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The ongoing and evolving usage of networks presents two critical challenges for current and future networks that require attention: (1) the task of effectively managing the vast and continually increasing data traffic and (2) the need to address the substantial number of end devices resulting from the rapid adoption of the Internet of Things. Besides these challenges, there is a mandatory need for energy consumption reduction, a more efficient resource usage, and streamlined processes without losing service quality. We comprehensively address these efforts, tackling the monitoring and quality assessment of streaming applications, a leading contributor to the total Internet traffic, as well as conducting an exhaustive analysis of the network performance within a Long Range Wide Area Network (LoRaWAN), one of the rapidly emerging LPWAN solutions.
The ongoing and evolving usage of networks presents two critical challenges for current and future networks that require attention: (1) the task of effectively managing the vast and continually increasing data traffic and (2) the need to address the substantial number of end devices resulting from the rapid adoption of the Internet of Things. Besides these challenges, there is a mandatory need for energy consumption reduction, a more efficient resource usage, and streamlined processes without losing service quality. We comprehensively address these efforts, tackling the monitoring and quality assessment of streaming applications, a leading contributor to the total Internet traffic, as well as conducting an exhaustive analysis of the network performance within a Long Range Wide Area Network (LoRaWAN), one of the rapidly emerging LPWAN solutions.
This work aims at elucidating chemical processes involving homogeneous catalysis and photo–physical relaxation of excited molecules in the solid state. Furthermore, compounds with supposedly small singlet–triplet gaps and therefore biradicaloid character are investigated with respect to their electro–chemical behavior. The work on hydroboration catalysis via a reduced 9,10–diboraanthracene (DBA) was preformed in collaboration with the Wagner group in Frankfurt, more specifically Dr. Sven Prey, who performed all laboratory experiments. The investigation of delayed luminescence properties in arylboronic esters in their solid state was conducted in collaboration with the Marder group in Würzburg. The author of this work took part in the synthesis of the investigated compounds while being supervised by Dr. Zhu Wu. The final project was a collaboration with the group of Anukul Jana from Hyderabad, India who provided the experimental data.
The fact that photovoltaics is a key technology for climate-neutral energy production can be taken as a given. The question to what extent perovskite will be used for photovoltaic technologies has not yet been fully answered. From a photophysical point of view, however, it has the potential to make a useful contribution to the energy sector. However, it remains to be seen whether perovskite-based modules will be able to compete with established technologies in terms of durability and cost efficiency. The additional aspect of ionic migration poses an additional challenge. In the present work, primarily the interaction between ionic redistribution, capacitive properties and recombination dynamics was investigated. This was done using impedance spectroscopy, OCVD and IV characteristics as well as extensive numerical drift-diffusion simulations. The combination of experimental and numerical methods proved to be very fruitful. A suitable model for the description of solar cells with respect to mobile ions was introduced in chapter 4.4. The formal mathematical description of the model was transferred by a non-dimensionalization and suitable numerically solvable form. The implementation took place in the Julia language. By intelligent use of structural properties of the sparse systems of equations, automatic differentiation and the use of efficient integration methods, the simulation tool is not only remarkably fast in finding the solution, but also scales quasi-linearly with the grid resolution. The software package was released under an open source license. In conventional semiconductor diodes, capacitance measurements are often used to determine the space charge density. In the first experimental chapter 5, it is shown that although this is also possible for the ionic migration present in perovskites, it cannot be directly understood as doping related, since the space charge distribution strongly depends on the preconditions and can be manipulated by an externally applied voltage. The exact form of this behavior depends on the perovskite composition. This shows, among other things, that experimental results can only be interpreted within the framework of conventional semiconductors to a very limited extent. Nevertheless, the built-in 99 potential of the solar cell can be determined if the experiments are carried out properly. A statement concerning the type and charge of the mobile ions is not possible without further effort, while their number can be determined. The simulations were applied to experimental data in chapter 6. Thus, it could be shown that mobile ions make a significant contribution to the OCVD of perovskite solar cells. j-V characteristics and OCVD transients measured as a function of temperature and illumination intensities could be quantitatively modeled simultaneously using a single global set of parameters. By the simulations it was further possible to derive a simple experimental procedure to determine the concentration and the diffusivity of the mobile ions. The possibility of describing different experiments in a uniform temperaturedependent manner strongly supports the model of mobile ions in perovskites. In summary, this work has made an important contribution to the elucidation of ionic contributions to the (photo)electrical properties of perovskite solar cells. Established experimental techniques for conventional semiconductors have been reinterpreted with respect to ionic mass transport and new methods have been proposed to draw conclusions on the properties for ionic transport. As a result, the published simulation tools can be used for a number of further studies.
Development, Simulation and Evaluation of Mobile Wireless Networks in Industrial Applications
(2023)
Manyindustrialautomationsolutionsusewirelesscommunicationandrelyontheavail-
ability and quality of the wireless channel. At the same time the wireless medium is
highly congested and guaranteeing the availability of wireless channels is becoming
increasingly difficult. In this work we show, that ad-hoc networking solutions can be
used to provide new communication channels and improve the performance of mobile
automation systems. These ad-hoc networking solutions describe different communi-
cation strategies, but avoid relying on network infrastructure by utilizing the Peer-to-
Peer (P2P) channel between communicating entities.
This work is a step towards the effective implementation of low-range communication
technologies(e.g. VisibleLightCommunication(VLC), radarcommunication, mmWave
communication) to the industrial application. Implementing infrastructure networks
with these technologies is unrealistic, since the low communication range would neces-
sitate a high number of Access Points (APs) to yield full coverage. However, ad-hoc
networks do not require any network infrastructure. In this work different ad-hoc net-
working solutions for the industrial use case are presented and tools and models for
their examination are proposed.
The main use case investigated in this work are Automated Guided Vehicles (AGVs)
for industrial applications. These mobile devices drive throughout the factory trans-
porting crates, goods or tools or assisting workers. In most implementations they must
exchange data with a Central Control Unit (CCU) and between one another. Predicting
if a certain communication technology is suitable for an application is very challenging
since the applications and the resulting requirements are very heterogeneous.
The proposed models and simulation tools enable the simulation of the complex inter-
action of mobile robotic clients and a wireless communication network. The goal is to
predict the characteristics of a networked AGV fleet.
