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Procedural content generation, the generation of video game content using pseudo-random algorithms, is a field of increasing business and academic interest due to its suitability for reducing development time and cost as well as the possibility of creating interesting, unique game spaces. Although many contemporary games feature procedurally generated content, the author perceived a lack of games using this approach to create realistic outer-space game environments, and the feasibility of employing procedural content generations in such a game was examined. Using current scientific models, a real-time astronomical simulation was developed in Python which generates star and planets object in a fictional galaxy procedurally to serve as the game space of a simple 2D space exploration game where the player has to search for intelligent life.
Wild boars belong to the most wide spread ungulates in the world. They are characterized by a well performed adaption to their environment mainly due to their omnivorous dietary. The wild boar population in Germany increased during the past three decades. Nowadays their high density leads to problems in agricultural areas due to damage of crops and plays a significant role as disease vector as the classical swine fever. For an effective population management population size information is of crucial importance. Different traditional methods exist to estimate population sizes as direct sightnings, faecal drop counts or hunting harvest which provide only relative estimates and population trends. Absolute population sizes could be yielded by a Capture-Mark-Recapture (CMR) approach. However, capturing of wild boars is difficult to realize and costly in terms of personnel and field effort.
Furthermore the capture probabilities are heterogeneous due to the variable behaviour of individuals influenced by age, sex, and experience of the animals. Non-invasive genetic methods are a promising complement to the traditional methods for population size estimation particularly for wild boar. These methods reduce stress and capture bias and increase the number of re-captures. Faeces proved to be a suitable DNA source for wild boar genotyping, due to almost equal capture probability. However working with faeces implicates difficulties such as low DNA rnquality and quantity, genotyping errors as dropout and false alleles.
The main aim of the present study was to develop a reliable, cost-efficient, reproducible and practicable method for wild boar genotyping. This method should provide a reliable dataset of genotypes obtained from the collected faeces samples. Individual identification forms the basis for an improved mark-recapture approach. As there is no sound method for absolute population counts in free living wild boar, reference values for the validation of this new approach are missing. Therefore, different routines to reduce and to assess genotyping errors were compared within this thesis. For maximum amplification rate, the storage, the extraction methods and the PCR-procedure were optimised. A step by step procedure was evaluated in order to determine the minimum required microsatellite (MS) number for reliable individual identification including a test with family groups (female and embryo tissue) to distinguish even between close relatives. A multiple-tubes approach, post-amplification checking and different correction procedures were applied to reduce genotyping errors. In order to quantify real genotyping error rates (GER) of datasets derived from sampling in the Palatinate Forest in western Germany, different methods for GER determination were compared with each other, obtaining GERs between 0% and 57.5%. As a consequence, more strict criteria for the multi-tube approach and increased repetition number of homozygous samples were used. An additional method validation was the implementation of a blind test to achieve the reliability of the genotyping and error checking procedure. Finally a strict and practicable proposal for the lab procedure was developed, by beginning with faecal sample collection and ending with a reliable dataset with genotypes of each sample.
The results of the presented method were derived from two sampling periods in a 4000 ha area in the Palatinate Forest in Rhineland-Palatinate in December 2006 and 2007. Both provided high confidence intervals (CI) applying inaccurate estimates (eg. for 2006 population size amounted to 215 with CI 95% of 156-314 and for 2007 population size amounted to 415 with CI 95% of 318-561) due to low sampling sizes (for 2006 n = 141 and for 2007 n = 326), successfully analysed samples (for 2006 n = 89 and for 2007 n = 156) and recapture numbers (for 2006 n = 12 and for 2007 n = 24). Furthermore, the population estimates even for the lowest values were considerably higher than previously assumed by hunting statistics, which implicates an ineffective hunting regime in the study area. For the future prospect, to obtain more precise population size estimations the increase of sampling sizes is inevitable, because absolute and reliable estimates are highly desirable for wildlife management and the control of diseases transmission. Nevertheless, the method for individual genotyping of wild boars evaluated in this thesis could be successfully established resulting in reliable datasets for population estimation modelling with sufficiently low GER.
