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We compare results of simulations of solar facular-like conditions performed using the numerical codes MURaM and STAGGER. Both simulation sets have a similar setup, including the initial condition of ≈200 G vertical magnetic flux. After interpolating the output physical quantities to constant optical depth, we compare them and test them against inversion results from solar observations. From the snapshots, we compute the monochromatic continuum in the visible and infrared, and the full Stokes vector of the Fe i spectral line pair around 6301–6302 Å. We compare the predicted spectral lines (at the simulation resolution and after smearing to the HINODE SP/SOT resolution) in terms of their main parameters for the Stokes I line profiles, and of their area and amplitude asymmetry for the Stokes V profiles. The codes produce magnetoconvection with similar appearance and distribution in temperature and velocity. The results also closely match the values from recent relevant solar observations. Although the overall distribution of the magnetic field is similar in both radiation-magnetohydrodynamic (RMHD) simulation sets, a detailed analysis reveals substantial disagreement in the field orientation, which we attribute to the differing boundary conditions. The resulting differences in the synthetic spectra disappear after spatial smearing to the resolution of the observations. We conclude that the two sets of simulations provide robust models of solar faculae. Nevertheless, we also find differences that call for caution when using results from RMHD simulations to interpret solar observational data.
Short-term Incentives of Research Evaluations: Evidence from the UK Research Excellence Framework
(2023)
Learning Management Systems (LMS), such as Moodle, enable the rapid progress of digitisation in teaching, which is no longer only taking place in the lecture hall, but increasingly “online” and asynchronously. New didactic concepts (blended learning, “flipped classroom”) consist of alternating self-learning and face-to-face phases, with the former taking place in the LMS, i.e. online. However, no analysis has yet been carried out as to how students act with the material in the self-learning phase, or the teachers are not provided with any information about the learning progress of the students during the self-learning phase. In this paper, concepts of learning and teaching analytics are presented to answer these questions and to integrate the measures derived from them into the teaching processes in a sustainable manner.
Background: Most clinical studies report the symptoms experienced by those infected with coronavirus disease 2019 (COVID-19) via patients already hospitalized. Here we analyzed the symptoms experienced outside of a hospital setting.
Methods: The Vienna Social Fund (FSW; Vienna, Austria), the Public Health Services of the City of Vienna (MA15) and the private company Symptoma collaborated to implement Vienna's official online COVID-19 symptom checker. Users answered 12 yes/no questions about symptoms to assess their risk for COVID-19. They could also specify their age and sex, and whether they had contact with someone who tested positive for COVID-19. Depending on the assessed risk of COVID-19 positivity, a SARS-CoV‑2 nucleic acid amplification test (NAAT) was performed. In this publication, we analyzed which factors (symptoms, sex or age) are associated with COVID-19 positivity. We also trained a classifier to correctly predict COVID-19 positivity from the collected data.
Results: Between 2 November 2020 and 18 November 2021, 9133 people experiencing COVID-19-like symptoms were assessed as high risk by the chatbot and were subsequently tested by a NAAT. Symptoms significantly associated with a positive COVID-19 test were malaise, fatigue, headache, cough, fever, dysgeusia and hyposmia. Our classifier could successfully predict COVID-19 positivity with an area under the curve (AUC) of 0.74.
Conclusion: This study provides reliable COVID-19 symptom statistics based on the general population verified by NAATs.
Keywords: Chatbot; Machine learning; Self-reported; Symptom assessment; Symptom checker.
Rethinking in Education
(2010)
Teaching Security at the UAS Technikum Wien – an interdisciplinary approach in higher education
(2016)
The dynamics of proteins are crucial for their function. However, commonly used techniques for studying protein structures are limited in monitoring time-resolved dynamics at high resolution. Combining electric fields with existing techniques to study gas-phase proteins, such as single particle imaging using free-electron lasers and gas-phase small angle X-ray scattering, has the potential to open up a new era in time-resolved studies of gas-phase protein dynamics. Using molecular dynamics simulations, we identify well-defined unfolding pathways of a protein, induced by experimentally achievable external electric fields. Our simulations show that strong electric fields in conjunction with short-pulsed X-ray sources such as free-electron lasers can be a new path for imaging dynamics of gas-phase proteins at high spatial and temporal resolution.