Thesis--University of Florida. / Description based on print version record. Typescript. Vita. Includes bibliographical references (leaves 180-181).

Thesis--Harvard University. / Bibliography: leaves 167-176.

Thesis (Ph. D.)--University of Chicago, 1914. / "Private edition, distributed by the University of Chicago libraries, Chicago, Illinois." "Reprinted from the Astrophysical journal, vol. L, no. 3, October, 1919." Includes bibliographical references.

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In Part 1, a background to the project is provided by summarizing the general properties of Miras. A section is devoted to describing pulsating variables from a theoretical viewpoint, and this is followed by a discussion of past photometric work. Finally, a comparison is made between the theory and observations of Miras, which points out the well- known incompatibilities between the two approaches. Part 2 begins by underlining the desirability of observing Miras in UBVRI, and then goes on to describe the mechanical design of the St. Andrews ten colour, two photo multiplier, automated photometer. This photometer was designed as a general user instrument for the South African Astronomical Observatory, where the observing programme was carried out between May 1973 and October 1974. The photometric characteristics of Miras are then reconsidered, and the adopted ten colour photometric system, which adds five 200 Å wide filters to UBVRI, is described. The procedures used for obtaining the observations, and the reduction methods are summarized, along with the likely sources of error. This is followed by the analysis of the results, which consists mainly of a description of the properties of the various two-colour diagrams, particular emphasis being placed on those such as (V-R, R-l) and (V, R-I) which show unexpectedly narrow sequences. In addition, considerable attention is directed at the scatter in the (U-B, R-I) diagram, and also at the interesting R Aqr system. The data are then considered from the period-luminosity- colour viewpoint, enabling a discussion of absolute magnitudes and the value of the pulsation constant, as well as an investigation, of the possible harmonic relationships between Miras of differing periods. Finally, an attempt is made to place the Me Miras in the (M[sub]bol, log Te) diagram, and the conclusion is drawn that the discrepancies between theory and observation can probably be mostly attributed to the methods of interpretation of the latter.

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This thesis describes a method for analyzing and designing a continuously variable transmission (CVT). The analysis process is implemented in a software package that can be used to tune a CVT for a given application. The analysis is accomplished through the use of kinematic principles as well as equations developed from basic energy balances. Although the theory developed can be applied to any CVT, this thesis focuses on a case study using the Team Industries brand CVT applied to the Virginia Tech Mini Baja Team. The work was motivated by the team's need to have a reliable and inexpensive method for CVT tuning. Previous approaches to CVT tuning were strictly empirical and involved mechanical component replacement in a slow and expensive trialand- error optimization loop. The tuning software is intended to be a first step in the process of properly tuning a CVT for a specific application and not a complete replacement for the empirical methods employed previously. / Master of Science

Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale level or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types. Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale level or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analysing data from a study on RNA editing. Therefore the suggested method can be applied straightforwardly by scientists in bioinformatics research. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics