Made available in DSpace on 2014-10-09T12:34:16Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:10:20Z (GMT). No. of bitstreams: 0 / Este trabalho, sendo pioneiro no Brasil, teve o intuito de efetuar uma investigação das nuvens cirrus na região Metropolitana de São Paulo (23,33ºS / 46,44ºW), SP, através do sistema MSP-Lidar para o período de Junho à Julho de 2007. Durante este período, foi verificada uma ocorrência de cirrus de aproximadamente 54% sobre o total de medidas efetuadas pelo sistema Lidar. Medidas com Lidar nos forneceram uma alta resolução espacial e temporal destas nuvens, permitindo assim caracterizá-las e classificá-las de acordo com as suas propriedades macro- e microfísicas. Para obter tais parâmetros, uma metodologia própria foi desenvolvida na recuperação dos dados de Lidar e uma robusta estatística foi aplicada para determinar as diferentes classes de cirrus. A metodologia adotada se resumiu basicamente (a) na determinação de períodos estacionários (ou observações) durante a evolução temporal de detecção de cirrus, (b) determinação da base e topo através de um valor limiar para o cálculo das variáveis macrofísicas (altitudes, temperaturas, espessuras geométricas), (c) aplicação do método da transmitância para cada camada de nuvem e a determinação das variáveis microfísicas (profundidade óptica e razão de Lidar). Neste processo, a razão de Lidar é calculada iterativamente até que haja a convergência da mesma. Análises estatísticas de multivariáveis foram efetuadas para a determinação das classes de cirrus. Estas classes são baseadas na espessura geométrica, altitude média e sua respectiva temperatura, a altitude relativa (diferença entre a altura da tropopausa e topo da nuvem) e a profundidade óptica. O uso sucessivo da Análise de Componentes Principais (PCA), do Método de Cluster Hierárquico (MCH) e da Análise de Discriminantes (AD) permitiu a identificação de 4 classes. Vale ressaltar que tais métodos foram aplicados somente para os casos identificados como camadas únicas de nuvens, pois não se observou significativamente a ocorrência de nuvens com multicamadas. A origem de formação das classes de cirrus encontradas, embora apresentando propriedades macro- e microfísicas distintas, foi identificada basicamente como a mesma, isto é, provenientes da injeção de vapor dágua na atmosfera por meio de sistemas frontais e seu respectivo resfriamento para a formação dos cristais de gelo. O mesmo mecanismo de formação também é atribuído aos jatos subtropicais. Uma análise em relação ao perfil de temperatura e a comparação com a literatura mostrou que as cirrus classificadas apresentam possivelmente cristais em forma de placas e colunas hexagonais. As razões de lidar (RL) calculadas também estão de acordo com a literatura. / Tese (Doutoramento) / IPEN/T / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

