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The study aims to examine the influence of external shocks, such as sanctions, the pandemic, and oil price fluctuations, on tax revenue stress in the country’s federal discticts and individual consistuent entities. To achieve this, we have developed a tax revenue stress index, which represents the spread from the moving standard deviation to the sliding rate of growth of annual tax revenues, and a methodology to decompose the index by its sources Using the developed index, we estimate tax revenue stress in Russian regions from January 2013 to October 2022, considering the pre-pandemic, pandemic, and recovery periods. Our findings indicate a significant positive correlation between tax revenue stress and oil price stress with a lag of 4-7 months, although the response of regions to oil market conditions varies. Notably, the Far Eastern, Ural, and Siberian federal districts experience higher average tax revenue stress. Among the federal districts, the Ural district plays a major role in both increasing and reducing tax revenue stress, while the Central district acts as a strong stress buffer during external shocks. The pandemic had a significant impact on tax revenues in most regions. However, in 24 out of 85 examined constitent entities of the Russian Federation, the average stress decreased, and in 36 regions, the maximum stress value decreased. During recovery, tax revenue stress was lower in 70 entities compared to the previous two periods. Nevertheless, metropolitan cities and certain regions have already felt the effects of new sanctions imposed in 2022. Notably, stress patterns behave abnormally in the Far Eastern border regions and Murmansk Oblast , which can be attributed to specific resilience factors related to their regional economies and specialization.

In the context of regional development, innovation and diversification are currently recognized as main strategic drivers. Particularly promising is the convergence of these areas, known as diversification based on innovation. For the resource sector of the economy and resource regions, such diversification entails the development of knowledge-intensive suppliers and high-tech services, often within specialized clusters. Given the increasing complexities of the resource base in the oil and gas sector and the pressures of sanctions, these objectives have gained significant relevance in Russia. A compelling illustration can be found in the establishment of a competitive oil industry cluster in the southern part of Tyumen Oblast. It is shown that this cluster has the potential to be a crucial catalyst for the socio-economic progress of the region. However, for the oil industry cluster to contribute sustainably to the region’s economic growth in the long term, it necessitates a substantial share of innovative projects. These projects should be science-intensive and deliver high added value, while effectively addressing all the emerging challenges associated with changes in the primary assets of the oil and gas sector in Western Siberia.

This article presents the findings of an empirical study conducted by the authors on cooperative network interactions among organizations in the production sector of Altai Krai. The study focuses on the region’s socio-economic development and, based on a brief review of other works, explores the aspects contemporary of empirical research on cooperative-network relations among organizations. We discuss and systematize the theoretical and methodological aspects of the study, provide our interpretation of basic concepts, and outline the key areas of analysis.The study methodology is described, highlighting its thus predetermined specific features that involve a comprehensive examination of organizations in the region. This includes small, medium, and large enterprises engaged in various types of activities, both within the region and across different regions and countries through cooperative-networking linkages. A questionnaire survey has been the primary tool used for data collection. We explain the principles for selecting the sample population and provide details about the information base of the study, which enables the achievement of its main objectives. The substantial results of the study’s first stage are presented, derived from testing the methodology. They offer a general overview of the scale, nature, and driving forces behind the development of industrial cooperation in Altai Krai, a typical agro-industrial region. Additionally, the study addresses the challenges related to the formation and development of industrial cooperation in the region.

State policies increasingly focus on fostering an effective innovation ecosystem and promoting the sustainable growth of high-tech and knowledge-intensive companies. A critical aspect influencing these objectives is the urban environment, which serves as a primary interface for most companies. In this regard, the Smart City Initiative at the departmental level and the federal project “Formation of Comfortable Urban Environment" are striving to establish modern smart cities. Smart cities create a growing demand for high-tech products and knowledge-intensive services, consequently driving their supply. The establishment of a smart, comfortable, and technologically advanced environment, coupled with an openness to new technologies, acts as a catalyst for the emergence and expansion of high-tech companies, as well as the development of an effective innovation ecosystem. Smart cities also facilitate the creation of innovation exchange hubs such as technology parks, business incubators, etc. Moreover, the presence of numerous high-tech companies reinforces the utilization of digital technologies and the Internet of Things within the urban landscape. The technologies developed and implemented must be relevant and user-friendly for citizens. This research aims to assess the reciprocal influence between high-tech and knowledge-intensive companies (HT&KICs) and smart cities, building upon a modified triple helix model as the theoretical framework. Through econometric analysis, we have observed that in large cities with a high level of digitalization in the urban economy and environment, there exists a positive impact on the development of HT&KICs.