Theproposedtoolswereusedtoimplement, testandexaminedifferentad-hocnetwork-
ing solutions for industrial applications using AGVs. These communication solutions
handle time-critical and delay-tolerant communication. Additionally a control method
for the AGVs is proposed, which optimizes the communication and in turn increases the
transport performance of the AGV fleet. Therefore, this work provides not only tools
for the further research of industrial ad-hoc system, but also first implementations of
ad-hoc systems which address many of the most pressing issues in industrial applica-
tions.
We consider a multi-species gas mixture described by a kinetic model. More precisely, we are interested in models with BGK interaction operators. Several extensions to the standard BGK model are studied.
Firstly, we allow the collision frequency to vary not only in time and space but also with the microscopic velocity. In the standard BGK model, the dependence on the microscopic velocity is neglected for reasons of simplicity. We allow for a more physical description by reintroducing this dependence. But even though the structure of the equations remains the same, the so-called target functions in the relaxation term become more sophisticated being defined by a variational procedure.
Secondly, we include quantum effects (for constant collision frequencies). This approach influences again the resulting target functions in the relaxation term depending on the respective type of quantum particles.
In this thesis, we present a numerical method for simulating such models. We use implicit-explicit time discretizations in order to take care of the stiff relaxation part due to possibly large collision frequencies. The key new ingredient is an implicit solver which minimizes a certain potential function. This procedure mimics the theoretical derivation in the models. We prove that theoretical properties of the model are preserved at the discrete level such as conservation of mass, total momentum and total energy, positivity of distribution functions and a proper entropy behavior. We provide an array of numerical tests illustrating the numerical scheme as well as its usefulness and effectiveness.
In this doctoral thesis we cover the performance evaluation of next generation data plane architectures, comprised of complex software as well as programmable hardware components that allow fine granular configuration. In the scope of the thesis we propose mechanisms to monitor the performance of singular components and model key performance indicators of software based packet processing solutions. We present novel approaches towards network abstraction that allow the integration of heterogeneous data plane technologies into a singular network while maintaining total transparency between control and data plane. Finally, we investigate a full, complex system consisting of multiple software-based solutions and perform a detailed performance analysis. We employ simulative approaches to investigate overload control mechanisms that allow efficient operation under adversary conditions. The contributions of this work build the foundation for future research in the areas of network softwarization and network function virtualization.
How genomic and ecological traits shape island biodiversity - insights from individual-based models
(2020)
Life on oceanic islands provides a playground and comparably easy\-/studied basis
for the understanding of biodiversity in general. Island biota feature many
fascinating patterns: endemic species, species radiations and species with
peculiar trait syndromes. However, classic and current island biogeography
theory does not yet consider all the factors necessary to explain many of these
patterns. In response to this, there is currently a shift in island biogeography
research to systematically consider species traits and thus gain a more
functional perspective. Despite this recent development, a set of species
characteristics remains largely ignored in island biogeography, namely genomic
traits. Evidence suggests that genomic factors could explain many of the
speciation and adaptation patterns found in nature and thus may be highly
informative to explain the fascinating and iconic phenomena known for oceanic
islands, including species radiations and susceptibility to biotic invasions.
Unfortunately, the current lack of comprehensive meaningful data makes studying
these factors challenging. Even with paleontological data and space-for-time
rationales, data is bound to be incomplete due to the very environmental
processes taking place on oceanic islands, such as land slides and volcanism,
and lacks causal information due to the focus on correlative approaches. As
promising alternative, integrative mechanistic models can explicitly consider
essential underlying eco\-/evolutionary mechanisms. In fact, these models have
shown to be applicable to a variety of different systems and study questions.
In this thesis, I therefore examined present mechanistic island models to
identify how they might be used to address some of the current open questions in
island biodiversity research. Since none of the models simultaneously considered
speciation and adaptation at a genomic level, I developed a new genome- and
niche-explicit, individual-based model. I used this model to address three
different phenomena of island biodiversity: environmental variation, insular
species radiations and species invasions.
Using only a single model I could show that small-bodied species with flexible
genomes are successful under environmental variation, that a complex combination
of dispersal abilities, reproductive strategies and genomic traits affect the
occurrence of species radiations and that invasions are primarily driven by the
intensity of introductions and the trait characteristics of invasive
species. This highlights how the consideration of functional traits can promote
the understanding of some of the understudied phenomena in island biodiversity.
The results presented in this thesis exemplify the generality of integrative
models which are built on first principles. Thus, by applying such models to
various complex study questions, they are able to unveil multiple biodiversity
dynamics and patterns. The combination of several models such as the one I
developed to an eco\-/evolutionary model ensemble could further help to identify
fundamental eco\-/evolutionary principles. I conclude the thesis with an outlook
on how to use and extend my developed model to investigate geomorphological
dynamics in archipelagos and to allow dynamic genomes, which would further
increase the model's generality.
The present dissertation investigates the management of RFID implementations in retail trade. Our work contributes to this by investigating important aspects that have so far received little attention in scientific literature. We therefore perform three studies about three important aspects of managing RFID implementations. We evaluate in our first study customer acceptance of pervasive retail systems using privacy calculus theory. The results of our study reveal the most important aspects a retailer has to consider when implementing pervasive retail systems. In our second study we analyze RFID-enabled robotic inventory taking with the help of a simulation model. The results show that retailers should implement robotic inventory taking if the accuracy rates of the robots are as high as the robots’ manufacturers claim. In our third and last study we evaluate the potentials of RFID data for supporting managerial decision making. We propose three novel methods in order to extract useful information from RFID data and propose a generic information extraction process. Our work is geared towards practitioners who want to improve their RFID-enabled processes and towards scientists conducting RFID-based research.
Lifetime techniques are applied to diverse fields of study including materials sciences, semiconductor physics, biology, molecular biophysics and photochemistry.