Regarding the rapidly growing amount of data produced every year and the increasing acceptance of Enterprise 2.0 enterprises have to care about the management of their data more and more. Content created and stored in an uncoordinated manner can lead to data-silos (Williams & Hardy 2011, p.57), which result in long search times, inaccessible data and in consequence monetary losses. The "expanding digital universe" forces enterprises to develop new archiving solutions and records management policies (Gantz et al. 2007, p.13). Enterprise Content Management (ECM) is the research field that deals with these challenges. It is placed in the scientific context of Enterprise Information Management. This thesis aims to find out to what extent current Enterprise Content Management Systems (ECMS) support these new requirements, especially concerning the archiving of Enterprise 2.0 data. For this purpose, three scenarios were created to evaluate two different kinds of ECMS (one Open Source - and one proprietary system) chosen on the basis of a short marketrnresearch. The application of the scenarios reveals that the system vendors actually face the industry- concerns: both tools provide functionality for the archiving of data arising from online collaboration and also business records management capabilities but the integration of those topics is not, or is only inconsistently solved. At this point new questions - such as, "Which datarngenerated in an Enterprise 2.0 is worth being a record?" - arise and should be examined in future research.
Schema information about resources in the Linked Open Data (LOD) cloud can be provided in a twofold way: it can be explicitly defined by attaching RDF types to the resources. Or it is provided implicitly via the definition of the resources´ properties.
In this paper, we analyze the correlation between the two sources of schema information. To this end, we have extracted schema information regarding the types and properties defined in two datasets of different size. One dataset is a LOD crawl from TimBL- FOAF profile (11 Mio. triple) and the second is an extract from the Billion Triples Challenge 2011 dataset (500 Mio. triple). We have conducted an in depth analysis and have computed various entropy measures as well as the mutual information encoded in this two manifestations of schema information.
Our analysis provides insights into the information encoded in the different schema characteristics. It shows that a schema based on either types or properties alone will capture only about 75% of the information contained in the data. From these observations, we derive conclusions about the design of future schemas for LOD.
In automated theorem proving, there are some problems that need information on the inequality of certain constants. In most cases this information is provided by adding facts which explicitly state that two constants are unequal. Depending on the number of constants, a huge amount of this facts can clutter the knowledge base and distract the author and readers of the problem from its actual proposition. For most cases it is save to assume that a larger knowledge base reduces the performance of a theorem prover, which is another drawback of explicit inequality facts. Using the unique name assumption in those reasoning tasks renders the introduction of inequality facts obsolete as the unique name assumptions states that two constants are identical iff their interpretation is identical. Implicit handling of non-identical constants makes the problems easier to comprehend and reduces the execution time of reasoning. In this thesis we will show how to integrate the unique name assumption into the E-hyper tableau calculus and that the modified calculus is sound and complete. The calculus will be implemented into the E-KRHyper theorem prover and we will show, by empiric evaluation, that the changed implementation, which is able to use the unique name assumption, is superior to the traditional version of E-KRHyper.
In this thesis the feasibility of a GPGPU (general-purpose computing on graphics processing units) approach to natural feature description on mobile phone GPUs is assessed. To this end, the SURF descriptor [4] has been implemented with OpenGL ES 2.0/GLSL ES 1.0 and evaluated across different mobile devices. The implementation is multiple times faster than a comparable CPU variant on the same device. The results proof the feasibility of modern mobile graphics accelerators for GPGPU tasks especially for the detection phase in natural feature tracking used in augmented reality applications. Extensive analysis and benchmarking of this approach in comparison to state of the art methods have been undertaken. Insights into the modifications necessary to adapt and modify the SURF algorithm to the limitations of a mobile GPU are presented. Further, an outlook for a GPGPU-based tracking pipeline on a mobile device is provided.
