The current COVID-19 pandemic has shown how important digitalization is to the world economies and how networks and connectivity, artificial intelligence, as well as basic and advanced digital skills sustain economies and societies. Digitalization involves an increase in the use of information and communication technologies (ICT) in all areas of the economy and all domains of a functioning society. This study was based on the Digital Economy and Society Index (DESI) dimensions: Connectivity; Human capital; Use of internet service; Integration of digital technology; Digital public services for 2020. The data included in the index were mostly collected from the relevant authorities of the Member States by the European Commission (Directorate-General for Communications Networks, Content and Technology as well as Eurostat). The Kohonen neural network technique, which uses an iterative algorithm, was used to group countries according to the DESI dimension. It is a method that enables grouping of objects into sets due to the similarity of the data they contain and characterizes them. In this study, the EU countries were divided into groups according to the similarity of DESI levels. For the set of 28 countries and 5 DESI dimensions the appropriate structure of the Kohonen network and its parameters were selected, in order to determine the most satisfactory results. The following were the groups of countries classified: Class ID1: Denmark, Finland, Netherlands, Sweden, Class ID2: Belgium, Estonia, Ireland, Malta, United Kingdom, Class ID3: Austria, Germany, Spain, France, Lithuania, Luxembourg, Latvia Class ID4: Czechia, Hungary, Italy, Poland, Portugal, Slovenia, Class ID5: Bulgaria, Cyprus, Greece, Croatia, Romania, Slovakia. The distribution of the separate classes of the state indicates that we can treat them due to the specificity of their features. In the ID 1 and ID2 classes the considered features assume very high numerical values. ID class 3 is characterized by an average level of DESI parameters. Low values of the considered features are characteristic of the ID4 and ID 5 classes. The classification result is helpful in distinguishing the current level of digitalization of European Union countries.

Since Middle Age Holy Cross Mts. region in Central Poland was very important mining and industrial district called Old Polish Industrial District. Therefore, a very large number of post-mining areas occurred here. The problem of their transformation has so far been poorly understood. Therefore, the aim of the study was to identify the changes taking place in selected post-mining areas of this region and to determine the role of natural and anthropogenic factors in their transformation. The application aim was to set directions for the revitalization of these areas, as they have great scientific, educational, landscape, recreation and geoturistic values. The results of the interdisciplinary research indicate that changes are a result of natural factors (e.g., present-day morphogenetic processes, especially mass movements; secondary plant succession) and anthropogenic, both planned (reclamation, development, access, adaptation and revitalization of post-mining areas) and unplanned (uncontrolled effects of tourist traffic, wild dumps). In undeveloped post-mining areas, natural factors and littering of facilities play the main role in their transformation, while in developed areas anthropogenic factors dominate. The best opportunities for multi-faceted use the post-mining sites are education, landscape, tourism and recreation, and economic fields. The directions of development of these areas proposed in the work may complement each other, and the decision to select the leading one should take into account, i.e., target functions they are to perform in the future. On the example of the revitalization of the Szewce quarry, a model of geotouristic management and access was developed and implemented, which can be used for other similar post-mining facilities in Poland.