Here we present DDRS4PALS, a software for the acquisition and simulation of lifetime spectra using the DRS4 evaluation board (Paul Scherrer Institute, Switzerland) for time resolved measurements and digitization of detector output pulses. Artifact afflicted pulses can be corrected or rejected prior to the lifetime calculation to provide the generation of high-quality lifetime spectra, which are crucial for a profound analysis, i.e. the decomposition of the true information. Moreover, the pulses can be streamed on an (external) hard drive during the measurement and subsequently downloaded in the offline mode without being connected to the hardware. This allows the generation of various lifetime spectra at different configurations from one single measurement and, hence, a meaningful comparison in terms of analyzability and quality. Parallel processing and an integrated JavaScript based language provide convenient options to accelerate and automate time consuming processes such as lifetime spectra simulations.
This paper presents a measurement of the polarisation of tau leptons produced in Z/gamma* -> tau tau decays which is performed with a dataset of proton-proton collisions at root s = 8 TeV, corresponding to an integrated luminosity of 20.2 fb(-1) recorded with the ATLAS detector at the LHC in 2012. The Z/gamma* -> tau tau decays are reconstructed from a hadronically decaying tau lepton with a single charged particle in the final state, accompanied by a tau lepton that decays leptonically. The tau polarisation is inferred from the relative fraction of energy carried by charged and neutral hadrons in the hadronic tau decays. The polarisation is measured in a fiducial region that corresponds to the kinematic region accessible to this analysis. The tau polarisation extracted over the full phase space within the Z/gamma* mass range of 66 < mZ/gamma* < 116GeVis found to be P-tau = -0.14 +/- 0.02(stat)+/- 0.04(syst). It is in agreement with the Standard Model prediction of Pt = -0.1517 +/- 0.0019, which is obtained from the ALP-GEN event generator interfaced with the PYTHIA 6 parton shower modelling and the TAUOLA tau decay library.
Neurobiology is widely supported by bioinformatics. Due to the big amount of data generated from the biological side a computational approach is required. This thesis presents four different cases of bioinformatic tools applied to the service of Neurobiology.
The first two tools presented belong to the field of image processing. In the first case, we make use of an algorithm based on the wavelet transformation to assess calcium activity events in cultured neurons. We designed an open source tool to assist neurobiology researchers in the analysis of calcium imaging videos. Such analysis is usually done manually which is time consuming and highly subjective. Our tool speeds up the work and offers the possibility of an unbiased detection of the calcium events. Even more important is that our algorithm not only detects the neuron spiking activity but also local spontaneous activity which is normally discarded because it is considered irrelevant. We showed that this activity is determinant in the calcium dynamics in neurons and it is involved in important functions like signal modulation and memory and learning.
The second project is a segmentation task. In our case we are interested in segmenting the neuron nuclei in electron microscopy images of c.elegans. Marking these structures is necessary in order to reconstruct the connectome of the organism. C.elegans is a great study case due to the simplicity of its nervous system (only 502 neurons). This worm, despite its simplicity has taught us a lot about neuronal mechanisms. There is still a lot of information we can extract from the c.elegans, therein lies the importance of reconstructing its connectome. There is a current version of the c.elegans connectome but it was done by hand and on a single subject which leaves a big room for errors. By automatizing the segmentation of the electron microscopy images we guarantee an unbiased approach and we will be able to verify the connectome on several subjects.
For the third project we moved from image processing applications to biological modeling. Because of the high complexity of even small biological systems it is necessary to analyze them with the help of computational tools. The term in silico was coined to refer to such computational models of biological systems. We designed an in silico model of the TNF (Tumor necrosis factor) ligand and its two principal receptors. This biological system is of high relevance because it is involved in the inflammation process. Inflammation is of most importance as protection mechanism but it can also lead to complicated diseases (e.g. cancer). Chronic inflammation processes can be particularly dangerous in the brain. In order to better understand the dynamics that govern the TNF system we created a model using the BioNetGen language. This is a rule based language that allows one to simulate systems where multiple agents are governed by a single rule. Using our model we characterized the TNF system and hypothesized about the relation of the ligand with each of the two receptors. Our hypotheses can be later used to define drug targets in the system or possible treatments for chronic inflammation or lack of the inflammatory response.
The final project deals with the protein folding problem. In our organism proteins are folded all the time, because only in their folded conformation are proteins capable of doing their job (with some very few exceptions). This folding process presents a great challenge for science because it has been shown to be an NP problem. NP means non deterministic Polynomial time problem. This basically means that this kind of problems cannot be efficiently solved. Nevertheless, somehow the body is capable of folding a protein in just milliseconds. This phenomenon puzzles not only biologists but also mathematicians. In mathematics NP problems have been studied for a long time and it is known that given the solution to one NP problem we could solve many of them (i.e. NP-complete problems). If we manage to understand how nature solves the protein folding problem then we might be able to apply this solution to many other problems. Our research intends to contribute to this discussion. Unfortunately, not to explain how nature solves the protein folding problem, but to explain that it does not solve the problem at all. This seems contradictory since I just mentioned that the body folds proteins all the time, but our hypothesis is that the organisms have learned to solve a simplified version of the NP problem. Nature does not solve the protein folding problem in its full complexity. It simply solves a small instance of the problem. An instance which is as simple as a convex optimization problem. We formulate the protein folding problem as an optimization problem to illustrate our claim and present some toy examples to illustrate the formulation. If our hypothesis is true, it means that protein folding is a simple problem. So we just need to understand and model the conditions of the vicinity inside the cell at the moment the folding process occurs. Once we understand this starting conformation and its influence in the folding process we will be able to design treatments for amyloid diseases such as Alzheimer's and Parkinson's.
In summary this thesis project contributes to the neurobiology research field from four different fronts. Two are practical contributions with immediate benefits, such as the calcium imaging video analysis tool and the TNF in silico model. The neuron nuclei segmentation is a contribution for the near future. A step towards the full annotation of the c.elegans connectome and later for the reconstruction of the connectome of other species. And finally, the protein folding project is a first impulse to change the way we conceive the protein folding process in nature. We try to point future research in a novel direction, where the amino code is not the most relevant characteristic of the process but the conditions within the cell.
Nowadays, data centers are becoming increasingly dynamic due to the common adoption of virtualization technologies. Systems can scale their capacity on demand by growing and shrinking their resources dynamically based on the current load. However, the complexity and performance of modern data centers is influenced not only by the software architecture, middleware, and computing resources, but also by network virtualization, network protocols, network services, and configuration. The field of network virtualization is not as mature as server virtualization and there are multiple competing approaches and technologies. Performance modeling and prediction techniques provide a powerful tool to analyze the performance of modern data centers. However, given the wide variety of network virtualization approaches, no common approach exists for modeling and evaluating the performance of virtualized networks.
