iraj teymouri

Today, smart tourism is growing and developing as a new type of tourism industry. Due to the growth of new technologies, countries can not act on tourism development without considering their impact and at the same time guarantee its sustainability. However, there are problems and challenges in this regard. Aras Free Zone as an example of this study is no exception and the main purpose of this study is to identify the challenges of smart tourism development in this area. In this regard, the research method of the present article is exploratory in nature and practical in purpose. The required information has been collected through library and field studies (questionnaire). The statistical population of the study was the experts in the field of tourism in Aras Free Zone. Using snowball method, 10 people were interviewed as a statistical sample of the research. Also, for the validity of extractive indicators, content analysis (CVR) method and for identifying the most important challenges of smart tourism, structural-interpretive modeling (ISM) and Mick Mac analysis have been used. In this regard, the research findings have shown that out of 20 challenges extracted from theoretical foundations, 14 challenges have acceptable validity. The challenges of manpower, organizational-management, information and communication technology, financial resources and infrastructure are considered as key challenges in the development of smart tourism in the Aras Free Zone, which have a great impact on the development of smart tourism.

With the global surge of urbanization and population growth, the well-being of children within modern urban developments has been largely overlooked, necessitating an urgent need for attentiveness towards their needs in cities. Consequently, the assessment of urban neighborhoods in terms of child-friendly city components becomes an essential area of research. This study employs a survey method to evaluate the dimensions and components associated with a child-friendly city. Utilizing a cluster-spatial sampling approach, ten neighborhoods from five regions of Ardabil are selected as representative samples (two neighborhoods per region). A total of 400 researcher-developed questionnaires are then distributed proportionally based on the population of each selected neighborhood. The collected data is subsequently analyzed using one-sample t-statistics in SPSS software, as well as multi-criteria decision-making models to prioritize the neighborhoods according to the research criteria. The findings reveal distinctive characteristics of child-friendly city dimensions and components across the various neighborhoods of Ardabil. Interestingly, when comparing the dimensions, the environmental-physical and organizational-managerial dimensions emerge as the most problematic areas within the studied neighborhoods. Additionally, it is noteworthy that despite the inherent spatial disparities between neighborhoods, Neighborhood No. 1 in District 4 and Neighborhood No. 3 in District 2 of Ardabil Municipality exhibit greater challenges compared to other neighborhoods under investigation.

In recent years, several algorithms have been proposed for detection in complex networks. It should be noted that wuth regarding the features of these communities, one of the existing methods for identifying communities, is providing an algorithms for weighting the edges of the network as the weight of the edges of the communities are incresed and at the same time the weight of the edges between communities are reduced. Therefore the distinction between communities could be identified simply. In the proposed method with useing the process of weighting the edges, we distinguish between the nodes that are more similar to each other and the nodes that have slight similarity. i.e. by assigning the weight with using the proposed criteria in some algorithms, the edges with more weight will have a greater role in determining the population.According that there is a positive correlation between similarity measures and community structures, the results of the tests shows that using local similarity measures as the weight of edges for some algorithms, causes an increase in the accuracy of communities recognition. These algorithms use the degree of the nodes as one of the network characteristics for computing the cores absorbing ability for communities formation. For example, in the case of Real Networks, running WHD-EM algorithm on the High school network, discovers the communities with the NMI=0.6652 and the purity criteria equal to 0.9845. Also it should be noted that some algorithms such as CPM, OSLOM and LINK in terms of NMI criteria, are better.