Novus fossilium index

Arkit: 1 arkintunnukseton lehti, H4.

Index fossilium analysibus chemicis examinatorum

Arkit: 2 arkintunnuksetonta lehteä, B4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, I4 K1.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, F4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, C4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, A4.

Novus fossilium index

Arkit: 1 arkintunnukseton lehti, L4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, E4.

Index fossilium analysibus chemicis examinatorum

Arkit: 1 arkintunnukseton lehti, G4 H2.

Quantitative structure-retention relationships analysis of retention index of essential oils

Genetic algorithm and multiple linear regression (GA-MLR), partial least square (GA-PLS), kernel PLS (GA-KPLS) and Levenberg-Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention index (RI) and descriptors for 116 diverse compounds in essential oils of six Stachys species. The correlation coefficient LGO-CV (Q²) between experimental and predicted RI for test set by GA-MLR, GA-PLS, GA-KPLS and L-M ANN was 0.886, 0.912, 0.937 and 0.964, respectively. This is the first research on the QSRR of the essential oil compounds against the RI using the GA-KPLS and L-M ANN.

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

Effect of Fusarium graminearum and infection index on germination and vigor of maize seeds

Pathogens in maize (Zea mays) seeds cause serious problems, such as the loss of their capacity to germinative. The objectives of this study were to identify the optimal period for infection of maize seeds on agar colonized by Fusarium graminearum, when incubated for 4, 8, 16 and 32 h, and to evaluate the effect of the fungus on the germination and vigor of seeds with different infection levels. After the respective incubation periods, the seeds were removed from the culture medium and submitted to the blotter test for 3 min with and without superficial disinfection with 1% solution of sodium hypochlorite. Once the optimal period for seed incubation was identified, seeds from the same sample were again placed on the colonized agar for infection. Germination and vigor tests (accelerated aging and cold test) were performed with a mixture of healthy seeds (placed on PDA medium) and inoculated seeds, resulting in seeds with 0, 20, 40, 60, 80 and 100% rates of infection. The results showed that a period of 32 h was long enough to obtain seeds infected by the pathogen. There were no significant effects of fungal infection on seed germination at any of the infection levels, probably due to the high vigor of the maize seed lot tested. Regarding vigor tests, infection levels differed significantly from the control (0% infection), but there were no significant differences among the infection levels.

Is it possible to utilize VIX for a profitable trading strategy of S&P 500 Index?

The goal of this research was to make an overall sight to VIX® and how it can be used as a stock market indicator. Volatility index often referred as the fear index, measures how much it costs for investor to protect his/her S&P 500 position from fluctuations with options. Over the relatively short history of VIX it has been a successful timing coordinator and it has given incremental information about the market state adding its own psychological view of the amount of fear and greed. Correctly utilized VIX information gives a considerable advantage in timing market actions. In this paper we test how VIX works as a leading indicator of broad stock market index such as S&P 500 (SPX). The purpose of this paper is to find a working way to interpret VIX. The various tests are made on time series data ranging from the year 1990 to the year 2010. The 10-day simple moving average strategy gave significant profits from the whole time when VIX data is available. Strategy was able to utilize the increases of SPX in example portfolio value and was able to step aside when SPX was declining. At the times when portfolio was aside of S it was on safety fund like on treasury bills getting an annual yield of 3 percent. On the other side just a static number’s of VIX did not work as indicators in a profit making way.

Enhanced Vegetation Index (EVI) na análise da dinâmica da vegetação da reserva biológica de Sooretama, ES

Técnicas de análises de séries temporais são utilizadas para caracterizar o comportamento de fenômenos naturais no domínio do tempo. Neste artigo, segundo a metodologia proposta por Box et al. (1994), 125 observações do Enhanced Vegetation Index (EVI) foram analisadas. Os valores modelados correspondem às variações temporais ocorridas no dossel florestal da reserva biológica de Sooretama, localizada ao Norte do Estado do Espírito Santo, no Município de Linhares. Os resultados indicaram que a metodologia foi adequada. Os resíduos do modelo ajustado são não correlacionados com distribuição normal, média zero e variância s². Com o menor valor do Critério de Informação de Akaike (AIC) -570,51, o modelo ajustado foi o Sazonal Auto-Regressivo Integrado de Médias Móveis (1,0,1)(1,0,1)12.