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Exporting agricultural products is considered as one of the strategies for developing non-oil exports and achieving sustainable economic growth in developing countries. Saffron, as an export commodity, holds particular significance in Iran's non-oil exports. Given Iran's position among the top four saffron-exporting countries globally, this study aims to prioritize Iran's saffron target markets based on market competition indices and calculate its relative advantage and export stability index in the world market and Iran's export target countries. Comparison of the global market structure of the product during 2003 to 2022 revealed that despite the significant shares of Iran, Spain, England, and Nigeria in most years, the market structure has been characterized by a multi-sided monopoly, open and closed, and in some years dominated by oligopoly, indicating an increase in the number of competitors and the competitiveness of the export market for this product. Iran, with an average share of 13.6% in the saffron export market and producing over 80% of saffron, does not have a direct share in global exports, and most of Iran's saffron is exported to countries such as the UAE, Spain, China, and Oman, and then re-exported to other countries, for which strategies such as market expansion and branding need to be prioritized. The results showed that in 2022, four countries, Nigeria, Sri Lanka, Iran, and Spain, accounted for 93% of the total world exports, and Iran ranked second in terms of export volume in the saffron export market during the study period. Also, Iran had an export stability index of less than one (0.96) but the trend of this index indicates a decrease in Iran's stability. The results showed that the majority of Iran's saffron exports are concentrated in only four countries, with the composition of these countries varying over time. To enhance market stability and growth, it is crucial to expand the target export markets. Prioritization should be given to China, UAE, Spain, India, USA, Germany, France, Italy, Sweden, and Kuwait, with average priority ranks of 4.15, 6.85, 7.7, 7.95, 8.9, 12.3, 14.35, 15.25, 15.5, and 16.45 respectively. Furthermore, the results indicated that the export market for saffron is oligopolistic. Therefore, it is essential for all exporting countries to collaborate in determining the price and market share for each country. This collaborative approach can help in stabilizing the market, ensuring fair pricing, and promoting sustainable growth in the saffron industry.

 Urban agriculture promotes sustainable development and fosters societal participation, including the rehabilitation of various groups, such as inmates. Therefore, this research aimed to analyze the effectiveness of prison-farm rehabilitation programs in Dizel Abad Prison in Iran. The research population consisted of two groups: one with 130 inmates, of whom 97 were selected and studied through systematic sampling via Cochran's formula, and the second group included 14 managers, offenders, and prison experts interviewed through a census. The data collection instrument was a researcher-developed questionnaire based on the Kirkpatrick evaluation model. The reliability of the questionnaire was confirmed using Cronbach's alpha coefficient, while its face and content validity were validated by a panel of experts. The findings at the first level of evaluation (i.e., reaction) showed that, with a confidence interval of 99%, the inmates were satisfied with participating in the urban agriculture training course. At the second level (i.e. learning), results indicated a significant increase in the inmates’ knowledge, with a statistically significant difference between their pre- and post-test scores (p<0.01). At the third level (i.e., behavior), the findings suggested that urban agriculture in prisons could lead to significant behavioral changes in inmates, with 95% confidence. At the fourth level of evaluation (i.e., results), the findings showed that although the urban agriculture training course in Dizel Abad Prison caused moderate changes in the inmates, the difference in pre-and-post-test scores was not statistically significant. Overall, the findings demonstrated that the urban agriculture training course in Dizel Abad Prison could bring about changes in inmates’ behavior, knowledge, and attitude. These findings support the potential of prison-farm programs within urban agriculture initiatives as viable methods for rehabilitation. 

The main objective of this article is to investigate the impact of various financial, economic, and political risks and economic complexity on the development of the Sukuk market in the Iranian economy. The data required to conduct this research based on the variables of the proposed model were used from the Capital Market Central Asset Management Company, the International Country Risk Guide (ICRG) database, and the MIT University website. The data relating to 2010-2022 is seasonal, and REVIEWS 13 software was used. The model estimation results using the Nonlinear Autoregressive Distributed Lag Model Approach (NARDL) show that the negative shock of political risk reduces the development of the Sukuk market in the short and long term. The negative shock of financial risk in the long term has a negative impact on the development of the Sukuk market. The negative shock of economic complexity reduces the development of the Sukuk market in the short term. The positive shocks of political risk, financial risk, economic risk, and economic complexity in the short and long term led to the development of the Sukuk market. Among the three types of risk, political risk and financial risk have the most impact on sukuk market development. The error correction coefficient in this estimate is negative and statistically significant, which shows that 0.42% of the short-term imbalance is adjusted to reach the long-term balance every year.

Portfolio optimization which means choosing the right stocks based on the highest return and lowest risk, is one of the most effective steps in making optimal investment decisions. Deciding which stock is in a better position compared to other stocks and deserves to be selected and placed in one's investment portfolio and how to allocate capital between these stocks, are complex issues. Theoretically, the issue of choosing a portfolio in the case of minimizing risk in the case of fixed returns can be solved by using mathematical formulas and through a quadratic equation; but in practice and in the real world, due to the large number of choices in capital markets, the mathematical approach used to solve this model, requires extensive calculations and planning. Considering that the behavior of the stock market does not follow a linear pattern, the common linear methods cannot be used and useful in describing this behavior. In this research, portfolio optimization using the gray wolf algorithm and the Markowitz model based on CO-GARCH modeling has been investigated. The statistical population of the current research included the information of 698 companies from the companies admitted to the Tehran Stock Exchange for the period of 2011 to 2020. First, the optimal investment model is presented based on the gray wolf algorithm, and After extracting the optimal model, the efficiency of the gray wolf algorithm is compared with the Markowitz model based on CO-GARCH modeling.

As we are looking for knowledge of stock future returns in portfolio optimization, we are practically faced with two principal concepts: Uncertainty and Information about variables. This paper attempts to introduce a pragmatic bi-objective investment model based on uncertainty, instead of probability space and information theory, instead of variance and other moments as a risk measure for portfolio optimization. Not only is uncertainty space expected to be more in line with investment theory, but also, applying and learning this approach seems more straightforward and practical for novice investors. The proposed model simultaneously maximizes the uncertain mean of stock returns and minimizes uncertain entropy as a measure of portfolio risk. The uncertain zigzag distribution has been used for variables to avoid the complexity of fitting distributions for data. This uncertain mean-entropy portfolio optimization (UMEPO) has been solved by three meta-heuristic methods of multi-objective optimization: NSGA-II, MOPS, and MOICA. Finally, it was observed that the optimal portfolio obtained from the proposed model has a higher return and a lower entropy as a risk measure compared to the same model in the probability space.

The purpose of this research is to provide a growth model of fixed assets based on the financial criteria of companies admitted to the Tehran Stock Exchange. The current research is applied in terms of objective classification and descriptive-correlation in terms of method. The research method is de-ductive-inductive. The statistical population of the current research is all the companies admitted to the Tehran Stock Exchange in the period from 2012-2021 and the financial information of 101 companies are use. Research hypotheses were tested using artificial intelligence algorithm. In this research, investment in fixed assets has been consider as a dependent variable, and financial criteria has been considered as primary independent variables. The results of research hypotheses testing using the methods of linear and non-linear algorithms of artificial intelligence PINSVR and KPLSR in predicting fixed asset investors of companies and by calculating the three errors criteria MAE, MSE and SMAPE in annual fixed assets. The asset forecasting in the next year of companies showed that the error difference between linear models and non-linear models is not so great that it can be claim that linear models are ineffective in predicting asset growth so that artificial intelligence algorithms are capable of predicting investment in company assets.