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Using Ramsey – Cass – Koopmans optimal growth model and specifying government’ tax behavior, this study analyzes the effect of tax policies on steady-state and optimal dynamic path of consumption, capital stock and  output for Iran and a group of East Asian countries. In this regard, after specifying consumption and capital stock dynamic behaviors, a model is calibrated and simulated for the selected economies by using annul data during 1980 -2010. Based on the simulation results for Iran, and compared to East Asian region, reductions in tax rates have no significant effects on steady- state and optimal dynamic path of capital stock, consumption and per capita output. Hence, tax policies are not effective in stimulating the real sector of the Iranian economy. The results also show that reductions in rates of income tax, capital gain tax and profit tax have positive and significant effects on the long-run steady-state path of consumption, capital stock and output, especially in less developed East Asian countries. The simulation results show that reduction in consumption tax rate, in particular across the highly developed East Asian countries, has positive and significant effect on steady-state of consumption; however, it has no effect on capital stock and output.   

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During recent decades, Energy as one of the most important factors of production and also as one of the most important end products, a special place in the country's economic development and growth development. Hence, the country authorities should try to predict anything more precise energy consumption in the proper planning and guidance consumption, to control the way they desired energy demand and supply parameters. The purpose of this paper is Evaluation Hybrid model of artificial neural networks and genetic algorithms in the forecast consumption energy of Iran. Therefore in this study, data from the annual energy consumption as the output forecasting model range and was used as input variables, data of the annual total population, GDP, imports and exports.The end results were assessed with of different models (RSE), (ME) and (RMSE). Evaluation results showed that the hybrid model of neural networks and genetic algorithm (ANN-GA), compared to other models with the highest accuracy in predicting consumption energy of Iran.

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Abstract: Concern about the environment has led to a new segment of consumers called green consumers. Because not all the consumers are equally green, using target marketing for persuading them to buy green product is essential. The first step in target marketing strategy is to segment the market and then develop profiles of the resulting market segments. This study aims to identify distinct green market segments based on demographic, psychographic and behavioral variables and also investigate the relationship between each variable and green consumer behavior. This study uses self-organizing maps (SOM) to segment and then develop profiles of Iranian green consumers. Based on the results four market segments have identified and were named intense greens, potential greens, egoist browns and intense browns. The results of this study also indicate that demographic variables including gender, age and income and psychographic and behavioral variables including personal values, religiosity, environmental attitude and knowledge have significant role in predicting green consumers’ behavior.

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Nowadays, the building ventilation is an essential process, due to need of improving the air quality and thermal comfort conditions for occupants. Providing the mentioned conditions is more complex for crowded and larger spaces. In this study, the effects of air change rate per hour (ACH) on thermal comfort, indoor air quality and energy consumption in an amphitheater with under floor air distribution system have been investigated by using the computational fluid dynamics and Open Foam numerical solver. For this issue, an amphitheater with 50 occupants has been modeled under the conditions that the air inlet diffusers located in front of seats. Also, the air change rate per hour is assumed to be 5, 10 and 15. For better comparison between the results, inlet air temperature is controlled until the mean of thermal comfort index (TSENS) in the occupied zone equals to zero. The results indicate that for air change rates of 15, 10 in comparison with ACH of 5, the CO2 concentrations in the occupied zone are respectively reduced about 36 and 46 percent and so the indoor air quality is improved. On the other hand, the energy consumption is increased about 28 and 69 percent, respectively. Also, based on the results, by increasing the ACH rate, the draft local discomfort is significantly increased and can be reached at the amount of 15%.

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Economic theories suggest that increasing uncertainty induces households to reduce the growth of their consumption expenditure. This study aims to examine how to change the consumption expenditure of Iranian households due to uncertainty in government expenditure. To do this, using annual data for 1978-2012, first, a measure for government expenditure uncertainty was introduced, and then its effects on household consumption behavior were analyzed. The results indicate that uncertainty in government expenditure has a negative and significant effect on growth of household consumption expenditure. On the other hand, the effect of government spending uncertainty on consumption expenditure of durable goods is positive. In other words, Iranian households in increasing uncertainty settings face with the growth of consumption spending on durable goods. Thus, the government needs to create transparency in fiscal policy, to reduce policy uncertainty for households as far as possible.

