caracterização in situ e estrutura genética de populações de Gossypium mustelinum

Gossypium mustelinum Miers ex Watt is the only cotton species native from Brazil. It is endemic of the semi-arid region from North-east of the country, where it occur near from resilient water sources. The threats to the in situ conservation of the populations are caused by human interference in its habitat, mainly by excessive cattle graze and deforestation. Establish efficient strategies of in situ conservation depend on the accomplishment of a diagnosis of how the specie is found in its natural environment, and the knowledge about the genetic structure of the populations. The objectives of this work were i) to determine the in situ conditions of two populations present in rivers from basin of Rio Paraguaçu at the Bahia State, ii) to evaluate the structure and genetic variability presented in both populations, iii) to establish in situ and ex situ conservation strategies. It were realized collection in november 2007, when was realized in situ characterization of G. mustelinum. SSR markers were used for analyze 218 genotypes deriving from two populations of the G. mustelinum, localized at Tocó river and the Capivara river. The allelic frequencies, the heterozigosity and the F statics were estimated. All the plants were classified as wild and natives, and there was no evidence of the use the plants or its parts. The populations showed different conservation conditions in situ. Few plantlets were found in sites with excessive cattle feed, an indication that the damages in young plants should be high enough to compromise the renovation of the populations. On the other hand, populations were well preserved when the anthropic damages was low or inexistent. The 14 SSR primer pairs amplified 17 loci with a medium number of 5 alleles per locus (a total of 85 alleles). The high level of endogamy estimated (FIS=0,808) and the low observed heterozygosity (H0=0,093) were indicatives that the populations reproduce mainly by selfing, geitonogamy and crosses between related individuals. The genetic diversity was high (HE=0,482) and the differentiation between the populations was very high (FST=0,328). At least two sites from both populations of G. mustelinum must be preserved to achieve suitable in situ conservation. Actions that preserve the gallery forest and keep the cattle away should implemented, and could be as simple as erecting a fence. It is not possible anticipated if the in situ preservation will be possible. Therefore collections and ex situ preservation of representative specimens are essential to conserve the genetic diversity of native G. mustelinum

Utilizando Pesos estáticos e dinâmicos em sistemas multi-classificadores com diferentes níveis de diversidade

Although some individual techniques of supervised Machine Learning (ML), also known as classifiers, or algorithms of classification, to supply solutions that, most of the time, are considered efficient, have experimental results gotten with the use of large sets of pattern and/or that they have a expressive amount of irrelevant data or incomplete characteristic, that show a decrease in the efficiency of the precision of these techniques. In other words, such techniques can t do an recognition of patterns of an efficient form in complex problems. With the intention to get better performance and efficiency of these ML techniques, were thought about the idea to using some types of LM algorithms work jointly, thus origin to the term Multi-Classifier System (MCS). The MCS s presents, as component, different of LM algorithms, called of base classifiers, and realized a combination of results gotten for these algorithms to reach the final result. So that the MCS has a better performance that the base classifiers, the results gotten for each base classifier must present an certain diversity, in other words, a difference between the results gotten for each classifier that compose the system. It can be said that it does not make signification to have MCS s whose base classifiers have identical answers to the sames patterns. Although the MCS s present better results that the individually systems, has always the search to improve the results gotten for this type of system. Aim at this improvement and a better consistency in the results, as well as a larger diversity of the classifiers of a MCS, comes being recently searched methodologies that present as characteristic the use of weights, or confidence values. These weights can describe the importance that certain classifier supplied when associating with each pattern to a determined class. These weights still are used, in associate with the exits of the classifiers, during the process of recognition (use) of the MCS s. Exist different ways of calculating these weights and can be divided in two categories: the static weights and the dynamic weights. The first category of weights is characterizes for not having the modification of its values during the classification process, different it occurs with the second category, where the values suffers modifications during the classification process. In this work an analysis will be made to verify if the use of the weights, statics as much as dynamics, they can increase the perfomance of the MCS s in comparison with the individually systems. Moreover, will be made an analysis in the diversity gotten for the MCS s, for this mode verify if it has some relation between the use of the weights in the MCS s with different levels of diversity

Incorporação do vínculo de suavidade no ajuste de histórico de reservatórios de petróleo

The history match procedure in an oil reservoir is of paramount importance in order to obtain a characterization of the reservoir parameters (statics and dynamics) that implicates in a predict production more perfected. Throughout this process one can find reservoir model parameters which are able to reproduce the behaviour of a real reservoir.Thus, this reservoir model may be used to predict production and can aid the oil file management. During the history match procedure the reservoir model parameters are modified and for every new set of reservoir model parameters found, a fluid flow simulation is performed so that it is possible to evaluate weather or not this new set of parameters reproduces the observations in the actual reservoir. The reservoir is said to be matched when the discrepancies between the model predictions and the observations of the real reservoir are below a certain tolerance. The determination of the model parameters via history matching requires the minimisation of an objective function (difference between the observed and simulated productions according to a chosen norm) in a parameter space populated by many local minima. In other words, more than one set of reservoir model parameters fits the observation. With respect to the non-uniqueness of the solution, the inverse problem associated to history match is ill-posed. In order to reduce this ambiguity, it is necessary to incorporate a priori information and constraints in the model reservoir parameters to be determined. In this dissertation, the regularization of the inverse problem associated to the history match was performed via the introduction of a smoothness constraint in the following parameter: permeability and porosity. This constraint has geological bias of asserting that these two properties smoothly vary in space. In this sense, it is necessary to find the right relative weight of this constrain in the objective function that stabilizes the inversion and yet, introduces minimum bias. A sequential search method called COMPLEX was used to find the reservoir model parameters that best reproduce the observations of a semi-synthetic model. This method does not require the usage of derivatives when searching for the minimum of the objective function. Here, it is shown that the judicious introduction of the smoothness constraint in the objective function formulation reduces the associated ambiguity and introduces minimum bias in the estimates of permeability and porosity of the semi-synthetic reservoir model