Divya Ramesh, Sriram Venkatachalam^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.069854 - 29 January 2026

Abstract A hybrid model combining Fully Non-Linear Potential Flow Theory (FNPT) based on the Finite Element Method (FEM) and the Unified Navier-Stokes equation, using the 3D Improved Meshless Local Petrov Galerkin method with Rankine Source (IMLPG_R), is developed to study wave interactions with a porous layer. In previous studies, the above formulations are applied to wave interaction with fixed cylindrical structures. The present study extends this framework by integrating a unified governing equation within the hybrid modeling approach to capture the dynamics of wave interaction with porous media. The porous layers are employed to replicate the… More >

Weiqi Mao^1,2,3, Enbo Yu^1,*, Guoji Xu³, Xiaozhen Li³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.069733 - 29 January 2026

Abstract Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration. This study presents a novel machine learning model that integrates clustering, deep learning, and transfer learning to mitigate accuracy degradation in 24-h forecasting. Initially, an optimized DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm clusters wind fields based on wind direction, probability density, and spectral features, enhancing physical interpretability and reducing training complexity. Subsequently, a ResNet (Residual Network) extracts multi-scale patterns from decomposed wind signals, while transfer learning adapts the backbone network across clusters, cutting training time by over… More >

Abdulrazzak Akroot¹, Kayser Aziz Ameen², Haitham M. Ibrahim³, Hasanain A. Abdul Wahhab^3,*, Miqdam T. Chaichan⁴

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073702 - 27 January 2026

Abstract Improving energy efficiency and lowering negative environmental impact through waste heat recovery (WHR) is a critical step toward sustainable cement manufacturing. This study analyzes advanced cogeneration systems for recovering waste heat from the Fallujah White Cement Plant in Iraq. The novelty of this work lies in its direct application and comparative thermodynamic analysis of three distinct cogeneration cycles—the Organic Rankine Cycle, the Single-Flash Steam Cycle, and the Dual-Pressure Steam Cycle—within the Iraqi cement industry, a context that has not been widely studied. The main objective is to evaluate and compare these models to determine the… More > Graphic Abstract

Benali Touhami¹, Bennaceur Said¹, Atouani Toufik¹, Lammari Khelifa², Ouradj Boudjamaa², Bounaama Fateh², Belkacem Draoui², Lyes Bennamoun^3,*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073329 - 27 January 2026

Abstract The aim of this study is to design, build, and evaluate an indirect forced convection solar dryer adapted to semi-arid climate, such as that of Béchar situated in the west south region of Algeria. The tested drying system consists of a flat-plate solar collector, an insulated two-chamber drying unit, and an Arduino-controlled device that ensures uniform temperature distribution and real-time monitoring using DHT22 sensors. Drying tests were conducted on locally grown beet slices at air temperatures of 45°C, 60°C, and 80°C, with a constant air velocity of 1.2 m/s and a mass flow rate of… More > Graphic Abstract

Wei Chen, Yang Wu^*, Tingting Pei, Jie Lin, Guojing Yuan

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070905 - 27 January 2026

Abstract In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic (PV) modules, this study proposes a predictive maintenance (PdM) strategy based on Remaining Useful Life (RUL) estimation. First, a RUL prediction model is established using the Transformer architecture, which enables the effective processing of sequential degradation data. By employing the historical degradation data of PV modules, the proposed model provides accurate forecasts of the remaining useful life, thereby supplying essential inputs for maintenance decision-making. Subsequently, the RUL information obtained from the prediction process is… More >

Jun Guo^1,*, Maoyuan Chen¹, Yuyang Li¹, Sibo Feng^2,3, Guangyu Fu³

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.068894 - 27 January 2026

Abstract The author proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network. The model plans the configuration of photovoltaic (3.8 MW), wind power (2.5 MW), energy storage (2.2 MWh), and SVC (1.2 Mvar) through interaction between upper and lower layers, and modifies lines 2–3, 8–9, etc. to improve transmission capacity and voltage stability. The author uses normal distribution and Monte Carlo method to model load uncertainty, and combines Weibull distribution to describe wind speed characteristics. Compared to the traditional… More >

