Immersive systems, like Augmented and Virtual Reality, offer new paradigms fordigital interaction, but confining users to a single reality often presents drawbacksfor complex tasks. Cross-Reality systems, which integrate multiple realities into asingle experience, have significant potential to enhance existing professional workflows by combining the unique strengths of physical and virtual environments. Thisdissertation investigates how Cross-Reality can enhance professional workflows byusing the traditional office as a primary use case, focusing on the central question:How can CR enhance existing workflows in physical settings by extendingthe physical environment with virtual content and environments?To address this, the dissertation presents a body of empirical work structuredaround isolating and investigating one core design challenge for each of the threeprimary types of Cross-Reality systems. The work first addresses transitionalCross-Reality systems, which allow users to switch between different realities, byexamining how to design effective transitions. It demonstrates that in task-drivenscenarios, users prioritize efficient transitions that minimize cognitive disruptionover more elaborate or interactive ones. Next, the dissertation tackles the fundamental problem of unwanted occlusion in Augmented Virtuality, a form of substitutional Cross-Reality systems, which integrate objects from one reality intoanother. It introduces and evaluates technical strategies to ensure physical toolsremain accessible within virtual spaces, revealing a critical trade-off between theefficacy of these solutions and user experience factors like cybersickness. Finally,the research explores multi-user Cross-Reality systems that enable collaborationbetween multiple users who may be experiencing different degrees of virtualitysimultaneously, and the complexities of enabling collaboration across multiplestages, underscoring the unique challenges of supporting shared awareness andmanaging asymmetric roles.These findings are grounded by a detailed analysis of the underlying hardware, which highlights how technical and perceptual issues inherent to VideoSee-Through and Optical See-Through Head-Mounted Displays directly impactthe feasibility and design of Cross-Reality systems. The overarching contributionof this dissertation is to provide a set of empirically-grounded design principlesfor applying Cross-Reality in productivity-focused environments. By shifting thedesign focus from entertainment to pragmatic qualities, this work offers valuableinsights into creating Cross-Reality systems that genuinely enhance workflows, prioritizing efficiency, usability, and seamless interaction while navigating technical

From Gene to Ecosystem – Retrospective molecular biodiversity monitoring of plant-associated arthropod communities and abiotic stress on plants

This thesis serves as proof of concept for the tensile strength simulation-based nonwoven material design. Objective is the adjustment of the parameters of an underlying production process with regard to a desired tensile strength behavior (optimization). As an example, we focus on the nonwoven airlay production and consider a thermobonding procedure for the consolidation of the nonwoven fabrics. To be able to map production parameters to the associated tensile strength behavior, we present a model-simulation framework composed of a model for the nonwoven fiber structure generation and a model for the nonwovens’ mechanical behavior under vertical load. The model for the fiber structure generation replicates the stochastic fiber lay-down of the airlay production and results in a random three-dimensional fiber web. This web is consolidated using a virtual bonding procedure that mimics the thermobonding of the nonwoven material. The topology of the resulting adhered fiber structure can be described by a graph, which serves as basis for the subsequent tensile strength simulation. The model used for this purpose describes the mechanical behavior of the material at fiber network level. Therefore, the considered fiber structure sample is interpreted as truss and the fiber connections are equipped with a nonlinear material law, which allows to describe the elastic phase of the nonwovens’ tensile strength behavior. The existence and uniqueness of a solution to the model as well as its numerical treatment are discussed. Moreover, we present data reduction strategies that enable more efficient simulations by removing fiber structure parts that do not contribute to the tensile strength behavior. As it becomes evident from the numerical experiments, a single tensile strength simulation for a production-like virtual sample is already computational demanding. Costs accumulate further, since Monte-Carlo simulations are required to account for the randomness in the fiber structure generation. Thus, direct simulations provide an infeasible basis for the nonwoven material design. This motivates the use of a predictive surrogate for optimization. Therefore, we consider regression-based approaches at different levels of information within the simulation framework. It turns out that the coupling of a polynomial model, for the fiber structure feature inference, with a linear one, for the stress-strain curve inference, yields accurate predictions. Once trained, the regression models allow for efficient evaluations and thus represent a suitable surrogate for the nonwoven material design. In this context, we discuss two exemplary problems of interest for the application: First, a tracking-type problem that aims to find the production parameters that result in a desired tensile strength behavior, expressed in terms of stress-strain curves. Second, an in-corridor maximization problem, which aims to identify the production parameters that maximize the probability of ending up in a specified stress-strain corridor.