The sustainable development of the Russian Arctic faces primarily socio-demographic risks: low population, migration outflow, subpar quality of life compared to the national average, and a mismatch between available educational opportunities and the needs of the Arctic economy and social sphere. Given that young peopleplay a crucial role in this area’s human and intellectual potential, their increased mobility in Arctic regions highlights the urgent need to find effective solutions to reduce youth migration. This can be achieved through the utilization of rapidly advancing digital technologies and smart solutions. This article presents the findings of a pilot study that aimed to test the hypothesis regarding the possible benefits of digital technologies in improving the quality of life for Arctic youth and mitigating their migration outflow. The results revealed significant potential for digital technologies to address the issue of youth migration outflow. In particular, the use of distance technologies to enhance the accessibility and quality of education can greatly influence young people’s decision to choose Arctic territories as their place of residence, work, and personal growth.

Human capital represents a crucial component for each enterprise, acting as a competitive advantage in achieving favorable outcomes. This study puts forward a hypothesis regarding the influence of factors causal to the emergent property on the self-assessment of firm-specific human capital within specific segments (individual categories) of the employed population, including business structure leaders, entrepreneurs, and employees. To measure firm-specific human capital, we employ a methodology adapted by the team of authors led by G.F. Romashkina from A. Moses’ research on human capital. The findings from our tests establish that social behavior factors, alongside traditional ones, significantly influence the self-assessment of firm-specific human capital, serving as the underlying conditions for the emergence effect.

The authors propose the galena flotation mechanism based on the joint work of chemisorbed collector and physisorbed lead xanthate in molecular form. It is proved experimentally that the products of interaction between xanthate and lead ions possess surface activity dependent on the concentration ratio and on solution pH. In alkaline range, they spread over the gas-water interface and can remove water from the interlayer between a mineral particle and a gas bubble. In sub-acid medium and at the increased mole ratio of lead ions to xanthate anions, the spreading velocity of the interaction products decreases. The physisorption mechanism of the collector has disclosed the causes of high floatability of galena in the alkaline range of pH and the decreased floatability in the sub-acid domain.

The causes of coincidence between pH of a solution of collectors, at which the maximal recovery of a target component is reached, and pH of the solution with minimal surface tension are discussed. Based on the physisorption mechanism of a collector, the floatability connection with the surface tension of the solution is explained. It is shown that extraction of ion-molecular associates from the solution and the presumptive increase in the mineral surface hydrophobicity are not an explanation of the increased recovery. The floatability improvement is achieved via removal of the kinetic constraint of the flotation contact by surface-active species of collectors. The increase in the collecting activity of a blend of collectors is explained by the synergetic effect of the decreased surface tension and reduced induction time. The criterion of flotation activity of a physisorbable collector is proposed.

This paper studies the effect of using cationic/mixed anionic collectors in the practical application of quartz sand flotation. The combined use of Dodecylamine(DDA)/sodium oleate(NaOL)/sodium dodecyl sulfonate(SDS) is the innovative point of this paper. This experiment used quartz sand from Hanzhong, Shaanxi Province, China. The results showed that the combined use of DDA, NaOL and SDS was better than the single use. Using DDA/NaOL/SDS mixed collector, the removal efficiency of Al₂O₃ can reach 75.1%, compared with DDA/NaOL mixed collector, the purification effect of Al₂O₃ can be increased by 11.1%. Compared with DDA/SDS mixed collector, the use of this mixed collector can greatly improve the purification effect and recovery rate. And the quartz concentrate has a good recovery rate in practical application.

By combining the advantages of InSAR, Probabilistic Integral Method and Genetic Algorithm, an improved method for dynamic prediction of probability integral parameters is proposed to realize subsidence inversion and prediction in insufficient mining. Firstly, InSAR is used to obtain the time series information of surface deformation in goaf. Then, a genetic algorithm-based parameter inversion model is constructed to invert the subsidence parameters such as subsidence coefficient and influence radius. After that, a dynamic prediction function is established to obtain the complete surface subsidence pattern and dynamic change trend of the mining area. Taking a goaf in Shanxi Province as the experimental object, Sentinel-1A(S-1A) image as the data source, combined with PIM and InSAR data, the parameter inversion model is used to successfully obtain the dynamic change process of mining subsidence parameters. The results show that the dynamic prediction function can achieve a certain effect on surface prediction in insufficient mining, and the parameter inversion model based on genetic algorithm has a high inversion accuracy, which provides a basis for surface prediction in insufficient mining.