Chen Yu, Jingjing Tan, Wenwu Zhao, Ke Gu^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075457 - 12 March 2026

Abstract Although federated learning (FL) improves privacy-preserving by updating parameters without collecting original user data, their shared gradients still leak sensitive user information. Existing differential privacy and encryption techniques typically focus on whether the aggregated gradient is correctly processed and verified only, rather than whether each user is honestly trained locally. To address these above issues, we propose a gradient feature-based collaborative filtering scheme in verification federated learning, where the authenticity of user training is verified using the collaborative filtering (CF) method based on gradient features. Compared with single user gradient detection (such as similarity detection More >

Oksana Ivanova^*, Roman Cherepanov, Sergey Zelepugin

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075451 - 12 March 2026

Abstract This study presents an investigation into shock-induced exothermic reactions within three distinct aluminum-based energetic mixtures: aluminum/sulfur (Al/S), aluminum/copper oxide (Al/CuO), and aluminum/polytetrafluoroethylene (Al/PTFE). A challenge in current modeling efforts is accurately capturing the complex physical and chemical coupling under extreme loading, especially the influence of rapidly forming gaseous products in Al/PTFE mixtures on material integrity. To address this, a wide-range numerical model based on the Smoothed Particle Hydrodynamics (SPH) method was developed. This mesh-free approach manages large deformations and incorporates elastic-plastic flow, heat transfer, component diffusion, and chemical kinetics simulated using both zero- and first-order… More >

Xuehan Li¹, Tao Jing², Yang Wang², Bo Gao³, Jing Ai⁴, Minghao Zhu^5,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075426 - 12 March 2026

Abstract With the deep integration of cloud computing, edge computing and the Internet of Things (IoT) technologies, smart manufacturing systems are undergoing profound changes. Over the past ten years, an extensive body of research on cloud-edge-end systems has been generated. However, challenges such as heterogeneous data fusion, real-time processing and system optimization still exist, and there is a lack of systematic review studies. In this paper, we review a cloud-edge-end collaborative sensing-communication-computing-control (SC3) system. This system integrates four layers of sensing, communication, computing and control to address the complex challenges of real-time decision making, resource… More >

Xinyue Zhang¹, Yuxuan Zhao², Jeremy S. Smith³, Yuechun Wang⁴, Gabriela Mogos⁵, Ka Lok Man¹, Yutao Yue^6,7,8,9, Young-Ae Jung^10,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075415 - 12 March 2026

Abstract Intelligent Transportation Systems (ITS) represent a cornerstone in modern traffic management, leveraging surveillance cameras as primary visual sensors to monitor road conditions. However, the fixed characteristics of public surveillance cameras, coupled with inherent image resolution limitations, pose significant challenges for Small Object Detection (SOD) in traffic surveillance. To address these challenges, this paper proposes Ghost-Attention YOLO (GA-YOLO), a lightweight model derived from YOLOv8 and specifically designed for traffic SOD. To enhance the attention of small targets and critical features, a novel channel-spatial attention mechanism, termed Small-object Extend Attention (SEA), is introduced. In addition, the original… More >

Hongyu Xu^1,*, Xiaofeng Liu¹, Zhao Li¹, Shuai Zhang², Jintao Cui¹, Zongshuai Zhou¹, Longlong Chen¹, Mengen Zhang¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075413 - 12 March 2026

Abstract In two-scale topology optimization, enhancing the connectivity between adjacent microstructures is crucial for achieving the collaborative optimization of micro-scale performance and macro-scale manufacturability. This paper proposes a two-scale concurrent topology optimization strategy aimed at improving the interface connection strength. This method employs a parametric approach to explicitly divide the micro-design domain into a “boundary connection region” and a “free design domain” at the initial stage of optimization. The boundary connection region is used to generate a connection layer that enhances the interface strength, while the free design domain is not constrained by this layer, thus… More >

Chenglong Lu¹, Jiehui Li¹, Tonglin Chen^1,2,*, Changhua Zhou¹, Yixin Fan¹, Xinlin Ren¹, Ziyi Ju¹, Wei Wang¹

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075400 - 12 March 2026

Abstract Existing methods for tracing water pollution sources typically integrate three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy with similarity-based matching algorithms. However, these approaches exhibit high error rates in borderline cases and necessitate expert manual review, which limits scalability and introduces inconsistencies between algorithmic outputs and expert judgment. To address these limitations, we propose a large vision-language model (VLM) designed as an “expert agent” to automatically refine similarity scores, ensuring alignment with expert decisions and overcoming key application bottlenecks. The model consists of two core components: (1) rule-based similarity calculation module generate initial spectral similarity scores, and More >

Chih-Yung Huang^*, Hong-Ru Shi, Min-Yan Xie

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075398 - 12 March 2026

Abstract Quality control plays a critical role in modern manufacturing. With the rapid development of electric vehicles, 5G communications, and the semiconductor industry, high-speed and high-precision detection of surface defects on silicon carbide (SiC) wafers has become essential. This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage. The complex machining textures on wafer surfaces hinder conventional machine vision models, often leading to misjudgment. To address this, deep learning algorithms were applied for defect classification. Because defects are rare and imbalanced across categories, data augmentation was performed… More >

Jingjing Liu¹, Chuanyang Liu^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075348 - 12 March 2026

Abstract Intelligent inspection of transmission lines enables efficient automated fault detection by integrating artificial intelligence, robotics, and other related technologies. It plays a key role in ensuring power grid safety, reducing operation and maintenance costs, driving the digital transformation of the power industry, and facilitating the achievement of the dual-carbon goals. This review focuses on vision-based power line inspection, with deep learning as the core perspective to systematically analyze the latest research advancements in this field. Firstly, at the technical foundation level, it elaborates on deep learning algorithms for intelligent transmission line inspection based on image… More >

Ahmad Salehiyan¹, Nuria Serrano², Francisco Hernando-Gallego³, Diego Martín^2,*, José Vicente Álvarez-Bravo²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075336 - 12 March 2026

Abstract The increasing integration of cyber-physical components in Industry 4.0 water infrastructures has heightened the risk of false data injection (FDI) attacks, posing critical threats to operational integrity, resource management, and public safety. Traditional detection mechanisms often struggle to generalize across heterogeneous environments or adapt to sophisticated, stealthy threats. To address these challenges, we propose a novel evolutionary optimized transformer-based deep reinforcement learning framework (Evo-Transformer-DRL) designed for robust and adaptive FDI detection in smart water infrastructures. The proposed architecture integrates three powerful paradigms: a transformer encoder for modeling complex temporal dependencies in multivariate time series, a… More >

Wenchao Shi¹, Tao Wei², Chuan Yang³, Qichao Fan³, Hongxi Liu⁴, Bin Shao^5,*, Peng Jing^4,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075293 - 12 March 2026

Abstract Enhancing the strength of nanotwinned aluminum (Al) is essential for the development of next-generation high-end chip technology. To better understand the detwinning behavior of nanotwinned Al under conditions with no resolved shear stress acting on the twin boundaries, we conducted molecular dynamics simulations of uniaxial tensile deformation in nanotwinned single-crystal Al at room temperature. Detwinning is observed only when the twin boundary spacing is 7.01 Å. At larger spacings, twin boundaries remain parallel to the loading direction, with no rotation or bending, indicating negligible migration. Detwinning is triggered by localized stress from dislocation interactions, with More >