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- Kaiserslautern - Fachbereich Architektur (1)
- Landau - Fachbereich Erziehungswissenschaften (1)
Distributed message-passing systems have become ubiquitous and essential for our daily lives. Hence, designing and implementing them correctly is of utmost importance. This is, however, very challenging at the same time. In fact, it is well-known that verifying such systems is algorithmically undecidable in general due to the interplay of asynchronous communication (messages are buffered) and concurrency. When designing communication in a system, it is natural to start with a global protocol specification of the desired communication behaviour. In such a top-down approach, the implementability problem asks, given such a global protocol, if the specified behaviour can be implemented in a distributed setting without additional synchronisation. This problem has been studied from two perspectives in the literature. On the one hand, there are Multiparty Session Types (MSTs) from process algebra, with global types to specify protocols. Key to the MST approach is a so-called projection operator, which takes a global type and tries to project it onto every participant: if successful, the local specifications are safe to use. This approach is efficient but brittle. On the other hand, High-level Message Sequence Charts (HMSCs) study the implementability problem from an automata-theoretic perspective. They employ very few restrictions on protocol specifications, making the implementability problem for HMSCs undecidable in general. The work in this thesis is the first to formally build a bridge between the world of MSTs and HMSCs. To start, we present a generalised projection operator for sender-driven choice. This allows a sender to send to different receivers when branching, which is crucial to handle common communication patterns from distributed computing. Despite this first step, we also show that the classical MST projection approach is inherently incomplete. We present the first formal encoding from global types to HMSCs. With this, we prove decidability of the implementability problem for global types with sender-driven choice. Furthermore, we develop the first direct and complete projection operator for global types with sender-driven choice, using automata-theoretic techniques, and show its effectiveness with a prototype implementation. We are the first to provide an upper bound for the implementability problem for global types with sender-driven (or directed) choice and show it to be in PSPACE. We also provide a session type system that uses the results from our projection operator. Last, we introduce protocol state machines (PSMs) – an automata-based protocol specification formalism – that subsume both global types from MSTs and HMSCs with regard to expressivity. We use transformations on PSMs to show that many of the syntactic restrictions of global types are not restrictive in terms of protocol expressivity. We prove that the implementability problem for PSMs with mixed choice, which requires no dedicated sender for a branch but solely all labels to be distinct, is undecidable in general. With our results on expressivity, this answers an open question: the implementability problem for mixed-choice global types is undecidable in general.
Coastal port-industrial areas are becoming increasingly significant due to urban shrinkage, population
decline, and climate change. To address social and economic issues and enhance climate resilience, it
is crucial to anticipate urban shrinkage in both stable and growing coastal areas that are undergoing
economic transformation. Urban planning can better understand the dynamics of planning for urban
shrinkage and climate resilience, as port-industrial areas have a large economic impact on nearby
coastal communities.
This dissertation examines the long-term implications of urban shrinkage in coastal port-industrial
areas in the context of climate change and sea level rise in England. The research problem is that
current urban policy does not adequately address the challenges of urban shrinkage and climate
resilience in these areas. The research questions are: What are the population changes in local areas
in England? What effect does population decline have on changing urbanisation patterns in older
industrial areas? What type of adaptation efforts were made in North East Lincolnshire, England, and
Bremerhaven, Germany, in response to the 2013 tidal surge, and how did this affect urban
shrinkage?
The dissertation applies an integrated concept of Shrinkage-Resilience as a framework for analysis.
The methodology includes a review of existing models and frameworks, as well as case studies of
international and local contexts. The findings suggest that between 2013-2019, 68% of older
industrial areas (including coastal ports) in England are undergoing changing urbanisation patterns
relative to population, land use, and green belt areas, and are key areas for urban policy, such as the
Levelling Up agenda. One of the areas, North East Lincolnshire is discussed and compared to
Bremerhaven. These examples demonstrate the link between Shrinkage-Resilience approaches and
their practical implementation in coastal port-industrial areas affected by urban shrinkage.
This research advances the scientific practice of urban planning and policy-making for shrinking cities
by introducing the approach of Shrinkage-Resilience, which emphasises the importance of
considering long-term social, economic, and environmental impacts in urban shrinkage contexts. This
approach is crucial in the transition to a more sustainable and inclusive society, where the welfare of
present and future generations, the environment, and economic development are taken into
account. The dissertation provides recommendations for urban planning to incorporate policy
changes for shrinking cities and coastal port-industrial areas worldwide, to include disaster risk
reduction and climate change adaptation approaches.
To increase situational awareness of the crane operator, the aim of this thesis is to develop a vision-based deep learning object detection from crane load-view using an adaptive perception in the construction area. Conventional worker detection methods are based on simple shape or color features from the worker's appearances. Nonetheless, these methods can fail to recognize the workers who do not wear the protective gears. To find out an image representation of the object from the top view manually or handcrafted feature is crucial. We, therefore, employed deep learning methods to automatically learn those features.
To yield optimal results, deep learning methods require mass amount of data.
Due to the data deficit especially in the construction domain, we developed the photorealistic world to create the data in addition to our samples collected from the real construction area. The simulated platform does not benefit only from diverse data types, but also concurrent research development which speeds up the pipeline at a low cost.
Our research findings indicate that the combination of synthetic and real training samples improved the state-of-the-art detector. In line with previous studies to bridge the gap between synthetic and real data, the results of preprocessed synthetic images are substantially better than using the raw data by approximately 10%.
Finding the right deep learning model for load-view detection is challenging.
By investigating our training data, it becomes evident that the majority of bounding box sizes are very small with a complex background.
In addition, we gave the priority to speed over accuracy based on the construction safety criteria. Finally, RetinaNet is chosen out of the three primary object detection models.
Nevertheless, the data-driven detection algorithm can fail to handle scale invariance, especially for detectors whose input size is changed in an extremely wide range.
The adaptive zoom feature can enhance the quality of the worker detection.
To avoid further data gathering and extensive retraining, the proposed automatic zoom method of the load-view crane camera supports the deep learning algorithm, specifically in the high scale variant problem. The finite state machine is employed for control strategies to adapt the zoom level to cope not only with inconsistent detection but also abrupt camera movement during lifting operation. Consequently, the detector is able to detect a small size object by smooth continuous zoom control without additional training.
The adaptive zoom control not only enhances the performance of the top-view object detection but also reduces the interaction of the crane operator with camera system, reducing the risk of fatality during load lifting operation.
Aquatic habitats are closely linked to the adjacent riparian area. Fluxes of nutrients, energy and matter through emerging aquatic insects are a key component of the aquatic subsidy to terrestrial systems. In fact, adult insects serve as high-quality prey for riparian predators. Stressors impacting the aquatic subsidy can thus translate to consequences for the receiving terrestrial food web, while mechanistic knowledge is extremely limited. Against this background, this thesis aimed at (i) assessing the impact of a model stressor specifically targeting insect emergence, that is the mosquito control agent Bacillus thuringiensis var. israelensis, on quantity, temporal dynamics and (ii) quality of emerging aquatic insects. For this purpose, outdoor floodplain pond mesocosms (n = 6) were employed. Since emergence is, in most cases, no point event but occurs over a longer period emergence was monitored over 3.5 months. The model stressor, i.e., Bti applied three times during spring at 2.88 × 10^9 ITU/ha, shifted the emergence time of aquatic insects, especially of non-biting midges (Diptera: Chironomidae), by ten days with a 26% reduced peak, while the nutrient content was not altered. On this basis, (ii) the propagation of the effects in aquatic subsidy emergence to riparian predators was investigated. Stable isotope analyses were used to assess the diet of a model predator, that is the web-building riparian spider Tetragnatha extensa. Results suggested changes in the composition of the spider’s diet to replace missing Chironomidae by other aquatic and terrestrial prey organisms pointing to further negative consequences. Finally, the thesis aimed at (iii) the understanding of processes underlying an altered emergence of aquatic subsidy mainly consisting of chironomids. Using a laboratory-based test design, populations of Chironomus riparius (n = 6) were assessed for their sensitivity towards Bti under different food qualities (high and low nutritious) before and after a long-term (six months) Bti exposure. Signs of phenotypic adaptation were observed in emergence time and nutrient content over multiple generations, resulting in changes in chironomids’ quantity and quality as food source. Overall, it can be concluded that direct and indirect effects of an aquatic stressor, as well as the adaptive response to it, can alter ecosystems at different levels, including individual, population and community level. Furthermore, this thesis highlights the importance of a temporal perspective when investigating the impact of aquatic stressors beyond ecosystem boundaries. It illustrates potential bottom-up effects on riparian predators through altered emergence of aquatic insects, feeding our understanding of meta-ecosystems and how stressors and their effects are transferred across systems. These insights will support efforts to protect and conserve natural ecosystems.
The German energy mix, which provides an overview of the sources of electricity available in Germany, is changing as a result of the expansion of renewable energy sources. With this shift towards sustainable energy sources such as wind and solar power, the electricity market situation is also in flux. Whereas in the past there were few uncertainties in electricity generation and only demand was subject to stochastic uncertainties, generation is now subject to stochastic fluctuations as well, especially due to weather dependency. To provide a supportive framework for this different situation, the electricity market has introduced, among other things, the intraday market, products with half-hourly and quarter-hourly time slices, and a modified balancing energy market design. As a result, both electricity price forecasting and optimization issues remain topical.
In this thesis, we first address intraday market modeling and intraday index forecasting. To do so, we move to the level of individual bids in the intraday market and use them to model the limit order books of intraday products. Based on statistics of the modeled limit order books, we present a novel estimator for the intraday indices. Especially for less liquid products, the order book statistics contain relevant information that allows for significantly more accurate predictions in comparison to the benchmark estimator.
Unlike the intraday market, the day ahead market allows smaller companies without their own trading department to participate since it is operated as a market with daily auctions. We optimize the flexibility offer of such a small company in the day ahead market and model the prices with a stochastic multi-factor model already used in the industry. To make this model accessible for stochastic optimization, we discretize it in time and space using scenario trees. Here we present existing algorithms for scenario tree generation as well as our own extensions and adaptations. These are based on the nested distance, which measures the distance between two distributions of stochastic processes. Based on the resulting scenario trees, we apply the stochastic optimization methods of stochastic programming, dynamic programming, and reinforcement learning to illustrate in which context the methods are appropriate.
Virtual Possibilities: Exploring the Role of Emerging Technologies in Work and Learning Environments
(2024)
The present work aims to investigate whether virtual reality can support learning as well as vocational work environments. To this end, four studies were conducted, with the first set investigating the demands for vocational workers and the impact of input methods on participant performance. These studies laid the foundation needed to create studies incorporating virtual reality research. The second set of studies was concerned with the impact of virtual reality on learning performance as well as the influence of binaural stimuli presentation on task performance. Results of each study are discussed individually and in conjunction with one another. The four studies are further supplemented with further research conducted by the author as well as an analysis of the growing field of virtual reality-based research. The thesis closes by embedding the discussed work into the scientific landscape and tries to give an outlook for virtual reality-based use cases in the future.
In recent years, there has been a growing need for accurate 3D scene reconstruction. Recent developments in the automotive industry have led to the increased use of ADAS where 3D reconstruction techniques are used, for example, as part of a collision detection system. For such applications, scene geometry reconstruction is usually performed in the form of depth estimation, where distances to scene objects are obtained.
In general, depth estimation systems can be divided into active and passive. Both systems have their advantages and disadvantages, but passive systems are usually cheaper to produce and easier to assemble and integrate than active systems. Passive systems can be stereo- or multiple-view based. Up to a certain limit, increasing the number of views in multi-view systems usually results in improved depth estimation accuracy.
One potential problem for ensuring the reliability of multi-view systems is the need to accurately estimate the orientation of their optical sensors. One way to ensure sensor placement for multi-view systems is to rigidly fix the sensors at the manufacturing stage. Unlike arbitrary sensor placement, using of a simplified and known sensor placement geometry further simplifies the depth estimation.
We meet with the concept of light field, which parameterizes all visible light passing through all viewpoints by their intersection with angular and spatial planes. When applied to computer vision, this gives us a 2D set of 2D images, where the physical distances between each image are fixed and proportional to each other.
Existing light field depth estimation methods provide good accuracy, which is suitable for industrial applications. However, the main problems of these methods are related to their running time and resource requirements. Most of the algorithms presented in the literature are typically sharpened for accuracy, can only be run on high-performance machines and often require a significant amount of time to process and obtain results.
Real-world applications often have running time requirements. Also, often there is a power-consumption limitation. In this dissertation, we investigate the problem of building a depth estimation system with an light field camera that satisfies the operating time and power consumption constraints without significant loss of estimation accuracy.
First, an algorithm for calibrating light field cameras is proposed, together with an algorithm for automatic calibration refinement, that works on arbitrary captured scenes. An algorithm for classical geometric depth estimation using light field cameras is proposed. Ways to optimize the algorithm for real-time use without significant loss of accuracy are presented. Finally, the ways how the presented depth estimation methods can be extended using modern deep learning paradigms under the two previously mentioned constraints are shown.
With the expansion of the electromobility and wind energy, the number of frequency inverter-controlled electric motors and generators is increasing. In parallel, the number of the rolling bearing failures caused by inverter-induced parasitic currents also shows an increasing trend. In order to determine the electrical state of the rolling bearing, to develop preventive measures against damages caused by parasitic currents and to support system-level calculations, electrical rolling bearing models have been developed. The models are based on the electrical insulating ability of the lubricant film that develops in the rolling contacts. For the capacitance calculation of the rolling contacts, different correction factors were developed to simplify the complex tribological and electrical interactions of this region. The state-of-the-art correction factors vary widely, and their validity range also differ significantly, which leads to uncertainty in their general application and to the demand for further investigations of this field. In the present work, a combined simulation method is developed that can determine the rolling bearing capacitance of axially loaded rolling bearings. The simulation consists of an electrically extended EHL simulation for calculating the capacitance of the rolling contact, and an electrical FEM simulation for the capacitance calculation of the non-contact regions. With the combination of the resulted capacitance values of the two simulation methods, the total rolling bearing capacitance can be determined with high accuracy and without using correction factors. In addition, due to experimental investigations, the different capacitance sources of the rolling bearing are identified. After the validation of the combined simulation method, it can be applied for the investigation of the different capacitance sources, i.e., to determine their significance compared to the total rolling bearing capacitance. The developed simulation method allows a detailed analysis of the rolling bearing capacitances, taking into account influencing factors that could not be considered before (e.g., oil quantity in the environment of the rolling bearing). As a result, the accurate calculation of the rolling bearing capacitance can improve the prediction of the harmful parasitic currents and help to develop preventive measures against them.
