Soil erosion is a key issue in rangelands, but current approaches for predicting soil erosion are based on research in croplands and may not be appropriate for rangelands. An improved model is needed that accounts for the dominant erosion processes that operate in rangelands rather than croplands. In addition, effective application of such a model of rangeland erosion requires improved methods for assessing both model sensitivity and uncertainty if the model is to be applied confidently in natural resources management.I developed a new equation for calculating the combined rate of splash and sheet erosion (Dss, kg/m2) using existing rainfall-simulation data sets from the western United States that is distinct from that for croplands: Dss = Kss I 1.052q0.592, where Kss is the splash and sheet erosion coefficient, I (m/s) is rainfall intensity, and q (mm/hr) is runoff rate. This equation, which accounts for inter-relationship between I and q, was incorporated into a new model, the Rangeland Hydrology and Erosion Model (RHEM). This new model was better at predicting observed erosion rates than the commonly used, existing soil erosion model Water Erosion Prediction Project (WEPP).New approaches for assessing model uncertainty and sensitivity were developed and applied to the model. The new approach for quantifying localized sensitivity indices, when combined with techniques such as correlation analysis and scatter plots, can be used effectively to compare the sensitivity of different inputs, locate sensitive regions in the parameter space, decompose the dependency of the model response on the input parameters, and identify nonlinear and incorrect relationships in the model. The approach for assessing model predictive uncertainty, called "Dual-Monte-Carlo" (DMC), uses two Monte-Carlo sampling loops to not only calculate predictive uncertainty for one input parameter set, but also examine the predictive uncertainty as a function of model inputs across the full range of parameter space. Both approaches were applied to RHEM and yielded insights into model behavior.Collectively, this research provides an important advance in developing improved predictions of erosion rates in rangelands and simultaneously provides new approaches for model sensitivity and uncertainty analyses that can be applied to other models and disciplines.

Diagnostic testing is essential to distinguish non-diseased individuals from diseased individuals. The sensitivity and specificity are two important indices for the diagnostic accuracy of continuous-scale diagnostic tests. If we want to compare the effectiveness of two tests, it is of interest to construct a confidence interval for the difference of the two sensitivities at a fixed level of specificity. In this thesis, we propose two empirical likelihood based confidence intervals (HBELI and HBELII) for the difference of two sensitivities at a predetermined specificity level. Simulation studies show that when correlation between the two test results exists, HBELI and HBELII intervals perform better than the existing bootstrap based BCa, BTI and BTII intervals due to shorter interval lengths. However, when there is no correlation, BCa, BTI and BTII intervals outperform HBELI and HBELII intervals due to better coverage probability in most simulation settings.

Fourier Transform Infrared (FTIR) has been use to discriminate different pathogens by signals from cells infected with these versus normal cells as references. To do the statistical analysis, Partial Least Square Regression (PLSR) was utilized to distinguish any two kinds of virus‐infected cells and normal cells. Validation using Bootstrap method and Cross‐validations were employed to calculate the shrinkages of Area Under the ROC Curve (AUC) and specificities corresponding to 80%, 90%, and 95% sensitivities. The result shows that our procedure can significantly discriminate these pathogens when we compare infected cells with the normal cells. On the height of this success, PLSR was applied again to simultaneously compare two kinds of virus‐infected cells and the normal cells. The shrinkage of Volume Under the Surface (VUS) was calculated to do the evaluation of model diagnostic performance. The high value of VUS demonstrates that our method can effectively differentiate virus‐infected cells and normal cells.

Purpose: To examine the relationship between visual acuity (VA) and radial deformation acuity (RDA) in children 6 to 12 years of age with amblyopia. Methods: RDA was measured in 35 participants with the Manchester RDA charts. VA was measured with the Early Treatment Diabetic Retinopathy Research Study (ETDRS) chart. Results: Median VA in non-amblyopic and amblyopic eyes was 0.04 logMAR (IQF -0.06 – 0.12) and 0.24 (IQF 0.12 – 0.04), respectively (Wilcoxon Signed Ranks test, z = -5.07, p < 0.001). Median RDA in non-amblyopic and amblyopic eyes was 2.73 log (IQF 2.53 – 2.87) and 2.63 log (IQF 2.53 – 2.77), respectively (Wilcoxon, z = -2.56, p < 0.05). Spearman correlation suggested that the amblyopic deficits in VA and RDA were related, r = -0.42, p < 0.05. Conclusion: A deficit in RDA was present in most children with amblyopia. A moderate relationship was noted between the amblyopic deficits found in VA and RDA.

