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Graphs and flow networks are important mathematical concepts that enable the modeling and analysis of a large variety of real world problems in different domains such as engineering, medicine or computer science. The number, sizes and complexities of those problems permanently increased during the last decades. This led to an increased demand of techniques that help domain experts in understanding their data and its underlying structure to enable an efficient analysis and decision making process.
To tackle this challenge, this work presents several new techniques that utilize concepts of visual analysis to provide domain scientists with new visualization methodologies and tools. Therefore, this work provides novel concepts and approaches for diverse aspects of the visual analysis such as data transformation, visual mapping, parameter refinement and analysis, model building and visualization as well as user interaction.
The presented techniques form a framework that enriches domain scientists with new visual analysis tools and help them analyze their data and gain insight from the underlying structures. To show the applicability and effectiveness of the presented approaches, this work tackles different applications such as networking, product flow management and vascular systems, while preserving the generality to be applicable to further domains.

The simulation of physical phenomena involving the dynamic behavior of fluids and gases
has numerous applications in various fields of science and engineering. Of particular interest
is the material transport behavior, the tendency of a flow field to displace parts of the
medium. Therefore, many visualization techniques rely on particle trajectories.
Lagrangian Flow Field Representation. In typical Eulerian settings, trajectories are
computed from the simulation output using numerical integration schemes. Accuracy concerns
arise because, due to limitations of storage space and bandwidth, often only a fraction
of the computed simulation time steps are available. Prior work has shown empirically that
a Lagrangian, trajectory-based representation can improve accuracy [Agr+14]. Determining
the parameters of such a representation in advance is difficult; a relationship between the
temporal and spatial resolution and the accuracy of resulting trajectories needs to be established.
We provide an error measure for upper bounds of the error of individual trajectories.
We show how areas at risk for high errors can be identified, thereby making it possible to
prioritize areas in time and space to allocate scarce storage resources.
Comparative Visual Analysis of Flow Field Ensembles. Independent of the representation,
errors of the simulation itself are often caused by inaccurate initial conditions,
limitations of the chosen simulation model, and numerical errors. To gain a better understanding
of the possible outcomes, multiple simulation runs can be calculated, resulting in
sets of simulation output referred to as ensembles. Of particular interest when studying the
material transport behavior of ensembles is the identification of areas where the simulation
runs agree or disagree. We introduce and evaluate an interactive method that enables application
scientists to reliably identify and examine regions of agreement and disagreement,
while taking into account the local transport behavior within individual simulation runs.
Particle-Based Representation and Visualization of Uncertain Flow Data Sets. Unlike
simulation ensembles, where uncertainty of the solution appears in the form of different
simulation runs, moment-based Eulerian multi-phase fluid simulations are probabilistic in
nature. These simulations, used in process engineering to simulate the behavior of bubbles in
liquid media, are aimed toward reducing the need for real-world experiments. The locations
of individual bubbles are not modeled explicitly, but stochastically through the properties of
locally defined bubble populations. Comparisons between simulation results and physical
experiments are difficult. We describe and analyze an approach that generates representative
sets of bubbles for moment-based simulation data. Using our approach, application scientists
can directly, visually compare simulation results and physical experiments.

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.

Cyanobacteria of biological soil crusts (BSCs) represent an important part of circumpolar
and Alpine ecosystems, serve as indicators for ecological condition and climate
change, and function as ecosystem engineers by soil stabilization or carbon and nitrogen
input. The characterization of cyanobacteria from both polar regions remains
extremely important to understand geographic distribution patterns and community
compositions. This study is the first of its kind revealing the efficiency of combining
denaturing gradient gel electrophoresis (DGGE), light microscopy and culture-based
16S rRNA gene sequencing, applied to polar and Alpine cyanobacteria dominated
BSCs. This study aimed to show the living proportion of cyanobacteria as an extension
to previously published meta-transcriptome
data of the same study sites.
Molecular fingerprints showed a distinct clustering of cyanobacterial communities
with a close relationship between Arctic and Alpine populations, which differed from
those found in Antarctica. Species richness and diversity supported these results,
which were also confirmed by microscopic investigations of living cyanobacteria
from the BSCs. Isolate-based
sequencing corroborated these trends as cold biome
clades were assigned, which included a potentially new Arctic clade of Oculatella.
Thus, our results contribute to the debate regarding biogeography of cyanobacteria
of cold biomes.

Indentation into a metastable austenite may induce the phase transformation to the bcc phase. We study this process using
atomistic simulation. At temperatures low compared to the equilibrium transformation temperature, the indentation triggers the
transformation of the entire crystallite: after starting the transformation, it rapidly proceeds throughout the simulation crystallite.
The microstructure of the transformed sample is characterized by twinned grains. At higher temperatures, around the equilibrium
transformation temperature, the crystal transforms only locally, in the vicinity of the indent pit. In addition, the indenter
produces dislocation plasticity in the remaining austenite. At intermediate temperatures, the crystal continuously transforms
throughout the indentation process.

Influence of the Crystal Surface on the Austenitic and Martensitic Phase Transition in Pure Iron
(2018)

Using classical molecular dynamics simulations, we studied the influence that free
surfaces exert on the austenitic and martensitic phase transition in iron. For several single-indexed
surfaces—such as (100)bcc and (110)bcc as well as (100)fcc and (110)fcc surfaces—appropriate
pathways exist that allow for the transformation of the surface structure. These are the Bain,
Mao, Pitsch, and Kurdjumov–Sachs pathways, respectively. Tilted surfaces follow the pathway
of the neighboring single-indexed plane. The austenitic transformation temperature follows the
dependence of the specific surface energy of the native bcc phase; here, the new phase nucleates at
the surface. In contrast, the martensitic transformation temperature steadily decreases when tilting
the surface from the (100)fcc to the (110)fcc orientation. This dependence is caused by the strong
out-of-plane deformation that (110)fcc facets experience under the transformation; here, the new
phase also nucleates in the bulk rather than at the surface.

Carotenoids are organic lipophilic tetraterpenes ubiquitously present in Nature and found across the three domains of life (Archaea, Bacteria and Eukaryotes). Their structure is characterized by an extensive conjugated double-bond system, which serves as a light-absorbing chromophore, hence determining its colour, and enables carotenoids to absorb energy from other molecules and to act as antioxidant agents. Humans obtain carotenoids mainly via the consumption of fruits and vegetables, and to a smaller extent from other food sources such as fish and eggs. The concentration of carotenoids in the human plasma and tissues has been positively associated with a lower incidence of several chronic diseases including, cancer, diabetes, macular degeneration and cardiovascular conditions, likely due to their antioxidant properties. However, an important aspect of carotenoids, namely β- and α-carotene and β-cryptoxanthin, in human health and development, is their potential to be converted by the body into Vitamin A.
Yet, bioavailability of carotenoids is relatively low (< 30%) and dependent, among others, on dietary factors, such as amount and type of dietary lipids and the presence of dietary fibres. One dietary factor that has been found to negatively impact carotenoid bioaccessibility and cellular uptake in vitro is high concentrations of divalent cations during simulated gastro-intestinal digestion. Nevertheless, the mechanism of action of divalent cations remains unclear. The goal of this thesis was to better understand how divalent cations act during digestion and modulate carotenoid bioavailability. In vitro trials of simulated gastro-intestinal digestion and cellular uptake were run to investigate how varying concentrations of calcium, magnesium and zinc affected the bioaccessibility of both pure carotenoids and carotenoids from food matrices. In order to validate or refute results obtained in vitro, a randomized and double blinded placebo controlled cross-over postprandial trial (24 male participants) was carried out, testing the effect of 3 supplementary calcium doses (0 mg, 500 mg and 1000 mg) on the bioavailability of carotenoids from a spinach based meal. In vitro trials showed that addition of the divalent cations significantly decreased the bioaccessibility of both pure carotenoids (P < 0.001) and those from food matrices (P < 0.01). This effect was dependent on the type of mineral and its concentration. Strongest effects were seen for increasing concentrations of calcium followed by magnesium and zinc. The addition of divalent cations also altered the physico-chemical properties, i.e. viscosity and surface tension, of the digestas. However, the extent of this effect varied according to the type of matrix. The effects on bioaccessibility and physico-chemical properties were accompanied by variations of the zeta-potential of the particles in solution. Taken together, results from the in vitro trials strongly suggested that divalent cations were able to bind bile salts and other surfactant agents, affecting their solubility. The observed i) decrease in macroviscosity, ii) increase in surface tension, and the iii) reduction of the zeta-potential of the digesta, confirmed the removal of surfactant agents from the system, most likely due to precipitation as a result of the lower solubility of the mineral-surfactant complexes. As such, micellarization of carotenoids was hindered, explaining their reduced bioaccessibility. As for the human trial, results showed that there was no significant influence of supplementation with either 500 or 1000 mg of supplemental calcium (in form of carbonate) on the bioavailability of a spinach based meal, as measured by the area-under curve of carotenoid concentrations in the plasma-triacylglycerol rich fraction, suggesting that the in vitro results are not supported in such an in vivo scenario, which may be explained by the initial low bioaccessibility of spinach carotenoids and the dissolution kinetics of the calcium pills. Further investigations are necessary to understand how divalent cations act during in vivo digestion and potentially interact with lipophilic nutrients and food constituents.

