Mingqi Liu, Ying Wang^*, Wujiang Li, Juyong Cao, Fuyong Yang

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.074783 - 12 July 2026

Abstract Accurately predicting battery life is essential for performance management and system safety. Due to the complexity and diversity of internal mechanisms in lithium-ion batteries, their nonlinear characteristics directly give rise to uncertainty in the battery degradation process. However, most existing prediction methods do not fully account for the uncertainty caused by various factors and only provide a point estimate finally. To address this issue, this paper proposes a new framework that combines Random Forest and Conformal Prediction to predict battery life and quantify the uncertainty of the results. This approach leverages the efficiency of Random… More >

Soran Manguri^1,2, Kasim Mermerdaş¹, Briar Esmail^3,4, Ahmed Manguri^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083537 - 30 June 2026

Abstract Geopolymer materials are increasingly recognized as sustainable alternatives to conventional cementitious materials due to their lower environmental impact and promising engineering performance. Recent studies have demonstrated that incorporating nanomaterials can further enhance the properties of geopolymer systems. In particular, nano-silica has been reported to significantly improve the mechanical performance of geopolymer materials. However, accurate prediction of compressive strength remains challenging because of the complex nonlinear interactions among mix design parameters, activator chemistry, and curing conditions. This study develops a machine learning framework to predict the 28-day compressive strength of nanosilica-modified hybrid geopolymer mortar using a… More >

Menghua Li^1,2,3, Bin Ou^1,2,3,4, Jiahao Li^1,2,3, Sitong Jin^1,2,3, Yanming Zhang^1,2,3, Shuyan Fu^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082514 - 30 June 2026

Abstract Accurate prediction of seepage discharge in earth-rock dams remains challenging due to the strong non-stationary and nonlinear characteristics, limited robustness of individual models, and poor interpretability of black-box approaches. To address these issues, this paper proposes an interpretable hybrid model that integrates Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM) networks, and Support Vector Machine (SVM). The model first decomposes the seepage discharge sequence and relevant lagged features using VMD. The LSTM network then captures temporal dependencies of the decomposed components, while the SVM performs regression on the original sequences and features. An adaptive fusion… More >

Naseer Muhammad Khan^1,2, Liqiang Ma^3,*, Majid Khan⁴, Sajjad Hussain⁵, Waleed Inqiad⁶, Tariq Feroze², Danial Jahed Armaghani^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081660 - 30 June 2026

Abstract Rock fracture behavior under stress is vital for risk evaluation in underground engineering excavation because the presence of water can significantly increase the extent of cracks and fractures in rock, leading to structural damage. This can result in catastrophic failures, including rock bursts, coal bursts, and water inrush. Hence, reliable prediction of rock damage and fracture processes is still lacking, which, in turn, enables the safe and efficient conduct of engineering projects in rock-mass environments. Thus, this study examines both dry and saturated sandstone samples under loading using Infrared Radiation (IR), Acoustic Emission (AE) monitoring,… More >

Cesar Guevara^*, Victoria Lopez

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.081473 - 30 June 2026

Abstract This study presents a mobile expert system for on-device detection and short-horizon forecasting of aggression using affordable edge hardware. The proposed framework combines lightweight on-body and ambient signals, compact sequential predictors, and an interpretable fuzzy decision layer that converts calibrated probabilities into actionable and auditable alerts. In a subject-held-out pilot study with 10 independent participants, the system achieved a macro-averaged F1 score of 98.3% and an area under the receiver operating characteristic curve of 0.998 on the held-out test split. These results should be interpreted as pilot-scale held-out estimates rather than as definitive evidence of… More >

Qin Zhao¹, Yuxin Wang¹, Gang Chen¹, Hui Zhang¹, Lei Wang¹, Mulin Zhou¹, Songfei Zhang¹, Yu Weng^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.6, 2026, DOI:10.32604/fdmp.2026.083174 - 30 June 2026

