José Velázquez, Dolores Ojados, Adrián Semitiel, Francisco Cavas^*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.071131

Abstract This research establishes a methodological framework for generating geometrically accurate 3D representations of human crystalline lenses through scanning technologies and digital reconstruction. Multiple scanning systems were evaluated to identify optimal approaches for point cloud processing and subsequent development of parameterized solid models, facilitating comprehensive morpho-geometric characterization. Experimental work was performed at the 3D Scanning Laboratory of SEDIC (Industrial Design and Scientific Calculation Service) at the Technical University of Cartagena, employing five distinct scanner types based on structured light, laser, and infrared technologies. Test specimens—including preliminary calibration using a lentil and biological analysis of a human… More >

Yonggang Tong^1,*, Kai Yang¹, Pengfei Li¹, Yongle Hu¹, Xiubing Liang^2,*, Jian Liu³, Yejun Li⁴, Jingzhong Fang¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071890

Abstract (NbZrHfTi)C high-entropy ceramics, as an emerging class of ultra-high-temperature materials, have garnered significant interest due to their unique multi-principal-element crystal structure and exceptional high-temperature properties. This study systematically investigates the mechanical properties of (NbZrHfTi)C high-entropy ceramics by employing first-principles density functional theory, combined with the Debye-Grüneisen model, to explore the variations in their thermophysical properties with temperature (0–2000 K) and pressure (0–30 GPa). Thermodynamically, the calculated mixing enthalpy and Gibbs free energy confirm the feasibility of forming a stable single-phase solid solution in (NbZrHfTi)C. The calculated results of the elastic stiffness constant indicate that the… More >

Binchang Ma¹, Xinhai Yu², Gang Huang^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071320

Abstract Vacancy defects, as fundamental disruptions in metallic lattices, play an important role in shaping the mechanical and electronic properties of aluminum crystals. However, the influence of vacancy position under coupled thermomechanical fields remains insufficiently understood. In this study, transmission and scanning electron microscopy were employed to observe dislocation structures and grain boundary heterogeneities in processed aluminum alloys, suggesting stress concentrations and microstructural inhomogeneities associated with vacancy accumulation. To complement these observations, first-principles calculations and molecular dynamics simulations were conducted for seven single-vacancy configurations in face-centered cubic aluminum. The stress response, total energy, density of states More >

Zeyu Chen¹, Jian Sun^2,*, Zhengda Huan¹, Ziyi Zhang¹

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.071182

Abstract To address the issues of poor adaptability in resource allocation and low multi-agent cooperation efficiency in Joint Radar and Communication (JRC) systems under dynamic environments, an intelligent optimization framework integrating Deep Reinforcement Learning (DRL) and Graph Neural Network (GNN) is proposed. This framework models resource allocation as a Partially Observable Markov Game (POMG), designs a weighted reward function to balance radar and communication efficiencies, adopts the Multi-Agent Proximal Policy Optimization (MAPPO) framework, and integrates Graph Convolutional Networks (GCN) and Graph Sample and Aggregate (GraphSAGE) to optimize information interaction. Simulations show that, compared with traditional methods More >

Haoxin Sun¹, Xiao Yu^1,*, Jiale Li¹, Yitong Xu¹, Jie Yu¹, Huanhuan Li¹, Yuanzhang Li², Yu-An Tan²

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070930

Abstract Since the advent of smart contracts, security vulnerabilities have remained a persistent challenge, compromsing both the reliability of contract execution and the overall stability of the virtual currency market. Consequently, the academic community has devoted increasing attention to these security risks. However, conventional approaches to vulnerability detection frequently exhibit limited accuracy. To address this limitation, the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks (GNNs). The proposed method first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts. More >

Malvinder Singh Bali¹, Weiwei Jiang^2,*, Saurav Verma³, Kanwalpreet Kour⁴, Ashwini Rao³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070866

Abstract In recent years, Blockchain Technology has become a paradigm shift, providing Transparent, Secure, and Decentralized platforms for diverse applications, ranging from Cryptocurrency to supply chain management. Nevertheless, the optimization of blockchain networks remains a critical challenge due to persistent issues such as latency, scalability, and energy consumption. This study proposes an innovative approach to Blockchain network optimization, drawing inspiration from principles of biological evolution and natural selection through evolutionary algorithms. Specifically, we explore the application of genetic algorithms, particle swarm optimization, and related evolutionary techniques to enhance the performance of blockchain networks. The proposed methodologies More >

Abu Tayab^1,*, Yanwen Li¹, Ahmad Syed², Ghanshyam G. Tejani^3,4,*, Doaa Sami Khafaga⁵, El-Sayed M. El-kenawy⁶, Amel Ali Alhussan⁷, Marwa M. Eid^8,9

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070583

Abstract Autonomous connected vehicles (ACV) involve advanced control strategies to effectively balance safety, efficiency, energy consumption, and passenger comfort. This research introduces a deep reinforcement learning (DRL)-based car-following (CF) framework employing the Deep Deterministic Policy Gradient (DDPG) algorithm, which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning. Utilizing real-world driving data from the highD dataset, the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios. The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control (MPC-ACC) controller. Results show that the… More >

Ameer Hamza, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069721

Abstract This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities, focusing on recent trends from 2022 to 2025. The primary objective is to evaluate methodological advancements, model performance, dataset usage, and existing challenges in developing clinically robust AI systems. We included peer-reviewed journal articles and high-impact conference papers published between 2022 and 2025, written in English, that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification. Excluded were non-open-access publications, books, and non-English articles. A structured search was… More >

Rasit Dinc¹, Nurittin Ardic^2,*

BIOCELL, DOI:10.32604/biocell.2025.072337

Abstract Neutrophil extracellular traps (NETs) have emerged as key mediators of cardiovascular diseases (CVDs), linking innate immune activation to vascular injury, thrombosis, and maladaptive remodeling. This review synthesizes recent insights into the molecular and cellular pathways driving NET formation, including post-translational modifications, metabolic reprogramming, inflammasome signaling, and autophagy. It highlights the role of NETs in atherosclerosis, thrombosis, myocardial ischemia-reperfusion injury, and hypertension, emphasizing common control points such as peptidylarginine deiminase 4 (PAD4)-dependent histone citrullination and nicotinamide adenine dinucleotide phosphate oxidases 2 (NOX2)-mediated oxidative stress. Mechanistic interpretation of circulating biomarkers, including myeloperoxidase (MPO)-DNA complexes, citrullinated histone H3,… More >

Jawad Ahmad¹, Mujeeb Ur Rehman^2,*, Wadii Boulila³

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074581

Abstract This article has no abstract. More >