UWCScholar
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Implications of financial literacy on financial wellness: a case study from a selected University in Cape Town, South Africa
(Universty of the Western Cape, 2025) Johnson, Ian Lyndon
This study examined the implications of financial literacy on the financial wellness of students at a selected university in Cape Town, South Africa. Given the increasing financial pressures on students, the research focused on four constructs of financial literacy as identified in the literature. This included financial knowledge, financial attitudes, financial behaviours, and financial self-efficacy. The study followed a post-positivist paradigm and employed a quantitative survey approach. A non-probability convenience sampling method was used to select participants. Following ethical clearance from the university, data were collected via a structured online questionnaire designed to measure students’ financial literacy. The target population comprised 11,771 commerce students at the selected university out of a total of 35,541 registered students at the time, with 163 completing the survey in full. Although the response rate was relatively low, the data provided valuable insights into respondents’ financial literacy. Descriptive and inferential statistics, which included ANOVA and correlation analysis, were used to examine relationships between financial literacy and financial wellness outcomes. Participation was voluntary, and ethical protocols of anonymity, confidentiality, and informed consent were strictly followed.
Development, evaluation, and In-vitro assessment of artificial intelligence antidiabetic predictive models from α-glucosidase inhibitors
(University of the Western Cape, 2024) Odugbemi Adeshina Isaiah
Background: The global rise in the prevalence of diabetes mellitus (DM) poses a significant health challenge, necessitating effective therapeutic interventions. Type 2 DM (T2DM) remains the most prevalent form of diabetes, accounting for over 90% of cases. Intestinal α-glucosidase enzymes, located at the small intestine's brush border, are important treatment targets in T2DM as they are involved in the terminal point of carbohydrate digestion in the gut, liberating glucose molecules, which are then transported into the bloodstream. Controlled inhibition of these enzymes is pivotal for managing postprandial hyperglycemia, which contributes to vascular complications in T2DM. The localized action of α-glucosidase inhibitors at the brush border eliminates the need for systemic absorption, directly mitigating postprandial hyperglycemic spikes. Acarbose and Miglitol are FDA-approved drugs that target and inhibit α-glucosidase. Despite their benefits, these drugs face some drawbacks. For example, acarbose has been shown to have many side effects and undergo gastrointestinal degradation, which may limit its efficacy. Conversely, Miglitol is less localized to the gastrointestinal tract due to its rapid absorption and high bioavailability, potentially diminishing its efficacy. These drawbacks underscore the need to search for viable alternatives. Traditional drug discovery approaches have primarily relied on empirical compound screening, which has been historically successful but is laborious, time-consuming, and expensive. In recent years, many strategies have been applied to drug discovery to mitigate the time and cost required as well as increase the chance of success. One such strategy is introducing artificial intelligence (AI) in drug discovery. The integration of machine learning (ML) and deep learning (DL) techniques, which are aspects of AI, into drug discovery has emerged as an advancement in Quantitative Structure-Activity Relationship (QSAR) approaches for drug discovery. The QSAR approach can analyze vast chemical datasets and potentially uncover novel α-glucosidase inhibitors more rapidly and cost-effectively than traditional methods. Therefore, this project set out to analyze the chemical space of existing α-glucosidase inhibitors that has experimentally determined IC50, develop ML and DL models for predicting α-glucosidase inhibitors, utilize these models for virtual screening to identify potential α-glucosidase inhibitors, and perform in vitro assays on the identified hits to validate predictions.
Method: The study begins with the assembling and preparation of a library of α-glucosidase inhibitors with experimentally determined IC50. The prepared data was categorized as active and inactive to enable machine learning classification tasks. A stringent IC50 threshold of ≤ 2.04 μM was used to label compounds active and higher IC50 compounds were labelled inactive. An extensive exploration of the chemical space of the prepared data was carried out to understand the properties influencing their reported bioactivity. Random Forest (RF) and Support Vector Machine (SVM) models were developed using 2D and 3D molecular descriptors and extended-connectivity fingerprints (ECFP). Additionally, state-of-the-art deep learning models were created using Graph Neural Networks (GNNs) architectures, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Isomorphism Networks (GIN), and Attentive Fingerprints (AFP). The GNNs work directly with molecular graphs, where atoms are nodes and bonds are edges, capturing detailed structural and relational information within molecules, making them effective for modelling complex biochemical interactions. These models were rigorously analyzed for performance across multiple metrics to identify the most effective techniques for predicting potent inhibitors. The top-performing models were then used for virtual screening against the DrugBank and Coconut databases. Promising candidates identified from the screening were subsequently subjected to in-vitro validation to confirm their inhibitory activity against α-glucosidase and evaluate their therapeutic potential.
Results: The findings of the chemical space exploration underscored the importance of properties such as molecular weight, cLogP, hydrogen bonding, and molecular rigidity in distinguishing compounds’ activities as defined by the set IC50 threshold. Compounds with an IC50 ≤ 2.04 μM are generally characterized by higher hydrophilicity, lower clogP, more negative clogS, greater polarity, and higher hydrogen bonding capacity, and vice versa. The RF and SVM models trained on the descriptors exhibited robust evaluation metrics performance, with 2D descriptors and ECFP4 molecular representations outperforming 3D descriptors. Virtual screening of the DrugBank database using these models identified potential α-glucosidase inhibitor drugs, demonstrating the potential of ML models in drug repurposing efforts. However, the RF model with ECFP4 fingerprints was highly conservative, predicting 0.3% of virtual screening DrugBank data to be active. In contrast, GNNs models showed less conservative active prediction rate of 5.3% on its Coconut database virtual screening predictions.
