Real-time operating systems for mixed-criticality systems must support different types of software, such as real-time applications and general purpose applications, and, at the same time, must provide strong spatial and temporal isolation between independent software components. Therefore, state-of-the-art real-time operating systems focus mainly on predictability and bounded worst-case behavior. However, general purpose operating systems such as Linux often feature more efficient---but less deterministic---mechanisms that significantly improve the average execution time. This thesis addresses the combination of the two contradicting requirements and shows thread synchronization mechanisms with efficient average-case behavior, but without sacrificing predictability and worst-case behavior. This thesis explores and evaluates the design space of fast paths in the implementation of typical blocking synchronization mechanisms, such as mutexes, condition variables, counting semaphores, barriers, or message queues. The key technique here is to avoid unnecessary system calls, as system calls have high costs compared to other processor operations available in user space, such as low-level atomic synchronization primitives. In particular, the thesis explores futexes, the state-of-the-art design for blocking synchronization mechanisms in Linux that handles the uncontended case of thread synchronization by using atomic operations in user space and calls into the kernel only to suspend and wake up threads. The thesis also proposes non-preemptive busy-waiting monitors that use an efficient priority ceiling mechanism to prevent the lock holder preemption problem without using system calls, and according low-level kernel primitives to construct efficient wait and notify operations. The evaluation shows that the presented approaches improve the average performance comparable to state-of-the-art approaches in Linux. At the same time, a worst-case timing analysis shows that the approaches only need constant or bounded temporal overheads at the operating system kernel level. Exploiting these fast paths is a worthwhile approach when designing systems that not only have to fulfill real-time requirements, but also best-effort workloads.

Based on dual process models of information processing, the present research addressed how explicit disgust sensitivity is re-adapted according to implicit disgust sensitivity via self-perception of automatic behavioral cues. Contrary to preceding studies (Hofmann, Gschwendner, & Schmitt, 2009) that concluded that there was a "blind spot" for self- but not for observer perception of automatic behavioral cues, in the present research, a re-adaption process was found for self-perceivers and observers. In Study 1 (N = 75), the predictive validity of an indirect disgust sensitivity measure was tested with a double-dissociation strategy. Study 2 (N = 117) reinvestigated the hypothesis that self-perception of automatic behavioral cues, predicted by an indirect disgust sensitivity measure, led to a re-adaption of explicit disgust sensitivity measures. Using a different approach from Hofmann et al. (2009), the self-perception procedure was modified by (a) feeding back the behavior several times while a small number of cues had to be rated for each feedback condition, (b) using disgust sensitivity as a domain with clearly unequivocal cues of automatic behavior (facial expression, body movements) and describing these cues unambiguously, and (c) using a specific explicit disgust sensitivity measure in addition to a general explicit disgust sensitivity measure. In Study 3 (N = 130), the findings of Study 2 were replicated and display rules and need for closure as moderator effects of predictive validity and cue utilization were additionally investigated. The moderator effects give hints that both displaying a disgusted facial expression and self-perception of one- own disgusted facial expression are subject to a self-serving bias, indicating that facial expression may not be an automatic behavior. Practical implications and implications for future research are discussed.

Wikipedia is the biggest, free online encyclopaedia that can be expanded by any-one. For the users, who create content on a specific Wikipedia language edition, a social network exists. In this social network users are categorised into different roles. These are normal users, administrators and functional bots. Within the networks, a user can post reviews, suggestions or send simple messages to the "talk page" of another user. Each language in the Wikipedia domain has this type of social network. In this thesis characteristics of the three different roles are analysed in order to learn how they function in one language network of Wikipedia and apply them to another Wikipedia network to identify bots. Timestamps from created posts are analysed to reveal noticeable characteristics referring to continuous messages, message rates and irregular behaviour of a user are discovered. Through this process we show that there exist differences between the roles for the mentioned characteristics.

With the appearance of modern virtual reality (VR) headsets on the consumer market, there has been the biggest boom in the history of VR technology. Naturally, this was accompanied by an increasing focus on the problems of current VR hardware. Especially the control in VR has always been a complex topic. One possible solution is the Leap Motion, a hand tracking device that was initially developed for desktop use, but with the last major software update it can be attached to standard VR headsets. This device allows very precise tracking of the user’s hands and fingers and their replication in the virtual world. The aim of this work is to design virtual user interfaces that can be operated with the Leap Motion to provide a natural method of interaction between the user and the VR environment. After that, subject tests are performed to evaluate their performance and compare them to traditional VR controllers.

While the 1960s and 1970s still knew permanent education (Council of Europe), recurrent education (OECD) and lifelong education (UNESCO), over the past 20 years, lifelong learning has become the single emblem for reforms in (pre-) primary, higher and adult education systems and international debates on education. Both highly industrialized and less industrialized countries embrace the concept as a response to the most diverse economic, social and demographic challenges - in many cases motivated by international organizations (IOs). Yet, literature on the nature of this influence, the diffusion of the concept among IOs and their understanding of it is scant and usually focuses on a small set of actors. Based on longitudinal data and a large set of education documents, the work identifies rapid diffusion of the concept across a heterogeneous, expansive and dynamic international field of 88 IOs in the period 1990-2013, which is difficult to explain with functionalist accounts. Based on the premises of world polity theory, this paper argues that what diffuses resembles less the bundle of systemic reforms usually associated with the concept in the literature and more a surprisingly detailed model of a new actor " the lifelong learner.

