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In this dissertation, we discuss how to price American-style options. Our aim is to study and improve the regression-based Monte Carlo methods. In order to have good benchmarks to compare with them, we also study the tree methods.
In the second chapter, we investigate the tree methods specifically. We do research firstly within the Black-Scholes model and then within the Heston model. In the Black-Scholes model, based on Müller's work, we illustrate how to price one dimensional and multidimensional American options, American Asian options, American lookback options, American barrier options and so on. In the Heston model, based on Sayer's research, we implement his algorithm to price one dimensional American options. In this way, we have good benchmarks of various American-style options and put them all in the appendix.
In the third chapter, we focus on the regression-based Monte Carlo methods theoretically and numerically. Firstly, we introduce two variations, the so called "Tsitsiklis-Roy method" and the "Longstaff-Schwartz method". Secondly, we illustrate the approximation of American option by its Bermudan counterpart. Thirdly we explain the source of low bias and high bias. Fourthly we compare these two methods using in-the-money paths and all paths. Fifthly, we examine the effect using different number and form of basis functions. Finally, we study the Andersen-Broadie method and present the lower and upper bounds.
In the fourth chapter, we study two machine learning techniques to improve the regression part of the Monte Carlo methods: Gaussian kernel method and kernel-based support vector machine. In order to choose a proper smooth parameter, we compare fixed bandwidth, global optimum and suboptimum from a finite set. We also point out that scaling the training data to [0,1] can avoid numerical difficulty. When out-of-sample paths of stock prices are simulated, the kernel method is robust and even performs better in several cases than the Tsitsiklis-Roy method and the Longstaff-Schwartz method. The support vector machine can keep on improving the kernel method and needs less representations of old stock prices during prediction of option continuation value for a new stock price.
In the fifth chapter, we switch to the hardware (FGPA) implementation of the Longstaff-Schwartz method and propose novel reversion formulas for the stock price and volatility within the Black-Scholes and Heston models. The test for this formula within the Black-Scholes model shows that the storage of data is reduced and also the corresponding energy consumption.
Redox-neutral decarboxylative coupling reactions have emerged as a powerful strategy for C-C bond formation. However, the existing reaction conditions possess limitations, such as the coupling of aryl halides restricted to ortho-substituted benzoic acids; alkenyl halides were not applicable in decarboxylative coupling reaction. Within this thesis, the developments of Pd/Cu bimetallic catalyst systems are presented to overcome the limitations.
In the first part of the PhD work, a customized bimetallic PdII/CuI catalyst system was successfully developed to facilitate the decarboxylative cross-coupling of non-ortho-substituted aromatic carboxylates with aryl chlorides. The restriction of decarboxylative cross-coupling reactions to ortho-substituted or heterocyclic carboxylate substrates was overcome by holistic optimization of this bimetallic Cu/Pd catalyst system. All kinds of benzoic acids regardless of their substitution pattern now can be applied in decarboxylative cross-coupling reaction. This confirms prediction by DFT studies that the previously observed limitation to certain activated carboxylates is not intrinsic. The catalyst system also presents higher performance in the coupling of ortho-substituted benzoates, giving much higher yields than those previously reported. ortho-Methyl benzoate and ortho-phenyl benzoate which have never before been converted in decarboxylative coupling reactions, gave reasonable yields. These together further confirm the superiority of the new protocol.
In the second part of the PhD work, arylalkenes syntheses via two different Pd/Cu bimetallic-catalyzed decarboxylative couplings have been developed. This part consists of two projects: 2a) decarboxylative coupling of alkenyl halides; 2b) decarboxylative Mizoroki-Heck coupling of aryl halides with α,β-unsaturated carboxylic acids.
In project 2a, widely available, inexpensive, bench-stable aromatic carboxylic acids are used as nucleophile precursors instead of expensive and sensitive organometallic reagents that are commonly used in previously reported transition-metal catalyzed cross-couplings of alkenyl halides. With this protocol, alkenyl halides for the first time are used in decarboxylative coupling reaction, allowing regiospecific synthesis of a broad range of (hetero)arylalkenes in high yields. Unwanted double bond isomerization, a common side reaction in the alternative Heck reactions especially in the coupling of cycloalkenes or aliphatic alkenes, did not take place in this decarboxylative coupling reaction. Polysubstituted alkenes that hard to access with Heck reaction are also produced in good yields. The reaction can easily be scaled up to gram scale. The synthetic utility of this reaction was also demonstrated by synthesizing an important intermediate of fungicidal compound in high yield within 2 steps.
In project 2b, a Cu/Pd bimetallic catalyzed decarboxylative Mizoroki-Heck coupling of aryl halides with α, β-unsaturated carboxylic acids was successfully developed in which the carboxylate group directs the arylation into its β-position before being tracelessly removed via protodecarboxylation. It opens up a convenient synthesis of unsymmetrical 1,1-disubstituted alkenes from widely available precursors. This reaction features good regioselectivity, which is complementary to that of traditional Heck reactions, and also presents excellent functional group tolerance. Moreover, a one-pot 3-step 1,1-diarylethylene synthesis from methyl acrylate was achieved, where solvent changes or isolation of intermediates are not required. This subproject presents an example of carboxylic acids utility in synthesizing valuable compounds which are hard to access via conventional methodologies.
The scientific intention of this work was to synthesize and characterize new bidentate, tridentate and multidentate ligands and to apply them in heterogenous catalysis. For each type of the ligands, new methods of synthesis were developed. Starting from 1,1'-(pyridine-2,6-diyl)diethanone and dimethylpyridine-2,6-dicarboxylate different bispyrazolpyridines were
synthesized and novel ruthenium complexes of the type (L)(NNN)RuCl2 could be obtained. The complexes with L = triphenylphosphine turned out to be highly efficient
catalyst precursors for the transfer hydrogenation of aromatic ketones. Introduction of a butyl group in the 5-positions of the pyrazoles leads to a pronounced increase of catalytic activity.
To find a method for the synthesis of bispyrimidinepyridines, different reactants and condition were applied and it was found that these tridentate ligands can be obtained by mixing and grinding the tetraketone with guanidinium carbonate and silica, which plays the role of a catalyst in this ring closing reaction.
The bidentate 2-amino-4-(2-pyridinyl)pyrimidines were synthesized from different substrates according to the desired substituent on the pyrimidine ring.
Reacting these bidentate ligands with the ruthenium(II) precursor [(η6-cymene)Ru(Cl)(μ
2-Cl)]2 gave cationic ruthenium(II) complexes of the type [(η6-cymene)Ru(Cl)(adpm)]Cl (adpm = chelating 2-amino-4-(2-yridinyl)pyrimidine ligand). Stirring the freshly prepared complexes with either NaBPh4, NaBF4 or KPF6, the chloride anion was exchanged against other coordinating anions (BF4-, PF6-, BPh4-).Some of these ruthenium complexes have shown very special activities in the transfer hydrogenation of ketones by reacting them in the absence of the base. This led to detailed investigations on the mechanism of this reaction. According to the activities and with the help
of ESI-MS experiments and DFT calculations, a mechanism was proposed for the transfer hydrogenation of acetophenone in the absence of the base. It shows that in the absence of the base, a C-H bond activation at the pyrimidine ring should occur to activate the catalyst.
The palladium complexes of bidentate N,N ligands were examined in coupling reactions. As expected, they did not show very special activities.
Multidentate ligands, having pyrimidine groups as relatively soft donors for late transition metals and simultaneously possessing a binding position for a hard Lewis-acid, could be obtained using the new synthesized bidentate and tridentate ligands.
Competing Neural Networks as Models for Non Stationary Financial Time Series -Changepoint Analysis-
(2005)
The problem of structural changes (variations) play a central role in many scientific fields. One of the most current debates is about climatic changes. Further, politicians, environmentalists, scientists, etc. are involved in this debate and almost everyone is concerned with the consequences of climatic changes. However, in this thesis we will not move into the latter direction, i.e. the study of climatic changes. Instead, we consider models for analyzing changes in the dynamics of observed time series assuming these changes are driven by a non-observable stochastic process. To this end, we consider a first order stationary Markov Chain as hidden process and define the Generalized Mixture of AR-ARCH model(GMAR-ARCH) which is an extension of the classical ARCH model to suit to model with dynamical changes. For this model we provide sufficient conditions that ensure its geometric ergodic property. Further, we define a conditional likelihood given the hidden process and a pseudo conditional likelihood in turn. For the pseudo conditional likelihood we assume that at each time instant the autoregressive and volatility functions can be suitably approximated by given Feedfoward Networks. Under this setting the consistency of the parameter estimates is derived and versions of the well-known Expectation Maximization algorithm and Viterbi Algorithm are designed to solve the problem numerically. Moreover, considering the volatility functions to be constants, we establish the consistency of the autoregressive functions estimates given some parametric classes of functions in general and some classes of single layer Feedfoward Networks in particular. Beside this hidden Markov Driven model, we define as alternative a Weighted Least Squares for estimating the time of change and the autoregressive functions. For the latter formulation, we consider a mixture of independent nonlinear autoregressive processes and assume once more that the autoregressive functions can be approximated by given single layer Feedfoward Networks. We derive the consistency and asymptotic normality of the parameter estimates. Further, we prove the convergence of Backpropagation for this setting under some regularity assumptions. Last but not least, we consider a Mixture of Nonlinear autoregressive processes with only one abrupt unknown changepoint and design a statistical test that can validate such changes.
The complexity of modern real-time systems is increasing day by day. This inevitable rise in complexity predominantly stems from two contradicting requirements, i.e., ever increasing demand for functionality, and required low cost for the final product. The development of modern multi-processors and variety of network protocols and architectures have enabled such a leap in complexity and functionality possible. Albeit, efficient use of these multi-processors and network architectures is still a major problem. Moreover, the software design and its development process needs improvements in order to support rapid-prototyping for ever changing system designs. Therefore, in this dissertation, we provide solutions for different problems faced in the development and deployment process of real-time systems. The contributions presented in this thesis enable efficient utilization of system resources, rapid design & development and component modularity & portability.
In order to ease the certification process, time-triggered computation model is often used in distributed systems. However, time-triggered scheduling is NP-hard, due to which the process of schedule generation for complex large systems becomes convoluted. Large scheduler run-times and low scalability are two major problems with time-triggered scheduling. To solve these problems, we present a modular real-time scheduler based on a novel search-tree pruning technique, which consumes less time (compared to the state-of-the-art) in order to schedule tasks on large distributed time-triggered systems. In order to provide end-to-end guarantees, we also extend our modular scheduler to quickly generate schedules for time-triggered network traffic in large TTEthernet based networks. We evaluate our schedulers on synthetic but practical task-sets and demonstrate that our pruning technique efficiently reduces scheduler run-times and exhibits adequate scalability for future time-triggered distributed systems.
In safety critical systems, the certification process also requires strict isolation between independent components. This isolation is enforced by utilizing resource partitioning approach, where different criticality components execute in different partitions (each temporally and spatially isolated from each other). However, existing partitioning approaches use periodic servers or tasks to service aperiodic activities. This approach leads to utilization loss and potentially leads to large latencies. On the contrary to the periodic approaches, state-of-the-art aperiodic task admission algorithms do not suffer from problems like utilization loss. However, these approaches do not support partitioned scheduling or mixed-criticality execution environment. To solve this problem, we propose an algorithm for online admission of aperiodic tasks which provides job execution flexibility, jitter control and leads to lower latencies of aperiodic tasks.
For safety critical systems, fault-tolerance is one of the most important requirements. In time-triggered systems, modes are often used to ensure survivability against faults, i.e., when a fault is detected, current system configuration (or mode) is changed such that the overall system performance is either unaffected or degrades gracefully. In literature, it has been asserted that a task-set might be schedulable in individual modes but unschedulable during a mode-change. Moreover, conventional mode-change execution strategies might cause significant delays until the next mode is established. In order to address these issues, in this dissertation, we present an approach for schedulability analysis of mode-changes and propose mode-change delay reduction techniques in distributed system architecture defined by the DREAMS project. We evaluate our approach on an avionics use case and demonstrate that our approach can drastically reduce mode-change delays.
In order to manage increasing system complexity, real-time applications also require new design and development technologies. Other than fulfilling the technical requirements, the main features required from such technologies include modularity and re-usability. AUTOSAR is one of these technologies in automotive industry, which defines an open standard for software architecture of a real-time operating system. However, being an industrial standard, the available proprietary tools do not support model extensions and/or new developments by third-parties and, therefore, hinder the software evolution. To solve this problem, we developed an open-source AUTOSAR toolchain which supports application development and code generation for several modules. In order to exhibit the capabilities of our toolchain, we developed two case studies. These case studies demonstrate that our toolchain generates valid artifacts, avoids dirty workarounds and supports application development.
In order to cope with evolving system designs and hardware platforms, rapid-development of scheduling and analysis algorithms is required. In order to ease the process of algorithm development, a number of scheduling and analysis frameworks are proposed in literature. However, these frameworks focus on a specific class of applications and are limited in functionality. In this dissertation, we provide the skeleton of a scheduling and analysis framework for real-time systems. In order to support rapid-development, we also highlight different development components which promote code reuse and component modularity.
To increase situational awareness of the crane operator, the aim of this thesis is to develop a vision-based deep learning object detection from crane load-view using an adaptive perception in the construction area. Conventional worker detection methods are based on simple shape or color features from the worker's appearances. Nonetheless, these methods can fail to recognize the workers who do not wear the protective gears. To find out an image representation of the object from the top view manually or handcrafted feature is crucial. We, therefore, employed deep learning methods to automatically learn those features.
To yield optimal results, deep learning methods require mass amount of data.
Due to the data deficit especially in the construction domain, we developed the photorealistic world to create the data in addition to our samples collected from the real construction area. The simulated platform does not benefit only from diverse data types, but also concurrent research development which speeds up the pipeline at a low cost.
Our research findings indicate that the combination of synthetic and real training samples improved the state-of-the-art detector. In line with previous studies to bridge the gap between synthetic and real data, the results of preprocessed synthetic images are substantially better than using the raw data by approximately 10%.
Finding the right deep learning model for load-view detection is challenging.
By investigating our training data, it becomes evident that the majority of bounding box sizes are very small with a complex background.
In addition, we gave the priority to speed over accuracy based on the construction safety criteria. Finally, RetinaNet is chosen out of the three primary object detection models.
Nevertheless, the data-driven detection algorithm can fail to handle scale invariance, especially for detectors whose input size is changed in an extremely wide range.
The adaptive zoom feature can enhance the quality of the worker detection.
To avoid further data gathering and extensive retraining, the proposed automatic zoom method of the load-view crane camera supports the deep learning algorithm, specifically in the high scale variant problem. The finite state machine is employed for control strategies to adapt the zoom level to cope not only with inconsistent detection but also abrupt camera movement during lifting operation. Consequently, the detector is able to detect a small size object by smooth continuous zoom control without additional training.
The adaptive zoom control not only enhances the performance of the top-view object detection but also reduces the interaction of the crane operator with camera system, reducing the risk of fatality during load lifting operation.
In the present work, various aspects of the mixed continuum-atomistic modelling of materials are studied, most of which are related to the problems arising due to a development of microstructures during the transition from an elastic to plastic description within the framework of continuum-atomistics. By virtue of the so-called Cauchy-Born hypothesis, which is an essential part of the continuum-atomistics, a localization criterion has been derived in terms of the loss of infinitesimal rank-one convexity of the strain energy density. According to this criterion, a numerical yield condition has been computed for two different interatomic energy functions. Therewith, the range of the Cauchy-Born rule validity has been defined, since the strain energy density remains quasiconvex only within the computed yield surface. To provide a possibility to continue the simulation of material response after the loss of quasiconvexity, a relaxation procedure proposed by Tadmor et al. leading necessarily to the development of microstructures has been used. Thereby, various notions of convexity have been overviewed in details. Alternatively to the above mentioned criterion, a stability criterion has been applied to detect the critical deformation. For the study in the postcritical region, the path-change procedure proposed by Wagner and Wriggers has been adapted for the continuum-atomistic and modified. To capture the deformation inhomogeneity arising due to the relaxation, the Cauchy-Born hypothesis has been extended by assumption that it represents only the 1st term in the Taylor's series expansion of the deformation map. The introduction of the 2nd, quadratic term results in the higher-order materials theory. Based on a simple computational example, the relevance of this theory in the postcritical region has been shown. For all simulations including the finite element examples, the development tool MATLAB 6.5 has been used.
The mechanical properties of semi-crystalline polymers depend extremely on their
morphology, which is dependent on the crystallization during processing. The aim of
this research is to determine the effect of various nanoparticles on morphology
formation and tensile mechanical properties of polypropylene under conditions
relevant in polymer processing and to contribute ultimately to the understanding of
this influence.
Based on the thermal analyses of samples during fast cooling, it is found that the
presence of nanoparticle enhances the overall crystallization process of PP. The results
suggest that an increase of the nucleation density/rate is a dominant process that
controls the crystallization process of PP in this work, which can help to reduce the
cycle time in the injection process. Moreover, the analysis of melting behaviors
obtained after each undercooling reveals that crystal perfection increases significantly
with the incorporation of TiO2 nanoparticles, while it is not influenced by the SiO2
nanoparticles.
This work also comprises an analysis of the influence of nanoparticles on the
microstructure of injection-molded parts. The results clearly show multi-layers along
the wall thickness. The spherulite size and the degree of crystallinity continuously
decrease from the center to the edge. Generally both the spherulite size and the degree
of crystallinity decrease with higher the SiO2 loading. In contrast, an increase in the
degree of crystallinity with an increasing TiO2 nanoparticle loading was detected.
The tensile properties exhibit a tendency to increase in the tensile strength as the core
is reached. The tensile strength decreases with the addition of nanoparticles, while the
elongation at break of nanoparticle-filled PP decreases from the skin to the core. With
increasing TiO2 loading, the elongation at break decreases.
Deep learning has achieved significant improvements in a variety of tasks in computer vision applications with an open image dataset which has a large amount of data. However, the acquisition of a large number of the dataset is a challenge in real-world applications, especially if they are new eras for deep learning. Furthermore, the distribution of class in the dataset is often imbalanced. The data imbalance problem is frequently bottlenecks of the neural network performance in classification. Recently, the potential of generative adversarial networks (GAN) as a data augmentation method on minority data has been studied.
This dissertation investigates using GAN and transfer learning to improve the performance of the classification under imbalanced data conditions. We first propose a classification enhancement generative adversarial networks (CEGAN) to enhance the quality of generated synthetic minority data and more importantly, to improve the prediction accuracy in data imbalanced condition. Our experiments show that approximating the real data distribution using CEGAN improves the classification performance significantly in data imbalanced conditions compared with various standard data augmentation methods.
To further improve the performance of the classification, we propose a novel supervised discriminative feature generation method (DFG) for minority class dataset. DFG is based on the modified structure of Generative Adversarial Network consisting of four independent networks: generator, discriminator, feature extractor, and classifier. To augment the selected discriminative features of minority class data by adopting attention mechanism, the generator for class-imbalanced target task is trained while feature extractor and classifier are regularized with the pre-trained ones from large source data. The experimental results show that the generator of DFG enhances the augmentation of label-preserved and diverse features, and classification results are significantly improved on the target task.
In this thesis, these proposals are deployed to bearing fault detection and diagnosis of induction motor and shipping label recognition and validation for logistics. The experimental results for bearing fault detection and diagnosis conclude that the proposed GAN-based framework has good performance on the imbalanced fault diagnosis of rotating machinery. The experimental results for shipping label recognition and validation also show that the proposed method achieves better performance than many classical and state-of-the-art algorithms.
This thesis consists of five chapters. Chapter one elaborates on the principle of cognitive consistency and provides an overview of what extant research refers to as cognitive consistency theories (e.g., Abelson et al., 1968; Harmon-Jones & Harmon-Jones, 2007; Simon, Stenstrom, & Read, 2015). Moreover, it describes the most prominent theoretical representatives in this context, namely balance theory (Heider, 1946, 1958), congruity theory (Osgood & Tannenbaum, 1955), and cognitive dissonance theory (Festinger, 1957). Chapter one further outlines the role of individuals’ preference for cognitive consistency in the context of financial resource acquisition, the recruitment of employees and the acquisition of customers in the entrepreneurial context.
Chapter two is co-authored by Prof. Dr. Matthias Baum and presents two separate studies in which we empirically investigate the hypothesis that social entrepreneurs face a systematic disadvantage, compared to for-profit entrepreneurs, when seeking to acquire financial resources. Further, our work goes beyond existing research by introducing biased perceptions as a factor that may constrain social enterprise resource acquisition and therefore possibly stall the process of social value creation. On the foundation of role congruity theory (Eagly & Karau, 2002), we emphasize on the question whether social entrepreneurs provide signals which are less congruent with the stereotype of successful entrepreneurs and, in such, are perceived as less competent. We further test whether such biased competency perceptions feed forward into a lower probability to receive funding.
Chapter three is also co-authored by Prof. Dr. Matthias Baum as well as by Eva Henrich. The aim of this chapter is to further our understanding of the early recruitment phase and to contribute to the current debate about how firms should orchestrate their recruitment channels in order to enhance the creation of employer knowledge. We introduce the concept of integrated marketing communication into the recruitment field and examine how the level of consistency regarding job or organization information affects the recall and the recognition of that information. We additionally test whether information consistency among multiple recruitment channels influences information recognition failure quota. Answering this question is important as by failing to remember the source of recruitment information, job seekers may attribute job information to the wrong firm and thus create an incorrect employer knowledge.
Chapter four, which is co-authored by Prof. Dr. Matthias Baum, introduces customer congruity perceptions between a brand and a reward in the context of customer referral programs as an essential driver of the effectiveness of such programs. More precisely, we posit and empirically test a model according to which the decision-making process of the customer recommending a firm involves multiple mental steps and assumes reward perceptions to be an immediate antecedent of brand evaluation, which then, ultimately shapes the likelihood of recommendation. The level of congruity/incongruity is set up as an antecedent state and affects the perceived attractiveness of the reward. Our work contributes to the discussion on the optimal level of congruity between a prevailing schema in the mind of the customer and a stimulus presented. In addition, chapter four introduces customer referral programs as a strategic tool for brand managers. Chapter four is further published in Psychology & Marketing.
Chapter five first proposes that marketing strategies specifically designed to induce word-of-mouth (WOM) behavior are particular relevant for new ventures. Against the background that previous research suggests that customer perceptions of young firm age may influence customer behavior and the degree to which customers support new ventures (e.g., Choi & Shepherd, 2005; Stinchcombe, 1965), we secondly conduct an experiment to examine the causal mechanisms linking firm age and customer WOM. Chapter five, too, is co-authored by Prof. Dr. Matthias Baum.
