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,
    Livelihoods and natural resource management in fast-track lithium mining in Arcturus rural community in Zimbabwe
    (University of the Witwatersrand, Johannesburg, 2025-10) Guvakuva, Zanele; Musavengane, Regis
    The global shift towards clean energy technology, as one of the sustainable initiatives to curb climate change, has significantly increased the demand for lithium, which is one of the key minerals in the development of this technology. This surge in demand has led to a massive expansion of lithium mining in Zimbabwe, with new mines mushrooming across the country. With the lithium boom being recent in the country, studies around this phenomenon are scarce. Previous studies and articles have primarily focused on the struggles of displaced households and small scall miners due to the lithium boom. This study introduces a livelihoods perspective focusing on communities that continue to reside adjacent to the mines. This makes it the first to apply the Sustainable Livelihoods Framework to analyse how lithium mining simultaneously affects frontline community livelihoods and water availability in Zimbabwe. Thus, offering a holistic socio-environmental perspective missing from existing research. The research investigates the livelihood changes and environmental impacts in the Arcturus rural community following the onset of the Arcadia Lithium Mine project in Zimbabwe. A qualitative approach was followed in exploring this issue, using the Arcadia Lithium Mine project as a case study and focusing on communities surrounding the mine. Semi-structured interviews were conducted with 29 research participants, who were selected through purposive and snowball sampling methods. Grounded in the Sustainable Livelihoods Framework (SLF), livelihood changes were examined. Thematic analysis was used to interpret the data. By integrating environmental dimensions, particularly water availability into the SLF analysis, this study extends the framework’s application and demonstrates its utility in capturing the intertwined socioenvironmental consequences of resource extraction. The findings revealed that all five capitals of the SLF as well as the environment have been negatively affected by the mine. To a large extent, the communities’ livelihoods did not improve following the establishment of the mine in their locality. Key livelihood activities affected included the loss of farmlands, grazing areas, fishing grounds, and reduced social interactions. While the impacts were not uniform across communities, the most affected community with all 5 capitals affected was Lawfield, where the mine claim is located. Some communities experienced greater disruption to social cohesion and natural capitals, while others reported significant declines in social cohesion and physical resources. However, some positive infrastructural developments on roads and a local school were reported. Regardless, participants across all cases expressed dissatisfaction, noting that the mine had eroded their livelihoods without offering meaningful benefits in return. In light of these findings, it is recommended that a national lithium strategy be developed and a dedicated government department be established to monitor emerging lithium mines, ensuring a balance between economic gains and socioecological impacts. This study’s in-depth analysis of one of the newly established lithium mines provides valuable insights into the livelihood and environmental challenges associated with the current lithium mining boom. With several other mines under exploration in Zimbabwe, the findings from this research highlight critical issues that may be present elsewhere and underscore the urgency of a coordinated national response. Further research across new lithium mining communities would deepen understanding these impacts. Thus, helping determine whether the challenges experienced by the Arcadia mine communities are widespread across new lithium mines. This ensures the development of national mitigation strategies that are robust and applicable across diverse new lithium mining projects.
  • Thumbnail Image
    Item type:Item,
    Exploring the influence of geological characteristics, surface texture, and surface energy of crushed rock aggregates on the compressive strength of concrete
    (University of the Witwatersrand, Johannesburg, 2025-08) Govender, Damisha; Ballim, Yunus; Madlakana, Nonkuselo
    Concrete is a globally used construction material valued for its strength, which has long been primarily determinant on the water-to-cement ratio. However, studies indicate that aggregate properties influence concrete strength. Therefore, this study aimed at assessing how geological characteristics, surface energy, and surface texture of crushed rock aggregates affect the compressive strength of concrete. The methods used in this study included Petrographic and TIMA analysis, UCS tests, SFE tests and Bond Strength tests. Dolomite, andesite, quartzite, ferro-quartzite and felsite aggregates were used to mix concrete with water-to-cement ratios of 0.4 and 0.6. The surface energy was measured on South Africa’s first Contact Angle Meter DMs-401 instrument, and mechanical bond strength was measured using 3-point bending tests. Results show that high mica contents weaken compressive strength, and its influence becomes more prominent as the water-to-cement ratio decreases; low SFE does not always result in high compressive strengths and these energies do not correlate with previous studies performed with different probe liquids; rough surface texture enhances the macro-mechanical bond between aggregate and cement, but detrimental minerals and structural features hinder bond strength from developing. In conclusion, it was found that the compressive strength is dependent on a combined influence of mineral characteristics, structural integrity, surface energy and surface texture. Therefore, fine grain size and rough, intact texture, high mechanical bond, little to no detrimental minerals and structural defects, and low surface energy are the properties that make the most suitable aggregates for high-strength concrete.
