Artificial intelligence tools secure tomorrow’s electric grid
Scientists develop environmental justice lens to identify neighborhoods vulnerable to climate change
A new capability to identify urban neighborhoods, down to the block and building level, that are most vulnerable to climate change could help ensure that mitigation and resilience programs reach the people who need them the most.
How an Alvin M. Weinberg Fellow is increasing security for critical infrastructure components
It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
What’s getting Jim Szybist fired up these days? It’s the opportunity to apply his years of alternative fuel combustion and thermodynamics research to the challenge of cleaning up the hard-to-decarbonize, heavy-duty mobility sector — from airplanes to locomotives to ships and massive farm combines.
ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
When Andrew Sutton arrived at ORNL in late 2020, he knew the move would be significant in more ways than just a change in location.
Unequal access to modern infrastructure is a feature of growing cities, according to a study published this week in the Proceedings of the National Academy of Sciences
David McCollum is using his interdisciplinary expertise, international networks and boundless enthusiasm to lead Oak Ridge National Laboratory’s contributions to the Net Zero World initiative.
University of Pennsylvania researchers called on computational systems biology expertise at Oak Ridge National Laboratory to analyze large datasets of single-cell RNA sequencing from skin samples afflicted with atopic dermatitis.