Open Access
ARTICLE
Md Jalal Uddin Rumi, Xiaowei Zeng*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.079021
（This article belongs to the Special Issue: Advances in Fracture Mechanics, Damage Mechanics, and Fatigue Modeling)
Abstract Cohesive zone models (CZMs) are widely used to simulate interfacial fracture, where the post-peak softening branch of the traction–separation law (TSL) can strongly influence both the predicted response and the numerical behavior, particularly when the fracture process zone is not small relative to the structure. In Abaqus, however, cohesive elements are natively restricted to bilinear and linear–exponential TSLs, and implementing other softening behaviors typically requires user subroutines, which requires advanced knowledge and limits rapid model development and testing. This work exploits Abaqus’s tabular damage-evolution capability in a different way by constructing the damage variable analytically… More >
Open Access
ARTICLE
Yuxiao Shi1, Jinglin Zhang2, Yuxia Li2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.078601
Abstract Traffic light detection and fault identification using images from road traffic cameras are important for intelligent traffic management and urban safety monitoring. However, images collected in real traffic environments show clear differences in camera view, lighting conditions, weather, and background complexity. As a result, traffic lights vary greatly in scale, spatial location, and appearance, which reduces detection accuracy in complex scenes. To deal with this problem, this paper presents a multi-scene traffic light detection and fault identification framework based on dual-attention image fusion. Large-scale road camera data from the Chengdu Traffic Management Bureau are used,… More >
Open Access
ARTICLE
Sergio Isai Palomino-Resendiz1,2, César Ulises Solís-Cervantes1,*, Luis Alberto Cantera-Cantera1,3, Jorge de Jesús Morales-Mercado1, Diego Alonso Flores-Hernández4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.077726
（This article belongs to the Special Issue: Computational Modeling, Simulation, and Algorithmic Methods for Dynamical Systems)
Abstract Many linear-in-parameters models arising in identification and control can be expressed as single-layer artificial neural networks (ANNs) with linear activation, enabling online learning via first-order optimization. In practice, however, standard gradient descent often exhibits slow convergence, large intermediate weights, and stagnation when the regressor data are ill-conditioned or computations are performed under finite precision. This paper proposes Gradient Descent with Time-Decaying Regularization (GD-TDR), a training algorithm that augments the quadratic loss with a regularization term whose weight decays exponentially in time. The proposed schedule enforces uniform strong convexity during early iterations, effectively mitigating neural-paralysis-like behavior associated More >
Open Access
ARTICLE
Nai-Wei Lo1, Cheng-I Lin2, Chih-Chieh Chang3,*, Chi-Yang Chang4, Tran Thi Luu Ly1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.077316
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract The growing frequency of malicious attacks on Internet of Things (IoT) devices has rendered conventional approaches with static label-dependent risk assessment models obsolete, especially when coping with unknown and continuously evolving threats. To mitigate these challenges, a novel dynamic trust evaluation framework approach is proposed in this work. The proposed framework utilized unsupervised learning and zero-knowledge proofs to assess device risks in complex environments adaptively, with an accuracy rate of 98.96% for normal clustering and 95.39% for anomalies. K-means clustering algorithm is leveraged to distinguish risk patterns with an additional Decision Tree classification algorithm to More >
Open Access
ARTICLE
Taher Alzahrani1, Saima Rashid2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.076957
Abstract Air pollution poses a serious public health threat in developing countries such as Pakistan, where rapid urbanization and industrialization have intensified atmospheric contamination. Although mobile sensing deployed on public transportation expands spatial coverage beyond fixed monitoring stations, accurate high-resolution pollution mapping remains constrained by sparse observations, computational burden, neglected pollutant interactions, and limited interpretability. To address these challenges, this study proposes a unified physics-informed deep learning framework for fine-grained air pollution map reconstruction and joint multi-pollutant estimation. The framework integrates mobile and stationary monitoring data with atmospheric dispersion principles to enhance physical consistency under limited… More >
Open Access
ARTICLE
Tianzhe Jiao, Yuming Chen, Xiaoyue Feng, Chaopeng Guo, Jie Song*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080595
Abstract Knowledge distillation bridges the performance gap between camera-based and LiDAR-based 3D
detectors by leveraging the precise geometric information from LiDAR. However, cross-modal knowledge transfer
remains challenging due to the inherent modality heterogeneity between LiDAR and camera data, which often leads to
instability during training. In this work, we find that these instabilities are closely related to distribution mismatch in
the cross-modal feature space and noisy teacher signals. To address this issue, we propose a novel distribution-aware
cross-modal distillation framework, named DA-T3D. Specifically, we first explicitly model the LiDAR teacher’s Bird’s-
Eye-View (BEV) feature distribution and… More >
Open Access
ARTICLE
Segundo Esteban1,*, Matilde Santos2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.077663
