Avaliação da erosão hídrica por meio da Equação Universal de Perda de Solo Modificada (MEUPS) na bacia hidrográfica do Ribeirão Jacutinga, Rio Claro (SP)

Water erosion is one of the main processes responsible for soil degradation, resulting in loss of parcels of land suitable for agriculture, to the loss of agricultural inputs and the resulting drift of pesticides and excess sediment to rivers, causing phenomena such as the siltation and eutrophication of water bodies. Such a scenario makes it necessary to perform work of a technical and scientific to provide subsidies to land-use planning, in order to protect natural resources biotic and abiotic. To develop this work is necessary to find a unit of analysis capable of integrating the different elements of the landscape, hydrosphere, atmosphere, biosphere and lithosphere. Therefore we adopt for this work the watershed as main unit studies. From this question, this project will focus on the assessment of surface water erosion through MEUPS (Equation Modified Universal Soil Loss) predictive model. With the aid of maps, remote sensing products, and the use of geotechnology, this study aims to evaluate for the for Natural Erosion Potential the basin of the Jacutinga river, located in Rio Claro - SP

Equação de Estado para o Universo Primordial

Pós-graduação em Física - IFT

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model

Boa colocação da equação do calor semilinear em L^p-fraco

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)