Wei Zhu, Yan Song, Di Chen, Huimin Chen, Dingding Zhang, Lu Chen^*

International Journal of Mental Health Promotion, DOI:10.32604/ijmhp.2025.070461

Abstract Objectives: Fear of recurrence (FoR) is a common psychological burden in cerebral glioma patients. Spousal emotional support and self-disclosure may help mitigate FoR, yet their roles in this population are unclear. This study aimed to explore the association between FoR, spousal emotional support, and self-disclosure in patients with cerebral glioma. Methods: Patients with cerebral glioma were assessed using the Fear of Progression Questionnaire-Short Form (FoP-Q-SF), Perceived Social Support Scale (PSSS), Distress Disclosure Index (DDI), and Acceptance and Action Questionnaire (AAQ). Pearson correlation analysis was conducted to examine the relationships among the scale scores, while multiple linear… More >

Huanfang Wang^1,*, Xinyi Li¹, Fangfang Zhao², Ximeng Cui³, Weijian Li⁴

International Journal of Mental Health Promotion, DOI:10.32604/ijmhp.2025.069623

Abstract Background: In China, the policy of rotating teachers between urban and rural schools has been implemented to reduce educational disparities and ensure equitable access to quality education. These teachers face unique professional and emotional challenges during the rotation process, making their emotional labor a critical factor influencing their job performance. This study aimed to explore the relationship between rotating teachers’ emotional labor strategies and job performance. Methods: This study conducted a cross-sectional survey among 577 rotating teachers selected through stratified random sampling from primary and secondary schools in mainland China. Date were collected using the Teacher… More >

Xueting Cheng¹, Rui Xu^2,*, Liming Bo¹, Cheng Liu², Huiping Zheng¹, Zhichong Cao²

Energy Engineering, DOI:10.32604/ee.2025.072350

Abstract To address the issue of transient low-voltage instability in AC-DC hybrid power systems following large disturbances, conventional voltage assessment and control strategies typically adopt a sequential “assess-then-act” paradigm, which struggles to simultaneously meet the requirements for both high accuracy and rapid response. This paper proposes a transient voltage assessment and control method based on a hybrid neural network incorporated with an improved snow ablation optimization (ISAO) algorithm. The core innovation of the proposed method lies in constructing an intelligent “physics-informed and neural network-integrated” framework, which achieves the integration of stability assessment and control strategy generation.… More >

Kashif Ahmed Soomro^1,2,3,*, Rahool Rai^1,3,4, S. R. Qureshi², Sudhakar Kumarasamy^4,5,6, Abdul Hameed Memon¹, Rabiya Jamil¹

Energy Engineering, DOI:10.32604/ee.2025.069847

Abstract Plate heat exchangers suffer from significant energy losses, which adversely affect the overall efficiency of thermal systems. To address this challenge, various heat transfer enhancement techniques have been investigated. Notably, the incorporation of surface corrugations is widely recognized as both effective and practical. Chevron corrugation is the most employed design. However, there remains a need to investigate alternative geometries that may offer superior performance. This study aims to find a novel corrugation design by conducting a comparative CFD analysis of flat, square, chevron, and cylindrical corrugated surfaces, assessing their impact on heat transfer enhancement within… More >

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 >