Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xi, 148 p.; also includes graphics. Includes bibliographical references (p. 146-148). Available online via OhioLINK's ETD Center

Habilitationsschrift - Universität Bonn.

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references.

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains x, 65 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 62-65).

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (Ph.D.)--Baylor University, 2006. / Includes bibliographical references (p. 168-171).

O objetivo deste trabalho e determinar a solução de algumas equações de coeficientes intervalares. Este estudo utiliza uma Teoria das Aproximações Intervalares, a qual foi descrita por [ACI91]. Nesta teoria a igualdade para intervalos e substituída pela relação de aproximação . Esta substituição deve-se ao fato da igualdade utilizada na Teoria Clássica dos Intervalos para resolução de equações de coeficientes intervalares não apresentar uma solução satisfatória, visto que a solução encontrada não contem todas as soluções das equações reais que compõe a equação intervalar. Pela substituição da igualdade intervalar por uma relação de aproximação é possível determinar a solução de equações de coeficientes intervalares, de maneira que esta solução contenha todas as possíveis soluções das equações reais pertencentes a equação intervalar. Apresenta-se alguns conceitos básicos, bem como analisa-se algumas propriedades no espaço solução ( /(R), +, •, C, 1). São representadas graficamente diferentes tipos de funções neste espaço intervalar, com os objetivos de obtenção da imagem, caracterização da solução e identificação gráfica da região de solução (ótima e externa), para cada tipo de função. Como a representação de intervalos de /(R) esta determinada num semiplano de eixos X - X+, onde X - representa o extremo inferior de cada intervalo e X+ representa o extremo superior dos intervalos, apresenta-se o espaço intervalar estendido /(R). Neste espaço intervalar estão definidos os intervalos não-regulares, representados no outro semi-piano de eixos X - X+ Em /(R) serão apresentados alguns conceitos fundamentais, assim como operações aritméticas e algumas considerações referentes aos intervalos não-regulares. No espaço intervalar /(R) e possível resolver equações de coeficientes intervalares de maneira análoga a resolução de equações reais no espaço real, pois este espaço intervalar possui a estrutura semelhante a de um corpo. Com isto apresenta-se a solução de equações de coeficientes intervalares lineares, obtida diretamente, assim como determina-se a Formula de Bascara Intervalar para resolução da Equação Quadrática Intervalar. Para funções que possuem grau maior que 2 apresenta-se alguns métodos iterativos intervalares, tais como o Método de Newton Intervalar, o Método da Secante Intervalar e o Método híbrido Intervalar, que permitem a obtenção do intervalo solução para funções intervalares. Por fim apresenta-se alguns conceitos básicos no espaço intervalar matricial M„,„(/(R)), bem como apresenta-se alguns métodos diretos para resolução de sistemas de equações lineares intervalares. / The aim of this work is to determine the solution set of some Equations of Interval Coefficients. The study use a Theory of Interval Approximation. The begining of this theory was described by [ACI91]. In this theory the equality for intervals is replaced by an approximation relation. When we make use of that relation to solve interval equations, it's possible to obtain an optimal solution, i.e., to get an interval solution that contain all of real solutions of the real equations envolved in the interval equation. By using the equality of Classical Interval Theory for solving interval equations we can not get an optimal solution, that is, the interval solution in the most of equations not consider some real solutions of real equations that belong to the interval equation. We present some basic concepts and analyse some properties at the interval space (1(R), E, -a x , 1). Different kind of functions are showed in this space in order to obtain the range, the solution caracterization and the graphic identification of the optimal and external solution region, for each kind of function. The representation of intervals in /(R) is determined in a half plane of axes X - , X+, where X - represent the lower endpoint and X+ represent the upper endpoint of the intervals. The nonregular intervals are defined in /(R), which are determined in an other half plane. In this interval space are presenting some specific concepts, as well as arithmetical operations and some remarks about nonregular intervals. The interval space (1(R), +, •, C, Ex , 1) have a similar structure to a field, so it's possible to solve interval coefficients equations analogously as to solve real equations in the real space. We present the solution of linear interval equations and we determine an interval formula to solve square interval equation. We present some intervals iterated methods for functions that have degree greater than 2 that allow to get an interval solution of interval functions. Finally we show some basic concepts about the interval matrix space Af,„„(IR)) and present direct methods for the resolution of linear interval sistems.

