Caracterização estatística de processos sísmicos via Distribuição Generalizada de Pareto. Estudo de caso: João Câmara-RN

The work is to make a brief discussion of methods to estimate the parameters of the Generalized Pareto distribution (GPD). Being addressed the following techniques: Moments (moments), Maximum Likelihood (MLE), Biased Probability Weighted Moments (PWMB), Unbiased Probability Weighted Moments (PWMU), Mean Power Density Divergence (MDPD), Median (MED), Pickands (PICKANDS), Maximum Penalized Likelihood (MPLE), Maximum Goodness-of-fit (MGF) and the Maximum Entropy (POME) technique, the focus of this manuscript. By way of illustration adjustments were made for the Generalized Pareto distribution, for a sequence of earthquakes intraplacas which occurred in the city of João Câmara in the northeastern region of Brazil, which was monitored continuously for two years (1987 and 1988). It was found that the MLE and POME were the most efficient methods, giving them basically mean squared errors. Based on the threshold of 1.5 degrees was estimated the seismic risk for the city, and estimated the level of return to earthquakes of intensity 1.5°, 2.0°, 2.5°, 3.0° and the most intense earthquake never registered in the city, which occurred in November 1986 with magnitude of about 5.2º

Estudo da água produzida em diferentes zonas de produção de petróleo, utilizando a hidroquímica e a análise estatística de parâmetros químicos

Over exploitation of oil deposits on land onshore or offshore, there is simultaneous generation of waste water, known as produced water, which represents the largest waste stream in the production of crude oil. The relationship between the chemical composition of oil and water production and the conditions in which this process occurs or is favored are still poorly studied. The area chosen for the study has an important oil reserve and an important aquifer saturated with freshwater meteoric. The aim of this work is to study some chemical parameters in water produced for each reservoir zone of production in mature oil fields of Açu Formation, using the hydrochemical and statistical analysis to serve as a reference and be used as tools against the indicator ranges water producers in oil producing wells. Samples were collected from different wells in 6 different areas of production and were measured 50 parameters, which can be classified into three groups: anions, cations and physicochemical properties (considering only the parameters that generated values above detection limits in all samples). Through the characterization hydrochemistry observed an area of water and chlorinated sodium, chlorinated calcium or magnesium (mixed) in well water in different areas of Açu, by applying a statistical treatment, we obtained a discriminant function that distinguishes chemically production areas. Thus, it was possible to calculate the rate of correct classification of the function was 76.3%. To validate this model the accuracy rate was 86%

Fundamentação cinética da estatística não gaussiana : efeitos em politrópicas

Considering a non-relativistic ideal gas, the standard foundations of kinetic theory are investigated in the context of non-gaussian statistical mechanics introduced by Kaniadakis. The new formalism is based on the generalization of the Boltzmann H-theorem and the deduction of Maxwells statistical distribution. The calculated power law distribution is parameterized through a parameter measuring the degree of non-gaussianity. In the limit = 0, the theory of gaussian Maxwell-Boltzmann distribution is recovered. Two physical applications of the non-gaussian effects have been considered. The first one, the -Doppler broadening of spectral lines from an excited gas is obtained from analytical expressions. The second one, a mathematical relationship between the entropic index and the stellar polytropic index is shown by using the thermodynamic formulation for self-gravitational systems

Física estatística aplicada a sistemas sociais através do estudo de redes complexas

In this work a study of social networks based on analysis of family names is presented. A basic approach to the mathematical formalism of graphs is developed and then main theoretical models for complex networks are presented aiming to support the analysis of surnames networks models. These, in turn, are worked so as to be drawn leading quantities, such as aggregation coefficient, minimum average path length and connectivity distribution. Based on these quantities, it can be stated that surnames networks are an example of complex network, showing important features such as preferential attachment and small-world character

Estudo da previsão da carga de ruptura de estacas pré-moldadas de concreto

The piles are one of the most important types of solution adopted for the foundation of buildings. They are responsible for transmitting to the soil in deepe r and resistant layers loads from structures. The interaction of the foundation element with the soil is a very important variable, making indispensable your domain in order to determine the strength of the assembly and establish design criteria for each c ase of application of the pile. In this research analyzes were performed f rom experiments load tests for precast concrete piles and inve stigations of soil of type SPT, a study was performed for obtaining the ultimate load capacity of the foundation through methods extrapolation of load - settlement curve , semi - empirical and theoretic . After that, were realized comparisons between the different methods used for two types of soil a granular behavior and other cohesive. For obtaining soil paramet ers to be used i n the methods were established empirical correlations with the standard penetration number (NSPT). The charge - settlement curves of the piles are also analyzed. In the face of established comparisons was indicated the most reliable semiempirical method Déco urt - Quaresma as the most reliable for estimating the tensile strength for granular and cohesive soils. Meanwhile, among the methods studied extrapolation is recommended method of Van der Veen as the most appropriate for predicting the tensile strength.