The fact that long fibre reinforced thermoplastic composites (LFT) have higher tensile
strength, modulus and even toughness, compared to short fibre reinforced
thermoplastics with the same fibre loading has been well documented in literature.
These are the underlying factors that have made LFT materials one of the most
rapidly growing sectors of plastics industry. New developments in manufacturing of
LFT composites have led to improvements in mechanical properties and price
reduction, which has made these materials an attractive choice as a replacement for
metals in automobile parts and other similar applications. However, there are still
several open scientific questions concerning the material selection leading to the
optimal property combinations. The present work is an attempt to clarify some of
these questions. The target was to develop tools that can be used to modify, or to
“tailor”, the properties of LFT composite materials, according to the requirements of
automobile and other applications.
The present study consisted of three separate case studies, focusing on the current
scientific issues on LFT material systems. The first part of this work was focused on
LGF reinforced thermoplastic styrenic resins. The target was to find suitable maleic
acid anhydride (MAH) based coupling agents in order to improve the fibre-matrix
interfacial strength, and, in this way, to develop an LGF concentrate suitable for
thermoplastic styrenic resins. It was shown that the mechanical properties of LGF
reinforced “styrenics” were considerably improved when a small amount of MAH
functionalised polymer was added to the matrix. This could be explained by the better fibre-matrix adhesion, revealed by scanning electron microscopy of fracture surfaces.
A novel LGF concentrate concept showed that one particular base material can be
used to produce parts with different mechanical and thermal properties by diluting the
fibre content with different types of thermoplastic styrenic resins. Therefore, this
concept allows a flexible production of parts, and it can be used in the manufacturing
of interior parts for automobile components.The second material system dealt with so called hybrid composites, consisting of
long glass fibre reinforced polypropylene (LGF-PP) and mineral fillers like calcium
carbonate and talcum. The aim was to get more information about the fracture
behaviour of such hybrid composites under tensile and impact loading, and to
observe the influence of the fillers on properties. It was found that, in general, the
addition of fillers in LGF-PP, increased stiffness but the strength and fracture
toughness were decreased. However, calcium carbonate and talcum fillers resulted
in different mechanical properties, when added to LGF-PP: better mechanical
properties were achieved by using talcum, compared to calcium carbonate. This
phenomenon could be explained by the different nucleation effect of these fillers,
which resulted in a different crystalline morphology of polypropylene, and by the
particle orientation during the processing when talc was used. Furthermore, the
acoustic emission study revealed that the fracture mode of LGF-PP changed when
calcium carbonate was added. The characteristic acoustic signals revealed that the
addition of filler led to the fibre debonding at an earlier stage of fracture sequence
when compared to unfilled LGF-PP.
In the third material system, the target was to develop a novel long glass fibre
reinforced composite material based on the blend of polyamide with thermoset
resins. In this study a blend of polyamide-66 (PA66) and phenol formaldehyde resin
(PFR) was used. The chemical structure of the PA66-PFR resin was analysed by
using small molecular weight analogues corresponding to PA66 and PFR
components, as well as by carrying out experiments using the macromolecular
system. Theoretical calculations and experiments showed that there exists a strong
hydrogen bonding between the carboxylic groups of PA66 and the hydroxylic groups
of PFR, exceeding even the strength of amide-water hydrogen bonds. This was
shown to lead to the miscible blends, when PFR was not crosslinked. It was also
found that the morphology of such thermoplastic-thermoset blends can be controlled
by altering ratio of blend components (PA66, PFR and crosslinking agent). In the
next phase, PA66-PFR blends were reinforced by long glass fibres. The studies
showed that the water absorption of the blend samples was considerably decreased,
which was also reflected in higher mechanical properties at equilibrium state.
Wie man aus zahlreichen Untersuchungen und Anwendungsbeispielen entnehmen
kann, besitzen langfaserverstärkte Thermoplaste (LFT) eine bessere Zugfestigkeit,
Biege- und Schlagzähigkeit im Vergleich zu kurzfaserverstärkten Thermoplasten. Die
Vorteile in den mechanischen Eigenschaften haben die LFT zu einem
schnellwachsenden Bereich in der Kunststoffindustrie gemacht. Neue Entwicklungen
in Bereich der Herstellung von LFT haben für zusätzliche Verbesserungen der
mechanischen Eigenschaften sowie eine Preisreduzierung der Materialien in den
vergangenen Jahren gesorgt, was die LFT zu einer attraktiven Wahl u.a. als Ersatz
von Metallen in Automobilteilen macht. Es stellen sich allerdings immer noch einige
offene wissenschaftliche Fragen in Bezug auf z.B. die Materialbeschaffenheit, um
optimale Eigenschaftskombinationen zu erreichen. Die vorliegende Arbeit versucht,
einige dieser Fragen zu beantworten. Ziel war es, Vorgehensweisen zu entwickeln,
mit denen man die Eigenschaften von LFT gezielt beeinflussen und so den
Anforderungen von Automobilen oder anderen Anwendungen anpassen oder
„maßschneidern“ kann.
Die vorliegende Arbeit besteht aus drei Teilen, welche sich auf unterschiedliche
Materialsysteme, angepasst an den aktuellen Bedarf und das Interesse der Industrie,
konzentrieren.
Der erste Teil der Arbeit richtet sich auf die Eigenschaftsoptimierung von
langglasfaserverstärkten (LGF) thermoplastischen Styrolcopolymeren und von
Blends aus diesen Materialien. Es wurden passende, auf Maleinsäureanhydride
(MAH) basierende Kopplungsmittel gefunden, um die Faser-Matrix-Haftung zu
optimieren. Weiterhin wurde ein LGF Konzentrat entwickelt, welches mit
verschiedenen thermoplastischen Styrolcopolymeren kompatibel ist und somit als
„Verstärkungsadditiv“ eingesetzt werden kann.Das Konzept für ein neues LGF-Konzentrat auf Basis des kompatiblen
Materialsystems konzentriert sich insbesondere darauf, dass ein Basismaterial für
die Herstellung von Bauteilen bereit gestellt werden kann, mit dessen Hilfe gezielt
verschiedene mechanische und thermomechanischen Eigenschaften durch das
Zumischen von verschiedenen Styrolcopoylmeren und Blends verbessert werden
können. Dieses Konzept ermöglicht eine sehr flexible Produktion von Bauteilen und
wird seine Anwendung bei der Herstellung von Bauteilen u.a. im Interieur von Autos
finden.
Das zweite Materialsystem basiert auf sogenannten hybriden Verbundwerkstoffen,
welche aus Langglasfasern und mineralischen Füllstoffen wie Kalziumkarbonat und
Talkum in einer Polypropylen (PP) - Matrix zusammengesetzt sind. Ziel war es, durch
detaillierte bruchmechanische Analysen genaue Informationen über das
Bruchverhalten dieser hybriden Verbundwerkstoffe bei Zug- und Schlagbelastung zu
bekommen, um dann die Unterschiede zwischen den verschiedenen Füllstoffen in
Bezug auf ihre Eigenschaften zu dokumentieren. Es konnte beobachtet werden, dass
bei Zugabe der Füllstoffe zum LGF-PP normalerweise die Steifigkeit weiter
verbessert wurde, jedoch die Festigkeit und Schlagzähigkeit abnahmen. Weiterhin
zeigten die verschiedenen Füllstoffe wie Kalziumkarbonat und Talkum
unterschiedliche mechanische Eigenschaften auf, wenn sie zusammen mit LGF
Verstärkung eingesetzt wurden: Bei der Zugabe von Talkum wurde u.a. eine deutlich
bessere Schlagzähigkeit als bei der Zugabe von Kalziumkarbonat festgestellt. Dieses
Phänomen konnte durch das unterschiedliche Nukleierungsverhalten des PPs erklärt
werden, welches in einer unterschiedlichen Kristallmorphologie von Polypropylen
resultierte. Weiterhin konnte man durch Messungen der akustischen Emmissionen
während der Zugbelastung eines bruchmechanischen Versuchskörpers aufzeigen,
dass die höhere Bruchzähigkeit von LGF-PP ohne Füllstoffe daraus resultiert, dass
Faser-Pullout schon bei geringeren Kräften vorhanden war.

Magnetoelastic coupling describes the mutual dependence of the elastic and magnetic fields and can be observed in certain types of materials, among which are the so-called "magnetostrictive materials". They belong to the large class of "smart materials", which change their shape, dimensions or material properties under the influence of an external field. The mechanical strain or deformation a material experiences due to an externally applied magnetic field is referred to as magnetostriction; the reciprocal effect, i.e. the change of the magnetization of a body subjected to mechanical stress is called inverse magnetostriction. The coupling of mechanical and electromagnetic fields is particularly observed in "giant magnetostrictive materials", alloys of ferromagnetic materials that can exhibit several thousand times greater magnitudes of magnetostriction (measured as the ratio of the change in length of the material to its original length) than the common magnetostrictive materials. These materials have wide applications areas: They are used as variable-stiffness devices, as sensors and actuators in mechanical systems or as artificial muscles. Possible application fields also include robotics, vibration control, hydraulics and sonar systems.
Although the computational treatment of coupled problems has seen great advances over the last decade, the underlying problem structure is often not fully understood nor taken into account when using black box simulation codes. A thorough analysis of the properties of coupled systems is thus an important task.
The thesis focuses on the mathematical modeling and analysis of the coupling effects in magnetostrictive materials. Under the assumption of linear and reversible material behavior with no magnetic hysteresis effects, a coupled magnetoelastic problem is set up using two different approaches: the magnetic scalar potential and vector potential formulations. On the basis of a minimum energy principle, a system of partial differential equations is derived and analyzed for both approaches. While the scalar potential model involves only stationary elastic and magnetic fields, the model using the magnetic vector potential accounts for different settings such as the eddy current approximation or the full Maxwell system in the frequency domain.
The distinctive feature of this work is the analysis of the obtained coupled magnetoelastic problems with regard to their structure, strong and weak formulations, the corresponding function spaces and the existence and uniqueness of the solutions. We show that the model based on the magnetic scalar potential constitutes a coupled saddle point problem with a penalty term. The main focus in proving the unique solvability of this problem lies on the verification of an inf-sup condition in the continuous and discrete cases. Furthermore, we discuss the impact of the reformulation of the coupled constitutive equations on the structure of the coupled problem and show that in contrast to the scalar potential approach, the vector potential formulation yields a symmetric system of PDEs. The dependence of the problem structure on the chosen formulation of the constitutive equations arises from the distinction of the energy and coenergy terms in the Lagrangian of the system. While certain combinations of the elastic and magnetic variables lead to a coupled magnetoelastic energy function yielding a symmetric problem, the use of their dual variables results in a coupled coenergy function for which a mixed problem is obtained.
The presented models are supplemented with numerical simulations carried out with MATLAB for different examples including a 1D Euler-Bernoulli beam under magnetic influence and a 2D magnetostrictive plate in the state of plane stress. The simulations are based on material data of Terfenol-D, a giant magnetostrictive materials used in many industrial applications.

