Collection D


Recent Submissions

Now showing 1 - 5 of 53
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    Flood frequency analysis – Part 2: Development of a modified plotting position
    (South African Water Research Commission., 2022-04-27) Van der Spuy D; Du Plessis JA
    The original plotting position concept was suggested more than a century ago. Since then, many alternative plotting position approaches have been developed. Despite a general lack of agreement around which plotting position is theoretically ‘correct’ and the ‘best’ to use, all plotting positions fail to adequately address outliers and data of similar magnitude. Hydrologists generally fail to acknowledge that the plotting position primarily offers an informative display of data, against which distributions can be compared, rather than an absolute measure of probability. This paper does not intend to challenge any of the many lengthy theoretical mathematical arguments, utilised to ‘prove’ why one plotting position is superior to the others. These theoretical arguments may very well be valid for a ‘population’ of flood peaks – the reality, however, is that hydrologists are confronted with the challenge of analysing very limited ‘samples’ of the population. Consequently, the plotting position issue demands a more pragmatic approach, rather than a purely theoretical approach. This paper illustrates various problems with existing plotting position techniques in use and offers an alternative approach and a more sensible plotting position technique, using Z-scores and referred to as the Z-set PP, against which distributions can be checked. The study further illustrates how effectively the Z‑set PP deals with outliers and its robustness with various record lengths. Although derived from a study of flood peak data obtained from South African flow-gauging sites, it is deemed that it will be universally applicable.
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    Deep Learning-Enabled Temperature Simulation of a Greenhouse Tunnel
    (IWACP, 2023) Jogunola, O.; Hull. K.J.; Mabitsela, M. M.; Phiri, E.E; Adebisi, B.; Booysen, MJ
    Agriculture is poised to suffer greatly from the effects of climate change. Prediction models, using deep learning, have been developed that can simulate and predict conditions in open field farming to combat the climate variability from climate change. However, deep learning used in precision agriculture, specifically greenhouse tunnels, is under-researched despite also being affected by this variability. Utilising tunnel data collected over 42 days, two hybrid deep learning models were designed. Specifically, a hybrid of convolutional neural network (CNN) and Long Short-Term Memory (LSTM), and a hybrid of CNN and Bidirectional LSTM (BLSTM). The models are designed to forecast the internal temperature of the tunnel to support its management. The cooling wet wall state, solar irradiance, inside and outside temperature of the tunnel are input variables to the developed deep-learning models. Two scenarios are discussed with the results, the first scenario includes all the external variables as input, while the second scenario only considers the internal temperature as input. Results show a performance improvement of 48% and 14% computation time for the CNN-LSTM compared to the CNN-BLSTM model for the two scenarios, respectively. In terms of the measured loss metrics, both models had varied performance and model fitness, with an average mean square error of 0.025 across the models and scenarios.
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    Towards a Cleaner Production of an Underutilised Legume, Bambara Groundnut
    (IWACP, 2023) Mabitsela, M. MA; Hull, K.JA; Mavengahama, SB.; Phiri, E.E.; Booysen, M.J
    Soilless cultivation systems such as aeroponics provide a more efficient, and clean food production of in areas where there is limited access to arable land for agricultural practices and drought-prone countries. The objective of this study was to evaluate the yield performance of seventy Bambara groundnut (BGN) landraces cultivated in aeroponics and compared with a traditional drip-irrigated hydroponic system with sawdust as a growing medium. The result showed that BGN landraces cultivated in aeroponics accumulated a high number of seeds, as compared to those landraces cultivated in hydroponics. However, BGN landraces cultivated in hydroponics recorded a high shoot dry weight and one hundred seed weight. The root length that could only be measured in BGN landraces cultivated in the aeroponics systems, showed that BGN root length can extend beyond one meter. Soilless cultivation systems with their high-water use efficiency have the potential of reducing production costs, thus making them accessible to farmers in countries where drought is a reality.
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    Simulating The Driving and Charging of Electric Minibus Taxis: A Case Study for Stellenbosch
    (IWACP, 2023) Pretorius, B. G.; Strauss, J. M.; Booysen, M. J.
    The Global North is increasing the drive for the electrification of the mobility industry. In sub-Saharan Africa, however, the adoption is yet to pick up steam due to various other challenges in the region. The viability of converting the paratransit fleet (which consists mostly of minibus taxis) to electric vehicles (EVs) with current combustion-based operations is investigated by making use of simulation software, and EV-Fleet-Sim. This developed software simulates the driving and charging of operationally tracked taxis in the Stellenbosch area. A charging algorithm, as well as a simple battery model, was included in the simulation to provide a more accurate representation of the scenario. Most of the taxis were found to still complete their required trips with the specified battery size of 70 kWh. However, new methods would need to be found, such as including a mixed fleet with some petrol or diesel taxis, to assure a 100% trip completion rate. The grid impact per vehicle was found with an expected maximum load appearing between the hours of 08h00 and 10h00 of 22 kW per vehicle, which corresponds to the time after the morning peak traffic of getting people to work. Furthermore, a minimum number of chargers can be implemented which will not affect the trip completion rate of the taxis. This was found to be for 4 chargers per 17 taxis. Future work is left to the testing of various parameters to find optimal solutions as well as including home charging and failed trip classification.
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    Clinical determinants distinguishing communicating and non-communicating hydrocephalus in childhood tuberculous meningitis at presentation
    (2022-12) Bovula, Siyabulela; Solomons, Regan; Van Toorn, Ronald
    ABSTRACT Introduction: Hydrocephalus occurs in up to 80% of children with tuberculous meningitis (TBM), of which the majority (70-80%) is of a communicating nature. Communicating hydrocephalus develops when cerebrospinal fluid (CSF) obstruction occurs at the level of the tentorium, whilst non-communicating hydrocephalus emanates from basal exudates that obstruct the outflow foramina of the fourth ventricle. Identifying the type of hydrocephalus is of critical importance since communicating hydrocephalus can be medically treated with diuretics whilst non-communicating hydrocephalus requires surgical CSF diversion. Conventional neuroimaging does not allow differentiation of the type of hydrocephalus. In resource-limited settings, air-encephalography is the only investigative modality that allows differentiation.