This Master of Applied Science thesis proposes a new design of low power, power factor corrected (PFC), power supplies. By lifting the hold up time restriction for devices that have a battery built in, the energy storage elements of the converter can be reduced, permitting a small and inexpensive power converter to be built. In addition, a new control technique for controlling cascaded converters is presented, named duty mode control (DMC). Its advantages are shown through simulations. The system was proven using a prototype developed in the laboratory designed for a universal ac input voltage (85 - 265VRMS at 50 - 60Hz) and a 40W output at 12V. It consisted of two interleaved phases sensed and digitally controlled on the isolated side of the converter. The prototype was able to achieve a power factor of greater than 0.98 for all operating conditions, and input harmonic current distortion well below any set of standards.

This Master of Applied Science thesis proposes a new design of low power, power factor corrected (PFC), power supplies. By lifting the hold up time restriction for devices that have a battery built in, the energy storage elements of the converter can be reduced, permitting a small and inexpensive power converter to be built. In addition, a new control technique for controlling cascaded converters is presented, named duty mode control (DMC). Its advantages are shown through simulations. The system was proven using a prototype developed in the laboratory designed for a universal ac input voltage (85 - 265VRMS at 50 - 60Hz) and a 40W output at 12V. It consisted of two interleaved phases sensed and digitally controlled on the isolated side of the converter. The prototype was able to achieve a power factor of greater than 0.98 for all operating conditions, and input harmonic current distortion well below any set of standards.

The solution of the Burgers' equation computed by the standard finite element method is degraded by oscillations, which are the manifestation of the Gibbs phenomenon. In this work we study the following numerical me- thods: Discontinuous Galerkin method, stable low order schemes and the flux corrected technique method in order to prevent the undesired Gibbs phenomenon. The focus is on the reduction of severe overshoots and under- shoots and the preservation of the smoothness of the solution. We consider a simple 1D problem on the interval (0, 1) with different initial conditions to demonstrate the properties of the presented methods. The numerical results of individual methods are provided. 1

Submitted by isabela.moljf@hotmail.com (isabela.moljf@hotmail.com) on 2017-08-09T11:18:45Z No. of bitstreams: 1 sebastiaomaurofilho.pdf: 637836 bytes, checksum: 0629945b7a9ce79385454dd5fb18c92f (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-08-09T13:36:52Z (GMT) No. of bitstreams: 1 sebastiaomaurofilho.pdf: 637836 bytes, checksum: 0629945b7a9ce79385454dd5fb18c92f (MD5) / Made available in DSpace on 2017-08-09T13:36:52Z (GMT). No. of bitstreams: 1 sebastiaomaurofilho.pdf: 637836 bytes, checksum: 0629945b7a9ce79385454dd5fb18c92f (MD5) Previous issue date: 2017-01-26 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Nesta tese aplicamos o método perturbativo, em nível clássico, à Gravidade de Quarta Ordem e à Relatividade Geral estendida pelo Grupo de Renormalização (RGGR). Para explorar as perturbações métricas, na teoria da Gravidade de Quarta Ordem, nós usamos a formulação de campos auxiliares para uma métrica de fundo curva e arbitrária. O caso em que a métrica de fundo é Ricci-plano foi elaborada em detalhes. Notamos que o uso de campos auxiliares tornará a análise perturbativa mais simples e os resultados mais claros. Como uma aplicação, nós reconsideramos os resultados para a estabilidade do buraco negro de Schwarzschild e discutimos alguns avanços para o buraco negro de Kerr na Gravidade de Quarta Ordem. Nós também usamos o método perturbativo para explorar os limites newtoniano e pós-newtoniano de RGGR. No Sistema Solar, RGGR depende de um único parâmetro adimensional /9, e ele é tal que para /9 = 0 a Relatividade Geral é obtida. Para estudar o limite newtoniano fizemos uso da técnica de transformação conforme e da dinâmica do vetor de Laplace-Runge-Lenz (LRL). Isso nos permitiu estimar o limite superior de P dentro do Sistema Solar em dois casos: um quando é levado em conta o efeito de potencial externo e outro quando ele não é considerado. Anteriormente, foi encontrado que