O objetivo deste trabalho foi estudar o papel dos aerossóis em modificar o desenvolvimento das nuvens e da precipitação. Esta tem sido uma das mais intrigantes questões no estudo das mudanças climáticas. Medidas da concentração de núcleos de condensação de nuvens (CCN) e distribuições de gotículas de nuvem durante o Experimento de Grande Escala da Biosfera-Atmosfera na Amazônia (LBA) revelaram características distintas entre condições atmosféricas limpas e poluídas. As medidas foram conduzidas no Sudoeste da Região Amazônica durante os meses de setembro e outubro de 2002, focando a transição do final da estação seca para o início da estação chuvosa. Durante a transição, a análise da concentração de CCN dentro da camada limite revelou um decréscimo geral, de valores acima de 1200 cm-3 para menos de 300 cm-3. A comparação entre áreas limpas e poluídas mostrou concentrações de CCN cerca de 5 vezes maiores em áreas poluídas. As diferenças não foram tão grandes nos níveis acima da camada limite. As medidas também mostraram um ciclo diurno acompanhando a atividade de queima de biomassa. Distribuições de tamanho de gotículas medidas em duas regiões com concentrações de aerossóis extremamente diferentes foram analisadas. Em condições poluídas pela queima de biomassa foi encontrada alta concentração de gotículas, com diâmetro médio e conteúdo de água de nuvem aumentando muito pouco com a altura, em comparação com regiões limpas. A função gama foi usada para ajustar as distribuições de gotículas e o parâmetro de forma da função foi usado como critério para definir adequadamente a melhor representação das distribuições de gotículas. De acordo com os valores encontrados, distribuições gama estreitas (parâmetro de forma em torno de 5) são mais bem indicadas para representar condições poluídas enquanto que aquelas mais largas se ajustam melhor em condições limpas (parâmetro de forma em torno de 2). Com base nesses resultados, experimentos numéricos foram conduzidos com o Brazilian Regional Atmospheric Modeling System (BRAMS) para investigar os efeitos da concentração de CCN e do parâmetro de forma das distribuições de gotículas no desenvolvimento da precipitação em nuvens convectivas tropicais. Os resultados mostraram uma grande sensibilidade devido às mudanças nesses parâmetros. Altas concentrações de CCN e distribuições de gotículas estreitas (parâmetros de forma maiores), típicas de dias poluídos, produziram baixos valores médios para água líquida integrada na coluna e precipitação acumulada na superfície. Por outro lado, tendência oposta a este efeito foi encontrada em condições limpas (baixos valores para ambos, a concentração de CCN e o parâmetro de forma). O parâmetro de forma se mostrou ser mais importante que a concentração de CCN. Os efeitos da concentração de CCN e do parâmetro de forma também influenciaram a distribuição espacial dos campos de nuvem e precipitação. Embora o valor médio desses campos tenha diminuído em condições poluídas, o valor máximo aumentou. Como conseqüência da menor dispersão nas nuvens em condições poluídas, mais radiação solar esteve disponível na superfície. Isto é oposto aos resultados dos modelos globais que mostram redução na radiação solar como conseqüência do segundo efeito indireto dos aerossóis. Da mesma forma, este estudo encontrou que as diferenças são reduzidas quando é incluído o efeito direto dos aerossóis em absorver ou refletir a radiação solar. Sobretudo, os resultados sugerem que um maior número de modelos com tratamento explícito dos processos microfísicos de nuvem são necessários. Esses modelos permitem comparações, podendo mostrar o melhor tratamento numérico a ser usado na representação dos efeitos dos aerossóis no processo de precipitação como um todo. Estes resultados são importantes porque melhoram a compreensão de como o clima será afetado como conseqüência das mudanças futuras. / The objective of this work was to study the role of aerosols in modifying clouds and precipitation. This is one of the most difficult aspects in the study of climate changes. Field measurements of cloud condensation nuclei (CCN) and cloud size distributions performed during the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) campaign revealed distinct characteristics between clean and polluted atmospheric conditions. Measurements were conducted over the southwestern Amazon region during September-October 2002 focusing the transition from dry to wet seasons. During this period, analysis of CCN concentrations in the boundary layer revealed a general decreasing trend from mean values higher than 1200 cm-3 to values lower than 300 cm-3. The comparison between clean and polluted areas showed CCN concentrations 5 times higher than in polluted areas. These differences were not so strong above the boundary layer. Measurements also showed a diurnal cycle following the biomass burning activity. Cloud droplet size distributions at two regions with extremely different aerosols loading were also analyzed. During biomass-burning conditions, at high concentrations of cloud droplets, the mean diameter and liquid water content increased very little with altitude when compared with unpolluted conditions. A gamma distribution was used to fit the measured droplet spectra and the shape parameter was used as a criterion to define the best choice of spectra representation. According to the found values, narrow gamma distributions optimally fit polluted conditions (shape parameter around 5), while broad distributions are best fits for unpolluted conditions (shape parameter around 2). Based on these results, numerical experiments were carried out using the Brazilian Regional Atmospheric Modeling System (BRAMS) to investigate the effects of CCN concentrations and shape parameters of droplet spectra on the development of precipitation in tropical convective clouds. The results showed large sensitivity due to changes in these parameters. It was observed that high CCN concentrations and narrower cloud droplet distributions (high values for shape parameter), typical of the polluted days, produced low mean values of liquid water path and accumulated surface precipitation. On the other hand, an opposite trend to this effect was found under clean conditions (low CCN concentration and shape parameter values). Shape parameter showed to be much more important than CCN concentration. The effects of CCN concentration and shape parameter also influenced the spatial distribution of cloud and precipitation fields. Although mean values of these fields decreased under polluted conditions, maximum values were increased. Consequently, the less dispersion in clouds under polluted conditions, the more surface solar radiation was found. This is opposite to the results of global climate models, which predict reduction in solar radiation as a consequence of the second aerosol indirect effect. Also, it was found that the differences were reduced when the aerosols direct effect to absorb or reflect solar radiation is included. Moreover, the results suggest that additional models with explicit microphysics process treatment are necessary in order to allow further comparisons, which could show the best numerical treatment to be used in representing the aerosol effects on precipitation process. The importance of these results is to improve the understanding of future climate changes.

