26 resultados para Vizinhança Comunicante
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Peng was the first to work with the Technical DFA (Detrended Fluctuation Analysis), a tool capable of detecting auto-long-range correlation in time series with non-stationary. In this study, the technique of DFA is used to obtain the Hurst exponent (H) profile of the electric neutron porosity of the 52 oil wells in Namorado Field, located in the Campos Basin -Brazil. The purpose is to know if the Hurst exponent can be used to characterize spatial distribution of wells. Thus, we verify that the wells that have close values of H are spatially close together. In this work we used the method of hierarchical clustering and non-hierarchical clustering method (the k-mean method). Then compare the two methods to see which of the two provides the best result. From this, was the parameter � (index neighborhood) which checks whether a data set generated by the k- average method, or at random, so in fact spatial patterns. High values of � indicate that the data are aggregated, while low values of � indicate that the data are scattered (no spatial correlation). Using the Monte Carlo method showed that combined data show a random distribution of � below the empirical value. So the empirical evidence of H obtained from 52 wells are grouped geographically. By passing the data of standard curves with the results obtained by the k-mean, confirming that it is effective to correlate well in spatial distribution
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Neste trabalho estudamos o comportamento das estrelas pertencentes a sistemas planetários no que diz respeito às suas características infravermelho e à distribuição espectral de energia (SED). Nosso estudo tem como base uma análise detalhada do comportamento da emissão no infravermelho de 48 estrelas com planetas, classificadas como estrelas da seqüência principal, subgigantes ou gigantes. Foram analisados dados de fotometria infravermelho nas bandas 12, 25 e 60µm do catálogo de fontes IRAS puntiformes (IPSC) e nas bandas JHK do projeto 2 Micron All Sky Survey (2MASS). A partir do cálculo da discrepância na posição de apontamento da fonte e do cálculo do índice de cor, selecionamos e localizamos os objetos no diagrama de cor-cor do IRAS. Este diagrama permite-nos identificar possíveis objetos detentores de disco de poeira. Fizemos também uma análise da distribuição espectral de energia onde observamos também traços de excesso de fluxo no infravermelho, com isso, confirmarmos a presença do disco de poeira nos objetos identificados no diagrama de cor. Apesar da atual amostra de estrelas com planetas incluir apenas um subconjunto de estrelas com planetas detectadas na vizinhança solar, a presente análise do fluxo infravermelho nesses objetos oferecem uma possibilidade única de estudar as características infravermelho das estrelas pertencentes aos sistemas planetários extra-solar. Neste contexto, nosso estudo aponta resultados interessantes, entre outros destacamos o fato de algumas estrelas com planetas apresentarem um peculiar fluxo IRAS [60-25], indicando a co-existência de poeira juntamente com os planetas destes sistemas extra solar
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
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Técnicas de otimização conhecidas como as metaheurísticas tem conseguido resolversatisfatoriamente problemas conhecidos, mas desenvolvimento das metaheurísticas écaracterizado por escolha de parâmetros para sua execução, na qual a opção apropriadadestes parâmetros (valores). Onde o ajuste de parâmetro é essencial testa-se os parâmetrosaté que resultados viáveis sejam obtidos, normalmente feita pelo desenvolvedor que estaimplementando a metaheuristica. A qualidade dos resultados de uma instância1 de testenão será transferida para outras instâncias a serem testadas e seu feedback pode requererum processo lento de “tentativa e erro” onde o algoritmo têm que ser ajustado para umaaplicação especifica. Diante deste contexto das metaheurísticas surgiu a Busca Reativaque defende a integração entre o aprendizado de máquina dentro de buscas heurísticaspara solucionar problemas de otimização complexos. A partir da integração que a BuscaReativa propõe entre o aprendizado de máquina e as metaheurísticas, surgiu a ideia dese colocar a Aprendizagem por Reforço mais especificamente o algoritmo Q-learning deforma reativa, para selecionar qual busca local é a mais indicada em determinado instanteda busca, para suceder uma outra busca local que não pode mais melhorar a soluçãocorrente na metaheurística VNS. Assim, neste trabalho propomos uma implementação reativa,utilizando aprendizado por reforço para o auto-tuning do algoritmo implementado,aplicado ao problema do caixeiro viajante simétrico e ao problema escalonamento sondaspara manutenção de poços.
