Medicine is rapidly evolving from statistical, evidence-based approaches to predictive, genotype-directed care, driven by ...

Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this ...

With peak wildfire season underway in California, PG & E's Chief Meteorologist Scott Stenfel held a virtual Wildfire Season ...

Simulating catalytic reactivity under operative conditions poses a significant challenge due to the dynamic nature of the catalysts and the high computational cost of electronic structure calculations ...

Climate and ocean models use a series of equations to represent complex natural processes. However, the equations used in ...

As semiconductor technologies advance, device structures are becoming increasingly complex. New materials and architectures introduce intricate physical effects requiring accurate modeling to ensure ...

The biopharmaceutical industry is rapidly moving from empirical, trial and error process development toward digitalized and ...

A rotating cylinder with its side cut away to expose the core, showing patches of purple, blue, green, yellow, and orange that are dense in the middle and more diffuse toward the edges. This rotating ...

A machine learning (ML)-based model may aid in-hospital community-acquired pneumonia (CAP) mortality prediction, according to study findings published in Respiratory Medicine. Res ...

Acoustic analysis of routine patient-clinician conversations may accurately identify cognitive impairment. Read more.

MIT researchers created a technique that captures chemical arrangements across materials to improve predictions of how metal ...

Researchers at Georgia Tech say they have developed a system to help answer that question. Known as FIRA, the tool analyzes ...