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The measurement of dielectrons (electron-positron pairs) allows to investigate the properties of strongly interacting matter, in particular the Quark-Gluon Plasma (QGP), which is created in relativistic heavy-ion collisions at the LHC. The evolution of the collision can be probed via dielectrons since electrons do not interact strongly and are created during all stages of the collision. One of the interests in dielectron measurements is motivated by possible modifications of the electromagnetic emission spectrum in the QGP, where pp collisions are used as a medium-free reference. The dielectron spectrum consists of contributions from various processes. In order to estimate contributions of known dielectron sources, simulations of the so-called dielectron cocktail are performed. In this thesis, dielectron cocktails in minimum bias pp collisions at p s = 7 TeV, p–Pb collisions at p sNN = 5.02 TeV and in central (0-10%) and semi-central (20-50%) Pb–Pb collisions at p sNN = 2.76 TeV at the LHC are presented.
In April and May 2012 data on Au+Au collisions at beam energies of Ekin = 1.23A GeV were collected with the High Acceptance Di-Electron Spectrometer (HADES) at the GSI Helmholtzzentrum für Schwerionenforschung facility in Darmstadt, Germany. In this thesis, the production of deuterons in this collision system is investigated.
A total number of 2.1 × 109 Au+Au events is selected, containing the most central 0-40% of events. After particle identification, based on a mass determination via time-of-flight and momentum and on a measurement of the energy loss, the transverse mass spectra of the deuteron candidates are extracted for various rapidities and subsequently corrected for acceptance and efficiency.
The inverse slope parameter of a Boltzmann fit applied to the transverse mass spectra at midrapidity, which is referred to as the effective temperature, is extracted. For a static thermal source, this parameter corresponds to the kinetic freeze-out temperature Tkin and is therefore expected to be smaller or equal to the chemical freeze-out temperature Tchem. The extracted effective temperature of Tef f = (190 ± 10) MeV however exceeds the chemical freeze-out temperature that was obtained by a statistical model fit to different particle yields. The effective temperatures of various particle species, obtained in previous analyses, suggest a systematic rise with increasing particle mass, which is confirmed by the deuteron results.
An explanation can be the influence of a collective expansion with a radial expansion velocity βr. By fitting a Siemens-Rasmussen function to the transverse mass spectra, the global temperature of T = (100 ± 8) MeV and radial expansion velocity βr = 0.37 ± 0.01 are obtained. This temperature is still very high and only takes into account the production of deuteron nuclei.
The simultaneous fit of a blast-wave function to the transverse mass spectra of deuterons and other particles, as obtained by previous analyses, considers a velocity profile for the radial expansion velocity and takes into account the production of various particle species. The resulting global temperature Tkin = (68 ± 1) MeV and average transverse expansion velocity hβri = 0.341 ± 0.003 are within the expected range for the collision energy.
The Siemens-Rasmussen fits are also used to extrapolate the transverse mass spectra into unmeasured regions, to integrate them and obtain a rapidity-dependent count rate. This count rate exhibits a thermal shape for central events and shows increasing spectator contributions for more peripheral events.
The invariant yield spectra of the deuterons are compared to those of protons, as obtained by a previous analysis, in the context of a nucleon coalescence model. The hereby extracted nucleon coalescence factor B2 = (4.6 ± 0.1) × 10−3 agrees with the expected result for the beam energy that was studied.
Frankfurt as a global international city is home to transcultural people with diverse linguistic biographies and migration backgrounds. As teachers exert significant influence on the language practice of their students and their awareness of self and others, it is crucial to examine the language ideologies and attitudes on multilingualism of teachers who work in different schools in Frankfurt. The online questionnaire was selected as the data collection
method for the combination of qualitative and quantitative analysis where teachers were asked to select their opinion on statements that were designed to represent concurring viewpoints of separate bilingualism and flexible bilingualism. The study builds on existing evidence that multiple factors dynamically shape teachers' attitudes towards multilingualism.
School-level support and cooperation between educational institutions seems to be necessary to establish horizontal continuity and help students benefit from language-sensitive didactic methods, such as translanguaging.
In situ rainwater harvesting has a long history in arid and semi-arid regions of the world buffering water shortages for human consumption and agriculture. In the context of an Integrated Water Resource Management (IWRM) in the Cuvelai Basin in northern Namibia, roof top rainwater harvesting is being introduced to a rural community for the irrigation of household scale gardens for the cultivation of horticulture products. This study elaborates how harvested rainwater can be used for garden irrigation in a sustainable manner evaluating ecologic, economic and social implications. Considering local conditions eight cropping scenarios were designed, including different criteria as well as one and two annual planting seasons. These schemes were tested under present climate conditions and under three future climate change scenarios for 2050 with the help of a tank model designed to model monthly tank inflows and outflows. Special attention was laid on risk and uncertainty aspects of varying inter-annual and interseasonal precipitation and future climate change. A framework for the assessment of sustainability was adapted to the purposes of this study and indicators have been developed in order to assess the cropping and irrigation schemes for sustainability.
