مطالب

This study is a structural equation modeling study. It has investigated the effect of literary tourism during the Covid-19 virus pandemic on tourists' behavioral goals through the attitude of mental norms. The present study is a one-section survey. The statistical population of this study includes all tourists who visited the cultural and spiritual sites of Shiraz, like Hafez and Saadi tombs. Literary tourism is a concept that praises the culture and heritage of a destination. The concept is to go to locations worldwide highlighted in some non/fictional novels. They can fascinate new tourists, increase the number of domestic travelers, and promote tourism destinations. Because the sample size is unknown through Cochran's formula with a 95% confidence level, 384 people were selected by the available sampling method. They answered the questionnaire about the effect of literary tourism during the Covid-19 virus pandemic on tourists' behavioral goals. The collected data were analyzed by using Amos software and SPSS. Confirmatory factor analysis, the reliability and validity of the contract measurement model, Cronbach's alpha results (above 0.7), and composite reliability (greater than 0.7) for the reliability and mean of the extracted variance (greater than 0.5) were confirmed. The structural equation model was used to test research hypotheses and the relationships between research variables (spiritual factors, behavioral purpose). The results showed that spiritual variables, in general, have a positive effect on the behavioral goal of tourists through the attitude of tourists' mental norms. This attitude was also recognized as a significant mediator between spiritual perception and behavioral goals. The findings of this study indicate that literary tourism has a positive, significant effect on increasing re-visit tourism destinations through attitude, mental norms, and perceived behavioral control.

The Industrial Internet of Things (IIoT) is a potential platform for developing industry 4.0 and its related applications, especially in cyber-physical systems. Such a new trend in manufacturing sectors offers further potential to optimize operations, realize business models, and reduce costs. Such accomplish may also lead to complex and complicated tasks; hence, to deal with such issues, Reference Architecture Model Industry 4.0 (RAMI 4.0) is developed to structure Industry 4.0. In this paper, the standardized framework is considered RAMI 4.0 and its integration with an IIoT software named Software Platform Embedded Systems (SPES). Integrating Model-Based Engineering (MBE) with a framework requires using a deep learning model called Recurrent Neural Network (RNN). The RNN-MBE, which optimizes the entire process, is responsible for optimizing the process and reducing industry costs. The optimization problem has been fixed, and the MBE simulation has shown that using the proposed MBE is efficient.