este parâmetro satisfaz o seguinte limite /9 < 10-21, quando o efeito de potencial externo é ignorado. Entretanto, como nós mostramos esse limite cresce cinco ordens de magnitude P < 10-16 quando tal efeito é considerado. Além disso, mostramos que para um certo limite, RGGR pode ser facilmente testada usando o formalismo parametrizado pós-newtoniano (PPN). / In this thesis we applied the perturbative method, on a classical level, to the fourth-order gravity and the Renormalization Group extended General Relativity (RGGR). We will consider auxiliary fields formulation for the general fourth-order gravity on an arbitrary curved back-ground to analyze the metric perturbations in this theory. The case of a Ricci-flat background was elaborated in detail. We noticed that the use of auxiliary fields helps to make the pertur-bative analysis easier and the results more clear. As an application we reconsider the stability problem of the Schwarzschild and Kerr black holes in the fourth-order gravity. We also used the perturbative method to develop the Newtonian and post-Newtonian limits of RGGR. In the Solar System, RGGR depends on a single dimensionless parameter 0, and this parameter is such that for 0 = 0 one fully recovers General Relativity in the Solar System. In order to study the Newtonian limit we used the conformal transformation technique and the dynamics of the Laplace-Runge-Lenz vector (LRL). In this way, we could estimate the upper bound for 0 within the Solar System in two case: the case where the external potential effect is considered and the another when it is not considered. Previously this parameter was constrained to be 0 < 10-21, without considering the external potential effect. However, as we showed, when such an effect is considered this bound increases by five orders of magnitude, O < 10-16. Moreover, we showed that under a certain approximation RGGR can be easily tested using the parametrized post-Newtonian (PPN) formalism.

Introduction: À la naissance, les signatures du microbiote intestinal sont différentes entre le nourrisson né à terme et le Preterme (PT), signalant un possible explication des déficits neurodéveloppementaux chez le PT via l’axe de communication entre le cerveau et l’intestin. Cette étude vise à démontrer que le microbiote intestinal peut être un biomarqueur précoce d’une évaluation neuromotrice anormale à l’âge corrigé du terme chez les PT. Méthodes: Des échantillons fécaux ont été collectés à la naissance et à l’âge corrigé du terme dans cette cohorte de PT. L’ADN génomique a été extrait pour chaque échantillon et amplifié dans la région V3-V4 de l’ARNr 16S. L’analyse bio-informatique du microbiote a été réalisée grâce à QIIME2 (v2020.2). À l’âge corrigé du terme, l’évaluation neuromotrice sera faite à l’aide de deux tests neuromoteurs: « Amiel-Tison Test » (ENTAT) et « Global Motor Assessment » (GMA). Résultats: L’analyse PCoA a permis de délimiter l’évaluation neuromotrice anormale à l’âge corrigé du terme en utilisant l’ENTAT combiné au GMA comme évaluation neuromotrice. En utilisant l’indice de dissimilarité du Bray-Curtis comme mesure de β-diversité, la PCoA démontre une plus forte similarité au niveau des MI des PTs à évaluation neuromotrice normaux à la naissance. L’analyse corrélationnelle nous montre que l’indice Shannon de diversité-α est plus élevé à la naissance chez les PTs avec une évaluation neuromotrice normal (*P=0.56), qui continue d’augmenter à l’âge corrigé du terme, se traduisant par un gain de biodiversité dans le MI de l’enfant avec une évaluation neuromotrice normal. Les mesures d’abondance relatives de genres bactériens nous démontrent une plus forte prévalence de pathogènes opportunistes, ainsi qu’une faible présence de bactéries commensales à la naissance et à l’âge corrigé du terme dans le groupe à évaluation neuromotrice anormale. Conclusion: Les niveaux de bactéries opportunistes sont plus élevés à l’âge corrigé du terme chez les PT à évaluation neuromotrice anormal de même qu’une raréfaction de bactéries commensales. La diversité du microbiote intestinale à l’âge corrigé du terme est aussi réduite chez les PT à évaluation neuromotrice anormale. / Background: The early developmental window of the gut microbiome occurs in parallel with the nervous system development, which may be influenced by early life exposure including variation in gut microbiota colonization. Emerging evidence are now highlighting how gut microbiota signatures are distinct