In the wake of 21 year old Don Sanderson’s death (January 1st, 2009), the direct result of head injuries sustained during an on-ice hockey fight, the social and political appetite for eliminating violence in amateur hockey appears to be at an all time high. Unfortunately, due to a variety of methodological and conceptual limitations previous research is currently unable to provide a unified and valid explanation for sport-specific aggression (Kirker, Tenenbaum, & Mattson, 2000). One of the primary impediments facing our understanding of sport-specific aggression is the descriptive and fragmented nature of the current body of literature. Over the years a number of independent lines of research have been undertaken, through which several psychological and social factors have been identified as potential determinants. However, in many cases these constructs have yet to be tested against athletes’ actual aggressive behaviour in sport and thus their predictive contribution to our understanding is still unknown. Consequently, the purpose of the current investigation was to assess the predictive influence of several commonly cited psychosocial constructs on amateur hockey players actual within-competition use of aggressive behaviour over a competitive season. A trait aggressive personality disposition emerged as the strongest and most stable predictor of athletes’ aggressive behaviour, accounting for 10 – 40% of the statistical variance depending upon the age and competitive level of the athletes under investigation. Differences in the overt expression of the this trait aggressive disposition between age cohorts (bantam / midget) and competitive levels (house league / rep) suggests that environmental and contextual factors also play a significant role in facilitating or repressing athletes’ aggressive behaviour. As such, the results of the current study support an interactive explanation for hockey-related aggression, whereby situational (e.g., team norms, perceived reinforcement) and personal factors (e.g., trait aggressive disposition, ego orientation) interact to either increase or decrease an athlete’s likelihood for committing aggressive penalty infractions over a competitive season. The current results are plotted and discussed within the parameters of Anderson and Bushman’s (2002) General Aggression Model (GAM), which is a frequently cited interactionist framework used in the broader study of human aggression.

Process control has been playing an increasingly important role in many industrial applications as an effective way to improve product quality, process costeffectiveness and safety. Simple linear dynamic models are used extensively in process control practice, but they are limited to the type of process behavior they can approximate. It is well-documented that simple nonlinear models can often provide much better approximations to process dynamics than linear models. It is evident that there is a potential of significant improvement of control quality through the implementation of the model-based control procedures. However, such control applications are still not widely implemented because mathematical process models in model-based control could be very difficult and expensive to obtain due to the complexity of those systems and poor understanding of the underlying physics. The main objective of this thesis is to develop new approaches to modeling and control of nonlinear processes. In this thesis, the multivariable nonlinear processes are approximated using a model with a static nonlinearity and a linear dynamics. In particular, the Hammerstein model structure, where the nonlinearity is on the input, is used. Cardinal spline functions are used to identify the multivariable input nonlinearity. Highlycoupled nonlinearity can also be identified due to flexibility and versatility of cardinal spline functions. An approach that can be used to identify both the nonlinearity and linear dynamics in a single step has been developed. The condition of persistent excitation has also been derived. Nonlinear control design approaches for the above models are then developed in this thesis based on: (1) a nonlinear compensator; (2) the extended internal model control (IMC); and (3) the model predictive control (MPC) framework. The concept of passivity is used to guarantee the stability of the closed-loop system of each of the approaches. In the nonlinear compensator approach, the passivity of the process is recovered using an appropriate static nonlinearity. The non-passive linear system is passified using a feedforward system, so that the passified overall system can be stabilized by a passive linear controller with the nonlinear compensator. In the extended IMC approach, dynamic inverses are used for both the input nonlinearity and linear dynamics. The concept of passive systems and the passivity-based stability conditions are used to obtain the invertible approximations of the subsystems and guarantee the stability of the nonlinear closed-loop system. In the MPC approach, a numerical inverse is implemented. The condition for which the numerical inversion is guaranteed to converge is derived. Based on these conditions, the input space in which the numerical inverse can be obtained is identified. This constitutes new constraints on the input space, in addition to the physical input constraints. The total input constraints are transformed into linear input constraints using polytopic descriptions and incorporated in the MPC design.