Made available in DSpace on 2014-06-12T22:56:34Z (GMT). No. of bitstreams: 2 arquivo1229_1.pdf: 2037381 bytes, checksum: 76ef1d886b39ab00e1420e5b4c061499 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011 / Propusemos nesse estudo que a Psicologia pode contribuir para a produção de conhecimento na área de Design e, para isso, nos apoiamos na perspectiva pragmática para analisar a produção de sentidos sobre tag clouds. Este termo refere-se a um recurso criado para representar o processo de tagging, de atribuir palavras-chave aos conteúdos da web, frequentemente utilizado em blogs. Objetivamos, então, identificar os sentidos produzidos por blogueiros sobre tag clouds a respeito dos motivos de incorporação desse recurso nos blogs e dos padrões de utilização. Para a construção de dados, pesquisamos por posts que discutiram sobre a utilização de tag clouds, visando a identificação dos sentidos sobre os motivos de incorporação; e, por outro lado, para investigar os sentidos sobre os padrões de utilização, convidamos blogueiros para a participação de entrevistas sobre o tema. Encontramos que os sentidos produzidos sobre os motivos de incorporação são direcionados a comparações intrarecurso, entre tipos de tag clouds, e inter-recurso, entre tag clouds e outros recurso como lista de tags e menu. Em relação aos padrões de utilização identificamos que estes estiveram relacionados aos sistemas de informação hospedeiros dos blogs. No 'Blogger.com', tag clouds caracterizam-se como menus para navegação nas páginas pessoais e, em geral, os blogueiros optaram por utilizá-lo nos blogs, e, por outro lado, no 'Wordpress.com', os blogueiros, em sua maioria, não optaram por utilizar tag clouds em suas páginas, já que as tags criadas direcionam para a navegação no sistema de informação. Concluimos, então, que representar tags através de tag clouds no próprio blog significa utilizar esses dados parcialmente, isto é, considerando apenas seu aspecto individual. Contudo, ao representar o conjunto de tags de um sistema de informação em uma única tag clouds, prioriza-se o aspecto coletivo. Porém, as duas dimensões, individual e coletiva, não são excludentes e ao precisar optar por uma dessas, os blogueiros são afastados do que parece ser a característica e função diferenciadora de tags e tag clouds na atividade, isto é, a união entre informações individuais e coletivas, entre interesses e conhecimentos que falam sobre um sujeito e, ao mesmo tempo, sobre uma comunidade

We present a new study of the spatial distribution and ages of the star clusters in the Small Magellanic Cloud (SMC). To detect and estimate the ages of the star clusters we rely on the new fully automated method developed by Bitsakis et al. Our code detects 1319 star clusters in the central 18 deg(2) of the SMC we surveyed (1108 of which have never been reported before). The age distribution of those clusters suggests enhanced cluster formation around 240 Myr ago. It also implies significant differences in the cluster distribution of the bar with respect to the rest of the galaxy, with the younger clusters being predominantly located in the bar. Having used the same setup, and data from the same surveys as for our previous study of the LMC, we are able to robustly compare the cluster properties between the two galaxies. Our results suggest that the bulk of the clusters in both galaxies were formed approximately 300 Myr ago, probably during a direct collision between the two galaxies. On the other hand, the locations of the young (<= 50 Myr) clusters in both Magellanic Clouds, found where their bars join the H I arms, suggest that cluster formation in those regions is a result of internal dynamical processes. Finally, we discuss the potential causes of the apparent outside-in quenching of cluster formation that we observe in the SMC. Our findings are consistent with an evolutionary scheme where the interactions between the Magellanic Clouds constitute the major mechanism driving their overall evolution.

We present our new, fully automated method to detect and measure the ages of star clusters in nearby galaxies, where individual stars can be resolved. The method relies purely on statistical analysis of observations and Monte-Carlo simulations to define stellar overdensities in the data. It decontaminates the cluster color-magnitude diagrams and, using a revised version of the Bayesian isochrone fitting code of Ramirez-Siordia et al., estimates the ages of the clusters. Comparisons of our estimates with those from other surveys show the superiority of our method to extract and measure the ages of star clusters, even in the most crowded fields. An application of our method is shown for the high-resolution, multiband imaging of the Large Magellanic Cloud. We detect 4850 clusters in the 7 deg(2) we surveyed, 3451 of which have not been reported before. Our findings suggest multiple epochs of star cluster formation, with the most probable occurring similar to 310 Myr ago. Several of these events are consistent with the epochs of the interactions among the Large and Small Magellanic Clouds, and the Galaxy, as predicted by N-body numerical simulations. Finally, the spatially resolved star cluster formation history may suggest an inside-out cluster formation scenario throughout the LMC, for the past 1 Gyr.

