Combining teaching and research: a BIP on geophysical and archaeological prospection of North Frisian medieval settlement patterns

We performed a research-oriented EU Erasmus+ Blended Intensive Program (BIP) with participants from four countries focused on North Frisian terp settlements from Roman Iron Age and medieval times. We show that the complex terp structure and environment can be efficiently prospected using combined magnetic and EMI mapping, and seismic and geoelectric profiling and drilling. We found evidence of multiple terp phases and a harbor at the Roman Iron Age terp of Tofting. In contrast, the medieval terp of Stolthusen is more simply constructed, probably uni-phase. The BIP proved to be a suitable tool for high-level hands-on education adding value to the research conducted in on-going projects

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures