A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. 21 October, 2014. / Three decades ago, the discovery of the Human Immunodeficiency Virus (HIV) was announced. The subsequent HIV pandemic has continued to devastate the global community, and many countries have set ambitious HIV reduction targets over the years. Reliable methods for measuring incidence, the rate of new infections, are essential for monitoring the virus, allocating resources, and assessing interventions. The estimation of incidence from single cross-sectional surveys using tests that distinguish between ‘recent’ and ‘non-recent’ infection has therefore attracted much interest. The approach provides a promising alternative to traditional estimation methods which often require more complex survey designs, rely on poorly known inputs, and are prone to bias. More specifically, the prevalence of HIV and ‘recent’ HIV infection, as measured in a survey, are used together with relevant test properties to infer incidence. However, there has been a lack of methodological consensus in the field, caused by limited applicability of proposed estimators, inconsistent test characterisation (or estimation of test properties) and uncertain test performance. This work aims to address these key obstacles. A general theoretical framework for incidence estimation is developed, relaxing unrealistic assumptions used in earlier estimators. Completely general definitions of the required test properties emerge from the analysis. The characterisation of tests is then explored: a new approach, that utilises specimens from subjects observed only once after infection, is demonstrated; and currently-used approaches, that require that subjects are followed-up over time after infection, are systematically benchmarked. The first independent and consistent characterisation of multiple candidate tests is presented, and was performed on behalf of the Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA), which was established to provide guidance and foster consensus in the field. Finally, the precision of the incidence estimator is presented as an appropriate metric for evaluating, optimising and comparing tests, and the framework serves to counter existing misconceptions about test performance. The contributions together provide sound theoretical and methodological foundations for the application, characterisation and optimisation of recent infection tests for HIV incidence surveillance, allowing the focus to now shift towards practical application.

Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and di↵erent tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherrently sparse in some bases, due to their inherrant structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competetive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by `1 pooling, and correct patient identification is consistently achieved 100% over 1000 trials, when either the face data or fingerprint data are implemented as a classification basis. The algorithm gets 100% classification when faces and fingerprints are concatenated into multimodal datasets. This suggests that 100% patient identification will be achievable in the clinal setting. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

The practice of evaluating situations with the Riverside Situational Q-Sort (RSQ:Wagerman & Funder, 2009) is relatively new. The present study aimed to investigate the theoretical framework supporting the RSQ with regards to the potential confounds of emotional state and the use of Likert-type ratings. Data were collected from a sample of Florida Atlantic University students (N = 206). Participants were primed for either a positive or negative mood state and asked to evaluate a situation with the RSQ in either the Q-Sort or Likert-type response format. Results suggested that response format has a significant influence on RSQ evaluations, but mood and the interaction between mood and response format do not. Exploratory analyses were conducted to determine the underlying mechanisms responsible. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

This study sought to determine the effect upon Tukey's Honestly Significant Difference (HSD) statistic of concurrently violating the assumptions of homogeneity of variance and equal sample sizes. Two forms for the unequal sample size problem were investigated. Kramer's form and the harmonic mean approach were the two unequal sample size procedures studied. The study employed a Monte Carlo simulation procedure which varied sample sizes with a heterogeneity of variance condition. Four thousand experiments were generated. Findings of this study were based upon the empirically obtained significance levels. Five conclusions were reached in this study. The first conclusion was that for the conditions of this study the Kramer form of the HSD statistic is not robust at the .05 or .01 nominal level of significance. A second conclusion was that the harmonic mean form of the HSD statistic is not robust at the .05 and .01 nominal level of significance. A general conclusion reached from all the findings formed the third conclusion. It was that the Kramer form of the HSD test is the preferred procedure under combined assumption violations of variance heterogeneity and unequal sample sizes. Two additional conclusions are based on related findings. The fourth conclusion was that for the combined assumption violations in this study, the actual significance levels (probability levels) were less-than the nominal significance levels when the magnitude of the unequal variances were positively related to the magnitude of the unequal sample sizes. The fifth and last conclusion was that for the concurrent assumption violation of variance heterogeneity and unequal sample sizes, the actual significance levels significantly exceed the nominal significance levels when the magnitude of the unequal variances are negatively related to the magnitude of the unequal sample sizes.

