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The semantic web and model-driven engineering are changing the enterprise computing paradigm. By introducing technologies like ontologies, metadata and logic, the semantic web improves drastically how companies manage knowledge. In counterpart, model-driven engineering relies on the principle of using models to provide abstraction, enabling developers to concentrate on the system functionality rather than on technical platforms. The next enterprise computing era will rely on the synergy between both technologies. On the one side, ontology technologies organize system knowledge in conceptual domains according to its meaning. It addresses enterprise computing needs by identifying, abstracting and rationalizing commonalities, and checking for inconsistencies across system specifications. On the other side, model-driven engineering is closing the gap among business requirements, designs and executables by using domain-specific languages with custom-built syntax and semantics. In this scenario, the research question that arises is: What are the scientific and technical results around ontology technologies that can be used in model-driven engineering and vice versa? The objective is to analyze approaches available in the literature that involve both ontologies and model-driven engineering. Therefore, we conduct a literature review that resulted in a feature model for classifying state-of-the-art approaches. The results show that the usage of ontologies and model-driven engineering together have multiple purposes: validation, visual notation, expressiveness and interoperability. While approaches involving both paradigms exist, an integrated approach for UML class-based modeling and ontology modeling is lacking so far. Therefore, we investigate the techniques and languages for designing integrated models. The objective is to provide an approach to support the design of integrated solutions. Thus, we develop a conceptual framework involving the structure and the notations of a solution to represent and query software artifacts using a combination of ontologies and class-based modeling. As proof of concept, we have implemented our approach as a set of open source plug-ins -- the TwoUse Toolkit. The hypothesis is that a combination of both paradigms yields improvements in both fields, ontology engineering and model-driven engineering. For MDE, we investigate the impact of using features of the Web Ontology Language in software modeling. The results are patterns and guidelines for designing ontology-based information systems and for supporting software engineers in modeling software. The results include alternative ways of describing classes and objects and querying software models and metamodels. Applications show improvements on changeability and extensibility. In the ontology engineering domain, we investigate the application of techniques used in model-driven engineering to fill the abstraction gap between ontology specification languages and programming languages. The objective is to provide a model-driven platform for supporting activities in the ontology engineering life cycle. Therefore, we study the development of core ontologies in our department, namely the core ontology for multimedia (COMM) and the multimedia metadata ontology. The results are domain-specific languages that allow ontology engineers to abstract from implementation issues and concentrate on the ontology engineering task. It results in increasing productivity by filling the gap between domain models and source code.
More than 10,000 organic chemicals such as pharmaceuticals, ingredients of personal care products and biocides are ubiquitously used in every day life. After their application, many of these chemicals enter the domestic sewer. Research has shown that conventional biological wastewater treatment in municipal wastewater treatment plants (WWTPs) is an insufficient barrier for the release of most of these anthropogenic chemicals into the receiving waters.
This bears unforeseen risks for aquatic wildlife and drinking water resources. Especially for recently introduced and/or detected compounds (so called emerging micropollutants), there is a growing need to investigate the occurrence and fate in WWTPs. In order to get a comprehensive picture on the behavior in municipal wastewater treatment, the following groups of emerging organic micropollutants, spanning a broad range of applications and physico-chemical properties, were selected as target compounds: pharmaceuticals (beta blockers, psycho-active drugs), UV-filters, vulcanization accelerators (benzothiazoles), biocides (anti-dandruffs, preservatives, disinfectants) and pesticides (phenylurea and triazine herbicides).
