Since Markowitz's substantial work, the mean-variance model has revolutionized the way people think about portfolio of assets. According to the modern portfolio theory, the fundamental principle of financial investments is a diversification where investors diversify their investments into different types of assets. Constructing an optimal risky portfolio is a high-dimensional constrained optimization problem where financial investors look for an optimal combination of their investments among different financial assets with the aim of achieving a maximum reward-to-variability ratio. Among the various methodologies suggested, the most popular one is based on maximizing the well-known Sharpe ratio.In this study, we apply particle swarm optimization (PSO) for constructing optimal risky portfolios based on Sharpe ratio for financial investments. A particle swarm solver is developed and tested on a risky investment portfolio. The method is applied to a sample of stocks in Tehran Stock Exchange. Experimental results reveal that the proposed PSO algorithm provides a very feasible and useful tool to assist the investors in planning their investment strategy and constructing their portfolio.

The aim of this study was to predict the positive reaction of buyers by examining the effectiveness of advertising on social networks with emphasis on teaching mental norms and expressing empathy. Methodology: This research was applied in terms of purpose and experimental in nature. The statistical population was users of the social network Instagram, the exact number of whom was unlimited. And by simple random sampling method, 150 online questionnaires were collected. The face and content validity of the final questionnaire was confirmed by marketing experts and in a pre-test on 30 users, Cronbach's alpha of 0.7 was observed. The total reliability of the questionnaires was confirmed by Cronbach's alpha and the reliability of each structure was confirmed by composite reliability. Data analysis was performed using structural equation modeling technique and PLS2 software. Findings: The model used in this article examines the concept of the formation of online user behavioral reactions with respect to social media advertising and the effect of teaching mental norms. This study conceptualizes the effectiveness of social media advertising as a concept that includes emotional attraction, information content, creativity, and interaction, all of which have the potential to contribute to a positive online behavior. Conclusion:The results showed that informational content, emotion and advertising creativity were the key drivers of desirable behavioral reactions to a social media ad and the intention to participate in the user's favorable responses was positively related to the purchase intention.

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The purpose of this study was to provide a model of job requirements and job resources affecting employees' job engagement. The research is of mixed exploratory (qualitative and quantitative) type. To collect qualitative data, themes analysis method and semi-structured interview (with 17 experts using purposive sampling method) and to collect quantitative data, two researcher-made questionnaires, titled (the importance of items and the current situation questionnaire( and the standard Utrecht questionnaire, were also used. The statistical population includes all 200 employees of the Organization of Cinema and Audiovisual Affairs in Iran. In the quantitative section, 127 people were randomly selected as a sample using Morgan's sampling table. The validity of the questionnaires was measured through face validity and confirmatory factor analysis, and the reliability of the questionnaires through Cronbach's alpha was obtained at 0.768, 0.930, and 0.942, respectively. The themes analysis method was used to analyze the qualitative data, confirmatory factor analysis and Lisrel software were used to validate the qualitative model, and the structural equation model was used to test the hypothesis using Smart PLS software. Based on the qualitative results of the research, seven main components were identified, and a themes network was drawn. The results of the quantitative analysis have also confirmed the significance of the model's relationship between the indices, components, and dimensions. Also, the findings showed that job requirements and job resources had a positive effect on job engagement.

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