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This thesis presents a model for the dynamical description of deconfined quark matter created in ultra-relativistic heavy ion collisions, treating quarks and antiquarks as classical point particles subject to a colour-dependent, Cornell-type potential interaction. The model provides a dynamical handle for hadronization via the recombination of quarks and antiquarks in colour neutral clusters. Gluons are not included explicitly in the model,but are described in an effective manner by the means of the potential interaction. The model includes four different quark flavours (up, down, strange and charm) and uses current masses for the quarks. The dynamical evolution of a system of colour charges subject to the Hamiltonian equations of motion of the model yields the formation of colour neutral clusters of quarks and antiquarks, which are subject only to a small remaining interaction, the strong interquark potential notwithstanding. These clusters can be mapped onto hadrons and hadronic resonances. Thus, the model allows a dynamical description of quarks degrees of freedom in heavy ion collisions, including a recombination scheme for hadronization. The thermal properties of the model turn pout to be very satisfying. The model shows a transition from a confining phase to a deconfined phase with rising temperature, going hand in hand with a softest point in the equation of state and a rise of energy density and pressure to the Stefan-Boltzmann limit of a gas of quarks and antiquarks. Moreover, the potential interaction is screened in the deconfined phase. For the dynamical description of ultra-relativistic heavy ion collision, the qMD model is coupled to UrQMD as a generator for its initial conditions. In this way, a fully dynamical description of the expansion and hadronization of the fireball created in such collisions can be achieved. Non-equilibrium aspects of the expansion dynamics and hadronization by recombination of quarks and antiquarks are discussed in detail, and a comparison with experimental data of collisions at the CERN-SPS is presented. The big advantage of the qMD model is the possibility to study cluster formation, including exotic clusters, and fluctuations in a dynamical manner. As an example, event-by-event fluctuations in electric charge are studied. Such fluctuations have been proposed as a clear criterion to distinguish a deconfined system from a hadrons gas. However, experimental data show hadron gas fluctuation measures even at RHIC, where deconfinement is taken for granted. We will see how the dynamics of quark recombination washes out the quark-gluon plasma signal in the fluctuation criterion. Moreover, we will discuss briefly the problem of entropy at recombination. In a second application, the formation of exotic hadronic clusters, larger than usual mesons and baryons, is studied. Such clusters could provide new measures for the thermalization and homogenization of a deconfined gas of colour charges. Moreover, number estimates for exotic clusters from recombination are considerably lower than corresponding predictions from thermal models, providing a clear difference between statistical hadronization and hadronization via quark recombination. A detailed analysis is provided for pentaquark candidates such as the Theta-Plus. It turns out that the distribution of exotic states over strangeness, isospin, and spin could provide a sensitive measure for thermalization and decorrelation in the deconfined quark phase, if it could be measured.
We study various fluctuation and correlation signals of the deconfined state using a dynamical recombination approach (quark Molecular Dynamics, qMD). We analyse charge ratio fluctuations, charge transfer fluctuations and baryon-strangeness correlations as a function of the center of mass energy with a set of central Pb+Pb/Au+Au events from AGS energies on (Elab = 4 AGeV) up to the highest RHIC energy available (V sNN = 200 GeV) and as a function of time with a set of central Au+Au qMD events at V sNN = 200 GeV with and without applying our hadronization procedure. For all studied quantities, the results start from values compatible with a weakly coupled QGP in the early stage and end with values compatible with the hadronic result in the final state. We show that the loss of the signal occurs at the same time as hadronization and trace it back to the dynamical recombination process implemented in our model.
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.
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.