Abstract Wind turbines are highly efficient energy converters that exploit locally available renewable resources across many regions. In modern floating offshore wind turbines (FOWTs), strong aerodynamic and hydrodynamic loads give rise to nonlinear and tightly coupled dynamics, which typically require dedicated—and computationally demanding—simulation tools for analysis and control design. This work introduces a simplified, control-oriented mathematical model of a FOWT, derived directly from fundamental force and torque balances and explicitly incorporating the gyroscopic effect, which is often neglected in onshore wind turbines due to its comparatively lower significance. Model parameters are identified for the NREL 5-MW… More >
Graphic Abstract
Open Access
ARTICLE
Sobhan Manjili1, Saeid Jafarzadeh Ghoushchi1, Mohammad Reza Maghami2,*, Mazlan Mohamed3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.076217
Abstract Accurate electricity load forecasting is crucial for optimizing power distribution networks, especially in rapidly growing cities like Tabriz (annual consumption growth of 7.2%). This study presents a hybrid AI framework integrating the Temporal Fusion Transformer (TFT) and XGBoost for residual error correction. The model is trained and evaluated using actual consumption data from Tabriz’s distribution network (2021–2023). Compared to a baseline TFT model, the proposed framework demonstrates a 11.2% reduction in RMSE (from 0.1249 to 0.1109) and a 10.7% decrease in MAE (from 0.0998 to 0.0891). Attention mechanism analysis reveals temperature (importance coefficient = 0.32), More >
Open Access
REVIEW
Rebika Rai1,*, Totan Bharasa2, Arunita Das3, Krishna Gopal Dhal3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.079037
Abstract Snake Optimizer (SO) is a popular optimization algorithm developed by Hashim and Hussien, based on the competitive and selective mating nature of snakes. By emulating such natural methods, SO presents an intelligent method to solve complicated optimization problems, making it a valuable tool in various scientific and technological applications. This paper provides an extensive review of the SO, its inception, the development of different variants, and applications. This paper identifies several SO variants, such as improved SO variants using different strategies, hybridized SO variants with other metaheuristics, Binary SO variants to solve discrete optimization problems,… More >
Open Access
ARTICLE
Shih-Lin Lin*, Yi-Hsuan Chen
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.078862
（This article belongs to the Special Issue: Advances in Deep Learning and Computer Vision for Intelligent Systems: Methods, Applications, and Future Directions)
Abstract Urban intersections contain severe blind zones where buildings and roadside obstacles block line-of-sight sensing, limiting the ability of autonomous vehicles to anticipate hidden hazards. This paper presents an urban-intersection-oriented non-line-of-sight (NLOS) perception framework that exploits specular reflections from building surfaces using 77 GHz frequency-modulated continuous-wave (FMCW) automotive radar. All evaluations are conducted in a MATLAB-based simulation environment that models intersection geometry, building-induced occlusions, and specular reflection-assisted propagation, and generates 77-GHz FMCW radar echoes under controllable interference; real-world validation with measured radar data and richer multipath/material modeling is planned as future work. To improve robustness under… More >
Open Access
ARTICLE
Liangchao Chen1, Yan Qiao1,*, Siwei Zhang1,*, Bin Liu2, Yonghua Shao3, Sijun Zhan3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.076245
（This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
Abstract This article examines wafer lots scheduling in the diffusion area in semiconductor manufacturing. The diffusion area comprises multiple tool groups. Each of them contains non-identical semiconductor tools. All tools can process multiple wafer lots simultaneously, and wafer lots processed together in a tool are called a wafer batch. Besides, each wafer lot has specific queue time limits (QTLs) between consecutive processing operations, making the scheduling problem more complicated. To solve it, a discrete backtracking search optimization algorithm (DBSA) is designed for optimizing both wafer lot assignments and wafer batch processing sequences. Once the processing sequence of wafer… More >
Open Access
ARTICLE
Jun Yu1,2,*, Lun-Ping Zhang1,2, Xian-Pi Zhang1,3, Teng Xie2,3, Wei-Di Wu1,2, Fang-Zhou Zhu2,3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.078976
（This article belongs to the Special Issue: Modeling and Applications of Bubble and Droplet in Engineering and Sciences)
Abstract Bubble dynamics near complex boundaries is critical for engineering applications like underwater explosions and cavitation control. This study investigates the collapsing behavior of near-wall bubbles adjacent to three boundary conditions (planar, elliptical convex, and elliptical concave surfaces) using a compressible multi-component flow model. The finite volume method combined with fifth-order Weighted Essentially Non-Oscillation (WENO) reconstruction and the Harten-Lax-van Leer Contact (HLLC) Riemann solver is employed for spatial discretization, while the third-order Total Variation Diminishing (TVD) Runge-Kutta scheme handles temporal discretization. Results show that elliptical convex and concave surfaces exhibit opposite regulatory effects: the convex surface More >
Open Access
ARTICLE
Jaeseung Lee1, Jehyeok Rew2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.076888
（This article belongs to the Special Issue: Deep Learning for Energy Systems)
