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Operational modelling of geomagnetic fields and geomagnetically induced currents
Published 2022“…For GIC modelling in networks with sparse magnetic Field data coverage, the well-defined and often used planar spherical elementary current systems interpolation method is adapted to use low-cost variometers and describe mid-latitude current systems. …”
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Beam ripple modelling in wideband dual-reflector antenna systems
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FORMALISING THE LOGIC OF SPATIAL QUALIFICATION USING A QUALITATIVE REASONING APPROACH
Published 2014-04ArticleFull Text AvailableAccess Repository -
Modelling credit spreads in an illiquid South African corporate debt market
Published 2019“…This key feature is used in a Bayesian methodology to impute missing credit spread data for calibration, for more meaningful inference. On sparse simulated data and market observed credit spread time series, the model proves to deliver an improved quality of the estimations, with probabilities that are now statistically founded. …”
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MODELING OF A THIN-LIQUID FALLING-FILM IN ABSORPTION COOLING SYSTEMS
Published 2012ArticleFull Text AvailableAccess Repository -
MATHEMATICAL MODELLING OF PNEUMATIC CLEANING OF COWPEA (Vigna unguiculata (L).Walp)
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Modelling the EM properties of dipole reflections with application to uniform chaff clouds
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Latent class modelling of respiratory outcomes in a South African birth cohort
Published 2025“…Random-effect LCA improved the modelling of recurrent wheezing trends but faced limitations with rare or sparse data patterns. …”
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Bayesian analysis of historical functional linear models with application to air pollution forecasting
Published 2023“…In this work, we develop a Bayesian framework for the analysis of the historical functional linear model with multiple predictors. Different from existing Bayesian approaches to historical functional linear models, our proposed methodology is able to handle multiple functional covariates with measurement error and sparseness. …”
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Development of a patient-specific unicompartmental knee replacement
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The application of spatial capture-recapture models to investigate leopard ecology and conservation in South Africa
Published 2022“…I then fit multisession spatial capture-recapture models to time series data from seven of these leopard populations to assess their threat level based on the estimated probability of population declines. …”
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Language modelling for code-switched automatic speech recognition in five South African languages
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Link prediction in knowledge graphs using latent feature modelling and neural tensor factorisation
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Incremental inference on higher-order probabilistic graphical models applied to constraint satisfaction problems
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A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data
Published 2021“…A prognostic model is a formal combination of multiple predictors from which risk probability of a specific diagnosis can be modelled for patients. …”
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Facilitating golden mole conservation in South African highland grasslands : a predictive modelling approach
Published 2016“…This study employed species distribution modelling to predict the distributional ranges of these taxa, and involved four main processes: (i) creating initial models trained on sparse museum data records; (ii) ground-truthing field surveys during austral spring/summer to gather additional specimens at additional localities; (iii) genetic analyses (using cytochrome-b) to determine the species identities of the newly-acquired specimens, as these taxa are morphologically indistinguishable; and (iv) refining the models and determining the conservation status of these Highveld golden moles. …”
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Martial eagles and the national power grid in South Africa: the implications of pylon-nesting for conservation management
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Locus-coeruleus norepinephrine system function in a developmental animal model of schizophrenia: the socially isolated rat
Published 2017“…Social isolation rearing (SIR) is a well-established developmental adversity paradigm which is used as an animal model of schizophrenia and usually studied in male rats. …”
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Evaluation of Physics-informed Neural Network Solution Accuracy and Efficiency for Modelling Aortic Transvalvular Blood Flow.
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