902 resultados para Explanation of the reasoning


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Explanation for some sheets issued in combined numbers.

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First edition published under title: Outline of Louis F. Post's lectures; second and third editions, The single tax; fourth and fifth editions, Taxation of land values.

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Mode of access: Internet.

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Mode of access: Internet.

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Includes bibliographical references and index.

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Bioenergetics differ between males and females of many species. Human females apportion a substantial proportion of energy resources towards gynoid fat storage, to support the energetic burden of reproduction. Similarly, axial calcium accrual is favoured in females compared with males. Nutritional status is a prognostic indicator in cystic fibrosis (CF), but girls and young women are at greater risk of death despite equivalent nutritional status to males. The aim of this study was to compare fat (energy) and calcium stores (bone density) in males and females with CF over a spectrum of disease severity. Methods: Fat as % body weight (fat%) and lumbar spine (LS) and total body (TB) bone mineral density (BMD) were measured using dual absorption X-ray photometry in 127(59M) control and 101(54M) CF subjects, aged 9–25 years. An equation for predicted age at death had been determined using survival data and history of pulmonary function for the whole clinic, based on a trivariate normal model using maximum likelihood methods (1). For the CF group, a disease severity index (predicted age at death) was calculated from the derived equations according to each subjects history of pulmonary function, current age, and gender. Disease severity was classified according to percentile of predicted age at death (‘mild’ ≥75th, ‘moderate’ 25th–75th, ‘severe’ ≤25th percentile). Wt for age z-score was calculated. Serum testosterone and oestrogen were measured in males and females respectively. Fat% and LSBMD were compared between the groups using ANOVA. Results: There was an interaction between disease severity and gender: increasing disease severity was associated with greater deficits in TB (p=0.01), LSBMD (p

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This thesis presents an investigation into the application of methods of uncertain reasoning to the biological classification of river water quality. Existing biological methods for reporting river water quality are critically evaluated, and the adoption of a discrete biological classification scheme advocated. Reasoning methods for managing uncertainty are explained, in which the Bayesian and Dempster-Shafer calculi are cited as primary numerical schemes. Elicitation of qualitative knowledge on benthic invertebrates is described. The specificity of benthic response to changes in water quality leads to the adoption of a sensor model of data interpretation, in which a reference set of taxa provide probabilistic support for the biological classes. The significance of sensor states, including that of absence, is shown. Novel techniques of directly eliciting the required uncertainty measures are presented. Bayesian and Dempster-Shafer calculi were used to combine the evidence provided by the sensors. The performance of these automatic classifiers was compared with the expert's own discrete classification of sampled sites. Variations of sensor data weighting, combination order and belief representation were examined for their effect on classification performance. The behaviour of the calculi under evidential conflict and alternative combination rules was investigated. Small variations in evidential weight and the inclusion of evidence from sensors absent from a sample improved classification performance of Bayesian belief and support for singleton hypotheses. For simple support, inclusion of absent evidence decreased classification rate. The performance of Dempster-Shafer classification using consonant belief functions was comparable to Bayesian and singleton belief. Recommendations are made for further work in biological classification using uncertain reasoning methods, including the combination of multiple-expert opinion, the use of Bayesian networks, and the integration of classification software within a decision support system for water quality assessment.