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 >

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 >

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 >

Wenwen Zhang^*, Changgui Lei

Chalcogenide Letters, Vol.22, No.12, pp. 1031-1045, 2025, DOI:10.15251/CL.2025.2212.1031 - 08 December 2025

Abstract The development of quick, accurate, and field-deployable detection techniques is required due to the growing environmental pollution caused by heavy metals, especially lead (Pb) and cadmium (Cd). In this work, bismuth silver sulfide (AgBiS₂) nanoparticles are synthesized using a simple hydrothermal method aided by biomolecules, and they are used as a new modifier forPb(II) and Cd(II) simultaneous electrochemical detection using screen-printed electrodes (SPEs). After thorough material characterisation, pure, highly crystalline, quasi-spherical AgBiS₂ nanoparticles produced with an average diameter of around 20 nm. The AgBiS₂/SPE sensor demonstrated a threefold decrease in charge transfer resistance, indicating a considerable improvement More >

Xingkang Jiang^*, Jing Tian, Yong Xu

Canadian Journal of Urology, Vol.32, No.6, pp. 539-540, 2025, DOI:10.32604/cju.2025.073363 - 30 December 2025

Abstract This article has no abstract. More >

Charles H. Schlaepfer¹, Zubin Shetty¹, Vignesh T. Packiam¹, Chad R. Tracy¹, Elizabeth B. Takacs¹, Ruslan Korets², Ryan L. Steinberg^1,*

Canadian Journal of Urology, Vol.32, No.6, pp. 643-649, 2025, DOI:10.32604/cju.2025.064818 - 30 December 2025

Abstract Introduction: Point-of-care ultrasound (POCUS) is a valuable tool for clinicians, but little data exists regarding the perceptions of ideal POCUS utilization, as compared to actual use, amongst urologists. We aim to assess how perceptions align or diverge with actual practice. Methods: An institutional review board (IRB)-approved survey was developed and disseminated by email to 6 of 8 American Urologic Association Sections, program directors via the Society of Academic Urologists, and to 2 residency programs. The primary outcome was to assess differences in current and perceived optimal use. Data was collected via the University of Iowa… More >

Ilhong Son^1,2, Sun Jung Han², Dong Hwan Ho^1,3,*

BIOCELL, Vol.49, No.12, pp. 2283-2297, 2025, DOI:10.32604/biocell.2025.071119 - 24 December 2025

Abstract The potential of urinary biomarkers to facilitate non-invasive monitoring of Parkinson’s disease (PD) is a promising avenue, offering insights into the complex pathophysiology of the disease. The aggregation of α-synuclein, a central feature of PD, can be detected in urine, providing a diagnostic clue. Mutations in the LRRK2 gene, associated with increased kinase activity, can be estimated through the measurement of phosphorylated LRRK2 (pS1292) in urine. Oxidative stress, a hallmark of PD, is reflected in elevated levels of oxidized DJ-1 (oxDJ-1) in urine. Beyond these core biomarkers, other urinary components like DOPA decarboxylase, acetyl phenylalanine, More >

Zheng Yin, Zhao Zhang^*

Journal on Artificial Intelligence, Vol.7, pp. 633-645, 2025, DOI:10.32604/jai.2025.074450 - 24 December 2025

Abstract Power load forecasting load forecasting is a core task in power system scheduling, operation, and planning. To enhance forecasting performance, this paper proposes a dual-input deep learning model that integrates Convolutional Neural Networks, Gated Recurrent Units, and a self-attention mechanism. Based on standardized data cleaning and normalization, the method performs convolutional feature extraction and recurrent modeling on load and meteorological time series separately. The self-attention mechanism is then applied to assign weights to key time steps, after which the two feature streams are flattened and concatenated. Finally, a fully connected layer is used to generate More >