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Artificial IntelligenceBased Surveillance of Tuberculosis in South Africa Using Google trends Data
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Neurodevelopmental disorders at Chris Hani Baragwanath Academic Hospital a 4year retrospective database review
No full textThe Effects of Node Removal on Bayesian Network Resilience for ATM Network Transaction Vulnerabilities
No full textA dissertation submitted in fulfillment of the requirements for the degree of Master of Science, to the Faculty of Science, School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2025We investigate the evaluation of influence relationships in probabilistic graphical models, focusing on the impact of node removal (mutilation) within Bayesian networks. The central problem addressed is understanding how the joint probability distribution and influence structure among interconnected variables evolve when a subset of nodes is removed, an issue relevant to various real-world systems experiencing disruptions. We model these dynamics using Bayesian learning to provide insights into network resilience and dependencies. To explore these effects, we generate synthetic Bayesian network structures that are tree-like, sparse, and dense, each representing different real-world configurations found in machine learning, sensor networks, and financial modeling. Conditional Probability Distributions (CPDs) were assigned to nodes based on the Bernoulli distribution. The Kullback-Leibler (KL) divergence quantified the deviations in influence structures post-removal, with evaluation of structure recovery employing an exact inference technique. Our findings indicate that each network type exhibits distinct responses to node removal: tree-like structures stabilize quickly with increased data, sparse structures show higher sensitivity but recover efficiently, and dense structures offer robustness through redundancy, though they demand larger datasets. These findings have significant implication for optimizing complex systems, particularly those requiring resilient network architectures. As a real-world application, we model ATM transaction networks to analyze how the removal of ATMs (due to vandalism, load shedding, or maintenance) impacts transaction flows. Our results show that high-traffic ATMs serve as critical nodes, significantly influencing neighboring ATMs when removed. By applying Bayesian structure learning, we demonstrate that optimal ATM network configurations can be identified to minimize disruption and improve financial service resilience. This study contributes to the growing field of probabilistic graphical models by introducing a novel approach to understanding influence dynamics in mutilated networks. It provides practical insights and lays a foundation for further research into complex systems where node integrity and network stability are critical for decision-making and operational efficiency.MMM202
The Best Interests of the Child and South African Defamation Law: Reconsidering Le Roux v Dey
No full textA research report submitted in fulfillment of the requirements for the Master of Laws, in the Faculty of Commerce Law and Management, School of law, University of the Witwatersrand, Johannesburg, 2025This research report investigates whether the common law of defamation is consistent with the principle of the “best interests of the child” entrenched in section 28(2) of the Constitution. An analysis of the judgments in Le Roux v Dey considers whether a child-centred approach to defamation law involving a child wrongdoer and an adult victim requires development to preclude an adult victim’s claim for damages. In this report I first seek to understand the current law and its rationale, by reviewing how the judgments in Le Roux grappled with the child/adult dynamic and applied the prima facie elements of defamation as well as the possible defences available to child defendants. Secondly, I consider the constitutional imperatives when there are conflicting constitutional rights, focusing on the rights to freedom of expression and human dignity and a third complicator being the best interests of the child. By conducting a balancing act of these rights with guidance from leading defamation law cases, I conclude that there is no need to further develop the common law of defamation as it is compliant to the Constitution and strikes a delicate balance between the three main rightsMM202
Increases in humidity will intensify lethal hyperthermia risk for birds occupying humid lowlands
No full textArchiving Visibility Data Using Lossy Baseline-Dependent SVD Techniques