The performance community has proposed multiple formalisms and models for evaluating the performance of infrastructures based on different network virtualization technologies. The existing performance models can be divided into two main categories: coarse-grained analytical models and highly-detailed simulation models. Analytical performance models are normally defined at a high level of abstraction and thus they abstract many details of the real network and therefore have limited predictive power. On the other hand, simulation models are normally focused on a selected networking technology and take into account many specific performance influencing factors, resulting in detailed models that are tightly bound to a given technology, infrastructure setup, or to a given protocol stack.
Existing models are inflexible, that means, they provide a single solution method without providing means for the user to influence the solution accuracy and solution overhead. To allow for flexibility in the performance prediction, the user is required to build multiple different performance models obtaining multiple performance predictions. Each performance prediction may then have different focus, different performance metrics, prediction accuracy, and solving time.
The goal of this thesis is to develop a modeling approach that does not require the user to have experience in any of the applied performance modeling formalisms. The approach offers the flexibility in the modeling and analysis by balancing between: (a) generic character and low overhead of coarse-grained analytical models, and (b) the more detailed simulation models with higher prediction accuracy.
The contributions of this thesis intersect with technologies and research areas, such as: software engineering, model-driven software development, domain-specific modeling, performance modeling and prediction, networking and data center networks, network virtualization, Software-Defined Networking (SDN), Network Function Virtualization (NFV). The main contributions of this thesis compose the Descartes Network Infrastructure (DNI) approach and include:
• Novel modeling abstractions for virtualized network infrastructures. This includes two meta-models that define modeling languages for modeling data center network performance. The DNI and miniDNI meta-models provide means for representing network infrastructures at two different abstraction levels. Regardless of which variant of the DNI meta-model is used, the modeling language provides generic modeling elements allowing to describe the majority of existing and future network technologies, while at the same time abstracting factors that have low influence on the overall performance. I focus on SDN and NFV as examples of modern virtualization technologies.
• Network deployment meta-model—an interface between DNI and other meta- models that allows to define mapping between DNI and other descriptive models. The integration with other domain-specific models allows capturing behaviors that are not reflected in the DNI model, for example, software bottlenecks, server virtualization, and middleware overheads.
• Flexible model solving with model transformations. The transformations enable solving a DNI model by transforming it into a predictive model. The model transformations vary in size and complexity depending on the amount of data abstracted in the transformation process and provided to the solver. In this thesis, I contribute six transformations that transform DNI models into various predictive models based on the following modeling formalisms: (a) OMNeT++ simulation, (b) Queueing Petri Nets (QPNs), (c) Layered Queueing Networks (LQNs). For each of these formalisms, multiple predictive models are generated (e.g., models with different level of detail): (a) two for OMNeT++, (b) two for QPNs, (c) two for LQNs. Some predictive models can be solved using multiple alternative solvers resulting in up to ten different automated solving methods for a single DNI model.
• A model extraction method that supports the modeler in the modeling process by automatically prefilling the DNI model with the network traffic data. The contributed traffic profile abstraction and optimization method provides a trade-off by balancing between the size and the level of detail of the extracted profiles.
• A method for selecting feasible solving methods for a DNI model. The method proposes a set of solvers based on trade-off analysis characterizing each transformation with respect to various parameters such as its specific limitations, expected prediction accuracy, expected run-time, required resources in terms of CPU and memory consumption, and scalability.
• An evaluation of the approach in the context of two realistic systems. I evaluate the approach with focus on such factors like: prediction of network capacity and interface throughput, applicability, flexibility in trading-off between prediction accuracy and solving time. Despite not focusing on the maximization of the prediction accuracy, I demonstrate that in the majority of cases, the prediction error is low—up to 20% for uncalibrated models and up to 10% for calibrated models depending on the solving technique.
In summary, this thesis presents the first approach to flexible run-time performance prediction in data center networks, including network based on SDN. It provides ability to flexibly balance between performance prediction accuracy and solving overhead. The approach provides the following key benefits:
• It is possible to predict the impact of changes in the data center network on the performance. The changes include: changes in network topology, hardware configuration, traffic load, and applications deployment.
• DNI can successfully model and predict the performance of multiple different of network infrastructures including proactive SDN scenarios.
• The prediction process is flexible, that is, it provides balance between the granularity of the predictive models and the solving time. The decreased prediction accuracy is usually rewarded with savings of the solving time and consumption of resources required for solving.
• The users are enabled to conduct performance analysis using multiple different prediction methods without requiring the expertise and experience in each of the modeling formalisms.
The components of the DNI approach can be also applied to scenarios that are not considered in this thesis. The approach is generalizable and applicable for the following examples: (a) networks outside of data centers may be analyzed with DNI as long as the background traffic profile is known; (b) uncalibrated DNI models may serve as a basis for design-time performance analysis; (c) the method for extracting and compacting of traffic profiles may be used for other, non-network workloads as well.
In this thesis two main projects are presented, both aiming at the overall goal
of particle detector development. In the first part of the thesis detailed shielding
studies are discussed, focused on the shielding section of the planned New Small
Wheel as part of the ATLAS detector upgrade. Those studies supported the discussions
within the upgrade community and decisions made on the final design of
the New Small Wheel. The second part of the thesis covers the design, construction
and functional demonstration of a test facility for gaseous detectors at the
University of Würzburg. Additional studies on the trigger system of the facility are
presented. Especially the precision and reliability of reference timing signals were
investigated.
Following the early experiences in aviation, medical simulation has rapidly
evolved into one of the most novel educational tools of the last three decades. In addition to its
use in training individuals or teams in crisis resource management, simulation has been studied as
a tool to evaluate technical and non-technical skills of individuals as well as, more recently,
entire medical teams.
