13 resultados para NEOPLASIAS (ESTATÍSTICAS E DADOS NUMÉRICOS)

em Universidade Federal do Rio Grande do Norte(UFRN)


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Breast cancer, despite being one of the leading causes of death among women worldwide is a disease that can be cured if diagnosed early. One of the main techniques used in the detection of breast cancer is the Fine Needle Aspirate FNA (aspiration puncture by thin needle) which, depending on the clinical case, requires the analysis of several medical specialists for the diagnosis development. However, such diagnosis and second opinions have been hampered by geographical dispersion of physicians and/or the difficulty in reconciling time to undertake work together. Within this reality, this PhD thesis uses computational intelligence in medical decision-making support for remote diagnosis. For that purpose, it presents a fuzzy method to assist the diagnosis of breast cancer, able to process and sort data extracted from breast tissue obtained by FNA. This method is integrated into a virtual environment for collaborative remote diagnosis, whose model was developed providing for the incorporation of prerequisite Modules for Pre Diagnosis to support medical decision. On the fuzzy Method Development, the process of knowledge acquisition was carried out by extraction and analysis of numerical data in gold standard data base and by interviews and discussions with medical experts. The method has been tested and validated with real cases and, according to the sensitivity and specificity achieved (correct diagnosis of tumors, malignant and benign respectively), the results obtained were satisfactory, considering the opinions of doctors and the quality standards for diagnosis of breast cancer and comparing them with other studies involving breast cancer diagnosis by FNA.

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This work aims to study the drying of cashew-nut pulp with different lay-out of dryers using conventional and solar energy. It concerns with the use of exceeding of the regional raw material and the suitable knowledge for the applicability of the drying systems as pathway for food conservation. Besides, it used renewable sources as solar energy to dry these agroindustrial products. Runs were carried out using a conventional tray-dryer with temperature, air velocity control and cashew slice thickness of 55°C, 65°C, 75°C; 3.0; 4.5, 6.0 m s-1; 1.0; 1.5 and 2.0 cm, respectively, in order to compare the studied systems. To evaluate the conventional tray-dryer, it was used a diffusional model of 2nd Fick´s law, where the drying curves were quite well fitted to an infinite flat plate design. For the drying runs where the room temperature had no control, it was developed a phenomenological-mathematical model for the solar dryer with indirect radiation under natural and forced convection based on material and energy balances of the system. Besides, it was carried out assays in the in natura as well as dehydrated, statistic analysis of the experimental drying data, sensorial analysis of the final dry product and a simplified economical analysis of the systems studied

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Produced water is characterized as one of the most common wastes generated during exploration and production of oil. This work aims to develop methodologies based on comparative statistical processes of hydrogeochemical analysis of production zones in order to minimize types of high-cost interventions to perform identification test fluids - TIF. For the study, 27 samples were collected from five different production zones were measured a total of 50 chemical species. After the chemical analysis was applied the statistical data, using the R Statistical Software, version 2.11.1. Statistical analysis was performed in three steps. In the first stage, the objective was to investigate the behavior of chemical species under study in each area of production through the descriptive graphical analysis. The second step was to identify a function that classify production zones from each sample, using discriminant analysis. In the training stage, the rate of correct classification function of discriminant analysis was 85.19%. The next stage of processing of the data used for Principal Component Analysis, by reducing the number of variables obtained from the linear combination of chemical species, try to improve the discriminant function obtained in the second stage and increase the discrimination power of the data, but the result was not satisfactory. In Profile Analysis curves were obtained for each production area, based on the characteristics of the chemical species present in each zone. With this study it was possible to develop a method using hydrochemistry and statistical analysis that can be used to distinguish the water produced in mature fields of oil, so that it is possible to identify the zone of production that is contributing to the excessive elevation of the water volume.

