Fabiano Thulu, Zeyun Wu^*

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.076328 - 27 April 2026

Abstract Real-time prediction of temperature distribution in the pressurizer walls of Pressurized Water Reactors (PWRs) during severe accidents, such as Station Blackout (SBO) and Loss-of-Coolant Accident (LOCA) is vital for structural integrity assessment. However, conventional thermal-hydraulic simulations used for such predictions are computationally intensive, limiting their applicability for real-time analysis. This study develops and compares three surrogate models: Polynomial Regression, Deep Neural Network (DNN), and a Physics-Informed Neural Network (PINN). Thermal-hydraulic simulation data generated by RELAP5-3D are integrated with physics-constrained learning techniques to model transient heat conduction in the pressurizer wall. The internal wall temperature evolution… More >

Kadriye Simsek Alan^*, Busra Senel Kahyaoglu

Journal on Artificial Intelligence, Vol.8, pp. 203-230, 2026, DOI:10.32604/jai.2026.078359 - 15 April 2026

Abstract Cardiovascular diseases remain one of the leading causes of mortality worldwide, making early and reliable diagnosis a critical challenge for modern healthcare systems. In this study, a systematic comparative performance analysis of widely used machine learning algorithms is conducted for the early detection of heart disease using tabular clinical data. Rather than proposing a novel model architecture, the primary objective is to provide a fair, reproducible, and clinically meaningful evaluation of commonly adopted classifiers under consistent experimental conditions. The Kaggle Heart Failure dataset is employed, and multiple machine learning models—including tuned Random Forest, tuned XGBoost,… More >

Man Zhang¹, Cheng Chen¹, Hai-Xia Guo¹, Yi-Ming Xiao¹, Xin-Jian Zhao^2,*

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.071943 - 27 February 2026

Abstract Traditional oilfields face increasing extraction challenges, primarily due to reservoir quality degradation and production decline, which are further exacerbated by volatile international crude oil prices—illustrated by Brent Crude’s trajectory from pandemic-induced negative pricing to geopolitically driven surges exceeding USD 100 per barrel. This study addresses these complexities through an integrated methodological framework applied to medium-permeability sandstone reservoirs in the Xinjiang oilfield by combining advanced numerical simulations with multivariate regression analysis. The methodology employs Latin Hypercube Sampling (LHS) to stratify geological parameter distributions and constructs heterogeneous reservoir models using Petrel software, rigorously validated through historical production… More >

Viorel Mînzu^1,*, Iulian Arama²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.075619 - 10 February 2026

Abstract This work addresses optimality aspects related to composite laminates having layers with different orientations. Regression Neural Networks can model the mechanical behavior of these laminates, specifically the stress-strain relationship. If this model has strong generalization ability, it can be coupled with a metaheuristic algorithm–the PSO algorithm used in this article–to address an optimization problem (OP) related to the orientations of composite laminates. To solve OPs, this paper proposes an optimization framework (OFW) that connects the two components, the optimal solution search mechanism and the RNN model. The OFW has two modules: the search mechanism (Adaptive… More >

Hyunki Lim^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074138 - 10 February 2026

Abstract High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements. In particular, in a multi-label environment, higher complexity is required as much as the number of labels. Moreover, an optimization problem that fully considers all dependencies between features and labels is difficult to solve. In this study, we propose a novel regression-based multi-label feature selection method that integrates mutual information to better exploit the underlying data structure. By incorporating mutual information into the regression formulation, the model captures not only linear relationships but also complex non-linear dependencies. The proposed… More >

Sin-Ye Jhong¹, Hui-Che Hsu^1,2, Hsin-Hua Huang², Chih-Hsien Hsia^3,4,*, Yulius Harjoseputro^2,5, Yung-Yao Chen^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.072633 - 10 February 2026

Abstract Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations. Standard classification methods fail to address these dual challenges, limiting their real-world performance. In this paper, a novel, three-phase training framework is proposed that learns a robust ordinal classifier directly from noisy labels. The approach synergistically combines a rank-based ordinal regression backbone with a cooperative, semi-supervised learning strategy to dynamically partition the data into clean and noisy subsets. A hybrid training objective is… More >

Yanzhao Yang¹, Xiaowen Song², Zhiying Deng^2,*, Jianyang Yu³

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.074345 - 06 February 2026

Abstract In this study, a Gaussian Process Regression (GPR) surrogate model coupled with a Bayesian optimization algorithm was employed for the single-objective design optimization of fan-shaped film cooling holes on a concave wall. Fan-shaped holes, commonly used in gas turbines and aerospace applications, flare toward the exit to form a protective cooling film over hot surfaces, enhancing thermal protection compared to cylindrical holes. An initial hole configuration was used to improve adiabatic cooling efficiency. Design variables included the hole injection angle, forward expansion angle, lateral expansion angle, and aperture ratio, while the objective function was the More >

Eun Hae Lee^1,2,3, Minjae Choi^4,5, Hanul Park^3,6, Joon Hee Han^3,6,7, Sujeong Yu^3,8, Joshua Kirabo Sempungu^1,2,3,6, Inbae Sohn^4,6, Yo Han Lee^3,6,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.073735 - 28 January 2026

Abstract Background: That Central and Eastern Europe and Central Asia (CEECA) experienced a major mortality crisis in the 1990s is a well-established finding, with most analyses focusing on singular causes like alcohol-related deaths. However, the utility of the integrated “deaths of despair” framework, which views alcohol, drug, and suicide deaths as a unified socio-economic phenomenon, remains under-explored in this context. Crucially, the long-term evolution of the composition of despair within the region remains a largely unexplored area of inquiry. Therefore, this study aims to analyze the long-term trends, changing composition, and regional heterogeneity of deaths from despair… More >

Shengkang Zhang¹, Yong Jin^2,*, Soon Poh Yap^1,*, Haoyun Fan¹, Shiyuan Li³, Ahmed El-Shafie⁴, Zainah Ibrahim¹, Amr El-Dieb⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075351 - 29 January 2026

Abstract Concrete-filled steel tubes (CFST) are widely utilized in civil engineering due to their superior load-bearing capacity, ductility, and seismic resistance. However, existing design codes, such as AISC and Eurocode 4, tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core. To address this limitation, this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer (PKO), a nature-inspired algorithm, to enhance the accuracy of shear strength prediction for CFST columns. Additionally, quantile regression is employed to construct prediction intervals for… More >

Hongrong Wang^1,2, Xinjian Li^3,4, Xingjing She¹, Wenjian Ma^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2039-2055, 2025, DOI:10.32604/cmes.2025.072572 - 26 November 2025

Abstract In modern complex systems, real-time regression prediction plays a vital role in performance evaluation and risk warning. Nevertheless, existing methods still face challenges in maintaining stability and predictive accuracy under complex conditions. To address these limitations, this study proposes an online prediction approach that integrates event tracking sensitivity analysis with machine learning. Specifically, a real-time event tracking sensitivity analysis method is employed to capture and quantify the impact of key events on system outputs. On this basis, a mutual-information–based self-extraction mechanism is introduced to construct prior weights, which are then incorporated into a LightGBM prediction More >