Destructive diseases of the lung like lung cancer or fibrosis are still often lethal. Also in case of fibrosis in the liver, the only possible cure is transplantation. In this thesis, we investigate 3D micro computed synchrotron radiation (SR\( \mu \)CT) images of capillary blood vessels in mouse lungs and livers. The specimen show so-called compensatory lung growth as well as different states of pulmonary and hepatic fibrosis. During compensatory lung growth, after resecting part of the lung, the remaining part compensates for this loss by extending into the empty space. This process is accompanied by an active vessel growing. In general, the human lung can not compensate for such a loss. Thus, understanding this process in mice is important to improve treatment options in case of diseases like lung cancer. In case of fibrosis, the formation of scars within the organ's tissue forces the capillary vessels to grow to ensure blood supply. Thus, the process of fibrosis as well as compensatory lung growth can be accessed by considering the capillary architecture. As preparation of 2D microscopic images is faster, easier, and cheaper compared to SR\( \mu \)CT images, they currently form the basis of medical investigation. Yet, characteristics like direction and shape of objects can only properly be analyzed using 3D imaging techniques. Hence, analyzing SR\( \mu \)CT data provides valuable additional information. For the fibrotic specimen, we apply image analysis methods well-known from material science. We measure the vessel diameter using the granulometry distribution function and describe the inter-vessel distance by the spherical contact distribution. Moreover, we estimate the directional distribution of the capillary structure. All features turn out to be useful to characterize fibrosis based on the deformation of capillary vessels. It is already known that the most efficient mechanism of vessel growing forms small torus-shaped holes within the capillary structure, so-called intussusceptive pillars. Analyzing their location and number strongly contributes to the characterization of vessel growing. Hence, for all three applications, this is of great interest. This thesis provides the first algorithm to detect intussusceptive pillars in SR\( \mu \)CT images. After segmentation of raw image data, our algorithm works automatically and allows for a quantitative evaluation of a large amount of data. The analysis of SR\( \mu \)CT data using our pillar algorithm as well as the granulometry, spherical contact distribution, and directional analysis extends the current state-of-the-art in medical studies. Although it is not possible to replace certain 3D features by 2D features without losing information, our results could be used to examine 2D features approximating the 3D findings reasonably well.

The growing computational power enables the establishment of the Population Balance Equation (PBE) to model the steady state and dynamic behavior of multiphase flow unit operations. Accordingly, the twophase flow behavior inside liquid-liquid extraction equipment is characterized by different factors. These factors include: interactions among droplets (breakage and coalescence), different time scales due to the size distribution of the dispersed phase, and micro time scales of the interphase diffusional mass transfer process. As a result of this, the general PBE has no well known analytical solution and therefore robust numerical solution methods with low computational cost are highly admired. In this work, the Sectional Quadrature Method of Moments (SQMOM) (Attarakih, M. M., Drumm, C., Bart, H.-J. (2009). Solution of the population balance equation using the Sectional Quadrature Method of Moments (SQMOM). Chem. Eng. Sci. 64, 742-752) is extended to take into account the continuous flow systems in spatial domain. In this regard, the SQMOM is extended to solve the spatially distributed nonhomogeneous bivariate PBE to model the hydrodynamics and physical/reactive mass transfer behavior of liquid-liquid extraction equipment. Based on the extended SQMOM, two different steady state and dynamic simulation algorithms for hydrodynamics and mass transfer behavior of liquid-liquid extraction equipment are developed and efficiently implemented. At the steady state modeling level, a Spatially-Mixed SQMOM (SM-SQMOM) algorithm is developed and successfully implemented in a onedimensional physical spatial domain. The integral spatial numerical flux is closed using the mean mass droplet diameter based on the One Primary and One Secondary Particle Method (OPOSPM which is the simplest case of the SQMOM). On the other hand the hydrodynamics integral source terms are closed using the analytical Two-Equal Weight Quadrature (TEqWQ). To avoid the numerical solution of the droplet rise velocity, an analytical solution based on the algebraic velocity model is derived for the particular case of unit velocity exponent appearing in the droplet swarm model. In addition to this, the source term due to mass transport is closed using OPOSPM. The resulting system of ordinary differential equations with