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The article analyzes the development of Moscow’s industrial sector. Despite the changes since the beginning of market reforms, the capital still hosts a significant industrial segment important not only to the city but the national economy at large, which makes selecting development vectors for this metropolis even more significant. We show that the existing competitive advantages in the form of concentrated production, financial, and intellectual resources are not exploited in their entirety. The article considers Moscow’s experience in pursuing an industrial policy based on the following combination: system-wide measures aimed at improving the investment attractiveness of the city with measures to support industrial sectors and certain economic activities, as well as to encourage investment in priority areas of industrial development, taking into account the established industrial potential, local social and economic needs, and promising technological trends. It is noted that the development of modern high-tech industrial sectors could contribute to the innovative growth potential being actualized by forming both demand for innovative products and their supply. We also examine the prospects of individual economic activities of an industrial kind for the city, their competitive advantages, limitations, and risks that may hinder investment in industries. Using the example of Moscow, we highlight several general principles that should be considered when preparing appropriate measures for the regions.

The main objective of this research is to identify which regional-level environment factors affect the emergence of high-tech companies and assess this impact’s orientation. As an empirical basis, the research uses the indicators of high-tech and knowledge-intensive companies by regions available in the SPARK Database of Russian Companies, regional economic development indicators issued by Rosstat, and public information provided by government authorities. We have evaluated how new companies form by the number of firms under three years of lifespan with non-zero revenues. Another factor analyzed was the ratio of this number to total employment in the region. Regional environment factors were considered in five sets: innovation potential, human resources potential, general characteristics of industrial production, state support of innovation activity, and partnerships among innovation companies. We paid special attention to the last two. Analyzing the general characteristics of the sample formed has proved that young companies are exceptionally unevenly distributed over the country, with 47.8% operating in five regions only. Of all firms, 96% belong to the category of microenterprises. The results of econometric calculations have revealed that the federal funding of innovation activity at the regional level is positively significant in general, as well as organizations being involved in joint R&D projects. Meanwhile, regional budget participation and channeling of federal resources to support innovative infrastructure for SMEs, similarly to whether regional firms are part of clusters and technology parks, do not yet have a noticeable impact on the emergence of new high-tech enterprises.

COVID-19 has led to significant changes around the globe. Many studies have already identified the factors influencing the disease spread and mortality, both at regional and national scales. The consistently large number of people infected and newly discovered strains show that these studies are as relevant as ever. Judging from global and domestic practices, we see the increasing use of digital technologies to combat the pandemic and its consequences. These can include technologies that facilitate detecting infected people, contact tracing, prediction, diagnosis and treatment, promoting public awareness, as well as helping to comply with social distancing, self-isolation rules, and transitioning to remote operations. Such technologies are better developed in smart cities due to the availability of suitable infrastructure, although they can be applied in any location. This study assesses what smart technologies and comfortable urban environments contribute to controlling coronavirus infection. The paper proposes a negative correlation between the smart urban environment and the number of deaths, which is tested with statistical methods. According to our findings, a developed urban infrastructure does reduce excess mortality, consisting not only of those who die directly from infection but also from its consequences. Its other benefits include reducing healthcare overload, difficulties with routine check-ups and planned operations, etc. At the same time, the degree of urban environment digitalization has no considerable impact on mortality.

This article aims to assess mortality by cause of death before the COVID-19 pandemic in Russia. The database consists of Rosstat data for the period from 1990 to 2019 characterized by multidirectional dynamics of life expectancy in the Russian population. We have found a significant increase in the degree and share of mortality from diseases of the digestive system, observed recently amid a decline in overall mortality, as well as from infectious and parasitic diseases. Other observations include an increase in mortality from neoplasms with its share reduced and a marked decrease in the degree and share of mortality from respiratory diseases, external causes, and circulatory diseases. As a result of countertrends, the main causes of death regrouped twice in 2006. The share of mortality from other causes, where neurodegenerative diseases play a major role in the aging society, has tripled; since 2016, it has been second only to mortality from cardiovascular diseases. Here we assess the gender peculiarities of mortality by cause of death and disparities between urban and rural areas. The study results can be used to improve the demographic policy in terms of mortality.