between term and preterm infants (PT). This study aims to explore if gut microbiota colonization, as an early biomarker, is associated with abnormal neuromotor examination at term-corrected age in infants PT. Methods: Fecal samples of thirty-four participants were collected at birth and from 26 participants at term-corrected age at term-corrected age from a longitudinal observational cohort of PT. Genomic DNA was extracted and V3-V4 region from 16S rRNA was amplified. Taxonomy abundance tables were generated from the FASTQ data obtained, using QIIME2 (v2020.2) for further analysis. At term-corrected age, two neuromotor examination were performed to evaluate neuromotor outcomes: The Amiel-Tison Test (ENTAT) and the Global Motor Assessment (GMA). Results: PCoA cluster analysis allowed to delineate abnormal neuromotor assessment at term-corrected age using ENTAT examination alone or in combination with GMA. In fact, using Bray-Curtis distance for β-diversity, PCoA shows a better clusterisation at birth of normal neuromotor examination than the abnormal neuromotor examination. Correlational analyses showed that Shannon diversity is higher at birth in PT with a normal neuromotor examination (*P=0.56), which increases at term-corrected age translating a gain in biodiversity in infant with a normal neuromotor examination. Relative abundance analysis showed a higher prevalence of opportunistic pathogens at birth and at term-corrected age in the abnormal neuromotor examination group, along with a rarefaction of commensal bacteria. Summary: Opportunistic pathogen levels are higher at term-corrected age in PT presenting an abnormal neuromotor examination along with a rarefaction of their commensal bacteria of their gut microbiota. This preliminary analysis also indicates that an early life poor bacterial diversity in the gut microbiota could be predictive of abnormal neuromotor examination at term-corrected age in infants PT.

Methods of slab-geometry molecular dynamics computer simulation were tested, compared, and applied to the prediction of activity coefficients, interfacial electrochemistry characterization, and ion transport through a model biological channel-membrane structure. The charged-sheets, 2-D Ewald, corrected 3-D Ewald, and corrected particle-particle-particle-mesh (P3M) methods were compared for efficiency and applicability to slab-geometry electrolyte systems with discrete water molecules. The P3M method was preferred for long-range force calculation in the problems of interest and was used throughout. The osmotic molecular dynamics method (OMD) was applied to the prediction of liquid mixture activity coefficients for six binary systems: methanol/n-hexane, n-hexane/n-pentane, methanol/water, chloroform/acetone, n-hexane/chloroform, methanol/ chloroform. OMD requires the establishment of chemical potential equilibrium across a semi-permeable membrane that divides the simulation cell between a pure solvent chamber and a chamber containing a mixture of solvent and solute molecules in order to predict the permeable component activity coefficient at the mixture side composition according to a thermodynamic identity. Chemical potential equilibrium is expedited by periodic adjustment of the mixture side chamber volume in response to the observed solvent flux. The method was validated and shown to be able to predict activity coefficients within the limitations of the simple models used. The electrochemical double layer characteristics for a simple electrolyte with discrete water molecules near a charged electrode were examined as a function of ion concentration, electrode charge, and ion size. The fluid structure and charge buildup near the electrode, the voltage drop across the double layer, and the double layer capacitance were studied and were found to be in reasonable agreement with experimental findings. Applied voltage non-equilibrium molecular dynamics was used to calculate the current-voltage relationship for a model biological pore. Ten 10-nanosecond trajectories were computed in each of 10 different conditions of concentration and applied voltage. The channel-membrane structure was bathed in electrolyte including discrete water molecules so that solvation, entry, and exit effects could be studied. Fluid structure, ion dynamics, channel selectivity, and potential gradients were examined. This work represents the first such channel study that does not neglect the vital contributions of discrete water molecules.

