Rivers play an important role in the global water cycle, support biodiversity and ecological integrity. However, river flow and thermal regimes are heavily altered in dammed rivers. These impacts are being exacerbated and become more apparent in rivers fragmented by multiple dams. Recent studies mainly focused on evaluating the cumulative impact of cascade reservoirs on flow or thermal regimes, but the role of upstream reservoirs in shaping the hydrology and hydrodynamics of downstream reservoirs remains poorly understood. To improve the understanding of the hydrodynamics in cascade reservoirs, long-term observational data are used in combination with numerical modeling to investigate the changes in flow and thermal regime in three cascade reservoirs at the upper reach of the Yangtze River. The three studied reservoirs are Xiluodu (XLD), Xiangjiaba (XJB) and Three Gorges Reservoir (TGR). In addition, the effects of single reservoir operation (at seasonal/daily time scale) on hydrodynamics are examined in a large tributary of TGR. The results show that the inflow of TGR has been substantially altered by the two upstream reservoirs with a higher discharge in spring and winter and a reduced peak flow in summer. XJB had no obvious contribution to the variations in inflow of TGR. The seasonal water temperature of TGR was also widely affected by the upstream two reservoirs, i.e., an increase in winter and decrease in spring, associated with a delay in water temperature rise and fall. These effects will probably be intensified in the coming years due to the construction of new reservoirs. The study also underlines the importance of reservoir operation in shaping the hydrodynamics of TGR. The seasonal dynamics of density currents in a tributary bay of TGR are closely related to seasonal reservoir operations. In addition, high-frequency water level fluctuations and flow velocity variations were observed in response to periodic tributary bay oscillations, which are driven by the diurnal discharge variations caused by the operation of TGR. As another consequence of operation of cascade reservoirs, the changes in TGR inflow weakened spring thermal stratification and caused warming in spring, autumn and winter. In response to this change, the intrusions from TGR occurred more frequently as overflow and earlier in spring, which caused a sharp reduction in biomass and frequency of phytoplankton blooms in tributary bays of TGR. This study suggests that high-frequency bay oscillations can potentially be used as an efficient management strategy for controlling algal blooms, which can be included in future multi-objective ecological conservation strategies.

As a consequence of the world population increase and the resulting water scarcity, water quality is the object of growing attention. In that context, organic anthropogenic molecules — often defined as micropollutants— represent a threat for water resources. Among them, pharmaceuticals are the object of particular concerns due to their permanent discharge, their increasing consumption and their effect-based structures. Pharmaceuticals are mainly introduced in the environment via wastewater treatment plants (WWTPs), along with their metabolites and the on-site formed transformation products (TPs). Once in the aquatic environment, they partition between the different environmental compartments in particular the aqueous phase, suspended particulate matter(SPM) and biota. In the last decades, pharmaceuticals have been widely investigated in the water phase. However, extreme polar pharmaceuticals have rarely been monitored due to the lack of robust analytical methods. Moreover, metabolites and TPs have seldom been included in routine analysis methods although their environmental relevance is proven. Furthermore, pharmaceuticals have been only sporadically investigated in SPM and biota and adequate multi-residue methods are lacking to obtain comprehensive results about their occurrence in these matrices. This thesis endeavors to cover these gaps of knowledge by the development of generic multi-residue methods for pharmaceuticals determination in the water phase, SPM and biota and to evaluate the occurrence and partition of pharmaceuticals into these compartments. For a complete overview, a particular focus was laid on extreme polar pharmaceuticals, pharmaceutical metabolites and TPs. In total, three innovative multi-residue methods were developed, they include analytes covering a broad range of physico-chemical properties. First, a reliable multi-residue method was developed for the analysis of extreme polar pharmaceuticals, metabolites and TPs dissolved in water. The selected analytes covered a significant range of elevated polarity and the method would be easily expendable to further analytes. This versatility could be achieved by the utilization of freeze-drying as sample preparation and zwitterionic hydrophilic interaction liquid chromatography (HILIC) in gradient elution mode. The suitability of HILIC chromatography to simultaneously quantify a large range of micropollutants in aqueous environmental samples was thoroughly studied. Several limitations were pointed out: a very complex and time-consuming method development, a very high sensitivity with regards to modification of the acetonitrile to water ratio in the eluent or the diluent and high positive matrix effects for certain analytes. However, these limitations can be overcome by the utilization of a precise protocol and appropriate labeled internal standards. They are overmatched by the benefits of HILIC which permits the chromatographic separation of extreme polar micropollutants. Investigation of environmental samples showed elevated concentrations of the analytes in the water phase. In particular, gabapentin, metformin, guanylurea and oxypurinol were measured at concentrations in the µg/L range in surface water. Subsequently, a reliable multi-residue method was established for the determination of 57 pharmaceuticals, 47 metabolites and TPs sorbed to SPM down to the low ng/g range. This method was conceived to cover a large range of polarity in particular with the inclusion of extreme polar pharmaceuticals. The extraction procedure was based on pressurized liquid extraction (PLE) followed by a clean-up via solvent exchange and detection via direct injection-reversed-phase LC-MS/MS and freeze-drying HILIC-MS/MS. Pharmaceutical sorption was examined using laboratory experiments. Derived distribution coefficients Kd varied by five orders of magnitude among the analytes and confirmed a high sorption potential for positively charged and nonpolar pharmaceuticals. The occurrence of pharmaceuticals in German rivers SPM was evaluated by the investigation of annual composite SPM samples taken at four sites at the river Rhine and one site at the river Saar between the years 2005 and 2015. It revealed the ubiquitous presence of pharmaceuticals sorbed to SPM in these rivers. In particular, positively charged analytes, even very polar and nonpolar pharmaceuticals showed appreciable concentrations. For many pharmaceuticals, a distinct correlation was observed between the annual quantities consumed in Germany and the concentrations measured in SPM. Studies of composite SPM spatial distribution permitted to get hints about specific industrial discharge by comparing the pollution pattern along the river. For the first time, these results showed the potential of SPM for the monitoring of positively charged and nonpolar pharmaceuticals in surface water. Finally, a reliable and generic multi residue method was developed to investigate 35 pharmaceuticals and 28 metabolites and TPs in fish plasma, fish liver and fish fillet. For this matrix, it was very challenging to develop an adequate clean-up allowing for the sufficient separation of the matrix disturbances from the analytes. In the final method, fish tissue extraction was performed by cell disruption followed by a non-discriminating clean-up based on silica gel solid-phase extraction(SPE) and restrictive access media (RAM) chromatography. Application of the developed method to the measurement of bream and carp tissues from German rivers revealed that even polar micropollutants such as pharmaceuticals are ubiquitously present in fish tissues. In total, 17 analytes were detected for the first time in fish tissues, including 10 metabolites/TPs. The importance of monitoring metabolites and TPs in fish tissues was confirmed with their detection at similar concentrations as their parents. Liver and fillet were shown to be appropriate for the monitoring of pharmaceuticals in fish, whereas plasma is more inconvenient due to very low concentrations and collection difficulties. Elevated concentrations of certain metabolites suggest possible formation of human metabolites in fish. Measured concentrations indicate a low bioaccumulation potential for pharmaceuticals in fish tissues.

Within the field of Business Process Management, business rules are commonly used to model company decision logic and govern allowed company behavior. An exemplary business rule in the financial sector could be for example: ”A customer with a mental condition is not creditworthy”. Business rules are usually created and maintained collaboratively and over time. In this setting, modelling errors can occur frequently. A challenging problem in this context is that of inconsistency, i.e., contradictory rules which cannot hold at the same time. For instance, regarding the exemplary rule above, an inconsistency would arise if a (second) modeller entered an additional rule: ”A customer with a mental condition is always creditworthy”, as the two rules cannot hold at the same time. In this thesis, we investigate how to handle such inconsistencies in business rule bases. In particular, we develop methods and techniques for the detection, analysis and resolution of inconsistencies in business rule bases

Graph-based data formats are flexible in representing data. In particular semantic data models, where the schema is part of the data, gained traction and commercial success in recent years. Semantic data models are also the basis for the Semantic Web - a Web of data governed by open standards in which computer programs can freely access the provided data. This thesis is concerned with the correctness of programs that access semantic data. While the flexibility of semantic data models is one of their biggest strengths, it can easily lead to programmers accidentally not accounting for unintuitive edge cases. Often, such exceptions surface during program execution as run-time errors or unintended side-effects. Depending on the exact condition, a program may run for a long time before the error occurs and the program crashes. This thesis defines type systems that can detect and avoid such run-time errors based on schema languages available for the Semantic Web. In particular, this thesis uses the Web Ontology Language (OWL) and its theoretic underpinnings, i.e., description logics, as well as the Shapes Constraint Language (SHACL) to define type systems that provide type-safe data access to semantic data graphs. Providing a safe type system is an established methodology for proving the absence of run-time errors in programs without requiring execution. Both schema languages are based on possible world semantics but differ in the treatment of incomplete knowledge. While OWL allows for modelling incomplete knowledge through an open-world semantics, SHACL relies on a fixed domain and closed-world semantics. We provide the formal underpinnings for type systems based on each of the two schema languages. In particular, we base our notion of types on sets of values which allows us to specify a subtype relation based on subset semantics. In case of description logics, subsumption is a routine problem. For the type system based on SHACL, we are able to translate it into a description logic subsumption problem.

