Jingxiang Meng¹, Xin Liu¹, Wenping Zeng¹, Jianjun Song², Songyi Liao¹, Yonggang Min^1,2,*, Jintao Huang^1,*

Journal of Renewable Materials, Vol.13, No.12, pp. 2297-2308, 2025, DOI:10.32604/jrm.2025.02025-0098 - 23 December 2025

Abstract Biomass-derived hard carbon has gradually become an important component of sodium-ion batteries’ anodes. In this work, Setaria viridis, a widely distributed plant, was employed as a precursor to synthesize hard carbon anodes for sodium-ion batteries. However, the hard carbon derived from raw precursors contains substantial impurities, which limit the performance of the obtained hard carbon. With different chemical etching processes, the content of impurities in the resultants was reduced to varying degrees. The optimized hard carbon anode delivered a reversible capacity of 198 mAh g⁻¹ at a current density of 0.04 A g⁻¹. This work shows the More > Graphic Abstract

Zhuoyuan Zheng^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-1, 2025, DOI:10.32604/icces.2025.010939

Abstract Silicon (Si) anodes are promising candidates for lithium-ion batteries due to their high theoretical capacity and low operating voltage. However, the significant volume expansion that occurs during lithiation presents challenges, including material degradation and decreased cycle life. This study employs an electrochemical-mechanical-thermal coupled finite element model, supported by experimental validation, to investigate the impact of lithiation-induced deformation on the energy dissipation of Si anodes. We quantitatively investigate the effects of several key design parameters—C-rate, Si layer thickness, and lithiation depth—on energy losses resulting from various mechanisms, such as mechanical energy loss, polarization, and joule heating.… More >

Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3169-3189, 2025, DOI:10.32604/cmes.2025.070225 - 30 September 2025

Abstract Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures More >

Suqiao Li¹, Taotao Cai², Lingling Li^3,*, Xuezhuan Zhao^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3583-3598, 2025, DOI:10.32604/cmc.2025.066909 - 23 September 2025

Abstract Rumor Control (RC), aimed at minimizing the spread of rumors in social networks, is of paramount importance, as the spread of rumors can lead to significant economic losses, societal disruptions, and even widespread panic. The RC problem has garnered extensive research attention, however, most existing solutions for rumor control face a trade-off between efficiency and effectiveness, which limits their practical application in real-world scenarios. In this light, this paper studies the Truth-spreading-based Rumor Control (TRC) problem, and introduces the Subgraph-based Greedy algorithm Optimized with CELF (SGOC), which employs subgraph techniques and the CELF strategy, as More >

Muhammad Salman Qamar^1,*, Muhammad Fahad Munir²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5463-5485, 2025, DOI:10.32604/cmc.2025.064442 - 30 July 2025

Abstract Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as clustering, multi-hop routing, and data aggregation, face challenges, including uneven energy depletion, high computational demands, and suboptimal cluster head (CH) selection. To address these limitations, this paper proposes a hybrid methodology that optimizes energy consumption (EC) while maintaining network performance. The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic (LEACH-D) protocol using More >

Li Ling^*, Danhao Song, Qianlong Hu, Zihao Xiang, Zeyu Zhang

Energy Engineering, Vol.122, No.8, pp. 3309-3328, 2025, DOI:10.32604/ee.2025.065824 - 24 July 2025

Abstract Rack-level loop thermosyphons have been widely adopted as a solution to data centers’ growing energy demands. While numerous studies have highlighted the heat transfer performance and energy-saving benefits of this system, its economic feasibility, water usage effectiveness (WUE), and carbon usage effectiveness (CUE) remain underexplored. This study introduces a comprehensive evaluation index designed to assess the applicability of the rack-level loop thermosyphon system across various computing hub nodes. The air wet bulb temperature T_a,w was identified as the most significant factor influencing the variability in the combination of PUE, CUE, and WUE values. The results indicate… More >

Cui Zhang¹, Lieping Zhang^2,*, Huaquan Gan³, Hongyuan Chen³, Zhihao Li³

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3125-3148, 2025, DOI:10.32604/cmc.2025.064499 - 03 July 2025

Abstract In large-scale Wireless Rechargeable Sensor Networks (WRSN), traditional forward routing mechanisms often lead to reduced energy efficiency. To address this issue, this paper proposes a WRSN node energy optimization algorithm based on regional partitioning and inter-layer routing. The algorithm employs a dynamic clustering radius method and the K-means clustering algorithm to dynamically partition the WRSN area. Then, the cluster head nodes in the outermost layer select an appropriate layer from the next relay routing region and designate it as the relay layer for data transmission. Relay nodes are selected layer by layer, starting from the… More >

Tawfeeq Shawly^1,2, Ahmed A. Alsheikhy^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3033-3064, 2025, DOI:10.32604/cmes.2025.065264 - 30 June 2025

Abstract Epilepsy is a long-term neurological condition marked by recurrent seizures, which result from abnormal electrical activity in the brain that disrupts its normal functioning. Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models, which inadequately represent the continuous dynamics of electroencephalogram (EEG) signals. To overcome this limitation, we introduce an innovative approach that employs Neural Ordinary Differential Equations (NODEs) to model EEG signals as continuous-time systems. This allows for effective management of irregular sampling and intricate temporal patterns. In contrast to conventional techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural… More >

María Belén Rocco, Clara Requena D’Alessio, Valeria Giselle Sánchez, Horacio Eduardo Romeo, María Laura Barreiro Arcos^*

BIOCELL, Vol.49, No.4, pp. 629-646, 2025, DOI:10.32604/biocell.2025.062525 - 30 April 2025

Abstract Introduction: Hyperthyroidism is known to affect various physiological systems, including the immune system. Thyroid hormones (THs) play a crucial role in regulating immune function, and alterations in THs levels can lead to immune dysregulation. Objective: Currently, we aimed to elucidate the effects of hyperthyroidism on immune function in BALB/c mice, with a focus on anatomical and histological changes in lymphoid organs, the immune response to mitogenic stimulation, mitochondrial dynamics, and reactive oxygen species (ROS) production. Methods: Hyperthyroidism was induced in BALB/c mice by administering thyroxine (T4; 14 mg/L) in their drinking water for 30 days. Thyroid… More >

Vijaya Krishna Akula^1,*, Tan Kuan Tak², Pravin Ramdas Kshirsagar³, Shrikant Vijayrao Sonekar⁴, Gopichand Ginnela⁵

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2449-2479, 2025, DOI:10.32604/cmc.2025.061486 - 16 April 2025

Abstract The rapid expansion of Internet of Things (IoT) networks has introduced challenges in network management, primarily in maintaining energy efficiency and robust connectivity across an increasing array of devices. This paper introduces the Adaptive Blended Marine Predators Algorithm (AB-MPA), a novel optimization technique designed to enhance Quality of Service (QoS) in IoT systems by dynamically optimizing network configurations for improved energy efficiency and stability. Our results represent significant improvements in network performance metrics such as energy consumption, throughput, and operational stability, indicating that AB-MPA effectively addresses the pressing needs of modern IoT environments. Nodes are More >