Agricultural sprayers are used to apply chemical treatments (pesticides and fertilizer) to crops. A sprayer distributes the chemical by employing many nozzles spaced evenly along a boom structure oriented perpendicular to the direction of travel to cover large areas with each machine pass. To maximize spray efficacy, the nozzles must be held a specific distance from the target to be sprayed. With diversification of crop types grown in Western Canada, foliar application of chemical treatments at multiple points during the plants life cycles are now required. This multi-growth-stage application process requires a machine with a large range of vertical adjustment; thus permitting the nozzles to be maintained the correct distance from the target (crop) as it grows. Suspended boom sprayers provide the range of adjustment required.<p> The suspended boom structure consists of three controlled sections which are positioned via use of hydraulic actuators. To reduce the effect of terrain inputs through the carrying frame on the booms orientation, most suspended boom sprayers incorporate a passive suspension system to limit coupling between the carrying frame and boom. By doing this however, a negative effect is created. During typical operation, the operator will use the actuator to reorient one section thereby maintaining the desired distance from the boom to the target; the opposing section will deviate from its desired position due to coupling of the boom sections through the passive suspension system. The quantification of this problem was the basis for this research. <p> A computer simulation model of the boom structure, passive suspension system, hydraulic actuator, and on/off type directional valve was created. Comparisons to experimental data showed the model was applicable for predicting trends in boom performance related to manipulation of actuator velocity profiles. Standardized changes in the actuated sections orientation were used to establish the existing performance baseline and quantify the problem. Alternative commercially available directional valves (proportional and pulse width modulated) were then simulated and used in conjunction with the boom model to determine if boom performance improvements may be realized by defining the actuators acceleration rate during orientation changes. <p> The proportional valve was able to limit the acceleration and deceleration of the actuated section to reduce the coupling effect and improve the non-actuated sections performance. However, the performance of the actuated section degraded more significantly in all trials regardless of input profile. The performance degradation resulted as slower acceleration and deceleration of the actuator required an increased amount of time for the desired orientation of the actuated section to be reached. It was also concluded that performance of the dynamic orientation of the boom structure was equivalent for orientation changes driven wither by pulse width modulation of an on/off valve or a true proportional valve. The boom structures large inertia and low natural frequency acted as a suitable filter for the flow and pressure pulsations introduced by pulse width modulation.

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Includes abstract. / Includes bibliographical references. / During December (2007), Cape Town’s peak tourism season, a pilot pedestrianisation project was run in the City of Cape Town. The 300 year old Greenmarket Square was pedestrianised and stakeholders on the square were surveyed six months after the implementation of the scheme to access the economic benefits, as well as the perception of predestination by the traders. A SATURN dynamic assignment model was used to simulate the effect of certain road closures to traffic as part of an extension to the pedestrian network. These road are vital to start forming a formalise pedestrian network within the CBD. From the results, a pedestrian network for the Cape Town CBD has been proposed.

Synapses communicate temporal sequences of action potentials between neurons with variant efficacy, allowing the same axon to convey independent messages to multiple post-synaptic targets. Several molecular mechanisms control information flow in neural networks. In the hippocampus, transmission responses are highly variable even at the level of individual synapses across different cell types and changes dynamically on the multiple time scales on which it operates. Modeling synaptic transmission and dynamics requires balancing the model’s interpretability and its ability to espouse experimental data. In this work, the high variability associated with synaptic responses is first considered at a synapse level. Taking a statistical approach to the phenomena, a biophysically tractable gamma-mixture model is developed to characterize postsynaptic responses from single synapse release events recorded by a sensor for the neurotransmitter glutamate as fluorescence transients. Here the development of this modeling framework leads to three different versions of the framework: two bimodal frameworks that take two different approaches to modeling the noisiness of synaptic releases, and a statistically validated unimodal approach. Variational inference techniques are applied to these frameworks through an expectation-maximization algorithm, which operates on the principles of maximum likelihood. This results in the extraction of latent variables for quantal size, number, and release probability, allowing for the characterization of release events at a synaptic level. A system identification approach is taken to capture the diverse types of synaptic response dynamics observed on short time scales. This extends work from previous phenomenological approaches to account for a nonlinearity and the kinetics evolving on multiple time scales present in this phenomenon. Gradient descent methods are used to estimate synaptic kinetics from complex firing patterns such as those observed \textit{in vivo}. The characterized dynamics in synaptic transmission all contribute to the transfer of information between cells and are assumed to strive for maximizing information transfer through reducing redundancies and optimizing cost-efficiency between the required energy input and the information transferred. The postsynapse has a seeming redundancy as it has two glutamate receptors with different detection thresholds, suggesting there should be a benefit to having both receptors; here this idea is explored here through numerical simulation. Taken together with the modeling of observed glutamate release dynamics, this creates an avenue for improved theory for information processing capabilities of synapses.