Folksonomies are Web 2.0 platforms where users share resources with each other. Furthermore, they can assign keywords (called tags) to the resources for categorizing and organizing the resources. Numerous types of resources like websites (Delicious), images (Flickr), and videos (YouTube) are supported by different folksonomies. The folksonomies are easy to use and thus attract the attention of millions of users. Together with the ease they offer, there are also some problems. This thesis addresses different problems of folksonomies and proposes solutions for these problems. The first problem occurs when users search for relevant resources in folksonomies. Often, the users are not able to find all relevant resources because they don't know which tags are relevant. The second problem is assigning tags to resources. Although many folksonomies (like Delicious) recommend tags for the resources, other folksonomies (like Flickr) do not recommend any tags. Tag recommendation helps the users to easily tag their resources. The third problem is that tags and resources are lacking semantics. This leads for example to ambiguous tags. The tags are lacking semantics because they are freely chosen keywords. The automatic identification of the semantics of tags and resources helps in reducing problems that arise from this freedom of the users in choosing the tags. This thesis proposes methods which exploit semantics to address the problems of search, tag recommendation, and the identification of tag semantics. The semantics are discovered from a variety of sources. In this thesis, we exploit web search engines, online social communities and the co-occurrences of tags as sources of semantics. Using different sources for discovering semantics reduces the efforts to build systems which solve the problems mentioned earlier. This thesis evaluates the proposed methods on a large scale data set. The evaluation results suggest that it is possible to exploit the semantics for improving search, recommendation of tags, and automatic identification of the semantics of tags and resources.
The aim of this dissertational work was to examine physiological (heart rate variability measures) and biomechanical parameters (step features) as possible anticipating indicators of psychological mood states. 420 participants (275 male and 145 female, age: M=34.7 years ± 9.7) engaged in a 60-minute slow endurance run while they were asked questions via a mobile answering and recording device. We measured several mood states, physiological measures, and biomechanical parameters. We used a latent growth curve analysis to examine the cross-lagged effects. Results demonstrated significant (p ≤.05) relationships between biomechanical shoe features anticipating psychological mood states, as well as psychological mood states anticipating physiological parameters.
Recent EU-frameworks enforce the implementation of risk mitigation measures for nonpoint-source pesticide pollution in surface waters. Vegetated surface flow treatments systems (VTS) can be a way to mitigate risk of adverse effects in the aquatic ecosystems following unavoidable pollution after rainfall-related runoff events. Studies in experimental wetland cells and vegetated ditch mesocosms with common fungicides, herbicides and insecticides were performed to assess efficiency of VTS. Comprehensive monitoring of fungicide exposure after rainfall-related runoff events and reduction of pesticide concentrations within partially optimised VTS was performed from 2006-2009 at five vegetated detention ponds and two vegetated ditches in the wine growing region of the Southern Palatinate (SW-Germany).
Influence of plant density, size related parameters and pesticide properties in the performance of the experimental devices, and the monitored systems were the focus of the analysis. A spatial tool for prediction of pesticide pollution of surface waters after rainfall-related runoff events was programmed in a geographic information system (GIS). A sophisticated and high resolution database on European scale was built for simulation. With the results of the experiments, the monitoring campaign and further results of the EU-Life Project ArtWET mitigation measures were implemented in a georeferenced spatial decision support system. The database for the GIS tools was built with open data. The REXTOX (ratio of exposure to toxicity) Risk Indicator, which was proposed by the OECD (Organisation for Economic Co-operation and Development), was extended, and used for modeling the risk of rainfall-related runoff exposure to pesticides, for all agricultural waterbodies on European scale. Results show good performance of VTS. The vegetated ditches and wetland cells of the experimental systems showed a very high reduction of more than 90% of pesticide concentrations and potential adverse effects. Vegetated ditches and wetland cells performed significantly better than devices without vegetation. Plant density and sorptivity of the pesticide were the variables with the highest explanatory power regarding the response variable reduction of concentrations. In the experimental vegetated ditches 65% of the reduction of peak concentrations was explained with plant density and KOC. The monitoring campaign showed that concentrations of the fungicides and potential adverse effects of the mixtures were reduced significantly within vegetated ditches (Median 56%) and detention ponds (Median 38%) systems. Regression analysis with data from the monitoring campaign identified plant density and size related properties as explanatory variables for mitigation efficiency (DP: R²=0.57, p<0.001; VD:
R²=0.19, p<0.001). Results of risk model runs are the input for the second tool, simulating three risk mitigation measures. VTS as risk mitigation measures are implemented using the results for plant density and size related performance of the experimental and monitoring studies, supported by additional data from the ArtWET project. Based on the risk tool, simulations can be performed for single crops, selected regions, different pesticide compounds and rainfall events. Costs for implementation of the mitigation measures are estimated. Experiments and monitoring, with focus on the whole range of pesticides, provide novel information on VTS for pesticide pollution. The monitoring campaign also shows that fungicide pollution may affect surface waters. Tools developed for this study are easy to use and are not only a good base for further spatial analysis but are also useful as decision support of the non-scientific community. On a large scale, the tools on the one hand can help to compute external costs of pesticide use with simulation of mitigation costs on three levels, on the other hand feasible measures mitigating or remediating the effects of nonpoint-source pollution can be identified for implementation. Further study of risk of adverse effects caused by fungicide pollution and long-time performance of optimised VTS is needed.
