Divya Ramesh, Sriram Venkatachalam^*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.069854

Abstract A hybrid model combining Fully Non-Linear Potential Flow Theory (FNPT) based on the Finite Element Method (FEM) and the Unified Navier-Stokes equation, using the 3D Improved Meshless Local Petrov Galerkin method with Rankine Source (IMLPG_R), is developed to study wave interactions with a porous layer. In previous studies, the above formulations are applied to wave interaction with fixed cylindrical structures. The present study extends this framework by integrating a unified governing equation within the hybrid modeling approach to capture the dynamics of wave interaction with porous media. The porous layers are employed to replicate the… 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, DOI:10.32604/cmes.2025.075064

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 >

Yewon Choi^1,2, Song Hyeon Ju², Jungsoo Nam^2,*, Min Ku Kim^1,3,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.071817

Abstract The composite material layering process has attracted considerable attention due to its production advantages, including high scalability and compatibility with a wide range of raw materials. However, changes in process conditions can lead to degradation in layer quality and non-uniformity, highlighting the need for real-time monitoring to improve overall quality and efficiency. In this study, an AI-based monitoring system was developed to evaluate layer width and assess quality in real time. Three deep learning models Faster Region-based Convolutional Neural Network (R-CNN), You Only Look Once version 8 (YOLOv8), and Single Shot MultiBox Detector (SSD) were… More >

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

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074679

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 >

Mohammed Al-Mahbashi^1,2,*, Ali Ahmed³, Abdolraheem Khader^4,*, Shakeel Ahmad³, Mohamed A. Damos⁵, Ahmed Abdu⁶

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075909

Abstract Reliable detection of traffic signs and lights (TSLs) at long range and under varying illumination is essential for improving the perception and safety of autonomous driving systems (ADS). Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions. To overcome these limitations, this research presents FED-YOLOv10s, an improved and lightweight object detection framework based on You Only look Once v10 (YOLOv10). The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations, an Efficient Multiscale Attention (EMA) mechanism to More >

Van-Viet Nguyen¹, Huu-Khanh Nguyen², Kim-Son Nguyen¹, Thi Minh-Hue Luong¹, Duc-Quang Vu¹, Trung-Nghia Phung³, The-Vinh Nguyen^1,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075442

Abstract It remains difficult to automate the creation and validation of Unified Modeling Language (UML) diagrams due to unstructured requirements, limited automated pipelines, and the lack of reliable evaluation methods. This study introduces a cohesive architecture that amalgamates requirement development, UML synthesis, and multimodal validation. First, LLaMA-3.2-1B-Instruct was utilized to generate user-focused requirements. Then, DeepSeek-R1-Distill-Qwen-32B applies its reasoning skills to transform these requirements into PlantUML code. Using this dual-LLM pipeline, we constructed a synthetic dataset of 11,997 UML diagrams spanning six major diagram families. Rendering analysis showed that 89.5% of the generated diagrams compile correctly, while… More >

Monalisa Jena¹, Noman Khan^2,*, Mi Young Lee^3,*, Seungmin Rho³

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.075119

Abstract Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises. When such risks go undetected, consequences can escalate to self-harm, long-term disability, reduced productivity, and significant societal and economic burden. Despite recent advances, detecting risk from online text remains challenging due to heterogeneous language, evolving semantics, and the sequential emergence of new datasets. Effective solutions must encode clinically meaningful cues, reason about causal relations, and adapt to new domains without forgetting prior knowledge. To address these challenges, this paper presents a Continual Neuro-Symbolic Graph… More >

Qi Li, Yifan Wang^*, Yuxi Liu, Xingjing She, Yixuan Wu

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074066

Abstract In data communication, limited communication resources often lead to measurement bias, which adversely affects subsequent system estimation if not effectively handled. This paper proposes a novel bias calibration algorithm under communication constraints to achieve accurate system states of the interested system. An output-based event-triggered scheme is first employed to alleviate transmission burden. Accounting for the limited-communication-induced measurement bias, a novel bias calibration algorithm following the Kalman filtering line is developed to restrain the effect of the measurement bias on system estimation, thereby achieving accurate system state estimates. Subsequently, the Field Programmable Gate Array (FPGA) implementation More >

Abdulaziz Almalaq¹, Ambe Harrison^2,*, Ibrahim Alsaleh¹, Abdullah Alassaf¹, Mashari Alangari¹

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074815

Abstract We present a computer-modeling framework for photovoltaic (PV) source emulation that preserves the exact single-diode physics while enabling iteration-free, real-time evaluation. We derive two closed-form explicit solvers based on the Lambert W function: a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator. Unlike Taylor-linearized explicit models, our proposed formulation retains the exponential nonlinearity of the PV equations. It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding, all while maintaining limited computational costs and a small… More >

Muhammad Farman^1,2,3,*, Muhammad Hashir Zubair⁴, Hua Li⁴, Kottakkaran Sooppy Nisar^5,6, Mohamad Hafez^7,8

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074160

Abstract Lassa Fever (LF) is a viral hemorrhagic illness transmitted via rodents and is endemic in West Africa, causing thousands of deaths annually. This study develops a dynamic model of Lassa virus transmission, capturing the progression of the disease through susceptible, exposed, infected, and recovered populations. The focus is on simulating this model using the fractional Caputo derivative, allowing both qualitative and quantitative analyses of boundedness, positivity, and solution uniqueness. Fixed-point theory and Lipschitz conditions are employed to confirm the existence and uniqueness of solutions, while Lyapunov functions establish the global stability of both disease-free and… More >