The purpose of this master thesis is to enable the Robot Lisa to process complex commands and extract the necessary information in order to perform a complex task as a sequence of smaller tasks. This is intended to be achieved by the improvement of the understanding that Lisa has of her environment by adding semantics to the maps that she builds. The complex command itself will be expected to be already parsed. Therefore the way the input is processed to become a parsed command is out of the scope of this work. Maps that Lisa builds will be improved by the addition of semantic annotations that can include any kind of information that might be useful for the performance of generic tasks. This can include (but not necessarily limited to) hierarchical classifications of locations, objects and surfaces. The processing of the command in addition to some information of the environment shall trigger the performance of a sequence of actions. These actions are expected to be included in Lisa- currently implemented tasks and will rely on the currently existing modules that perform them.
Nevertheless the aim of this work is not only to be able to use currently implemented tasks in a more complex sequence of actions but also make it easier to add new tasks to the complex commands that Lisa can perform.
The goal of this Bachelor thesis is to implement and evaluate the "Simulating of Collective Misbelief"-model into the NetLogo programming language. Therefore, the model requirements have to be specified and implemented into the NetLogo environment. Further tool-related re-quirements have to be specified to enable the model to work in NetLogo. After implementation several simulations will be conducted to answer the research question stated above.
To assess the effect of organic compounds on the aquatic environment, organisms are typically exposed to toxicant solutions and the adverse effects observed are linked to the concentration in the surrounding media. As compounds generally need to be taken up into the organism and distributed to the respective target sites for the induction of effects, the internal exposure is postulated to best represent the observed effects.
The aim of this work is to contribute to an improved effect assessment of organic compounds by describing experimental and modelling methods to obtain information on the internal exposure of contaminants in organisms.
Chapter 2 details a protocol for the determination of bioconcentration parameter for uptake (k1) and elimination (k2) of organic compounds in zebrafish (Danio rerio) eggs. This enables the simulation of the internal exposure in zebrafish eggs from an ambient exposure concentration over time. The accumulated contaminant amount in zebrafish eggs was also determined, using a biomimetic extraction method. Different bioconc-entration estimation models for the determination of internal steady-state concentrat-ion of pharmaceutical compounds in fish to an environmental exposure are presented in Chapter 3. Bioconcentration factors were estimated from the compounds octanol: water partition coefficient (KOW) to determine the internal exposure to an ambient concentration.
To assess the integral bioavailable fraction from the water and sediment phase of environmental contaminants for rooted aquatic plants, the internal exposure in river-living Myriophyllum aquaticum plants were determined over time, presented in Chapter 4. The plants were collected at different time points, with the accumulated organic contaminants determined using a liquid extraction method.
In Chapter 5 a protocol was established to enable the non-invasive observation of effects in M. aquaticum plants exposed to contaminated sediments over time. Since the toxicant effects are a result of all uptake and distribution processes to the target site and the toxico-dynamic process leading to an observed effect during static exposure, information on the internal exposure could thus be gained from the temporal effect expression.rn
The Multimedia Metadata Ontology (M3O) provides a generic modeling framework for representing multimedia metadata. It has been designed based on an analysis of existing metadata standards and metadata formats. The M3O abstracts from the existing metadata standards and formats and provides generic modeling solutions for annotations, decompositions, and provenance of metadata. Being a generic modeling framework, the M3O aims at integrating the existing metadata standards and metadata formats rather than replacing them. This is in particular useful as today's multimedia applications often need to combine and use more than one existing metadata standard or metadata format at the same time. However, applying and specializing the abstract and powerful M3O modeling framework in concrete application domains and integrating it with existing metadata formats and metadata standards is not always straightforward. Thus, we have developed a step-by-step alignment method that describes how to integrate existing multimedia metadata standards and metadata formats with the M3O in order to use them in a concrete application. We demonstrate our alignment method by integrating seven different existing metadata standards and metadata formats with the M3O and describe the experiences made during the integration process.