56 resultados para Logistic growth equation


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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Adjusting autoregressive and mixed models to growth data fits discontinuous functions, which makes it difficult to determine critical points. In this study we propose a new approach to determine the critical stability point of cattle growth using a first-order autoregressive model and a mixed model with random asymptote, using the deterministic portion of the models. Three functions were compared: logistic, Gompertz, and Richards. The Richards autoregressive model yielded the best fit, but the critical growth values were adjusted very early, and for this purpose the Gompertz model was more appropriate.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Delamination or crack propagation between plies is a critical issue for structural composites. In viewing this issue and the large application of woven fabrics in structural applications, especially the ones that requires high drapeability to be preformed in a RTM mold cavity such as the asymmetric ones, e.g HS series, this research aimed in dynamically testing the carbon fiber 5HS/RTM6 epoxy composites under opening mode using DCB set up in order to investigate the crack growth rate behavior in an irregular surface produced by the fabric waviness. The evaluation of the energy involved in each crack increment was based on the Irwin-Kies equation using compliance beam theory. The tests were conducted at constant stress ratio of R=0.1 with displacement control, frequency of 10 Hz, in accordance to ASTM E647-00 for measurement of crack growth rate. The results showed large scatter when compared to unidirectional carbon fiber composites due to damage accumulation at the fill tows.

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Pós-graduação em Biometria - IBB

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Objective: To evaluate the growth pattern of low birth weight preterm infants born to hypertensive mothers, the occurrence of growth disorders, and risk factors for inadequate growth at 24 months of corrected age (CA).Methods: Cohort study of preterm low birth weight infants followed until 24 months CA, in a university hospital between January 2009 and December 2010. Inclusion criteria: gestational age < 37 weeks and birth weight of 1,500-2,499g. Exclusion criteria: multiple pregnancies, major congenital anomalies, and loss to follow up in the 2nd year of life. The following were evaluated: weight, length, and BMI. Outcomes: growth failure and risk of overweight at 0, 12, and 24 months CA. Student's t-test, Repeated measures ANOVA (RM-ANOVA), and multiple logistic regression were used.Results: A total of 80 preterm low birth weight infants born to hypertensive mothers and 101 born to normotensive mothers were studied. There was a higher risk of overweight in children of hypertensive mothers at 24 months; however, maternal hypertension was not a risk factor for inadequate growth. Logistic regression showed that being born small for gestational age and inadequate growth in the first 12 months of life were associated with poorer growth at 24 months.Conclusion: Preterm low birth weight born infants to hypertensive mothers have an increased risk of overweight at 24 months CA. Being born small for gestational age and inadequate growth in the 1st year of life are risk factors for growth disorders at 24 months CA. (C) 2014 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

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In order to refine the solution given by the classical logistic equation and extend its range of applications in the study of tumor dynamics, we propose and solve a generalization of this equation, using the so-called Fractional Calculus, i.e., we replace the ordinary derivative of order 1, in one version of the usual equation, by a non-integer derivative of order 0 < α < 1, and recover the classical solution as a particular case. Finally, we analyze the applicability of this model to describe the growth of cancer tumors.