Yifan Li^*, Congzhe Zhu, Rong Gao^*, Bin Yang

Energy Engineering, Vol.122, No.11, pp. 4523-4539, 2025, DOI:10.32604/ee.2025.068480 - 27 October 2025

Abstract The local overheating issue is a serious threat to the safe operation of data centers (DCs). The chip-level liquid cooling with pool boiling is expected to solve this problem. The effect of nano configuration and surface wettability on the boiling characteristics of copper surfaces is studied using molecular dynamics (MD) simulation. The argon is chosen as the coolant, and the wall temperature is 300 K. The main findings and innovations are as follows. (1) Compared to the smooth surface and fin surface, the cylindrical nano cavity obtains the superior boiling performance with earlier onset of… More > Graphic Abstract

Cheng Peng^1,*, Hao Qi²

Energy Engineering, Vol.122, No.11, pp. 4579-4602, 2025, DOI:10.32604/ee.2025.067704 - 27 October 2025

Abstract Integrated energy systems (IES) are widely regarded as a key enabler of carbon neutrality, enabling the coordinated use of electricity, heat, and gas to support large-scale renewable integration. Yet their practical deployment still faces major challenges, including rigid thermoelectric coupling, insufficient operational flexibility, and fragmented carbon and certificate market mechanisms. To address these issues, this study proposes a low-carbon economic dispatch model for integrated energy systems (IES) that reduces emissions and costs while improving renewable energy utilization. A coordinated framework integrating carbon capture, utilization, and storage, two-stage power-to-gas, combined heat and power, and ground-source heat… More > Graphic Abstract

Xinjian Li¹, Yu Zhang^1,2,*, Zewen Wang¹, Zhenyun Song¹

Energy Engineering, Vol.122, No.11, pp. 4603-4619, 2025, DOI:10.32604/ee.2025.065799 - 27 October 2025

Abstract Power prediction has been critical in large-scale wind power grid connections. However, traditional wind power prediction methods have long suffered from problems, for instance low prediction accuracy and poor reliability. For this purpose, a hybrid prediction model (VMD-LSTM-Attention) has been proposed, which integrates the variational modal decomposition (VMD), the long short-term memory (LSTM), and the attention mechanism (Attention), and has been optimized by improved dung beetle optimization algorithm (IDBO). Firstly, the algorithm’s performance has been significantly enhanced through the implementation of three key strategies, namely the elite group strategy of the Logistic-Tent map, the nonlinear… More >

Boyan Cui^1,2, Huijuan Wang^1,*, Yongjun Qi^1,*, Hongce Chen¹, Quanbo Yuan^1,3, Dongran Liu¹, Xuehua Zhou¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5531-5553, 2025, DOI:10.32604/cmc.2025.070101 - 23 October 2025

Abstract The proliferation of malware and the emergence of adversarial samples pose severe threats to global cybersecurity, demanding robust detection mechanisms. Traditional malware detection methods suffer from limited feature extraction capabilities, while existing Vision Transformer (ViT)-based approaches face high computational complexity due to global self-attention, hindering their efficiency in handling large-scale image data. To address these issues, this paper proposes a novel hybrid enhanced Vision Transformer architecture, HERL-ViT, tailored for malware detection. The detection framework involves five phases: malware image visualization, image segmentation with patch embedding, regional-local attention-based feature extraction, enhanced feature transformation, and classification. Methodologically,… More >

Jiao Chen¹, Xiao Wang^1,*, Zhiqin He¹, Yi Chen², Chao Ma¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5423-5450, 2025, DOI:10.32604/cmc.2025.067952 - 23 October 2025

Abstract This research proposes an innovative solution to the inherent challenges faced by landslide displacement prediction models based on data-driven methods, such as the need for extensive historical datasets for training, the reliance on manual feature selection, and the difficulty in effectively utilizing landslide historical data. We have developed a dual-channel deep learning prediction model that integrates multimodal decomposition and an attention mechanism to overcome these challenges and improve prediction performance. The proposed methodology follows a three-stage framework: (1) Empirical Mode Decomposition (EMD) effectively segregates cumulative displacement and feature factors; (2) We have developed a Double… More >

Fang Yuan, Xueyao Gao^*, Chunxiang Zhang

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5037-5055, 2025, DOI:10.32604/cmc.2025.067760 - 23 October 2025

