2 resultados para 340402 Econometric and Statistical Methods
em QSpace: Queen's University - Canada
Resumo:
Cyclododecane (CDD) is a waxy, solid cyclic hydrocarbon (C12H24) that sublimes at room temperature and possesses strong hydrophobicity. In paper conservation CDD is used principally as a temporary fixative of water-soluble media during aqueous treatments. Hydrophobicity, ease of reversibility, low toxicity, and absence of residues are reasons often cited for its use over alternative materials although the latter two claims continue to be debated in the literature. The sublimation rate has important implications for treatment planning as well as health and safety considerations given the dearth of reliable information on its toxicity and exposure limits. This study examined how the rate of sublimation is affected by fiber type, sizing, and surface finish as well as delivery in the molten phase and as a saturated solution in low boiling petroleum ether. The effect of warming the paper prior to application was also evaluated. Sublimation was monitored using gravimetric analysis after which samples were tested for residues with gas chromatography-flame ionization detection (GC-FID) to confirm complete sublimation. Water absorbency tests were conducted to determine whether this property is fully reestablished. Results suggested that the sublimation rate of CDD is affected minimally by all of the paper characteristics and application methods examined in this study. The main factors influencing the rate appear to be the thickness and mass of the CDD over a given surface area as well as temperature and ventilation. The GC-FID results showed that most of the CDD sublimed within several days of its disappearance from the paper surface regardless of the application method. Minimal changes occurred in the water absorbency of the samples following complete sublimation.
Resumo:
Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.