The intermittent nature of solar energy poses challenges to grid stability, making accurate ultra-short-term solar irradiance forecasting crucial for balancing supply and demand. However, traditional ...

Researchers at Los Alamos National Laboratory have developed a new approach that addresses the limitations of generative AI ...

Tabular foundation models are the next major unlock for AI adoption, especially in industries sitting on massive databases of ...

As climate extremes intensify across Africa, the need for accurate and timely weather prediction has become increasingly ...

A poor night's sleep portends a bleary-eyed next day, but it could also hint at diseases that will strike years down the road ...

We are seeking a Data Scientist passionate about turning complex commercial challenges into actionable, high-impact solutions? We’re looking for someone to take the lead on our pricing and markdown ...

A new artificial intelligence model developed by Stanford Medicine researchers and their colleagues can use physiological ...

Buildings produce a large share of New York's greenhouse gas emissions, but predicting future energy demand—essential for ...

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

This forecasting study analyzes the impact of the Inflation Reduction Act (IRA) on diabetes drug costs for Medicare in Louisiana, USA. It finds that price negotiations for three non-insulin drugs are ...