It is usually fairly difficult to obtain clinical reference data from critical events to refute
claims that the management of actual events fell below what could reasonably be expected and we
demonstrated the use of rank order statistics to calculate quantiles with confidence limits for
management times of critical obstetrical events using data from realistic simulation. This approach
could be used to describe the distribution of treatment times in order to assist in deciding what
performance may constitute an outlier. It can also identify particular challenges of clinical
practice and allow the development of educational curricula. While the information derived from
simulation has to be interpreted with a high degree of caution for a clinical context, it may
represent a further ‘added value’ or important step in establishing simulation as a training tool
and to provide information that could be used in an appropriate clinical context for adverse
events. Large amounts of data (such as from a simulation registry) would allow the calculation of
acceptable confidence intervals for the required
outcome parameters as well as actual tolerance limits.
Continuously increasing energy prices have considerably influenced the cost of living over the last decades. At the same time increasingly extreme weather conditions, drought-filled summers as well as autumns and winters with heavier rainfall and worsening storms have been reported. These are possibly the harbingers of the expected approaching global climate change. Considering the depletability of fossil energy sources and a rising distrust in nuclear power, investigations into new and innovative renewable energy sources are necessary to prepare for the coming future.
In addition to wind, hydro and biomass technologies, electricity generated by the direct conversion of incident sunlight is one of the most promising approaches. Since the syntheses and detailed studies of organic semiconducting polymers and fullerenes were intensified, a new kind of solar cell fabrication became conceivable. In addition to classical vacuum deposition techniques, organic cells were now also able to be processed from a solution, even on flexible substrates like plastic, fabric or paper.
An organic solar cell represents a complex electrical device influenced for instance by light interference for charge carrier generation. Also charge carrier recombination and transport mechanisms are important to its performance. In accordance to Coulomb interaction, this results in a specific distribution of the charge carriers and the electric field, which finally yield the measured current-voltage characteristics. Changes of certain parameters result in a complex response in the investigated device due to interactions between the physical processes. Consequently, it is necessary to find a way to generally predict the response of such a device to temperature changes for example.
In this work, a numerical, one-dimensional simulation has been developed based on the drift-diffusion equations for electrons, holes and excitons. The generation and recombination rates of the single species are defined according to a detailed balance approach. The Coulomb interaction between the single charge carriers is considered through the Poisson equation. An analytically non-solvable differential equation system is consequently set-up. With numerical approaches, valid solutions describing the macroscopic processes in organic solar cells can be found. An additional optical simulation is used to determine the spatially resolved charge carrier generation rates due to interference.
Concepts regarding organic semiconductors and solar cells are introduced in the first part of this work. All chapters are based on previous ones and logically outline the basic physics, device architectures, models of charge carrier generation and recombination as well as the mathematic and numerical approaches to obtain valid simulation results.
In the second part, the simulation is used to elaborate issues of current interest in organic solar cell research. This includes a basic understanding of how the open circuit voltage is generated and which processes limit its value. S-shaped current-voltage characteristics are explained assigning finite surface recombination velocities at metal electrodes piling-up local space charges. The power conversion efficiency is identified as a trade-off between charge carrier accumulation and charge extraction. This leads to an optimum of the power conversion efficiency at moderate to high charge carrier mobilities. Differences between recombination rates determined by different interpretations of identical experimental results are assigned to a spatially inhomogeneous recombination, relevant for almost all low mobility semiconductor devices.
Staphylococcus aureus (SA) causes nosocomial infections including life threatening sepsis by multi-resistant strains (MRSA). It has the ability to form biofilms to protect it from the host immune system and from anti staphylococcal drugs. Biofilm and planctonic life style is regulated by a complex Quorum-Sensing (QS) system with agr as a central regulator. To study biofilm formation and QS mechanisms in SA a Boolean network was build (94 nodes, 184 edges) including two different component systems such as agr, sae and arl. Important proteins such as Sar, Rot and SigB were included as further nodes in the model. System analysis showed there are only two stable states biofilm forming versus planctonic with clearly different subnetworks turned on. Validation according to gene expression data confirmed this. Network consistency was tested first according to previous knowledge and literature. Furthermore, the predicted node activity of different in silico knock-out strains agreed well with corresponding micro array experiments and data sets. Additional validation included the expression of further nodes (Northern blots) and biofilm production compared in different knock-out strains in biofilm adherence assays. The model faithfully reproduces the behaviour of QS signalling mutants. The integrated model allows also prediction of various other network mutations and is supported by experimental data from different strains. Furthermore, the well connected hub proteins elucidate how integration of different inputs is achieved by the QS network. For in silico as well as in vitro experiments it was found that the sae-locus is also a central modulator of biofilm production. Sae knock-out strains showed stronger biofilms. Wild type phenotype was rescued by sae complementation. To elucidate the way in which sae takes influence on biofilm formation the network was used and Venn-diagrams were made, revealing nodes regulated by sae and changed in biofilms. In these Venn-diagrams nucleases and extracellular proteins were found to be promising nodes. The network revealed DNAse to be of great importance. Therefore qualitatively the DNAse amount, produced by different SA mutants was measured, it was tried to dissolve biofilms with according amounts of DNAse and the concentration of nucleic acids, proteins and polysaccharides were measured in biofilms of different SA mutants.
With its thorough validation the network model provides a powerful tool to study QS and biofilm formation in SA, including successful predictions for different knock-out mutant behaviour, QS signalling and biofilm formation. This includes implications for the behaviour of MRSA strains and mutants. Key regulatory mutation combinations (agr–, sae–, sae–/agr–, sigB+, sigB+/sae–) were directly tested in the model but also in experiments. High connectivity was a good guide to identify master regulators, whose detailed behaviour was studied both in vitro and in the model. Together, both lines of evidence support in particular a refined regulatory role for sae and agr with involvement in biofilm repression and/or SA dissemination. With examination of the composition of different mutant biofilms as well as with the examination of the reaction cascade that connects sae to the biofilm forming ability of SA and also by postulating that nucleases might play an important role in that, first steps were taken in proving and explaining regulatory links leading from sae to biofilms. Furthermore differences in biofilms of different mutant SA strains were found leading us in perspective towards a new understanding of biofilms including knowledge how to better regulate, fight and use its different properties.
