5,*, Kamel Zidi⁶, Wad Ghaban⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 571-596, 2025, DOI:10.32604/cmes.2025.071642 - 30 October 2025

Abstract Several optimization methods, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are used to select the most suitable Static Synchronous Compensator (STATCOM) technology for the optimal operation of the power system, as well as to determine its optimal location and size to minimize power losses. An IEEE 14 bus system, integrating three wind turbines based on Squirrel Cage Induction Generators (SCIGs), is used to test the applicability of the proposed algorithms. The results demonstrate that these algorithms are capable of selecting the most appropriate technology while optimally sizing and locating the STATCOM to More >