Results 161 to 170 of about 28,119 (261)

A Comprehensive Study to Compare Different Compound Representations for Predicting Carcinogenicity In Vivo

open access: yesJournal of Applied Toxicology, EarlyView.
ABSTRACT Carcinogenicity evaluation is a critical component of chemical risk assessment, yet traditional in vivo testing remains time consuming, costly, and ethically challenging. Computational approaches based on machine learning offer promising alternatives, but the relative contributions of different molecular representation strategies for ...
Iuri Barbosa Pereira   +2 more
wiley   +1 more source

Optimizing feather hydrolysate via machine learning for microbial recycling of waste concrete fines

open access: yesJournal of Chemical Technology &Biotechnology, EarlyView.
Abstract BACKGROUND The concrete industry faces significant challenges from CO2 emissions and the disposal of waste concrete fines (WCF). Microbially induced calcite precipitation (MICP) can bind WCF into bioconcrete, but the high cost of commercial culture media hinders its application.
Henrietta Ottová   +5 more
wiley   +1 more source

Bias Correction of ERA5 Temperature Across Five Topoclimatic Clusters: A Comparative Evaluation of Four Methods

open access: yesInternational Journal of Climatology, EarlyView.
ERA5 near‐surface temperature over South America exhibits systematic cold biases in daily maximum temperature and warm biases in daily minimum temperature. To address the continent's strong climatic and topographic heterogeneity, South America was partitioned into five topoclimatic clusters, and four bias‐correction approaches were evaluated. The Multi‐
José Roberto Rozante, Gabriela Rozante
wiley   +1 more source

Machine Learning‐Based Correction of Reanalysis Surface Radiation Fluxes Using Ground‐Measured Data in the Southern Brazilian Pampa Biome

open access: yesInternational Journal of Climatology, EarlyView.
This study demonstrates that ERA5 provides more accurate surface radiation flux estimates than MERRA2 across the Southern Brazilian Pampa. Machine learning models, particularly Random Forest, further improved the precision of reanalysis data for climate applications.
Olusola Samuel Ojo   +5 more
wiley   +1 more source

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review

open access: yesKnee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. Methods Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer‐reviewed studies ...
Amna Gillani   +5 more
wiley   +1 more source

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