Artificial intelligence (AI) is of rising importance in these days. AI is increasingly used in various company fields. Nonetheless, no high-quality scientific sources could be found stating the use of AI in the field of leadership. This research gap is addressed with this elaboration by performing expert interviews with leaders. In total seventeen companies could be questioned. The results indicate that AI is not widely used in leadership yet since only one company uses it currently and just about 10% of the participants plan the implementation in the closer feature. While the following items ex- plain why companies want to use AI in leadership: Chances for automation, time and cost savings, many important disadvantages and issues prevent companies from actively using it now: No areas of application are known, no need justifies the use, human interactions as a key aspect of leadership is reduced and it is hard to collect all necessary data. Beyond that, it was aimed to identify changes in the field of leadership through the use of AI. This objective could not be addressed due to the limited number of participants using AI in leadership. Keywords: Leadership, artificial intelligence, transformation, state-of-use

Despite widespread plans of big companies like Amazon and Google to develop unmanned delivery drones, scholarly research in this field is scarce, especially in the information systems field. From technical and legal perspectives, drone delivery in last-mile scenarios is in a quite mature state. However, estimates of user acceptance are varying between high skepticism and exaggerated optimism. This research follows a mixed method approach consisting both qualitative and quantitative research, to identify and test determinants of consumer delivery drone service adoption. The qualitative part rests on ten interviews among average consumers, who use delivery services on a regular basis. Insights gained from the qualitative part were used to develop an online survey and to assess the influence of associated risks on adoption intentions. The quantitative results show that especially financial and physical risks impede drone delivery service adoption. Delivery companies who are currently thinking about providing a delivery drone service may find these results useful when evaluating usage behaviors in the future market for delivery drones.

Nowadays, almost any IT system involves personal data processing. In such systems, many privacy risks arise when privacy concerns are not properly addressed from the early phases of the system design. The General Data Protection Regulation (GDPR) prescribes the Privacy by Design (PbD) principle. As its core, PbD obliges protecting personal data from the onset of the system development, by effectively integrating appropriate privacy controls into the design. To operationalize the concept of PbD, a set of challenges emerges: First, we need a basis to define privacy concerns. Without such a basis, we are not able to verify whether personal data processing is authorized. Second, we need to identify where precisely in a system, the controls have to be applied. This calls for system analysis concerning privacy concerns. Third, with a view to selecting and integrating appropriate controls, based on the results of system analysis, a mechanism to identify the privacy risks is required. Mitigating privacy risks is at the core of the PbD principle. Fourth, choosing and integrating appropriate controls into a system are complex tasks that besides risks, have to consider potential interrelations among privacy controls and the costs of the controls. This thesis introduces a model-based privacy by design methodology to handle the above challenges. Our methodology relies on a precise definition of privacy concerns and comprises three sub-methodologies: model-based privacy analysis, modelbased privacy impact assessment and privacy-enhanced system design modeling. First, we introduce a definition of privacy preferences, which provides a basis to specify privacy concerns and to verify whether personal data processing is authorized. Second, we present a model-based methodology to analyze a system model. The results of this analysis denote a set of privacy design violations. Third, taking into account the results of privacy analysis, we introduce a model-based privacy impact assessment methodology to identify concrete privacy risks in a system model. Fourth, concerning the risks, and taking into account the interrelations and the costs of the controls, we propose a methodology to select appropriate controls and integrate them into a system design. Using various practical case studies, we evaluate our concepts, showing a promising outlook on the applicability of our methodology in real-world settings.

Constituent parsing attempts to extract syntactic structure from a sentence. These parsing systems are helpful in many NLP applications such as grammar checking, question answering, and information extraction. This thesis work is about implementing a constituent parser for German language using neural networks. Over the past, recurrent neural networks have been used in building a parser and also many NLP applications. In this, self-attention neural network modules are used intensively to understand sentences effectively. With multilayered self-attention networks, constituent parsing achieves 93.68% F1 score. This is improved even further by using both character and word embeddings as a representation of the input. An F1 score of 94.10% was the best achieved by constituent parser using only the dataset provided. With the help of external datasets such as German Wikipedia, pre-trained ELMo models are used along with self-attention networks achieving 95.87% F1 score.

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.

The underlying characteristics of blockchain can facilitate data provenance, data integrity, data security, and data management. It has the potential to transform the healthcare sector. Since the introduction of Bitcoin in the fintech industry, the blcockhain technology has been gaining a lot of traction and its purpose is not just limited to finance. This thesis highlights the inner workings of blockchain technology and its application areas with possible existing solutions. Blockchain could lay the path for a new revolution in conventional healthcare systems. We presented how individual sectors within the healthcare industry could use blockchain and what solution persists. Also, we have presented our own concept to improve the existing paper-based prescription management system which is based on Hyperledger framework. The results of this work suggest that healthcare can benefit from blockchain technology bringing in the new ways patients can be treated.

The industry standard Decision Model and Notation (DMN) has enabled a new way for the formalization of business rules since 2015. Here, rules are modeled in so-called decision tables, which are defined by input columns and output columns. Furthermore, decisions are arranged in a graph-like structure (DRD level), which creates dependencies between them. With a given input, the decisions now can be requested by appropriate systems. Thereby, activated rules produce output for future use. However, modeling mistakes produces erroneous models, which can occur in the decision tables as well as at the DRD level. According to the Design Science Research Methodology, this thesis introduces an implementation of a verification prototype for the detection and resolution of these errors while the modeling phase. Therefore, presented basics provide the needed theoretical foundation for the development of the tool. This thesis further presents the architecture of the tool and the implemented verification capabilities. Finally, the created prototype is evaluated.

On-screen interactive presentations have got immense popularity in the domain of attentive interfaces recently. These attentive screens adapt their behavior according to the user's visual attention. This thesis aims to introduce an application that would enable these attentive interfaces to change their behavior not just according to the gaze data but also facial features and expressions. The modern era requires new ways of communications and publications for advertisement. These ads need to be more specific according to people's interests, age, and gender. When advertising, it's important to get a reaction from the user but not every user is interested in providing feedback. In such a context more, advance techniques are required that would collect user's feedback effortlessly. The main problem this thesis intends to resolve is, to apply advanced techniques of gaze and face recognition to collect data about user's reactions towards different ads being played on interactive screens. We aim to create an application that enables attentive screens to detect a person's facial features, expressions, and eye gaze. With eye gaze data we can determine the interests and with facial features, age and gender can be specified. All this information will help in optimizing the advertisements.

Amphibian populations are declining worldwide for multiple reasons such as habitat destruction and climate change. An example for an endangered European amphibian is the yellow-bellied toad Bombina variegata. Populations have been declining for decades, particularly at the northern and western range margin. One of the extant northern range centres is the Westerwald region in Rhineland-Palatinate, Germany. To implement informed conservation activities on this threatened species, knowledge of its life-history strategy is crucial. This study therefore focused on different developmental stages to test predictions of life-history theory. It addressed (1) developmental, (2) demographic and (3) genetic issues of Bombina variegata as a model organism: (1) Carry-over effects from larval environment to terrestrial stages and associated vulnerability to predators were investigated using mesocosm approaches, fitness tests and predation trials. (2) The dynamics and demography of B. variegata populations were studied applying a capture-mark-recapture analysis and skeletochronology. The study was complemented through (3) an analysis of genetic diversity and structuring of B. variegata populations using 10 microsatellite loci. In order to reveal general patterns and characteristics among B. variegata populations, the study focused on three geographical scales: local (i.e. a former military training area), regional (i.e. the Westerwald region) and continental scale (i.e. the geographical range of B. variegata). The study revealed carry-over effects of larval environment on metamorph phenotype and behaviour causing variation in fitness in the early terrestrial stage of B. variegata. Metamorph size and condition are crucial factors for survival, as small-sized individuals were particularly prone to predator attacks. Yellow-bellied toads show a remarkable fast-slow continuum of the life-history trait longevity. A populations’ position within this continuum may be determined by local environmental stochasticity, i.e. an extrinsic source of variation, and the efficiency of chemical antipredator protection, i.e. an intrinsic source of variation. Extreme longevity seems to be an exception in B. variegata. Senescence was absent in this study. Weather variability affected reproductive success and thus population dynamics. The dispersal potential was low and short-term fragmentation of populations caused significant genetic differentiation at the local scale. Long-term isolation resulted in increased genetic distance at the regional scale. At the continental scale, populations inhabiting the marginal regions were deeply structured with reduced allelic richness. As consequence of environmental changes, short-lived and isolated B. variegata populations at the range margin may face an increased risk of extinction. Conservation measures should thus improve the connectivity among local populations and reinforce annual reproductive success. Further research on the intraspecific variation in B. variegata skin toxins is required to reveal potential effects on palatability and thus longevity.