Foliicolous lichens are one of the most abundant epiphytes in tropical rainforests and one of the few groups of organisms that characterize these forests. Tropical rainforests are increasingly affected by anthropogenic disturbance resulting in forest destruction and degradation. However, not much is known on the effects of anthropogenic disturbance on the diversity of foliicolous lichens. Understanding such effects is crucial for the development of appropriate measures for the conservation of such organisms. In this study, foliicolous lichens diversity was investigated in three tropical rainforests in East Africa. Godere Forest in Southwest Ethiopia is a transitional rainforest with a mixture of Afromontane and Guineo-Congolian species. The forest is secondary and has been affected by shifting cultivation, semi-forest coffee management and commercial coffee plantation. Budongo Forest in West Uganda is a Guineo-Congolian rainforest consisting of primary and secondary forests. Kakamega Forest in western Kenya is a transitional rainforest with a mixture of Guineo-Congolian and Afromontane species. The forest is a mosaic of near-primary forest, secondary forests of different seral stages, grasslands, plantations, and natural glades.

This thesis addresses the automated identification and localization of a time-varying number of objects in a stream of sensor data. The problem is challenging due to its combinatorial nature: If the number of objects is unknown, the number of possible object trajectories grows exponentially with the number of observations. Random finite sets are a relatively new theory that has been developed to derive at principled and efficient approximations. It is based around set-valued random variables that contain an unknown number of elements which appear in arbitrary order and are themselves random. While extensively studied in theory, random finite sets have not yet become a leading paradigm in practical computer vision and robotics applications. This thesis explores random finite sets in visual tracking applications. The first method developed in this thesis combines set-valued recursive filtering with global optimization. The problem is approached in a min-cost flow network formulation, which has become a standard inference framework for multiple object tracking due to its efficiency and optimality. A main limitation of this formulation is a restriction to unary and pairwise cost terms. This circumstance makes integration of higher-order motion models challenging. The method developed in this thesis approaches this limitation by application of a Probability Hypothesis Density filter. The Probability Hypothesis Density filter was the first practically implemented state estimator based on random finite sets. It circumvents the combinatorial nature of data association itself by propagation of an object density measure that can be computed efficiently, without maintaining explicit trajectory hypotheses. In this work, the filter recursion is used to augment measurements with an additional hidden kinematic state to be used for construction of more informed flow network cost terms, e.g., based on linear motion models. The method is evaluated on public benchmarks where a considerate improvement is achieved compared to network flow formulations that are based on static features alone, such as distance between detections and appearance similarity. A second part of this thesis focuses on the related task of detecting and tracking a single robot operator in crowded environments. Different from the conventional multiple object tracking scenario, the tracked individual can leave the scene and later reappear after a longer period of absence. Therefore, a re-identification component is required that picks up the track on reentrance. Based on random finite sets, the Bernoulli filter is an optimal Bayes filter that provides a natural representation for this type of problem. In this work, it is shown how the Bernoulli filter can be combined with a Probability Hypothesis Density filter to track operator and non-operators simultaneously. The method is evaluated on a publicly available multiple object tracking dataset as well as on custom sequences that are specific to the targeted application. Experiments show reliable tracking in crowded scenes and robust re-identification after long term occlusion. Finally, a third part of this thesis focuses on appearance modeling as an essential aspect of any method that is applied to visual object tracking scenarios. Therefore, a feature representation that is robust to pose variations and changing lighting conditions is learned offline, before the actual tracking application. This thesis proposes a joint classification and metric learning objective where a deep convolutional neural network is trained to identify the individuals in the training set. At test time, the final classification layer can be stripped from the network and appearance similarity can be queried using cosine distance in representation space. This framework represents an alternative to direct metric learning objectives that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large scale person re-identification datasets where competitive results are achieved overall. In particular, the proposed method better generalizes to the test set compared to a network trained with the well-established triplet loss.

The Internet of Things is still one of the most relevant topics in the field of economics and research powered by the increasing demand of innovative services. Cost reductions in manufacturing of IoT hardware and the development of completely new communication ways has led to the point of bil-lions of devices connected to the internet. But in order to rule this new IoT landscape a standardized solution to conquer these challenges must be developed, the IoT Architecture. This thesis examines the structure, purpose and requirements of IoT Architecture Models in the global IoT landscape and proposes an overview across the selected ones. For that purpose, a struc-tured literature analysis on this topic is conducted within this thesis, including an analysis on three existing research approaches trying to frame this topic and a tool supported evaluation of IoT Archi-tecture literature with over 200 accessed documents. Furthermore, a coding of literature with the help of the specialised coding tool ATLAS.ti 8 is conduct-ed on 30 different IoT Architecture Models. In a final step these Architecture Models are categorized and compared to each other showing that the environment of IoT and its Architectures gets even more complex the further the research goes.