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Produced water is characterized as one of the most common wastes generated during exploration and production of oil. This work aims to develop methodologies based on comparative statistical processes of hydrogeochemical analysis of production zones in order to minimize types of high-cost interventions to perform identification test fluids - TIF. For the study, 27 samples were collected from five different production zones were measured a total of 50 chemical species. After the chemical analysis was applied the statistical data, using the R Statistical Software, version 2.11.1. Statistical analysis was performed in three steps. In the first stage, the objective was to investigate the behavior of chemical species under study in each area of production through the descriptive graphical analysis. The second step was to identify a function that classify production zones from each sample, using discriminant analysis. In the training stage, the rate of correct classification function of discriminant analysis was 85.19%. The next stage of processing of the data used for Principal Component Analysis, by reducing the number of variables obtained from the linear combination of chemical species, try to improve the discriminant function obtained in the second stage and increase the discrimination power of the data, but the result was not satisfactory. In Profile Analysis curves were obtained for each production area, based on the characteristics of the chemical species present in each zone. With this study it was possible to develop a method using hydrochemistry and statistical analysis that can be used to distinguish the water produced in mature fields of oil, so that it is possible to identify the zone of production that is contributing to the excessive elevation of the water volume.

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Foi avaliada, no presente estudo, a prevalência dos casos de tumores benignos e malignos de glândulas salivares maiores e menores que ocorreram na população atendida no hospital Dr. Luiz Antônio Natal/RN, no período de 1989 a 2005, bem como as possíveis associações existentes entre os aspectos clínico e patológicos exibidos pelos referidos tumores, visando à obtenção de parâmetros indicadores de diagnóstico e/ou prognóstico. Dos prontuários dos pacientes foram obtidas todas as informações clínicas necessárias para a realização do trabalho. A análise dos dados revelou que dos 303 tumores de glândula salivar estudados, a maioria (71%) foram benignos, o mais comum foi adenoma Pleomórfico. As médias de idades para os tumores benignos e malignos foi de 49,2 e 58,5 anos, respectivamente. Diferenças estatisticamente significativas entre estes tumores foram observadas para as seguintes variáveis: idade média, o tamanho do tumor e duração da doença. Em relação ao tamanho do tumor, carcinoma mucoepidermoide mostrou-se 1,74 vezes menor que o de outros tumores malignos. Uma associação entre o diagnóstico histológico e variável consistência do tumor foi observada. Os dados apresentados neste estudo são relevantes para a compreensão das diversas características exibidas por estes tumores, já que corroboram uma série de estudos anteriores

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Nowadays, telecommunications is one of the most dynamic and strategic areas in the world. Organizations are always seeking to find new management practices within an ever increasing competitive environment where resources are getting scarce. In this scenario, data obtained from business and corporate processes have even greater importance, although this data is not yet adequately explored. Knowledge Discovery in Databases (KDD) appears then, as an option to allow the study of complex problems in different areas of management. This work proposes both a systematization of KDD activities using concepts from different methodologies, such as CRISP-DM, SEMMA and FAYYAD approaches and a study concerning the viability of multivariate regression analysis models to explain corporative telecommunications sales using performance indicators. Thus, statistical methods were outlined to analyze the effects of such indicators on the behavior of business productivity. According to business and standard statistical analysis, equations were defined and fit to their respective determination coefficients. Tests of hypotheses were also conducted on parameters with the purpose of validating the regression models. The results show that there is a relationship between these development indicators and the amount of sales

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In recent decades, changes have been occurring in the telecommunications industry, allied to competition driven by the policies of privatization and concessions, have fomented the world market irrefutably causing the emergence of a new reality. The reflections in Brazil have become evident due to the appearance of significant growth rates, getting in 2012 to provide a net operating income of 128 billion dollars, placing the country among the five major powers in the world in mobile communications. In this context, an issue of increasing importance to the financial health of companies is their ability to retain their customers, as well as turn them into loyal customers. The appearance of infidelity from customer operators has been generating monthly rates shutdowns about two to four percent per month accounting for business management one of its biggest challenges, since capturing a new customer has meant an expenditure greater than five times to retention. For this purpose, models have been developed by means of structural equation modeling to identify the relationships between the various determinants of customer loyalty in the context of services. The original contribution of this thesis is to develop a model for loyalty from the identification of relationships between determinants of satisfaction (latent variables) and the inclusion of attributes that determine the perceptions of service quality for the mobile communications industry, such as quality, satisfaction, value, trust, expectation and loyalty. It is a qualitative research which will be conducted with customers of operators through simple random sampling technique, using structured questionnaires. As a result, the proposed model and statistical evaluations should enable operators to conclude that customer loyalty is directly influenced by technical and operational quality of the services offered, as well as provide a satisfaction index for the mobile communication segment