Initial goal of the current dissertation was the determination of image-based biomarkers sensitive for neurodegenerative processes in the human brain. One such process is the demyelination of neural cells characteristic for Multiple sclerosis (MS) - the most common neurological disease in young adults for which there is no cure yet. Conventional MRI techniques are very effective in localizing areas of brain tissue damage and are thus a reliable tool for the initial MS diagnosis. However, a mismatch between the clinical fndings and the visualized areas of damage is observed, which renders the use of the standard MRI diffcult for the objective disease monitoring and therapy evaluation. To address this problem, a novel algorithm for the fast mapping of myelin water content using standard multiecho gradient echo acquisitions of the human brain is developed in the current work. The method extents a previously published approach for the simultaneous measurement of brain T1, T∗ 2 and total water content. Employing the multiexponential T∗ 2 decay signal of myelinated tissue, myelin water content is measured based on the quantifcation of two water pools (myelin water and rest) with different relaxation times. Whole brain in vivo myelin water content maps are acquired in 10 healthy controls and one subject with MS. The in vivo results obtained are consistent with previous reports. The acquired quantitative data have a high potential in the context of MS. However, the parameters estimated in a multiparametric acquisition are correlated and constitute therefore an ill-posed, nontrivial data analysis problem. Motivated by this specific problem, a new data clustering approach is developed called Nuclear Potential Clustering, NPC. It is suitable for the explorative analysis of arbitrary dimensional and possibly correlated data without a priori assumptions about its structure. The developed algorithm is based on a concept adapted from nuclear physics. To partition the data, the dynamic behavior of electrically even charged nucleons interacting in a d-dimensional feature space is modeled. An adaptive nuclear potential, comprised of a short-range attractive (Strong interaction) and a long-range repulsive term (Coulomb potential), is assigned to each data point. Thus, nucleons that are densely distributed in space fuse to build nuclei (clusters), whereas single point clusters are repelled (noise). The algorithm is optimized and tested in an extensive study with a series of synthetic datasets as well as the Iris data. The results show that it can robustly identify clusters even when complex configurations and noise are present. Finally, to address the initial goal, quantitative MRI data of 42 patients are analyzed employing NPC. A series of experiments with different sets of image-based features show a consistent grouping tendency: younger patients with low disease grade are recognized as cohesive clusters, while those of higher age and impairment are recognized as outliers. This allows for the definition of a reference region in a feature space associated with phenotypic data. Tracking of the individual's positions therein can disclose patients at risk and be employed for therapy evaluation.

The history of human kind is characterized by social conflict. Every conflict can be the starting point of social change or the escalation into more destructive forms. The social conflict in regard to rising numbers of refugees and their acceptance that arose in most host countries in 2015 already took on destructive forms – in Germany, right-wing extremists attacked refugee shelters and even killed multiple people, including political leaders who openly supported refugees. Thus, incompatible expectancies and values of different parts of the society led to violent action tendencies, which tremendously threaten intergroup relations. Psychological research has developed several interventions in past decades to improve intergroup relations, but they fall short, for example, when it comes to the inclusion of people with extreme attitudes and to precisely differentiate potential prosocial outcomes of the interventions. Thus, this dissertation aimed to a) develop psychological interventions, that could also be applied to people with more extreme attitudes, thereby putting a special emphasis on collecting a diverse sample; b) gain knowledge about target- and outcome specific effects: Who benefits from which intervention and how can specific prosocial actions be predicted in order to develop interventions that guide needs-based actions; and c) shed light on potential underlying mechanisms of the interventions. The dissertation will be introduced by the socio-political background that motivated the line of research pursued, before providing an overview of the conceptualization of social conflicts and potential psychological inhibitors and catalyzers for conflict transformation. Based on past research on socio-psychological interventions and their limitations, the aims of the dissertation will be presented in more detail, followed by a short summary of each manuscript. Overall, the present thesis comprises four manuscripts that were summarized in the general discussion into a road map for social-psychological interventions to put them into a broader perspective. The road map aspires to provide recommendations for increasing – either approach-oriented or support-oriented actions – by the socio-psychological interventions for a variety of host society groups depending on their pre-existing attitude towards refugees. A Paradoxical Intervention targeting central beliefs of people with negative attitudes towards refugees influenced inhibitory and catalyzing factors for conflict transformation over the course of three experiments – thereby providing an effective tool to establish approach-oriented action tendencies, such as the willingness to get in contact with refugees. Further, the dissertation presents a novel mechanism – namely Cognitive Flexibility – which could explain the Paradoxical Interventions’ effect of past research. By positively affecting a context-free mindset, the Paradoxical Intervention could impact more flexible thought processes in general, irrespective of the topic tackled in the Paradoxical Intervention itself. For people with rather positive attitudes addressing emotions may increase specific support-oriented action tendencies. The dissertation provides evidence of a positive relation between moral outrage and hierarchy-challenging actions, such as solidarity-based collective action, and sympathy with prosocial hierarchy-maintaining support-oriented actions, such as dependency-oriented helping. These exclusive relations between specific emotions and action intentions provide important implications for the theorizing of emotion-behavior relations, as well as for practical considerations. In addition, a diversity workshop conducted with future diplomats showed indirect effects on solidarity-based collective action via diversity perception and superordinate group identification, thereby extending past research by including action intentions and going beyond the focus on grassroot-initiatives by presenting an implementable intervention for future leaders in a real world context. Taken together, this dissertation provides important insights for the development of socio-psychological interventions. By integrating a diverse sample, including members of institutions on meso- and macro-levels (non-governmental organizations and future politicians) of our society, this dissertation presents a unique multi-perspective of host society members on the social conflict of refugee acceptance and support. Thereby, this work contributes to theoretical and practical advancement of how social psychology can contribute not only to negative peace – by for example (indirectly) reducing support of violence against refugees – but also to positive peace – by for example investigating precursors of hierarchy-challenging actions that enable equal rights.

In Western personnel psychology, competence- and control beliefs (CCB) are of widespread use to predict typical work-related outcomes such as well-being, achievement motivation and job performance. The predictive value and comprehension of CCB in East Africa is examined, comparing a Kenyan target with a German source sample (N=143). Responses to personality tests included qualitative interviews on items capturing control orientations (self concept of ability, internality, powerful others, and chance). Linear regression analyses, explorative factor analyses, and a procrustean target rotation showed comparable, but not fully congruent predictability for the connection of CCB with outcome variables. Factor structures of control responses did not resemble each other sufficiently. Content analyses including scale intercorrelations, quantitative and qualitative item information served for an explanation of this predictability gap, specifying differences between the German and Kenyan samples that are associated with the social-relational domain of personality. Results fit in the picture depicted by the African Ubuntu philosophy and the South African Personality Inventory project (SAPI), both emphasizing social-relational aspects. In particular, the powerful others control orientation diverges the most between the cultures. Being perceived as a negative and external factor in the German sample with its individualistic culture, powerful others is of mixed emotional quality and just as well internal, when asked for in the Kenyan sample with its Ubuntu-worldview. An uncritical transfer of CCB measures from one culture to another is assumed to be inappropriate. More emic-etic based research is demanded concerning intra- and intercultural variability of CCB to depict a transcultural applicable model.

The Material Point Method (MPM) has proven to be a very capable simulation method in computer graphics that is able to model materials that were previously very challenging to animate [1, 2]. Apart from simulating singular materials, the simulation of multiple materials that interact with each other introduces new challenges. This is the focus of this thesis. It will be shown that the self-collision capabilities of the MPM can naturally handle multiple materials interacting in the same scene on a collision basis, even if the materials use distinct constitutive models. This is then extended by porous interaction of materials as in[3], which also integrates easily with MPM.It will furthermore be shown that regular single-grid MPM can be viewed as a subset of this multi-grid approach, meaning that its behavior can also be achieved if multiple grids are used. The porous interaction is generalized to arbitrary materials and freely changeable material interaction terms, yielding a flexible, user-controllable framework that is independent of specific constitutive models. The framework is implemented on the GPU in a straightforward and simple way and takes advantage of the rasterization pipeline to resolve write-conflicts, resulting in a portable implementation with wide hardware support, unlike other approaches such as [4].

