http://hdl.handle.net/11189/1933 Wed, 02 Sep 2026 04:57:19 GMT 2026-09-02T04:57:19Z http://hdl.handle.net/11189/10773 Title: Optimization and regression analysis of friction stir processing parameters of AA5083/Coal composites for marine applications Authors: Muribwathoho, Oritonda; Msomi, Velaphi; Mabuwa, Sipokazi Abstract: This study aimed to optimize friction stir processing parameters to enhance the mechanical properties of AA 5083/coal composites, a novel material combination with potential applications in marine environments. By systematically varying process parameters such as tilt angle, traverse speed, and rotational speed using a Taguchi experimental design, the FSP process was optimized. Signal-to-noise ratio and analysis of variance techniques were used to determine the most influential parameters on microhardness and ultimate tensile strength. A regression model was developed to predict composite behavior under these optimal conditions. This study found that a combination of 900 rpm, 60 mm/min, and a 2° tilt angle significantly improved mechanical properties. This research contributes to the advancement of FSP for producing high-performance, lightweight, and corrosion-resistant aluminum composites, particularly for marine applications. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11189/10773 2025-01-01T00:00:00Z http://hdl.handle.net/11189/10772 Title: Applications, technologies, and evaluation methods in smart aquaponics: a systematic literature review Authors: Anila, Mundackal; Daramola, Olawande Abstract: Smart aquaponics systems are gaining popularity as they contribute immensely to sustainable food production. These systems enhance traditional farming with advanced technologies like the Internet of Things (IoT), solar energy, and Artificial Intelligence (AI) for increased proficiency and productivity. However, assessing the performance and effectiveness of these systems is challenging. A systematic literature review (SLR) was conducted to examine the applications, technologies, and evaluation methods used in smart aquaponics. The study sourced peer-reviewed publications from IEEE Xplore, Scopus, SpringerLink and Science Direct. After applying inclusion and exclusion criteria, a total of 105 primary studies were selected for the SLR. The findings show that aquaponics predictions (27%) have been under-explored compared to applications that involved monitoring or monitoring and controlling aquaponics (73%). IoT technologies have been used to create prototype aquaponic systems and collect data, while machine learning/deep learning (predictive analytics) are used for prediction, abnormality detection, and intelligent decision-making. So far, predictive analytics solutions for aquaponics yield prediction, return-on-investment (ROI) estimates, resource optimisation, product marketing, security of aquaponics systems, and sustainability assessment have received very little attention. Also, few studies (37.7%) incorporated any form of evaluation of the proposed solutions, while expert feedback and usability evaluation, which involved stakeholders and end-users of aquaponics solutions, have been rarely used for their assessment. In addition, existing smart aquaponics studies have limitations in terms of their short-term focus (monitoring and controlling of aquaponics not undertaken over a long time to assess performance and sustainability), being conducted mostly in controlled settings (which limits applicability to diverse conditions), and being focused on specific geographical contexts(which limits their generalizability). These limitations provide opportunities for future research. Generally, this study provides new insights and expands discussion on the topic of smart aquaponics. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11189/10772 2025-01-01T00:00:00Z http://hdl.handle.net/11189/10764 Title: Analysing the market value of land accommodating logistics facilities in the city of Cape Town Municipality, South Africa Authors: Mokhele, Masilonyane Abstract: The world is characterised by the growing volumes and flow of goods, which, amid benefits to economic development, result in negative externalities affecting the sustainability of cities. Although numerous studies have analysed the locational patterns of logistics facilities in cities, further research is required to examine their real estate patterns and trends. The aim of the paper is, therefore, to analyse the value of land accommodating logistics facilities in the City of Cape Town municipality, South Africa. Given the lack of dedicated geo-spatial data, logistics firms were searched on Google Maps, utilising a combination of aerial photography and street view imagery. Three main attributes of land parcels hosting logistics facilities were thereafter captured from the municipal cadastral information: property extent, street address, and property number. The latter two were used to extract the 2018 and 2022 property market values from the valuation rolls on the municipal website, followed by statistical, spatial, and geographically weighted regression (GWR) analyses. Zones near the central business district and seaport, as well as areas with prime road-based accessibility, had high market values, while those near the railway stations did not stand out. However, GWR yielded weak relationships between market values and the locational variables analysed, arguably showing a disconnect between spatial planning and logistics planning. Towards augmenting sustainable logistics, it is recommended that relevant stakeholders strategically integrate logistics into spatial planning, and particularly revitalise freight rail to attract investment to logistics hubs with direct railway access. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11189/10764 2025-01-01T00:00:00Z http://hdl.handle.net/11189/10736 Title: Optimising knowledge management practices for improved collaboration in disaster response Authors: Matekenya, Teurai; Ruhode, Ephias Abstract: Disaster response operations are inherently complex, requiring effective collaboration between various stakeholders, including government agencies, non-governmental organisations, first responders and local communities. However, these operations are often hindered by fragmented communication, siloed information and inefficiencies in knowledge sharing. This study explores the optimisation of knowledge management (KM) practices to enhance collaboration in disaster response, adopting a transdisciplinary approach that integrates insights from natural, social, economic and technological systems. Drawing on data that were collected from Zimbabwe’s Department of Civil Protection and the Civil Protection Committees, the study used thematic analysis to identify key KM strategies, including the development of a single knowledge repository, investment in local indigenous knowledge for early warning systems, capacity building, use of technologies and fostering a knowledge culture. Implementing these strategies can enhance collaboration, streamline communication and lead to more effective disaster response and management. Transdisciplinary contribution: The findings provide actionable insights for practitioners and organisations aiming to optimise disaster response capabilities through effective KM. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11189/10736 2025-01-01T00:00:00Z