Indonesia has been implementing methadone maintenance treatment (MMT) since January 2003 as a strategy to minimize HIV transmission among injecting drug users (IDU). Previous studies have shown the effectiveness of the program and also showed that the program had attracted many IDU to participate. However, the dropout rate, particularly in Jakarta clinics, was relatively high. The first aim of this study was to investigate the MMT retention rate and its predictive variables. The second aim was to examine the effects of remaining in the program on treatment outcomes. A six-month longitudinal prospective cohort study was conducted at the client level and a cross-sectional survey was carried out at the clinic level. Information from this study provides significant inputs for developing drug treatment policy and improving its quality of service in Indonesia. It also contributes to a better understanding of the substitution treatment implementation in Indonesia. The average 3-month treatment retention rate was 74.2 percent and the 6-month retention rate was 61.3 percent. There was no significant difference in retention rates between clinics. Significant predictors of treatment retention in MMT in Indonesia were size of dose, the interaction between take-home dose and clinic experience, age of participant, participant’s belief towards the program and perceived accessibility, while a variable representing perceived peer support unexpectedly predicted an increased likelihood of prematurely leaving the treatment. This study showed a marked reduction in the use of heroin and depression status and a significant improvement of self-efficacy at the follow up times among participants who continued in treatment. There were no significant differences in criminal involvement and physical health status between those who remained in treatment and the treatment dropouts in both follow-up interviews. Nevertheless, there was a significant improvement in physical health from baseline to follow-up in both groups. The study concluded that retention rates of MMT in Indonesia were comparable to those of similar programs in other countries. As previously reported in other settings, dose was the primary predictor of treatment retention in Indonesia. A policy of providing take-home doses, prescribed in experienced clinics,was also found to be a significant predictor of remaining in treatment. Further research, however, is still needed to explain some of the unexpected observations. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1522114 / Thesis (Ph.D.) -- University of Adelaide, School of Medical Sciences, 2010

Thesis (MScIng)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 104-110).

Thesis (M.Eng.). / Written for the Dept. of Biomedical Engienering and Montreal Neurological Institute. Title from title page of PDF (viewed 2009/06/17). Includes bibliographical references.

Thesis (M. Eng.)(Electronic)--University of Pretoria, 2004. / Includes summary. Includes bibliographical references.

This thesis explores the spatiotemporal network dynamics underlying natural speech comprehension, as measured by electro-magnetoencephalography (E/MEG). I focus on the transient effects of incrementality and constraints in speech on access to lexical semantics. Through three E/MEG experiments I address two core issues in systems neuroscience of language: 1) What are the network dynamics underpinning cognitive computations that take place when we map sounds to rich semantic representations? 2) How do the prior semantic and syntactic contextual constraints facilitate this mapping? Experiment 1 investigated the cognitive processes and relevant networks that come online prior to a word’s recognition point (e.g. “f” for butterfly) as we access meaning through speech in isolation. The results revealed that 300 ms before the word is recognised, the speech incrementally activated matching phonological and semantic representations resulting in transient competition. This competition recruited LIFG, and modality specific regions (LSMG, LSTG for the phonological; LAG and MTG for the semantic domain). Immediately after the word’s recognition point the semantic representation of the target concept was boosted, and rapidly accessed recruiting bilateral MTG and AG. Experiment 2 explored the cortical networks underpinning contextual semantic processing in speech. Participant listened to two-word spoken phrases where the semantic constraint provided by the modifier was manipulated. To separate out cognitive networks that are modulated by semantic constraint from task positive networks I performed a temporal independent component analysis. Among 14 networks extracted, only the activity of bilateral AG was modulated by semantic constraint between -400 to -300 ms before the noun’s recognition point. Experiment 3 addressed the influence of sentential syntactic constraint on anticipation and activation of upcoming syntactic frames in speech. Participants listened to sentences with local syntactic ambiguities. The analysis of the connectivity dynamics in the left frontotemporal syntax network showed that the processing of sentences that contained the less anticipated syntactic structure showed early increased feedforward information flow in 0-100 ms, followed by increased recurrent connectivity between LIFG and LpMTG from the 200-500 ms from the verb onset. Altogether the three experiments reveal novel insights into transient cognitive networks recruited incrementally over time both in the absence of and with context, as the speech unfolds, and how the activation of these networks are modulated by contextual syntactic and semantic constraints. Further I provide neural evidence that contextual constraints serve to facilitate speech comprehension, and how the speech networks recover from failed anticipations.

