Efforts to induce customers to buy groceries through the Internet have existed for around twenty years. Early on, the market structures of the digital grocery trade were still strongly fragmented and poorly coordinated. Due to the technological advancement in the past decade, the digital purchase of groceries has become more attractive. The adoption rate of these services varies greatly between different regions. In Germany in particular, the digital grocery trade is stagnating at a comparatively low level. In this regard, this dissertation analyzes both the retail-side market structures and the expectations and obstacles of German consumers. The year 2020 connotes a turning point for the online grocery trade, as daily routines such as grocery shopping were subject to strict regulations imparted at a governmental level in order to reduce COVID-19 infections. At the same time, despite this opportunity, the digital grocery trade has not yet established itself nationwide in Germany. This can be attributed to a lack of investments, but also to inadequate digitization measures. A stronger synchronization between the digital and stationary offer, better integration of digital food services at a regional level as well as adapted, target group-appropriate digital solutions for the efficient breakdown of usage barriers will benefit service usage. The importance of stable food chains and distribution channels was illustrated by the COVID-19 pandemic. Further research should help to develop the digital food trade into a stable and sustainable supplementation of the stationary store.

Invasive species play increasing roles worldwide. Invasions are considered successful when species establish and spread in their exotic range. Subsequently, dispersal is a major determinant of species’ range dynamics. Mermessus trilobatus, native to North America, has rapidly spread in Europe via aerial dispersal. Here we investigated the interplay of ecological and evolutionary processes behind its colonisation success. First, we examined two possible ecological mechanisms. Similar to other invasive invertebrates, the colonisation success of Mermessus trilobatus might be related to human-induced habitat disturbance. Opposite to this expectation, our results showed that densities of Mermessus trilobatus decreased with soil disturbance in grasslands suggesting that its invasion success was not connected to a ruderal strategy. Further, invasive species often escape the ecological pressures from novel enemies in their exotic ranges. Unexpectedly, invasive Mermessus trilobatus was more sensitive to a native predator than native Erigone dentipalpis during our predator susceptibility trials. This indicates that the relation between the invasive spider and its native predator is dominated by prey naïveté rather than enemy release. The remaining three chapters of the thesis investigated the dispersal behaviour of this invasive species. Hitherto, studies of passive aerial dispersal used wind as the primary dispersal-initiating factor despite a recent demonstration of the effects of the atmospheric electric fields on spiders’ pre-dispersal behaviour. During our experiments, only the wind facilitated the flight, although electric fields induced pre-dispersal behaviour in spiders. Consequently, studies around passive aerial dispersal should control electric fields but use wind as a stimulating factor. Rapidly expanding species might be disproportionately distributed in their exotic range, with an accumulation of dispersive genotypes at the leading edge of their range. Such imbalanced spatial segregation is possible when the dispersal behaviour of expanding species is heritable. Our results showed that the dispersal traits of Mermessus trilobatus were heritable through both parents and for both sexes with recessive inheritance of high dispersal ability in this species. Following the heritability experiments, we documented an accelerated spread of Mermessus trilobatus in Europe and tested whether dispersal, reproduction or competing ability was at the source of this pattern. Our results showed that the accumulation of more mobile but not reproductive or competitive genotypes at the expansion front of this invasive species gave rise to an accelerated range expansion by more than 1350 km in under 45 years. Invasive Mermessus trilobatus is inferior to native sympatric species with respect to competing ability (Eichenberger et al., 2009), disturbance tolerance and predation pressure. Nevertheless, the species successfully established in its exotic range and spread by accelerating its expansion rate. Rapid reproduction that balances the high ecological pressures might be the other potential mechanism behind its colonisation success in Europe and deserves further investigation.

