This simulation study examined the performance of the curve-of-factors growth model when serial correlation and growth processes were present in the first-level factor structure. As previous research has shown (Ferron, Dailey, & Yi, 2002; Kwok, West, & Green, 2007; Murphy & Pituch, 2009) estimates of the fixed effects and their standard errors were unbiased when serial correlation was present in the data but unmodeled. However, variance components were estimated poorly across the examined serial correlation conditions. Two new models were also examined: one curve-of-factors model was fitted with a first-order autoregressive serial correlation parameter, and a second curve-of-factors model was fitted with first-order autoregressive and moving average serial correlation parameters. The models were developed in an effort to measure growth and serial correlation processes within the same data set. Both models fitted with serial correlation parameters were able to accurately reproduce the serial correlation parameter and approximate the true growth trajectory. However, estimates of the variance components and the standard errors of the fixed effects were problematic. The two models also produced inadmissible solutions across all conditions. Of the three models, the curve-of-factors model had the best overall performance. / text

Hydraulic fracturing is a physically complex phenomenon, and there are many variables, both environmental and operational, that affect the overall success of a fracture treatment. Amongst the operational variables, the process of proppant selection is key to ensuring that the induced fractures remain open and permeable. A variety of physical mechanisms act to degrade the permeability of a given proppant packing after deposition in a fracture, the most important of which is the magnitude of the confining stress. The goal of this work is to understand how mixtures of unlike proppants behave under various stress conditions. Specifically, the permeability and conductivity of various mixtures of unlike proppants are measured as a function of confining stress. A secondary investigation is also made into the dependence of permeability on the areal concentration of proppant. Choices of proppants are restricted to those which are currently most common in industry, in terms of both material and size. To that end, mixtures consisted of primarily ceramics and sands with appropriate grain size distributions. Additionally, a light-weight plastic proppant was included in the study. Simple laboratory methods are employed to measure the permeability of the various proppant packings. Values obtained from direct experimentation are compared with values obtained from an independent analytical model. Given the assumptions which are inherent in the analytical model, the experimental and analytical results are in satisfactory agreement. Also, a correlation is developed for single proppants and binary mixtures which predicts permeability as a function of stress, grain size, material, and weight fraction. One key conclusion is that for a binary mixture of proppants, the mixture permeability will not generally be a weighted linear combination of the pure proppant permeabilities. In other words, the permeability of a mixture comprised of 50% (by weight) of one component and 50% of the second component will generally not be halfway between the permeabilities of the single components. A hypothesis is presented which posits that there are threshold weight fractions for each proppant pair that control the permeability of the mixture. / text

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A frequent problem in physics and engineering is that of determining a mathematical model for the dynamic performance of a system. It is particularly useful to be able to make measurements which enable such a model to follow changes in the system dynamics in the course of normal operation. Linear control theory, although now being replaced by a more general approach, can still form the basis for such system analysis. Cross correlating signals from a linear process can give a great deal of information about the process dynamics without injecting any test disturbances, or, when test signals are possible, cross correlation can be used to recover dynamic information in the presence of considerable background noise. The use of specially constructed test signals can make cross correlation a powerful technique in the identification of dynamic systems.

Environmental concerns caused by burning fossil fuel and the safety concerns associated with nuclear power plants have led to increased interest and investment in wind power. Wind penetration in power systems is rapidly increasing world-wide and creating significant impacts on the overall system performance. The impact of wind generation on the overall system performance increases substantially as wind penetration in power systems continues to increase to relatively high levels. It becomes increasingly important to accurately model the wind behavior, the interaction with other wind sources and conventional sources, and incorporate the characteristics of the energy demand in order to carry out a realistic evaluation of system reliability. Analytical methods using annual wind models have generally been used for reliability evaluation of wind integrated power systems. These methods do not recognize the seasonal and diurnal load following capability of wind. In this thesis, the system adequacy indices are first evaluated on an annual and seasonal basis and then a technique is developed to incorporate the diurnal load following capability of wind. Power systems with high wind penetrations are often connected to multiple wind farms at different geographic locations. Wind speed correlations between the different wind farms largely affect the total wind power generation characteristics of such systems, and therefore should be an important parameter in the wind modeling process. Another concern that arises is the lack of time-synchronized data, especially at the planning phase, which limits the capability of system planners to accurately model multiple correlated wind farms using simple analytical methods. A simple and appropriate probabilistic analytical wind model which can be used for adequacy evaluation of multiple wind-integrated power systems is proposed in the thesis. A simple analytical method to develop an approximate wind model of multiple correlated wind farms when time-synchronized wind data is not available is also proposed in the thesis. The methods to incorporate correlations in the adequacy evaluations of wind integrated power systems presented in the thesis are expected to be highly useful for system planners and policy makers as wind penetration continues to increase.

