A research report submitted in partial fulfilment of the requirements for the degree of Master in Science in Physiotherapy in the Faculty of Medicine of the University of the Witwatersrand, Johannesburg. Johannesburg, 1994 / The manual resuscitation bag is a modality which is commonly used by physiotherapists to manually hyperinflate the lungs of mechanically ventilated patients. There is limited scientific evidence to support its use and the literature is not in agreement as to the effects of manual hyperinflation. A meta-analysis of the current research has been conducted to investigate the effects of this modality on arterial oxygen tension and lung compliance. All studies evaluating the effects of manual hyperinflation (or bagging) on arterial oxygen tensions andlor lung compliance on mechanically ventilated patients have been retrieved. Only studies which reported results in terms of mean values and standard deviation or standard error of the mean could be used in this analysis. Twelve studies were identified between the time period 1968 - 1994. Seven of these studies fitted the inclusion criteria. The mean and standard error (If the mean values for arterial oxygen tensions (Pa02) and lung compliance (Ci) have been used to calculate the 95% confidence intervals and these results were plotted on a graph. A comparative analysis has been performed un the results of the seven studies. A generally non-significant association between bagging and the Pa02 and Ci values was demonstrated. Great discrepancies were identified in the designs of the seven studies.Since the seven studies included ill this meta-analysis show an overall non-significant association, it is reasonable to assume that the manual resuscitation bag has limited capacity for increasing the Pa02 and Ci values. It is unfortunate that in the studies where a positive outcome of bagging was demonstrated. inadequate data was presented in the trials. Therefore, these studies could not be included in this meta-analysis. The studies which have been included, however, presented such divergent designs that they do not offer conclusive evidence. Recommendations are presented for a standardised, multicentre study which hopefully will clarify the therapeutic value of this elusive modality

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 302-308). Available also in a digital version from Dissertation Abstracts.

Thesis (MScIng)--University of Stellenbosch, 2007. / Bibliography. Also available via the Internet.

Thesis (M.Eng.). / Written for the Dept. of Mechanical Engineering. Title from title page of PDF (viewed 2008/07/29). Includes bibliographical references.

Thesis (M.S.)--Ohio University, June, 1994. / Title from PDF t.p.

Thesis (M. Sc. (Computer Science))--University of Pretoria, 2000. / Summaries in Afrikaans and English. Includes bibliographical references.

Thesis (M.S.)--University of Wisconsin--Madison, 1969. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 166-171). Available also in a digital version from Dissertation Abstracts.

Thesis (MScIng)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

The optimization of parameter driven simulations has been the focus of many research papers. Algorithms like Hill Climbing, Tabu-Search and Simulated Annealing have been thoroughly discussed and analyzed. However, these algorithms do not take into account the fact that simulations can have dynamic scenarios. In this dissertation, the possibility of using the classical optimization methods just mentioned, combined with clustering techniques, in order to optimize parameter driven simulations having dynamic scenarios, will be analyzed. This will be accomplished by optimizing simulations in several random static scenarios. The optimum results of each of these optimizations will be clustered in order to find a set of typical solutions for the simulation. These typical solutions can then be used in dynamic scenario simulations as references that will help the simulation adapt to scenario changes. A generic optimization and clustering system was developed in order to test the method just described. A simple traffic simulation system, to be used as a testbed, was also developed. The results of this approach show that, in some cases, it is possible to improve the outcome of simulations in dynamic environments and still use the classical methods developed for static scenarios.