  • Thumbnail Image
    Item type:Item,
    Investigating the integration of interoception in a reinforcement learning pipeline
    (University of the Witwatersrand, Johannesburg, 2025-09) Gebreselassie, Marrian Haileselassie; Williams, Victoria; Jarvis, Devon
    Reinforcement learning (RL), inspired by neuroscience, trains artificial agents to maximise external rewards. However, decision-making in humans involves complex evaluation and planning processes that initiate actions beyond external reward including the incentive to maintain internal homeostasis. Interoception is the brain’s processing of signals from the body which convey hunger, thirst and body temperature. The initiation of actions beyond external reward is demonstrated in behaviours which address interoceptive needs like hunger. The prefrontal cortex (PFC), in coordination with other brain regions, balances responding to these intrinsically rewarding internal needs while engaging in externally rewarding activities. This study models this holistic evaluation process conducted by the PFC to manage an organism’s hunger levels alongside extrinsically rewarding activities. The study draws on an analogy of a mouse in a flat room with access to a running wheel (extrinsic reward) and a block of cheese (intrinsic reward), to an RL agent in a MiniGrid environment with a green goal square and a food location. It does this by equipping an RL agent with ghrelin levels and the capacity to manage its subsequent hunger thresholds by visiting the predefined food location. The behaviour of the RL agent is consistent with the naturally observed behaviour of a hungry mouse, in that a “hungry” RL agent (i.e. when its ghrelin levels equal the hunger threshold) prioritises visiting the food location before the green goal square, emulating survival-based behaviours. By varying key parameters, an RL agent which incorporates interoception can reproduce behaviours consistent with organisms suffering from obesity and anhedonia. Hence, this study demonstrates the potential of integrating the holistic role of the PFC in human decision making onto the RL pipeline.
  • Thumbnail Image
    Item type:Item,
    Seasonal trends in trace metals and anions and their human health risk assessment in the surface water of the Vaal River, South Africa
    (University of the Witwatersrand, Johannesburg, 2025-06) Fusirai, Fadzanai; Chimuka, Luke; Richards, Heidi L.
    The Vaal River has been previously reported to be polluted beyond acceptable limits. The present study aimed to monitor the seasonal variation of metals and anions in the Vaal River and assess the health risks associated with using this water. Water samples (VRS1-VRS9) from selected sampling locations covering approximately 180 km of the river were collected over four seasons and analysed to determine their physical and chemical properties (pH, TDS, EC, Al, Cr, Cu, Fe, Zn, Mn, Ni, Cd, Ca, Mg, Na, F-, Cl, NO3-, NO2-, SO4 2-, Br-, PO4 3-). Only Fe, Al, and Mn concentrations and EC exceeded standard limits by both the World Health Organization (WHO) and South African National Standards (SANS). One-way ANOVA revealed no statistically significant differences in the seasonal analyte concentrations except for Cu, while the Kruskal Wallis test revealed statistically significant differences in concentrations of Br-, Cd, and Cr. To assess the quality of the water for drinking purposes, water quality index (WQI) evaluations were employed. The overall WQI ranged from 74.6 to 255.3, indicating that the quality of water varied from good water to very poor water, with one site (VRS3) in Standerton and the other (VRS7) in Midvaal exhibiting very poor water quality. Al and Fe were found to be the major contributors to poor water quality at all sampling sites. Heavy metal pollution index (HPI) revealed that heavy metal pollution was low at all sampling sites, except at VRS3 and VRS7, where it was within a moderate degree of pollution. Hierarchical cluster analysis (HCA) grouped upstream and downstream sampling sites at approximately 40% similarity level. Pearson correlation coefficient matrix tables and principal component analysis (PCA) revealed how various parameters correlate to each other and determined sites affected by either natural or anthropogenic activities. Pollution source apportionment was done using PCA results, this revealed that variables in PC1 mostly originated from natural sources while variables in PC2 were from anthropogenic activities. Furthermore, human health risk assessments were conducted considering the results from the analysed metals in comparison to the WHO limits. Human health risk assessments revealed that children were at serious risk of both non-carcinogenic (HI>1) and carcinogenic (CR>1×10-4) health risks at all sampling sites upon exposure to water by ingestion, as compared to adults. Generally, downstream sampling sites were at a higher risk for non-carcinogenic risks, while VRS3 and VRS7 were the most hazardous sites for carcinogenic risk. The major contributor to cancer risk was found to be Cr, contributing 92.7%, while Cd contributed 7.3%. The human health risk assessment for the dermal exposure pathway indicated no significant carcinogenic or non-carcinogenic health risks for both adults and children, suggesting that ingestion remains the primary route of concern for exposure to contaminants in the Vaal River.
  • Thumbnail Image
    Item type:Item,
    A Novel Data Augmentation Framework for Contrastive Self-Supervised Learning
    (University of the Witwatersrand, Johannesburg, 2024-09) Fokam, Manuel Arnol; Klein, Richard
    Self-supervised learning (SSL) has emerged as a leading paradigm in visual representation learning, employing techniques that encourage similarity among latent representations produced from diverse transformations of the same image. Despite the proven impact of data augmentation on such methods, the design of a suitable data augmentation pipeline for training such methods disregards the downstream tasks for which the learned representation is intended. To overcome this challenge, we introduce a novel data augmentation framework called Rank-Update (RU). This framework periodically assesses the relevance of different augmentation techniques for various downstream tasks, enabling the construction of an augmentation pipeline tailored to the specific requirements of those tasks without sacrificing generalization. This approach is appropriate in cases where we know the downstream tasks in advance but do not have enough data to train a model on these tasks directly through supervised learning. We first present an empirical study on the influence of different data augmentation techniques on SSL models across various downstream tasks. Furthermore, we then demonstrate the effectiveness of our approach in comparison to other methods on the same set of downstream tasks. Finally, we showcase the robustness of our method across different configurations, while presenting its limitations.