Hefei Wang, Ruichun Gu^*, Jingyu Wang, Xiaolin Zhang, Hui Wei

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069251 - 10 November 2025

Abstract Graph Federated Learning (GFL) has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information. However, existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization, particularly in non-independent and identically distributed (NON-IID) scenarios where balancing global structural understanding and local node-level detail remains a challenge. To this end, this paper proposes a novel framework called GFL-SAR (Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement), which enhances the representation learning capability of graph data through a dual-branch… More >

Wenxiang Zhang^1,2, Wenning Cui¹, Juan Huang³, Zhangen Lu¹, Kuijing Liang¹, Lingbao Wang^1,2, Shanshan Wei^1,2, Liran Shi¹, Huifen Li¹, Xiaoli Guo^1,2,*, Jianhui Ma^4,*

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 3893-3912, 2025, DOI:10.32604/phyton.2025.073736 - 29 December 2025

Abstract UDP-glycosyltransferases (UGTs) play essential roles in plant secondary metabolism and stress responses, yet their composition and functions in Sorghum bicolor, a model C4 plant, remain inadequately characterized. This study identified 196 SbUGT genes distributed across all 10 chromosomes and classified them into 16 subfamilies (A–P) through phylogenetic analysis. Among these, 61.2% were intronless, and 10 conserved motifs, including the UGT-specific PSPG box, were identified. Synteny analysis using MCScanX revealed 12 segmental duplication events and conserved syntenic relationships with other Poaceae species (rice, maize, and barley). Promoter analysis uncovered 125 distinct cis-acting elements, predominantly associated with stress and… More >

Suppakit Eiadtrong^1,2,#, Tan N. Nguyen^3,#,*, Mohamed-Ouejdi Belarbi⁴, Nuttawit Wattanasakulpong^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2819-2848, 2025, DOI:10.32604/cmes.2025.069481 - 30 September 2025

Abstract An improved meshfree moving-Kriging (MK) formulation for free vibration analysis of functionally graded material-functionally graded carbon nanotube-reinforced composite (FGM-FGCNTRC) sandwich shells is first proposed in this article. The proposed sandwich structure consists of skins of FGM layers and an FGCNTRC core. This structure possesses all the advantages of FGM and FGCNTRC, including high electrical or thermal insulating properties, high fatigue resistance, good corrosion resistance, high stiffness, low density, high strength, and high aspect ratios. Such sandwich structures can be used to replace conventional FGM structures. The present formulation has been established by using an improved More >

Saeed Alzahrani¹, Nazar Hussain², Farah Mohammad^3,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4825-4849, 2025, DOI:10.32604/cmc.2025.066322 - 30 July 2025

Abstract Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients. This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling. By integrating real-time images, electronic health records, and genomic information, the system enables personalized simulations for disease progression modeling, treatment response prediction, and preventive care strategies. In dysarthric speech, which is characterized by articulation imprecision, temporal misalignments, and phoneme distortions, existing models struggle to capture these irregularities. Traditional approaches, often relying solely on audio features, fail to address… More >

Ya-Jie Sun¹, Li-Wei Qiao¹, Sai Ji^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1769-1785, 2025, DOI:10.32604/cmc.2025.062950 - 09 June 2025

Abstract Vehicle re-identification involves matching images of vehicles across varying camera views. The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images, which increases the complexity of re-identification tasks. To tackle these challenges, this study proposes AG-GCN (Attention-Guided Graph Convolutional Network), a novel framework integrating several pivotal components. Initially, AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically, thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones. Moreover, AG-GCN adopts More >

Yuxin Gao¹, Xiaodong Duan^2,3, Qiguo Dai^2,3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 701-718, 2025, DOI:10.32604/cmc.2025.060137 - 26 March 2025

Abstract Graph convolutional network (GCN) as an essential tool in human action recognition tasks have achieved excellent performance in previous studies. However, most current skeleton-based action recognition using GCN methods use a shared topology, which cannot flexibly adapt to the diverse correlations between joints under different motion features. The video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation algorithms. In this work, we propose a novel graph convolutional learning framework, called PCCTR-GCN, which integrates pose correction and channel topology refinement for skeleton-based human action… More >

Lijuan Zhang^1,2, Xiaoyu Wang^1,2, Songtao Zhang³, Yutong Jiang^4,*, Dongming Li¹, Weichen Sun⁴

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1219-1237, 2025, DOI:10.32604/cmc.2025.059856 - 26 March 2025

Abstract In this paper, a reasoning enhancement method based on RGCN (Relational Graph Convolutional Network) is proposed to improve the detection capability of UAV (Unmanned Aerial Vehicle) on fast-moving military targets in urban battlefield environments. By combining military images with the publicly available VisDrone2019 dataset, a new dataset called VisMilitary was built and multiple YOLO (You Only Look Once) models were tested on it. Due to the low confidence problem caused by fuzzy targets, the performance of traditional YOLO models on real battlefield images decreases significantly. Therefore, we propose an improved RGCN inference model, which improves More >

Xin Liu^1,*, Meng Sun¹, Bo Lin², Shibo Gu¹

Energy Engineering, Vol.122, No.3, pp. 1053-1072, 2025, DOI:10.32604/ee.2025.060489 - 07 March 2025

Abstract Long-term petroleum production forecasting is essential for the effective development and management of oilfields. Due to its ability to extract complex patterns, deep learning has gained popularity for production forecasting. However, existing deep learning models frequently overlook the selective utilization of information from other production wells, resulting in suboptimal performance in long-term production forecasting across multiple wells. To achieve accurate long-term petroleum production forecast, we propose a spatial-geological perception graph convolutional neural network (SGP-GCN) that accounts for the temporal, spatial, and geological dependencies inherent in petroleum production. Utilizing the attention mechanism, the SGP-GCN effectively captures… More >

Baoquan Liu^1,3, Xi Chen^2,3, Qingjun Yuan^2,3, Degang Li^2,3, Chunxiang Gu^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3179-3201, 2025, DOI:10.32604/cmc.2024.059688 - 17 February 2025

Abstract With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not… More >

Xinlu Zong^*, Fan Yu, Zhen Chen, Xue Xia

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3517-3537, 2025, DOI:10.32604/cmc.2024.057494 - 17 February 2025

Abstract Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a More >