Refine
Year of publication
- 2018 (37) (remove)
Document Type
- Doctoral Thesis (20)
- Master's Thesis (7)
- Bachelor Thesis (6)
- Part of Periodical (3)
- Conference Proceedings (1)
Language
- English (37) (remove)
Is part of the Bibliography
- no (37) (remove)
Keywords
- 1H-NMR Relaxometry (1)
- Analysis of social platform (1)
- Bodenchemie (1)
- Bodenphysik (1)
- Cloud Point Extraction (1)
- Collaboration (1)
- Computer Vision (1)
- Deep Metric Learning (1)
- Documents (1)
- Economic potential (1)
Institute
- Institute for Web Science and Technologies (9)
- Institut für Umweltwissenschaften (6)
- Fachbereich 7 (5)
- Institut für Wirtschafts- und Verwaltungsinformatik (5)
- Institut für Computervisualistik (4)
- Fachbereich 4 (3)
- Institut für Management (2)
- Arbeitsbereich Entwicklungspsychologie und Pädagogische Psychologie (1)
- Fachbereich 8 (1)
- Institut für Informatik (1)
Entrepreneurship plays a vital role in scientific literature and in public debates. Especially in these hightech and digitized times it happens more and more frequently that young entrepreneurs with a good idea make the breakthrough and set up an established company. Basically, there are an increasing number of start-ups and a trend towards independence. The economy of a country depends on young entrepreneurs in order to remain economically competitive in international competition. It follows that young entrepreneurs must be encouraged and supported. This support is expressed in various stages of foundation and through various fields of action. In the meantime, there are many offers for start-up support. These networks satisfy different fields of action along a foundation. However, a structured overview of these networks on which a young founder can orient himself and gain easily access to the offers of the networks, is missing until then.
This work attempts to present these offers clearly on a map and to categorize and present the commitment in the respective fields of action. In addition to this main objective, the following three key questions are investigated and answered in this work:
1. How can the Entrepreneurship Networks be assigned to the respective fields of action of Entrepreneurship Education?
2. What is the benefit of such a classification for potential entrepreneurs in detail?
3. Are these Entrepreneurship networks missing an important step? Might they improve their offer? Does the value chain cover every need a young entrepreneur might have?
For this purpose, the respective fields of action of the networks are first separated from each other along a founding and defined individually. Subsequently, a combination of quantitative and qualitative approaches was used to filter and analyze the contents of the websites of the networks. The results of this investigation were transformed in a classification
The aim of this work is to produce a map that displays the existing networks in the world clearly. The map also contains information that is more detailed and the classifica-tion of the networks in the respective fields of action.
Ontologies are valuable tools for knowledge representation and important building blocks of the Semantic Web. They are not static and can change over time. Changing an ontology can be necessary for various reasons: the domain that is represented by an ontology can change or an ontology is reused and must be adapted to the new context. In addition, modeling errors could have been introduced into the ontology which must be found and removed. The non-triviality of the change process has led to the emerge of ontology change as an own field of research. The removal of knowledge from ontologies is an important aspect of this change process, because even the addition of new knowledge to an ontology potentially requires the removal of older, conflicting knowledge. Such a removal must be performed in a thought-out way. A naïve change of concepts within the ontology can easily remove other, unrelated knowledge or alter the semantics of concepts in an unintended way [2]. For these reasons, this thesis introduces a formal operator for the fine-grained retraction of knowledge from EL concepts which is partially based on the postulates for belief set contraction and belief base contraction [3, 4, 5] and the work of Suchanek et al. [6]. For this, a short introduction to ontologies and OWL 2 is given and the problem of ontology change is explained. It is then argued why a formal operator can support this process and why the Description Logic EL provides a good starting point for the development of such an operator. After this, a general introduction to Description Logic is given. This includes its history, an overview of its applications and common reasoning tasks in this logic. Following this, the logic EL is defined. In a next step, related work is examined and it is shown why the recovery postulate and the relevance postulate cannot be naïvely employed in the development of an operator that removes knowledge from EL concepts. Following this, the requirements to the operator are formulated and properties are given which are mainly based on the postulates for belief set and belief base contraction. Additional properties are developed which make up for the non-applicability of the recovery and relevance postulates. After this, a formal definition of the operator is given and it is shown that the operator is applicable to the task of a fine-grained removal of knowledge from EL concepts. In a next step, it is proven that the operator fulfills all the previously defined properties. It is then demonstrated how the operator can be combined with laconic justifications [7] to assist a human ontology editor by automatically removing unwanted consequences from an ontology. Building on this, a plugin for the ontology editor Protégé is introduced that is based on algorithms that were derived from the formal definition of the operator. The content of this work is then summarized and a final conclusion is drawn. The thesis closes with an outlook into possible future work.
