Site specific growth of metal catalyzed silica nanowires for biological and chemical sensing

In this research the integration of nanostructures and micro-scale devices was investigated using silica nanowires to develop a simple yet robust nanomanufacturing technique for improving the detection parameters of chemical and biological sensors. This has been achieved with the use of a dielectric barrier layer, to restrict nanowire growth to site-specific locations which has removed the need for post growth processing, by making it possible to place nanostructures on pre-pattern substrates. Nanowires were synthesized using the Vapor-Liquid-Solid growth method. Process parameters (temperature and time) and manufacturing aspects (structural integrity and biocompatibility) were investigated. Silica nanowires were observed experimentally to determine how their physical and chemical properties could be tuned for integration into existing sensing structures. Growth kinetic experiments performed using gold and palladium catalysts at 1050°C for 60 minutes in an open-tube furnace yielded dense and consistent silica nanowire growth. This consistent growth led to the development of growth model fitting, through use of the Maximum Likelihood Estimation (MLE) and Bayesian hierarchical modeling. Transmission electron microscopy studies revealed the nanowires to be amorphous and X-ray diffraction confirmed the composition to be SiO2 . Silica nanowires were monitored in epithelial breast cancer media using Impedance spectroscopy, to test biocompatibility, due to potential in vivo use as a diagnostic aid. It was found that palladium catalyzed silica nanowires were toxic to breast cancer cells, however, nanowires were inert at 1μg/mL concentrations. Additionally a method for direct nanowire integration was developed that allowed for silica nanowires to be grown directly into interdigitated sensing structures. This technique eliminates the need for physical nanowire transfer thus preserving nanowire structure and performance integrity and further reduces fabrication cost. Successful nanowire integration was physically verified using Scanning electron microscopy and confirmed electrically using Electrochemical Impedance Spectroscopy of immobilized Prostate Specific Antigens (PSA). The experiments performed above serve as a guideline to addressing the metallurgic challenges in nanoscale integration of materials with varying composition and to understanding the effects of nanomaterials on biological structures that come in contact with the human body.

A Case Study of the Ellison Model's Use of Mentoring as an Approach Toward Inclusive Community Building

The Ellison Executive Mentoring Inclusive Community Building (ICB) Model is a paradigm for initiating and implementing projects utilizing executives and professionals from a variety of fields and industries, university students, and pre-college students. The model emphasizes adherence to ethical values and promotes inclusiveness in community development. It is a hierarchical model in which actors in each succeeding level of operation serve as mentors to the next. Through a three-step process--content, process, and product--participants must be trained with this mentoring and apprenticeship paradigm in conflict resolution, and they receive sensitivitiy and diversity training, through an interactive and dramatic exposition. The content phase introduces participants to the model's philosophy, ethics, values and methods of operation. The process used to teach and reinforce its precepts is the mentoring and apprenticeship activities and projects in which the participants engage and whose end product demontrates their knowledge and understanding of the model's concepts. This study sought to ascertain from the participants' perspectives whether the model's mentoring approach is an effective means of fostering inclusiveness, based upon their own experiences in using it. The research utilized a qualitative approach and included data from field observations, individual and group interviews, and written accounts of participants' attitudes. Participants complete ICB projects utilizing the Ellison Model as a method of development and implementation. They generally perceive that the model is a viable tool for dealing with diversity issues whether at work, at school, or at home. The projects are also instructional in that whether participants are mentored or seve as apprentices, they gain useful skills and knowledge about their careers. Since the model is relatively new, there is ample room for research in a variety of areas including organizational studies to dertmine its effectiveness in combating problems related to various kinds of discrimination.

Discovering complex interrelationships between socioeconomic status and health in Europe: A case study applying Bayesian Networks

Studies assume that socioeconomic status determines individuals’ states of health, but how does health determine socioeconomic status? And how does this association vary depending on contextual differences? To answer this question, our study uses an additive Bayesian Networks model to explain the interrelationships between health and socioeconomic determinants using complex and messy data. This model has been used to find the most probable structure in a network to describe the interdependence of these factors in five European welfare state regimes. The advantage of this study is that it offers a specific picture to describe the complex interrelationship between socioeconomic determinants and health, producing a network that is controlled by socio demographic factors such as gender and age. The present work provides a general framework to describe and understand the complex association between socioeconomic determinants and health.

