954 resultados para Models, Statistical


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Background elimination models are widely used in motion tracking systems. Our aim is to develop a system that performs reliably under adverse lighting conditions. In particular, this includes indoor scenes lit partly or entirely by diffuse natural light. We present a modified "median value" model in which the detection threshold adapts to global changes in illumination. The responses of several models are compared, demonstrating the effectiveness of the new model.

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To develop a mathematical model to predict the probability of having community-acquired pneumonia and to evaluate an already developed prediction rule that has not been validated in a clinical scenario.

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The purpose of the present study was to establish reference values for hemoglobins (Hb) using HPLC, in samples containing normal Hb (AA), sickle cell trait without alpha-thalassemia (AS), sickle cell trait with alpha-thalassemia (ASH), sickle cell anemia (SS), and Hb SC disease (SC). The blood samples were analyzed by electrophoresis, HPLC and molecular procedures. The Hb A2 mean was 4.30 ± 0.44% in AS, 4.18 ± 0.42% in ASH, 3.90 ± 1.14% in SS, and 4.39 ± 0.35% in SC. They were similar, but above the normal range. Between the AS and ASH groups, only the amount of Hb S was higher in the AS group. The Hb S mean in the AS group was 38.54 ± 3.01% and in the ASH it was 36.54 ± 3.76%. In the qualitative analysis, using FastMap, distinct groups were seen: AA and SS located at opposite extremes, AS and ASH with overlapping values and intermediate distribution, SC between heterozygotes and the SS group. Hb S was confirmed by allele-specific polymerase chain reaction. The Hb values established will be available for use as a reference for the Brazilian population, drawing attention to the increased levels of Hb A2, which should be considered with caution to prevent incorrect diagnoses. ©FUNPEC-RP.

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Incluye Bibliografía

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Dosage and frequency of treatment schedules are important for successful chemotherapy. However, in this work we argue that cell-kill response and tumoral growth should not be seen as separate and therefore are essential in a mathematical cancer model. This paper presents a mathematical model for sequencing of cancer chemotherapy and surgery. Our purpose is to investigate treatments for large human tumours considering a suitable cell-kill dynamics. We use some biological and pharmacological data in a numerical approach, where drug administration occurs in cycles (periodic infusion) and surgery is performed instantaneously. Moreover, we also present an analysis of stability for a chemotherapeutic model with continuous drug administration. According to Norton & Simon [22], our results indicate that chemotherapy is less eficient in treating tumours that have reached a plateau level of growing and that a combination with surgical treatment can provide better outcomes.

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Pós-graduação em Engenharia Elétrica - FEIS

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Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated margional residual vector by the Cholesky decomposition of the inverse of the estimated margional variance matrix. The resulting "rotated" residuals are used to construct an empirical cumulative distribution function and pointwise standard errors. The theoretical framework, including conditions and asymptotic properties, involves technical details that are motivated by Lange and Ryan (1989), Pierce (1982), and Randles (1982). Our method appears to work well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series). Our methods can produce satisfactory results even for models that do not satisfy all of the technical conditions stated in our theory.