The study found that with the given tank size of 30 m³, depending on crop scenario, under optimized conditions a garden area of 60 to 90 m³ can be irrigated. The choice of crops highly impacts water use efficiency and economic profitability, compared to the considerably lower impact of amount of annual planting seasons and future climate change. In the case of worsening future climate conditions, adaptation measures need to be taken as especially the economic as well as the environmental situation are expected to exacerbate due to expected decreases in yields and revenues. Already under present conditions however, the economic dimension represents the most limiting factor to sustainability, particularly due to the excessive investment costs of the rainwater harvesting and gardening facility. Nonetheless, rainwater harvesting in combination with gardening can be regarded as successful in securing household nutrition, providing sufficient horticulture products for household consumption or market sale. At the same time with the optimal choice of crops the investment costs can be recovered within the end of the lifespan of the facility.
In this work we study basic properties of unstable particles and scalar hadronic resonances, respectively, within simple quantum mechanical and quantum field theoretical (effective) models. The term 'particle' is usually assigned to entities, described by physical theories, that are able to propagate over sufficiently large time scales (e.g. from a source to a detector) and hence could be identified in experiments - one especially should be able to measure some of their distinct properties like spin or charge. Nevertheless, it is well known that there exists a huge amount of unstable particles to which it seems difficult to allocate such definite values for their mass and decay width. In fact, for extremely short-lived members of that species, so called resonances, the theoretical description turns out to be highly complicated and requires some very interesting concepts of complex analysis.
In the first chapter, we start with the basic ideas of quantum field theory. In particular, we introduce the Feynman propagator for unstable scalar resonances and motivate the idea that this kind of correlation function should possess complex poles which parameterize the mass and decay width of the considered particle. We also brie
y discuss the problematic scalar sector in particle physics, emphasizing that hadronic loop contributions, given by strongly coupled hadronic intermediate states, dominate its dynamics. After that, the second chapter is dedicated to the method of analytic continuation of complex functions through branch cuts. As will be seen in the upcoming sections, this method is crucial in order to describe physics of scalar resonances because the relevant functions to be investigated (namely, the Feynman propagator of interacting quantm field theories) will also have branch cuts in the complex energy plane due to the already mentioned loop contributions. As is consensus among the physical community, the understanding of the physical behaviour of resonances requires a deeper insight of what is going on beyond the branch cut. This will lead us to the idea of a Riemann surface, a one-dimensional complex manifold on which the Feynman propagator is defined.
We then apply these concepts to a simple non-relativistic Lee model in the third chapter and demonstrate the physical implications, i.e., the motion of the propagator poles and the behaviour of the spectral function. Besides that, we investigate the time evolution of a particle described by such a model. All this will serve as a detailed preparation in order to encounter the rich phenomena occuring on the Riemann surface in quantum field theory. In the last chapter, we finally concentrate on a simple quantm field theoretical model which describes the decay of a scalar state into two (pseudo)scalar ones. It is investigated how the motion of the propagator poles is in
uenced by loop contributions of the two (pseudo)scalar particles. We perform a numerical study for a hadronic system involving a scalar seed state (alias the σ-meson) that couples to pions. The unexpected emergence of a putative stable state below the two-pion threshold is investigated and it is claeifieed under which conditions such a stable state appears.
Computing the diameter of a graph is a fundamental part of network analysis. Even if the data fits into main memory the best known algorithm needs O(n2) [3] with high probability to compute the exact diameter. In practice this is usually too costly. Therefore, heuristics have been developed to approximate the diameter much faster. The heuristic “double sweep lower bound” (dslb) has reasonably good results and needs only two Breadth-First Searches (BFS). Hence, dslb has a complexity of O(n+m). If the data does not fit into main memory, an external-memory algorithm is needed. In this thesis the I/O model by Vitter and Shriver [4] is used. It is widely accepted and has produced suitable results in the past. The best known external-memory BFS implementation has an I/O-complexity of W(pn B + sort(n)) for sparse graphs [5]. But this is still very expensive compared to the I/O complexity of sorting with O(N/B * logM/B (N/B)). While there is no improvement for the external-memory computation of BFS yet, Meyer published a different approach called “Parallel clustering growing approach” (PAR_APPROX) that is a trade-off between the I/O complexity and the approximation guarantee [6].
