Hang Wen^1,2, Kai Zeng^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072909 - 12 January 2026

Abstract Federated learning often experiences slow and unstable convergence due to edge-side data heterogeneity. This problem becomes more severe when edge participation rate is low, as the information collected from different edge devices varies significantly. As a result, communication overhead increases, which further slows down the convergence process. To address this challenge, we propose a simple yet effective federated learning framework that improves consistency among edge devices. The core idea is clusters the lookahead gradients collected from edge devices on the cloud server to obtain personalized momentum for steering local updates. In parallel, a global momentum… More > Graphic Abstract

Binchang Ma¹, Xinhai Yu², Gang Huang^3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.071320 - 10 November 2025

Abstract Vacancy defects, as fundamental disruptions in metallic lattices, play an important role in shaping the mechanical and electronic properties of aluminum crystals. However, the influence of vacancy position under coupled thermomechanical fields remains insufficiently understood. In this study, transmission and scanning electron microscopy were employed to observe dislocation structures and grain boundary heterogeneities in processed aluminum alloys, suggesting stress concentrations and microstructural inhomogeneities associated with vacancy accumulation. To complement these observations, first-principles calculations and molecular dynamics simulations were conducted for seven single-vacancy configurations in face-centered cubic aluminum. The stress response, total energy, density of states More >

Raghavendra Kulkarni¹, Venkata Satya Suresh kumar Kondeti¹, Binu Sudhakaran Pillai², Surendran Rajendran^3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069835 - 10 November 2025

Abstract The next-generation RAN, known as Open Radio Access Network (ORAN), allows for several advantages, including cost-effectiveness, network flexibility, and interoperability. Now ORAN applications, utilising machine learning (ML) and artificial intelligence (AI) techniques, have become standard practice. The need for Federated Learning (FL) for ML model training in ORAN environments is heightened by the modularised structure of the ORAN architecture and the shortcomings of conventional ML techniques. However, the traditional plaintext model update sharing of FL in multi-BS contexts is susceptible to privacy violations such as deep-leakage gradient assaults and inference. Therefore, this research presents a… More >

Haoran Wang¹, Shuhong Yang², Kuan Shao², Tao Xiao², Zhenyong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069404 - 10 November 2025

Abstract With the rapid development of the Artificial Intelligence of Things (AIoT), convolutional neural networks (CNNs) have demonstrated potential and remarkable performance in AIoT applications due to their excellent performance in various inference tasks. However, the users have concerns about privacy leakage for the use of AI and the performance and efficiency of computing on resource-constrained IoT edge devices. Therefore, this paper proposes an efficient privacy-preserving CNN framework (i.e., EPPA) based on the Fully Homomorphic Encryption (FHE) scheme for AIoT application scenarios. In the plaintext domain, we verify schemes with different activation structures to determine the… More >

Ghulam Mujtaba^1,2,#, Wenbiao Liu^1,#, Mohammed Alshehri³, Yahya AlQahtani⁴, Nouf Abdullah Almujally⁵, Hui Liu^1,6,7,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.067895 - 10 November 2025

Abstract As urban landscapes evolve and vehicular volumes soar, traditional traffic monitoring systems struggle to scale, often failing under the complexities of dense, dynamic, and occluded environments. This paper introduces a novel, unified deep learning framework for vehicle detection, tracking, counting, and classification in aerial imagery designed explicitly for modern smart city infrastructure demands. Our approach begins with adaptive histogram equalization to optimize aerial image clarity, followed by a cutting-edge scene parsing technique using Mask2Former, enabling robust segmentation even in visually congested settings. Vehicle detection leverages the latest YOLOv11 architecture, delivering superior accuracy in aerial contexts… More >

Ahmed El Maloufy, Mohamed Amine Tahiri, Ahmed Bencherqui, Hicham Karmouni, Mhamed Sayyouri^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5789-5822, 2025, DOI:10.32604/cmc.2025.068885 - 23 October 2025

