Qiuhong Wang¹, Hao Yin^1,*, Qingping Sun^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012656

Abstract Tubular NiTi is a promising candidate of eco-friendly solid-state refrigerant for elastocaloric cooling, but the severe functional degradation of NiTi material during cyclic phase transition (PT) is a key concern in the technology development. Here, plastic deformation of 6.7% is applied on the NiTi tube by overstress training under 1900 MPa for five cycles to improve the cyclic PT stability without losing cooling efficiency. It is found that after 106 compressive cycles under an applied stress of 1000 MPa, the overstress-trained NiTi tube exhibits small residual strain (0.5%), stable adiabatic temperatures drop (T=11K) and improved… More >

Mingxing Wu¹, Chengzhen Li¹, Xinyan Feng¹, Fei Chen², Yingchun Feng¹, Huihui Song¹, Wenyu Wang³, Faye Zhang^3,*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1473-1487, 2025, DOI:10.32604/sdhm.2025.069811 - 17 November 2025

Abstract As a core component of power systems, the operational status of transformers directly affects grid stability. To address the problem of “domain shift” in cross-domain fault diagnosis, this paper proposes a memory-enhanced dual-stream network (MemFuse-DSN). The method reconstructs the feature space by selecting and enhancing multi-source domain samples based on similarity metrics. An adaptive weighted dual-stream architecture is designed, integrating gradient reversal and orthogonality constraints to achieve efficient feature alignment. In addition, a novel dual dynamic memory module is introduced: the task memory bank is used to store high-confidence class prototype information, and adopts an More >

Abdulaziz A. Alsulami¹, Qasem Abu Al-Haija^2,*, Badraddin Alturki³, Ayman Yafoz¹, Ali Alqahtani⁴, Raed Alsini¹, Sami Saeed Binyamin⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1117-1140, 2025, DOI:10.32604/cmes.2025.070745 - 30 October 2025

Abstract QR codes are widely used in applications such as information sharing, advertising, and digital payments. However, their growing adoption has made them attractive targets for malicious activities, including malware distribution and phishing attacks. Traditional detection approaches rely on URL analysis or image-based feature extraction, which may introduce significant computational overhead and limit real-time applicability, and their performance often depends on the quality of extracted features. Previous studies in malicious detection do not fully focus on QR code security when combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs). This research proposes a deep learning… More >

Jin Wang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011894

Abstract Shape-memory elastomers (SMEs) have emerged as promising smart-materials platforms for soft actuators and intelligent structures due to their programmable thermally-induced reversible shape transformations. However, four critical scientific and technological challenges impede their practical engineering implementation. First, the thermodynamic and molecular mechanisms governing their thermomechanical behavior remain incompletely elucidated. Second, achieving large reversible deformations requires retention of molecular orientation during thermal actuation cycles- a persistent challenge given their large strain recovery at the heating temperature. Third, while biological muscles achieve sub-second actuation, current SME systems exhibit response times spanning several seconds, necessitating at least one order More >

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 >

Kadriye Simsek Alan^*, Ayca Durgut, Helin Doga Demirel

Journal on Artificial Intelligence, Vol.7, pp. 397-415, 2025, DOI:10.32604/jai.2025.071133 - 20 October 2025

Abstract Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures. While earlier research has emphasized CPU utilization forecasting, joint prediction of CPU and memory usage under real workload conditions remains underexplored. This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset. The framework combines Extreme Gradient Boosting (XGBoost) with a MultiOutputRegressor (MOR) to capture nonlinear interactions across multiple resource dimensions, supported by a leakage-safe imputation strategy that prevents bias from missing values. Nested… More >

Muhammad Salman Qamar^1,2,*, Muhammad Fahad Munir², Athar Waseem²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3557-3584, 2025, DOI:10.32604/cmes.2025.067447 - 30 September 2025

Abstract Air pollution, specifically fine particulate matter (PM2.5), represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems. Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks; however, the inherent nonlinearity and dynamic variability of air quality data present significant challenges. This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and the hybrid CNN-LSTM as well as statistical models, AutoRegressive Integrated Moving Average (ARIMA) and Maximum Likelihood Estimation (MLE) for hourly PM2.5 forecasting. Model performance is… More >

Yixin Zhang, Yongbin Ma^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012268

Abstract In this paper, a novel porous thermoelastic model is developed, building upon the existing framework of thermoelastic model. The objective of this study is to investigate the thermoelastic response behavior of porous materials. The Klein-Gordon (KG) operator is employed to describe the effect of spatio-temporal non-localization in the constitutive equation, and the memory-dependent derivative (MDD) is incorporated into the Moore-Gibson-Thompson (MGT) heat conduction equation. The model is applied to study the thermoelastic response of hollow porous cylinders under thermal shock, which accurately captures the complex micro-interaction characteristics and memory-dependent properties of the porous structure. Subsequently,… More >

Jing Zhang, Liang Meng^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.011468

Abstract Shape memory alloys (SMA), with their unique phase transformation capability, can deform under external force and recover their original shape through a martensite-to-austenite phase transformation triggered by heating [1]. Utilizing this characteristic, SMA wires can be pre-stretched and fixed, generating internal stress during shape recovery, which increases the natural frequency of SMA wire structures [2]. This property is of significant importance in structural dynamics design. Based on this, structures incorporating SMA wires and SMA-reinforced plate structures can be designed to dynamically adjust their natural frequencies and control structural dynamic responses. Furthermore, the vibration modes of More >

Siyi Wang, Yan Zhuang^*, Zhizhuang Zhou, Xinhao Wang, Menglan Li

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3041-3066, 2025, DOI:10.32604/cmc.2025.067636 - 23 September 2025

Abstract Heap memory anomalies, such as Use-After-Free (UAF), Double-Free, and Memory Leaks, pose critical security threats including system crashes, data leakage, and remote exploits. Existing methods often fail to handle multiple anomaly types and meet real-time detection demands. To address these challenges, this paper proposes MemHookNet, a real-time multi-class heap anomaly detection framework that combines log hooking with deep learning. Without modifying source code, MemHookNet non-intrusively captures memory operation logs at runtime and transforms them into structured sequences encoding operation types, pointer identifiers, thread context, memory sizes, and temporal intervals. A sliding-window Long Short-Term Memory (LSTM) More >