Abstract for the print-book: Walden, R. (2008). Architectural Psychology: School, University Campus, and Office Building of the Future. Lengerich: Pabst Science Publishers (in German). The need for display of self in architecture and for users' self-regulation of stress factors, which demonstrate that users crave individual control of their environment (cf. Flammer, 1990; Burger, 1992) motivated this study to use the concept of environmental control as a central criterion for the evaluation of built environment. It was applied to three case studies: a school, a university campus, and an office building. Advantages and disadvantages of the data- gathering methods of architectural Programming, User-Needs Analysis, and Post-Occupancy Evaluation were analyzed to highlight their significance in terms of Building Performance Evaluation as described by Preiser and Sc hramm (1997, 2005). The “Koblenz Architecture Questionnaire” was used as an instrument for assessing the built environment of the three case studies, and the study reports selected findings from these questionnaires. The investigation seeks to determine the effect of architecture - especially buildings' provisions for user control of environmental conditions - on user performance (cf. BOSTI studies, 1984, 2001) in three innovative buildings: the Waldorf School in Cologne, the new campus for the University in Koblenz, and the Office Tower of the Deutsche Post World Net AG in Bonn. Performance is measured in terms of (1) Lear ning and Work Efficiency, (2) Well-being, (3) Environmental Control, (4) Social Behavior (the latter just for the school project), and by means of 21 and 16 additional psychological criteria for success of the organization in the cases of the university and the office building, respectively. The study aims, among other things, at reassessing the theoretical concept of 'environmental control' and at making recommendations for both improvement of existing buildings and the design of new projects. Two central questions are: In User-Needs Analysis, what is the difference between the assessment of a building for its current use and its estimated performance in future? Do certain architectural features influence user assessments on the given performance criteria? In the studies, three mapping sentences were developed according to the 'facet approach' (Borg, 1996) as well as two systems to judge the quality of school and office buildings. Using these systems, information was obtained in all three studies to construct questionnaires. In the school study, teachers were asked 139 questions, pupils 86 questions. Responses were obtained from 26 teachers and 122 pupils. For the university, 147 students and 28 faculty members responded to 203 questions. For the office building, 56 student-experts were asked 254 questions. Characteristics of the built environment were rated using the following scale: +2 ☺☺ (very good “at present”, and accordingly very important “in the future”) down to –2 // (very bad “at present”, and very unimportant “in the future”). A general finding was a high and significant co rrelation between the responses for the three main performance criteria in all three case studies, especially for the 'importance for the future' aspect. This supports the conclusion that a perception of higher degree of environmental control by users will lead to an increased sense of well-being and consequently, there will also be a higher expectation of improved work or learning efficiency 'in the future'. The three studies further show for example that users in all three environments desire 'retreat opportunities' which may take the form of student offices in schools, niches and small group seating in classrooms, and sheltered seating in outdoor areas and work tables in the cafeteria for the university. For the offices, users wanted more visual privacy (less transparent office partitions in Combi Offices) for less visual control of their activities by supervisors and co-workers. The relationships found by the studies between the responses on the central performance criteria and the spatial characteristics of the three buildings support the contention that focused improvements in the built environment, especially with respect to features that enhance user control of environmental conditions, will influence users’ well-being as well as work performance and work or learning efficiency in a positive way.

For software engineers, conceptually understanding the tools they are using in the context of their projects is a daily challenge and a prerequisite for complex tasks. Textual explanations and code examples serve as knowledge resources for understanding software languages and software technologies. This thesis describes research on integrating and interconnecting existing knowledge resources, which can then be used to assist with understanding and comparing software languages and software technologies on a conceptual level. We consider the following broad research questions that we later refine: What knowledge resources can be systematically reused for recovering structured knowledge and how? What vocabulary already exists in literature that is used to express conceptual knowledge? How can we reuse the online encyclopedia Wikipedia? How can we detect and report on instances of technology usage? How can we assure reproducibility as the central quality factor of any construction process for knowledge artifacts? As qualitative research, we describe methodologies to recover knowledge resources by i.) systematically studying literature, ii.) mining Wikipedia, iii.) mining available textual explanations and code examples of technology usage. The theoretical findings are backed by case studies. As research contributions, we have recovered i.) a reference semantics of vocabulary for describing software technology usage with an emphasis on software languages, ii.) an annotated corpus of Wikipedia articles on software languages, iii.) insights into technology usage on GitHub with regard to a catalog of pattern and iv.) megamodels of technology usage that are interconnected with existing textual explanations and code examples.

Social media provides a powerful way for people to share opinions and sentiments about a specific topic, allowing others to benefit from these thoughts and feelings. This procedure generates a huge amount of unstructured data, such as texts, images, and references that are constantly increasing through daily comments to related discussions. However, the vast amount of unstructured data presents risks to the information-extraction process, and so decision making becomes highly challenging. This is because data overload may cause the loss of useful data due to its inappropriate presentation and its accumulation. To this extent, this thesis contributed to the field of analyzing and detecting feelings in images and texts. And that by extracting the feelings and opinions hidden in a huge collection of image data and texts on social networks After that, these feelings are classified into positive, negative, or neutral, according to the features of the classified data. The process of extracting these feelings greatly helps in decision-making processes on various topics as will be explained in the first chapter of the thesis. A system has been built that can classify the feelings inherent in the images and texts on social media sites, such as people’s opinions about products and companies, personal posts, and general messages. This thesis begins by introducing a new method of reducing the dimension of text data based on data-mining approaches and then examines the sentiment based on neural and deep neural network classification algorithms. Subsequently, in contrast to sentiment analysis research in text datasets, we examine sentiment expression and polarity classification within and across image datasets by building deep neural networks based on the attention mechanism.