Abstract This study presents an integrated multi-scale framework for predicting gas entrainment and flow behavior in coalbed methane production systems under gas-liquid two-phase flow conditions. The approach combines three-dimensional computational fluid dynamics simulations, reduced-order modeling, and machine-learning-based prediction to achieve both high physical fidelity and computational efficiency. Such an improved strategy stems from a specific need. As coalbed methane extraction increasingly encounters complex multiphase flow conditions, accurate characterization of gas entrainment has become essential for improving production stability and optimizing downstream gathering and separation systems. In practice, the flow entering rod pumps frequently deviates from the… More >

Nayeemuddin Mohammed^1,2, Tahar Ayadat^1,2,*, Andi Asiz^1,2, Nadeem Pasha³

Structural Durability & Health Monitoring, Vol.20, No.4, 2026, DOI:10.32604/sdhm.2026.080495 - 30 June 2026

Abstract Significant efforts have been made to increase the strength of concrete by using industrial waste such as fly ash and steel slag as partial substitutes for concrete in concrete. However, predicting the concrete’s compressive strength is a challenge as it is influenced by several factors such as the shape and size of the aggregate, the water-ratio balance. This study examines the predictive capability of three deep learning models: Bagging Extreme Gradient Boosted Model (BXGBM), Deep Random Vector Functional Link (DRVFL), and Kernel Extreme Learning Machine (KELM) on the prediction for compressive strength of concrete. The… More >

Daxun Luo^#, Bixiao Wang^#, Haifeng Song, Chaoyue Ji, Weiguo Hu, Bo Xiao, Boxing Su, Yubao Liu^*, Jianxing Li^*

Canadian Journal of Urology, Vol.33, No.3, pp. 623-634, 2026, DOI:10.32604/cju.2026.076790 - 29 June 2026

Abstract Objectives: Urinary stone composition critically influences treatment selection and recurrence prevention, yet current intraoperative assessment remains imprecise. This study aims to achieve intraoperative prediction of stone composition by applying a deep convolutional neural network (CNN) to routinely captured endoscopic images. Methods: We retrospectively studied endoscopic images from stone-breaking surgeries in Beijing Tsinghua Changgung Hospital during 2022-12–2024-12. Images were captured before and after laser lithotripsy. Based on postoperative infrared spectroscopy, stones were divided into five categories. In total, 1780 images (1167 from RIRS, 613 from PCNL) were included and split into training and testing sets at… More >

Jakkrawut Techo¹, Panupon Trairat¹, Karthikeyan Velmurugan^2,*

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.080474 - 18 June 2026

Abstract In this study, selective and non-selective absorber-coated trays were employed to dry carrots and pears. Two trays with a selective absorber coating (1 mm thickness) were used, each loaded with 600 g of sliced carrots and pears. Similarly, two additional trays with a non-selective absorber coating were utilised. Furthermore, the performance of both selective and non-selective absorber-coated trays was compared with conventional open sun drying. The selective absorber-coated tray demonstrated higher thermal energy absorption and enabled the drying of carrots within 2 days, resulting in a weight loss of 529 g. In contrast, owing to… More >

Jesús Águila-León¹, Carlos Vargas-Salgado^2,*, Dácil Díaz-Bello², Fabián Lara-Vargas³

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.078087 - 18 June 2026

Abstract Integrating renewable energy sources presents technical challenges due to their variable nature, particularly in predicting and managing microgrid operational modes. Accurate identification of grid states—interconnected or islanded—is essential for maintaining stability and optimizing performance under fluctuating environmental conditions to meet energy demand. This work proposes a bio-inspired, optimized binary classification model based on Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN), with the architecture and hyperparameters tuned using the novel Mosquito Mating Swarm Optimization (MMSO) algorithm, inspired by mosquito mating behavior and swarm dynamics. The model employs an MLP-ANN with a variable number of hidden layers and… More > Graphic Abstract