In this thesis we address the problem of understanding the star formation process in giant molecular clouds in a galactic context. Most simulations of molecular clouds to date use an oversimplified set of initial conditions (turbulent spheres/boxes or colliding flows). Full galactic scale models are able to generate molecular clouds with complex morphologies and velocity fields but they fail to reproduce in detail the effects that occur at sub-pc scales (e.g. stellar feedback). Our goal is to build the bridge between these two scenarios, and to model the star formation process in molecular clouds produced in a galactic context. We extract our molecular clouds from full-scale galactic simulations, hence we need to increase the resolution by two orders of magnitude. We introduce the details of the program used to simulate molecular clouds in Chapter 2, and describe in detail the method we follow to increase the resolution of the galactic clouds. In Chapter 3 we compare our simulated galactic clouds with the more conventional approach of using turbulent spheres. We create turbulent spheres to match the virial state of three galactic clouds. We perform isothermal simulations and find that the velocity field inherited from the full-scale galactic simulations plays an important role in the star formation process. Clouds affected by strong galactic shear produce less stars compared with clouds that are compressed. We define (and test) a set of parameters to characterise the dynamical state of our clouds. To include stellar feedback in our simulations we need to introduce a cooling/heating algorithm. In Chapter 4 we analyse how the different velocity fields of our clouds change the temperature distribution even in the absence of feedback. To study the formation of molecules we need to model the chemistry of H2 in our clouds. We also add CO chemistry, and produce synthetic observations of our clouds. The effect of feedback from winds and supernovae in galactic clouds is studied in Chapter 5. We analyse the effect of winds in clouds with very different velocity fields. We find that the effect of winds is stronger in highly virialised, high star forming clouds, with clouds with weak galactic shear, compared to unbound shear-dominated clouds. The steady and continuous action of the winds appears to have a greater effect than the supernovae. In summary, the inherited properties from the galaxy have an impact on many relevant processes in star formation, influencing gravitational collapse, the formation of filamentary structures, the temperature field of the cloud, and have a considerable effect on the impact of feedback in the clouds.

The formation of stars begins with the fragmentation of molecular clouds and the formation of dense cores. This fragmentation process can either be the result of classical gravitational instabilities or triggered by some external event. The gas and dust of young protoclusters often hold the imprints of the initial conditions and triggers of that specific star forming episode. In this context, my thesis work is a study of the gas properties of young protoclus- ters within the Gould Belt. The first part of my work consists of a detailed study of the young Serpens star forming region with CO isotopologues. This study has revealed a complex temperature, column density and velocity structure. I proposed a scenario where a collision between two filamentary clouds or flows is responsible for the observed complex structure and the most recent burst of star formation in Serpens. This hypothesis was tested with SPH simulations and provides a plausible scenario. I am currently extending this work to other regions with a variety of star formation efficiencies, in search of the particular physical properties and dynamics of a molecular cloud that allow or prevent clouds to be in the verge of forming stars. As such, I have included in this manuscript my study of the gas in the B59 star forming region, the only active clump in the Pipe Nebula. The results from this study have shown it to be very different from Serpens, even though further studies are needed to provide a complete picture of the region. B59 was taken as the starting point for a larger study of the entire Pipe Nebula, driven by the peculiarly low star formation efficiency in the cloud and a test to the physical properties of cores prior to star formation.

