A research team from Wuhan University has developed an innovative machine learning framework that accelerates the discovery of materials with tailored thermal properties. By combining interpretable deep learning with multiscale computational techniques, the team achieved highly accurate predictions of lattice thermal conductivity (LTC) while also shedding light on the underlying physical mechanisms. Their approach maintains the predictive power of traditional "black-box" models but adds the physical interpretability. As result, several high-performance materials for thermal management were identified. This advancement not only speeds up the development of efficient thermoelectric devices and thermal control systems but also provides deeper insights into the fundamental process of heat transfer at the atomic scale.

The National Institute of Information and Communications Technology (NICT) and the Nagoya Institute of Technology (NITech), collaborated with the Japan Aerospace Exploration Agency (JAXA), have achieved the world’s first successful demonstration of next-generation error correction codes, mitigating the impact of atmospheric turbulence on ground-to-satellite laser communications.

Atmospheric turbulence in ground-to-satellite laser links is known to cause fading, resulting in burst data errors. Error correction codes are one of the key technologies to mitigate such effects. In this experiment, we transmitted next-generation error correction codes with high correction capability (5G NR LDPC and DVB-S2) and successfully corrected burst data errors caused by atmospheric turbulence in the laser link. This result confirmed that both codes can significantly improve communication quality compared to conventional schemes.

This achievement is expected to contribute to the practical implementation of ground-to-satellite laser communications by applying these codes.

What if we told you that the secret to healthier soil, cleaner ecosystems, and smarter farming isn’t buried in a high-tech lab—but hidden in the data behind crop residues, wood chips, and food waste?

Meet the future of sustainable agriculture: a powerful new machine learning tool that can predict exactly how much biochar—a carbon-rich, soil-boosting material—can be made from any type of biomass, and how much nitrogen, phosphorus, and potassium it will contain. No crystal ball needed. Just smart science, powered by data.

Georgia State computer science students are building robotics to deliver tomorrow’s solutions — and learning as they do it.

A team of researchers used airborne radar to map out an unexplored region in the deep interior of East Antarctica in search of old ice that could record clues from Earth's ancient climate. Although they haven’t found a suitable ice sample yet, their survey revealed new insights about the geologic processes happening under the Antarctic ice sheet and how ice is moving around beneath it.

By combining ground-based direct imaging and radial velocity measurements with precise astrometric acceleration data from the Gaia space telescope, astronomers have discovered a brown dwarf companion orbiting a nearby red dwarf star about 55 light-years from Earth. The team accurately determined its mass (≈60 Jupiter masses) and orbital distance (≈4.3 AU) using Subaru’s Infrared Doppler instrument (IRD) under the IRD Strategic Program, Keck high-contrast imaging, and Gaia data. The object, classified as a late-T-type brown dwarf, shows roughly 30% variability in near-infrared brightness, offering a valuable benchmark for studying atmospheric clouds and circulation. This marks the first successful application of Gaia-only astrometric acceleration to precisely characterize a companion beyond Hipparcos’ detection limits.