Indicador de vulnerabilidade agropecuária a extremos climáticos para o nordeste do Brasil

The effects of climate change on human societies have become the focus of many researchers for their research. Understanding weather patterns (circulation of the atmosphere, precipitation, temperature) is essences for predicting extreme weather, but analyze how these extreme events act in our society and look for ways to reduce the impact caused by these events is the great challenge. Using a concept very in the humanities and social sciences to understand these impacts and the adaptation of the society's vulnerability. The objective of this work is to develop and apply a methodology for evaluating fining scale and quantify the vulnerability of the Brazilian Northeast to climatic extremes, developing a methodology that combines aspects of vulnerability to drought, as well as socioeconomic and climatic indicators used to assess exposure, ability to adaptation and the sensitivity of geographical microregions of the region. The assessment of the susceptibility or degree of exposure to risk is the regional using the SPI (Standardized Precipitation Index) by the degree of magnitude dried (MD), the rate of precipitation such as PCD (Precipitation Concentration Degree) and PCP (Precipitation Period Concentration) helped characterize and regional climatology, these indices showed satisfactory results in the pilot study of Rio Grande do Norte to assess the degree of exposure to drought. Regarding sensitivity agricultural / livestock multivariate statistical technique to factor analysis showed acceptable results for the proposed model using data for the period 1990-1999 (P1). The application of the analysis of vulnerability considering the adaptive capacity, as the adaptive disability have almost similar results with much of the region's vulnerability to extreme south of Bahia state as a part of the semiarid region has a degree of vulnerability among moderate and mean

Modelagem estatística e atribuições dos eventos de precipitação extrema na Amazônia brasileira

Intense precipitation events (IPE) have been causing great social and economic losses in the affected regions. In the Amazon, these events can have serious impacts, primarily for populations living on the margins of its countless rivers, because when water levels are elevated, floods and/or inundations are generally observed. Thus, the main objective of this research is to study IPE, through Extreme Value Theory (EVT), to estimate return periods of these events and identify regions of the Brazilian Amazon where IPE have the largest values. The study was performed using daily rainfall data of the hydrometeorological network managed by the National Water Agency (Agência Nacional de Água) and the Meteorological Data Bank for Education and Research (Banco de Dados Meteorológicos para Ensino e Pesquisa) of the National Institute of Meteorology (Instituto Nacional de Meteorologia), covering the period 1983-2012. First, homogeneous rainfall regions were determined through cluster analysis, using the hierarchical agglomerative Ward method. Then synthetic series to represent the homogeneous regions were created. Next EVT, was applied in these series, through Generalized Extreme Value (GEV) and the Generalized Pareto Distribution (GPD). The goodness of fit of these distributions were evaluated by the application of the Kolmogorov-Smirnov test, which compares the cumulated empirical distributions with the theoretical ones. Finally, the composition technique was used to characterize the prevailing atmospheric patterns for the occurrence of IPE. The results suggest that the Brazilian Amazon has six pluvial homogeneous regions. It is expected more severe IPE to occur in the south and in the Amazon coast. More intense rainfall events are expected during the rainy or transitions seasons of each sub-region, with total daily precipitation of 146.1, 143.1 and 109.4 mm (GEV) and 201.6, 209.5 and 152.4 mm (GPD), at least once year, in the south, in the coast and in the northwest of the Brazilian Amazon, respectively. For the south Amazonia, the composition analysis revealed that IPE are associated with the configuration and formation of the South Atlantic Convergence Zone. Along the coast, intense precipitation events are associated with mesoscale systems, such Squall Lines. In Northwest Amazonia IPE are apparently associated with the Intertropical Convergence Zone and/or local convection.