As Enterprise 2.0 (E2.0) initiatives are gradually moving out of the early experimentation phase it is time to focus greater attention on examining the structures, processes and operations surrounding E2.0 projects. In this paper we present the findings of an empirical study to investigate and understand the reasons for initiating E2.0 projects and the benefits being derived from them. Our study comprises seven in-depth case studies of E2.0 implementations. We develop a classification and means of visualising the scope of E2.0 initiatives and use these methods to analyse and compare projects. Our findings indicate a wide range of motivations and combinations of technology in use and show a strong emphasis towards the content management functionality of E2.0 technologies.

Agricultural land-use may lead to brief pulse exposures of pesticides in edge-of-field streams, potentially resulting in adverse effects on aquatic macrophytes, invertebrates and ecosystem functions. The higher tier risk assessment is mainly based on pond mesocosms which are not designed to mimic stream-typical conditions. Relatively little is known on exposure and effect assessment using stream mesocosms. Thus the present thesis evaluates the appliacability of the stream mesocosms to mimic stream-typical pulse exposures, to assess resulting effects on flora and fauna and to evaluate aquatic-terrestrial food web coupling. The first objective was to mimic stream-typical pulse exposure scenarios with different durations (≤ 1 to ≥ 24 hours). These exposure scenarios established using a fluorescence tracer were the methodological basis for the effect assessment of an herbicide and an insecticide. In order to evaluate the applicability of stream mesocosms for regulatory purposes, the second objective was to assess effects on two aquatic macrophytes following a 24-h pulse exposure with the herbicide iofensulfuron-sodium (1, 3, 10 and 30 µg/L; n = 3). Growth inhibition of up to 66 and 45% was observed for the total shoot length of Myriophyllum spicatum and Elodea canadensis, respectively. Recovery of this endpoint could be demonstrated within 42 days for both macrophytes. The third objective was to assess effects on structural and functional endpoints following a 6-h pulse exposure of the pyrethroid ether etofenprox (0.05, 0.5 and 5 µg/L; n = 4). The most sensitive structural (abundance of Cloeon simile) and functional (feeding rates of Asellus aquaticus) endpoint revealed significant effects at 0.05 µg/L etofenprox. This concentration was below field-measured etofenprox concentrations and thus suggests that pulse exposures adversely affect invertebrate populations and ecosystem functions in streams. Such pollutions of streams may also result in decreased emergence of aquatic insects and potentially lead to an insect-mediated transfer of pollutants to adjacent food webs. Test systems capable to assess aquatic-terrestrial effects are not yet integrated in mesocosm approaches but might be of interest for substances with bioaccumulation potential. Here, the fourth part provides an aquatic-terrestrial model ecosystem capable to assess cross-ecosystem effects. Information on the riparian food web such as the contribution of aquatic (up to 71%) and terrestrial (up to 29%) insect prey to the diet of the riparian spider Tetragnatha extensa was assessed via stable isotope ratios (δ13C and δ15N). Thus, the present thesis provides the methodological basis to assess aquatic-terrestrial pollutant transfer and effects on the riparian food web. Overall the results of this thesis indicate, that stream mesocosms can be used to mimic stream-typical pulse exposures of pesticides, to assess resulting effects on macrophytes and invertebrates within prospective environmental risk assessment (ERA) and to evaluate changes in riparian food webs.

More than 10,000 organic chemicals such as pharmaceuticals, ingredients of personal care products and biocides are ubiquitously used in every day life. After their application, many of these chemicals enter the domestic sewer. Research has shown that conventional biological wastewater treatment in municipal wastewater treatment plants (WWTPs) is an insufficient barrier for the release of most of these anthropogenic chemicals into the receiving waters. This bears unforeseen risks for aquatic wildlife and drinking water resources. Especially for recently introduced and/or detected compounds (so called emerging micropollutants), there is a growing need to investigate the occurrence and fate in WWTPs. In order to get a comprehensive picture on the behavior in municipal wastewater treatment, the following groups of emerging organic micropollutants, spanning a broad range of applications and physico-chemical properties, were selected as target compounds: pharmaceuticals (beta blockers, psycho-active drugs), UV-filters, vulcanization accelerators (benzothiazoles), biocides (anti-dandruffs, preservatives, disinfectants) and pesticides (phenylurea and triazine herbicides).

To construct a business process model manually is a highly complex and error-prone task which takes a lot of time and deep insights into the organizational structure, its operations and business rules. To improve the output of business analysts dealing with this process, different techniques have been introduced by researchers to support them during construction with helpful recommendations. These supporting recommendation systems vary in their way of what to recommend in the first place as well as their calculations taking place under the hood to recommend the most fitting element to the user. After a broad introduction into the field of business process modeling and its basic recommendation structures, this work will take a closer look at diverse proposals and descriptions published in current literature regarding implementation strategies to effectively and efficiently assist modelers during their business process model creation. A critical analysis of presentations in the selected literature will point out strengths and weaknesses of their approaches, studies and descriptions of those. As a result, the final concept matrix in this work will give a precise and helpful overview about the key features and recommendation methods used and implemented in previous research studies to pinpoint an entry into future works without the downsides already spotted by fellow researchers.

