This study examines the relationship between Russian FDI carried out by large MNCs and investment development path (IDP). Although statistical analysis does not establish a significant relationship between outward FDI and GDP, the behavior of Russian outward FDI contradicts traditional models. Two primary factors contribute to this paradox. First, the complex business environment in Russia, characterized by a combination of both improvements and contradictions, has a significant impact on outward FDI behavior. Secondly, the duality of the Russian economy and society plays a decisive role. This segment resembles a high-income country with ample resources, while most face lower income levels, raising concerns about wealth distribution. Historical factors, including Russia’s transition from a state-controlled to a market-oriented economy, contribute to the internationalization of Russian MNCs. Both state-owned enterprises and privatized firms are influenced by the state, although to varying degrees. Government involvement in international business strategies increases the knowledge and experience of Russian MNCs, but also raises concerns about political influence.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The main long-term goal of international communities is to achieve sustainable development. This issue is currently highly topical in most European Union (EU) countries due to the ongoing energy crisis. Building Integrated Photovoltaics (BIPV), which can be integrated into the building surface (roof or facade), thereby replacing conventional building materials, contributes significantly to achieving zero net energy buildings. However, fire safety is important when using BIPV as a structural system in buildings, and it is essential that the application of BIPV as building facades and roofs does not adversely affect the safety of the buildings, their occupants, or the responding firefighters. As multifunctional products, BIPV modules must meet fire safety requirements in the field of electrical engineering as well as in the construction industry. In terms of building regulations, the fire safety requirements of the BIPV must comply with national building regulations. Within this article, aspects and fire hazards associated with BIPV system installations will be defined, including proposals for installation and material requirements that can help meet fire safety.

In order to strengthen the study of soil-landscape relationships in mountain areas, a digital soil mapping approach based on fuzzy set theory was applied. Initially, soil properties were estimated with the regression kriging (RK) method, combining soil data and auxiliary information derived from a digital elevation model (DEM) and satellite images. Subsequently, the grouping of soil properties in raster format was performed with the fuzzy c-means (FCM) algorithm, whose final product resulted in a fuzzy soil class variation model at a semi-detailed scale. The validation of the model showed an overall reliability of 88% and a Kappa index of 84%, which shows the usefulness of fuzzy clustering in the evaluation of soil-landscape relationships and in the correlation with soil taxonomic categories.