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Institute
Culture and violence
(2010)
The basic assumption of this study is that specific cultural conditions may lead to psychopathological reactions through which an increase in interpersonal violence may happen. The objective of this study was to define to what extent homicide rates across national cultures might be associated with the strength of their attitudes toward specific beliefs and values, and their scores in specific cultural dimensions. To answer this question, nine independent variables were defined six of which were related to the people- attitudes pertaining importance of religion (Religiosity), excessive feeling of choice and control (Omnipotence), clear-cut distinction between good and evil (Absolutism), proud of their nationality (Nationalism), approval of competition (Competitiveness), and high respect for authorities and emphasis on obedience (Authoritarianism). The data for these variables were collected from World Values Survey. For two cultural dimensions, Collectivism, and Power Distance, Hofstede- scores were used. The 9th variable was GNI per capita. After estimation of 7% missing values in the whole data through multiple imputation, a sample of 81 nations was used for further statistical analyses.
Results: Stepwise regression analysis indicated Omnipotence and GNI as the strongest predictors of homicide (β = .44 P = .000; β = -.27 P = .006 respectively). The 9 independent variables were loaded on two factors, socio-economic development (SED) and psycho-cultural factor (Psy-Cul), which were negatively correlated (-.47). The Psy-Cul was interpreted as an indicator of narcissism, and a mediator between SED and homicide. Hierarchical cluster analysis made a clear distinction among three main groups of Western, Developing, and post-Communist nations on the basis of the two factors.
Specifying behaviors of multi-agent systems (MASs) is a demanding task, especially when applied in safety-critical systems. In the latter systems, the specification of behaviors has to be carried out carefully in order to avoid side effects that might cause unwanted or even disastrous behaviors. Thus, formal methods based on mathematical models of the system under design are helpful. They not only allow us to formally specify the system at different levels of abstraction, but also to verify the consistency of the specified systems before implementing them. The formal specification aims a precise and unambiguous description of the behavior of MASs, whereas the verification aims at proving the satisfaction of specified requirements. A behavior of an agent can be described as discrete changes of its states with respect to external or internal actions. Whenever an action occurs, the agent moves from one state to another one. Therefore, an efficient way to model this type of discrete behaviors is to use a kind of state transition diagrams such as finite automata. One remarkable advantage of such transition diagrams is that they lend themselves formal analysis techniques using model checking. The latter is an automatic verification technique which determines whether given properties are satisfied within a model underlying a particular system. In realistic physical environments, however, it is necessary to consider continuous behaviors in addition to discrete behaviors of MASs. Examples of those type of behaviors include the movement of a soccer agent to kick off or to go to the ball, the process of putting out the fire by a fire brigade agent in a rescue scenario, or any other behaviors that depend on any timed physical law. The traditional state transition diagrams are not sufficient to combine these types of behaviors. Hybrid automata offer an elegant method to capture such types of behaviors. Hybrid automata extend regular state transition diagrams with methods that deal with those continuous actions such that the state transition diagrams are used to model the discrete changes of behaviors, while differential equations are used to model the continuous changes. The semantics of hybrid automata make them accessible to formal verification by means of model checking. The main goal of this thesis is to approach hybrid automata for specifying and verifying behaviors of MASs. However, specifying and and verifying behaviors of MASs by means of hybrid automata raises several issues that should be considered. These issues include the complexity, modularity, and the expressiveness of MASs' models. This thesis addresses these issues and provides possible solutions to tackle them.
Investigating the environmental fate of iodinated X-ray contrast media in the urban water cycle
(2010)
Iodinated X-ray contrast media (ICM) are a group of emerging contaminants which have been detected at elevated concentrations in the aquatic environment. These compounds are excreted unmetabolized into hospital wastewater, and eventually treated at wastewater treatment plants (WWTPs). The removal of ICM in WWTPs has not been very effective and therefore the ICM enter the aquatic environment via WWTP effluent discharges. Research has investigated the removal of selected ICM via abiotic and biotic processes, however limited work has attempted to determine the fate of these compounds once released into the environment. This thesis investigates the biotransformation of four selected ICM (diatrizoate, iohexol, iomeprol, and iopamidol) in aerobic soil-water and sediment-water systems as well as in different environmental matrices. Iohexol, iomeprol and iopamidol were biotransformed to several TPs in the aerobic batch systems, while no biotransformation was observed for the ionic ICM, diatrizoate. In total 34 biotransformation products (TPs) of the three non-ionic ICM were identified. The combination of semi-preparative HPLC-UV, hybrid triple quadrupole-linear ion trap mass spectrometry (Qq-LIT-MS) was found to be an accurate approach for the structural elucidation of ICM TPs. The ICM TPs resulted in microbial transformation occurring at the side chains of the parent ICM, with the iodinated aromatic ring unmodified.
This dissertation provides an interdisciplinary contribution to the project ReGLaN-Health & Logistics. ReGLaN-Health & Logistics, is an international cooperation deriving benefits from the capabilities of scientists working on different fields. The aim of the project is the development of a socalled SDSS that supports decision makers working within health systems with a special focus on rural areas. In this dissertation, one important component for the development of the DSS named EWARS is proposed and described in detail. This component called SPATTB is developed with the intention of dealing with spatial data, i.e. data with additional geocoded information with regard to the special requirements of the EWARS.rnrnAn important component in the process of developing the EWARS is the concept of GIS. Classically, geocoded information with a vectorial character numerically describing spatial phenomena is managed and processed in a GIS. For the development of the EWARS, the manageability of the type of data exemplarily given by (x,y,o) with coordinates x,y ) and Ozon-concentration o is not sufficient. It is described, that the manageable data has to be extended to data of type (x,y,f ), where (x,y) are the geocoded information, but where f is not only a numerical value but a functional description of a certain phenomenom. An example for the existence and appearance of that type of data is the geocoded information about the variation of the Ozon-concentration in time or depending on temperature. A knowledge-base as important subsystem of DSS containing expert knowledge is mentioned. This expert-knowledge can be made manageable when using methods from the field of fuzzy logic. Thereby mappings, socalled fuzzy-sets, are generated. Within the EWARS, these mappings will be used with respect to additional geocoded data. The knowledge about the geocoded mapping information only at a finite set of locations (x,y) associated with mapping information f is not sufficient in applications that need continuous statements in a certain geographical area. To provide a contribution towards solving this problem, methods from the field of computer geometry and CAD, so-called Bezier-methods, are used for interpolating this geocoded mapping information. Classically, these methods operates on vectors a the multidimensional vector-space whose elements contain real-valued components but in terms of dealing with mapping information, there has to be an extension on topological vector spaces since mapping spaces can be defined as such spaces. This builds a new perspective and possibility in the application of these methods. Therefore, the according algorithms have to be extended; this work is presented. The field of Artificial Neural Networks plays an important role for the processing and management of the data within the EWARS, where features of biological processes and structures are modeled and implemented as algorithms. Generally, the developed methods can be divided as usable in terms of interpolation or approximation functional coherences and in such being applicable to classification problems. In this dissertation one method from each type is regarded in more detailed. Thereby, the classical algorithms of the so-called Backpropagation-Networks for approximation and the Kohonen-Networks for classification are described. Within the thesis, an extension of these algorithms is then proposed using coherences from mathematical measure-theory and approximation theory. The mentioned extension of these algorithms is based on a preprocessing of the mapping data using integration methods from measure theory.