The Angola Low and Botswana High pressure systems are thought to play a crucial role in the variability of summer rainfall over southern Africa. However, very little is known about their variability during the summer half of the year and how their influence on rainfall patterns during ENSO and non-ENSO summers may vary. In simple terms, a weaker Angola Low is expected to lead to decreased rainfall as is a stronger Botswana High. This study looks at the monthly evolution of the Angola Low and the Botswana High during the neutral summer of 2012/13 and the two strong El Ni˜no summers of 1997/98 and 2015/16 using the WRF model. CFSR and CFSv2 reanalyses, satellite derived winds, GPCC rainfall and TRMM satellite-derived rainfall estimates are used to validate the model. The model was integrated from September through to April for each run with observed sea surface temperature and reanalyses as boundary conditions. During the neutral summer of 2012/13, the Angola Low became clearly evident in the model during the pentad of 6-10 October whereas during the 1997/98 and 2015/16 El Ni˜no summers, it became evident during the pentads of 6-10 November and 16-20 November respectively. In addition to these differences in onset, there were also differences in the date after which the Low was no longer present in the model fields. These dates were 26-28 February 2013 and 26-31 March 1998 while in the 2015/16 case, the Low remained present throughout the whole of March. In each year, the Botswana High was present throughout the entire summer half of the year. The WRF simulation indicated that during the strong El Ni˜no event of 1997/98, the Angola Low did not weaken whereas the Botswana High was weaker than normal. However, during the strong El Ni˜no event of 2015/16, the Angola Low was weaker and the Botswana High was relatively strong. The strengthening of the Angola Low and the weakening of the Botswana High during the strong 1997/98 El Ni˜no led to substantial rainfall over southern Africa. The near to above average rainfall over subtropical southern Africa during 1997/98 was unexpected given the strength of the El Ni˜no and the SST anomalies in the Indian and Pacific Oceans. The weaker Angola Low and stronger Botswana High during the strong 2015/16 El Ni˜no led to severe drought over the region. The study highlights the importance of modulations in the Angola Low and the Botswana High for rainfall anomalies during ENSO and non-ENSO summers as very different rainfall patterns may occur over southern Africa during similar strength ENSO events. The significance of these regional circulation systems is reinforced by the fact that during the 2012/13 neutral summer, the Angola Low was stronger than average and the Botswana High was relatively weak leading to good rainfall. The relationship between the Angola Low, the Botswana High and southern Africa rainfall is found to be relatively strong through the 1979-2017 period. Thus, monitoring and better understanding these regional circulation systems is important and complements ongoing efforts to monitor and predict ENSO.

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A urine model has been developed. The model is used in the computation of chemical speciation and degree of precipitation by means of equilibrium constants and solubility products. The urine model was developed in order to investigate some of the factors associated with urolithiasis. A specific model development procedure was used. This procedure was based on iterative construction, validation, refinement and usage of the model. One refinement stage involved the potentiometric characterisation of the solution equilibria of the Mg-P0 4-NH 3-H system. Whereas the subsystems; H-P0 4 , H-NH 3 and Mg-P0 4 -H, were rigorously characterised, the overall system was only tentatively explored. The results obtained, however, indicate that the characterisation of the three subsystems is likely to be sufficient for the purposes of the urine model. The validation of the basic urine model was achieved by comparing the model's predicted precipitation with experimental observations in another project currently being pursued in this department. This validation has indicated that the kinetics of precipitation should be taken into account for some of the important urinary so 1 id. The urine mode 1 was nevertheless successfully used to investigate the effect of pH on urinary precipitation, the influence of some urinary components on calcium oxalate precipitation and the role of cadmium in urolithiasis. The results obtained were in general agreement with other observed and predicted results. Finally, a coherent procedure for investigating the problems of urolithiasis by means of such a model is proposed.

Work on developing team models for use in adaptive systems generally and intelligent tutoring systems more specifically has largely focused on the task skills or learning efficacy of teams working on short-term projects in highly-controlled virtual environments. In this work, we report on the development of a balanced team model that takes into account task skills, teamwork behaviours and team workflow that has been empirically evaluated via an uncontrolled real-world long-term pilot study of student software engineering teams. We also discuss the use of the the J4.8 machine learning algorithm with our team model in the construction of a team performance prediction system.

Work on developing team models for use in adaptive systems generally and intelligent tutoring systems more specifically has largely focused on the task skills or learning efficacy of teams working on short-term projects in highly-controlled virtual environments. In this work, we report on the development of a balanced team model that takes into account task skills, teamwork behaviours and team workflow that has been empirically evaluated via an uncontrolled real-world long-term pilot study of student software engineering teams. We also discuss the use of the the J4.8 machine learning algorithm with our team model in the construction of a team performance prediction system.