Education is the Achilles heel of successful resuscitation in cardiac arrest. Therefore, we aim to contribute to the educational efficiency by providing a novel augmented-reality (AR) guided interactive cardiopulmonary resuscitation (CPR) "trainer". For this trainer, a mixed reality smart glass, Microsoft HoloLens, and a CPR manikin covered with pressure sensors were used. To introduce the CPR procedure to a learner, an application with an intractable virtual teacher model was designed. The teaching scenario consists of the two main parts, theory and practice. In the theoretical part, the virtual teacher provides all information about the CPR procedure. Afterward, the user will be asked to perform the CPR cycles in three different stages. In the first two stages, it is aimed to gain the muscle memory with audio and optical feedback system. In the end, the performance of the participant is evaluated by the virtual teacher.

We present a study comparing the effect of real-time wearable feedback with traditional training methods for cardiopulmonary resuscitation (CPR). The aim is to ensure that the students can deliver CPR with the right compression speed and depth. On the wearable side, we test two systems: one based on a combination of visual feedback and tactile information on a smart-watch and one based on visual feedback and audio information on a Google Glass. In a trial with 50 subjects (23 trainee nurses and 27 novices,) we compare those modalities to standard human teaching that is used in nurse training. While a single traditional teaching session tends to improve only the percentage of correct depth, it has less effect on the percentage of effective CPR (depth and speed correct at the same time). By contrast, in a training session with the wearable feedback device, the average percentage of time when CPR is effective improves by up to almost 25%.

Materials in general can be divided into insulators, semiconductors and conductors,
depending on their degree of electrical conductivity. Polymers are classified as
electrically insulating materials, having electrical conductivity values lower than 10-12
S/cm. Due to their favourable characteristics, e.g. their good physical characteristics,
their low density, which results in weight reduction, etc., polymers are also
considered for applications where a certain degree of conductivity is required. The
main aim of this study was to develop electrically conductive composite materials
based on epoxy (EP) matrix, and to study their thermal, electrical, and mechanical
properties. The target values of electrical conductivity were mainly in the range of
electrostatic discharge protection (ESD, 10-9-10-6 S/cm).
Carbon fibres (CF) were the first type of conductive filler used. It was established that
there is a significant influence of the fibre aspect ratio on the electrical properties of
the fabricated composite materials. With longer CF the percolation threshold value
could be achieved at lower concentrations. Additional to the homogeneous CF/EP
composites, graded samples were also developed. By the use of a centrifugation
method, the CF created a graded distribution along one dimension of the samples.
The effect of the different processing parameters on the resulting graded structures
and consequently on their gradients in the electrical and mechanical properties were
systematically studied.
An intrinsically conductive polyaniline (PANI) salt was also used for enhancing the
electrical properties of the EP. In this case, a much lower percolation threshold was
observed compared to that of CF. PANI was found out to have, up to a particular
concentration, a minimal influence on the thermal and mechanical properties of the
EP system.
Furthermore, the two above-mentioned conductive fillers were jointly added to the EP
matrix. Improved electrical and mechanical properties were observed by this
incorporation. A synergy effect between the two fillers took place regarding the
electrical conductivity of the composites.
The last part of this work was engaged in the application of existing theoretical
models for the prediction of the electrical conductivity of the developed polymer composites. A good correlation between the simulation and the experiments was
observed.
Allgemein werden Materialien in Bezug auf ihre elektrische Leitfähigkeit in Isolatoren,
Halbleiter oder Leiter unterteilt. Polymere gehören mit einer elektrischen Leitfähigkeit
niedriger als 10-12 S/cm in die Gruppe der Isolatoren. Aufgrund vorteilhafter
Eigenschaften der Polymere, wie z.B. ihren guten physikalischen Eigenschaften,
ihrer geringen Dichte, welche zur Gewichtsreduktion beiträgt, usw., werden Polymere
auch für Anwendungen in Betracht gezogen, bei denen ein gewisser Grad an
Leitfähigkeit gefordert wird. Das Hauptziel dieser Studie war, elektrisch leitende
Verbundwerkstoffe auf der Basis von Epoxidharz (EP) zu entwickeln und deren
elektrische, mechanische und thermische Eigenschaften zu studieren. Die Zielwerte
der elektrischen Leitfähigkeit lagen hauptsächlich im Bereich der Vermeidung
elektrostatischer Aufladungen (ESD, 10-9-10-6 S/cm).
Bei der Herstellung elektrisch leitender Kunststoffen wurden als erstes
Kohlenstofffasern (CF) als leitfähige Füllstoffe benutzt. Bei den durchgeführten
Experimenten konnte man beobachten, dass das Faserlängenverhältnis einen
bedeutenden Einfluss auf die elektrischen Eigenschaften der fabrizierten
Verbundwerkstoffe hat. Mit längeren CF wurde die Perkolationsschwelle bereits bei
einer niedrigeren Konzentration erreicht. Zusätzlich zu den homogenen CF/EP
Verbundwerkstoffen, wurden auch Gradientenwerkstoffe entwickelt. Mit Hilfe einer
Zentrifugation konnte eine gradierte Verteilung der CF entlang der Probenlängeachse
erreicht werden. Die Effekte der unterschiedlichen Zentrifugationsparameter
auf die resultierenden Gradientenwerkstoffe und die daraus
resultierenden, gradierten elektrischen und mechanischen Eigenschaften wurden
systematisch studiert.
Ein intrinsisch leitendes Polyanilin-Salz (PANI) wurde auch für das Erhöhen der
elektrischen Eigenschaften des EP benutzt. In diesem Fall wurde eine viel niedrigere
Perkolationsschwelle verglichen mit der von CF beobachtet. Der Einsatz von PANI hat bis zu einer bestimmten Konzentration nur einen minimalen Einfluß auf die
thermischen und mechanischen Eigenschaften des EP Systems.
In einem dritte Schritt wurden die zwei oben erwähnten, leitenden Füllstoffe
gemeinsam der EP Matrix hinzugefügt. Erhöhte elektrische und mechanische
Eigenschaften wurden in diesem Fall beobachtet, wobei sich ein Synergie-Effekt
zwischen den zwei Füllstoffen bezogen auf die elektrische Leitfähigkeit der
Verbundwerkstoffe ergab.
Im letzten Teil dieser Arbeit fand die Anwendung von theoretischen Modelle zur
Vorhersage der elektrischen Leitfähigkeit der entwickelten Verbundwerkstoffe statt.
Dabei konnte eine gute Übereinstimmung mit den experimentellen Ergebnissen
festgestellt werden .

For modeling approaches in systems biology, knowledge of the absolute abundances of cellular proteins is essential. One way to gain this knowledge is the use of quantification concatamers (QconCATs), which are synthetic proteins consisting of proteotypic peptides derived from the target proteins to be quantified. The QconCAT protein is labeled with a heavy isotope upon expression in E. coli and known amounts of the purified protein are spiked into a whole cell protein extract. Upon tryptic digestion, labeled and unlabeled peptides are released from the QconCAT and the native proteins, respectively, and both are quantified by LC-MS/MS. The labeled Q-peptides then serve as standards for determining the absolute quantity of the native peptides/proteins. Here we have applied the QconCAT approach to Chlamydomonas reinhardtii for the absolute quantification of the major proteins and protein complexes driving photosynthetic light reactions in the thylakoid membranes and carbon fixation in the pyrenoid. We found that with 25.2 attomol/cell the Rubisco large subunit makes up 6.6% of all proteins in a Chlamydomonas cell and with this exceeds the amount of the small subunit by a factor of 1.56. EPYC1, which links Rubisco to form the pyrenoid, is eight times less abundant than RBCS, and Rubisco activase is 32-times less abundant than RBCS. With 5.2 attomol/cell, photosystem II is the most abundant complex involved in the photosynthetic light reactions, followed by plastocyanin, photosystem I and the cytochrome b6/f complex, which range between 2.9 and 3.5 attomol/cell. The least abundant complex is the ATP synthase with 2 attomol/cell. While applying the QconCAT approach, we have been able to identify many potential pitfalls associated with this technique. We analyze and discuss these pitfalls in detail and provide an optimized workflow for future applications of this technique.

This thesis addresses several challenges for sustainable logistics operations and investigates (1) the integration of intermediate stops in the route planning of transportation vehicles, which especially becomes relevant when alternative-fuel vehicles with limited driving range or a sparse refueling infrastructure are considered, (2) the combined planning of the battery replacement infrastructure and of the routing for battery electric vehicles, (3) the use of mobile load replenishment or refueling possibilities in environments where the respective infrastructure is not available, and (4) the additional consideration of the flow of goods from the end user in backward direction to the point of origin for the purpose of, e.g., recapturing value or proper disposal. We utilize models and solution methods from the domain of operations research to gain insights into the investigated problems and thus to support managerial decisions with respect to these issues.

