ABSTRACT This study presents a state-of-the-art ensemble machine learning (ML) model-based comprehensive evaluation of chloride ion penetration resistance of Self-Compacting Concrete (SCC) durability, simulated on an extensive dataset of 396 Rapid Chloride Penetration Test (RCPT), incorporating variations in cement, fly ash (FA), silica fume (SF), aggregate content, and temperature. Descriptive statistical analysis and visualizations revealed that moderate cement content, higher SF levels, and optimized FA replacement significantly enhance durability by reducing RCPT values. Elevated temperatures lead to increased permeability, although their impact can be mitigated in the mixes with higher SCM content. Four ensemble-based ML algorithms, Random Forest (RF), Gradient Boosting Machine (GBM), XGBoost, and CATBoost, were developed and compared for predictive modelling of RCPT values. The comparative analysis reveals XGBoost as the top performer with an R 2 of 0.9989, followed by RF (R 2 = 0.987), with CATBoost (R 2 = 0.963) and GBM (R 2 = 0.956) also performing satisfactorily. XGBoost excels in the validation with an R 2 of 0.9799. The study also presents a GUI framework for user-friendly, rapid, and cost-effective preliminary assessments. The study highlights the combined role of SCMs and ML-driven predictive modelling in developing durable, sustainable, and cost-effective SCCs for modern infrastructure.

Insight into the oxidation mechanism of low-salt Sichuan-style sausages treated with inclusion complexes of tea-polyphenol/β-cyclodextrin/NaCl and electron beam irradiation using machine learning algorithms.

Machine learning for the prediction of atrial fibrillation recurrence after catheter ablation: A systematic review and meta-analysis.

High-selectivity phenol detection in cumene process wastewater via bromination and dynamic optical path.

Spatial-temporal patterns and drivers of coastal water quality dynamics: Insights from explainable machine learning and PLS-SEM analysis in Xiamen Bay, China.

Automatic multi-language analysis of SOLID compliance via machine learning algorithms

Prediction of flexural and split tensile strength of waste glass-concrete composite using machine learning algorithms

Identification of mitochondrial dysfunction-related biomarkers and immune infiltration in liver ischemia-reperfusion injury via integrated bioinformatics and machine learning.

Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal interstitial lung disease characterized by excessive extracellular matrix (ECM) deposition and tissue stiffening. Matrix stiffness is a key driver of fibrosis, yet diagnostic biomarkers directly linked to this physical property are lacking. This study aimed to identify robust matrix stiffness-related diagnostic biomarkers and potential therapeutic targets for IPF using an integrated machine learning approach. Gene expression profiles were obtained from the GEO database (Training set: GSE33566; Validation set: GSE93606). Differentially expressed genes (DEGs) were intersected with a matrix stiffness-related gene set. Three machine learning algorithms (SVM-RFE, LASSO, and Naive Bayes) were employed to screen diagnostic feature genes. A diagnostic nomogram was constructed and evaluated. Functional enrichment (GO/KEGG/GSEA), immune infiltration (ssGSEA), and molecular docking analyses were performed to explore biological functions and predict therapeutic drugs. Eighteen matrix stiffness-related DEGs were identified. Through machine learning screening, GSN and ARG1 were determined as robust key genes, exhibiting high diagnostic accuracy (AUC>0.7) in both training and validation cohorts. Functional analysis revealed that GSN is involved in actin cytoskeleton regulation, while ARG1 participates in immune response modulation. Both genes showed strong positive correlations with the infiltration of macrophages and neutrophils. Furthermore, molecular docking identified RA-2 as a potential therapeutic agent targeting ARG1 with high binding affinity (-9.2kcal/mol). We identified GSN and ARG1 as novel matrix stiffness-related diagnostic biomarkers for IPF, linking mechanotransduction to immune microenvironment remodeling. The diagnostic nomogram offers high clinical predictive value, and RA-2 emerged as a putative ARG1-targeting compound with favorable docking energy and warrants further experimental validation as a potential antifibrotic agent.

Intelligent recognition of the fermentation stage of baijiu based on multi-dimensional data fusion and interpretable machine learning.

Predictive value of tear lipidomics biomarkers for TAO activity and relationship with clinical characteristics.

Association between red cell distribution width-albumin ratio and osteoarthritis in middle-aged and older adults: Analysis of NHANES data (1999-2018).

Tracing the geographical origin of tiger nut (Cyperus esculentus L) in China based on stable isotopes and mineral elements combined with multi-modal recognition.

Therapeutic drug monitoring is essential for ensuring the efficacy and safety of vancomycin therapy in critically ill patients. This study aimed to develop a machine learning model for individualized prediction of vancomycin concentration-time curves in ICU patients. Adult ICU patients who received intravenous vancomycin and underwent therapeutic drug monitoring at Peking Union Medical College Hospital between January 2014 and December 2023 were retrospectively included. A total of 401 patients were randomly divided into training (n = 280) and testing (n = 121) cohorts. Individual pharmacokinetic parameters were estimated using Bayesian posterior inference and served as reference targets. Five machine learning algorithms were evaluated, and the two with the best predictive performance, Lasso Regression and LightGBM, were integrated with a one-compartment pharmacokinetic model to construct the final predictive model. In the internal testing cohort, the model achieved a mean absolute percentage error (MAPE) of 39.5% for vancomycin concentration prediction. External validation in an independent cohort of 2283 patients showed consistent performance (MAPE = 35.6%). The machine learning-based model significantly outperformed the classic pharmacokinetic model (p < 0.001) in both internal and external validations. A user-friendly software tool based on the model was also developed to facilitate clinical implementation. These findings suggest that the proposed model offers a robust and practical decision-support tool for optimizing individualized vancomycin dosing in ICU settings. Trial Registration: ClinicalTrials.gov identifier: NCT06431412.

Iatrogenic plasticizer Di(2-ethylhexyl) phthalate (DEHP) exposure increases Sepsis mortality risk: Machine learning implicates monocyte-driven immune dysregulation.

Establishment of a quantitative GC-MS method for acrylamide detection and in situ kinetic study of acrylamide formation in fried potato slices.

Multifunctional conductive hydrogel based on carboxymethyl cellulose/oxidized sodium alginate for machine learning-guided sports training.

Quantifying contributions of climate and rice phenological changes to SOC dynamics based on the integration of biogeochemical model and machine learning algorithm

NIPSNAP3B elevates mitochondrial biogenesis to attenuate lipid accumulation in childhood obesity via AMPK pathway.

DrowsyDG-Phys: Generalizable driver drowsiness estimation in conditional automated vehicles using physiological signals.