Knowledge workers face an ever increasing flood of information in their daily work. They live in a “multi-tasking craziness”, involving activities like creating, finding, processing, assessing or organizing information while constantly switching from one context to another, each being associated with different tasks, documents, mails, etc. Hence, their personal information sphere consisting of file, mail and bookmark folders as well as their content, calendar entries, etc. is cluttered with information that has become irrelevant. Finding important information thus gets harder and much of previously gained knowledge is practically lost.
This thesis explores new ways of solving this problem by investigating the potential of self-(re)organizing and especially forgetting-enabled personal knowledge assistants in the given scenario. It utilizes so-called Managed Forgetting, which is an escalating set of measures to overcome the binary keep-or-delete paradigm, ranging from temporal hiding, to condensation, to adaptive reorganization, synchronization, archiving and deletion. Managed Forgetting is combined with two other major ideas: First, it uses the Semantic Desktop as an ecosystem, which brings Semantic Web and thus knowledge graph technologies to a user’s desktop, making it possible to capture and represent major parts of a user’s personal mental model in a machine-understandable way and exploit it in many different applications. Second, the system uses explicated context information – so-called Context Spaces: context is seen as an explicit interaction element users can work with (i.e. a “tangible” object similar to a folder) and in (immersion). The thesis is structured according to the basic interaction cycle with such a system, ranging from evidence collection to information extraction and context elicitation, followed by information value assessment and the actual support measures consisting of self-(re)organization decisions (back-end) and user interface updates (front-end). The system’s data foundation are personal or group knowledge graphs as well as native data. This work makes contributions to all of these aspects, whereas several of them have been investigated and developed in interdisciplinary research with cognitive scientists. On a more general level, searching and trust in such highly autonomous assistants have also been investigated.
In summary, a self-(re)organizing and especially forgetting-enabled support system for information management and knowledge work has been realized. Its different features vary in maturity: the most mature ones are already in practical use (also in industry), while the latest are just well elaborated (position papers) or rough ideas. Different evaluation strategies have been applied ranging from mere data-driven experiments to various user studies. Some of them were rather short-term with controlled laboratory conditions, others less controlled but spanning several months. Different benefits of working with such a system could be quantified, e.g. cognitive offloading effects and reduced task switching/resumption time. Other benefits were gathered qualitatively, e.g. tidiness of the information sphere and its better alignment with the user’s mental model. The presented approach has been shown to hold a lot of potential. In some aspects, however, only first steps have been taken towards tapping it, e.g. several support measures can be further refined and automation further increased.
This thesis focuses on the operation of reliability-constrained routes in wireless ad-hoc networks. A complete communication protocol that is capable of guaranteeing a statistical minimum reliability level would have to support several functionalities: first, routes that are capable of supporting the specified Quality of Service requirement have to be discovered. During operation of discovered routes, the current Quality of Service level has to be monitored continuously. Whenever significant deviations are detected and the required level of Quality of Service is endangered, route maintenance has to ensure continuous operation. All four functionalities, route discovery, route operation, route maintenance and collection and distribution of network status information, will be addressed in this thesis.
In the first part of the thesis, we propose a new approach for Quality-of- Service routing in wireless ad-hoc networks called rmin-routing, with the provision of statistical minimum route reliability as main route selection criterion. To achieve specified minimum route reliabilities, we improve the reliability of individual links by well-directed retransmissions, to be applied during the operation of routes. To select among a set of candidate routes, we define and apply route quality criteria concerning network load.
High-quality information about the network status is essential for the discovery and operation of routes and clusters in wireless ad-hoc networks. This requires permanent observation and assessment of nodes, links, and link metrics, and the exchange of gathered status data. In the second part of the thesis, we present cTEx, a configurable topology explorer for wireless ad-hoc networks that efficiently detects and exchanges high-quality network status information during operation.
In the third part, we propose a decentralized algorithm for the discovery and operation of reliability-constrained routes in wireless ad-hoc networks called dRmin-routing. The algorithm uses locally available network status information about network topology and link properties that is collected proactively in order to discover a preliminary route candidate. This is followed by a distributed, reactive search along this preselected route to remove imprecisions of the locally recorded network status before making a final route selection. During route operation, dRmin-routing monitors routes and performs different kinds of route repair actions to maintain route reliability in order to overcome varying link reliabilities.
Modeling and Simulation of Internet of Things Infrastructures for Cyber-Physical Energy Systems
(2024)
This dissertation presents a novel approach to the model-based development and simulation-based validation of Internet of Things (IoT) infrastructures within the context of Cyber-Physical Energy Systems (CPES). CPES represents an evolution in energy management, seamlessly blending physical and cyber components for efficient, secure, and dependable energy distribution. However, the intricate interplay of these components demands innovative modeling and simulation strategies.
The work begins by establishing a robust foundation, exploring essential background elements such as requirements engineering, model-based systems engineering, digitalization approaches, and the intricacies of IoT platforms. It introduces the novel concept of homomorphic encryption, a critical enabler for securing IoT data within CPES.
In the exploration of the state of the art, the dissertation delves into the multifaceted landscape of IoT simulation, emphasizing the significance of versatility, community support, scalability, and synchronization.
The core contribution emerges in the chapter on simulating IoT networks. It introduces a sophisticated framework that encompasses hardware-in-the-loop, software-in-the-loop, and human-in-the-loop simulation. This innovative framework extends the boundaries of conventional simulation, enabling holistic evaluations of IoT systems.
A practical case study on smart energy usage showcases the application of the framework. Detailed SysML models, including requirements, package diagrams, block definition diagrams, internal block diagrams, state machine diagrams, and activity diagrams, are meticulously examined. The performance evaluation encompasses diverse aspects, from hardware and software validation to human interaction.
In conclusion, this dissertation represents a significant leap forward in the integration of IoT infrastructures within CPES. Its contributions extend from a comprehensive understanding of foundational elements to the practical implementation of a holistic simulation framework. This work not only addresses the current challenges but also outlines a path for future research, shaping the landscape of IoT integration within the dynamic realm of CPES. It offers invaluable insights for researchers, engineers, and stakeholders working towards resilient, secure, and energy-efficient infrastructures.
Aflatoxins, a group of mycotoxins produced by various mold species within the genus Aspergillus, have been extensively investigated for their potential to contaminate food and feed, rendering them unfit for consumption. Nevertheless, the role of aflatoxins as environmental contaminants in soil, which represents their natural habitat, remains a relatively unexplored area in aflatoxin research. This knowledge gap can be attributed, in part, to the methodological challenges associated with detecting aflatoxins in soil. The main objective of this PhD project was to develop and validate an analytical method that allows monitoring of aflatoxins in soil, and scrutinize the mechanisms and extent of occurrence of aflatoxins in soil, the processes governing their dissipation, and their impact on the soil microbiome and associated soil functions. By utilizing an efficient extraction solvent mixture comprising acetonitrile and water, coupled with an ultrasonication step, recoveries of 78% to 92% were achieved, enabling reliable determination of trace levels in soil ranging from 0.5 to 20 µg kg-1. However, in a field trial conducted in a high-risk model region for aflatoxin contamination in Sub-Saharan Africa, no aflatoxins were detected using this procedure, underscoring the complexities of field monitoring. These challenges encompassed rapid degradation, spatial heterogeneity, and seasonal fluctuations in aflatoxin occurrence. Degradation experiments revealed the importance of microbial and photochemical processes in the dissipation of aflatoxins in soil with half-lives of 20 - 65 days. The rate of dissipation was found to be influenced by soil properties, most notably soil texture and the initial concentration of aflatoxins in the soil. An exposure study provided evidence that aflatoxins do not pose a substantial threat to the soil microbiome, encompassing microbial biomass, activity, and catabolic functionality. This was particularly evident in clayey soils, where the toxicity of aflatoxins diminished significantly due to their strong binding to clay minerals. However, several critical questions remain unanswered, emphasizing the necessity for further research to attain a more comprehensive understanding of the ecological importance of aflatoxins. Future research should prioritize the challenges associated with field monitoring of aflatoxins, elucidate the mechanisms responsible for the dissipation of aflatoxins in soil during microbial and photochemical degradation, and investigate the ecological consequences of aflatoxins in regions heavily affected by aflatoxins, taking into account the interactions between aflatoxins and environmental and anthropogenic stressors. Addressing these questions contributes to a comprehensive understanding of the environmental impact of aflatoxins in soil, ultimately contributing to more effective strategies for aflatoxin management in agriculture.
Understanding human crowd behaviour has been an intriguing topic of interdisciplinary research in recent decades. Modelling of crowd dynamics using differential equations is an indispensable approach to unraveling the various complex dynamics involved in such interacting particle systems. Numerical simulation of pedestrian crowd via these mathematical models allows us to study different realistic scenarios beyond the limitations of studies via controlled experiments.
In this thesis, the main objective is to understand and analyse the dynamics in a domain shared by both pedestrians and moving obstacles. We model pedestrian motion by combining the social force concept with the idea of optimal path computation. This leads to a system of ordinary differential equations governing the dynamics of individual pedestrians via the interaction forces (social forces) between them. Additionally, a non-local force term involving the optimal path and desired velocity governs the pedestrian trajectory. The optimal path computation involves solving a time-independent Eikonal equation, which is coupled to the system of ODEs. A hydrodynamic model is developed from this microscopic model via the mean-field limit.
To consider the interaction with moving obstacles in the domain, we model a set of kinematic equations for the obstacle motion. Two kinds of obstacles are considered - "passive", which move in their predefined trajectories and have only a one-way interaction with pedestrians, and "dynamic", which have a feedback interaction with pedestrians and have their trajectories changing dynamically. The coupled model of pedestrians and obstacles is used to discern pedestrian collision avoidance behaviour in different computational scenarios in a long rectangular domain. We observe that pedestrians avoid collisions through route choice strategies that involve changes in speed and path. We extend this model to consider the interaction between pedestrians and vehicular traffic. We appropriately model the interactions of vehicles, following lane traffic, based on the car-following approach. We observe how the deceleration and braking mechanism of vehicles is executed at pedestrian crossings depending on the right of way on the roads.
As a second objective, we study the disease contagion in moving crowds. We consider the influence of the crowd motion in a complex dynamical environment on the course of infection of pedestrians. A hydrodynamic model for multi-group pedestrian flow is derived from the kinetic equations based on a social force model. It is coupled along with an Eikonal equation to a non-local SEIS contagion model for disease spread. Here, apart from the description of local contacts, the influence of contact times has also been modelled. We observe that the nature of the flow and the geometry of the domain lead to changes in density which affect the contact time and, consequently, the rate of spread of infection.
Finally, the social force model is compared to a variable speed based rational behaviour pedestrian model. We derive a hierarchy of the heuristics-based model from microscopic to macroscopic scales and numerically investigate these models in different density scenarios. Various numerical test cases are considered, including uni- and bi-directional flows and scenarios with and without obstacles. We observe that in low-density scenarios, collision avoidance forces arising from the behavioural heuristics give valid results. Whereas in high-density scenarios, repulsive force terms are essential.
The numerical simulations of all the models are carried out using a mesh-free particle method based on least square approximations. The meshfree numerical framework provides an efficient and elegant way to handle complex geometric situations involving boundaries and stationary or moving obstacles.
Mechanistic disease spread models for different vector borne diseases have been studied from the 19th century. The relevance of mathematical modeling and numerical simulation of disease spread is increasing nowadays. This thesis focuses on the compartmental models of the vector-borne diseases that are also transmitted directly among humans. An example of such an arboviral disease that falls under this category is the Zika Virus disease. The study begins with a compartmental SIRUV model and its mathematical analysis. The non-trivial relationship between the basic reproduction number obtained through two methods have been discussed. The analytical results that are mathematically proven for this model are numerically verified. Another SIRUV model is presented by considering a different formulation of the model parameters and the newly obtained model is shown to be clearly incorporating the dependence on the ratio of mosquito population size to human population size in the disease spread. In order to incorporate the spatial as well as temporal dynamics of the disease spread, a meta-population model based on the SIRUV model was developed. The space domain under consideration are divided into patches which may denote mutually exclusive spatial entities like administrative areas, districts, provinces, cities, states or even countries. The research focused only on the short term movements or commuting behavior of humans across the patches. This is incorportated in the multi-patch meta-population model using a matrix of residence time fractions of humans in each patches. Mathematically simplified analytical results are deduced by which it is shown that, for an exemplary scenario that is numerically studied, the multi-patch model also admits the threshold properties that the single patch SIRUV model holds. The relevance of commuting behavior of humans in the disease spread has been presented using the numerical results from this model. The local and non-local commuting are incorporated into the meta-population model in a numerical example. Later, a PDE model is developed from the multi-patch model.
In this thesis, material removal mechanisms in grinding are investigated considering a gritworkpiece interaction as well as a grinding-wheel workpiece interaction. In grit-workpiece interaction in a micrometer scale, single grit scratch experiments were performed to investigate material removal mechanism in grinding namely rubbing, plowing, and cutting. Experiments performed were analyzed based on material removal, process forces and specific energy. A finite element model is developed to simulate a single-grit scratch process. As part of the development of the finite element scratch model a 2D and 3D model is developed. A 2D model is utilized to test
material parameters and test various mesh discretizational approaches. A 3D model undertaking the tested material parameters from the 2D model is developed and is tested against experimental results for various mesh discretization. The simulation model is validated based on process forces and ground topography from experiments. The model is also further scaled to simulate multiple grit-workpiece interaction validated against experimental results. As a final step, simulation models are developed to simulate material removal, due to the interaction of grinding wheel and workpiece. A developed virtual grinding wheel topographical model is employed to display
an approach, to upscale a grinding process from grit-workpiece interaction to wheel-workpiece
interaction. In conclusion, practical conclusions drawn and scope for future studies are derived
based on the developed simulation models.
