The CW-Net technique explains the behavior of an autonomous vehicle, using concepts a human can easily understand. Researchers found these explanations helped drivers predict how a vehicle would behave in surprising situations.

Researchers at Helmholtz Munich and the Technical University of Munich (TUM) have developed Synthetic Transfer Vehicles (STVs), an entirely new class of RNA transporters. To do so, they combined naturally occurring protein building blocks with synthetic protein structures designed using generative AI. Systematic screening identified STV-C8 as the most efficient candidate: In cell culture, it transports RNA far more efficiently than the lipid nanoparticles tested and can be adapted to different RNA cargoes and target cells. In animal models, the team demonstrated that the system also works in a living organism.

The United Nations hold well-developed and safe roads as an important part of infrastructure in their Sustainable Development Goals. Until now, however, there has been no benchmark for the condition and state of road networks worldwide. Researchers at the Institute of Geography of Heidelberg University and HeiGIT (Heidelberg Institute of Geoinformation Technology) have addressed this gap by using artificial intelligence and satellite imagery to create an open-access dataset that maps and classifies more than nine million kilometers of roads worldwide. The dataset also captures changes in road conditions over time, providing valuable information for humanitarian applications and serving as an important indicator for assessing socioeconomic development, particularly in data-scarce regions.