Although most plastic pollution originates on land, current research largely remains focused on aquatic ecosystems. Studies pioneering terrestrial microplastic research have adapted analytical methods from aquatic research without acknowledging the complex nature of soil. Meanwhile, novel methods have been developed and further refined. However, methodical inconsistencies still challenge a comprehensive understanding of microplastic occurrence and fate in and on soil. This review aims to disentangle the variety of state-of-the-art sample preparation techniques for heterogeneous solid matrices to identify and discuss best-practice methods for soil-focused microplastic analyses. We show that soil sampling, homogenization, and aggregate dispersion are often neglected or incompletely documented. Microplastic preconcentration is typically performed by separating inorganic soil constituents with high-density salt solutions. Not yet standardized but currently most used separation setups involve overflowing beakers to retrieve supernatant plastics, although closed-design separation funnels probably reduce the risk of contamination. Fenton reagent may be particularly useful to digest soil organic matter if suspected to interfere with subsequent microplastic quantification. A promising new approach is extraction of target polymers with organic solvents. However, insufficiently characterized soils still impede an informed decision on optimal sample preparation. Further research and method development thus requires thorough validation and quality control with well-characterized matrices to enable robust routine analyses for terrestrial microplastics.

Schizophrenia is a chronic mental health disorder, which changes rapidly the life of the persons and their families, who suffer from it. It causes high biological and psychological vulnerability as well as cognitive, emotional and behavioral disorders. Nowadays, evidence-based pharmacotherapy and psychotherapy are available aiming the rehabilitation and recovery of individuals with schizophrenia. A democratic society is obliged to give these people the opportunity to have an access to those treatments. The following three published studies present this dissertation thesis and have a common focus on the implementation of evidence-based psychotherapy in individuals with schizophrenia. The first study evaluates the efficacy of the Integrated Psychological Therapy (IPT) in Greece, one of the most evaluated rehabilitation programs. IPT was compared to Treatment as Usual (TAU) in a randomized controlled trial (RCT) with 48 individuals with schizophrenia. Significant effects favouring IPT were found in working memory, in social perception, in negative symptoms, in general psychopathology and in insight. This study supports evidence for the efficacy of IPT in Greece. The second study evaluates a second hypothesis, when IPT is more and less effective regarding treatment resistant schizophrenia (TRS) and non treatment resistant schizophrenia (NTRS). It is a part of the first paper. Significant effects favouring NTRS were found for verbal memory, for symptoms, for functioning and quality of life. Effect sizes showed superiority of NTRS in comparison to TRS. IPTTRS showed on the other side some significant improvements. This study presents the initial findings of a larger study to be conducted internationally for the first time. The third study is a systematic review, which aims to evaluate the efficacy of Cognitive Behavioral Therapy (CBT), of Meta Cognitive Therapy (MCT), Metacognitive Training (MCTR), Metacognitive Reflection and Insight Therapy (MERIT), of various Rehabilitation Programs and Recovery Programs in individuals with schizophrenia. 41 RCTs and 12 Case Studies were included. The above interventions are efficacious in the improvement of cognitions, symptoms, functional outcome, insight, self-esteem, comorbid disorders and metacognitive capacity. The three studies provide insight regarding the importance of evidence-based psychotherapy in persons with schizophrenia leading to recovery and reintegration into society. Future RCTs with larger samples and long-term follow up, combining evidence-based psychotherapies for individuals with schizophrenia need to be done.

This work describes a novel software tool for visualizing anatomical segmentations of medical images. It was developed as part of a bachelor's thesis project, with a view to supporting research into automatic anatomical brain image segmentation. The tool builds on a widely-used visualization approach for 3D image volumes, where sections in orthogonal directions are rendered on screen as 2D images. It implements novel display modes that solve common problems with conventional viewer programs. In particular, it features a double-contour display mode to aid the user's spatial orientation in the image, as well as modes for comparing two competing segmentation labels pertaining to one and the same anatomical region. The tool was developed as an extension to an existing open-source software suite for medical image processing. The visualization modes are, however, suitable for implementation in the context of other viewer programs that follow a similar rendering approach. The modified code can be found here: soundray.org/mm-segmentation-visualization.tar.gz.

The goal of this thesis is to create a recommender system (RS) for business processes, based on the existing ProM plugin RegPFA. To accomplish this task, firstly an interface must be created that sets up and expands a database receiving probabilistic finite automata (PFA) created by RegPFA in tsml format as input. Secondly, a Java program must be designed that uses said database to recommend the process elements that are most likely to follow a given sequence of process elements.

Most social media platforms allow users to freely express their opinions, feelings, and beliefs. However, in recent years the growing propagation of hate speech, offensive language, racism and sexism on the social media outlets have drawn attention from individuals, companies, and researchers. Today, sexism both online and offline with different forms, including blatant, covert, and subtle lan- guage, is a common phenomenon in society. A notable amount of work has been done over identifying sexist content and computationally detecting sexism which exists online. Although previous efforts have mostly used peoples’ activities on social media platforms such as Twitter as a public and helpful source for collecting data, they neglect the fact that the method of gathering sexist tweets could be biased towards the initial search terms. Moreover, some forms of sexism could be missed since some tweets which contain offensive language could be misclassified as hate speech. Further, in existing hate speech corpora, sexist tweets mostly express hostile sexism, and to some degree, the other forms of sexism which also appear online was disregarded. Besides, the creation of labeled datasets with manual exertion, relying on users to report offensive comments with a tremendous effort by human annotators is not only a costly and time-consuming process, but it also raises the risk of involving discrimination under biased judgment. This thesis generates a novel sexist and non-sexist dataset which is constructed via "UnSexistifyIt", an online web-based game that incentivizes the players to make minimal modifications to a sexist statement with the goal of turning it into a non-sexist statement and convincing other players that the modified statement is non-sexist. The game applies the methodology of "Game With A Purpose" to generate data as a side-effect of playing the game and also employs the gamification and crowdsourcing techniques to enhance non-game contexts. When voluntary participants play the game, they help to produce non-sexist statements which can reduce the cost of generating new corpus. This work explores how diverse individual beliefs concerning sexism are. Further, the result of this work highlights the impact of various linguistic features and content attributes regarding sexist language detection. Finally, this thesis could help to expand our understanding regarding the syntactic and semantic structure of sexist and non-sexist content and also provides insights to build a probabilistic classifier for single sentences into sexist or non-sexist classes and lastly find a potential ground truth for such a classifier.

The erosion of the closed innovation paradigm in conjunction with increasing competitive pressure has boosted the interest of both researchers and organizations in open innovation. Despite such rising interest, several companies remain reluctant to open their organizational boundaries to practice open innovation. Among the many reasons for such reservation are the pertinent complexity of transitioning toward open innovation and a lack of understanding of the procedures required for such endeavors. Hence, this thesis sets out to investigate how organizations can open their boundaries to successfully transition from closed to open innovation by analyzing the current literature on open innovation. In doing so, the transitional procedures are structured and classified into a model comprising three phases, namely unfreezing, moving, and institutionalizing of changes. Procedures of the unfreezing phase lay the foundation for a successful transition to open innovation, while procedures of the moving phase depict how the change occurs. Finally, procedures of the institutionalizing phase contribute to the sustainability of the transition by employing governance mechanisms and performance measures. Additionally, the individual procedures are characterized along with their corresponding barriers and critical success factors. As a result of this structured depiction of the transition process, a guideline is derived. This guideline includes the commonly employed actions of successful practitioners of open innovation, which may serve as a baseline for interested parties of the paradigm. With the derivation of the guideline and concise depiction of the individual transitional phases, this thesis consequently reduces the overall complexity and increases the comprehensibility of the transition and its implications for organizations.

Business Process Querying (BPQ) is a discipline in the field of Business Process Man- agement which helps experts to understand existing process models and accelerates the development of new ones. Its queries can fetch and merge these models, answer questions regarding the underlying process, and conduct compliance checking in return. Many languages have been deployed in this discipline but two language types are dominant: Logic-based languages use temporal logic to verify models as finite state machines whereas graph-based languages use pattern matching to retrieve subgraphs of model graphs directly. This thesis aims to map the features of both language types to features of the other to identify strengths and weaknesses. Exemplarily, the features of Computational Tree Logic (CTL) and The Diagramed Modeling Language (DMQL) are mapped to one another. CTL explores the valid state space and thus is better for behavioral querying. Lacking certain structural features and counting mechanisms it is not appropriate to query structural properties. In contrast, DMQL issues structural queries and its patterns can reconstruct any CTL formula. However, they do not always achieve exactly the same semantic: Patterns treat conditional flow as sequential flow by ignoring its conditions. As a result, retrieved mappings are invalid process execution sequences, i.e. false positives, in certain scenarios. DMQL can be used for behavioral querying if these are absent or acceptable. In conclusion, both language types have strengths and are specialized for different BPQ use cases but in certain scenarios graph-based languages can be applied to both. Integrating the evaluation of conditions would remove the need for logic-based languages in BPQ completely.