The aim of this thesis is to introduce an equilibrium insurance market model and study its properties and possible applications in risk class management.
First, an insurance market model based on an equilibrium approach is developed. Depending on the premium, the insured will choose the amount of coverage they buy in order to maximize their expected utility. The behavior of the insurer in different market regimes is then compared. While the premiums in markets with perfect competition are calculated in order to make no profit at all, insurers try to maximize their margins in a monopolistic market.
In markets modeled in this way several phenomena become evident. Perhaps the most important one is the so-called push-out effect. When customers with different attributes are insured together, insurance might become so expensive for one type of customers that those agents are better off with buying no insurance at all. The push-out effect was already shown for theoretical examples in the literature. We present a comprehensive analysis of the equilibrium insurance market model and the push-out effect for different insurance products such as life, health and disability insurance contracts using real-life data from different sources. In a concluding chapter we formulate indicators when a push-out can be expected and when not.
Machine learning regression approaches such as neural networks have gained vast popularity in recent years. The exponential growth of computing power has enabled larger and more evolved networks that can perform increasingly complex tasks. In our feasibility study about the use of neural networks in the regression of equilibrium insurance premiums it is shown that this regression is quite robust and the risk of overfitting can almost be excluded -- as long as the regression is performed on at least a few thousand data points.
Grouping customers of different risk types into contracts is important for the stability and the robustness of an insurance market. This motivates the study of the optimal assignment of risk classes into contracts, also known as rating classes. We provide a theoretical framework that makes use of techniques from different mathematical fields such as non-linear optimization, convex analysis, herding theory, game theory and combinatorics. In addition, we are able to show that the market specifications have a large impact on the optimal allocation of risk classes to contracts by the insurer. However, there does not need to be an optimal risk class assignment for each of these specifications.
To address this issue, we present two different approaches, one more theoretical and another that can easily be implemented in practice. An extension of our model to markets with capacity constraints rounds off the topic and extends the applicability of our approach.
Climate change will have severe consequences on Eastern Boundary Upwelling Systems (EBUS). They host the largest fisheries in the world supporting the life of millions of people due to their tremendous primary production. Therefore, it is of utmost importance to better understand predicted impacts like alternating upwelling intensities and light impediment on the structure and the trophic role of protistan plankton communities as they form the basis of the food web. Numerical models estimate the intensification of the frequency in eddy formation. These ocean features are of particular importance due to their influence on the distribution and diversity of plankton communities and the access to resources, which are still not well understood even to the present day. My PhD thesis entails two subjects conducted during large-scaled cooperation projects REEBUS (Role of Eddies in Eastern Boundary Upwelling Systems) and CUSCO (Coastal Upwelling System in a Changing Ocean).
Subject I of my study was conducted within the multidisciplinary framework REEBUS to investigate the influence of eddies on the biological carbon pump in the Canary Current System (CanCS). More specifically, the aim was to find out how mesoscale cyclonic eddies affect the regional diversity, structure, and trophic role of protistan plankton communities in a subtropical oligotrophic oceanic offshore region.
Samples were taken during the M156 and M160 cruises in the Atlantic Ocean around Cape Verde during July and December 2019, respectively. Three eddies with varying ages of emergence and three water layers (deep chlorophyll maximum DCM, right beneath the DCM and oxygen minimum zone OMZ) were sampled. Additional stations without eddy perturbation were analyzed as references. The effect of oceanic mesoscale cyclonic eddies on protistan plankton communities was analyzed by implementing three approaches. (i) V9 18S rRNA gene amplicons were examined to analyze the diversity and structure of the plankton communities and to infer their role in the biological carbon pump. (ii) By assigning functional traits to taxonomically assigned eDNA sequences, functional richness and ecological strategies (ES) were determined. (iii) Grazing experiments were conducted to assess abundance and carbon transfer from prokaryotes to phagotrophic protists.
All three eddies examined in this study differed in their ASV abundance, diversity, and taxonomic composition with the most pronounced differences in the DCM. Dinoflagellates were the most abundant taxa in all three depth layers. Other dominating taxa were radiolarians, Discoba and haptophytes. The trait-approach could only assign ~15% of all ASVs and revealed in general a relatively high functional richness. But no unique ES was determined within a specific eddy. This indicates pronounced functional redundancy, which is recognized to be correlated with ecosystem resilience and robustness by providing a degree of buffering capacity in the face of biodiversity loss. Elevated microbial abundances as well as bacterivory were clearly associated to mesoscale eddy features, albeit with remarkable seasonal fluctuations. Since eddy activity is expected to increase on a global scale in future climate change scenarios, cyclonic eddies could counteract climate change by enhancing carbon sequestration to abyssal depths. The findings demonstrate that cyclonic eddies are unique, heterogeneous, and abundant ecosystems with trapped water masses in which characteristic protistan plankton develop as the eddies age and migrate westward into subtropical oligotrophic offshore waters. Therefore, eddies influence regional protistan plankton diversity qualitatively and quantitatively.
Subject II of my PhD project contributed to the CUSCO field campaign to identify the influence of varying upwelling intensities in combination with distinct light treatments on the whole food web structure and carbon pump in the Humboldt Current System (HCS) off Peru. To accomplish such a task, eight offshore-mesocosms were deployed and two light scenarios (low light, LL; high light, HL) were created by darkening half of the mesocosms. Upwelling was simulated by injecting distinct proportions (0%, 15%, 30% and 45%) of collected deep-water (DW) into each of the moored mesocosms. My aim was to examine the changes in diversity, structure, and trophic role of protistan plankton communities for the induced manipulations by analyzing the V9 18S rRNA gene amplicons and performing short-term grazing experiments.
The upwelling simulations induced a significant increase in alpha diversity under both light conditions. In austral summer, reflected by HL conditions, a generally higher alpha diversity was recorded compared to the austral winter simulation, instigated by LL treatment. Significant alterations of the protistan plankton community structure could likewise be observed. Diatoms were associated to increased levels of DW addition in the mimicked austral winter situation. Under nutrient depletion, chlorophytes exhibited high relative abundances in the simulated austral winter scenario. Dinoflagellates dominated the austral summer condition in all upwelling simulations. Tendencies of reduced unicellular eukaryotes and increased prokaryotic abundances were determined under light impediment. Protistan-mediated mortality of prokaryotes also decreased by ~30% in the mimicked austral winter scenario.
The findings indicate that the microbial loop is a more relevant factor in the structure of the food web in austral summer and is more focused on the utilization of diatoms in austral winter in the HCS off Peru. It was evident that distinct light intensities coupled with multiple upwelling scenarios could lead to alterations in biochemical cycles, trophic interactions, and ecosystem services. Considering the threat of climate change, the predicted relocation of EBUS could limit primary production and lengthen the food web structure with severe socio-economic consequences.
Since their introduction, robots have primarily influenced the industrial world, providing new opportunities and challenges for humans and machinery. With the introduction of lightweight robots and mobile robot platforms, the field of robot applications has been expanded, diversified, and brought closer to society. The increased degree of digitalization and the personalization of goods and products require an enhanced and flexible robot deployment by operating several multi-robot systems along production processes, industrial applications, assembly and packaging lines, transport systems, etc.
Efficient and safe robot operation relies on successful task planning followed by the computation and execution of task-performing motion trajectories. This thesis addresses these issues by developing, implementing, and validating optimization-based methods for task and trajectory planning in robotics, considering certain optimality and performance criteria. The focus is mainly on the time optimality of the presented approaches with respect to both execution and computation time without compromising safe robot use.
Driven by a systematic approach, the basis for the algorithm development is established first by modeling the kinematics and dynamics of the considered robots and identifying required dynamic parameters. In a further step, time-optimal task and trajectory planning algorithms for a single robotic arm are developed. Initially, a hierarchical approach is introduced consisting of two decoupled optimization-based control policies, a binary problem for task planning, and a continuous model predictive trajectory planning problem. The two layers of the hierarchical structure are then merged into a monolithic layer, resulting in a hybrid structure in the form of a mixed-integer optimization problem for inherent task and trajectory planning.
Motivated by a multi-robot deployment, the hierarchical control structure for time-optimal task and trajectory planning is extended for the case of a two-arm robotic system with highly overlapping operational spaces, leading to challenging robot motions with high inter-robot collision potential. To this end, a novel predictive approach for collision avoidance is proposed based on a continuous approximation of the robot geometry, resulting in a nonlinear optimization problem capable of online applications with real-time requirements. Towards a mobile and flexible robot platform, a model predictive path-following controller for an omnidirectional mobile robot is introduced. Here, a time-minimal approach is also applied, which consists of the robot following a given parameterized path as accurately as possible and at maximum speed.
The performance of the proposed algorithms and methods is experimentally analyzed and validated under real conditions on robot demonstrators. Implementation details, including the resulting hardware and software architecture, are presented, followed by a detailed description of the results. Concrete and industry-oriented demonstrators for integrating robotic arms in existing manual processes and the indoor navigation of a mobile robot complete the work.
Cancer, a complex and multifaceted disease, continues to challenge the boundaries of biomedical research. In this dissertation, we explore the complexity of cancer genesis, employing multiscale modeling, abstract mathematical concepts such as stability analysis, and numerical simulations as powerful tools to decipher its underlying mechanisms. Through a series of comprehensive studies, we mainly investigate the cell cycle dynamics, the delicate balance between quiescence and proliferation, the impact of mutations, and the co-evolution of healthy and cancer stem cell lineages. The introductory chapter provides a comprehensive overview of cancer and the critical importance of understanding its underlying mechanisms. Additionally, it establishes the foundation by elucidating key definitions and presenting various modeling perspectives to address the cancer genesis. Next, cell cycle dynamics have been explored, revealing the temporal oscillatory dynamics that govern the progression of cells through the cell cycle.
The first half of the thesis investigates the cell cycle dynamics and evolution of cancer stem cell lineages by incorporating feedback regulation mechanisms. Thereby, the pivotal role of feedback loops in driving the expansion of cancer stem cells has been thoroughly studied, offering new perspectives on cancer progression. Furthermore, the mathematical rigor of the model has been addressed by deriving wellposedness conditions, thereby strengthening the reliability of our findings and conclusions. Then, expanding our modeling scope, we explore the interplay between quiescent and proliferating cell populations, shedding light on the importance of their equilibrium in cancer biology. The models developed in this context offer potential avenues for targeted cancer therapies, addressing perspective cell populations critical for cancer progression. The second half of the thesis focuses on multiscale modeling of proliferating and quiescent cell populations incorporating cell cycle dynamics and the extension thereof with mutation acquisition. Following rigorous mathematical analysis, the wellposedness of the proposed modeling frameworks have been studied along with steady-state solutions and stability criteria.
In a nutshell, this thesis represents a significant stride in our understanding of cancer genesis, providing a comprehensive view of the complex interplay between cell cycle dynamics, quiescence, proliferation, mutation acquisition, and cancer stem cells. The journey towards conquering cancer is far from over. However, this research provides valuable insights and directions for future investigation, bringing us closer to the ultimate goal of mitigating the impact of this formidable disease.
Mixed Isogeometric Methods for Hodge–Laplace Problems induced by Second-Order Hilbert Complexes
(2024)
Partial differential equations (PDEs) play a crucial role in mathematics and physics to describe numerous physical processes. In numerical computations within the scope of PDE problems, the transition from classical to weak solutions is often meaningful. The latter may not precisely satisfy the original PDE, but they fulfill a weak variational formulation, which, in turn, is suitable for the discretization concept of Finite Elements (FE). A central concept in this context is the
well-posed problem. A class of PDE problems for which not only well-posedness statements but also suitable weak formulations are known are the so-called abstract Hodge–Laplace problems. These can be derived from Hilbert complexes and constitute a central aspect of the Finite Element Exterior Calculus (FEEC).
This thesis addresses the discretization of mixed formulations of Hodge-Laplace problems, focusing on two key aspects. Firstly, we utilize Isogeometric Analysis (IGA) as a specific paradigm for discretization, combining geometric representations with Non-Uniform Rational B-Splines (NURBS) and Finite Element discretizations.
Secondly, we primarily concentrate on mixed formulations exhibiting a saddle-point structure and generated from Hilbert complexes with second-order derivative operators. We go beyond the well-known case of the classical de Rham
complex, considering complexes such as the Hessian or elasticity complex. The BGG (Bernstein–Gelfand–Gelfand) method is employed to define and examine these second-order complexes. The main results include proofs of discrete well-posedness and a priori error estimates for two different discretization approaches. One approach demonstrates, through the introduction of a Lagrange multiplier, how the so-called isogeometric discrete differential forms can be reused.
A second method addresses the question of how standard NURBS basis functions, through a modification of the mixed formulation, can also lead to convergent procedures. Numerical tests and examples, conducted using MATLAB and the open-source software GeoPDEs, illustrate the theoretical findings. Our primary application extends to linear elasticity theory, extensively
discussing mixed methods with and without strong symmetry of the stress tensor.
The work demonstrates the potential of IGA in numerical computations, particularly in the challenging scenario of second-order Hilbert complexes. It also provides insights into how IGA and FEEC can be meaningfully combined, even for non-de Rham complexes.
Distributed Optimization of Constraint-Coupled Systems via Approximations of the Dual Function
(2024)
This thesis deals with the distributed optimization of constraint-coupled systems. This problem class is often encountered in systems consisting of multiple individual subsystems, which are coupled through shared limited resources. The goal is to optimize each subsystem in a distributed manner while still ensuring that system-wide constraints are satisfied. By introducing dual variables for the system-wide constraints the system-wide problem can be decomposed into individual subproblems. These resulting subproblems can then be coordinated by iteratively adapting the dual variables. This thesis presents two new algorithms that exploit the properties of the dual optimization problem. Both algorithms compute a quadratic surrogate function of the dual function in each iteration, which is optimized to adapt the dual variables. The Quadratically Approximated Dual Ascent (QADA) algorithm computes the surrogate function by solving a regression problem, while the Quasi-Newton Dual Ascent (QNDA) algorithm updates the surrogate function iteratively via a quasi-Newton scheme. Both algorithms employ cutting planes to take the nonsmoothness of the dual function into account. The proposed algorithms are compared to algorithms from the literature on a large number of different benchmark problems, showing superior performance in most cases. In addition to general convex and mixed-integer optimization problems, dual decomposition-based distributed optimization is applied to distributed model predictive control and distributed K-means clustering problems.
Lubricated tribological contact processes are important in both nature and in many technical applications. Fluid lubricants play an important role in contact processes, e.g. they reduce friction and cool the contact zone. The fundamentals of lubricated contact processes on the atomistic scale are, however, today not fully understood. A lubricated contact process is defined here as a process, where two solid bodies that are in close proximity and eventually in parts in direct contact, carry out a relative motion, whereat the remaining volume is submersed by a fluid lubricant. Such lubricated contact processes are difficult to examine experimentally. Atomistic simulations are an attractive alternative for investigating the fundamentals of such processes. In this work, molecular dynamics simulations were used for studying different elementary processes of lubricated tribological contacts. A simplified, yet realistic simulation setup was developed in this work for that purpose using classical force fields. In particular, the two solid bodies were fully submersed in the fluid lubricant such that the squeeze-out was realistically modeled. The velocity of the relative motion of the two solid bodies was imposed as a boundary condition. Two types of cases were considered in this work: i) a model system based on synthetic model substances, which enables a direct, but generic, investigation of molecular interaction features on the contact process; and ii) real substance systems, where the force fields describe specific real substances. Using the model system i), also the reproducibility of the findings obtained from the computer experiments was critically assessed. In most cases, also the dry reference case was studied. Both mechanical and thermodynamic properties were studied -- focusing on the influence of lubrication. The following properties were studied: The contact forces, the coefficient of friction, the dislocation behavior in the solid, the chip formation and the formation of the groove, the squeeze-out behavior of the fluid in the contact zone, the local temperature and the energy balance of the system, the adsorption of fluid particles on the solid surfaces, as well as the formation of a tribofilm. Systematic studies were carried out for elucidating the influence of the wetting behavior, the influence of the molecular architecture of the lubricant, and the influence of the lubrication gap height on the contact process. As expected, the presence of a fluid lubricant reduces the temperature in the vicinity of the contact zone. The presence of the lubricant is, moreover, found to have a significant influence on the friction and on the energy balance of the process. The presence of a lubricant reduces the coefficient of friction compared to a dry case in the starting phase of a contact process, while lubricant molecules remain in the contact zone between the two solid bodies. This is a result of an increased normal and slightly decreased tangential force in the starting phase. When the fluid molecules are squeezed out with ongoing contact time and the contact zone is essentially dry, the coefficient of friction is increased by the presence of a fluid compared to a dry case. This is attributed to an imprinting of individual fluid particles into the solid surface, which is energetically unfavorable. By studying the contact process in a wide range of gap height, the entire range of the Stribeck curve is obtained from the molecular simulations. Thereby, the three main lubrication regimes of the Stribeck curve and their transition regions are covered, namely boundary lubrication (significant elastic and plastic deformation of the substrate), mixed lubrication (adsorbed fluid layers dominate the process), and hydrodynamic lubrication (shear flow is set up between the surface and the asperity). The atomistic effects in the different lubrication regimes are elucidated. Notably, the formation of a tribofilm is observed, in which lubricant molecules are immersed into the metal surface. The formation of a tribofilm is found to have important consequences for the contact process. The work done by the relative motion is found to mainly dissipate and thereby heat up the system. Only a minor part of the work causes plastic deformation. Finally, the assumptions, simplifications, and approximations applied in the simulations are critically discussed, which highlights possible future work.
Reactive absorption with amines is the most important technique for the removal of CO2
from gas streams, e.g. from flue gas, natural gas or off-gas from the cement industry.
In this work a rigorous simulation model for the absorption and desorption of CO2 with
an amine-containing solvent is validated using data from pilot plants of various sizes.
This model was then coupled with a detailed simulation of a coal-fired power plant.
The power generation efficiency drop with CO2 capture was determined and process
parameters in the power plant and separation process were optimized. It was shown
that the high energy demand of CO2 separation significantly reduces power generation
efficiencies, which underlines the need for improvements. This can be achieved by better
solvents or by advanced process designs. In this work such improved CO2 separation
processes are described and evaluated by detailed simulation studies.
In order to develop detailed rigorous simulation models for reactive absorption with novel
solvent systems, a precise knowledge of the liquid phase reaction kinetics is necessary.
There are well established techniques for measuring species distributions in equilibirated
aqueous amine solutions by NMR spectrosopy. However, the existing NMR techniques
cannot be used for monitoring fast reactions in these solutions. Therefore, in this work
a novel temperature-controlled micro-reactor NMR probe head was developed which
enables studying reaction kinetics with time constants in the range of seconds.
On this basis, modern solvent systems for CO2 absorption can be characterized and
the scale-up of separation process for future plants can be accompanied using rigorous
process simulation.
Pervasive human impacts rapidly change freshwater biodiversity. Frequently recorded exceedances of regulatory acceptable thresholds by pesticide concentrations suggest that pesticide pollution is a relevant contributor to broad-scale trends in freshwater biodiversity. A more precise pre-release Ecological Risk Assessment (ERA) might increase its protectiveness, consequently reducing the likelihood of unacceptable effects on the environment. European ERA currently neglects possible differences in sensitivity between exposed ecosystems. If the taxonomic composition of assemblages would differ systematically among certain types of ecosystems, so might their sensitivity toward pesticides. In that case, a single regulatory threshold would be over- or underprotective.
In this thesis, we evaluate (1) whether the assemblage composition of macroinvertebrates, diatoms, fishes, and aquatic macrophytes differs systematically between the types of a European river typology system, and (2) whether these taxonomical differences engender differences in sensitivity toward pesticides. While a selection of ecoregions is available for Europe, only a single typology system that classifies individual river segments is available at this spatial scale - the Broad River Types (BRT).
In the first two papers of this thesis, we compiled and prepared large databases of macroinvertebrate (paper one), diatom, fish, and aquatic macrophyte (paper two) occurrences throughout Europe to evaluate whether assemblages are more similar within than among BRT types. Additionally, we compared its performance to that of different ecoregion systems. We employed multiple tests to evaluate the performances, two of which were also designed in the studies. All typology systems failed to reach common quality thresholds for the evaluated metrics for most taxa. Nonetheless, performance differed markedly between typology systems and taxa, with the BRT often performing worst. We showed that currently available, European freshwater typology systems are not well suited to capture differences in biotic communities and suggest several possible amelioration.
In the third study, we evaluated whether ecologically meaningful differences in sensitivity exist between BRT types. To this end, we predicted the sensitivity of macroinvertebrate assemblages across Europe toward Atrazine, copper, and Imidacloprid using a hierarchical species sensitivity distribution model. The predicted assemblage sensitives differed only marginally between BRT types. The largest difference between
median river type sensitivities was a factor of 2.6, which is far below the assessment factor suggested for such models (6), as well as the factor of variation commonly observed between toxicity tests of the same species-compound pair (7.5 for copper). Our results don’t support the notion that a type-specific ERA might improve the accuracy of thresholds. However, in addition to the taxonomic composition the bioavailability of chemicals, the interaction with other stressors, and the sensitivity of a given species might differ between river types.
Weak memory consistency models capture the outcomes of concurrent
programs that appear in practice and yet cannot be explained by thread
interleavings. Such outcomes pose two major challenges to formal
methods. First, establishing that a memory model satisfies its
intended properties (e.g., supports a certain compilation scheme) is
extremely error-prone: most proposed language models were initially
broken and required multiple iterations to achieve soundness. Second,
weak memory models make verification of concurrent programs much
harder, as a result of which there are no scalable verification
techniques beyond a few that target very simple models.
This thesis presents solutions to both of these problems.
First, it shows that the relevant metatheory of weak memory
models can be effectively decided (sparing years of manual proof
efforts), and presents Kater, a tool that can answer metatheoretic
queries in a matter of seconds. Second, it presents GenMC, the first
(and only) scalable stateless model checker that is parametric in the
choice of the memory model, often improving the prior state of the art
by orders of magnitude.
This thesis outlines the development of thermoplastic-graphite based plate heat exchangers from material screening to operation including performance evaluation and fouling investi-gations. Polypropylene and polyphenylene sulfide as matrix and graphite as filler were cho-sen as feedstock materials, as they possess a low density and excellent corrosion resistance at a comparatively low price.
For the purpose of material screening, custom-made polymer composite plates with a plate thickness of 1-2 mm and a filler content of up to 80 wt.% were investigated for their thermal and mechanical suitability with regard to their use in plate heat exchangers. Three-point flexural tests show that the loading of polypropylene with graphite leads to mechanical prop-erties that allow the composites to be applied as corrugated heat exchanger plates. The simu-lated maximum overpressure is greater than 7 bar, depending on the wall thickness. The thermal conductivity of the composites was increased by a factor of 12.5 compared to pure polypropylene, resulting in thermal conductivities of up to 2.74 W/mK.
The fabrication of the developed corrugated heat exchanger plates, with a thickness between 0.85 mm and 2.5 mm and a heat transfer surface area of 11.13·10-3 m² was carried out via processes that can be automized, namely extrusion and embossing. With the manufactured plate heat exchanger, overall heat transfer coefficients are determined over a wide range of operating conditions (Re = 200 - 1600), which are used to validate a plate heat exchanger model and consequently to compare the composites with conventional materials. The em-bossing, which seems to result in a shift of the internal graphite structure, leads to a further improvement of the thermal conductivity by 7-20 %, in addition to the impact of the filler. With low plate thicknesses, overall heat transfer coefficients of up to 1850 W/m²K could be obtained. Considering the low density of the manufactured thermal plates, this ensures com-parable performance with metallic materials over a wide range of process conditions (Re = 200 - 4000).
The fouling kinetics and amount of calcium sulfate and calcium carbonate, respectively, on different polypropylene/graphite composites in a flat plate heat exchanger and the developed chevron type plate heat exchanger are determined and compared to the reference material stainless steel. For a straight evaluation of the fouling susceptibility of the materials the for-mation of bubbles on the materials is considered by optical imaging or excluded by a degas-ser. The results are interpreted using surface free energy and roughness of the surfaces. The results show that if bubble formation is avoided, the polymer composites have a very low fouling tendency compared to stainless steel, which is attributed to the low surface free ener-gies of approximately 25 mN/m. This is particularly the case when turbulent flows are pre-sent, as is in plate heat exchangers or when sandblasted specimen are used. Sandblasting also continues to increase heat transfer compared to untreated samples by increasing thermal conductivity and creating local turbulences. Depending on the test conditions, the fouling resistance formed on the stainless steel surface is an order of magnitude greater than on the flat plate polymer composites. In addition, the fouling layers adhere only weakly to the com-posites, which indicates an easy cleaning in place after the formation of deposits. The fouling investigations in the plate heat exchanger reveal sensitivity to calcium sulfate fouling, how-ever, CFD simulations indicate that this is due to flow maldistribution and not the actual pol-ymer composite materials.
Machine Learning (ML) is expected to become an integrated part of future mobile networks due to its capacity for solving complex problems. During inference, ML algorithms extract the hidden knowledge of their input data which is delivered to them through wireless links in many scenarios. Transmission of a massive amount of such input data can impose a huge burden on the mobile network. On the other hand, it is known that ML algorithms can tolerate different levels of distortion on their input components, while the quality of their predictions remains unaffected. Therefore, utilization of the conventional approaches
implies a waste of radio resources, since they target an exact reconstruction of transmitted data, i.e., the input of ML algorithms. In this thesis, we propose a novel relevance based framework that focuses on the quality of final ML outputs instead of such syntax based reconstruction of transmitted inputs. To this end, we quantify the semantics or relevancy of input components in terms of the bit allocation aspect of data compression, where a higher tolerance for distortion implies less relevancy. A lower relevance level is translated into the allocation of less radio resources, e.g., bandwidth. The introduced formulation provides the foundations for the efficient support of ML models with their required data in the inference phase, while wireless resources are employed efficiently.
In this dissertation, a generic relevance based framework utilizing the Kullback-Leibler Divergence (KLD) is developed that is applicable to many realistic scenarios. The system model under study contains multiple sources transmitting correlated multivariate input components of a ML algorithm. The ML model is seen as a black box, which is trained and has fixed parameters while operating in the inference phase. Our proposed bit allocation accounts for the rate-distortion tradeoff. Hence, it is simply adjustable for application to
other problems. Here, an extended version of the proposed bit allocation strategy is introduced for signaling overhead reduction, in which the relevancy level of each input attribute changes instantaneously. In another expansion, to take the effect of dynamic channel states into account, a resource allocation approach for ML based centralized control systems is proposed. The novel quality of service metric takes outputs of ML algorithms into consideration,
and in combination with the designed greedy algorithm, provides significantly
improved end-to-end performance for a network of cart inverted pendulums.
The introduced relevance based framework is comprehensively investigated by considering various case studies, real and synthetic data, regression and classification, different estimators for the KLD, various ML models and codebook designs. Furthermore, the reliability of this proposed solution is explored in presence of packet drops, indicating robustness of the relevance based compression. In all of the simulations, the relevance based solutions deliver the best outcome in terms of the carefully chosen key performance indicators. In most of them, significantly high gains are also achieved compared to the conventional techniques, motivating further research on the subject.
