ABSTRACT It may be of no surprise that water quality data is right-skewed, but what appears to be overlooked by some is that the arithmetic mean and standard deviation most often fail as measures of central tendency in skewed data. When using the arithmetic mean and arithmetic standard deviation with nutrient data, one standard deviation about the arithmetic mean can capture nearly all of the data and extend into negative values. Representing nutrient data this way can be misleading to viewers who are using the statistics, and making assumptions, to understand the characteristics of those waters. Through an in-depth statistical analysis of Florida's nitrogen and phosphorus data, I have found the geometric mean and multiplicative standard deviation capture a better representation of the central region of skewed data. Including the geometric mean and multiplicative standard deviation in the descriptive statistics of nutrient data is relatively simple with today's tools and helps to better describe the data. Adding these statistics can contribute to more effective understanding of nutrient concentrations, better application of data, and the development of better data-derived policy. While the suggestions of this paper are by no means original, it is with added evidence provided by the study of the skewness, distributions, and central regions of 53 nutrient data sets that I intend to help reiterate the argument that a few additional descriptive statistics can greatly empower the communication of data, and because of the ease with which they can now be calculated, there is no excuse to ignore them.

Defect-driven transport of impurities plays a key role in the microstructure evolution of alloys, and has a great impact on the mechanical properties at the macroscopic scale. This phenomenon is greatly enhanced in irradiated materials because of the large amount of radiation-induced crystal defects (vacancies and interstitials). For instance, the formation of nanosized solute clusters in neutron-irradiated reactor pressure vessel (RPV) ferritic steels has been shown to hinder dislocation motion and induce hardening and embrittlement. In Swedish RPV steels, this mechanical-property degradation is enhanced by the high content of manganese and nickel impurities. It has been suggested that the formation of Mn-Ni-rich clusters (which contain also Cu, Si, and P) might be the outcome of a dynamic process, where crystal defects act both as nucleation sites and solute carriers. Solute transport by point defects is therefore a crucial mechanism to understand the origin and the dynamics of the clustering process. The first part of this work aims at modeling solute transport by point defects in dilute iron alloys, to identify the intrinsic diffusion mechanisms for a wide range of impurities. Transport and diffusion coefficients are obtained by combining accurate ab initio calculations of defect transition rates with an exact mean-field model. The results show that solute drag by single vacancies is a common phenomenon occurring at RPV temperature (about 300 °C) for all impurities found in the solute clusters, and that transport of phosphorus and manganese atoms is dominated by interstitial-type defects. These transport tendencies confirm that point defects can indeed carry impurities towards nucleated solute clusters. Moreover, the obtained flux-coupling tendencies can also explain the observed radiation-induced solute enrichment on grain boundaries and dislocations. In the second part of this work, the acquired knowledge about solute-transport mechanisms is transferred to kinetic Monte Carlo (KMC) models, with the aim of simulating the RPV microstructure evolution. Firstly, the needed parameters in terms of solute-defect cluster stability and mobility are calculated by means of dedicated KMC simulations. Secondly, an innovative approach to the prediction of transition rates in complex multicomponent alloys is introduced. This approach relies on a neural network based on ab initio-computed migration barriers. Finally, the evolution of the Swedish RPV steels is simulated in a "gray-alloy" fashion, where impurities are introduced indirectly as a modification of the defect-cluster mobilities. The latter simulations are compared to the experimental characterization of the Swedish RPV surveillance samples, and confirm the possibility that solute clusters might form on small interstitial clusters. In conclusion, this work identifies from a solid theoretical perspective the atomic-transport phenomena underlying the formation of embrittling nanofeatures in RPV steels. In addition, it prepares the ground for the development of predictive KMC tools that can simulate the microstructure evolution of a wide variety of irradiated alloys. This is of great interest not only for reactor pressure vessels, but also for many other materials in extreme environments. / <p>QC 20151123</p>