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The ecosystem is a fundamental pillar of human life, which has been changed due to the progress and development of the world. The emission of air pollutants is a key factor in environmental degradation. Air pollutants impose the so-called "degradation costs" on different sectors, which these costs are not included in official calculations. Consumption of energy carriers is the main cause of air pollutants emissions in Iran. Therefore, this research seeks to examine the degradation costs of air pollutants emitted by the use of energy carriers in Iran. One way to reduce the emissions of air pollutants and their degradation costs is to impose tax on the consumption of energy carriers. This study considers a scenario for raising the price of energy carriers to the level of FOB price of Persian Gulf. For this purpose, the standard computable general equilibrium model of Lofgren et al () is used. The statistical basis of the CGE model is the 2006 social accounting matrix (SAM). The statistical data on energy consumption and emissions of air pollutants are derived from the energy balance sheets over the period 2006-2012. In addition, economic sectors are divided into 25 sections according to the ISIC classification. Degradation cost in the baseline scenario is equivalent of 14.43% of GDP (at constant prices) in 2006, which by applying the scenario for increasing the price of energy carriers, this cost declines by 23% and amounts to 10 percent of GDP. Results also reveal that the road transportation and electricity sectors experience the greatest reductions in degradation cost.

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Inefficiency and injustice of subsidy system in Iran over the years induced the government to implement targeted subsidy law since 2010. As a result, the share of poor households receiving government subsidies decreased. Utilizing the micro-data of Households’ Income and Expenditure Surveys (HIES) from 2005 to 2014 and applying seemingly unrelated regression(SUR) technique through feasible generalized least squares (FGLS) method, this study examines the effect of targeting subsidies on households’ consumption combination. The results show that the shares of necessary goods in households’ expenditure have increased. According to Engle theory, these indicate reductions in households’ welfare. In addition, the “subsidy” dummy variable has the most effect on food expenditure share among different goods groups. One reason for the welfare reduction is higher increase in relative prices compared to increase in income due to subsidy. Since, the permanent income is of the highest effect on allocation of households’ budget among different goods groups; therefore, policy makers should focus on increase in the households’ real permanent income instead of inflationary policies, which increase nominal cash subsidies.

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Windows, as elements connecting built and natural environment, play an important role in providing internal comfort. During winter, solar heat gain through windows reduces heating demand, heating load and energy consumption of the building. On the other hand, it increases cooling load in summer. Hence, using blinds is common in office buildings to control solar radiation. Although using blinds prevents from entering part of the solar radiation, simultaneously, it improves comfort conditions for the employees. It should be mentioned that an appropriate control of blinds, regarding changes in external and internal environmental conditions, will lead to a decrease of energy consumption and discomfort caused by direct solar radiation. In this paper, the use of blinds on windows is simulated for cardinal orientations and different blind angles and positions; finally, the total thermal load of the space and the amount of glare is studied. According to the results, blinds have a significant impact on spaces total load, as well as reduction of interior glare compared to the reference case with no blinds.

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Nowadays, Hybrid Electric Vehicles (HEVs) are introduced in order to reduce fuel consumption and emissions. The issue that is very important in HEVs, is how to split power between main components of powertrain. Best energy management can be obtained when all future conditions are available. With the advancement of the intelligent systems, access to the road conditions, traffic and other online information has been provided up to the limited prediction horizon. In this paper, a combination of predictive control and Dynamic Programming methods have been used for obtaining online sub-optimal trajectory. Change in the state of traffic in the path has great effect on reduction of fuel consumption. Therefore, According to the state of traffic, a fuzzy logic system is proposed for the online estimating of the vehicle speed. Unlike many energy management methods that use historical data, the proposed strategy leads to reducing the dependence of the controller on the drive cycle. The simulation is implemented on a Plug-in Hybrid Electric Vehicle with parallel structure. The proposed method is compared with Dynamic Programming and instantaneous optimization. Evaluation of results shows that the proposed method, while simplicity and avoiding complicated mathematical relationships, in addition to fuel consumption reduction compared with instantaneous optimization, can manage SOC, properly. The results of this method are close to the global optimal solution of Dynamic Programming.

