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In urban planning, both measuring and communicating sustainability are among the most recent concerns. Therefore, the primary emphasis of this thesis concerns establishing metrics and visualization techniques in order to deal with indicators of sustainability.
First, this thesis provides a novel approach for measuring and monitoring two indicators of sustainability - urban sprawl and carbon footprints – at the urban neighborhood scale. By designating different sectors of relevant carbon emissions as well as different household categories, this thesis provides detailed information about carbon emissions in order to estimate impacts of daily consumption decisions and travel behavior by household type. Regarding urban sprawl, a novel gridcell-based indicator model is established, based on different dimensions of urban sprawl.
Second, this thesis presents a three-step-based visualization method, addressing predefined requirements for geovisualizations and visualizing those indicator results, introduced above. This surface-visualization combines advantages from both common GIS representation and three-dimensional representation techniques within the field of urban planning, and is assisted by a web-based graphical user interface which allows for accessing the results by the public.
In addition, by focusing on local neighborhoods, this thesis provides an alternative approach in measuring and visualizing both indicators by utilizing a Neighborhood Relation Diagram (NRD), based on weighted Voronoi diagrams. Thus, the user is able to a) utilize original census data, b) compare direct impacts of indicator results on the neighboring cells, and c) compare both indicators of sustainability visually.

The safety of embedded systems is becoming more and more important nowadays. Fault Tree Analysis (FTA) is a widely used technique for analyzing the safety of embedded systems. A standardized tree-like structure called a Fault Tree (FT) models the failures of the systems. The Component Fault Tree (CFT) provides an advanced modeling concept for adapting the traditional FTs to the hierarchical architecture model in system design. Minimal Cut Set (MCS) analysis is a method that works for qualitative analysis based on the FTs. Each MCS represents a minimal combination of component failures of a system called basic events, which may together cause the top-level system failure. The ordinary representations of MCSs consist of plain text and data tables with little additional supporting visual and interactive information. Importance analysis based on FTs or CFTs estimates the contribution of each potential basic event to a top-level system failure. The resulting importance values of basic events are typically represented in summary views, e.g., data tables and histograms. There is little visual integration between these forms and the FT (or CFT) structure. The safety of a system can be improved using an iterative process, called the safety improvement process, based on FTs taking relevant constraints into account, e.g., cost. Typically, relevant data regarding the safety improvement process are presented across multiple views with few interactive associations. In short, the ordinary representation concepts cannot effectively facilitate these analyses.
We propose a set of visualization approaches for addressing the issues above mentioned in order to facilitate those analyses in terms of the representations.
Contribution:
1. To support the MCS analysis, we propose a matrix-based visualization that allows detailed data of the MCSs of interest to be viewed while maintaining a satisfactory overview of a large number of MCSs for effective navigation and pattern analysis. Engineers can also intuitively analyze the influence of MCSs of a CFT.
2. To facilitate the importance analysis based on the CFT, we propose a hybrid visualization approach that combines the icicle-layout-style architectural views with the CFT structure. This approach facilitates to identify the vulnerable components taking the hierarchies of system architecture into account and investigate the logical failure propagation of the important basic events.
3. We propose a visual safety improvement process that integrates an enhanced decision tree with a scatter plot. This approach allows one to visually investigate the detailed data related to individual steps of the process while maintaining the overview of the process. The approach facilitates to construct and analyze improvement solutions of the safety of a system.
Using our visualization approaches, the MCS analysis, the importance analysis, and the safety improvement process based on the CFT can be facilitated.

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.

This thesis is devoted to furthering the tropical intersection theory as well as to applying the
developed theory to gain new insights about tropical moduli spaces.
We use piecewise polynomials to define tropical cocycles that generalise the notion of tropical Cartier divisors to higher codimensions, introduce an intersection product of cocycles with tropical cycles and use the connection to toric geometry to prove a Poincaré duality for certain cases. Our
main application of this Poincaré duality is the construction of intersection-theoretic fibres under a
large class of tropical morphisms.
We construct an intersection product of cycles on matroid varieties which are a natural
generalisation of tropicalisations of classical linear spaces and the local blocks of smooth tropical
varieties. The key ingredient is the ability to express a matroid variety contained in another matroid variety by a piecewise polynomial that is given in terms of the rank functions of the corresponding
matroids. In particular, this enables us to intersect cycles on the moduli spaces of n-marked abstract
rational curves. We also construct a pull-back of cycles along morphisms of smooth varieties, relate
pull-backs to tropical modifications and show that every cycle on a matroid variety is rationally
equivalent to its recession cycle and can be cut out by a cocycle.
Finally, we define families of smooth rational tropical curves over smooth varieties and construct a tropical fibre product in order to show that every morphism of a smooth variety to the moduli space of abstract rational tropical curves induces a family of curves over the domain of the morphism.
This leads to an alternative, inductive way of constructing moduli spaces of rational curves.