Poor posture in childhood and adolescence is held responsible for the occurrence
of associated disorders in adult age. This study aimed to verify whether body
posture in adolescence can be enhanced through the improvement of neuromuscular
performance, attained by means of targeted strength, stretch, and body perception
training, and whether any such improvement might also transition into adulthood. From
a total of 84 volunteers, the posture development of 67 adolescents was checked
annually between the age of 14 and 20 based on index values in three posture
situations. 28 adolescents exercised twice a week for about 2 h up to the age of 18, 24
adolescents exercised continually up to the age of 20. Both groups practiced other
additional sports for about 1.8 h/week. Fifteen persons served as a non-exercising
control group, practicing optional sports of about 1.8 h/week until the age of 18,
after that for 0.9 h/week. Group allocation was not random, but depended on the
participants’ choice. A linear mixed model was used to analyze the development
of posture indexes among the groups and over time and the possible influence of
anthropometric parameters (weight, size), of optional athletic activity and of sedentary
behavior. The post hoc pairwise comparison was performed applying the Scheffé test.
The significance level was set at 0.05. The group that exercised continually (TR20)
exhibited a significant posture parameter improvement in all posture situations from
the 2nd year of exercising on. The group that terminated their training when reaching
adulthood (TR18) retained some improvements, such as conscious straightening of the
body posture. In other posture situations (habitual, closed eyes), their posture results
declined again from age 18. The effect sizes determined were between Eta² = 0.12 and
Eta² = 0.19 and represent moderate to strong effects. The control group did not exhibit
any differences. Anthropometric parameters, additional athletic activities and sedentary
behavior did not influence the posture parameters significantly. An additional athletic
training of 2 h per week including elements for improved body perception seems to
have the potential to improve body posture in symptom free male adolescents and
young adults.

To investigate whether participants can activate only one spatially oriented number line at a time or
multiple number lines simultaneously, they were asked to solve a unit magnitude comparison task
(unit smaller/larger than 5) and a parity judgment task (even/odd) on two-digit numbers. In both these
primary tasks, decades were irrelevant. After some of the primary task trials (randomly), participants
were asked to additionally solve a secondary task based on the previously presented number. In
Experiment 1, they had to decide whether the two-digit number presented for the primary task was
larger or smaller than 50. Thus, for the secondary task decades were relevant. In contrast, in Experiment
2, the secondary task was a color judgment task, which means decades were irrelevant. In Experiment
1, decades’ and units’ magnitudes influenced the spatial association of numbers separately. In contrast,
in Experiment 2, only the units were spatially associated with magnitude. It was concluded that
multiple number lines (one for units and one for decades) can be activated if attention is focused on
multiple, separate magnitude attributes.

The demand of sustainability is continuously increasing. Therefore, thermoplastic
composites became a focus of research due to their good weight to performance
ratio. Nevertheless, the limiting factor of their usage for some processes is the loss of
consolidation during re-melting (deconsolidation), which reduces the part quality.
Several studies dealing with deconsolidation are available. These studies investigate
a single material and process, which limit their usefulness in terms of general
interpretations as well as their comparability to other studies. There are two main
approaches. The first approach identifies the internal void pressure as the main
cause of deconsolidation and the second approach identifies the fiber reinforcement
network as the main cause. Due to of their controversial results and limited variety of
materials and processes, there is a big need of a more comprehensive investigation
on several materials and processes.
This study investigates the deconsolidation behavior of 17 different materials and
material configurations considering commodity, engineering, and performance
polymers as well as a carbon and two glass fiber fabrics. Based on the first law of
thermodynamics, a deconsolidation model is proposed and verified by experiments.
Universal applicable input parameters are proposed for the prediction of
deconsolidation to minimize the required input measurements. The study revealed
that the fiber reinforcement network is the main cause of deconsolidation, especially
for fiber volume fractions higher than 48 %. The internal void pressure can promote
deconsolidation, when the specimen was recently manufactured. In other cases the
internal void pressure as well as the surface tension prevents deconsolidation.
During deconsolidation the polymer is displaced by the volume increase of the void.
The polymer flow damps the progress of deconsolidation because of the internal
friction of the polymer. The crystallinity and the thermal expansion lead to a
reversible thickness increase during deconsolidation. Moisture can highly accelerate
deconsolidation and can increase the thickness by several times because of the
vaporization of water. The model is also capable to predict reconsolidation under the
defined boundary condition of pressure, time, and specimen size. For high pressure
matrix squeeze out occur, which falsifies the accuracy of the model.The proposed model was applied to thermoforming, induction welding, and
thermoplastic tape placement. It is demonstrated that the load rate during
thermoforming is the critical factor of achieving complete reconsolidation. The
required load rate can be determined by the model and is dependent on the cooling
rate, the forming length, the extent of deconsolidation, the processing temperature,
and the final pressure. During induction welding deconsolidation can tremendously
occur because of the left moisture in the polymer at the molten state. The moisture
cannot fully diffuse out of the specimen during the faster heating. Therefore,
additional pressure is needed for complete reconsolidation than it would be for a dry
specimen. Deconsolidation is an issue for thermoplastic tape placement, too. It limits
the placement velocity because of insufficient cooling after compaction. If the
specimen after compaction is locally in a molten state, it deconsolidates and causes
residual stresses in the bond line, which decreases the interlaminar shear strength. It
can be concluded that the study gains new knowledge and helps to optimize these
processes by means of the developed model without a high number of required
measurements.
Aufgrund seiner guten spezifischen Festigkeit und Steifigkeit ist der
endlosfaserverstärkte Thermoplast ein hervorragender Leichtbauwerkstoff. Allerdings
kann es während des Wiederaufschmelzens durch Dekonsolidierung zu einem
Verlust der guten mechanischen Eigenschaften kommen, daher ist Dekonsolidierung
unerwünscht. In vielen Studien wurde die Dekonsolidierung mit unterschiedlichen
Ergebnissen untersucht. Dabei wurde meist ein Material und ein Prozess betrachtet.
Eine allgemeine Interpretation und die Vergleichbarkeit unter den Studien sind
dadurch nur begrenzt möglich. Aus der Literatur sind zwei Ansätze bekannt. Dem
ersten Ansatz liegt der Druckunterschied zwischen Poreninnendruck und
Umgebungsdruck als Hauptursache der Dekonsolidierung zu Grunde. Beim zweiten
Ansatz wird die Faserverstärkung als Hauptursache identifiziert. Aufgrund der
kontroversen Ergebnisse und der begrenzten Anzahl der Materialien und
Verarbeitungsverfahren, besteht die Notwendigkeit einer umfassenden Untersuchung
über mehrere Materialien und Prozesse. Diese Studie umfasst drei Polymere
(Polypropylen, Polycarbonat und Polyphenylensulfid), drei Gewebe (Köper, Atlas und
Unidirektional) und zwei Prozesse (Autoklav und Heißpressen) bei verschiedenen
Faservolumengehalten.
Es wurde der Einfluss des Porengehaltes auf die interlaminare Scherfestigkeit
untersucht. Aus der Literatur ist bekannt, dass die interlaminare Scherfestigkeit mit
der Zunahme des Porengehaltes linear sinkt. Dies konnte für die Dekonsolidierung
bestätigt werden. Die Reduktion der interlaminaren Scherfestigkeit für
thermoplastische Matrizes ist kleiner als für duroplastische Matrizes und liegt im
Bereich zwischen 0,5 % bis 1,5 % pro Prozent Porengehalt. Außerdem ist die
Abnahme signifikant vom Matrixpolymer abhängig.
Im Falle der thermisch induzierten Dekonsolidierung nimmt der Porengehalt
proportional zu der Dicke der Probe zu und ist ein Maß für die Dekonsolidierung. Die
Pore expandiert aufgrund der thermischen Gasexpansion und kann durch äußere
Kräfte zur Expansion gezwungen werden, was zu einem Unterdruck in der Pore
führt. Die Faserverstärkung ist die Hauptursache der Dickenzunahme
beziehungsweise der Dekonsolidierung. Die gespeicherte Energie, aufgebaut während der Kompaktierung, wird während der Dekonsolidierung abgegeben. Der
Dekompaktierungsdruck reicht von 0,02 MPa bis 0,15 MPa für die untersuchten
Gewebe und Faservolumengehalte. Die Oberflächenspannung behindert die
Porenexpansion, weil die Oberfläche vergrößert werden muss, die zusätzliche
Energie benötigt. Beim Kontakt von benachbarten Poren verursacht die
Oberflächenspannung ein Verschmelzen der Poren. Durch das bessere Volumen-
Oberfläche-Verhältnis wird Energie abgebaut. Der Polymerfluss bremst die
Entwicklung der Dickenzunahme aufgrund der erforderlichen Energie (innere
Reibung) der viskosen Strömung. Je höher die Temperatur ist, desto niedriger ist die
Viskosität des Polymers, wodurch weniger Energie für ein weiteres Porenwachstum
benötigt wird. Durch den reversiblen Einfluss der Kristallinität und der
Wärmeausdehnung des Verbundes wird während der Erwärmung die Dicke erhöht
und während der Abkühlung wieder verringert. Feuchtigkeit kann einen enormen
Einfluss auf die Dekonsolidierung haben. Ist noch Feuchtigkeit über der
Schmelztemperatur im Verbund vorhanden, verdampft diese und kann die Dicke um
ein Vielfaches der ursprünglichen Dicke vergrößern.
Das Dekonsolidierungsmodell ist in der Lage die Rekonsolidierung vorherzusagen.
Allerdings muss der Rekonsolidierungsdruck unter einem Grenzwert liegen
(0,15 MPa für 50x50 mm² und 1,5 MPa für 500x500 mm² große Proben), da es sonst
bei der Probe zu einem Polymerfluss aus der Probe von mehr als 2 % kommt. Die
Rekonsolidierung ist eine inverse Dekonsolidierung und weist die gleichen
Mechanismen in der entgegengesetzten Richtung auf.
Das entwickelte Modell basiert auf dem ersten Hauptsatz der Thermodynamik und
kann die Dicke während der Dekonsolidierung und der Rekonsolidierung
vorhersagen. Dabei wurden eine homogene Porenverteilung und eine einheitliche,
kugelförmige Porengröße angenommen. Außerdem wurde die Massenerhaltung
angenommen. Um den Aufwand für die Bestimmung der Eingangsgrößen zu
reduzieren, wurden allgemein gültige Eingabeparameter bestimmt, die für eine
Vielzahl von Konfigurationen gelten. Das simulierte Materialverhalten mit den
allgemein gültigen Eingangsparametern erzielte unter den definierten
Einschränkungen eine gute Übereinstimmung mit dem tatsächlichen
Materialverhalten. Nur bei Konfigurationen mit einer Viskositätsdifferenz von mehr als 30 % zwischen der Schmelztemperatur und der Prozesstemperatur sind die
allgemein gültigen Eingangsparameter nicht anwendbar. Um die Relevanz für die
Industrie aufzuzeigen, wurden die Effekte der Dekonsolidierung für drei weitere
Verfahren simuliert. Es wurde gezeigt, dass die Kraftzunahmegeschwindigkeit
während des Thermoformens ein Schlüsselfaktor für eine vollständige
Rekonsolidierung ist. Wenn die Kraft zu langsam appliziert wird oder die finale Kraft
zu gering ist, ist die Probe bereits erstarrt, bevor eine vollständige Konsolidierung
erreicht werden kann. Auch beim Induktionsschweißen kann Dekonsolidierung
auftreten. Besonders die Feuchtigkeit kann zu einer starken Zunahme der
Dekonsolidierung führen, verursacht durch die sehr schnellen Heizraten von mehr als
100 K/min. Die Feuchtigkeit kann während der kurzen Aufheizphase nicht vollständig
aus dem Polymer ausdiffundieren, sodass die Feuchtigkeit beim Erreichen der
Schmelztemperatur in der Probe verdampft. Beim Tapelegen wird die
Ablegegeschwindigkeit durch die Dekonsolidierung begrenzt. Nach einer scheinbar
vollständigen Konsolidierung unter der Walze kann die Probe lokal dekonsolidieren,
wenn das Polymer unter der Oberfläche noch geschmolzen ist. Die daraus
resultierenden Poren reduzieren die interlaminare Scherfestigkeit drastisch um 5,8 %
pro Prozent Porengehalt für den untersuchten Fall. Ursache ist die Kristallisation in
der Verbindungszone. Dadurch werden Eigenspannungen erzeugt, die in der
gleichen Größenordnung wie die tatsächliche Scherfestigkeit sind.

