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Most Virtual Reality systems employ some form of parallel processing, making use of multiple processors which are often distributed over large areas geographically, and which communicate via various forms of message passing. The approaches to parallel decomposition differ for each system, as do the performance implications of each approach. Previous comparisons have only identified and categorized the different approaches. None have examined the performance issues involved in the different parallel decompositions. Performance measurement for a Virtual Reality system differs from that of other parallel systems in that some measure of the delays involved with the interaction of the separate components is required, in addition to the measure of the throughput of the system. Existing performance analysis approaches are typically not well suited to providing both these measures. This thesis describes the development of a performance analysis technique that is able to provide measures of both interaction latency and cycle time for a model of a Virtual Reality system. This technique allows performance measures to be generated as symbolic expressions describing the relationships between the delays in the model. It automatically generates constraint regions, specifying the values of the system parameters for which performance characteristics change. The performance analysis technique shows strong agreement with values measured from implementation of three common decomposition strategies on two message passing architectures. The technique is successfully applied to a range of parallel decomposition strategies found in Parallel and Distributed Virtual Reality systems. For each system, the primary decomposition techniques are isolated and analysed to determine their performance characteristics. This analysis allows a comparison of the various decomposition techniques, and in many cases reveals trends in their behaviour that would have gone unnoticed with alternative analysis techniques. The work described in this thesis supports the Performance Analysis and Comparison of Parallel and Distributed Virtual Reality systems. In addition it acts as a reference, describing the performance characteristics of decomposition strategies used in Virtual Reality systems.

One of the most important industrial uses of pyrotechnic compositions is as delay fuses in electric detonators. Many factors influence the rate of burning of such fuses. These include (a) the primary choice of chemical components, followed by (b) the physical properties of these components, particularly the particle-size and distribution of the fuel, (c) the composition of the system chosen and (d) the presence of additives and/or impurities. A full experimental study of the influences of even a few of these factors, while attempting to hold other potential variables constant, would be extremely time consuming and hence attention has been focused on the possibilities of modelling pyrotechnic combustion. Various approaches to the modelling of pyrotechnic combustion are discussed. These include:- (i) one-dimensional finite-difference models; (ii) two-dimensional finite-element models; (iii) particle-packing considerations; (iv) Monte Carlo models. Predicted behaviour is compared with extensive experimental information for the widely-used antimony/potassium permanganate pyrotechnic system, and the tungsten /potassium dichromate pyrotechnic system. The one-dimensional finite-difference model was investigated to give a simple means of investigating the effects of some parameters on the combustion of a pyrotechnic. The two-dimensional finite-difference model used similar inputs, but at the expense of considerably more computer power, gave more extensive information such as the shape of the burning front and the temperature gradients throughout the column and within the casing material. Both these models gave improved results when allowance was made for autocatalytic kinetics in place of the usual assumption of an "order-of-reaction", n ≤ 1. The particle-packing model investigated the qualitative relationship between the maximum burning rate of a pyrotechnic system and the maximum number of contact points (per 1.00 g composition) calculated for that system. Qualitative agreement was found for those systems which are presumed to burn mainly via solid-solid reactions. The Monte Carlo model investigated the effect of the random packing of fuel and oxidant particles on the variability of the burning rate of a pyrotechnic composition.

This thesis is concerned with grouping complex systems by means of concurrent model, in order to aid in (i) formulation of classifications and (ii) induction of parallel simulation programs. It observes, and seeks f~ furmalize _ and then exploit, the strong structural resemblance between complex systems and occam programs. The thesis hypothesizes that groups of complex systems may be discriminated according to shared structural and behavioural characteristics. Such an analysis of the complex systems domain may be performed in the abstract with the aid of a model for capturing interesting features of complex systems. The resulting groups would form a classification of complex systems. An additional hypothesis is that, insofar as the model is able to capture sufficient . programmatic information, these groups may be used to define, automatically, algorithmic skeletons for the concurrent simulation of complex systems. In order to test these hypotheses, a specification model and an accompanying formal notation are developed. The model expresses properties of complex systems in a mixture of object-oriented and process-oriented styles .. The model is then used as the basis for performing both classification and automatic induction of parallel simulation programs. The thesis takes the view that specification models should not be overly complex, especially if the specifications are meant to be executable. Therefore the requirement for explicit consideration of concurrency on the part of specifiers is minimized. The thesis formulates specifications of classes of cellular automata and neural networks according to the proposed model. Procedures for verificati6If - and induction of parallel simulation programs are also included.

