Scientific Highlights

Saharan dust storms played havoc with weather predictions. Invertible neural networks to retrieve aerosol properties from light scattering data may help.

Andreas Wallraff talks about moving in, refrigerators and measuring the first superconducting qubit at the ETHZ-PSI Quantum Computing hub.

The prestigious Fellow Award “Fellow of The Combustion Institute” was allotted to Dr. Mantzaras for “Pioneering Experimental and Modeling Research in Hetero-/Homogeneous and Catalytic Combustion”. The combustion activities at LSM emphasize on non-intrusive laser-based measurements in a high-pressure optically accessible catalytic reactor, while the modeling activities encompass advanced multidimensional numerical simulations and theoretical work based on activation energy asymptotics.

With the imminent relaxation of socio-economic restrictions, it becomes vital to assess its effect on the prevalence of acute infections within the population, as rapidly as possible. Currently available monitoring instruments for the COVID-19 pandemic have an inherent time delay of about 14 days, as they rely on confirmed infections, hospitalizations, and death numbers. These methods give R_eff(t) (the number of infections caused by a single infected person), but their delay is a significant disadvantage when restrictions are released. If after relaxation, R_eff(t) rises above 1, one will not be able to react adequately before two weeks have passed during which time the prevalence could significantly rise. Here, we propose the use of random testing to shorten this reaction time, by obtaining direct and modeling dependent information on R_eff(t). Through random testing of between 2500 and 20000 people per day, we find that over periods significantly shorter than two weeks, it becomes possible to detect a dangerous increase in R_eff with reasonable confidence.