Student Number : 0002066T - MSc dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment / Investigations of uni-directional, horizontally polarized waveguide antennas with longitudinal slotted arrays operating at 2.45GHz and their applications to wireless local area networks (WLAN) are presented in this paper. Requirements, considerations, and limitations associated with the design process of this particular waveguide are discussed and presented. Various antenna parameters were simulated using MATLAB® and SuperNEC® software simulation programs, and were applied to a sensitivity analysis of antenna design. End-fed and center-fed antennas were designed, built, and measured at WLAN frequencies. Measured antennas had high gain above 15dBi, broad beam around the azimuth, and high efficiency, but were limited by their impedance dependency and narrow bandwidth. The center-fed antenna had 3dBi higher gain than the 18dBi gain of the end-fed antenna. The VSWR ratio of both antennas was less than 1:1.5 at the operating frequency. The center-fed antenna had broader azimuth and elevation patterns by 40° and 10°, respectively. The end- fed antenna had more stable gain and VSWR, 50% wider VSWR bandwidth of 100MHz, and more directional elevation pattern. The design criteria generated using waveguide theory and simulated analysis was validated by the physical design and performance of the measured antennas.

Submitted by Rosina Valeria Lanzellotti Mattiussi Teixeira (rosina.teixeira@unisantos.br) on 2016-02-22T16:47:21Z No. of bitstreams: 1 Marcia Matos Bezerra Gomes.pdf: 1029140 bytes, checksum: 1effa71011338847db6ac10a5d8d28c9 (MD5) / Made available in DSpace on 2016-02-22T16:47:21Z (GMT). No. of bitstreams: 1 Marcia Matos Bezerra Gomes.pdf: 1029140 bytes, checksum: 1effa71011338847db6ac10a5d8d28c9 (MD5) Previous issue date: 2015-08-24 / Introduction - Several studies have demonstrated an association between air pollution and adverse effects of pregnancy-related low birth weight (LBW) and preterm delivery, affecting growth and neurological development. This association has contributed to intrauterine growth retardation (IUGR), likely to cause over time various diseases in adulthood. Objective - This study aimed to investigate the association between low birth weight and premature birth and maternal exposure to carbon monoxide (CO) in Campina Grande -PB, Brazil. Methods - This is a population longitudinal study, investigating all newborn babies of women living in Campina Grande, Paraiba, Brazil, from 2007 to 2011. The birth data were obtained from the Live Birth Information System (SINASC) of the Ministry of Health. The exposure information was provided by CATT- BRAMS - SISAM. The associations between the outcomes of interest and changes in CO concentrations, controlling for several risk factors during pregnancy were done using the regression models univariate and multiple logistic, using trimesters of pregnancy as display windows. The level of significance was 5%. The study covered 31,030 births from 1st. January 2007 to 31 December 2011. Results - The low birth weight accounted for 8.1% of all newborns. Prematurity was observed in 21.8% of all newborns. To BPN was increased risks of maternal exposure to CO in the 1st., 2nd. and 3rd. quarter respectively of: (RR 1.59, 95% CI 1.34; 1.89), (RR 1.58, 95% CI 1.30; 1.92) and (RR 1.34, 95% CI 1, 21; 1.54). To prematurity, the risks to the 1st, 2nd and 3rd trimesters of pregnancy were RR 1.50; 95% CI 0.61; 3.66), 0.47 RR and CI 0.26; 8.69) and (RR 0.97 CI 0.12; 7.81). The variables settings included in multiple models were maternal age, marital status, pre-natal, childbirth and sex. Conclusion - This study suggests that exposure to CO, even below the standard of air quality in Brazil, for BPN outcome contributes to risk of low birth weight in all the analyzed quarters. Prematurity outcome for the risk of this exposure effect was only observed in the 1st. trimester. The use of CATT-BRAMS data can be an important tool in areas where monitoring stations are not available, although these models are preliminary, needing a validation. / Introdução - Vários estudos têm demonstrado a associação entre poluição atmosférica e efeitos adversos da gravidez, relacionada com baixo peso ao nascer (BPN) e parto prematuro, comprometendo o crescimento e desenvolvimento neuropsicomotor, contribuindo para um retardo do crescimento intrauterino (RCIU), com probabilidade de causar ao longo do tempo varias doenças na vida adulta. Objetivo - Este estudo objetivou investigar a associação entre o BPN, parto prematuro e exposição materna ao monóxido de carbono (CO) em Campina Grande -PB, Brasil. Métodos - Estudo longitudinal populacional, investigando todos os recém-nascidos de mulheres residentes em Campina Grande, Paraíba, Brasil, no período de 2007-2011. Os dados foram obtidos do Sistema de Informações sobre Nascidos Vivos (SINASC) do Ministério da Saúde. As Informações de exposição foram fornecidas por CATT- BRAMS - SISAM. Para avaliar a associação entre os desfechos de interesse e associação às variações das concentrações de CO, controlando para os diversos fatores de risco durante a gravidez, foram utilizados os modelos de regressão logística univariado e múltiplo, usando os trimestres da gravidez como janelas de exposição. O nível de significância adotado para confirmar associações e para identificar fatores de risco para prematuridade e baixo peso, tanto no modelo logístico univariado quanto nos modelos múltiplos, foi de 5%. O estudo abrangeu 31.030 nascimentos ocorridos entre 1º de Janeiro de 2007 e 31 de Dezembro de 2011. Resultados - O baixo peso ao nascimento representou 8,1% de todos os recém-nascidos. Prematuridade foi observada em 21,8% de todos os recém-nascidos. Para BPN houve aumento dos riscos de exposição materna ao CO no 1º. , 2º. e 3º. trimestre respectivamente de: (RR 1,59; IC 95% 1,34; 1,89), (RR 1,58; IC 95% 1,30; 1,92) e (RR 1,34; IC95% 1,21; 1,54). Para prematuridade, os riscos para os 1º, 2º e 3º trimestres gestacionais foram RR 1,50; IC 95% 0,61; 3,66), RR 0,47 e IC 0,26; 8,69) e (RR 0,97 IC 0,12; 7,81). As variáveis de ajustes incluídas nos modelos múltiplos foram a idade materna, estado civil, pré-natal, parto e sexo. Conclusão - Este estudo sugere que a exposição ao CO, mesmo abaixo do padrão de qualidade do ar do Brasil, para o desfecho BPN contribui para risco de baixo peso ao nascer em todos os trimestres analisados. Para o desfecho Prematuridade o risco de sofrer efeito dessa exposição só foi observado no 1º. trimestre gestacional. A utilização de dados CATT-BRAMS pode ser uma ferramenta importante em regiões onde estações de monitoramento não estejam disponíveis, embora esses modelos sejam preliminares, precisando de uma validação.