Aim of this study was the assessment of the conservation status of vascular plants in East African rain forests with the background of establishing an ex-situ culture of local endangered plants at the Botanic Garden of the Maseno University (Kenya).
For a sustainable implementation it was first necessary to learn more about the general species inventory, especially concerning species composition and abundance under human impact, and to assess the conservation priority of each plant species. Representative for East African rain forests, Kakamega Forest (Kenya) and Budongo Forest (Uganda) were selected to serve as model forests.
Beside the general floristic investigations including all vascular plants, a special focus was laid on vascular epiphytes and their vulnerability to forest disturbance. To assess the conservation priority of the plants, a rating system was developed based on seven threat criteria. By carrying out first plant collections, the exsitu culture in Maseno Botanic Garden was already initiated.
Studies have shown that wastewater treatment plant (WWTP) effluents are the major pathways of organic and inorganic chemicals of anthropogenic use (=micropollutants) into aquatic environments. There, micropollutants can be transferred to ground water bodies - and may finally end up in drinking water - or cause various effects in aquatic organisms like multiple resistances of bacteria. Hence, the upgrading of WWTPs with the aim to reduce the load of those micropollutants is currently under discussion.
Therefore, the primary objective of this thesis was to assess ecotoxicological effects of wastewater ozonation, a tertiary treatment method, using specifically developed toxicity tests with Gammarus fossarum (Koch) at various levels of ecological complexity. Several studies were designed in the laboratory and under semi-field conditions to cope with this primary objective. Prior to the investigations with ozone treated wastewater, the ecotoxicity of secondary treated (=non-ozone treated) wastewater from WWTP Wüeri, Switzerland, for the test species was assessed by a four-week experiment. This experiment displayed statistically significant impairments in feeding, assimilation and physiological endpoints related to population development and reproduction. The first experiment investigating ecotoxicological implications of ozone application in wastewater from the same WWTP displayed a preference of G. fossarum for leaf discs conditioned in ozone treated wastewater when offered together with leaf discs conditioned in non-ozone treated wastewater. This effect seems to be mainly driven by an alteration in the leaf associated microbial community. Another series of laboratory experiments conducted also with wastewater from WWTP Wüeri treated with ozone at the lab- or full-scale, revealed significantly increased feeding rates of G. fossarum exposed to ozone treated wastewater compared to non-ozone treated wastewater. These laboratory experiments also indicated that any alteration in the organic matrix potentially caused by ozone treatment is not related to the effects in feeding as this endpoint showed only negligible deviation in secondary treated wastewater, which contained hardly any (micro)pollutants (i.e. pharmaceuticals), from the same wastewater additionally treated with ozone. Moreover, it was shown that shifts in the dissolved organic carbon (DOC) profile do not affect the feeding rate of gammarids. In situ bioassays conducted in the receiving stream of the WWTP Wüeri confirmed the results of the laboratory experiments by displaying significantly reduced feeding rates of G. fossarum exposed below the WWTP effluent if non-ozone treated wastewater was released. However, at the time the ozonation was operating, no adverse effects in feeding rates were observed below the effluent compared to the unaffected upstream sites. Also population studies in on-site flow-through stream microcosms displayed an increased feeding and a statistically significantly higher population size after ten weeks when exposed to ozone treated wastewater compared to non-ozone treated wastewater.