Transnational protest movements continue to expose the enduring legacies of colonial exploitation and institutionalised racism within and beyond European cities. They foreground the systemic conditions under which Black lives are rendered disproportionately vulnerable to premature death. In doing so, they expose the enduring entanglements of racial capitalism, state violence and spatial exclusion. Through their ongoing political agitation these movements highlight the need for spatio-temporally situated and relationally embedded engagements with Black urban lives. My thesis responds to that call by examining place-making practices of enclosure and refusal throughout Black London’s post-World War II development. Grounded in the ethnographic narrative of “being halfway while shooting”, I explore how Black lives are enclosed by institutional racism, how this enclosure is spatialised and how Black and differently racialised Londoners refuse these spatial enclosures through everyday and collective place-making practices. At the intersection of structural constraint and the desire to enact Black freedom in London, I specifically foreground the emergence of fugitive place-making practices. Conceptually, I bring (critical) urban geography scholarship, Black studies and Black (British)Geographies scholarship into conversation. I develop “being halfway while shooting” as a relational concept that foregrounds the production of racialised urban knowledges, the multiplicity of Black enclosures, and the plurality of place-based strategies committed to refusal. I do so by stressing the relevance of Black fugitive thinking to account for the ongoing refusals that mark the relationship between Blackness and the British city. Methodologically, I adopt a research-activist ethnographic approach, grounded in my long-term engagement with a housing campaign in East London that organises around the housing needs of London’s racialised and gendered urban poor. Using qualitative methods - archival research, interviews, (non-)participant observations, document and media analysis - I embed contemporary struggles into long and ongoing histories of racial-capitalist urban development as well as Black and multi-ethnic refusal. The empirical chapters trace place-making practices of enclosure and refusal across London’s post-World War II urban development. By examining the aftermath of urban revolts and changing urban welfare regimes, I explore how racialised urban governance has been historically materialised in and through the city. At the same time, I foreground how within this racialised construction of the British city, Black and differently racialised Londoners continue to hold open the possibility of refusal through places in which communal care and self-determination can be enacted. I then turn to the struggle over housing in East London, showing how contemporary processes of racialised dehumanisation and ongoing displacement are both historically rooted and actively contested. In the final empirical chapter I accentuate the relevance of these findings for German-speaking critical urban geography debates. The research shows that racial capitalist urbanism reproduces enclosures through practices of value extraction, spatial displacement, and the policing of Black subjectivities. In response, Black and differently racialised Londoners engage in fugitive place-making. Rooted in communal care, political organisation, collective education and cultural affirmation, these practices reassert Black presence and belonging. They offer an enduring mode of place-based refusal and the ongoing possibility to stay in the city differently. These findings not only demonstrate the academic significance of my research but also underscore the urgent need to support the place-making practices of Black and differently racialised urban communities, who continue to refuse the racialised enclosure of the British city from within. From these empirical insights, I propose the concept of a fugitive sense of place - a theoretical lens that accounts for the racialised reproduction of urban space and the transformative place-making practices of those who refuse its logics. Rather than offering prescriptive policy recommendations, I call for a reorientation of urban geographical enquiry by centring Black spatial practices, knowledges and imaginations. Through the lens of “being halfway while shooting”, I argue for a rethinking of human habitation and urban theory through the lived experiences of Black survival and refusal. Attending to a fugitive sense of place, I propose new avenues for human geography research to explore how fugitive place-making practices reshape the meanings, conditions, and possibilities of urban life.

