Chronic low back pain (CLBP) is a challenging problem in Nova Scotia and is a leading cause of disability and a contributor to high health related costs to the system. The primary objective of this thesis is to develop and test a methodology for the creation of an electronic standardized assessment tool for chronic conditions such as CLBP using a triangulation method. The methodology involves evidence-based, expert and explicit clinical knowledge in the development of the tool. The outcome of this research is the development of a methodology model for the generation of electronic standardized assessment form for CLBP with 30 predictive factors. Experts evaluated the form for its use and usefulness, usability, and standardized terminologies. Intra-Class Correlation (ICC) and Cronbach’s alpha were used to measure inter-rater reliabilities among experts. The results were in the fair and moderate levels of agreement due to the limitation in sample size and the variation of disciplines among participants.

Our behaviour is guided by the ability to predict future events. The predictive saccade paradigm has been shown to be a valuable tool that uses eye movements to measure the control of predictive behaviour. In this task, subjects follow a visual target that alternates or “steps” between two fixed locations at either predictable or unpredictable inter-stimulus time intervals (ISIs). Response times can be measured by subtracting the time of saccade initiation from the time of target appearance. When the ISI is predictable, saccadic reaction times (SRTs) become predictive (SRT <100ms) within 3-4 target steps, but when the ISI is unpredictable, the SRTs remain reactive to target appearance (SRT >100ms). The goal of our study was to investigate neural mechanisms controlling prediction by contrasting areas in the brain that were more active for predictive (PRED) versus reactive (REACT) saccades in young healthy adults using functional magnetic resonance imaging (fMRI). fMRI analysis revealed two distinct neural networks more recruited for REACT and PRED tasks. We observed greater activation for the REACT task compared to the PRED task in oculomotor network areas including the frontal, supplementary, parietal eye fields, dorsolateral prefrontal cortex, thalamus, and putamen. These structures are all involved with the control of saccades. We also observed greater activation for the PRED task compared to the REACT task in default network areas, including the medial prefrontal cortex, posterior cingulate cortex, inferior parietal lobule, and hippocampus. These structures are known to be involved with passive thinking when subjects are not focused on their external environments. We also observed greater activation for the PRED task in the cerebellum (crus I), which may serve as the internal clock that drives the regular rhythmic behaviour observed for predictive saccades. In summary, our findings suggest brain activation in the PRED task reflects automated and motor-timed responses, while that for the REACT task reflects externally-driven responses. Therefore, the predictive saccade task is an excellent tool for measuring prediction involving fast internally-guided responses. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2011-08-12 10:21:37.744

As computing matures, it is becoming increasingly obvious that a change is necessary for the manner in which web services interact with users. Server-centric models are inconvenient for users. A new paradigm, Smart Interactions, provides a web service architecture which is centered around the user's needs, rather than the simplistic server view currently being used. The system responds to the individual user and is able to adapt to changes to better serve the user. The Smart Internet system helps the user accomplish their tasks efficiently and intuitively. An important aspect of Smart Interactions is that of cognitive support, which provides enhanced information and guidance to the system or user linked to the current task. This thesis examines predictive analytics and its application to cognitive support in Smart Interactions, and presents and evaluates a framework for using predictive analytic support within the Smart Internet model. / Thesis (Master, Computing) -- Queen's University, 2011-09-29 18:11:02.374

No description available.

South Africa has faced tremendous changes over the past decade, which has had a huge impact on the working environment. Organisations are compelled to address the societal disparities between various cultural groups. However, previously disadvantaged groups have had to face inequalities of the education system in the past, such as a lack of qualified teachers (especially in the natural sciences), and poor educational books and facilities. This has often resulted in poor grade 12 results. Social responsibility and social investment programmes are an attempt to rectify these inequalities. The objective of this research was to investigate the validity of the current selection battery of the Youth Foundation Training Programme (YFTP) in terms of academic performance of the students on the bridging programme. A correlational design was used in this research in order to investigate predictive validity whereby data on the assessment procedure was collected at about the time applicants were hired. The scores obtained from the Advanced Progressive Matrices (APM), which forms part of the Raven's Progressive Matrices as well as the indices of the Potential Index Battery (PIB) tests, acted as the independent variables, while the Matric results of the participants served as the criterion measure ofthe dependent variable. The data was analysed using the Statistical Package for Social Sciences (SPSS) software programme by means of correlations and regression analyses. The results showed that although the current selection battery used for the bridging students does indeed have some value, it only appears to be a poor predictor of the Matric results. Individually, the SpEEx tests used in the battery evidently were not good predictors of the Matric results, while the respective beta weights of the individual instruments did confirm that the APM was the strongest predictor. Limitations were identified and recommendations for further research were discussed. / Thesis (M.A. (Industrial Psychology))--North-West University, Potchefstroom Campus, 2007.

No description available.

