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  • Open Access

    ARTICLE

    A Micromechanics-Based Softening Hyperelastic Model for Granular Materials: Multiscale Insights into Strain Localization and Softening

    Chenxi Xiu1,2,*, Xihua Chu2, Ao Mei1, Liangfei Gong1

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-39, 2026, DOI:10.32604/cmc.2025.073193 - 09 December 2025

    Abstract Granular materials exhibit complex macroscopic mechanical behaviors closely related to their micro-scale microstructural features. Traditional macroscopic phenomenological elasto-plastic models, however, usually have complex formulations and lack explicit relations to these microstructural features. To avoid these limitations, this study proposes a micromechanics-based softening hyperelastic model for granular materials, integrating softening hyperelasticity with microstructural insights to capture strain softening, critical state, and strain localization behaviors. The model has two key advantages: (1) a clear conceptualization, straightforward formulation, and ease of numerical implementation (via Abaqus UMAT subroutine in this study); (2) explicit incorporation of micro-scale features (e.g., contact… More >

  • Open Access

    REVIEW

    Implementation of Human-AI Interaction in Reinforcement Learning: Literature Review and Case Studies

    Shaoping Xiao1,*, Zhaoan Wang1, Junchao Li2, Caden Noeller1, Jiefeng Jiang3, Jun Wang4

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-62, 2026, DOI:10.32604/cmc.2025.072146 - 09 December 2025

    Abstract The integration of human factors into artificial intelligence (AI) systems has emerged as a critical research frontier, particularly in reinforcement learning (RL), where human-AI interaction (HAII) presents both opportunities and challenges. As RL continues to demonstrate remarkable success in model-free and partially observable environments, its real-world deployment increasingly requires effective collaboration with human operators and stakeholders. This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies. We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration: computational trust modeling, system usability, and decision understandability. Our… More >

  • Open Access

    ARTICLE

    An Improved Variant of Multi-Population Cooperative Constrained Multi-Objective Optimization (MCCMO) for Multi-Objective Optimization Problem

    Muhammad Waqar Khan1,*, Adnan Ahmed Siddiqui1, Syed Sajjad Hussain Rizvi2

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.070858 - 09 December 2025

    Abstract The multi-objective optimization problems, especially in constrained environments such as power distribution planning, demand robust strategies for discovering effective solutions. This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization (MCCMO) Algorithm, termed Adaptive Diversity Preservation (ADP). This enhancement is primarily focused on the improvement of constraint handling strategies, local search integration, hybrid selection approaches, and adaptive parameter control. The improved variant was experimented on with the RWMOP50 power distribution system planning benchmark. As per the findings, the improved variant outperformed the original MCCMO across the eleven performance metrics, particularly in terms… More >

  • Open Access

    ARTICLE

    HDFPM: A Heterogeneous Disk Failure Prediction Method Based on Time Series Features

    Zhongrui Jing1, Hongzhang Yang1,*, Jiangpu Guo2

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.067759 - 09 December 2025

    Abstract Hard disk drives (HDDs) serve as the primary storage devices in modern data centers. Once a failure occurs, it often leads to severe data loss, significantly degrading the reliability of storage systems. Numerous studies have proposed machine learning-based HDD failure prediction models. However, the Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes differ across HDD manufacturers. We define hard drives of the same brand and model as homogeneous HDD groups, and those from different brands or models as heterogeneous HDD groups. In practical engineering scenarios, a data center is often composed of a heterogeneous population of… More >

  • Open Access

    ARTICLE

    Multi-Constraint Generative Adversarial Network-Driven Optimization Method for Super-Resolution Reconstruction of Remote Sensing Images

    Binghong Zhang, Jialing Zhou, Xinye Zhou, Jia Zhao, Jinchun Zhu, Guangpeng Fan*

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068309 - 10 November 2025

    Abstract Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring, urban planning, and disaster assessment. However, traditional methods exhibit deficiencies in detail recovery and noise suppression, particularly when processing complex landscapes (e.g., forests, farmlands), leading to artifacts and spectral distortions that limit practical utility. To address this, we propose an enhanced Super-Resolution Generative Adversarial Network (SRGAN) framework featuring three key innovations: (1) Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing; (2) A multi-loss joint optimization strategy… More >

  • Open Access

    ARTICLE

    Federated Multi-Label Feature Selection via Dual-Layer Hybrid Breeding Cooperative Particle Swarm Optimization with Manifold and Sparsity Regularization

    Songsong Zhang1, Huazhong Jin1,2,*, Zhiwei Ye1,2, Jia Yang1,2, Jixin Zhang1,2, Dongfang Wu1,2, Xiao Zheng1,2, Dingfeng Song1

