Kyoto, Japan -- Japan is well known for its large earthquakes, but not all regions show the same patterns of earthquake activity. One way to understand which places tend to experience large or small earthquakes is the b-value, a key statistical measure long used by researchers for understanding seismicity and assessing earthquake occurrence patterns.

The b-value describes the relative numbers of small and large earthquakes in a given area. A high b-value means that small earthquakes are comparatively more frequent, whereas a low b-value indicates a relative increase in the proportion of larger earthquakes.

Many previous studies have suggested that b-values may decrease before -- or increase after -- large earthquakes, possibly reflecting changes in stress before and after rupture. Other studies have proposed that large earthquakes tend to occur in areas where b-values are low. Until now, however, it has remained unclear whether time-dependent changes or spatial differences are more important when comparing many large earthquake sequences.

Groundbreaking modelling has revealed that as many as 220,000 Indigenous Australians died in a devastating 1789 smallpox outbreak that originated in Sydney, by tracing its origins to the First Fleet. Soon after British ships arrived, smallpox swept through First Nations communities in the Sydney area and many died as a result.

INQUBATOR, Fraunhofer's quantum computing consulting and testing center, offers businesses an practical and cost-effective access to quantum computing. Starting immediately, companies of all sizes can apply with their use cases to develop customized solutions in collaboration with Fraunhofer partners and test the potential of quantum algorithms. The application deadline is August 31, 2026.

RNA design is central to next-generation therapeutics, yet identifying sequences that reliably fold into desired structures remains a major computational challenge, often constrained by high cost and time. A new study from Keio University explores the use of factorization machine with quadratic-optimization annealing (FMQA) for RNA inverse folding, while also examining how different encoding strategies may influence artificial intelligence (AI)-driven design performance, revealing an underexplored dimension of biomolecular engineering.