O objetivo deste trabalho e determinar a solução de algumas equações de coeficientes intervalares. Este estudo utiliza uma Teoria das Aproximações Intervalares, a qual foi descrita por [ACI91]. Nesta teoria a igualdade para intervalos e substituída pela relação de aproximação . Esta substituição deve-se ao fato da igualdade utilizada na Teoria Clássica dos Intervalos para resolução de equações de coeficientes intervalares não apresentar uma solução satisfatória, visto que a solução encontrada não contem todas as soluções das equações reais que compõe a equação intervalar. Pela substituição da igualdade intervalar por uma relação de aproximação é possível determinar a solução de equações de coeficientes intervalares, de maneira que esta solução contenha todas as possíveis soluções das equações reais pertencentes a equação intervalar. Apresenta-se alguns conceitos básicos, bem como analisa-se algumas propriedades no espaço solução ( /(R), +, •, C, 1). São representadas graficamente diferentes tipos de funções neste espaço intervalar, com os objetivos de obtenção da imagem, caracterização da solução e identificação gráfica da região de solução (ótima e externa), para cada tipo de função. Como a representação de intervalos de /(R) esta determinada num semiplano de eixos X - X+, onde X - representa o extremo inferior de cada intervalo e X+ representa o extremo superior dos intervalos, apresenta-se o espaço intervalar estendido /(R). Neste espaço intervalar estão definidos os intervalos não-regulares, representados no outro semi-piano de eixos X - X+ Em /(R) serão apresentados alguns conceitos fundamentais, assim como operações aritméticas e algumas considerações referentes aos intervalos não-regulares. No espaço intervalar /(R) e possível resolver equações de coeficientes intervalares de maneira análoga a resolução de equações reais no espaço real, pois este espaço intervalar possui a estrutura semelhante a de um corpo. Com isto apresenta-se a solução de equações de coeficientes intervalares lineares, obtida diretamente, assim como determina-se a Formula de Bascara Intervalar para resolução da Equação Quadrática Intervalar. Para funções que possuem grau maior que 2 apresenta-se alguns métodos iterativos intervalares, tais como o Método de Newton Intervalar, o Método da Secante Intervalar e o Método híbrido Intervalar, que permitem a obtenção do intervalo solução para funções intervalares. Por fim apresenta-se alguns conceitos básicos no espaço intervalar matricial M„,„(/(R)), bem como apresenta-se alguns métodos diretos para resolução de sistemas de equações lineares intervalares. / The aim of this work is to determine the solution set of some Equations of Interval Coefficients. The study use a Theory of Interval Approximation. The begining of this theory was described by [ACI91]. In this theory the equality for intervals is replaced by an approximation relation. When we make use of that relation to solve interval equations, it's possible to obtain an optimal solution, i.e., to get an interval solution that contain all of real solutions of the real equations envolved in the interval equation. By using the equality of Classical Interval Theory for solving interval equations we can not get an optimal solution, that is, the interval solution in the most of equations not consider some real solutions of real equations that belong to the interval equation. We present some basic concepts and analyse some properties at the interval space (1(R), E, -a x , 1). Different kind of functions are showed in this space in order to obtain the range, the solution caracterization and the graphic identification of the optimal and external solution region, for each kind of function. The representation of intervals in /(R) is determined in a half plane of axes X - , X+, where X - represent the lower endpoint and X+ represent the upper endpoint of the intervals. The nonregular intervals are defined in /(R), which are determined in an other half plane. In this interval space are presenting some specific concepts, as well as arithmetical operations and some remarks about nonregular intervals. The interval space (1(R), +, •, C, Ex , 1) have a similar structure to a field, so it's possible to solve interval coefficients equations analogously as to solve real equations in the real space. We present the solution of linear interval equations and we determine an interval formula to solve square interval equation. We present some intervals iterated methods for functions that have degree greater than 2 that allow to get an interval solution of interval functions. Finally we show some basic concepts about the interval matrix space Af,„„(IR)) and present direct methods for the resolution of linear interval sistems.

The first aim of this study was to determine the effect of irrigation intervals on the survival of L. monocytogenes on spray irrigated broccoli under field trial conditions, and subsequent survival of the pathogen on broccoli during postharvest processing procedures. The nonpathogenic L. innocua was used as surrogate organism to L. monocytogenes. Broccoli in the field was treated with irrigation water inoculated with L. innocua, during intervals over a period of five weeks and the growth and survival of the organism was monitored weekly. L. innocua numbers remained similar over intervals that received consecutive inoculations and L. innocua numbers decreased by at least 2.3 log cfu/g after inoculation ceased, which showed an inoculation effect and that time had an influence on organism survival. Cessation of irrigation before harvest was found to effectively reduce pathogen contamination levels on the crop, whilst repeated irrigation with contaminated water contributes to maintenance of L. innocua as well as elevated total microbial counts on the broccoli. A lack of correlation between the L. innocua counts and the recorded environmental temperatures in the field, including temperature and relative humidity, suggested that survival is not solely dependent on and influenced by, nor can it be predicted by these parameters. It was found that the presence of high levels of contamination (with, in this case L. innocua) in irrigation water used for vegetable crops, can be associated with an increased microbial population on the crop surface. Secondly, the effect of processing on organism survival post-harvest was assessed. Washing with water caused a 1 log reduction of L. innocua, whilst washing with 200 ppm chlorinated water facilitated a further 1 log reduction. Cooking reduced L. innocua numbers on broccoli by an average of 1.1 log units and aerobic plate counts by between 1 and 2 log units. A combined treatment of washing with chlorine, storage in MAP (5% CO2, 5% O2) for two days at 4°C and final microwave heating resulted in the lowest pathogen numbers, causing a 5.13 log cfu/g log reduction. Therefore, even though chlorine isprocessing, it does not suffice alone to eliminate pathogens (with L. innocua being representative of L. monocytogenes) from vegetables, just as MAP storage is only effective as part of a hurdle procedure. Cooking is essential in destroying L. innocua present on broccoli and to ensure vegetables that are safe for consumption in terms of pathogenic exposure. With this knowledge on the behaviour of L. monocytogenes on broccoli, the risk associated with the application of contaminated irrigation water to fresh produce can be better understood and the hazard managed. Copyright / Dissertation (MSc)--University of Pretoria, 2012. / Food Science / unrestricted

