WIReDSpace

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Communities in WIReDSpace

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Now showing 1 - 5 of 20

Recent Submissions

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    Kuva Mutowra Kwakaoma: A systematic investigation of the factors affecting the provision of quality health care to foreign nationals by public health care providers: The case of migrant woman in Alexandra, South Africa
    (University of the Witwatersrand, Johannesburg, 2025-02) Pasurayi, Sihle Faith; Silhlogonyane, Mfaniseni
    This research report delves into the pressing issue of injustices faced by migrant women in accessing public healthcare in Alexandra, Johannesburg. The focus is on understanding the experiences of these women to inform planners' interventions in addressing the growing challenge. Drawing on critical theories of xenophobia and intersectionality (Atrey, 2022), the study examines South Africa's current public health system, government policies, and hospital regulations, particularly their impact on migrant women. The methodology includes a critical review of literature and interviews, aiming to uncover, evaluate, and synthesize relevant findings on immigrant women's access to public health care. Central research questions explore the structural factors fostering xenophobic practices among healthcare practitioners and delve into the definition of a migrant woman, existing legislation, xenophobic elements in government policies, theoretical underpinnings of xenophobia in healthcare, and practitioners' attitudes towards migrant women. Objectives include examining legislation and regulations, assessing xenophobic tendencies in government policies, reviewing theoretical frameworks, gauging healthcare sector attitudes, and determining urban planning's role in mitigating xenophobia. The rationale for this study is grounded in the overlooked role of women in migration studies and the need for a gendered understanding of migration. While existing literature discusses migrants' health conditions and service utilization, less is known about healthcare practitioners' perspectives and practices regarding migrant women. Central to the study is an analysis of the legislation and urban planning's potential role in mitigating xenophobia, informed by frameworks such as Barned and Nel (2017). The key findings reveal the complex realities and challenges migrant women face in accessing healthcare, marked by xenophobic tendencies in policies and healthcare practitioners' varied attitudes. The study emphasizes the need for enhanced training and systemic reforms in healthcare. Conclusively, the report underscores the vital strides needed to understand and address the disparities migrant women face in public healthcare. It presents their experiences as a foundation for future recommendations and interventions, aspiring to foster a more inclusive, empathetic, and equitable healthcare landscape.
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    Digital addressing in South Africa: As a form of citizenship, inclusivity, and catalyst for faster service delivery in informal settlements
    (University of the Witwatersrand, Johannesburg, 2025-10) Makwela, Michael; Klug, Neil
    This study examines the significance of granting digital addresses addressing unrecognised areas, such as informal settlements in South Africa. The focus of the research is to explore how having an address in an informal settlement affects residents' quality of life. By using a case study approach, this research will compare the Thembelihle and Skoonplaas informal settlements. The study argues that the absence of an address in informal settlements undermines the residents' right to dignity, citizenship, and overall access to the right to the city. It will analyze the effects of the addressing system in informal settlements and identify the system's deficiencies. The research draws upon international literature, observations, and interviews conducted for the study. It also references South Africa's progressive legislation and post-1994 programs aimed at upgrading informal settlements, which emphasize the need for incremental improvements. The empirical data for this research will be based on interviews with municipal officials, policymakers, intermediary organizations, community leaders, and a review of relevant literature.
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    BRICS (original 5) Policy Outlook, Transitional Electricity Production Technologies, Social Impact and Funding Models
    (University of the Witwatersrand, Johannesburg, 2025-08) Ramluckun, Rajesh; Ngubevana, Lwazi; Malumbazo, Nandi