In conclusion, the present thesis documents that ozonation might be a suitable tool to reduce both the load of micropollutants as well as the ecotoxicity of wastewaters. Thus, this technology may help to meet the requirements of the Water Framework Directive also under predicted climate change scenarios, which may lead to elevated proportions of wastewater in the receiving stream during summer discharge. However, as ozone application may also produce by-products with a higher toxicity than their parent compounds, the implementation of this technique should be assessed further both via chemical analysis and ecotoxicological bioassays.
In this thesis, I study the spectral characteristics of large dynamic networks and formulate the spectral evolution model. The spectral evolution model applies to networks that evolve over time, and describes their spectral decompositions such as the eigenvalue and singular value decomposition. The spectral evolution model states that over time, the eigenvalues of a network change while its eigenvectors stay approximately constant.
I validate the spectral evolution model empirically on over a hundred network datasets, and theoretically by showing that it generalizes arncertain number of known link prediction functions, including graph kernels, path counting methods, rank reduction and triangle closing. The collection of datasets I use contains 118 distinct network datasets. One dataset, the signed social network of the Slashdot Zoo, was specifically extracted during work on this thesis. I also show that the spectral evolution model can be understood as a generalization of the preferential attachment model, if we consider growth in latent dimensions of a network individually. As applications of the spectral evolution model, I introduce two new link prediction algorithms that can be used for recommender systems, search engines, collaborative filtering, rating prediction, link sign prediction and more.
The first link prediction algorithm reduces to a one-dimensional curve fitting problem from which a spectral transformation is learned. The second method uses extrapolation of eigenvalues to predict future eigenvalues. As special cases, I show that the spectral evolution model applies to directed, undirected, weighted, unweighted, signed and bipartite networks. For signed graphs, I introduce new applications of the Laplacian matrix for graph drawing, spectral clustering, and describe new Laplacian graph kernels. I also define the algebraic conflict, a measure of the conflict present in a signed graph based on the signed graph Laplacian. I describe the problem of link sign prediction spectrally, and introduce the signed resistance distance. For bipartite and directed graphs, I introduce the hyperbolic sine and odd Neumann kernels, which generalize the exponential and Neumann kernels for undirected unipartite graphs. I show that the problem of directed and bipartite link prediction are related by the fact that both can be solved by considering spectral evolution in the singular value decomposition.
Model-Driven Engineering (MDE) aims to raise the level of abstraction in software system specifications and increase automation in software development. Modelware technological spaces contain the languages and tools for MDE that software developers take into consideration to model systems and domains. Ontoware technological spaces contain ontology languages and technologies to design, query, and reason on knowledge. With the advent of the Semantic Web, ontologies are now being used within the field of software development, as well. In this thesis, bridging technologies are developed to combine two technological spaces in general. Transformation bridges translate models between spaces, mapping bridges relate different models between two spaces, and, integration bridges merge spaces to new all-embracing technological spaces. API bridges establish interoperability between the tools used in the space. In particular, this thesis focuses on the combination of modelware and ontoware technological spaces. Subsequent to a sound comparison of languages and tools in both spaces, the integration bridge is used to build a common technological space, which allows for the hybrid use of languages and the interoperable use of tools. The new space allows for language and domain engineering. Ontology-based software languages may be designed in the new space where syntax and formal semantics are defined with the support of ontology languages, and the correctness of language models is ensured by the use of ontology reasoning technologies. These languages represent a core means for exploiting expressive ontology reasoning in the software modeling domain, while remaining flexible enough to accommodate varying needs of software modelers. Application domains are conceptually described by languages that allow for defining domain instances and types within one domain model. Integrated ontology languages may provide formal semantics for domain-specific languages and ontology technologies allow for reasoning over types and instances in domain models. A scenario in which configurations for network device families are modeled illustrates the approaches discussed in this thesis. Furthermore, the implementation of all bridging technologies for the combination of technological spaces and all tools for ontology-based language engineering and use is illustrated.