Summary Historicully, stress analysis used for component design assames "ideal" materials, i.e. with isotropic, homogeneous and uniform metallurgical properties. We know that this is untrue, no matter how good the design. As design cannot provide fo, defect-free materials or components, ok appropriute defect tolerance should be the aim of the designer. Hence the concept of fuil-safe components has been introduced fo, safety-critical purts. Automotive wheels, howeveF, ure not considered fail-safe and fatigue lW prediction techniques fo, these components need to be improved in an endeavour to provide light-weight, attractive, but still safe und durable wheels. This applied approuch, where the proposed lW prediction model employs relutionships given by Gerber und a proposed lW prediction model derived from combining aspects of lW prediction models according to Collins und Juvinall & Marshek. The results show good coruelation with that of uctual wheel fatigue data.

This thesis presents a computational model of some of the knowledge and processing capabilities required by a speaker to produce fluent, coherent discourse in a natural language. The main purpose of the work is to study the representation of linguistic knowledge and the way in which this knowledge is used to produce utterances which achieve a communicative task in a given discourse situation. The model defines a simple world in which two actors communicate with each other in the course of attempting to achieve practical goals. The actors' behaviour is organised at three levels the level of non-linguistic planning and action, the level of the organisation of self-other interactions (turn-taking), and the level of the construction of individual utterances. Actions at lower levels gain functional relevance by their contribution to the achievement of goals defined at higher levels. At all levels, the model performs principled computations based on explicitly represented knowledge, thus embodying an account of how a non-linguistic goal might give rise to a particular form of words under appropriate circumstances

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Infection with Campylobacter jejuni is one of the leading causes of bacterial gastroenteritis, causing an estimated 2.1 million cases annually. Although infections with C. jejuni resolve naturally, over 13,000 hospitalizations and 100 deaths are attributed to this organism. Despite these alarming numbers, relatively little is known about C. jejuni pathogenesis, when compared to other enteric pathogens. This dissertation outlines the identification and characterization of a novel virulence factor in C. jejuni, the protein expressed by the Cj1534c gene. Using microarray and RT real time PCR, Cj1534c was found to be greater than 10 fold over expressed in both swine and poultry. Employing immunoelectron microscopy, we determined that at least a subset of the protein is surface localized. Based on the surface localization and up regulation in poultry, colonization studies were performed. Results demonstrate a significant reduction in colonization by a Cj1534c deficient mutant as compared to wild type. In vitro binding assays using both biotic and abiotic surfaces indicate this protein is involved with attachment to surfaces, as well as the invasion of cultured epithelial cells. In vitro findings were confirmed in vivo using swine, with the Cj1534c mutant being highly attenuated as compared to wild type strains. Additionally, the Cj1534c protein was tested for its potential as a vaccine in poultry. Studies demonstrated that Cj1534c recombinantly expressed in a Salmonella expression vector partially protected chickens, reducing cecal colonization three logs as compared to wild type. Taken together, this data demonstrates a major role of Cj1534c in both chicken cecal colonization and infection of swine.

The main philosophy underlying this research is that a model should constitute a representation of both what we know and what we do not know about the structure and behavior of a system. In other words it should summarize, as far as possible, both our degree of certainty and degree of uncertainty, so that it facilitates statements about prediction uncertainty arising from model structural uncertainty. Based on this philosophy, the following issues were explored in the dissertation: Identification of a hydrologic system model based on assumption about perceptual and conceptual models structure only, without strong additional assumptions about its mathematical structure Development of a novel data assimilation method for extraction of mathematical relationships between modeled variables using a Bayesian probabilistic framework as an alternative to up-scaling of governing equations Evaluation of the uncertainty in predicted system response arising from three uncertainty types: o uncertainty caused by initial conditions, o uncertainty caused by inputs, o uncertainty caused by mathematical structure Merging of theory and data to identify a system as an alternative to parameter calibration and state-updating approaches Possibility of correcting existing models and including descriptions of uncertainty about their mapping relationships using the proposed method Investigation of a simple hydrological conceptual mass balance model with two-dimensional input, one-dimensional state and two-dimensional output at watershed scale and different temporal scales using the method

The U.S. wool industry has long received lower prices for comparable wool types than those of Australia. In order to better understand such price differences, economic evaluations of both the U.S. and Australian wool markets were conducted. This research focused on two primary objectives. The first objective was to determine what price differences existed between the Australian and U.S. wool markets and measure that difference. The second objective was to calculate price differences attributable to wool characteristics, as well as those resulting from regional, seasonal, and yearly differences. In order to accomplish the objectives, the study was set up into three different hedonic pricing models: U.S., Australian, and combined. In the U.S. model, there were significant price differences in season, year, region, level of preparation, and wool description. In addition, average fiber diameter (AFD) had a negative nonlinear relationship with price and lot weight had a positive linear relationship with price. The Australian model was notably different than the U.S. model in that there were only three variables. The yearly variable follows the same general pattern as the U.S. data but with a smaller span of difference. The seasonal price differences were distinctly different than the U.S. because of the difference in seasonal patterns. In addition, the AFD had a similar negative nonlinear relationship with price. The final model combines both the U.S. data and the Australian data. The combined model had only three variables: season, year, AFD and country. As in the case of the previous two models, AFD had the same negative nonlinear relationship and similar price elasticity. Overall, there was a -30.5 percent discount for U.S. wool when compared to Australian wool. This can be attributed to several different factors. One of which is that the Australian wool industry has a more extensive marketing scheme when compared to the U.S wool market as a whole. However, this is only a beginning to future research that needs to be conducted. Continuing this study for future years, having more descriptive categories, and additional countries would further add explanation to wool prices.