Vaskar Chakma^1,#, Xiaolin Ju^1,#, Heling Cao², Xue Feng³, Xiaodong Ji³, Haiyan Pan^3,*, Gao Zhan^1,*

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 37-86, 2026, DOI:10.32604/jimh.2026.075201 - 23 January 2026

Abstract Wide QRS Complex Tachycardia (WCT) is a life-threatening cardiac arrhythmia requiring rapid and accurate diagnosis. Traditional manual ECG interpretation is time-consuming and subject to inter-observer variability, while existing AI models often lack the clinical interpretability necessary for trusted deployment in emergency settings. We developed CardioForest, an optimized Random Forest ensemble model, for automated WCT detection from 12-lead ECG signals. The model was trained, tested, and validated using 10-fold cross-validation on 800,000 ten-second-long 12-lead Electrocardiogram (ECG) recordings from the MIMIC-IV dataset (15.46% WCT prevalence), with comparative evaluation against XGBoost, LightGBM, and Gradient Boosting models. Performance was… More >

Vaskar Chakma^1,*, Md Jaheid Hasan Nerab¹, Abdur Rouf¹, Abu Sayed², Hossem Md Saim³, Md. Nournabi Khan³

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 1-35, 2026, DOI:10.32604/jimh.2026.074347 - 23 January 2026

Abstract Smoking continues to be a major preventable cause of death worldwide, affecting millions through damage to the heart, metabolism, liver, and kidneys. However, current medical screening methods often miss the early warning signs of smoking-related health problems, leading to late-stage diagnoses when treatment options become limited. This study presents a systematic comparative evaluation of machine learning approaches for smoking-related health risk assessment, emphasizing clinical interpretability and practical deployment over algorithmic innovation. We analyzed health screening data from 55,691 individuals, examining various health indicators including body measurements, blood tests, and demographic information. We tested three advanced… More >

Suman Kunwar^*

Journal on Artificial Intelligence, Vol.8, pp. 39-49, 2026, DOI:10.32604/jai.2026.076674 - 22 January 2026

Abstract The rise in convenience packaging has led to generation of enormous waste, making efficient waste sorting crucial for sustainable waste management. To address this, we developed DWaste, a computer vision-powered platform designed for real-time waste sorting on resource-constrained smartphones and edge devices, including offline functionality. We benchmarked various image classification models (EfficientNetV2S/M, ResNet50/101, MobileNet) and object detection (YOLOv8n, YOLOv11n) including our purposed YOLOv8n-CBAM model using our annotated dataset designed for recycling. We found a clear trade-off between accuracy and resource consumption: the best classifier, EfficientNetV2S, achieved high accuracy (≈96%) but suffered from high latency More >

Williams Kyei^*, Chunyong Yin, Kelvin Amos Nicodemas, Khagendra Darlami

Journal on Artificial Intelligence, Vol.8, pp. 19-38, 2026, DOI:10.32604/jai.2026.074988 - 20 January 2026

Abstract The COVID-19 pandemic has underscored the need for rapid and accurate diagnostic tools to differentiate respiratory infections from normal cases using chest X-rays (CXRs). Manual interpretation of CXRs is time-consuming and prone to errors, particularly in distinguishing COVID-19 from viral pneumonia. This research addresses these challenges by proposing a customized EfficientNet-B0 model for ternary classification (COVID-19, Viral Pneumonia, Normal) on the COVID-19 Radiography Database. Employing transfer learning with architectural modifications, including a tailored classification head and regularization techniques, the model achieves superior performance. Evaluated via accuracy, F1-score (macro-averaged), AUROC (macro-averaged), precision (macro-averaged), recall (macro-averaged), inference… More >