For some optimization problems on a graph \(G=(V,E)\), one can give a general formulation: Let \(c\colon E \to \mathbb{R}_{\geq 0}\) be a cost function on the edges and \(X \subseteq 2^E\) be a set of (so-called feasible) subsets of \(E\), one aims to minimize \(\sum_{e\in S} c(e)\) among all feasible \(S\in X\). This formulation covers, for instance, the shortest path problem by choosing \(X\) as the set of all paths between two vertices, or the minimum spanning tree problem by choosing \(X\) to be the set of all spanning trees. This bachelor thesis deals with a parametric version of this formulation, where the edge costs \(c_\lambda\colon E \to \mathbb{R}_{\geq 0}\) depend on a parameter \(\lambda\in\mathbb{R}_{\geq 0}\) in a concave and piecewise linear manner. The goal is to investigate the worst case minimum size of a so-called representation system \(R\subseteq X\), which contains for each scenario \(\lambda\in\mathbb{R}_{\geq 0}\) an optimal solution \(S(\lambda)\in R\). It turns out that only a pseudo-polynomial size can be ensured in general, but smaller systems have to exist in special cases. Moreover, methods are presented to find such small systems algorithmically. Finally, the notion of a representation system is relaxed in order to get smaller (i.e. polynomial) systems ensuring a certain approximation ratio.

Cutting-edge cancer therapy involves producing individualized medicine for many patients at the same time. Within this process, most steps can be completed for a certain number of patients simultaneously. Using these resources efficiently may significantly reduce waiting times for the patients and is therefore crucial for saving human lives. However, this involves solving a complex scheduling problem, which can mathematically be modeled as a proportionate flow shop of batching machines (PFB). In this thesis we investigate exact and approximate algorithms for tackling many variants of this problem. Related mathematical models have been studied before in the context of semiconductor manufacturing.

The focus of this work is to provide and evaluate a novel method for multifield topology-based analysis and visualization. Through this concept, called Pareto sets, one is capable to identify critical regions in a multifield with arbitrary many individual fields. It uses ideas found in graph optimization to find common behavior and areas of divergence between multiple optimization objectives. The connections between the latter areas can be reduced into a graph structure allowing for an abstract visualization of the multifield to support data exploration and understanding.
The research question that is answered in this dissertation is about the general capability and expandability of the Pareto set concept in context of visualization and application. Furthermore, the study of its relations, drawbacks and advantages towards other topological-based approaches. This questions is answered in several steps, including consideration and comparison with related work, a thorough introduction of the Pareto set itself as well as a framework for efficient implementation and an attached discussion regarding limitations of the concept and their implications for run time, suitable data, and possible improvements.
Furthermore, this work considers possible simplification approaches like integrated single-field simplification methods but also using common structures identified through the Pareto set concept to smooth all individual fields at once. These considerations are especially important for real-world scenarios to visualize highly complex data by removing small local structures without destroying information about larger, global trends.
To further emphasize possible improvements and expandability of the Pareto set concept, the thesis studies a variety of different real world applications. For each scenario, this work shows how the definition and visualization of the Pareto set is used and improved for data exploration and analysis based on the scenarios.
In summary, this dissertation provides a complete and sound summary of the Pareto set concept as ground work for future application of multifield data analysis. The possible scenarios include those presented in the application section, but are found in a wide range of research and industrial areas relying on uncertainty analysis, time-varying data, and ensembles of data sets in general.

The size congruity effect involves interference between numerical magnitude and physical size of visually presented numbers: congruent numbers (either both small or both large in numerical magnitude and physical size) are responded to faster than incongruent ones (small numerical magnitude/large physical size or vice versa). Besides, numerical magnitude is associated with lateralized response codes, leading to the Spatial Numerical Association of Response Codes (SNARC) effect: small numerical magnitudes are preferably responded to on the left side and large ones on the right side. Whereas size congruity effects are ascribed to interference between stimulus dimensions in the decision stage, SNARC effects are understood as (in)compatibilities in stimulus-response combinations. Accordingly, size congruity and SNARC effects were previously found to be independent in parity and in physical size judgment tasks. We investigated their dependency in numerical magnitude judgment tasks. We obtained independent size congruity and SNARC effects in these tasks and replicated this observation for the parity judgment task. The results confirm and extend the notion that size congruity and SNARC effects operate in different representational spaces. We discuss possible implications for number representation.

Novel image processing techniques have been in development for decades, but most
of these techniques are barely used in real world applications. This results in a gap
between image processing research and real-world applications; this thesis aims to
close this gap. In an initial study, the quantification, propagation, and communication
of uncertainty were determined to be key features in gaining acceptance for
new image processing techniques in applications.
This thesis presents a holistic approach based on a novel image processing pipeline,
capable of quantifying, propagating, and communicating image uncertainty. This
work provides an improved image data transformation paradigm, extending image
data using a flexible, high-dimensional uncertainty model. Based on this, a completely
redesigned image processing pipeline is presented. In this pipeline, each
step respects and preserves the underlying image uncertainty, allowing image uncertainty
quantification, image pre-processing, image segmentation, and geometry
extraction. This is communicated by utilizing meaningful visualization methodologies
throughout each computational step.
The presented methods are examined qualitatively by comparing to the Stateof-
the-Art, in addition to user evaluation in different domains. To show the applicability
of the presented approach to real world scenarios, this thesis demonstrates
domain-specific problems and the successful implementation of the presented techniques
in these domains.

Der Fokus der vorliegenden Arbeit liegt auf endlosfaser- und langfaserverstärkten
thermoplastischen Materialien. Hierfür wurde das „multilayered hybrid
(MLH)“ Konzept entwickelt und auf zwei Halbzeuge, den MLH-Roving und die MLHMat
angewendet. Der MLH-Roving ist ein Roving (bestehend aus Endlosfasern), der
durch thermoplastische Folien in mehrere Schichten geteilt wird. Der MLH-Roving
wird durch eine neuartige Spreizmethode mit anschließender thermischen Fixierung
und abschließender mehrfacher Faltung hergestellt. Dadurch können verschiedene
Faser-Matrix-Konfigurationen realisiert werden. Die MLH-Mat ist ein
glasmattenverstärktes thermoplastisches Material, das für hohe Fasergehalte bis 45
vol. % und verschiedene Matrixpolymere, z.B. Polypropylen (PP) und Polyamide 6
(PA6) geeignet ist. Sie zeichnet sich durch eine hohe Homogenität in der
Flächendichte und in der Faserrichtung aus. Durch dynamische Crashversuche mit
auf MLH-Roving und MLH-Mat basierenden Probekörpern wurden das
Crashverhalten und die Performance untersucht. Die Ergebnisse der Crashkörper
basierend auf langfaserverstärktem Material (MLH-Mat) und endlosfaserverstärktem
Material (MLH-Roving) waren vergleichbar. Die PA6-Typen zeigten eine bessere
Crashperformance als PP-Typen.
The present work deals with continuous fiber- and long fiber reinforced thermoplastic
materials. The concept of multilayered hybrid (MLH) structure was developed and
applied to the so-called MLH-roving and MLH-mat. The MLH-roving is a continuous
fiber roving separated evenly into several sublayers by thermoplastic films, through
the sequential processes of spreading with a newly derived equation, thermal fixing,
and folding. It was aimed to satisfy the variety of material configuration as well as the
variety in intermediate product. The MLH-mat is a glass mat reinforced thermoplastic
(GMT)-like material that is suitable for high fiber contents up to 45 vol. % and various
matrix polymers, e.g. polypropylene (PP), polyamide 6 (PA6). It showed homogeneity
in areal density, random directional fiber distribution, and reheating stability required
for molding process. On the MLH-roving and MLH-mat materials, the crash behavior
and performance were investigated by dynamic crash test. Long fiber reinforced
materials (MLH-mat) were equivalent to continuous fiber reinforced materials (MLHroving),
and PA6 grades showed higher crash performance than PP grades.

