Este trabalho tem como objetivo estudar alguns modelos matemáticos em Epidemiologia, sob o ponto de vista das aplicações, com particular referência a epidemia da Covid-19. São apresentados os modelos clássicos como SIS (Suscetível -Infectado -Suscetível), SIR (Suscetível -Infectado -Removido) e SIRS (Suscetível -Infectado-Removido -Suscetível), considerando a população total constante. Como aplicação, o modelo SIR foi utilizado para projetar a evolução epidêmica da Covid-19 no Brasil e no Estado da Paraíba. Para tanto, utilizou-se métodos numéricos (Euler ou Runge-Kutta) para resolver os sistemas de equações diferenciais e os parâmetros foram obtidos através de processo de otimização. De acordo com os resultados obtidos, o modelo descreve bem a população de infectados nos períodos analisados. Verificou-se mudança na taxa de reprodução (𝑅 0 ) da Covid-19 no Brasil, que passou de 3.7 (no início da epidemia) para 1.72.
Simulate the rice drying process at specific drying conditions is of great interest to optimize the process and ensure a better quality of the final product. In the present work, experimental drying procedures of rough rice grains (BRSMG CONAI variety) was reported and drying kinetic was obtained at temperature using 40°C. The results were compared with simulated data by means of the liquid diffusion model equilibrium boundary condition. The geometry used to represent the rice grain was prolate spheroid. For this purpose, the diffusion equation, written in cylindrical coordinates, and solved via Galerkin-based integral method considering the constant diffusion coefficient. A good agreement was observed between predicted and experimental data. It was also possible to observe that the highest moisture gradients occur at the tip of the grain, which is region more affected by thermal and hydric stresses. The studied model can be used to solve problems involving diffusion processes, such as: drying, wetting, heating and cooling, provided that the geometrical shape of the body is similar to prolate spheroid.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.