درخت حوزه‌های تخصصی

The present study aims to examine and explain the role of rail transport in Iran’s economic development from the customers' perspective. This research was conducted using a survey method, employing a questionnaire distributed among 384 rail transport customers. The validity of the questionnaire was confirmed by five experts, and its reliability was assessed through Cronbach’s alpha coefficient, with values exceeding 0.7 for all variables, indicating satisfactory reliability of the measurement tool. Data analysis was performed using SPSS software. The results revealed that rail transport has a significant impact on attracting global business opportunities, economic competitiveness, traffic congestion reduction, and road maintenance. Additionally, this study examined the relationship between rail infrastructure development and its impact on economic productivity. The findings indicated that increased investment in the rail transport sector can enhance transportation efficiency and improve Iran's trade conditions. One of the most critical outcomes of this study was identifying the challenges hindering the expansion of the rail system, including high development costs and a lack of attractiveness for private investors. This research can assist policymakers in developing appropriate strategies to improve rail transport. The findings align with international studies in this field, emphasizing the importance of this sector in sustainable economic development. It is recommended that precise planning be undertaken to expand rail networks and enhance service quality in this domain

Predicting stock price fluctuations has always been one of the most important financial challenges due to the complexities of financial data and nonlinear market behavior. This research aimed to analyze and compare the performance of machine learning algorithms in predicting the closing price jump of Iran Khodro Company shares. Two different methods of managing unbalanced data, NearMiss and SMOTE, were used to overcome the challenge of unbalanced data. The results showed that the NearMiss method outperformed SMOTE by balancing precision and recall in machine learning models. The CatBoost model was recognized as the best machine learning model in this study due to its stable performance in NearMiss and SMOTE methods. The CatBoost model showed a perfect balance between evaluation indicators in the NearMiss method, with an accuracy of 91.46% and an F1 score of 91.29%. This model also had high precision (93.18%) and acceptable recall (89.52%), which showed the ability to detect jumps and avoid wrong predictions correctly. On the other hand, in the SMOTE method, the Random Forest model was superior, with an accuracy of 85.08%. These results show that a combination of unbalanced data management methods and advanced machine learning algorithms can significantly improve the accuracy of price volatility prediction. The results of this research can help investors and financial analysts make better decisions in risk management and optimizing investment strategies.

Livestock grazing pressure in East Azerbaijan province, located in the northwest of Iran, is 2.5 times higher than the capacity of rangelands. This study employed a two-stage Delphi technique to identify and rank supervisory, natural, economic, cultural, legal, administrative, and infrastructure factors. The second Delphi round results indicated that, from the experts’ point of view, poverty among rural residents on the outskirts of rangelands (scoring 4.7), insufficient support of the Agriculture Organization in providing livestock feed and allocation of loans to livestock farmers (scoring 4.53), conversion of rangelands into agricultural and horticultural lands to gain higher profits (scoring 4.43), overgrazing and long term grazing (scoring 4.4), were the important factors contributing to rangeland degradation. Comprehensive legal, legislative, executive, and monitoring measures to prevent overgrazing and premature grazing were identified as key solutions to reduce rangeland degradation. Among economic factors, poverty among rural residents on the outskirts of rangelands and their livelihood dependence on rangelands were recognized as the most significant factors in the destruction of rangelands and forests. Additionally, collaboration with the Agriculture Organization in providing alternative forage for livestock, strengthening the monitoring mechanisms of relevant organizations, and enhancing education and awareness levels among the public, regarding the protection of natural resources have been highlighted as other important strategies in reducing the level of rangeland degradation.