The ability to sense and respond to different environmental conditions allows living organisms to adapt quickly to their surroundings. In order to use light as a source of information, plants, fungi, and bacteria employ phytochromes. With their ability to detect far-red and red light, phytochromes constitute a major photoreceptor family. Bacterial phytochromes (BphPs) are composed of an apo-phytochrome and an open-chain tetrapyrrole, the chromophore biliverdin IXα, which mediates the photosensory properties. Depending on the photoexcitation and the quality of the incident light, phytochromes interconvert between two photoconvertible parental states: the red light-absorbing Pr-form and the far-red light-absorbing Pfr-form. In contrast to prototypical phytochromes, with a thermal stable Pr ground state, there is a group of bacterial phytochromes that exhibit dark reversion from the Pr- to the Pfr-form. These special proteins are classified as bathy phytochromes and range across different classes of bacteria. Moreover, the majority of BphPs act as sensor histidine kinases in two-component regulatory systems. The light-triggered conformational change results in the autophosphorylation of the histidine kinase domain and the transphosphorylation of an associated response regulator, inducing a cellular response. Spectroscopic analysis utilizing homologously produced protein identified PaBphP, the histidine kinase of the human opportunistic pathogen Pseudomonas aeruginosa, as a bathy phytochrome. Intensive research on PaBphP revealed evidence that the interconversion between its physiological active and inactive states is influenced by light and darkness rather than far-red and red light. In order to conduct a comprehensive systematic analysis, further bacterial phytochromes were investigated regarding their biochemical and spectroscopic behavior, as well as their autokinase activity. In addition to PaBphP, this work employs the bathy phytochromes AtBphP2, AvBphP2, XccBphP from the non-photosynthetic plant pathogens Agrobacterium tumefaciens, Allorhizobium vitis, Xanthomonas campestris, as well as RtBphP2 from the soil bacterium Ramlibacter tataouinensis. All investigated BphPs displayed a bathy-typical behavior by developing a distinct Pr-form under far-red light conditions and undergoing dark reversion to their Pfr-form. Different Pr/Pfr-fractions can be identified among the BphP populations in varying natural light conditions, including red or blue light. The Pr-form is considered as the active form due to autophosphorylation activity in the heterologously produced phytochromes when exposed to light. In the absence of light, associated with the development of the Pfr-form, the phytochromes exhibited disabled or strongly reduced autokinase activity. Additionally, light-triggered phosphorylation was observed for the response regulator PaAlgB, which is linked to the phytochrome of P. aeruginosa. This study presents the first comparative investigation of numerous bathy phytochromes under identical conditions. The work addressed a gap in the literature by providing quantitative correlation between kinase activity and calculated Pr/Pfr-fractions obtained from spectroscopic measurements. The biological role of PaBphP was partially elucidated through phenotypic characterization employing P. aeruginosa mutant and overexpression strains. The generation of a functional model was possible by considering the postulated functions of the other phytochromes found in the literature. In summary, bathy BphPs are hypothesized to modulate bacterial virulence according to the circadian day/night rhythm of their hosts. The pathogens are believed to reduce their virulence during daylight hours to evade immune and defense reactions, while increasing their virulence during the evening and night, enabling more effective infections.
In contrast to motorbike tyres, whose friction during cornering has to be as high as possible, the desired effect in skiing is the opposite, that of low friction. The reduced friction between skis and ice or snow is made possible by a film of meltwater that forms as a function of friction power. To support this friction mechanism, skis are waxed with different waxes in both hobby and professional sports, depending on a variety of conditions. Waxes with fluorine additives show best performance in most conditions, corresponding to the lowest friction coefficients. However, for health and environmental reasons, the International Ski Federation (FIS) and the Biathlon Un-ion (IBU) have imposed a complete ban on fluorine additives at all FIS races and IBU events with effect from the 2023/2024 season. As a result, wax manufacturers are required to develop and extensively test fluorine-free waxes in order to remain competitive.
Traditional tests take place either indoors or outdoors in the field. Athletes, who complete a particular distance and whose time is measured, also note the impres-sions that the prepared skis provide to the skiers. The time and cost involved in nu-merous individual tests is a drawback, and the presence of only a single type of snow in the hall or field, air resistance, changing environmental conditions and var-iations in the athlete's movement, limit the depth of information. For the need of re-ducing the time-consuming procedure of indoor and outdoor tests, a tribometer of-fers a solution where friction measurements can be performed on a laboratory scale. Due to the consistent adjustable conditions such as temperature, speed and load applied to the friction partners, scientific studies can be carried out with reduced dis-turbance variables. At present, the tribometric results of laboratory instruments for predicting friction values do not translate into application in practice. The reasons for this are the compromises that have to be made in the design of the tribometers.
This work reviews the existing tribometers for their operating conditions and con-firms the need for a scientific method of characterising different waxes. In order to fill the gap between friction results obtained in laboratory tests which cannot yet be used in the selection of waxes, and traditional field tests, this thesis is dedicated to the methodical design and manufacture of a linear tribometer capable of measuring friction between a ski base made of UHMWPE (ultra high molecular weight polyeth-ylene) and an ice sample. The tribometer provides for the first time results that allow differentiating be-tween different modified waxes with regard to their running performance. Friction-influencing factors such as speed, temperature and the surface pressure below the ski base can be adjusted within the range relevant for ski sports. Furthermore, the laboratory-scale test stand, which is located in a cold chamber, is capable of ac-commodating not only typical ski jumping base lengths and widths, but also cross-country and alpine ski bases. To verify the tribometer, a ski base is treated with three waxes of different fluorine content and measured comparatively. With a minimum of 95% confidence, the friction differences between the tested waxes depending on their fluorine content is validated and proven at the end of this work.
Functional structures as well as materials provided by nature have always been a great source of inspiration for new technologies. Adapting and improving the discovered concepts, however, demands a detailed understanding of their working principles, while employing natural materials for fabrication tasks requires suitable functionalization and modification.
In this thesis, the white scales of the beetle Cyphochilus are examined in order to reveal unknown aspects of their light transport properties. In addition, the monomer of the material they are made of is utilized for 3D microfabrication.
White beetle scales have been fascinating scientists for more than a decade because they display brilliant whiteness despite their small thickness and the low refractive index contrast. Their optical properties arise from highly efficient light scattering within the disordered intra-scale network structure.
To gain a better understanding of the scattering properties, several previous studies have investigated the light transport and its connection to the structural anisotropy with the aid of diffusion theory. While this framework allows to relate the light scattering to macroscopic transport properties, an accurate determination of the effective refractive index of the structure is required. Due to its simplicity, the Maxwell-Garnett mixing rule is frequently used for this task, although its constraint to particle and feature sizes much smaller than the wavelength is clearly violated for the scales.
To provide a correct calculation of the effective refractive index, here, finite-difference time-domain simulations are used to systematically examine the impact of size effects on the effective refractive index. Deploying this simulation approach, the Maxwell-Garnett mixing rule is shown to break down for large particles. In contrast, it is found that a quadratic polynomial function describes the effective refractive index in close approximation, while its coefficients can be obtained from an empirical linear function. As a result, a simple mixing rule is reported that unambiguously surpasses classical mixing rules when composite media containing large feature sizes are considered. This is important not only for the accurate description of white beetle scales, but also for other turbid media, such as biological tissues in opto-biomedical diagnostics.
Describing light transport by means of diffusion theory moreover neglects any coherent effects, such as interference. Hence, their impact on the generation of brilliant whiteness is currently unknown. To shed a light on their role, spatial- and time-resolved light scattering spectromicroscopy is applied to investigate the scales and a model structure of them based on disordered Bragg stacks. For both structures the occurrence of weakly localized photonic modes, i.e., closed scattering loops, is observed, which is further verified in accompanying simulations. As shown in this thesis, leakage from these random photonic modes contributes at least 20% to the overall reflected light. This reveals the importance of coherent effects for a complete description of the underlying light transport properties; an aspect that is entirely missing in the purely diffusive transport presumed so far. Identifying the importance of weak localization for the generation of brilliant whiteness paves the way to further enhance the design of efficient optical scattering media, an issue that recently drawn great attention.
Unlike their plant-based counterparts, rigid carbohydrates, such as chitin, are currently unavailable for 3D microfabrication via direct laser writing, despite their great significance in the animal kingdom for the construction of functional microstructures. To overcome this gap, the monomeric unit of chitin, N-acetyl-D-glucosamine, is here functionalized to serve as a photo-crosslinkable monomer in a non-hydrogel photoresist. Since all previous photoresists based on animal carbohydrates are in the form of hydrogel formulations, a new group of photoresists is established for direct laser writing.
Moreover, it is exhibited that the sensitization effect, previously used only in the context of UV curing, can be successfully transferred to direct laser writing to increase the maximum writing speed. This effect is based on the beneficial combination of two photoinitiators.
In this, one photoinitiator is an efficient crosslinking agent for the monomer used, but a rather poor two-photon absorber. The other photoinitiator (called sensitizer) possesses, conversely, a much higher two-photon absorption coefficient at the applied wavelength but is not well suited as a crosslinking agent. In combination, the energy absorbed by the sensitizer is passed to the photoinitiator, resulting in the formation of radicals needed to start the polymerization. As this greatly increases the rate at which the photoinitiator is radicalized, resists containing a photoinitiator and a sensitizer are shown to outperform resists containing only one of the components. Deploying the sensitization effect in direct laser writing therefore offers a simple way to individually tune the crosslinking ability and the two-photon absorption properties by combining existing compounds, compared to the costly chemical synthesis of novel, customized photoinitiators.
Production, purification and analysis of novel peptide antibiotics from terrestrial cyanobacteria
(2024)
Cyanobacteria are a known source for bioactive compounds, of which several also show antibiotic activity. In regard to the growing number of multi-resistant pathogens, the search for novel antibiotic substances is of great importance and unexploited sources should be explored. So, this thesis initially dealt with the identification of productive strains, especially within the group of the terrestrial cyanobacteria, which are less well studied than marine and freshwater strains. Amongst these, Chroococcidiopsis cubana, an extremely desiccation and radiation tolerant, unicellular cyanobacterium was found to produce an extracellular antimicrobial metabolite effective against the Gram-positive indicator bacterium Micrococcus luteus as well as the pathogenic yeast Candida auris. However, as the sole identification of a productive cyanobacterium is not sufficient for further analysis and a future production scale-up, the second part of this thesis targeted the identification of compound synthesis prerequisites. As a result, a limitation of nitrogen was shown to be the production trigger, a finding that was used for the establishment of a continuous production system. The increased compound formation was then used for purification and analysis steps. As a second approach, in silico identified bacteriocin gene clusters from C. cubana were cloned and heterologously expressed in Escherichia coli. By this, the bacteriocin B135CC was identified as a strong bacteriolytic agent, active predominantly against the Gram-positive strains Staphylococcus aureus and Mycobacterium phlei. The peptide showed no cytotoxic effects against mouse neuroblastoma (N2a-) cells and a high temperature tolerance up to 60 °C. In order to facilitate the whole project, two standard protocols, specifically adapted for the work with cyanobacteria, were established. First, a method for a quick and easy in vivo vitality estimation of phototrophic cells and second, an approach for a high throughput determination of nitrate concentrations in microalgal cultures. Both methods greatly helped to proceed the main objectives of this work, the first one by simplifying the development of suitable cryopreservation protocols for individual cyanobacteria strains and the second one by accelerating the determination of the optimal nitrate concentration for the production of the antimicrobial compound from C. cubana. In the course of this cultivation optimization, the ability of cyanobacteria to utilize organic carbon sources for an accelerated cell growth was examined in greater detail. It could be shown that C. cubana reaches significantly higher growth rates when mixotrophically cultivated with fructose or glucose. Interestingly, this effect was even further enhanced when light intensity was decreased. Under these low-light conditions, phototrophically cultivated C. cubana cells showed a clearly decreased cell growth. This effect might be extremely useful for a quick and economic preparation of precultures.
Plant-specific factors affecting short-range attraction and oviposition of European grapevine moths
(2024)
The spread of pests and pathogens is increasingly intensified by climate change and globalization. Two of the most serious insect pests threating European viticulture are the European grape berry moth, Eupoecilia ambiguella (Hübner) and the European grapevine moth Lobesia botrana (Denis & Schiffermüller). Larvae feed on fructiferous organs of grapevine Vitis vinifera, resulting in high yield and quality losses. Under the aspects of integrated pest management, insecticide measures are only reasonable when other control strategies become ineffective. In order to support the development of novel decision support system for the application of insecticides, the aim of this thesis was to decipher plant-specific factors, which affect the short-range attraction and oviposition of L. botrana and E. ambiguella.
The focus was set on the visual, volatile, tactile and gustatory stimuli provided by their host plant after settlement. The use of artificial surfaces as model plant showed that oviposition of both species is affected by the color, the shape and the texture of the oviposition site. To explain a susceptibility of certain grapevine cultivars and phenological stages of the berries to egg infestations, we analysed and compared the chemical composition of the epicuticular waxes of the berry surface as well as the volatile organic compounds emitted by the berries. Thereby it turned out that the attractiveness to wax extracts decreased during ripening of the berries, highlighting a preference of earlier phenological stages of the berries for oviposition. In addition, grapevine cultivars exhibited variations in their volatile composition. The principle components perceived by female’s antennae could not explain the differentiation between cultivars, suggesting volatiles do not trigger orientation to certain cultivars. Furthermore, a method was developed to measure real-time behavioural response of female moths to volatiles. The setup allowed to quantify the orientation to a volatile source as well as movements of the antennae and ovipositor. They could be linked to the olfactory and gustatory perception of volatiles during the evaluation of suitable host plants for oviposition. In addition, the risk of potential alternative host plants in the vicinity of the vineyard was investigated. This confirmed that L. botrana in particular prefers the stimuli provided by some plants to those of grapevine. Overall, the results suggest that during oviposition, volatiles emitted by the plants and the composition of the plant surface are the most important factors for host plant differentiation.
Streams and their adjacent terrestrial ecosystem are tightly linked via the flux of organisms and matter. Emergent aquatic insects can be an important food source for riparian predators like bats, birds, spiders, and lizards. Information about the quality, quantity and phenology of emergent aquatic insects is necessary to estimate how riparian predators can benefit from them as food source. Though intensive agriculture is a globally dominant land use, little is known about how agricultural land use affects the quantity, quality as well as phenology of emergent aquatic insects. Typically, emergent aquatic insects contain more long-chain polyunsaturated fatty acids (PUFA) than terrestrial insects. Especially long-chain PUFA, were shown to enhance growth and immune response of spiders and birds.
In chapter 2, the PUFA transfer to spiders and the effect of food sources differing in their PUFA profiles on spiders was examined in outdoor microcosms under environmentally realistic conditions (i.e., normal weather conditions, possibility to construct orb webs as in their natural habitat). The environmental context determined how PUFA can affect the spiders. For instance, besides PUFA profiles of food sources, environmental variables like the temperature were important for the growth and body condition of spiders.