The role of implicit motives for affective, cognitive and behavioral processes has been a focal part of psychological research for decades. Yet, the majority of research in this field has been concentrated on processes involving implicit motives in adulthood. The systematic investigation of developmental correlates of implicit motives remains largely uncharted. The studies cumulated in this thesis aim to add to the sparse research on implicit motives in childhood and adolescence. Specifically, the development of the implicit power motive in the transition of middle to late childhood as a function of parenting behavior (Chapter 4), the predictive value of the implicit achievement motive for objective swimming performance in children and adolescents (Chapter 5) and the role of motive congruence for successful goal realization in adolescent samples across two cultures (Chapter 6) were investigated. Results of Study 1 (Chapter 4) indicate a negative longitudinal association of authoritarian parenting with the implicit power motive in children that is moderated by children’s perception of psychologically controlling parenting. Study 2 (Chapter 5) extends existing research on the assumed positive association of the implicit achievement motive and sports performance and demonstrates the moderating role of competitive anxiety on this association. Finally, Study 3 (Chapter 6) illustrates a moderating effect of implicit motives on the association of goal commitment and successful goal realization in German and Zambian adolescents, however, this effect was only observed in the domain of power motivation. Findings from all three studies are discussed in the context of the significance of implicit motives for psychological research.

This dissertation examines how individuals unlock their personal power by investigating individual differences in self-regulation, in particular, how situational conditions interact with the personality dispositions of action versus state orientation. Action-oriented individuals are well able to regulate their affective states and to bridge the intention–behavior gap, showing initiative, implementing demanding intentions, and resisting temptations. State-oriented individuals, by contrast, often struggle to regulate affect and experience difficulties enacting intentions, especially under demanding conditions, tending to hesitate and ruminate. While extensive research has highlighted the advantages of action orientation across various domains such as education and health, this thesis challenges the prevailing one-sided perspective that presents action orientation as inherently superior and frames state orientation negatively. Drawing on Personality Systems Interactions theory, the dissertation adopts a dynamic view that understands these dispositions as context-sensitive rather than fixed. The central assumption is that action and state orientation each require different kinds of situational conditions to fully unlock their potential. Across six empirical studies (overall N = 1,067) using a multimethod approach that combines experimental and survey-based research in diverse populations and contextual settings, this dissertation examines (1) action and state orientation as distinct dispositions, (2) their dynamic interaction with situational factors, and (3) ways to support each in mobilizing personal power. Overall, the findings show that each disposition offers unique advantages - they simply require different situational conditions for their potential to unfold.

Bilevel problems are optimization problems for which parts of the variables are constrained to be an optimal solution to another nested optimization problem. This structure renders bilevel problems particularly well-suited for modeling hierarchical decision-making processes. They are widely applicable in areas such as energy markets, transportation systems, security planning, and pricing. However, the hierarchical nature of these problems also makes them inherently challenging to solve, both in theory and in practice. In this thesis, we study different nonlinear problem settings for the nested optimization problem. First, we focus on nonlinear but convex bilevel problems with purely integer variables. We propose a solution algorithm that uses a branch-and-cut framework with tailored cutting planes. We prove correctness and finite termination of the method under suitable assumptions and put it into context of existing literature. Moreover, we provide an extensive numerical study to showcase the applicability of our method and we compare it to the state-of-the-art approach for a less general setting on suitable instances from the literature. Furthermore, we discuss challenges that arise when we try to generalize our approach to the mixed-integer setting. Next, we study mixed-integer bilevel problems for which the nested problem has a nonconvex and quadratic objective function, linear constraints, and continuous variables. We state and prove a complexity-theoretical hardness result for this problem class and develop a lower and upper bounding scheme to solve these problems. We prove correctness and finite termination of the proposed method under suitable assumptions and test its applicability in a numerical study. Finally, we consider bilevel problems with continuous variables, where the nested problem has a convex-quadratic objective function and linear constraints. We reformulate them as single-level optimization problems using necessary and sufficient optimality conditions for the nested problem. Then, we explore the family of so-called P-split reformulations for this single-level problem and test their applicability in a preliminary numerical study.