No full textA dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Physics, Faculty of Science, School of Physics, University of the Witwatersrand, Johannesburg, 2025Modern radio interferometer arrays, such as the MeerKAT [1], the Australian Square Kilometre Array Pathfinder (ASKAP) [2, 3], the Low Frequency Array (LOFAR) [4], the Murchison Widefield Array (MWA) [5, 6], and the upcoming Square Kilometre Array Observatory (SKAO) [7], generate large volumes of data due to their high temporal and spectral resolutions, large number of baseline configurations, and wide bandwidths. Managing these data volumes poses substantial challenges in terms of storage and processing. To address the growing costs, averaging techniques are widely used to reduce data sizes. However, averaging leads to signal loss in radio interferometric images, resulting in smeared or blurred source emissions and reduced source amplitudes. Moreover, the extent of this smearing is baseline-dependent, as the signal phase depends on baseline length. Specifically, longer baselines are more affected than shorter ones. This is addressed by Baseline Dependent Averaging (BDA), which applies variable averaging intervals - longer for shorter baselines and shorter for longer baselines. BDA achieves high data volume reduction since radio interferometers generally have more shorter baselines, which can be aggressively averaged with minimal smearing effects. However, BDA changes the time-frequency grid structure of the data, making it incompatible with the standard storage format in the field, the Measurement Set (MS). A promising approach to data compression was presented by Atemkeng et al. [8], who developed a compression technique based on Singular Value Decomposition (SVD). This approach exploits the inherent structure of raw visibility data, representing it as a low-rank matrix approximation where each component corresponds to a specific Fourier component of the sky distribution. By approximating the data with a reduced rank, the essential features of the original data can be captured using fewer components, effectively reducing data size. In this work, we build on the methods introduced by Atemkeng et al. [8], specifically evaluating the effectiveness of SVD in compressing large volumes of data while preserving image quality and data fidelity for long-term archival. Although our study focuses on the MeerKAT telescope, the approach can be adapted for use with any other radio telescope. Our findings demonstrate that for a bright point source (1 Jy), whether located at the phase centre or away from it, the data features can effectively be captured using a single component, recovering over 99.90% of the source amplitude and achieving a data size reduction of over 97%. For fields with multiple sources, the features can be fully captured using 3-4 components out of 24, recovering over 99.90% of the source amplitude for a source at the edge of the Field of View (FoV), which is around 1.1 deg for the MeerKAT at a frequency of 1.4 GHz. This results in a data size reduction of over 91%. Additionally, we found that the source or field direction does not impact SVD compression. On the other hand, the Signal to Noise Ratio (SNR) significantly affects SVD compression. For sources with low SNR or faint sources, all components are required to recover more than 97% of the source amplitude, making the compression ineffective. In such scenarios, it would be more advantageous to first denoise the data or to use BDA.National Research Foundation (NRF)South African Radio Astronomy Observatory (SARAO)South African Radio Astronomy Observatory-Human Capital Development (SARAO-HCD) Group GrantMMM202
Factors influencing utilization of physiotherapy services by health care providers at Busia County Referral Hospital in Kenya
No full textFactors that affect the adoption of buy now pay later technologies amongst South African SMME's
No full textA research report submitted in fulfillment of the requirements for the Master of Management in the field of Digital Business, in the Faculty of Commerce Law and Management, Wits Business School, University of the Witwatersrand, Johannesburg, 2025The adoption of Buy-Now-Pay-Later (BNPL) financing among Small, Medium, and Micro Enterprises (SMMEs) in South Africa is an emerging trend with significant implications for business growth and financial inclusion. Despite the global rise of BNPL as a flexible payment solution, its adoption within the South African SMME sector remains limited. This study explores the key factors influencing BNPL adoption by drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A quantitative research approach was employed, utilizing survey data collected from South African SMMEs operating across various industries. The study investigated the roles of performance expectancy, effort expectancy, and social influence in shaping the behavioural intention to adopt BNPL financing. Additionally, demographic variables such as age, gender, and business experience were analysed as potential moderators of these relationships. The findings indicate that performance expectancy and social influence significantly impact BNPL adoption, with effort expectancy playing a lesser role. Younger and more tech-savvy entrepreneurs demonstrated a higher likelihood of adoption, emphasizing the role of digital literacy in financial technology acceptance. The study also highlights the challenges faced by SMMEs, including regulatory uncertainties, financial literacy gaps, and accessibility constraints, which hinder widespread BNPL adoption. The research contributes to both academic literature and practical applications by providing insights that can guide BNPL providers, policymakers, and industry stakeholders in fostering a more inclusive financial ecosystem. It underscores the need for targeted financial education programs and regulatory frameworks that support responsible lending while promoting digital payment solutions tailored to the needs of SMMEs.MM202