Neste trabalho apresentamos o desenvolvimento de dois modelos metapopula ionais heterogêneos para uma úni a espé ie, onde em ada modelo trabalhamos om dois agrupamentos de sítios que se diferen iam pela taxa de migração ( onstante ou dependente da densidade) e dinâmi a lo al. São apresentadas as ondições ne essárias para a o orrên ia de dinâmi as par ialmente sin ronizadas, o asionando a formação de dois clusters, onde em ada cluster a dinâmi a de todos os sítios é sin ronizada. Com o propósito de analisar a estabilidade assintóti a do estado par ialmente sín rono, obtemos uma expressão para o ál ulo do número de Lyapunov transversal do atrator par ialmente sin ronizado e simulamos numeri amente esse número para diversas possibilidades de parâmetros e redes de onexão. Por m, fazemos uma omparação entre ambos os modelos desenvolvidos. / In this work we present the development of two heterogeneous metapopulation models for a single spe ie, where in ea h model we work with two groups of pat hes that di er by the rate of migration ( onstant or density dependent) and by the lo al dynami s. The ne essary onditions for the o urren e of partially syn hronized dynami s are presented, resulting in the formation of two lusters, where in ea h luster the dynami s of all the pat hes are syn hronized. In order to analyze the asymptoti stability of the partially syn hronous state, we obtain an expression for the al ulation of the transverse Lyapunov number of the partially syn hronized attra tor and numeri ally simulate this number for several possibilities of parameters and onne tion networks. Finally, we make a omparison between both developed models.

Categorical data are common in various research areas, and clustering is a prevalent technique used for analyse them. However, two challenging problems are encountered in categorical data clustering analysis. The first is that most categorical data distance metrics were actually proposed for nominal data (i.e., a categorical data set that comprises only nominal attributes), ignoring the fact that ordinal attributes are also common in various categorical data sets. As a result, these nominal data distance metrics cannot account for the order information of ordinal attributes and may thus inappropriately measure the distances for ordinal data (i.e., a categorical data set that comprises only ordinal attributes) and mixed categorical data (i.e., a categorical data set that comprises both ordinal and nominal attributes). The second problem is that most hierarchical clustering approaches were actually designed for numerical data and have very high computation costs; that is, with time complexity O(N2) for a data set with N data objects. These issues have presented huge obstacles to the clustering analysis of categorical data. To address the ordinal data distance measurement problem, we studied the characteristics of ordered possible values (also called 'categories' interchangeably in this thesis) of ordinal attributes and propose a novel ordinal data distance metric, which we call the Entropy-Based Distance Metric (EBDM), to quantify the distances between ordinal categories. The EBDM adopts cumulative entropy as a measure to indicate the amount of information in the ordinal categories and simulates the thinking process of changing one's mind between two ordered choices to quantify the distances according to the amount of information in the ordinal categories. The order relationship and the statistical information of the ordinal categories are both considered by the EBDM for more appropriate distance measurement. Experimental results illustrate the superiority of the proposed EBDM in ordinal data clustering. In addition to designing an ordinal data distance metric, we further propose a unified categorical data distance metric that is suitable for distance measurement of all three types of categorical data (i.e., ordinal data, nominal data, and mixed categorical data). The extended version uniformly defines distances and attribute weights for both ordinal and nominal attributes, by which the distances measured for the two types of attributes of a mixed categorical data can be directly combined to obtain the overall distances between data objects with no information loss. Extensive experiments on all three types of categorical data sets demonstrate the effectiveness of the unified distance metric in clustering analysis of categorical data. To address the hierarchical clustering problem of large-scale categorical data, we propose a fast hierarchical clustering framework called the Growing Multi-layer Topology Training (GMTT). The most significant merit of this framework is its ability to reduce the time complexity of most existing hierarchical clustering frameworks (i.e., O(N2)) to O(N1.5) without sacrificing the quality (i.e., clustering accuracy and hierarchical details) of the constructed hierarchy. According to our design, the GMTT framework is applicable to categorical data clustering simply by adopting a categorical data distance metric. To make the GMTT framework suitable for the processing of streaming categorical data, we also provide an incremental version of GMTT that can dynamically adopt new inputs into the hierarchy via local updating. Theoretical analysis proves that the GMTT frameworks have time complexity O(N1.5). Extensive experiments show the efficacy of the GMTT frameworks and demonstrate that they achieve more competitive categorical data clustering performance by adopting the proposed unified distance metric.

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An important research area in modern astrophysics is understanding how molecular clouds form stars and star clusters. These rich clusters within molecular clouds are the dominant mode of star formation in our galaxy, but we know very little about these areas of space due to incomplete observational data. The MYStIX (Massive Young Star-Forming Complex Study in Infrared and X-Ray) project was started to create a detailed catalogue of these regions and the rich star clusters embedded within them. Once the observational data was available, the evolution of these clusters could be investigated in more detail. Current cluster simulations investigate the stars in detail but usually ignore the gas entirely which can be inaccurate, especially in gas mass dominated clusters. We use AMUSE (Astrophysical Multi-purpose Software Environment) to model embedded young clusters with stars and gas, similar to those found by the MYStIX project, and track their evolution over the first few million years of their lifespan while allowing the stars and gas to interact. We are particularly interested in non-spheroidal subclusters and how they can evolve into the spherical structures that we see today. / Thesis / Master of Science (MSc)

This research project focuses on using a multiproxy approach to discriminate between overwash and non-hurricane marsh sediments within the bed of a coastal lake. 3 marsh cores were collected in an area of McFaddin National Wildlife Refuge just south of Clam Lake that are known to contain 4 hurricane overwash deposits, Ike, Rita, Carla, and Audrey. LOI and XRF analysis were used to determine the signature of the hurricane overwash layers. 3 more cores were collected from Clam Lake where there are no visible sand layers. The elemental signature of the overwash layers found in the marsh cores was used to run a hierarchical cluster analysis on the lake cores. This was able to determine the effectiveness of XRF's ability to distinguish between hurricane overwash and marsh sediments. The combination of cluster analysis, LOI, and XRF can tentatively identify hurricane overwash deposits in a coastal lake, however, it is more successful in the marsh cores. Results in the lake cores are somewhat inconsistent and uncertain, possibly because there may have not been enough overwash deposits to identity or that the XRF analysis needs more distinct sand layers to distinguish between overwash and marsh.

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