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Nowadays, the world is facing to increasing loss of fossil resources, energy crisis and environmental problems. On the other hand, diesel engines due to wide application in various sectors such as transport, agriculture, industry, etc., are the main sources of emissions and fuel consumption. Accurate measurement of fuel consumption and engine pollution is time-consuming and costly. Hence, the main objective of this study was to develop proper linear regression models of some important performance parameters of ITM285 tractor engine based on engine torque and engine speed. Experiments were carried out in 11 levels of primary engine speed (1063, 1204, 1346, 1488, 1629, 1771, 1818, 1913 and 2054 rpm) by 10 N.m steps of torque from zero (no load) to full load. The measured parameters include fuel consumption mass flow, exhaust temperature, instantaneous engine speed, maximum and mean exhaust opacities. Four different linear regression models were used to estimate the parameters. The results of regression models performance evaluation showed that quadratic model had the highest efficiency and the lowest RMSE for all parameters. The maximum and minimum effects of engine torque were on exhaust temperature and instantaneous engine speed, respectively; while, this result was completely reverse for primary engine speed. The results of regression models evaluation showed a high adaptation between the output of each model and the desired output. Also, the fuel mass flow and exhaust temperature were highly correlated to the maximum and mean exhaust opacity with correlation coefficients of 0.96 and 0.99, respectively.

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Full factorial investigation is necessary in the study of the hydraulic phenomena which are function of different variables with different levels. It is logical to use the full factorial method when the number of variables and their levels are low. However, sometimes due to lack of time and shortage of financial restrictions, using the full factorial method is not possible. The Taguchi method, which is used for design of experiments, uses the fractional factorial instead of full factorial. This method not only decrease the number of studies but also guaranties the correlated comparison of all variables. In this paper the Taguchi method is used for finding the optimized hydraulic parameters like length and location of spur dike in different Froude Numbers in 90 degree bend. Furthermore, the comparison between the Taguchi method and full factorial is done for the number of investigations, finding the optimized level for each parameter and the time needed for study. In order to get the results, the parameters of length, location of spur dike in 90 degree bend, and the Froude Number are considered with three different levels. The SSIIM numerical model is applied to simulate the studies designed by Taguchi and full factorial methods. The results show that the Taguchi method, could predict the optimum parameters only with 9 studies whereas with full factorial method 27 studies was necessary. Also, using Taguchi method leads to more than 66% decrease in the total running time. For studies designed by Taguchi method, the optimum value of length of spur dike is the as one designed by Full factorial method. Also, the length of spur dike is the most effective parameter on flow pattern around spur dike and the position of spur dike and Froude Number are next in rank, respectively. These results are the same for two methods used to design of studies. Using 9 studies designed by Taguchi method, investigation of the effect of other parameter such as the angle of the spur dike is possible without changing the number of studies, whereas 81 studies should be done by full factorial method.
Full factorial investigation is necessary in the study of the hydraulic phenomena which are function of different variables with different levels. It is logical to use the full factorial method when the number of variables and their levels are low. However, sometimes due to lack of time and shortage of financial restrictions, using the full factorial method is not possible. The Taguchi method, which is used for design of experiments, uses the fractional factorial instead of full factorial. This method not only decrease the number of studies but also guaranties the correlated comparison of all variables. In this paper the Taguchi method is used for finding the optimized hydraulic parameters like length and location of spur dike in different Froude Numbers in 90 degree bend. Furthermore, the comparison between the Taguchi method and full factorial is done for the number of investigations, finding the optimized level for each parameter and the time needed for study. In order to get the results, the parameters of length, location of spur dike in 90 degree bend, and the Froude Number are considered with three different levels. The SSIIM numerical model is applied to simulate the studies designed by Taguchi and full factorial methods. The results show that the Taguchi method, could predict the optimum parameters only with 9 studies whereas with full factorial method 27 studies was necessary. Also, using Taguchi method leads to more than 66% decrease in the total running time. For studies designed by Taguchi method, the optimum value of length of spur dike is the as one designed by Full factorial method. Also, the length of spur dike is the most effective parameter on flow pattern around spur dike and the position of spur dike and Froude Number are next in rank, respectively. These results are the same for two methods used to design of studies. Using 9 studies designed by Taguchi method, investigation of the effect of other parameter such as the angle of the spur dike is possible without changing the number of studies, whereas 81 studies should be done by full factorial method.