Extracellular enzymes in microbial communities play a central role in nutrient cycling and the degradation of (pollutant) substances in various natural and anthropogenic systems. Bound in aquatic biofilms, sludge aggregates, or even unbound at their interfaces, they are of great importance for both the environment and human health. In particular, in wastewater treatment plants and inland waters, hydrolytic activities influence the wide-reaching efficiency of nutrient removal and self-purification, thus contributing significantly to overall water quality. The main goal of this dissertation project was to investigate the factors that influence enzymatic activity and the health of microbial communities in activated sludge and river systems, particularly in relation to anthropogenic influences and natural environmental conditions. The aim was to contribute to a better understanding of the sensitivity of our freshwater ecosystems and to support the long-term preservation of water quality and ecological stability. The development and optimization of appropriate methods, as well as their testing and applicability, were the focal points. For this purpose, a fluorometric microplate assay was developed and adapted to determine both extracellular enzyme activities (EEAs) in activated sludge samples and in intact biofilms. Its suitability for field studies was subsequently tested. Inhibition and activity of selected hydrolases under different conditions were investigated to better understand the mechanisms and potential environmental risks posed by anthropogenic influences and seasonal fluctuations of hydrochemical and climatic parameters. The first phase of the doctoral thesis involved studies on the inhibition of alkaline phosphatase in activated sludge by oxyanions. Using the fluorometric microplate assay, the inhibitory effect was sensitively detected over a pH range of 7.0 to 8.5. IC50- and IC20-concentrations were calculated from modeled dose-response functions. It was found that vanadate and tungstate caused strong inhibitory effects, while molybdate moderately inhibited the enzyme. An increasing pH led to a reduction in the inhibitory effect of tungstate and molybdate. The inhibition effects of vanadate were not significantly affected by the pH. In municipal wastewater, the concentrations of such metal ions are usually low, but industrial wastewater may have pollutant loads that can significantly impact the removal of phosphorus-containing compounds, and thus the efficiency of treatment plants. In the second phase, an attempt was made to further adapt the developed methodology to investigate EEA and kinetics in intact freshwater biofilms. Four different types of bead materials (lava, glass, sintered quartz, and ceramics) fitting into a 96-well microplate were tested as carriers for biofilms on both the laboratory and field scale. The analysis included a total of seven hydrolases as representatives of key nutrient cycles such as phosphorus, carbon, and nitrogen: phosphatases, glucosidases, peptidases (two different types), and sulfatase. Experiments with increasing substrate concentrations led to classical kinetic profiles according to the Michaelis-Menten mechanism. This allowed for the prediction of the biofilm enzymes’ response to different substrate concentrations. Parameters such as Vmax and Km could be derived from the modeled curves. Ceramic beads are particularly suitable for long-term studies due to their high stability, while sintered quartz beads should be preferred for the use in stagnant media (material loss under turbulent conditions). Lava and glass beads, on the other and, proved suboptimal for uniform biofilm development due to their surface properties. The potential use of this fast and sensitive test for ecotoxicological or even human-toxicological studies was demonstrated by the effects of caffeine on the activity of PDE. The result of this part of the research represents a powerful tool for assessing environmental pollution and monitoring water quality. The high application potential was clearly highlighted in the final phase of the project. The goal here was to deepen the understanding of interactions between seasonal factors, anthropogenic influences, and biofilm processes in rivers by investigating EEA and biofilm parameters such as biomass and relating them to hydrochemical and climatic factors. Ceramic beads were exposed both upstream and downstream of a wastewater treatment plant discharge and sampled over a period of seven months. EEAs and biomass varied depending on the season and location, with higher microbial activity observed upstream in winter. Winter conditions led to the dilution of most nutrients as well as in an increse of dissolved oxygen. Nutrient concentrations analyzed downstream were significantly higher in the summer. Accumulation of nutrient or pollutants during the summer months cannot be excluded, which may have led to a general reduction in enzyme activities. Potential causes could be inhibitory effects on the enzymes, or a reduced enzyme activity due to a sufficiently high nutrient supply. In general, the sampling site upstream showed a more pronounced seasonal dynamics, with a significant proportion of the variance in biological parameters (activity and biomass) attributable to seasonal factors. A secondary component, likely reflecting the impact of the treatment plant discharge, explained another portion of the data variance. Regardless of the season, high correlations between biological parameters were observed upstream, while downstream the data were more decorrelated. This could be because the biofilms, under chronic stress, respond less dynamically to seasonal fluctuations. This dissertation illustrates that in addition to anthropogenic stress factors, seasonal fluctuations of hydrochemical and climatic parameters should also be considered in "stress downstream the pipe" studies. The selected methods are recommended for explaining and considering the data variance, as they highlight the complex interplay between microbial enzymatic activity, environmental factors, and pollutants in the activated sludge of wastewater treatment plants and also in aquatic systems. The novel bead assay could pave the way for the future standardization of effect-oriented studies on intact aquatic biofilms.