The software development industry is enhancing day by day. The introduction of agile software development methodologies was a tremendous structural change in companies. Agile transformation provides unlimited opportunities and benefits to the existing and new developing companies. Along with benefits, agile conversion also brings many unseen challenges. New entrants have the advantage of being flexible and cope with the environmental, consumer, and cultural changes, but existing companies are bound to rigid structure. The goal of this research is to have deep insight into agile software development methodology, agile manifesto, and principles behind the agile manifesto. The prerequisites company must know for agile software development implementation. The benefits a company can achieve by implementing agile software development. Significant challenges that a company can face during agile implementation in a company. The research objectives of this study help to generate strong motivational research questions. These research questions cover the cultural aspects of company agility, values and principles of agile, benefits, and challenges of agile implementation. The project management triangle will show how benefits of cost, benefits of time, and benefits of quality can be achieved by implementing agile methodologies. Six significant areas have been explored, which shows different challenges a company can face during implementation agile software development methodology. In the end, after the in depth systematic literature review, conclusion is made following some open topics for future work and recommendations on the topic of implementation of agile software development methodology in a company.

Streams are coupled with their riparian area. Emerging insects from streams can be an important prey in the riparian area. Such aquatic subsidies can cause predators to switch prey or increase predator abundances. This can impact the whole terrestrial food web. Stressors associated with agricultural land use can alter insect communities in water and on land, resulting in complex response patterns of terrestrial predators that rely on prey from both systems. This thesis comprises studies on the impact of aquatic nsects on a terrestrial model ecosystem (Objective 1, hapter 2), the influence of agricultural land use on riparian spiders’ traits and community (Objective 2, Chapter 3), and on the impact of agricultural land use on the contribution of different prey to spider diet (Objective 3, Chapter 4). In chapter 2, I present a study where we conducted a mesocosm experiment to examine the effects of aquatic subsidies on a simplified terrestrial food web consisting of two types of herbivores (leafhoppers and weevils), plants and predators (spiders). I focused on the prey choice of the spiders by excluding predator immigration and reproduction. In accordance with predator switching, survival of leafhoppers increased in the presence of aquatic subsidies. By contrast, the presence of aquatic subsidies indirectly reduced weevils and herbivory. In chapter 3, I present the results on the taxonomic and trait response of riparian spider communities to gradients of agricultural stressors and environmental variables, with a particular emphasis on pesticides. To capture spiders with different traits and survival strategies, we used multiple collection methods. Spider community composition was best explained by in-stream pesticide toxicity and shading of the stream bank, a proxy for the quality of the habitat. Species richness and the number of spider individuals, as well as community ballooning ability, were negatively associated with in-stream pesticide toxicity. In contrast, mean body size and shading preference of spider communities responded strongest to shading, whereas mean niche width (habitat preference for moisture and shading) responded strongest to other environmental variables. In chapter 4, I describe aquatic-terrestrial predator-prey relations with gradients of agricultural stressors and environmental variables. I sampled spiders, as well as their aquatic and terrestrial prey along streams with an assumed pesticide pollution gradient and determined their stable carbon and nitrogen signals. Potential aquatic prey biomass correlated positively with an increasing aquatic prey contribution of T. montana. The contribution of aquatic prey to the diet of P. amentata showed a positive relationship with increasing toxicity in streams. Overall, this thesis contributes to the emerging discipline of cross-ecosystem ecology and shows that aquatic-terrestrial linkages and riparian food webs can be influenced by land use related stressors. Future manipulative field studies on aquatic-terrestrial linkages are required that consider the quality of prey organisms, fostering mechanistic understanding of such crossecosystem effects. Knowledge on these linkages is important to improve understanding of consequences of anthropogenic stressors and to prevent further losses of ecosystems and their biodiversity.

Commonsense reasoning can be seen as a process of identifying dependencies amongst events and actions. Understanding the circumstances surrounding these events requires background knowledge with sufficient breadth to cover a wide variety of domains. In the recent decades, there has been a lot of work in extracting commonsense knowledge, a number of these projects provide their collected data as semantic networks such as ConceptNet and CausalNet. In this thesis, we attempt to undertake the Choice Of Plausible Alternatives (COPA) challenge, a problem set with 1000 questions written in multiple-choice format with a premise and two alternative choices for each question. Our approach differs from previous work by using shortest paths between concepts in a causal graph with the edge weight as causality metric. We use CausalNet as primary network and implement a few design choices to explore the strengths and drawbacks of this approach, and propose an extension using ConceptNet by leveraging its commonsense knowledge base.

Belief revision is the subarea of knowledge representation which studies the dynamics of epistemic states of an agent. In the classical AGM approach, contraction, as part of the belief revision, deals with the removal of beliefs in knowledge bases. This master's thesis presents the study and the implementation of concept contraction in the Description Logic EL. Concept contraction deals with the following situation. Given two concept C and D, assuming that C is subsumed by D, how can concept C be changed so that it is not subsumed by D anymore, but is as similar as possible to C? This approach of belief change is different from other related work because it deals with contraction in the level of concepts and not T-Boxes and A-Boxes in general. The main contribution of the thesis is the implementation of the concept contraction. The implementation provides insight into the complexity of contraction in EL, which is tractable since the main inference task in EL is also tractable. The implementation consists of the design of five algorithms that are necessary for concept contraction. The algorithms are described, illustrated with examples, and analyzed in terms of time complexity. Furthermore, we propose an new approach for a selection function, adapt for the concept contraction. The selection function uses metadata about the concepts in order to select the best from an input set. The metadata is modeled in a framework that we have designed, based on standard metadata frameworks. As an important part of the concept contraction, the selection function is responsible for selecting the best concepts that are as similar as possible to concept C. Lastly, we have successfully implemented the concept contraction in Python, and the results are promising.

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.

Tracking is an integral part of many modern applications, especially in areas like autonomous systems and Augmented Reality. For performing tracking there are a wide array of approaches. One that has become a subject of research just recently is the utilization of Neural Networks. In the scope of this master thesis an application will be developed which uses such a Neural Network for the tracking process. This also requires the creation of training data as well as the creation and training of a Neural Network. Subsequently the usage of Neural Networks for tracking will be analyzed and evaluated. This includes several aspects. The quality of the tracking for different degrees of freedom will be checked as well as the the impact of the Neural Network on the applications performance. Additionally the amount of required training data is investigated, the influence of the network architecture and the importance of providing depth data as part of the networks input. This should provide an insight into how relevant this approach could be for its adoption in future products.

The mitral valve is one of four human heart valves. It is located in the left heart and acts as a unidirectional passageway for blood between the left atrium and the left ventricle. A correctly functioning mitral valve prevents a backflow of blood into the pulmonary circulation (lungs) and thus constitutes a vital part of the cardiac cycle. Pathologies of the mitral valve can manifest in a variety of symptoms with severity ranging from chest pain and fatigue to pulmonary edema (fluid accumulation in the tissue and air space of lungs), which may ultimately cause respiratory failure. Malfunctioning mitral valves can be restored through complex surgical interventions, which greatly benefit from intensive planning and pre-operative analysis. Visualization techniques provide a possibility to enhance such preparation processes and can also facilitate post-operative evaluation. The work at hand extends current research in this field, building upon patient-specific mitral valve segmentations developed at the German Cancer Research Center, which result in triangulated 3D models of the valve surface. The core of this work will be the construction of a 2D-view of these models through global parameterization, a method that can be used to establish a bijective mapping between a planar parameter domain and a surface embedded in higher dimensions. A flat representation of the mitral valve provides physicians with a view of the whole surface at once, similar to a map. This allows assessment of the valve's area and shape without the need for different viewing angles. Parts of the valve that are occluded by geometry in 3D become visible in 2D. An additional contribution of this work will be the exploration of different visualizations of the 3D and 2D mitral valve representations. Features of the valve can be highlighted by associating them with specified colors, which can for instance directly convey pathology indicators. Quality and effectiveness of the proposed methods were evaluated through a survey conducted at the Heidelberg University Hospital.

Lakes and reservoirs are important sources of methane, a potent greenhouse gas. Although freshwaters cover only a small fraction of the global surface, their contribution to global methane emission is significant and this is expected to increase, as a positive feedback to climate warming and exacerbated eutrophication. Yet, global estimates of methane emission from freshwaters are often based on point measurements that are spatio-temporally biased. To better constrain the uncertainties in quantifying methane fluxes from inland waters, a closer examination of the processes transporting methane from sediment to atmosphere is necessary. Among these processes, ebullition (bubbling) is an important transport pathway and is a primary source of uncertainty in quantifying methane emissions from freshwaters. This thesis aims to improve our understanding of ebullition in freshwaters by studying the processes of methane bubble formation, storage and release in aquatic sediments. The laboratory experiments demonstrate that aquatic sediments can store up to ~20% (volumetric content) gas and the storage capacity varies with sediment properties. The methane produced is stored as gas bubbles in sediment with minimal ebullition until the storage capacity is reached. Once the sediment void spaces are created by gas bubble formation, they are stable and available for future bubble storage and transport. Controlled water level drawdown experiments showed that the amounts of gas released from the sediment scaled with the total volume of sediment gas storage and correlated linearly to the drop in hydrostatic pressure. It was hypothesized that not only the timing of ebullition is controlled by sediment gas storage, but also the spatial distribution of ebullition. A newly developed freeze corer, capable of characterizing sediment gas content under in situ environments, enabled the possibility to test the hypothesis in a large subtropical lake (Lake Kinneret, Israel). The results showed that gas content was variable both vertically and horizontally in the lake sediment. Sediment methane production rate and sediment characteristics could explain these variabilities. The spatial distribution of ebullition generally was in a good agreement with the horizontal distribution of depth-averaged (surface 1 m) sediment gas content. While discrepancies were found between sediment depth-integrated methane production and the snapshot ebullition rate, they were consistent in a long term (multiyear average). These findings provide a solid basis for the future development of a process-based ebullition model. By coupling a sediment transport model with a sediment diagenetic model, general patterns of ebullition hotspots can be predicted at a system level and the uncertainties in ebullition flux measurements can be better constrained both on long-term (months to years) and short-term (minutes to hours) scales.