The topic of this thesis is the coupling of an atomistic and a coarse scale region in molecular dynamics simulations with the focus on the reflection of waves at the interface between the two scales and the velocity of waves in the coarse scale region for a non-equilibrium process. First, two models from the literature for such a coupling, the concurrent coupling of length scales and the bridging scales method are investigated for a one dimensional system with harmonic interaction. It turns out that the concurrent coupling of length scales method leads to the reflection of fine scale waves at the interface, while the bridging scales method gives an approximated system that is not energy conserving. The velocity of waves in the coarse scale region is in both models not correct. To circumvent this problems, we present a coupling based on the displacement splitting of the bridging scales method together with choosing appropriate variables in orthogonal subspaces. This coupling allows the derivation of evolution equations of fine and coarse scale degrees of freedom together with a reflectionless boundary condition at the interface directly from the Lagrangian of the system. This leads to an energy conserving approximated system with a clear separation between modeling errors an errors due to the numerical solution. Possible approximations in the Lagrangian and the numerical computation of the memory integral and other numerical errors are discussed. We further present a method to choose the interpolation from coarse to atomistic scale in such a way, that the fine scale degrees of freedom in the coarse scale region can be neglected. The interpolation weights are computed by comparing the dispersion relations of the coarse scale equations and the fully atomistic system. With this new interpolation weights, the number of degrees of freedom can be drastically reduced without creating an error in the velocity of the waves in the coarse scale region. We give an alternative derivation of the new coupling with the Mori-Zwanzig projection operator formalism, and explain how the method can be extended to non-zero temperature simulations. For the comparison of the results of the approximated with the fully atomistic system, we use a local stress tensor and the energy in the atomistic region. Examples for the numerical solution of the approximated system for harmonic potentials are given in one and two dimensions.
Dealing with uncertain structures or data has lately been getting much attention in discrete optimization. This thesis addresses two different areas in discrete optimization: Connectivity and covering.
When discussing uncertain structures in networks it is often of interest to determine how many vertices or edges may fail in order for the network to stay connected.
Connectivity is a broad, well studied topic in graph theory. One of the most important results in this area is Menger's Theorem which states that the minimum number of vertices needed to separate two non-adjacent vertices equals the maximum number of internally vertex-disjoint paths between these vertices. Here, we discuss mixed forms of connectivity in which both vertices and edges are removed from a graph at the same time. The Beineke Harary Conjecture states that for any two distinct vertices that can be separated with k vertices and l edges but not with k-1 vertices and l edges or k vertices and l-1 edges there exist k+l edge-disjoint paths between them of which k+1 are internally vertex-disjoint. In contrast to Menger's Theorem, the existence of the paths is not sufficient for the connectivity statement to hold. Our main contribution is the proof of the Beineke Harary Conjecture for the case that l equals 2.
We also consider different problems from the area of facility location and covering. We regard problems in which we are given sets of locations and regions, where each region has an assigned number of clients. We are now looking for an allocation of suppliers into the locations, such that each client is served by some supplier. The notable difference to other covering problems is that we assume that each supplier may only serve a fixed number of clients which is not part of the input. We discuss the complexity and solution approaches of three such problems which vary in the way the clients are assigned to the suppliers.
The cytosolic Fe65 adaptor protein family, consisting of Fe65, Fe65L1 and Fe65L2 is involved in many intracellular signaling pathways linking via its three interaction domains a continuously growing list of proteins by facilitating functional interactions. One of the most important binding partners of Fe65 family proteins is the amyloid precursor protein (APP), which plays an important role in Alzheimer Disease.
To gain deeper insights in the function of the ubiquitously expressed Fe65 and the brain enriched Fe65L1, the goal of my study was I) to analyze their putative synaptic function in vivo, II) to examine structural analysis focusing on a putative dimeric complex of Fe65, III) to consider the involvement of Fe65 in mediating LRP1 and APP intracellular trafficking in murine hippocampal neurons. By utilizing several behavioral analyses of Fe65 KO, Fe65L1 KO and Fe65/Fe65L1 DKO mice I could demonstrate that the Fe65 protein family is essential for learning and memory as well as grip strength and locomotor activity. Furthermore, immunohistological as well as protein biochemical analysis revealed that the Fe65 protein family is important for neuromuscular junction formation in the peripheral nervous system, which involves binding of APP and acting downstream of the APP signaling pathway. Via Co-immunoprecipitation analysis I could verify that Fe65 is capable to form dimers ex vivo, which exclusively occur in the cytosol and upon APP expression are shifted to membrane compartments forming trimeric complexes. The influence of the loss of Fe65 and/or Fe65L1 on APP and/or LRP1 transport characteristics in axons could not be verified, possibly conditioned by the compensatory effect of Fe65L2. However, I could demonstrate that LRP1 affects the APP transport independently of Fe65 by shifting APP into slower types of vesicles leading to changed processing and endocytosis of APP.
The outcome of my thesis advanced our understanding of the Fe65 protein family, especially its interplay with APP physiological function in synapse formation and synaptic plasticity.
Despite their “weak nature” London dispersion interactions are omnipresent and of fundamental importance for many aspects of chemistry and biology and have often been underestimated in the description of intra- and intermolecular interactions. In this thesis, London dispersion is investigated in the gas phase with molecular beam experiments and quantum chemistry. The focus of this work lies in the investigation of London dispersion in the electronic ground state and the electronically excited state. For the electronic ground state, dispersion-bound dimers of triphenylmethane derivatives were analyzed. Depending on the dispersion energy donor, a tail-to-tail (TPM), head-to-tail (iPrTPM) or head-to-head (tBuTPM) arrangement can be assumed for the minimum structure. The tBuTPM dimer exhibits an exceptionally small C-H·· H-C contact which is stabilized by strong London dispersion interactions which was quantified by energy composition analysis. For the characterization of the dimer, the calculation of anharmonic frequencies was of high importance and was also validated with literature data. The second system, the chromone-MeOH balance represents an ideal molecular balance with two competing docking sites at the carbonyl oxygen. The experimental results are compared to theoretical predictions obtained from (TD)DFT-, DLPNO-CCSD(T) and SAPT-calculations to study the balance between electrostatics, induction and dispersion interaction in the S0 and T1 state. The chromone-solvent system was identified as an ideal system for studying London dispersion in multiple electonic states. Furthermore, candidates for derivivatives of chromone were analyzed with quantum chemical methods in the electronic ground and electronically excited state in an attempt to identify suitable candidates for further experiments. The 6-methylchromone shows promising behavior in stabilizing the inside pocket regardless of the electronic state and was analyzed in more detail with a variety of methods. Similar analysis of 2-CF3chromone and the 2-CF3, 6-methylchromone showed no special effect of a substitution in 2-position or possible cooperative effects.
This thesis comprises investigations on the interaction of \(N_2\) adsorbate molecules with size-selected iron metal clusters under cryo conditions. All investigations were performed at the customized fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer FRITZ. This setup serves a laser vaporization (LVAP) source to generate the investigated iron cluster ions. Cryo kinetic studies investigate the stepwise \(N_2\) adsorption on size selected \(Fe_n^+\) clusters under well-defined isothermal conditions. The adsorption behavior and the adsorption limits lead to information about the cluster structure and its reactivity. By coupling a tunable IR laser in the ICR cell, it is possible to perform cryo infrared photon dissociation (IR-PD) spectroscopy experiments. This provides information on binding motifs of the \(N_2\) adsorbates and the cluster structure. Combining both methods with quantum chemical calculations via density functional theory (DFT) substantiates the experimental results and deepens the fundamental insights into the cluster structure, their reactivity, and the metal-adsorbate bonding.
In this dissertation we consider mesoscale based models for flow driven fibre orientation dynamics in suspensions. Models for fibre orientation dynamics are derived for two classes of suspensions. For concentrated suspensions of rigid fibres the Folgar-Tucker model is generalized by incorporating the excluded volume effect. For dilute semi-flexible fibre suspensions a novel moments based description of fibre orientation state is introduced and a model for the flow-driven evolution of the corresponding variables is derived together with several closure approximations. The equation system describing fibre suspension flows, consisting of the incompressible Navier-Stokes equation with an orientation state dependent non-Newtonian constitutive relation and a linear first order hyperbolic system for the fibre orientation variables, has been analyzed, allowing rather general fibre orientation evolution models and constitutive relations. The existence and uniqueness of a solution has been demonstrated locally in time for sufficiently small data. The closure relations for the semiflexible fibre suspension model are studied numerically. A finite volume based discretization of the suspension flow is given and the numerical results for several two and three dimensional domains with different parameter values are presented and discussed.
In this work two main approaches for the evaluation of credit derivatives are analyzed: the copula based approach and the Markov Chain based approach. This work gives the opportunity to use the advantages and avoid disadvantages of both approaches. For example, modeling of contagion effects, i.e. modeling dependencies between counterparty defaults, is complicated under the copula approach. One remedy is to use Markov Chain, where it can be done directly. The work consists of five chapters. The first chapter of this work extends the model for the pricing of CDS contracts presented in the paper by Kraft and Steffensen (2007). In the widely used models for CDS pricing it is assumed that only borrower can default. In our model we assume that each of the counterparties involved in the contract may default. Calculated contract prices are compared with those calculated under usual assumptions. All results are summarized in the form of numerical examples and plots. In the second chapter the copula and its main properties are described. The methods of constructing copulas as well as most common copulas families and its properties are introduced. In the third chapter the method of constructing a copula for the existing Markov Chain is introduced. The cases with two and three counterparties are considered. Necessary relations between the transition intensities are derived to directly find some copula functions. The formulae for default dependencies like Spearman's rho and Kendall's tau for defined copulas are derived. Several numerical examples are presented in which the copulas are built for given Markov Chains. The fourth chapter deals with the approximation of copulas if for a given Markov Chain a copula cannot be provided explicitly. The fifth chapter concludes this thesis.
The goal of this thesis is to find ways to improve the analysis of hyperspectral Terahertz images. Although it would be desirable to have methods that can be applied on all spectral areas, this is impossible. Depending on the spectroscopic technique, the way the data is acquired differs as well as the characteristics that are to be detected. For these reasons, methods have to be developed or adapted to be especially suitable for the THz range and its applications. Among those are particularly the security sector and the pharmaceutical industry.
Due to the fact that in many applications the volume of spectra to be organized is high, manual data processing is difficult. Especially in hyperspectral imaging, the literature is concerned with various forms of data organization such as feature reduction and classification. In all these methods, the amount of necessary influence of the user should be minimized on the one hand and on the other hand the adaption to the specific application should be maximized.
Therefore, this work aims at automatically segmenting or clustering THz-TDS data. To achieve this, we propose a course of action that makes the methods adaptable to different kinds of measurements and applications. State of the art methods will be analyzed and supplemented where necessary, improvements and new methods will be proposed. This course of action includes preprocessing methods to make the data comparable. Furthermore, feature reduction that represents chemical content in about 20 channels instead of the initial hundreds will be presented. Finally the data will be segmented by efficient hierarchical clustering schemes. Various application examples will be shown.
Further work should include a final classification of the detected segments. It is not discussed here as it strongly depends on specific applications.
Properties of vapor-liquid interfaces play an important role in many processes, but corresponding data is scarce, especially for mixtures. Therefore, two independent routes were employed in the present work to study them: molecular dynamics (MD) simulations using classical force fields as well as density gradient theory (DGT) in combination with theoretically-based equations of state (EOS). The investigated interfacial properties include: interfacial tension, adsorption, and the enrichment of components, which
quantifies the interesting effect that in many systems the density of certain components in the interfacial region is much higher than in either of the bulk phases. As systematic investigations of the enrichment were lacking, it was comprehensively studied here by considering a large number of Lennard-Jones (LJ) mixtures with different phase behavior; also the dependence of the enrichment on temperature and concentration was elucidated and a conformal solution theory for describing the interfacial properties of LJ mixtures was developed. Furthermore, general relations of interfacial properties and the phase behavior were revealed and the relation between the enrichment and the wetting behavior of fluid interfaces was elucidated. All studies were carried out by both MD and DGT, which were found to agree well in most cases. The results were extended to real mixtures, which were studied not only by simulations but also in laboratory experiments. In connection with these investigations, three literature reviews were prepared which cover: a) simulation data on thermophysical properties of the LJ fluid; b) the performance of different EOS of the LJ fluid on that simulation data; c) data on the enrichment at vapor-liquid interfaces. Electronic databases were established for a) - c). Based on c), a short-cut method for the prediction of the enrichment from readily available vapor-liquid equilibrium data was developed. Last not least, an MD method for studying the influence of mass transfer on interfacial properties was developed and applied to investigate the influence of the enrichment on the mass transfer.
Lubricated tribological contact processes are important in both nature and in many technical applications. Fluid lubricants play an important role in contact processes, e.g. they reduce friction and cool the contact zone. The fundamentals of lubricated contact processes on the atomistic scale are, however, today not fully understood. A lubricated contact process is defined here as a process, where two solid bodies that are in close proximity and eventually in parts in direct contact, carry out a relative motion, whereat the remaining volume is submersed by a fluid lubricant. Such lubricated contact processes are difficult to examine experimentally. Atomistic simulations are an attractive alternative for investigating the fundamentals of such processes. In this work, molecular dynamics simulations were used for studying different elementary processes of lubricated tribological contacts. A simplified, yet realistic simulation setup was developed in this work for that purpose using classical force fields. In particular, the two solid bodies were fully submersed in the fluid lubricant such that the squeeze-out was realistically modeled. The velocity of the relative motion of the two solid bodies was imposed as a boundary condition. Two types of cases were considered in this work: i) a model system based on synthetic model substances, which enables a direct, but generic, investigation of molecular interaction features on the contact process; and ii) real substance systems, where the force fields describe specific real substances. Using the model system i), also the reproducibility of the findings obtained from the computer experiments was critically assessed. In most cases, also the dry reference case was studied. Both mechanical and thermodynamic properties were studied -- focusing on the influence of lubrication. The following properties were studied: The contact forces, the coefficient of friction, the dislocation behavior in the solid, the chip formation and the formation of the groove, the squeeze-out behavior of the fluid in the contact zone, the local temperature and the energy balance of the system, the adsorption of fluid particles on the solid surfaces, as well as the formation of a tribofilm. Systematic studies were carried out for elucidating the influence of the wetting behavior, the influence of the molecular architecture of the lubricant, and the influence of the lubrication gap height on the contact process. As expected, the presence of a fluid lubricant reduces the temperature in the vicinity of the contact zone. The presence of the lubricant is, moreover, found to have a significant influence on the friction and on the energy balance of the process. The presence of a lubricant reduces the coefficient of friction compared to a dry case in the starting phase of a contact process, while lubricant molecules remain in the contact zone between the two solid bodies. This is a result of an increased normal and slightly decreased tangential force in the starting phase. When the fluid molecules are squeezed out with ongoing contact time and the contact zone is essentially dry, the coefficient of friction is increased by the presence of a fluid compared to a dry case. This is attributed to an imprinting of individual fluid particles into the solid surface, which is energetically unfavorable. By studying the contact process in a wide range of gap height, the entire range of the Stribeck curve is obtained from the molecular simulations. Thereby, the three main lubrication regimes of the Stribeck curve and their transition regions are covered, namely boundary lubrication (significant elastic and plastic deformation of the substrate), mixed lubrication (adsorbed fluid layers dominate the process), and hydrodynamic lubrication (shear flow is set up between the surface and the asperity). The atomistic effects in the different lubrication regimes are elucidated. Notably, the formation of a tribofilm is observed, in which lubricant molecules are immersed into the metal surface. The formation of a tribofilm is found to have important consequences for the contact process. The work done by the relative motion is found to mainly dissipate and thereby heat up the system. Only a minor part of the work causes plastic deformation. Finally, the assumptions, simplifications, and approximations applied in the simulations are critically discussed, which highlights possible future work.
Numerical Godeaux surfaces are minimal surfaces of general type with the smallest possible numerical invariants. It is known that the torsion group of a numerical Godeaux surface is cyclic of order \(m\leq 5\). A full classification has been given for the cases \(m=3,4,5\) by the work of Reid and Miyaoka. In each case, the corresponding moduli space is 8-dimensional and irreducible.
There exist explicit examples of numerical Godeaux surfaces for the orders \(m=1,2\), but a complete classification for these surfaces is still missing.
In this thesis we present a construction method for numerical Godeaux surfaces which is based on homological algebra and computer algebra and which arises from an experimental approach by Schreyer. The main idea is to consider the canonical ring \(R(X)\) of a numerical Godeaux surface \(X\) as a module over some graded polynomial ring \(S\). The ring \(S\) is chosen so that \(R(X)\) is finitely generated as an \(S\)-module and a Gorenstein \(S\)-algebra of codimension 3. We prove that the canonical ring of any numerical Godeaux surface, considered as an \(S\)-module, admits a minimal free resolution whose middle map is alternating. Moreover, we show that a partial converse of this statement is true under some additional conditions.
Afterwards we use these results to construct (canonical rings of) numerical Godeaux surfaces. Hereby, we restrict our study to surfaces whose bicanonical system has no fixed component but 4 distinct base points, in the following referred to as marked numerical Godeaux surfaces.
The particular interest of this thesis lies on marked numerical Godeaux surfaces whose torsion group is trivial. For these surfaces we study the fibration of genus 4 over \(\mathbb{P}^1\) induced by the bicanonical system. Catanese and Pignatelli showed that the general fibre is non-hyperelliptic and that the number \(\tilde{h}\) of hyperelliptic fibres is bounded by 3. The two explicit constructions of numerical Godeaux surfaces with a trivial torsion group due to Barlow and Craighero-Gattazzo, respectively, satisfy \(\tilde{h} = 2\).
With the method from this thesis, we construct an 8-dimensional family of numerical Godeaux surfaces with a trivial torsion group and whose general element satisfy \(\tilde{h}=0\).
Furthermore, we establish a criterion for the existence of hyperelliptic fibres in terms of a minimal free resolution of \(R(X)\). Using this criterion, we verify experimentally the
existence of a numerical Godeaux surface with \(\tilde{h}=1\).
Integrating Security Concerns into Safety Analysis of Embedded Systems Using Component Fault Trees
(2016)
Nowadays, almost every newly developed system contains embedded systems for controlling system functions. An embedded system perceives its environment via sensors, and interacts with it using actuators such as motors. For systems that might damage their environment by faulty behavior usually a safety analysis is performed. Security properties of embedded systems are usually not analyzed at all. New developments in the area of Industry 4.0 and Internet of Things lead to more and more networking of embedded systems. Thereby, new causes for system failures emerge: Vulnerabilities in software and communication components might be exploited by attackers to obtain control over a system. By targeted actions a system may also be brought into a critical state in which it might harm itself or its environment. Examples for such vulnerabilities, and also successful attacks, became known over the last few years.
For this reason, in embedded systems safety as well as security has to be analyzed at least as far as it may cause safety critical failures of system components.
The goal of this thesis is to describe in one model how vulnerabilities from the security point of view might influence the safety of a system. The focus lies on safety analysis of systems, so the safety analysis is extended to encompass security problems that may have an effect on the safety of a system. Component Fault Trees are very well suited to examine causes of a failure and to find failure scenarios composed of combinations of faults. A Component Fault Tree of an analyzed system is extended by additional Basic Events that may be caused by targeted attacks. Qualitative and quantitative analyses are extended to take the additional security events into account. Thereby, causes of failures that are based on safety as well as security problems may be found. Quantitative or at least semi-quantitative analyses allow to evaluate security measures more detailed, and to justify the need of such.
The approach was applied to several example systems: The safety chain of the off-road robot RAVON, an adaptive cruise control, a smart farming scenario, and a model of a generic infusion pump were analyzed. The result of all example analyses was that additional failure causes were found which would not have been detected in traditional Component Fault Trees. In the analyses also failure scenarios were found that are caused solely by attacks, and that are not depending on failures of system components. These are especially critical scenarios which should not happen in this way, as they are not found in a classical safety analysis. Thus the approach shows its additional benefit to a safety analysis which is achieved by the application of established techniques with only little additional effort.
Termination of Rewriting
(1994)
More and more, term rewriting systems are applied in computer science aswell as in mathematics. They are based on directed equations which may be used as non-deterministic functional programs. Termination is a key property for computing with termrewriting systems.In this thesis, we deal with different classes of so-called simplification orderings which areable to prove the termination of term rewriting systems. Above all, we focus on the problemof applying these termination methods to examples occurring in practice. We introduce aformalism that allows clear representations of orderings. The power of classical simplifica-tion orderings - namely recursive path orderings, path and decomposition orderings, Knuth-Bendix orderings and polynomial orderings - is improved. Further, we restrict these orderingssuch that they are compatible with underlying AC-theories by extending well-known methodsas well as by developing new techniques. For automatically generating all these orderings,heuristic-based algorithms are given. A comparison of these orderings with respect to theirpowers and their time complexities concludes the theoretical part of this thesis. Finally, notonly a detailed statistical evaluation of examples but also a brief introduction into the designof a software tool representing the integration of the specified approaches is given.
Light is an essential aspect of daily life, exerting a profound influence on various physiological and behavioral processes, including circadian rhythms, alertness, cognition, mood, and behavior. Technological advances, particularly the widespread adoption of light-emitting diodes (LEDs), have significantly accelerated the impact of lighting on the human experience. With the increasing global accessibility to electric and modern lighting systems, there is a pressing need to scientifically investigate the human-centered effects of lighting for the billions of people worldwide who encounter natural and electric lighting in their daily lives. Extensive interdisciplinary research across fields such as physics, engineering, psychology, medicine, business administration, and architecture has explored the biological and psychological effects of lighting, underscoring the immense potential for further advancements in this domain. Notably, innovative lighting technologies and strategies hold tremendous promise in enhancing human health, performance, and overall well-being.
Beyond physical spaces, three-dimensional virtual environments, including metaverse platforms, are becoming increasingly important. Simulated lighting in virtual spaces can have visual and non-visual effects on users. As technological progress and digitalization extend globally, more individuals will be exposed to virtual lighting scenarios. Consequently, exploring the human-centered lighting effects in virtual environments offers a compelling opportunity to improve the quality of user experiences. This thesis demonstrates the adaptability of established measurement methods from physical illumination and perception research for virtual environments.
This thesis comprises three parts. The first part reviews the current state of research on lighting and its influences on humans, examines research methods in lighting research, and identifies research gaps. The second part investigates the effects of lighting on complex emotional and behavioral constructs, specifically conflict handling. Elaborate laboratory experiments explore lighting as an independent variable, including realistic correlated color temperature (CCT) levels and enhanced CCT changes. Statistical analyses provide in-depth examination and critical discussion of the effects. The third part explores lighting in virtual spaces, considering literature, methodological approaches, and challenges. Two studies investigate visual and non-visual effects, and preferences in virtual environment design. Comparative analysis of the data yields implications for research and practice, including the interdisciplinary perspective of a novel approach called human-centric virtual lighting (HCVL).