SHIM is a concurrent deterministic programming language for embedded systems built on rendezvous communication. It abstracts away many details to give the developer a high-level view that includes virtual shared variables, threads as orthogonal statements, and deterministic concurrent exceptions.
In this paper, we present a new way to compile a SHIM-like language into a set of asynchronous guarded actions, a well-established intermediate representation for concurrent systems. By doing so, we build a bridge to many other tools, including hardware synthesis and formal verification. We present our translation in detail, illustrate it through examples, and show how the result can be used by various other tools.

By natural or man-made disasters, the evacuation of a whole region or city may become necessary. Apart from private traffic, the evacuation from collection points to secure shelters outside the endangered region will be realized by a bus fleet made available by emergency relief. The arising Bus Evacuation Problem (BEP) is a vehicle scheduling problem, in which a given number of evacuees needs to be transported from a set of collection points to a set of capacitated shelters, minimizing the total evacuation time, i.e., the time needed until the last person is brought to safety.
In this paper we consider an extended version of the BEP, the Robust Bus Evacuation Problem (RBEP), in which the exact numbers of evacuees are not known, but may stem from a set of probable scenarios. However, after a given reckoning time, this uncertainty is eliminated and planners are given exact figures. The problem is to decide for each bus, if it is better to send it right away -- using uncertain numbers of evacuees -- or to wait until the numbers become known.
We present a mixed-integer linear programming formulation for the RBEP and discuss solution approaches; in particular, we present a tabu search framework for finding heuristic solutions of acceptable quality within short computation time. In computational experiments using both randomly generated instances and the real-world scenario of evacuating the city of Kaiserslautern, we compare our solution approaches.

Due to their N-glycosidase activity, ribosome-inactivating proteins (RIPs) are attractive candidates as antitumor and antiviral agents in medical and biological research. In the present study, we have successfully cloned two different truncated gelonins into pET-28a(+) vectors and expressed intact recombinant gelonin (rGel), recombinant C-terminally truncated gelonin (rC3-gelonin) and recombinant N- and C-terminally truncated gelonin (rN34C3-gelonin). Biological experiments showed that all these recombinant gelonins have no inhibiting effect on MCF-7 cell lines. These data suggest that the truncated-gelonins are still having a specific structure that does not allow for internalization into cells. Further, truncation of gelonin leads to partial or complete loss of N-glycosidase as well as DNase activity compared to intact rGel. Our data suggest that C-and N-terminal amino acid residues are involved in the catalytic and cytotoxic activities of rGel. In addition, the intact gelonin should be selected as a toxin in the immunoconjugate rather than truncated gelonin.
In the second part, an immunotoxin composed of gelonin, a basic protein of 30 kDa isolated from the Indian plant Gelonium multiflorum and the cytotoxic drug MTX has been studied as a potential tool of gelonin delivery into the cytoplasm of cells. Results of many experiments showed that, on the average, about 5 molecules of MTX were coupled to one molecule of gelonin. The MTX-gelonin conjugate is able to reduce the viability of MCF-7 cell in a dose-dependent manner (ID50, 10 nM) as shown by MTT assay and significantly induce direct and oxidative DNA damage as shown by the alkaline comet assay. However, in-vitro translation toxicity MTX-gelonin conjugates have IC50, 50.5 ng/ml which is less toxic than that of gelonin alone IC50, 4.6 ng/ml. It can be concluded that the positive charge plays an important role in the N-glycosidase activity of gelonin. Furthermore, conjugation of MTX with gelonin through α- and γ- carboxyl groups leads to the partial loss of its anti-folate activity compared to free MTX. These results, taken together, indicate that conjugation of MTX to gelonin permits delivery of the gelonin into the cytoplasm of cancer cells and exerts a measurable toxic effect.
In the third part, we have isolated and characterized two ribosome-inactivating proteins (RIPs) type I, gelonin and GAP31, from seeds of Gelonium multiflorum. Both proteins exhibit RNA-N-glycosidase activity. The amino acid sequences of gelonin and GAP31 were identified by MALDI and ESI mass spectrometry. Gelonin and GAP31 peptides - obtained by proteolytic digestion (trypsin and Arg-C) - are consistent with the amino acid sequence published by Rosenblum and Huang, respectively. Further structural characterization of gelonin and GAP31 (tryptic and Arg-C peptide mapping) showed that the two RIPs have 96% similarity in their sequence. Thus, these two proteins are most probably isoforms arisen from the same gene by alternative splicing. The ESI-MS analysis of gelonin and GAP31 exhibited at least three different post-translational modified forms. A standard plant paucidomannosidic N-glycosylation pattern (GlcNAc2Man2-5Xyl0-1 and GlcNAc2Man6-12Fuc1-2Xyl0-2) was identified using electrospray ionization MS for gelonin on N196 and GAP31 on N189, respectively. Based on these results, both proteins are located in the vacuoles of Gelonium multiflorum seeds.

