Mir Majid Etghani¹, Homayoun Boodaghi^2,*

Energy Engineering, Vol.122, No.11, pp. 4385-4474, 2025, DOI:10.32604/ee.2025.070668 - 27 October 2025

Abstract Energy system optimization has become crucial for enhancing efficiency and environmental sustainability. This comprehensive review examines the synergistic application of Artificial Neural Networks (ANN) and Taguchi methods in optimizing diverse energy systems. While previous reviews have focused on these methods separately, this paper presents the first integrated analysis of both approaches across multiple energy applications. We systematically analyze their implementation in: Internal combustion engines, Thermal energy storage systems, Solar energy systems, Wind and tidal turbines, Heat exchangers, and hybrid energy systems. Our findings reveal that ANN models consistently achieve prediction accuracies exceeding 90% when compared More > Graphic Abstract

Sandra Gajević¹, Slavica Miladinović¹, Jelena Jovanović¹, Onur Güler², Serdar Özkaya², Blaža Stojanović^1,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4341-4361, 2025, DOI:10.32604/cmc.2025.065645 - 30 July 2025

Abstract This paper presents an investigation of the tribological performance of AA2024–B₄C composites, with a specific focus on the influence of reinforcement and processing parameters. In this study three input parameters were varied: B₄C weight percentage, milling time, and normal load, to evaluate their effects on two output parameters: wear loss and the coefficient of friction. AA2024 alloy was used as the matrix alloy, while B₄C particles were used as reinforcement. Due to the high hardness and wear resistance of B₄C, the optimized composite shows strong potential for use in aerospace structural elements and automotive brake components. The… More >

Muhammad Salman Qamar^1,*, Muhammad Fahad Munir²

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5463-5485, 2025, DOI:10.32604/cmc.2025.064442 - 30 July 2025

Abstract Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as clustering, multi-hop routing, and data aggregation, face challenges, including uneven energy depletion, high computational demands, and suboptimal cluster head (CH) selection. To address these limitations, this paper proposes a hybrid methodology that optimizes energy consumption (EC) while maintaining network performance. The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic (LEACH-D) protocol using More >

Fatma Bunyan Unel^1,*, Lutfiye Kusak¹, Murat Yakar¹, Abdullah Sahin², Hakan Dogan³, Fikret Demir⁴

Revue Internationale de Géomatique, Vol.34, pp. 433-460, 2025, DOI:10.32604/rig.2025.065502 - 29 July 2025

Abstract This study aimed to create a model for calculating the total reference crop evapotranspiration (ETo) in Mersin Province from May 2015 to 2020 and to generate maps using spatial analysis. Lemons from citrus play a significant role in Mersin’s agriculture, and because of lemons’ sensitivity to temperature, ETo is essential for them. It was observed that the ETo value () calculated using the Penman-Monteith (PM) method increased over the years. A model was developed using data from 36 Automated Weather Observing Systems (AWOS) in Mersin, Türkiye, which is located in a semi-arid climate zone. The… More >

Ridha Djebali^1,*, Bernard Pateyron², Mokhtar Ferhi¹, Mohamed Ouerhani³, Karim Khemiri¹, Montassar Najari¹, M. Ammar Abbassi⁴, Chohdi Amri⁵, Ridha Ennetta⁶, Zied Driss⁷

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 441-461, 2025, DOI:10.32604/fhmt.2025.063375 - 25 April 2025

Abstract This paper investigates the application of Direct Current Atmospheric Plasma Spraying (DC-APS) as a versatile thermal spray technique for the application of coatings with tailored properties to various substrates. The process uses a high-speed, high-temperature plasma jet to melt and propel the feedstock powder particles, making it particularly useful for improving the performance and durability of components in renewable energy systems such as solar cells, wind turbines, and fuel cells. The integration of nanostructured alumina (Al₂O₃) thin films into multilayer coatings is considered a promising advancement that improves mechanical strength, thermal stability, and environmental resistance. The More >

Hamayun Khan^1,*, Muhammad Atif Imtiaz², Hira Siddique³, Muhammad Tausif Afzal Rana⁴, Arshad Ali⁵, Muhammad Zeeshan Baig⁶, Saif ur Rehman⁷, Yazed Alsaawy⁵

Computer Systems Science and Engineering, Vol.49, pp. 317-331, 2025, DOI:10.32604/csse.2025.061118 - 19 March 2025

Abstract The migration of tasks aided by machine learning (ML) predictions IN (DPM) is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor. In this paper, we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling (EA-EDF). ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system. The proposed system model allocates processors… More >

Abdul Ahad^1,2, Ira Puspitasari^1,3,*, Jiangbin Zheng², Shamsher Ullah⁴, Farhan Ullah⁵, Sheikh Tahir Bakhsh⁶, Ivan Miguel Pires^7,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025

Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >

Moein Tosan¹, Mohammad Reza Gharib^2,*, Nasrin Fathollahzadeh Attar³, Ali Maroosi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1109-1154, 2025, DOI:10.32604/cmes.2025.058595 - 27 January 2025

Abstract Accurate estimation of evapotranspiration (ET) is crucial for efficient water resource management, particularly in the face of climate change and increasing water scarcity. This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers, selected according to PRISMA guidelines, to evaluate the performance of Hybrid Artificial Neural Networks (HANNs) in ET estimation. The findings demonstrate that HANNs, particularly those combining Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), are highly effective in capturing the complex nonlinear relationships and temporal dependencies characteristic of hydrological processes. These… More >

Linlin Sun^1,2, Zihui Wang³, Shukun Cui^1,2, Ziquan Yan^1,2,*, Weiping Hu³, Qingchun Meng³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 555-577, 2025, DOI:10.32604/cmes.2024.056023 - 17 December 2024

Abstract Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed railways. They can cause significant wheel-rail dynamic interactions, leading to wheel-rail noise, component damage, and deterioration. Few researchers have employed the vehicle-track interaction dynamic model to study the dynamic interactions between wheel and rail induced by rail weld geometry irregularities. However, the cosine wave model used to simulate rail weld irregularities mainly focuses on the maximum value and neglects the geometric shape. In this study, novel theoretical models were developed for three categories of rail weld irregularities, based… More >

Petr Opěla^1,*, Josef Walek^1,*, Jaromír Kopeček²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 713-732, 2025, DOI:10.32604/cmes.2024.055219 - 17 December 2024

Abstract In engineering practice, it is often necessary to determine functional relationships between dependent and independent variables. These relationships can be highly nonlinear, and classical regression approaches cannot always provide sufficiently reliable solutions. Nevertheless, Machine Learning (ML) techniques, which offer advanced regression tools to address complicated engineering issues, have been developed and widely explored. This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials. The ML-based regression methods of Artificial Neural Networks (ANNs), Support Vector Machine (SVM), Decision Tree Regression (DTR), and Gaussian Process Regression More >