The recent Generalised Predictive Control algorithm (Clarke et al, 1984,87) is a self-tuning/ adaptive control algorithm that is based upon long-range prediction, and is thus claimed to be particularly suitable for process control application. The complicated nature of GPC prevents the application of standard analytical techniques. Therefore an alternative technique is developed where an equivalent closed loop expression is repeatedly calculated for various control scenarios. The properties of GPC are investigated and, in particular, it is shown that 'default' values for GPC's design parameters give a mean-level type of control law that can reasonably be expected to provide robust control for a wide variety of processes. Two successful industrial applications of GPC are then reported. The first series of trials involve the SISO control of soap moisture for a full-scale drying process. After a brief period of PRBS assisted self-tuning default GPC control performance is shown to be significantly better than the existing manual control, despite the presence of a large time-delay, poor measurements and severe production restrictions. The second application concerns the MIMO inner loop control of a spray drying tower using two types of GPC controller: full multivariable MGPC, and multi-loop DGPC. Again after only a brief period of PRBS assisted self-tuning both provide dramatically superior control compared to the existing multi-loop gain-scheduled PID control scheme. In particular the use of MGPC successfully avoids any requirement for a priori knowledge of the process time-delay structure or input-output pairing. The decoupling performance of MGPC is improved by scaling and that of DGPC by the use of feed-forward. The practical effectiveness of GPC's design parameters (e.g. P, T and λ) is also demonstrated. On the estimation side of adaptive control the current state-of-the-art algorithms are reviewed and shown to suffer from problems such as 'blowup', parameter drift and sensitivity to unmeasurable load disturbances. To overcome these problems two novel estimation algorithms (CLS and DLS) are developed that extend the RLS cost-function to include weighting of estimated parameters. The exploitation of the 'fault detection' properties of CLS is proposed as a more realistic estimation philosophy for adaptive control than the 'continuous retention of adaptivity'.

This thesis reports the successful application of the recently introduced Generalised Predictive Control self-tuner to the high-performance positioning of a real flexible single-link robot arm. The large amount of experimental time available on this high bandwidth system allowed exhaustive testing of the 'tuning-knobs' and 'design-filters' available to the user for tailoring the closed-loop. Based upon these experiments a coherent philosophy for configuring GPC in practice is generated. Configuration details found to be necessary for satisfactory GPC control of this high-order neutrally stable and non-minimum-phase plant, with its lightly damped resonant modes, are isolated. In particular it is found that band-pass filtering of data is essential for stable offset-free control using finite-order models of the plant. These aspects are considered in detail both theoretically and experimentally. In this application, as is often the case in practice, some information about the plant dynamics is available beforehand. Novel methods for the inclusion of this prior knowledge are introduced and their beneficial effects on the convergence of the recursive least squares estimation scheme, upon which most self-tuners are based, are demonstrated in simulation and experiment. A novel high-speed 68010/20 multi-processor computer system is described which allows the implementation of GPC at the required high sample rate (60Hz). The software division of the self-tuning algorithm into concurrently and sequentially executing tasks is discussed in detail.

Predictive control has attracted much attention from both industry and academia alike due to its intuitive time domain formulation and since it easily affords adaption. The time domain formulation enables the user to build in prior knowledge of the operating constraints and thus the process can be controlled more efficiently, and the adaptive mechanism provides tighter control for systems whose behaviour changes with time. This thesis presents a fusion of technologies for dealing with the more practical aspects of obtaining suitable models for predictive control, especially in the adaptive sense. An accurate model of the process to be controlled is vital to the success of a predictive control scheme, and most the of work to date has assumed that this model is of fixed order, a restriction which can lead to poor controller performance associated with under/overparameterisation of the estimated model. To overcome this restriction a strategy which estimates both the parameters and the order of a linear model of the time-varying plant online is suggested. This Multiple Model Least-Squares technique is based on the recent work of Niu and co-workers who have ingeniously extended Bierman's method of UD updating so that, with only a small change to the existing UD update code, a wealth of additional information can be obtained directly from the U and D matrices including estimates of all the lower order models and their loss functions. The algorithm is derived using Clarke's Lagrange multiplier approach leading to a neater derivation and possibly a more direct understanding of Niu's Augmented UD Identification algorithm. An efficient and robust forgetting mechanism is then developed by analysing the properties of the continuous-time differential equations corresponding to existing parameter tracking methods. The resulting Multiple Model Recursive Least-Squares estimator is also ported to the δ-domain in order to obtain models for predictive controllers that employ fast sampling. The MMRLS estimator is then used in an adaptive multiple model based predictive controller for a coupled tanks system to compare performance with the fixed model order case.

Government regulations and growing concerns regarding global warming has lead to an increasing number of passenger vehicles on the roads today that are not powered by the conventional internal combustion (IC) engine. Automotive manufacturers have introduced electric powertrains over the last 10 years which have introduced new challenges regarding powering accessory loads historically reliant on the mechanical energy of the IC engine. High density batteries are used to store the electrical energy required by an electric powertrain and due to their relatively narrow acceptable temperature range, require liquid cooling. The cooling system in place currently utilizes the A/C compressor for cooling and a separate electric element for heating which is energy expensive when the source of energy is electricity. The proposed solution is a thermoelectric heat pump for both heating and cooling. A model predictive controller (MPC) is designed, implemented and tested to optimize the operation of the thermoelectric heat pump. The model predictive controller is chosen due to its ability to accept multiple constrained inputs and outputs as well as optimize the system according to a cost function which may consist of any parameters the designer chooses. The system is highly non-linear and complex therefore both physical modelling and system identi cation were used to derive an accurate model of the system. A steepest descent algorithm was used for optimization of the cost function. The controller was tested in a test bench environment. The results show the thermoelectric heat pump does hold the battery at the speci ed set point however more optimization was expected from the controller. The controller fell short of expectation due to operational restriction enforced during design meant to simplify the problem. The MPC controller is capable of much better performance through adding more detail to the model, an improved optimization algorithm and allowing more flexibility in set point selection.