Bayesian nonparametric latent variable models

L’un des problèmes importants en apprentissage automatique est de déterminer la complexité du modèle à apprendre. Une trop grande complexité mène au surapprentissage, ce qui correspond à trouver des structures qui n’existent pas réellement dans les données, tandis qu’une trop faible complexité mène au sous-apprentissage, c’est-à-dire que l’expressivité du modèle est insuffisante pour capturer l’ensemble des structures présentes dans les données. Pour certains modèles probabilistes, la complexité du modèle se traduit par l’introduction d’une ou plusieurs variables cachées dont le rôle est d’expliquer le processus génératif des données. Il existe diverses approches permettant d’identifier le nombre approprié de variables cachées d’un modèle. Cette thèse s’intéresse aux méthodes Bayésiennes nonparamétriques permettant de déterminer le nombre de variables cachées à utiliser ainsi que leur dimensionnalité. La popularisation des statistiques Bayésiennes nonparamétriques au sein de la communauté de l’apprentissage automatique est assez récente. Leur principal attrait vient du fait qu’elles offrent des modèles hautement flexibles et dont la complexité s’ajuste proportionnellement à la quantité de données disponibles. Au cours des dernières années, la recherche sur les méthodes d’apprentissage Bayésiennes nonparamétriques a porté sur trois aspects principaux : la construction de nouveaux modèles, le développement d’algorithmes d’inférence et les applications. Cette thèse présente nos contributions à ces trois sujets de recherches dans le contexte d’apprentissage de modèles à variables cachées. Dans un premier temps, nous introduisons le Pitman-Yor process mixture of Gaussians, un modèle permettant l’apprentissage de mélanges infinis de Gaussiennes. Nous présentons aussi un algorithme d’inférence permettant de découvrir les composantes cachées du modèle que nous évaluons sur deux applications concrètes de robotique. Nos résultats démontrent que l’approche proposée surpasse en performance et en flexibilité les approches classiques d’apprentissage. Dans un deuxième temps, nous proposons l’extended cascading Indian buffet process, un modèle servant de distribution de probabilité a priori sur l’espace des graphes dirigés acycliques. Dans le contexte de réseaux Bayésien, ce prior permet d’identifier à la fois la présence de variables cachées et la structure du réseau parmi celles-ci. Un algorithme d’inférence Monte Carlo par chaîne de Markov est utilisé pour l’évaluation sur des problèmes d’identification de structures et d’estimation de densités. Dans un dernier temps, nous proposons le Indian chefs process, un modèle plus général que l’extended cascading Indian buffet process servant à l’apprentissage de graphes et d’ordres. L’avantage du nouveau modèle est qu’il admet les connections entres les variables observables et qu’il prend en compte l’ordre des variables. Nous présentons un algorithme d’inférence Monte Carlo par chaîne de Markov avec saut réversible permettant l’apprentissage conjoint de graphes et d’ordres. L’évaluation est faite sur des problèmes d’estimations de densité et de test d’indépendance. Ce modèle est le premier modèle Bayésien nonparamétrique permettant d’apprendre des réseaux Bayésiens disposant d’une structure complètement arbitraire.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Site Specifc Growth of Metal Catalyzed Silica Nanowires for Biological and Chemical Sensing