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The existence of huge gas resource in Iran and the global demand for the replacement of fossil fuels with this cleaner energy resource has caused that the large-scale gas export becomes an interesting topic. One of the methods for large-scale gas exports is liquefaction which is done by refrigeration cycle. Considering the importance of the efficient use and the reduction in energy consumption, particularly in large energy consumers like liquefaction plants, it is imperative to optimize the refrigeration cycles used in these plants. While there have been many studies focusing on the power consumption minimization of refrigeration cycles, however, in most of these studies the performance limitations of the refrigeration cycle components have not been considered. Therefore, the results of such studies are not practical for in-use refrigeration cycles in gas refineries. The main goal of this paper is to propose a systematic method to minimize the power consumption of in-use refrigeration cycles in gas liquefaction processes by taking into account the performance limitations of refrigeration cycle components and the interactions between the refrigeration cycle and the core process. In this regard, a combination of thermodynamic viewpoints and pinch technology is used as well as considering the above mentioned limitations, to express the multi-stage refrigeration cycles’ power consumption minimization problem as a function of several independent variables. Up to 15% reduction in the specific power consumption is achieved when the proposed method are implemented on the optimization of a typical in-use three-stage refrigeration cycle, used in a propane liquefaction plant.

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Dielectric barrier discharge (DBD) plasma actuators are one of the new devices for active flow control, which has received substantial attention during the last decade. The performance of the actuator is optimum when it induces the highest velocity per unit of power consumption. Since the induced velocity and the power consumption of the actuator depend on many different variables, finding the optimal set, which results in the best performance, is of immense importance. In this paper, in order to optimize the performance of these actuators, at first, by using full factorial design of experiments the effect of electrical variables (including voltage and frequency) and geometrical variables (including the gap between electrodes, dielectric thickness, and covered electrode width) on induced flow velocity and power consumption in steady actuation is experimentally investigated. Then, by using the multi-layer perceptron neural network, a model is created for the ratio of induced velocity to power consumption. The model is validated both statistically and experimentally. The results indicate that the coefficient of determination for training and test data is higher than 95 percent. Finally, the surrogate model is optimized by genetic algorithm and the optimal value of electrical and geometrical variables is determined. In order to validate the result, an actuator is designed based on the optimal set of variables and it’s ratio of velocity to power is measured to be
29.71 (m/s)/(kW/m). The difference of 3 percent between the measured and the predicted value demonstrates high accuracy and correctness of the proposed model and method.

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The robot has to adapt its movement with the various condition of surfaces and ensuring the stability with the proper motion of its bust and legs in order to be able to move on different surfaces. A lot of basic parameters of the gait can be expressed by the planned seven-linked bipedal robot. One of the issues that have always attracted the attention of researchers, in this field, is to predict the motion path that guarantees the stability and minimizes the energy. In this study, parameter optimization being used which means that at the first, joint angles are defined as a parameter functions and then with respect to kinematics constraints that define maximum and minimum of joint angles, the problem of motion obtained in the way that maximizing stability of robot and minimizing energy and puts the robot in the permitted stable region. Also, we tried to have zero moment point scale used to calculate the stability of robot. Experimental tests in order to motion analysis for walking healthy were performed and the results were validated. Finally, due to the presented model and predicted path, the robot can move like a person. Comparison of experimental results and the result of presented model were used in each step to validate the accuracy of the proposed method.

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In the present study, the influence of drying temperature on energy consumption and qualitative characteristics of onion including rehydration capacity, vitamin C content and total phenolic content (TPC) was investigated. Onion slices with 3 mm thickness was dried in a hot air dryer at temperatures 0f 40‒70 °C. Specific energy consumption and energy efficiency were significantly (P < 0.05) improved by increasing temperature and obtained to be in the range of 35.83‒59.33 MJ/kg and 4.01‒6.52%, respectively. Increasing air temperature resulted in significant (p < 0.05) improvement in energy consumption indices. Rehydration capacity in the dried onion samples varied from 4.01% (at drying temperature of 40 °C) to 6.52 (at drying temperature of 70 °C). Vitamin C content in fresh samples was 50.19, and in dried samples varied from 14.92 to 21.38 mg/100 g _{dry matter}. TPC was measured using Foline–Ciocalteu reagent and found that the TPC in fresh onions (389.6 mg GAE/100 _{dry matter}) was significantly (p < 0.05) decreased in the dried samples (212.3‒295.8 mg GAE/100 _{dry matter}). Based on the obtained results, drying of the onions at higher temperatures led to more deterioration in vitamin C content and the TPC.

