Yasir Khan¹, Arshad Riaz^2,*, Iqra Batool², Safia Akram³, A. Alameer¹, Ghaliah Alhamzi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078060 - 30 March 2026

Abstract Tangent hyperbolic fluids characterized by shear-thinning behavior, are widely utilized in diverse industrial and scientific fields such as polymer engineering, inkjet printing, biofluids modeling, thermal insulation materials, and chemical manufacturing. Additionally, double-diffusive convection involving simultaneous heat and mass transfer driven by temperature and concentration gradients plays a critical role in many natural and industrial systems, including oceanic circulation, geothermal energy extraction, crystal solidification, alloy formation, and enhanced oil recovery. The current work examines the peristaltic transport of a tangent hyperbolic nanofluid under the concurrent effects of thermal radiation, electroosmotic forces, slip boundary conditions, and double… More >

Najat Almutairi¹, Mohammed Messaoudi², Faisal Muteb K. Almalki³, Sayed Saber^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076371 - 30 March 2026

Abstract This paper introduces a novel fractional-order model based on the Caputo–Fabrizio (CF) derivative for analyzing computer virus propagation in networked environments. The model partitions the computer population into four compartments: susceptible, latently infected, breaking-out, and antivirus-capable systems. By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems, aspects inadequately represented by traditional integer-order models. Under Lipschitz continuity and boundedness assumptions, the existence and uniqueness of solutions are rigorously established via fixed-point theory. We develop a tailored two-step Adams–Bashforth numerical scheme for the CF framework More >

Zhengyu Wu¹, Kejun Kang², Yixiu Liu^3,*, Chenpu Li³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075593 - 12 March 2026

Abstract Binary segmentation tasks in computer vision exhibit diverse appearance distributions and complex boundary characteristics. To address the limited generalization and adaptability of existing models across heterogeneous tasks, we propose Abel-Net, an Aggregated Bilateral Edge Localization Network designed as a universal framework for multi-task binary segmentation. Abel-Net integrates global and local contextual cues to enhance feature learning and edge precision. Specifically, a multi-scale feature pyramid fusion strategy is implemented via an Aggregated Skip Connection (ASC) module to strengthen feature adaptability, while the Edge Dual Localization (EDL) mechanism performs coarse-to-fine refinement through edge-aware supervision. Additionally, Edge Attention More >

Maria Tănase^1,*, Gennadiy Lvov²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.078844 - 26 February 2026

Abstract The increasing occurrence of corrosion-related damage in steel pipelines has led to the growing use of composite-based repair techniques as an efficient alternative to traditional replacement methods. Computer modeling and structural analysis were performed for the repair reinforcement of a steel pipeline with a composite bandage. A preliminary analysis of possible contact interaction schemes was implemented based on the theory of cylindrical shells, taking into account transverse shear deformations. The finite element method was used for a detailed study of the stress state of the composite bandage and the reinforced section of the pipeline. The… More >

Mubarak Alanazi^1,*, Junaid Rashid²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077956 - 26 February 2026

Abstract Wind turbine blade defect detection faces persistent challenges in separating small, low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints. Conventional image-processing pipelines struggle with scalability and robustness, and recent deep learning methods remain sensitive to class imbalance and acquisition variability. This paper introduces TurbineBladeDetNet, a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types. Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability. The model achieves 97.14% accuracy, 98.65% More >

Waseem Akhtar^1,2, Mahwish Ilyas³, Romana Aziz^4,*, Ghadah Aldehim⁴, Tassawar Iqbal⁵, Muhammad Ramzan⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.071669 - 26 February 2026

Abstract Human Activity Recognition (HAR) is a novel area for computer vision. It has a great impact on healthcare, smart environments, and surveillance while is able to automatically detect human behavior. It plays a vital role in many applications, such as smart home, healthcare, human computer interaction, sports analysis, and especially, intelligent surveillance. In this paper, we propose a robust and efficient HAR system by leveraging deep learning paradigms, including pre-trained models, CNN architectures, and their average-weighted fusion. However, due to the diversity of human actions and various environmental influences, as well as a lack of… More >

Nataliya Kazantseva^1,2,*, Nikolai Saharov¹, Denis Davydov^1,2, Nikolai Popov², Maxim Il’inikh¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.073078 - 10 February 2026

Abstract Because of the developed surface of the Triply Periodic Minimum Surface (TPMS) structures, polylactide (PLA) products with a TPMS structure are thought to be promising bio soluble implants with the potential for targeted drug delivery. For implants, mechanical properties are key performance characteristics, so understanding the deformation and failure mechanisms is essential for selecting the appropriate implant structure. The deformation and fracture processes in PLA samples with different interior architectures have been studied through computer simulation and experimental research. Two TPMS topologies, the Schwarz Diamond and Gyroid architectures, were used for the sample construction by… More >

Nouf Abdullah Almujally¹, Tanvir Fatima Naik Bukht², Shuaa S. Alharbi³, Asaad Algarni⁴, Ahmad Jalal^2,5, Jeongmin Park^6,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072243 - 10 February 2026

Abstract Recognising human-object interactions (HOI) is a challenging task for traditional machine learning models, including convolutional neural networks (CNNs). Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI. The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity. HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability. This work proposes a Novel quantum gate-enabled hybrid CNN (QEH-CNN) for effective HOI recognition. The model enhances CNN performance by integrating quantum computing components. The framework begins with bilateral image filtering,… More >

Abdulaziz Almalaq¹, Ambe Harrison^2,*, Ibrahim Alsaleh¹, Abdullah Alassaf¹, Mashari Alangari¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074815 - 29 January 2026

Abstract We present a computer-modeling framework for photovoltaic (PV) source emulation that preserves the exact single-diode physics while enabling iteration-free, real-time evaluation. We derive two closed-form explicit solvers based on the Lambert W function: a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator. Unlike Taylor-linearized explicit models, our proposed formulation retains the exponential nonlinearity of the PV equations. It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding, all while maintaining limited computational costs and a small… More >

Weishan Gao^1,2, Ye Wang^1,2, Xiaoyin Wang^1,2, Xiaochuan Jing^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073850 - 12 January 2026

Abstract In the field of intelligent surveillance, weakly supervised video anomaly detection (WSVAD) has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels. Although multiple instance learning (MIL) has dominated the WSVAD for a long time, its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events. In addition, insufficient temporal modeling obscures causal relationships between events, making anomaly decisions reactive rather than reasoning-based. To overcome the limitations above, this paper proposes an adaptive knowledge-based guidance method that integrates external structured… More >