This study tests the size effect in the London Stock Exchange, using data for all nonfinancial listed firms from January 1985 to December 1995. The initial tests indicate that average stock returns are negatively related to firm size and that small firm portfolios earn returns in excess of the market risk. Further, the study tests whether the size effect is a proxy for variables such as the Book-to- Market Value and the Borrowing Ratio, as well as the impact of the dividend and the Bid- Ask spread on the return of the extreme size portfolios. The originality of this study is in the application of the Markov Chain Model to testing the Random Walk and Bubbles hypotheses, and the Vector Autoregression (VAR) framework for testing the relationship of macroeconomic variables with size portfolio returns.

Application and implication of using complex vectors and complex transformations in solutions of Maxwell’s equations is investigated. Complex vectors are used in complex plane waves and help to represent this type of waves geometrically. It is shown that they are also useful in representing inhomogeneous plane waves in plasma, single-negative and double-negative metamaterials. In specific I will investigate the Otto configuration and Kretschmann configuration and I will show that in order to observe the minimum in reflection coefficient it is necessary for the metal to be lossy. We will compare this to the case of plasmon-like resonance when a PEC periodic structure is illuminated by a plane wave. Complex transformations are crucial in deriving Gaussian beam solutions of paraxial Helmholtz equation from spherical wave solution of Helmholtz equation. Vector Gaussian beams also will be discussed shortly.

Classifying pipe cracks by size from their pulse-echo ultrasonic signal is difficult but highly significant for the defect evaluation required in pipe testing and maintenance decision making. For this thesis, a binary Support Vector Machine (SVM) classifier, which divides pipe cracks into two categories: large and small, was developed using collected ultrasonic signals. To improve the performance of this SVM classifier in terms of reducing test errors, we first combined the Sequential Backward Selection and Sequential Forward Selection schemes for input feature reduction. Secondly, we used the data dependent kernel instead of the Gaussian kernel as the kernel function in the SVM classifier. Thirdly, as it is time-consuming to use the classic grid-search method for parameter selection of SVM, this work proposes a Kernel Fisher Discriminant Ratio (KFD Ratio) which makes it possible to more quickly select parameters for the SVM classifier.

Typescript. / Thesis (Ph. D.)--University of Hawaii, 1971. / Bibliography: leaves 73-74. / vi, 74 l

Fowlpox viruses (FPV) are attractive platform vaccine vector candidates because their capacity for insertion of multiple heterologous genes makes them favourable for genetic modification. They also have strong adjuvant activity in their own right. As FPV does not replicate in mammalian cells, there is significantly less opposition associated with their clinical application, with a number already in use. However, a thorough understanding of the immunological relationship between FPV and the mammalian immune system is still lacking. The aim of this thesis was to construct a series of recombinant FPV vectors that co-expressed the nominal antigen chicken ovalbumin (OVA), (FPV[subscript]OVA), and/or murine interleukin-4 (mIL-4). These constructs were used for the characterisation of the relationship between FPV and the mammalian immune system and how this is altered by the co-expression of mIL-4. Immunisation with FPV[subscript]OVA resulted in rapid and highly localized OVA expression which induced strong CD8⁺ cytotoxic T cell (CTL) activity but only weak CD4⁺ T helper and antibody responses. In addition, presentation of FPV-derived antigen and the priming of antigen-specific CTL responses required a permissive bone marrow (BM)-derived cell as the antigen presenting cell (APC). Co-administration with FPV[subscript]mIL-4 resulted in a dramatic reduction in CTL activity that remained largely non-functional throughout the infection and a skewing of the T helper (Th) response towards Th2 with a reduction in interferon (IFN)-γ production by OVA-specific Th cells. These findings provide a sound basis for further characterization of how FPV interacts with the innate and adaptive arms of the immune system, how these can be manipulated via the co-administration of cytokines, and discovering if future rationally designed modifications result in FPV vectored vaccines that induce durable cellular and humoral immunity. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1352466 / Thesis (M.Med.Sc.) - University of Adelaide, School of Medicine, 2009