Social entrepreneurship is a form of entrepreneurship that marries a social mission to a competitive value proposition. Notably, social entrepreneurship fosters a more equitable society by addressing social issues and trying to achieve an ongoing sustainable impact through a social mission rather than purely profit maximization. The topic of social entrepreneurship has appealed considerably to many different streams of research. The focus on understanding how and why entrepreneurs think and act is a significant justification for future research. Nevertheless, the theoretical examination of this phenomenon is in its infancy. Social entrepreneurship research is still largely phenomenon-driven. Specifically, Social Entrepreneurial Intention is in an early stage and lacks quantitative research. Therefore, this thesis proposes to address this need. The thesis’ objectives are twofold: (1) develop a formation model for Social Entrepreneurial Intentions in general and (2) test the model by conducting an empirical study. Based on these objectives, the two research questions guiding the thesis are (1) what factors influence the intention of a person to become a social entrepreneur and (2) what relationships exist among these factors.
In order to answer these two research questions, this thesis uses purposeful research design, which is a combination of literature review and empirical study. The literature review is based on a comprehensive range of books, articles, and research papers published in leading academic journals and conference proceedings in different disciplines such as entrepreneurship, social entrepreneurship, entrepreneurship education, management, social psychology, and social economics. The empirical study is conducted via a survey of 600 last-year students from four universities in three regions in Vietnam: Hanoi, Da Nang, and Ho Chi Minh. The data are analyzed with SPSS-AMOS version 24, using screening data, scale development, exploratory factor analysis, and confirmation factor analysis. The thesis ascertains that Entrepreneurship Experience/Extra-curricular Activity, Role Model, Social Entrepreneurial Self-Efficacy, and Social Entrepreneurial Outcome Expectation directly and positively affect the intention of the Vietnamese students to be social entrepreneurs. Entrepreneurship Education also influences the Social Entrepreneurial Intention, but not directly, otherwise indirectly via Social Entrepreneurial Self-Efficacy and Social Entrepreneurial Outcome Expectation. Similarly, Perceived Support has no direct relationship to Social Entrepreneurial Intention; however, it shows an indirect link via the mediator ‘Social Entrepreneurial Outcome Expectation’. Furthermore, the dissertation brings new insights to the social entrepreneurship literature and provides important implications for practice. Limitations and future directions are also provided in the thesis.
The primary aims of the study are (1) to identify classroom instructional factors which have a crucial effect on the academic growth of ninth-graders in EFL in Vietnam, and (2) to gain insight into their interplay with each other and with context factors. Besides, this study has a strong focus on methodological approaches: (a) using multiple methods in order to deal with the “large p, small n” problem, (b) to understand the relevance of the scaling model used for the results.