respect to space is solved using the MATLAB adaptive Runge–Kutta method (ODE45). At the dynamic modeling level, the SQMOM is extended to a one-dimensional physical spatial domain and resolved using the finite volume method. To close the mathematical model, the required quadrature nodes and weights are calculated using the analytical solution based on the Two Unequal Weights Quadrature (TUEWQ) formula. By applying the finite volume method to the spatial domain, a semi-discreet ordinary differential equation system is obtained and solved. Both steady state and dynamic algorithms are extensively validated at analytical, numerical, and experimental levels. At the numerical level, the predictions of both algorithms are validated using the extended fixed pivot technique as implemented in PPBLab software (Attarakih, M., Alzyod, S., Abu-Khader, M., Bart, H.-J. (2012). PPBLAB: A new multivariate population balance environment for particulate system modeling and simulation. Procedia Eng. 42, pp. 144-562). At the experimental validation level, the extended SQMOM is successfully used to model the steady state hydrodynamics and physical and reactive mass transfer behavior of agitated liquid-liquid extraction columns under different operating conditions. In this regard, both models are found efficient and able to follow liquid extraction column behavior during column scale-up, where three column diameters were investigated (DN32, DN80, and DN150). To shed more light on the local interactions among the contacted phases, a reduced coupled PBE and CFD framework is used to model the hydrodynamic behavior of pulsed sieve plate columns. In this regard, OPOSPM is utilized and implemented in FLUENT 18.2 commercial software as a special case of the SQMOM. The dropletdroplet interactions (breakage and coalescence) are taken into account using OPOSPM, while the required information about the velocity field and energy dissipation is calculated by the CFD model. In addition to this, the proposed coupled OPOSPM-CFD framework is extended to include the mass transfer. The proposed framework is numerically tested and the results are compared with the published experimental data. The required breakage and coalescence parameters to perform the 2D-CFD simulation are estimated using PPBLab software, where a 1D-CFD simulation using a multi-sectional gird is performed. A very good agreement is obtained at the experimental and the numerical validation levels.

In recent years, enormous progress has been made in the field of Artificial Intelligence (AI). Especially the introduction of Deep Learning and end-to-end learning, the availability of large datasets and the necessary computational power in form of specialised hardware allowed researchers to build systems with previously unseen performance in areas such as computer vision, machine translation and machine gaming. In parallel, the Semantic Web and its Linked Data movement have published many interlinked RDF datasets, forming the world’s largest, decentralised and publicly available knowledge base. Despite these scientific successes, all current systems are still narrow AI systems. Each of them is specialised to a specific task and cannot easily be adapted to all other human intelligence tasks, as would be necessary for Artificial General Intelligence (AGI). Furthermore, most of the currently developed systems are not able to learn by making use of freely available knowledge such as provided by the Semantic Web. Autonomous incorporation of new knowledge is however one of the pre-conditions for human-like problem solving. This work provides a small step towards teaching machines such human-like reasoning on freely available knowledge from the Semantic Web. We investigate how human associations, one of the building blocks of our thinking, can be simulated with Linked Data. The two main results of these investigations are a ground truth dataset of semantic associations and a machine learning algorithm that is able to identify patterns for them in huge knowledge bases. The ground truth dataset of semantic associations consists of DBpedia entities that are known to be strongly associated by humans. The dataset is published as RDF and can be used for future research. The developed machine learning algorithm is an evolutionary algorithm that can learn SPARQL queries from a given SPARQL endpoint based on a given list of exemplary source-target entity pairs. The algorithm operates in an end-to-end learning fashion, extracting features in form of graph patterns without the need for human intervention. The learned patterns form a feature space adapted to the given list of examples and can be used to predict target candidates from the SPARQL endpoint for new source nodes. On our semantic association ground truth dataset, our evolutionary graph pattern learner reaches a Recall@10 of > 63 % and an MRR (& MAP) > 43 %, outperforming all baselines. With an achieved Recall@1 of > 34% it even reaches average human top response prediction performance. We also demonstrate how the graph pattern learner can be applied to other interesting areas without modification.