In-vitro assessment revealed that the compounds predicted in virtual screening by the RF model did not show measurable IC50 values, highlighting a limitation in the corroboration of its predictive capability with experimental validation. Conversely, the in-vitro assessment of selected compounds predicted by the GAT model showed better conformation with computational predictions, identifying compounds with measurable IC50 values as active, though these compounds did not satisfactorily meet the stringent threshold we have set for active classification (IC50 ≤ 2.04 μM).
Conclusion: The chemical space exploration of the compound library with in vitro inhibitory activities against intestinal α-glucosidase revealed the importance of polarity and hydrogen bonding capacity in potent inhibitors of α-glucosidase. The top performing ML (RF) and DL (GAT) models built on the structural data of the compound library were used for virtual screening of DrugBank database and Coconut database of natural compounds, respectively, to rapidly mine potential α-glucosidase inhibitors. In vitro assessment of hit compounds showed that GAT model predictions have better alignment with empirical IC50 data.
Multipoles of the redshift-space power spectrum from SKAO and other surveys
(University of the Western Cape, 2025) Baatjes Ron Ryan
The large-scale distribution of neutral hydrogen in the late universe provides a probe of the underlying distribution of matter, which is a powerful complement to the standard probe delivered by galaxy number counts. Measuring the neutral hydrogen distribution with MeerKAT, SKAO and other radio telescopes through the hydrogen 21 cm line emission has the potential to become a key cosmological probe in the coming years. We investigate the cosmological constraining power of the 21 cm intensity mapping power spectrum in redshift space. To this end, we decompose the power spectrum into multipoles, modelling the effects of foreground avoidance, telescope beam, instrumental noise and the bias of 21 cm intensity fluctuations. Then we use Fisher forecasting methods to estimate the precision that is obtainable by proposed surveys and on the full SKAO. Measurements of the power spectrum multipoles enable measurements of key cosmological parameters and we estimate the precision of such measurements. In particular, the multipoles provide an estimate of the growth rate of large-scale structure, which can deliver tests of the theory of gravity.
How postgraduate students at University of the Western Cape experienced and coped with the COVID-19 pandemic: A biographical exploration
(University of the Western Cape, 2023) Mohutsiwa Omphile Doctor
The right of access to higher education in South Africa is enshrined in the country’s Constitution. Access to higher education has been a key issue on the national political agenda since the advent of democracy in South Africa. Prior to 1994, access to tertiary education in South Africa was divided along racial and class lines with the majority of the population excluded from higher education. While significant advances have been made in widening access, this is a legacy that persists today. Unfortunately, the COVID-19 pandemic has once again exposed inequalities in tertiary education. Universities’ migration to remote teaching and learning due to the COVID-19 pandemic has limited students’ access and full participation in their studies. The pandemic has also exposed the negative aspects of South Africa’s education system, such as inequities that are the legacy of apartheid and colonialism. The study explores the experiences of students from a historically disadvantaged institution as it relates to access and full participation in their studies. The research adopted a qualitative research design. It targeted students at a historically disadvantaged institution namely, University of the Western Cape (UWC). The rationale of the research was to investigate and critically interrogate the challenges faced by students during the COVID-19 pandemic. Students had to cope with the conditions and realities that they found themselves in as a result of the measures that universities implemented during the pandemic. The study examines the impact of those measures on students’ access and participation as it explores the experiences of the students interviewed for the study. Given the location of the study, it also reflects on whether and how structural issues of class and race affected the ability of students to adapt and cope with the massive changes that occurred as a result of the pandemic. In summary, the study found that the pandemic induced isolation, stress, and limited access to support, among others, exacerbating academic difficulties due to remote learning. Despite this, postgraduate students adopted virtual learning, sought mental health support, and established routines and peer communities to cope with the challenges. The UWC implemented initiatives, including mental health services, study data and devices provision, while supervisor guidance and online libraries were crucial for sustaining academic pursuits, demonstrating the complex interplay of challenges and coping strategies in higher education during the pandemic.
Land, livelihoods and belonging on redistributed land of former labour tenants in South Africa
(Universty of the Western Cape, 2024) Yeni, Sithandiwe
This thesis explores what happens to the lives of the ‘relative surplus population’ when they repossess land in the context of neoliberal capitalism characterised by the crisis of social reproduction. It explains how land repossession has shaped tenure arrangements, livelihoods, and notions of belonging among the former labour tenants and their descendants. It draws on Marxist and feminist political economy theories and applies the concepts of racial capitalism and belonging. The combination of these theories and concepts, such as primitive accumulation, relative surplus population, social reproduction, gendered labour, belonging and racism, helps to explain the history of land dispossession and its outcomes and the position of working-class women in the process. I use these theoretical tools and concepts to analyse contemporary processes of agrarian change under neoliberal capitalism. The research was conducted in Mhlopheni in KwaZulu-Natal province, using qualitative and quantitative research methods. This was done through a survey of 32 out of 41 households, 25 life histories, nine focus groups, and 56 in-depth interviews, mainly between 2021 and 2022. Additional telephonic interviews were conducted between 2023 and 2024. Respondents included people living in Mhlopheni, predominantly former labour tenants and their descendants, government officials, land activists and people who currently and previously worked at the land rights nongovernment organisation that supports former labour tenants in this region. This data was supplemented with the literature.