When and where the next large earthquake will strike remains one of the most difficult questions in geosciences. Researchers from the GFZ Helmholtz Centre for Geosciences around Dr Sadegh Karimpouli and Prof. Dr Patricia Martínez-Garzón have now – together with international partners – developed a new data-driven approach that can identify characteristic changes in seismic activity before some major earthquakes occur. The team used unsupervised machine learning to detect previously hidden patterns in earthquake catalogues, without relying on predefined assumptions. They applied their method to several well-documented major earthquakes, including the Kahramanmaraş (Türkiye, 2023), Iquique (Chile, 2014) and L’Aquila (Italy, 2009) events, and were able to identify distinct patterns in the foreshock activity in these cases, which occurred weeks to months before the mainshock. For other earthquakes for which no precursor phenomena were known – the Noto (Japan, 2024) and Amatrice (Italy, 2016) events – the method found no patterns. The researchers therefore believe that their new approach has potential for the further development of operational earthquake forecasting approaches. The study has been published in the journal Nature Communications.

Researchers from Tongji University present a comprehensive review of emerging optical techniques for sorting and detecting chiral particles. The review highlights advances in engineered light fields, nanophotonic platforms, and artificial intelligence that enhance the sensitivity, selectivity, and efficiency of chiral analysis. By comparing optical sorting and detection strategies across diverse platforms, the work outlines current challenges and future directions, providing a valuable roadmap for developing practical, high-performance optical technologies for chemistry, biomedicine, and materials science.

A pioneering curriculum will integrate shared learning for Enrolled Nurse and Registered Nurse students across nursing programmes of NUS and ITE