The study presents the development of an alternative noise current term and novel voltage dependent current noise algorithm for conductance based stochastic auditory nerve fibre (ANF) models. ANFs are known to have significant variance in threshold stimulus which affects temporal characteristics such as latency. This variance is primarily caused by the stochastic behaviour or microscopic fluctuations of the node of Ranvier's voltage dependent sodium channels of which the intensity is a function of membrane voltage. Though easy to implement and low in computational cost, existing current noise models have two deficiencies: it is independent of membrane voltage and it is unable to inherently determine the noise intensity required to produce in vivo measured discharge probability functions. The proposed algorithm overcomes these deficiencies whilst maintaining its low computational cost and ease of implementation compared to other conductance and Markovian based stochastic models. The algorithm is applied to a Hodgkin-Huxley based compartmental cat ANF model and validated via comparison of the threshold probability and latency distributions to measured cat ANF data. Simulation results show the algorithm's adherence to in vivo stochastic fibre characteristics such as an exponential relationship between the membrane noise and transmembrane voltage, a negative linear relationship between the log of the relative spread of the discharge probability and the log of the fibre diameter and a decrease in latency with an increase in stimulus intensity.

This paper presents a near optimal hoist scheduling and control program for rock winders found in South African deep level mines in the context of demand side management and time-of-use (TOU) tariffs. The objective is to achieve a set hoist target at minimum energy cost within various system constraints. The development of a discrete dynamic and constrained mixed integer linear programming model for a twin rock winder system is presented on which a half-hourly model predictive control (MPC) algorithm containing an adapted branch and bound methodology is applied for near optimal scheduling. Simulation results illustrate the effectiveness of the control program by minimising the energy costs through scheduling according to the TOU tariff and controlling output and ore levels within their boundaries even in the case of significant random delays in the system. Scheduling according to the TOU tariff shows a possible 30.8% reduction in energy cost while approximately 6 h of delays in the system resulted in a mere 14% increase in energy cost.

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Facial nerve stimulation (FNS) is a side-effect of cochlear implantation that can result in severe discomfort for the user and essentially limits the optimal use of the implant. In recent years, research in the field of three-dimensional cochlear implant modelling has led to the progression from generic models to user-specific models with one of the intentions to develop model-based diagnostic tools. The objective of this study is to investigate the mechanisms that underlie the manifestation of FNS in the post-meningitic cochleae of a specific CI user through computational modelling. Bilateral models (right and left) were created using a method previously developed for the construction of a three-dimensional user-specific volume conduction model of the cochlea and was expanded to include the facial nerve geometry. Reduced temporal bone density based on bone densitometry, cochlear duct ossification and degenerate auditory neural fibres were incorporated into a comprehensive FNS model. Auditory and facial nerve thresholds were predicted with the models showing good correspondence to perceptual thresholds and the user's FNS experience. Ossified cochlear ducts appear to aggravate the increase in thresholds caused by the otic capsule's decreased resistivity. This translational case study demonstrates the application of computational modelling as a clinical instrument in the assessment and management of complications with CI implantation.

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