Die Risikoprämie einer Unternehmensanleihe dient prinzipiell der wirtschaftlichen Kompensation für die Übernahme zusätzlicher Risiken gegenüber den Risiken der Benchmark. Allerdings findet sich in der bisher veröffentlichen Literatur eine Vielzahl von den praktischen Messkonzepten, die in vielen Fällen nicht fehlerfrei und problemlos zustande gekommen sind. Daher ist die präzise und quantitative Messung der Risikoprämien von Unternehmensanleihen eine betriebswirtschaftliche Notwendigkeit. In der vorliegenden Arbeit werden im Hinblick auf die Erreichbarkeit drei alternative Messkonzepte bezüglich der Risikoprämien von Unternehmensanleihen vorgestellt und miteinander verglichen. Einige bisherige Studien sind der Auffassung, dass die Risikoprämien von Unternehmensanleihen zumeist von den Nicht-Kreditkomponenten beeinflusst werden. Um diese Marktanomalien zu erklären, verwenden die vorliegenden Untersuchungen das statistische lineare Faktor-Modell. In diesem Zusammenhang wird die Untersuchung von LITTERMAN/SCHEINKMAN (1991) auf die risikobehafteten Unternehmensanleihen übertragen. Im Kern steht die Frage, welche Risikoarten bzw. wie viele Einflussfaktoren wirken sich auf die Risikoprämien von Unternehmensanleihen in wieweit aus. Das Ziel ist ein sparsames lineares Faktor-Modell mit wirtschaftlicher Bedeutung aufzubauen. Somit leistet diese Dissertationsschrift einen wesentlichen Beitrag zur Gestaltung der Anleiheanalyse bzw. zur Portfolioverwaltung.

Disregard for patient-specific joint-level variability may be related to decreased functional ability, poor implant longevity and dissatisfaction post-TKA. The purpose of this study was to, 1) compare pre and post-implant intraoperative passive knee adduction angle kinematic patterns and characterize the effect of surgical intervention on each pattern, 2) examine the association between passive pre and post-implant knee kinematics measured intraoperatively and dynamic knee kinematics and kinetics pre and post-TKA measured during gait, and 3) compare dynamic post-TKA kinematic and kinetic patterns between patient-specific knee recipients and traditional TKA recipient. Patients received a TKA using the Stryker Precision Knee navigation system capturing pre/post-implant kinematics through a passive range of flexion. One-week prior and 1-year post-TKA patients underwent three-dimensional gait analysis. Knee joint waveforms were calculated according to the joint coordinate system. Principal component analysis (PCA) was applied to frontal plane gait angles, moments and navigation angles. Paired two- tailed t-tests were used to compare principal component (PC) scores between pre and post-implant patterns, and a one-way ANOVA was used to test if post-implant patterns were significantly different from zero. Two-tailed Pearson correlation coefficients tested for associations between navigation and gait PCscores, and an un-paired two-tailed t-test was used to compare PCscores between patient-specific and traditional TKA groups. Six different passive kinematic phenotypes were captured pre-implant. Although some waveform patterns persisted at small magnitudes post-implant (PC1 and PC3: p<0.001), curves remained within the clinically acceptable alignment range through passive motion. A positive correlation was found between navigation adduction angle PC1 and gait adduction moment PC1 pre and post-TKA (p<0.001, r=0.79; p<0.01 r=0.67), and a negative correlation between navigation adduction angle PC1 and gait adduction angle PC1 post-TKA (p=0.03, r=-0.53). The patient-specific group showed significantly lower PC2 scores than the traditional TKA group (p=0.03), describing a lower flexion moment magnitude during early stance phase, possibly representing a functional limitation or non- confidence during gait. These results were an important first step to assess patient- specific approaches to TKA, suggesting possible applications for patient-specific intraoperative kinematics to aid in surgical decision-making and influence functional outcomes.

Age related macular degeneration (AMD) is a public health concern in an aging society. The retinal pigment epithelium (RPE) layer of the eye is a principal site of pathogenesis for AMD. Morphological characteristics of the cells in the RPE layer can be used to discriminate age and disease status of individuals. In this thesis three genotypes of mice of various ages are used to study the predictive abilities of these characteristics. The disease state is represented by two mutant genotypes and the healthy state by the wild-type. Classification analysis is applied to the RPE morphology from the different spatial regions of the RPE layer. Variable reduction is accomplished by principal component analysis (PCA) and classification analysis by the k-nearest neighbor (k-NN) algorithm. In this way the differential ability of the spatial regions to predict age and disease status by cellular variables is explored.