Groundwater is essential for the provision of drinking water in many areas around the world. The ecosystem services provided by groundwater-related organisms are crucial for the quality of groundwater-bearing aquifers. Therefore, if remediation of contaminated groundwater is necessary, the remediation method has to be carefully selected to avoid risk-risk trade-offs that might impact these valuable ecosystems. In the present thesis, the ecotoxicity of the in situ remediation agent Carbo-Iron (a composite of zero valent nano-iron and active carbon) was investigated, an estimation of its environmental risk was performed, and the risk and benefit of a groundwater remediation with Carbo-Iron were comprehensively analysed. At the beginning of the work on the present thesis, a sound assessment of the environmental risks of nanomaterials was impeded by a lack of guidance documents, resulting in many uncertainties on selection of suitable test methods and a low comparability of test results from different studies with similar nanomaterials. The reasons for the low comparability were based on methodological aspects of the testing procedures before and during the toxicity testing. Therefore, decision trees were developed as a tool to systematically decide on ecotoxicity test procedures for nanomaterials. Potential effects of Carbo-Iron on embryonic, juvenile and adult life stages of zebrafish (Danio rerio) and the amphipod Hyalella azteca were investigated in acute and chronic tests. These tests were based on existing OECD and EPA test guidelines (OECD, 1992a, 2013a, 2013b; US EPA, 2000) to facilitate the use of the obtained effect data in the risk assessment. Additionally, the uptake of particles into the test organisms was investigated using microscopic methods. In zebrafish embryos, effects of Carbo-Iron on gene expression were investigated. The obtained ecotoxicity data were complemented by studies with the waterflea Daphnia magna, the algae Scenedesmus vacuolatus, larvae of the insect species Chironomus riparius and nitrifying soil microorganisms. In the fish embryo test, no passage of Carbo-Iron particles into the perivitelline space or the embryo was observed. In D. rerio and H. azteca, Carbo-Iron was detected in the gut at the end of exposure, but no passage into the surrounding tissue was detected. Carbo-Iron had no significant effect on soil microorganisms and on survival and growth of fish. However, it had significant effects on the growth, feeding rate and reproduction of H. azteca and on survival and reproduction in D. magna. Additionally, the development rate of C. riparius and the cell volume of S. vacuolatus were negatively influenced. A predicted no effect concentration of 0.1 mg/L was derived from the ecotoxicity studies based on the no-effect level determined in the reproduction test with D. magna and an assessment factor of 10. It was compared to measured and modelled environmental concentrations for Carbo-Iron after application to an aquifer contaminated with chlorohydrocarbons in a field study. Based on these concentrations, risk quotients were derived. Additionally, the overall environmental risk before and after Carbo-Iron application was assessed to verify whether the chances for a risk-risk trade-off by the remediation of the contaminated site could be minimized. With the data used in the present study, a reduced environmental risk was identified after the application of Carbo-Iron. Thus, the benefit of remediation with Carbo-Iron outweighs potential negative effects on the environment.

Pelagic oxyclines, the transition zone between oxygen rich surface waters and oxygen depleted deep waters, are a common characteristic of eutrophic lakes during summer stratification. They can have tremendous effects on the biodiversity and the ecosystem functioning of lakes and, to add insult to injury, are expected to become more frequent and more pronounced as climate warming progresses. On these grounds, this thesis endeavors to advance the understanding of formation, persistence, and consequences of pelagic oxyclines: We test, whether the formation of metalimnetic oxygen minima is intrinsically tied to a locally enhanced oxygen consuming process, investigate the relative importance of vertical physical oxygen transport and biochemical oxygen consumption for the persistence of pelagic oxyclines, and finally assess their potential consequences for whole lake cycling. To pursue these objectives, the present thesis nearly exclusively resorts to in situ measurements. Field campaigns were conducted at three lakes in Germany featuring different types of oxyclines and resolved either a short (hours to days) or a long (weeks to months) time scale. Measurements comprised temperature, current velocity, and concentrations of oxygen and reduced substances in high temporal and vertical resolution. Additionally, vertical transport was estimated by applying the eddy correlation technique within the pelagic region for the first time. The thesis revealed, that the formation of metalimnetic oxygen minima does not necessarily depend on locally enhanced oxygen depletion, but can solely result from gradients and curvatures of oxygen concentration and depletion and their relative position to each other. Physical oxygen transport was found to be relevant for oxycline persistence when it considerably postponed anoxia on a long time scale. However, its influence on oxygen dynamics was minor on short time scales, although mixing and transport were highly variable. Biochemical consumption always dominated the fate of oxygen in pelagic oxyclines. It was primarily determined by the oxidative breakdown of organic matter originating from the epilimnion, whereas in meromictic lakes, the oxidation of reduced substances dominated. Beyond that, the results of the thesis emphasize that pelagic oxyclines can be a hotspot of mineralization and, hence, short-circuit carbon and nutrient cycling in the upper part of the water column. Overall, the present thesis highlights the importance of considering physical transport as well as biochemical cycling in future studies.