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This work presents a new model for the Heterogeneous p-median Problem (HPM), proposed to recover the hidden category structures present in the data provided by a sorting task procedure, a popular approach to understand heterogeneous individual’s perception of products and brands. This new model is named as the Penalty-free Heterogeneous p-median Problem (PFHPM), a single-objective version of the original problem, the HPM. The main parameter in the HPM is also eliminated, the penalty factor. It is responsible for the weighting of the objective function terms. The adjusting of this parameter controls the way that the model recovers the hidden category structures present in data, and depends on a broad knowledge of the problem. Additionally, two complementary formulations for the PFHPM are shown, both mixed integer linear programming problems. From these additional formulations lower-bounds were obtained for the PFHPM. These values were used to validate a specialized Variable Neighborhood Search (VNS) algorithm, proposed to solve the PFHPM. This algorithm provided good quality solutions for the PFHPM, solving artificial generated instances from a Monte Carlo Simulation and real data instances, even with limited computational resources. Statistical analyses presented in this work suggest that the new algorithm and model, the PFHPM, can recover more accurately the original category structures related to heterogeneous individual’s perceptions than the original model and algorithm, the HPM. Finally, an illustrative application of the PFHPM is presented, as well as some insights about some new possibilities for it, extending the new model to fuzzy environments
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The pioneering work proposed by Skumanich (1972) has shown that the projected mean rotational velocity < v sini > for solar type stars follows a rotation law decreases with the time given by t −1/2 , where t is the stellar age. This relationship is consistent with the theories of the angular momentum loss through the ionized stellar wind, which in turn is coupled to the star through its magnetic field. Several authors (e.g.: Silva et al. 2013 and de Freitas et al. 2014) have analyzed the possible matches between the rotational decay and the profile of the velocity distribution. These authors came to a simple heuristic relationship, but did not build a direct path between the exponent of the rotational decay (j) and the exponent of the distribution of the rotational velocity (q). The whole theoretical scenario has been proposed using an efficient and strong statistical mechanics well known as non-extensive statistical mechanics. The present dissertation proposes effectively to close this issue by elaborating a theoretical way to modify the q-Maxwellians’ distributions into q-Maxwellians with physics links extracted from the theory of magnetic braking. In order to test our distributions we have used the GenevaCapenhagen Survey data with approximately 6000 F and G field stars limited by age. As a result, we obtained that the exponents of the decay law and distribution follow a similar relationship to that proposed by Silva et al. (2013).
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Population aging is a global demographic trend. This process is a reality that merits attention and importance in recent years, and cause considerable impact in terms of greater demands on the health sector, social security and special care and attention from families and society as a whole. Thus, in the context of addressing the consequences of demographic transition, population aging is characterized as a major challenge for Brazilian society. Therefore, this study was conducted in two main objectives. In the first article, variables of socioeconomic and demographic contexts were employed to identify multidimensional profiles of elderly residents in the Northeast capitals, from specific indicators from the 2010 Census information Therefore, we used the Grade of Membership Method (GoM), whose design profiles admits that an individual belongs to different degrees of relevance to multiple profiles in order to identify socioeconomic and demographic factors associated with living conditions of the elderly in the Northeastern capitals. The second article examined the possible relationship between mortality from chronic diseases and socio-economic indicators in the elderly population, of the 137 districts in Natal, broken down by ten-year age groups (60 to 69 years, 70-79 years and 80 and over. The microdata from the Mortality Information System (SIM), was used, provided by the Health Secretariat of Christmas, and population information came from the Population Census 2010. The method refers to the Global and Local Index neighborhood logic (LISA) Moran, whose spatial distribution from the choropleth maps allowed us to analyze the mortality of the elderly by neighborhoods, according to socioeconomic and demographic indicators, according to the presence of special significance. In the first article, the results show the identification of three extreme profiles. The Profile 1 which is characterized by median socioeconomic status and contributes 35.5% of elderly residents in the area considered. The profile 2 which brings together seniors with low socioeconomic status characteristics, with a percentage of 24.8% of cases. And the Profile 3 composing elderly with features that reveal better socioeconomic conditions, about 29.7% of the elderly. Overall, the results point to poor living conditions represented by the definition of these profiles, mainly expressed by the results observed in more than half of the northeastern elderly experience a situation of social vulnerability given the large percentage that makes up the Profile 1 and Profile 2, adding 60% of the elderly. In the second article, the results show a higher proportion of elderly concentrated in the neighborhoods of higher socioeconomic status, such as Petrópolis and LagoaSeca. Mortality rates, according to the causes of death and standardized by the empirical Bayesian method were distributed locally as follows: Neoplasms (Reis Santos, New Discovery, New Town, Grass Soft and Ponta Negra); Hypertensive diseases (Blue Lagoon, Potengi, Redinha, Reis Santos, Riverside, Lagoa Nova, Grass Soft, Neópolis and Ponta Negra); Acute Myocardial Infarction (Northeast, Guarapes and grass Soft); Cerebrovascular diseases (Petrópolis and Mother Luiza); Pneumonia (Ribeira, Praia do Meio, New Discovery, Grass Soft and Ponta Negra); Chronic Diseases of the Lower Way Airlines (Igapó, Northeast and Thursdays). The present findings at work may contribute to other studies on the subject and development of specific policies for the elderly.
