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Confinement of charge carriers in gapped bilayer graphene within magnetic and electrostatic barriers

Local Cation-Ordered Superlattice Stabilizing Ni-Rich Single-Crystalline Cathodes

Bioprospecting of six polyphenol-rich Mediterranean wild edible plants reveals antioxidant, antibiofilm and bactericidal properties against Methicillin resistant Staphylococcus aureus

BNB-Modified Perylene, Terrylene, and Quaterrylene Diimides: Introduction of the π-Accepting and Aggregation-Suppressing Diborinic Imide Group in Rylene Dyes

Alzheimer’s disease risk prediction using machine learning for survival analysis with a comorbidity-based approach

Uncovering the Pressure-Dependent Mechanism of CO₂ Hydrogenation to Methanol on Ga-Promoted Cu/ZrO₂ Using <i>Operando</i> Modulation-Excitation DRIFTS

Identifying ferroptosis-related genes in lung adenocarcinoma using random walk with restart in the PPI network

Parent-of-origin effects found for gene variants that affect human growth and metabolism

Design and Synthesis of Hetero-Bicephalic Detergents for Native Mass Spectrometry of Membrane Proteins

Optimizing equivalent testing for scaled projectile penetration into multilayer concrete targets with fuse overload via layer-by-layer chasing-catching methodology

Distinctive Kinetic Signatures of Surface Segregation Processes in Bimetallic Nanoparticle Catalysis

Machine learning analysis of greenhouse gas sources impacting Africa’s food security nexus

Basicity–Controlled C–H Bond Activation by a Structurally Characterized Ni(III)–Hydroxo Complex

Unraveling the luminescence secrets of turquoise nucleus cultivated pearls

DNA Framework Nanoruler-Directed Surface Fluorescence Enhancement as a Sensing Platform for MicroRNA Detection

Distinguishing the flow of airborne microorganisms along with environmental conditions and their influence on historic heritage buildings

Emerging Complex Behavior Driven by Self-Organization: Dynamic Covalent Libraries of Acylhydrazones in Water

Machine learning approaches for predicting the structural number of flexible pavements based on subgrade soil properties

Abstract This study presents a machine learning approach to predict the structural number of flexible pavements using subgrade soil properties and environmental conditions. Four algorithms were evaluated, including random forest, extreme gradient boosting, gradient boosting, and K nearest neighbors. The dataset was prepared by converting resilient modulus values into structural numbers using the bisection method applied to the American Association of State Highway and Transportation Officials 1993 design equation. Input variables included moisture content, dry unit weight, weighted plasticity index, and the number of freeze and thaw cycles. Each model was trained and tested using standard performance metrics. Gradient boosting achieved the highest accuracy with a determination coefficient of 0.917. Moisture content was identified as the most significant predictor in most models. The findings demonstrate that machine learning models can accurately predict pavement thickness requirements based on readily available soil and environmental data. This approach reduces reliance on expensive and time-consuming laboratory tests and provides a practical and efficient tool for pavement design. This study highlights the potential of machine learning models in enhancing pavement design by accurately predicting structural performance parameters based on soil and environmental factors.

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Super-Resolution Imaging of Semiconductor Nanopatterns Using the Non-Bridging-Oxygen-Hole-Center-Based Photoluminescence Enhancement Effect