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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.
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.
In this thesis we exercise a wide variety of libraries, frameworks and other technologies that are available for the Haskell programming language. We show various applications of Haskell in real-world scenarios and contribute implementations and taxonomy entities to the 101companies system. That is, we cover a broad range of the 101companies feature model and define related terms and technologies. The implementations illustrate how different language concepts of Haskell, such as a very strong typing system, polymorphism, higher-order functions and monads, can be effectively used in the development of information systems. In this context we demonstrate both advantages and limitations of different Haskell technologies.
Distance vector routing protocols are interior gateway protocols in which every router sets up a routing table with the help of the information it receives from its neighboring routers. The routing table contains the next hops and associated distances on the shortest paths to every other router in the network. Security mechanisms implemented in distance vector routing protocols are insufficient. It is rather assumed that the environment is trustworthy. However, routers can be malicious for several reasons and manipulate routing by injecting false routing updates. Authenticity and integrity of transmitted routing updates have to be guaranteed and at the same time performance and benefits should be well-balanced.
In this paper several approaches that aim at meeting the above mentioned conditions are examined and their advantages and disadvantages are compared.
Cloud Computing is a topic that has gained momentum in the last years. Current studies show that an increasing number of companies is evaluating the promised advantages and considering making use of cloud services. In this paper we investigate the phenomenon of cloud computing and its importance for the operation of ERP systems. We argue that the phenomenon of cloud computing could lead to a decisive change in the way business software is deployed in companies. Our reference framework contains three levels (IaaS, PaaS, SaaS) and clarifies the meaning of public, private and hybrid clouds. The three levels of cloud computing and their impact on ERP systems operation are discussed. From the literature we identify areas for future research and propose a research agenda.
This paper describes results of the simulation of social objects, the dependence of schoolchildren's professional abilities on their personal characteristics. The simulation tool is the artificial neural network (ANN) technology. Results of a comparison of the time expense for training the ANN and for calculating the weight coefficients with serial and parallel algorithms, respectively, are presented.
An estimation of the number of multiplication and addition operations for training artififfcial neural networks by means of consecutive and parallel algorithms on a computer cluster is carried out. The evaluation of the efficiency of these algorithms is developed. The multilayer perceptron, the Volterra network and the cascade-correlation network are used as structures of artififfcial neural networks. Different methods of non-linear programming such as gradient and non-gradient methods are used for the calculation of the weight coefficients.
Identifying reusable legacy code able to implement SOA services is still an open research issue. This master thesis presents an approach to identify legacy code for service implementation based on dynamic analysis and the application of data mining techniques. rnrnAs part of the SOAMIG project, code execution traces were mapped to business processes. Due to the high amount of traces generated by dynamic analyses, the traces must be post-processed in order to provide useful information. rnrnFor this master thesis, two data mining techniques - cluster analysis and link analysis - were applied to the traces. First tests on a Java/Swing legacy system provided good results, compared to an expert- allocation of legacy code.