In the third chapter, the effect of agricultural land use on the quantity in terms of biomass as well as abundance, phenology and composition of emergent aquatic insects was assessed. Previous studies were limited to seasons or single time points, which hampered determining annual biomass export and shifts in phenology. Therefore, emergent aquatic insects were sampled continuously over the primary emergence period of one year and environmental variables associated with agricultural land use were monitored. The biomass and abundance in total were higher (61 – 68 and 79 – 86%, respectively) in agricultural than forested sites. In addition to that, a turn-over of emergent aquatic insect assemblages and a shift in phenology of aquatic insects was identified. In agricultural sites, 71% families of aquatic insects emerged earlier than in forested sites. Pesticide toxicity was associated with different aquatic insect order biomass and abundances. During the same experiment spiders were sampled in spring, summer, and autumn. Additionally, the fatty acid (FA) content of the spiders and emergent aquatic insects was determined. These results are presented in chapter 4. The FA export via emergent aquatic insects was higher (26 – 29%) in forested than agricultural sites, which indicated a reduced quality of aquatic insects as food source for riparian predators in agricultural sites. The FA profiles of mayflies, flies and caddisflies differed between land-use types, but not for spiders. Shading and pool habitats were the most important environmental variables for the FA profiles, though environmental variables explained only little variation in FA profiles. Overall, the quantity, quality and phenology of emergent aquatic insects differed between land-use types, which can affect population dynamics in the adjacent terrestrial ecosystem. Our results can be used in modeling food-web dynamics or meta-ecosystems to improve understanding of linked ecosystems.
Biodiversity has declined by approximately 70% in the last 50 years for vertebrate and invertebrate species. This loss in biodiversity is strongly connected with anthropogenic activities, such as agricultural intensification and pollution. Currently, pesticides are needed to secure the growing global food demand, although they are recognized as one of the main drivers of biodiversity loss, mainly in agricultural areas.
In the European Union, pesticides are regulated within the risk assessment framework, which aims to protect both the environment and human health from undesirable effects. The effects on non-target organisms are mostly assessed following a “one-size-fits-all” approach, focused on sensitive species tests. However, it has been recognized that the current methodology can be improved in order to minimize undesirable effects. Aiming to provide valuable data to inform future risk assessment, this thesis focused on two terrestrial organism groups that play beneficial roles, especially in agroecosystems: earthworms and spiders.
Although the earthworm Eisenia fetida is included in pesticide regulation, its use as the only earthworm representative may lead to uncertainties for the risk assessment. Therefore, we collected ecotoxicological data on field-captured earthworm species via acute exposure to imidacloprid and copper. In addition, we investigated the relationships between earthworm chemical sensitivity, biological traits and habitat preferences, and potential links with their ecosystem services (Chapter 2). We found that earthworms sampled from extremely acidic soils were less sensitive to copper than earthworms from neutral soils. Moreover, anecic and endogeic earthworms were more sensitive to imidacloprid than epigeic earthworms.
Spiders have, thus far, been understudied in regulatory risk assessment in comparison to other non-target arthropods. Thus, we aimed to collect ecotoxicological data of spider species sampled in different European climates via acute exposure to lambda-cyhalothrin. Moreover, we explored relationships between spider chemical sensitivity, phylogeny, biological traits and habitat preferences, as well as potential links with their ecosystem services (Chapter 3). Spiders showed a high sensitivity to lambda-cyhalothrin. Furthermore, our results showed that spider sensitivity varies depending on climate. We confirmed this relationship by incorporating different rearing and test temperatures into the toxicity testing protocol (Chapter 4).
The outcomes of this thesis contribute to informing pesticide regulatory practices, allowing for an improved protection and conservation of terrestrial organism groups and the ecosystem services they provide. The consideration of ecological traits, habitat variability and related plasticity, key species, and ecological network structure could improve the risk assessment framework and minimize the effects of pesticides and other stressors on an ecosystem-level.
The vast majority of all mitochondrial proteins are synthesized in the cytosol. These proteins carry characteristic targeting motifs within their sequence, which allows for the binding of chaperones, that in turn usher precursors to the mitochondrial surface for import and assembly. Though, our understanding of these early reactions is still lacking, recent efforts have shown that the ER surface can facilitate the import of mitochondrial proteins (ER-SURF) with the help of the J-protein Djp1. Close cooperation of organelles in form of membrane contact sites is crucial for cellular function. The aim of my work was to investigate whether ER-mitochondria contact sites are critical for the transfer of proteins from the ER to mitochondria.
Several contact sites have been characterized between ER and mitochondria in S. cerevisiae. One contact site is called the ER mitochondria encounter structure (ERMES) and another is partly formed by Tom70. Owing to the high propensity of suppressor mutations in ERMES, I employed a knockdown approach to deplete this contact site. Using an inducible CRISPR interference (CRISPRi) system, I could rapidly and efficiently deplete Mdm34, which is a part of ERMES. I could show that depletion of Mdm34 had a synthetic negative effect in combination with a deletion of TOM70. Loss of both contact sites led to a strong decrease of many mitochondrial proteins in the whole cell proteome. Using affinity purification of ER and mitochondria in conjunction with mass spectrometry I could demonstrate that a specific set of mitochondrial proteins are enriched on the ER upon loss of Mdm34 and Tom70, which mainly were proteins of the inner membrane e.g., Oxa1 and Cox5A. Moreover, I was able to validate that the import of these proteins was hampered upon loss of both contact sites. Also, in vivo the biogenesis of Oxa1 was impeded upon single loss of Mdm34 or Tom70 and strongly impaired if both were lost. Analysis of the maximum hydrophobicity of inner membrane proteins in the ER-SURF set revealed on average a significantly higher peak compared to other inner membrane proteins. I could show that deleting or swapping the transmembrane domain of Cox5A would make it contact site independent or reliant on contact sites respectively, as revealed by an in vitro import assay.
In this study I was able to demonstrate the involvement of membrane contact sites in ER-SURF and identify a list of putative clients. Furthermore, I could show that hydrophobicity of the transmembrane segment of inner membrane proteins is one determinant for ER-SURF dependence.
Chemical pollution is a ubiquitous stressor affecting streams and their linkages to riparian forests. Contaminants act by altering the emergence of aquatic insects from streams. Emergent insects can also take up contaminants and transfer them into the terrestrial ecosystem. Emergent insects are an important source of prey for riparian insectivores and changes in the emergence flux or contamination of insects can affect the riparian food web. However, little is knownabout the implications of emerging contaminants such as agricultural pesticides and wastewatereffluent on the terrestrial food web. In this dissertation, I address possible consequences ofagricultural and wastewater stream pollution for riparian insectivores, namely bats and spiders.
The contribution of aquatic prey to riparian spider diets has mainly been determined by stableisotope analysis, but DNA metabarcoding, a highly sensitive method of identifying consumedprey using DNA, promises to further detangle changes in these trophic interactions. In Chapter2, we tested a bleaching decontamination protocol to determine the suitability of using metabarcoding on spiders contaminated during sampling. We confirmed the applicability of metabarcoding, but also found that the wolf spiders (Lyocsidae) collected in riparian areas did not appear to rely strongly on aquatic prey. This informed our choice of Tetragnatha montana, which are highly reliant on aquatic prey, for the field study in Chapter 3.
We then conducted three field studies. Chapters 3 and 4 evaluate indirect trophic effects of chemical stream pollution on spiders and bats, respectively. Chapter 5 quantifies the accumulation of pesticides from the stream to riparian spiders via emergent insects. We found that riparian bats foraged more and that spiders consumed more Chironomidae at more polluted sites, indicating that there was no overall decrease in emergence due to chemical pollution. We also found that certain pesticides accumulated in emergent insects and riparian spiders. Together, this suggests that chemical stream pollution resulted in an increased dietary exposure of riparian insectivores to contaminants, rather than a decrease in prey availability.
These results demonstrate the role of streams and aquatic-terrestrial linkages in propagating stressors across ecosystem boundaries. They also show the benefit of using sensitive methods like DNA metabarcoding to unveil trophic effects of chemical pollution. Future studies should focus on quantifying the risk of contaminant uptake and potential effects for riparian bats, as well as considering how the observed drivers change in different contamination scenarios and ecosystems. This knowledge is important to protect the functionality of the riparian ecosystem and its inhabitants.
Since the turn of the millennium, character research has been on the rise among psychological researchers. In 2004, the field of positive psychology introduced the Values in Action (VIA) framework encompassing 24 theoretically justified and empirically supported character strengths intended for the measurement of good character. Their assignment to six "core virtues" according to Linnaean principles links the 24 character strengths to philosophical and religious theories of virtue. However, the originally developed proprietary VIA Inventory of Strengths (VIA-IS) for the measurement of the 24 character strengths and its public domain counterpart, the IPIP-VIA, are based on a relatively crude scale development approach. Yet, the VIA-IS and the IPIP-VIA dominated (applied) character research for a long time. While researchers recently refined the proprietary VIA instruments, no character strength scales developed according to the state of the art are available in the public domain, thwarting progress in character research. Furthermore, most factor-analytic studies on the hierarchical structure of the 24 VIA character strengths yielded inconsistent results regarding the number and nature of global VIA constructs due to differing methodological standards and strategies. Only recently, a growing body of research consonantly has suggested that three global constructs span the VIA trait space. Consequently, there is only one proprietary inventory for measuring global VIA constructs and none that is available in the public domain. Against this backdrop, this dissertation addressed three methodological challenges in character assessment, taking an open-science approach, a (cross-country) replicability approach, and an integrative approach (i.e., integrating the results into the larger picture of personality science, particularly linking the VIA character traits to the Big Five and value traits).
Study 1 revised the English-language IPIP-VIA and concurrently translated/adapted it to German to yield character strength scales especially suitable for cross-cultural large-scale assessment: The 96-item IPIP-VIA-R measures each character strength with four balanced-keyed, content-valid, and cross-culturally adaptable items building scales that showed satisfactory reliability, (partial) scalar measurement invariance across Germany and the UK, and evidence of construct and criterion validity. Study 2 applied the IPIP-VIA-R and a rigorous factor-analytic approach to revisit the hierarchical structure of the 24 VIA character strengths, revealing three well-interpretable global “core strengths” that were replicable across Germany and the UK: positivity, dependability, and mastery. Study 3 applied an Ant Colony Optimization algorithm to select an optimal 18-item subset of the IPIP-VIA-R to measure each core strength with a balanced-keyed, content-valid six-item scale that again showed satisfactory reliability, scalar measurement invariance across Germany and the UK, and evidence of construct and criterion validity.
Taken as a whole, the dissertation advanced the measurement of VIA character traits in the public domain, the understanding of the VIA character trait space (especially its intersection with Big Five personality and basic human values), and the establishment of the VIA trait hierarchy. To address its research questions framed as methodological challenges, the dissertation introduced and elaborated methodological approaches that researchers might adapt to other individual differences constructs. Even though there remain challenges to be taken up in future work (e.g., adapting the IPIP-VIA-R character and core strength scales for use in a more diverse set of cultures; multi-informant assessment), researchers and survey programs can readily apply the character scales developed as part of this dissertation.
One of the main tasks of molecular biology is understanding the mechanisms of molecular biological processes. This brings the problem of creating regulatory networks and therefore finding key regulators. In order to do it, it is important to have such representation of the data that can reveal the distinct patterns within the big groups. On one side, there are numerous experimentally determined kinetic information about the alteration of molecular presence in the observed system. On the other side, there are documented throughout the years evidences of the involvement of molecules in different biological processes. Both sources of the information have their drawbacks: experimental data reflect only a fleeting molecular state of each individual organism and therefore are often high-variant and noisy; functional groups were determined as generalization of known roles of molecules in biological processes and therefore can be not complete and only partially relevant to certain experimental conditions and individual organisms. Our goal is to get the overview of the experimentally observed molecules and extract the knowledge from both sources, avoiding constrains of noise distractions and generalization bias. The resulted optimal representation of the experimental data then would help to pinpoint potential regulators.
The proposed method is called the Signature Topology (ST) approach, as it uses the functional topology as the prior knowledge source and creates a specific signature for the given experimental data. The ST approach is based on knowledge-and-data-driven machine learning algorithm, that is implemented via a dynamic programming approach. Based on both prior knowledge and learning from the data, the proposed approach represents a combination of supervised and unsupervised machine learning. The resulting network structure deals with data abundance and avoids an over-detailed description that may lead to misinterpretation and is able to pick out elements with minor behavior patterns.
The method is tested with artificial data and applied to real-world mass-spectrometry proteome data and NGS-transcriptome data of Chlamydomonas reinhardtii. The proposed approach helps with identification of the potential regulatory genes, whose roles are not explicitly provided in the used functional ontology. Moreover, it shows a successful reduction in data complexity while preserving all individual molecular information reported in the literature and stored in the functional ontology. If the proposed approach analyzes different experimental data with the same ontology, the resulting networks are uniform and therefore can be compared. That gives an opportunity to compare between a great variety of experimental conditions, from different organisms to different
system levels.
Industrial robots are vital in automation technology, but their limitations become evident in applications requiring high path accuracy. This research focuses on improving the dynamic path accuracy of industrial robots by integrating additional sensor technology and employing intelligent feed-forward control. Specifically, the inclusion of secondary encoder sensors enables explicit measurement and compensation of robot gear deformations. Three types of model-based feed-forward controllers, namely physics-based, data-based, and hybrid, are developed to effectively counteract dynamic effects.
Firstly, a physics-based feed-forward control method is proposed, explicitly modeling joint deformations, hydraulic weight compensation, and other relevant features. Nonlinear friction parameters are accurately identified using a globally optimized design of experiments. The resulting physics-based model is fully continuously differentiable, facilitating its transformation into a code-optimized flatness-based feed-forward control.
Secondly, a data-based feed-forward control approach is introduced, leveraging a continuous-time neural network. The continuous-time approach demonstrates enhanced model generalization capabilities even with limited data. Furthermore, a time domain normalization method is introduced, significantly improving numerical properties by concurrently normalizing measurement timelines, robot states, and state derivatives. Based on previous work, a method ensuring input-to-state and global-asymptotic stability is presented, employing a Lyapunov function. Model stability is enforced already during training using constrained optimization techniques. Moreover, the data-based methods are evaluated on public benchmarks, extending its applicability beyond the field of robotics.
