26 research outputs found
ΠΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° ΡΠ΅Π°Π±ΠΈΠ»ΠΈΡΠ°ΡΠΈΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΡΡΠ°ΡΡΠ΅Π³ΠΎ Π²ΠΎΠ·ΡΠ°ΡΡΠ° Ρ ΠΎΡΡΠ΅ΠΎΠ°ΡΡΡΠΈΡΠΎΠΌ
Objective: to develop a personalized rehabilitation program for improving age-related resilience (AR), antioxidant status (AOS), the quality of life and reducing pain in elderly patients with osteoarthritis (OA).Patients and methods. The program consisted of two parts. In the first part, we conducted a comparative study to assess the AR (Mahnach test), geriatric status, and AOS (amperometric flow-injection analysis) in 181 subjects in total, with coxarthrosis (n=92) and without it (n=89). The average age of patients in two groups was comparable: 72.1Β±1.1 and 71.9Β±1.1 years. Using factor analysis, we developed the personalized rehabilitation program based on the obtained data. The effectiveness of the program was evaluated in the second part of our work. We conducted an additional comparative study of changes in AR, AOS, severity of joint pain (by visual analogue scale, VAS) and quality of life (according to SF-36 questionnaire) in patients with coxarthrosis (n=114).Results and discussion. Patients with coxarthrosis had significantly lower level of AR, total antioxidant, and antiradical activity, and a higher content of Schiff bases as compared to subjects without coxarthrosis (p<0.05). The program of rehabilitation, which included a course of treatment with Chondroquard, significantly improved AR, AOS, quality of life and reduced hip pain compared to the standard OA therapy (p<0.05).Conclusion. The personalized rehabilitation program has a complex positive effect on pain, quality of life, AR and AOS in elderly patients with OA.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ β ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΡΠ΅Π°Π±ΠΈΠ»ΠΈΡΠ°ΡΠΈΠΈ, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΠΉ Π½Π° ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ Π²ΠΎΠ·ΡΠ°ΡΡΠ½ΠΎΠΉ ΠΆΠΈΠ·Π½Π΅ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΠΈ (ΠΠ), ΡΠ»ΡΡΡΠ΅Π½ΠΈΠ΅ Π°Π½ΡΠΈΠΎΠΊΡΠΈΠ΄Π°Π½ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΡΡΡΠ° (ΠΠΠ‘), ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ Π±ΠΎΠ»ΠΈ ΠΈ ΡΠ»ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΆΠΈΠ·Π½ΠΈ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΡΡΠ°ΡΡΠ΅Π³ΠΎ Π²ΠΎΠ·ΡΠ°ΡΡΠ° Ρ ΠΎΡΡΠ΅ΠΎΠ°ΡΡΡΠΈΡΠΎΠΌ (ΠΠ).ΠΠ°ΡΠΈΠ΅Π½ΡΡ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° ΡΠΎΡΡΠΎΡΠ»Π° ΠΈΠ· Π΄Π²ΡΡ
ΡΠ°ΡΡΠ΅ΠΉ. Π ΠΏΠ΅ΡΠ²ΠΎΠΉ ΡΠ°ΡΡΠΈ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠ΅Π·Π΅ΡΠ²Π½ΡΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠ° Π±ΡΠ»ΠΎ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΠ (ΡΠ΅ΡΡ ΠΠ°Ρ
Π½Π°ΡΠ°), Π³Π΅ΡΠΈΠ°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΡΠ°ΡΡΡΠ° ΠΈ ΠΠΠ‘ (Π°ΠΌΠΏΠ΅ΡΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΏΡΠΎΡΠΎΡΠ½ΠΎ-ΠΈΠ½ΠΆΠ΅ΠΊΡΠΈΠΎΠ½Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ·) Ρ 181 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ° Ρ ΠΊΠΎΠΊΡΠ°ΡΡΡΠΎΠ·ΠΎΠΌ (n=92) ΠΈ Π±Π΅Π· ΡΠ°ΠΊΠΎΠ²ΠΎΠ³ΠΎ (n=89). Π‘ΡΠ΅Π΄Π½ΠΈΠΉ Π²ΠΎΠ·ΡΠ°ΡΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π΄Π²ΡΡ
Π³ΡΡΠΏΠΏ Π±ΡΠ» ΡΠΎΠΏΠΎΡΡΠ°Π²ΠΈΠΌ: 72,1Β±1,1 ΠΈ 71,9Β±1,1 Π³ΠΎΠ΄Π°. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠ°ΠΊΡΠΎΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π±ΡΠ»Π° ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° ΡΠ΅Π°Π±ΠΈΠ»ΠΈΡΠ°ΡΠΈΠΈ. ΠΠΎ Π²ΡΠΎΡΠΎΠΉ ΡΠ°ΡΡΠΈ ΡΠ°Π±ΠΎΡΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»Π°ΡΡ ΠΎΡΠ΅Π½ΠΊΠ° Π΅Π΅ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ. ΠΡΠ»ΠΎ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΡΡΠ΅Ρ
ΠΌΠ΅ΡΡΡΠ½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΠ, ΠΠΠ‘, Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΡΡΠΈ Π±ΠΎΠ»ΠΈ Π² ΡΡΡΡΠ°Π²Π°Ρ
(ΠΏΠΎ Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΠΎΠΉ Π°Π½Π°Π»ΠΎΠ³ΠΎΠ²ΠΎΠΉ ΡΠΊΠ°Π»Π΅) ΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΆΠΈΠ·Π½ΠΈ (ΠΏΠΎ SF-36), Π² ΠΊΠΎΡΠΎΡΠΎΠ΅ Π²ΠΎΡΠ»ΠΈ 114 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΊΠΎΠΊΡΠ°ΡΡΡΠΎΠ·ΠΎΠΌ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅. Π£ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΊΠΎΠΊΡΠ°ΡΡΡΠΎΠ·ΠΎΠΌ ΠΎΡΠΌΠ΅ΡΠ°Π»ΠΈΡΡ Π·Π½Π°ΡΠΈΠΌΠΎ Π±ΠΎΠ»Π΅Π΅ Π½ΠΈΠ·ΠΊΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΠΠ, ΠΎΠ±ΡΠ΅ΠΉ Π°Π½ΡΠΈΠΎΠΊΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ, Π°Π½ΡΠΈΡΠ°Π΄ΠΈΠΊΠ°Π»ΡΠ½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΈ Π±ΠΎΠ»Π΅Π΅ Π²ΡΡΠΎΠΊΠΎΠ΅ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅ ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΡΠ°ΠΊΠΎΠ²ΡΠΌΠΈ Ρ Π»ΠΈΡ Π±Π΅Π· ΠΊΠΎΠΊΡΠ°ΡΡΡΠΎΠ·Π° (Ρ<0,05). Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½Π°Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° ΡΠ΅Π°Π±ΠΈΠ»ΠΈΡΠ°ΡΠΈΠΈ, Π²ΠΊΠ»ΡΡΠ°Π²ΡΠ°Ρ ΠΊΡΡΡΠΎΠ²ΠΎΠ΅ Π»Π΅ΡΠ΅Π½ΠΈΠ΅ Π₯ΠΎΠ½Π΄ΡΠΎΠ³Π°ΡΠ΄ΠΎΠΌ, ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΡΠΎ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΠΎΠΉ ΡΠ΅ΡΠ°ΠΏΠΈΠ΅ΠΉ ΠΠ Π·Π½Π°ΡΠΈΠΌΠΎ ΡΠ»ΡΡΡΠ°Π»Π° ΠΠ, ΠΠΠ‘, ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ ΠΆΠΈΠ·Π½ΠΈ ΠΈ ΡΠΌΠ΅Π½ΡΡΠ°Π»Π° Π±ΠΎΠ»Ρ Π² ΡΠ°Π·ΠΎΠ±Π΅Π΄ΡΠ΅Π½Π½ΡΡ
