978 resultados para ocean heat content
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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As respostas às mudanças de temperatura de plantas aclimatadas e não aclimatadas de E. grandis cultivadas in vitro foram avaliadas considerando alterações dos níveis de prolina e proteínas solúveis totais. Análises de proteínas solúveis através de SDS-PAGE e prolina foram realizadas após 12h a 12ºC (aclimatação ao frio) ou a 33ºC (aclimatação ao calor), e imediatamente depois dos choques térmicos a 41ºC e 0ºC. Análises também foram realizadas após um período de 24h depois dos choques térmicos (período de recuperação). O tratamento de temperatura a 0ºC não alterou o padrão de proteínas nas plantas aclimatadas e não aclimatadas, entretanto a temperatura baixa induziu altos níveis de prolina, que se mantiveram relativamente altos após o período de recuperação. Três novas proteínas (90,5, 75 e 39 kDa), provavelmente HSPs, foram observadas nas plantas aclimatadas e não aclimatadas submetidas às temperaturas altas. As plantas expostas a 41ºC foram capazes de recuperar-se dos choques após o período de recuperação, entretanto não houve recuperação completa das plantas expostas às baixas temperaturas. O efeito da aclimatação sobre a recuperação (homeostasis) pode variar dependendo do parâmetro avaliado, tipo e duração do choque térmico.
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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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Specific heat, thermal conductivity, thermal diffusivity, and density of coffee extract were experimentally determined in the range of 0.49 to 0.90 (wet basis) water content and at temperatures varying from 30 to 82 degreesC. Thermal conductivity and specific heat were measured by means of the same apparatus- a cell constituted of two concentric cylinders - operating at steady and unsteady state, respectively. The thermal diffusivity was measured by the well-known Dickerson's method and density was determined by picnometry. The results obtained were used to derive mathematical models for predicting these properties as a function of concentration and temperature.
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The accurate determination of thermophysical properties of milk is very important for design, simulation, optimization, and control of food processing such as evaporation, heat exchanging, spray drying, and so forth. Generally, polynomial methods are used for prediction of these properties based on empirical correlation to experimental data. Artificial neural networks are better Suited for processing noisy and extensive knowledge indexing. This article proposed the application of neural networks for prediction of specific heat, thermal conductivity, and density of milk with temperature ranged from 2.0 to 71.0degreesC, 72.0 to 92.0% of water content (w/w), and 1.350 to 7.822% of fat content (w/w). Artificial neural networks presented a better prediction capability of specific heat, thermal conductivity, and density of milk than polynomial modeling. It showed a reasonable alternative to empirical modeling for thermophysical properties of foods.
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The thermal properties of plums (Prunus domestica) and prunes were investigated in the moisture content of 14.2-80.4% (wet basis) near room temperature (approximately 28 degrees C). The apparent density of the fruits increased from 1042.9 to 1460.0 kg/m(3), and the bulk density increased from 706.6 to 897.5 kg/m(3) as the plums were dried, following classical empirical models as a function of moisture content. It was found that specific heat, effective thermal diffusivity, and effective thermal conductivity of the prunes increased with the moisture content of the samples, which can be represented by using different empirical models.