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  • Open Access

    ARTICLE

    Numerical Mesoscale Analysis of Rubber Size, Rubber Content, and Specimen Size Effects on Crumb Rubber Concrete Using BFEM

    Mahmoud M. A. Kamel1,2, Yu Fu3, S. Z. Abeer4, Zaman Mohamed Al-Delfi4, Yijiang Peng1,*

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078775 - 09 April 2026

    Abstract Crumb rubber concrete (CRC) has emerged as a sustainable solution to the environmental challenges posed by rubber waste. This study introduces an advanced mixed-random-aggregate mesoscale model for CRC based on the Base Force Element Method (BFEM) and the complementary energy principle. The model incorporates different rubber substitution ratios (0%–30%), rubber particle sizes (2 mm and 4 mm), and specimen dimensions (edge lengths of 100, 150, and 300 mm). These parameters are considered to investigate their effects on the mechanical properties and failure mechanisms of CRC. Accordingly, the numerical results include stress–strain responses, elastic modulus, and… More >

  • Open Access

    ARTICLE

    Bonding Properties of the Graphene/Aluminum Interface with Transition Metal Coating: A First-Principles Study

    Xiaoming Du1, Jiahui Guo1, Gaohan Liao1, Tianfu Li2,*, Haicheng Liang1,*

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078760 - 09 April 2026

    Abstract Graphene has excellent mechanical, electrical and optical properties, which make it an ideal reinforcement phase for aluminum matrix composites. However, graphene is easy to agglomerate and has poor wettability with the aluminum matrix, resulting in unsatisfactory effects when added to the aluminum matrix. In this paper, the effects of transition metals (Cu, Ni, Co) on the bonding properties at the graphene/aluminum interface were systematically investigated using first-principles calculations. The computational results reveal significant differences in the effects of various metals and their crystal plane orientations on interface stability and bonding strength. Among Cu, Ni, Co… More >

  • Open Access

    ARTICLE

    Phase-Dependent Structural, Optical, and Thermodynamic Behavior of BaTiO3: Insights from First-Principles Calculations

    Yasemin O. Ciftci1, İlknur K. Durukan1, Upasana Rani2, Peeyush Kumar Kamlesh3,*

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078722 - 09 April 2026

    Abstract This study examines the phase-dependent structural, electronic, optical, and thermodynamic characteristics of the cubic, tetragonal, and orthorhombic phases of BaTiO3 using DFT simulations. Lattice parameters and bulk moduli computed through structural optimizations within the GGA-PBE framework are in good agreement with existing experimental and theoretical studies. All phases exhibit negative formation energies, indicating thermodynamic stability, with the orthorhombic phase being the most stable. Electronic structure calculations reveal indirect band gaps of 2.86, 2.96, and 3.43 eV for the cubic, tetragonal, and orthorhombic phases, respectively. The density of states analysis indicates that O-p states dominate the valence… More >

  • Open Access

    ARTICLE

    Trustworthy Personalized Federated Recommender System with Blockchain-Assisted Decentralized Reward Management

    Waqar Ali1, May Altulyan2, Ghulam Farooque3, Siyuan Li4, Jie Shao4,5,*

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078599 - 09 April 2026

    Abstract Federated recommender systems (FedRS) enable collaborative model training while preserving user privacy, yet they remain vulnerable to adversarial attacks, unreliable client updates, and misaligned incentives in decentralized environments. Existing approaches struggle to jointly preserve personalization, robustness, and trust when user data are highly non-IID and recommendation quality is governed by ranking-oriented objectives. To address these challenges, we propose a Trustworthy Federated Recommender System (T-FedRS) that extends federated neural collaborative filtering by integrating a ranking-aware reputation mechanism and a lightweight blockchain layer for transparent incentive allocation. Personalization is preserved through locally maintained user embeddings, while item parameters… More >

  • Open Access

    ARTICLE

    Artificial Neural Network-Based Prediction and Validation of Drill Flank Wear in GFRP Machining for Sustainable and Smart Manufacturing

    Sathish Rao Udupi, Gururaj Bolar, Manjunath Shettar*, Ashwini Bhat

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078574 - 09 April 2026

    Abstract Glass fiber-reinforced polymer composites (GFRPCs) are extensively utilized in the aerospace, automotive, and structural sectors; nevertheless, their heterogeneous and abrasive characteristics result in rapid tool wear during drilling. Drill flank wear among various wear mechanisms notably influences hole quality and dimensional accuracy. This research investigates the impact of spindle speed, feed rate, and drill diameter on flank wear during dry drilling of GFRPC laminates with high-speed steel (HSS) twist drills. A full-factorial design with 81 experiments is used to create a comprehensive dataset. ANOVA indicates that spindle speed is the dominant factor affecting wear changes,… More >

