Thesis (Ph. D.)--University of New South Wales, 1997. / Completed at: University of New South Wales, School of Mathematics. Title from electronic deposit form.

Thesis (Ph. D.)--University of Chicago, Dept. of Statistics, June 1999. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 45-48). Also available in electronic version.

Made available in DSpace on 2014-06-11T19:35:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2003-03Bitstream added on 2014-06-13T19:05:51Z : No. of bitstreams: 1 napoleao_das_dr_guara.pdf: 1233136 bytes, checksum: e1780c6a0f02e82cd3c85543e0c3f36c (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Os sistemas de refrigeração por absorção da mistura amônia-água têm sido amplamente utilizados e referenciados na literatura. Normalmente, os modelos encontrados para análises são baseados considerando-se, dados de tabelas e diagramas para a entalpia em função da concentração. Para a análise de tabelas e diagramas de entropia em função da concentração de amônia na mistura, não se detém a mesma atenção, existindo ausência de dados disponíveis na literatura para esta propriedade termodinâmica. Estes sistemas apresentam-se complexos quanto aos parâmetros físico-químicos, tornando-se necessário um estudo mais aprofundado e detalhado da termodinâmica associada, corroborando para a determinação da inter-relação da entropia com outras propriedades associadas a refrigeração. Neste trabalho são formulados modelos de cálculos para o levantamento da entropia em função da temperatura, pressão e concentração de misturas amônia-água S = S (T, P, x). A confecção dos diagramas de entropia na fase líquido-vapor foram determinados visando auxiliar projetos industriais através de análises energéticas e exergéticas, simulação e otimização de processos relacionados ao sistema de refrigeração por absorção. / Refrigeration systems of the ammonia-water mixtures by absorption have been widely used and referred in the literature. Usually the models found for analyses are based taking in to account data of tables and diagrams for the enthalpy as a function of the concentration. For the analysis of tables and entropy diagrams in function of the concentration of ammonia in the mixture, it has not been taken the same attention, existing lack of available data in the literature for that thermodynamic property. These systems seem to be complex as for the physiochemical parameters, becoming necessary a deeper and detailed study on the associated thermodynamics, corroborating to determine the interrelation of the entropy with other associated refrigeration properties. In this work, calculation models to improve the entropy are formulated in function of temperature, pressure and concentration of the ammonia-water mixtures S = S (T, P, x). The performance of the entropy diagrams in the liquid-vapor phase was surely aiming to aid industrial projects through energy analyses and exergetic simulation and optimization of processes related to the system of cooling by absorption.

This study was performed under the motivation to find a scheme that could describe the complex behavior of cardiovascular homeostasis. This is hypothesized to be manifested in a thermodynamic description of the cardiovascular system (CVS). Seen from a thermodynamic framework, the mechanics of blood flow can be gauged in similar terms as metabolic exchange at the capillaries - thereby providing a holistic and novel perspective on overall CVS function. Entropy generation, a thermodynamic calculation, represents lost work and is hypothesized to reveal something about the "optimal" state of the CVS. In particular, it is hypothesized that the CVS state that generates minimal entropy, given certain constraints, will be physiologically preferred and that cardiovascular control operates to find this state. This will be tested by first proposing a method to calculate entropy generation in the CVS, and secondly characterizing entropy generation across unique CVS states by simulation of a mathematical model.

Why is the term information in English an uncountable noun, whereas in information theory it is a well-defined quantity? Since the amount of information can be quantified, what is the meaning of a fraction of that amount? This dissertation introduces a quasi-entropy matrix which developed from Claude Shannon's information measure as an analytical tool for behavioral studies. Such matrix emphasizes the role of relative characteristics of individual level data across different collections. The real challenge in the big data era is never the size of the dataset, but how data lead scientists to individuals rather than arbitrarily divided statistical groups. This proposed matrix, when combining with other statistical measures, provides a new and easy-to-do method for identifying pattern in a well-defined system because it is built on the idea that uneven probability distributions lead to decrease in system entropy. Although the matrix is not superior to classical correlation techniques, it allows an interpretation not available with traditional standard statistics. Finally, this matrix connects heterogeneous datasets because it is a frequency-based method and it works on the modes of data rather than the means of values. It also visualizes clustering in data although this type of clustering is not measured by the squared Euclidean distance of the numerical attributes.

Information theoretical measures are used to design, from first principles, an objective function that can drive a learning machine process to a solution that is robust to perturbations in parameters. Full analytic derivations are given and tested with computational examples showing that indeed the procedure is successful. The final solution, implemented by a robust learning machine, expresses a balance between Shannon differential entropy and Fisher information. This is also surprising in being an analytical relation, given the purely numerical operations of the learning machine.

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