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 >

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 >

Faten S. Alamri¹, Adil Ali Saleem², Muhammad I. Khan³, Hafeez Ur Rehman Siddiqui², Amjad Rehman^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074679 - 29 January 2026

Abstract Motor imbalance is a critical failure mode in rotating machinery, potentially causing severe equipment damage if undetected. Traditional vibration-based diagnostic methods rely on direct sensor contact, leading to installation challenges and measurement artifacts that can compromise accuracy. This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar. A dataset of 1802 experimental trials was sourced, covering four imbalance levels (0, 10, 20, 30 g) across varying motor speeds (500–1500 rpm) and load torques (0–3 Nm). Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second… More >

Hosam O. Elansary¹, Naveed A. Noor², Syed M. Ahmad³, Humza Riaz³, Sohail Mumtaz^4,*

Chalcogenide Letters, Vol.23, No.1, 2026, DOI:10.32604/cl.2026.076592 - 26 January 2026

Abstract Ferrites are remarkable compounds for energy harvesting and spintronic applications. For this purpose, mechanically stable, thermodynamic, photo-catalytic, and ferromagnetic characteristics of ferrites Al₂Mn(S/Se)₄ have been investigated significantly using PBEsol-GGA and modified Becke Johnson potential (TB-mBJ). In order to determine structural stability, we calculate formation energy (E_f) and Born stability criteria that confirm the structural stability of the Al₂Mn(S/Se)₄. 2D and 3D plots of Poisson’s ratio (υ) and linear compressibility are also used to indicate the stability of these materials. Additionally, thermodynamic characteristics reveal that both ferrites are stable. Spin-polarized electronic properties indicate that both ferrites are ferromagnetic More >

Cheng Zhang, Rui Zhang, Yu Shao, Jiabin Wang, Qianyue Yang, Fang Xie, Rongling Yang, Hongzhen Luo^*

Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0157 - 23 January 2026

Abstract Biomass is a resource whose organic carbon is formed from atmospheric carbon dioxide. It has numerous characteristics such as low carbon emissions, renewability, and environmental friendliness. The efficient utilization of biomass plays a significant role in promoting the development of clean energy, alleviating environmental pressures, and achieving carbon neutrality goals. Among the numerous processing technologies of biomass, hydrothermal carbonization (HTC) is a promising thermochemical process that can decompose and convert biomass into hydrochar under relatively mild conditions of approximately 180°C–300°C, thereby enabling its efficient resource utilization. In addition, HTC can directly process feedstocks with high… More >

Tahir Mahmood^1,*, Muhammad Waseem Ashraf^1,*, Shahzadi Tayyaba², Muhammad Munir³, Babiker M. A. Abdel-Banat³, Hassan Ali Dinar³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072436 - 12 January 2026

Abstract Artificial intelligence (AI) based models have been used to predict the structural, optical, mechanical, and electrochemical properties of zinc oxide/graphene oxide nanocomposites. Machine learning (ML) models such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and hybrid, along with fuzzy logic tools, were applied to predict the different properties like wavelength at maximum intensity (444 nm), crystallite size (17.50 nm), and optical bandgap (2.85 eV). While some other properties, such as energy density, power density, and charge transfer resistance, were also predicted with the help of datasets of 1000 (80:20). In… More >

M. N. Abd-Al Ameer, Iman S. Kareem, Ali A. Ismaeel^*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.073437 - 27 December 2025

Abstract Electrical and electronic devices face significant challenges in heat management due to their compact size and high heat flux, which negatively impact performance and reliability. Conventional cooling methods, such as forced air cooling, often struggle to transfer heat efficiently. In contrast, thermoelectric coolers (TECs) provide an innovative active cooling solution to meet growing thermal management demands. In this research, a refrigerant based on mono ethylene glycol and distilled water was used instead of using gases, in addition to using thermoelectric cooling units instead of using a compressor in traditional refrigeration systems. This study evaluates the… More > Graphic Abstract

K. Naga Venkata Siva¹, Damodhar Reddy², P. Krishna Murthy³, Kiran Kumar Pulamolu⁴, M. Dharani⁵, T. Venkatakrishnamoorthy^6,*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.072982 - 27 December 2025

Abstract Conventional multilevel inverters often suffer from high harmonic distortion and increased design complexity due to the need for numerous power semiconductor components, particularly at elevated voltage levels. Addressing these shortcomings, this work presents a robust 15-level Packed U Cell (PUC) inverter topology designed for renewable energy and grid-connected applications. The proposed system integrates a sensor less proportional-resonant (PR) controller with an advanced carrier-based pulse width modulation scheme. This approach efficiently balances capacitor voltage, minimizes steady-state error, and strongly suppresses both zero and third-order harmonics resulting in reduced total harmonic distortion and enhanced voltage regulation. Additionally, More >

Guangyu Huo, Chang Su, Xiaoyu Zhang^*, Xiaohui Cui, Lizhong Zhang

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.072147 - 09 December 2025

Abstract Long-term traffic flow prediction is a crucial component of intelligent transportation systems within intelligent networks, requiring predictive models that balance accuracy with low-latency and lightweight computation to optimize traffic management and enhance urban mobility and sustainability. However, traditional predictive models struggle to capture long-term temporal dependencies and are computationally intensive, limiting their practicality in real-time. Moreover, many approaches overlook the periodic characteristics inherent in traffic data, further impacting performance. To address these challenges, we introduce ST-MambaGCN, a State-Space-Based Spatio-Temporal Graph Convolution Network. Unlike conventional models, ST-MambaGCN replaces the temporal attention layer with Mamba, a state-space More >

Lei Wang^1,2, Menghan Wei², Ziwei Huangfu³, Shunjie Zhu², Xuejian Ge^1,*, Zhengquan Li⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-32, 2026, DOI:10.32604/cmc.2025.071295 - 09 December 2025

Abstract Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control More >