This study measures resident student perceptions of on-campus living and study environments at the University of Namibia campus residence and their relation to student academic performance. Data were obtained from a stratified random sample of resident students with hostels (individual dormitory) as strata. Student academic performance was measured by grade point average obtained from the university registrar. Student perceptions of living and study environments were obtained from a survey. Inferences were made from the sample to the population concerning: student perceptions of the adequacy of the library and campus safety, and differences in perceptions between students living in old-style and new-style hostels. To relate student perceptions to academic performance, a model regressing GPA on student perception variables was constructed. The principal findings of the analyses were that (1) Student perceptions do not differ between old and new hostels; (2) There is an association between time spent in the hostel and the type of room, ability to study in room during the day and the type of room, ability to study in room at night and the type of room, time spent in hostel and number of times student change blocks, ability to study in room at night and availability of study desk in room, ability to study in room at night and availability of study lamp in room, effectiveness of UNAM security personnel and safety studying at classes at night and also between effectiveness of UNAM security personnel and student perception on whether security on campus should remain unchanged respectively; (3) Mean GPA differs with respect to the type of room, ability to study in room during the day, time spent in hostel, number of times student change blocks, current year of study, time spent on study, students who are self-catering, sufficiency of water supply in blocks and also with students who are enrolled in Law and B.Commerce field of study and with students receiving financial support in the form of loans. (4) The variables found to be significant in the regression model were Law field of study, double rooms, inability to study in room during the day and self-catering respectively.

Spatial heterogeneity of the environment, as measured by floral diversity, composition and structure, is known to influence the distribution and diversity of invertebrates. Heterogeneity brought about by anthropogenic disturbance may be a threat to invertebrate diversity. This thesis investigates the impacts of vegetation heterogeneity at a range of scales on the diversity of invertebrate populations in modified high-altitude indigenous grasslands on the Rock and Pillar Range, Central Otago. Invertebrates were sampled in and on the edges of snow tussock fragments to assess whether species richness increased systematically with fragment area. Invertebrate composition was poorly related to fragment area, plant composition and environmental variables. Taxon richness, abundance and/or diversity for three invertebrate groups increased as fragment area decreased, perhaps reflecting an influx of species from the surrounding matrix. For snow tussock leaf invertebrates in autumn, richness and abundance were at least two times lower in tussocks exposed to the wind than those in the centre of fragments, suggesting selection of habitat may be based on microclimatic characteristics. Invertebrates were sampled from the bases of tussocks after they were clipped to simulate three levels of vertebrate grazing. Invertebrate community composition differed between sites and sampling dates but was unaffected by clipping treatment. At the higher altitude site invertebrate abundance was 1.45 times greater and Shannon-Wiener diversity (H�) 1.22 times lower than at the lower altitude site. The latter sampling date had higher abundance (2.12 times) and taxon richness (1.14 times) than the earlier date. Pitfall-trapped invertebrates in cushionfield, herbfield and snow tussock differed in community composition and often by taxon richness, abundance and diversity. Across habitats, plant composition, plant diversity and some environmental variables were correlated with invertebrate variables, but could not be separated from vegetation type. The invertebrates collected in the course of the study are listed. Four Phyla, eight Classes, 24 orders and over 300 taxa were recorded. Almost all taxa are endemic and many have limited distributions and/or are undescribed. A species list is provided with collection altitude, method and habitat type. Invertebrate assemblages from sites differing in altitude, vegetation type and level of habitat modification on the Rock and Pillar Range are compared. Sites differed in species composition and rank orders of abundance and richness. At lower elevations, invertebrate richness was at least 25% less, and standardised trap abundance at least 44% less, than that at the highest elevation. Richness and abundance of exotic invertebrates decreased with increasing altitude. This thesis highlights several points concerning the study of grassland invertebrates and heterogeneity on the Rock and Pillar Range. First, there are differences in invertebrate assemblages at a range of scales. Conserving invertebrate diversity will therefore require altitudinal sequences and different habitat types, including disturbed areas. At high elevations, tussock habitat may be disproportionately important due to its relative rarity. Second, the effects of disturbance on invertebrates were only visible at large spatial scales. Third, there is a paucity of research on New Zealand invertebrates, especially in regard to terrestrial disturbance, which has resulted in a shortfall of biological, distributional, taxonomic and ecological knowledge.

Bibliography: p.63-65.

The objective of this research effort is to provide a methodology for assessing the effectiveness of sampling techniques used to gather different types of geo-physical information by a robotic agent. We focus on assessing how well unique real-time sampling strategies acquire information that is, otherwise, too dangerous or costly to collect by human scientists. Traditional sampling strategies and informed search tech- niques provide the underlying structure for a navigating robotic surveyor whose goal is to collect samples that yield an accurate representation of the measured phenomena under realistic constraints. These sampling strategies are alternative improvements that provide greater information gain than current sampling technology allows. The contributions of this work include the following: 1) A method for estimating spa- tially distributed phenomena, using a partial sample set of information, that shows improvement over that of a more traditional estimation method. 2) A method for sampling this phenomena in the form of a navigation scheme for a mobile robotic survey system. 3) A method of ranking and comparing different navigation algorithms relative to one another based on performance (reconstruction error) and resource (distance) constraints. We introduce a specific class of navigation algorithms as example sampling strategies to demonstrate how our methodology allows different robot navigation options to be contrasted and the most practical strategy selected.

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published_or_final_version / Applied Statistics / Master / Master of Social Sciences