Accurate snow simulation is key to capture snow's iconic visuals. Intricate methods exist that attempt to grasp snow behaviour in a holistic manner. Computational complexity prevents them from reaching real-time performance. This thesis presents three techniques making use of the GPU that focus on the deformation of a snow surface in real-time. The approaches are examined by their ability to scale with an increasing number of deformation actors and their visual portrayal of snow deformation. The findings indicate that the approaches maintain real-time performance well into several hundred individual deformation actors. However, these approaches each have their individual restrictions handicapping the visual results. An experimental approach is to combine the techniques at reduced deformation actor count to benefit from the detailed, merged deformation pattern.

Eye-trackers have been used in the past to identify visual foci in images, find task-related image regions, or localize affective regions in images. However, they have not been used for identifying specific objects in images. In this paper, we investigate whether it is possible to assign image regions showing specific objects with tags describing these objects by analyzing the users' gaze paths. To this end, we have conducted an experiment with 20 subjects viewing 50 image-tag-pairs each. We have compared the tag-to-region assignments for nine existing and four new fixation measures. In addition, we have investigated the impact of extending region boundaries, weighting small image regions, and the number of subjects viewing the images. The paper shows that a tag-to-region assignment with an accuracy of 67% can be achieved by using gaze information. In addition, we show that multiple regions on the same image can be differentiated with an accuracy of 38%.

This dissertation provides an interdisciplinary contribution to the project ReGLaN-Health & Logistics. ReGLaN-Health & Logistics, is an international cooperation deriving benefits from the capabilities of scientists working on different fields. The aim of the project is the development of a socalled SDSS that supports decision makers working within health systems with a special focus on rural areas. In this dissertation, one important component for the development of the DSS named EWARS is proposed and described in detail. This component called SPATTB is developed with the intention of dealing with spatial data, i.e. data with additional geocoded information with regard to the special requirements of the EWARS.rnrnAn important component in the process of developing the EWARS is the concept of GIS. Classically, geocoded information with a vectorial character numerically describing spatial phenomena is managed and processed in a GIS. For the development of the EWARS, the manageability of the type of data exemplarily given by (x,y,o) with coordinates x,y ) and Ozon-concentration o is not sufficient. It is described, that the manageable data has to be extended to data of type (x,y,f ), where (x,y) are the geocoded information, but where f is not only a numerical value but a functional description of a certain phenomenom. An example for the existence and appearance of that type of data is the geocoded information about the variation of the Ozon-concentration in time or depending on temperature. A knowledge-base as important subsystem of DSS containing expert knowledge is mentioned. This expert-knowledge can be made manageable when using methods from the field of fuzzy logic. Thereby mappings, socalled fuzzy-sets, are generated. Within the EWARS, these mappings will be used with respect to additional geocoded data. The knowledge about the geocoded mapping information only at a finite set of locations (x,y) associated with mapping information f is not sufficient in applications that need continuous statements in a certain geographical area. To provide a contribution towards solving this problem, methods from the field of computer geometry and CAD, so-called Bezier-methods, are used for interpolating this geocoded mapping information. Classically, these methods operates on vectors a the multidimensional vector-space whose elements contain real-valued components but in terms of dealing with mapping information, there has to be an extension on topological vector spaces since mapping spaces can be defined as such spaces. This builds a new perspective and possibility in the application of these methods. Therefore, the according algorithms have to be extended; this work is presented. The field of Artificial Neural Networks plays an important role for the processing and management of the data within the EWARS, where features of biological processes and structures are modeled and implemented as algorithms. Generally, the developed methods can be divided as usable in terms of interpolation or approximation functional coherences and in such being applicable to classification problems. In this dissertation one method from each type is regarded in more detailed. Thereby, the classical algorithms of the so-called Backpropagation-Networks for approximation and the Kohonen-Networks for classification are described. Within the thesis, an extension of these algorithms is then proposed using coherences from mathematical measure-theory and approximation theory. The mentioned extension of these algorithms is based on a preprocessing of the mapping data using integration methods from measure theory.