Though environmental inequality research has gained extensive interest in the United States, it has received far less attention in Europe and Germany. The main objective of this book is to extend the research on environmental inequality in Germany. This book aims to shed more light on the question of whether minorities in Germany are affected by a disproportionately high burden of environmental pollution, and to increase the general knowledge about the causal mechanisms, which contribute to the unequal distribution of environmental hazards across the population. To improve our knowledge about environmental inequality in Germany, this book extends previous research in several ways. First, to evaluate the extent of environmental inequality, this book relies on two different data sources. On the on hand, it uses household-level survey data and self-reports about the impairment through air pollution. On the other hand, it combines aggregated census data and objective register-based measures of industrial air pollution by using geographic information systems (GIS). Consequently, this book offers the first analysis of environmental inequality on the national level that uses objective measures of air pollution in Germany. Second, to evaluate the causes of environmental inequality, this book applies a panel data analysis on the household level, thereby offering the first longitudinal analysis of selective migration processes outside the United States. Third, it compares the level of environmental inequality between German metropolitan areas and evaluates to which extent the theoretical arguments of environmental inequality can explain differing levels of environmental inequality across the country. By doing so, this book not only investigates the impact of indicators derived by the standard strand of theoretical reasoning but also includes structural characteristics of the urban space. All studies presented in this book confirm the disproportionate exposure of minorities to environmental pollution. Minorities live in more polluted areas in Germany but also in more polluted parts of the communities, and this disadvantage is most severe in metropolitan regions. Though this book finds evidence for selective migration processes contributing to the disproportionate exposure of minorities to environmental pollution, it also stresses the importance of urban conditions. Especially cities with centrally located industrial facilities yield a high level of environmental inequality. This poses the question of whether environmental inequality might be the result of two independent processes: 1) urban infrastructure confines residential choices of minorities to the urban core, and 2) urban infrastructure facilitates centrally located industries. In combination, both processes lead to a disproportionate burden of minority households.

Computational problems that involve dynamic data, such as physics simulations and program development environments, have been an important subject of study in programming languages. Recent advances in self-adjusting computation made progress towards achieving efficient incremental computation by providing algorithmic language abstractions to express computations that respond automatically to dynamic changes in their inputs. Selfadjusting programs have been shown to be efficient for a broad range of problems via an explicit programming style, where the programmer uses specific primitives to identify, create and operate on data that can change over time. This dissertation presents implicit self-adjusting computation, a type directed technique for translating purely functional programs into self-adjusting programs. In this implicit approach, the programmer annotates the (toplevel) input types of the programs to be translated. Type inference finds all other types, and a type-directed translation rewrites the source program into an explicitly self-adjusting target program. The type system is related to information-flow type systems and enjoys decidable type inference via constraint solving. We prove that the translation outputs well-typed self-adjusting programs and preserves the source program’s input-output behavior, guaranteeing that translated programs respond correctly to all changes to their data. Using a cost semantics, we also prove that the translation preserves the asymptotic complexity of the source program. As a second contribution, we present two techniques to facilitate the processing of large and dynamic data in self-adjusting computation. First, we present a type system for precise dependency tracking that minimizes the time and space for storing dependency metadata. The type system improves the scalability of self-adjusting computation by eliminating an important assumption of prior work that can lead to recording spurious dependencies. We present a type-directed translation algorithm that generates correct selfadjusting programs without relying on this assumption. Second, we show a probabilistic-chunking technique to further decrease space usage by controlling the fundamental space-time tradeoff in self-adjusting computation. We implement implicit self-adjusting computation as an extension to Standard ML with compiler and runtime support. Using the compiler, we are able to incrementalize an interesting set of applications, including standard list and matrix benchmarks, ray tracer, PageRank, sparse graph connectivity, and social circle counts. Our experiments show that our compiler incrementalizes existing code with only trivial amounts of annotation, and the resulting programs bring asymptotic improvements to large datasets from real-world applications, leading to orders of magnitude speedups in practice.