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Arbitrage is a known economic term, implying simultaneous transaction on commodities. For instance, a Generation Company (GenCo) may involve itself in selling fuel, emission allowance and etc., instead of generating either active or reactive power. Moreover, arbitrage between active and reactive powers can, in turn, be considered. In this paper, arbitrage between active and reactive power is considered from Independent System Operator (ISO) viewpoint. A pool model is employed. ISO runs an optimization problem, by which, it could determine and predict GenCos behaviours; in terms of participating in active and reactive power markets for the next twelve months. A normal distribution function is used for price forecasting. Two 3-bus and 14-bus test systems are used to demonstrate the capabilities of the proposed algorithm.

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The aim of this study is to examine the main determinants of visitors' willingness to Pay (WTP) for the Pasargad Historical Palaces using contingent valuation method (CVM). To this end, the data are collected using questionnaire throughout a random sample of the citizens of Isfahan, Shiraz and Marvdasht. The data include socio-economic information and visitors' willingness to pay. The variables considered in the WTP model comprise gender, age, education, occupation, income, household size, environmental attitude and distance. Considering the characteristics of the data, the Probit and Tobit techniques are employed to estimate the model. The empirical results indicate that the average visitors’ willingness to pay is 114530 Rials. Moreover, the results confirm that the gender, household size, distance and income have statistically significant effects on visitors’ willingness to pay. However, the results show that the women are more likely to pay more than men. Based on the individuals willingness to pay and the number of visitors, the value of Pasargad Palaces from the point of view of the domestic visitors and consumption surplus exceed to 6640 and 6060 million Rials, respectively.

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Fried food products are very popular due to their unique characteristics such as color, smell, taste and desirable texture. Controlling frying conditions and using edible hydrocolloid coatings (gums) is one of the best ways in reduction of oil uptake, moisture retention and improving the appearance properties of fried foods. In this study, different concentrations of wild sage seed gum (0, 0.5, 1 and 1.5%) were used to coating of zucchini slices during deep frying at 155, 170 and 185°C and the relationship between process parameters and the quality of final product were modeled by genetic algorithm-artificial neural network method. The results of this study showed that coating with wild sage seed gum reduced the oil uptake of the final product and in terms of appearance characteristics, the coated samples were lighter. Coating pretreatment maintained the final product moisture and the size of the samples coated with 1.5% gum was larger than the other samples (lower surface changes percent). This process was modeled by genetic algorithm-artificial neural network method with 2 inputs include wild sage seed gum concentration and frying temperature and 7 outputs  include oil percentage, moisture content, yellowness index (b^*), redness index (a^*), lightness index (L^*), color changes intensity (ΔE) and surface changes. The results of modeling showed that a network with 5 neurons in a hidden layer and using the sigmoid activation function can predict the physicochemical properties of fried zucchini slices. Sensitivity analysis results showed that the changes in the concentration of wild sage seed gum had the highest effect on the yellowness index and then on the surface color changes intensity index of fried zucchini slices. Also, the change of frying temperature has the highest effects on the color changes intensity and lightness indexes of fried samples.