In this research the integration of nanostructures and micro-scale devices was investigated using silica nanowires to develop a simple yet robust nanomanufacturing technique for improving the detection parameters of chemical and biological sensors. This has been achieved with the use of a dielectric barrier layer, to restrict nanowire growth to site-specific locations which has removed the need for post growth processing, by making it possible to place nanostructures on pre-pattern substrates. Nanowires were synthesized using the Vapor-Liquid-Solid growth method. Process parameters (temperature and time) and manufacturing aspects (structural integrity and biocompatibility) were investigated. Silica nanowires were observed experimentally to determine how their physical and chemical properties could be tuned for integration into existing sensing structures. Growth kinetic experiments performed using gold and palladium catalysts at 1050 ˚C for 60 minutes in an open-tube furnace yielded dense and consistent silica nanowire growth. This consistent growth led to the development of growth model fitting, through use of the Maximum Likelihood Estimation (MLE) and Bayesian hierarchical modeling. Transmission electron microscopy studies revealed the nanowires to be amorphous and X-ray diffraction confirmed the composition to be SiO2 . Silica nanowires were monitored in epithelial breast cancer media using Impedance spectroscopy, to test biocompatibility, due to potential in vivo use as a diagnostic aid. It was found that palladium catalyzed silica nanowires were toxic to breast cancer cells, however, nanowires were inert at 1µg/mL concentrations. Additionally a method for direct nanowire integration was developed that allowed for silica nanowires to be grown directly into interdigitated sensing structures. This technique eliminates the need for physical nanowire transfer thus preserving nanowire structure and performance integrity and further reduces fabrication cost. Successful nanowire integration was physically verified using Scanning electron microscopy and confirmed electrically using Electrochemical Impedance Spectroscopy of immobilized Prostate Specific Antigens (PSA). The experiments performed above serve as a guideline to addressing the metallurgic challenges in nanoscale integration of materials with varying composition and to understanding the effects of nanomaterials on biological structures that come in contact with the human body.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

One-dimensional Model for Fluids of Third-grade in Tubes with Constant Radius

Our goal in this paper is to extend previous results obtained for Newtonian and secondgrade fluids to third-grade fluids in the case of an axisymmetric, straight, rigid and impermeable tube with constant cross-section using a one-dimensional hierarchical model based on the Cosserat theory related to fluid dynamics. In this way we can reduce the full threedimensional system of equations for the axisymmetric unsteady motion of a non-Newtonian incompressible third-grade fluid to a system of equations depending on time and on a single spatial variable. Some numerical simulations for the volume flow rate and the the wall shear stress are presented.

Atividade fisica e aptidão fisica de adolescentes : similaridades e contrastes em uma decada

Universidade Estadual de Campinas . Faculdade de Educação Física

Aleitamento materno, alimentação complementar, sobrepeso e obesidade em pré-escolares

OBJETIVO: Analisar a associação do sobrepeso e da obesidade com o aleitamento materno e a alimentação complementar em pré-escolares. MÉTODOS: Estudo transversal envolvendo 566 crianças matriculadas em escolas particulares no município de São Paulo, SP, 2004-2005. A variável dependente foi sobrepeso e obesidade. Para a classificação do estado nutricional das crianças foram utilizadas as curvas de percentis do Índice de Massa Corporal para idade, classificando como sobrepeso valores e"P85 e modelo hierarquizado. RESULTADOS: A prevalência de sobrepeso e obesidade da população estudada foi de 34,4%. Foram fatores de proteção contra sobrepeso e obesidade o aleitamento materno exclusivo por seis meses ou mais (IC 95% [0,38;0,86]; OR=0,57; p=0,02) e o aleitamento materno por mais de 24 meses (IC 95% [0,05;0,37]; OR=0,13; p=0,00). CONCLUSÕES: Os resultados sugerem que o aleitamento materno pode proteger as crianças contra o sobrepeso e a obesidade, agregando mais uma vantagem ao leite materno.

Anemia e deficiência de ferro em pré-escolares da Amazônia Ocidental brasileira: prevalência e fatores associados

Estudo transversal de base populacional que investigou prevalênciasde anemia e fatores associados à anemia, anemia ferropriva e deficiência de ferro entre crianças de 6 a 60 meses da área urbana de dois municípios do Acre, Brasil (N = 624). Dosagens de hemoglobina sanguínea, ferritina e receptor solúvel de transferrina plasmáticas foram realizadas mediante sangue venoso. Condições sócio-econômicas, demográficas e de morbidade foram obtidas por questionário. Razões de prevalências foram calculadas por regressão de Poisson em modelo hierárquico. As prevalências de anemia, anemia ferropriva e deficiência de ferro foram de 30,6%, 20,9% e 43,5%, respectivamente. Menores de 24 meses apresentaram maior risco para anemia, anemia ferropriva e deficiência de ferro. Pertencer ao maior tercil do índice de riqueza conferiu proteção contra anemia ferropriva (RP = 0,62; IC95%: 0,40-0,98). Pertencer ao maior quartil do índice estatura/idade foi protetor contra anemia (0,62; 0,44-0,86) e anemia ferropriva (0,51; 0,33-0,79), e ocorrência recente de diarréia representou risco (anemia: 1,47; 1,12-1,92 e anemia ferropriva: 1,44; 1,03-2,01). A infestação por geohelmintos conferiu risco para anemia, anemia ferropriva e deficiência de ferro.

