Mong-Fong Horng^1,#, Thanh-Lam Nguyen^1,#, Thanh-Tuan Nguyen^2,*, Chin-Shiuh Shieh^1,*, Lan-Yuen Guo³, Chen-Fu Hung⁴, Chun-Chih Lo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075064 - 29 January 2026

Abstract The global population is rapidly expanding, driving an increasing demand for intelligent healthcare systems. Artificial intelligence (AI) applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend. Among these applications, mouth motion tracking and mouth-state detection represent an important direction, providing valuable support for diagnosing neuromuscular disorders such as dysphagia, Bell’s palsy, and Parkinson’s disease. In this study, we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices. The proposed system integrates the Facial… More >

Guebin Choi^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073985 - 29 January 2026

Abstract This study proposes a novel forecasting framework that simultaneously captures the strong periodicity and irregular meteorological fluctuations inherent in solar radiation time series. Existing approaches typically define inter-regional correlations using either simple correlation coefficients or distance-based measures when applying spatio-temporal graph neural networks (STGNNs). However, such definitions are prone to generating spurious correlations due to the dominance of periodic structures. To address this limitation, we adopt the Elastic-Band Transform (EBT) to decompose solar radiation into periodic and amplitude-modulated components, which are then modeled independently with separate graph neural networks. The periodic component, characterized by strong More >

Yongmei Zhang^*, Tianxin Zhang, Linghua Tian

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073159 - 12 January 2026

Abstract Marine forecasting is critical for navigation safety and disaster prevention. However, traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features. To address the limitations, the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism. The model treats target forecast values as internal variables, and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer. Using a self-attention structure, the model captures correlations between exogenous variables and target sequences, explores intrinsic More >

José Arturo Ramírez-Fernández¹, Henevith G. Méndez-Figueroa¹, Sebastián Ossandón^2,*, Ricardo Galván-Martínez³, Miguel Ángel Hernández-Pérez³, Ricardo Orozco-Cruz³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072707 - 12 January 2026

Abstract In this study, artificial neural networks (ANNs) were implemented to determine design parameters for an impressed current cathodic protection (ICCP) prototype. An ASTM A36 steel plate was tested in 3.5% NaCl solution, seawater, and NS4 using electrochemical impedance spectroscopy (EIS) to monitor the evolution of the substrate surface, which affects the current required to reach the protection potential (Eprot). Experimental data were collected as training datasets and analyzed using statistical methods, including box plots and correlation matrices. Subsequently, ANNs were applied to predict the current demand at different exposure times, enabling the estimation of electrochemical More >

Jinfeng Ji¹, Geunseok Yang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071733 - 12 January 2026

Abstract Automated Program Repair (APR) techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects. Despite considerable progress in APR methodologies, existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code. In this paper, we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework, explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis. Our approach begins with an innovative preprocessing pipeline, where code segments and stack traces are… More >

Ruidong Chen^1,#, Guopeng Wang^2,#, Jingyuan Yang¹, Ziyu Wang¹, Fang Zou¹, Jia Sun¹, Xingpeng Tang¹, Ting Chen^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.068878 - 12 January 2026

Abstract The performance of data restore is one of the key indicators of user experience for backup storage systems. Compared to the traditional offline restore process, online restore reduces downtime during backup restoration, allowing users to operate on already restored files while other files are still being restored. This approach improves availability during restoration tasks but suffers from a critical limitation: inconsistencies between the access sequence and the restore sequence. In many cases, the file a user needs to access at a given moment may not yet be restored, resulting in significant delays and poor user… More >

Jianhua Du¹, Shaofeng Wang¹, Ting Gao², Huiwen Sun², Wenjing Liu^1,*

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071189 - 08 January 2026

Abstract In ultrasonic non-destructive testing of high-temperature industrial equipment, sound velocity drift induced by non-uniform temperature fields can severely compromise defect localization accuracy. Conventional approaches that rely on room-temperature sound velocities introduce systematic errors, potentially leading to misjudgment of safety-critical components. Two primary challenges hinder current methods: first, it is difficult to monitor real-time changes in sound velocity distribution within a thermal gradient; second, traditional uniform-temperature correction models fail to capture the nonlinear dependence of material properties on temperature and their effect on ultrasonic velocity fields. Here, we propose a defect localization correction method based on… More >

Himadri Nath Saha¹, Dipanwita Chakraborty Bhattacharya^2,*, Sancharita Dutta³, Arnab Bera³, Srutorshi Basuray⁴, Satyasaran Changdar⁵, Saptarshi Banerjee⁶, Jon Turdiev⁷

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-56, 2026, DOI:10.32604/cmc.2025.070507 - 09 December 2025

Abstract The emergence of Medical Large Language Models has significantly transformed healthcare. Medical Large Language Models (Med-LLMs) serve as transformative tools that enhance clinical practice through applications in decision support, documentation, and diagnostics. This evaluation examines the performance of leading Med-LLMs, including GPT-4Med, Med-PaLM, MEDITRON, PubMedGPT, and MedAlpaca, across diverse medical datasets. It provides graphical comparisons of their effectiveness in distinct healthcare domains. The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making, documentation, drug discovery, research, patient interaction, and public health. The paper addresses deployment challenges of Medical-LLMs, More >

Qiuru Fu¹, Shumao Zhang¹, Shuang Zhou¹, Jie Xu^1,*, Changming Zhao², Shanchao Li³, Du Xu^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070493 - 09 December 2025

Abstract Knowledge graphs often suffer from sparsity and incompleteness. Knowledge graph reasoning is an effective way to address these issues. Unlike static knowledge graph reasoning, which is invariant over time, dynamic knowledge graph reasoning is more challenging due to its temporal nature. In essence, within each time step in a dynamic knowledge graph, there exists structural dependencies among entities and relations, whereas between adjacent time steps, there exists temporal continuity. Based on these structural and temporal characteristics, we propose a model named “DKGR-DR” to learn distributed representations of entities and relations by combining recurrent neural networks More >

Zheyuan Jia, Fenglin Jin^*, Jun Xie, Yuan He

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.069128 - 10 November 2025

Abstract This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks (SAGIN) through a novel Recursive Multi-Agent Proximal Policy Optimization (RMAPPO) algorithm. The exponential growth of mobile devices and data traffic has substantially increased network congestion, particularly in urban areas and regions with limited terrestrial infrastructure. Our approach jointly optimizes unmanned aerial vehicle (UAV) trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput, minimize energy consumption, and maintain equitable resource distribution. The proposed RMAPPO framework incorporates recurrent neural networks (RNNs) to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent More >