Crimea is a very attractive tourist destination with a high rank of tourist values, being natural and cultural heritage, with high recognition on the international tourist market. Since 2014, the tourism economy has been in a protracted crisis, caused by both internal and external reasons. The annexation of Crimea by the Russian Federation in March 2014 significantly exacerbated the problems of the functioning of the tourism economy, preventing its sustainable development. The Russian authorities are taking political as well as socioeconomic measures to rebuild the tourism economy of Crimea, by taking steps to increase tourist traffic in Crimea, especially within the country, i.e., from other regions of Russia. One of the directions of actions to overcome the crisis is information support by the Russian media, the analysis of which is presented in this article. The aim of the study is therefore an attempt to evaluate the effectiveness of information support by the Russian media for the tourist destination of Crimea in 2014–2019. Media activities are part of the Russian government’s tourism policy in this area. The article deals with the following theoretical issues: tourism policy, sustainable tourism development, heritage as the subject of tourism policy impact, information management, crisis management, the role of information in crisis management. The empirical part presents: the genesis and development of the crisis in the tourist destination of Crimea, the activity of the Russian authorities in terms of impact on the crisis, an analysis of the use of information by the Russian media in overcoming the crisis in the Crimean tourism economy.

Інтервальне оцінювання кількості учасників масових протестних акцій

The method of forecasting the time of overloading of data transfer routes in the mobile radio link (MRL), which is constructed using the neural network of Elman based on the calculation of the potential of neurons in the network is presented. The essence of the method is to make a decision on finding new routes based on the predicted time of overloading the data transfer routes in the MRL. This method allows predicting the time of overloading of data transfer routes in the MRL due to the reduction of the computational complexity of the neural network and the application of the algorithm for training the neural network.