Au cours des vingt dernières années, de nombreux algorithmes de reconstruction de surface ont été développés. Néanmoins, des données additionnelles telles que les normales orientées sont souvent requises et la robustesse aux données imparfaites est encore un vrai défi. Dans cette thèse, nous traitons de nuages de points non-orientés et imparfaits, et proposons deux nouvelles méthodes gérant deux différents types de surfaces. La première méthode, adaptée au bruit, s'applique aux surfaces lisses et fermées. Elle prend en entrée un nuage de points avec du bruit variable et des données aberrantes, et comporte trois grandes étapes. Premièrement, en supposant que la surface est lisse et de dimension connue, nous calculons une fonction distance adaptée au bruit. Puis nous estimons le signe et l'incertitude de la fonction sur un ensemble de points-sources, en minimisant une énergie quadratique exprimée sur les arêtes d'un graphe uniforme aléatoire. Enfin, nous calculons une fonction implicite signée par une approche dite « random walker » avec des contraintes molles choisies aux points-sources de faible incertitude. La seconde méthode génère des surfaces planaires par morceaux, potentiellement non-variétés, représentées par des maillages triangulaires simples. En faisant croitre des primitives planaires convexes sous une erreur de Hausdorff bornée, nous déduisons à la fois la surface et sa connectivité et générons un complexe simplicial qui représente efficacement les grandes régions planaires, les petits éléments et les bords. La convexité des primitives est essentielle pour la robustesse et l'efficacité de notre approche. / Over the last two decades, a high number of reliable algorithms for surface reconstruction from point clouds has been developed. However, they often require additional attributes such as normals or visibility, and robustness to defect-laden data is often achieved through strong assumptions and remains a scientific challenge. In this thesis we focus on defect-laden, unoriented point clouds and contribute two new reconstruction methods designed for two specific classes of output surfaces. The first method is noise-adaptive and specialized to smooth, closed shapes. It takes as input a point cloud with variable noise and outliers, and comprises three main steps. First, we compute a novel noise-adaptive distance function to the inferred shape, which relies on the assumption that this shape is a smooth submanifold of known dimension. Second, we estimate the sign and confidence of the function at a set of seed points, through minimizing a quadratic energy expressed on the edges of a uniform random graph. Third, we compute a signed implicit function through a random walker approach with soft constraints chosen as the most confident seed points. The second method generates piecewise-planar surfaces, possibly non-manifold, represented by low complexity triangle surface meshes. Through multiscale region growing of Hausdorff-error-bounded convex planar primitives, we infer both shape and connectivity of the input and generate a simplicial complex that efficiently captures large flat regions as well as small features and boundaries. Imposing convexity of primitives is shown to be crucial to both the robustness and efficacy of our approach.

We present the discovery of a very faint stellar system, SMASH 1, that is potentially a satellite of the Large Magellanic Cloud. Found within the Survey of the MAgellanic Stellar History (SMASH), SMASH 1 is a compact (r(h) 9.1(-3.4)(+5.9)pc) and very low luminosity (M-V = -1.0 +/- 0.9, L-V = 10(2.3 +/- 0.4) L-circle dot) stellar system that is revealed by its sparsely populated main sequence and a handful of red giant branch candidate member stars. The photometric properties of these stars are compatible with a metal-poor ([Fe/H] = -2.2) and old (13 Gyr) isochrone located at a distance modulus of similar to 18.8, i.e., a distance of similar to 57 kpc. Situated at 11 degrees.3 from the LMC in projection, its three-dimensional distance from the Cloud is similar to 13 kpc, consistent with a connection to the LMC, whose tidal radius is at least 16 kpc. Although the nature of SMASH 1 remains uncertain, its compactness favors it being a stellar cluster and hence dark-matter free. If this is the case, its dynamical tidal radius is only less than or similar to 19 pc at this distance from the LMC, and smaller than the system's extent on the sky. Its low luminosity and apparent high ellipticity (epsilon = 0.62(-0.21)(+0.17)) with its major axis pointing toward the LMC may well be the tell-tale sign of its imminent tidal demise.

Large scale semantic segmentation is considered as one of the fundamental tasks in 3D scene understanding. Point clouds provide a basic and rich geometric representation of scenes and tangible objects. Convolutional Neural Networks (CNNs) have demonstrated an impressive success in processing regular discrete data such as 2D images and 1D audio. However, CNNs do not directly generalize to point cloud processing due to their irregular and un-ordered nature. One way to extend CNNs to point cloud understanding is to derive an intermediate euclidean representation of a point cloud by projecting onto image domain, voxelizing, or treating points as vertices of an un-directed graph. Graph-CNNs (GCNs) have demonstrated to be a very promising solution for deep learning on irregular data such as social networks, biological systems, and recently point clouds. Early works in literature for graph based point networks relied on constructing dynamic graphs in the node feature space to define a convolution kernel. Later works constructed hierarchical static graphs in 3D space for an encoder-decoder framework inspired from image segmentation. This thesis takes a closer look at both dynamic and static graph neighborhoods of graph- based point networks for the task of semantic segmentation in order to: 1) discuss a potential cause for why going deep in dynamic GCNs does not necessarily lead to an improved performance, and 2) propose a new approach in treating points in a static graph neighborhood for an improved information aggregation. The proposed method leads to an efficient graph based 3D semantic segmentation network that is on par with current state-of-the-art methods on both indoor and outdoor scene semantic segmentation benchmarks such as S3DIS and Semantic3D.