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Abstract: This study presents an adaptive intelligent algorithm for forecasting gasoline demand based of artificial neural network (ANN), conventional regression and design of experiment (DOE). To show the superiority and applicability of the proposed algorithm it has been applied for monthly demand estimation of gasoline in Japan, USA, Kuwait, Canada and Iran within 1992–2005. The economic indicators used in this paper are price, GDP (Gross Domestic Production), population, number of vehicles, gasoline demand in the last periods and correlation coefficient between variables. The proposed algorithm may be used to estimate gasoline demand in the future by optimizing parameter values. The proposed algorithm uses ANOVA to select either ANN or regression for future demand estimation. Furthermore, if the null hypothesis in ANOVA and F-test is rejected, the Duncan method is used to identify which model is closer to actual data at α level of significance. Neural network is an intelligent, adaptive and optimizing method for solving the complicated problems. The results show that ANN provides far less error than regression. Moreover, the proposed algorithm provides near optimal solution for gasoline demand estimation due to its flexibility and intelligibility. The intelligent algorithm is therefore ideal for policy making with respect to gasoline consumption estimation. [ABSTRACT FROM AUTHOR]

Within- and between-person variation in nutrient intake has been characterized in different adult populations, but little is known of country, age, or sex differences among children. The objectives of this study were as follows: 1) to describe the mean intake, within- and between-individual CV and variance ratios of nutrient intake among children ages 9-18 y old in Russia and the United States in 1996; 2) to compare the age and sex-related differences in nutrient intake variance within and between countries; and 3) to hypothesize about the feasibility of using within-individual variance estimates from one nationally representative sample to adjust the usual intake distributions in another nationally representative sample. Mean intakes of all nutrients except magnesium were significantly higher among U.S. children (P < 0.001); within-person variation was higher among the U.S. children, possibly indicating greater access to a wide array of foods. Strong differentials existed in variance components by sex in both countries, although not in the same direction, and differed by age in U.S. girls. Ratios of within- to between-person variance in 8 of 11 nutrients were lower among Russian (range: 0.9-1.6) than U.S. children (range: 1.4-1.7), suggesting that day-to-day bias may not affect Russian dietary recalls as strongly as in the United States. Researchers are encouraged to use these estimates to conduct sensitivity analyses of usual intake distributions in their own data when multiple days of data collection are not feasible. [ABSTRACT FROM AUTHOR]