972 resultados para Precipitação (Meteorologia) - Previsão - Paraná


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The sugarcane industry has been important in the Brazilian economy since the colonial period. The search for alternative energy sources has gained more prominence, by offering a product generating clean energy. With the opening of the Brazilian economy, the sector has undergone transformations operating in a free market environment requiring greater efficiency and competitiveness of those involved in order to stay in business. This scenario is producer/supplier independent, and social aspects related to their stay in the market. Although its share in sugarcane production is smaller than the plant itself, it is still considerable having reached around 20% to 25% in 2008 by employing labor, also production factors had an important economic impact in the regions where they operate. Therefore, this study aimed to estimate the economic efficiency and production of independent sugarcane producers in the state of Paraná through the DEA model. The Data envelopment analysis (DEA) is a nonparametric technique that, using linear programming constructs production borders from production units that employ similar technological processes to transform inputs into outputs.The results showed that of the total surveyed, 13.56% had maximum efficiency (an efficiency score equal to 1). The average efficiency under variable returns to scale (BCC-DEA) was 0.71024. One can thus conclude that for the majority of the samples collected, it might be better use of available resources to the in order to obtain the economic efficiency of the production process.

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Knowing the annual climatic conditions is of great importance for appropriate planning in agriculture. However, the systems of climatic classification are not widely used in agricultural studies because of the wide range of scales in which they are used. A series with data from 20 years of observations from 45 climatological stations in all over the state of Pernambuco was used. The probability density function of the incomplete gamma distribution was used to evaluate the occurrence of dry, regular and rainy years. The monthly climatic water balance was estimated using the Thornthwaite and Mather method (1955), and based on those findings, the climatic classifications were performed using the Thornthwaite (1948) and Thornthwaite and Mather (1955) for each site. The method of Kriging interpolation was used for the spatialization of the results. The study classifications were very sensitive to the local reliefs, to the amount of rainfall, and to the temperatures of the regions resulting in a wide number of climatic types. The climatic classification system of Thornthwaite and Mather (1955) allowed efficient classification of climates and a clearer summary of the information provided. In so doing, it demonstrated its capability to determine agro climatic zones.

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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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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Geologia Regional - IGCE

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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The 1988 constitution makes an life is a supreme good when increased health as the fundamental condition requiring that all ill patient has the right to be treated in a public hospital (CF, art. 196). In this sense, the goal of this work is to generate a weekly forecast of hospital care by means of an advanced prediction model. It is expected that the model of self-regressivas seasonal moving averages SARIMA generate reliable and adherent to issue forecasts analyzed, thus enabling better resource allocation and more efficient hospital management

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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

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The national truck fleet has expanded strongly in recent decades. However, due to fluctuations in the demand that the market is exposed, it needed up making more effective strategic decisions of automakers. These decisions are made after an evaluation of guaranteed sales forecasts. This work aims to generate an annual forecast of truck production by Box and Jenkins methodology. They used annual data for referring forecast modeling from the year 1957 to 2014, which were obtained by the National Association of Motor Vehicle Manufacturers (Anfavea). The model used was Autoregressive Integrated Moving Average (ARIMA) and can choose the best model for the series under study, and the ARIMA (2,1,3) as representative for conducting truck production forecast

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Considering the high competitiveness in the industrial chemical sector, demand forecast is a relevant factor for decision-making. There is a need for tools capable of assisting in the analysis and definition of the forecast. In that sense, the objective is to generate the chemical industry forecast using an advanced forecasting model and thus verify the accuracy of the method. Because it is time series with seasonality, the model of seasonal autoregressive integrated moving average - SARIMA generated reliable forecasts and acceding to the problem analyzed, thus enabling, through validation with real data improvements in the management and decision making of supply chain

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In Geotechnical engineering the foundation projects depend on the bearing capacity and the acceptable displacements. One of the possible ways to predict the bearing capacity of foundations is through semi-empirical statistical methods which correlate in-situ tests (SPT and CPT). The piles breaking loads are defined by the interpretation of the load x head displacement curve and the experimental data acquired through the load test. In this work it is studied the behavior of bored piles executed in the Araquari/SC region, comparing the bearing capacity values predicted by the methods DECOURT & QUARESMA MODIFICADO (1996), AOKI & VELLOSO MODIFICADO MONTEIRO (2000), MILITITISKY E ALVES (1985), DECOURT & QUARESMA (1978), MÉTODO DE AOKI & VELLOSO (1975) e PHILOPANNAT (1986), with the results of the load test, evaluating their differences and discussing parameters that have direct effects on the prediction

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