World’s ecosystems are under great pressure satisfying anthropogenic demands, with freshwaters being of central importance. The Millennium Ecosystem Assessment has identified anthropogenic land use and associated stressors as main drivers in jeopardizing stream ecosystem functions and the biodiversity supported by freshwaters. Adverse effects on the biodiversity of freshwater organisms, such as macroinvertebrates, may propagate to fundamental ecosystem functions, such as organic matter breakdown (OMB) with potentially severe consequences for ecosystem services. In order to adequately protect and preserve freshwater ecosystems, investigations regarding potential and observed as well as direct and indirect effects of anthropogenic land use and associated stressors (e.g. nutrients, pesticides or heavy metals) on ecosystem functioning and stream biodiversity are needed. While greater species diversity most likely benefits ecosystem functions, the direction and magnitude of changes in ecosystem functioning depends primarily on species functional traits. In this context, the functional diversity of stream organisms has been suggested to be a more suitable predictor of changes in ecosystem functions than taxonomic diversity. The thesis aims at investigating effects of anthropogenic land use on (i) three ecosystem functions by anthropogenic toxicants to identify effect thresholds (chapter 2), (ii) the organic matter breakdown by three land use categories to identify effects on the functional level (chapter 3) and (iii)on the stream community along an established land-use gradient to identify effects on the community level. In chapter 2, I reviewed the literature regarding pesticide and heavy metal effects on OMB, primary production and community respiration. From each reviewed study that met inclusion criteria, the toxicant concentration resulting in a reduction of at least 20% in an ecosystem function was standardized based on laboratory toxicity data. Effect thresholds were based on the relationship between ecosystem functions and standardized concentration-effect relationships. The analysis revealed that more than one third of pesticide observations indicated reductions in ecosystem functions at concentrations that are assumed being protective in regulation. However, high variation within and between studies hampered the derivation of a concentration-effect relationship and thus effect thresholds. In chapter 3, I conducted a field study to determine the microbial and invertebrate-mediated OMB by deploying fine and coarse mesh leaf bags in streams with forested, agricultural, vinicultural and urban riparian land use. Additionally, physicochemical, geographical and habitat parameters were monitored to explain potential differences in OMB among land use types and sites. Regarding results, only microbial OMB differed between land use types. The microbial OMB showed a negative relationship with pH while the invertebrate-mediated OMB was positively related to tree cover. OMB responded to stressor gradients rather than directly to land use. In chapter 4, macroinvertebrates were sampled in concert with leaf bag deployment and after species identification (i) the taxonomic diversity in terms of Simpson diversity and total taxonomic richness (TTR) and (ii) the functional diversity in terms of bio-ecological traits and Rao’s quadratic entropy was determined for each community. Additionally, a land-use gradient was established and the response of the taxonomic and functional diversity of invertebrate communities along this gradient was investigated to examine whether these two metrics of biodiversity are predictive for the rate of OMB. Neither bio-ecological traits nor the functional diversity showed a significant relationship with OMB. Although, TTR decreased with increasing anthropogenic stress and also the community structure and 26 % of bio-ecological traits were significantly related to the stress gradient, any of these shifts propagated to OMB. Our results show that the complexity of real-world situations in freshwater ecosystems impedes the effect assessment of chemicals and land use for functional endpoints, and consequently our potential to predict changes. We conclude that current safety factors used in chemical risk assessment may not be sufficient for pesticides to protect functional endpoints. Furthermore, simplifying real-world stressor gradients into few land use categories was unsuitable to predict and quantify losses in OMB. Thus, the monitoring of specific stressors may be more relevant than crude land use categories to detect effects on ecosystem functions. This may, however, limit the large scale assessment of the status of OMB. Finally, despite several functional changes in the communities the functional diversity over several trait modalities remained similar. Neither taxonomic nor functional diversity were suitable predictors of OMB. Thus, when understanding anthropogenic impacts on the linkage between biodiversity and ecosystem functioning is of main interest, focusing on diversity metrics that are clearly linked to the stressor in question (Jackson et al. 2016) or integrating taxonomic and functional metrics (Mondy et al., 2012) might enhance our predictive capacity.

Autonomous systems such as robots already are part of our daily life. In contrast to these machines, humans an react appropriately to their counterparts. People can hear and interpret human speech, and interpret facial expressions of other people. This thesis presents a system for automatic facial expression recognition with emotion mapping. The system is image-based and employs feature-based feature extraction. This thesis analyzes the common steps of an emotion recognition system and presents state-of-the-art methods. The approach presented is based on 2D features. These features are detected in the face. No neutral face is needed as reference. The system extracts two types of facial parameters. The first type consists of distances between the feature points. The second type comprises angles between lines connecting the feature points. Both types of parameters are implemented and tested. The parameters which provide the best results for expression recognition are used to compare the system with state-of-the-art approaches. A multiclass Support Vector Machine classifies the parameters. The results are codes of Action Units of the Facial Action Coding System. These codes are mapped to a facial emotion. This thesis addresses the six basic emotions (happy, surprised, sad, fearful, angry, and disgusted) plus the neutral facial expression. The system presented is implemented in C++ and is provided with an interface to the Robot Operating System (ROS).

The term “Software Chrestomaty” is defined as a collection of software systems meant to be useful in learning about or gaining insight into software languages, software technologies, software concepts, programming, and software engineering. 101companies software chrestomathy is a community project with the attributes of a Research 2.0 infrastructure for various stakeholders in software languages and technology communities. The core of 101companies combines a semantic wiki and confederated open source repositories. We designed and developed an integrated ontology-based knowledge base about software languages and technologies. The knowledge is created by the community of contributors and supported with a running example and structured documentation. The complete ecosystem is exposed by using Linked Data principles and equipped with the additional metadata about individual artifacts. Within the context of software chrestomathy we explored a new type of software architecture – linguistic architecture that is targeted on the language and technology relationships within a software product and based on the megamodels. Our approach to documentation of the software systems is highly structured and makes use of the concepts of the newly developed megamodeling language MegaL. We “connect” an emerging ontology with the megamodeling artifacts to raise the cognitive value of the linguistic architecture.