As a multilingual system,Wikipedia provides many challenges for academics and engineers alike. One such challenge is cultural contextualisation of Wikipedia content, and the lack of approaches to effectively quantify it. Additionally, what seems to lack is the intent of establishing sound computational practices and frameworks for measuring cultural variations in the data. Current approaches seem to mostly be dictated by the data availability, which makes it difficult to apply them in other contexts. Another common drawback is that they rarely scale due to a significant qualitative or translation effort. To address these limitations, this thesis develops and tests two modular quantitative approaches. They are aimed at quantifying culture-related phenomena in systems which rely on multilingual user-generated content. In particular, they allow to: (1) operationalise a custom concept of culture in a system; (2) quantify and compare culture-specific content- or coverage biases in such a system; and (3) map a large scale landscape of shared cultural interests and focal points. Empirical validation of these approaches is split into two parts. First, an approach to mapping Wikipedia communities of shared co-editing interests is validated on two large Wikipedia datasets comprising multilateral geopolitical and linguistic editor communities. Both datasets reveal measurable clusters of consistent co-editing interest, and computationally confirm that these clusters correspond to existing colonial, religious, socio economic, and geographical ties. Second, an approach to quantifying content differences is validated on a multilingual Wikipedia dataset, and a multi-platform (Wikipedia and Encyclopedia Britannica) dataset. Both are limited to a selected knowledge domain of national history. This analysis allows, for the first time on the large scale, to quantify and visualise the distribution of historical focal points in the articles on national histories. All results are cross-validated either by domain experts, or external datasets. Main thesis contributions. This thesis: (1) presents an effort to formalise the process of measuring cultural variations in user-generated data; (2) introduces and tests two novel approaches to quantifying cultural contextualisation in multilingual data; (3) synthesises a valuable overview of literature on defining and quantifying culture; (4) provides important empirical insights on the effect of culture on Wikipedia content and coverage; demonstrates that Wikipedia is not contextfree, and these differences should not be treated as noise, but rather, as an important feature of the data. (5) makes practical service contributions through sharing data and visualisations.

