Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches to improve ...

Researchers have developed a hybrid CFD-neural network model for predicting TAIs in hydrogen-fueled turbines, improving ...

The ROAR trial tested the hypothesis that returning familial hypercholesterolemia-associated genetic results leads to ...

Nitrate pollution has become one of the most widespread water quality challenges in intensively farmed regions around the world, threatening drinking water safety, aquatic ecosystems, and downstream ...

This important study combines optogenetic manipulations and wide-field imaging to show that the retrosplenial cortex controls behavioral responses to whisker deflection in a context-dependent manner.

In a remote corner of East Greenland, a section of mountain gave way, collapsing into a narrow glacial fjord bounded by steep ...

Combining microscopy and machine-learning techniques leads to faster, more precise analyses of critical coating materials ...

This study provides a useful application of computational modelling to examine how people with chronic pain learn under uncertainty, contributing to efforts to link pain with motivational processes.

A new study reveals how biological branching networks use surface geometry to shape blood vessels, brains, and plants.

Training artificial intelligence models is costly. Researchers estimate that training costs for the largest frontier models ...