Therapeutic Assessment (TA) is a hybrid of assessment and therapy techniques in which assessors actively collaborate with clients during an individualized assessment. TA is centered around client assessment questions and provides a safe environment where clients can create shifts in their ‘story’ of self. More specifically, TA with children and their parents has demonstrated more confident parenting and parents’ better understanding of their child’s difficulties, while children have shown decreased problem behaviors and improved social/emotional functioning. The theoretical framework behind TA emphasizes the importance of the interpersonal interactions between the assessor and client, such as the development of a strong assessor client relationship and collaboration. These interpersonal processes are conceptualized as catalysts for greater depth of parent investment in the assessment and deeper levels of feedback results. The need for greater parent involvement and partnership in child mental health services is increasingly recognized in the client/parent satisfaction literature. Parent feedback to child mental health services is most often acquired through satisfaction questionnaires. However, the satisfaction literature has well known limitations, specifically a lack of unifying theory and methodological issues in scale development. Parent satisfaction research indicates that interpersonal experiences are more related to satisfaction than outcomes or client characteristics, and that more psychometrically sound measures are needed. Currently, satisfaction surveys do not provide a detailed understanding of parents’ experiences to inform practice and research. The current study outlines the development of the Parent Experience of Assessment Scale (PEAS). The PEAS is anchored in the theoretical orientation of TA and provides a more quantifiable measure of hypothesized underlying TA constructs. Moreover, the development of the PEAS uses advanced statistical techniques, such as Confirmatory Factor Analysis and invariance testing, to provide a higher level of psychometric rigor. The resulting scale consists of 24 items divided among 5 subscales with demonstrated relationships to general satisfaction. Structural equation modeling provides insight via direct and indirect effects among the PEAS subscales and their relationship to general satisfaction. Through the development of the PEAS, this study provides empirical evidence and support for TA theory and a more nuanced understanding of parent experiences related to satisfaction. / text

Use of imaging systems for performing various situational awareness tasks in militaryand commercial settings has a long history. There is increasing recognition,however, that a much better job can be done by developing non-traditional opticalsystems that exploit the task-specific system aspects within the imager itself. Insome cases, a direct consequence of this approach can be real-time data compressionalong with increased measurement fidelity of the task-specific features. In others,compression can potentially allow us to perform high-level tasks such as direct trackingusing the compressed measurements without reconstructing the scene of interest.In this dissertation we present novel advancements in feature-specific (FS) imagersfor large field-of-view surveillence, and estimation of temporal object-scene changesutilizing the compressive imaging paradigm. We develop these two ideas in parallel.In the first case we show a feature-specific (FS) imager that optically multiplexesmultiple, encoded sub-fields of view onto a common focal plane. Sub-field encodingenables target tracking by creating a unique connection between target characteristicsin superposition space and the target's true position in real space. This isaccomplished without reconstructing a conventional image of the large field of view.System performance is evaluated in terms of two criteria: average decoding time andprobability of decoding error. We study these performance criteria as a functionof resolution in the encoding scheme and signal-to-noise ratio. We also includesimulation and experimental results demonstrating our novel tracking method. Inthe second case we present a FS imager for estimating temporal changes in the objectscene over time by quantifying these changes through a sequence of differenceimages. The difference images are estimated by taking compressive measurementsof the scene. Our goals are twofold. First, to design the optimal sensing matrixfor taking compressive measurements. In scenarios where such sensing matrices arenot tractable, we consider plausible candidate sensing matrices that either use theavailable <italic>a priori</italic> information or are non-adaptive. Second, we develop closed-form and iterative techniques for estimating the difference images. We present results to show the efficacy of these techniques and discuss the advantages of each.

No description available.

This report covers a study testing the possibility of adding portfolio optimization by mean-variance analysis as a tool to extend the concept of momentum strategies in contrast to naive allocation formed by Jegadeesh &amp; Titman (1993). Further these active investment strategies are compared with a passive benchmark as well as a randomly selected portfolio over the entire study-period. The study showed that the naive allocation model outperformed the mean-variance model both economically as well as statistically. No indication where obtained for a lagged return effect when letting a mean-variance model choose weights for a quarterly holding period and the resulting investment recommendation is to follow a naive investment strategy within a momentum framework.