Understanding the emergence of species' ranges is one of the most fundamental challenges in ecology. Early on, geographical barriers were identified as obvious natural constraints to the spread of species. However, many range borders occur along gradually changing landscapes, where no sharp barriers are obvious. Mechanistic explanations for this seeming contradiction incorporate environmental gradients that either affect the spatio-temporal variability of conditions or the increasing fragmentation of habitat. Additionally, biological mechanisms like Allee effects (i.e. decreased growth rates at low population sizes or densities), condition-dependent dispersal, and biological interactions with other species have been shown to severely affect the location of range margins. The role of dispersal has been in the focus of many studies dealing with range border formation. Dispersal is known to be highly plastic and evolvable, even over short ecological time-scales. However, only few studies concentrated on the impact of evolving dispersal on range dynamics. This thesis aims at filling this gap. I study the influence of evolving dispersal rates on the persistence of spatially structured populations in environmental gradients and its consequences for the establishment of range borders. More specially I investigate scenarios of range formation in equilibrium, periods of range expansion, and range shifts under global climate change ...
This thesis deals with the chaotic dynamics of nonlinear networks consisting of semiconductor lasers which have time-delayed self-feedbacks or mutual couplings. These semiconductor lasers are simulated numerically by the Lang-Kobayashi equations. The central issue is how the chaoticity of the lasers, measured by the maximal Lyapunov exponent, changes when the delay time is changed. It is analysed how this change of chaoticity with increasing delay time depends on the reflectivity of the mirror for the self-feedback or the strength of the mutal coupling, respectively. The consequences of the different types of chaos for the effect of chaos synchronization of mutually coupled semiconductor lasers are deduced and discussed. At the beginning of this thesis, the master stability formalism for the stability analysis of nonlinear networks with delay is explained. After the description of the Lang-Kobayashi equations and their linearizations as a model for the numerical simulation of semiconductor lasers with time-delayed couplings, the artificial sub-Lyapunov exponent $\lambda_{0}$ is introduced. It is explained how the sign of the sub-Lyapunov exponent can be determined by experiments. The notions of "strong chaos" and "weak chaos" are introduced and distinguished by their different scaling properties of the maximal Lyapunov exponent with the delay time. The sign of the sub-Lyapunov exponent $\lambda_{0}$ is shown to determine the occurence of strong or weak chaos. The transition sequence "weak to strong chaos and back to weak chaos" upon monotonically increasing the coupling strength $\sigma$ of a single laser's self-feedback is shown for numerical calculations of the Lang-Kobayashi equations. At the transition between strong and weak chaos, the sub-Lyapunov exponent vanishes, $\lambda_{0}=0$, resulting in a special scaling behaviour of the maximal Lyapunov exponent with the delay time. Transitions between strong and weak chaos by changing $\sigma$ can also be found for the Rössler and Lorenz dynamics. The connection between the sub-Lyapunov exponent and the time-dependent eigenvalues of the Jacobian for the internal laser dynamics is analysed. Counterintuitively, the difference between strong and weak chaos is not directly visible from the trajectory although the difference of the trajectories induces the transitions between the two types of chaos. In addition, it is shown that a linear measure like the auto-correlation function cannot unambiguously reveal the difference between strong and weak chaos either. Although the auto-correlations after one delay time are significantly higher for weak chaos than for strong chaos, it is not possible to detect a qualitative difference. If two time-scale separated self-feedbacks are present, the shorter feedback has to be taken into account for the definition of a new sub-Lyapunov exponent $\lambda_{0,s}$, which in this case determines the occurence of strong or weak chaos. If the two self-feedbacks have comparable delay times, the sub-Lyapunov exponent $\lambda_{0}$ remains the criterion for strong or weak chaos. It is shown that the sub-Lyapunov exponent scales with the square root of the effective pump current $\sqrt{p-1}$, both in its magnitude and in the position of the critical coupling strengths. For networks with several distinct sub-Lyapunov exponents, it is shown that the maximal sub-Lyapunov exponent of the network determines whether the network's maximal Lyapunov exponent scales strongly or weakly with increasing delay time. As a consequence, complete synchronization of a network is excluded for arbitrary networks which contain at least one strongly chaotic laser. Furthermore, it is demonstrated that the sub-Lyapunov exponent of a driven laser depends on the number of the incoherently superimposed inputs from unsynchronized input lasers. For networks of delay-coupled lasers operating in weak chaos, the condition $|\gamma_{2}|<\mathrm{e}^{-\lambda_{\mathrm{m}}\,\tau}$ for stable chaos synchronization is deduced using the master stability formalism. Hence, synchronization of any network depends only on the properties of a single laser with self-feedback and the eigenvalue gap of the coupling matrix. The characteristics of the master stability function for the Lang-Kobayashi dynamics is described, and consequently, the master stability function is refined to allow for precise practical prediction of synchronization. The prediction of synchronization with the master stability function is demonstrated for bidirectional and unidirectional networks. Furthermore, the master stability function is extended for two distinct delay times. Finally, symmetries and resonances for certain values of the ratio of the delay times are shown for the master stability function of the Lang-Kobyashi equations.
This thesis analyzes the 2001-2006 labor market reforms in Germany. The aim of this work is twofold. First, an overview of the most important reform measures and the intended effects is given. Second, two specific and very fundamental amendments, namely the merging of unemployment assistance and social benefits, as well as changes in the duration of unemployment insurance benefits, are analyzed in detail to evaluate their effects on individuals and the entire economy. Using a matching model with optimal search intensity and Semi-Markov methods, the effects of these two amendments on the duration of unemployment, optimal search intensity and unemployment are analyzed.