Due to rapid growth in computing power, the collection of high dimensional and complex datasets is increasingly feasible. To reap their full benefit, novel analytic strategies may be required. Application of such methods remains limited in certain epidemiologic research areas. The overarching aim of this dissertation was to apply novel analytic strategies with close ties to causal inference and statistical learning theory to gain new insights into well-studied areas of perinatal epidemiology. In Study 1, we explored whether the association between short interpregnancy intervals (i.e., the end of one pregnancy to the start of the next) and increased risk of preterm birth may be due to residual confounding in three populations (n=693 American Indian and n=728 white women from the Northern Plains, U.S., and n=783 mixed ancestry women from the Western Cape, South Africa). Using data from the prospective Safe Passage cohort (2007-2015), we applied propensity score methods to control for a variety of sociodemographic and reproductive factors. A third-to-half of women with <6 months intervals had propensity scores that largely did not overlap with those of women with 18-23 months intervals. Since the propensity score models included factors related to both interpregnancy interval and preterm birth, these findings suggest the possibility of strong confounding in all three populations. The pooled associational estimate with preterm birth was attenuated in the propensity score trimmed and weighted data (risk ratio 1.4, 95% CI 0.75-2.6) compared with the crude results (risk ratio 1.7, 95% CI 1.1-2.7). However, the sample size and precision were reduced after propensity score trimming, and several covariates remained imbalanced. The data demonstrated the complexity of the processes leading to interpregnancy interval length. These issues may have been difficult to identify without comprehensive confounder data and with other methods, such as traditional regression adjustment. In Study 2, we examined the relative importance of timing (first trimester versus second/third trimesters) and degree of gestational weight gain in relation to infant size at birth (small-and-large-for-gestational age) among women with obesity using data from a medical records-based case-cohort study (Pittsburgh, PA, 1998-2010). We operationalized serial antenatal weight measurements as above, below, or within the current recommended ranges for U.S. pregnancies, i.e., 0.2-2.0 kg total gain in the first trimester and 0.17-0.27 kg per week in the second and third trimesters (based on group based trajectory modeling). Data were analyzed by obesity class (n=1290 in the class I subcohort, n=1247 class II, n=1198 class III). Our findings supported the current clinical guidelines, except for women with class III obesity. Among women with class III obesity, lower than recommended gain in the second and third trimesters was associated with decreased risk of having a large-for-gestational age infant (adjusted risk ratio 0.76, 95% CI 0.51-1.1), while not increasing small-for-gestational age (SGA) risk (adjusted risk ratio 1.0, 95% CI 0.63-1.7). Our results were in agreement with findings from several other studies of women with obesity using other methodologies to operationalize gestational weight gain. In Study 3, we used hierarchical clustering to explore latent groups of placental pathology features. We also investigated whether the placental clusters, in addition to birthweight percentiles, were beneficial to explain the variability of select adverse pregnancy outcomes. Data were from the Safe Passage Study (same as Study 1, n=2005). We identified one cluster with low prevalence of abnormalities (60.9%) and three clusters that mapped well to the expert consensus-based Amsterdam criteria: severe maternal vascular malperfusion (5.8%), fetal vascular malperfusion (11.1%), and inflammation (22.1%). The clusters were weakly-to-moderately associated with certain antenatal risk factors, pregnancy complications, and neonatal outcomes. Birthweight percentiles plus the placental clusters was better able to explain the variance of select adverse outcomes, compared with using small-for-gestational age only. This study serves as proof-of-concept that machine learning methods, and placental data, may aid in the identification and etiologic study of certain adverse pregnancy outcomes. In sum, all three studies support that the application of novel analytic methods to high-dimensional datasets may expand our understanding of certain causal questions, even ones that have been broached before, although, as seen in Study 2, such research may not always yield novel insights.