  • Open Access

    REVIEW

    A Survey of Pixhawk/PX4 Autopilot and Its Impact on Research and Education

    Nourdine Aliane*

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078545 - 09 April 2026

    Abstract The rapid advancement of unmanned aerial vehicle (UAV) technologies has increased demand for flexible autopilot platforms suitable for both research and education. Among available options, the open-source Pixhawk/PX4 autopilot has emerged as a leading solution due to its modular architecture and robust software ecosystem. This survey examines the adoption of the Pixhawk/PX4 platform in research and education. The survey covers the analysis of the Pixhawk/PX4 autopilot software development APIs, its compatibility with ROS middleware and MATLAB/Simulink environments, and environments for software/hardware-in-the-loop simulations. Additionally, it explores the integration of Cutting-Edge technologies to enhance UAVs performance. By More >

  • Open Access

    ARTICLE

    From Stability to Hardness: High-Throughput First-Principles Screening Reveals Promising MAB Phases for Advanced Engineering Applications

    Jiamin Xue1, Jiexi Song2,*, Diwei Shi1,*

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078225 - 09 April 2026

    Abstract MAB phases are a class of layered ternary transition-metal borides, characterized by hard M-B slabs interleaved with softer A-element layers, and thus hold promise for wear-resistant and high-temperature structural applications. However, their compositional space and structural diversity remain insufficiently explored, limiting guidance for synthesis and property optimization. In this work, we perform a comprehensive exploration and screening of the MAB family using high-throughput first-principles calculations. We systematically identify 855 candidate MAB compounds with orthorhombic and hexagonal structures across multiple transition-metal families, which form the starting pool for subsequent stability and property evaluation. The workflow evaluates… More >

  • Open Access

    ARTICLE

    Edge-Intelligent Photovoltaic Fault Localization via NAS-Optimized Feature-Space Sub-Pixel Matching

    Hongjiang Wang1, Jian Yu2, Tian Zhang3, Na Ren4, Nan Zhang2, Zhenyu Liu1,*

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077997 - 09 April 2026

    Abstract The rapid deployment of Industrial Internet of Things (IIoT) systems, such as large-scale photovoltaic (PV) power stations in modern power grids, has created a strong demand for edge-intelligent fault localization methods that can operate reliably under strict computational and memory constraints. In this work, we propose an edge-intelligent photovoltaic fault localization framework that integrates intelligent computation with classical sub-pixel optimization. The framework adopts a modular, edge-oriented design in which a radial basis function (RBF) network is first employed as a lightweight screening module to enable conditional execution, thereby reducing unnecessary computation for non-faulty samples. For… More >

  • Open Access

    ARTICLE

    A Robust Design Method for Low-Pressure Die Casting Process Based on Surrogate Models

    Yunlang Zhan1,2, Fuhao Fan1,2, Xilin Li1,2, Zhenfei Zhan1,*, Yongzhi Jiang1, Yutong Yang2,*, Shiyao Huang2

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077966 - 09 April 2026

    Abstract The batch-to-batch variability in low-pressure die casting (LPDC), caused by inherent process parameter fluctuations, poses a significant challenge to consistent quality. However, traditional single-point optimization methods ignore parameter fluctuations. This study presents a robust design framework to overcome this limitation. First, an integrated simulation workflow was established by coupling ProCAST casting simulation with Abaqus finite element analysis to predict shrinkage pore volume and load-bearing capacity (LBC). Subsequently, a dataset was constructed from the integrated simulations, and then served to develop a surrogate model using the Extreme Gradient Boosting algorithm. Finally, robust process windows were derived… More >

  • Open Access

    ARTICLE

    Structured Random Cycle-Guided Algorithm (SRCA): An Adaptive Metaheuristic Combining Directionally-Guided and Stochastic Search Strategies

    Giuseppe Marannano*, Antonino Cirello, Tommaso Ingrassia

    CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077884 - 09 April 2026

    Abstract In response to the growing need for adaptive optimization algorithms capable of handling complex, multimodal, and high-dimensional search spaces, this paper introduces the Structured Random Cycle-guided Algorithm (SRCA). SRCA is not presented as a fundamentally new optimization paradigm, but rather as an architectural synthesis and a unified adaptive framework for dynamic operator selection. Based on a cycle-structured architecture, directional and stochastic search behaviors are dynamically selected at the individual level. The algorithm orchestrates well-established structured movements with a diverse pool of stochastic exploration strategies, enabling a coherent and adaptive balance between exploration and exploitation throughout More >

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