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068044 - 10 November 2025

    Abstract Multi-label feature selection (MFS) is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels. However, traditional centralized methods face significant challenges in privacy-sensitive and distributed settings, often neglecting label dependencies and suffering from low computational efficiency. To address these issues, we introduce a novel framework, Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization (DHBCPSO-MSR). Leveraging the federated learning paradigm, Fed-MFSDHBCPSO allows clients to perform local feature selection (FS) using DHBCPSO-MSR. Locally selected feature subsets are encrypted with differential privacy (DP) and transmitted… More >

  • Open Access

    ARTICLE

    Fatigue Assessment of Large-Diameter Stiffened Tubular Welded Joints Using Effective Notch Strain and Structural Strain Approach

    Dan Jiao1,2, Yan Dong1,2,*, Hao Xie3, Yordan Garbatov4,*, Jiancheng Liu5, Hui Zhang5

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3197-3216, 2025, DOI:10.32604/cmes.2025.074239 - 23 December 2025

    Abstract Floating offshore wind turbine platforms typically use stiffened tubular joints at the connections between columns and braces. These joints are prone to fatigue due to complex weld geometries and the additional stress concentrations caused by the stiffeners. Existing hot-spot stress approaches may be inadequate for analysing these joints because they do not simultaneously address weld-toe and weld-root failures. To address these limitations, this study evaluates the fatigue strength of stiffened tubular joints using the effective notch strain approach and the structural strain approach. Both methods account for fatigue at the weld toe and weld root… More >

  • Open Access

    ARTICLE

    Optimization and Sensitivity Analysis of Non-Isothermal Carreau Fluid Flow in Roll Coating Systems with Fixed Boundary Constraints: A Comparative Investigation

    Mujahid Islam1, Fateh Ali1,*, Xinlong Feng1,*, M. Zahid2, Sana Naz Maqbool1

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3511-3561, 2025, DOI:10.32604/cmes.2025.073678 - 23 December 2025

    Abstract Roll coating is a vital industrial process used in printing, packaging, and polymer film production, where maintaining a uniform coating is critical for product quality and efficiency. This work models non-isothermal Carreau fluid flow between a rotating roll and a stationary wall under fixed boundary constraints to evaluate how non-Newtonian and thermal effects influence coating performance. The governing equations are transformed into non-dimensional form and simplified using lubrication approximation theory. Approximate analytical solutions are obtained via the perturbation technique, while numerical results are computed using both the finite difference method and the BVP-Midrich technique. Furthermore, More >

  • Open Access

    ARTICLE

    Development of a CNT/Bi2S3/PVDF composite waterproof film-based strain sensor for motion monitoring

    A. X. Yanga, L. F. Huangb,*, Y. Y. Liuc

    Chalcogenide Letters, Vol.22, No.7, pp. 649-663, 2025, DOI:10.15251/CL.2025.227.649

    Abstract An innovative flexible electronic device was developed by integrating functionalized carbon nanotubes, bismuth sulfide nanostructures, and a polyvinylidene fluoride matrix to create a highly water‐resistant strain detection platform. The fabricated film exhibited a remarkable static water contact angle of 141°, with only a 3–4° reduction after 48 hours of immersion, confirming its excellent hydrophobic performance. Mechanical testing revealed a tensile strength of 43.2 MPa and maintained over 96% of its original strength following 1000 bending cycles, thereby demonstrating outstanding durability under repetitive deformation. Electrical characterization showed an initial conductivity of 12.3 S/m and a baseline resistance near… More >

  • Open Access

    ARTICLE

    Securing IoT Ecosystems: Experimental Evaluation of Modern Lightweight Cryptographic Algorithms and Their Performance

    Mircea Ţălu1,2,*

    Journal of Cyber Security, Vol.7, pp. 565-587, 2025, DOI:10.32604/jcs.2025.073690 - 11 December 2025

    Abstract The rapid proliferation of Internet of Things (IoT) devices has intensified the demand for cryptographic solutions that balance security, performance, and resource efficiency. However, existing studies often focus on isolated algorithmic families, lacking a comprehensive structural and experimental comparison across diverse lightweight cryptographic designs. This study addresses that gap by providing an integrated analysis of modern lightweight cryptographic algorithms spanning six structural classes—Substitution–Permutation Network (SPN), Feistel Network (FN), Generalized Feistel Network (GFN), Addition–Rotation–XOR (ARX), Nonlinear Feedback Shift Register (NLFSR), and Hybrid models—evaluated on resource-constrained IoT platforms. The key contributions include: (i) establishing a unified benchmarking… More >

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