This study was concerned with the most common reverse flow type of cyclones where the flow enters the cyclone through a tangential inlet and leaves via an axial outlet pipe at the top of the cyclone. Numerical computations of two different cyclones were based on the so-called Stairmand cyclone. The difference in geometry between these two cyclones was basically characterized by the geometrical swirl number Sg of 3.5 and 4. Turbulent secondary flows inside a straight square channel have been studied numerically by using Large Eddy Simulation (LES) in order to verify the implementation process. Prandtl’s secondary motion calculated by LES shows satisfying agreement with both, Direct Numerical Simulation (DNS) and experimental results. Numerical calculations were carried out at various axial positions and at the apex cone of a gas cyclone separator. Two different NS-solvers (a commercial one, and a research code), based on a pressure correction algorithm of the SIMPLE method have been applied to predict the flow behavior. The flow was assumed as unsteady, incompressible and isothermal. A k − epsilon turbulence model has been applied first using the commercial code to investigate the gas flow. Due to the nature of cyclone flows, which exhibit highly curved streamlines and anisotropic turbulence, advanced turbulence models such as RSM (Reynolds Stress Model) and LES (Large Eddy Simulation) have been used as well. The RSM simulation was performed using the commercial package CFX4.4, while for the LES calculations the research code MISTRAL/PartFlow-3D code developed in our multiphase research group has been applied utilizing the Smagorinsky model. It was found that the k − epsilon model cannot predict flow phenomena inside the cyclone properly due to the strong curvature of the streamlines. The RSM results are comparable with LES results in the area of the apex cone plane. However, the application of the LES reveals qualitative agreement with the experimental data, but requires higher computer capacity and longer running times than RSM. These calculations of the continuous phase flow were the basis for modeling the behavior of the solid particles in the cyclone separator. Particle trajectories, pressure drop and the cyclone separation efficiency have been studied in some detail. This thesis is organized into five chapters. After an introduction and overview, chapter 2 deals with continuous phase flow turbulence modeling including the governing equations. The emphasis will be based on LES modelling. Furthermore, the disperse phase motion is treated in chapter 3. In chapter 4, the validation process of LES implementation with channel flow is presented. Moreover, prediction profiles of the gas flow are presented and discussed. In addition, disperse phase flow results are presented and discussed such as particle trajectories; pressure drop and cyclone separation efficiency are also discussed. Chapter 5 summarizes and concludes the thesis.

Emerging from discussions within gender, contextual embeddedness, and migration, this research addresses the issue of the labor market integration of highly educated migrant women in Sweden. The thesis seeks to broaden the understanding of the gender gap in entrepreneurship by contextualizing the decisions of starting a business, analyzing the different strategies employed by migrant women to develop their businesses as well as the role of entrepreneurship in their lives in relation to gender norms. The study uses the life course approach and methods as well as a gendered multi-context framework (Welter et al., 2014) to investigate the following research questions: 1) Under what circumstances do migrant women turn to entrepreneurship in their life course and what are the associated changes on their career? 2) How do they mobilize social ties across different spatial contexts and business stages? 3) How do migrant women make sense of entrepreneurship in relation to gendered societal norms from the country of origin and destination? The results suggest that complex temporal, spatial, social, and institutional dynamics condition the decision to start a business, and the different strategies employed. Furthermore, entrepreneurship appears as a way to challenge and overcome gender norms.

