Refine
Document Type
- Preprint (7)
- Article (6)
- Conference Proceeding (3)
- Doctoral Thesis (1)
Has Fulltext
- yes (17)
Is part of the Bibliography
- no (17)
Keywords
- Beruf (3)
- Bologna-Prozess (3)
- Exzellenzinitiative (3)
- Germanistik (3)
- Germanistikstudium (3)
- Theorie (3)
- Zukunft (3)
- Recombination (2)
- Advanced stage (1)
- COVID-19 (1)
Institute
Das Internationale Colloquium "Perspektiven der Germanistik im 21. Jahrhundert" fand vom 4. bis 6. April 2013 im SchlossHerrenhausen in Hannover statt.
Der Autor hat den Herausgebern den vorliegenden Text nach der Konferenz zur Verfügung gestellt. Er antwortet auf die Ausgangsfragen zum Diskussionsforum B.2 "Germanistik studieren – Perspektiven in Ausbildung und Beruf", die im Programm zur Veranstaltung formuliert worden waren.
Ausgangsfragen: Bologna: Segen und/oder Fluch? Bedeuten Modularisierung und Ausrichtung auf Kompetenzen das Ende der Humboldt'schen Bildungsidee? Und wenn: Ist das ein Verlust oder ein Gewinn? Wie ist die Modularisierung der Studienordnung mit Blick auf praktische Erfahrungen zu bewerten? Hat sie zu einer Verbesserung des Studienverlaufs geführt? Welches Curriculum müsste die Germanistik der Zukunft haben? Welche 'Schlüsselqualifikationen' sollte sie vermitteln? Sollte das Studium (noch) stärker berufsorientiert strukturiert sein? Sind auch hier Anpassungen an das medientechnische Umfeld erforderlich? Wie steht es generell um die Berufsaussichten von Germanisten? Kommt den Fächern bzw. den Universitäten selbst eine höhere Verantwortung für die Vermittlung in Berufe zu ('employability')?
Dieser Text wurde verlesen als Statement auf dem Internationalen Colloquium "Perspektiven der Germanistik im 21. Jahrhundert", das vom 4. bis 6. April 2013 im Schloss Herrenhausen in Hannover stattfand. Er bildete die Grundlage für eine Podiumsdiskussion zum Thema "Germanistik studieren – Perspektiven in Ausbildung und Beruf" in der Sektion "Jenseits von Bologna – Studium und Beruf".
Purpose: While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.
Methods: We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).
Results: The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.
Conclusion: We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.
Abstract Geant4 is a toolkit for simulating the passage of particles through matter. It includes a complete range of functionality including tracking, geometry, physics models and hits. The physics processes offered cover a comprehensive range, including electromagnetic, hadronic and optical processes, a large set of long-lived particles, materials and elements, over a wide energy range starting, in some cases, from 250 eV and extending in others to the TeV energy range. It has been designed and constructed to expose the physics models utilised, to handle complex geometries, and to enable its easy adaptation for optimal use in different sets of applications. The toolkit is the result of a worldwide collaboration of physicists and software engineers. It has been created exploiting software engineering and object-oriented technology and implemented in the C++ programming language. It has been used in applications in particle physics, nuclear physics, accelerator design, space engineering and medical physics. PACS: 07.05.Tp; 13; 23
Noneequilibrium models (three-fluid hydrodynamics and UrQMD) use to discuss the uniqueness of often proposed experimental signatures for quark matter formation in relativistic heavy ion collisions. It is demonstrated that these two models - although they do treat the most interesting early phase of the collisions quite differently(thermalizing QGP vs. coherent color fields with virtual particles) - both yields a reasonable agreement with a large variety of the available heavy ion data.
Within a dynamical quark recombination model, we explore various proposed event-by-event observables sensitive to the microscopic structure of the QCD-matter created at RHIC energies. Charge ratio fluctuations, charge transfer fluctuations and baryon-strangeness correlations are computed from a sample of central Au + Au events at the highest RHIC energy available (sNN=200 GeV). We find that for all explored observables, the calculations yield the values predicted for a quark–gluon plasma only at early times of the evolution, whereas the final state approaches the values expected for a hadronic gas. We argue that the recombination-like hadronization process itself is responsible for the disappearance of the predicted deconfinement signals. This might explain why no fluctuation signatures for the transition between quark and hadronic matter was ever observed in the experimental data up to now.