Thousands of chemicals from daily use are being discharged from civilization into the water cycle via different pathways. Ingredients of personal care products, detergents, pharmaceuticals, pesticides, and industrial chemicals thus find their way into the aquatic ecosystems and may cause adverse impacts on the ecology. Pharmaceuticals for instance, represent a central group of anthropogenic chemicals, because of their designed potency to interfere with physiological functions in organisms. Ecotoxicological effects from pharmaceutical burden have been verified in the past. Therapeutic groups with pronounced endocrine disrupting potentials such as steroid hormones gain increasing focus in environmental research as it was reported that they cause endocrine disruption in aquatic organisms even when exposed to environmentally relevant concentrations. This thesis considers the comprehensive investigation of the occurrence of corticosteroids and progestogens in wastewater treatment plant (WWTP) effluents and surface waters as well as the elucidation of the fate and biodegradability of these steroid families during activated sludge treatment. For the first goal of the thesis, a robust and highly sensitive analytical method based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) was developed in order to simultaneously determine the occurrence of around 60 mineralocorticoids, glucocorticoids and progestogens in the aquatic environment. A special focus was set to the compound selection due to the diversity of marketed synthetic steroids. Some analytical challenges have been approved by individual approaches regarding sensitivity enhancement and compound stabilities. These results may be important for further research in environmental analysis of steroid hormones. Reliable and low quantification limits are the perquisite for the determination of corticosteroids and progestogens at relevant concentrations due to low consumption volumes and simultaneously low effect-based trigger values. Achieved quantification limits for all target analytes ranged between 0.02 ng/L and 0.5 ng/L in surface water and 0.05 ng/L to 5 ng/L in WWTP effluents. This sensitivity enabled the detection of three mineralocorticoids, 23 glucocorticoids and 10 progestogens within the sampling campaign around Germany. Many of them were detected for the first time in the environment, particularly in Germany and the EU. To the best of our knowledge, this in-depth steroid screening provided a good overview of single steroid burden and allowed for the identification of predominantly steroids of each steroid type analyzed for the first time. The frequent detection of highly potent synthetic steroids (e.g. triamcinolone acetonide, clobetasol propionate, betamethasone valerate, dienogest, cyproterone acetate) highlighted insufficient removal during conventional Summary wastewater treatment and indicated the need for regulation to control their emission since the steroid concentrations were found to be above the reported effect-based trigger values for biota. Overall, the study revealed reliable environmental data of poorly or even not analyzed steroids. The results complement the existing knowledge in this field but also providednew information which can beused particularly for compound prioritization in ecotoxicological research and environmental analysis. Based on the data obtained from the monitoring campaign, incubation experiments were conducted to enable the comparison of the biodegradability and transformation processes in activated sludge treatment for structure-related steroids under aerobic and standardized experimental conditions. The compounds were accurately selected to cover manifold structural moieties of commonly used glucocorticoids, including non-halogenated and halogenated steroids, their mono- and diesters, and several acetonide-type steroids. This approach allowed for a structure-based interpretation of the results. The obtained biodegradation rate constants suggested large variations in the biodegradability (half-lifes ranged from < 0.5 h to > 14 d). An increasing stability was identified in the order from non-halogenated steroids (e.g. hydrocortisone), over 9α-halogenated steroids (e.g. betamethasone), to C17-monoesters (e.g. betamethasone 17-valerate, clobetasol propionate), and finally to acetonides (e.g. triamcinolone acetonide), thus suggesting a strong relationship of the biodegradability with the glucocorticoid structure. Some explanations for this behavior have been received by identifying the transformation products (TPs) and elucidating individual transformation pathways. The results revealed the identification of the likelihood of transformation reactions depending on the chemical steroid structure for the first time. Among the identified TPs, the carboxylates (e.g. TPs of fluticasone propionate, triamcinolone acetonide) have been shown persistency in the subsequent incubation experiments. The newly identified TPs furthermore were frequently detected in the effluents of full-scale wastewater treatment plants. These findings emphasized i) the transferability of the lab-scale degradation experiments to real world and that ii) insufficient removals may cause adverse effects in the aquatic environment due to the ability of the precursor steroids and TPs to interact with the endocrine system in biota. For the last goal, the conceptual study for glucocorticoids was applied to progestogens. Here, two sub-types of the steroid family frequently used for hormonal contraception were selected (17α-hydroxyprogesterone and 19-norstestosterone type). The progestogens showed a fast and complete degradation within six hours, and thus empathizes pronounced biodegradability. However, cyproterone acetate and dienogest Summary have been found to be more recalcitrant in activated sludge treatment. This was consistent with their ubiquitously occurrence during the previous monitoring campaign. The elucidation of TPs again revealed some crucial information regarding the observed behavior and highlighted furthermore the formation of hazardous TPs. It was shown that 19-nortestosterone type steroids are able to undergo aromatization at ring A in contact with activated sludge, leading to the formation of estrogen-like TPs with a phenolic moiety at ring A. In the case of norethisterone the formation of 17α-ethinylestradiol was confirmed, which is a well-known potent synthetic estrogen with elevated ecotoxicological potency. Thus, the results indicated for the very first time an unknown source of estrogenic compounds, particularly for 17α-ethinylestradiol. In conclusion, some steroids were found to be very stable in activated sludge treatment, others degrade well, and others which do degrade but predominantly to active TPs depending on their chemical structure. Fluorinated acetal steroids such as triamcinolone acetonide and fluocinolone acetonide are poorly biodegradable, which is reflected in high concentrations detected ubiquitously in WWTP effluents. Endogenous steroids and their most related synthetic once such as hydrocortisone, prednisolone or 17α-hydroxyprogesterone are readily biodegradable. Regardless their high influent concentrations, they are almost completely removed in conventional WWTPs. Steroids between this range have been found to form elevated quantities of TPs which are partially still active, which particularly the case for betamethasone, fluticasone propionate, cyproterone acetate or dienogest. The thesis illustrates the need for an extensive evaluation of the environmental risks and carried out that corticosteroids and progestogens merit more attention in environmental regulatory and research than it is currently the case

