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Recent Submissions

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    Assessing perceptions of environmental management in the construction industry: a case study of construction companies in the Gauteng Province
    (University of the Witwatersrand, Johannesburg, 2025-10) Hlungwane, Rhulani Hlulani; Kubanza, Nzalalemba Serge
    This study looks into the perceptions of construction industry professionals working for grade 9 CIDB registered companies within the Gauteng Province of South Africa. Given that construction is not only of high economic value but also of high environmental impact, understanding the perceptions of construction professionals regarding their obligation towards the environment is crucial for ensuring sustainability in the construction industry. Informed by Stakeholder Theory, Institutional Theory and the Theory of Planned Behavior, the investigation used a quantitative cross-sectional design entailing structured questionnaires among Environmental Officers (EOs) and Project Managers (PMs). There were 66 valid responses (35 EOs and 31 PMs) which were analyzed with descriptive and inferential statistics. Respondents showed a very high sense of consciousness and positive attitudes towards environmental regulations, with a 90% of them rating compliance at "extremely important." Yet environmental practice application remains reactive and inconsistent, with prevalence of the obsessions of audits rather than inherent commitment. Key inhibitions reported were lack of funds, insufficiency of commitment by management, lack of coordination and ambiguity of regulations. Notably, cost perception was a key inhibition reported and environmental compliance was viewed as an added cost burden. The study concludes that while environmental stewardship commitment at the theoretical level does exist, application is limited by structural, institutional and perceptual problems. Its recommendation for greater policy conformity, capacity building initiatives and organizational leadership acts to promote mainstreaming of sustainable construction practice. Such a result seeks to bring environmental policy closer to on-site practice for South Africa's construction industry.
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    Taxonomy, ecology and conservation of selected rare and threatened Kniphofia species
    (University of the Witwatersrand, Johannesburg, 2025-10) Hlongwane, Ndumiso; Witkowski, Ed T.F.; Victor, Janine
    Kniphofia is a genus of 71 species with an African distribution. Forty-eight of these occur in southern Africa. The genus has several taxonomic issues that require investigation, and these difficulties are related to intermediate forms that put these species' taxonomic status in doubt. There are currently 14 threatened species of Kniphofia as reported on the SANBI Redlist, however, a detailed study on the threats and population size structure has only been conducted on one Critically Endangered (CR) species, Kniphofia umbrina from Eswatini. This study was prompted by a lack of knowledge concerning the current population sizes, structures, threats, and distribution of 13 other threatened taxa. Most of these 13 taxa are poorly known taxonomically. The aim was to revise the taxonomic status of five threatened Kniphofia taxa (K. leucocephala, K. pauciflora, K. drepanophylla, K. ichopensis var. aciformis, and K. crassifolia), and their five morphologically similar congeneric species (K. baurii, K. ichopensis var. ichopensis, K. laxiflora, K. angustifolia, and K. fibrosa) (eight species plus two varieties), conduct population studies on these species, and assess their conservation status. Morphological characters (qualitative and quantitative) were evaluated in 191 specimens from South African herbaria. The data were analysed using the Statistical Package for the Social Sciences (SPSS) and Paleontological Statistics (PAST). The findings relating to K. leucocephala and K. pauciflora suggest that the two species are morphologically similar, although they exhibit different flower colours. The two species show a different altitudinal range, differ in flowering time and are distributed in different vegetation types. Morphological and flowering similarities were also noted between K. baurii and K. drepanophylla. These two species differed in altitude and vegetation type. A close morphological similarity between K. baurii and K. fibrosa was noted but needs further investigation. Kniphofia ichopensis var. ichopensis was morphologically similar to K. ichopensis var. aciformis and K. angustifolia but significantly different to K. laxiflora. Similar flowering times were noted between K. ichopensis var. ichopensis and K. ichopensis var.aciformis, while those of K. angustifolia and K. laxiflora differed. There was no altitudinal difference between K. ichopensis var. ichopensis and both K. ichopensis var. aciformis and K. laxiflora. However, K. angustifolia differed in altitude from K. ichopensis var. ichopensis. Their distribution within vegetation types also differed among all these species. Due to the absence of herbarium material for K. crassifolia, the study could not establish its taxonomic relationship with K. fibrosa. A neotype was designated for K. leucocephala in this study due to a missing type specimen. Although the study recognised most of the compared Kniphofia species as morphologically indistinguishable species, taxonomic changes were not made due to a lack of a detailed phylogenetic study inclusive of all the species in this genus. Therefore, the status quo of the currently recognised morphological units and Codd species concept is maintained at present. The study also recognised the work done by Ramdhani et al. (2009) but suggests that the work needs to be expanded to better understand monophyly and species relationships within this genus. A single population of each of the paired Kniphofia species was assessed in KwaZulu-Natal, and the combined population sizes were found to be very low (220 individuals), of which 44% were reproductive adults. Least Concern (LC) species had higher population sizes compared to threatened species, with sizes ranging from 14 to 40 plants. Bimodal and J-shaped size class distribution (SCD) were the most common across all populations, suggesting episodic recruitment and the domination of adult individuals. Of the three measures of SCD explored, plant height was the preferred measure for these species. Most of these populations were in areas dominated by grassy vegetation, with disturbance factors such as the prevalence of invasive alien plant species (IAPs), rubbish dumping, trampling, and overgrazing, which may cause habitat degradation. Field search of K. crassifolia subpopulations was fruitless, suggesting a possible extinction in the wild. Using population data from this current study, the status of K. leucocephala and K. pauciflora was confirmed to be Critically Endangered (CR). This is due to high risks from IAP species such as Eucalyptus, urban expansion, reduction in habitat quality and low Area of Occupancy (AOO) and Extent of Occurrence (EOO). Although K. drepanophylla currently has 15 populations, only one is known to be in a conserved area, and the populations outside the nature reserves face a higher risk of trampling, uncontrolled fires, overgrazing, and harvesting on communal land. Therefore, the study proposes a Vulnerable (VU) category and immediate re-assessment of the other populations that were not surveyed during this study to confirm this proposed status. There was limited information on the status of K. ichopensis var. aciformis prior to this study; however, it has been established that there are only four currently known populations of this species. The surveyed population is threatened by cattle trampling, overgrazing and unmanaged IAP, and three of its populations occur in habitats that are poorly protected, which also makes it vulnerable to extinction. Therefore, a VU category is proposed for this species. Although K. baurii, K. ichopensis var. ichopensis, K. laxiflora, K. angustifolia, and K. fibrosa are listed as LC, there were few individuals (all <40) in each of these populations. These populations were mainly threatened by trampling from humans and livestock, harvesting, rubbish dumping, IAP and overgrazing. The results suggest that although these species are categorised as LC, they are facing a serious decline due to multiple threats and will also need conservation and monitoring directed towards them. The current conservation status for these species was provisionally retained, but more extensive surveys, preferably of all known populations, are a priority. The study provided baseline information for suitable conservation adaptive management plans, with fencing populations outside conserved areas proposed. Further studies are needed to assess suitable fire regimes and the potential to augment them with additional seedlings to increase their viability, given the very small population sizes.
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    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.
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    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.
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    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.