Data from a research project carried out in Vietnam during the school year 2006–2007 were used in this study. Besides a longitudinal design with two measurement points (MPs) using adapted English tests and questionnaires from the DESI-study in Germany, a video study was conducted in the middle of the school year between two MPs. The recorded video data were transcribed, micro-analytically coded, and lessons were rated to gain indicators of classroom instruction. Different IRT scaling models were chosen to estimate student ability in the pretest and posttest. For the C-test, the unidimensional 1PL and 2PL models, the Rasch testlet model, and testlet 2PL model were selected to model student ability. To estimate student ability via the listening comprehension test (LC-test), the Rasch model, the unidimensional 2PL, and 3PL models were applied. The student ability estimates at the two MPs were linked to one common scale using the concurrent calibration approach with different a priori ability distributions. The plausible values (PVs) were generated and treated as student ability estimates for all analyses. To understand the relationship between the instructional variables and student growth, we explored the hypothesized linear and nonlinear, additive and interactive effects of classroom instructional factors. To examine these hypothetical effects, OLS and regularized regression models using lasso (least absolute shrinkage and selection operators) were applied, including main effects as well as quadratic and interaction terms of instructional variables. Initial student ability and the socioeconomic status of students were treated as context variables.
The results show, on the one hand, a positive view of important general instructional quality dimensions of teaching effectiveness and, on the other hand, a strongly teacher-centered and textbook-driven instruction and poor instructional quality from the point of view of EFL didactics. The most important instructional factors of student growth in the C-test were quality aspects of motivation in instruction as well as aspects related to the teaching language. Regarding the LC-test results, language-related aspects together with the relative frequency of repeated questions were the most important predictors of student growth. While the findings confirmed all the hypothesized instructional effects on student growth, aptitude treatment interaction effects of instruction were only confirmed with regard to student growth in the C-test. The different scaling models produced significant differences in the results regarding instructional effects on student growth.
Navigation is a natural way to explore and discover content in a digital environment. Hence, providers of online information systems such as Wikipedia---a free online encyclopedia---are interested in providing navigational support to their users. To this end, an essential task approached in this thesis is the analysis and modeling of navigational user behavior in information networks with the goal of paving the way for the improvement and maintenance of web-based systems. Using large-scale log data from Wikipedia, this thesis first studies information access by contrasting search and navigation as the two main information access paradigms on the Web. Second, this thesis validates and builds upon existing navigational hypotheses to introduce an adaptation of the well-known PageRank algorithm. This adaptation is an improvement of the standard PageRank random surfer navigation model that results in a more "reasonable surfer" by accounting for the visual position of links, the information network regions they lead to, and the textual similarity between the link source and target articles. Finally, using agent-based simulations, this thesis compares user models that have a different knowledge of the network topology in order to investigate the amount and type of network topological information needed for efficient navigation. An evaluation of agents' success on four different networks reveals that in order to navigate efficiently, users require only a small amount of high-quality knowledge of the network topology. Aside from the direct benefits to content ranking provided by the "reasonable surfer" version of PageRank, the empirical insights presented in this thesis may also have an impact on system design decisions and Wikipedia editor guidelines, i.e., for link placement and webpage layout.
The concept of hard and soft news (HSN) is regarded as one of the most important concepts in journalism research. Despites this popularity, two major research voids can be assigned to the concept. First, it lacks conceptual clarity: the concept gets used interchangeably with related concepts such as sensationalism, which has led to fuzzy demarcations of HSN. Also, it is still not agreed on of which dimensions the concept in composed. Second, little is known about the factors that influence the production of news in terms of their hard or soft nature. The present disserta-tion casts a twofold glance on the HSN concept – it aims to assess the conceptual status of the concept and production of hard and soft news.
At the outset, this dissertation delineates the theoretical base for three manuscripts in total and presented considerations on concepts in social sciences in general and hard and soft news in particular as well as the production of news, particularly of hard and soft news. The first paper proposed a theoretical frame-work model to distinguish HSN and related concepts. Based on a literature review of in total five concepts, this model suggested a hierarchy in which these concepts can be discerned according to their occurrence in media content. The second pa-per focused on the inner coherence of the HSN concept in its most recent academ-ic understanding. The results of a factorial survey with German newspaper jour-nalists showed that, indeed, four out of five dimensions of the HSN concept com-prised what the journalists understood by it. Hence, the most recent academic un-derstanding is to a great extent coherent. The third study shed light on the produc-tion of HSN, focusing on the influence of individual journalists’ and audience’s characteristics on whether news was presented in hard or soft way. The findings of a survey with simulated decision scenarios among German print journalists showed that the HSN dimensions were susceptible to different journalistic influ-ences and that a perceived politically uninterested audience led to a softer cover-age. The dissertation concluded with connecting these findings with the considera-tions on concept evaluation and the production of news. Implications for research on and with the concept of HSN were presented, before concluding with limitations and suggestions for future research.