Abstract In the current digital context, safeguarding copyright is a major issue, particularly for architectural drawings produced by students. These works are frequently the result of innovative academic thinking combining creativity and technical precision. They are particularly vulnerable to the risk of illegal reproduction when disseminated in digital format. This research suggests, for the first time, an innovative approach to copyright protection by embedding a double digital watermark to address this challenge. The solution relies on a synergistic fusion of several sophisticated methods: Krawtchouk Optimized Octonion Moments (OKOM), Quaternion Singular Value Decomposition (QSVD), and Discrete Waveform… More >

Shuoshuo Shen^1,2, Jin Cheng^1,2,*, Zhenyu Liu², Jianrong Tan^1,2

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-2, 2025, DOI:10.32604/icces.2025.011752

Abstract Motion stability assessment of industrial robots subject to complex dynamic properties and multi-source uncertainties in open environments registers an important yet challenging task [1–5]. To tackle this task, this study proposes a new reliability-based motion stability analysis method for industrial robots, which incorporates the moment-based method and Bayesian inference-guided probabilistic model updating strategy. To start with, the comprehensive motion system model of industrial robots is established by integrating the control, drive, and multi-body motion models. The reliability-based stability model of industrial robots is presented considering the uncertainty of parameters. Subsequently, the fractional exponential moments are… More >

Shifa Fan^1,*, Yuanwen Gao^2,3, Zhiqiang Li¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-2, 2025, DOI:10.32604/icces.2025.010668

Abstract In recent years, wearable technology has burst onto the scene as a game-changer, completely transforming multiple facets of our daily lives—from keeping tabs on our health to facilitating communication for staying connected. It has found its way into diverse fields such as healthcare, education, the military, engineering, and sports. However, a major challenge hindering the popularization of wearable devices is the need for a reliable power source. Conventional batteries, though widely used, have limitations, including the need for frequent recharging or replacement, which hinder the seamless integration of wearable technology into everyday life [1]. To… More >

Ksenia Saerova^*, Ruslan Safin

Journal of Renewable Materials, Vol.13, No.9, pp. 1829-1840, 2025, DOI:10.32604/jrm.2025.02025-0076 - 22 September 2025

Abstract This article presents research focused on developing and scientifically substantiating a technology for producing environmentally friendly glued structures from wood treated through a two-stage process. The methodology involves preliminary thermal modification followed by high-frequency low-temperature plasma treatment. Thermal modification enhances performance characteristics such as resistance to rot, lowers hygroscopicity, and increases dimensional stability. However, it can diminish the adhesive properties of wood, complicating the bonding process. To address this challenge, the study introduces high-frequency low-temperature plasma treatment, which activates the wood surface, improving wettability and adhesion while minimizing glue consumption. Experimental results indicate that plasma More >

Yun Wang^1,2,#, Xin-Yu Li^1,3,#, Sheng-Li Wu^1,3, Pianchou Gongpan¹, Da-Hong Li², Chang-An Geng^1,3,*

Phyton-International Journal of Experimental Botany, Vol.94, No.8, pp. 2563-2574, 2025, DOI:10.32604/phyton.2025.068185 - 29 August 2025

Abstract Twelve lignans (1–12) isolated from Amomum tsao-ko leaves were evaluated for the inhibitory effects against α-glucosidase and PTP1B. Compounds 1−4 and 10 showed inhibition on α-glucosidase with inhibitory ratios ranging from 53.8% to 90.0%, while compound 10 demonstrated 56.1% inhibition on PTP1B at 200 μM. Notably, erythro-5-methoxy-dadahol A (2) and threo-5-methoxy-dadahol A (3) displayed obvious inhibition on α-glucosidase with IC₅₀ values of 33.3 μM and 22.1 μM, significantly outperforming acarbose (IC₅₀ = 344.0 μM). Kinetic study revealed that compound 3 maintained a mixed-type mode, engaging with both free enzyme and enzyme-substrate complex via non-competitive and uncompetitive mechanisms. Molecular docking simulations further clarified its More >