Open Access
ARTICLE
Natalia Prieto-Fernández1, Martín Bayón-Gutiérrez1,*, Sergio Fernández-Blanco1, Álvaro Fernández-Blanco1, Francisco Carro-De-Lorenzo2, José Alberto Benítez-Andrades1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080540
（This article belongs to the Special Issue: Environment Modeling for Applications of Mobile Robots)
Abstract Feature-based Simultaneous Localization and Mapping (SLAM) using 2D Light Detection and Ranging (LiDAR) in structured indoor environments commonly relies on the extraction of straight segments and corners from raw scan data. The quality of these landmarks depends not only on the fitting algorithm, but also on how uncertainty is modeled and propagated from line estimates to derived corner features. Although the magnitude of LiDAR uncertainty has been widely studied, the influence of line parameterization and geometric conditioning on uncertainty propagation has received less attention. In particular, the scale ambiguity inherent to implicit line representations can… More >
Open Access
ARTICLE
Angel Prado1,*, Alejandro Alañón2, Ricardo Castedo3, Anastasio Pedro Santos3, Lina María López3, María Chiquito3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.079804
（This article belongs to the Special Issue: Modeling and Simulation of Explosive Effects on Structural Elements and Materials)
Abstract This study presents a numerical investigation into the influence of reinforcement layout on the blast response of a reinforced concrete (RC) slab subjected to a close-in explosion. The reference scenario is based on a blast test from the SEGTRANS project using a 15 kg TNT equivalent charge. A validated LS-DYNA model was used, applying the Load Blast Enhanced method and the Continuous Surface Cap Model for concrete behaviour. Forty-nine reinforcement configurations were assessed, all with constant steel mass but varying numbers of longitudinal bars and stirrups. Damage metrics such as eroded elements and internal energy… More >
Open Access
ARTICLE
Zafer Serin1,*, Cihan Karakuzu2, Uğur Yüzgeç2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.079254
（This article belongs to the Special Issue: AI-Enhanced Computational Methods in Engineering and Physical Science)
Abstract This study proposes SWAGE-3D (Spectral Wasserstein Attention Generative Ensemble), an enhanced 3D-VAE-GAN framework for single-view 3D object reconstruction using voxel-based representations. The proposed model integrates RGB-D encoding, Wasserstein adversarial learning with hybrid Lipschitz regularization, and a self-attention–augmented generator to improve structural coherence and training stability. By combining variational latent modeling with stabilized Wasserstein optimization, the framework aims to address common challenges in 3D generative modeling, including mode collapse, unstable convergence, and insufficient global consistency. The encoder employs a depth-aware feature extraction strategy, while the discriminator utilizes a hybrid spectral normalization and gradient penalty mechanism to More >
Graphic Abstract
Open Access
ARTICLE
Yousra Abudaqqa*, Zulaiha Ali Othman, Azuraliza Abu Bakar
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.079034
Abstract Signal categorization is a critical component of the Dendritic Cell Algorithm (DCA), as it directly influences its anomaly detection capability. Conventional DCA implementations typically rely on heuristic or optimization-based approaches, such as Grouping Particle Swarm Optimization (GPSO), Grouping Genetic Algorithms (GGA), Principal Component Analysis (PCA), and Support Vector Machines (SVM), to determine mappings between input features and the three immunological signal categories: Pathogen-Associated Molecular Patterns (PAMP), Danger Signals (DS), and Safe Signals (SS). These approaches depend heavily on domain expertise and predefined rules, making the resulting signal mappings static and often dataset specific. Consequently, the… More >
Open Access
ARTICLE
Nasser Firouzi*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.075681
Abstract This study investigates finite transient deformations in hyperelastic beam structures based on the Gent material model. To enable its application within beam formulations, the Gent model is expressed in a linearized form. A five-parameter beam element, incorporating two displacement variables, two difference parameters, and one thickness parameter, is adopted. The nonlinear dynamic response is solved using the implicit Newmark method, allowing efficient analysis of beams subjected to complex loading and boundary conditions. The results show that the proposed approach accurately captures the response of geometrically nonlinear beams and reproduces the behavior of neo-Hookean hyperelastic beams More >
Open Access
ARTICLE
Salamat Ullah1,2,*, Muhammad Zahid3, Khaled Aati4, Abdulrahman Abbadi4, Haroon Ijaz5, Ali Qabur4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080484
（This article belongs to the Special Issue: Emerging Artificial Intelligence & Data-Driven Modeling in Civil Engineering)
Abstract Designing thin-walled plate structures is challenging due to their susceptibility to various forms of structural instability. In addition, the substantial computational cost of finite element analyses, especially in optimization scenarios, underscores the need for efficient and reliable surrogate models. To address this challenge, the present study employs machine learning (ML) techniques to predict the buckling response of thin plates under complex boundary conditions. Four ML models, including XGBoost, CatBoost, Light GBM, and Random Forest, are developed to predict the buckling coefficient based on input features, including aspect ratio, boundary condition, and compressive loading pattern. The… More >
Open Access
ARTICLE
Sarna Soren1, Samrat Hansda1,*, Umair Khan2,3, Anuar Ishak4, Ahmed Kadhim Hussein5, Md Mamun Molla6,7
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.079635
（This article belongs to the Special Issue: Computational Methods in Mono/hybrid nanofluids: Innovative Applications and Future Trends)
Abstract This study presents a numerical investigation of thermosolutal convection within a baffled porous cavity filled with a radiative Casson-based ternary aqueous nanofluid. The ternary hybrid nanofluid is formulated by dispersing three distinct nanoparticles into a water-based solution, aiming to enhance the thermal and solute transport characteristics. The cavity includes internal baffles that modulate convective flow and facilitate improved energy transport. The governing equations for momentum, energy, species concentration, and entropy generation are discretized and solved using a higher-order compact (HOC) finite difference scheme, ensuring superior numerical precision. The novelty of the present study lies in… More >