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.
This thesis addresses the automated identification and localization of a time-varying number of objects in a stream of sensor data. The problem is challenging due to its combinatorial nature: If the number of objects is unknown, the number of possible object trajectories grows exponentially with the number of observations. Random finite sets are a relatively new theory that has been developed to derive at principled and efficient approximations. It is based around set-valued random variables that contain an unknown number of elements which appear in arbitrary order and are themselves random. While extensively studied in theory, random finite sets have not yet become a leading paradigm in practical computer vision and robotics applications. This thesis explores random finite sets in visual tracking applications. The first method developed in this thesis combines set-valued recursive filtering with global optimization. The problem is approached in a min-cost flow network formulation, which has become a standard inference framework for multiple object tracking due to its efficiency and optimality. A main limitation of this formulation is a restriction to unary and pairwise cost terms. This circumstance makes integration of higher-order motion models challenging. The method developed in this thesis approaches this limitation by application of a Probability Hypothesis Density filter. The Probability Hypothesis Density filter was the first practically implemented state estimator based on random finite sets. It circumvents the combinatorial nature of data association itself by propagation of an object density measure that can be computed efficiently, without maintaining explicit trajectory hypotheses. In this work, the filter recursion is used to augment measurements with an additional hidden kinematic state to be used for construction of more informed flow network cost terms, e.g., based on linear motion models. The method is evaluated on public benchmarks where a considerate improvement is achieved compared to network flow formulations that are based on static features alone, such as distance between detections and appearance similarity. A second part of this thesis focuses on the related task of detecting and tracking a single robot operator in crowded environments. Different from the conventional multiple object tracking scenario, the tracked individual can leave the scene and later reappear after a longer period of absence. Therefore, a re-identification component is required that picks up the track on reentrance. Based on random finite sets, the Bernoulli filter is an optimal Bayes filter that provides a natural representation for this type of problem. In this work, it is shown how the Bernoulli filter can be combined with a Probability Hypothesis Density filter to track operator and non-operators simultaneously. The method is evaluated on a publicly available multiple object tracking dataset as well as on custom sequences that are specific to the targeted application. Experiments show reliable tracking in crowded scenes and robust re-identification after long term occlusion. Finally, a third part of this thesis focuses on appearance modeling as an essential aspect of any method that is applied to visual object tracking scenarios. Therefore, a feature representation that is robust to pose variations and changing lighting conditions is learned offline, before the actual tracking application. This thesis proposes a joint classification and metric learning objective where a deep convolutional neural network is trained to identify the individuals in the training set. At test time, the final classification layer can be stripped from the network and appearance similarity can be queried using cosine distance in representation space. This framework represents an alternative to direct metric learning objectives that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large scale person re-identification datasets where competitive results are achieved overall. In particular, the proposed method better generalizes to the test set compared to a network trained with the well-established triplet loss.
Systemic neonicotinoids are one of the most widely used insecticide classes worldwide. In addition to their use in agriculture, they are increasingly applied on forest trees as a protective measure against insect pests. However, senescent leaves containing neonicotinoids might, inter alia during autumn leaf fall, enter nearby streams. There, the hydrophilic neonicotinoids may be remobilized from leaves to water resulting in waterborne exposure of aquatic non-target organisms. Despite the insensitivity of the standard test species Daphnia magna (Crustacea, Cladocera) toward neonicotinoids, a potential risk for aquatic organisms is evident as many other aquatic invertebrates (in particular insects and amphipods) display adverse effects when exposed to neonicotinoids in the ng/L- to low µg/L-range. In addition to waterborne exposure, in particular leaf-shredding invertebrates (= shredders) might be adversely affected by the introduction of neonicotinoid-contaminated leaves into the aquatic environment since they heavily rely on leaf litter as food source. However, dietary neonicotinoid exposure of aquatic shredders has hardly received any attention from researchers and is not considered during aquatic environmental risk assessment. The primary aim of this thesis is, therefore, (1) to characterize foliar neonicotinoid residues and exposure pathways relevant for aquatic shredders, (2) to investigate ecotoxicological effects of waterborne and dietary exposure on two model shredders, namely Gammarus fossarum (Crustacea, Amphipoda) and Chaetopteryx villosa (Insecta, Trichoptera), and (3) to identify biotic and abiotic factors potentially modulating exposure under field conditions.