In this thesis different existing approaches will be evaluated. Also, PAR_APPROX will be implemented and analyzed if it is viable in practice. One main result will be that it is difficult to choose the parameter in a way that PAR_APPROX is reasonably fast for every graph class without using the semi external-memory Single Source Shortest Path (SSSP) implementation by [1]. However, the gain is small compared to external-memory BFS using this approach. Therefore, the approach PAR_APPROX_R will be developed. Furthermore, a lower bound for the expected error of PAR_APPROX_R will be proved on a carefully chosen difficult input class. With PAR_APPROX_R the desired gain will be reached.
Analysis of machine learning prediction quality for automated subgroups within the MIMIC III dataset
(2023)
The motivation for this master’s thesis is to explore the potential of predictive data analytics in the field of medicine. For this, the MIMIC-III dataset offers an extensive foundation for the construction of prediction models, including Random Forest, XGBOOST, and deep learning networks. These models were implemented to forecast the mortality of 2,655 stroke patients.
The first part of the thesis involved conducting a comprehensive data analysis of the filtered MIMIC-III dataset.
Subsequently, the effectiveness and fairness of the predictive models were evaluated. Although the performance levels of the developed models did not match those reported in related research, their potential became evident. The results obtained demonstrated promising capabilities and highlighted the effectiveness of the applied methodologies. Moreover, the feature relevance within the XGBOOST model was examined to increase model explainability.
Finally, relevant subgroups were identified to perform a comparative analysis of the prediction performance across these subgroups. While this approach can be regarded as a valuable methodology, it was not possible to investigate underlying reasons for potential unfairness across clusters. Inside the test data, not enough instances remained per subgroup for further fairness or feature relevance analysis.
In conclusion, the implementation of an alternative use case with a higher patient count is recommended.
The code for this analysis is made available via a GitHub repository and includes a frontend to visualize the results.
Recently, Aumüller and Dietzfelbinger proposed a version of a dual-pivot Quicksort, called "Count", which is optimal among dual-pivot versions with respect to the average number of key comparisons required. In this master's thesis we provide further probabilistic analysis of "Count". We derive an exact formula for the average number of swaps needed by "Count" as well as an asymptotic formula for the variance of the number of swaps and a limit law. Also for the number of key comparisons the asymptotic variance and a limit law are identified. We also consider both complexity measures jointly and find their asymptotic correlation.
AI-based computer vision systems play a crucial role in the environment perception for autonomous driving. Although the development of self-driving systems has been pursued for multiple decades, it is only recently that breakthroughs in Deep Neural Networks (DNNs) have led to their widespread application in perception pipelines, which are getting more and more sophisticated. However, with this rising trend comes the need for a systematic safety analysis to evaluate the DNN's behavior in difficult scenarios as well as to identify the various factors that cause misbehavior in such systems. This work aims to deliver a crucial contribution to the lacking literature on the systematic analysis of Performance Limiting Factors (PLFs) for DNNs by investigating the task of pedestrian detection in urban traffic from a monocular camera mounted on an autonomous vehicle. To investigate the common factors that lead to DNN misbehavior, six commonly used state-of-the-art object detection architectures and three detection tasks are studied using a new large-scale synthetic dataset and a smaller real-world dataset for pedestrian detection. The systematic analysis includes 17 factors from the literature and four novel factors that are introduced as part of this work. Each of the 21 factors is assessed based on its influence on the detection performance and whether it can be considered a Performance Limiting Factor (PLF). In order to support the evaluation of the detection performance, a novel and task-oriented Pedestrian Detection Safety Metric (PDSM) is introduced, which is specifically designed to aid in the identification of individual factors that contribute to DNN failure. This work further introduces a training approach for F1-Score maximization whose purpose is to ensure that the DNNs are assessed at their highest performance. Moreover, a new occlusion estimation model is introduced to replace the missing pedestrian occlusion annotations in the real-world dataset. Based on a qualitative analysis of the correlation graphs that visualize the correlation between the PLFs and the detection performance, this study identified 16 of the initial 21 factors as being PLFs for DNNs out of which the entropy, the occlusion ratio, the boundary edge strength, and the bounding box aspect ratio turned out to be most severely affecting the detection performance. The findings of this study highlight some of the most serious shortcomings of current DNNs and pave the way for future research to address these issues.
This work derived the value of α-induced production cross sections of 77Kr and 77Br at α-energies of 12 MeV and 14 MeV, the thick target yields of 77Kr and 77Br at α-energies of 11.19 MeV, 13 MeV and 15.1 MeV and the thick target yield of 80Br as well as 80mBr at an α-energy of 15.1 MeV using the activation technique...