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Population aging is a global demographic trend. This process is a reality that merits attention and importance in recent years, and cause considerable impact in terms of greater demands on the health sector, social security and special care and attention from families and society as a whole. Thus, in the context of addressing the consequences of demographic transition, population aging is characterized as a major challenge for Brazilian society. Therefore, this study was conducted in two main objectives. In the first article, variables of socioeconomic and demographic contexts were employed to identify multidimensional profiles of elderly residents in the Northeast capitals, from specific indicators from the 2010 Census information Therefore, we used the Grade of Membership Method (GoM), whose design profiles admits that an individual belongs to different degrees of relevance to multiple profiles in order to identify socioeconomic and demographic factors associated with living conditions of the elderly in the Northeastern capitals. The second article examined the possible relationship between mortality from chronic diseases and socio-economic indicators in the elderly population, of the 137 districts in Natal, broken down by ten-year age groups (60 to 69 years, 70-79 years and 80 and over. The microdata from the Mortality Information System (SIM), was used, provided by the Health Secretariat of Christmas, and population information came from the Population Census 2010. The method refers to the Global and Local Index neighborhood logic (LISA) Moran, whose spatial distribution from the choropleth maps allowed us to analyze the mortality of the elderly by neighborhoods, according to socioeconomic and demographic indicators, according to the presence of special significance. In the first article, the results show the identification of three extreme profiles. The Profile 1 which is characterized by median socioeconomic status and contributes 35.5% of elderly residents in the area considered. The profile 2 which brings together seniors with low socioeconomic status characteristics, with a percentage of 24.8% of cases. And the Profile 3 composing elderly with features that reveal better socioeconomic conditions, about 29.7% of the elderly. Overall, the results point to poor living conditions represented by the definition of these profiles, mainly expressed by the results observed in more than half of the northeastern elderly experience a situation of social vulnerability given the large percentage that makes up the Profile 1 and Profile 2, adding 60% of the elderly. In the second article, the results show a higher proportion of elderly concentrated in the neighborhoods of higher socioeconomic status, such as Petrópolis and LagoaSeca. Mortality rates, according to the causes of death and standardized by the empirical Bayesian method were distributed locally as follows: Neoplasms (Reis Santos, New Discovery, New Town, Grass Soft and Ponta Negra); Hypertensive diseases (Blue Lagoon, Potengi, Redinha, Reis Santos, Riverside, Lagoa Nova, Grass Soft, Neópolis and Ponta Negra); Acute Myocardial Infarction (Northeast, Guarapes and grass Soft); Cerebrovascular diseases (Petrópolis and Mother Luiza); Pneumonia (Ribeira, Praia do Meio, New Discovery, Grass Soft and Ponta Negra); Chronic Diseases of the Lower Way Airlines (Igapó, Northeast and Thursdays). The present findings at work may contribute to other studies on the subject and development of specific policies for the elderly.
