914 resultados para Riemann, superfici, genere, curve


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Introduction Jatropha gossypifolia has been used quite extensively by traditional medicine for the treatment of several diseases in South America and Africa. This medicinal plant has therapeutic potential as a phytomedicine and therefore the establishment of innovative analytical methods to characterise their active components is crucial to the future development of a quality product. Objective To enhance the chromatographic resolution of HPLC-UV-diode-array detector (DAD) experiments applying chemometric tools. Methods Crude leave extracts from J. gossypifolia were analysed by HPLC-DAD. A chromatographic band deconvolution method was designed and applied using interval multivariate curve resolution by alternating least squares (MCR-ALS). Results The MCR-ALS method allowed the deconvolution from up to 117% more bands, compared with the original HPLC-DAD experiments, even in regions where the UV spectra showed high similarity. The method assisted in the dereplication of three C-glycosylflavones isomers: vitexin/isovitexin, orientin/homorientin and schaftoside/isoschaftoside. Conclusion The MCR-ALS method is shown to be a powerful tool to solve problems of chromatographic band overlapping from complex mixtures such as natural crude samples. Copyright © 2013 John Wiley & Sons, Ltd. Extracts from J. gossypifolia were analyzed by HPLC-DAD and, dereplicated applying MCR-ALS. The method assisted in the detection of three C-glycosylflavones isomers: vitexin/isovitexin, orientin/homorientin and schaftoside/isoschaftoside. The application of MCR-ALS allowed solving problems of chromatographic band overlapping from complex mixtures such as natural crude samples. Copyright © 2013 John Wiley & Sons, Ltd.

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

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Pós-graduação em Matemática - IBILCE

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Pós-graduação em Matemática Universitária - IGCE

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Com o objetivo de ajustar modelos não-lineares, foram utilizados registros mensais do peso de 10 fêmeas de cateto (Pecari tajacu) coletados durante dois anos, no criatório do campo experimental Álvaro Adolfo da Embrapa Amazônia Oriental, Belém, PA. Utilizaram-se os modelos de Von Bertalanffy, Brody, Gompertz e Logístico. Os parâmetros foram estimados usando o procedimento NLIN do aplicativo SAS. Os critérios utilizados para verificar o ajuste dos modelos foram: desvio padrão assintótico (ASD); coeficiente de determinação (R2); desvio médio absoluto dos resíduos (ARD) e o índice assintótico (AR). Os modelos Brody e Logístico estimaram, respectivamente, o maior (19,44kg) e o menor (19,18kg) peso assintótico (A), caracterizando a menor (0,0064kg/dia) e a maior (0,0113kg/dia) taxa de maturação (K), haja vista a natureza antagônica entre estes parâmetros, comprovada pela correlação fenotípica variando entre -0,75 à -0,47. O modelo Brody estimou o menor valor para o ARD, fator limitante para caracterizar o menor valor para o AR por este modelo. Considerando o AR, o modelo Brody apresentou o melhor ajuste, contudo, pelos valores encontrados, os demais modelos também apresentaram ajuste adequando aos dados ponderais da referida espécie/sexo. Com base no AR adotado neste trabalho, recomenda-se o modelo Brody para ajustar a curva de crescimento de fêmeas de cateto (Pecari tajacu). Em razão dos valores estimados, sobretudo, para a K, essa característica pode ser incluída em um índice de seleção. Contudo, estudos com grupos mais representativos e criados em outras condições se faz oportuno.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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

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In this Note it is worked out a new set of Laplace-Like equations for quaternions through Riemann-Cauchy hypercomplex relations otained earlier [1]. As in the theory of functions of a complex variable, it is expected that this new set of Laplace-Like equations might be applied to a large number of Physical problems, providing new insights in the Classical Fields Theory.

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The objective of this study was to estimate growth parameters of carcass components (wing, thighs and drumsticks, back and breast) and organs (heart, liver, gizzard and gut) in males and females of one meat-type quail strain (Coturnix coturnix coturnix) and two laying strains (Coturnix coturnix japonica) designated either yellow or red.A total of 1350 quail from 1 to 42d old were distributed in a completely randomised design, with 5 replicates of each strain. The carcass component weights and body organs were analysed weekly and evaluated using the Gompertz function; growth rates were evaluated through derivative equations.The meat-type strain presented the highest growth rates in carcass components and organs. Across strains, females showed the highest weight of internal organs at maturity compared to males.Females had greater growth potential in breast, wings and back than males for both yellow and red laying quail.

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The security of the two party Diffie-Hellman key exchange protocol is currently based on the discrete logarithm problem (DLP). However, it can also be built upon the elliptic curve discrete logarithm problem (ECDLP). Most proposed secure group communication schemes employ the DLP-based Diffie-Hellman protocol. This paper proposes the ECDLP-based Diffie-Hellman protocols for secure group communication and evaluates their performance on wireless ad hoc networks. The proposed schemes are compared at the same security level with DLP-based group protocols under different channel conditions. Our experiments and analysis show that the Tree-based Group Elliptic Curve Diffie-Hellman (TGECDH) protocol is the best in overall performance for secure group communication among the four schemes discussed in the paper. Low communication overhead, relatively low computation load and short packets are the main reasons for the good performance of the TGECDH protocol.

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In active learning, a machine learning algorithmis given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active learning affects AUC. We examine two existing algorithms from the literature and present our own active learning algorithms designed to maximize the AUC of the hypothesis. One of our algorithms was consistently the top performer, and Closest Sampling from the literature often came in second behind it. When good posterior probability estimates were available, our heuristics were by far the best.