A bottom-up approach quantifies the contributions of human-caused heating from building energy use during extreme heat events.
Computational work uses a Chicago neighborhood to understand and quantify climate effects on building energy use from changes in urban design.
A new way of representing river-groundwater exchanges paves the way for next-generation river network modeling.
Researchers find that fungal spores are most abundant during initial growth, while bacteria predominate during flowering and fruit development.
Using an ensemble of artificial intelligence (AI) agents enabled faster, more accurate data analysis of synchrotron x-ray data.
Machine Learning offers New Insights and New Parameterization for the path from Drizzle Drops to Warm Rain
Noise estimation circuits, in conjunction with other error mitigation methods, produce reliable results for quantum computer-based materials simulations
Adding a little oxygen to particle accelerator structures may make them more efficient and easier to build.
Monitoring photo-excited electrons in real time with nanometer sensitivity reveals strengths and weaknesses in a common light-harvesting material.
Researchers enable real-time adjustments to communication among three remote nodes in a quantum network.
Fine roots grow dramatically faster in an experimentally warmed peatland
Computers learn from a combination of experimental and evolutionary data to enhance the function of useful proteins.