Both the physics-based and data-based models are combined into a hybrid model. Comparative analysis of the three models reveals that the continuous-time neural network yields the highest model accuracy, while the physics-based model delivers the best safety properties. The effectiveness of all three models is experimentally validated using an industrial robot.
The present thesis reports on studies of atomically precise, size-selected tantalum
cluster ions \(Ta_n^±\) under cryogenic conditions in a FT-ICR mass spectrometer with respect to surface adsorbate interactions at the fundamental level, focusing on \(N_2\) and \(H_2\) adsorption and activation. The wealth of results presented here is the result of systematic studies that have revealed valuable kinetic, spectroscopic, and quantum chemical information, which together paint a comprehensive picture of the elementary adsorption steps and mechanisms in detail.
The \(N_2\) and \(H_2\) adsorption processes to \(Ta_n^+\) clusters exhibit dependencies on cluster size n and on adsorbate load. In terms of \(N_2\) adsorption, there is evidence for spontaneous \(N_2\) activation and cleavage by \(Ta_2^+\) - \(Ta_4^+\), while it appears to be suppressedby \(Ta_5^+\) - \(Ta_8^+\). The activation and cleavage of \(N_2\) molecules proceeds across
surmountable barriers and along much-involved multidimensional reaction paths.
Underlying reaction processes and involved intermediates are elucidated. Two different processes are characteristic of \(H_2\) adsorption: There are fast adsoprtion processes without competing desorption reactions at low \(H_2\) loadings, indicating dissociative adsorption processes, followed by slow adsorption reactions accompanied by multiple desorption reactions at high \(H_2\) loadings, indicating molecular \(H_2\) adsorption. The threshold is the completion of the first adsorbate shell. The \(N_2\) adsorption study of \(Ta_n^-\) clusters revealed that the \(N_2\) adsorption ability of anionic tantalum clusters depends strongly on cluster size n. The cluster size n = 9 is the minimum size for \(N_2\) adsorption onto \(Ta_n^-\) clusters to yield stable and detectable cluster adsorbate species \([Ta_n(N_2)_m]^-\).
The booming global market of nanomaterials in the last few decades has led to the inevitable emission of these materials into aquatic environments; hence, understanding their physical, chemical, and biological transformations has become a big concern for environmental scientists. Despite a great deal of effort made to understand the mobility, fate, and risk assessment of e.g, TiO2 nanoparticles, it is still unclear if the obtained results, under lab-controlled conditions, can be generalized to realistic released nanoparticles in aquatic environments since the complex dynamics of environmental conditions are not completely reproducible under controlled conditions.
In the present study, we proposed a new approach to expose TiO2 nanoparticles to environmental conditions of natural surface waters by making use of dialysis membranes as passive reactors. The function of these reactors is based on the permeability of the membrane to the dissolved matter of surface waters while TiO2 nanoparticles do not pass through the membrane. These systems benefit from the fact that although the complexity and temporal variability of most of the environmental parameters of surface waters are reproducible inside the reactors, colloidal and particulate interferences remain separated. Furthermore, no significant reduction in pore size i.e., membrane fouling is observed in dialysis bags after exposure to surface waters which validates the efficiency of the system.
Taking advantage of these reactors to expose nanoparticles to surface waters, we investigated the influential physicochemical parameters of the surface waters on the formation of natural coating onto nanoparticles. Hence, dialysis bags were used to expose TiO2 nanoparticles, in situ, to ten different surface waters in the spring and summer of 2019. Due to the complexity of the natural dissolved matter of the surface waters as long as their low natural concentrations, we needed to use a combination of analytical techniques and multivariate data analysis to investigate the coatings. The initial findings were similar to the lab-controlled exposure studies in the literature showing pH, electrical conductivity, and Ca2+- Mg2+ concentration as the three most important parameters of surface waters controlling the formation of coatings. Nonetheless, we came across a phenomenon being overlooked under lab-controlled conditions; natural coatings are composed of not only organics (DOM: dissolved organic matter) but also inorganics (carbonate) which implies that their realistic coatings are more complex than what the previous studies described.
The second part of this thesis focused on investigating the interactions of more realistic nanoparticles (extracted TiO2 nanoparticles from 11 sunscreens) with DOM. Using ToF-SIMS combined with high-dimensional data analysis, we tried to find a general DOM-sorption pattern among TiO2 nanoparticles since finding this pattern could have ultimately opened a way to assess the fate of (more) realistic nanoparticles in aquatic environments. Contrary to our expectations, the results showed a unique sorption pattern for each sunscreen controlled by the composition of the sunscreens implying that the sorption pattern of each sunscreen should be investigated individually. In the next step of this study, we used random forest to extract the most important fragments of DOM sorbed onto each sunscreen followed by an effort to assign these important masses to chemical fragments.
Trying to provide a comprehensive understanding of interactions of the released n-TiO2 in aquatic environments, in future studies, we are going to expand our coating research to different types of TiO2 nanoparticles, such as extracted particles from paint, where the reaction media (surface waters) are covering a wide range of water parameters representative of various ecosystems. Making use of state-of-the-art techniques as long as multivariate data analysis, we will try to achieve a model describing the sorption mechanisms of dissolved matter of surface waters onto nanoparticles. Such studies can eventually lead us to a better understanding of the fate of the released nanoparticles under natural conditions.
The massive use of chemicals by humans is increasing pollution of the world’s ecosystems. Yet, knowledge about exposure and effects of chemicals in real-world ecosystems remains limited. Prediction of chemical effects in the context of ecotoxicological research and chemical regulation continues to focus on organism- or population-level responses established under simplified conditions while aiming to protect the functioning of ecosystems. A unified, comprehensive framework for the prediction of chemical effects in real-world ecosystems is still lacking. A major limitation of ecotoxicological studies considered in predictive modelling is that they rarely consider spatial dynamics (e.g. gene flow or species dispersal) as relevant processes influencing the trajectory of populations or communities, respectively. For instance, the spatial propagation of pesticide effects from polluted to least impacted sites has been predicted in several modelling studies but has not yet been characterised in the field.
The thesis starts in Chapter 1 with a brief introduction to chemical pollution in ecosystems, chemical effect prediction in ecotoxicology, and pesticides in freshwater ecosystems, then outlines the main objectives of the thesis. Subsequently, Chapter 2 presents a conceptual study about the current prediction of chemical effects in ecotoxicology and potential future avenues to improve ecological relevance of effect predictions by addressing the integration of different levels of biological organisation (termed biological levels). The study shows that approaches and tools that currently contribute to the prediction of chemical effects can be attributed to three idealised perspectives: the suborganismal, organismal and ecological perspective. The perspectives focus on different biological levels and are associated with distinct scientific concepts and communities. They complement each other so theoretical and empirical links between them may enhance prediction by capturing the entire phenomenon of chemical effects, from chemical uptake to ecosystem effects. Complex experimental studies accounting for eco-evolutionary dynamics are needed to cross barriers between biological levels as well as spatiotemporal scales. Overall, the conclusions of Chapter 2 may help to develop overarching frameworks for predicting chemical effects in ecosystems, including for untested species. Chapters 3 and 4 present a field study combined with laboratory analyses on the potential propagation of pesticides and their effects from agricultural stream sections to the edge of least impacted upstream sections, that can serve as refuges for many species. The study examines exposure and effects for different biological levels at three site types, the pesticide-polluted agricultural sites (termed agriculture), least impacted upstream sites (termed refuge) and transitional sites (termed edge) in six small streams of south-west Germany. The results in Chapter 3 show that regional transport of pesticides can lead to ecologically relevant pesticide exposure in forested sections within a few kilometres upstream of agricultural areas (i.e. at both edge and refuge sites). As further demonstrated in Chapter 3, the tested indicators of community responses (Jaccard Index, taxonomic richness, total abundance, SPEARpesticides) together suggest a species turnover from upstream refuge to downstream agricultural sites and a potential influence of adjacent agriculture on the edge sites. In contrast, Chapter 4 does not identify any particular edge effect that distinguish edge organisms and populations in edge sites from those in more upstream refuge sites. Gammarus fossarum populations at edges show equal levels of imidacloprid tolerance, energy reserves (i.e. lipid content) and genetic diversity to populations further upstream. Gammarus spp. from agricultural sites exhibit a lower imidacloprid tolerance compared to edge and refuge, potentially due to energy trade-offs in a multiple stressor environment, but related effects do not propagate to the edges (Chapter 4). Notwithstanding, the results of Chapter 4 indicate bidirectional gene flow between site types, supporting the hypothesis that adapted genotypes – if present at locally polluted sites – could spread to populations at least impacted sites. Taken together, Chapters 3 and 4, illustrate that pesticides and their effects can potentially propagate to least impacted upstream sections, empirically novel findings to our knowledge. These results of this thesis can help in predicting or explaining population and community dynamics in least impacted habitats and can ultimately inform pesticide management as well as freshwater restoration and protection of biodiversity.
Single-phase flows are attracting significant attention in Digital Rock Physics (DRP), primarily for the computation of permeability of rock samples. Despite the active development of algorithms and software for DRP, pore-scale simulations for tight reservoirs — typically characterized by low multiscale porosity and low permeability — remain challenging. The term "multiscale porosity" means that, despite the high imaging resolution, unresolved porosity regions may appear in the image in addition to pure fluid regions. Due to the enormous complexity of pore space geometries, physical processes occurring at different scales, large variations in coefficients, and the extensive size of computational domains, existing numerical algorithms cannot always provide satisfactory results.
Even without unresolved porosity, conventional Stokes solvers designed for computing permeability at higher porosities, in certain cases, tend to stagnate for images of tight rocks. If the Stokes equations are properly discretized, it is known that the Schur complement matrix is spectrally equivalent to the identity matrix. Moreover, in the case of simple geometries, it is often observed that most of its eigenvalues are equal to one. These facts form the basis for the famous Uzawa algorithm. However, in complex geometries, the Schur complement matrix can become severely ill-conditioned, having a significant portion of non-unit eigenvalues. This makes the established Uzawa preconditioner inefficient. To explain this behavior, we perform spectral analysis of the Pressure Schur Complement formulation for the staggered finite-difference discretization of the Stokes equations. Firstly, we conjecture that the no-slip boundary conditions are the reason for non-unit eigenvalues of the Schur complement matrix. Secondly, we demonstrate that its condition number increases with increasing the surface-to-volume ratio of the flow domain. As an alternative to the Uzawa preconditioner, we propose using the diffusive SIMPLE preconditioner for geometries with a large surface-to-volume ratio. We show that the latter is much more efficient and robust for such geometries. Furthermore, we show that the usage of the SIMPLE preconditioner leads to more accurate practical computation of the permeability of tight porous media.
As a central part of the work, a reliable workflow has been developed which includes robust and efficient Stokes-Brinkman and Darcy solvers tailored for low-porosity multiclass samples and is accompanied by a sample classification tool. Extensive studies have been conducted to validate and assess the performance of the workflow. The simulation results illustrate the high accuracy and robustness of the developed flow solvers. Their superior efficiency in computing permeability of tight rocks is demonstrated in comparison with the state-of-the-art commercial solver for DRP.
Additionally, the Navier-Stokes solver for binary images from tight sandstones is discussed.
Particulate matter has been considered an indicator for the pollution of urban stormwater runoff for quite some time. There are only few studies that have investigated the contamination with organic micropollutants and metals both in the dis-solved and particulate phase as well as across different particle size classes. Yet, this distribution plays an important role in better understanding and optimising urban stormwater treatment measures. Therefore, this work aimed at assessing the composition of particulate matter in urban stormwater in terms of the physico-chemical properties (particle size distribution and organic content), as well as the occurrence of organic micropollutants and metals, their association to particulate matter and their removal from urban runoff. An intensive long term monitoring campaign at a centralised stormwater treatment facility of an industrial area was conducted. The stormwater runoff was sampled with large volume sampling tanks filled volume-proportional to the runoff at the two outlets of the facility. This allowed the determination of the event mean concentrations as well as the load-related removal efficiencies of the treatment facility for different parameters. Within each sample the concentrations of total suspended solids across different particle size fractions (< 63 µm, 63 – 125 µm, 125 – 250 µm, 250 – 2000 µm) were measured as well as their organic content. Furthermore, the concentrations and the phase distribution of 5 metals (Chromium, Copper, Zinc, Cadmium, Lead) and 29 organic micropollutants including polycyclic aromatic hydrocarbons, industrial chemicals (e.g. organophosphates, alkyphenols) and biocides were ana-lysed across different particle size fractions. In this study, over a period of almost 2.5 years, a total of 36 sampling events were recorded and investigated within two sampling periods (2015 – 2016 and 2017 – 2019) at the rainwater treatment facility in Freiburg Haid. The occurrence of organic micropollutants was determined in 22 of these events and the occurrence of metals in 17. The evaluation of the event mean concentration of total suspended solids showed that the fine fraction of the solids is of particular importance, as it showed an event mean concentration more than twice as high (34 mg \(L^{-1}\)) as the coarser particle fraction (14.9 mg \(L^{-1}\)). Regarding the occurrence of total suspended solids in terms of the transported solid load, the solids < 63 µm accounted for a mean proportion of 61 %, the fraction 63 – 125 µm for 13 %, the fraction 125 – 250 µm for 6 % and the fraction 250 – 2000 µm for 9 % of the total solid mass. In terms of the organic content of the solids, the results showed a clear increase of the organic content with increasing particle size (measured as loss on ignition).