No description available.

The vast majority of oil and gas reserves are trapped in fractured carbonate reservoirs. Most carbonate reservoirs are naturally fractured, with fractures ranging from millimeter- to kilometer-scale. These fractures create complex flow behaviors which impact reservoir characterization, production performance, and, eventually, total recovery. As we know, bridging the gas from plug to near-wellbore, eventually to field scales, is a persisting challenge in modeling Naturally Fractured Reservoirs (NFRs). This dissertation will focus on assessing the fundamental flow mechanisms in fractured rocks at the plug scale, understanding the governing upscaling parameters, and ultimately, developing fit-for-purpose upscaling tools for field-scale implementation. In this dissertation, we first focus on the upscaling of rock fractures under the laminar flow regime. A novel analytical model is presented by incorporating the effects of normal aperture, roughness, and tortuosity. We then investigate the stress-dependent hydraulic behaviors of rock fractures. A new and generalized theoretical model is derived and verified by a dataset collected from public experimental resources. In addition, an efficient coupled flow-geomechanics algorithm is developed to further validate the proposed analytical model. The physics of matrix-fracture interaction and fluid leakage is modeled by a high-resolution, micro-continuum approach, called extended Darcy-Brinkman-Stokes (DBS) equations. We observe the back-flow phenomena for the first time. Machine learning is then implemented into our traditional upscaling work under complex physics (e.g., initial and Klinkenberg effects). We finally consolidate the lab-scale upscaling tools and scale them up to the field scale. We develop a fully coupled hydro-mechanical model based on the Discrete-Fracture Model (DFM) in fractured reservoirs, in which we incorporate localized effects of fracture roughness at the field-scale.

本論文主要在估計門檻式自動迴歸模型之參數的信賴區間。由線性自動迴歸 模型衍生出來的非線性自動迴歸模型中，門檻式自動迴歸模型是其中一種經常會被應用到的模型。雖然，門檻式自動迴歸模型之參數的漸近理論已經發展了許多；但是，相較於大樣本理論，有限樣本下參數的性質討論則較少。對於有限樣本的研究，Woodroofe (1989) 提出一種近似法：非常弱近似法。 Woodroofe 和 Coad (1997) 則利用此方法去架構一適性化線性模型之參數的修正信賴區間。Weng 和 Woodroofe (2006) 則將此近似法應用於線性自動迴歸模型。這個方法的應用始於定義一近似樞紐量，接著利用此方法找出近似樞紐量的近似期望值及近似變異數，並對此近似樞紐量標準化，則標準化後的樞紐量將近似於標準常態分配，因此得以架構參數的修正信賴區間。而在線性自動迴歸模型下，利用非常弱展開所導出的近似期望值及近似變異數僅會與一階動差及二階動差的微分有關。因此，本論文的研究目的就是在樣本數為適當的情況下，將線性自動迴歸模型的結果運用於門檻式自動迴歸模型。由於大部分門檻式自動迴歸模型的動差並無明確之形式；因此，本研究採用蒙地卡羅法及插分法去近似其動差及微分。最後，以第一階門檻式自動迴歸模型去配適美國的國內生產總值資料。 / Threshold autoregressive (TAR) models are popular nonlinear extension of the linear autoregressive (AR) models. Though many have developed the asymptotic theory for parameter estimates in the TAR models, there have been less studies about the finite sample properties. Woodroofe (1989) and Woodroofe and Coad (1997) developed a very weak approximation and used it to construct corrected confidence sets for parameters in an adaptive linear model. This approximation was further developed by Woodroofe and Coad (1999) and Weng and Woodroofe (2006), who derived the corrected confidence sets for parameters in the AR(p) models and other adaptive models. This approach starts with an approximate pivot, and employs the very weak expansions to determine the mean and variance corrections of the pivot. Then, the renormalized pivot is used to form corrected confidence sets. The correction terms have simple forms, and for AR(p) models it involves only the first two moments of the process and the derivatives of these moments. However, for TAR models the analytic forms for moments are known only in some cases when the autoregression function has special structures. The goal of this research is to extend the very weak method to the TAR models to form corrected confidence sets when sample size is moderate. We propose using the difference quotient method and Monte Carlo simulations to approximate the derivatives. Some simulation studies are provided to assess the accuracy of the method. Then, we apply the approach to a real U.S. GDP data.

