Studienarbeit aus dem Jahr 2010 im Fachbereich Politik - Thema: Entwicklungspolitik, Note: 1,3, Ruprecht-Karls-Universität Heidelberg (Institut für Politische Wissenschaft), Veranstaltung: Entwicklungstheorien und Entwicklungspolitik, Sprache: Deutsch, Abstract: Die Situation im subsaharischen Afrika wirft unweigerlich die Frage nach den Ursachen für diese (Fehl-)Entwicklung auf, deren Klärung essentieller Bestandteil dieser Arbeit ist. Explizit lautet die Frage: Warum kam es trotz etwa einer Billion US$ Entwicklungshilfe in den letzten 50 Jahren zu dieser (Fehl-)Entwicklung Afrikas und worin ruhen Hoffnungen für das subsaharische Afrika? Die aktuelle Forschungsliteratur geht diesbezüglich weit auseinander. Die "linke Seite" sieht Subsahara-Afrika in der Armutsfalle gefangen, aus der es nur durch Entwicklungshilfe zu befreien sei. Die Armutsfalle wird vor allem durch geografische und ökologische Faktoren bedingt, wie Krankheiten, extremes Klima, Binnenstaatlichkeit und "Von schlechten Nachbarn umgeben". Dabei sei Staatsversagen vor allem die Folge und weniger die Ursache der wirtschaftlichen Krise. "Die Regierungen in Afrika versagen, weil Afrika arm ist." Zu nennen ist hier vor allem Jefrey Sachs' "Das Ende der Armut". Die "rechte Seite" hingegen sieht das Problem in der Entwicklungshilfe selbst. "(Aid is) No longer part of the potential solution, it's part of the problem - in fact aid is the problem." Entwicklungshilfe hatte keine Auswirkung - die Armut wuchs und die Wachstumsraten fielen. Wachstum wäre jedoch immer möglich, wenn sich die Gesellschaften, insbesondere die korrupten Regierungen, nur zusammenreißen würden. Zu nennen sind hier "Dead Aid" von Dambisa Moyo und "Wir retten die Welt zu Tode" von William Easterly. Diese Arbeit wird versuchen, die Extreme zu vermeiden. Sie wählt den Weg der Mitte. Entwicklungshilfe reicht allein nicht aus, um einen Umschwung für die Gesellschaften des subsaharischen Afrikas zu bewirken. Aber sie ist eher Teil der Lösung a

In the context of current surgical techniques, the classification of 3D organs based on two-dimensional cross-sections is a decisive and still challenging task. The goal of this paper is to explore an approach to address this problem. By this means, the expectation is to go further in the direction of patient-specific surgery. Based on two-dimensional image data, we analyze different clustering results assuming specific evaluation criteria. By doing so, a determination of the most appropriate number of clusters is possible. As an example, we use this method to classify the shape of the neck of the pancreas of humans, which is relevant for different types of distal pancreatectomy. Hereby, scaling issues of the available data are a key point. Therefore, an overall protocol needs to care for comparable data.

The structure of cells is a key to understanding cellular function, diagnosis of pathological conditions, and development of new treatments. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Ongoing improvements in faster acquisition times increase demand for accelerated image analysis. Currently, the automatic segmentation of cellular structures is a major bottleneck in the SXT data analysis pipeline. In this paper, we introduce an automated 3D cytoplasm segmentation model - ACSeg - by use of semi-automatically segmented labels and 3D U-Net, implemented in the online platform Biomedisa. The segmentation model is trained on semi-automatically labeled datasets and shows rapid convergence to high-accuracy segmentation, therefore reducing time and labor. ACSeg trained on 43 SXT tomograms of human immune T cells, the model successfully segmented unseen SXT tomograms of human hepatocyte-derived carcinoma cells, mouse microglia, and embryonic fibroblast cells. Furthermore, we could diversify the model by adding only 6 specific SXT tomograms, showing the potential for the development of an optimal experimental design. The ACSeg is published on the open image segmentation platform Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types. The approach can be expanded for automatic segmentation of other organelles visualized by SXT, providing means for structural analysis of cell remodeling under different pathogens at statistically significant sizes, therefore enabling the development of novel drug treatments.

Employee absences cannot be avoided but excessive and uncontrolled absences affect not only the companies and employees but also impact the economy, government and society. Though actual losses are hard to compute, absenteeism has been estimated to cost billions in direct and indirect costs. Addressing employee absences is difficult because the underlying reasons and causes are complex and not straightforward. Compounding this, companies do not have tools to analyze and predict the future risk of employee absences, relying instead on retrospective data that may not be relevant to the current situation at hand. In this study, we show how machine learning methods can be used to predict employee absence risks. Results show that Neural Networks give best accuracy (77%) over Random Forest (72%) and Support Vector Machines (62%). The effect of training data size and varied feature sets on the models' performances were also tested. Also, a method allowing for ranking the sensitivity of a Neural Network to each feature is presented.