This Thesis is concerned with model-based control, where models of linear nonminimal state-space (NMSS) and nonlinear state-dependent parameter (SDP) form are considered. In particular, the focus is on model-based predictive control (MPC) in conjunction with the linear NMSS model and on proportional-integralplus (PIP) pole-assignment control in conjunction with the SDP model. The SDP-PIP pole-assignment controller is based on a nonlinear SDP model, however, the approach uses a linear pole-assignment controller design technique. This ‘potential paradox’ is addressed in this Thesis. A conceptual approach to realising the SDP-PIP pole-assignment control is proposed, where an additional conceptual time-shift operator is introduced. This allows the SDPPIP, at each sampling time instance, to be considered as an equivalent linear controller, while operating, in fact, in a nonlinear overall context. Additionally, an attempt to realise SDP-PIP control, where the SDP model exhibits equivalent linear system numerator zeros, is proposed. Regarding the NMSS MPC, emphasis is on square, i.e. equal number of inputs and outputs, multi-input multi-output (MIMO) modelled systems, which exhibit system output cross-coupling effects. Moreover, the NMSS MPC in incremental input form and making use of an integral-of-errors state variable, is considered. A strategy is proposed, that allows decoupling of the system outputs by diagonalising the closed-loop system model via an input transformation. A modification to the NMSS MPC in incremental input form is proposed such that the transformed system input - system output pairs can be considered individually, which allows the control and prediction horizons to be assigned to the individual pairs separately. This modification allows imposed constraints to be accommodated such that the cross-coupling effects do not re-emerge. A practical example is presented, namely, a DC-DC boost converter operating in discontinuous conduction mode (DCM), for which a SDP model is developed. This model is based on measured input-output data rather than on physical relationships. The model incorporates the output current so that the requirements for the load, driven by the converter, is constrained to remain within a predefined output current range. The proposed SDP model is compared to an alternative nonlinear Hammerstein-bilinear structured (HBS) model. The HBS model is, in a similar manner to the SDP model, also based on measured input-output data. Moreover, the differences as well as the similarities of the SDP and HBS model are elaborated. Furthermore, SDP-PIP pole-assignment control, based on the developed SDP model, is applied to the converter and the performance is compared to baseline linear PIP control schemes.

The control of flotation circuits is a complicated problem, since flotation circuits are nonlinear multivariable processes with a significant degree of interaction between the variables. Isolated PID controllers usually do not perform adequately. The application of a nonlinear model predictive algorithm based on second order Volterra models was investigated. Volterra series models are a higher order extension of linear impulse response models. The nonlinear model predictive control algorithm can also be seen as a linear model predictive controller with higher order correction terms. A dynamic model of a flotation circuit based on the governing continuity equations was developed. The responses obtained represented the qualitative relationships between the model inputs and the controlled variables. This model exhibited strong nonlinearities, including asymmetrical responses to symmetrical inputs and gain sign changes. This dynamic model was treated as the plant to be identified and from which second order Volterra models were obtained. Full Volterra models required excessively large data sets, but significant reductions in the size of the required data set could be achieved if some of the second order coefficients were constrained to zero. These "pruned" Volterra models represented the plant dynamics significantly better than linear models. In particular, these second order Volterra models were able to model asymmetrical responses including gain sign changes. A special case of "pruned" second order Volterra models are diagonal second order models, where all the off-diagonal coefficients (hij where i ≠ j) are constrained to zero. These models required less data than pruned Volterra models containing off-diagonal coefficients, but were less accurate. The performance of nonlinear model predictive controllers based on a pruned second order and diagonal second order Volterra models was evaluated. The performance of these controllers was also compared to the performance obtained with a first order (linear) Volterra model. All three controllers gave equivalent results for large manipulated variable weights. However, when the controllers were tuned more aggressively, results obtained from the three controllers differed considerably. The pruned nonlinear controller performed well even when tuned aggressively while the performance of the linear controller deteriorated. For the case of disturbance rejection, the linear controller performed slightly better than the nonlinear controllers. / Dissertation (MEng (Control Engineering))--University of Pretoria, 2006. / Chemical Engineering / unrestricted