Cette thèse comporte trois articles dont un est publié et deux en préparation. Le sujet central de la thèse porte sur le traitement des valeurs aberrantes représentatives dans deux aspects importants des enquêtes que sont : l’estimation des petits domaines et l’imputation en présence de non-réponse partielle. En ce qui concerne les petits domaines, les estimateurs robustes dans le cadre des modèles au niveau des unités ont été étudiés. Sinha & Rao (2009) proposent une version robuste du meilleur prédicteur linéaire sans biais empirique pour la moyenne des petits domaines. Leur estimateur robuste est de type «plugin», et à la lumière des travaux de Chambers (1986), cet estimateur peut être biaisé dans certaines situations. Chambers et al. (2014) proposent un estimateur corrigé du biais. En outre, un estimateur de l’erreur quadratique moyenne a été associé à ces estimateurs ponctuels. Sinha & Rao (2009) proposent une procédure bootstrap paramétrique pour estimer l’erreur quadratique moyenne. Des méthodes analytiques sont proposées dans Chambers et al. (2014). Cependant, leur validité théorique n’a pas été établie et leurs performances empiriques ne sont pas pleinement satisfaisantes. Ici, nous examinons deux nouvelles approches pour obtenir une version robuste du meilleur prédicteur linéaire sans biais empirique : la première est fondée sur les travaux de Chambers (1986), et la deuxième est basée sur le concept de biais conditionnel comme mesure de l’influence d’une unité de la population. Ces deux classes d’estimateurs robustes des petits domaines incluent également un terme de correction pour le biais. Cependant, ils utilisent tous les deux l’information disponible dans tous les domaines contrairement à celui de Chambers et al. (2014) qui utilise uniquement l’information disponible dans le domaine d’intérêt. Dans certaines situations, un biais non négligeable est possible pour l’estimateur de Sinha & Rao (2009), alors que les estimateurs proposés exhibent un faible biais pour un choix approprié de la fonction d’influence et de la constante de robustesse. Les simulations Monte Carlo sont effectuées, et les comparaisons sont faites entre les estimateurs proposés et ceux de Sinha & Rao (2009) et de Chambers et al. (2014). Les résultats montrent que les estimateurs de Sinha & Rao (2009) et de Chambers et al. (2014) peuvent avoir un biais important, alors que les estimateurs proposés ont une meilleure performance en termes de biais et d’erreur quadratique moyenne. En outre, nous proposons une nouvelle procédure bootstrap pour l’estimation de l’erreur quadratique moyenne des estimateurs robustes des petits domaines. Contrairement aux procédures existantes, nous montrons formellement la validité asymptotique de la méthode bootstrap proposée. Par ailleurs, la méthode proposée est semi-paramétrique, c’est-à-dire, elle n’est pas assujettie à une hypothèse sur les distributions des erreurs ou des effets aléatoires. Ainsi, elle est particulièrement attrayante et plus largement applicable. Nous examinons les performances de notre procédure bootstrap avec les simulations Monte Carlo. Les résultats montrent que notre procédure performe bien et surtout performe mieux que tous les compétiteurs étudiés. Une application de la méthode proposée est illustrée en analysant les données réelles contenant des valeurs aberrantes de Battese, Harter & Fuller (1988). S’agissant de l’imputation en présence de non-réponse partielle, certaines formes d’imputation simple ont été étudiées. L’imputation par la régression déterministe entre les classes, qui inclut l’imputation par le ratio et l’imputation par la moyenne sont souvent utilisées dans les enquêtes. Ces méthodes d’imputation peuvent conduire à des estimateurs imputés biaisés si le modèle d’imputation ou le modèle de non-réponse n’est pas correctement spécifié. Des estimateurs doublement robustes ont été développés dans les années récentes. Ces estimateurs sont sans biais si l’un au moins des modèles d’imputation ou de non-réponse est bien spécifié. Cependant, en présence des valeurs aberrantes, les estimateurs imputés doublement robustes peuvent être très instables. En utilisant le concept de biais conditionnel, nous proposons une version robuste aux valeurs aberrantes de l’estimateur doublement robuste. Les résultats des études par simulations montrent que l’estimateur proposé performe bien