In this thesis, I present the results of my studies on taxonomy, systematics, and biogeography of Impatiens (Balsaminaceae) in Madagascar and the Comoro islands. In Chapter 1 I reviewed the literature on taxonomy and classification of Balsaminaceae, on habitat, world distribution, morphology, molecular phylogenetics and infrageneric classification of the genus Impatiens. In Chapters 2-15 (Fischer & Rahelivololona 2002, 2003, 2004, 2007, 2015a, 2015b, 2015c, 2016, Fischer et al. 2004. 2017, 2018a, b submitted, Rahelivololona et al. 2003) I presented the first results of a revision of Balsaminaceae of Madagascar and the Comoro islands including the description of 78 new species. In Chapter 16 (Yuan et al. 2004) we worked on the phylogeny and biogeography of Balsaminaceae inferred from ITS sequences using combined results from molecular phylogenetic and morphological analyses. In Chapter 17 (Rahelivololona et al. 2018) we conducted a phylogeny and assessment of the infrageneric classification of species in the Malagasy Impatiens (Balsaminaceae) with a particular emphasis on taxa collected from Marojejy. Below I summarise the most important findings of each chapter and provide an outlook for future studies. How many species of Impatiens occur in Madagascar and the Comoro islands? To provide additional information on the taxonomic revision of Impatiens in Madagascar and the Comoro islands, the identification of already described species as well as the description of new species was conducted. Based on herbarium specimens from BR, G, K, NEU, P, TAN and on living plants collected during several field trips, 78 new species and 6 nomina nova have been published and another 70 new taxa are already identified. Actually more than 260 species occur in Madagascar and the Comoro islands and all of them are endemic. For each species, a description of the morphology, phenology, ecology and known distribution range was provided. Apart from new taxa, the delimitation of already described species like Impatiens firmula Baker and Impatiens hildebrandtii Baill. could be clarified by studying the types and by observing the variability in the field. Are the groups of Impatiens in Madagascar monophyletic, and what is the systematic position of Trimorphopetalum? Yuan & al. (2004) conducted a molecular phylogenetic study to examine the morphological and karyological evolution, and the historical biogeography of the Balsaminaceae family by using nucleotide sequence data of internal transcribed spacer regions of nuclear ribosomal DNA. The results support the monophyly of the Malagasy endemic section Trimorphopetalum and show that the cleistogamous Impatiens inaperta should be included in the sect. Trimorphopetalum which is the most derived within Impatiens. Therefore, the section Preimpatiens proposed by Perrier de la Bâthie (1934) is paraphyletic. Rahelivololona & al. (2018) provided a phylogenetic study focused on three subdivisions (based on macromorphological characters) proposed by Perrier de la Bâthie (1934). The analysis was done using two nuclear AP3/DEF homologues (ImpDEF1 and ImpDEF2) and the plastid atpB-rbcL spacer to reassess or assess the monophyly of the Malagasy Impatiens, of the sections Preimpatiens (Humblotianae and Vulgare groups) and Trimorphopetalum. A focus was on the species of Impatiens from the Marojejy National Park and of the morphologically variable species I. elatostemmoides, I. “hammarbyoides”, I. inaperta and I. manaharensis, using monophyly as the primary criterion. As results the Malagasy Impatiens are paraphyletic and the section Preimpatiens sensu Perrier de la Bâthie (1934) (= subgen. Impatiens sensu Fischer & Rahelivololona 2002) was not resolved as a monophyletic group. The section Trimorphopetalum sensu Perrier de la Bâthie (1934) (= subgen. Trimorphopetalum sensu Fischer & Rahelivololona 2002), however, was strongly confirmed as a monophyletic lineage (BS: 92; BPP: 1). Neither the Humblotianae group nor the Vulgare group was supported as monophyletic. None of the morphologically variable species appeared to be monophyletic and the sampled species of Impatiens from the Marojejy National Park do also not form a monophyletic group. What are the biogeographical position and the distribution patterns of Impatiens in Madagascar and the Comoro islands? Investigation of the geographical affinities and species distribution of section Impatiens (including Humblotianae group and Vulgare group) and section Trimorphopetalum were conducted and the origin and evolution as well as species richness and endemism were discussed. The isolation, the climate and the complex topography of Madagascar have generated the microhabitats and ecological niches favourable to the diversification of Impatiens species. Impatiens of Madagascar with 260 endemic species is actually the largest genus in Madagascar. Therefore, Madagascar and the Comoro islands are among the most species-rich regions in the world for Impatiens. Future studies In Impatiens on Madagascar, there remain numerous unresolved questions that need to be adressed: • A further study based on a much larger molecular data set and sampling from the entire geographic ranges of Impatiens in Madagascar is needed to retest the monophyly of the different subgenera and sections, as well as a molecular dating of the Malagasy Impatiens. • The study of pollinators as a key for understanding the radiation and species richness is required: Within Impatiens the different shapes of spur are related to pollinators (bees, birds, butterflies and moths). Therefore pollinator observation of specific species need to be done to understand the radiation of species by adaptation and coevolution with these pollinators. A pollination study with a large number of species within section Trimorphopetalum will help to understand the mechanism of complete disappearance of the spur, the shift of pollinators and the evolution of species richness. • The destruction of the natural habitats of Impatiens and the subsequent reduction of humidity in logged area constitute a severe threat for the survival of many species. The conservation and reforestation of vulnerable areas such as Ankaratra, Daraina, Mandraka and Col des Tapia near Antsirabe is required. • In terms of conservation and to mitigate the threat on the genus, a study on the ex-situ-conservation of Malagasy Impatiens species is very important as long as some species are suitable for horticultural purposes (e.g. Impatiens mayae-valeriae, Impatiens emiliae and species with broad red spur). • Finally, the publication of the revision of Impatiens of Madagascar and the Comoro islands will help other botanists to identify the species and will thus increase our knowledge on the group.

Over the past few decades society’s dependence on software systems has grown significantly. These systems are utilized in nearly every matter of life today and often handle sensitive, private data. This situation has turned software security analysis into an essential and widely researched topic in the field of computer science. Researchers in this field tend to make the assumption that the quality of the software systems' code directly affects the possibility for security gaps to arise in it. Because this assumption is based on properties of the code, proving it true would mean that security assessments can be performed on software, even before a certain version of it is released. A study based on this implication has already attempted to mathematically assess the existence of such a correlation, studying it based on quality and security metric calculations. The present study builds upon that study in finding an automatic method for choosing well-fitted software projects as a sample for this correlation analysis and extends the variety of projects considered for the it. In this thesis, the automatic generation of graphical representations both for the correlations between the metrics as well as for their evolution is also introduced. With these improvements, this thesis verifies the results of the previous study with a different and broader project input. It also focuses on analyzing the correlations between the quality and security metrics to real-world vulnerability data metrics. The data is extracted and evaluated from dedicated software vulnerability information sources and serves to represent the existence of proven security weaknesses in the studied software. The study discusses some of the difficulties that arise when trying to gather such information and link it to the difference in the information contained in the repositories of the studied projects. This thesis confirms the significant influence that quality metrics have on each other. It also shows that it is important to view them together as a whole and suppose that their correlation could influence the appearance of unwanted vulnerabilities as well. One of the important conclusions I can draw from this thesis is that the visualization of metric evolution graphs, helps the understanding of the values as well as their connection to each other in a more meaningful way. It allows for better grasp of their influence on each other as opposed to only studying their correlation values. This study confirms that studying metric correlations and evolution trends can help developers improve their projects and prevent them from becoming difficult to extend and maintain, increasing the potential for good quality as well as more secure software code.

Our work finds the fine grained edits in context of neighbouring tokens in Wikipedia articles. We cluster those edits according to similar neighbouring context. We encode neighbouring context into vector space using word vectors. We evaluate clusters returned by our algorithm on extrinsic and intrinsic metric and compare it with previous work. We analyse the relation between extrinsic and intrinsic measurements of fine grained edit tokens.

