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Historically, difference in performance across dominant and subordinate groups on tests of cognitive ability have been observed. Of particular importance is the underperformance of groups on tests. It is proposed that this results from the underlying assumptions of conventional tests. Contextual models (which argue that cognitive ability is socially determined) and research pertaining to Piagetian theory argue that understanding children's socio-ecological contexts, as well as their underlying cognitive processes, enhances assessment of cognitive ability/ competence. Additionally, the multitude of factors influencing performance in an intra- and intercultural assessment situation should be considered. A new test of cognitive ability based on Piagetian Genetic Epistemology, the Counter test, has been developed by Dr V. Grover. Previous exploratory research has indicated performance differences across designated racial groups attending different education systems. This research focuses on understanding performance (on the Counter Test) across "black" and "coloured" groups within the same education system. A fortuitous sampling technique was used to select a sample consisting of 20 "black" and 20 "coloured" children, aged 8 and 9 years. Demographic data was obtained to understand their socio-ecological context. They were administered the Draw-A-Man and Counter Test (first administration). Results obtained on the Counter Test indicated that designated racial group does not influence performance on the test (1 = 0.203, d.£ = 38, Q > 0.01) or significant underperformance within each group (1 = 6.901 "coloured", 1 = 9.68 "black", d.£= 19, l2. < 0.01). The latter is similar to findings on previous research. Possible explanations are given, but further vigorous investigations are indicated. On second administration, the unstandardized, structured steps based on Feuerstein's Learning Potential Assessment Device were administered to 14 children - criterion underperformance from 1 yr. 6mo to 2 yr. 9mo on the Counter test. The clinical interpretation of 6 children's performance across administrations enhanced understanding of their cognitive processes and the contingencies affecting their use and indicated overall improvement in performance. This indicates that "actual" competence can be assessed through more appropriate assessment procedures. Implications for assessment in a multi-cultural context are considered.

This report presents the study of two-equation turbulence modeling. The primary objective of this study is to implement two-equation k-ε and k-ω turbulence models as a part of the incompressible flow solver, U²NCLE, on unstructured grids. There are several two-equation models but the selection of one which is in par with the model in UNCLE solver is required so that this model can be compared for robustness and accuracy as dem-onstrated by turbulence models in UNCLE. The selection also requires that the pre-defined arrays and variables can be used to avoid overhead and deviation from the solution procedure used in U²NCLE. The present study deals with the two-equation k-ε model contributed by Shih and Lumley and the two-equation k-ω model contributed by Wilcox. Implementation of these models will give the user multiple options of two-equation turbulence modeling for solution purpose. Particular attention is paid to the efficiency of the implementation. Various approximations to the source terms are considered and the most optimal and accurate approximation is identified. These models are validated via relatively small model problems, for example a flat plate case, by comparing the results with the results obtained from the respective models in UNCLE and the existing two-equation q-ω model in U²NCLE. Further validation is carried out by comparing computed forces and moments with experi-mental data for the SUBOFF model with sail and stern appendages.

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Submitted in fulfillment of the requirements of the Degree of Master of Technology: Civil Engineering, Durban University of Technology, 2014. / Streamflow modelling remains crucial to decision-making especially when it concerns planning and management of water resources systems in water-stressed regions. This study proposes a suitable method for streamflow modelling irrespective of the limited availability of historical datasets. Two data-driven modelling techniques were applied comparatively so as to achieve this aim. Genetic programming (GP), an evolutionary algorithm approach and a differential evolution (DE)-trained artificial neural network (ANN) were used for streamflow prediction in the upper Mkomazi River, South Africa. Historical records of streamflow and meteorological variables for a 19-year period (1994- 2012) were used for model development and also in the selection of predictor variables into the input vector space of the models. In both approaches, individual monthly predictive models were developed for each month of the year using a 1-year lead time. Two case studies were considered in development of the ANN models. Case study 1 involved the use of correlation analysis in selecting input variables as employed during GP model development, while the DE algorithm was used for training and optimizing the model parameters. However in case study 2, genetic programming was incorporated as a screening tool for determining the dimensionality of the ANN models, while the learning process was further fine-tuned by subjecting the DE algorithm to sensitivity analysis. Altogether, the performance of the three sets of predictive models were evaluated comparatively using three statistical measures namely, Mean Absolute Percent Error (MAPE), Root Mean-Squared Error (RMSE) and coefficient of determination (R2). Results showed better predictive performance by the GP models both during the training and validation phases when compared with the ANNs. Although the ANN models developed in case study 1 gave satisfactory results during the training phase, they were unable to extensively replicate those results during the validation phase. It was found that results from case study 1 were considerably influenced by the problems of overfitting and memorization, which are typical of ANNs when subjected to small amount of datasets. However, results from case study 2 showed great improvement across the three evaluation criteria, as the overfitting and memorization problems were significantly minimized, thus leading to improved accuracy in the predictions of the ANN models. It was concluded that the conjunctive use of the two evolutionary computation methods (GP and DE) can be used to improve the performance of artificial neural networks models, especially when availability of datasets is limited. In addition, the GP models can be deployed as predictive tools for the purpose of planning and management of water resources within the Mkomazi region and KwaZulu-Natal province as a whole.

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published_or_final_version / Community Medicine / Master / Master of Philosophy

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