WIReDSpace

Welcome to WIReDSpace (Wits Institutional Repository on DSpace)

For queries relating to content and technical issues, please contact IR specialists via this email address : openscholarship.library@wits.ac.za,
Tel: 011 717 4652 or 011 717 1954

Photo by @inspiredimages

Communities in WIReDSpace

Select a community to browse its collections.

Now showing 1 - 5 of 19

Recent Submissions

  • Thumbnail Image
    Item type:Item,
    Toolkit for high-dimensional quantum light
    (University of the Witwatersrand, Johannesburg, 2025) Gounden, Neelan
    The manipulation of light’s various degrees of freedom, known as structuring light, has become useful in the development of both classical and quantum technologies. Two-dimensional quantum states have been investigated optically for many years, but have a limited amount of information which can be encoded into them. In order to surpass this limitation, high-dimensional quantum states have also been studied using light in quantum optics experiments. High-dimensional quantum states al- low for large amounts of information to be encoded into the states and bi-photon quantum states present an added advantage of non-local measurements. However, high-dimensional bi-photon states impose challenges such as efficient generation and detection of the states. In this dissertation we explore a generation technique for high dimensional states as well efficient detection techniques and methods which can be employed to com- bat challenges which may arise. The high-dimensional degrees of freedom that will be used are orbital angular momentum and transverse spatial position. The cor- relations between a pair of entangled photons are exploited in order to perform measurements on a bi-photon high-dimensional quantum state. The theory and experimental implementation of characterisation techniques for high-dimensional quantum states are discussed such as OAM bandwidth measurements, Bell inequal- ity violations and reconstructing the information of a quantum state using quantum state tomography. Quantum ghost imaging is a quantum imaging technique that uses the transverse position correlations between the entangled photons. The theory and experimental implementation of ghost imaging using high-dimensional bi-photon position states is explored. The uncertainty principle between angular position and OAM is inves- tigated for a bi-photon quantum state and how a non-local induced OAM spread is limited by the spread in OAM of the initial quantum state produced from spon- taneous parametric down-conversion. A link between a quantum search algorithm (Grover’s algorithm) and quantum ghost imaging is investigated and similarities are drawn. The complexity of Grover’s algorithm is alleviated by leveraging high- dimensional bi-photon position states.
  • Thumbnail Image
    Item type:Item,
    Status prediction and risk stratification of COVID-19 by machine and statistical learning analysis of routine laboratory tests
    (University of the Witwatersrand, Johannesburg, 2022) Chironda, Cyril Tamuka; Mlambo, F.; George, J.
    The COVID-19 pandemic has placed a huge stress on an already overburdened health system. Diagnosis is based on detection of a positive RT-PCR test which may be delayed during peaks. Rapid diagnosis and risk stratification of high risk patients allows for prioritisation of resources for patient care. The study aims were to classify patients as COVID-19 positive or negative, and to further classify patients as severe or not severe based on outcomes using machine learning on routine laboratory tests. Data was extracted for all individuals who had at least one SARS-CoV-2 PCR test done via the NHLS between the periods of 1 March 2020 to 7 July 2020. Exclusion criteria: those 18 years, and those with indeterminate PCR tests. Results for 15437 patients (3301 positive and 12136 negative) were used to fit 6 machine learning models namely the logistic regression (LR) (the base model), decision trees (DT), random forest (RF), extreme gradient boosting (XGB), convolutional neural network (CNN) and self-normalising neural network (SNN). Model development was carried out by splitting the data into training and testing set of a ratio 70:30, together with 10- fold cross validation re-sampling technique. For risk stratification admission to high care or ICU was the outcome for severe disease. Performance of the models varied with diagnostic sensitivity ranging from 85.25% for SNN to 97.73% for the RF models. Area under curve ranged from 70% for DT to 93% for LR, with accuracy ranging from 84% for LR to 88% for SNN. For risk stratification: sensitivity was best for RF at 75% and accuracy of 75% for CNN. Area under curve ranged from 57% for CNN to 75% for RF. The models performed better on diagnostics as compared to risk stratification. In both tasks RF and SNN were the best performing models. Machine Learning (ML) can be incorporated into the laboratory information system and offers promise for early identification and risk stratification of COVID-19 patients, particularly in areas of resource-poor settings.
  • Thumbnail Image
    Item type:Item,
    The role of stripes: testing possible drivers of geographical variation in zebra (Equus quagga and Equus zebra) stripe patterns
    (University of the Witwatersrand, Johannesburg, 2025) Chen, Amy; Reynolds, Chevonne
    Zebras exhibit one of the most striking patterns in the natural world, yet the adaptive function of their black and white stripes remain largely undetermined. These stripe patterns are unique to each species, with mountain zebras (Equus zebra) displaying thinner and more densely packed stripes and plains zebras (Equus quagga), which display thicker but more spread apart stripes. Furthermore, there is also geographical variation of striping patterns within each species, for example, some individuals of mountain zebras display larger rump stripes and some individuals of plains zebras displaying more contrasted and defined stripes. Multiple hypotheses have been proposed to explain the adaptive functions of zebra stripes, including roles in social interactions, thermoregulation, predator avoidance, and protection against biting flies, but research is yet to reach a consensus on the drivers of these patterns. To test several of these competing hypotheses, I aimed to quantify geographical variation in zebra stripes and to determine whether environmental factors (i.e. temperature, solar radiation, woody cover and rainfall), as well as the probability of lion and biting fly occurrence could explain the geographical variation in stripe patterns for two zebra species, Equus quagga and Equus zebra. Unlike previous studies that assessed single predictors, we used a multimodel approach within