As in the case of solids, the highest concentrations of the organic micropollutants and metals investigated were found in the particle size fraction < 63 µm. This fine fraction of the particles also accounted for the largest load of organic micropollutants and metals. Therefore, the particle loading with organic micropollutants or metals respectively the particle-bound micropollutant/metal concentration was calculated in this study. For most substances, a rather equal distribution over the smallest three particle size fractions was found. A certain correlation of the organic content with the occurrence of organic micropollutants and metals could be shown, therefore it can be assumed that the particle-bound concentration is certainly influenced by the organic content of the particulate matter. However, due to the fact that, among other things, the largest particle-bound pollutant loads are transported with particles < 63 µm, the fine fraction represents the relevant particle size in urban stormwater runoff. Regarding the total treatment efficiency (including sedimentation efficiency and volume retention), the investigated facility in this study was able to reduce the load of fine particles by only a quarter. The larger particle size classes were reduced by far more than half in most cases. If total suspended solids in its entire particle size range were used as a proxy to estimate the removal efficiency of metals and organic micropollutants, the efficiency would be overestimated and the actual pollutant load released into the environment would thus be underestimated. However, the investigation, weather the particle size fraction < 63 µm would be more suitable, showed that even for substances with a high tendency to adsorb onto particles (e.g. Cr, Cu, IND, GHI), the total treatment efficiency was still overestimated by the fine fraction.
Previous research has shown the importance of early science, technology, engineering, and math education for children’s knowledge, as it establishes a groundwork for their later learning and academic achievement. However, the engagement of preschool teachers especially in science learning activities is infrequent, and some teachers still pronounce the belief that science education is inappropriate for the early childhood years. Furthermore, there is a lack of clarity regarding the connections between teachers' attitudes (including their knowledge, beliefs, and willingness) towards teaching early science and their actual teaching practice, as well as the subsequent effects of teacher practice on children's learning outcomes. This dissertation primarily aims to clarify these associations. Block play offers the possibility to link scientific concepts (e.g., stability) to children’s everyday activities and thus represents an age-appropriate way to examine young children’s STEM-learning. The present dissertation encompasses three research articles, focusing specifically on the interplay between preschool teachers’ dispositions and practice in block play and 4- to 6-year old children’s knowledge. The first article focused on the validation of a self-developed instrument to assess preschool teachers’ willingness to engage in science teaching and examined the predictive power of teachers’ willingness for teachers’ practice. Results suggested that the instrument measured teachers’ willingness reliably and validly, however, teachers’ willingness did not predict their practice in block play. The second article examined the relationship between the preschool teachers’ instructional quality during block play and various aspects of children's knowledge. Specifically, the study explored how instructional quality in block play influenced children's knowledge in stability, math, and spatial language. Additionally, children’s academic self-concept and cognitive aspects (i.e., intelligence, working memory) were considered. Results implied that preschool teachers’ scaffolding activities were related to children’s stability knowledge in block play. Moreover, teachers’ instructional quality was positively correlated with children’s academic self-concept in block play. The primary focus of the third article was on implementing a block play curriculum. Therefore, study 3 employed a longitudinal design to assess the effectiveness of a teacher training on teachers’ practice with the curriculum, which included both, guided and free play. Teachers were randomly assigned to either a control group or an experimental group. The experimental groups received training with the block play curriculum, while the control group did not receive any training. Results showed no change in teachers’ knowledge before and after training. Nonetheless, teachers in the experimental group applied more scaffolding after the training. Furthermore, preschool teachers applied more scaffolding during guided than during free play. Children’s math score in the experimental group, but not in the control group, significantly improved from pre- to post-test. In the general discussion, the findings of the three articles are reflected in the light of the interplay between teachers’ dispositions and their teaching practice as well as the impact of teacher practice on children’s knowledge. Besides, the discussion reflects on methodological difficulties of empirical studies in early childcare settings, providing a prospective view on multimethod approaches for future research. Taken together, the present dissertation contributes to a more profound understanding of how teacher practices and children's knowledge interact. Further, the research holds great relevance for practical application as it illustrates the differential effects of teacher training on preschool teachers’ knowledge and their teaching practice.
Nitrogen removal from wastewater is increasingly important to protect natural water sources and has proven a challenge for wastewater treatment plants in different countries. Strict discharge norms for nitrogen components and unfavourable wastewater quality are among the main challenges observed.
An example WWTP (450,000 PECOD,120), representative of these challenges (i.e. strict discharge norm for NH4-N and TN, partially unfavourable wastewater composition for upstream denitrification) was modelled with the software SIMBA. The model was calibrated, and validated, using different statistical parameters. The model was used for dynamic simulation to test different operational and automation strategies, to improve nitrogen removal.
The tested strategies considered the bypass of primary clarifiers, changes in the anaerobic, anoxic, and aerobic reactors configuration, changes in the aeration system (DO setpoint, the inclusion of online sensors and different control approaches in the aeration loop), the adjustment of the internal recirculation rate, the implementation of intermittent denitrification, among others. The addition of an anaerobic digestion stage, considering the adjustment of the sludge age in the biological treatment and the treatment of the centrate (including nitrogen backload), was tested as well.
To evaluate the strategies' performance, an evaluation criteria chart was created to select the best strategies from an overall perspective, considering the improvements or deterioration in norm compliance, aeration requirements, pollutant emissions to the environment, and biogas production (if applicable).
The best overall results were obtained with strategies that aimed to improve the denitrification capacity (e.g. increase anoxic volume by reducing aerobic volume), adjusted the air requirements (e.g. inclusion of an NH4-N online measurement in the aeration control loop), and provided flexibility (e.g. intermittent denitrification). With the right combination of strategies, the norm compliance was significantly improved e.g. reduced from 31 to 4 in a year, as well as the emissions to the environment.
The inclusion of an anaerobic digestion stage for sewage sludge treatment challenges the nitrogen removal even further, but similar optimisation strategies, based on the same approach were able to improve norm compliance.
However, none of the combinations, with or without anaerobic digestion, achieved total norm compliance. Therefore, a different technology than A2/O, an SBR treatment stage was designed, providing increased operational flexibility. The A2/O system in the computer model was replaced by an SBR process. This showed the best results, based on the criteria previously defined, with total norm compliance.
Based on the learnings of the design, redesign, and strategies tested, a guideline for an integral optimisation of nitrogen removal was developed, based on six pillars, considering a detailed WWTP operational analysis, the use of dynamic simulation as a tool, the testing of known and simple optimization approaches, the definition of clear and objective evaluation criteria, the consideration of anaerobic digestion (and the backload) and finally the re-evaluation of the type of technology for biological wastewater treatment.
This dissertation project aims to examine the potential of network modelling, an increasingly popular methodology in emotion research (e.g., Fried et al., 2016), to better comprehend age-related differences in structural connections between cognitive processes such as fluid intelligence and executive control functions. Furthermore, it aims to identify the key variables that link self-regulation to executive control functions and age-related discrepancies. Lastly, it seeks to delve into the key variables and correlations between executive control functions, self-regulation, and affect utilizing a longitudinal design in combination with machine learning as a data-driven method.
In study 1, differences between the cognitive performance networks of younger (M = 38.0 years of age, SD = 9.9) and older (M = 64.1 years of age, SD = 7.7) adults were explored. Network modelling showed that while speeded attention is essential throughout the life-span, connections between fluid intelligence and working memory were stronger, and intelligence was more central in the older group. Additionally, confirmatory factor modelling demonstrated that latent correlations were highest between working memory and intelligence, particularly in older adults, whereas inhibition had the lowest correlations with other abilities. This research suggests that the relations of cognitive abilities may differ between younger and older adults, indicating process-specific changes in the cognitive performance network.
In study 2, we investigated the connections of self-regulation (SR) and executive control functions (EF), which are theoretical concepts encompassing various cognitive abilities supporting the regulation of behavior, thoughts, and emotions (Inzlicht et al., 2021; Wiebe & Karbach, 2017). Evidence, however, implies that correlations between self-report measures and performance-based tasks are often difficult to observe (e.g., Eisenberg et al., 2019). We investigated connections and overlap between different aspects of SR and EF in a life-span sample (14-82 years). Participants completed several self-report measures and behavioral tasks, such as sensation seeking, mindfulness, grit, or eating behavior questionnaires and working memory, inhibition, and shifting tasks. Network models for a youth, middle-aged, and older-aged group were estimated to identify key variables that are well connected in the SR and EF construct space. In general, stronger connections were observed within the clusters of SR and EF than between them, and older adults appeared to have more connections between SR and EF than younger individuals, probably because of declining cognitive resources.
In study 3, we analyzed the intricate links between EF, SR and affect, as well as individual differences in these relations. Bridgett et al. (2013) proposed that EF and self-regulation SR are psychological constructs to support the regulation of cognition and affect. A total of 315 participants, aged 14 to 80, answered questionnaires and took part in behavioral tasks which evaluated EF, SR, and both positive and negative affect two times (one-month apart). Combined X-means and deep learning algorithms aided in the separation of two distinct groups who featured different EF performances, SR tendencies, and affective experiences. Network model analysis was then utilized to confirm the connections between the EF, SR, and affect variables in each of the two groups. The two groups displayed a maximal centrality for variables linked to SR and positive affect. Group membership remained mostly consistent (85%) across both measurement occasions. Logistic regression indicated that age and personality (conscientiousness, neuroticism, and agreeableness) predicted group membership. This sheds light on stable individual differences in the complex relations of EF, SR, and affect.
This dissertation project utilized a combination of standard approaches (such as confirmatory factor analysis; CFA) and advanced approaches (such as network models, machine learning algorithms, and deep learning) to explore the connections between cognitive abilities, EF, SR, and affect. Our findings are in line with the theory of process specific changes in age-dedifferentiation. Findings suggested that connections between SR and EF were stronger within clusters, and positive affect was better connected to SR than EF measures. Lastly, age and personality traits were found to predict the clusters. These findings suggest that computational modelling is an effective exploratory tool in understanding how cognitive abilities and other psychological constructs may interact. Further research is necessary to gain further insights on the mechanisms behind differences in network structures.
This thesis deals with modeling and simulation of district heating networks (DHN) and the mathematical analysis of the proposed DHN model. We provide a detailed derivation of the complete system of governing equations, starting from a brief exposition of the physical quantities of interest, continued with the components to set up a graph based network model accounting for fluxes and coupling conditions, the transport equations for water and thermal energy in pipelines, and the terms representing consumers and producers. On this basis, we perform an analysis of the solvability of the model equations, starting from the scalar advection problem in a single–consumer single–producer network, to a generalized problem suitable to model simple networks without loops. We also derive an abstract formulation of the problem, which serves as a rigorous mathematical model that can be utilized for optimization problems. The theoretical results can be utilized to perform tran- sient simulations of real world DHN and optimize their performance by optimal control, as indicated in a case study.
In one-dimensional (1-D) Ultrasound (US) measurements, signals are
acquired that form the basis of more sophisticated two-dimensional (2-D) or
three-dimensional (3-D) US imaging. These 1-D signals contain a lot of raw
information about the US wave propagation and interaction with the
medium that is only processed in parts during image generation. While
image representations are easy to interpret for humans, the analysis of US
wave signals is hard to perform without applying algorithms to extract
desired features.
This work investigates reliable and fast 1-D US signal classifications to
distinguish between different stages or states in biomedical US scenarios and
shows how the new field of Machine Learning (ML) on raw US wave data
provides advantages and different applications. To achieve good results, the
input signals are treated as time series, which requires the deployment of
comparatively complex Time Series Classification (TSC) algorithms.
The literature shows that a lot of research efforts have previously only
tackled the classification and segmentation of US Brightness mode (B-Mode)
images, while neglecting approaches to classify 1-D signals to a large extent.
This research contributes by developing, deploying and evaluating
classification approaches for three distinct biomedical US classification tasks
and finds that respective signal classifications for different scenarios are
possible with varying degrees of accuracies. It entails the comparison of
several combinations of data types (e.g. temporal, spectral and statistical
features or raw signals), ML models and pre-processing steps to provide a
strong foundation for robust, binary classifications of 1-D US signals for
scenarios based on low-cost wearable, mobile and stationary devices. This
research addresses scientific questions not answered before by informing on
detailed descriptions of beneficial domain specific knowledge (domain specific
knowledge (DSK)), achieved accuracies and times needed for training and
evaluation of the examined ML models.
The resulting ML pipelines includes solutions based on data acquired from
custom experimental setups or clinical trials. Possible real-world applications
might include muscle contraction trackers, muscle fatigue detectors,
epiphyseal radius bone closure detectors or devices providing information
about advanced liver disease stages.
Automated machine-assisted
classifications requiring as little DSK as possible from the end user enable
application scenarios ranging from fitness or rehabilitation trackers as
consumer devices to solutions providing diagnostic support without requiring
extensive knowledge from professional medical practitioners. For example,
decision support systems for bone age assessments in clinical use or liver
health assessment systems for gastroenterologists.
This work shows that reliable, robust and fast classifications based on 1-D
US signals are possible with high degrees of accuracies depending on the
examined scenario with achieved F 1 -scores ranging from ≈ 70% to ≈ 87%.
These results prove that real-life applications for recreational purposes are
already possible and that critical applications for clinical use are highly likely
to be achieved once the presented approaches are further optimized in the future.
A new class of amines that are promising solvents for reactive CO2-absorption processes was thoroughly investigated in a comprehensive experimental study. The amines are all derivatives of triacetoneamine and differ only in the substituent of the triacetoneamine ring structure. These amines are abbreviated by the acronym EvA with a consecutive number that designates the derivatives. About 50 EvAs were considered in the present study, from which 26 were actually synthesized and investigated as aqueous solvents. The investigated properties were: solubility of CO2, rate of absorption of CO2, liquidliquid and solid-liquid equilibrium, speciation (qualitative and quantitative), pK-values, pH-values, foaming behavior, density, dynamic viscosity, vapor pressure, and liquid heat capacity. All 26 EvAs were assessed in an experimental screening. The results were compared with the results of two standard solvents from industry: aqueous solvents of monoethanolamine (MEA) and a solvent blend of methyl-diethanolamine and piperazine (MDEA/PZ). Detailed studies were carried out for two EvAs that revealed significantly improved performance compared to MEA and MDEA/PZ: EvA34 combines favorable properties of MEA and MDEA/PZ in one molecule. EvA25 reveals a liquid-liquid phase split that reduces the solubility of CO2 in the solvent and shifts the CO2 into the aqueous phase. This allowed the design of a new CO2-absorption process, that takes advantage of the liquid-liquid phase split. Finally, the chemical speciation in 16 EvAs was investigated by NMR spectroscopy. From the results, relationships between the chemical structure of the EvAs and the observed speciation, basicity, and application properties were established. This enabled giving guidelines for the design of new amines and proposing new types of amines, which were called ADAMs.