In this PhD thesis, we study the heteroepitaxial crystal growth by means of Monte Carlo simulations. Of particular interest in this work is the influence of the lattice mismatch of the adsorbates relative to the substrate on surface structures. In the framework of an off-lattice model, we consider one monolayer of adsorbate and investigate the emerging nanopatterns in equilibrium and their formation during growth. In chapter 1, a brief introduction is given, which describes the role of computer simulations in the field of the physics of condensed matter. Chapter 2 is devoted to some technical basics of experimental methods of molecular beam epitaxy and the theoretical description. Before a model for the simulation can be designed, it is necessary to make some considerations of the single processes which occur during epitaxial growth. For that purpose we look at an experimental setup and extract the main microscopic processes. Afterwards a brief overview of different theoretical concepts describing that physical procedures is given. In chapter 3, the model used in the simulations is presented. The aim is to investigate the growth of an fcc crystal in the [111] direction. In order to keep the simulation times within a feasible limit a simple pair potential, the Lennard-Jones potential, with continuous particle positions is used, which are necessary to describe effects resulting from the atomic mismatch in the crystal. Furthermore the detailed algorithm is introduced which is based on the idea to calculate the barrier of each diffusion event and to use the barriers in a rejection-free method. Chapter 4 is attended to the simulation of equilibrium. The influence of different parameters on the emerging structures in the first monolayer upon the surface, which is completely covered with two adsorbate materials, is studied. Especially the competition between binding energy and strain leads to very interesting pattern formations like islands or stripes. In chapter 5 the results of growth simulations are presented. At first, we introduce a model in order to realize off-lattice Kinetic Monte Carlo simulations. Since the costs in simulation time are enormous, some simplifications in the calculation of diffusion barriers are necessary and therefore the previous model is supplemented with some elements from the so-called ball and spring model. The next point is devoted to the calculation of energy barriers followed by the presentation of the growth simulations. Binary systems with only one sort of adsorbate are investigated as well as ternary systems with two different adsorbates. Finally, a comparison to the equilibrium simulations is drawn. Chapter 6 contains some concluding remarks and gives an outlook to possible further investigations.
In this PhD thesis, the effect of strain on heteroepitaxial growth is investigated by means of Kinetic Monte Carlo simulations. In this context the lattice misfit, arising from the different lattice constants of the adsorbate and the substrate material, is of particular interest. As a consequence, this lattice misfit leads to long-range elastic strain effects having strong influence on the entire growing crystal and its resulting surface morphology. The main focus of this work is the investigation of different strain relaxation mechanisms and their controlling parameters, revealing interesting consequences on the subsequent growth. Since epitaxial growth is carried out under conditions far away from thermodynamic equilibrium, it is strongly determined by surface kinetics. At this point the relevant kinetic microscopic processes are described, followed by theoretical considerations of heteroepitaxial growth disclosing an overview over several independent methodological streams, used to model epitaxy in different time and length scales, as well as the characterization of misfit dislocations and the classification of epitaxial growth modes based on thermodynamic considerations. The epitaxial growth is performed by means of Kinetic Monte Carlo simulations which allows for the consideration of long range effects in systems with lateral extension of few hundred atoms. By using an off-lattice simulation model the particles are able to leave their predefined lattice sites, which is an indispensable condition for simulating strain relaxation mechanisms. The main idea of our used model is calculating the activation energy of all relevant thermally activated processes by using simple pair potentials and then realizing the dynamics by performing each event according to its probability by means of a rejection-free algorithm method. In addition, the crystal relaxation procedure, the grid-based particle access method, which accelerates the simulation enormously, and the efficient implementation of the algorithm are discussed. To study the influence of long range elastic strain effects, the main part of this work was realized on the two dimensional triangular lattice, which can be treated as a cross section of the real three dimensional case. Chapter 4 deals with the formation of misfit dislocations as a strain relaxation mechanism and the resulting consequences on the subsequent heteroepitaxial growth. We can distinguish between two principally different dislocation formation mechanisms, depending strongly on the sign as well as on the magnitude of the misfit, but also the surface kinetics need to be taken into account. Additionally, the dislocations affect the lattice spacings of the crystal whose observed progression is in qualitative good agreement with experimental results. Furthermore, the dislocations influence the subsequent growth of the adsorbate film, since the potential energy of an adatom is modulated by buried dislocations. A clear correlation between the lateral positions of buried dislocations and the positions of mounds grown on the surface can be observed. In chapter 5, an alternative strain relaxation mechanism is studied: the formation of three dimensional islands enables the particles to approach their preferred lattice spacing. We demonstrate that it is possible to adjust within our simulation model each of the three epitaxial growth modes: Volmer–Weber, Frank–van der Merve or layer-by-layer, and Stranski–Krastanov growth mode. Moreover, we can show that the emerging growth mode depends in principle on two parameters: on the one hand the interaction strength of adsorbate particles with each other, compared to the interaction of adsorbate with substrate particles, and on the other hand the lattice misfit between adsorbate and substrate particles. A sensible choice of these two parameters allows the realization of each growth mode within the simulations. In conclusion, the formation of nanostructures controlled by an underlying dislocation network can be applied in the concept of self-organized pattern formation as well as by the tendency to form ordered arrays of strain-induced three dimensional grown islands. In chapter 6, we extend our model to three dimensions and investigate the effect of strain on growth on bcc(100) surfaces. We introduce an anisotropic potential yielding a stable bcc lattice structure within the off-lattice representation. We can show that the strain built up in submonolayer islands is mainly released at the island edges and the lattice misfit has strong influence on the diffusion process on the plane surface as well as on the situation at island edges with eminent consequences on the appearance of submonolayer islands.
The investigation of multivariate generalized Pareto distributions (GPDs) in the framework of extreme value theory has begun only lately. Recent results show that they can, as in the univariate case, be used in Peaks over Threshold approaches. In this manuscript we investigate the definition of GPDs from Section 5.1 of Falk et al. (2004), which does not differ in the area of interest from those of other authors. We first show some theoretical properties and introduce important examples of GPDs. For the further investigation of these distributions simulation methods are an important part. We describe several methods of simulating GPDs, beginning with an efficient method for the logistic GPD. This algorithm is based on the Shi transformation, which was introduced by Shi (1995) and was used in Stephenson (2003) for the simulation of multivariate extreme value distributions of logistic type. We also present nonparametric and parametric estimation methods in GPD models. We estimate the angular density nonparametrically in arbitrary dimension, where the bivariate case turns out to be a special case. The asymptotic normality of the corresponding estimators is shown. Also in the parametric estimations, which are mainly based on maximum likelihood methods, the asymptotic normality of the estimators is shown under certain regularity conditions. Finally the methods are applied to a real hydrological data set containing water discharges of the rivers Altmühl and Danube in southern Bavaria.