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The objective of this work was the development and improvement of the mathematical models based on mass and heat balances, representing the drying transient process fruit pulp in spouted bed dryer with intermittent feeding. Mass and energy balance for drying, represented by a system of differential equations, were developed in Fortran language and adapted to the condition of intermittent feeding and mass accumulation. Were used the DASSL routine (Differential Algebraic System Solver) for solving the differential equation system and used a heuristic optimization algorithm in parameter estimation, the Particle Swarm algorithm. From the experimental data food drying, the differential models were used to determine the quantity of water and the drying air temperature at the exit of a spouted bed and accumulated mass of powder in the dryer. The models were validated using the experimental data of drying whose operating conditions, air temperature, flow rate and time intermittency, varied within the limits studied. In reviewing the results predicted, it was found that these models represent the experimental data of the kinetics of production and accumulation of powder and humidity and air temperature at the outlet of the dryer

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

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Orbital remote sensing has been used as a beneficial tool in improving the knowledge on oceanographic and hydrodynamic aspects in northern portion of the continental shelf of Rio Grande do Norte, offshore Potiguar Basin. Aspects such as geography, temporal and spatial resolution combined with a consistent methodology and provide a substantial economic advantage compared to traditional methods of in situ data collecting. Images of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA's AQUA satellite were obtained to support systematic data collections related to the campaign of environmental monitoring and characterization of Potiguar Basin, held in May 2004. Images of Total Suspension Matter (TSM) and values of radiance standard were generated for the calculation of concentrations of total suspension matter (TSM), chlorophyll-a and sea surface temperature (SST). These data sets were used for statistical comparisons between measures in situ and satellite estimates looking validate algorithms or develop a comprehensive regional approach empirically. AQUA-MODIS images allowed the simultaneous comparison of two-dimensional water quality (total suspension matter), phytoplankton biomass (chlorophyll-a) variability and physical (temperature). For images of total suspension matter, the generated models showed a good correlation with the field data, allowing quantitative and qualitative analysis. The images of chlorophyll-a showed a consistent correlation with the in situ values of concentration. The algorithms adjusted for these images obtained a correlation coefficient fairly well with the data field in order that the sensor can be having an effect throughout the water column and not just the surface. This has led to a fit between the data of chlorophyll-the integration of the average sampling interval of the entire water column up to the level of the first optical depth, with the data generated from the images. This method resulted in higher values of chlorophyll concentration to greater depths, due to the fact that we are integrating more values of chlorophyll in the water column. Thus we can represent the biomass available in the water column. Images SST and SST measures in situ showed a mean difference DT (SST insitu - SST sat) around -0.14 ° C, considered low, making the results very good. The integration of total suspension matter, chlorophyll-a, the temperature of the sea surface (SST) and auxiliary data enabled the recognition of some of the main ways to fund the continental shelf. The main features highlighted were submerged canyons of rivers Apodi and Açu, some of the lines and beachrocks reefs, structural highs and the continental shelf break which occurs at depths around -60 m. The results confirmed the high potential for use of the AQUA-MODIS images to environmental monitoring of sea areas due to ease of detection of the field two-dimensional material in suspension on the sea surface, temperature and the concentration of chlorophyll-a