Abstract The engine serves as the primary component that generates power and drives vehicle movement. Given its critical role, accurately diagnosing engine faults is essential for ensuring vehicle safety and reliability. Recent advances in machine learning (ML) have enabled the development of artificial intelligence (AI)-based diagnostic models with strong predictive performance. However, the lack of transparency in these models constrains user confidence in their diagnostic outcomes. While explainable AI (XAI) methods such as local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) have been introduced to improve interpretability, their reliance on visual outputs requires manual… More >
Open Access
ARTICLE
Rafael de Oliveira Silva1,2,*, Roberto Outa3, Fábio Roberto Chavarette4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080607
（This article belongs to the Special Issue: Numerical Modeling in Technical Diagnostics and Predictive Maintenance)
Abstract The reliable detection of cracks in engineering materials remains a fundamental challenge in nondestructive testing, especially in applications that require automated inspection, reduced instrumentation costs, and robustness under noisy operational conditions. Traditional nondestructive evaluation techniques often rely on complex sensing setups or expert-dependent interpretation, which can limit scalability and real-time applicability. In this context, this study addresses the scientific problem of achieving reliable and automated crack detection using simplified sensing architectures combined with intelligent data-driven analysis. This work proposes an intelligent signal classification framework for crack detection in polymeric materials based on machine learning and… More >
Open Access
EDITORIAL
Nishu Gupta1,*, Manuel J. C. S. Reis2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.082568
（This article belongs to the Special Issue: Next-Generation Intelligent Networks and Systems: Advances in IoT, Edge Computing, and Secure Cyber-Physical Applications)
Abstract This article has no abstract. More >
Open Access
ARTICLE
Faten S. Alamri1, Noor Ayesha2, Afia Zafar3, Adil Ali Saleem4,*, Amjad R. Khan5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080335
（This article belongs to the Special Issue: Advances in Deep Learning and Computer Vision for Intelligent Systems: Methods, Applications, and Future Directions)
Abstract The use of automated skin lesion classification is still a disadvantage, since there is a great visual similarity between benign and malignant lesions. The majority of deep learning methods utilize dermoscopic images only, without taking into account clinical metadata employed by dermatologists on a regular basis. The following paper proposes a vision-graph multimodal framework that links Image encoding to graph neural networks based on metadata representation through the fusion of learnable attention. The framework focuses on three limitations, which are underutilization of clinical context, absence of interpretability, and suboptimal incorporation of modalities. Gradient-weighted Class Activation… More >
Graphic Abstract
Open Access
ARTICLE
Xuan Sun1,2, Yueying Zhu3, Jiaxi Jin1, Zhitong Li1,*, Leizhi Wang4, Zhaobo Chen1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.078891
（This article belongs to the Special Issue: Modeling, Control and Application of Smart Materials)
Abstract Piezoelectric smart materials have been widely used in applications such as soft robotic actuation, vibration control and sensing of aerospace structures. In such contexts, the smart structures are typically subjected to significant large deformations and strong electromechanical coupling effects, which pose considerable challenges for conventional analytical approaches and classical finite element models in accurately predicting their nonlinear dynamic responses and capturing multiphysics coupling behaviors. To address these challenges in modeling and analysis, this work develops a flexible–electrical coupled computational framework with a unified mesh description based on the absolute nodal coordinate formulation (ANCF). This coupling… More >
Open Access
ARTICLE
Wei-Teng Chang1, Ben-Jye Chang2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.077118
（This article belongs to the Special Issue: Computer Modeling for Future Communications and Networks)
Abstract Some critical applications of emergency, Active Safe Driving (ASD), eV2X, and LEO communications require ultra-low delay and highly reliable transmission according to beyond 5G-Advanced (5G-A), 6G, and LEO specifications. Related studies proposed various scheduling algorithms in terms of single and multiple QoS requirements. However, these approaches tend to prioritize traditional QoS requirements while neglecting crucial considerations such as bearer costs and associated benefits. Moreover, most scheduling neglects the carrying cost according to the radio resource state and the bringing reward from different types of flows. Thus, this paper proposes a novel cost-based flow scheduling (eSCFS)… More >
Open Access
ARTICLE
Minjeong Kang1, Jung Hoon Lee1,*, Il-Gu Lee2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.078247
（This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)
Abstract In next-generation non-terrestrial network environments, the increasing risk of detection by unauthorized observers has motivated extensive research on covert communication approaches that minimize the probability of detection. In particular, jamming-assisted cooperative covert communication has attracted significant attention as an effective approach to simultaneously ensure communication performance and security, leading to growing interest in cooperative architectures among heterogeneous platforms. This study investigates covert communication in Low Earth Orbit (LEO) satellite–unmanned aerial vehicle (UAV) cooperative networks, where the LEO satellite serves a legitimate user, while the UAV acts as a cooperative jammer to enhance covertness. A network… More >
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