pour un choix approprié de la constante de robustesse. / This thesis focuses on the treatment of representative outliers in two important aspects of surveys: small area estimation and imputation for item non-response. Concerning small area estimation, robust estimators in unit-level models have been studied. Sinha & Rao (2009) proposed estimation procedures designed for small area means, based on robustified maximum likelihood parameters estimates of linear mixed model and robust empirical best linear unbiased predictors of the random effect of the underlying model. Their robust methods for estimating area means are of the plug-in type, and in view of the results of Chambers (1986), the resulting robust estimators may be biased in some situations. Biascorrected estimators have been proposed by Chambers et al. (2014). In addition, these robust small area estimators were associated with the estimation of the Mean Square Error (MSE). Sinha & Rao (2009) proposed a parametric bootstrap procedure based on the robust estimates of the parameters of the underlying linear mixed model to estimate the MSE. Analytical procedures for the estimation of the MSE have been proposed in Chambers et al. (2014). However, their theoretical validity has not been formally established and their empirical performances are not fully satisfactorily. Here, we investigate two new approaches for the robust version the best empirical unbiased estimator: the first one relies on the work of Chambers (1986), while the second proposal uses the concept of conditional bias as an influence measure to assess the impact of units in the population. These two classes of robust small area estimators also include a correction term for the bias. However, they are both fully bias-corrected, in the sense that the correction term takes into account the potential impact of the other domains on the small area of interest unlike the one of Chambers et al. (2014) which focuses only on the domain of interest. Under certain conditions, non-negligible bias is expected for the Sinha-Rao method, while the proposed methods exhibit significant bias reduction, controlled by appropriate choices of the influence function and tuning constants. Monte Carlo simulations are conducted, and comparisons are made between: the new robust estimators, the Sinha-Rao estimator, and the bias-corrected estimator. Empirical results suggest that the Sinha-Rao method and the bias-adjusted estimator of Chambers et al (2014) may exhibit a large bias, while the new procedures offer often better performances in terms of bias and mean squared error. In addition, we propose a new bootstrap procedure for MSE estimation of robust small area predictors. Unlike existing approaches, we formally prove the asymptotic validity of the proposed bootstrap method. Moreover, the proposed method is semi-parametric, i.e., it does not rely on specific distributional assumptions about the errors and random effects of the unit-level model underlying the small-area estimation, thus it is particularly attractive and more widely applicable. We assess the finite sample performance of our bootstrap estimator through Monte Carlo simulations. The results show that our procedure performs satisfactorily well and outperforms existing ones. Application of the proposed method is illustrated by analyzing a well-known outlier-contaminated small county crops area data from North-Central Iowa farms and Landsat satellite images. Concerning imputation in the presence of item non-response, some single imputation methods have been studied. The deterministic regression imputation, which includes the ratio imputation and mean imputation are often used in surveys. These imputation methods may lead to biased imputed estimators if the imputation model or the non-response model is not properly specified. Recently, doubly robust imputed estimators have been developed. However, in the presence of outliers, the doubly robust imputed estimators can be very unstable. Using the concept of conditional bias as a measure of influence (Beaumont, Haziza and Ruiz-Gazen, 2013), we propose an outlier robust version of the doubly robust imputed estimator. Thus this estimator is denoted as a triple robust imputed estimator. The results of simulation studies show that the proposed estimator performs satisfactorily well for an appropriate choice of the tuning constant.