Fowlpox viruses (FPV) are attractive platform vaccine vector candidates because their capacity for insertion of multiple heterologous genes makes them favourable for genetic modification. They also have strong adjuvant activity in their own right. As FPV does not replicate in mammalian cells, there is significantly less opposition associated with their clinical application, with a number already in use. However, a thorough understanding of the immunological relationship between FPV and the mammalian immune system is still lacking. The aim of this thesis was to construct a series of recombinant FPV vectors that co-expressed the nominal antigen chicken ovalbumin (OVA), (FPV[subscript]OVA), and/or murine interleukin-4 (mIL-4). These constructs were used for the characterisation of the relationship between FPV and the mammalian immune system and how this is altered by the co-expression of mIL-4. Immunisation with FPV[subscript]OVA resulted in rapid and highly localized OVA expression which induced strong CD8⁺ cytotoxic T cell (CTL) activity but only weak CD4⁺ T helper and antibody responses. In addition, presentation of FPV-derived antigen and the priming of antigen-specific CTL responses required a permissive bone marrow (BM)-derived cell as the antigen presenting cell (APC). Co-administration with FPV[subscript]mIL-4 resulted in a dramatic reduction in CTL activity that remained largely non-functional throughout the infection and a skewing of the T helper (Th) response towards Th2 with a reduction in interferon (IFN)-γ production by OVA-specific Th cells. These findings provide a sound basis for further characterization of how FPV interacts with the innate and adaptive arms of the immune system, how these can be manipulated via the co-administration of cytokines, and discovering if future rationally designed modifications result in FPV vectored vaccines that induce durable cellular and humoral immunity. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1352466 / Thesis (M.Med.Sc.) - University of Adelaide, School of Medicine, 2009

Fowlpox viruses (FPV) are attractive platform vaccine vector candidates because their capacity for insertion of multiple heterologous genes makes them favourable for genetic modification. They also have strong adjuvant activity in their own right. As FPV does not replicate in mammalian cells, there is significantly less opposition associated with their clinical application, with a number already in use. However, a thorough understanding of the immunological relationship between FPV and the mammalian immune system is still lacking. The aim of this thesis was to construct a series of recombinant FPV vectors that co-expressed the nominal antigen chicken ovalbumin (OVA), (FPV[subscript]OVA), and/or murine interleukin-4 (mIL-4). These constructs were used for the characterisation of the relationship between FPV and the mammalian immune system and how this is altered by the co-expression of mIL-4. Immunisation with FPV[subscript]OVA resulted in rapid and highly localized OVA expression which induced strong CD8⁺ cytotoxic T cell (CTL) activity but only weak CD4⁺ T helper and antibody responses. In addition, presentation of FPV-derived antigen and the priming of antigen-specific CTL responses required a permissive bone marrow (BM)-derived cell as the antigen presenting cell (APC). Co-administration with FPV[subscript]mIL-4 resulted in a dramatic reduction in CTL activity that remained largely non-functional throughout the infection and a skewing of the T helper (Th) response towards Th2 with a reduction in interferon (IFN)-γ production by OVA-specific Th cells. These findings provide a sound basis for further characterization of how FPV interacts with the innate and adaptive arms of the immune system, how these can be manipulated via the co-administration of cytokines, and discovering if future rationally designed modifications result in FPV vectored vaccines that induce durable cellular and humoral immunity. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1352466 / Thesis (M.Med.Sc.) - University of Adelaide, School of Medicine, 2009

