Development of a Cu-Sn based brazing system with a low brazing and a high remelting temperature

Objective of the project presented is the development of a joining process for hot working steel components at low brazing temperatures leading to a bond with a much higher remelting temperature. This basically is achieved by the use of a Cu-Sn melt spinning foil combined with a pure Cu foil. During brazing, the Sn content of the foil is decreased by diffusion of Sn into the additional Cu resulting in a homogenious joint with a increased remelting temperature of the filler metal. Within this project specimens were brazed and diffusion annealed in a vacuum furnace at 850 °C varying the processing times (0 - 10 h). The samples prepared were studied metallographically and diffusion profiles of Sn were recorded using EDX line scans. The results are discussed in view of further investigations and envisaged applications.German Ministry of Economic Affairs and EnergyIGF/IGF 18.706 N/DV

Predicting lethal courses in critically ill COVID-19 patients using a machine learning model trained on patients with non-COVID-19 viral pneumonia

In a pandemic with a novel disease, disease-specific prognosis models are available only with a delay. To bridge the critical early phase, models built for similar diseases might be applied. To test the accuracy of such a knowledge transfer, we investigated how precise lethal courses in critically ill COVID-19 patients can be predicted by a model trained on critically ill non-COVID-19 viral pneumonia patients. We trained gradient boosted decision tree models on 718 (245 deceased) non-COVID-19 viral pneumonia patients to predict individual ICU mortality and applied it to 1054 (369 deceased) COVID-19 patients. Our model showed a significantly better predictive performance (AUROC 0.86 [95% CI 0.86-0.87]) than the clinical scores APACHE2 (0.63 [95% CI 0.61-0.65]), SAPS2 (0.72 [95% CI 0.71-0.74]) and SOFA (0.76 [95% CI 0.75-0.77]), the COVID-19-specific mortality prediction models of Zhou (0.76 [95% CI 0.73-0.78]) and Wang (laboratory: 0.62 [95% CI 0.59-0.65]; clinical: 0.56 [95% CI 0.55-0.58]) and the 4C COVID-19 Mortality score (0.71 [95% CI 0.70-0.72]). We conclude that lethal courses in critically ill COVID-19 patients can be predicted by a machine learning model trained on non-COVID-19 patients. Our results suggest that in a pandemic with a novel disease, prognosis models built for similar diseases can be applied, even when the diseases differ in time courses and in rates of critical and lethal courses

Introduction. Ruptures in the Everyday

Ruptures in the Everyday was jointly written by ATG26, a scholarly collective comprising the following authors:Jonathan Bach, Andrew Stuart Bergerson (lead author), Susanne Beer, Mark E. Blum, Michaela Christ, Cristina Cuevas-Wolf, Mary Fulbrook, Eva Giloi, Thomas Gurr, Jason Johnson, Craig Koslofsky, Dani Kranz, Phil Leask, Wendy Lower, Elissa Mailänder, Josie McLellan, Alexandra Oeser, Steve Ostovich, Will Rall, Leonard Schmieding (lead author), Johannes Schwartz, Sara Ann Sewell, Paul Steege, Maximilian Strnad, Julia Timpe, Heléna TóthInternational audienc