Since the invention of U-net architecture in 2015, convolutional networks based on its encoder-decoder approach significantly improved results in image analysis challenges. It has been proven that such architectures can also be successfully applied in different domains by winning numerous championships in recent years. Also, the transfer learning technique created an opportunity to push state-of-the-art benchmarks to a higher level. Using this approach is beneficial for the medical domain, as collecting datasets is generally a difficult and expensive process. In this thesis, we address the task of semantic segmentation with Deep Learning and make three main contributions and release experimental results that have practical value for medical imaging. First, we evaluate the performance of four neural network architectures on the dataset of the cervical spine MRI scans. Second, we use transfer learning from models trained on the Imagenet dataset and compare it to randomly initialized networks. Third, we evaluate models trained on the bias field corrected and raw MRI data. All code to reproduce results is publicly available online.

The European landscape is dominated by intensive agriculture which leads to widespread impact on the environment. The frequent use of agricultural pesticides is one of the major causes of an ongoing decline in flower-visiting insects (FVIs). The conservation of this ecologically diverse assemblage of mobile, flying insect species is required by international and European policy. To counteract the decrease in species numbers and their abundances, FVIs need to be protected from anthropogenic stressors. European pesticide risk assessment was devised to prevent unacceptable adverse consequences of pesticide use on FVIs. However, there is an ongoing discussion by scientists and policy-makers if the current risk assessment actually provides adequate protection for FVI species. The first main objective of this thesis was to investigate pesticide impact on FVI species. The scientific literature was reviewed to identify groups of FVIs, summarize their ecology, and determine their habitat. This was followed by a synthesis of studies about the exposure of FVIs in their habitat and subsequent effects. In addition, the acute sensitivity of one FVI group, bee species, to pesticides was studied in laboratory experiments. The second main objective was to evaluate the European risk assessment for possible deficits and propose improvements to the current framework. Regulatory documents were screened to assess the adequacy of the guidance in place in light of the scientific evidence. The suitability of the honey bee Apis mellifera as the currently only regulatory surrogate species for FVIs was discussed in detail. The available scientific data show that there are far more groups of FVIs than the usually mentioned bees and butterflies. FVIs include many groups of ecologically different species that live in the entire agricultural landscape. Their habitats in crops and adjacent semi-natural areas can be contaminated by pesticides through multiple pathways. Environmentally realistic exposure of these habitats can lead to severe effects on FVI population parameters. The laboratory studies of acute sensitivity in bee species showed that pesticide effects on FVIs can vary greatly between species and pesticides. The follow-up critical evaluation of the European FVI risk assessment revealed major shortcomings in exposure and effect assessment. The honey bee proved to be a sufficient surrogate for bee species in lower tier risk assessment. Additional test species may be chosen for higher tier risk assessment to account for ecological differences. This thesis shows that the ecology of FVIs should generally be considered to a greater extent to improve the regulatory process. Data-driven computational approaches could be used as alternative methods to incorporate ecological trait data in spatio-temporal scenarios. Many open questions need to be answered by further research to better understand FVI species and promote necessary changes to risk assessment. In general, other FVI groups than bees need to be investigated. Furthermore, comprehensive data on FVI groups and their ecology need to be collected. Contamination of FVI habitat needs to be linked to exposure of FVI individuals and ecologically complex effects on FVI populations should receive increased attention. In the long term, European FVI risk assessment would benefit from shifting its general principles towards more scientifically informed regulatory decisions. This would require a paradigm shift from arbitrary assumptions and unnecessarily complicated schemes to a substantiated holistic framework.