We develop and test asset pricing model formulations that are simultaneously conditional and nonlinear. Formulations based upon five popular asset pricing models are tested against the widely studied Fama and French (1993) twenty-five size and book-to-market sorted portfolios. Test results indicate that the conditional nonlinear specification of the Fama and French (1993) three state variable model (FF3) is the only specification not rejected by the data and thus capable of pricing the "size" and "book-to-market" effects simultaneously. The pricing performance of the FF3 conditional nonlinear pricing kernel is corifirmed by robustness tests on out-of-sample data as well as tests with alternative instrumental and conditioning variables. While Bansal and Viswanathan (1993) and Chapman (1997) find unconditional nonlinear pricing kernels sufficient to capture the size effect alone, our results indicate that similar unconditional nonlinear pricing kernels considered here do not price the size and book-to-market effects simultaneously. However, nested model tests indicate that, in isolation, both conditioning information and nonlinearity significantly improve the pricing kernel performance for all five asset pricing models. The success of the conditional nonlinear FF3 model also suggests that the combination of conditioning and nonlinearity is critical to pricing kernel design. Implications for both academic researchers and practitioners are considered.

This thesis consists of two essays which contribute to different but related aspects of the empirical asset pricing literature. The common theme is that incorrect restrictions can lead to inaccurate decisions. The first essay demonstrates that failure to account for the Federal Reserve experiment can lead to incorrect assumptions about the explosiveness of short-term interest rate volatility, while the second essay demonstrates that we need to incorporate skewness to develop models that adequately account for the cross-section of equity returns. Essay 1 empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the elasticity of volatility parameter for single-regime models unanimously indicate an explosive volatility process, whereas the Markov-switching models estimates are reasonable. We find that either Markov-switching or stochastic volatility, but not both, is needed to adequately fit the data. A robust conclusion is that volatility depends on the level of the short rate. Finally, the Markov-switching model is the best for forecasting. A technical contribution of this paper is a presentation of quasi-maximum likelihood estimation techniques for the Markov-switching stochastic-volatility model. Essay 2 proposes a new approach to estimating and testing nonlinear pricing models using GMM. The methodology extends the GMM based conditional mean-variance asset pricing tests of Harvey (1989) and He et al (1996) to include preferences over moments higher than variance. In particular we explore the empirical usefulness of the conditional coskewness of an assets return with the market return in explaining the cross-section of equity returns. The methodology is both flexible and parsimonious. We avoid modelling any asset specific parameters and avoid making restrictive assumptions on the dynamics of co-moments. By using GMM to estimate the models' parameters we also avoid making any assumptions about the distribution of the data. The empirical results indicate that coskewness is useful in explaining the cross-section of equity returns, and that both covariance and coskewness are time varying. We also find that the usefulness of coskewness is robust to the inclusion of Fama and French's (1993) SMB and HML factor returns. There is an interesting debate raging in the empirical asset pricing literature comparing the SDF versus beta methodologies. This paper's technique is a conditional version of the beta methodology, which turns out to be directly comparable with the SDF methodology with only minor modifications. Our SDF version imposes the CAPM's restrictions that the coefficients in the pricing kernel are known functions of the moments of market returns, which are modelled using macro-variables. We find that the SDF implied by the three-moment CAPM provides a better fit in this data set than current practice of parameterizing the coefficients on market returns in the SDF. This has an interesting application to the current SDF versus beta methodology debate.

Using a multiple stage age of infection model, we derive an expression for the basic reproduction number, Ro. We apply this method to find Ro in analogous treatment models. We find, in the model without treatment, Ro depends only on the mean infective period, and not on the infective distribution. In treatment models, Ro depends on the mean infective and mean treatment period, as well as the distribution of the infective period, but not on the distribution of the treatment period. With an explicit formula for Ro and the final size relation, we provide a practical alternative to evaluating the effect of treatment and other control measures. We compare our models to previous models of SARS and TB.

Cross validation (CV) is widely used for model assessment and comparison. In this thesis, we first review and compare three v-fold CV strategies: best single CV, repeated and averaged CV and double CV. The mean squared errors of the CV strategies in estimating the best predictive performance are illustrated by using simulated and real data examples. The results show that repeated and averaged CV is a good strategy and outperforms the other two CV strategies for finite samples in terms of the mean squared error in estimating prediction accuracy and the probability of choosing an optimal model. In practice, when we need to compare many models, conducting repeated and averaged CV strategy is not computational feasible. We develop an efficient sequential methodology for model comparison based on CV. It also takes into account the randomness in CV. The number of models is reduced via an adaptive, multiplicity-adjusted sequential algorithm, where poor performers are quickly eliminated. By exploiting matching of individual observations, it is sometimes even possible to establish the statistically significant inferiority of some models with just one execution of CV. This adaptive and computationally efficient methodology is demonstrated on a large cheminformatics data set from PubChem. Cross validated mean squared error (CVMSE) is widely used to estimate the prediction mean squared error (MSE) of statistical methods. For linear models, we show how CVMSE depends on the number of folds, v, used in cross validation, the number of observations, and the number of model parameters. We establish that the bias of CVMSE in estimating the true MSE decreases with v and increases with model complexity. In particular, the bias may be very substantial for models with many parameters relative to the number of observations, even if v is large. These results are used to correct CVMSE for its bias. We compare our proposed bias correction with that of Burman (1989), through simulated and real examples. We also illustrate that our method of correcting for the bias of CVMSE may change the results of model selection.