During the course of this thesis, ecotoxicologically relevant foliar residues of the neonicotinoids imidacloprid, thiacloprid and acetamiprid were quantified in black alder trees treated at field relevant levels. A worst-case model – developed to simulate imidacloprid water concentrations resulting from an input of contaminated leaves into a stream – predicted only low aqueous imidacloprid concentrations (i.e., ng/L-range). However, the model identified dietary uptake as an additional exposure pathway relevant for shredders up to a few days after the leaves’ introduction into the stream. When test organisms were simultaneously exposed (= combined exposure) to neonicotinoids leaching from leaves into the water and via the consumption of contaminated leaves, adverse effects exceeded those observed under waterborne exposure alone. When exposure pathways were separated using a flow-through system, dietary exposure towards thiacloprid-contaminated leaves caused similar sublethal adverse effects in G. fossarum as observed under waterborne exposure. Moreover, the effect sizes observed under combined exposure were largely predictable using the reference model “independent action”, which assumes different molecular target sites to be affected. Dietary toxicity for shredders might, however, be reduced under field conditions since UV-induced photodegradation and leaching decreased imidacloprid residues in leaves and thereby the toxicity for G. fossarum. In contrast, both shredders were found unable to actively avoid dietary exposure. This thesis thus recommends considering dietary exposure towards systemic insecticides, such as neonicotinoids, already during their registration to safeguard aquatic shredders, associated ecosystem functions (e.g., leaf litter breakdown) and ultimately ecosystem integrity.
Fresh water resources like rivers and reservoirs are exposed to a drastically changing world. In order to safeguard these lentic ecosystems, they need stronger protection in times of global change and population growth. In the last years, the exploitation pressure on drinking water reservoirs has increased steadily worldwide. Besides securing the demands of safe drinking water supply, international laws especially in Europe (EU Water Framework Directive) stipulate to minimize the impact of dams on downstream rivers. In this study we investigate the potential of a smart withdrawal strategy at Grosse Dhuenn Reservoir to improve the temperature and discharge regime downstream without jeopardizing drinking water production. Our aim is to improve the existing withdrawal strategy for operating the reservoir in a sustainable way in terms of water quality and quantity. First, we set-up and calibrated a 1D numerical model for Grosse Dhuenn Reservoir with the open-source community model “General Lake Model” (GLM) together with its water quality module “Aquatic Ecodynamics” library (AED2). The reservoir model reproduced water temperatures and hypolimnetic dissolved oxygen concentrations accurately over a 5 year period. Second, we extended the model source code with a selective withdrawal functionality (adaptive offtake) and added operational rules for a realistic reservoir management. Now the model is able to autonomously determine the best withdrawal height according to the temperature and flow requirements of the downstream river and the raw water quality objectives. Criteria for the determination of the withdrawal regime are selective withdrawal, development of stratification and oxygen content in the deep hypolimnion. This functionality is not available in current reservoir models, where withdrawal heights are generally provided a priori to the model and kept fixed during the simulation. Third, we ran scenario simulations identifying an improved reservoir withdrawal strategy to balance the demands for downstream river and raw water supply. Therefore we aimed at finding an optimal parallel withdrawal ratio between cold hypolimnetic water and warm epilimnetic or metalimnetic water in order to provide a pre-defined temperature in the downstream river. The reservoir model and the proposed withdrawal strategy provide a simple and efficient tool to optimize reservoir management in a multi-objective view for mastering future reservoir management challenges.