No abstract

Este trabalho objetivou estudar a distribuição espacial do café entre as 558microrregiões brasileiras, no período de 1991 a 2003, ou seja, identificar regimes espaciaisou clusters desta commodity e sua dinâmica no decorrer dos anos em estudo. Para isto,foram utilizados o quociente locacional para medir a concentração da produção do cafée a análise exploratória de dados espaciais. É possível destacar que a estrutura espacialde produção do café persiste espacialmente concentrada, principalmente nos estados deMinas Gerais e Espírito Santo, que, em 2004, contribuíram com 70,66% da produçãonacional do café, e em microrregiões do estado de Rondônia, que apresentaram quocienteslocacionais muito elevados na produção desse produto.

Six yeast isolates were obtained from rotting wood samples in Brazil and frass of a cerambycid beetle larva in French Guiana. Sequence analysis of the ITS-5.8S region and the D1/D2 domains of the large subunit rRNA gene showed that the isolates represent a novel species of Cyberlindnera. This novel species is related to Cyberlindnera japonica, Cyberlindnera xylosilytica, Candida easanensis and Candida maesa. It is heterothallic and produces asci with two or four hat-shaped ascospores. The name Cyberlindnera dasilvae sp. nov. is proposed to accommodate the novel species. The holotype of Cy. dasilvae is CBS 16129T and the designated paratype is CBS 16584. The MycoBank number is 838252. All isolates of Cy. dasilvae were able to convert xylose into xylitol with maximum xylitol production within 60 and 72 h. The isolates produced xylitol with values ranging from 12.61 to 31.79 g l−1 in yeast extract–peptone–xylose medium with 5% xylose. When the isolates were tested in sugarcane bagasse hydrolysate containing around 35–38 g l−1 d-xylose, isolate UFMG-CM-Y519 showed maximum xylitol production.

Purpose: The objective of this article is to model a minute series of exchange rates for the EUR/USD pair using the singular spectrum analysis (SSA) and ARIMA-GARCH methods and evaluate which one offers better forecasts for a five-minute horizon. Originality/value: Despite being a successful technique in other branches of science, the application of SSA in finance is quite new. Furthermore, exchange rate modeling is a complex problem, comprising statistical concepts and properties. However, despite the complexity, the analysis of this series is extremely important for several agents playing, directly or indirectly, a role in the economy and the financial market. Design/methodology/approach: Time series models were estimated using the ARIMA-GARCH and SSA techniques, taking into account three samples of the ask exchange rate (closing): uptrend, downtrend, and no well-defined trend. Findings: The forecasts carried out by the SSA were the ones closest to the original observations for the three cases. Regarding the quality measurements, SSA obtained the best results for both uptrend and downtrend samples; for the sample with no well-defined trend, the findings indicated that the ARIMA-GARCH technique attained better results. However, it was concluded that the SSA forecasts, regarding exchange rates during the studied period, are more appropriate than the ones obtained by the ARIMA-GARCH model, regardless of the market movement.

ABSTRACT. This paper proposes a method (denoted by WD-ANN) that combines the Artificial Neural Networks (ANN) and the Wavelet Decomposition (WD) to generate short-term global horizontal solar radiation forecasting, which is an essential information for evaluating the electrical power generated from the conversion of solar energy into electrical energy. The WD-ANN method consists of two basic steps: firstly, it is performed the decomposition of level p of the time series of interest, generating p + 1 wavelet orthonormal components; secondly, the p + 1 wavelet orthonormal components (generated in the step 1) are inserted simultaneously into an ANN in order to generate short-term forecasting. The results showed that the proposed method (WD-ANN) improved substantially the performance over the (traditional) ANN method.