The gas phase infrared and fragmentation spectra of a systematic group of trimetallic oxo-centered
transition metal complexes are shown and discussed, with formate and acetate bridging ligands and
pyridine and water as axial ligands.
The stability of the complexes, as predicted by appropriate ab initio simulations, is demonstrated to
agree with collision induced dissociation (CID) measurements.
A broad range of DFT calculations are shown. They are used to simulate the geometry, the bonding
situation, relative stability and flexibility of the discussed complexes, and to specify the observed
trends. These simulations correctly predict the trends in the band splitting of the symmetric and
asymmetric carboxylate stretch modes, but fail to account for anharmonic effects observed specifically
in the mid IR range.
The infrared spectra of the different ligands are introduced in a brief literature review. Their changes
in different environments or different bonding situations are discussed and visualized, especially the
interplay between fundamental-, overtone-, and combination bands, as well as Fermi resonances
between them.
A new variation on the infrared multi photon dissociation (IRMPD) spectroscopy method is proposed
and evaluated. In addition to the commonly considered total fragment yield, the cumulative fragment
yield can be used to plot the wavelength dependent relative abundance of different fragmentation
products. This is shown to include valuable additional information on the excited chromophors, and
their coupling to specific fragmentation channels.
High quality homo- and heterometallic IRMPD spectra of oxo centered carboxylate complexes of
chromium and iron show the impacts of the influencing factors: the metal centers, the bridging ligands,
their carboxylate stretch modes and CH bend modes, and the terminal ligands.
In all four formate spectra, anharmonic effects are necessary to explain the observed spectra:
combination bands of both carboxylate stretch modes and a Fermi resonance of the fundamental of
the CH stretch mode, and a combination band of the asymmetric carboxylate stretch mode with the
CH bend mode of the formate bridging ligand.
For the water adduct species, partial hydrolysis is proposed to account for the changes in the observed
carboxylic stretch modes.
Appropriate experiments are suggested to verify the mode assignments that are not directly explained
by the ab initio calculations, the available experimental results or other means like deuteration
experiments.

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

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 \(\tilde{h}=1\).

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

The Symbol Grounding Problem (SGP) is one of the first attempts to proposed a hypothesis about mapping abstract concepts and the real world. For example, the concept "ball" can be represented by an object with a round shape (visual modality) and phonemes /b/ /a/ /l/ (audio modality).
This thesis is inspired by the association learning presented in infant development.
Newborns can associate visual and audio modalities of the same concept that are presented at the same time for vocabulary acquisition task.
The goal of this thesis is to develop a novel framework that combines the constraints of the Symbol Grounding Problem and Neural Networks in a simplified scenario of association learning in infants. The first motivation is that the network output can be considered as numerical symbolic features because the attributes of input samples are already embedded. The second motivation is the association between two samples is predefined before training via the same vectorial representation. This thesis proposes to associate two samples and the vectorial representation during training. Two scenarios are considered: sample pair association and sequence pair association.
Three main contributions are presented in this work.
The first contribution is a novel Symbolic Association Model based on two parallel MLPs.
The association task is defined by learning that two instances that represent one concept.
Moreover, a novel training algorithm is defined by matching the output vectors of the MLPs with a statistical distribution for obtaining the relationship between concepts and vectorial representations.
The second contribution is a novel Symbolic Association Model based on two parallel LSTM networks that are trained on weakly labeled sequences.
The definition of association task is extended to learn that two sequences represent the same series of concepts.
This model uses a training algorithm that is similar to MLP-based approach.
The last contribution is a Classless Association.
The association task is defined by learning based on the relationship of two samples that represents the same unknown concept.
In summary, the contributions of this thesis are to extend Artificial Intelligence and Cognitive Computation research with a new constraint that is cognitive motivated. Moreover, two training algorithms with a new constraint are proposed for two cases: single and sequence associations. Besides, a new training rule with no-labels with promising results is proposed.

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

SDE-driven modeling of phenotypically heterogeneous tumors: The influence of cancer cell stemness
(2018)

We deduce cell population models describing the evolution of a tumor (possibly interacting with its
environment of healthy cells) with the aid of differential equations. Thereby, different subpopulations
of cancer cells allow accounting for the tumor heterogeneity. In our settings these include cancer
stem cells known to be less sensitive to treatment and differentiated cancer cells having a higher
sensitivity towards chemo- and radiotherapy. Our approach relies on stochastic differential equations
in order to account for randomness in the system, arising e.g., by the therapy-induced decreasing
number of clonogens, which renders a pure deterministic model arguable. The equations are deduced
relying on transition probabilities characterizing innovations of the two cancer cell subpopulations,
and similarly extended to also account for the evolution of normal tissue. Several therapy approaches
are introduced and compared by way of tumor control probability (TCP) and uncomplicated tumor
control probability (UTCP). A PDE approach allows to assess the evolution of tumor and normal
tissue with respect to time and to cell population densities which can vary continuously in a given set
of states. Analytical approximations of solutions to the obtained PDE system are provided as well.

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

Tables or ranked lists summarize facts about a group of entities in a concise and structured fashion. They are found in all kind of domains and easily comprehensible by humans. Some globally prominent examples of such rankings are the tallest buildings in the World, the richest people in Germany, or most powerful cars. The availability of vast amounts of tables or rankings from open domain allows different ways to explore data. Computing similarity between ranked lists, in order to find those lists where entities are presented in a similar order, carries important analytical insights. This thesis presents a novel query-driven Locality Sensitive Hashing (LSH) method, in order to efficiently find similar top-k rankings for a given input ranking. Experiments show that the proposed method provides a far better performance than inverted-index--based approaches, in particular, it is able to outperform the popular prefix-filtering method. Additionally, an LSH-based probabilistic pruning approach is proposed that optimizes the space utilization of inverted indices, while still maintaining a user-provided recall requirement for the results of the similarity search. Further, this thesis addresses the problem of automatically identifying interesting categorical attributes, in order to explore the entity-centric data by organizing them into meaningful categories. Our approach proposes novel statistical measures, beyond known concepts, like information entropy, in order to capture the distribution of data to train a classifier that can predict which categorical attribute will be perceived suitable by humans for data categorization. We further discuss how the information of useful categories can be applied in PANTHEON and PALEO, two data exploration frameworks developed in our group.

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

The transfer of substrates between to enzymes within a biosynthesis pathway is an effective way to synthesize the specific product and a good way to avoid metabolic interference. This process is called metabolic channeling and it describes the (in-)direct transfer of an intermediate molecule between the active sites of two enzymes. By forming multi-enzyme cascades the efficiency of product formation and the flux is elevated and intermediate products are transferred and converted in a correct manner by the enzymes.
During tetrapyrrole biosynthesis several substrate transfer events occur and are prerequisite for an optimal pigment synthesis. In this project the metabolic channeling process during the pink pigment phycoerythrobilin (PEB) was investigated. The responsible ferredoxin-dependent bilin reductases (FDBR) for PEB formation are PebA and PebB. During the pigment synthesis the intermediate molecule 15,16-dihydrobiliverdin (DHBV) is formed and transferred from PebA to PebB. While in earlier studies a metabolic channeling of DHBV was postulated, this work revealed new insights into the requirements of this protein-protein interaction. It became clear, that the most important requirement for the PebA/PebB interaction is based on the affinity to their substrate/product DHBV. The already high affinity of both enzymes to each other is enhanced in the presence of DHBV in the binding pocket of PebA which leads to a rapid transfer to the subsequent enzyme PebB. DHBV is a labile molecule and needs to be rapidly channeled in order to get correctly further reduced to PEB. Fluorescence titration experiments and transfer assays confirmed the enhancement effect of DHBV for its own transfer.
More insights became clear by creating an active fusion protein of PebA and PebB and comparing its reaction mechanism with standard FDBRs. This fusion protein was able to convert biliverdin IXα (BV IXα) to PEB similar to the PebS activity, which also can convert BV IXα via DHBV to PEB as a single enzyme. The product and intermediate of the reaction were identified via HPLC and UV-Vis spectroscopy.
The results of this work revealed that PebA and PebB interact via a proximity channeling process where the intermediate DHBV plays an important role for the interaction. It also highlights the importance of substrate channeling in the synthesis of PEB to optimize the flux of intermediates through this metabolic pathway.

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.