Meeting growing demands for agricultural products requires management solutions that enhance food production, whilst minimizing negative environmental impacts. Conventional agricultural intensification jeopardizes farmland biodiversity and associated ecosystem services through excessive anthropogenic inputs and landscape simplification. Agri-environment schemes (AES) are commonly implemented to mitigate the adverse effects of conventional intensification on biodiversity. However the moderate success of such schemes thus far would strongly benefit from more explicit goals regarding ecosystem service provisioning. Providing key resources to beneficial organisms may improve their abundance, fitness, diversity and the ecosystem services they provide. With targeted habitat management, AES may synergistically enhance biodiversity and agricultural production and thus contribute to ecological intensification. We demonstrate that sown perennial wildflower strips, as implemented in current AES focusing on biodiversity conservation also benefit biological pest control in nearby crops (Chapter 2). Comparing winter wheat fields adjacent to wildflower strips with fields without wildflower strips we found strongly reduced cereal leaf beetle (Oulema sp.) density and plant damage near wildflower strips. In addition, winter wheat yield was 10 % higher when fields adjoined wildflower strips. This confirms previous assumptions that wildflower strips, known for positive effects on farmland biodiversity, can also enhance ecosystem services such as pest control and the positive correlation of yield with flower abundance and diversity suggests that floral resources are key. Refining sown flower strips for enhanced service provision requires mechanistic understanding of how organisms benefit from floral resources. In climate chamber experiments investigating the impact of single and multiple flowering plant species on fitness components of three key arthropod natural enemies of aphids, we demonstrate that different natural enemies benefit differently from the offered resources (Chapter 3). Some flower species were hereby more valuable to natural enemies than others overall. Additionally, the mixture with all flowers generally performed better than monocultures, yet with no transgressive overyielding. By explicitly tailoring flower strips to the requirements of key natural enemies of crop pests we aimed to maximise natural enemy mediated pest control in winter wheat (Chapter 4)and potato (Chapter 5) crops. Respecting the manifold requirements of diverse natural enemies but not pests, in terms of temporal and spatial provisioning of floral, extra floral and structural resources, we designed targeted annual flower strips that can be included in crop rotation to support key arthropods at the place and time they are needed. Indeed, field experiments revealed that cereal leaf beetle density and plant damage in winter wheat can be reduced by 40 % to 61 % and aphid densities in potatoes even by 77 %, if a targeted flower strip is sown into the field. These effects were not restricted to the vicinity of flower strips and, in contrast to fields without flower strip, often prevented action thresholds from being reached. This suggests that targeted flower strips could replace insecticides. All adult natural enemies were enhanced inside targeted flower strips when compared to control strips. Yet, spillover to the field was restricted to key natural enemies such as ground beetles (winter wheat), hoverflies (potato) and lacewings (winter wheat and potato), suggesting their dominant role in biological control. In potatoes, targeted flower strips also enhanced hoverfly species richness in strips and crop, highlighting their additional benefits for diversity. The present results provide more insights into the mechanisms underlying conservation biological control and highlight the potential of tailored habitat management for ecological intensification.

This paper originates from the FP6 project "Emergence in the Loop (EMIL)" which explores the emergence of norms in artificial societies. Part of work package 3 of this project is a simulator that allows for simulation experiments in different scenarios, one of which is collaborative writing. The agents in this still prototypical implementation are able to perform certain actions, such as writing short texts, submitting them to a central collection of texts (the "encyclopaedia") or adding their texts to texts formerly prepared by other agents. At the same time they are able to comment upon others' texts, for instance checking for correct spelling, for double entries in the encyclopaedia or for plagiarisms. Findings of this kind lead to reproaching the original authors of blamable texts. Under certain conditions blamable activities are no longer performed after some time.

Social entrepreneurship is a form of entrepreneurship that marries a social mission to a competitive value proposition. Notably, social entrepreneurship fosters a more equitable society by addressing social issues and trying to achieve an ongoing sustainable impact through a social mission rather than purely profit maximization. The topic of social entrepreneurship has appealed considerably to many different streams of research. The focus on understanding how and why entrepreneurs think and act is a significant justification for future research. Nevertheless, the theoretical examination of this phenomenon is in its infancy. Social entrepreneurship research is still largely phenomenon-driven. Specifically, Social Entrepreneurial Intention is in an early stage and lacks quantitative research. Therefore, this thesis proposes to address this need. The thesis’ objectives are twofold: (1) develop a formation model for Social Entrepreneurial Intentions in general and (2) test the model by conducting an empirical study. Based on these objectives, the two research questions guiding the thesis are (1) what factors influence the intention of a person to become a social entrepreneur and (2) what relationships exist among these factors. In order to answer these two research questions, this thesis uses purposeful research design, which is a combination of literature review and empirical study. The literature review is based on a comprehensive range of books, articles, and research papers published in leading academic journals and conference proceedings in different disciplines such as entrepreneurship, social entrepreneurship, entrepreneurship education, management, social psychology, and social economics. The empirical study is conducted via a survey of 600 last-year students from four universities in three regions in Vietnam: Hanoi, Da Nang, and Ho Chi Minh. The data are analyzed with SPSS-AMOS version 24, using screening data, scale development, exploratory factor analysis, and confirmation factor analysis. The thesis ascertains that Entrepreneurship Experience/Extra-curricular Activity, Role Model, Social Entrepreneurial Self-Efficacy, and Social Entrepreneurial Outcome Expectation directly and positively affect the intention of the Vietnamese students to be social entrepreneurs. Entrepreneurship Education also influences the Social Entrepreneurial Intention, but not directly, otherwise indirectly via Social Entrepreneurial Self-Efficacy and Social Entrepreneurial Outcome Expectation. Similarly, Perceived Support has no direct relationship to Social Entrepreneurial Intention; however, it shows an indirect link via the mediator ‘Social Entrepreneurial Outcome Expectation’. Furthermore, the dissertation brings new insights to the social entrepreneurship literature and provides important implications for practice. Limitations and future directions are also provided in the thesis.