Ahmed Ibrahim Hassanin Mohamed^1,2,*, Nourhan M. Amin^2,3, Heba Elsaid Matter², Ibrahim F. Eldemary², Ahmed F. Oan²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079630 - 27 April 2026

Abstract The design of foundations for high-voltage electrical network lattice towers depends on reliable prediction of resistance to uplift and lateral forces. Because foundation works contribute substantially to the total project cost, a clear understanding of ultimate pullout capacity and the associated failure mechanism is required to support safe and economical design. This paper presents a three-dimensional finite element investigation using PLAXIS 3D to quantify the influence of soil type (pure sand and sand with 8% fines), footing dimensions ((3.5 × 7), (5 × 10), (7.5 × 15)), relative compaction RC are 92% and 100%, and… More >

Sobhan Manjili¹, Saeid Jafarzadeh Ghoushchi¹, Mohammad Reza Maghami^2,*, Mazlan Mohamed^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076217 - 27 April 2026

Abstract Accurate electricity load forecasting is crucial for optimizing power distribution networks, especially in rapidly growing cities like Tabriz (annual consumption growth of 7.2%). This study presents a hybrid AI framework integrating the Temporal Fusion Transformer (TFT) and XGBoost for residual error correction. The model is trained and evaluated using actual consumption data from Tabriz’s distribution network (2021–2023). Compared to a baseline TFT model, the proposed framework demonstrates a 11.2% reduction in RMSE (from 0.1249 to 0.1109) and a 10.7% decrease in MAE (from 0.0998 to 0.0891). Attention mechanism analysis reveals temperature (importance coefficient = 0.32), More >

Hong Zhang¹, Bin Xu¹, Jinzhong Li¹, Xiaoxiao Meng^2,*, Cheng Qian², Wei Ma¹, Yuguang Xie¹

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2025.073542 - 27 April 2026

Abstract The increasing integration of distributed renewable energy sources in the distribution network leads to unbalanced load rates in the distribution network. The traditional load balancing methods are mainly based on network reconfiguration, which have problems such as a long time scale and poor adaptability. In response to these issues, this paper proposes a distributed iterative learning control (ILC) strategy for load balancing in flexible AC/DC hybrid distribution systems. This method combines the consensus algorithm with the ILC mechanism to construct a multi-terminal AC/DC flexible interconnection system model. It is only necessary to measure the load… More >

Lanyue Liu¹, Chunzhi Zhang^1,*, Zhongyi Yu²

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2025.071463 - 27 April 2026

Abstract This study develops an optimized integrated system for full-capacity hot water supply in hotels by combining solar thermal energy and air-source heat pumps. Using a hotel in Wuhan as a case study, a four-season × four-occupancy multidimensional working-condition matrix was established. Dynamic simulation and multi-objective optimization were performed on TRNSYS-TRNOPT, with the cost-benefit ratio (CBR) as the core evaluation metric. Key parameters—including collector area, tilt and azimuth angles, heat pump capacity, and storage tank volume—were jointly optimized. Model calibration against measured data yielded a deviation of less than 8%. The results demonstrate that the optimized More >

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 >