Singular Spectrum Analysis (SSA) is a method for decomposing and forecasting time series that recently has had major developments but it is not yet routinely included in introductory time series courses. An international conference on the topic was held in Beijing in 2012. The basic SSA method decomposes a time series into trend, seasonal component and noise. However there are other more advanced extensions and applications of the method such as change-point detection or the treatment of multivariate time series. The purpose of this work is to understand the basic SSA method through its application to the monthly average sea temperature in a point of the coast of South America, near where “EI Ni˜no” phenomenon originates, and to artificial time series simulated using harmonic functions. The output of the basic SSA method is then compared with that of other decomposition methods such as classic seasonal decomposition, X-11 decomposition using moving averages and seasonal decomposition by Loess (STL) that are included in some time series courses.

AIM: To examine associations of prenatal maternal smoking and second-hand smoke (SHS) exposure with the development of adolescent obesity. METHODS: Longitudinal data (1991-2007) from National Institute of Child Health and Human Development Study of Early Child Care and Youth Development involving mothers that smoked and or exposed to SHS during the year before birth were analysed. Adolescent obesity in ages 12.0-15.9 years was defined as a BMI ≥ 95th percentile. Generalised estimating equations (GEE) were used for the analyses. RESULTS: Obesity was more prevalent among adolescents whose mothers smoked or had SHS exposure than those that did not smoke or exposed to SHS. After adjusting for maternal and child factors, GEE models showed that odds of adolescent obesity increased with prenatal maternal smoking (OR = 1.57, 95% CI = 1.03-2.39) and SHS exposure (OR = 1.53, 95% CI = 1.04-2.27). The odds for obesity increased more than two times among adolescents exposed to both maternal smoking and SHS (OR = 2.10, 95% CI = 1.24, 3.56) compared with those without exposure. Additionally, not breastfeeding, maternal obesity, and longer screen viewing hours per day were associated with increased odds of obesity. CONCLUSIONS: There is possibly a long-term joint effect of prenatal maternal smoke (smoking and SHS) exposure on obesity among adolescent offspring, and the effect is independent of birthweight. These findings suggest that adolescent obesity could possibly be curtailed with the development and promotion of smoking cessation programmes for families during the year before birth.

