Demanda energética em diferentes manejos de solo e híbridos de milho

This study aimed to account the energy balance of six different farming systems: CMT (minimum tillage with transgenic corn); CMNT (minimum tillage with non-transgenic corn); PCT (conventional tillage with transgenic corn); PCNT (conventional tillage with non-transgenic corn); PDT (no-tillage system with transgenic corn) and PDNT (no-tillage system with non-transgenic corn). The study was carried out at the Experimental Farm Lageado FCA/Unesp, Botucatu - SP. The energy balance was performed by subtracting the total energy inputs by the total energy output. Energy efficiency was calculated by dividing the total energy output by the total energy inputs. The total values ​​of energy inputs and outputs were expressed in MJ.ha-1 (mega joules per hectare). To calculate the energy inputs were accounted the fuel consumption and lubricants, depreciation energy of machinery and implements, manpower and agricultural inputs. The energy outputs were calculated according to the maize yield. The result shows that the system PDT was the system that had better energy efficiency and the system that had the lowest efficiency was the PCT system. The system with lower energy demand was the PDT and the greatest demand was PCNT.

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

Modelagem auto-regressiva na previsão de demanda dos produtos de uma empresa do setor químico

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