Artificial intelligence and machine learning are rapidly transforming scientific research, with researchers across multiple areas of physics leading the development and application of these tools. To meet the needs of this fast-growing research community, the American Physical Society is launching PRX Intelligence, a highly selective open access journal that will welcome manuscripts on AI and machine learning methods and their application to advance scientific understanding. The journal will accept submissions starting February 2026.

Sustainable city management recognises the importance of not only the busy above-ground urban systems but also the invisible underground infrastructure, utilities, and hidden heritage and their stories. The Hong Kong Polytechnic University (PolyU) is at the fore-front of geospatial and near-surface geophysical technologies, advancing land surveying research that supports cultural preservation and the sustainable development urban environment.

Wiley announces the release of additional data to its IR, Raman, LC-MS, and SmartSpectra libraries, significantly broadening compound coverage.

The Hațeg Basin in Transylvania is world-famous for its dinosaur remains, which have been unearthed from dozens of sites over the past century. Despite the high number of fossil localities, dinosaur finds are generally considered rare in the area. An exception is the newly discovered site, where researchers found more than a hundred vertebrate fossils per square meter — with the large dinosaur bones lying almost on top of each other.

Two Virginia high school students have discovered how sea anemones respond to salinity changes caused by hurricane rainfall. As hurricanes worsen due to climate change, this important information could help conservation efforts and inspire advocacy efforts. The research was shared in a paper published in Ocean-Land-Atmosphere Research on October 7.

Kyoto, Japan -- Predicting earthquakes has long been an unattainable fantasy. Factors like odd animal behaviors that have historically been thought to forebode earthquakes are not supported by empirical evidence. As these factors often occur independently of earthquakes and vice versa, seismologists believe that earthquakes occur with little or no warning. At least, that's how it appears from the surface.

Earthquake-generating zones lie deep within the Earth's crust and thus cannot be directly observed, but scientists have long proposed that faults may undergo a precursory phase before an earthquake during which micro-fracturing and slow slip occur. Yet, despite their obvious potential, exactly how these processes could enable prediction of a main shock remains unclear. Furthermore, observational studies have suggested that small and large earthquakes appear indistinguishable during the beginning of their rupture, raising doubts about the usefulness of short-term precursors.

These difficulties have prompted interest in the use of machine learning to search for potentially predictive fault signals. Machine learning models have demonstrated an ability to predict stick-slip laboratory earthquakes  in small, centimeter-scale experiments, but this approach has not yet been applied to larger, more complex systems that more closely mimic natural faults.