Human action recognition from a video has received growing attention in computer vision and has made significant progress in recent years. Action recognition is described as a requirement to decide which human actions appear in videos. The difficulties involved in distinguishing human actions are due to the high complexity of human behaviors as well as appearance variation, motion pattern variation, occlusions, etc. Many applications use human action recognition on captured video from cameras, resulting in video surveillance systems, health monitoring, human-computer interaction, and robotics. Action recognition based on RGB-D data has increasingly drawn more attention to it in recent years. RGB-D data contain color (Red, Green, and Blue (RGB)) and depth data that represent the distance from the sensor to every pixel in the object (object point). The main problem that this thesis deals with is how to automate the classification of specific human activities/actions through RGB-D data. The classification process of these activities utilizes a spatial and temporal structure of actions. Therefore, the goal of this work is to develop algorithms that can distinguish these activities by recognizing low-level and high-level activities of interest from one another. These algorithms are developed by introducing new features and methods using RGB-D data to enhance the detection and recognition of human activities. In this thesis, the most popular state-of-the-art techniques are reviewed, presented, and evaluated. From the literature review, these techniques are categorized into hand-crafted features and deep learning-based approaches. The proposed new action recognition framework is based on these two categories that are approved in this work by embedding novel methods for human action recognition. These methods are based on features extracted from RGB-D data that are evaluated using machine learning techniques. The presented work of this thesis improves human action recognition in two distinct parts. The first part focuses on improving current successful hand-crafted approaches. It contributes into two significant areas of state-of-the-art: Execute the existing feature detectors, and classify the human action in the 3D spatio-temporal domains by testing a new combination of different feature representations. The contributions of this part are tested based on machine learning techniques that include unsupervised and supervised learning to evaluate this suitability for the task of human action recognition. A k-means clustering represents the unsupervised learning technique, while the supervised learning technique is represented by: Support Vector Machine, Random Forest, K-Nearest Neighbor, Naive Bayes, and Artificial Neural Networks classifiers. The second part focuses on studying the current deep-learning-based approach and how to use it with RGB-D data for the human action recognition task. As the first step of each contribution, an input video is analyzed as a sequence of frames. Then, pre-processing steps are applied to the video frames, like filtering and smoothing methods to remove the noisy data from each frame. Afterward, different motion detection and feature representation methods are used to extract features presented in each frame. The extracted features are represented by local features, global features, and feature combination besides deep learning methods, e.g., Convolutional Neural Networks. The feature combination achieves an excellent accuracy performance that outperforms other methods on the same RGB-D datasets. All the results from the proposed methods in this thesis are evaluated based on publicly available datasets, which illustrate that using spatiotemporal features can improve the recognition accuracy. The competitive experimental results are achieved overall. In particular, the proposed methods can be better applied to the test set compared to the state-of-the-art methods using the RGB-D datasets.

This thesis addresses the reduced basis methods for parametrized quasilinear elliptic and parabolic partial differential equations with strongly monotone differential operator. It presents all of the ingredients of the reduced basis method: basis generation for reduced basis approximation, certification of the approximation error by suitable a-posteriori error control and an Offine-Online decomposition. The methodology is further applied to the magnetostatic and magnetoquasistatic approximations of Maxwell’s equations and its validity is confirmed by numerical examples.

Vertebrate biodiversity is rapidly decreasing worldwide with amphibians being the most endangered vertebrate group. In the EU, 21 of 89 amphibian species are recognized as being endangered. The intensively used European agricultural landscape is one of the major causes for these declines. As agriculture represents an essential habitat for amphibians, exposure to pesticides can have adverse effects on amphibian populations. Currently, the European risk assessment of pesticides for vertebrates requires specific approaches for fish regarding aquatic vertebrate toxicity and birds as well as mammals for terrestrial vertebrate toxicity but does not address the unique characteristics of amphibians. Therefore, the overall goal of this thesis was to investigate the ecotoxicological effects of pesticides on Central European anuran amphibians. For this, effects on aquatic and terrestrial amphibian life stages as well as on reproduction were investigated. Then, in anticipation of a risk assessment of pesticides for amphibians, this thesis discussed potential regulatory risk assessment approaches. For the investigated pesticides and amphibian species, it was observed that the acute aquatic toxicity of pesticides can be addressed using the existing aquatic risk assessment approach based on fish toxicity data. However, lethal as well as sublethal effects were observed in terrestrial juveniles after dermal exposure to environmentally realistic pesticide concentrations, which cannot be covered using an existing risk assessment approach. Therefore, pesticides should also be evaluated for potential terrestrial toxicity using risk assessment tools before approval. Additionally, effects of co-formulants and adjuvants of pesticides need specific consideration in a future risk assessment as they can increase toxicity of pesticides to aquatic and terrestrial amphibian stages. The chronic duration of combined aquatic and terrestrial exposure was shown to affect amphibian reproduction. Currently, such effects cannot be captured by the existing risk assessment as data involving field scenarios analysing effects of multiple pesticides on amphibian reproduction are too rare to allow comparison to data of other terrestrial vertebrates such as birds and mammals. In the light of these findings, future research should not only address acute and lethal effects, but also chronic and sublethal effects on a population level. As pesticide exposure can adversely affect amphibian populations, their application should be considered even more carefully to avoid further amphibian declines. Overall, this thesis emphasizes the urgent need for a protective pesticide risk assessment for amphibians to preserve and promote stable amphibian populations in agricultural landscapes.