This dissertation explored the way in which bumblebees' visual system helps them discover their first flower. Previous studies found bees have unlearned preferences for parts of a flower, such as its colour and shape. The first study pitted two variables against each other: pattern type: sunburst or bull's eye, versus the location of the pattern: shapes appeared peripherally or centrally. We observed free-flying bees in a flight cage using Radio-Frequency Identification (RFID) tracking. The results show two distinct behavioural preferences: Pattern type predicts landing: bees prefer radial over concentric patterns, regardless of whether the radial pattern is on the perimeter or near the centre of the flower. Pattern location predicts exploration: bees were more likely to explore the inside of artificial flowers if the shapes were displayed near the centre of the flower, regardless of whether the pattern was radial or concentric. As part of the second component, we implemented a mathematical model aimed at explaining how bees come to prefer radial patterns, leafy backgrounds and symmetry. The model was based on unsupervised neural networks used to describe cognitive mechanisms. The results captured with the results of multiple behavioural experiments. The model suggests that bees choose computationally "cheaper" stimuli, those that contain less information. The third study tested the computational load hypothesis generated by the artificial neural networks. Visual properties of symmetry, and spatial frequency were tested. Studying free-flying bees in a flight cage using motion-sensitive video recordings, we found that bees preferred 4-axis symmetrical patterns in both low and high frequency displays.

The extensive agricultural usage of organohalogenated pesticides has raised many concerns about their potential hazards especially in the soil environment. Environmental metabolomics is an emerging field that investigates the changes in the metabolic profile of native organisms in their environment due to the presence of an environmental stressor. Research presented here explores the potential of Nuclear Magnetic Resonance (NMR)-based metabolomics to examine the sub-lethal exposure of the earthworm, Eisenia fetida to sub-lethal concentrations of organohalogenated pesticides. Various one-dimensional (1-D) and two dimensional (2-D) NMR techniques were compared in a contact filter paper test earthworm metabolomic study using endosulfan, a prevalent pesticide in the environment. The results determined that both the 1H Presaturation Utilizing Gradients and Echos (PURGE) and the 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR techniques were most effective in discriminating and identifying significant metabolites in earthworms due to contaminant exposure. These two NMR techniques were further explored in another metabolomic study using various sub-lethal concentrations of endosulfan and an organofluorine pesticide, trifluralin to E. fetida. Principal component analysis (PCA) tests showed increasing separation between the exposed and unexposed earthworms as the concentrations for both contaminants increased. A neurotoxic mode of action (MOA) for endosulfan and a non-polar narcotic MOA for trifluralin were delineated as many significant metabolites, arising from exposure, were identified. The earthworm tissue extract is commonly used as the biological medium for metabolomic studies. However, many overlapping resonances are apparent in an earthworm tissue extract NMR spectrum due to the abundance of metabolites present. To mitigate this spectral overlap, the earthworm’s coelomic fluid (CF) was tested as a complementary biological medium to the tissue extract in an endosulfan exposure metabolomic study to identify additional metabolites of stress. Compared to tests on the tissue extract, a plethora of different metabolites were identified in the earthworm CF using 1-D PURGE and 2-D HSQC NMR techniques. In addition to the neurotoxic MOA identified previously, an apoptotic MOA was also postulated due to endosulfan exposure. This thesis also explored the application of 1-D and 2-D NMR techniques in a soil metabolomic study to understand the exposure of E. fetida to sub-lethal concentrations of endosulfan and its main degradation product, endosulfan sulfate. The earthworm’s CF and tissue extract were both analyzed to maximize the significant metabolites identified due to contaminant exposure. The PCA results identified similar toxicity for both organochlorine contaminants as the same separation, between exposed to the unexposed earthworms, were detected at various concentrations. Both neurotoxic and apopotic MOAs were observed as identical fluctuations of significant metabolites were found. This research demonstrates the potential of NMR-based metabolomics as a powerful environmental monitoring tool to understand sub-lethal organohalogenated pesticide exposure in soil using earthworms as living probes.

A face authentication system based on principal component analysis and neural networks is developed in this thesis. The system consists of three stages / preprocessing, principal component analysis, and recognition. In preprocessing stage, normalization illumination, and head orientation were done. Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set. New faces are projected onto the space expanded by eigenfaces and represented by weighted sum of the eigenfaces. These weights are used to identify the faces. Neural network is used to create the face database and recognize and authenticate the face by using these weights. In this work, a separate network was build for each person. The input face is projected onto the eigenface space first and new descriptor is obtained. The new descriptor is used as input to each person&amp / #8217 / s network, trained earlier. The one with maximum output is selected and reported as the host if it passes predefined recognition threshold. The algorithms that have been developed are tested on ORL, Yale and Feret Face Databases.