The status of Business Process Management (BPM) recommender systems is not quite clear as research states. The use of recommenders familiarized itself with the world during the rise of technological evolution in the past decade.Ever since then, several BPM recommender systems came about. However, not a lot of research is conducted in this field. It is not well known to what broad are the technologies used and how are they used. Moreover, this master’s thesis aims at surveying the BPM recommender systems existing. Building on this, the recommendations come in different shapes. They can be positionbased where an element is to be placed at an element’s front, back or to autocomplete a missing link. On the other hand, Recommendations can be textual, to fill the labels of the elements. Furthermore, the literature review for BPM recommender systems took place under the guides of a literature review framework. The framework suggests 5stages of consecutive stages for this sake. The first stage is defining a scope for the research. Secondly, conceptualizing the topic by choosing key terms for literature research. After that in the third stage, comes the research stage.As for the fourth stage, it suggests choosing analysis features over which the literature is to be synthesized and compared. Finally, it recommends defining the research agenda to describe the reason for the literature review. By invoking the mentioned methodology, this master’s thesis surveyed 18 BPM recommender systems. It was found as a result of the survey that there are not many different technologies for implementing the recommenders. It was also found that the majority of the recommenders suggest nodes that are yet to come in the model, which is called forward recommending. Also, one of the results of the survey indicated the scarce use of textual recommendations to BPM labels. Finally, 18 recommenders are considered less than excepted for a developing field therefore as a result, the survey found a shortage in the number of BPM recommender systems. The results indicate several shortages in several aspects in the field of BPM recommender systems. On this basis, this master’s thesis recommends the future work on it the results.

Despite the inception of new technologies at a breakneck pace, many analytics projects fail mainly due to the use of incompatible development methodologies. As big data analytics projects are different from software development projects, the methodologies used in software development projects could not be applied in the same fashion to analytics projects. The traditional agile project management approaches to the projects do not consider the complexities involved in the analytics. In this thesis, the challenges involved in generalizing the application of agile methodologies will be evaluated, and some suitable agile frameworks which are more compatible with the analytics project will be explored and recommended. The standard practices and approaches which are currently applied in the industry for analytics projects will be discussed concerning enablers and success factors for agile adaption. In the end, after the comprehensive discussion and analysis of the problem and complexities, a framework will be recommended that copes best with the discussed challenges and complexities and is generally well suited for the most data-intensive analytics projects.

Sediment transport contributes to the movement of inorganic and organic material in rivers. The construction of a dam interrupts the continuity of this sediment transport through rivers, causing sediments to accumulate within the reservoir. Reservoirs can also act as carbon sinks and methane can be released when organic matter in the sediment is degraded under anoxic conditions. Reservoir sedimentation poses a great threat to the sustainability of reservoirs worldwide, and can emit the potent greenhouse gas methane into the atmosphere. Sediment management measures to rehabilitate silted reservoirs are required to achieve both better water quantity and quality, as well as to mitigate greenhouse gas emissions. This thesis aims at the improvement of sediment sampling techniques to characterize sediment deposits as a basis for accurate and efficient water jet dredging and to monitor the dredging efficiency by measuring the sediment concentration. To achieve this, we investigated freeze coring as a method to sample (gas-bearing) sediment in situ. The freeze cores from three reservoirs obtained were scanned using a non-destructive X-Ray CT scan technique. This allows the determination of sediment stratification and character-ization of gas bubbles to quantify methane emissions and serve as a basis for the identi-fication of specific (i.e. contaminated) sediment layers to be dredged. The results demon-strate the capability of freeze coring as a method for the characterization of (gas-bearing) sediment and overcomes certain limitations of commonly used gravity cores. Even though the core’s structure showed coring disturbances related to the freezing process, the general core integrity seems to not have been disturbed. For dredging purposes, we analyzed the impact pressure distribution and spray pattern of submerged cavitating wa-ter jets and determined the effects of impinging distances and angles, pump pressures and spray angles. We used an adapted Pressure Measurement Sensing technique to enhance the spatial distribution, which proved to be a comparatively easy-to-use meas-urement method for an improved understanding of the governing factors on the erosional capacity of cavitating water jets. Based on this data, the multiple linear regression model can be used to predict the impact pressure distribution of those water jets to achieve higher dredging accuracy and efficiency. To determine the dredging operational efficien-cy, we developed a semi-continuous automated measurement device to measure the sediment concentration of the slurry. This simple and robust device has lower costs, compared to traditional and surrogate sediment concentration measurement technolo-gies, and can be monitored and controlled remotely under a wide range of concentrations and grain-sizes, unaffected by entrained gas bubbles

This paper describes the robots TIAGo and Lisa used by team homer@UniKoblenz of the University of Koblenz-Landau, Germany, for the participation at the RoboCup@Home 2019 in Sydney, Australia. We ended up first at RoboCup@Home 2019 in the Open Platform League and won the competition in our league now three times in a row (four times in total) which makes our team the most successful in RoboCup@Home. We demonstrated approaches for learning from demonstration, touch enforcing manipulation and autonomous semantic exploration in the finals. A special focus is put on novel system components and the open source contributions of our team. We have released packages for object recognition, a robot face including speech synthesis, mapping and navigation, speech recognition interface, gesture recognition and imitation learning. The packages are available (and new packages will be released) on http://homer.uni-koblenz.de.

Current political issues are often reflected in social media discussions, gathering politicians and voters on common platforms. As these can affect the public perception of politics, the inner dynamics and backgrounds of such debates are of great scientific interest. This thesis takes user generated messages from an up-to-date dataset of considerable relevance as Time Series, and applies a topic-based analysis of inspiration and agenda setting to it. The Institute for Web Science and Technologies of the University Koblenz-Landau has collected Twitter data generated beforehand by candidates of the European Parliament Election 2019. This work processes and analyzes the dataset for various properties, while focusing on the influence of politicians and media on online debates. An algorithm to cluster tweets into topical threads is introduced. Subsequently, Sequential Association Rules are mined, yielding wide array of potential influence relations between both actors and topics. The elaborated methodology can be configured with different parameters and is extensible in functionality and scope of application.

Hands-free text editing using multimodal approach (Voice and Gaze) can improve the text editing process than using unimodal approach (Voice only)

Software systems have an increasing impact on our daily lives. Many systems process sensitive data or control critical infrastructure. Providing secure software is therefore inevitable. Such systems are rarely being renewed regularly due to the high costs and effort. Oftentimes, systems that were planned and implemented to be secure, become insecure because their context evolves. These systems are connected to the Internet and therefore also constantly subject to new types of attacks. The security requirements of these systems remain unchanged, while, for example, discovery of a vulnerability of an encryption algorithm previously assumed to be secure requires a change of the system design. Some security requirements cannot be checked by the system’s design but only at run time. Furthermore, the sudden discovery of a security violation requires an immediate reaction to prevent a system shutdown. Knowledge regarding security best practices, attacks, and mitigations is generally available, yet rarely integrated part of software development or covering evolution. This thesis examines how the security of long-living software systems can be preserved taking into account the influence of context evolutions. The goal of the proposed approach, S²EC²O, is to recover the security of model-based software systems using co-evolution. An ontology-based knowledge base is introduced, capable of managing common, as well as system-specific knowledge relevant to security. A transformation achieves the connection of the knowledge base to the UML system model. By using semantic differences, knowledge inference, and the detection of inconsistencies in the knowledge base, context knowledge evolutions are detected. A catalog containing rules to manage and recover security requirements uses detected context evolutions to propose potential co-evolutions to the system model which reestablish the compliance with security requirements. S²EC²O uses security annotations to link models and executable code and provides support for run-time monitoring. The adaptation of running systems is being considered as is round-trip engineering, which integrates insights from the run time into the system model. S²EC²O is amended by prototypical tool support. This tool is used to show S²EC²O’s applicability based on a case study targeting the medical information system iTrust. This thesis at hand contributes to the development and maintenance of long-living software systems, regarding their security. The proposed approach will aid security experts: It detects security-relevant changes to the system context, determines the impact on the system’s security and facilitates co-evolutions to recover the compliance with the security requirements.

Topic models are a popular tool to extract concepts of large text corpora. These text corpora tend to contain hidden meta groups. The size relation of these groups is frequently imbalanced. Their presence is often ignored when applying a topic model. Therefore, this thesis explores the influence of such imbalanced corpora on topic models. The influence is tested by training LDA on samples with varying size relations. The samples are generated from data sets containing a large group differences i.e language difference and small group differences i.e. political orientation. The predictive performance on those imbalanced corpora is judged using perplexity. The experiments show that the presence of groups in training corpora can influence the prediction performance of LDA. The impact varies due to various factors, including language-specific perplexity scores. The group-related prediction performance changes for groups when varying the relative group sizes. The actual change varies between data sets. LDA is able to distinguish between different latent groups in document corpora if differences between groups are large enough, e.g. for groups with different languages. The proportion of group-specific topics is under-proportional to the share of the group in the corpus and relatively smaller for minorities.