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As part of this project and in preparation for future experimental studies of gas-phase ion-molecule reactions, extensive modification and characterization of the crossed molecular beam machine in the Department of Chemistry, University of Canterbury has been carried out. This instrument has been configured and some preliminary testing completed to enable the future study of gas-phase ion-molecule collisions of H⁺₃ and Y⁻ (Y = F, Cl, Br) with dipole-oriented CZ₃X (Z = H, F and X = F, Cl, Br). Theoretical calculations (ab initio and density functional theory) are reported on previously experimentally characterized Na + CH₃NO₂, Na + CH₃NC, and K + CH₃NC systems, and several other systems of relevance. All gas-phase experimental and theoretical studies have the common theme of studying collision orientation dependence of reaction under singlecollision conditions. Experimental measurements, theoretical simulations and calculations are also reported on some selected ferrous (Fe²⁺) high-spin (S=2) crystals, in an attempt to resolve microscopic contributions of two fundamental macroscopic tensor properties: the electric-field gradient (efg); and the mean square displacement (msd) in the case when more than one symmetry related site of low local point-group symmetry contributes to the same quadrupole doublet. These determinations have been made using the nuclear spectroscopic technique of Mössbauer spectroscopy, and complemented with X-ray crystallographic measurements.

A number of topics related to Fourier imaging are investigated. Relationships between the magnitude of errors in the amplitude and phase of the Fourier transform of images and the mean square error in reconstructed images are derived. The differing effects of amplitude and phase errors are evaluated, and "equivalent" amplitude and phase errors are derived. A model of the probability density function of the Fourier amplitudes of images is derived. The fundamental basis of phase dominance is studied and quantitated. Inconsistencies in published counter-examples of phase dominance are highlighted. The key characteristics of natural images that lead to their observed power spectral behaviour with spatial frequency are determined.

Within the scope of this thesis, the Black-Litterman Asset Allocation Model (as presented in He &amp; Litterman, 1999) is compared to the classical mean-variance framework by simulating past performance of portfolios constructed by both models using identical input data. A quantitative investment strategy which favours stocks with high dividend yield rates is used to generate private views about the expected excess returns for a fraction of the stocks included in the sample. By comparing the ex-post risk-return characteristics of the portfolios and performing ample sensitivity analysis with respect to the numerical values assigned to the input variables, we evaluate the two models’ suitability for different categories of portfolio managers. As a neutral benchmark towards which both portfolios can be measured, a third market-capitalization-weighted portfolio is constructed from the same investment universe. The empirical data used for the purpose of our simulations consists of total return indices for 23 of the 30 stocks included in the OMXS30 index as of the 21st of February 2014 and stretches between January of 2003 and December of 2013.   The results of our simulations show that the Black-Litterman portfolio has delivered risk-adjusted return which is superior not only to that of its market-capitalization-weighted counterpart but also to that of the classical mean-variance portfolio. This result holds true for four out of five simulated strengths of the investment strategy under the assumption of zero transaction costs, a rebalancing frequency of 20 trading days, an estimated risk aversion parameter of 2.5 and a five per cent uncertainty associated with the CAPM prior. Sensitivity analysis performed by examining how the results are affected by variations in these input variables has also shown notable differences in the sensitivity of the results obtained from the two models. While the performance of the Black-Litterman portfolio does undergo material changes as the inputs are varied, these changes are nowhere near as profound as those exhibited by the classical mean-variance portfolio.   In the light of our empirical results, we also conclude that there are mainly two aspects which the portfolio manager ought to consider before committing to one model rather than the other. Firstly, the nature behind the views generated by the investment strategy needs to be taken into account. For the implementation of views which are of an α-driven character, the dynamics of the Black-Litterman model may not be as appropriate as for views which are believed to also influence the expected return on other securities. Secondly, the soundness of using market-capitalization weights as a benchmark towards which the final solution will gravitate needs to be assessed. Managers who strive to achieve performance which is fundamentally uncorrelated to that of the market index may want to either reconsider the benchmark weights or opt for an alternative model.