Peng Guan^*, Ming-Ran Li, Si-Bo-Wen Wang

Frontiers in Heat and Mass Transfer, Vol.24, No.4, 2026, DOI:10.32604/fhmt.2026.082595 - 31 August 2026

Abstract This paper develops a thermomechanical optimization method for turbo guide vane (TGV) weight reduction under restrictive conditions and highly sensitive variables. The proposed method integrates a flow-thermo-structural model, orthogonal experimental design (OED), and an optimization framework based on response surface methodology (RSM) and a genetic algorithm (GA). To address both the plastic limit and temperature distribution of the TGV, a new parameter termed the stress ratio is introduced as a constraint during optimization. Six highly sensitive variables were selected from ten cooling channel diameters using OED. Simulation results based on the thermal-fluid coupling model were… More >

Bai Xiao^1,*, Jingjun Bu¹, Binbin Du², Yulin Ge², Jian Gao²

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2026.081408 - 30 August 2026

Abstract In order to solve the problem of the mismatch between the supply of electric vehicle charging stations and charging demand of electric vehicle charging stations caused by the rapid growth of electric vehicles, and the difficulty in determining the optimal site and capacity of electric vehicle charging stations, an optimisation method for the siting and sizing of electric vehicle charging stations considering the accessibility of user charging was proposed. Firstly, the road network topology structure is established in the GIS environment, and the shortest time path of the user is planned by establishing the road… More >

Yi Pan, Mingshen Wang, Ye Xue, Huiyu Miao, Kemin Dai, Xiaodong Yuan^*, Fei Zeng

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2025.074882 - 30 August 2026

Abstract Electric vehicles (EVs), characterized by their large-scale deployment and flexible charging–discharging scheduling, represent a growing form of transportation. However, their widespread adoption poses considerable challenges to the security and stability of the power grid during peak charging periods, highlighting the need for effective management of the coupled traffic–grid system. To address this issue, this paper proposes a blockchain-driven optimization model for charging scheduling in dynamic traffic networks. Blockchain technology is introduced to ensure data transparency and security in decentralized decision-making. First, a queuing model integrating the Bureau of Public Roads (BPR) function with the M/M/c/K… More > Graphic Abstract

Min Huang¹, Cuichen Zhou¹, Yuming Wang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085152 - 28 August 2026

Abstract With the complexity of the aviation equipment mission environment, aircraft are inevitably exposed to a high-intensity and stable complex electromagnetic radiation environment for a long time in some mission areas. It is difficult to effectively reduce electromagnetic risk by relying solely on compatible technologies such as electromagnetic shielding and flight path avoidance. Aiming at the problem that aircraft are vulnerable to electromagnetic interference in a harsh electromagnetic environment, this paper proposes an aircraft attitude angle optimization method based on an intelligent optimization algorithm under the framework of digital twin modeling, according to the characteristics that… More >

Rana Muhammad Adnan Ikram¹, Jing-Cheng Han^1,*, Ahmed A. Ewees², Mo Wang³, Ozgur Kisi^4,5,6,*, Salim Heddam⁷, Mohammad Zounemat-Kermani⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083313 - 27 July 2026

Abstract Two-phase optimized machine learning and deep learning models play a key role in enhancing the prediction accuracy of nonlinear time series modeling. This study assesses the performance of a novel two-phase optimized Long Short-Term Memory (LSTM) model with integration of Aquila Optimizer (AO) and Wild Horse Optimizer (WHO) in predicting monthly streamflow in a snow-fed catchment. The two-phase optimized LSTM-WHOAO model is compared with single-phase optimized models such as LSTM-GA (Genetic Algorithm), LSTM-GWO (Grey Wolf Optimizer), LSTM-WOA (Whale Optimization Algorithm), LSTM-AO, and LSTM-WHO. The outcomes acquired from the deep learning models were compared using four… More >

Osama Al-Haj Hassan^1,*, Ammar Odeh¹, Abdullah Aref ², Ghassan Samara³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083861 - 23 July 2026

Abstract Mashups are among the key web technologies that provide end-users with customizable and personalized tools. Most mashup platforms are based on centralized architectures or do not employ fully decentralized architectures; therefore, in this paper, we propose a decentralized architecture for mashups that combines the strengths of structured and unstructured peer-to-peer networks. For the structured part, we rely on the Chord lookup protocol, and for the unstructured part, we build groups of nodes via two flavors of network flooding, namely, sequence number flooding and reverse path flooding. Brokers in the unstructured part would be responsible for More >

Reen-Cheng Wang¹, Hong-Sheng Wang², Kuo-Chun Tseng^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083124 - 23 July 2026

Abstract Network microsegmentation has become a key mechanism for enforcing zero-trust architecture in enterprise environments, yet its effectiveness remains closely tied to initialization quality. This study formulates network microsegmentation as a state-dependent combinatorial optimization problem in which optimization behavior depends on the availability of structural guidance. A comparative analysis is conducted across four representative optimization paradigms, including genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO), and amplitude-ensemble quantum-inspired tabu search (AE-QTS), under both structured and unstructured conditions. Experiments are conducted on a representative brownfield enterprise network using 30 independent runs per configuration. In addition… More >

Karim Gasmi^1,*, Afrah Alanazi², Inam Alanazi², Sahar Almenwer¹, Norah Alanazi¹, Sarah Almaghrabi³, Samia Yahyaoui⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081705 - 30 June 2026

Abstract Cardiac magnetic resonance imaging (MRI) segmentation is an essential aspect of quantitative cardiovascular analysis, facilitating accurate evaluation of ventricular volumes, myocardial mass, and functional parameters. Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC, but they remain challenging to deploy in real-world multi-centre settings. Data privacy laws make it hard to share data across institutions, and differences in imaging protocols and patient populations mean that data is not always distributed in the same way (non-IID). This can have a big impact on how well models work together and how well… More >

Chengyu Hou^1,2, Wenzao Li², Hanyun Li³, Kui Liu¹, Zhuoning Zhao¹, Hongping Shu^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081575 - 15 June 2026

Abstract The rapid growth of Internet of Things (IoT) and 5G technologies has led to a sharp increase in computing demands from wireless devices, making efficient task offloading a critical challenge. Key issues include reducing application latency, lowering the energy consumption of terminal devices, and improving overall system performance, all of which directly affect user experience. Traditional genetic algorithms (GA), inspired by biological evolution, have been widely used in task offloading, but they often suffer from slow convergence and a tendency to fall into local optima in complex scenarios, limiting their effectiveness. To address these drawbacks,… More >

Maryem Lachgar^1,*, Mansour Lmkaiti¹, Ibtissam Larhlimi¹, Imad Aattouri², Hicham Ouchitachen¹, Hicham Mouncif¹

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.075234 - 08 May 2026

Abstract This study introduces a hybrid routing protocol, Low Energy Adaptive Clustering Hierarchy—Ant Colony Optimization—Genetic Algorithm (LEACH-ACO-GA), for wireless sensor networks. It combines regional ant colony optimization for cluster head selection with inter-cluster routing based on a genetic algorithm. The proposed method reduces energy consumption from 6.9 J (LEACH Classic) to 5.6 J (LEACH-ACO-GA) and decreases latency from 460 to 390 ms, while maintaining a packet delivery ratio of 0.97. These values are averaged over 70 rounds based on 30 independent simulation runs conducted on networks with 50 and 200 nodes. The hybrid method extends network More >