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In the present study, an infrared-assisted solar dryer was used to determine the drying kinetics, energy consumption and quality parameters evaluation of Echium amoenum. Experiments were conducted with two levels of drying air flow rate (0.0025 and 0.005 m³s^-1) and three levels of IR lamp power (100, 150 and 213 W). Drying time, energy consumption and evaluation of quality properties in different air flow rates and lamp powers were compared to the conventional method (shade drying). Five empirical models were fitted on the experimental data and the goodness of regression models were evaluated using coefficient of determination (R²), root mean square error (RMSE), and Chi square (χ²). Results of drying time in the different experiments showed highly significant differences respect to the conventional method (p-value<0.01). Also results showed that increasing the air flow rate and IR power caused a reduction of 37% and 17% in drying time, respectively. Best empirical model to describe the drying behavior was the Page model. The lowest specific energy consumptions (SEC) was 4.63 MJ kg^-1, which was occurred at the air flow rate and IR power of 0.005 m³s^-1 and 150 W and the highest SEC was 5.26 MJ kg^-1 and occurred at 0.0025 m³s^-1 of air flow rate and 213 W of IR lamp, respectively. Finally, the air flow rate of 0.005 m³s^-1 and the IR power of 150 W was recommended for Echium amoenum drying in the IR-ASD because of the fair energy consumption and the suitable product color.
 

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The main goal of this paper is to analyze the exchange rate pass-through, the relationship between exchange rate and prices, provided that a shock occurs and changes exchange rate and prices. The key point in this study is that exchange rate is considered as an endogenous variable. This issue is important because exchange rate pass-through due to specific shocks differs from case to case. Hence a dynamic stochastic general equilibrium model is presented and simulated for Iran. The accuracy of the model is analyzed by comparing the moments of the model and the moments of the quarterly data from 1988 to 2010. Then, exchange rate pass-through conditional on each shock (technology, oil revenue, foreign output, and demand for money, foreign interest rate and monetary policy shocks) is calculated by the ratio of covariance of the impulse response of price and exchange rate to variance of the impulse response of exchange rate. Finally, aggregate exchange rate pass-through is computed as the sum of conditional pass-through coefficients in each time weighted by the contribution of each shock. The biggest exchange rate pass-through to consumer prices belongs to oil revenue and foreign output shocks which amounts to about 1, and the smallest one is related to technology shock.

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Population age structure is a main factor affecting government consumption expenditure. This paper examines the effects of changes in population age structure on government consumption expenditure by using a Mixed Frequency Data Sampling (MIDAS) approach. The estimation results indicate that population age structure are of positive and significant effects on government consumption expenditure. In addition, government consumption expenditure is forecasted for 2014. To assess the predictive power of the model, the actual data in 2014 was not used. The expenditure forecasted by the model is 1437079 billion Rials, and corresponding real value is 1438316 billion Rials. This indicates the goodness of fit of model.

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Over the past decades, increasing in consumption of goods and services led to the destruction of natural resources and severe damage to the environment, which has led many consumers to reduce environmental impact caused by their consumption. In this study, it has been tried to investigate the effect of knowledge on environmental issues, consumer perception and consumer recycling behavior on the purchasing behavior of green products using structural equation modeling method. For this purpose, it was used 215 consumers in the city of Mashhad in 1396.
The results of structural equation modeling suggest that consumers' perception or attitude is the most important predictor of the purchasing behavior of green food products, which attempts to raise awareness of consumers can be one of the most important measures through advertising tools to introduce their various abilities to reduce environmental degradation.
 

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Bread is an important and influential element in our Iranian culture of nutrition and lifestyle. This study addresses the sociological explanation of the consumption of this essential commodity among the citizens of Tehran. The statistical population consists of all the citizens of Tehran living in areas 3, 7 and 19. 184 people were questioned and data were collected through a questionnaire. Data analysis method is Structural equation modeling that has been one of the few multivariate methods. Data collected has been analyzed through the Amos software. Accordingly, a theoretical model is estimated in which economic, cultural and social capital (social trust) forms external and independent variables, and the bread consumption variable is also included as an internal variable dependent on the model. Also, the lifestyle variable plays the role of mediator variable in the model. The basis for the formation of this theoretical model has been Pierre Bourdieu. The findings show that, in general, experimental data supports theoretical model. That is, bread consumption can be explained within the framework of the model. Among the different hypotheses of the hypothesis that have shown the relationship of lifestyle and social trust with the consumption of bread have been confirmed. Other variables are directly related to the consumption of bread, but this relationship is weak and therefore their related hypotheses are not statistically valid. Of course, two variables of cultural capital and economic capital have a significant relationship with the variable of lifestyle. This could mean their indirect impact on the bread consumption variable