Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter interactions. To address this, this study integrates first-principles calculations with machine learning (ML) for rapid gas sensitivity prediction.

The XGBoost model predicts hyperglycemia risk in psoriasis patients with high accuracy, achieving an AUC of 0.821 in the training set. A web-based calculator was developed to facilitate personalized treatment strategies for psoriasis patients at risk of hyperglycemia.

A new study shows that machine-learning models can accurately predict daily crop transpiration using direct plant measurements and environmental data. By training models on seven years of high-resolution lysimeter data, the researchers demonstrate strong ...