The thesis presented here utilizes a variety of methods and study systems to address how ecological promote plasticity in reproductive behaviours. We study mate choice, copulation and parental care as the different reproductive behaviours, as they can be envisioned as representatives of different stages of the reproductive cycle and can be subject to different selection pressures. With the use of computer simulations we study the conditions of sex ratio and cost of courting under which a learned mate preference in males or in both sexes can evolve. We found that for males, maternal imprinting is the most advantageous imprinting strategy, but when both sexes imprint, paternal imprinting in both sexes is the most advantageous strategy. We show that environmental change can lead to the evolution of sexual imprinting by both sexes. A study using mesocosm and mating trial experiments, measuring female survival and male mating success was used to study the role of intra- and interspecific interactions in mating behaviour (competition and harassment) in Calopteryx splendens. We showed that intense intraspecific male-male competition reduces harassment over females and increases female survival. On the other side, interspecific reproductive interference can reduce male mating success and can increase female survival. Finally, theory on the use of social learning was tested using Drosophila melanogaster oviposition site choice. We show that fruit flies use social learning more after they experience heterogeneous environments. However, our results suggest that the use of social learning was driven by fruit flies signalling more when they experience heterogeneous environments, instead of driven by copying others decisions, as theoretical predictions suggest. We also show that the use of social learning is an innate trait, opening the opportunity for the study of the genomic basis of social learning.

Lam Suk Yee Eva. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 44-47). / Abstracts in English and Chinese ; questionnaire and appendix also in Chinese. / ABSTRACT --- p.2 / TABLE OF CONTENTS --- p.4 / Chapter CHAPTER 1: --- INTRODUCTION --- p.6 / Time discounting --- p.7 / Causes of the time discounting effect --- p.8 / Time discounting in job choice decision --- p.9 / Counteracting the time-discounting effect by influencing perceived probability --- p.12 / Summary of hypotheses --- p.12 / Chapter CHAPTER 2: --- METHOD --- p.15 / Participants --- p.15 / Procedure --- p.15 / Chapter CHAPTER 3: --- RESULTS --- p.20 / Manipulation Checking --- p.20 / Effect of feedback messages on job acceptance decisions --- p.20 / Effect of feedback messages on perceived value and probability of getting the jobs and the test scores --- p.22 / Effect of perceived value and probability of getting the jobs and written test scores on job acceptance decision --- p.25 / Effect of perceived probability of getting the job offer as mediator --- p.31 / Effect of feedback messages on the length of time that people need to make up their job acceptance decision --- p.33 / Chapter CHAPTER 4 : --- DISCUSSION --- p.34 / Effect of feedback message on perceived value of getting the jobs --- p.34 / Effect of feedback messages on job acceptance decision --- p.35 / Limitation --- p.39 / Implications and future research --- p.41 / REFERENCE --- p.44 / APPENDIX A --- p.48 / APPENDIX B --- p.49 / APPENDIX C --- p.53 / APPENDIX D --- p.57 / APPENDIX E --- p.59 / APPENDIX F --- p.63 / APPENDIX G --- p.64 / APPENDIX H --- p.67 / APPENDIX I --- p.69 / APPENDIX J --- p.71 / APPENDIX K --- p.75

Speciation is a process by which 1 population splits into 2 distinct populations that in time acquire reproductive isolation (RI). Interestingly, the speed at which RI is attained can be influenced by several factors. My thesis work focuses on how protein-protein interactions affect this process, either by impeding or expediting the acquisition of RI. Here, I present an analysis of cascading positive selection in a group of centromere associated proteins in the Drosophila melanogaster subgroup. Within this group, there are proteins known to interact with rapidly evolving DNA elements located at the centromere, as well as other proteins. While previous work has shown that proteins binding rapidly evolving DNA elements are prone to rapid evolution, I provide evidence that this cascade of positive selection can further extend to other interacting partners. This group of rapidly evolving proteins provides a prime example of protein-protein interactions driving speciation. I also present evidence of protein-protein interactions impeding the process of speciation. Specifically, this work focuses on the sister species D. yakuba and D. santomea. This species pair is of particular interest because previous work has demonstrated that he mitochondrial genome completely introgressed from D. yakuba to D. santomea replacing the native form. By assessing nuclear genes encoding proteins that interact with mitochondrial proteins in the Oxidative Phosphorylation (OXPHOS) pathway, I have identified specific cases of co-introgression in which nuclear encoded mitochondrial partners have introgressed with the mitochondrial genome to allow for the maintenance of physical function, thus maintaining similarities between species, ultimately impeding the process of speciation.

Decision making is a fundamental tool for managers and enable them to make logical decisions in critical situation between different options. This thesis concentrates mainly on seller selection problem or in some cases it also refers to vendor or supplier selection problem (SSP) and demonstrates how the multiple attribute decision making (MADM) methods can be effectively used for vendor selection decision in various situations of project management and supply chain environment. A case study has been carried out within two different countries (Sweden and Iran) in order to help practically managers to choose the best alternatives among their preferences. In this regard, AHP as one of major tools of MADM is also deployed.

