Fuzzy time series Hollister Canada Coupon models have been applied to handle nonlinear problems. To forecast fuzzy time series, this study applies a backpropagation neural network because of its nonlinear structures. We propose two models: a basic model using a neural network approach to forecast all of the observations,
and a hybrid model consisting of a neural network approach to forecast the known patterns as well as a simple method to forecast the unknown patterns. The stock index in Taiwan for the years 1991–2003 is chosen as the forecasting target. The empirical results show that the hybrid model outperforms both the basic and a conventional fuzzy time series models.
Determination of tin (Sn) concentrations in human blood samples was examined by atomic absorption spectrometry with a graphite furnace. The matrix modifiers, 120 μg/ml Ni, 0.1% H3PO4, and 10% ascorbic acid, in the furnace increased the sensitivity; the detection limit was 2.5 ng/ml when 10 μl of the sample solution was injected into furnace, namely 25 pg of Sn. When Sn in blood samples was determined, addition of ascorbic acid was necessary. Hollister Toronto Canada
The highest absorbance was observed when Ni, H3PO4, and ascorbic acid were added. Sn concentrations in blood of male smelter workers were very low, ranging from below the detection limit to 20 ng/ml, which were considered to be normal values.