an AIC framework to test several competing hypotheses for the function of stripes. I analysed crowd-sourced images (n = 283) of zebras from across their distribution (from Kenya in the north, Namibia in the west, to southern Africa in the south) and used ImageJ to measure features such as the number of stripes, proportion of dark stripes, perimeter/area ratio of stripes, contrast of stripes and presence or absence of shadow stripes. My results showed that 1) certain stripe characteristics such as the number of stripes, perimeter/area ratio, contrast of stripes and presence/absence of shadow stripes varied across geographical space and 2) environmental variables such as solar radiation, woody cover and rainfall may drive this geographic cline. Zebras appeared duller and darker overall in areas with higher solar radiation as the contrast on the torso and rump decreased while shadow stripe presence increased, potentially as a 10 protection mechanism against UV radiation. Increased contrast on belly stripes and presence of shadow stripes in regions with higher woody cover were thought to help zebras better camouflage to their background. Shadow stripe presence was also associated with regions of low rainfall and offered weak support to Gloger’s rule in zebras. My findings indicate that zebra stripe variation is likely driven by multiple selective pressures and highlights the complexity of adaptive traits. My analysis showcases the need for a more integrative approach to understanding one of nature’s most iconic patterns by incorporating multiple ecological, environmental, and evolutionary factors.
  • Thumbnail Image
    Item type:Item,
    Assessing the effectiveness of green infrastructure in urban areas - for mitigation of climate change-induced extreme events in South Africa: a case study of the Johannesburg CBD
    (University of the Witwatersrand, Johannesburg, 2025) Thobejane, Kagiso; Kubanza, Nzalalemba Serge
    The rationale of this research study is to examine the effectiveness of green infrastructure in urban areas for mitigation of climate change, with specific interest in this case study being the Johannesburg Central Business District. The study asserts that green infrastructure should be implemented as a measure to mitigate the effects of the urban heat island effect and urban flooding. The study formulates evidence from municipal authorities, private sector specialists, and local community members to understand the local context and institutional processes influencing the adoption of green infrastructure. The study reveals that the urban heat island effect and flood risk are crucial threats to Johannesburg’s urban environment, and that there are vulnerable populations that are disproportionately affected. Through assessing the effectiveness of green infrastructure, such as green vegetation-covered roofs, bioswales, wetlands, permeable pavements, and urban trees, the study demonstrates green infrastructure’s ability to provide holistic benefits spanning from urban temperature regulation to stormwater management, to social equity improvement. Furthermore, the study presents notable barriers such as institutional silos, fiscal constraints, and regulatory gaps to the adoption of green infrastructure in the city of Johannesburg. The study concludes with recommendations for integrated policy strategies, clear custodianship, and community engagement in the development of a climate-resilient urban environment in Johannesburg, thus informing future research and broader discussions on urban resilience and climate adaptation in the Global South.
  • Thumbnail Image
    Item type:Item,
    Hydrogeochemical characterisation of groundwater in fractured aquifers at Konkola mine in Chililabombwe, Zambia
    (University of the Witwatersrand, Johannesburg, 2025) Sakala, Vincent; Abiye, Tamiru
    An extensive hydrogeological investigation was carried out at the Konkola Copper Mine (KCM), a major mining operation located in Chililabombwe, Zambia, to explore the hydrogeochemical properties of groundwater in fractured aquifers. The approach integrated multiple datasets, including a review of the regional and local geological settings, site-specific meteorological data, environmental isotopes, hydrochemical analyses and previous hydrogeological studies conducted at KCM. The primary aim was to enhance the understanding of both fractured and unfractured aquifers at KCM, which will contribute to the development of a more effective dewatering strategy for the mine. The study established a hydraulic link between fractured and unfractured aquifers as supported by similarities in δ²H and δ¹⁸O isotopic signatures that indicate shared recharge sources and groundwater mixing. In the investigated site, the prevailing hydrochemical facies is classified as Ca-HCO3, indicating that the chemistry of groundwater is dominantly influenced by rock-water interactions, particularly with the carbonaceous and siliceous rock formations affected by percolating water. The hydrochemical results revealed aquifer connectivity and inflow direction, supporting dewatering planning. The stable isotope ( δ²H and δ¹⁸O ) analysis suggests that groundwater recharge primarily occurs through rainfall originating from high latitudes and regional air circulations associated with cooler climate conditions, as evidenced by the depleted isotope signatures. Additionally, the similarities in isotopic (stable) signatures indicate a possible hydraulic linkage among the aquifers at KCM. Tritium analysis reveals ranges between 0.1 and 1.0 T.U., suggesting a mix of recently recharged and sub-modern water, further supporting the presence of hydraulic connections between the aquifers. Higher tritium levels indicate rapid infiltration within permeable aquifer systems, while lower values suggest deep- circulating or confined groundwater. Variations in tritium concentrations across and within aquifers, especially in the Hangingwall Aquifer (HWA) and fractured aquifer, highlight the mixing of distinct water sources. Based on the integrated hydrogeological investigation, the fractured aquifer at KCM has been identified as a zone of groundwater mixing. The overall groundwater flow is directed from the HWA and Lower Porous Conglomerate (LPC) towards the Footwall Aquifer (FWA) and the fractured aquifer. This report recommends that ongoing sampling of isotopes and water chemistry from rainfall, surface water bodies, and underground sources within the study area should continue. Such efforts are important for gaining a better understanding of the area’s hydrogeochemistry. Additionally, measures to exclude surface water should be improved iii to minimize seepage, as this study has observed a connection between surface water and groundwater.