Open Access
ARTICLE
Ezz El-Din Hemdan1, Haitham Elwahsh2,3, Samah Alshathri4,*, Amged Sayed5,6,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.079305
（This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)
Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >
Open Access
ARTICLE
Basma Souayeh*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.081163
（This article belongs to the Special Issue: Computational Advances in Nanofluids: Modelling, Simulations, and Applications)
Abstract This study conducts a comprehensive numerical investigation of magnetohydrodynamic (MHD) mixed convection and entropy generation in a two-dimensional square cavity filled with a ternary hybrid nanofluid. The working fluid consists of Multi-Walled Carbon Nanotubes (MWCNT), Copper (Cu), and Ferric Oxide (Fe3O4) nanoparticles dispersed in water, selected for their superior thermal properties. Two vertically aligned, saw-tooth-shaped cooling structures are embedded along the left and right walls of the cavity, with four distinct configurations considered based on their vertical positioning. An externally imposed uniform magnetic field is applied to assess its influence on fluid flow, heat transfer, and… More >
Open Access
ARTICLE
Md Al Rifat Anan1, Donghyeon Ryu2, Yu-Lin Shen1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080371
（This article belongs to the Special Issue: Advances in Numerical Modeling of Composite Structures and Repairs)
Abstract The development of surface wrinkles on thin films bonded to compliant substrates is recognized as a form of mechanical instability. While this wrinkling behavior is widely studied when the thin film is under direct compression, much less attention has been devoted to the prediction of wrinkle formation caused by tension and cyclic compression-tension deformations. This work focuses on compression vs. tension-induced wrinkles using a relatively stiff polymeric film on an elastomeric substrate as the model system. Experimental observations show that parallel wrinkles formed during unidirectional compression gradually disappear under reverse loading. When the thin film… More >
Open Access
EDITORIAL
Bo Yang1,*, Chao Liu2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083726
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
Abstract This article has no abstract. More >
Open Access
ARTICLE
Menahil Rahman1, Waqas Ishtiaq2, Amerah Alabrah3,*, Arif Mehmood4, Rana Faraz Ahmed4, Iqra Khalid5, Farhan Amin6,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.078549
（This article belongs to the Special Issue: Explainable AI, Digital Twin, and Hybrid Deep Learning Approaches for Urban–Regional Hydrology, Water Quality, and Risk Modeling under Uncertainty)
Abstract Access to safe drinking water is a fundamental determinant of global health. The presence of contaminated water affects the citizens’ health. Per- and polyfluoroalkyl substances (PFAS) are often referred to as forever chemicals. They pose a persistent and growing threat to drinking water. In the literature, machine learning methods are used to identify the forever chemicals in water. However, traditional methods are not efficient and scalable. Thus, to solve this issue. This study develops a large-scale machine-learning framework for PFAS risk screening in US public water systems. The proposed framework incorporates data ingestion, preprocessing, and More >
Open Access
ARTICLE
Munthir Qasaimeh1, Mostafa Ali1, Qasem Abu Al-Haija2,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.080864
（This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)
Abstract Vision Transformers (ViTs) have recently achieved high performance in retinal Optical Coherence Tomography (OCT) classification studies. However, ViT models continue to face significant challenges, including high computational cost, vulnerability to adversarial attacks, and pronounced sensitivity to preprocessing techniques. This study introduces GreenShield, a unified framework designed to produce an efficient and robust ViT model, referred to as GreenShield-ViT, which outperforms existing lightweight ViT variants in terms of adversarial robustness for retinal OCT classification. The framework integrates a gradient-based block-importance pruning strategy to compress the ViT/B-16 architecture, and adversarial training with proper ImageNet normalization and anti-saturation… More >
Open Access
ARTICLE
Abdulrahman Dira Khalaf1,2,*, Hazlina Hamdan1,*, Alfian Abdul Halin1, Noridayu Manshor1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.075537
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Current automated lesion segmentation methods have limited success, particularly for segmenting small, irregular, or heterogeneous lesions. Moreover, such models require significant computational power, which restricts their scalability and clinical application. To overcome these limitations, a lightweight LANET, which is a layer-attention network based on an encoder–decoder deep-learning architecture, has the explicit goal of increasing the segmentation performance and computational efficiency. The LANET is coupled with three new modules: (i) an attention module that includes a depthwise separable convolution operator to reduce the number of parameters, (ii) a custom attention mechanism, and (iii) an atrous spatial… More >
Graphic Abstract
Open Access
EDITORIAL
Ji Su Park1,*, Pan Yi2, Jong Hyuk (James) Park3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.083347
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract This article has no abstract. More >
Open Access
ARTICLE
Roshana Mukhtar1, Chuan-Yu Chang2, Muhammad Asif Zahoor Raja1,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2026.079390
（This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
Abstract Parkinson’s disease (PD) is a complex neurodegenerative disease associated with the accumulation of α-synuclein, which is linked to the dysfunctional ubiquitin–proteasome system. Fractional calculus has emerged as a powerful tool for modeling complex disease dynamics due to its promising features that inherently capture memory and hereditary effects. This paper presents a fractional-order Proteasome-Fibril interaction model (F-PFIM) for the dynamics of PD, represented by three fractional differential classes, showing concentrations of fibrils (F), proteasomes (P), and proteasome fibril complex (C). The three classes of the F-PFIM collectively make a controlling system that works for the clearance… More >
Graphic Abstract
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