A key problem in model-based object recognition is selection, namely, the problem of isolating regions in an image that are likely to come from a single object. This isolation can be either based solely on image data (data-driven) or can incorporate the knowledge of the model object (model-driven). In this paper we present an approach that exploits the property of closely-spaced parallelism between lines on objects to achieve data and model-driven selection. Specifically, we present a method of identifying groups of closely-spaced parallel lines in images that generates a linear number of small-sized and reliable groups thus meeting several of the desirable requirements of a grouping scheme for recognition. The line groups generated form the basis for data and model-driven selection. Data-driven selection is achieved by selecting salient line groups as judged by a saliency measure that emphasizes the likelihood of the groups coming from single objects. The approach to model-driven selection, on the other hand, uses the description of closely-spaced parallel line groups on the model object to selectively generate line groups in the image that are likely to eb the projections of the model groups under a set of allowable transformations and taking into account the effect of occlusions, illumination changes, and imaging errors. We then discuss the utility of line groups-based selection in the context of reducing the search involved in recognition, both as an independent selection mechanism, and when used in combination with other cues such as color. Finally, we present results that indicate a vast improvement in the performance of a recognition system that is integrated with parallel line groups-based selection.

This work focuses on the evaluation of a bagging EB method in terms of its ability to select a subset of QTL-related markers for accurate EBV prediction. Experiments were performed on several simulated and real datasets consisting of SNP genotypes and phenotypes. The simulated datasets modeled different dominance levels and different levels of background noises. Our results show that the bagging EB method is able to detect most of the simulated QTL, even with large background noises. The average recall of QTL detection was $0.71$. When using the markers detected by the bagging EB method to predict EBVs, the prediction accuracy improved dramatically on the simulation datasets compared to using the entire set of markers. However, the prediction accuracy did not improve much when doing the same experiments on the two real datasets. The best accuracy of EBV prediction we achieved for the dairy dataset is 0.57 and the best accuracy for the beef dataset is 0.73.

Thesis (Ph. D.)--Oregon State University, 1981. / Typescript (photocopy). Includes bibliographical references (leaves 122-133). Also available on the World Wide Web.

This research consists of two parts. The first part examines the posterior probability integrals for a family of linear models which arises from the work of Hart, Koen and Lombard (2003). Applying Laplace's method to these integrals is not entirely straightforward. One of the requirements is to analyze the asymptotic behavior of the information matrices as the sample size tends to infinity. This requires a number of analytic tricks, including viewing our covariance matrices as tending to differential operators. The use of differential operators and their Green's functions can provide a convenient and systematic method to asymptotically invert the covariance matrices. Once we have found the asymptotic behavior of the information matrices, we will see that in most cases BIC provides a reasonable approximation to the log of the posterior probability and Laplace's method gives more terms in the expansion and hence provides a slightly better approximation. In other cases, a number of pathologies will arise. We will see that in one case, BIC does not provide an asymptotically consistent estimate of the posterior probability; however, the more general Laplace's method will provide such an estimate. In another case, we will see that a naive application of Laplace's method will give a misleading answer and Laplace's method must be adapted to give the correct answer. The second part uses numerical methods to compute the "exact" posterior probabilities and compare them to the approximations arising from BIC and Laplace's method.

Replicator dynamics are an increasingly popular device for obtaining (local) solutions of considerably high quality to so-called standard quadratic optimization problems, which consist of finding maxima of (possibly indefinite) quadratic forms over the standard simplex. In the simplest version of portfolio selection, the quadratic form is theoretically negative-semidefinite, so that any local solution automatically is a global one. However, if it comes to more realistic set-ups, then (i) no market portfolio is available, so that one ends up with an indefinite theoretical problem, (ii) estimated covariance matrices modelling risk may be indefinite also. This paper deals with both problems in a different way: (i) will be solved via escape steps to avoid low-quality local solutions while (ii) is dealt with by several projection strategies which convert the indefinite estimated covariance matrix into a positive-semidefinite one. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Multiple comparisons and selection procedures are commonly studied in research and employed in application. Clinical trial is one of popular fields to which the subject of multiple comparisons is extensively applied. Based on the Federal Food, Drug, and Cosmetic Act, drug manufacturers need to not only demonstrate safety of their drug products but also establish effectiveness by substantial evidence in order to obtain marketing approval. However, the problem of error inflation occurs when there are more than two groups to compare with at the same time. How to design a test procedure with high power while controlling type I error becomes an important issue. The treatment with the largest population mean is considered to be the best one in the study. Potentially the best treatments can receive increased resources and further investigation by excluding clearly inferior treatments. Hence, a small number of possibly the best treatments is preferred. This thesis focuses on the problem of eliminating the less effective treatments among three in clinical trials. The goal is to increase the ability to identify any inferior treatment providing that the probability of excluding any best treatment is guaranteed to be less than or equal to alpha. A step-down procedure is applied to solve the problem. The general step-down procedure with fixed thresholds is conservative in our problem. The test is not efficient in rejecting the less effective treatments. We propose two methods with sharper thresholds to improve current procedures and construct a subset containing strictly inferior treatments. The first method, the restricted parameter space approach, is designed for the scenario when prior information about range of treatment means is known. The second method, the step-down procedure with feedback, utilizes observations to modify the threshold and controls error rate for the whole parameter space. The new procedures have greater ability to detect more inferior treatments than the standard procedure. In addition, type I error is also controlled under mild violation of the assumptions demonstrated by simulation.