This article is devoted to the issues of modeling and forecasting mortality rates in the regions of the Russian Federation. The analysis showed that 2020 was characterized by a significant deterioration in the demographic situation and that the key reason was an increase in the number of deaths by 18%. Although excess mortality was also observed in most other countries, the authors do not set the task of assessing the impact of such an atypical situation as the coronavirus pandemic. The primary task is to develop measures to reduce the death rate under so-called “normal conditions”. Ensuring a high quality of life, in a sense, would alone be a factor to reduce the consequences of unforeseen situations. Our hypothesis is that the level of social infrastructure advancement largely determines the regional mortality and life expectancy. The goal is to develop an approach to modeling this impact and forming appropriate forward-looking estimates for the medium term. For that, we use methods of comparative, factor, correlation, and regression analysis, grouping methods, and scenario forecasting. As a result, we have formed a set of econometric models which describe the relationship between the death rate, life expectancy, the level of social infrastructure advancement, and how well the region’s potential benefits are actualized. The obtained forecast shows that the baseline scenario allows a decrease in the death rate in most Russian regions by 1.0-2.5% compared to 2019 and an increase in the average life expectancy up to 74.7 years. However, one needs to consider the law of diminishing marginal utility, which states that resources required to increase life expectancy will ever grow. Part of such resources will be allocated to developing social infrastructure. These results can be applied by public authorities to draft demographic policy measures.

The article examines the factors that determine the risks and depth of poverty for households with disabled people. The empirical basis of this study is the 2018 Comprehensive Survey of Living Conditions. The study findings show that when one working-age adult is disabled, poverty risks increase for the entire household, whereas having a child or elderly person with a disability does not significantly affect poverty risks compared to households without disabilities. Residing in rural areas or small towns, managing large dwellings, and having children, especially of preschool age, increase the risk of poverty for such households. To reduce the risks, it is necessary to shift the focus from the individual recipient in need of assistance to the household as an economic unit, and to switch from the categorical principle of social protection of vulnerable groups to targeted social support based on an individual assessed need. This change will make it possible to consider the diversity of living circumstances in households that include people with disabilities.

The article discusses the theoretical, methodological, and applied aspects of ensuring that the results of discontinuous surveys at the national, regional, and municipal levels are representative. It reviews the principles, methods, forms, and technologies of sample observations implemented by state and public institutions when various mass sample surveys are arranged and conducted. We also define the problem of obtaining non-random samples in the survey process and assess the practice of spreading their results to the general population. Without the basis for a general population (i.e., a unit listing), researchers must solve the inverse problem of the sampling method; this circumstance does not allow obtaining random samples, and besides, often there is no reasoning for the representativeness of the data obtained. Viewing these problems with adequate statistical inference allows us to find an acceptable solution. We have developed and tested an algorithm to generate representative non-random samples on a wide range of surveys. The proposed methodology is used in arranging and conducting a sociological survey on enforcing the critical provisions of several federal laws. Ensuring the representativeness of the obtained non-random sample has made it possible to assess the level and degree of differentiation of citizens’ satisfaction with the measures taken to regulate public relations in the studied laws across federal districts and constituent entities of the Russian Federation.

The practices of the social and economic development of the south of Tyumen Oblast (STO, federal constituent entity, without autonomous okrugs) since the mid-2000s exemplify generally successful economic growth. The STO industry’s build-up was largely associated with the extraction of hydrocarbons (primarily oil) and their processing (petrochemistry and oil refining). But these growth drivers are losing their meaning. Today, the potential for further growth due to the expansion of hydrocarbon production and processing is close to being exhausted. The region needs to search for new sources of economic growth. They can be related to the knowledge economy and interregional ties, which includes manufacturing high-tech equipment and providing high-tech services not only for STO but also for the oil and gas sector throughout Western Siberia. It is advisable to pursue activities in this regard within the newly established oil cluster, whose successful functioning has all the necessary prerequisites in STO. Its generation and development must be carried out considering the peculiarities of transforming the main assets of the oil and gas sector, Western Siberian mineral reserves.

This paper aims to encapsulate the trends in the variation of speed with real-time parameters, i.e., temperature and the corresponding change in humidity thereof for the underground mine ventilation system. The purpose mentioned above is fulfilled using a precise and vigorous estimation method of the speed for the sensorless induction motor drive (IMD). The developed model reference adaptive scheme (MRAS) speed estimator can be utilized to control the IM speed for the underground mine ventilation system based on the real-time parameters. The present work is on temperature and humidity-dependent MRAS based sensorless speed estimation technique for IM used in mine ventilation drives. This methodology has been tested analytically and experimentally using MATLAB/Simulink and LabVIEW-2013 laboratory interfaces. Furthermore, a statistically validated empirical relation between the temperature, humidity of the underground mine and speed of the ventilation system has also been developed to facilitate calculations of the same. However, implementing the proposed methodology in an actual underground mine remains a thing of the future.

The aim of the research is to propose that artificial neural networks be applied in the process of identification of structurally altered coal. The results suggest that the proposed methodology of classification, due to its high effectiveness exceeding 90% of correct identifications, may be successfully used as a tool supporting the observer's decisions concerning the description of coal from near-fault zones.