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Large-scale transcriptomics data studies revolutionised the fields of systems biology and medicine, allowing to generate deeper mechanistic insights into biological pathways and molecular functions. However, conventional bulk RNA-sequencing results in the analysis of an averaged signal of many input cells, which are homogenised during the experimental procedure. Hence, those insights represent only a coarse-grained picture, potentially missing information from rare or unidentified cell types. Allowing for an unprecedented level of resolution, single-cell transcriptomics may help to identify and characterise new cell types, unravel developmental trajectories, and facilitate inference of cell type-specific networks. Besides all these tempting promises, there is one main limitation that currently hampers many downstream tasks: single-cell RNA-sequencing data is characterised by a high degree of sparsity. Due to this limitation, no reliable network inference tools allowed to disentangle the hidden information in the single-cell data. Single-cell correlation networks likely hold previously masked information and could allow inferring new insights into cell type-specific networks. To harness the potential of single-cell transcriptomics data, this dissertation sought to evaluate the influence of data dropout on network inference and how this might be alleviated. However, two premisses must be met to fulfil the promise of cell type-specific networks: (I) cell type annotation and (II) reliable network inference. Since any experimentally generated scRNA-seq data is associated with an unknown degree of dropout, a benchmarking framework was set up using a synthetic gold data set, which was subsequently affected with different defined degrees of dropout. Aiming to desparsify the dropout-afflicted data, the influence of various imputations tools on the network structure was further evaluated. The results highlighted that for moderate dropout levels, a deep count autoencoder (DCA) was able to outperform the other tools and the unimputed data. To fulfil the premiss of cell type annotation, the impact of data imputation on cell-cell correlations was investigated using a human retina organoid data set. The results highlighted that no imputation tool intervened with cell cluster annotation. Based on the encouraging results of the benchmarking analysis, a window of opportunity was identified, which allowed for meaningful network inference from imputed single-cell RNA-seq data. Therefore, the inference of cell type-specific networks subsequent to DCA-imputation was evaluated in a human retina organoid data set. To understand the differences and commonalities of cell type-specific networks, those were analysed for cones and rods, two closely related photoreceptor cell types of the retina. Comparing the importance of marker genes for rods and cones between their respective cell type-specific networks exhibited that these genes were of high importance, i.e. had hub-gene-like properties in one module of the corresponding network but were of less importance in the opposing network. Furthermore, it was analysed how many hub genes in general preserved their status across cell type-specific networks and whether they associate with similar or diverging sub-networks. While a set of preserved hub genes was identified, a few were linked to completely different network structures. One candidate was EIF4EBP1, a eukaryotic translation initiation factor binding protein, which is associated with a retinal pathology called age-related macular degeneration (AMD). These results suggest that given very defined prerequisites, data imputation via DCA can indeed facilitate cell type-specific network inference, delivering promising biological insights. Referring back to AMD, a major cause for the loss of central vision in patients older than 65, neither the defined mechanisms of pathogenesis nor treatment options are at hand. However, light can be shed on this disease through the employment of organoid model systems since they resemble the in vivo organ composition while reducing its complexity and ethical concerns. Therefore, a recently developed human retina organoid system (HRO) was investigated using the single-cell toolbox to evaluate whether it provides a useful base to study the defined effects on the onset and progression of AMD in the future. In particular, different workflows for a robust and in-depth annotation of cell types were used, including literature-based and transfer learning approaches. These allowed to state that the organoid system may reproduce hallmarks of a more central retina, which is an important determinant of AMD pathogenesis. Also, using trajectory analysis, it could be detected that the organoids in part reproduce major developmental hallmarks of the retina, but that different HRO samples exhibited developmental differences that point at different degrees of maturation. Altogether, this analysis allowed to deeply characterise a human retinal organoid system, which revealed in vivo-like outcomes and features as pinpointing discrepancies. These results could be used to refine culture conditions during the organoid differentiation to optimise its utility as a disease model. In summary, this dissertation describes a workflow that, in contrast to the current state of the art in the literature enables the inference of cell type-specific gene regulatory networks. The thesis illustrated that such networks indeed differ even between closely related cells. Thus, single-cell transcriptomics can yield unprecedented insights into so far not understood cell regulatory principles, particularly rare cell types that are so far hardly reflected in bulk-derived RNA-seq data.

Indiana University-Purdue University Indianapolis (IUPUI) / Action recognition has been an active research topic for over three decades. There are various applications of action recognition, such as surveillance, human-computer interaction, and content-based retrieval. Recently, research focuses on movies, web videos, and TV shows datasets. The nature of these datasets make action recognition very challenging due to scene variability and complexity, namely background clutter, occlusions, viewpoint changes, fast irregular motion, and large spatio-temporal search space (articulation configurations and motions). The use of local space and time image features shows promising results, avoiding the cumbersome and often inaccurate frame-by-frame segmentation (boundary estimation). We focus on two state of the art methods for the action classification problem: dense trajectories and recurrent neural networks (RNN). Dense trajectories use typical supervised training (e.g., with Support Vector Machines) of features such as 3D-SIFT, extended SURF, HOG3D, and local trinary patterns; the main idea is to densely sample these features in each frame and track them in the sequence based on optical flow. On the other hand, the deep neural network uses the input frames to detect action and produce part proposals, i.e., estimate information on body parts (shapes and locations). We compare qualitatively and numerically these two approaches, indicative to what is used today, and describe our conclusions with respect to accuracy and efficiency.

This (Diplom-) thesis deals with the particle trajectories of an incompressible and ideal fluid flow in 𝑛 ≥ 2 dimensions. It presents a complete and detailed proof of the surprising fact that the trajectories of a smooth solution of the incompressible Euler equations are locally analytic in time. In following the approach of P. Serfati, a complex ordinary differential equation (ODE) is investigated which can be seen as a complex extension of a partial differential equation, which is solved by the trajectories. The right hand side of this ODE is in fact given by a singular integral operator which coincides with the pressure gradient along the trajectories. Eventually, we may apply the Cauchy-Lipschitz existence theorem involving holomorphic maps between complex Banach spaces in order to get a unique solution for the above mentioned ODE. This solution is real-analytic in time and coincides with the particle trajectories.

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The expansion of higher education and changes in the labour markets seem to have reached a point where training for an M.Ed is affected by a number of issues traditionally raised in the context of graduate employment and work. Growing emphasis is placed on general skills and flexibility, which is briefly mentioned in this report. Labour market theory, human capital development, social capital and career development are explored in detail. Management is no longer perceived in terms of maintaining the business machine but is evolving into a motivator and leader of staff, an inspirational entity that is quintessential to the retention of highly qualified staff . This has resulted in the language of business changing, thus the „efficient company‟ has become a „learning organisation‟ (Germishuys, 2006). Therefore it is of great interest to actually conduct a case study of 11 M.Ed. graduates to track their career trajectories to see where this prestigious qualification has taken them. The researcher traces a general group of M.Ed graduates from the class of 1999 from the University of the Witwatersrand (Wits). The main idea of this study is to ascertain exactly what it is that the sample M.Ed graduates have accomplished in their professional lives. It was found that the colour of one‟s skin, background, contextual factors, opportunities, social networking and career aspirations have played a pivotal role in the career progression of the M.Ed. graduates.