Epidemiology of leisure, transportation, occupational, and household physical activity: prevalence and associated factors

Background: To estimate the prevalence of and identify factors associated with physical activity in leisure, transportation, occupational, and household settings. Methods: This was a cross-sectional study aimed at investigating living and health conditions among the population of São Paulo, Brazil. Data on 1318 adults aged 18 to 65 years were used. To assess physical activity, the long version of the International Physical Activity Questionnaire was applied. Multivariate analysis was conducted using a hierarchical model. Results: The greatest prevalence of insufficient activity related to transportation (91.7%), followed by leisure (77.5%), occupational (68.9%), and household settings (56.7%). The variables associated with insufficient levels of physical activity in leisure were female sex, older age, low education level, nonwhite skin color, smoking, and self-reported poor health; in occupational settings were female sex, white skin color, high education level, self-reported poor health, nonsmoking, and obesity; in transportation settings were female sex; and in household settings, with male sex, separated, or widowed status and high education level. Conclusion: Physical activity in transportation and leisure settings should be encouraged. This study will serve as a reference point in monitoring different types of physical activities and implementing public physical activity policies in developing countries

Retardo estatural em menores de cinco anos: um estudo "baseline"

OBJETIVO: Descrever a prevalência e analisar fatores associados ao retardo estatural em menores de cinco anos. MÉTODOS: Estudo “baseline”, que analisou 2.040 menores de cinco anos, verificando possíveis associações entre o retardo estatural (índice altura/idade ≤ 2 escores Z) e variáveis hierarquizadas em seis blocos: socioeconômicas, do domicílio, do saneamento, maternas, biológicas e de acesso aos serviços de saúde. A análise multivariada foi realizada por regressão de Poisson, com opção de erro padrão robusto, obtendo-se as razões de prevalência ajustadas, com IC 95por cento e respectivos valores de significância. RESULTADOS: Entre as variáveis não dicotômicas, houve associação positiva com tipo de teto e número de moradores por cômodo e associação negativa com renda, escolaridade da mãe e peso ao nascer. A análise ajustada indicou ainda como variáveis significantes: abastecimento de água, visita do agente comunitário de saúde, local do parto, internação por diarréia e internação por pneumonia. CONCLUSÃO: Os fatores identificados como de risco para o retardo estatural configuram a multicausalidade do problema, implicando na necessidade de intervenções multisetoriais e multiníveis para o seu controle

Epidemiology of Leisure, Transportation, Occupational, and Household Physical Activity: Prevalence and Associated Factors

Background: To estimate the prevalence of and identify factors associated with physical activity in leisure, transportation, occupational, and household settings. Methods: This was a cross-sectional study aimed at investigating living and health conditions among the population of So Paulo, Brazil. Data on 1318 adults aged 18 to 65 years were used. To assess physical activity, the long version of the International Physical Activity Questionnaire was applied. Multivariate analysis was conducted using a hierarchical model. Results: The greatest prevalence of insufficient activity related to transportation (91.7%), followed by leisure (77.5%), occupational (68.9%), and household settings (56.7%). The variables associated with insufficient levels of physical activity in leisure were female sex, older age, low education level, nonwhite skin color, smoking, and self-reported poor health; in occupational settings were female sex, white skin color, high education level, self-reported poor health, nonsmoking, and obesity; in transportation settings were female sex; and in household settings, with male sex, separated, or widowed status and high education level. Conclusion: Physical activity in transportation and leisure settings should be encouraged. This study will serve as a reference point in monitoring different types of physical activities and implementing public physical activity policies in developing countries.