Abstract 3D model classification has emerged as a significant research focus in computer vision. However, traditional convolutional neural networks (CNNs) often struggle to capture global dependencies across both height and width dimensions simultaneously, leading to limited feature representation capabilities when handling complex visual tasks. To address this challenge, we propose a novel 3D model classification network named ViT-GE (Vision Transformer with Global and Efficient Attention), which integrates Global Grouped Coordinate Attention (GGCA) and Efficient Channel Attention (ECA) mechanisms. Specifically, the Vision Transformer (ViT) is employed to extract comprehensive global features from multi-view inputs using its self-attention More >

Yuanyuan Xiong¹, Tong Wu¹, Lixin Sun¹, Mingyu Hu², Jie Yu^1,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4473-4489, 2025, DOI:10.32604/cmc.2025.067722 - 23 October 2025

Abstract Ag/Al₂O₃ powders are highly effective catalytic materials utilized in the epoxidation of ethylene to produce ethylene oxide. One of the critical challenges in this catalytic process is the stability of nano-sized Ag particles, especially during high-temperature catalysis. However, this issue can be effectively addressed through in-situ reaction synthesis. To gain a deeper understanding of the underlying mechanisms, the phase transformation process and the thermodynamic mechanism of the oxidation reaction in the Ag/Al₂O₃ system have been investigated using first-principles thermodynamic calculations in conjunction with traditional thermodynamic data. These calculations, whose accuracy has been verified, provide valuable insights into… More >

Lincong Zhao¹, Liandong Chen¹, Peipei Shen¹, Zizhou Liu¹, Chengzhu Li¹, Fanqin Zhou^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5057-5071, 2025, DOI:10.32604/cmc.2025.067521 - 23 October 2025

Abstract The rapid growth of Internet of things devices and the emergence of rapidly evolving network threats have made traditional security assessment methods inadequate. Federated learning offers a promising solution to expedite the training of security assessment models. However, ensuring the trustworthiness and robustness of federated learning under multi-party collaboration scenarios remains a challenge. To address these issues, this study proposes a shard aggregation network structure and a malicious node detection mechanism, along with improvements to the federated learning training process. First, we extract the data features of the participants by using spectral clustering methods combined… More >

Chengru Yang^1,#, Zhiyu Wang^1,#, Shaowu Bi¹, Xinmiao Zhang¹, Zhaoqiang Xu¹, Yifei Ge¹, Tianjie Zhang¹, Nan Wang¹, Yi Xu^{1,2,3,4,5,6,7,8,9,*}, Xiangyu Zhong^1,*

Oncology Research, Vol.33, No.11, pp. 3207-3229, 2025, DOI:10.32604/or.2025.066150 - 22 October 2025

Abstract Pancreatic cancer (PC) is an extremely aggressive cancer of the digestive system with insidious onset and the lack of effective biomarkers, resulting in late-stage diagnosis and poor prognosis. Exosomal non-coding RNAs (ncRNAs) are key mediators of intercellular communication that drive PC initiation and advancement. By modulating gene expression, they impact tumor microenvironment (TME) remodeling, proliferation, migration, apoptosis, and immune evasion. Critically, exosomal ncRNAs serve as promising biomarkers for early diagnosis and prognostic assessment. This review summarizes the current research achievements regarding exosomal ncRNAs in PC, systematically elaborating on their roles in tumor occurrence, metastasis, chemoresistance More >

Nuria Malena Tebez¹, María Cecilia Terrile^1,*, María Elisa Picco¹, María José Iglesias^2,*

BIOCELL, Vol.49, No.10, pp. 1913-1928, 2025, DOI:10.32604/biocell.2025.067833 - 22 October 2025

Abstract In plants, the ubiquitin–proteasome system (UPS) plays a central role in hormonal regulation, including the action of the phytohormone auxin, which orchestrates numerous aspects of growth and development. Auxin modulates redox metabolism and promotes the accumulation of nitric oxide (NO) in various tissues and physiological contexts. NO functions as a redox signaling molecule, exerting its effects in part through the reversible oxidation of cysteine residues via a post-translational modification known as S-nitrosylation. Recent findings highlight a dynamic interplay between S-nitrosylation and the ubiquitination machinery, shaping critical aspects of auxin-mediated plant responses. In this review, we More >