This thesis contains two major parts: The first part introduces the reader into three independent concepts of treating strongly correlated many body physics. These are, on the analytical side the SO(5)-theory (Chap.3), which poses the general frame. On the numerical side these are the Stochastic Series Expansion (SSE) (Chap.1) and the Contractor Renormalization Group (CORE) approach (Chap. 2}). The central idea of this thesis was to combine these above concepts, in order to achieve a better understanding of the high-T_c superconductors (HTSC). The results obtained by this combination can be found in the second major part of this thesis (chapters 4 and 5). The main idea of this thesis, i.e., to combine the SO(5)-theory with the capabilities of bosonic Quantum-Monte Carlo simulations and those of the CORE approach, has been proven to be a very successful Ansatz. Two different approaches, one based on symmetry and one on renormalization-group arguments, motivate an effective bosonic Hamiltonian. In a subsequent step the effective Hamiltonian has been simulated efficiently using the SSE. The results reproduce salient experiments on high-T_c superconductors. In addition, it has been shown that the model can be extended to capture also charge ordering. These results also form a profound basis for further studies, for example one could address the open question of SO(5)-symmetry restoration at a multicritical point in the extended pSO(5) model, where longer ranged interactions are included.
In this thesis a new and powerful approach for modeling laser cavity eigenmodes is presented. This approach is based on an eigenvalue problem for singularly perturbed partial differential operators with complex coefficients; such operators have not been investigated in detail until now. The eigenvalue problem is discretized by finite elements, and convergence of the approximate solution is proved by using an abstract convergence theory also developed in this dissertation. This theory for the convergence of an approximate solution of a (quadratic) eigenvalue problem, which particularly can be applied to a finite element discretization, is interesting on its own, since the ideas can conceivably be used to handle equations with a more complex nonlinearity. The discretized eigenvalue problem essentially is solved by preconditioned GMRES, where the preconditioner is constructed according to the underlying physics of the problem. The power and correctness of the new approach for computing laser cavity eigenmodes is clearly demonstrated by successfully simulating a variety of different cavity configurations. The thesis is organized as follows: Chapter 1 contains a short overview on solving the so-called Helmholtz equation with the help of finite elements. The main part of Chapter 2 is dedicated to the analysis of a one-dimensional model problem containing the main idea of a new model for laser cavity eigenmodes which is derived in detail in Chapter 3. Chapter 4 comprises a convergence theory for the approximate solution of quadratic eigenvalue problems. In Chapter 5, a stabilized finite element discretization of the new model is described and its convergence is proved by applying the theory of Chapter 4. Chapter 6 contains computational aspects of solving the resulting system of equations and, finally, Chapter 7 presents numerical results for various configurations, demonstrating the practical relevance of our new approach.
In this PhD thesis, we develop models for the numerical simulation of epitaxial crystal growth, as realized, e.g., in molecular beam epitaxy (MBE). The basic idea is to use a discrete lattice gas representation of the crystal structure, and to apply kinetic Monte Carlo (KMC) simulations for the description of the growth dynamics. The main advantage of the KMC approach is the possibility to account for atomistic details and at the same time cover MBE relevant time scales in the simulation. In chapter 1, we describe the principles of MBE, pointing out relevant physical processes and the influence of experimental control parameters. We discuss various methods used in the theoretical description of epitaxial growth. Subsequently, the underlying concepts of the KMC method and the lattice gas approach are presented. Important aspects concerning the design of a lattice gas model are considered, e.g. the solid-on-solid approximation or the choice of an appropriate lattice topology. A key element of any KMC simulation is the selection of allowed events and the evaluation of Arrhenius rates for thermally activated processes. We discuss simplifying schemes that are used to approximate the corresponding energy barriers if detailed knowledge about the barriers is not available. Finally, the efficient implementation of the MC kinetics using a rejection-free algorithm is described. In chapter 2, we present a solid-on-solid lattice gas model which aims at the description of II-VI(001) semiconductor surfaces like CdTe(001). The model accounts for the zincblende structure and the relevant surface reconstructions of Cd- and Te-terminated surfaces. Particles at the surface interact via anisotropic nearest and next nearest neighbor interactions, whereas interactions in the bulk are isotropic. The anisotropic surface interactions reflect known properties of CdTe(001) like the small energy difference between the c(2x2) and (2x1) vacancy structures of Cd-terminated surfaces. A key element of the model is the presence of additional Te atoms in a weakly bound Te* state, which is motivated by experimental observations of Te coverages exceeding one monolayer at low temperatures and high Te fluxes. The true mechanism of binding excess Te to the surface is still unclear. Here, we use a mean-field approach assuming a Te* reservoir with limited occupation. In chapter 3, we perform KMC simulations of atomic layer epitaxy (ALE) of CdTe(001). We study the self-regulation of the ALE growth rate and demonstrate how the interplay of the Te* reservoir occupation with the surface kinetics results in two different regimes: at high temperatures the growth rate is limited to one half layer of CdTe per ALE cycle, whereas at low enough temperatures each cycle adds a complete layer. The temperature where the transition between the two regimes occurs depends mainly on the particle fluxes. The temperature dependence of the growth rate and the flux dependence of the transition temperature are in good qualitative agreement with experimental results. Comparing the macroscopic activation energy for Te* desorption in our model with experimental values we find semiquantitative agreement. In chapter 4, we study the formation of nanostructures with alternating stripes during submonolayer heteroepitaxy of two different adsorbate species on a given substrate. We evaluate the influence of two mechanisms: kinetic segregation due to chemically induced diffusion barriers, and strain relaxation by alternating arrangement of the adsorbate species. KMC simulations of a simple cubic lattice gas with weak inter-species binding energy show that kinetic effects are sufficient to account for stripe formation during growth. The dependence of the stripe width on control parameters is investigated. We find an Arrhenius temperature dependence, in agreement with experimental investigations of phase separation in binary or ternary material systems. Canonical MC simulations show that the observed stripes are not stable under equilibrium conditions: the adsorbate species separate into very large domains. Off-lattice simulations which account for the lattice misfit of the involved particle species show that, under equilibrium conditions, the competition between binding and strain energy results in regular stripe patterns with a well-defined width depending on both misfit and binding energies. In KMC simulations, the stripe-formation and the experimentally reported ramification of adsorbate islands are reproduced. To clarify the origin of the island ramification, we investigate an enhanced lattice gas model whose parameters are fitted to match characteristic off-lattice diffusion barriers. The simulation results show that a satisfactory explanation of experimental observations within the lattice gas framework requires a detailed incorporation of long-range elastic interactions. In the appendix we discuss supplementary topics related to the lattice gas simulations in chapter 4.