A basic introduction to the techniques of writing computer simulations for particle physics experiments concerning rare decays is given. A very brief outline of the theoretical basis for cross-section calculations is followed by more in-depth discussions of Monte Carlo sampling techniques, including the use of the CERN package DIVON. The reader is introduced to the concepts involved in detector simulation; specifically, the CERN package GEANT in presented and discussed in some detail. Finally, methods of modelling external conversion processes are taken up. Illustrations for the discussions are taken from the various rare decay modes of the π⁰. Example code and some sample results comparing simulated and real data are given in the appendices. / Science, Faculty of / Physics and Astronomy, Department of / Graduate

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Healthcare social media is emerging in recent years with increasing attention on people’s health. Online review websites are not only diversi.ed with medicine, hospitals, or doctors but abundant in amount. To discover knowledge from these online reviews, several computational models are proposed. Online healthcare review websites are facing challenges in con.ict of interests among various healthcare stakeholders. To avoid legal complaints and better sustain under such circumstance, we propose a decoupling approach for designing healthcare review websites. Objective components such as medical condition and treatment are remained as the primary parts, as they are generic, impersonal and directly related to patients themselves. Subjective components, however, such as comments to doctors or hospitals are decoupled as secondary parts for sensitive and controversial informa­tion and are optional to reviewers. Our proposed approach shows better .exibility in managing of contents in different levels of details and ability of balancing the right of expression of reviewers with other stakeholders. To identity the patient-reported adverse reactions in drug reviews, we propose a consumer-oriented coding scheme using wordnet synonym and derivational related form. Signi.cant discrepancy of incidences of adverse reactions is discovered be­tween online reviews and clinical trials. We proposed an adverse reaction report ratio model for integrated interpretation of adverse reactions reported in online re­views versus those from clinical trial. Our estimation on average adverse reactions shows high correlation with drug acceptability score obtained from a large-scale meta-analysis. To investigate the impact of key adverse reactions in patients’ perspective, we propose a topic model named Fisher’s Linear Discriminant Analysis Projected Non­negative Matrix Factorization (FLDA-projected-NMF) for discovering discrimina­tive features and topics with additional class information. With satisfaction scores provided in the reviews, discriminative features and topics on satisfaction are dis­covered and polarities of adverse reactions are estimated based on the discriminative feature weights. Discriminative features and topics on medication duration and on age group are obtained as well. Our method outperforms other supervised methods in evaluation of topic sentiment score and topic interpretation measured by entropy. Patient-reported adverse reaction terms are mined from reviews with comment class label. Some new adverse reactions in depression drug and statin drug are also dis­covered. To further study patients’ behaviors, we use structural equation modeling for studying the relationship of factors in patients’ treatment experience with patients’ quality of life. In covariance model, most adverse reactions are found of small co­variance except nausea, headache and dizziness. In measurement model, coef.cients of individual adverse reactions on latent adverse reaction are correlated to the inci­dence of adverse reactions. In structural model, we model the relationship of latent adverse reaction, rating score, positive sentiment and negative sentiment. Compari­son between the measurement models of rating scores of depression drug and statin drug shows that there could be latent factors to account for the variances of latent rating, which shows correlations with the severity of adverse reactions.

In this paper, we present an overview of a number of existing flow visualization methods, developed by the authors in the recent past, that are specifically aimed at integrating and leveraging domain-specific knowledge into the visualization process. These methods transcend the traditional divide between interactive exploration and featurebased schemes and allow a visualization user to benefit from the abstraction properties of feature extraction and topological methods while retaining intuitive and interactive control over the visual analysis process, as we demonstrate on a number of examples.