Bin Ou^1,2,3,4, Haoquan Chi^1,3, Xu’an Qian^1,3, Shuyan Fu^1,3, Zhirui Miao^1,3, Dingzhu Zhao^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.074757 - 29 January 2026

Abstract Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation. To address the challenges of complex monitoring data, the uneven spatial distribution of deformation, and the construction and optimization of a prediction model for deformation prediction, a multipoint ultrahigh arch dam deformation prediction model, namely, the CEEMDAN-KPCA-GSWOA-KELM, which is based on a clustering partition, is proposed. First, the monitoring data are preprocessed via variational mode decomposition (VMD) and wavelet denoising (WT), which effectively filters out noise and improves the signal-to-noise ratio of the data, providing high-quality input data for subsequent prediction… More > Graphic Abstract

Mohammad Shokouhifar^1,*, Fakhrosadat Fanian², Mehdi Hosseinzadeh^3,4,*, Aseel Smerat^5,6, Kamal M. Othman⁷, Abdulfattah Noorwali⁷, Esam Y. O. Zafar⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.073789 - 29 January 2026

Abstract Wireless Sensor Networks (WSNs) have become foundational in numerous real-world applications, ranging from environmental monitoring and industrial automation to healthcare systems and smart city development. As these networks continue to grow in scale and complexity, the need for energy-efficient, scalable, and robust communication protocols becomes more critical than ever. Metaheuristic algorithms have shown significant promise in addressing these challenges, offering flexible and effective solutions for optimizing WSN performance. Among them, the Grey Wolf Optimizer (GWO) algorithm has attracted growing attention due to its simplicity, fast convergence, and strong global search capabilities. Accordingly, this survey provides… More >

Weiqi Mao^1,2,3, Enbo Yu^1,*, Guoji Xu³, Xiaozhen Li³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.069733 - 29 January 2026

Abstract Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration. This study presents a novel machine learning model that integrates clustering, deep learning, and transfer learning to mitigate accuracy degradation in 24-h forecasting. Initially, an optimized DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm clusters wind fields based on wind direction, probability density, and spectral features, enhancing physical interpretability and reducing training complexity. Subsequently, a ResNet (Residual Network) extracts multi-scale patterns from decomposed wind signals, while transfer learning adapts the backbone network across clusters, cutting training time by over… More >

Bu Zou^1,#, Yi-En Xu^2,#, Hui-Chan He³, Zu-Lu Ye², Da-Lei Zhou², Cai-Yun He^2,*, Chan Huang^4,*

Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.069453 - 19 January 2026

Abstract Objectives: Gastric cancer (GC) remains a major global health concern, and Phosphoinositide-3-Kinase Regulatory Subunit 1 (PIK3R1), a regulatory subunit of the PI3K signaling pathway, may play a critical yet underexplored role in GC progression. This study aimed to investigate the prognostic significance of PIK3R1 in GC and its association with the tumor immune microenvironment. Methods: PIK3R1 expression and its clinical relevance were analyzed using datasets from GC patients who underwent gastrectomy, including cohorts from The Cancer Genome Atlas (TCGA) and the Sun Yat-sen University Cancer Center (SYSUCC). Prognostic models integrating PIK3R1 expression with clinical parameters… More >

Danping Niu¹, Yuan Ping^1,*, Chun Guo², Xiaojun Wang³, Bin Hao⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073630 - 12 January 2026

Abstract With the increasing complexity of malware attack techniques, traditional detection methods face significant challenges, such as privacy preservation, data heterogeneity, and lacking category information. To address these issues, we propose Federated Dynamic Prototype Learning (FedDPL) for malware classification by integrating Federated Learning with a specifically designed K-means. Under the Federated Learning framework, model training occurs locally without data sharing, effectively protecting user data privacy and preventing the leakage of sensitive information. Furthermore, to tackle the challenges of data heterogeneity and the lack of category information, FedDPL introduces a dynamic prototype learning mechanism, which adaptively adjusts the More >

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

Pham Huy Thong¹, Hoang Thi Canh^2,3,*, Nguyen Tuan Huy⁴, Nguyen Long Giang^1,*, Luong Thi Hong Lan⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071776 - 12 January 2026

Abstract Satellite image segmentation plays a crucial role in remote sensing, supporting applications such as environmental monitoring, land use analysis, and disaster management. However, traditional segmentation methods often rely on large amounts of labeled data, which are costly and time-consuming to obtain, especially in large-scale or dynamic environments. To address this challenge, we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering (SS-MPFC) algorithm, which improves segmentation accuracy and robustness, particularly in complex and uncertain remote sensing scenarios. SS-MPFC unifies three paradigms: semi-supervised learning, multi-view clustering, and picture fuzzy set theory. This integration allows the model to effectively… More >

Yuyao Huang, Hui Shu, Fei Kang^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071552 - 12 January 2026

Abstract Message structure reconstruction is a critical task in protocol reverse engineering, aiming to recover protocol field structures without access to source code. It enables important applications in network security, including malware analysis and protocol fuzzing. However, existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery, resulting in imprecise and incomplete reconstructions. In this paper, we propose ProRE, a novel method for reconstructing protocol field structures based on program execution slice embedding. ProRE extracts code slices from protocol parsing at runtime, converts them into embedding vectors using a data flow-sensitive assembly language model, More >

Karim Boudjebbour^1,2, Abdelkader Belkhir¹, Hamza Kheddar^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071532 - 12 January 2026

Abstract Identifying the community structure of complex networks is crucial to extracting insights and understanding network properties. Although several community detection methods have been proposed, many are unsuitable for social networks due to significant limitations. Specifically, most approaches depend mainly on user–user structural links while overlooking service-centric, semantic, and multi-attribute drivers of community formation, and they also lack flexible filtering mechanisms for large-scale, service-oriented settings. Our proposed approach, called community discovery-based service (CDBS), leverages user profiles and their interactions with consulted web services. The method introduces a novel similarity measure, global similarity interaction profile (GSIP), which… More >

Binjiang Hu^1,*, Yihua Zhu², Liang Tu^1,2, Zun Ma³, Xian Meng³, Kewei Xu³

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069777 - 27 December 2025

Abstract This paper proposes an equivalent modeling method for photovoltaic (PV) power stations via a particle swarm optimization (PSO) K-means clustering (KMC) algorithm with passive filter parameter clustering to address the complexities, simulation time cost and convergence problems of detailed PV power station models. First, the amplitude–frequency curves of different filter parameters are analyzed. Based on the results, a grouping parameter set for characterizing the external filter characteristics is established. These parameters are further defined as clustering parameters. A single PV inverter model is then established as a prerequisite foundation. The proposed equivalent method combines the… More >