The main task in this thesis is to design a robot vision system with face detection and tracking capability. Hence there are two main works in the thesis: Firstly, the detection of the face on an image that is taken from the camera on the robot must be achieved. Hence this is a serious real time image processing task and time constraints are very important because of this reason. A processing rate of 1 frame/second is tried to be achieved and hence a fast face detection algorithm had to be used. The Eigenface method and the Subspace LDA (Linear Discriminant Analysis) method are implemented, tested and compared for face detection and Eigenface method proposed by Turk and Pentland is decided to be used. The images are first passed through a number of preprocessing algorithms to obtain better performance, like skin detection, histogram equalization etc. After this filtering process the face candidate regions are put through the face detection algorithm to understand whether there is a face or not in the image. Some modifications are applied to the eigenface algorithm to detect the faces better and faster. Secondly, the robot must move towards the face in the image. This task includes robot motion. The robot to be used for this purpose is a Pioneer 2-DX8 Plus, which is a product of ActivMedia Robotics Inc. and only the interfaces to move the robot have been implemented in the thesis software. The robot is to detect the faces at different distances and arrange its position according to the distance of the human to the robot. Hence a scaling mechanism must be used either in the training images, or in the input image taken from the camera. Because of timing constraint and low camera resolution, a limited number of scaling is applied in the face detection process. With this reason faces of people who are very far or very close to the robot will not be detected. A background independent face detection system is tried to be designed. However the resultant algorithm is slightly dependent to the background. There is no any other constraints in the system.

Different approaches to the face recognition are studied in this thesis. These approaches are PCA (Eigenface), Kernel Eigenface and Fisher LDA. Principal component analysis extracts the most important information contained in the face to construct a computational model that best describes the face. In Eigenface approach, variation between the face images are described by using a set of characteristic face images in order to find out the eigenvectors (Eigenfaces) of the covariance matrix of the distribution, spanned by a training set of face images. Then, every face image is represented by a linear combination of these eigenvectors. Recognition is implemented by projecting a new image into the face subspace spanned by the Eigenfaces and then classifying the face by comparing its position in face space with the positions of known individuals. In Kernel Eigenface method, non-linear mapping of input space is implemented before PCA in order to handle non-linearly embedded properties of images (i.e. background differences, illumination changes, and facial expressions etc.). In Fisher LDA, LDA is applied after PCA to increase the discrimination between classes. These methods are implemented on three databases that are: Yale face database, AT&amp / T (formerly Olivetti Research Laboratory) face database, and METU Vision Lab face database. Experiment results are compared with respect to the effects of changes in illumination, pose and expression. Kernel Eigenface and Fisher LDA show slightly better performance with respect to Eigenfaces method under changes in illumination. Expression differences did not affect the performance of Eigenfaces method. From test results, it can be observed that Eigenfaces approach is an adequate method that can be used in face recognition systems due to its simplicity, speed and learning capability. By this way, it can easily be used in real time systems.

This thesis applies an electromagnetic feature extraction technique to design electromagnetic target classifiers for conductors, dielectrics and dielectric coated conductors using their natural resonance related late-time scattered responses. Classifier databases contain scattered data at only a few aspects for each candidate target. The targets are dielectric spheres of varying sizes and refractive indices, perfectly conducting spheres varying sizes and dielectric coated conducting spheres of varying refractive indices and thickness in coating. The applied classifier design technique is suitable for real-time target classification because of the computational efficiency of feature extraction and decision making approaches. The Wigner-Ville Distribution (WD) is employed in this study in addition to the Principal Components Analysis (PCA) technique to extract target features mainly from late-time target responses. WD is applied to the back-scattered responses at different aspects. To decrease aspect dependency, feature vectors are extracted from selected late-time portions of the WD outputs that include natural resonance related information. Principal components analysis is also used to fuse the feature vectors and/or late-time target responses extracted from reference aspects of a given target into a single characteristic feature vector for each target to further reduce aspect dependency.

In the present study, for estimating the Central Asia contribution to the Anatolia, nine Alu insertion polymorphisms (ACE, PV92, FXIIIB, APO, A25, B65, TPA25, D1, HS4.32 ) in 100 individuals from Anatolia were examined. Alu insertion frequency for these loci were calculated as 0,410 / 0,220 / 0,579 / 0,963 / 0,067 / 0,667 / 0,390 / 0,427 / and 0,637 respectively and they were found to be in Hardy-Weinberg equilibrium (p&lt / 0,05). Observed insertion frequencies of each loci were compared with those of the previous observations (Din&ccedil / , 2003 / Comas et al., 2004) and it was found that the present study results were not different than those obtained by Comas et al. (2004). Thus, these two data were pooled (N = 143) and used to examine genetic relationships between populations from Eurasia and Africa. Pairwise Fst statistics indicated that there is higher genetic similarity between Anatolia and all of the Balkans and some of the Caucasian populations. Neighbor Joining (NJ) tree based on Reynold&rsquo / s genetic distances and Principal Component Analysis (PCA) both grouped the Anatolian populations with Balkans and some of the Caucasian populations and show clear differentiation of Asian populations from the Anatolian population. The relative genetic contribution of Central Asian genes to the current Anatolian gene pool was quantified using Admix analysis, considering for comparison populations of Balkans (Greek, Romania, Albania and Hungarian) and Central Asia (Uighur, Uzbeks, Tajicks, Kazaks, Kyrgyzes, Dungans). Estimates suggest roughly 28 % contribution from Asia to Anatolia in concordance with the previous estimation (Benedetto et al., 2001).