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Currently, the growing aging population challenges the society and public health policies, for increased longevity need to be associated with quality of life. Adequate physical and social environment are key factors for the welfare of the elderly, particularly the housing environment - this thesis understood as the home (dwelling unit) and its surroundings (close proximity). In addition, Brazilian legislation in this sector indicates the importance of the elderly remain at home and in the family. In addition, Brazilian legislation in this sector indicates the importance of the elderly remain at home and in the family. Based on this framework mortar, the thesis was starting questions: How do you live the elderly population aged 80 and over which is served by the Health Family Strategy of the Unified Health System? That social and environmental conditions of the place of residence act more directly on their quality of life? How do these people get housing conditions experienced? The research aimed to investigate how the residential environment (social and physical) influence everyday activities and quality of life of the elderly. Exploratory qualitative study highlighting the home visits, developed based on multimethod strategy. The empirical study was conducted in the city of Cabedelo-PB, Nov/2013 to Feb/2014. Participants were 36 elderly people (31 women and 5 men) aged between 80 and 99 years, little education, who live 39 years in the area (average). In the research first stage were applied questionnaires for socio-demographics and livability of the residence and the surroundings. In the second stage we used semi-structured interview and a tour accompanied in the neighborhood (with those who have accepted to do so). Throughout work it was kept a diary by the researcher and held naturalistic observations of the behavior of the elderly. Quantitative data were described using descriptive statistics, and information from the interviews were analyzed through the Collective Subject Discourse technique. Among the key ideas that emerged from them are: the representation of home, neighborhood support and related issues dyad independence / autonomy. The study showed that the elderly develop strong attachment to the place where he lives, the importance of it for your health and the desire to stay there. Thus, despite experiencing many barriers (more physical than the social), at the place where they live, they say they are satisfied, even when unfavorable conditions are evident. Concluding that as the houses are environmentally more docile, simple changes ensure autonomy, independence and mobility for the elderly. In turn, the barriers of the urban environment show it more difficult to deal with, making this space inhospitable to most survey participants, a condition that hinders your physical activities and social participation, and negatively influence their quality of life.
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The main objective of this study is to apply recently developed methods of physical-statistic to time series analysis, particularly in electrical induction s profiles of oil wells data, to study the petrophysical similarity of those wells in a spatial distribution. For this, we used the DFA method in order to know if we can or not use this technique to characterize spatially the fields. After obtain the DFA values for all wells, we applied clustering analysis. To do these tests we used the non-hierarchical method called K-means. Usually based on the Euclidean distance, the K-means consists in dividing the elements of a data matrix N in k groups, so that the similarities among elements belonging to different groups are the smallest possible. In order to test if a dataset generated by the K-means method or randomly generated datasets form spatial patterns, we created the parameter Ω (index of neighborhood). High values of Ω reveals more aggregated data and low values of Ω show scattered data or data without spatial correlation. Thus we concluded that data from the DFA of 54 wells are grouped and can be used to characterize spatial fields. Applying contour level technique we confirm the results obtained by the K-means, confirming that DFA is effective to perform spatial analysis
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In recent years, the DFA introduced by Peng, was established as an important tool capable of detecting long-range autocorrelation in time series with non-stationary. This technique has been successfully applied to various areas such as: Econophysics, Biophysics, Medicine, Physics and Climatology. In this study, we used the DFA technique to obtain the Hurst exponent (H) of the profile of electric density profile (RHOB) of 53 wells resulting from the Field School of Namorados. In this work we want to know if we can or not use H to spatially characterize the spatial data field. Two cases arise: In the first a set of H reflects the local geology, with wells that are geographically closer showing similar H, and then one can use H in geostatistical procedures. In the second case each well has its proper H and the information of the well are uncorrelated, the profiles show only random fluctuations in H that do not show any spatial structure. Cluster analysis is a method widely used in carrying out statistical analysis. In this work we use the non-hierarchy method of k-means. In order to verify whether a set of data generated by the k-means method shows spatial patterns, we create the parameter Ω (index of neighborhood). High Ω shows more aggregated data, low Ω indicates dispersed or data without spatial correlation. With help of this index and the method of Monte Carlo. Using Ω index we verify that random cluster data shows a distribution of Ω that is lower than actual cluster Ω. Thus we conclude that the data of H obtained in 53 wells are grouped and can be used to characterize space patterns. The analysis of curves level confirmed the results of the k-means