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The Russian Federation is one of the world’s largest exporters of fossil-based energy sources such as oil, natural gas and coal. Approximately 90% of the energy production in the Russian Federation consists of oil, natural gas and coal. Renewable energy (RE) in the Russian Federation mainly comprises hydroelectric energy. The purpose of this paper is to identify the factors that influenced the growth of RE resources in the Russian Federation between 1990 and 2020.

The unit root tests augmented Dickey and Fuller and Phillips and Perron, as well as Johansen cointegration and Granger causality approaches, were used. This study was conducted using vector error correction models for the years 1990–2020.

The cointegration method's findings demonstrate that while a rise in non-RE sources has a negative impact on RE development, an increase in income, energy consumption, trade openness and CO₂ emissions has a favorable impact on RE expansion. The vector error correction model Granger causality test also shows a unidirectional relationship between RE and non-RE sources, gross domestic product, energy consumption and CO₂ emissions. Trade openness, on the other hand, has no causal association with RE.

The Russian Federation must consider the practical implications of RE sources. However, there is a greater need for the Russian Federation to frame sound energy policies for RE development.

This paper aims to fill a gap in the literature on Russian RE development. Furthermore, the results of the methodological analysis can be used to guide policymakers in the field of RE development. This paper is also more policy-relevant and is quite useful in the context of sustainable energy development.

In the age of DarkNetMarkets proliferation, combatting money laundering has become even more complicated. Constantly evolving technologies add a new layer of difficulty to already intricated schemes of hiding the cryptocurrency’s origin. Considering the latest development of cryptocurrency- and blockchain-related use cases, this study aims to scrutinize Italian and Russian antimoney laundering regulations to understand their preparedness for a new era of laundering possibilities.

One of the most recommended ways to buy and sell cryptocurrencies for illegal drug trade on DarkNet was discovered using machine learning, i.e. natural language processing and topic modeling. This study compares how current Italian and Russian laws address this technique.

Despite differences in cryptocurrency regulation, both the Italian Republic and the Russian Federation fall behind on preventing cryptolaundering.

The main contributions of this paper: consideration of noncustodial wallet projects and nonfungible token platforms through the lens of money laundering opportunities, comparison of Italian and Russian antimoney laundering regulations related to cryptocurrency, empirical analysis of the preferred method of trading/exchanging cryptocurrency for DarkNet illegal trade using machine learning techniques and the assessment of how Italian and Russian regulations address these money laundering methods.

The paper aims to reveal the attitude of the Russian competition authorities towards cross-border mergers involving foreign buyers. The study addresses the following question: Is the probability of Russian competition authorities' intervention significantly different when a foreign buyer takes part in the merger? This is the key test to reveal whether competition authorities gravitate towards “economic nationalism” or “promotion of foreign investments”.

The discrete choice model is applied to the dataset of 7,607 merger cases investigated by the Russian competition authorities between 2012 and 2017. The probability of competition authorities' intervention, such as merger correction by using remedies or deal rejection, is used as a measure of special attention.

The study finds out favoritism patterns of the regulator with regard to foreign companies. In particular, the deals involving a foreign buyer had less chance of intervention, i.e. imposition of remedies, from national competition authorities. The sanctions period does not moderate the probability of approval of a cross-border merger with foreign buyers by the Russian competition authorities.

The paper contributes to merger control literature by addressing the political economy issues. It discovers that, besides regulation by the law, there are hidden motives, such as protectionism or favoritism of foreign companies, which could drive the regulator's decision. Therefore, the studies of cross-border mergers provide an opportunity to investigate the political issues of merger control through the identification of a special attitude to foreign companies and analysis of regularities that might explain such a policy.

The paper examines whether, along with the financial performance, the disclosure of research and development (R&D) expenses, patent portfolios, patent citations and innovation activities affect the market capitalization of Russian companies.

The paper opted for a set of techniques including bag-of-words (BoW) to retrieve additional innovation-related data from companies' annual reports, self-organizing maps (SOM) to perform visual exploratory analysis and panel data regression (PDR) to conduct confirmatory analysis using data on 74 Russian publicly traded companies for the period 2013–2019.

The paper observes that the disclosure of nonfinancial data on R&D, patents and primarily product and marketing innovations positively affects the market capitalization of the largest Russian companies, which are mainly focused on energy, raw materials and utilities and are operating on international markets. The study suggests that these companies are financially well-resourced to innovate at risk and thus to provide positive signals to stakeholders and external agents.

Our findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the compiled lexicons.

The study aims to expand the domains of signaling theory and market valuation by providing new insights into the impact that companies' reporting on R&D, patents and innovation activities has on market capitalization. New nonfinancial factors that previous research does not investigate – innovation disclosure indicators (IDI) – are tested.

The research objectives are to interview rising preschool teachers studying at Russian universities, highlight the categories of information technologies used in preschool institutions based on the survey results and possible difficulties that may arise when working with them and develop tips for working with interactive technologies for preschool teachers.

The results of the survey showed a high level of involvement of information technology in the educational process. The majority of respondents (87%) use information technology in teaching and learning. The analysis of the answers shows relatively identical indicators of the use of different types of information technologies: 65% prefer more technological and 35% – applied. It was shown that the use of technological and applied ICT categories isn't similar among participants of the survey. To increase the level of students' and teachers’ knowledge with modern information technologies the program was created.

