This work deals with estimation of temperature dependent binary interaction parameters (kij) for binary systems containing CO2 using the Soave-Redlich-Kwong equation of state with a GROUP CONTRIBUTION method. In this paper six GROUPs, namely CH3, CH2, CH, CH4 (methane), C2H6 (ethane), and CO2 (carbon dioxide) are defined and their relevant values of GROUP interaction parameters are optimized. Using this method, it is possible to estimate the kij of any mixture containing carbon dioxide and hydrocarbons at any temperature along the coexistence curve. The results obtained in this study are, in most cases, accurate.

In this paper, vapor pressure for pure compounds is estimated using the Artificial Neural Networks and a simple GROUP CONTRIBUTION Method (ANN–GCM). For model comprehensiveness, materials were chosen from various families. Most of materials are from 12 families. Vapor pressure data of 100 compounds is used to train, validate and test the ANN-GCM model. Vapor pressure data were taken from literature for wide ranges of temperature (68.55-559.15 K). Based on results, the best structure for feed-forward back propagation neural network is Levenberg-Marquardt back propagation training algorithm, logsig transfer function for hidden layer and linear transfer function for output layer. The multiplayer network model consists of temperature, acentric factor, critical temperature, critical pressure and the structure of molecules as inputs, 10 neurons in the hidden layer and one neuron in the output layer corresponding to vapor pressure. The weights are optimized to minimize error between experimental and calculated data. Results show that optimum neural network architecture is able to predict vapor pressure data with an acceptable level. The trained network predicts the vapor pressure data with average relative deviation percent of 1.18%.

Reviewing the literature of inequality decomposition by subGROUPs shows that between-GROUP inequality (urban and rural areas) often accounts for a small share of total inequality. Therefore, in interpreting the result, researchers give less emphasis on designing appropriate policies for removing inequality across those areas. In this paper, we employed maximum between-GROUP inequality approach for getting a better understanding of the importance and the role of between urban and rural areas inequality in overall inequality in Iran. The analyses have been done using annual data for 2004, 2008, 2012, and 2016 drawn from the Iranian Urban and Rural Household Income and Expenditure Survey. As expected, the results of conventional decomposition method (Theil index) show that inequality between urban and rural areas have a CONTRIBUTION of only 11. 25 percent to the overall inequality. However, the results of maximum between-GROUP inequality method show that the between-GROUP inequality plays a more important role in overall inequality. In particular, the results of this technique show that the CONTRIBUTION of between-GROUP inequality to the overall inequality is almost twice that obtained from conventional decomposition method. The important policy implication of the results is that economic policies aiming at reducing inequality should be much more concerned about this component of overall inequality in Iran. Furthermore, the findings of this paper reveal that overall inequality has been reduced after the implementation of Targeted Subsidies Reform in Iran.

Context and purpose. Obtaining the private GAP standard for healthy product certification due to costs is out of reach in many small and mediumsized farms among smallholder farmers. GROUPGAP has provided a much-anticipated alternative where operators and farmers' cooperatives can pay the cost of certification on a cost-sharing basis. Therefore, the purpose of this research was to explain the development strategies of GAP standard with emphasis on GROUPGAP in the rice production system of Mazandaran province. Methodology/approach. Current research is qualitative and data analysis was conducted in the grounded theory method, during a three-stage process of open coding, central coding, and selective coding. The data collection tool included an in-depth and semi-structured interview with a qualitative content analysis technique, which was analyzed through Maxquda software. The statistical population of the research was 18 experts and specialists of the Agricultural Jihad Organization and managers of the Rice Farmers Cooperative Company of Mazandaran Province, who were selected by the purposeful sampling method. Findings and conclusions. After extracting the concepts of the content analysis of the interviews, 9 conceptual codes out of 30 initial codes were identified in the first stage of coding. After refining and merging, the codes were classified into 3 subcategories. These components include the GROUP certification system, the development of publicGAP programs and the development of private GAP. In the GROUP certificates, the two dimensions of contract system development and guaranteed purchase, the development of the cooperative company for the production of healthy products have been taken into consideration. It is recommended to strengthen the organizations and cooperatives of farmers and exporters, especially to strengthen young farmers and women organizations. Originality. Originality/innovation: Due to the importance of the GAP standard issue and farmers' problems in providing the audit fees and establishment of GAP certificate, so far, no comprehensive study has been carried out in Iran on the development of operational strategies for the establishment of GAP and GROUPGAP. Therefore, the current research is an attempt to provide a useful framework in explaining GAP standard development strategies with the participation of farmers in adopting the government, private, GROUP and cooperative certification system.