Research Doctorate - Doctor of Philosophy (PhD) / This Thesis addresses the problem of robust experiment design, i.e., how to design an input signal to maximise the amount of information obtained from an experiment given limited prior knowledge of the true system. The majority of existing literature on experiment design specifically considers optimal experiment design, which, typically depends on the true system parameters, that is, the very thing that the experiment is intended to find. This obviously gives rise to a paradox. The results presented in this Thesis, on robust experiment design, are aimed at resolving this paradox. In the robust experiment design problem, we assume that the parameter vector is a-priori known to belong to a given compact set, and study the design of an input spectrum which maximises the worst case scenario over this set. We also analyse the problem from a different perspective where, given the same assumption on the parameter vector, we examine cost functions that give rise to an optimal input spectrum independent of the true system features. As a first approach to this problem we utilise an asymptotic (in model order) expression for the variance of the system transfer function estimator. To enable the extension of these results to finite order models, we digress from the main topic and develop several fundamental integral limitations on the variance of estimated parametric models. Based on these results, we then return to robust experiment design, where the input design problems are reformulated using the fundamental limitations as constraints. In this manner we establish that our previous results, obtained from asymptotic variance formulas, are valid also for finite order models. Robustness issues in experiment design also arise in the area of `identification for (robust) control'. In particular, a new paradigm has recently been developed to deal with experiment design for control, namely `least costly experiment design'. In the Thesis we analyse least costly experiment design and establish its equivalence with the standard formulation of experiment design problems. Next we examine a problem involving the cost of complexity in system identification. This problem consists of determining the minimum amount of input power required to estimate a given system with a prescribed degree of accuracy, measured as the maximum variance of its frequency response estimator over a given bandwidth. In particular, we study the dependence of this cost on the model order, the required accuracy, the noise variance and the size of the bandwidth of interest. Finally, we consider the practical problem of how to optimally generate an input signal given its spectrum. Our solution is centered around a Model Predictive Control (MPC) based algorithm, which is straightforward to implement and exhibits fast convergence that is empirically verified.

Research Doctorate - Doctor of Philosophy (PhD) / This Thesis addresses the problem of robust experiment design, i.e., how to design an input signal to maximise the amount of information obtained from an experiment given limited prior knowledge of the true system. The majority of existing literature on experiment design specifically considers optimal experiment design, which, typically depends on the true system parameters, that is, the very thing that the experiment is intended to find. This obviously gives rise to a paradox. The results presented in this Thesis, on robust experiment design, are aimed at resolving this paradox. In the robust experiment design problem, we assume that the parameter vector is a-priori known to belong to a given compact set, and study the design of an input spectrum which maximises the worst case scenario over this set. We also analyse the problem from a different perspective where, given the same assumption on the parameter vector, we examine cost functions that give rise to an optimal input spectrum independent of the true system features. As a first approach to this problem we utilise an asymptotic (in model order) expression for the variance of the system transfer function estimator. To enable the extension of these results to finite order models, we digress from the main topic and develop several fundamental integral limitations on the variance of estimated parametric models. Based on these results, we then return to robust experiment design, where the input design problems are reformulated using the fundamental limitations as constraints. In this manner we establish that our previous results, obtained from asymptotic variance formulas, are valid also for finite order models. Robustness issues in experiment design also arise in the area of `identification for (robust) control'. In particular, a new paradigm has recently been developed to deal with experiment design for control, namely `least costly experiment design'. In the Thesis we analyse least costly experiment design and establish its equivalence with the standard formulation of experiment design problems. Next we examine a problem involving the cost of complexity in system identification. This problem consists of determining the minimum amount of input power required to estimate a given system with a prescribed degree of accuracy, measured as the maximum variance of its frequency response estimator over a given bandwidth. In particular, we study the dependence of this cost on the model order, the required accuracy, the noise variance and the size of the bandwidth of interest. Finally, we consider the practical problem of how to optimally generate an input signal given its spectrum. Our solution is centered around a Model Predictive Control (MPC) based algorithm, which is straightforward to implement and exhibits fast convergence that is empirically verified.

Thesis (Ph. D.)--University of Adelaide, Dept. of Pure Mathematics. 1985. / Includes bibliographical references (leaves 225-227).