In preschool education, the kindergarten teacher or supervisor should act as the leader of the learning process, including the interactive one. The teacher should encourage children to independently use the ICT tools and guide the process.

Given the rapid pace of development of science and technology, the curriculum needs to be deepened and expanded by diversifying activities. It is worth paying attention to modern methods of teaching and learning, which require the use of pedagogical innovations, the acquaintance of students and teachers with possible ways to expand the activities and update the latest teaching methods in educational process.

There are common problems in the identification of uncertain nonlinear systems, nonparametric approximation, state estimation, and automatic control. Dynamic neural network (DNN) approximation can simplify the development of all the aforementioned problems in either continuous or discrete systems. A DNN is represented by a system of differential or recurrent equations defined in the space of vector activation functions with weights and offsets that are functionally associated with the input data.

This study describes the version of the toolbox, that can be used to identify the dynamics of the black box and restore the laws underlying the system using known inputs and outputs. Depending on the completeness of the information, the toolbox allows users to change the DNN structure to suit specific tasks.

The toolbox consists of three main components: user layer, network manager, and network instance. The user layer provides high-level control and monitoring of system performance. The network manager serves as an intermediary between the user layer and the network instance, and allows the user layer to start and stop learning, providing an interface to indirectly access the internal data of the DNN.

Control capability is limited to adjusting a small number of numerical parameters and selecting functional parameters from a predefined list.

The key feature of the toolbox is the possibility of developing an algorithmic semi-automatic selection of activation function parameters based on optimization problem solutions.

This paper aims that digital transformation (DT) is crucial for companies to stay competitive. While research on DT has quickly gained great popularity, the intersection of trade associations (TAs) and their role in the DT of their members is not yet researched.

In this paper, the authors conducted 20 interviews with Belgian TAs to investigate the role of a TAs in the DT of its members, and how they drive the DT of its members. In addition, the authors investigate the core tasks of TAs, the need of the different industries to digitalize, and the digital projects the different industries are working on.

The findings indicate that TAs can be in a prime position to steer the DT of their members, especially for industries comprised of smaller players. Their roles can range from informing roles to true leaders of DT by creating novel products, such as online platforms and driving the entire sector forwards.

These findings call for more research into TAs and how their role can be optimized for steering DT of their members.

This is the first study to extensively study the role of TAs on the DT of their members.

This paper seeks to assess the capabilities and maturity of supply chain planning and product quality management systems implemented by the dairy industries in three different countries: Russia, Kazakhstan, and Lithuania.

Through a systematic analysis of statistical information, the descriptors of the logistics supply chain efficiency were identified. Directions for dairy supply chain management improvement were also highlighted. The study uses secondary statistics from open sources as a basis for the comparative analysis across a range of indicators, including the Logistics Performance Index (LPI), the dairy products quantity, the production volume of milk and dairy products, and other indicators characterizing the dairy industry.

The results of this study suggest the inconsistency and narrowness of single indexes and ranking, which are traditionally used to evaluate the logistics system. LPI values confirmed that the internal efficiency of the Lithuanian logistics system exceeds similar systems in Russia and Kazakhstan.

In this paper, ways to improve supply chain management of dairy products in the context of globalization have been determined, the prerequisites for the formation of the perishable goods market according to the balance of supply and demand and the institutional mechanism of this process, which represents important information for all interested economic agents, have been analyzed.

The proposed approach points to the importance of having detailed information on the supply chain infrastructure and the need to introduce a single information space based on modern information and communication technologies.

The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government interference amid the ongoing pandemic.

The design of this study has several tracks, namely, a macro-level track, which is represented by the government measures to halt the pandemic; a micro-level track, which is followed by textual analysis of IPO prospectuses; and, finally, a machine learning track, in which the authors use state-of-the-art tools to improve their linear regression model.

The authors found that strict government anti-COVID-19 measures indeed contribute to the reduction of the IPO underpricing. Interestingly, the mere fact of such measures taking place is enough to take effect on financial markets, regardless of the resulting efficiency of such measures. At the micro-level, the authors show that prospectus sentiments and their significance differ across prospectus sections. Using linear regression and machine learning models, the authors find robust evidence that such sections as “Risk factors”, “Prospectus summary”, “Financial Information” and “Business” play a crucial role in explaining the underpricing. Their effect is different, namely, it turns out that the more negative “Risk factors” and “Financial Information” sentiment, the higher the resulting underpricing. Conversely, the more positive “Prospectus summary” and “Business” sentiments appear, the lower the resulting underpricing is. In addition, we used machine learning methods. Consisting of more than 580 IPO prospectuses, the study sample required modern and powerful machine learning tools like Isolation Forest for pre-processing or Random Forest Regressor and Light Gradient Boosting Model for modelling purposes, which enabled the authors to gain better results compared to the classic linear regression model.

At the micro level, this study is not confined to 2020, but also embraces 2021, the year of the record number of IPOs held. Moreover, in this paper, these were prospectuses that served as a source of management sentiment. In addition, the authors used a tailor-made government stringency index. At the micro level, basing the study on behavioural finance hypotheses, the authors conducted both separate and holistic analysis of prospectuses to assess investors’ reaction to different aspects of IPO companies as well as to the characteristics of the IPOs themselves. Lastly, the authors introduced a few innovations to the research methodology. Textual analysis was conducted on a corpus of prospectuses included in a study sample. However, the authors did not use pre-trained dictionaries, but instead opted for FLAIR, a modern open-source framework for natural language processing.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.