درخت حوزه‌های تخصصی

Purpose: Considering the role and importance of establishing knowledge management in various organizations, the purpose of this research was to provide a model of factors affecting the establishment of knowledge management in the sailing federation. Methodology: This study was of applied method in terms of purpose and quantitative in terms of execution. The study community was the employees of the Iran Rowing Federation in 1401, 20 of whom were selected as a sample using targeted and snowball sampling methods having conditions to enter the study. The samples responded to the researcher-made questionnaire of the factors affecting the establishment of knowledge management in the sailing federation (44 items) and its validity and reliability were evaluated appropriately. Data were analyzed with exploratory factor analysis and structural equation modeling methods in SPSS and Smart PLS software. Findings: The findings showed that the factors affecting the establishment of knowledge management in the sailing federation have 14 factors of understanding and insight in employees, expertise of employees, committed managers, participation of employees, motivation of human resources, expertise in knowledge management processes, strategic processes, rules and internal processes, functional processes, innovative processes, technological capabilities, technological infrastructures, technological communication and recognition of knowledge management technologies. Also, the factor load of all factors was higher than 0.50, the convergent validity of all factors with the average variance extracted method was higher than 0.40, and the reliability of all factors with the combined method was higher than 0.70. In addition, the model of factors affecting the establishment of knowledge management in the sailing federation had a good fit, and the effect of all paths was evaluated as appropriate. Conclusion: According to the results of this study, an effective step can be taken to improve the establishment of knowledge management in the sailing federation through the identified factors.

Recent studies on financial markets have demonstrated that technical analysis can help us effectively predict the stock market index trend. Business systems are widely used for stock market analysis. This paper uses a genetic algorithm (GA) to develop a stock market trading optimization system. Our proposed system can generate a decision-making strategy for buying, holding, and selling stocks for each day and generate high returns for each stock. The system consists of two stages: removing restricted stocks and producing a stock trading strategy. Accordingly, evolutionary computation, like GA, is highly promising because of its intelligence, flexibility, and search strength (fast and efficient). The multiple-objective nature of the utilized algorithm can be regarded as the center of gravity of the research question. The proper functioning or malfunctioning of the resulting portfolio management can be employed as a benchmark for selecting or discarding the algorithm. On the other hand, the research question is focused on the application of technical analysis indicators. Therefore, both aspects of the research question, namely the multiple-objective nature of the algorithm in terms of the analysis method and technical indicators in terms of features selected for analysis, must be taken into account.

Background: The 2024 Iranian presidential election intensified political polarization on Persian Twitter (X), where ideological factions engaged in networked contestation. This study employs social network analysis to examine polarization mechanics, mapping key actors, echo chambers, and discursive strategies. Findings illuminate digital factionalism, algorithmic amplification, and the role of influencers in political mobilization.Aims: This study explores the mechanisms driving political polarization on Persian Twitter (X) during the 2024 Iranian presidential election.Methodology: Utilizing computational social science methodologies, the research combines social network analysis (SNA) and thematic analysis to examine over 133,000 active users and numerous election-related hashtags. Data collection spanned June 1–16, 2024, leveraging Twitter's API to identify clusters, user interactions, and thematic trends. Key tools included the Louvain algorithm for community detection and centrality measures for network analysis, visualized through Gephi software.Findings: Findings reveal a fragmented political landscape characterized by ideological divides, echo chambers, and limited interaction between opposing factions. Analysis identified six major clusters, each aligned with distinct political affiliations, including reformists, conservatives, and opposition groups. Thematic analysis further highlighted the rhetoric surrounding leading candidates Masoud Pezeshkian and Saeed Jalili, revealing polarized sentiments and distinct narratives among their supporters and detractors. The study also underscores the role of platform algorithms, influencer strategies, and group identities in deepening polarization.Conclusions: This research contributes to the understanding of digital polarization within Iran's socio-political context. It emphasizes the dual role of social media as both a space for public discourse and a driver of ideological segregation. Recommendations include strategies for promoting digital literacy, fostering inclusive discussions, and enhancing algorithmic transparency to mitigate polarization in Iran.