By studying how seismic waves travel through frozen soil on Earth, a University of Maryland geophysicist helped develop a better way to detect water ice on the moon—a critical resource for future astronauts and extended lunar missions.

UCF's College of Medicine has opened a new clinic to help pilots meet all levels of FAA medical requirements to fly safely. 

Black holes are often portrayed as cosmic gluttons that swallow everything that comes too close. But new observations of a dramatic black hole outburst – led by Warwick Postdoctoral Fellow Dr Noel Castro Segura – suggest the reality is much messier.

SAN ANTONIO — July 29, 2025 — NASA’s Juno mission has made the first direct measurements of the temperature below the surface of Jupiter's moon Io, which reveal significant heating in the upper few feet of the most volcanically active world in the solar system.

Despite our knowledge of supermassive blackholes far from complete, astronomers have long known that they produce violent winds. But a recent discovery has shown that these winds are more powerful than previously thought, 100 times more powerful, to be precise. High-precision observations of gas surrounding a black hole 3.4 billion light-years from Earth showed that the winds carry energy across distances of approximately 300,000 light-years, impacting surrounding galaxies. 

A team of astronomers at the University of North Carolina at Chapel Hill has observed an extremely rare “wandering” black hole located tens of thousands of light-years from the center of its galaxy.

Normally invisible as it whizzes around outer space, this supermassive black hole revealed itself to UMD astronomers by shimmering as it swallowed a star.

A new study published in Big Earth Data proposes an operational framework that integrates multiple machine learning and deep learning models with high-resolution Sentinel-2 data to estimate agricultural drought conditions in irrigated and non-irrigated maize fields in Eastern Austria. Results from 2018–2023 show that deep learning models, particularly the Deep Neural Network (DNN), achieved the best performance, while non-irrigated fields exhibited higher prediction accuracy due to clearer drought signals, demonstrating the value of Sentinel-2–based deep learning for operational drought monitoring.