The application of machine learning and deep learning methods to hydrological modelling has advanced significantly in recent years, offering alternatives to traditional conceptual and physically based approaches. Within the numerous algorithms, long short-term memory (LSTM) networks have proven themselves particularly useful for the task of streamflow modelling. This thesis provides a collection of publications that investigate the capabilities, limitations and interpretability of LSTM for the purpose of streamflow modelling and climate change impact assessment within the lowland Ems catchment in Northwest Germany. Within a comparative performance evaluation, LSTM and its predecessor, the recurrent neural network, demonstrate superior accuracy compared to the conceptual HBV model across various statistical performance metrics. However, a decline in performance was observed during low-flow conditions in certain sub-catchments. The evaluation of the flow duration curve revealed that the ML models more effectively capture the water balance, while HBV better represents streamflow dynamics. To enhance the interpretability of LSTM, six explainable artificial intelligence techniques were applied. These methods consistently identified seasonal patterns in the temporal relevance of hydroclimatic input data. In combination with an observed correlation between the internal LSTM states and catchment-scale soil moisture dynamics, the findings suggest that LSTM models are capable of implicitly learning the relevant hydrological processes. Following, the capabilities of LSTM to model climate change impact scenarios, particularly when they extend beyond historically observed climate conditions, are addressed. An ensemble of climate change projections is provided as hydroclimatic input to evaluate the performance of LSTMs and conceptual models. While all models reveal heterogeneous alterations in streamflow under future climate conditions, significant differences emerge based on the model type. Results provide evidence that LSTMs, in combination with the temperature-based Haude formula for estimating potential evaporation, work inadequately under altered climatic regimes, raising concerns about their applicability in long-term projections. The study also indicates the potential need to incorporate physical constraints into LSTM architectures to ensure model robustness and hydrological plausibility beyond the historical training range. Collectively, this thesis contributes important insights into the applicability and interpretability of LSTM models in streamflow modelling. Despite the presence of a physically realistic representation of soil moisture dynamics of the Ems catchment, no robust change signals for streamflow under climate change can be derived. Those results underscore the potential of LSTM model approaches for accurate streamflow simulation, however, they require us to always critically question LSTM results, particularly when they are applied outside the training range.

Spatial microsimulation is an important tool for integrating geographical information into the evaluation of public policies and the analysis of social phenomena in urban regions. These models simulate the behavior and interaction between units of the region, such as individuals, households or firms, under specific conditions that may or not involve projections over time. This requires a representative base data set for their respective units. In this thesis, we focus on the geo-referencing step of the population in the construction of this data set, where we define the location of the individuals so that the allocation obtained is representative in relation to the population of the region. To do this, we consider the assignment of households to dwellings with specific coordinates by solving a maximum weight matching problem where side constraints are included so that the allocation obtained satisfies statistical structures intrinsic to the considered region. The model of this problem represents each feasible assignment of household to dwelling as a binary variable, which results in billions of variables for medium-sized municipalities such as the city of Trier, Germany. Therefore, standard solvers for mixed-integer linear optimization are not able to solve it due to their high time and memory consumption. Hence, we develop two approaches capable of producing high-quality allocations using a reasonable amount of computational resources, one based on specific decomposition algorithms, and the other characterized by the application of an approximation algorithm in the framework of Lagrangian relaxation of the side constraints. We theoretically explore the allocations obtained by both approaches and perform an extensive computational study using synthetic data sets and real-world data sets associated with the city of Trier. The results show that the developed methods are able to obtain near-optimal solutions using significantly less memory and time than the solver Gurobi, which enables them to tackle significantly larger instances, with approximately 100 000 households and dwellings. Furthermore, the allocations obtained for the real-world data sets correspond to a realistic population distribution, which strengthens the practical applicability of our methods.