Fatih Selimefendigil¹, Kaouther Ghachem^2,*, Hind Albalawi³, Badr M. AlShammari⁴, Lioua Kolsi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 899-925, 2025, DOI:10.32604/cmes.2025.066555 - 31 July 2025

Abstract Cooling system design applicable to more than one photovoltaic (PV) unit may be challenging due to the arrangement and geometry of the modules. Different cooling techniques are provided in this study to regulate the temperature of conductive panels that are arranged perpendicular to each other. The model uses two vented cavity systems and one L-shaped channel with ternary nanofluid enhanced non-uniform magnetic field. Their cooling performances and comparative results between different systems are provided. The finite element method is used to conduct a numerical analysis for a range of values of the following: the strength… More >

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 >

Nawaf Waqas¹, Muhammad Islam^2,*, Muhammad Yahya³, Shabana Habib⁴, Mohammed Aloraini², Sheroz Khan⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3029-3051, 2025, DOI:10.32604/cmc.2025.064660 - 03 July 2025

Abstract Deep learning (DL), derived from the domain of Artificial Neural Networks (ANN), forms one of the most essential components of modern deep learning algorithms. DL segmentation models rely on layer-by-layer convolution-based feature representation, guided by forward and backward propagation. A critical aspect of this process is the selection of an appropriate activation function (AF) to ensure robust model learning. However, existing activation functions often fail to effectively address the vanishing gradient problem or are complicated by the need for manual parameter tuning. Most current research on activation function design focuses on classification tasks using natural… More >

Muhammad Asif Zahoor Raja¹, Aqsa Zafar Abbasi², Kottakkaran Sooppy Nisar^3,*, Ayesha Rafiq², Muhammad Shoaib⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2133-2153, 2025, DOI:10.32604/cmes.2025.058020 - 30 May 2025

Abstract Being a nonlinear operator, fractional derivatives can affect the enforcement of existence at any given time. As a result, the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations (FODEs). The goal of this study is to increase the fractional model of the TB virus’s (FMTBV) accuracy. Stochastic solvers have never been used to solve FMTBV previously. The Bayesian regularized artificial (BRA) method and neural networks (NNs), often referred to as BRA-NNs, were used to solve the FMTBV model. Each scenario features five occurrences that each reflect a different… More >

Kun Li¹, Tianfu Li^2,*, Xiuwei Wang¹, Qingchun Meng¹, Zhenjie Wang¹, Jinyang Luo^1,2, Zhaohui Wang¹, Yuedong Yao²

Energy Engineering, Vol.122, No.6, pp. 2215-2238, 2025, DOI:10.32604/ee.2025.062876 - 29 May 2025

Abstract CO₂ flooding for enhanced oil recovery (EOR) not only enables underground carbon storage but also plays a critical role in tertiary oil recovery. However, its displacement efficiency is constrained by whether CO₂ and crude oil achieve miscibility, necessitating precise prediction of the minimum miscibility pressure (MMP) for CO₂-oil systems. Traditional methods, such as experimental measurements and empirical correlations, face challenges including time-consuming procedures and limited applicability. In contrast, artificial intelligence (AI) algorithms have emerged as superior alternatives due to their efficiency, broad applicability, and high prediction accuracy. This study employs four AI algorithms—Random Forest Regression (RFR), Genetic… More >

Muhammad Qasim^1,2, Syed M. Adnan Shah¹, Qamas Gul Khan Safi¹, Danish Mahmood², Adeel Iqbal^3,*, Ali Nauman³, Sung Won Kim^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4429-4445, 2025, DOI:10.32604/cmc.2025.065060 - 19 May 2025

Abstract Grains are the most important food consumed globally, yet their yield can be severely impacted by pest infestations. Addressing this issue, scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods. Traditional approaches often rely on preprocessed datasets, but there is a growing need for solutions that utilize real-time images of pests in their natural habitat. Our study introduces a novel two-step approach to tackle this challenge. Initially, raw images with complex backgrounds are captured. In the subsequent step, feature extraction is performed using both hand-crafted algorithms (Haralick, LBP, and Color… 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 >

Yi-Feng Luo¹, Guan-Jhu Chen^2,*, Chun-Liang Liu³, Yen-Tse Chung⁴

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 823-844, 2025, DOI:10.32604/cmc.2025.061498 - 26 March 2025

Abstract As lithium-ion batteries become increasingly prevalent in electric scooters, vehicles, mobile devices, and energy storage systems, accurate estimation of remaining battery capacity is crucial for optimizing system performance and reliability. Unlike traditional methods that rely on static alternating internal resistance (SAIR) measurements in an open-circuit state, this study presents a real-time state of charge (SOC) estimation method combining dynamic alternating internal resistance (DAIR) with artificial neural networks (ANN). The system simultaneously measures electrochemical impedance |Z| at various frequencies, discharge C-rate, and battery surface temperature during the discharge process, using these parameters for ANN training. The… More >