Wenfeng Zheng¹, Guangyu Xu², Siyu Lu³, Junmin Lyu⁴, Feng Bao^5,*, Lirong Yin^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075741 - 29 January 2026

Abstract Graph Neural Networks (GNNs), as a deep learning framework specifically designed for graph-structured data, have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis. However, current reviews on GNN models are mainly focused on smaller domains, and there is a lack of systematic reviews on the classification and applications of GNN models. This review systematically synthesizes the three canonical branches of GNN, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sampling Aggregation Network (GraphSAGE), and analyzes their integration pathways More >

Yuerou Feng, Panpan Tong, Shuwen Fu, Xiaofan Lu, Liquan Zheng, Jielan Lai^*, Renchun Lai^*

Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.072563 - 19 January 2026

Abstract Background: The efficacy of standard 5-fluorouracil (5-FU) chemotherapy for colorectal cancer is limited by drug resistance and adverse effects, prompting research into esketamine, a potent ketamine variant with analgesic, antidepressant, and recently discovered anti-tumor properties, to determine if it can enhance 5-FU’s chemosensitivity. This study investigates whether esketamine synergizes with 5-FU to enhance therapeutic efficacy in colorectal adenocarcinoma cell models. Methods: We performed functional assays to evaluate proliferation (CCK-8), migration (wound healing), invasion (Transwell), and apoptosis (flow cytometry) in colorectal adenocarcinoma cell lines treated with 5-FU alone or in combination with esketamine. Transcriptomic profiling was… More >

Girish Hariharan¹, Vinyas¹, Gowrishankar Mandya Chennegowda¹, Nitesh Kumar¹, Shiva Kumar¹, Deepak Doreswamy², Subraya Krishna Bhat^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073772 - 12 January 2026

Abstract This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite–epoxy (G–E) composites reinforced with cast iron (CI) filler particles. Hybrid G–E composite with added cast iron (CI) filler particles enhances stiffness, strength, and vibration damping, offering enhanced performance for vibration-sensitive engineering applications. Unlike conventional approaches, this work simultaneously employs Artificial Neural Networks (ANN) for high-accuracy property prediction and Response Surface Methodology (RSM) for in-depth analysis of factor interactions and optimization. A total of 24 experimental test data sets of varying input… More >

Nikita Golovkin^1,2, Olesya Nikulenkova³, Vsevolod Pobezhimov¹, Alexander Nesmelov¹, Sergei Chvalun¹, Fedor Sorokin³, Arthur Krupnin^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073161 - 12 January 2026

Abstract This study presents and verifies a hybrid methodology for reliable determination of parameters in structural rheological models (Zener, Burgers, and Maxwell) describing the viscoelastic behavior of polyurethane specimens manufactured using extrusion-based 3D printing. Through comprehensive testing, including cyclic compression at strain rates ranging from 0.12 to 120 mm/min (0%–15% strain) and creep/relaxation experiments (10%–30% strain), the lumped parameters were independently determined using both analytical and numerical solutions of the models’ differential equations, followed by cross-verification in additional experiments. Numerical solutions for creep and relaxation problems were obtained using finite element analysis, with the three-parameter Mooney-Rivlin… More > Graphic Abstract

Tao Geng¹, Shuaibing Li^1,*, Yunyun Yun¹, Yongqiang Kang¹, Hongwei Li², Junmin Zhu²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071644 - 12 January 2026

Abstract In order to address the challenges posed by complex background interference, high miss-detection rates of micro-scale defects, and limited model deployment efficiency in photovoltaic (PV) module defect detection, this paper proposes an efficient detection framework based on an improved YOLOv11 architecture. First, a Re-parameterized Convolution (RepConv) module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency. Second, a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism (MSFF-CBAM) is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial… More >

Jin Lin^1,*, Bin Yu², Chao Chen¹, Jiezhen Cai¹, Yifan Wu², Cunping Wang³

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069310 - 27 December 2025

Abstract With the increasing integration of renewable energy, microgrids are increasingly facing stability challenges, primarily due to the lack of inherent inertia in inverter-dominated systems, which is traditionally provided by synchronous generators. To address this critical issue, Virtual Synchronous Generator (VSG) technology has emerged as a highly promising solution by emulating the inertia and damping characteristics of conventional synchronous generators. To enhance the operational efficiency of virtual synchronous generators (VSGs), this study employs small-signal modeling analysis, root locus methods, and synchronous generator power-angle characteristic analysis to comprehensively evaluate how virtual inertia and damping coefficients affect frequency… More > Graphic Abstract

Xiaorui Zhang^1,2,*, Chunlin Yuan³, Wei Sun⁴, Ting Wang⁵

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.071043 - 09 December 2025

Abstract Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression, but existing models have significant shortcomings. On the one hand, Transformer multi-head self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity. On the other hand, fixed-size convolution kernels are often used, which have weak perception ability for emotional regions of different scales. Therefore, this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling. In terms of space, multi-branch large-kernel stripe convolution is used to More >

Bing Wei, Ming Zhong, Qian Chen, Yi Wu^*, Yubin Li

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069910 - 09 December 2025

Abstract In erasure-coded storage systems, updating data requires parity maintenance, which often leads to significant I/O amplification due to “write-after-read” operations. Furthermore, scattered parity placement increases disk seek overhead during repair, resulting in degraded system performance. To address these challenges, this paper proposes a Cognitive Update and Repair Method (CURM) that leverages machine learning to classify files into write-only, read-only, and read-write categories, enabling tailored update and repair strategies. For write-only and read-write files, CURM employs a data-difference mechanism combined with fine-grained I/O scheduling to minimize redundant read operations and mitigate I/O amplification. For read-write files,… More >

Agit Amrullah, Ferda Ernawan^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069229 - 09 December 2025

Abstract This paper proposes a tamper detection technique for semi-fragile watermarking using Quantization-based Discrete Cosine Transform (DCT) for tamper localization. In this study, the proposed embedding strategy is investigated by experimental tests over the diagonal order of the DCT coefficients. The cover image is divided into non-overlapping blocks of size 8 × 8 pixels. The DCT is applied to each block, and the coefficients are arranged using a zig-zag pattern within the block. In this study, the low-frequency coefficients are selected to examine the impact of the imperceptibility score and tamper detection accuracy. High accuracy of… More >

Muhammad Adil¹, Nadeem Javaid^1,*, Imran Ahmed², Abrar Ahmed³, Nabil Alrajeh^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071215 - 10 November 2025

Abstract Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention. However, existing Deep Learning (DL) approaches often face several limitations, including inefficient feature extraction, class imbalance, suboptimal classification performance, and limited interpretability, which collectively hinder their deployment in clinical settings. To address these challenges, we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture. The preprocessing stage involves label encoding and feature scaling. To address the issue of… More >