Increasing costs due to the rising attrition of drug candidates in late developmental phases alongside post-marketing withdrawal of drugs challenge the pharmaceutical industry to further improve their current preclinical safety assessment strategies. One of the most common reasons for the termination of drug candidates is drug induced hepatotoxicity, which more often than not remains undetected in early developmental stages, thus emphasizing the necessity for improved and more predictive preclinical test systems. One reason for the very limited value of currently applied in vitro test systems for the detection of potential hepatotoxic liabilities is the lack of organotypic and tissue-specific physiology of hepatocytes cultured in ordinary monolayer culture formats.
The thesis at hand primarily deals with the evaluation of both two- and three-dimensional cell culture approaches with respect to their relative ability to predict the hepatotoxic potential of drug candidates in early developmental phases. First, different hepatic cell models, which are routinely used in pharmaceutical industry (primary human hepatocytes as well as the three cell lines HepG2, HepaRG and Upcyte hepatocytes), were investigated in conventional 2D monolayer culture with respect to their ability to detect hepatotoxic effects in simple cytotoxicity studies. Moreover, it could be shown that the global protein expression levels of all cell lines substantially differ from that of primary human hepatocytes, with the least pronounced difference in HepaRG cells.
The introduction of a third dimension through the cultivation of spheroids enables hepatocytes to recapitulate their typical native polarity and furthermore dramatically increases the contact surface of adjacent cells. These differences in cellular architecture have a positive influence on hepatocyte longevity and the expression of drug metabolizing enzymes and transporters, which could be proven via immunofluorescent (IF) staining for at least 14 days in PHH and at least 28 days in HepaRG spheroids, respectively. Additionally, the IF staining of three different phase III transporters (MDR1, MRP2 and BSEP) indicated a bile canalicular network in spheroids of both cell models. A dose-dependent inducibility of important cytochrome P450 isoenzymes in HepaRG spheroids could be shown on the protein level via IF for at least 14 days. CYP inducibility of HepaRG cells cultured in 2D and 3D was compared on the mRNA level for up to 14 days and inducibility was generally lower in 3D compared to 2D under the conditions of this study. In a comparative cytotoxicity study, both PHH and HepaRG spheroids as well as HepaRG monolayers have been treated with five hepatotoxic drugs for up to 14 days and viability was measured at three time points (days 3, 7 and 14). A clear time- and dose-dependent onset of the drug-induced hepatotoxic effects was observable in all conditions tested, indicated by a shift of the respective EC50 value towards lower doses by increasing exposure. The observed effects were most pronounced in PHH spheroids, thus indicating those as the most sensitive cell model in this study. Moreover, HepaRG cells were more sensitive in spheroid culture compared to monolayers, which suggests a potential application of spheroids as long-term test system for the detection of hepatotoxicities with slow onset. Finally, the basal protein expression levels of three antigens (CYP1A2, CYP3A4 and NAT 1/2) were analyzed via Western Blotting in HepaRG cells cultured in three different cell culture formats (2D, 3D and QV) in order to estimate the impact of the cell culture conditions on protein expression levels. In the QV system enables a pump-driven flow of cell culture media, which introduces both mechanical stimuli through shear and molecular stimuli through dynamic circulation to the monolayer. Those stimuli resulted in a clearly positive effect on the expression levels of the selected antigens by an increased expression level in comparison to both 2D and 3D. In contrast, HepaRG spheroids showed time-dependent differences with the overall highest levels at day 7.
The studies presented in this thesis delivered valuable information on the increased physiological relevance in dependence on the cell culture format: three-dimensionality as well as the circulation of media lead to a more differentiated phenotype in hepatic cell models. Those cell culture formats are applicable in preclinical drug development in order to obtain more relevant information at early developmental stages and thus help to create a more efficient drug development process. Nonetheless, further studies are necessary to thoroughly characterize, validate and standardize such novel cell culture approaches prior to their routine application in industry.

Road accidents remain as one of the major causes of death and injuries globally. Several million people die every year due to road accidents all over the world. Although the number of accidents in European region have reduced in the past years, road safety still remains a major challenge. Especially in case of commercial trucks, due to the size and load of the vehicle, even minor collisions with other road users would lead to serious injuries or death. In order to reduce number of accidents, automotive industry is rapidly developing advanced driver assistance systems (ADAS) and automated driving technologies. Efficient and reliable solutions are required for these systems to sense, perceive and react to different environmental conditions. For vehicle safety applications such as collision avoidance with vulnerable road users (VRUs), it is not only important for the system to efficiently detect and track the objects in the vicinity of the vehicle but should also function robustly.
An environment perception solution for application in commercial truck safety systems and for future automated driving is developed in this work. Thereby a method for integrated tracking and classification of road users in the near vicinity of the vehicle is formulated. The drawbacks in conventional multi-object tracking algorithms with respect to state, measurement and data association uncertainties have been addressed with the recent advancements in the field of unified multi-object tracking solutions based on random finite sets (RFS). Gaussian mixture implementation of the recently developed labeled multi-Bernoulli (LMB) filter [RSD15] is used as the basis for multi-object tracking in this work. Measurement from an high-resolution radar sensor is used as the main input for detecting and tracking objects.
On one side, the focus of this work is on tracking VRUs in the near vicinity of the truck. As it is beneficial for most of the vehicle safety systems to also know the category that the object belongs to, the focus on the other side is also to classify the road users. All the radar detections believed to originate from a single object are clustered together with help of density based spatial clustering for application with noise (DBSCAN) algorithm. Each cluster of detections would have different properties based on the respective object characteristics. Sixteen distinct features based on radar detections, that are suitable for separating pedestrians, bicyclists and passenger car categories are selected and extracted for each of the cluster. A machine learning based classifier is constructed, trained and parameterised for distinguishing the road users based on the extracted features.
The class information derived from the radar detections can further be used by the tracking algorithm, to adapt the model parameters used for precisely predicting the object motion according to the category of the object. Multiple model labeled multi-Bernoulli filter (MMLMB) is used for modelling different object motions. Apart from the detection level, the estimated state of an object on the tracking level also provides information about the object class. Both these informations are fused using Dempster-Shafer theory (DST) of evidence, based on respective class probabilities Thereby, the output of the integrated tracking and classification with MMLMB filter are classified tracks that can be used by truck safety applications with better reliability.
The developed environment perception method is further implemented as a real-time prototypical system on a commercial truck. The performance of the tracking and classification approaches are evaluated with the help of simulation and multiple test scenarios. A comparison of the developed approaches to a conventional converted measurements Kalman filter with global nearest neighbour association (CMKF-GNN) shows significant advantages in the overall accuracy and performance.

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 research explores the development of web based reference software for
characterisation of surface roughness for two-dimensional surface data. The reference software used for verification of surface characteristics makes the evaluation methods easier for clients. The algorithms used in this software
are based on International ISO standards. Most software used in industrial measuring
instruments may give variations in the parameters calculated due to numerical changes in
calculation. Such variations can be verified using the proposed reference software.
The evaluation of surface roughness is carried out in four major steps: data capture, data
align, data filtering and parameter calculation. This work walks through each of these steps
explaining how surface profiles are evaluated by pre-processing steps called fitting and
filtering. The analysis process is then followed by parameter evaluation according to DIN EN
ISO 4287 and DIN EN ISO 13565-2 standards to extract important information from the
profile to characterise surface roughness.

If gradient based derivative algorithms are used to improve industrial products by reducing their target functions, the derivatives need to be exact.
The last percent of possible improvement, like the efficiency of a turbine, can only be gained if the derivatives are consistent with the solution process that is used in the simulation software.
It is problematic that the development of the simulation software is an ongoing process which leads to the use of approximated derivatives.
If a derivative computation is implemented manually, it will be inconsistent after some time if it is not updated.
This thesis presents a generalized approach which differentiates the whole simulation software with Algorithmic Differentiation (AD), and guarantees a correct and consistent derivative computation after each change to the software.
For this purpose, the variable tagging technique is developed.
The technique checks at run-time if all dependencies, which are used by the derivative algorithms, are correct.
Since it is also necessary to check the correctness of the implementation, a theorem is developed which describes how AD derivatives can be compared.
This theorem is used to develop further methods that can detect and correct errors.
All methods are designed such that they can be applied in real world applications and are used within industrial configurations.
The process described above yields consistent and correct derivatives but the efficiency can still be improved.
This is done by deriving new derivative algorithms.
A fixed-point iterator approach, with a consistent derivation, yields all state of the art algorithms and produces two new algorithms.
These two new algorithms include all implementation details and therefore they produce consistent derivative results.
For detecting hot spots in the application, the state of the art techniques are presented and extended.
The data management is changed such that the performance of the software is affected only marginally when quantities, like the number of input and output variables or the memory consumption, are computed for the detection.
The hot spots can be treated with techniques like checkpointing or preaccumulation.
How these techniques change the time and memory consumption is analyzed and it is shown how they need to be used in selected AD tools.
As a last step, the used AD tools are analyzed in more detail.
The major implementation strategies for operator overloading AD tools are presented and implementation improvements for existing AD tools are discussed.\
The discussion focuses on a minimal memory consumption and makes it possible to compare AD tools on a theoretical level.
The new AD tool CoDiPack is based on these findings and its design and concepts are presented.
The improvements and findings in this thesis make it possible, that an automatic, consistent and correct derivative is generated in an efficient way for industrial applications.

Neuronal inhibition is mediated by glycine and/or GABA. Inferior colliculus (IC) neurons receive glycinergic and GABAergic
inputs, whereas inhibition in hippocampus (HC) predominantly relies on GABA. Astrocytes heterogeneously
express neurotransmitter transporters and are expected to adapt to the local requirements regarding neurotransmitter
homeostasis. Here we analyzed the expression of inhibitory neurotransmitter transporters in IC and HC astrocytes using
whole-cell patch-clamp and single-cell reverse transcription-PCR. We show that most astrocytes in both regions expressed
functional glycine transporters (GlyTs). Activation of these transporters resulted in an inward current (IGly) that
was sensitive to the competitive GlyT1 agonist sarcosine. Astrocytes exhibited transcripts for GlyT1 but not for
GlyT2. Glycine did not alter the membrane resistance (RM) arguing for the absence of functional glycine receptors (GlyRs).
Thus, IGly was mainly mediated by GlyT1. Similarly, we found expression of functional GABA transporters (GATs) in all IC
astrocytes and about half of the HC astrocytes. These transporters mediated an inward current (IGABA) that was sensitive to
the competitive GAT-1 and GAT-3 antagonists NO711 and SNAP5114, respectively. Accordingly, transcripts for GAT-1 and
GAT-3 were found but not for GAT-2 and BGT-1. Only in hippocampal astrocytes, GABA transiently reduced
RM demonstrating the presence of GABAA receptors (GABAARs). However, IGABA was mainly not contaminated
by GABAAR-mediated currents as RM changes vanished shortly after GABA application. In both regions, IGABA
was stronger than IGly. Furthermore, in HC the IGABA/IGly ratio was larger compared to IC. Taken together, our
results demonstrate that astrocytes are heterogeneous across and within distinct brain areas. Furthermore, we
could show that the capacity for glycine and GABA uptake varies between both brain regions.