Within aquatic environments sediment water interfaces (SWIs) are the most important areas concerning exchange processes between the water body and the sediment. These spatially restricted regions are characterized by steep biogeochemical gradients that determine the speciation and fate of natural or artificial substances. Apart from biological mediated processes (e.g., burrowing organisms, photosynthesis) the determining exchange processes are diffusion or a colloid-mediated transport. Hence, methods are required enabling to capture the fine scale structures at the boundary layer and to distinguish between the different transport pathways. Regarding emerging substances that will probably reach the aquatic environment engineered nanomaterials (ENMs) are of great concern due to their increased use in many products and applications. Since they are determined based on their size (<100 nm) they include a variety of different materials behaving differently in the environment. Once released, they will inevitable mix with naturally present colloids (< 1 μm) including natural nanomaterials. With regard to existing methodological gaps concerning the characterization of ENMs (as emerging substances) and the investigation of SWIs (as receiving environmental compartments), the aim of this thesis was to develop, validate and apply suitable analytical tools. The challenges were to i) develop methods that enable a high resolution and low-invasive sampling of sediment pore water. To ii) develop routine-suitable methods for the characterization of metal-based engineered nanoparticles and iii) to adopt and optimize size-fractionation approaches for pore water samples of sediment depth profiles to obtain size-related information on element distributions at SWIs. Within the first part, an available microprofiling system was combined with a novel micro sampling system equipped with newly developed sample filtration-probes. The system was thoroughly validated and applied to a freshwater sediment proving the applicability for an automatic sampling of sediment pore waters in parallel to microsensor measurements. Thereby, for the first time multi-element information for sediment depth profiles were obtained at a millimeter scale that could directly be related to simultaneously measured sediment parameters. Due to the expected release of ENMs to the environment the aim was to develop methods that enable the investigation of fate and transport of ENMs at sediment water interfaces. Since standardized approaches are still lacking, methods were developed for the determination of the total mass concentration and the determination of the dissolved fraction of (nano)particle suspensions. Thereby, validated, routine suitable methods were provided enabling for the first time a routine-suitable determination of these two, among the most important properties regarding the analyses of colloidal systems, also urgently needed as a basis for the development of appropriate (future) risk assessments and regulatory frameworks. Based on this methodological basis, approaches were developed enabling to distinguish between dissolved and colloidal fractions of sediment pore waters. This made it possible for the first time to obtain fraction related element information for sediment depth profiles at a millimeter scale, capturing the fine scale structures and distinguishing between diffusion and colloid-mediated transport. In addition to the research oriented parts of this thesis, questions concerning the regulation of ENPs in the case of a release into aquatic systems were addressed in a separate publication (included in the Appendix) discussing the topic against the background of the currently valid German water legislation and the actual state of the research.

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.

The content aggregator platform Reddit has established itself as one of the most popular websites in the world. However, scientific research on Reddit is hindered as Reddit allows (and even encourages) user anonymity, i.e., user profiles do not contain personal information such as the gender. Inferring the gender of users in large-scale could enable the analysis of gender-specific areas of interest, reactions to events, and behavioral patterns. In this direction, this thesis suggests a machine learning approach of estimating the gender of Reddit users. By exploiting specific conventions in parts of the website, we obtain a ground truth for more than 190 million comments of labeled users. This data is then used to train machine learning classifiers to use them to gain insights about the gender balance of particular subreddits and the platform in general. By comparing a variety of different approaches for classification algorithm, we find that character-level convolutional neural network achieves performance with an 82.3% F1 score on a task of predicting a gender of a user based on his/her comments. The score surpasses 85% mark for frequent users with more than 50 comments. Furthermore, we discover that female users are less active on Reddit platform, they write fewer comments and post in fewer subreddits on average, when compared to male users.

Business documents contain valuable information. In order to comply with legal requirements, to serve as organisational knowledge and to prevent risks they need to be managed. However, changes in technology with which documents are being produced introduced new kinds of documents and new ways of interacting with documents. Thereby, the web 2.0 led to the development of Enterprise Collaboration Systems (ECS), which enable employees to use wiki, blog or forum applications for conducting their business. Part of the content produced in ECS can be called Social Business Documents (SBD). Compared to traditional digital documents SBD are different in their nature and structure as they are, for example, less well-structured and do not follow a strict lifecycle. These characteristics bring along new management challenges. However, currently research literature lacks investigations on the characteristics of SBD, their peculiarities and management. This dissertation uses document theory and documentary practice as theoretical lenses to investigate the new challenges of the long-term management of SBD in ECS. By using an interpretative, exploratory, mixed methods approach the study includes two major research parts. First, the nature and structure of Social Business Documents is addressed by analysing them within four different systems using four different modelling techniques each. The findings are used to develop general SBD information models, outlining the basic underlying components, structure, functions and included metadata, as well as a broad range of SBD characteristics. The second phase comprises a focus group, a case study including in-depth interviews and a questionnaire, all conducted with industry representatives. The focus group identified that the kind of SBD used for specific content and the actual place of storage differ between organisations as well as that there are currently nearly no management practices for SBD at hand. The case study provided deep insights into general document management activities and investigated requirements, challenges and actions for managing SBD. Finally, the questionnaire consolidated and deepened the previous findings. It provides insights about the value of SBD, their current management practices as well as management challenges and needs. Despite all participating organisations storing information worth managing in SBD most are not addressing them with management activities and many challenges remain. Together, the investigations enable a contribution to practice and theory. The progress in practice is summarised through a framework, addressing the long-term management of Social Business Documents. The framework identifies and outlines the requirements and challenges of and the actions for SBD management. It also indicates the dependencies of the different aspects. Furthermore, the findings enable the progress in theory within documentary practice by discussing the extension of document types to include SBD. Existing problems are outlined along the definitions of records and the newly possible characteristics of documents emerging through Social Business Documents are taken into account.

During the last couple of years the extension of the internet into the real world, also referred to as the Internet of Things (IoT), was positively affected by an ongoing digitalization (Mattern and Floerkemeier, 2010; Evans, 2013). Furthermore, one of the most active IoT domains is the personal health ecosystem (Steele and Clarke, 2013). However, this thesis proposes a gamification framework which is supported and enabled by IoT to bring personal health and IoT together in the context of health-insurances. By examining gamification approaches and identifying the role of IoT in such, a conceptual model of a gamification approach was created which indicates where and how IoT is ap-plicable to it. Hence, IoT acts as enabler and furthermore as enhancer of gamified activities. Especial-ly the necessity of wearable devices was highlighted. A stakeholder analysis shed light on respective benefits which concluded in the outcome, that IoT enabled two paradigm shifts for both, the insur-ance and their customer. While taking the results of the examination and the stakeholder analysis as input, the previously made insights were used to develop an IoT supported gamification framework. The framework includes a multi-level structure which is meant to guide through the process of creat-ing an approach but also to analyze already existing approaches. Additionally, the developed frame-work was instantiated based on the application Pokémon Go to identify occurring issues and explain why it failed to retain their customer in the long term. The thesis provides a foundation on which fur-ther context related research can be orientated.

Carabids, which are frequently distributed in agricultural landscapes, are natural enemies of different pests including slugs. Semi-natural habitats are known to affect carabids and thus, their potential to support natural pest control. The impact of semi-natural habitats was investigated on carabids and slugs within different non-crop habitats (chapter 2). Most carabids and Deroceras reticulatum showed preferences for herbaceous semi-natural habitats, while Arion spp. occured mainly in woody habitats. An increase of predatory carabid abundance, which was linked to an inclining amount of semi-natural habitats in the landscape, and a decrease of Arion spp. densities, indicated a high potential for slug control in structural rich landscapes. Effects of semi-natural habitats were investigated on predatory carabids and slugs in 18 wheat fields (chapter 3). Predatory carabid species richness was positively affected by the increasing amount of semi-natural habitats in the landscape, whereas predatory carabid abundance was neither influenced by adjacent habitat type nor by the proportion of semi-natural habitats in the landscape. The target pest species showed divergent patterns, whereas Arion spp. densities were highest in structural poor landscapes near woody margins. D. reticulatum was not affected by habitat type or landscape, reflecting its adaptation to agriculture. Results indicate an increased control of Arion spp. by carabids in landscapes with a high amount of semi-natural habitats. Effects of semi-natural habitats and the influence of farming system was tested on carabid distribution within 18 pumpkin fields (chapter 4). Carabid species richness generally increased with decreasing distance to the field margins, whereas carabid abundance responded differently according to the adjacent habitat type. Farming system had no effect on carabids and landscape heterogeneity only affected carabids in organic pumpkin fields. Slug and slug egg predation of three common carabid species was tested in single and double species treatments in the laboratory (chapter 5). Results show additive and synergistic effects depending on the carabid species. In general, semi-natural habitats can enhance the potential of slug control by carabids. This counts especially for Arionid slugs. Semi-natural habitats can support carabid communities by providing shelter, oviposition and overwintering sites as wells as complementary food sources. Therefore, it is important to provide a certain amount of non-crop habitats in agricultural landscapes.