Análise da imunoexpressão de OCT4 e CD44 em neoplasias de glândulas salivares menores e maiores

Salivary gland neoplasms exhibit a wide variety of biological behavior and a high morphological diversity raises the interest in researching these lesions. The stem cells are the main source for the generation and maintenance of cell diversity, disorders in the regulation of these cells can lead to the production of altered stem cells, termed cancer stem cells capable of generate the tumor. Researches on cancer stem cells and associated proteins have been developed in some oral cancers; however, their role in salivary gland neoplasms is not well established. Thus, the aim of this study was to identify the tumor parenchyma cells exhibiting stem cell characteristics, by evaluating the immunoreactivity of OCT4 and CD44, in a number of cases of salivary gland neoplasms. The sample consisted of 20 pleomorphic adenomas, 20 mucoepidermoid carcinomas and 20 adenoid cystic carcinoma located in minor and major salivary glands. The expression of OCT4 and CD44 was evaluated by the percentage of positive cells (PP) and the intensity of expression (IE), it is realized the sum of the scores, resulting in the total score immunostaining (PIT) ranging 0-7. All studied cases showed positive expression of OCT4 and CD44 and higher values than the control groups. It was observed that for OCT4 luminal cells and non-luminal were immunostained in the case of pleomorphic adenomas and adenoid cystic carcinoma. Already the immunoreactivity of CD44 was particularly evident in the non-luminal cells of these lesions. In mucoepidermoid carcinomas for both markers, there was immunoreactivity in squamous and intermediate cells and absence of staining mucous cells. For both markers, a statistically significant higher immunostaining was verified in neoplasms located in the major salivary glands compared with lesions in the minor salivary (p<0.001). At the total sample and in the group of minor salivary glands, malignant neoplasms exhibited higher immunoreactivity for OCT4 than pleomorphic adenoma. However, there was no statistically significant difference between the lesions and between their classifications histomorphologic. Analyzing the correlation between OCT4 and CD44 immunoexpressions, a statistically significant moderate positive correlation (r = 0.444) was observed. The high expression of OCT4 and CD44 may indicate that these proteins play an important role in identifying cancer stem cells, allowing a prediction of biological behavior of salivary gland neoplasms.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Análise de volatilidade, integração de preços e previsibilidade para o mercado brasileiro de camarão

The present paper has the purpose of investigate the dynamics of the volatility structure in the shrimp prices in the Brazilian fish market. Therefore, a description of the initial aspects of the shrimp price series was made. From this information, statistics tests were made and selected univariate models to be price predictors. Then, it was verified the existence of relationship of long-term equilibrium between the Brazilian and American imported shrimp and if, confirmed the relationship, whether or not there is a causal link between these assets, considering that the two countries had presented trade relations over the years. It is presented as an exploratory research of applied nature with quantitative approach. The database was collected through direct contact with the Companhia de Entrepostos e Armazéns Gerais de São Paulo (CEAGESP) and on the official website of American import, National Marine Fisheries Service - National Oceanic and Atmospheric Administration (NMFS- NOAA). The results showed that the great variability in the active price is directly related with the gain and loss of the market agents. The price series presents a strong seasonal and biannual effect. The average structure of price of shrimp in the last 12 years was R$ 11.58 and external factors besides the production and marketing (U.S. antidumping, floods and pathologies) strongly affected the prices. Among the tested models for predicting prices of shrimp, four were selected, which through the prediction methodologies of one step forward of horizon 12, proved to be statistically more robust. It was found that there is weak evidence of long-term equilibrium between the Brazilian and American shrimp, where equivalently, was not found a causal link between them. We concluded that the dynamic pricing of commodity shrimp is strongly influenced by external productive factors and that these phenomena cause seasonal effects in the prices. There is no relationship of long-term stability between the Brazilian and American shrimp prices, but it is known that Brazil imports USA production inputs, which somehow shows some dependence productive. To the market agents, the risk of interferences of the external prices cointegrated to Brazilian is practically inexistent. Through statistical modeling is possible to minimize the risk and uncertainty embedded in the fish market, thus, the sales and marketing strategies for the Brazilian shrimp can be consolidated and widespread

Modelagem e previsão da perda de injetividade em poços canhoneados

Waterflooding is a technique largely applied in the oil industry. The injected water displaces oil to the producer wells and avoid reservoir pressure decline. However, suspended particles in the injected water may cause plugging of pore throats causing formation damage (permeability reduction) and injectivity decline during waterflooding. When injectivity decline occurs it is necessary to increase the injection pressure in order to maintain water flow injection. Therefore, a reliable prediction of injectivity decline is essential in waterflooding projects. In this dissertation, a simulator based on the traditional porous medium filtration model (including deep bed filtration and external filter cake formation) was developed and applied to predict injectivity decline in perforated wells (this prediction was made from history data). Experimental modeling and injectivity decline in open-hole wells is also discussed. The injectivity of modeling showed good agreement with field data, which can be used to support plan stimulation injection wells

Um Modelo híbrido para previsão de curvas de produção de petróleo

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