Optimal control of partial differential equations is an important task in applied mathematics where it is used in order to optimize, for example, industrial or medical processes. In this thesis we investigate an optimal control problem with tracking type cost functional for the Cattaneo equation with distributed control, that is, \(\tau y_{tt} + y_t - \Delta y = u\). Our focus is on the theoretical and numerical analysis of the limit process \(\tau \to 0\) where we prove the convergence of solutions of the Cattaneo equation to solutions of the heat equation.
We start by deriving both the Cattaneo and the classical heat equation as well as introducing our notation and some functional analytic background. Afterwards, we prove the well-posedness of the Cattaneo equation for homogeneous Dirichlet boundary conditions, that is, we show the existence and uniqueness of a weak solution together with its continuous dependence on the data. We need this in the following, where we investigate the optimal control problem for the Cattaneo equation: We show the existence and uniqueness of a global minimizer for an optimal control problem with tracking type cost functional and the Cattaneo equation as a constraint. Subsequently, we do an asymptotic analysis for \(\tau \to 0\) for both the forward equation and the aforementioned optimal control problem and show that the solutions of these problems for the Cattaneo equation converge strongly to the ones for the heat equation. Finally, we investigate these problems numerically, where we examine the different behaviour of the models and also consider the limit \(\tau \to 0\), suggesting a linear convergence rate.

The aim of this dissertation is to explain processes in recruitment by gaining a better understanding of how perceptions evolve and how recruitment outcomes and perceptions are influenced. To do so, this dissertation takes a closer look at the formation of fit perceptions, the effects of top employer awards on pre-hire recruitment outcomes, and on how perceptions about external sources are influenced.

Fast Internet content delivery relies on two layers of caches on the request path. Firstly, content delivery networks (CDNs) seek to answer user requests before they traverse slow Internet paths. Secondly, aggregation caches in data centers seek to answer user requests before they traverse slow backend systems. The key challenge in managing these caches is the high variability of object sizes, request patterns, and retrieval latencies. Unfortunately, most existing literature focuses on caching with low (or no) variability in object sizes and ignores the intricacies of data center subsystems.
This thesis seeks to fill this gap with three contributions. First, we design a new caching system, called AdaptSize, that is robust under high object size variability. Second, we derive a method (called Flow-Offline Optimum or FOO) to predict the optimal cache hit ratio under variable object sizes. Third, we design a new caching system, called RobinHood, that exploits variances in retrieval latencies to deliver faster responses to user requests in data centers.
The techniques proposed in this thesis significantly improve the performance of CDN and data center caches. On two production traces from one of the world's largest CDN AdaptSize achieves 30-91% higher hit ratios than widely-used production systems, and 33-46% higher hit ratios than state-of-the-art research systems. Further, AdaptSize reduces the latency by more than 30% at the median, 90-percentile and 99-percentile.
We evaluate the accuracy of our FOO analysis technique on eight different production traces spanning four major Internet companies.
We find that FOO's error is at most 0.3%. Further, FOO reveals that the gap between online policies and OPT is much larger than previously thought: 27% on average, and up to 43% on web application traces.
We evaluate RobinHood with production traces from a major Internet company on a 50-server cluster. We find that RobinHood improves the 99-percentile latency by more than 50% over existing caching systems.
As load imbalances grow, RobinHood's latency improvement can be more than 2x. Further, we show that RobinHood is robust against server failures and adapts to automatic scaling of backend systems.
The results of this thesis demonstrate the power of guiding the design of practical caching policies using mathematical performance models and analysis. These models are general enough to find application in other areas of caching design and future challenges in Internet content delivery.

The simulation of cutting process challenges established methods due to large deformations and topological changes. In this work a particle finite element method (PFEM) is presented, which combines the benefits of discrete modeling techniques and methods based on continuum mechanics. A crucial part of the PFEM is the detection of the boundary of a set of particles. The impact of this boundary detection method on the structural integrity is examined and a relation of the key parameter of the method to the eigenvalues of strain tensors is elaborated. The influence of important process parameters on the cutting force is studied and a comparison to an empirical relation is presented.

In modern algebraic geometry solutions of polynomial equations are studied from a qualitative point of view using highly sophisticated tools such as cohomology, \(D\)-modules and Hodge structures. The latter have been unified in Saito’s far-reaching theory of mixed Hodge modules, that has shown striking applications including vanishing theorems for cohomology. A mixed Hodge module can be seen as a special type of filtered \(D\)-module, which is an algebraic counterpart of a system of linear differential equations. We present the first algorithmic approach to Saito’s theory. To this end, we develop a Gröbner basis theory for a new class of algebras generalizing PBW-algebras.
The category of mixed Hodge modules satisfies Grothendieck’s six-functor formalism. In part these functors rely on an additional natural filtration, the so-called \(V\)-filtration. A key result of this thesis is an algorithm to compute the \(V\)-filtration in the filtered setting. We derive from this algorithm methods for the computation of (extraordinary) direct image functors under open embeddings of complements of pure codimension one subvarieties. As side results we show
how to compute vanishing and nearby cycle functors and a quasi-inverse of Kashiwara’s equivalence for mixed Hodge modules.
Describing these functors in terms of local coordinates and taking local sections, we reduce the corresponding computations to algorithms over certain bifiltered algebras. It leads us to introduce the class of so-called PBW-reduction-algebras, a generalization of the class of PBW-algebras. We establish a comprehensive Gröbner basis framework for this generalization representing the involved filtrations by weight vectors.

Analyzing Centrality Indices in Complex Networks: an Approach Using Fuzzy Aggregation Operators
(2018)

The identification of entities that play an important role in a system is one of the fundamental analyses being performed in network studies. This topic is mainly related to centrality indices, which quantify node centrality with respect to several properties in the represented network. The nodes identified in such an analysis are called central nodes. Although centrality indices are very useful for these analyses, there exist several challenges regarding which one fits best
for a network. In addition, if the usage of only one index for determining central
nodes leads to under- or overestimation of the importance of nodes and is
insufficient for finding important nodes, then the question is how multiple indices
can be used in conjunction in such an evaluation. Thus, in this thesis an approach is proposed that includes multiple indices of nodes, each indicating
an aspect of importance, in the respective evaluation and where all the aspects of a node’s centrality are analyzed in an explorative manner. To achieve this
aim, the proposed idea uses fuzzy operators, including a parameter for generating different types of aggregations over multiple indices. In addition, several preprocessing methods for normalization of those values are proposed and discussed. We investigate whether the choice of different decisions regarding the
aggregation of the values changes the ranking of the nodes or not. It is revealed that (1) there are nodes that remain stable among the top-ranking nodes, which
makes them the most central nodes, and there are nodes that remain stable
among the bottom-ranking nodes, which makes them the least central nodes; and (2) there are nodes that show high sensitivity to the choice of normalization
methods and/or aggregations. We explain both cases and the reasons why the nodes’ rankings are stable or sensitive to the corresponding choices in various networks, such as social networks, communication networks, and air transportation networks.

Certain brain tumours are very hard to treat with radiotherapy due to their irregular shape caused by the infiltrative nature of the tumour cells. To enhance the estimation of the tumour extent one may use a mathematical model. As the brain structure plays an important role for the cell migration, it has to be included in such a model. This is done via diffusion-MRI data. We set up a multiscale model class accounting among others for integrin-mediated movement of cancer cells in the brain tissue, and the integrin-mediated proliferation. Moreover, we model a novel chemotherapy in combination with standard radiotherapy.
Thereby, we start on the cellular scale in order to describe migration. Then we deduce mean-field equations on the mesoscopic (cell density) scale on which we also incorporate cell proliferation. To reduce the phase space of the mesoscopic equation, we use parabolic scaling and deduce an effective description in the form of a reaction-convection-diffusion equation on the macroscopic spatio-temporal scale. On this scale we perform three dimensional numerical simulations for the tumour cell density, thereby incorporating real diffusion tensor imaging data. To this aim, we present programmes for the data processing taking the raw medical data and processing it to the form to be included in the numerical simulation. Thanks to the reduction of the phase space, the numerical simulations are fast enough to enable application in clinical practice.

We studied the development of cognitive abilities related to intelligence and creativity
(N = 48, 6–10 years old), using a longitudinal design (over one school year), in order
to evaluate an Enrichment Program for gifted primary school children initiated by
the government of the German federal state of Rhineland-Palatinate (Entdeckertag
Rheinland Pfalz, Germany; ET; Day of Discoverers). A group of German primary school
children (N = 24), identified earlier as intellectually gifted and selected to join the
ET program was compared to a gender-, class- and IQ- matched group of control
children that did not participate in this program. All participants performed the Standard
Progressive Matrices (SPM) test, which measures intelligence in well-defined problem
space; the Creative Reasoning Task (CRT), which measures intelligence in ill-defined
problem space; and the test of creative thinking-drawing production (TCT-DP), which
measures creativity, also in ill-defined problem space. Results revealed that problem
space matters: the ET program is effective only for the improvement of intelligence
operating in well-defined problem space. An effect was found for intelligence as
measured by SPM only, but neither for intelligence operating in ill-defined problem space
(CRT) nor for creativity (TCT-DP). This suggests that, depending on the type of problem
spaces presented, different cognitive abilities are elicited in the same child. Therefore,
enrichment programs for gifted, but also for children attending traditional schools,
should provide opportunities to develop cognitive abilities related to intelligence,
operating in both well- and ill-defined problem spaces, and to creativity in a parallel,
using an interactive approach.