مطالب

The registration of Qeshm Geopark as the first geopark of Iran and the Middle East region in the list of global geoparks by UNESCO along with its natural, cultural, historical, economic, commercial and military capabilities has brought Qeshm Island to a unique position. It has made it have international value in addition to national and regional value. One of the ways to achieve coordinated development with the principles of sustainability and spatial planning is to evaluate the ecological potential of areas; this article examines the potential of using Qeshm Geopark for various activities. The current research has evaluated the potential of Qeshm Geopark in relation to agricultural activities, environmental protection, tourism, urban and commercial development, and industrial development using a systemic perspective and providing an ecological model. In this research, documentary information was collected from available library sources, and in the next step, maps related to the shape of the land in Qeshm Geopark were prepared in the GIS environment, after combining and analyzing the maps and combining them with existing social information ؛Economic information, in the final exploitation capacity of Qeshm Geopark, based on the ecological model of exploitation and using the multi-factor evaluation method, has been investigated at the level of the whole island. The results show that Qeshm Geopark has a suitable and high capacity for tourism development and extensive and concentrated industrial development and lacks sufficient capacity for agricultural development And the ecological potential and urban development and environmental protection has been assessed as moderate.

Financial statements are critical to users, as the increasing fraud cases have left behind irreversible impacts. Hence, this study aims to identify the appropriate financial ratios for fraud risk prediction in the financial statements of companies listed on the Tehran Stock Exchange within the 2014–2021 period. The study is based on data from 180 companies listed on the Tehran Stock Exchange, encompassing a total of 1440 financial statements. To select the most appropriate ratios for fraud risk prediction, all financial ratios were tested by three metaheuristic algorithms, i.e., genetic algorithm, grey wolf optimization, and particle swarm optimization. Metaheuristic and data mining methods were employed for data analysis, and these analyses were conducted using MATLAB R2020a (MATLAB 9.8). According to the research results, the fitness function yielded 0.2708 in particle swarm optimization (PSO). With an accuracy of 72.92% after 19 iterations, PSO was more accurate and converged faster than the other algorithms. It also extracted 11 financial ratios: total debts to total assets, working capital to total assets, stock to current asset, accounts receivables to sales, accounts receivables to total assets, gross income to total assets, net income to gross income, current assets to current debt, cash balance to current debt, retained earnings and loss to equity, and long-term debt to equity. The support vector machine (SVM) classifier was then employed for fraud risk detection at companies through the ratios extracted by the proposed algorithms. The accuracy and precision of financial ratios extracted by PSO and SVM were reported at 80,60% and 71,20%, respectively, which indicates the superiority of the proposed model to other models. Considering that the results obtained from the performance evaluation of financial ratios provided by PSO-SVM demonstrate the capability of this method in predicting the likelihood of fraud in financial statements, it can assist financial statement users. By incorporating these ratios about the performance of the target companies and comparing them with those of other companies, users can make more informed decisions in economic decision-making, investments, credit assessments, and more, ultimately minimizing potential losses and risks.

Sanctions increase managers' motivations to manage profits and the accumulation of bad news due to their negative impact on corporate profitability and cash flow. According to Jin and Myers (2006), this issue increases companies' stock price crash risk. The higher probability of stock price crashes indicates a stock price overvaluation. As a result, the expected return on such shares will be low. The current paper used the softmax model to calculate the probability of stock price crashes and the expected return calculated by the Fama-French three-factor model. The sample used in this paper includes 80 import- and export-oriented exchange companies from 2008-2021. The results of this paper indicate a positive and significant relationship between the sanctions variables and the probability of stock crashes. So, sanctions cause an increase in the accumulation of bad news and information asymmetry between managers and investors. The second part of the paper's results indicates a negative relationship between the probability of stock crashes and the expected return on the stocks in the Iranian capital market. Therefore, investors have relatively good analytical skills in the Iranian capital market due to its shallow depth and infrastructure problems. The results of this paper can be used in portfolio management to select stocks with a lower probability of crash and higher return.