Jiajia Liu^1,2, Shuchen Pang³, Peng Xie³, Haitao Zhou³, Chenxi Du³, Haoran Hu³, Bo Tang³, Jianhua Liu³, Fei Jia¹, Huibing Zhang^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077824 - 09 April 2026

Abstract In the unmanned aerial vehicle (UAV) assisted edge computing system, the broadcast characteristics of the UAV signal, the high mobility of the UAV, and the limited airborne energy make the task offloading strategy face challenges such as increased risk of information disclosure, limited computing resources, and the trade-off between energy consumption and flight time. To address these issues, we propose a K-means in-depth reinforcement learning algorithm based on Soft Actor-Critic (SAC). The proposed method first leverages the K-means clustering algorithm to determine the optimal deployment of ground jammers based on the final distribution of mobile… More >

Dmytro Tymoshchuk^1,*, Oleh Yasniy¹, Iryna Didych², Pavlo Maruschak^3,*, Yuri Lapusta⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077062 - 09 April 2026

Abstract This article presents an approach to predicting the hysteresis behavior of shape memory alloys (SMAs) using a Temporal Convolutional Network (TCN) deep learning model, followed by the interpretation of the results using Explainable Artificial Intelligence (XAI) methods. The experimental dataset was prepared based on cyclic loading tests of nickel-titanium wire at loading frequencies of 0.3, 0.5, 1, 3, and 5 Hz. For training, validation, and testing, 100–250 loading-unloading cycles were used. The input features of the models were stress σ (MPa), cycle number (Cycle parameter), and loading-unloading stage indicator, while the output variable was strain… More >

Hitesh Mohapatra^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076726 - 09 April 2026

Abstract This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge. Existing literature shows that edge-based distributed intelligence reduces cloud dependency. It addresses transmission latency, device energy use, and bandwidth limits. Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination. Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8% and energy efficiency gains of 27.4% compared to conventional cloud-centric models.… More >

Fangxiang Hu¹, Qi Fu^1,2,*, Shiwen Zhang¹, Jing Huang¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.074154 - 09 April 2026

Abstract In the Internet of Vehicles (IoV) environment, the growing demand for computational resources from diverse vehicular applications often exceeds the capabilities of intelligent connected vehicles. Traditional approaches, which rely on one or more computational resources within the cloud-edge-device computing model, struggle to ensure overall service quality when handling high-density traffic flows and large-scale tasks. To address this issue, we propose a computational offloading scheme based on a cloud-edge-device collaborative 6G IoV edge computing model, namely, Multi-Agent Deep Reinforcement Learning-based and Server-weighted scoring Selection (MADRLSS), which aims to optimize dynamic offloading decisions and resource allocation. The… More >

Chao Guo¹, Chao Deng², Xing Bai³, Ziyuan Fan⁴, Jintao Li², Yuan Ren^2,*

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.075363 - 31 March 2026

Abstract Hangers play a crucial role in transferring loads in suspension bridges, yet their condition often deteriorates faster than expected due to corrosion and fatigue effects. Premature hanger failure poses serious risks to bridge safety and results in significant economic loss due to frequent replacement and traffic interruption. To address these challenges, this study proposes an integrated framework to evaluate the life-cycle safety and operational cost of bridge hangers. Traffic data obtained from Weigh-in-Motion (WIM) systems are used to simulate dynamic hanger responses. A wire-to-hanger deterioration model is then employed to capture the time-dependent interaction between… More >

Shuigen Hu^1,2, Hao Wang³, Qingyang Wei^4,*, Maosen Cao^2,4, Drahomír Novák⁵

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.073665 - 31 March 2026

Abstract As vital hydraulic infrastructures, concrete dams demand uncompromising safety assurance. Seismic effect commonly serves as a potential factor contributing to the damage of concrete dams, making seismic performance analysis crucial for structural integrity. Numerical simulation based on damage mechanics is usually considered as the approach for investigating the seismic damage behavior of concrete dams. To address the limitations of existing studies and extract the key dynamic characteristics of concrete arch dams, a concrete elastoplastic damage mechanics model is adopted, a seismic load input technique involving the viscoelastic boundary along with equivalent nodal forces is generated,… More >

Yu Shen¹, Yi Liu¹, Hanchen Zhang², Liuyang Li^3,4, Kaiming Pan⁵, Qinghe Fang^2,*

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.072871 - 31 March 2026

Abstract This study presents a systematic numerical analysis of wind loads on offshore photovoltaic (PV) panels. A computational fluid dynamics (CFD) model, incorporating a free-surface wave boundary condition, is developed and validated against experimental data. Parametric investigations quantify the effects of wind speed, panel tilt angle, clearance, and wave characteristics on the aerodynamic coefficients (drag, lift, and moment). Results indicate that all force coefficients increase with wind speed, with the lift coefficient being most sensitive to wave action. While a larger tilt angle intensifies airflow disturbance and amplifies the coefficients, this effect is more pronounced over More >

Yang Cheng^*, Dajiang Wang, Zhiyang Sun

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.3, 2026, DOI:10.32604/fdmp.2026.077747 - 31 March 2026

Abstract Accurate diagnosis of liquid loading in gas wells is hindered by inconsistent criteria for identifying the critical liquid-loading condition and by reliance on subjective observation during the development of physical models. To address this issue, controlled laboratory experiments were conducted to investigate pressure fluctuations in gas–liquid two-phase flow under different flow regimes, with the aim of establishing a quantitative criterion to identify such critical conditions. High-frequency pressure signals were collected and analyzed using complementary ensemble empirical mode decomposition (CEEMD). Characteristic parameters describing slug flow, annular flow, and the critical liquid-loading condition were extracted accordingly, including More >

Zhixiang Zhang¹, Chao Luo², Chen Zhang^1,*, Zheng Li¹, Yihua Zhu², Xu Cai¹

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072678 - 27 March 2026

Abstract Fatigue loads on wind turbines are critical factors that significantly influence operational lifespan and reliability. The passive yaw control of wind turbines often fails to capture the dynamic gradient changes of wind speed and direction in the wind field, leading to an increased risk of load overload, severely affecting operational lifespan and reducing power generation efficiency. This impact is even more pronounced during the passage of a cold front. To address this issue, this paper proposes an independent variable-pitch control method that optimizes predictions by utilizing the spatiotemporal relationship between pre-observed cold front patterns and… More >

Oksana Ivanova^*, Roman Cherepanov, Sergey Zelepugin

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075451 - 12 March 2026

Abstract This study presents an investigation into shock-induced exothermic reactions within three distinct aluminum-based energetic mixtures: aluminum/sulfur (Al/S), aluminum/copper oxide (Al/CuO), and aluminum/polytetrafluoroethylene (Al/PTFE). A challenge in current modeling efforts is accurately capturing the complex physical and chemical coupling under extreme loading, especially the influence of rapidly forming gaseous products in Al/PTFE mixtures on material integrity. To address this, a wide-range numerical model based on the Smoothed Particle Hydrodynamics (SPH) method was developed. This mesh-free approach manages large deformations and incorporates elastic-plastic flow, heat transfer, component diffusion, and chemical kinetics simulated using both zero- and first-order… More >