<p>The 24-item scale has been used extensively to assess the attitudes towards AIDS precautions. This study investigated the usefulness and validity of the instrument in a South African setting, fourteen years after the development of the instrument. If a new structure could be found statistically, the HIV/AIDS prevention strategies could be more effective in aiding campaigns to change attitudes and sexual behaviour.</p>

Previous work has shown that the strongest concentrations of lunar crustal magnetic anomalies are located antipodal to four large, similarly aged impact basins (Orientale, Serenitatis, Imbrium, and Crisium). Here, we report results of a correlation study between magnetic anomaly clusters and geology in areas antipodal to Imbrium, Orientale, and Crisium. Unusual geologic terranes, interpreted to be of seismic or ejecta origin associated with the antipodal basins, have been mapped antipodal to both Orientale and Imbrium. All three antipode regions have many high-albedo swirl markings. Results indicate that both of the unusual antipode terranes and Mare Ingenii (antipodal to Imbrium) show a correlation with high-magnitude crustal magnetic anomalies. A statistical correlation between all geologic units and regions of medium to high magnetization when high-albedo features are present (antipodal to Orientale) may suggest a deep, possibly seismic origin to the anomalies. However, previous studies have provided strong evidence that basin ejecta units are the most likely sources of lunar crustal anomalies, and there is currently insufficient evidence to differentiate between an ejecta or seismic origin for the antipodal anomalies. Results indicate a strong correlation between the high-albedo markings and regions of high magnetization for the Imbrium, Orientale, and Crisium antipodes. Combined with growing evidence for an Imbrian age to the magnetic anomalies, this supports a solar wind deflection origin for the lunar swirls.

In recent years y-y angular correlations have been very useful in confirming the spins of excited nuclear states. Angular correlation techniques have also been employed to study the electric and magnetic character of excited nuclear states. With these things in mind, it was decided to design, construct, and test a precision angular correlation table.

A modelagem de equações estruturais é uma técnica estatística multivariada que permite analisar variáveis que não podem ser medidas diretamente, mas que podem ser estimadas através de indicadores. Dado o poder que esta técnica tem em acomodar diversas situações em um único modelo, sua aplicação vem crescendo nas diversas áreas do conhecimento. Diante disto, este trabalho teve por objetivo avaliar a incorporação de indicadores categóricos ordinais em modelos de equações estruturais, fazendo um resumo dos principais procedimentos teóricos e subjetivos presentes no processo de estimação de um modelo, avaliando as suposições violadas quando indicadores ordinais são utilizados para estimar variáveis latentes e criando diretrizes que devem ser seguidas para a correta estimação dos parâmetros do modelo. Mostramos que as correlações especiais (correlação tetracórica, correlação policórica, correlação biserial e correlação poliserial) são as melhores escolhas como medida de associação entre indicadores, que estimam com maior precisão a correlação entre duas variáveis, em comparação à correlação de Pearson, e que são robustas a desvios de simetria e curtose. Por fim aplicamos os conceitos apresentados ao longo deste estudo a dois modelos hipotéticos com o objetivo de avaliar as diferenças entre os parâmetros estimados quando um modelo é ajustado utilizando a matriz de correlações especiais em substituição à matriz de correlação de Pearson. / The structural equation modeling is a multivariate statistical technique that allows us to analyze variables that cant be measured directly but can be estimated through indicators. Given the power that this technique has to accommodate several situations in a single model, its application has increased in several areas of the knowledge. At first, this study aimed to evaluate the incorporation of ordinal categorical indicators in structural equation models, making a summary of the major theoretical and subjective procedures of estimating the present model, assessing the assumptions that are violated when ordinal indicators are used to estimate latent variables and creating guidelines to be followed to correct estimation of model parameters. We show that the special correlations (tetrachoric correlation, polychoric correlation, biserial correlation and poliserial correlation) are the best choices as a measure of association between indicators, that estimate more accurately the correlation between two variables, compared to Pearsons correlation, and that they are robust to deviations from symmetry and kurtosis. Finally, we apply the concepts presented in this study to two hypothetical models to evaluate the differences between the estimated parameters when a model is adjusted using the special correlation matrix substituting the Pearsons correlation matrix.