No description available.

abstract: Parkinson’s disease (PD) is a progressive neurodegenerative disorder, diagnosed late in the disease by a series of motor deficits that manifest over years or decades. It is characterized by degeneration of mid-brain dopaminergic neurons with a high prevalence of dementia associated with the spread of pathology to cortical regions. Patients exhibiting symptoms have already undergone significant neuronal loss without chance for recovery. Analysis of disease specific changes in gene expression directly from human patients can uncover invaluable clues about a still unknown etiology, the potential of which grows exponentially as additional gene regulatory measures are questioned. Epigenetic mechanisms are emerging as important components of neurodegeneration, including PD; the extent to which methylation changes correlate with disease progression has not yet been reported. This collection of work aims to define multiple layers of PD that will work toward developing biomarkers that not only could improve diagnostic accuracy, but also push the boundaries of the disease detection timeline. I examined changes in gene expression, alternative splicing of those gene products, and the regulatory mechanism of DNA methylation in the Parkinson’s disease system, as well as the pathologically related Alzheimer’s disease (AD). I first used RNA sequencing (RNAseq) to evaluate differential gene expression and alternative splicing in the posterior cingulate cortex of patients with PD and PD with dementia (PDD). Next, I performed a longitudinal genome-wide methylation study surveying ~850K CpG methylation sites in whole blood from 189 PD patients and 191 control individuals obtained at both a baseline and at a follow-up visit after 2 years. I also considered how symptom management medications could affect the regulatory mechanism of DNA methylation. In the last chapter of this work, I intersected RNAseq and DNA methylation array datasets from whole blood patient samples for integrated differential analyses of both PD and AD. Changes in gene expression and DNA methylation reveal clear patterns of pathway dysregulation that can be seen across brain and blood, from one study to the next. I present a thorough survey of molecular changes occurring within the idiopathic Parkinson’s disease patient and propose candidate targets for potential molecular biomarkers. / Dissertation/Thesis / Doctoral Dissertation Molecular and Cellular Biology 2019

abstract: Previous research has shown functional mixed-effects models and traditional mixed-effects models perform similarly when recovering mean and individual trajectories (Fine, Suk, & Grimm, 2019). However, Fine et al. (2019) showed traditional mixed-effects models were able to more accurately recover the underlying mean curves compared to functional mixed-effects models. That project generated data following a parametric structure. This paper extended previous work and aimed to compare nonlinear mixed-effects models and functional mixed-effects models on their ability to recover underlying trajectories which were generated from an inherently nonparametric process. This paper introduces readers to nonlinear mixed-effects models and functional mixed-effects models. A simulation study is then presented where the mean and random effects structure of the simulated data were generated using B-splines. The accuracy of recovered curves was examined under various conditions including sample size, number of time points per curve, and measurement design. Results showed the functional mixed-effects models recovered the underlying mean curve more accurately than the nonlinear mixed-effects models. In general, the functional mixed-effects models recovered the underlying individual curves more accurately than the nonlinear mixed-effects models. Progesterone cycle data from Brumback and Rice (1998) were then analyzed to demonstrate the utility of both models. Both models were shown to perform similarly when analyzing the progesterone data. / Dissertation/Thesis / Doctoral Dissertation Psychology 2019

abstract: In this dissertation, I examine the treatment and sentencing of American Indian defendants. This work contributes to research on cumulative disadvantage and the role race and social context play to influence federal sentencing outcomes. Disparities in federal sentencing for racial and ethnic minorities are an important concern to scholars and policy makers. Literature suggests that blacks and Latinos are sentenced more harshly than similarly situated white offenders. These findings are concerning because they suggest that minorities are treated unfairly by the criminal justice system, questions the legitimacy of how offenders are processed and treated, and defendants of color who are meted out tougher punishments face substantial social and economic difficulties thereafter. Although the black-white and Latino-white disparities have been identified and highlighted, less is known about whether disparities extend to other minority groups, and consequently little is known about the treatment of these neglected groups. I investigate whether American Indian defendants experience cumulative disadvantages at multiple decision points, disadvantage over time, and the effect of social context on drawing on American Indian disadvantage, the focal concerns and minority threat perspectives. The focal concerns perspective is used to develop hypotheses about how American Indian defendants will receive harsher punishments at multiple decision points. I also use this perspective to predict that American Indian disadvantages will increase over time. Lastly, I examine social context and its effect on punishment decisions for American Indians using the minority threat perspective. I hypothesize that 
social context impacts how American Indian defendants are sentenced at the federal level. Data come from the Federal Justice Statistics Program Data Series, the US Census, and the Uniform Crime Report, with a focus on data gathered from the Administrative Office of the United States Courts and the United States Sentencing Commission. A range of modeling strategies are used to test the hypotheses including multinomial logistic regression, ordinary least squares regression, and multilevel modeling. The results suggest that cumulative disadvantages against American Indian defendants is pronounced, American Indian disparity over time is significant for certain outcomes, and social context plays a limited role in American Indian sentencing disadvantage. / Dissertation/Thesis / Doctoral Dissertation Criminology and Criminal Justice 2019

No description available.

Abstract available in the American Journal of Pharmaceutical Education.