We consider a variant of the generalized assignment problem (GAP) where the amount of space used in each bin is restricted to be either zero (if the bin is not opened) or above a given lower bound (a minimum quantity). We provide several complexity results for different versions of the problem and give polynomial time exact algorithms and approximation algorithms for restricted cases.
For the most general version of the problem, we show that it does not admit a polynomial time approximation algorithm (unless P=NP), even for the case of a single bin. This motivates to study dual approximation algorithms that compute solutions violating the bin capacities and minimum quantities by a constant factor. When the number of bins is fixed and the minimum quantity of each bin is at least a factor \(\delta>1\) larger than the largest size of an item in the bin, we show how to obtain a polynomial time dual approximation algorithm that computes a solution violating the minimum quantities and bin capacities by at most a factor \(1-\frac{1}{\delta}\) and \(1+\frac{1}{\delta}\), respectively, and whose profit is at least as large as the profit of the best solution that satisfies the minimum quantities and bin capacities strictly.
In particular, for \(\delta=2\), we obtain a polynomial time (1,2)-approximation algorithm.

This paper considers supervised multi-class image segmentation: from a labeled set of
pixels in one image, we learn the segmentation and apply it to the rest of the image or to other similar images. We study approaches with p-Laplacians, (vector-valued) Reproducing Kernel Hilbert
Spaces (RKHSs) and combinations of both. In all approaches we construct segment membership
vectors. In the p-Laplacian model the segment membership vectors have to fulfill a certain probability simplex constraint. Interestingly, we could prove that this is not really a constraint in the case p=2 but is automatically fulfilled. While the 2-Laplacian model gives a good general segmentation, the case of the 1-Laplacian tends to neglect smaller segments. The RKHS approach has
the benefit of fast computation. This direction is motivated by image colorization, where a given
dab of color is extended to a nearby region of similar features or to another image. The connection
between colorization and multi-class segmentation is explored in this paper with an application to
medical image segmentation. We further consider an improvement using a combined method. Each
model is carefully considered with numerical experiments for validation, followed by medical image
segmentation at the end.

One of the fundamental problems in computational structural biology is the prediction of RNA secondary structures from a single sequence. To solve this problem, mainly two different approaches have been used over the past decades: the free energy minimization (MFE) approach which is still considered the most popular and successful method and the competing stochastic context-free grammar (SCFG) approach. While the accuracy of the MFE based algorithms is limited by the quality of underlying thermodynamic models, the SCFG method abstracts from free energies and instead tries to learn about the structural behavior of the molecules by training the grammars on known real RNA structures, making it highly dependent on the availability of a rich high quality training set. However, due to the respective problems associated with both methods, new statistics based approaches towards RNA structure prediction have become increasingly appreciated. For instance, over the last years, several statistical sampling methods and clustering techniques have been invented that are based on the computation of partition functions (PFs) and base pair probabilities according to thermodynamic models. A corresponding SCFG based statistical sampling algorithm for RNA secondary structures has been studied just recently. Notably, this probabilistic method is capable of producing accurate (prediction) results, where its worst-case time and space requirements are equal to those of common RNA folding algorithms for single sequences.
The aim of this work is to present a comprehensive study on how enriching the underlying SCFG by additional information on the lengths of generated substructures (i.e. by incorporating length-dependencies into the SCFG based sampling algorithm, which is actually possible without significant losses in performance) affects the reliability of the induced RNA model and the accuracy of sampled secondary structures. As we will see, significant differences with respect to the overall quality of generated sample sets and the resulting predictive accuracy are typically implied. In principle, when considering the more specialized length-dependent SCFG model as basis for statistical sampling, a higher accuracy of predicted foldings can be reached at the price of a lower diversity of generated candidate structures (compared to the more general traditional SCFG variant or sampling based on PFs that rely on free energies).