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Nanoparticle-Filled Thermoplastics and Thermoplastic Elastomer: Structure-Property Relationships
(2012)

The present work focuses on the structure-property relationships of
particulate-filled thermoplastics and thermoplastic elastomer (TPE). In this work
two thermoplastics and one TPE were used as polymer matrices, i.e. amorphous
bisphenol-A polycarbonate (PC), semi-crystalline isotactic polypropylene (iPP),
and a block copolymer poly(butylene terephthalate)-block-poly(tetramethylene
glycol) TPE(PBT-PTMG). For PC, a selected type of various Aerosil® nano-SiO2
types was used as filler to improve the thermal and mechanical properties by
maintaining the transparency of PC matrix. Different types of SiO2 and TiO2
nanoparticles with different surface polarity were used for iPP. The goal was to
examine the influence of surface polarity and chemical nature of nanoparticles on
the thermal, mechanical and morphological properties of iPP composites. For
TPE(PBT-PTMG), three TiO2 particles were used, i.e. one grade with hydroxyl
groups on the particle surface and the other two grades are surface-modified with
metal and metal oxides, respectively. The influence of primary size and dispersion
quality of TiO2 particles on the properties of TPE(PBT-PTMG)/TiO2 composites
were determined and discussed.
All polymer composites were produced by direct melt blending in a twin-screw
extruder via masterbatch technique. The dispersion of particles was examined by
using scanning electron microscopy (SEM) and micro-computerized tomography
(μCT). The thermal and crystalline properties of polymer composites were characterized by using thermogravimetric analysis (TGA) and differential
scanning calorimetry (DSC). The mechanical and thermomechanical properties
were determined by using mechanical tensile testing, compact tension and
Charpy impact as well as dynamic-mechanical thermal analysis (DMTA).
The SEM results show that the unpolar-surface modified nanoparticles are better
dispersed in polymer matrices as iPP than polar-surface nanoparticles, especially
in case of using Aeroxide® TiO2 nanoparticles. The Aeroxide® TiO2 nanoparticles
with a polar surface due to Ti-OH groups result in a very high degree of
agglomeration in both iPP and TPE matrices because of strong van der Waals
interactions among particles (hydrogen bonding). Compared to unmodified
Aeroxide® TiO2 nanoparticles, the other grades of surface modified TiO2 particles
are very homogenously dispersed in used iPP and TPE(PBT-PTMG). The
incorporation of SiO2 nanoparticles into bisphenol-A PC significantly increases
the mechanical properties of PC/SiO2 nanocomposites, particularly the resistance
against environmental stress crazing (ESC). However, the transparency of
PC/SiO2 nanocomposites decreases with increasing nanoparticle content and
size due to a mismatch of infractive indices of PC and SiO2 particles. The different
surface polarity of nanoparticles in iPP shows evident influence on properties of
iPP composites. Among iPP/SiO2 nanocomposites, the nanocomposite
containing SiO2 nanoparticles with a higher degree of hydrophobicity shows
improved fracture and impact toughness compared to the other iPP/SiO2
composites. The TPE(PBT-PTMG)/TiO2 composites show much better thermal and mechanical properties than neat TPE(PBT-PTMG) due to strong chemical
interactions between polymer matrix and TiO2 particles. In addition, better
dispersion quality of TiO2 particles in used TPE(PBT-PTMG) leads to dramatically
improved mechanical properties of TPE(PBT-PTMG)/TiO2 composites.

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.

Today, polygonal models occur everywhere in graphical applications, since they are easy
to render and to compute and a very huge set of tools are existing for generation and
manipulation of polygonal data. But modern scanning devices that allow a high quality
and large scale acquisition of complex real world models often deliver a large set of
points as resulting data structure of the scanned surface. A direct triangulation of those
point clouds does not always result in good models. They often contain problems like
holes, self-intersections and non manifold structures. Also one often looses important
surface structures like sharp corners and edges during a usual surface reconstruction.
So it is suitable to stay a little longer in the point based world to analyze the point cloud
data with respect to such features and apply a surface reconstruction method afterwards
that is known to construct continuous and smooth surfaces and extend it to reconstruct
sharp features.

