Fengxia Shi¹, Jian Zhao^2,3,*, Xiaodong Dai¹, Guoxin Zhang⁴, Yuan Lu⁴, Yuyan Shang¹

FDMP-Fluid Dynamics & Materials Processing, DOI:10.32604/fdmp.2025.073836

Abstract Gas–liquid two-phase jets exhibit markedly enhanced impact performance due to the violent collapse of entrained bubbles, which generates transient microjets and shock waves. The geometry of the nozzle is a decisive factor in controlling jet formation, flow modulation, and impact efficiency. In this work, the structural optimization of gas–liquid two-phase nozzles was investigated numerically using the Volume of Fluid (VOF). Simulation results show that the aero-shaped nozzle delivers a significantly stronger impact on the target surface than conventional geometries. Specifically, its impact pressure is 21% higher than that of a conical straight nozzle and 37%… More > Graphic Abstract

Zhengqing Zhang^1,2,3,*, Xiaojun Yuan², Hui Wu²

FDMP-Fluid Dynamics & Materials Processing, DOI:10.32604/fdmp.2025.071115

Abstract Frost accumulation on the evaporator fins of air source heat pumps (ASHPs) severely degrades heat transfer performance and overall system efficiency. To address this, the present study employs computational fluid dynamics (CFD) to investigate how fin spacing influences frosting behavior, emphasizing the coupled evolution of frost thickness, density, airflow, and temperature distribution within fin channels. Results reveal that fin spacing is a key parameter governing both the extent and rate of frost growth. Wider fin spacing enhances frost accumulation, with a final frost mass of 6.41 g at 12 mm, about 71.8% higher than at More >

Qiang Xu^1,*, Leyao Cong¹, Jianing Wang¹, Xingyu Zhu¹, Shaojun Cui¹, Guoqiang Sun², Xueheng Shi²

Energy Engineering, DOI:10.32604/ee.2025.073947

Abstract Power system faults can trigger a massive influx of complex alarm signals to the operation and maintenance center, posing significant challenges for dispatchers in accurately identifying the underlying faults. To address the issues of sample imbalance and low accuracy in traditional power grid monitoring alarm event identification methods, a power grid monitoring alarm event identification method based on BERT large language model is proposed. Firstly, information entropy is employed to filter effective monitoring alarm signals, and the k-means clustering algorithm is used to group all alarm signals into different event types, forming the initial power… More >

Jia Mi¹, Zhikang Liu¹, Chang Li², Jing Wan^1,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.074364

Abstract Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies. Despite notable progress, existing computational techniques struggle to represent the interplay between sequence-derived and structure-dependent protein features. To overcome this limitation, we introduce GLM-EP, a unified framework that fuses protein language models with equivariant graph neural networks. By merging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations, GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification. Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional More >

Shuangyuan Li^1,*, Zhengwei Wang², Jiaming Liang³, Yichen Wang⁴

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073896

Abstract Object detection plays a crucial role in the field of computer vision, and small object detection has long been a challenging issue within this domain. In order to improve the performance of object detection on small targets, this paper proposes an enhanced structure for YOLOv5, termed ATC-YOLOv5. Firstly, a novel structure, AdaptiveTrans, is introduced into YOLOv5 to facilitate efficient communication between the encoder and the detector. Consequently, the network can better address the adaptability challenge posed by objects of different sizes in object detection. Additionally, the paper incorporates the CBAM (Convolutional Block Attention Module) attention More >

José Armando Santas Ciavatta, Juan Ramón Bermejo Higuera^*, Javier Bermejo Higuera, Juan Antonio Sicilia Montalvo, Tomás Sureda Riera, Jesús Pérez Melero

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074566

Abstract As artificial Intelligence (AI) continues to expand exponentially, particularly with the emergence of generative pre-trained transformers (GPT) based on a transformer’s architecture, which has revolutionized data processing and enabled significant improvements in various applications. This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models (LLM). Our primary objective is to evaluate the effectiveness of Static Application Security Testing (SAST) by applying various techniques such as prompt persona, structure outputs and zero-shot. To the selection of the LLMs (CodeLlama 7B, DeepSeek coder 7B, Gemini 1.5 Flash,… More >

Sara Tehsin¹, Inzamam Mashood Nasir^1,*, Wiem Abdelbaki², Fadwa Alrowais³, Khalid A. Alattas⁴, Sultan Almutairi⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.074416

Abstract Objective: Deep learning is employed increasingly in Gastroenterology (GI) endoscopy computer-aided diagnostics for polyp segmentation and multi-class disease detection. In the real world, implementation requires high accuracy, therapeutically relevant explanations, strong calibration, domain generalization, and efficiency. Current Convolutional Neural Network (CNN) and transformer models compromise border precision and global context, generate attention maps that fail to align with expert reasoning, deteriorate during cross-center changes, and exhibit inadequate calibration, hence diminishing clinical trust. Methods: HMA-DER is a hierarchical multi-attention architecture that uses dilation-enhanced residual blocks and an explainability-aware Cognitive Alignment Score (CAS) regularizer to directly align… More >

Sghaier Guizani^1,*, Tehseen Mazhar^2,3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073883

Abstract The advent of quantum computing poses a significant challenge to traditional cryptographic protocols, particularly those used in Secure Multiparty Computation (MPC), a fundamental cryptographic primitive for privacy-preserving computation. Classical MPC relies on cryptographic techniques such as homomorphic encryption, secret sharing, and oblivious transfer, which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries. This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI, IEEE Explore, Springer, and Elsevier, examining the applications, types, and security issues with the solution of… More >

Xiang Luo¹, Yuxuan Peng², Renghong Xie¹, Peng Li³, Yuwen Qian^3,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073700

Abstract Deep learning has made significant progress in the field of oriented object detection for remote sensing images. However, existing methods still face challenges when dealing with difficult tasks such as multi-scale targets, complex backgrounds, and small objects in remote sensing. Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot. Therefore, we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture, specifically optimized for the characteristics of large target scale variations, diverse orientations, and numerous small objects… More >

Si-Woo Kim, Yong Soo Kim^*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073492

Abstract Predictive maintenance (PdM) is vital for ensuring the reliability, safety, and cost efficiency of heavy-duty vehicle fleets. However, real-world sensor data are often highly imbalanced, noisy, and temporally irregular, posing significant challenges to model robustness and deployment. Using multivariate time-series data from Scania trucks, this study proposes a novel PdM framework that integrates efficient feature summarization with cost-sensitive hierarchical classification. First, the proposed last_k_summary method transforms recent operational records into compact statistical and trend-based descriptors while preserving missingness, allowing LightGBM to leverage its inherent split rules without ad-hoc imputation. Then, a two-stage LightGBM framework is developed… More >