نازنین پیله وری

This article introduces an integrated approach for addressing uncertainty and improving human-oriented risk control by combining differential equation modeling with neural and fuzzy logic enhancements. Differential equation modeling provides a structured mathematical foundation for capturing price dynamics over time, while neural and fuzzy logic components adaptively adjust the model to account for nonlinear behaviors and uncertain market signals. The proposed framework is applied within volatility-aware trading strategies, comparing fixed-exposure and downside-scaled momentum approaches. Using daily data from five major digital currencies spanning 2016 to 2024, the model demonstrates improved prediction accuracy and controlled exposure under volatile conditions. While the adaptive strategy offers reduced drawdowns and more stable weight distributions, it does not universally outperform in return-to-risk metrics. However, the integrated system consistently shows better alignment with market risk regimes, particularly in directional accuracy, confidence calibration, and drawdown control enhancing its practical viability for real-world deployment.

Purpose: The objective of this research was to present an e-learning model with an emphasis on psychological capital at Mellat Insurance. Methodology: This research is qualitative in nature and employs thematic analysis as its method. The qualitative section of the study population included experts in education, e-learning, and psychology, focusing on psychological capital. Through purposive sampling, criterion-based, the desired sample was selected, and sampling continued until theoretical saturation of the data was reached. Thus, the participants in the study included 18 experts and managers. The data collection tool in this research was semi-structured interviews. It is noteworthy that the extraction of these indices was conducted by reviewing existing theoretical foundations and implementing interview transcripts using the thematic analysis method of Braun and Clarke in ATLAS.ti software. Findings: Based on the qualitative technique of thematic analysis, 141 initial codes were identified in 20 initial themes, and finally, 6 constructive themes were determined. The 6 constructive themes are: educational content, e-learning infrastructure, institutionalization of psychological capital, intra-organizational factors, extra-organizational factors, and effectiveness. Conclusion: One of the main necessities for implementing an e-learning model with an emphasis on psychological capital at Mellat Insurance is to increase organizational productivity. Organizational maturity refers to the ability of the organization to respond to challenges, adapt to changes, and continuously improve. This concept addresses the organization's ability to utilize internal and external knowledge, experiences, and resources to enhance processes, improve performance, and create added value. The primary necessity is that Mellat Insurance must seek to establish an advanced and flexible educational structure that enables employees to acquire the necessary knowledge, skills, and abilities to face emerging challenges and opportunities.

The automotive industry, as a job-creating and infrastructure industry, needs an executive model for success in the domestic and international markets. In this regard, the present study has been conducted with the aim of designing a model for implementing the fourth generation industry to achieve sustainable development goals in Iran Khodro Company. The study is an applied-developmental study in terms of purpose and cross-sectional survey research. Also, in this study, a mixed research method (qualitative-quantitative) was used. Content analysis method and MaxQDA software were used for data analysis in the qualitative section. Then, using Interpretive Structural Modeling (ISM) with MICMAC software, the initial pattern was drawn. In the quantitative section, one-sample t-test and SPSS software were used to measure the current situation. The research findings showed that the Collection and Analysis of Big Data affects the Simulation and Automatic Robots. These factors affect horizontally and vertically integration systems and thus lead to the Internet of Industrial Things, Augmented Reality and Cyber Security. Further, through the Cloud Computing system, Additive Manufacturing is affected and this Additive Manufacturing leads to Sustainable Development.

INTRODUCTION: Crisis management maintains security, stability, and flexibility in society by integrating different social resources with appropriate decisions and strengthening the ability of the community to recover from the negative impacts. Therefore, the present study aimed to conduct a model analysis of effective factors in implementing crisis management policies. METHODS: The present study is a descriptive survey research with a mixed methods design. A total of 18 experts were non-randomly and purposefully selected for the qualitative section based on expertise and availability. In addition, 248 individuals were selected from managers and experts (658 people) of the National Disaster Management Organization of Iran and partner organizations for the quantitative section based on Krejcie and Morgan Table using Cochran's formula and simple random sampling. The research instrument was a researcher-made questionnaire including 33 factors affecting the implementation of crisis management policy, the content validity of which was confirmed by professors, and its reliability by Cronbach's alpha test (α = 0.89). Data were analyzed using SPSS and AMOS software and the AHP method was used to prioritize the factors. FINDINGS: Findings indicated that 33 components in the form of structural, organizational, environmental, and management factors, as well as financial and human resources, uniform practices, communication, information, and inter-organizational coordination, regulations, and policy implementers successfully affect the implementation of crisis management policies. Structural, organizational, and environmental factors significantly affect the implementation of policies. CONCLUSION: Defining the responsibilities of partner organizations as well as developing transparency and designing specific goals contribute to the successful implementation of crisis management policies.

To improve the operation level of the companies tailored to industry 4.0 it is necessary to develop logistics system. In order to develop the logistics level, the first step is to measure the growth and evolution of all logistics process. For measuring the evolution and growth of the logistics system (LS), the processes and components of the logistics system and then the evolution level should be determined, at the end their level of evolution should be measured for each firms. The purpose of this paper is to determine the evolution level of the LS in the large industries of Iran. The research method is descriptive - applied and Data were gathered in two steps. First utilizing interviews with logistics experts for identifying the logistics process and components. Second step was data collection by check list in local visits and direct observation in firms. Checklist was completed in group sessions with the help of managers and experts in each firm. Using the mean of the logistics components and degree of adoptive (DOA) technique, evolution of LS was measured in industries. Results shown that firms with heavy products have little progress in the evolution of logistics compared to other firms and also the firms that have automatic production system will growth in logistic evolution better than others especially in intra-logistics and material handling. The method and result of this paper can be seen as a roadmap for managers and organizations and they will plan development and evolution of logistics processes.