In conclusion, this thesis comprehensively explores the impact of lighting on the human experience in both physical spaces and virtual environments. By addressing research gaps and employing contemporary methodologies, the findings contribute to our understanding of the effects of lighting on humans. Furthermore, the implications for research and practice offer valuable insights for the development of innovative lighting technologies and strategies aimed at enhancing the well-being and experiences of individuals worldwide. This work highlights the relevance of interdisciplinary research involving fields such as architecture, business management, event management, computer science, design, engineering, ergonomics, lighting research, medicine, physics, and psychology in advancing our understanding of visual and non-visual lighting effects.
This thesis, whose subject is located in the field of algorithmic commutative algebra and algebraic geometry, consists of three parts.
The first part is devoted to parallelization, a technique which allows us to take advantage of the computational power of modern multicore processors. First, we present parallel algorithms for the normalization of a reduced affine algebra A over a perfect field. Starting from the algorithm of Greuel, Laplagne, and Seelisch, we propose two approaches. For the local-to-global approach, we stratify the singular locus Sing(A) of A, compute the normalization locally at each stratum and finally reconstruct the normalization of A from the local results. For the second approach, we apply modular methods to both the global and the local-to-global normalization algorithm.
Second, we propose a parallel version of the algorithm of Gianni, Trager, and Zacharias for primary decomposition. For the parallelization of this algorithm, we use modular methods for the computationally hardest steps, such as for the computation of the associated prime ideals in the zero-dimensional case and for the standard bases computations. We then apply an innovative fast method to verify that the result is indeed a primary decomposition of the input ideal. This allows us to skip the verification step at each of the intermediate modular computations.
The proposed parallel algorithms are implemented in the open-source computer algebra system SINGULAR. The implementation is based on SINGULAR's new parallel framework which has been developed as part of this thesis and which is specifically designed for applications in mathematical research.
In the second part, we propose new algorithms for the computation of syzygies, based on an in-depth analysis of Schreyer's algorithm. Here, the main ideas are that we may leave out so-called "lower order terms" which do not contribute to the result of the algorithm, that we do not need to order the terms of certain module elements which occur at intermediate steps, and that some partial results can be cached and reused.
Finally, the third part deals with the algorithmic classification of singularities over the real numbers. First, we present a real version of the Splitting Lemma and, based on the classification theorems of Arnold, algorithms for the classification of the simple real singularities. In addition to the algorithms, we also provide insights into how real and complex singularities are related geometrically. Second, we explicitly describe the structure of the equivalence classes of the unimodal real singularities of corank 2. We prove that the equivalences are given by automorphisms of a certain shape. Based on this theorem, we explain in detail how the structure of the equivalence classes can be computed using SINGULAR and present the results in concise form. The probably most surprising outcome is that the real singularity type \(J_{10}^-\) is actually redundant.
This thesis deals with generalized inverses, multivariate polynomial interpolation and approximation of scattered data. Moreover, it covers the lifting scheme, which basically links the aforementioned topics. For instance, determining filters for the lifting scheme is connected to multivariate polynomial interpolation. More precisely, sets of interpolation sites are required that can be interpolated by a unique polynomial of a certain degree. In this thesis a new class of such sets is introduced and elements from this class are used to construct new and computationally more efficient filters for the lifting scheme.
Furthermore, a method to approximate multidimensional scattered data is introduced which is based on the lifting scheme. A major task in this method is to solve an ordinary linear least squares problem which possesses a special structure. Exploiting this structure yields better approximations and therefore this particular least squares problem is analyzed in detail. This leads to a characterization of special generalized inverses with partially prescribed image spaces.
This thesis aims to examine various determinants of perceived team diversity on the on hand, and, on the other hand, the individual consequences of perceived team diversity. To ensure a strong theoretical foundation, I integrate and discuss different conceptualizations of and theoretical approaches to team diversity, empirically examined in three independent studies. The first study investigates the relationship between objective team diversity and perceived team diversity, and as moderators individual attitudes toward diversity and perception of one’s own work team’s diversity. The second study answers the questions of why and when dirty-task frequency impairs employees’ work relations and the third study examines how different cognitive mechanisms mediate the relationships between employees’ perceptions of different types of subgroups and their elaboration of information and perspectives. Taken together, study results provide support for the selection-extraction-application model of people perception and the assumption that individuals can integrate objective team characteristics into their mental representation of teams, using them to judging the team. Moreover, results show that a fit between perceived supervisor support and perceived organizational value of diversity can buffer the effects of dirty-task frequency on perception of identity-based subgroups, as well as perceived relationship conflict and surface acting, through employees’ perceptions of identity-based subgroups. Also, perceived social-identity threat and perceived procedural fairness but not perceived distributive fairness and perceived transactive memory systems serve as cognitive mechanisms of the relationships between employees’ perceptions of different types of subgroups and their elaboration of information and perspectives. These results contribute to diversity literature, such as the theory of subgroups in work teams and the categorization-elaboration model. In addition, I propose the input-mediator-output-input model of perceived team diversity, based on the study results, and recommend practitioners to develop diversity mindsets in teams.
Characterization of neuronal activity in the auditory brainstem of rats: An optical imaging approach
(2004)
In this doctoral thesis, several aspects of neuronal activity in the rat superior olivary complex (SOC), an auditory brainstem structure, were analyzed using optical imaging with voltage-sensitive dyes (VSD). The thesis is divided into 5 Chapters. Chapter 1 is a general introduction, which gives an overview of the auditory brainstem and VSD imaging. In Chapter 2, an optical imaging method for the SOC was standardized, using the VSD RH795. To do so, the following factors were optimized: (1) An extracellular potassium concentration of 5 mM is necessary during the incubation and recording to observe synaptically evoked responses in the SOC. (2) Employing different power supplies reduced the noise. (3) Averaging of 10 subsequent trials yielded a better signal-to-noise ratio. (4) RH795 of 100 µM with 50 min prewash was optimal to image SOC slices for more than one hour. (5) Stimulus-evoked optical signals were TTX sensitive, revealing action potential-driven input. (6) Synaptically evoked optical signals were characterized to be composed of pre- and postsynaptic components. (7) Optical signals were well correlated with anatomical structures. Overall, this method allows the comparative measurement of electrical activity of cell ensembles with high spatio-temporal resolution. In Chapter 3, the nature of functional inputs to the lateral superior olive (LSO), the medial superior olive (MSO), and the superior paraolivary nucleus (SPN) were analyzed using the glycine receptor blocker strychnine and the AMPA/kainate receptor blocker CNQX. In the LSO, the known glutamatergic inputs from the ipsilateral, and the glycinergic inputs from the ipsilateral and contralateral sides, were confirmed. Furthermore, a CNQX-sensitive input from the contralateral was identified. In the MSO, the glutamatergic and glycinergic inputs from the ipsilateral and contralateral sides were corroborated. In the SPN, besides the known glycinergic input from the contralateral, I found a glycinergic input from the ipsilateral and I also identified CNQX-sensitive inputs from the contralateral and ipsilateral sides. Together, my results thus corroborate findings obtained with different preparations and methods, and provide additional information on the pharmacological nature of the inputs. In Chapter 4, the development of glycinergic inhibition for the LSO, the MSO, the SPN, and the medial nucleus of the trapezoid body (MNTB) was studied by characterizing the polarity of strychnine-sensitive responses. In the LSO, the high frequency region displayed a shift in the polarity at P4, whereas the low frequency region displayed at P6. In the MSO, both the regions displayed the shift at P5. The SPN displayed a shift in the polarity at E18-20 without any regional differences. The MNTB lacked a shift between P3-10. Together, these results demonstrate a differential timing in the development of glycinergic inhibition in these nuclei. In Chapter 5, the role of the MSO in processing bilateral time differences (t) was investigated. This was done by stimulating ipsilateral and contralateral inputs to the MSO with different t values. In preliminary experiments, the postsynaptic responses showed a differential pattern in the spread of activity upon different t values. This data demonstrates a possible presence of delay lines as proposed by Jeffress in the interaural time difference model of sound localization. In conclusion, this study demonstrates the usage of VSD imaging to analyze the neuronal activity in auditory brainstem slices. Moreover, this study expands the knowledge of the inputs to the SOC, and has identified one glycinergic and three AMPA/kainate glutamatergic novel inputs to the SOC nuclei.
In this thesis, material removal mechanisms in grinding are investigated considering a gritworkpiece interaction as well as a grinding-wheel workpiece interaction. In grit-workpiece interaction in a micrometer scale, single grit scratch experiments were performed to investigate material removal mechanism in grinding namely rubbing, plowing, and cutting. Experiments performed were analyzed based on material removal, process forces and specific energy. A finite element model is developed to simulate a single-grit scratch process. As part of the development of the finite element scratch model a 2D and 3D model is developed. A 2D model is utilized to test
material parameters and test various mesh discretizational approaches. A 3D model undertaking the tested material parameters from the 2D model is developed and is tested against experimental results for various mesh discretization. The simulation model is validated based on process forces and ground topography from experiments. The model is also further scaled to simulate multiple grit-workpiece interaction validated against experimental results. As a final step, simulation models are developed to simulate material removal, due to the interaction of grinding wheel and workpiece. A developed virtual grinding wheel topographical model is employed to display
an approach, to upscale a grinding process from grit-workpiece interaction to wheel-workpiece
interaction. In conclusion, practical conclusions drawn and scope for future studies are derived
based on the developed simulation models.
Thermodynamic Modeling of Poorly Specified Mixtures using NMR Fingerprinting and Machine Learning
(2023)
Poorly specified mixtures, i.e., mixtures of unknown or incompletely known composition,
are common in many fields of process engineering. Dealing with such mixtures in process
design is challenging as their properties cannot be described with classical thermodynamic
models, which require a full specification. As a workaround, pseudo-components
can be introduced, which are generally defined using ad-hoc assumptions. In the present
thesis, a new framework is developed for the thermodynamic modeling of such mixtures
using nuclear magnetic resonance (NMR) experiments in combination with machine-learning
(ML) methods. In the framework, a characterization of a mixture in terms of
structural groups (“NMR fingerprint”) is obtained by using the ML concept of support
vector classification. Based on the group-specific fingerprint, quantum-chemical descriptors
of the unknown part of the mixture as well as activity coefficients can already be
predicted. Furthermore, a meaningful definition of pseudo-components is achieved by
clustering the structural groups into pseudo-components with the K-medians algorithm
based on their self-diffusion coefficients measured by pulsed-field gradient (PFG) NMR.
It is demonstrated that the characterization of poorly specified mixtures in terms of
pseudo-components can be combined with several thermodynamic group-contribution
methods. The resulting thermodynamic models were applied to various poorly specified
mixtures and used for solving two typical tasks from conceptual fluid separation process
design: the solvent screening for liquid-liquid extraction processes and the simulation
of open evaporation processes. The predictions with the methods developed here show
very good agreement with the results obtained for the fully specified mixtures.
Economics of Downside Risk
(2019)
Ever since establishment of portfolio selection theory by Markowitz (1952), the use of Standard deviation as a measure of risk has heavily been criticized. The aim of this thesis is to refine classical portfolio selection and asset pricing theory by using a downside deviation risk measure. It is defined as below-target semideviation and referred to as downside risk.
Downside efficient portfolios maximize expected payoff given a prescribed upper bound for downside risk and, thus, are analogs to mean-variance efficient portfolios in the sense of Markowitz. The present thesis provides an alternative proof of existence of downside efficient portfolios and identifies a sufficient criterion for their uniqueness. A specific representation of their form brings structural similarity to mean-variance efficient portfolios to light. Eventually, a separation theorem for the existence and uniqueness of portfolios that maximize the trade-off between downside risk and return is established.
The notion of a downside risk asset market equilibrium (DRAME) in an asset market with finitely many investors is introduced. This thesis addresses the existence and uniqueness Problem of such equilibria and specifies a DRAME pricing formula. In contrast to prices obtained from the mean-variance CAPM pricing formula, DRAME prices are arbitrage-free and strictly positive.
The final part of this thesis addresses practical issues. An algorithm that allows for an effective computation of downside efficient portfolios from simulated or historical financial data is outlined. In a simulation study, it is revealed in which scenarios downside efficient portfolios
outperform mean-variance efficient portfolios.
An efficient multiscale approach is established in order to compute the macroscopic response of nonlinear composites. The micro problem is rewritten in an integral form of the Lippmann-Schwinger type and solved efficiently by Fast Fourier Transforms. Using realistic microstructure models complex nonlinear effects are reproduced and validated with measured data of fiber reinforced plastics. The micro problem is integrated in a Finite Element framework which is used to solve the macroscale. The scale coupling technique and a consistent numerical algorithm is established. The method provides an efficient way to determine the macroscopic response considering arbitrary microstructures, constitutive behaviors and loading conditions.
Esterases and lipases are widely used as industrial enzymes and for the synthesis of chiral drugs. Because of their rich secondary metabolism, Streptomyces species offer a relatively untapped source of interesting esterases and lipases. S. coelicolor and S. avermitilis contain 51 genes annotated as esterases and/or lipases. In this study I have cloned 14 different genes encoding for lipolytic enzymes from S. coelicolor (11 genes) and S. avermitilis (four genes). Some of these genes were over-expressed in E. coli. Three of the produced enzymes, which were produced by the genes SCO 7131, SCO6966 and SCO3644, were characterized biochemically and one of them was subjected for directed evolution. The gene estA (locus SCO 7131) was annotated as a putative lipase/esterase in the genome sequence of S. coelicolor A3(2), but does not have a homologue in the genome sequence of S. avermitilis or in other known Streptomyces sequences. estA was cloned and expressed in E. coli as a His-tagged protein. The protein was purified and could be recovered in its non-tagged form after digestion with factor Xa. The relative molecular weight was estimated to be 35.5kDa. The enzyme was only active towards acetate esters and not on larger substrates. It had a stereospecificity towards α-naphathylacetate. It was thermostable, with a half-life at 50C of 4.5 hours. Est A showed stability over pH range 5.5-10, and had optimum pH of 7.5. Its activity was drastically decreased when it was pre-incubated in 10mM PMSF, Cu+2 and Hg+2. It was not very stable in most organic solvents and had only slight enantioselectivity. Est A belongs to the HSL family whose founder member is the human hormone-sensitive lipase. I have developed a protein profile for the HSL family modifying the conserved motifs found by Arpigny and Jaeger (1999). Due to the presence of several HSL members with known 3D structure and good homology to Est A, I was able to make a homology model of Est A. Five different mutants of Est A were produced through site directed mutagenesis: W87F, V158A, W87F/V158A, M162L and S163A. The mutants M162L and S163A did not produce a significant change either in substrate specificity or enzyme kinetics. The mutants V158A and W87F/V158A could act on the larger substrates p-nitrophenylbutyrate and caproate and tributyrin. The mutant V158A had improved thermostability and its t1/2 at 50ºC increased to 24h. The affinity of V158A towards p-nitrophenyacetate increased 6-fold when compared with the wild type, whereas the affinity of W87F decreased 4-fold. Directed evolution of Est A was done through random mutagenesis and ER-PCR. A library of 6336 mutants was constructed and screened for mutants with a broader spectrum of substrate specificity. The mutant XXVF7 did show alteration in the substrate specificity of Est A. The mutant XXVF7 had 5 amino acids changes L76R, L146P, S196G, W213R and L267R. The gene locus SCO 6966 (estB gene) was cloned and expressed in E. coli as a His-tagged protein. It was not possible to remove the His-tag using factor Xa. The tagged protein had a molecular weight 31.9kDa. Est B was active against short chain fatty acid esters (C2-C6). Its optimum temperature was 30ºC and was stable for 1h at temperatures up to 37ºC. The enzyme had maximum activity at pH 8-8.5 and was stable over pH range 7.5-11 for 24h. It was highly sensitive for PMSF, Cu+2 and Hg+2. The enzymatic activity deceased in presence of organic solvents, however it was fairly stable for 1h in 20% organic solvents solutions. A third esterase was produced from the gene locus SCO 3644. This esterase was a thermosensitive one with optimum temperature of 35ºC. The three characterized enzymes included a thermophilic, mesophilic and psychrophilic ones. This indicates the high variation in the characters of Streptomyces lipolytic enzymes and highlighting Streptomyces as a source for esterases and lipases of interesting catalytic activity. This study was an initial trial to provide a strategy for a comprehensive use of genome data.
In this thesis we consider the directional analysis of stationary point processes. We focus on three non-parametric methods based on second order analysis which we have defined as Integral method, Ellipsoid method, and Projection method. We present the methods in a general setting and then focus on their application in the 2D and 3D case of a particular type of anisotropy mechanism called geometric anisotropy. We mainly consider regular point patterns motivated by our application to real 3D data coming from glaciology. Note that directional analysis of 3D data is not so prominent in the literature.
We compare the performance of the methods, which depends on the relative parameters, in a simulation study both in 2D and 3D. Based on the results we give recommendations on how to choose the methods´ parameters in practice.
We apply the directional analysis to the 3D data coming from glaciology, which consist in the locations of air-bubbles in polar ice cores. The aim of this study is to provide information about the deformation rate in the ice and the corresponding thinning of ice layers at different depths. This information is substantial for the glaciologists in order to build ice dating models and consequently to give a correct interpretation of the climate information which can be found by analyzing ice cores. In this thesis we consider data coming from three different ice cores: the Talos Dome core, the EDML core and the Renland core.
Motivated by the ice application, we study how isotropic and stationary noise influences the directional analysis. In fact, due to the relaxation of the ice after drilling, noise bubbles can form within the ice samples. In this context we take two classification algorithms into consideration, which aim to classify points in a superposition of a regular isotropic and stationary point process with Poisson noise.
We introduce two methods to visualize anisotropy, which are particularly useful in 3D and apply them to the ice data. Finally, we consider the problem of testing anisotropy and the limiting behavior of the geometric anisotropy transform.
Palladium-Catalyzed C–C Bond Formations via Activation of Carboxylic Acids and Their Derivatives
(2013)
Applications of carboxylic acids and their derivatives in transition metal-catalyzed cross-coupling reactions regio-selectively forming Csp3-Csp2, and Csp2-Csp2 bonds were explored in this thesis. Several important organic building blocks such as aryl acetates, diaryl acetates, imines, ketones, biaryls, styrenes and polysubstituted alkenes were successfully accessed from carboxylic acids and their derivatives by the means of C–H activation and decarboxylative cross-couplings.
An efficient and practical protocol for the synthesis of biologically important ethyl 2-arylacates through the dealkoxycarbonlative cross-coupling reaction between aryl halides and malonates was developed. Activation of the alpha-proton of alkyl esters by a copper catalyst allowed the deprotonation of esters even in the presence of mild bases, leading to a straightforward and efficient approach to alkyl alpha-diarylacetate from simple alkyl acetates and aryl halides.
The addition of a primary amine into the coupling reaction of alpha-oxocarboxylic acids and aryl halides led to an unprecedented low-temperature redox-neutral decarboxylative coupling process, providing a green and efficient method for the preparation of azomethines, in which all the three substituents can be independently varied. A minor modification of this protocol allowed us to easily access the corresponding ketones.
The decarboxylative coupling of robust aryl mesylates as well as polysubstituted alkenyl mesylates using our customized imidazolyl phosphine ligands was realized, further expanding the scope of carbon electrophiles in decarboxylative coupling reactions. Variation of the ligands led to two complementary protocols, providing the corresponding biaryls and polysubstituted olefins in high yields.
The use of a new class of pyrimidinyl phosphine ligands dramatically reduced the reaction temperatures of decarboxylative cross-coupling reactions between aromatic carboxylic acids and aryl or alkenyl triflates. The new catalyst system for the first time allowed the efficient decarboxylative biaryls synthesis at only 100 °C, representing a significant achievement in redox-neutral decarboxylative coupling reactions.
This thesis deals with the relationship between no-arbitrage and (strictly) consistent price processes for a financial market with proportional transaction costs
in a discrete time model. The exact mathematical statement behind this relationship is formulated in the so-called Fundamental Theorem of Asset Pricing (FTAP). Among the many proofs of the FTAP without transaction costs there
is also an economic intuitive utility-based approach. It relies on the economic
intuitive fact that the investor can maximize his expected utility from terminal
wealth. This approach is rather constructive since the equivalent martingale measure is then given by the marginal utility evaluated at the optimal terminal payoff.
However, in the presence of proportional transaction costs such a utility-based approach for the existence of consistent price processes is missing in the literature. So far, rather deep methods from functional analysis or from the theory of random sets have been used to show the FTAP under proportional transaction costs.
For the sake of existence of a utility-maximizing payoff we first concentrate on a generic single-period model with only one risky asset. The marignal utility evaluated at the optimal terminal payoff yields the first component of a
consistent price process. The second component is given by the bid-ask prices
depending on the investors optimal action. Even more is true: nearby this consistent price process there are many strictly consistent price processes. Their exact structure allows us to apply this utility-maximizing argument in a multi-period model. In a backwards induction we adapt the given bid-ask prices in such a way so that the strictly consistent price processes found from maximizing utility can be extended to terminal time. In addition possible arbitrage opportunities of the 2nd kind vanish which can present for the original bid-ask process. The notion of arbitrage opportunities of the 2nd kind has been so
far investigated only in models with strict costs in every state. In our model
transaction costs need not be present in every state.
For a model with finitely many risky assets a similar idea is applicable. However, in the single-period case we need to develop new methods compared
to the single-period case with only one risky asset. There are mainly two reasons
for that. Firstly, it is not at all obvious how to get a consistent price process
from the utility-maximizing payoff, since the consistent price process has to be
found for all assets simultaneously. Secondly, we need to show directly that the
so-called vector space property for null payoffs implies the robust no-arbitrage condition. Once this step is accomplished we can à priori use prices with a
smaller spread than the original ones so that the consistent price process found
from the utility-maximizing payoff is strictly consistent for the original prices.
To make the results applicable for the multi-period case we assume that the prices are given by compact and convex random sets. Then the multi-period case is similar to the case with only one risky asset but more demanding with regard to technical questions.
The present thesis deals with a novel air interface concept for beyond 3G mobile radio systems. Signals received at a certain reference cell in a cellular system which originate in neighboring cells of the same cellular system are undesired and constitute the intercell interference. Due to intercell interference, the spectrum capacity of cellular systems is limited and therefore the reduction of intercell interference is an important goal in the design of future mobile radio systems. In the present thesis, a novel service area based air interface concept is investigated in which interference is combated by joint detection and joint transmission, providing an increased spectrum capacity as compared to state-of-the-art cellular systems. Various algorithms are studied, with the aid of which intra service area interference can be combated. In the uplink transmission, by optimum joint detection the probability of erroneous decision is minimized. Alternatively, suboptimum joint detection algorithms can be applied offering reduced complexity. By linear receive zero-forcing joint detection interference in a service area is eliminated, while by linear minimum mean square error joint detection a trade-off is performed between interference elimination and noise enhancement. Moreover, iterative joint detection is investigated and it is shown that convergence of the data estimates of iterative joint detection without data estimate refinement towards the data estimates of linear joint detection can be achieved. Iterative joint detection can be further enhanced by the refinement of the data estimates in each iteration. For the downlink transmission, the reciprocity of uplink and downlink channels is used by joint transmission eliminating the need for channel estimation and therefore allowing for simple mobile terminals. A novel algorithm for optimum joint transmission is presented and it is shown how transmit signals can be designed which result in the minimum possible average bit error probability at the mobile terminals. By linear transmit zero-forcing joint transmission interference in the downlink transmission is eliminated, whereas by iterative joint transmission transmit signals are constructed in an iterative manner. In a next step, the performance of joint detection and joint transmission in service area based systems is investigated. It is shown that the price to be paid for the interference suppression in service area based systems is the suboptimum use of the receive energy in the uplink transmission and of the transmit energy in the downlink transmission, with respect to the single user reference system. In the case of receive zero-forcing joint detection in the uplink and transmit zero-forcing joint transmission in the downlink, i.e., in the case of linear unbiased data transmission, it is shown that the same price, quantified by the energy efficiency, has to be paid for interference elimination in both uplink and downlink. Finally it is shown that if the system load is fixed, the number of active mobile terminals in a SA and hence the spectrum capacity can be increased without any significant reduction in the average energy efficiency of the data transmission.
Wine and alcoholic fermentations are complex and fascinating ecosystems. Wine aroma is shaped by the wine’s chemical compositions, in which both microbes and grape constituents play crucial roles. Activities of the microbial community impact the sensory properties of the final product, therefore, the characterisation of microbial diversity is essential in understanding and predicting sensory properties of wine. Characterisation has been challenging with traditional approaches, where microbes are isolated and therefore analyzed outside from their natural environment. This causes a bias in the observed microbial composition structure. In addition, true community interactions cannot be studied using isolates. Furthermore, the multiplex ties between wine chemical and sensory compositions remain evasive due to their multivariate and nonlinear nature. Therefore, the sensorial outcome arising from different microbial communities has remained inconclusive.
In this thesis, microbial diversity during Riesling wine fermentations is investigated with the aim to understand the roles of microbial communities during fermentations and their links to sensory properties. With the advancement of high-throughput tools based ‘omic methods, such as next-generation sequencing (NGS) technologies, it is now possible to study microbial communities and their functions without isolation by culturing. This developing field and its potential to wine community is reviewed in Chapter 1. The standardisation of methods remains challenging in the field. DNA extraction is a key step in capturing the microbial diversity in samples for generating NGS data, therefore, DNA extraction methods are evaluated in Chapter 2. In Chapter 3, machine learning is utilized in guiding raw data mining generated by the untargeted GC-MS analysis. This step is crucial in order to take full advantages of the large scope of data generated by ‘omic methods. These lay a solid foundation for Chapters 4 and 5 where microbial community structures and their outputs - chemical and sensory compositions are studied by using approaches and tools based on multiple ‘omics methods.
The results of this thesis show first that by using novel statistical approaches, it is possible to extract meaningful information from heterogeneous biological, chemical and sensorial data. Secondly, results suggest that the variation in wine aroma, might be related
to microbial interactions taking place not only inside a single community, but also the
IV
interactions between communities, such as vineyard and winery communities. Therefore, the true sensory expression of terroir might be masked by the interaction between two microbial communities, although more work is needed to uncover this potential relationship. Such potential interaction mechanisms were uncovered between non- Saccharomyces yeast and bacteria in this work and unexpected novel bacterial growth was observed during alcohol fermentation. This suggests new layers in understanding of wine fermentations. In the future, multi-omic approaches could be applied to identify biological pathways leading to specific wine aroma as well as investigate the effects upon specific winemaking conditions. These results are relevant not just for the wine industry, but also to other industries where complex microbial networks are important. As such, the approaches presented in this thesis might find widely use in the food industry.
The construction of number fields with given Galois group fits into the framework of the inverse Galois problem. This problem remains still unsolved, although many partial results have been obtained over the last century.
Shafarevich proved in 1954 that every solvable group is realizable as the Galois group of a number field. Unfortunately, the proof does not provide a method to explicitly find such a field.
This work aims at producing a constructive version of the theorem by solving the following task: given a solvable group $G$ and a $B\in \mathbf N$, construct all normal number fields with Galois group $G$ and absolute discriminant bounded by $B$.
Since a field with solvable Galois group can be realized as a tower of abelian extensions, the main role in our algorithm is played by class field theory, which is the subject of the first part of this work.
The second half is devoted to the study of the relation between the group structure and the field through Galois correspondence.
In particular, we study the existence of obstructions to embedding problems and some criteria to predict the Galois group of an extension.
Automation of the Hazard and Operability Method Using Ontology-based Scenario Causation Models
(2022)
The hazard and operability (HAZOP) method is widely used in chemical and process industries to identify and evaluate hazards. Due to its human-centered nature, it is time-consuming, and the results depend on the team composition. In addition, the factors time pressure, type of implementation, experience of the participants, and participant involvement affect the results. This research aims to digitize the HAZOP method. The investigation shows that knowledge-based systems with ontologies for knowledge representation are suitable to achieve the objective. Complex interdisciplinary knowledge regarding facility, process, substance, and site information must be represented to perform the task. A result of this work is a plant part taxonomy and a developed object-oriented equipment entity library. During ontology development, typical HAZOP scenarios, as well as their structure, components, and underlying causal model, were investigated. Based on these observations, semantic relationships between the scenario components were identified. The likelihood of causes and severity of consequences were determined as part of an automatic risk assessment using a risk matrix to determine safeguards reliably. An inference algorithm based on semantic reasoners and case-based reasoning was developed to exploit the ontology and evaluate the input data object containing the plant representation. With consideration given to topology, aspects like the propagation of sub-scenarios through plant parts were considered. The results of the developed knowledge-based system were automatically generated HAZOP worksheets. Evaluation of the achieved results was based on representative case studies in which the relevance, comprehensibility, and completeness of the automatically identified scenarios were considered. The achieved results were compared with conventionally prepared HAZOP tables for benchmark purposes. By paying particular attention to the causal relationships between scenario components, the risk assessment, and with consideration of safeguards, the quality of the automatically generated results was comparable to conventional HAZOP worksheets. This research shows that formal ontologies are suitable for representing complex interdisciplinary knowledge in the field of process and plant safety. The results contribute to the use of knowledge-based systems for digitizing the HAZOP method. When used correctly, knowledge-based systems can help decrease the preparation time and repetitious nature of HAZOP studies and standardize results.
Traffic flow on road networks has been a continuous source of challenging mathematical problems. Mathematical modelling can provide an understanding of dynamics of traffic flow and hence helpful in organizing the flow through the network. In this dissertation macroscopic models for the traffic flow in road networks are presented. The primary interest is the extension of the existing macroscopic road network models based on partial differential equations (PDE model). In order to overcome the difficulty of high computational costs of PDE model an ODE model has been introduced. In addition, steady state traffic flow model named as RSA model on road networks has been dicsussed. To obtain the optimal flow through the network cost functionals and corresponding optimal control problems are defined. The solution of these optimization problems provides an information of shortest path through the network subject to road conditions. The resulting constrained optimization problem is solved approximately by solving unconstrained problem invovling exact penalty functions and the penalty parameter. A good estimate of the threshold of the penalty parameter is defined. A well defined algorithm for solving a nonlinear, nonconvex equality and bound constrained optimization problem is introduced. The numerical results on the convergence history of the algorithm support the theoretical results. In addition to this, bottleneck situations in the traffic flow have been treated using a domain decomposition method (DDM). In particular this method could be used to solve the scalar conservation laws with the discontinuous flux functions corresponding to other physical problems too. This method is effective even when the flux function presents more than one discontinuity within the same spatial domain. It is found in the numerical results that the DDM is superior to other schemes and demonstrates good shock resolution.
Inflation modeling is a very important tool for conducting an efficient monetary policy. This doctoral thesis reviewed inflation models, in particular the Phillips curve models of inflation dynamics. We focused on a well known and widely used model, the so-called three equation new Keynesian model which is a system of equations consisting of a new Keynesian Phillips curve (NKPC), an investment and saving (IS) curve and an interest rate rule.
We gave a detailed derivation of these equations. The interest rate rule used in this model is normally determined by using a Lagrangian method to solve an optimal control problem constrained by a standard discrete time NKPC which describes the inflation dynamics and an IS curve that represents the output gaps dynamics. In contrast to the real world, this method assumes that the policy makers intervene continuously. This means that the costs resulting from the change in the interest rates are ignored. We showed also that there are approximation errors made, when one log-linearizes non linear equations, by doing the derivation of the standard discrete time NKPC.
We agreed with other researchers as mentioned in this thesis, that errors which result from ignoring such log-linear approximation errors and the costs of altering interest rates by determining interest rate rule, can lead to a suboptimal interest rate rule and hence to non-optimal paths of output gaps and inflation rate.
To overcome such a problem, we proposed a stochastic optimal impulse control method. We formulated the problem as a stochastic optimal impulse control problem by considering the costs of change in interest rates and the approximation error terms. In order to formulate this problem, we first transform the standard discrete time NKPC and the IS curve into their high-frequency versions and hence into their continuous time versions where error terms are described by a zero mean Gaussian white noise with a finite and constant variance. After formulating this problem, we use the quasi-variational inequality approach to solve analytically a special case of the central bank problem, where an inflation rate is supposed to be on target and a central bank has to optimally control output gap dynamics. This method gives an optimal control band in which output gap process has to be maintained and an optimal control strategy, which includes the optimal size of intervention and optimal intervention time, that can be used to keep the process into the optimal control band.
Finally, using a numerical example, we examined the impact of some model parameters on optimal control strategy. The results show that an increase in the output gap volatility as well as in the fixed and proportional costs of the change in interest rate lead to an increase in the width of the optimal control band. In this case, the optimal intervention requires the central bank to wait longer before undertaking another control action.
Nanotechnology is now recognized as one of the most promising areas for technological
development in the 21st century. In materials research, the development of
polymer nanocomposites is rapidly emerging as a multidisciplinary research activity
whose results could widen the applications of polymers to the benefit of many different
industries. Nanocomposites are a new class of composites that are particle-filled
polymers for which at least one dimension of the dispersed particle is in the nanometer
range. In the related area polymer/clay nanocomposites have attracted considerable
interest because they often exhibit remarkable property improvements when
compared to virgin polymer or conventional micro- and macro- composites.
The present work addresses the toughening and reinforcement of thermoplastics via
a novel method which allows us to achieve micro- and nanocomposites. In this work
two matrices are used: amorphous polystyrene (PS) and semi-crystalline polyoxymethylene
(POM). Polyurethane (PU) was selected as the toughening agent for POM
and used in its latex form. It is noteworthy that the mean size of rubber latices is
closely matched with that of conventional toughening agents, impact modifiers.
Boehmite alumina and sodium fluorohectorite (FH) were used as reinforcements.
One of the criteria for selecting these fillers was that they are water swellable/
dispersible and thus their nanoscale dispersion can be achieved also in aqueous
polymer latex. A systematic study was performed on how to adapt discontinuousand
continuous manufacturing techniques for the related nanocomposites.
The dispersion of nanofillers was characterized by transmission, scanning electron
and atomic force microcopy (TEM, SEM and AFM respectively), X-ray diffraction
(XRD) techniques, and discussed. The crystallization of POM was studied by means
of differential scanning calorimetry and polarized light optical microscopy (DSC and
PLM, respectively). The mechanical and thermomechanical properties of the composites
were determined in uniaxial tensile, dynamic-mechanical thermal analysis
(DMTA), short-time creep tests, and thermogravimetric analysis (TGA).
PS composites were produced first by a discontinuous manufacturing technique,
whereby FH or alumina was incorporated in the PS matrix by melt blending with and
without latex precompounding of PS latex with the nanofiller. It was found that direct melt mixing (DM) of the nanofillers with PS resulted in micro-, whereas the latex mediated
pre-compounding (masterbatch technique, MB) in nanocomposites. FH was
not intercalated by PS when prepared by DM. On the other hand, FH was well dispersed
(mostly intercalated) in PS via the PS latex-mediated predispersion of FH following
the MB route. The nanocomposites produced by MB outperformed the DM
compounded microcomposites in respect to properties like stiffness, strength and
ductility based on dynamic-mechanical and static tensile tests. It was found that the
resistance to creep (summarized in master curves) of the nanocomposites were improved
compared to those of the microcomposites. Master curves (creep compliance
vs. time), constructed based on isothermal creep tests performed at different temperatures,
showed that the nanofiller reinforcement affects mostly the initial creep
compliance.
Next, ternary composites composed of POM, PU and boehmite alumina were produced
by melt blending with and without latex precompounding. Latex precompounding
served for the predispersion of the alumina particles. The related MB was produced
by mixing the PU latex with water dispersible boehmite alumina. The composites
produced by the MB technique outperformed the DM compounded composites in
respect to most of the thermal and mechanical characteristics.
Toughened and/or reinforced PS- and POM-based composites have been successfully
produced by a continuous extrusion technique, too. This technique resulted in
good dispersion of both nanofillers (boehmite) and impact modifier (PU). Compared
to the microcomposites obtained by conventional DM, the nanofiller dispersion became
finer and uniform when using the water-mediated predispersion. The resulting
structure markedly affected the mechanical properties (stiffness and creep resistance)
of the corresponding composites. The impact resistance of POM was highly
enhanced by the addition of PU rubber when manufactured by the continuous extrusion
manufacturing technique. This was traced to the dispersed PU particle size being
in the range required from conventional, impact modifiers.
On the complexity and approximability of optimization problems with Minimum Quantity Constraints
(2020)
During the last couple of years, there has been a variety of publications on the topic of
minimum quantity constraints. In general, a minimum quantity constraint is a lower bound
constraint on an entity of an optimization problem that only has to be fulfilled if the entity is
“used” in the respective solution. For example, if a minimum quantity \(q_e\) is defined on an
edge \(e\) of a flow network, the edge flow on \(e\) may either be \(0\) or at least \(q_e\) units of flow.
Minimum quantity constraints have already been applied to problem classes such as flow, bin
packing, assignment, scheduling and matching problems. A result that is common to all these
problem classes is that in the majority of cases problems with minimum quantity constraints
are NP-hard, even if the problem without minimum quantity constraints but with fixed lower
bounds can be solved in polynomial time. For instance, the maximum flow problem is known
to be solvable in polynomial time, but becomes NP-hard once minimum quantity constraints
are added.
In this thesis we consider flow, bin packing, scheduling and matching problems with minimum
quantity constraints. For each of these problem classes we provide a summary of the
definitions and results that exist to date. In addition, we define new problems by applying
minimum quantity constraints to the maximum-weight b-matching problem and to open
shop scheduling problems. We contribute results to each of the four problem classes: We
show NP-hardness for a variety of problems with minimum quantity constraints that have
not been considered so far. If possible, we restrict NP-hard problems to special cases that
can be solved in polynomial time. In addition, we consider approximability of the problems:
For most problems it turns out that, unless P=NP, there cannot be any polynomial-time
approximation algorithm. Hence, we consider bicriteria approximation algorithms that allow
the constraints of the problem to be violated up to a certain degree. This approach proves to
be very helpful and we provide a polynomial-time bicriteria approximation algorithm for at
least one problem of each of the four problem classes we consider. For problems defined on
graphs, the class of series parallel graphs supports this approach very well.
We end the thesis with a summary of the results and several suggestions for future research
on minimum quantity constraints.
We construct and study two surface measures on the space C([0,1],M) of paths in a compact Riemannian manifold M embedded into the Euclidean space R^n. The first one is induced by conditioning the usual Wiener measure on C([0,T],R^n) to the event that the Brownian particle does not leave the tubular epsilon-neighborhood of M up to time T, and passing to the limit. The second one is defined as the limit of the laws of reflected Brownian motions with reflection on the boundaries of the tubular epsilon-neighborhoods of M. We prove that the both surface measures exist and compare them with the Wiener measure W_M on C([0,T],M). We show that the first one is equivalent to W_M and compute the corresponding density explicitly in terms of the scalar curvature and the mean curvature vector of M. Further, we show that the second surface measure coincides with W_M. Finally, we study the limit behavior of the both surface measures as T tends to infinity.
Requirements-Aware, Template-Based Protocol Graphs for Service-Oriented Network Architectures
(2016)
Rigidness of the Internet causes its architectural design issues such as interdependencies among the layers, no cross-layer information exchange, and applications dependency on the underlying protocols implementation.
G-Lab (i.e., http://www.german-lab.de/) is a research project for Future Internet Architecture (FIA), which focuses on problems of the Internet such as rigidness, mobility, and addressing. Where the focus of ICSY (i.e., www.icsy) was on providing the flexibility in future network architectures. An approach so-called Service Oriented Network Architecture (SONATE) is proposed to compose the protocols dynamically. SONATE is based on principles of the service-oriented architecture (SOA), where protocols are decomposed in software modules and later they are put together on demand to provide the desired service.
This composition of functionalities can be performed at various time-epochs (e.g., run-time, design-time, deployment-time). However, these epochs have trade-off in terms of the time-complexity (i.e., required setup time) and the provided flexibility. The design-time is the least time critical in comparison to other time phases, which makes it possible to utilize human-analytical capability. However, the design-time lacks the real-time knowledge of requirements and network conditions, what results in inflexible protocol graphs, and they cannot be changed at later stages on changing requirements. Contrary to the design-time, the run-time is most time critical where an application is waiting for a connection to be established, but at the same time it has maximum information to generate a protocol graph suitable to the given requirements.
Considering limitations above of different time-phases, in this thesis, a novel intermediate functional composition approach (i.e., Template-Based Composition) has been presented to generate requirements aware protocol graphs. The template-based composition splits the composition process across different time-phases to exploit the less time critical nature and human-analytical availability of the design-time, ability to instantaneously deploy new functionalities of the deployment time and maximum information availability of the run-time. The approach is successfully implemented , demonstrated and evaluated based on its performance to know the implications for the practical use.
We present a numerical scheme to simulate a moving rigid body with arbitrary shape suspended in a rarefied gas micro flows, in view of applications to complex computations of moving structures in micro or vacuum systems. The rarefied gas is simulated by solving the Boltzmann equation using a DSMC particle method. The motion of the rigid body is governed by the Newton-Euler equations, where the force and the torque on the rigid body is computed from the momentum transfer of the gas molecules colliding with the body. The resulting motion of the rigid body affects in turn again the gas flow in the surroundings. This means that a two-way coupling has been modeled. We validate the scheme by performing various numerical experiments in 1-, 2- and 3-dimensional computational domains. We have presented 1-dimensional actuator problem, 2-dimensional cavity driven flow problem, Brownian diffusion of a spherical particle both with translational and rotational motions, and finally thermophoresis on a spherical particles. We compare the numerical results obtained from the numerical simulations with the existing theories in each test examples.
The work consists of two parts.
In the first part an optimization problem of structures of linear elastic material with contact modeled by Robin-type boundary conditions is considered. The structures model textile-like materials and possess certain quasiperiodicity properties. The homogenization method is used to represent the structures by homogeneous elastic bodies and is essential for formulations of the effective stress and Poisson's ratio optimization problems. At the micro-level, the classical one-dimensional Euler-Bernoulli beam model extended with jump conditions at contact interfaces is used. The stress optimization problem is of a PDE-constrained optimization type, and the adjoint approach is exploited. Several numerical results are provided.
In the second part a non-linear model for simulation of textiles is proposed. The yarns are modeled by hyperelastic law and have no bending stiffness. The friction is modeled by the Capstan equation. The model is formulated as a problem with the rate-independent dissipation, and the basic continuity and convexity properties are investigated. The part ends with numerical experiments and a comparison of the results to a real measurement.
Mechanistic disease spread models for different vector borne diseases have been studied from the 19th century. The relevance of mathematical modeling and numerical simulation of disease spread is increasing nowadays. This thesis focuses on the compartmental models of the vector-borne diseases that are also transmitted directly among humans. An example of such an arboviral disease that falls under this category is the Zika Virus disease. The study begins with a compartmental SIRUV model and its mathematical analysis. The non-trivial relationship between the basic reproduction number obtained through two methods have been discussed. The analytical results that are mathematically proven for this model are numerically verified. Another SIRUV model is presented by considering a different formulation of the model parameters and the newly obtained model is shown to be clearly incorporating the dependence on the ratio of mosquito population size to human population size in the disease spread. In order to incorporate the spatial as well as temporal dynamics of the disease spread, a meta-population model based on the SIRUV model was developed. The space domain under consideration are divided into patches which may denote mutually exclusive spatial entities like administrative areas, districts, provinces, cities, states or even countries. The research focused only on the short term movements or commuting behavior of humans across the patches. This is incorportated in the multi-patch meta-population model using a matrix of residence time fractions of humans in each patches. Mathematically simplified analytical results are deduced by which it is shown that, for an exemplary scenario that is numerically studied, the multi-patch model also admits the threshold properties that the single patch SIRUV model holds. The relevance of commuting behavior of humans in the disease spread has been presented using the numerical results from this model. The local and non-local commuting are incorporated into the meta-population model in a numerical example. Later, a PDE model is developed from the multi-patch model.
A Multi-Phase Flow Model Incorporated with Population Balance Equation in a Meshfree Framework
(2011)
This study deals with the numerical solution of a meshfree coupled model of Computational Fluid Dynamics (CFD) and Population Balance Equation (PBE) for liquid-liquid extraction columns. In modeling the coupled hydrodynamics and mass transfer in liquid extraction columns one encounters multidimensional population balance equation that could not be fully resolved numerically within a reasonable time necessary for steady state or dynamic simulations. For this reason, there is an obvious need for a new liquid extraction model that captures all the essential physical phenomena and still tractable from computational point of view. This thesis discusses a new model which focuses on discretization of the external (spatial) and internal coordinates such that the computational time is drastically reduced. For the internal coordinates, the concept of the multi-primary particle method; as a special case of the Sectional Quadrature Method of Moments (SQMOM) is used to represent the droplet internal properties. This model is capable of conserving the most important integral properties of the distribution; namely: the total number, solute and volume concentrations and reduces the computational time when compared to the classical finite difference methods, which require many grid points to conserve the desired physical quantities. On the other hand, due to the discrete nature of the dispersed phase, a meshfree Lagrangian particle method is used to discretize the spatial domain (extraction column height) using the Finite Pointset Method (FPM). This method avoids the extremely difficult convective term discretization using the classical finite volume methods, which require a lot of grid points to capture the moving fronts propagating along column height.
This thesis introduces a novel deformation method for computational meshes. It is based on the numerical path following for the equations of nonlinear elasticity. By employing a logarithmic variation of the neo-Hookean hyperelastic material law, the method guarantees that the mesh elements do not become inverted and remain well-shaped. In order to demonstrate the performance of the method, this thesis addresses two areas of active research in isogeometric analysis: volumetric domain parametrization and fluid-structure interaction. The former concerns itself with the construction of a parametrization for a given computational domain provided only a parametrization of the domain’s boundary. The proposed mesh deformation method gives rise to a novel solution approach to this problem. Within it, the domain parametrization is constructed as a deformed configuration of a simplified domain. In order to obtain the simplified domain, the boundary of the target domain is projected in the \(L^2\)-sense onto a coarse NURBS basis. Then, the Coons patch is applied to parametrize the simplified domain. As a range of 2D and 3D examples demonstrates, the mesh deformation approach is able to produce high-quality parametrizations for complex domains where many state-of-the-art methods either fail or become unstable and inefficient. In the context of fluid-structure interaction, the proposed mesh deformation method is applied to robustly update the computational mesh in situations when the fluid domain undergoes large deformations. In comparison to the state-of-the-art mesh update methods, it is able to handle larger deformations and does not result in an eventual reduction of mesh quality. The performance of the method is demonstrated on a classic 2D fluid-structure interaction benchmark reproduced by using an isogeometric partitioned solver with strong coupling.
This thesis is divided into two parts. Both cope with multi-class image segmentation and utilize
non-smooth optimization algorithms.
The topic of the first part, namely unsupervised segmentation, is the application of clustering
to image pixels. Therefore, we start with an introduction of the biconvex center-based clustering
algorithms c-means and fuzzy c-means, where c denotes the number of classes. We show that
fuzzy c-means can be seen as an approximation of c-means in terms of power means.
Since noise is omnipresent in our image data, these simple clustering models are not suitable
for its segmentation. To this end, we introduce a general and finite dimensional segmentation
model that consists of a data term stemming from the aforementioned clustering models plus a
continuous regularization term. We tackle this optimization model via an alternating minimiza-
tion approach called regularized c-centers (RcC). Thereby, we fix the centers and optimize the
segment membership of the pixels and vice versa. In this general setting, we prove convergence
in the sense of set-valued algorithms using Zangwill’s Theory [172].
Further, we present a segmentation model with a total variation regularizer. While updating
the cluster centers is straightforward for fixed segment memberships of the pixels, updating the
segment membership can be solved iteratively via non-smooth, convex optimization. Thereby,
we do not iterate a convex optimization algorithm until convergence. Instead, we stop as soon as
we have a certain amount of decrease in the objective functional to increase the efficiency. This
algorithm is a particular implementation of RcC providing also the corresponding convergence
theory. Moreover, we show the good performance of our method in various examples such as
simulated 2d images of brain tissue and 3d volumes of two materials, namely a multi-filament
composite superconductor and a carbon fiber reinforced silicon carbide ceramics. Thereby, we
exploit the property of the latter material that two components have no common boundary in
our adapted model.
The second part of the thesis is concerned with supervised segmentation. We leave the area
of center based models and investigate convex approaches related to graph p-Laplacians and
reproducing kernel Hilbert spaces (RKHSs). We study the effect of different weights used to
construct the graph. In practical experiments we show on the one hand image types that
are better segmented by the p-Laplacian model and on the other hand images that are better
segmented by the RKHS-based approach. This is due to the fact that the p-Laplacian approach
provides smoother results, while the RKHS approach provides often more accurate and detailed
segmentations. Finally, we propose a novel combination of both approaches to benefit from the
advantages of both models and study the performance on challenging medical image data.
Layout analysis--the division of page images into text blocks, lines, and determination of their reading order--is a major performance limiting step in large scale document digitization projects. This thesis addresses this problem in several ways: it presents new performance measures to identify important classes of layout errors, evaluates the performance of state-of-the-art layout analysis algorithms, presents a number of methods to reduce the error rate and catastrophic failures occurring during layout analysis, and develops a statistically motivated, trainable layout analysis system that addresses the needs of large-scale document analysis applications. An overview of the key contributions of this thesis is as follows. First, this thesis presents an efficient local adaptive thresholding algorithm that yields the same quality of binarization as that of state-of-the-art local binarization methods, but runs in time close to that of global thresholding methods, independent of the local window size. Tests on the UW-1 dataset demonstrate a 20-fold speedup compared to traditional local thresholding techniques. Then, this thesis presents a new perspective for document image cleanup. Instead of trying to explicitly detect and remove marginal noise, the approach focuses on locating the page frame, i.e. the actual page contents area. A geometric matching algorithm is presented to extract the page frame of a structured document. It is demonstrated that incorporating page frame detection step into document processing chain results in a reduction in OCR error rates from 4.3% to 1.7% (n=4,831,618 characters) on the UW-III dataset and layout-based retrieval error rates from 7.5% to 5.3% (n=815 documents) on the MARG dataset. The performance of six widely used page segmentation algorithms (x-y cut, smearing, whitespace analysis, constrained text-line finding, docstrum, and Voronoi) on the UW-III database is evaluated in this work using a state-of-the-art evaluation methodology. It is shown that current evaluation scores are insufficient for diagnosing specific errors in page segmentation and fail to identify some classes of serious segmentation errors altogether. Thus, a vectorial score is introduced that is sensitive to, and identifies, the most important classes of segmentation errors (over-, under-, and mis-segmentation) and what page components (lines, blocks, etc.) are affected. Unlike previous schemes, this evaluation method has a canonical representation of ground truth data and guarantees pixel-accurate evaluation results for arbitrary region shapes. Based on a detailed analysis of the errors made by different page segmentation algorithms, this thesis presents a novel combination of the line-based approach by Breuel with the area-based approach of Baird which solves the over-segmentation problem in area-based approaches. This new approach achieves a mean text-line extraction error rate of 4.4% (n=878 documents) on the UW-III dataset, which is the lowest among the analyzed algorithms. This thesis also describes a simple, fast, and accurate system for document image zone classification that results from a detailed comparative analysis of performance of widely used features in document analysis and content-based image retrieval. Using a novel combination of known algorithms, an error rate of 1.46% (n=13,811 zones) is achieved on the UW-III dataset in comparison to a state-of-the-art system that reports an error rate of 1.55% (n=24,177 zones) using more complicated techniques. In addition to layout analysis of Roman script documents, this work also presents the first high-performance layout analysis method for Urdu script. For that purpose a geometric text-line model for Urdu script is presented. It is shown that the method can accurately extract Urdu text-lines from documents of different layouts like prose books, poetry books, magazines, and newspapers. Finally, this thesis presents a novel algorithm for probabilistic layout analysis that specifically addresses the needs of large-scale digitization projects. The presented approach models known page layouts as a structural mixture model. A probabilistic matching algorithm is presented that gives multiple interpretations of input layout with associated probabilities. An algorithm based on A* search is presented for finding the most likely layout of a page, given its structural layout model. For training layout models, an EM-like algorithm is presented that is capable of learning the geometric variability of layout structures from data, without the need for a page segmentation ground-truth. Evaluation of the algorithm on documents from the MARG dataset shows an accuracy of above 95% for geometric layout analysis.
Characterization of a bacterial-like signal transduction phosphorelay in Methanosarcina acetivorans
(2021)
Signal transduction systems are of great importance for the adaptation of organisms to new conditions. These systems occur most frequently in bacteria and are well-understood thanks to research. It was not until 10 years after the discovery of two-component systems in bacteria that such a system was reported in archaea. This work provides new insights into signal transduction in archaea, through the characterization of a histidine kinase MA4377 and its multi-component system in the methanogenic archaeon Methanosarcina acetivorans. MA4377 is a hybrid kinase of bacterial origin and was probably integrated into M. acetivorans via horizontal gene transfer. Based on the fused receiver domains, MA4377 is classified as a hybrid kinase that regulates a cell response upon the perception of a signal through a multi-component system. These systems consist of four components, the first of which is the kinase. MA4377 has autophosphorylation activity and is phosphorylated at a conserved histidine residue (His497). While the kinase activity is independent of the redox state of the protein, the PAS domain is mandatory for autokinase activity. Using different protein variants, it could be shown that the two fused receiver domains are not involved in the phosphorelay. Rather, the single receiver domain MA4376 serves as the second component of the system. The signal is ultimately transmitted to a transcription factor (MA4375) of the Msr family. Factors of this family are involved in the regulation of methanogenesis, among other things. MA4377 could thus be involved in a multi-component system regulating methanogenesis. The receiver MA4376 plays a central role here since feedback regulation on the kinase was observed. Further investigations will show to what extent cross-regulation with other kinases takes place and in what way the receiver MA4376 plays a key role in this multi-component system.
Wireless Sensor Networks (WSN) are dynamically-arranged networks typically composed of a large number of arbitrarily-distributed sensor nodes with computing capabilities contributing to –at least– one common application. The main characteristic of these networks is that of being functionally constrained due to a scarce availability of resources and strong dependence on uncontrollable environmental factors. These conditions introduce severe restrictions on the applicability of classic real-time methods aiming at guaranteeing time-bounded communications. Existing real-time solutions tend to apply concepts that were originally not conceived for sensor networks, idealizing realistic application scenarios and overlooking at important design limitations. This results in a number of misleading practices contributing to approaches of restricted validity in real-world scenarios. Amending the confrontation between WSNs and real-time objectives starts with a review of the basic fundamentals of existing approaches. In doing so, this thesis presents an alternative approach based on a generalized timeliness notion suitable to the particularities of WSNs. The new conceptual notion allows the definition of feasible real-time objectives opening a new scope of possibilities not constrained to idealized systems. The core of this thesis is based on the definition and application of Quality of Service (QoS) trade-offs between timeliness and other significant QoS metrics. The analysis of local and global trade-offs provides a step-by-step methodology identifying the correlations between these quality metrics. This association enables the definition of alternative trade-off configurations (set points) influencing the quality performance of the network at selected instants of time. With the basic grounds established, the above concepts are embedded in a simple routing protocol constituting a proof of concept for the validity of the presented analysis. Extensive evaluations under realistic scenarios are driven on simulation environments as well as real testbeds, validating the consistency of this approach.
Most modern multiprocessors offer weak memory behavior to improve their performance in terms of throughput. They allow the order of memory operations to be observed differently by each processor. This is opposite to the concept of sequential consistency (SC) which enforces a unique sequential view on all operations for all processors. Because most software has been and still is developed with SC in mind, we face a gap between the expected behavior and the actual behavior on modern architectures. The issues described only affect multithreaded software and therefore most programmers might never face them. However, multi-threaded bare metal software like operating systems, embedded software, and real-time software have to consider memory consistency and ensure that the order of memory operations does not yield unexpected results. This software is more critical as general consumer software in terms of consequences, and therefore new methods are needed to ensure their correct behavior.
In general, a memory system is considered weak if it allows behavior that is not possible in a sequential system. For example, in the SPARC processor with total store ordering (TSO) consistency, all writes might be delayed by store buffers before they eventually are processed by the main memory. This allows the issuing process to work with its own written values before other processes observed them (i.e., reading its own value before it leaves the store buffer). Because this behavior is not possible with sequential consistency, TSO is considered to be weaker than SC. Programming in the context of weak memory architectures requires a proper comprehension of how the model deviates from expected sequential behavior. For verification of these programs formal representations are required that cover the weak behavior in order to utilize formal verification tools.
This thesis explores different verification approaches and respectively fitting representations of a multitude of memory models. In a joint effort, we started with the concept of testing memory operation traces in regard of their consistency with different memory consistency models. A memory operation trace is directly derived from a program trace and consists of a sequence of read and write operations for each process. Analyzing the testing problem, we are able to prove that the problem is NP-complete for most memory models. In that process, a satisfiability (SAT) encoding for given problem instances was developed, that can be used in reachability and robustness analysis.
In order to cover all program executions instead of just a single program trace, additional representations are introduced and explored throughout this thesis. One of the representations introduced is a novel approach to specify a weak memory system using temporal logics. A set of linear temporal logic (LTL) formulas is developed that describes all properties required to restrict possible traces to those consistent to the given memory model. The resulting LTL specifications can directly be used in model checking, e.g., to check safety conditions. Unfortunately, the derived LTL specifications suffer from the state explosion problem: Even small examples, like the Peterson mutual exclusion algorithm, tend to generate huge formulas and require vast amounts of memory for verification. For this reason, it is concluded that using the proposed verification approach these specifications are not well suited for verification of real world software. Nonetheless, they provide comprehensive and formally correct descriptions that might be used elsewhere, e.g., programming or teaching.
Another approach to represent these models are operational semantics. In this thesis, operational semantics of weak memory models are provided in the form of reference machines that are both correct and complete regarding the memory model specification. Operational semantics allow to simulate systems with weak memory models step by step. This provides an elegant way to study the effects that lead to weak consistent behavior, while still providing a basis for formal verification. The operational models are then incorporated in verification tools for multithreaded software. These state space exploration tools proved suitable for verification of multithreaded software in a weak consistent memory environment. However, because not only the memory system but also the processor are expressed as operational semantics, some verification approach will not be feasible due to the large size of the state space.
Finally, to tackle the beforementioned issue, a state transition system for parallel programs is proposed. The transition system is defined by a set of structural operational semantics (SOS) rules and a suitable memory structure that can cover multiple memory models. This allows to influence the state space by use of smart representations and approximation approaches in future work.
Faces deliver invaluable information about people. Machine-based perception can be of a great benefit in extracting that underlying information in face images if the problem is properly modeled. Classical image processing algorithms may fail to handle the diverse data available today due to several challenges related to varying capturing locations, and conditions. Advanced machine learning methods and algorithms are now highly beneficial due to the rapid development of powerful hardware, enabling feasible advanced solutions based on data learning and summarization into powerful models. In this thesis, novel solutions are provided to the problems of head orientation estimation and gender prediction. Initially, classical machine learning algorithms were used to address head orientation estimation but were limited by their inability to handle large datasets and poor generalization. To overcome these challenges, a new highly accurate head pose dataset was acquired to tackle the identified problems. Novel trained deep neural networks have been exploited, that use the acquired data and provide novel architectures. The information about head pose is then represented in the network weights, thus, allowing predicting the head orientation angles given a new unseen face. The acquired dataset, named AutoPOSE opens the door for further studies in the field of computer vision and especially, face analysis. The problem of gender prediction has also been explored, but unlike humans who can easily identify gender from a face, computers face difficulties due to facial similarities. Therefore, hand-crafted features are not effective for generalization. To address this, a new deep learning method was developed and evaluated on multiple public datasets, with identified challenges in both still images and videos addressed. Finally, the effect of facial appearance changes due to head orientation variation has been investigated on gender prediction accuracy. A novel orientation-guided feature maps recalibration method is presented, that significantly increased the accuracy of gender prediction.
In conclusion, two problems have been addressed in this thesis, independently and joined together. Existing methods have been enhanced with intelligent pre-processing methods and new approaches have been introduced to tackle existing challenges, that arise from pose, illumination, and occlusion variations. The proposed methods have been extensively evaluated, showing that head orientation and gender prediction can be estimated with high accuracy using machine learning-based methods. Also, the evaluations showed that the use of head orientation information consistently improved the gender prediction accuracy. Scientific contributions have been presented, and the new acquired highly accurate dataset motivates the research community to push the state-of-the-art forward.
Microsystem technology has been a fast evolving field over the last few years. Its ability to handle volumes in the sub-microliter range makes it very interesting for potential application in fields such as biology, medicine and pharmaceutical research. However, the use of micro-fabricated devices for the analysis of liquid biological samples still has to prove its applicability for many particular demands of basic research. This is particularly true for samples consisting of complex protein mixtures. The presented study therefore aimed at evaluating if a commonly used glass-coating technique from the field of micro-fluidic technology can be used to fabricate an analysis system for molecular biology. It was ultimately motivated by the demand to develop a technique that allows the analysis of biological samples at the single-cell level. Gene expression at the transcription level is initiated and regulated by DNA-binding proteins. To fully understand these regulatory processes, it is necessary to monitor the interaction of specific transcription factors with other elements - proteins as well as DNA sites - in living cells. One well-established method to perform such analysis is the Chromatin Immunoprecipitation (CHIP) assay. To map protein-DNA interactions, living cells are treated with formaldehyde in vivo to cross-link DNA-binding proteins to their resident sites. The chromatin is then broken into small fragments, and specific antibodies against the protein of interest are used to immunopurify the chromatin fragments to which those factors are bound. After purification, the associated DNA can be detected and analyzed using Polymerase Chain Reaction (PCR). Current CHIP technology is limited as it needs a relatively large number of cells while there is increasing interest in monitoring DNA-protein interactions in very few, if not single cells. Most notably this is the case in research on early organism development (embryogenesis). To investigate if microsystem technology can be used to analyze DNA-protein complexes from samples containing chromatin from only few cells, a new setup for fluid transport in glass capillaries of 75 µm inner diameter has been developed, forming an array of micro-columns for parallel affinity chromatography. The inner capillary walls were antibody-coated using a silane-based protocol. The remaining surface was made chemically inert by saturating free binding sites with suitable biomolecules. Variations of this protocol have been tested. Furthermore, the sensitivity of the PCR method to detect immunoprecipitated protein-DNA complexes was improved, resulting in the reliable detection of about 100 DNA fragments from chromatin. The aim of the study was to successively decrease the amount of analyzed chromatin in order to investigate the lower limits of this technology in regard to sensitivity and specificity of detection. The Drosophila GAGA transcription factor was used as an established model system. The protein has already been analyzed in several large-scale CHIP experiments and antibodies of excellent specificity are available. The results of the study revealed that this approach is not easily applicable to "real-world" biological samples in regard to volume reduction and specificity. Particularly, material that non-specifically adsorbed to capillary surfaces outweighed the specific antibody-antigen interaction, the system was designed for. It became clear that complex biological structures, such as chromatin-protein compositions, are not as easily accessible by techniques based on chemically modified glass surfaces as pre-purified samples. In the case of the investigated system, it became evident that there is a need for more research that goes beyond the scope of this work. It is necessary to develop novel coatings and materials to prevent non-specific adsorption. In addition to improving existing techniques, fundamentally new concepts, such as microstructures in biocompatible polymers or liquid transport on hydrophobic stripes on planar substrates to minimize surface contact, may also help to advance the miniaturization of biological experiments.
This thesis deals with 3 important aspects of optimal investment in real-world financial markets: taxes, crashes, and illiquidity. An introductory chapter reviews the portfolio problem in its historical context and motivates the theme of this work: We extend the standard modelling framework to include specific real-world features and evaluate their significance. In the first chapter, we analyze the optimal portfolio problem with capital gains taxes, assuming that taxes are deferred until the end of the investment horizon. The problem is solved with the help of a modification of the classical martingale method. The second chapter is concerned with optimal asset allocation under the threat of a financial market crash. The investor takes a worst-case attitude towards the crash, so her investment objective is to be best off in the most adverse crash scenario. We first survey the existing literature on the worst-case approach to optimal investment and then present in detail the novel martingale approach to worst-case portfolio optimization. The first part of this chapter is based on joint work with Ralf Korn. In the last chapter, we investigate optimal portfolio decisions in the presence of illiquidity. Illiquidity is understood as a period in which it is impossible to trade on financial markets. We use dynamic programming techniques in combination with abstract convergence results to solve the corresponding optimal investment problem. This chapter is based on joint work with Holger Kraft and Peter Diesinger.
In this thesis, mathematical research questions related to recursive utility and stochastic differential utility (SDU) are explored.
First, a class of backward equations under nonlinear expectations is investigated: Existence and uniqueness of solutions are established, and the issues of stability and discrete-time approximation are addressed. It is then shown that backward equations of this class naturally appear as a continuous-time limit in the context of recursive utility with nonlinear expectations.
Then, the Epstein-Zin parametrization of SDU is studied. The focus is on specifications with both relative risk aversion and elasitcity of intertemporal substitution greater that one. A concave utility functional is constructed and a utility gradient inequality is established.
Finally, consumption-portfolio problems with recursive preferences and unspanned risk are investigated. The investor's optimal strategies are characterized by a specific semilinear partial differential equation. The solution of this equation is constructed by a fixed point argument, and a corresponding efficient and accurate method to calculate optimal strategies numerically is given.
In recent years the field of polymer tribology experienced a tremendous development
leading to an increased demand for highly sophisticated in-situ measurement methods.
Therefore, advanced measurement techniques were developed and established
in this study. Innovative approaches based on dynamic thermocouple, resistive electrical
conductivity, and confocal distance measurement methods were developed in
order to in-situ characterize both the temperature at sliding interfaces and real contact
area, and furthermore the thickness of transfer films. Although dynamic thermocouple
and real contact area measurement techniques were already used in similar
applications for metallic sliding pairs, comprehensive modifications were necessary to
meet the specific demands and characteristics of polymers and composites since
they have significantly different thermal conductivities and contact kinematics. By using
tribologically optimized PEEK compounds as reference a new measurement and
calculation model for the dynamic thermocouple method was set up. This method
allows the determination of hot spot temperatures for PEEK compounds, and it was
found that they can reach up to 1000 °C in case of short carbon fibers present in the
polymer. With regard to the non-isotropic characteristics of the polymer compound,
the contact situation between short carbon fibers and steel counterbody could be
successfully monitored by applying a resistive measurement method for the real contact
area determination. Temperature compensation approaches were investigated
for the transfer film layer thickness determination, resulting in in-situ measurements
with a resolution of ~0.1 μm. In addition to a successful implementation of the measurement
systems, failure mechanism processes were clarified for the PEEK compound
used. For the first time in polymer tribology the behavior of the most interesting
system parameters could be monitored simultaneously under increasing load
conditions. It showed an increasing friction coefficient, wear rate, transfer film layer
thickness, and specimen overall temperature when frictional energy exceeded the
thermal transport capabilities of the specimen. In contrast, the real contact area between
short carbon fibers and steel decreased due to the separation effect caused by
the transfer film layer. Since the sliding contact was more and more matrix dominated,
the hot spot temperatures on the fibers dropped, too. The results of this failure
mechanism investigation already demonstrate the opportunities which the new
measurement techniques provide for a deeper understanding of tribological processes,
enabling improvements in material composition and application design.
The Event Segmentation Theory (Kurby & Zacks, 2008; Zacks, Speer, Swallow, Braver, & Reynolds, 2007) explains the perceptual organization of an ongoing activity into meaningful events. The classical event segmentation task (Newtson, 1973) involves watching an online video and indicating with key presses the event boundaries, i.e., when one event ends and the next one begins. The resulting hierarchical organization of object-based coarse events and action-based fine events gives insight into various cognitive processes. I used the Event Segmentation Theory to develop assistance and training systems for assembly workers in industrial settings at various levels - experts, new hires, and intellectually disabled people. Therefore, the first scientific question I asked was whether online and offline event segmentation result in the same event boundaries. This is important because assembly work requires not only watching activities online but processing the information offline, e.g., while performing the assembly task. By developing a special software tool that enables assessment of offline event boundaries, I established that online perception and offline elaboration lead to similar event boundaries. This study supports prior work suggesting that instructions should be structured around event boundaries.
Secondly, I investigated the importance of fine versus coarse event boundaries when learning the sequence of steps in virtual training, both for novices and experts in car door assembly. I found memory, tested by ability to predict the next frame, to be enhanced for object-based coarse events from the nearest fine event boundary. However, virtual training did not improve memory for action-based fine events from the nearest coarse event boundary. I conjecture that trainees primarily acquire the sequence of object-based coarse events in an initial training. Based on differences found in memory performance between experts and novices, I conclude that memory for action-based fine events is dependent on expertise.
Thirdly, I used the Event Segmentation Theory to investigate whether the simple and repetitive assembly tasks offered at workshops for intellectually disabled persons utilize their full cognitive potential. I analyzed event segmentation performance of 32 intellectually disabled persons compared to 30 controls using a variety of event segmentation measures. I found specific deficits in event boundary detection and hierarchical organization of events for the intellectually disabled group. However, results suggest that hierarchical organization is task-dependent. Because the event segmentation task accounted for differences in general cognitive ability, I propose the event segmentation task as diagnostic method for the need for support in executing assembly tasks.
Based on these three studies, I argue that the Event Segmentation Theory offers a framework for assessment and assistance of important attentional, perceptual, and memory processes related to assembly tasks. I demonstrate how practical applications can make use of this framework for the development of new computer-based assistance and training systems that are tailored to the users’ need for support and improve their quality of life.
Although today’s bipeds are capable of demonstrating impressive locomotion skills, in many aspects, there’s still a big gap compared to the capabilities observed in humans. Partially, this is due to the deployed control paradigms that are mostly based on analytical approaches. The analytical nature of those approaches entails strong model dependencies – regarding the robotic platform as well as the environment – which makes them prone to unknown disturbances. Recently, an increasing number of biologically-inspired control approaches have been presented from which a human-like bipedal gait emerges. Although the control structures only rely on proprioceptive sensory information, the smoothness of the motions and the robustness against external disturbances is impressive. Due to the lack of suitable robotic platforms, until today the controllers have been mostly applied to
simulations.
Therefore, as the first step towards a suitable platform, this thesis presents the Compliant Robotic Leg (CARL) that features mono- as well as biarticular actuation. The design is driven by a set of core-requirements that is primarily derived from the biologically-inspired behavior-based bipedal locomotion control (B4LC) and complemented by further functional aspects from biomechanical research. Throughout the design process, CARL is understood as a unified dynamic system that emerges from the interplay of the mechanics, the electronics, and the control. Thus, having an explicit control approach and the respective gait in mind, the influence of each subsystem on the characteristics of the overall system is considered
carefully.
The result is a planar robotic leg whose three joints are driven by five highly integrated linear SEAs– three mono- and two biarticular actuators – with minimized reflected inertia. The SEAs are encapsulated by FPGA-based embedded nodes that are designed to meet the hard application requirements while enabling the deployment of a full-featured robotic framework. CARL’s foot is implemented using a COTS prosthetic foot; the sensor information is obtained from the deformation of its main structure. Both subsystems are integrated into a leg structure that matches the proportions of a human with a size of 1.7 m.
The functionality of the subsystems, as well as the overall system, is validated experimentally. In particular, the final experiment demonstrates a coordinated walking motion and thereby confirms that CARL can produce the desired behavior – a natural looking, human-like gait is emerging from the interplay of the behavior-based walking control and the mechatronic system. CARL is robust regarding impacts, the redundant actuation system can render the desired joint torques/impedances, and the foot system supports the walking structurally while it provides the necessary sensory information. Considering that there is no movement of the upper trunk, the angle and torque profiles are comparable to the ones found in humans.
In an overall effort to contribute to the steadily expanding EO literature, this cumulative dissertation aims to help the literature to advance with greater clarity, comprehensive modeling, and more robust research designs. To achieve this, the first paper of this dissertation focuses on the consistency and coherence in variable choices and modeling considerations by conducting a systematic quantitative review of the EO-performance literature. Drawing on the plethora of previous EO studies, the second paper employs a comprehensive meta-analytic structural equation modeling approach (MASEM) to explore the potential for unique component-level relationships among EO’s three core dimensions in antecedent to outcome relationships. The third paper draws on these component-level insights and performs a finer-grained replication of the seminal MASEM of Rosenbusch, Rauch, and Bausch (2013) that proposes EO as a full mediator between the task environment and firm performance. The fourth and final paper of this cumulative dissertation illustrates exigent endogeneity concerns inherent in observational EO-performance research and provides guidance on how researchers can move towards establishing causal relationships.
Due to the steadily growing flood of data, the appropriate use of visualizations for efficient data analysis is as important today as it has never been before. In many application domains, the data flood is based on processes that can be represented by node-link diagrams. Within such a diagram, nodes may represent intermediate results (or products), system states (or snapshots), milestones or real (and possibly georeferenced) objects, while links (edges) can embody transition conditions, transformation processes or real physical connections. Inspired by the engineering sciences application domain and the research project “SinOptiKom: Cross-sectoral optimization of transformation processes in municipal infrastructures in rural areas”, a platform for the analysis of transformation processes has been researched and developed based on a geographic information system (GIS). Caused by the increased amount of available and interesting data, a particular challenge is the simultaneous visualization of several visible attributes within one single diagram instead of using multiple ones. Therefore, two approaches have been developed, which utilize the available space between nodes in a diagram to display additional information.
Motivated by the necessity of appropriate result communication with various stakeholders, a concept for a universal, dashboard-based analysis platform has been developed. This web-based approach is conceptually capable of displaying data from various data sources and has been supplemented by collaboration possibilities such as sharing, annotating and presenting features.
In order to demonstrate the applicability and usability of newly developed applications, visualizations or user interfaces, extensive evaluations with human users are often inevitable. To reduce the complexity and the effort for conducting an evaluation, the browser-based evaluation framework (BREF) has been designed and implemented. Through its universal and flexible character, virtually any visualization or interaction running in the browser can be evaluated with BREF without any additional application (except for a modern web browser) on the target device. BREF has already proved itself in a wide range of application areas during the development and has since grown into a comprehensive evaluation tool.
Semi-structured data is a common data format in many domains.
It is characterized by a hierarchical structure and a schema that is not fixed.
Efficient and scalable processing of this data is therefore challenging, as many existing indexing and processing techniques are not well-suited for this data format.
This dissertation presents a novel approach to processing large JSON datasets.
We describe a new data processor, JODA, that is designed to process semi-structured data by using all available computing resources and state-of-the-art techniques.
Using a custom query language and a vertically-scaling pipeline query execution engine, JODA can process large datasets with high throughput.
We optimize JODA by using a novel optimization for iterative query workloads called delta trees, which succinctly represent the changes between two documents.
This allows us to process iterative and exploratory queries efficiently.
We improve the filtering performance of JODA by implementing a holistic adaptive indexing approach that creates and improves structural and content indices on the fly, depending on the query load.
No prior knowledge about the data is required, and the indices are automatically improved over time.
JODA is also modularized and can be extended with new user-defined predicates, functions, indices, import, and export functionalities.
These modules can be written in an external programming language and integrated into the query execution pipeline at runtime.
To evaluate this system against competitors, we introduce a benchmark generator, coined BETZE, which aims to simulate data scientists exploring unknown JSON datasets.
The generator can be tweaked to generate query workload with different characteristics, or predefined presets can be used to quickly generate a benchmark.
We see that JODA outperforms competitors in most tasks over a wide range of datasets and use-cases.
Mobility has become an integral feature of many wireless networks. Along with this mobility comes the need for location awareness. A prime example for this development are today’s and future transportation systems. They increasingly rely on wireless communications to exchange location and velocity information for a multitude of functions and applications. At the same time, the technological progress facilitates the widespread availability of sophisticated radio technology such as software-defined radios. The result is a variety of new attack vectors threatening the integrity of location information in mobile networks.
Although such attacks can have severe consequences in safety-critical environments such as transportation, the combination of mobility and integrity of spatial information has not received much attention in security research in the past. In this thesis we aim to fill this gap by providing adequate methods to protect the integrity of location and velocity information in the presence of mobility. Based on physical effects of mobility on wireless communications, we develop new methods to securely verify locations, sequences of locations, and velocity information provided by untrusted nodes. The results of our analyses show that mobility can in fact be exploited to provide robust security at low cost.
To further investigate the applicability of our schemes to real-world transportation systems, we have built the OpenSky Network, a sensor network which collects air traffic control communication data for scientific applications. The network uses crowdsourcing and has already achieved coverage in most parts of the world with more than 1000 sensors.
Based on the data provided by the network and measurements with commercial off-the-shelf hardware, we demonstrate the technical feasibility and security of our schemes in the air traffic scenario. Moreover, the experience and data provided by the OpenSky Network allows us to investigate the challenges for our schemes in the real-world air traffic communication environment. We show that our verification methods match all
requirements to help secure the next generation air traffic system.
This thesis investigates the numerical and experimental characterization of the particle size
distribution in two different chemical engineering applications (liquid-gas and liquid-liquid).
First, a gas-liquid flow in a pseudo 2D bubble column is investigated using optical imaging
techniques. Objective of the experiment is the localized measurement of the bubble size
distribution, as well as the orientation and movement of the gas bubbles. This is achieved
using an automated camera traverse system, capturing high-speed images of different zones
in the bubble column. The images are then evaluated using an algorithmic approach consid-
ering the bubble size, movement direction and orientation of the gas bubbles. The measured
quantities are evaluated in each zone of the bubble column. The captured experimental data
is then used as a validation for the implementation of the Sectional Quadratic Method of
Moments (SQMoM), as a solution for the population balance equation, in the computational
fluid dynamics (CFD) solver multiphaseEulerFoam of the open-source software OpenFOAM.
The simulation is specifically compared to the zone-wise data of the bubble column experi-
ments. The adapted solver is able to simulate the development of the bubble size distribution
in the different zones of the bubble column.
The second investigation considers a liquid-liquid flow in a mixer-settler with the objective
to analyze knitted meshes as coalescing aids. The experiment is executed using an automated
mixer-settler set-up. To control the droplet size distribution, leaving the mixer and entering
the settler, a control loop is developed to set the mixer speed. The setup uses a convolutional
neural network (CNN) to segment droplet clusters, in the live captured camera images. The
CNN is trained in a two-step approach, which uses an algorithmic method for trainings
database generation, to segment a representative image, in the first step. In a second step,
the previously trained network is used to create a database of images to train a second network for the live evaluation. This allows a training without the need of for a large already
evaluated database. An adaption of the presented method and an alternative method using
a generative adversarial neural network (GAN) for trainings database generation is tested
considering gas-liquid multiphase flows.
The automated mixer-settler allows for an investigation of the knitted meshes as coalescing
aids under a controlled load, while the total flow rate, or the phase ratio, or the droplet
size distribution is gradually changed. The results of the experiments are used to adapt
an iterative design approach for settlers to implement the usage of knitted meshes as
coalescencing aids.
Molecular simulation is an important tool for investigating the behavior of fluids and solids. Nanoscopic processes and physical properties of the material can be studied predictively based on the description of the molecular interactions by force fields. This is used in the present work to tackle engineering questions that are hard to answer with other methods. First, mass transfer at fluid interfaces was investigated on the nanoscopic level. Therefore, two distinct simulation methods were developed and used to systematically investigate the mass transfer in mixtures of simple model fluids, described by the ‘Lennard-Jones truncated and shifted’ (LJTS) potential. The research question was whether the adsorption of components at the interface, which is observed also in many simple fluid mixtures, has an influence on the mass transfer. Such an influence was indeed found in the studies with both scenarios. Furthermore, explosions of nanodroplets caused by a spontaneous evaporation of the liquid phase were investigated with non-equilibrium molecular dynamics (NEMD) simulations. In these simulations, the interior of an LJTS droplet was superheated by a local thermostat, so that a vapor bubble nucleated inside of the droplet. Depending on the degree of superheating, different phenomena were observed, ranging from a simple evaporation of the droplet over oscillatory behavior of the bubble to an immediate droplet explosion. For molecular simulations of real mixtures, suitable force fields are needed. In this work, a set of molecular models for the alkali nitrates was developed and systematically compared to experimental data of thermophysical and structural properties of aqueous alkali nitrate solutions from the literature. Lastly, the structure and clustering of 1:1 electrolytes in aqueous solution was investigated for a broad concentration range starting from near infinite dilution up to high supersaturation. Based on the simulation results, an empirical rule was proposed to provide estimates of the solubility of salts with standard molecular dynamics simulations without the need of elaborate calculation schemes or significant additional computational effort.
In robotics, information is often regarded as a means to an end. The question of how to structure information and how to bridge the semantic gap between different levels of abstraction in a uniform way is still widely regarded as a technical issue. Ignoring these challenges appears to lead robotics into a similar stasis as experienced in the software industry of the late 1960s. From the beginning of the software crisis until today, numerous methods, techniques, and tools for managing the increasing complexity of software systems have evolved. The attempt to transfer several of these ideas towards applications in robotics yielded various control architectures, frameworks, and process models. These attempts mainly provide modularisation schemata which suggest how to decompose a complex system into less complex subsystems. The schematisation of representation and information flow however is mostly ignored. In this work, a set of design schemata is proposed which is embedded into an action/perception-oriented design methodology to promote thorough abstractions between distinct levels of control. Action-oriented design decomposes control systems top-down and sensor data is extracted from the environment as required. This comes with the problem that information is often condensed in a premature fashion. That way, sensor processing is dependent on the control system design resulting in a monolithical system structure with limited options for reusability. In contrast, perception-oriented design constructs control systems bottom-up starting with the extraction of environment information from sensor data. The extracted entities are placed into structures which evolve with the development of the sensor processing algorithms. In consequence, the control system is strictly dependent on the sensor processing algorithms which again results in a monolithic system. In their particular domain, both design approaches have great advantages but fail to create inherently modular systems. The design approach proposed in this work combines the strengths of action orientation and perception orientation into one coherent methodology without inheriting their weaknesses. More precisely, design schemata for representation, translation, and fusion of environmental information are developed which establish thorough abstraction mechanisms between components. The explicit introduction of abstractions particularly supports extensibility and scalability of robot control systems by design.
Augmented (AR), Virtual (VR) and Mixed Reality (MR) are on their way into everyday life. The recent emergence of consumer-friendly hardware to access this technology has greatly benefited the community. Research and application examples for AR, VR and MR can be found in many fields, such as medicine, sports, the area of cultural heritage, teleworking, entertainment and gaming. Although this technology has been around for decades, immersive applications using this technology are still in their infancy. As manufacturers increase accessibility to these technologies by introducing consumer grade hardware with natural input modalities such as eye gaze or hand tracking, new opportunities but also problems and challenges arise. Researchers strive to develop and investigate new techniques for dynamic content creation or novel interaction techniques. It has yet to be found out which interactions can be made intuitively by users. A major issue is that the possibilities for easy prototyping and rapid testing of new interaction techniques are limited and largely unexplored.
In this thesis, different solutions are proposed to improve gesture-based interaction in immersive environments by introducing gesture authoring tools and developing novel applications. Specifically, hand gestures should be made more accessible to people outside this specialised domain. First, a survey which explores one of the largest and most promising application scenario for AR, VR and MR, namely remote collaboration is introduced. Based on the results of this survey, the thesis focuses on several important issues to consider when developing and creating applications. At its core, the thesis is about rapid prototyping based on panorama images and the use of hand gestures for interactions. Therefore, a technique to create immersive applications with panorama based virtual environments including hand gestures is introduced. A framework to rapidly design, prototype, implement, and create arbitrary one-handed gestures is presented. Based on a user study, the potential of the framework as well as efficacy and usability of hand gestures is investigated. Next, the potential of hand gestures for locomotion tasks in VR is investigated. Additionally, it is analysed how lay people can adapt to the use of hand tracking technology in this context. Lastly, the use of hand gestures for grasping virtual objects is explored and compared to state of the art techniques. Within this thesis, different input modalities and techniques are compared in terms of usability, effort, accuracy, task completion time, user rating, and naturalness.
Small embedded devices are highly specialized platforms that integrate several pe- ripherals alongside the CPU core. Embedded devices extensively rely on Firmware (FW) to control and access the peripherals as well as other important functionality. Customizing embedded computing platforms to specific application domains often necessitates optimizing the firmware and/or the HW/SW interface under tight re- source constraints. Such optimizations frequently alter the communication between the firmware and the peripheral devices, possibly compromising functional correct- ness of the input/output behavior of the embedded system. This poses challenges to the development and verification of such systems. The system must be adapted and verified to each specific device configuration.
This thesis presents a formal approach to formulate these verification tasks at several levels of abstraction, along with corresponding HW/SW co-equivalence checking techniques for verifying correct I/O behavior of peripherals under a modified firmware. The feasibility of the approach is shown on several case studies, including industrial driver software as well as open-source peripherals. In addition, a subtle bug in one of the peripherals and several undocumented preconditions for correct device behavior were detected by the verification method.
Today’s digital world would be unthinkable without complex data sets. Whether in private, business or industrial environments, complex data provide the basis for important and critical decisions and determine many processes, some of which are automated. This is often associated with Big Data. However, often only one aspect of the usual Big Data definitions is sufficient and a human observer can no longer capture the data completely and correctly. In this thesis, different approaches are presented in order to master selected challenges in a more effective, efficient and userfriendly way. The approaches range from easier pre-processing of data sets for later analysis and the identification of design guidelines of such assistants, new visualization techniques for presenting uncertainty, extensions of existing visualizations for categorical data, concepts for time-saving selection methods for subsets of data points and faster navigation and zoom interaction–especially in the web-based area with enormous amounts of data–to new and innovative orientation-based interaction metaphors for mobile devices as well as stationary working environments. Evaluations and appropriate use case of the individual approaches show the usability also in comparison with state-of-the-art techniques.
Multidisciplinary optimizations (MDOs) encompass optimization problems that combine different disciplines into a single optimization with the aim of converging towards a design that simultaneously fulfills multiple criteria. For example, considering both fluid and structural disciplines to obtain a shape that is not only aerodynamically efficient, but also respects structural constraints. Combined with CAD-based parametrizations, the optimization produces an improved, manufacturable shape. For turbomachinery applications, this method has been successfully applied using gradient-free optimization methods such as genetic algorithms, surrogate modeling, and others. While such algorithms can be easily applied without access to the source code, the number of iterations to converge is dependent on the number of design parameters. This results in high computational costs and limited design spaces. A competitive alternative is offered by gradient-based optimization algorithms combined with adjoint methods, where the computational complexity of the gradient calculation is no longer dependent on the number of design parameters, but rather on the number of outputs. Such methods have been extensively used in single-disciplinary aerodynamic optimizations using adjoint fluid solvers and CAD parametrizations. However, CAD-based MDOs leveraging adjoint methods are just beginning to emerge.
This thesis contributes to this field of research by setting up a CAD-based adjoint MDO framework for turbomachinery design using both fluid and structural disciplines. To achieve this, the von Kármán Institute’s existing CAD-based optimization framework cado is augmented by the development of a FEM-based structural solver which has been differentiated using the algorithmic differentiation tool CoDiPack of TU Kaiserslautern. While most of the code could be differentiated in a black-box fashion, special treatment is required for the iterative linear and eigenvalue solvers to ensure accuracy and reduce memory consumption. As a result, the solver has the capability of computing both stress and vibration gradients at a cost independent on the number of design parameters. For the presented application case of a radial turbine optimization, the full gradient calculation has a computational cost of approximately 3.14 times the cost of a primal run and the peak memory usage of approximately 2.76 times that of a primal run.
The FEM code leverages object-oriented design such that the same base structure can be reused for different purposes with minimal re-differentiation. This is demonstrated by considering a composite material test case where the gradients could be easily calculated with respect to an extended design space that includes material properties. Additionally, the structural solver is reused within a CAD-based mesh deformation, which propagates the structural FEM mesh gradients through to the CAD parameters. This closes the link between the CAD shape and FEM mesh. Finally, the MDO framework is applied by optimizing the aerodynamic efficiency of a radial turbine while respecting structural constraints.
We introduce and investigate a product pricing model in social networks where the value a possible buyer assigns to a product is influenced by the previous buyers. The selling proceeds in discrete, synchronous rounds for some set price and the individual values are additively altered. Whereas computing the revenue for a given price can be done in polynomial time, we show that the basic problem PPAI, i.e., is there a price generating a requested revenue, is weakly NP-complete. With algorithm Frag we provide a pseudo-polynomial time algorithm checking the range of prices in intervals of common buying behavior we call fragments. In some special cases, e.g., solely positive influences, graphs with bounded in-degree, or graphs with bounded path length, the amount of fragments is polynomial. Since the run-time of Frag is polynomial in the amount of fragments, the algorithm itself is polynomial for these special cases. For graphs with positive influence we show that every buyer does also buy for lower prices, a property that is not inherent for arbitrary graphs. Algorithm FixHighest improves the run-time on these graphs by using the above property.
Furthermore, we introduce variations on this basic model. The version of delaying the propagation of influences and the awareness of the product can be implemented in our basic model by substituting nodes and arcs with simple gadgets. In the chapter on Dynamic Product Pricing we allow price changes, thereby raising the complexity even for graphs with solely positive or negative influences. Concerning Perishable Product Pricing, i.e., the selling of products that are usable for some time and can be rebought afterward, the principal problem is computing the revenue that a given price can generate in some time horizon. In general, the problem is #P-hard and algorithm Break runs in pseudo-polynomial time. For polynomially computable revenue, we investigate once more the complexity to find the best price.
We conclude the thesis with short results in topics of Cooperative Pricing, Initial Value as Parameter, Two Product Pricing, and Bounded Additive Influence.
In change-point analysis the point of interest is to decide if the observations follow one model
or if there is at least one time-point, where the model has changed. This results in two sub-
fields, the testing of a change and the estimation of the time of change. This thesis considers
both parts but with the restriction of testing and estimating for at most one change-point.
A well known example is based on independent observations having one change in the mean.
Based on the likelihood ratio test a test statistic with an asymptotic Gumbel distribution was
derived for this model. As it is a well-known fact that the corresponding convergence rate is
very slow, modifications of the test using a weight function were considered. Those tests have
a better performance. We focus on this class of test statistics.
The first part gives a detailed introduction to the techniques for analysing test statistics and
estimators. Therefore we consider the multivariate mean change model and focus on the effects
of the weight function. In the case of change-point estimators we can distinguish between
the assumption of a fixed size of change (fixed alternative) and the assumption that the size
of the change is converging to 0 (local alternative). Especially, the fixed case in rarely analysed
in the literature. We show how to come from the proof for the fixed alternative to the
proof of the local alternative. Finally, we give a simulation study for heavy tailed multivariate
observations.
The main part of this thesis focuses on two points. First, analysing test statistics and, secondly,
analysing the corresponding change-point estimators. In both cases, we first consider a
change in the mean for independent observations but relaxing the moment condition. Based on
a robust estimator for the mean, we derive a new type of change-point test having a randomized
weight function. Secondly, we analyse non-linear autoregressive models with unknown
regression function. Based on neural networks, test statistics and estimators are derived for
correctly specified as well as for misspecified situations. This part extends the literature as
we analyse test statistics and estimators not only based on the sample residuals. In both
sections, the section on tests and the one on the change-point estimator, we end with giving
regularity conditions on the model as well as the parameter estimator.
Finally, a simulation study for the case of the neural network based test and estimator is
given. We discuss the behaviour under correct and mis-specification and apply the neural
network based test and estimator on two data sets.
Data-driven and Sparse-to-Dense Concepts in Scene Flow Estimation for Automotive Applications
(2022)
Highly assisted driving and autonomous vehicles require a detailed and accurate perception of the environment. This includes the perception of the 3D geometry of the scene and the 3D motion of other road users. The estimation of both based on images is known as the scene flow problem in computer vision. This thesis deals with a solution to the scene flow problem that is suitable for application in autonomous vehicles. This application imposes strict requirements on accuracy, robustness, and speed. Previous work was lagging behind in at least one of these metrics. To work towards the fulfillment of those requirements, the sparse-to-dense concept for scene flow estimation is introduced in this thesis. The idea can be summarized as follows: First, scene flow is estimated for some points of the scene for which this can be done comparatively easily and reliably. Then, an interpolation is performed to obtain a dense estimate for the entire scene. Because of the separation into two steps, each part can be optimized individually. In a series of experiments, it is shown that the proposed methods achieve competitive results and are preferable to previous techniques in some aspects. As a second contribution, individual components in the sparse-to-dense pipeline are replaced by deep learning modules. These are a highly localized and highly accurate feature descriptor to represent pixels for dense matching, and a network for robust and generic sparse-to-dense interpolation. Compared to end-to-end architectures, the advantage of deep modules is that they can be trained more effciently with data from different domains. The recombination approach applies a similar concept as the sparse-to-dense approach by solving and combining less diffcult, auxiliary sub-problems. 3D geometry and 2D motion are estimated separately, the individual results are combined, and then also interpolated into a dense scene flow. As a final contribution, the thesis proposes a set of monolithic end-to-end networks for scene flow estimation.
Malaria is still a big problem globally causing more than 400,000 deaths each year. Although very effective antimalarial drugs are on the market, their mode of action is still not fully understood. In the last years some patients showed an increased clearance time from Plasmodium falciparum malaria parasites after a therapy with the most effective antimalarial compound artemisinin. It was shown that mutations in the propeller domain of a protein called PfKelch13 are directly linked to this decreased susceptibility towards the drug. To gain insights into the protein function, I produced different mutants of a truncated version of this protein containing the BTB and propeller domain in high yield and purity in insect cells. I showed a positive correlation between the solubility of the recombinant protein and the artemisinin susceptibility. Prominent PfKelch13 mutants from the field with decreased artemisinin susceptibility (I543T, R539T, C580Y) were insoluble when recombinantly expressed suggesting that improper folding of PfKelch13 leads to this decrease in sensitivity. The mutation C580Y is the most frequent mutation in South East Asia. The substitution of this cysteine residue does not allow the formation of an intramolecular disulfide bond with cysteine C532 according to an existing crystal structure. Interestingly, substitution of these cysteines to serines did not show improper folding in insect cells, arguing that rather specific substitutions than the residue position itself are responsible for the alteration of drug sensitivity. To test the impact of the disulfide bond on artemisinin susceptibility, I generated stable transgenic parasites expressing the corresponding serine mutations. Neither C580S nor C532S showed decreased artemisinin susceptibility. Therefore, it could be excluded that the formation of the intramolecular disulfide bond has an influence on artemisinin susceptibility. We further asked, if the protein abundance has an influence on the sensitivity towards the drug. I successfully generated stable transgenic parasites expressing His8-PFKELCH13 fused to the glmS riboswitch. I showed that protein levels could be efficiently down-regulated by more than 90% resulting in a very low parasite susceptibility towards artemisinin. This strain offers a basis for future experiments in the understanding of the impact of PfKelch13 protein levels on biochemical pathways in malaria parasites.
Peroxiredoxins (Prxs) play an important role in protecting the cell from high amounts hydroperoxides. Among the five known Prxs in P. falciparum our group took PfAOP as a model enzyme to study the catalytic cycle. It has been shown that PfAOP reduces hydroperoxides like H2O2 or tBuOOH with fast kinetics, and that reduction of the protein is linked to the GSH/Grx system (Djuika et al. 2013). However, no direct kinetic data was available for the reductive half-reaction of PfAOP and GSH. In this thesis, I qualitatively showed that oxidized PfAOP can be glutathionylated and that in a next step glutathione can be transferred to PfGrx. I further determined the rate constants of the glutathionylation of PfAOP by stopped-flow measurements. Rate constants of around 10^5 M-1s-1 indicate a fast kinetic that is able to protect the protein from hyperoxidation and inactivation. Furthermore, I determined the activation energy, entropy and enthalpy for this reaction of 41.1 kJ/mol, -0.79 J/mol and 39.8 kJ/mol, respectively. Hence, the activation energy of the glutathionylation of oxidized PfAOP suggests the break of two to three hydrogen bonds and is rather temperature-independent.
This thesis builds a bridge between singularity theory and computer algebra. To an isolated hypersurface singularity one can associate a regular meromorphic connection, the Gauß-Manin connection, containing a lattice, the Brieskorn lattice. The leading terms of the Brieskorn lattice with respect to the weight and V-filtration of the Gauß-Manin connection define the spectral pairs. They correspond to the Hodge numbers of the mixed Hodge structure on the cohomology of the Milnor fibre and belong to the finest known invariants of isolated hypersurface singularities. The differential structure of the Brieskorn lattice can be described by two complex endomorphisms A0 and A1 containing even more information than the spectral pairs. In this thesis, an algorithmic approach to the Brieskorn lattice in the Gauß-Manin connection is presented. It leads to algorithms to compute the complex monodromy, the spectral pairs, and the differential structure of the Brieskorn lattice. These algorithms are implemented in the computer algebra system Singular.
Predicting secondary structures of RNA molecules is one of the fundamental problems of and thus a challenging task in computational structural biology. Existing prediction methods basically use the dynamic programming principle and are either based on a general thermodynamic model or on a specific probabilistic model, traditionally realized by a stochastic context-free grammar. To date, the applied grammars were rather simple and small and despite the fact that statistical approaches have become increasingly appreciated over the past years, a corresponding sampling algorithm based on a stochastic RNA structure model has not yet been devised. In addition, basically all popular state-of-the-art tools for computational structure prediction have the same worst-case time and space requirements of O(n^3) and O(n^2) for sequence length n, limiting their applicability for practical purposes due to the often quite large sizes of native RNA molecules. Accordingly, the prime demand imposed by biologists on computational prediction procedures is to reach a reduced waiting time for results that are not significantly less accurate.
We here deal with all of these issues, by describing algorithms and performing comprehensive studies that are based on sophisticated stochastic context-free grammars of similar complexity as those underlying thermodynamic prediction approaches, where all of our methods indeed make use of the concept of sampling. We also employ the approximation technique known from theoretical computer science in order to reach a heuristic worst-case speedup for RNA folding.
Particularly, we start by describing a way for deriving a sequence-independent random sampler for an arbitrary class of RNAs by means of (weighted) unranking. The resulting algorithm may generate any secondary structure of a given fixed size n in only O(n·log(n)) time, where the results are observed to be accurate, validating its practical applicability.
With respect to RNA folding, we present a novel probabilistic sampling algorithm that generates statistically representative and reproducible samples of the entire ensemble of feasible structures for a particular input sequence. This method actually samples the possible foldings from a distribution implied by a suitable (traditional or length-dependent) grammar. Notably, we also propose several (new) ways for obtaining predictions from generated samples. Both variants have the same worst-case time and space complexities of O(n^3) and O(n^2) for sequence length n. Nevertheless, evaluations of our sampling methods show that they are actually capable of producing accurate (prediction) results.
In an attempt to resolve the long-standing problem of reducing the time complexity of RNA folding algorithms without sacrificing much of the accuracy of the results, we invented an innovative heuristic statistical sampling method that can be implemented to require only O(n^2) time for generating a fixed-size sample of candidate structures for a given sequence of length n. Since a reasonable prediction can still efficiently be obtained from the generated sample set, this approach finally reduces the worst-case time complexity by a liner factor compared to all existing precise methods. Notably, we also propose a novel (heuristic) sampling strategy as opposed to the common one typically applied for statistical sampling, which may produce more accurate results for particular settings. A validation of our heuristic sampling approach by comparison to several leading RNA secondary structure prediction tools indicates that it is capable of producing competitive predictions, but may require the consideration of large sample sizes.
Biological Soil Crusts (BSCs), composed of lichens, mosses, green algae, microfungi and cyanobacteria are an ecological important part of the perennial landcover of many arid and semiarid regions (Belnap et al. 2001a), (Büdel 2002). In many arid and hyperarid areas BSCs form the only perennial "vegetation cover" largely due to their extensive resistance to drought (Lange et al. 1975). For the Central Namib Desert (Namibia), BSCs consisting of extraordinary vast lichen communities were recently mapped and classified into six morphological classes for a coastal area of 350 km x 60 km. Embedded into the project "BIOTA" (www.biota-africa.org) financed by the German Federal Ministry of Education and Research the study was undertaken in the framework of the PhD thesis by Christoph Schultz. Some of these lichen communities grouped together in so called "lichen fields" have already been studied concerning their ecology and diversity in the past (Lange et al. 1994), (Loris & Schieferstein 1992), (Loris et al. 2004), (Ullmann & Büdel 2001a), (Wessels 1989). Multispectral LANDSAT 7 ETM+ and LANDSAT 5 TM satellite imagery was utilized for an unitemporal supervised classification as well as for the establishment of a monitoring based on a combined retrospective supervised classification and change detection approach (Bock 2003), (Weiers et al. 2003). Results comprise the analysis of the mapped distribution of lichen communities for the Central Namib Desert as of 2003 as well as reconstructed distributions for the years 2000, 1999, 1992 and 1991 derived from retrospective supervised classification. This allows a first monitoring of the disturbance, destruction and recovery of the lichen communities in these arid environments including the analysis of the major abiotic processes involved. Further analysis of these abiotic processes is key for understanding the influence of Namib lichen communities on overall aeolian and water induced erosion rates, nutrient cycles, water balance and pedogenic processes (Belnap & Gillette 1998), (Belnap et al. 2001b), (Belnap 2001c), (Evans & Lange 2001), (McKenna Neumann & Maxwell 1999). In order to aid the understanding of these processes SRTM digital elevation model data as well as climate data sets were used as reference. Good correlation between geomorphological form elements as well as hydrological drainage system and the disturbance patterns derived from individual post classification change comparisons between the timeframes could be observed. Conjoined with the climate data sets sporadic foehn-like windstorms as well as extraordinary precipitation events were identified to largely affect the distribution patterns of lichen communities. Therefore the analysis and monitoring of the diversity, distribution and spatiotemporal change of Central Namib BSCs with the means of Remote Sensing and GIS applications proof to be important tools to create further understanding of desertification and degradation processes in these arid regions.
The central topic of this thesis is Alperin's weight conjecture, a problem concerning the representation theory of finite groups.
This conjecture, which was first proposed by J. L. Alperin in 1986, asserts that for any finite group the number of its irreducible Brauer characters coincides with the number of conjugacy classes of its weights. The blockwise version of Alperin's conjecture partitions this problem into a question concerning the number of irreducible Brauer characters and weights belonging to the blocks of finite groups.
A proof for this conjecture has not (yet) been found. However, the problem has been reduced to a question on non-abelian finite (quasi-) simple groups in the sense that there is a set of conditions, the so-called inductive blockwise Alperin weight condition, whose verification for all non-abelian finite simple groups implies the blockwise Alperin weight conjecture. Now the objective is to prove this condition for all non-abelian finite simple groups, all of which are known via the classification of finite simple groups.
In this thesis we establish the inductive blockwise Alperin weight condition for three infinite series of finite groups of Lie type: the special linear groups \(SL_3(q)\) in the case \(q>2\) and \(q \not\equiv 1 \bmod 3\), the Chevalley groups \(G_2(q)\) for \(q \geqslant 5\), and Steinberg's triality groups \(^3D_4(q)\).
Undocumented enterprise data can easily pile up in companies in form of datasets and personal information. In absence of a data management strategy, such data becomes rather messy and may not fit for its intended use. Since there is often no documentation available, only a limited number of domain experts are aware of its contents. Therefore, for companies it becomes increasingly difficult to use such data to its full potential. To provide a solution, this PhD thesis investigates the construction of enterprise and personal knowledge graphs by semantically enriching messy data with meaning using semantic technologies. Since real world entities and their interrelations are organized in a graph, knowledge graphs serve as a semantic bridge between domain conceptualization and raw data. Spreadsheets are a prominent example of such enterprise data, since they are widely used by knowledge workers in the industrial sector. Two distinct approaches are investigated to construct knowledge graphs from them: a global extraction & annotation method and a local mapping technique. The latter is further complemented with a predictor of mapping rules on messy data. Different human-in-the-loop strategies are considered to include experts depending on their user group. Since non-technical users usually lack understanding of semantic technologies, they need appropriate tools to be able to give feedback. In case of developers, approaches are proposed to close the technology gap between industry and Semantic Web related concepts. Semantic Web practitioners participate with ontology modeling and linked data applications. Enterprise and personal data is typically confidential which is why it cannot be shared with a research community to discuss its challenges. However, for evaluation and reproducibility reasons publicly available datasets are mandatory. The thesis proposes ways to generate synthetic datasets with the goal to be as authentic as possible. Besides that, for internal evaluations a crawler of personal data on desktops is implemented. There are further contributions related to this thesis in diverse domains. One is about the motivation to support users in their daily work using personal knowledge assistants. Others are the agricultural field and the data science domain which also benefit from knowledge graph approaches. In conclusion, this PhD thesis contributes to the construction of knowledge graphs from especially messy enterprise data, while users from different groups take part in this process in various ways.
The nondestructive testing of multilayered materials is increasingly applied in
both scientific and industrial fields. In particular, developments in millimeter
wave and terahertz technology open up novel measurement applications, which
benefit from the nonionizing properties of this frequency range. One example is
the noncontact inspection of layer thicknesses. Frequently used measuring and
analysis methods lead to a resolution limit that is determined by the bandwidth
of the setup. This thesis analyzes the reliable evaluation of thinner layer thicknesses
using model-based signal processing.
In dieser Arbeit wird ein Ansatz zur Simulation von Initiierung und Wachstum von Rissen in spröden Materialien, basierend auf diffusen Grenzflächen, um seinen Anwendungsbereich erweitert. Durch die Ergänzung der inneren Energie eines bestehenden Phasenfeldmodells um einen zusätzlichen Anteil wird das Beschreiben von Ermüdungsrisswachstum
ermöglicht. Außerdem wird durch das Modifizieren des Gradienten des Phasenfeldparameters in der regularisierten Rissenergie erreicht, dass das entstehende Modell anisotropes
Risswachstum abbilden kann.
Einleitend werden die benötigten Grundlagen aus verschiedenen Feldern der Mechanik
beschrieben sowie die Phasenfeld Methode zur Simulation von Risswachstum eingeführt.
Danach werden die Modifikationen, welche im Rahmen dieser Arbeit eingebracht wurden
begründet. Es wird im Weiteren detailliert beschrieben, wie die entsprechenden Erweiterungen eingearbeitet wurden und die Evolutionsgleichungen der neuen Phasenfeldmodelle,
in Form von Ginzubug-Landau Gleichungen werden hergeleitet.
Das resultierende gekoppelte Differentialgleichungssystem wurde, unter Verwendung von
impliziter Zeitintegration, als ebenes nichtlineares Finite Elemente Problem implementiert.
Die starke als auch die schwache Form der beschreibenden Gleichungen des Phasenfeld
Modells werden beschrieben.
Verschiedene Test Szenarien wurden simuliert, um zu Untersuchen inwieweit reale Ergebnisse mit dem Modell erzielt werden können. Die Ergebnisse zeigen, dass Brüche in
anisotropen Medien beschrieben werden können. Außerdem erzielt das entwickelte Ermüdungsmodell reales Verhalten für wichtige Größen wie die Risswachstumsgeschwindigkeit
und Phänomene wie Mittelspannungseinfluss oder Reihenfolge Einflüsse werden korrekt
abgebildet. Simulationsergebnisse von beiden Modellen werden mit experimentellen Ergebnissen verglichen, wobei eine gute Übereinstimmung nachgewiesen werden kann.
This thesis is concerned with the modeling of the domain structure evolution in ferroelectric materials. Both a sharp interface model, in which the driving force on a domain wall is used to postulate an evolution law, and a continuum phase field model are treated in a thermodynamically consistent framework. Within the phase field model, a Ginzburg-Landau type evolution law for the spontaneous polarization is derived. Numerical simulations (FEM) show the influence of various kinds of defects on the domain wall mobility in comparison with experimental findings. A macroscopic material law derived from the phase field model is used to calculate polarization yield surfaces for multiaxial loading conditions.
Real-time systems are systems that have to react correctly to stimuli from the environment within given timing constraints.
Today, real-time systems are employed everywhere in industry, not only in safety-critical systems but also in, e.g., communication, entertainment, and multimedia systems.
With the advent of multicore platforms, new challenges on the efficient exploitation of real-time systems have arisen:
First, there is the need for effective scheduling algorithms that feature low overheads to improve the use of the computational resources of real-time systems.
The goal of these algorithms is to ensure timely execution of tasks, i.e., to provide runtime guarantees.
Additionally, many systems require their scheduling algorithm to flexibly react to unforeseen events.
Second, the inherent parallelism of multicore systems leads to contention for shared hardware resources and complicates system analysis.
At any time, multiple applications run with varying resource requirements and compete for the scarce resources of the system.
As a result, there is a need for an adaptive resource management.
Achieving and implementing an effective and efficient resource management is a challenging task.
The main goal of resource management is to guarantee a minimum resource availability to real-time applications.
A further goal is to fulfill global optimization objectives, e.g., maximization of the global system performance, or the user perceived quality of service.
In this thesis, we derive methods based on the slot shifting algorithm.
Slot shifting provides flexible scheduling of time-constrained applications and can react to unforeseen events in time-triggered systems.
For this reason, we aim at designing slot shifting based algorithms targeted for multicore systems to tackle the aforementioned challenges.
The main contribution of this thesis is to present two global slot shifting algorithms targeted for multicore systems.
Additionally, we extend slot shifting algorithms to improve their runtime behavior, or to handle non-preemptive firm aperiodic tasks.
In a variety of experiments, the effectiveness and efficiency of the algorithms are evaluated and confirmed.
Finally, the thesis presents an implementation of a slot-shifting-based logic into a resource management framework for multicore systems.
Thus, the thesis closes the circle and successfully bridges the gap between real-time scheduling theory and real-world implementations.
We prove applicability of the slot shifting algorithm to effectively and efficiently perform adaptive resource management on multicore systems.
This thesis addresses challenges faced by small package shipping companies and investigates the integration of 1) service consistency and driver knowledge aspects and 2) the utilization of electric vehicles into the route planning of small package shippers. We use Operations Research models and solution methods to gain insights into the newly arising problems and thus support managerial decisions concerning these issues.
Oxidative folding of proteins in the mitochondrial intermembrane space of Leishmania tarentolae
(2022)
Mitochondrial genes encode for a few proteins. Thus, the majority of proteins has to be imported to the organelles, which is only possible in the unfolded state. The subsequent folding guarantees functionality. One of the proteins responsible for folding in the intermembrane space is Mia40, which is known in opisthokonts. No ortholog for Mia40 is known in kinetoplastida such as Leishmania tarentolae. First, already known candidates for Mia40 orthologs were investigated. In previous work Mic20 had been identified in Trypanosomes.1–4 Gene editing cassettes to knock-out or modify the gene LtaPh_3313851, which encodes the Mic20 ortholog, could not be inserted homozygously. Thus, the gene is assumed to be essential. Another protein that plays an important role in mitochondrial protein import is Erv, a known interactor of Mia40. Erv is also found in Leishmania. Two proteins of so far unknown function had been identified as potential interactors of Erv and could be candidates for Mia40 orthologs.5 Potential knock-out strains of one protein-encoding gene each were investigated. The knock-out of LtaP32.0380 was assumed to be complete and the gene dispensable. The knock-out cassette for LtaP07.0980 could be shown to be inserted heterozygously, which could indicate the essentiality of the gene. To identify new candidates for Mia40 orthologs in Leishmania tarentolae, potential substrates6 of the Mia40/Erv pathway were used as baits in the present work. Gene editing via CRISPR/Cas9 included attempts to insert knock-out or tagging cassettes to five different genes. Homozygous insertion succeeded for the C-terminal His8-tagging cassettes for LtaP19.1110, and for the N-terminal His8-tagging cassettes for LtaP25.1620 and LtaP09.1390. No homozygous gene editing could be observed for LtaP35.0210. The knock-out of LtaP04.0060 was assumed to be complete. The presence of the N-terminal His8-tagged substrate 4 (LtaP09.1390) could be shown in cell lysates. The correct position of tagged substrate 4 in the cell was confirmed. Further cell lysates were purified in pull-downs on Ni-NTA to obtain tagged substrate 4 with its interaction partners. The presence of tagged proteins in the eluates could be confirmed. To identify interacting proteins, mass spectrometry analysis was performed. In further experiments, DTT and TMAD were used to alter the redox conditions in the cells before lysis and purification. The evaluation of the data included the comparison of the proteins identified in different experiments and the comparison with potential interactors of Erv.5,7 Also, properties of Mia40 that might be conserved were considered. Two characteristic motifs of known Mia40 orthologs are a CPC and a twin CX9C motif. Thus, proteins with these or similar motifs were specifically searched for. Different candidates for Mia40 orthologs were identified and discussed.
A Consistent Large Eddy Approach for Lattice Boltzmann Methods and its Application to Complex Flows
(2015)
Lattice Boltzmann Methods have shown to be promising tools for solving fluid flow problems. This is related to the advantages of these methods, which are among others, the simplicity in handling complex geometries and the high efficiency in calculating transient flows. Lattice Boltzmann Methods are mesoscopic methods, based on discrete particle dynamics. This is in contrast to conventional Computational Fluid Dynamics methods, which are based on the solution of the continuum equations. Calculations of turbulent flows in engineering depend in general on modeling, since resolving of all turbulent scales is and will be in near future far beyond the computational possibilities. One of the most auspicious modeling approaches is the large eddy simulation, in which the large, inhomogeneous turbulence structures are directly computed and the smaller, more homogeneous structures are modeled.
In this thesis, a consistent large eddy approach for the Lattice Boltzmann Method is introduced. This large eddy model includes, besides a subgrid scale model, appropriate boundary conditions for wall resolved and wall modeled calculations. It also provides conditions for turbulent domain inlets. For the case of wall modeled simulations, a two layer wall model is derived in the Lattice Boltzmann context. Turbulent inlet conditions are achieved by means of a synthetic turbulence technique within the Lattice Boltzmann Method.
The proposed approach is implemented in the Lattice Boltzmann based CFD package SAM-Lattice, which has been created in the course of this work. SAM-Lattice is feasible of the calculation of incompressible or weakly compressible, isothermal flows of engineering interest in complex three dimensional domains. Special design targets of SAM-Lattice are high automatization and high performance.
Validation of the suggested large eddy Lattice Boltzmann scheme is performed for pump intake flows, which have not yet been treated by LBM. Even though, this numerical method is very suitable for this kind of vortical flows in complicated domains. In general, applications of LBM to hydrodynamic engineering problems are rare. The results of the pump intake validation cases reveal that the proposed numerical approach is able to represent qualitatively and quantitatively the very complex flows in the intakes. The findings provided in this thesis can serve as the basis for a broader application of LBM in hydrodynamic engineering problems.
The massive use of chemicals by humans is increasing pollution of the world’s ecosystems. Yet, knowledge about exposure and effects of chemicals in real-world ecosystems remains limited. Prediction of chemical effects in the context of ecotoxicological research and chemical regulation continues to focus on organism- or population-level responses established under simplified conditions while aiming to protect the functioning of ecosystems. A unified, comprehensive framework for the prediction of chemical effects in real-world ecosystems is still lacking. A major limitation of ecotoxicological studies considered in predictive modelling is that they rarely consider spatial dynamics (e.g. gene flow or species dispersal) as relevant processes influencing the trajectory of populations or communities, respectively. For instance, the spatial propagation of pesticide effects from polluted to least impacted sites has been predicted in several modelling studies but has not yet been characterised in the field.
The thesis starts in Chapter 1 with a brief introduction to chemical pollution in ecosystems, chemical effect prediction in ecotoxicology, and pesticides in freshwater ecosystems, then outlines the main objectives of the thesis. Subsequently, Chapter 2 presents a conceptual study about the current prediction of chemical effects in ecotoxicology and potential future avenues to improve ecological relevance of effect predictions by addressing the integration of different levels of biological organisation (termed biological levels). The study shows that approaches and tools that currently contribute to the prediction of chemical effects can be attributed to three idealised perspectives: the suborganismal, organismal and ecological perspective. The perspectives focus on different biological levels and are associated with distinct scientific concepts and communities. They complement each other so theoretical and empirical links between them may enhance prediction by capturing the entire phenomenon of chemical effects, from chemical uptake to ecosystem effects. Complex experimental studies accounting for eco-evolutionary dynamics are needed to cross barriers between biological levels as well as spatiotemporal scales. Overall, the conclusions of Chapter 2 may help to develop overarching frameworks for predicting chemical effects in ecosystems, including for untested species. Chapters 3 and 4 present a field study combined with laboratory analyses on the potential propagation of pesticides and their effects from agricultural stream sections to the edge of least impacted upstream sections, that can serve as refuges for many species. The study examines exposure and effects for different biological levels at three site types, the pesticide-polluted agricultural sites (termed agriculture), least impacted upstream sites (termed refuge) and transitional sites (termed edge) in six small streams of south-west Germany. The results in Chapter 3 show that regional transport of pesticides can lead to ecologically relevant pesticide exposure in forested sections within a few kilometres upstream of agricultural areas (i.e. at both edge and refuge sites). As further demonstrated in Chapter 3, the tested indicators of community responses (Jaccard Index, taxonomic richness, total abundance, SPEARpesticides) together suggest a species turnover from upstream refuge to downstream agricultural sites and a potential influence of adjacent agriculture on the edge sites. In contrast, Chapter 4 does not identify any particular edge effect that distinguish edge organisms and populations in edge sites from those in more upstream refuge sites. Gammarus fossarum populations at edges show equal levels of imidacloprid tolerance, energy reserves (i.e. lipid content) and genetic diversity to populations further upstream. Gammarus spp. from agricultural sites exhibit a lower imidacloprid tolerance compared to edge and refuge, potentially due to energy trade-offs in a multiple stressor environment, but related effects do not propagate to the edges (Chapter 4). Notwithstanding, the results of Chapter 4 indicate bidirectional gene flow between site types, supporting the hypothesis that adapted genotypes – if present at locally polluted sites – could spread to populations at least impacted sites. Taken together, Chapters 3 and 4, illustrate that pesticides and their effects can potentially propagate to least impacted upstream sections, empirically novel findings to our knowledge. These results of this thesis can help in predicting or explaining population and community dynamics in least impacted habitats and can ultimately inform pesticide management as well as freshwater restoration and protection of biodiversity.
Wechselnde Umweltbedingungen wie Temperaturveränderungen oder der Zugang zu Nährstoffen erfordern spezielle genetische Anpassungsprogramme, vor allem von sessilen Organismen wie Pflanzen. Ein solcher hochkonservierter Mechanismus, der unter anderem vor Temperaturspitzen schützt, ist die von Hitzeschockfaktoren (HSF) kontrollierte Hitzeschockantwort (HSR). Dabei werden vermehrt spezifische Hitzestressproteine (HSPs, Chaperone) gebildet, die Proteine vor Denaturierung schützen. In Pflanzen hat sich ein hochkomplexes regulatorisches Netzwerk gebildet, das aus über 20 HSFs besteht, das eine genaue Feinabstimmung der HSR auf die jeweiligen Stressbedingungen erlaubt.
Das hohe Maß an Komplexität der HSR in Pflanzen erschwert die wissenschaftliche Zugänglichkeit jedoch erheblich. Um die grundlegenden Prinzipien der HSR in Pflanzen zu verstehen griffen wir deshalb auf einen einfacheren Modellorganismus zurück, der Pflanzen sehr nahe steht aber nur einen einzigen HSF (HSF1) enthält, der einzelligen Grünalge Chlamydomonas reinhardtii. Im Rahmen dieser Arbeit wurden dazu drei Ansätze verfolgt.
Als erstes wurden verschiedene chemische Substanzen eingesetzt die unterschiedliche Schritte während der Aktivierung und Abschaltung der HSR hemmen um darüber die Regulation der HSR aufzuklären. Dabei wurde festgestellt, dass die Phosphorylierung von HSF1 eine entscheidende Rolle in der Aktivierung der HSR spielt, das auslösende Momentum die Anhäufung von falsch gefalteten Proteinen ist und das HSP90A aus dem Cytosol eine wichtige modulierende Rolle bei der HSR spielt.
Als zweites wurde die Veränderung sämtlicher Transkripte mithilfe von Microarrays gemessen, um vor allem pflanzenspezifische Prozesse zu identifizieren, die auf erhöhte Temperaturen gezielt angepasst werden müssen. Dabei konnte die Chlorophyll Biosynthese und der Transport von Proteinen in den Chloroplasten als neue, pflanzenspezifische Ziele der Stressantwort identifiziert werden. Des Weiteren konnte direkt gezeigt werden, das HSF1 auch plastidäre Chaperone reguliert, im Gegensatz zu mitochondrialen Chaperonen die getrennt gesteuert werden.
Als letztes wurde gezielt die Expression wichtiger Gene für die Stressantwort (HSF1/HSP70B) unterdrückt, um den Einfluss dieser Gene auf die HSR genauer zu studieren. Dazu habe ich ein in der einzelligen Grünalge neuartiges System entwickelt, basierend auf dem RNAi Mechanismus, dass es erlaubt abhängig von der Stickstoffquelle im Nährmedium auch essentielle Gene gezielt auszuschalten. Dieses System erlaubte es zu zeigen, dass HSF1 selbst während des Stresses die Expression seiner RNA erhöht, und dies gezielt tut um die Stressantwort weiter zu verstärken. Es konnte weiter gezeigt werden, dass das Chloroplasten Chaperon HSP70B ein essentielles Protein für das Zellwachstum ist, welches mithilfe des induzierbaren RNAi Systems genauer untersucht werden kann. Dabei wurde festgestellt, dass die HSP70B vermittelte Assemblierung und Disassemblierung des VIPP1 Proteins entscheidend ist für dessen Funktion in der Zelle. Des Weiteren konnte gezeigt werde, dass HSP70B wahrscheinlich verantwortlich ist für die Faltung eines oder mehrerer noch unbekannter Enzyme der Arginin Biosynthese oder der Stickstofffixierung, und das diese Prozesse wahrscheinlich die essentielle Funktion von HSP70B darstellen.
Previous research has shown the importance of early science, technology, engineering, and math education for children’s knowledge, as it establishes a groundwork for their later learning and academic achievement. However, the engagement of preschool teachers especially in science learning activities is infrequent, and some teachers still pronounce the belief that science education is inappropriate for the early childhood years. Furthermore, there is a lack of clarity regarding the connections between teachers' attitudes (including their knowledge, beliefs, and willingness) towards teaching early science and their actual teaching practice, as well as the subsequent effects of teacher practice on children's learning outcomes. This dissertation primarily aims to clarify these associations. Block play offers the possibility to link scientific concepts (e.g., stability) to children’s everyday activities and thus represents an age-appropriate way to examine young children’s STEM-learning. The present dissertation encompasses three research articles, focusing specifically on the interplay between preschool teachers’ dispositions and practice in block play and 4- to 6-year old children’s knowledge. The first article focused on the validation of a self-developed instrument to assess preschool teachers’ willingness to engage in science teaching and examined the predictive power of teachers’ willingness for teachers’ practice. Results suggested that the instrument measured teachers’ willingness reliably and validly, however, teachers’ willingness did not predict their practice in block play. The second article examined the relationship between the preschool teachers’ instructional quality during block play and various aspects of children's knowledge. Specifically, the study explored how instructional quality in block play influenced children's knowledge in stability, math, and spatial language. Additionally, children’s academic self-concept and cognitive aspects (i.e., intelligence, working memory) were considered. Results implied that preschool teachers’ scaffolding activities were related to children’s stability knowledge in block play. Moreover, teachers’ instructional quality was positively correlated with children’s academic self-concept in block play. The primary focus of the third article was on implementing a block play curriculum. Therefore, study 3 employed a longitudinal design to assess the effectiveness of a teacher training on teachers’ practice with the curriculum, which included both, guided and free play. Teachers were randomly assigned to either a control group or an experimental group. The experimental groups received training with the block play curriculum, while the control group did not receive any training. Results showed no change in teachers’ knowledge before and after training. Nonetheless, teachers in the experimental group applied more scaffolding after the training. Furthermore, preschool teachers applied more scaffolding during guided than during free play. Children’s math score in the experimental group, but not in the control group, significantly improved from pre- to post-test. In the general discussion, the findings of the three articles are reflected in the light of the interplay between teachers’ dispositions and their teaching practice as well as the impact of teacher practice on children’s knowledge. Besides, the discussion reflects on methodological difficulties of empirical studies in early childcare settings, providing a prospective view on multimethod approaches for future research. Taken together, the present dissertation contributes to a more profound understanding of how teacher practices and children's knowledge interact. Further, the research holds great relevance for practical application as it illustrates the differential effects of teacher training on preschool teachers’ knowledge and their teaching practice.
Symplectic linear quotient singularities belong to the class of symplectic singularities introduced by Beauville in 2000.
They are linear quotients by a group preserving a symplectic form on the vector space and are necessarily singular by a classical theorem of Chevalley-Serre-Shephard-Todd.
We study \(\mathbb Q\)-factorial terminalizations of such quotient singularities, that is, crepant partial resolutions that are allowed to have mild singularities.
The only symplectic linear quotients that can possibly admit a smooth \(\mathbb Q\)-factorial terminalization are by a theorem of Verbitsky those by symplectic reflection groups.
A smooth \(\mathbb Q\)-factorial terminalization is in this context referred to as a symplectic resolution and over the past two decades, there is an ongoing effort to classify exactly which symplectic reflection groups give rise to quotients that admit symplectic resolutions.
We reduce this classification to finitely many, precisely 45, open cases by proving that for almost all quotients by symplectically primitive symplectic reflection groups no such resolution exists.
Concentrating on the groups themselves, we prove that a parabolic subgroup of a symplectic reflection group is generated by symplectic reflections as well.
This is a direct analogue of a theorem of Steinberg for complex reflection groups.
We further study divisor class groups of \(\mathbb Q\)-factorial terminalizations of linear quotients by finite subgroups \(G\) of the special linear group and prove that such a class group is completely controlled by the symplectic reflections - or more generally junior elements - contained in \(G\).
We finally discuss our implementation of an algorithm by Yamagishi for the computation of the Cox ring of a \(\mathbb Q\)-factorial terminalization of a linear quotient in the computer algebra system OSCAR.
We use this algorithm to construct a generating system of the Cox ring corresponding to the quotient by a dihedral group of order \(2d\) with \(d\) odd acting by symplectic reflections.
Although our argument follows the algorithm, the proof does not logically depend on computer calculations.
We are able to derive the \(\mathbb Q\)-factorial terminalization itself from the Cox ring in this case.
Reading as a cultural skill is acquired over a long period of training. This thesis supports the idea that reading is based on specific strategies that result from modification and coordination of earlier developed object recognition strategies. The reading-specific processing strategies are considered to be more analytic compared to object recognition strategies, which are described as holistic. To enable proper reading skills these strategies have to become automatized. Study 1 (Chapter 4) examined the temporal and visual constrains of letter recognition strategies. In the first experiment two successively presented stimuli (letters or non-letters) had to be classified as same or different. The second stimulus could either be presented in isolation or surrounded by a shape, which was either similar (congruent) or different (incongruent) in its geometrical properties to the stimulus itself. The non-letter pairs were presented twice as often as the letter pairs. The results demonstrated a preference for the holistic strategy also in letters, even if the non- letter set was presented twice as often as the letter set, showing that the analytic strategy does not replace the holistic one completely, but that the usage of both strategies is task-sensitive. In Experiment 2, we compared the Global Precedence Effect (GPE) for letters and non-letters in central viewing, with the global stimulus size close to the functional visual field in whole word reading (6.5◦ of visual angle) and local stimuli close to the critical size for fluent reading of individual letters (0.5◦ of visual angle). Under these conditions, the GPE remained robust for non-letters. For letters, however, it disappeared: letters showed no overall response time advantage for the global level and symmetric congruence effects (local-to-global as well as global-to-local interference). These results indicate that reading is based on resident analytic visual processing strategies for letters. In Study 2 (Chapter 5) we replicated the latter result with a large group of participants as part of a study in which pairwise associations of non-letters and phonological or non-phonological sounds were systematically trained. We investigated whether training would eliminate the GPE also for non-letters. We observed, however, that the differentiation between letters and non-letter shapes persists after training. This result implies that pairwise association learning is not sufficient to overrule the process differentiation in adults. In addition, subtle effects arising in the letter condition (due to enhanced power) enable us to further specify the differentiation in processing between letters and non-letter shapes. The influence of reading ability on the GPE was examined in Study 3 (Chapter 6). Children with normal reading skills and children with poor reading skills were instructed to detect a target in Latin or Hebrew Navon letters. Children with normal reading skills showed a GPE for Latin letters, but not for Hebrew letters. In contrast, the dyslexia group did not show GPE for either kind of stimuli. These results suggest that dyslexic children are not able to apply the same automatized letter processing strategy as children with normal reading skills do. The difference between the analytic letter processing and the holistic non-letter processing was transferred to the context of whole word reading in Study 4 (Chapter 7). When participants were instructed to detect either a letter or a non-letter in a mixed character string, for letters the reaction times and error rates increased linearly from the left to the right terminal position in the string, whereas for non-letters a symmetrical U-shaped function was observed. These results suggest, that the letter-specific processing strategies are triggered automatically also for more word-like material. Thus, this thesis supports and expands prior results of letter-specific processing and gives new evidences for letter-specific processing strategies.
The validity of formulas w.r.t. a specification over first-order logic with a semantics based on all models is semi-decidable. Therefore, we may implement a proof procedure which finds a proof for every valid formula fully automatically. But this semantics often lacks intuition: Some pathological models such as the trivial model may produce unexpected results w.r.t. validity. Instead, we may consider just a class of special models, for instance, the class of all data models. Proofs are then performed using induction. But, inductive validity is not semi-decidable -- even for first-order logic. This theoretical drawback manifests itself in practical limitations: There are theorems that cannot be proved by induction directly but only generalizations can be proved. For their definition, we may have to extend the specification. Therefore, we cannot expect to prove interesting theorems fully automatically. Instead, we have to support user-interaction in a suitable way. In this thesis, we aim at developing techniques that enhance automatic proof control of (inductive) theorem provers and that enable user-interaction in a suitable way. We integrate our new proof techniques into the inductive theorem prover QuodLibet and validate them with various case studies. Essentially, we introduce the following three proof techniques: -We integrate a decision procedure for linear arithmetic into QuodLibet in a close way by defining new inference rules that perform the elementary steps of the decision procedure. This allows us to implement well-known heuristics for automatic proof control. Furthermore, we are able to provide special purpose tactics that support the manual speculation of lemmas if a proof attempt gets stuck. The integration improves the ability of the theorem prover to prove theorems automatically as well as its efficiency. Our approach is competitive with other approaches regarding efficiency; it provides advantages regarding the speculation of lemmas. -The automatic proof control searches for a proof by applying inference rules. The search space is not only infinite, but grows dramatically with the depth of the search. In contrast to this, checking and analyzing performed proofs is very efficient. As the search space also has a high redundancy, it is reasonable to reuse subproofs found during proof search. We define new notions for the contribution of proof steps to a proof. These notions enable the derivation of pruned proofs and the identification of superfluous subformulas in theorems. A proof may be reused in two ways: upward propagation prunes a proof by eliminating superfluous proof steps; sideward reuse closes an open proof obligation by replaying an already found proof. -For interactive theorem provers, it is essential not only to prove automatically as many lemmas as possible but also to restrict proof search in such a way that the proof process stops within a reasonable amount of time. We introduce different markings in the goals to be proved and the lemmas to be applied to restrict proof search in a flexible way: With a forbidden marking, we can simulate well-known approaches for applying conditional lemmas. A mandatory marking provides a new heuristics which is inspired by local contribution of proof steps. With obligatory and generous markings, we can fine-tune the degree of efficiency and extent of proof search manually. With an elaborate case study, we show the benefits of the different techniques, in particular the synergetic effect of their combination.
This thesis is concerned with the modeling of the solid-solid phase transformation, such as the martensitic transformation. The allotropes austenite and martensite are important for industry applications. As a result of its ductility, austenite is desired in the bulk, as opposed to martensite, which desired in the near surface region. The phase field method is used to model the phase transformation by minimizing the free energy. It consists of a mechanical part, due to elastic strain and a chemical part, due to the martensitic transformation. The latter is temperature dependent. Therefore, a temperature dependent separation potential is presented here. To accommodate multiple orientation variants, a multivariant phase field model is employed. Using the Khachaturyan approach, the effective material parameters can be used to describe a constitutive model. This however, renders the nodal residual vector and elemental tangent matrix directly dependent on the phase, making a generalization complicated. An easier approach is the use of the Voigt/Taylor homogenization, in which the energy and their derivatives are interpolated creating an interface for material law of the individual phases.
The goal of this work is the development and investigation of an interdisciplinary and in itself closed hydrodynamic approach to the simulation of dilute and dense granular flow. The definition of “granular flow” is a nontrivial task in itself. We say that it is either the flow of grains in a vacuum or in a fluid. A grain is an observable piece of a certain material, for example stone when we mean the flow of sand. Choosing a hydrodynamic view on granular flow, we treat the granular material as a fluid. A hydrodynamic model is developed, that describes the process of flowing granular material. This is done through a system of partial differential equations and algebraic relations. This system is derived by the kinetic theory of granular gases which is characterized by inelastic collisions extended with approaches from soil mechanics. Solutions to the system have to be obtained to understand the process. The equations are so difficult to solve that an analytical solution is out of reach. So approximate solutions must be obtained. Hence the next step is the choice or development of a numerical algorithm to obtain approximate solutions of the model. Common to every problem in numerical simulation, these two steps do not lead to a result without implementation of the algorithm. Hence the author attempts to present this work in the following frame, to participate in and contribute to the three areas Physics, Mathematics and Software implementation and approach the simulation of granular flow in a combined and interdisciplinary way. This work is structured as follows. A continuum model for granular flow which covers the regime of fast dilute flow as well as slow dense flow up to vanishing velocity is presented in the first chapter. This model is strongly nonlinear in the dependence of viscosity and other coefficients on the hydrodynamic variables and it is singular because some coefficients diverge towards the maximum packing fraction of grains. Hence the second difficulty, the challenging task of numerically obtaining approximate solutions for this model is faced in the second chapter. In the third chapter we aim at the validation of both the model and the numerical algorithm through numerical experiments and investigations and show their application to industrial problems. There we focus intensively on the shear flow experiment from the experimental and analytical work of Bocquet et al. which serves well to demonstrate the algorithm, all boundary conditions involved and provides a setting for analytical studies to compare our results. The fourth chapter rounds up the work with the implementation of both the model and the numerical algorithm in a software framework for the solution of complex rheology problems developed as part of this thesis.