Image restoration and enhancement methods that respect important features such as edges play a fundamental role in digital image processing. In the last decades a large
variety of methods have been proposed. Nevertheless, the correct restoration and
preservation of, e.g., sharp corners, crossings or texture in images is still a challenge, in particular in the presence of severe distortions. Moreover, in the context of image denoising many methods are designed for the removal of additive Gaussian noise and their adaptation for other types of noise occurring in practice requires usually additional efforts.
The aim of this thesis is to contribute to these topics and to develop and analyze new
methods for restoring images corrupted by different types of noise:
First, we present variational models and diffusion methods which are particularly well
suited for the restoration of sharp corners and X junctions in images corrupted by
strong additive Gaussian noise. For their deduction we present and analyze different
tensor based methods for locally estimating orientations in images and show how to
successfully incorporate the obtained information in the denoising process. The advantageous
properties of the obtained methods are shown theoretically as well as by
numerical experiments. Moreover, the potential of the proposed methods is demonstrated
for applications beyond image denoising.
Afterwards, we focus on variational methods for the restoration of images corrupted
by Poisson and multiplicative Gamma noise. Here, different methods from the literature
are compared and the surprising equivalence between a standard model for
the removal of Poisson noise and a recently introduced approach for multiplicative
Gamma noise is proven. Since this Poisson model has not been considered for multiplicative
Gamma noise before, we investigate its properties further for more general
regularizers including also nonlocal ones. Moreover, an efficient algorithm for solving
the involved minimization problems is proposed, which can also handle an additional
linear transformation of the data. The good performance of this algorithm is demonstrated
experimentally and different examples with images corrupted by Poisson and
multiplicative Gamma noise are presented.
In the final part of this thesis new nonlocal filters for images corrupted by multiplicative
noise are presented. These filters are deduced in a weighted maximum likelihood
estimation framework and for the definition of the involved weights a new similarity measure for the comparison of data corrupted by multiplicative noise is applied. The
advantageous properties of the new measure are demonstrated theoretically and by
numerical examples. Besides, denoising results for images corrupted by multiplicative
Gamma and Rayleigh noise show the very good performance of the new filters.

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.

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.

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.

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.

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.

Filtering, Approximation and Portfolio Optimization for Shot-Noise Models and the Heston Model
(2012)

We consider a continuous time market model in which stock returns satisfy a stochastic differential equation with stochastic drift, e.g. following an Ornstein-Uhlenbeck process. The driving noise of the stock returns consists not only of Brownian motion but also of a jump part (shot noise or compound Poisson process). The investor's objective is to maximize expected utility of terminal wealth under partial information which means that the investor only observes stock prices but does not observe the drift process. Since the drift of the stock prices is unobservable, it has to be estimated using filtering techniques. E.g., if the drift follows an Ornstein-Uhlenbeck process and without
jump part, Kalman filtering can be applied and optimal strategies can be computed explicitly. Also in other cases, like for an underlying
Markov chain, finite-dimensional filters exist. But for certain jump processes (e.g. shot noise) or certain nonlinear drift dynamics explicit computations, based on discrete observations, are no longer possible or existence of finite dimensional filters is no longer valid. The same
computational difficulties apply to the optimal strategy since it depends on the filter. In this case the model may be approximated by
a model where the filter is known and can be computed. E.g., we use statistical linearization for non-linear drift processes, finite-state-Markov chain approximations for the drift process and/or diffusion approximations for small jumps in the noise term.
In the approximating models, filters and optimal strategies can often be computed explicitly. We analyze and compare different approximation methods, in particular in view of performance of the corresponding utility maximizing strategies.