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A fragilidade brasileira quanto à competitividade turística é um fato observável nos dados da Organização Mundial do Turismo. O Brasil caiu em 2011, da 45ª para a 52ª posição, apesar de liderar no atributo recursos naturais e estar colocado na 23° em recursos culturais. Assim, grandes interesses e esforços têm sido direcionados para o estudo da competitividade dos produtos e destinos turísticos. O destino turístico é caracterizado por um conjunto complexo e articulado de fatores tangíveis e intangíveis, apresentando alta complexidade, dados de elevada dimensionalidade, não linearidade e comportamento dinâmico, tornando-se difícil a modelagem desses processos por meio de abordagens baseadas em técnicas estatísticas clássicas. Esta tese investigou modelos de equações estruturais e seus algoritmos, aplicados nesta área, analisando o ciclo completo de análise de dados, em um processo confirmatório no desenvolvimento e avaliação de um modelo holístico da satisfação do turista; na validação da estrutura do modelo de medida e do modelo estrutural, por meio de testes de invariância de múltiplos grupos; na análise comparativa dos métodos de estimação MLE, GLS e ULS para a modelagem da satisfação e na realização de segmentação de mercado no setor de destino turístico utilizando mapas auto-organizáveis de Kohonen e sua validação com modelagem de equações estruturais. Aplicações foram feitas em análises de dados no setor de turismo, principal indústria de serviços do Estado do Rio Grande do Norte, tendo sido, teoricamente desenvolvidos e testados empiricamente, modelos de equações estruturais em padrões comportamentais de destino turístico. Os resultados do estudo empírico se basearam em pesquisas com a técnica de amostragem aleatória sistemática, efetuadas em Natal-RN, entre Janeiro e Março de 2013 e forneceram evidências sustentáveis de que o modelo teórico proposto é satisfatório, com elevada capacidade explicativa e preditiva, sendo a satisfação o antecedente mais importante da lealdade no destino. Além disso, a satisfação é mediadora entre a geração da motivação da viagem e a lealdade do destino e que os turistas buscam primeiro à satisfação com a qualidade dos serviços de turismo e, posteriormente, com os aspectos que influenciam a lealdade. Contribuições acadêmicas e gerenciais são mostradas e sugestões de estudo são dadas para trabalhos futuros.

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Data Visualization is widely used to facilitate the comprehension of information and find relationships between data. One of the most widely used techniques for multivariate data (4 or more variables) visualization is the 2D scatterplot. This technique associates each data item to a visual mark in the following way: two variables are mapped to Cartesian coordinates so that a visual mark can be placed on the Cartesian plane; the others variables are mapped gradually to visual properties of the mark, such as size, color, shape, among others. As the number of variables to be visualized increases, the amount of visual properties associated to the mark increases as well. As a result, the complexity of the final visualization is higher. However, increasing the complexity of the visualization does not necessarily implies a better visualization and, sometimes, it provides an inverse situation, producing a visually polluted and confusing visualization—this problem is called visual properties overload. This work aims to investigate whether it is possible to work around the overload of the visual channel and improve insight about multivariate data visualized through a modification in the 2D scatterplot technique. In this modification, we map the variables from data items to multisensoriy marks. These marks are composed not only by visual properties, but haptic properties, such as vibration, viscosity and elastic resistance, as well. We believed that this approach could ease the insight process, through the transposition of properties from the visual channel to the haptic channel. The hypothesis was verified through experiments, in which we have analyzed (a) the accuracy of the answers; (b) response time; and (c) the grade of personal satisfaction with the proposed approach. However, the hypothesis was not validated. The results suggest that there is an equivalence between the investigated visual and haptic properties in all analyzed aspects, though in strictly numeric terms the multisensory visualization achieved better results in response time and personal satisfaction.

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Survival models deals with the modelling of time to event data. In certain situations, a share of the population can no longer be subjected to the event occurrence. In this context, the cure fraction models emerged. Among the models that incorporate a fraction of cured one of the most known is the promotion time model. In the present study we discuss hypothesis testing in the promotion time model with Weibull distribution for the failure times of susceptible individuals. Hypothesis testing in this model may be performed based on likelihood ratio, gradient, score or Wald statistics. The critical values are obtained from asymptotic approximations, which may result in size distortions in nite sample sizes. This study proposes bootstrap corrections to the aforementioned tests and Bartlett bootstrap to the likelihood ratio statistic in Weibull promotion time model. Using Monte Carlo simulations we compared the nite sample performances of the proposed corrections in contrast with the usual tests. The numerical evidence favors the proposed corrected tests. At the end of the work an empirical application is presented.