    This thesis investigates the Just Energy Transition within the BRICS countries (Brazil, Russia, India, China, and South Africa), with a particular focus on energy policy, technological innovations, social impact, and green finance. As the world transitions towards sustainable energy, it is critical to ensure that this shift is equitable, especially for emerging economies where energy access, social equity, and economic stability are central concerns. This research examines the energy policies of BRICS nations, assessing their effectiveness in fostering inclusive, sustainable development while addressing the environmental and social dimensions of the energy transition. It also explores the role of technology and green finance in enabling the transition to cleaner energy systems, particularly in the context of developing economies. Using a comparative case study approach, the thesis highlights the lessons learned from BRICS countries’ experiences in implementing just energy transitions, focusing on best practices and challenges faced. Special attention is given to South Africa, where energy poverty, socioeconomic inequalities, and dependence on coal have created significant barriers to a just transition. Drawing on the experiences of other BRICS nations, this research provides targeted recommendations for South Africa, emphasizing the need for tailored energy policies, social inclusion strategies, and investment in green finance to ensure a just and sustainable energy future. The findings contribute to the broader discourse on just energy transitions, offering insights for policymakers, industry leaders, and international stakeholders seeking to balance sustainability with equity in emerging economies.
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    A Process Systems Approach on the Optimisation of Water and Power Networks using a Discrete-Time Framework
    (University of the Witwatersrand, Johannesburg, 2025-09) Kazengura, David; Majozi, Thokozani
    Rising water and energy demands due to the increasing global population and economies have necessitated solutions for sustainable management of water and power distribution networks. Drawing from the concept of the water-energy nexus, studies have integrated the operation of water and power distribution networks to increase efficiencies of the other. The inherent flexibility of power intensive operations in the water distribution network can improve power grid stability through demand-side management. Concomitantly, the water distribution network can take advantage of time-varying electricity prices offered by the power distribution network to reduce operating costs, as well as improve their energy efficiency through optimal scheduling. In this work, a discrete time MINLP model is proposed to solve a short-term scheduling problem resulting from the integration of water and power distribution networks. The developed model ensures that detailed modelling of storage facilities in the integrated networks, as well as complete capture of the water-energy nexus between water and power distribution networks are achieved. An illustrative example is used to show the applicability of the model. Results showed a 2.88% increase in cost savings for the detailed modelling of the storage facilities in comparison to a black-box model, whereas the complete capture of the water-energy nexus led to an increase of 0.57% in the overall operational costs.
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    Revolutionising Electricity Theft Detection in Smart Grids: An Edge-Centric Hybrid Machine Learning Framework with IoT Integration
    (University of the Witwatersrand, Johannesburg, 2025-03) Maboko, Malebo Legologela Gift; Dewa, Mncedisi Trinity
    Non-Technical Losses (NTLs), including electricity theft, meter tampering, and incorrect readings, significantly contributed to distributed electricity losses, accounting for nearly half of global losses resulting in substantial financial impacts annually. To address this issue, the study proposed a robust anomaly detection framework that integrated smart meters and Advanced Power Metering Infrastructure (APMI) with machine learning models to identify fraudulent energy consumption. Preprocessing techniques, such as data interpolation, Z-score Analysis, data normalisation, and Principal Component Analysis (PCA), were employed to handle inconsistencies, missing values, and outliers, ensuring accurate feature extraction and classification. A supervised learning approach was implemented, utilising Deep Neural Networks (DNN), Random Forest (RF), Logistic Regression, and a custom Hybrid Random Forest-DNN model. The models were trained and evaluated on publicly available smart meter data, augmented with synthetic anomalies at 25%, 50%, and 75% levels to assess robustness. The Hybrid RF-DNN model outperformed others, achieving an average F1-score of 96%, a test accuracy of 96.6%, and a low root mean squared error (RMSE) of 0.2065. It also reduced execution time by 14.3% compared to the standard Random Forest model. The Random Forest model followed closely, with an F1-score of 95.0% and test accuracy of 95.1%. Independent DNN models achieved an average F1-score and test accuracy of 92.6%, while Logistic Regression performed the weakest, with an F1-score of 65% and accuracy of 64.2%. A Flask based web application was developed for real-time anomaly detection, integrating automated data preprocessing, model deployment, and interactive visualisation. Additionally, a microcontroller-based transducer system was designed in an attempt to mitigate physical theft. The system, featuring an ESP32 microcontroller, Global System for Mobile Communications (GSM) module, sound sensor, and ultrasonic sensor, was optimised for Zone 2 environments and generated Short Message Service (SMS) alerts with a response time of 3.9 seconds. Future work aimed to explore explainable Artificial Intelligence (AI) methods, such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model agnostic Explanations (LIME), alongside unsupervised learning techniques like k-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to enhance anomaly detection and model interpretability.