Depletion calculations for nuclear reactors model the dynamic coupling between the material composition and neutron flux and help predict reactor performance and safety characteristics. In order to be trusted as reliable predictive tools and inputs to licensing and operational decisions, the simulations must include an accurate and holistic quantification of errors and uncertainties in its outputs. Uncertainty quantification is a formidable challenge in large, realistic reactor models because of the large number of unknowns and myriad sources of uncertainty and error. We present a framework for performing efficient uncertainty quantification in depletion problems using an adjoint approach, with emphasis on high-fidelity calculations using advanced massively parallel computing architectures. This approach calls for a solution to two systems of equations: (a) the forward, engineering system that models the reactor, and (b) the adjoint system, which is mathematically related to but different from the forward system. We use the solutions of these systems to produce sensitivity and error estimates at a cost that does not grow rapidly with the number of uncertain inputs. We present the framework in a general fashion and apply it to both the source-driven and k-eigenvalue forms of the depletion equations. We describe the implementation and verification of solvers for the forward and ad- joint equations in the PDT code, and we test the algorithms on realistic reactor analysis problems. We demonstrate a new approach for reducing the memory and I/O demands on the host machine, which can be overwhelming for typical adjoint algorithms. Our conclusion is that adjoint depletion calculations using full transport solutions are not only computationally tractable, they are the most attractive option for performing uncertainty quantification on high-fidelity reactor analysis problems.

Increased impulsivity and alterations in reward sensitivity co-occur in many psychiatric disorders. Moreover, individuals reporting more impulsive traits are less efficient in learning stimulus-reward associations. This suggests that impulsivity and reward sensitivity may be linked, consistent with evidence that the orbital frontal cortex (OFC) is implicated in both processes. This study examined the relationship between impulsive traits, assessed by the Barratt Impulsiveness Scale (BIS) and the Eysenck (EIQ), and performance on three behavioral tasks that measure impulsivity and reward sensitivity. The tasks included a Conditioned Pattern Preference (CPP) task, which measures the preference for abstract visual cues as an index of implicit emotional learning, a Probabilistic Reversal Learning (PRL) task that assessed the ability to alter behaviour when reward contingencies change and an Emotional Stroop task which assessed attentional control in response to emotionally salient stimuli. This study provided novel information on the relationship between processes that mediate impulsivity and reward sensitivity. In brief, subjects that were considered to have some explicit knowledge of experimental conditions showed a higher preference formation for the pattern paired with the reward on 90% of the conditioning trials. Although there was no overall effect of impulsivity, the medium impulsive group displayed the strongest preference formation (highest score for the 90% pattern and lowest score for the 10% pattern) compared to the low and high groups. Furthermore, there was an overall effect of Word Category in that participants made more errors for the emotional words (positive and negative) than the neutral words. There was no overall effect of Impulsivity on Stroop performance in this sample. Finally, for the PRL task more participants in the high impulsive group did not meet criterion for the Acquisition stage while more low impulsive subjects did not meet reversal criterion. Furthermore, high impulsive subjects made more overall errors in the Acquisition stage but not Reversal stage. In brief, low and high impulsive subjects performed sub-optimally on the CPP and PRL tasks but not on the Stroop task. This pattern reflects an inverted-U shaped relationship of the effects of impulsivity on associative learning. / Thesis (Master, Psychology) -- Queen's University, 2010-02-05 13:33:27.076

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The research analyzes the operating performance of three vehicle detection technologies for use in the City of Winnipeg. The technologies were: Autoscope Encore (video sensor), Iteris Vantage Edge2 (video sensor) and Matrix Wavetronix (microwave sensor). The sensors were tested in the tow eastbound lanes and two turning lanes on the intersection of Bishop Grandin Blvd and St.Mary's Road in Winnipeg, Manitoba. The research considered 24 weather, illumination, wind and traffic conditions. Testing and analysis was completed at the stop bar, and advance zone as well as for count performance. Sensitivity is a measure of the number of calls missed by the sensor. In terms of sensitivity, Iteris performed best overall, performing with greater sensitivity than Autoscope and Matrix in 17 of 24 conditions at the stop bar and outperforming in 11 of 12 conditions for advanced zone detection in this research. For count performance the Iteris had better accuracy when compared to ground truth established by Miovision Technologies, than Autoscope and Matrix.

This thesis examines the role of language and culture in international business. Through a theoretical framework, it investigates how these are integrated and argues that an understanding of the complexity of the relationship between language and culture in cross-cultural communication is crucial in international business, as essentially it provides an explanation as to what effective communication means. The premise that language and cultural barriers might be preventing New Zealand businesses from enhanced engagement in Asia was investigated using quantitative data obtained from an online survey of New Zealand exporters supported by qualitative data from case studies. The results revealed that New Zealand companies were expressing much apprehension regarding language and cultural barriers prior to entry into Asia but upon entry, they had found that the experience had not been as difficult as anticipated. English alone was sufficient for the most part, particularly during the early phases. However, if businesses wanted to commit fully on a long term engagement with Asia, then English alone was not enough because without the knowledge of the local language and culture, they could not communicate effectively to build long-term relationships. Faced with a rapidly changing, increasingly competitive multilingual global environment, this study suggests that New Zealand businesses will need to find practical solutions to best enhance their opportunities in Asia.