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Die Geschichte der Landtage in der SBZ und in der frühen DDR ist weithin in Vergessenheit geraten. Obwohl die allgemeine Forschungsmeinung ihnen bislang nur die Rolle einer Fußnote der Landesgeschichte beigemessen hat, besaßen die Parlamente in Wirklichkeit für die Nachkriegszeit eine nicht zu unterschätzende Bedeutung.
Die vorliegende Studie untersucht am Beispiel der Landtage von Brandenburg und Thüringen den Wandel der Volksvertretungen von ihren Anfängen 1946 bis zur Auflösung 1952. Im Sinne einer vergleichenden Landesgeschichte werden die Parlamente nicht nur in den von der Besatzungsmacht vorgegebenen politischen und administrativen Rahmen eingeordnet, sondern es wird auch nach ihren strukturellen Gemeinsamkeiten und Besonderheiten gefragt. Das Augenmerk richtet sich zudem auf die Wandlung der Landtagsfraktionen von CDU und LDP: Pochten diese anfangs auf Eigenständigkeit und Gleichberechtigung, wurden sie rasch einem Prozess der politischen Anpassung und schließlich der Ausschaltung unterworfen. An dessen Ende stand die vollständige Unterordnung unter den Willen der Einheitspartei. Die Publikation versteht sich somit als ein Beitrag zum besseren Verständnis der Diktaturdurchsetzung in der SBZ/DDR auf Landesebene.
Change points in time series are perceived as heterogeneities in the statistical or dynamical characteristics of the observations. Unraveling such transitions yields essential information for the understanding of the observed system’s intrinsic evolution and potential external influences. A precise detection of multiple changes is therefore of great importance for various research disciplines, such as environmental sciences, bioinformatics and economics. The primary purpose of the detection approach introduced in this thesis is the investigation of transitions underlying direct or indirect climate observations. In order to develop a diagnostic approach capable to capture such a variety of natural processes, the generic statistical features in terms of central tendency and dispersion are employed in the light of Bayesian inversion. In contrast to established Bayesian approaches to multiple changes, the generic approach proposed in this thesis is not formulated in the framework of specialized partition models of high dimensionality requiring prior specification, but as a robust kernel-based approach of low dimensionality employing least informative prior distributions.
First of all, a local Bayesian inversion approach is developed to robustly infer on the location and the generic patterns of a single transition. The analysis of synthetic time series comprising changes of different observational evidence, data loss and outliers validates the performance, consistency and sensitivity of the inference algorithm. To systematically investigate time series for multiple changes, the Bayesian inversion is extended to a kernel-based inference approach. By introducing basic kernel measures, the weighted kernel inference results are composed into a proxy probability to a posterior distribution of multiple transitions. The detection approach is applied to environmental time series from the Nile river in Aswan and the weather station Tuscaloosa, Alabama comprising documented changes. The method’s performance confirms the approach as a powerful diagnostic tool to decipher multiple changes underlying direct climate observations.
Finally, the kernel-based Bayesian inference approach is used to investigate a set of complex terrigenous dust records interpreted as climate indicators of the African region of the Plio-Pleistocene period. A detailed inference unravels multiple transitions underlying the indirect climate observations, that are interpreted as conjoint changes. The identified conjoint changes coincide with established global climate events. In particular, the two-step transition associated to the establishment of the modern Walker-Circulation contributes to the current discussion about the influence of paleoclimate changes on the environmental conditions in tropical and subtropical Africa at around two million years ago.
We do magnetohydrodynamic (MHD) simulations of local box models of turbulent Interstellar Medium (ISM) and analyse the process of amplification and saturation of mean magnetic fields with methods of mean field dynamo theory. It is shown that the process of saturation of mean fields can be partially described by the prolonged diffusion time scales in presence of the dynamically significant magnetic fields. However, the outward wind also plays an essential role in the saturation in higher SN rate case. Algebraic expressions for the back reaction of the magnetic field onto the turbulent transport coefficients are derived, which allow a complete description of the nonlinear dynamo. We also present the effects of dynamically significant mean fields on the ISM configuration and pressure distribution. We further add the cosmic ray component in the simulations and investigate the kinematic growth of mean fields with a dynamo perspective.