ΡΡΡΡΠ°Π²Π°Ρ
(Ρ<0,05).ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ° ΡΠ΅Π°Π±ΠΈΠ»ΠΈΡΠ°ΡΠΈΠΈ ΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ΅ ΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° Π±ΠΎΠ»Ρ, ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ ΠΆΠΈΠ·Π½ΠΈ, ΠΠ ΠΈ ΠΠΠ‘ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΡΡΠ°ΡΡΠΈΡ
Π²ΠΎΠ·ΡΠ°ΡΡΠ½ΡΡ
Π³ΡΡΠΏΠΏ Ρ ΠΠ
Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues
Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described
THE METHODICAL APPROACHES TO PERFECTION OF CASH SERVICE IN SYSTEM Β«BANK β CLIENTΒ»
In clause the problems of cash service in system "bank β client", reason of failures(refusals) by establishments of Bank of Russia in reception to execution(performance) of the money checks and announcements on a payment in cash are considered(examined), the discrepancy of formats cash ΠΊΠ»ΠΈΠ΅Π½ΡΡΠΊΠΈΡ
of the documents to modern technologies and customs of a business revolution is marked. The modification in system of cash service of the clients is offered by the edition of the certain Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎ-legal documents, the opinion expresses, that all this will raise efficiency, culture and quality of service, both in establishments of Bank of Russia, and in credit organizations
PROBLEMS AND DIRECTIONS OF THE FURTHER PERFECTION OF LOCAL SELF-MANAGEMENT
In clause the theoretical aspects of limits of independence of local self-management, his dependence on a level Development a civil society are considered, the importance of activization of local self-management is opened, including through use of public hearings, the necessity of a material embodiment of local self-management is shown by way of strategic development of territories, the inclusion of local self-management in expansion innovation and Economy of energy of managing in territory is proved
ANTICRISIS SELF-DEVELOPMENT OF TERRITORIES
In clause the work of bodies of local self-management on overcoming the negative crisis phenomena is analyzed. The anticrisis measures spent by municipalities of the Ural region are opened. Is shown, that the self-development of territories depends on financial, organizational and administrative resources available at municipal formations
Individualization of City Development Strategies: Case of Ekaterinburg and Birmingham)
In the XXI century, a strategic approach to the management of individual territories development is becoming increasingly popular worldwide. The creation of scenarios and targets for large urban agglomerations is the most difficult aspect of strategic planning, as the complex structure of economy and social relations generates a variety of interests of different population groups. For such territories, it is inadvisable to use a unified planning template, because the conditions and demands of local communities require individual planning process and approaches. In this context, we decided to compare the development strategies of the Russian city Ekaterinburg and the English city Birmingham. Firstly, they have similar industrial, social and cultural, and demographic characteristics. Secondly, both cities strategic development plans are aimed at increasing their individualization. We demonstrate the necessity and feasibility of individual city development strategies, suggesting a methodology for its creation. Development strategies vary depending on technological and social progress. While technological progress increases the productive forces of cities, social progress ensures individualization of their development strategies by creating specific mentality, sociocultural, ethical and other values. We hypothesise that individual city development strategies depend on the territorial specificity of social progress, based on the needs of a cityβs residents. Thus, the emergence of new needs requires updated strategies, adjusting resources in accordance with these new needs. Based on the study and comparison of strategic plans of two cities, we have suggested methodological approaches to the individualization and implementation of city development strategies. We have determined a system of measures ensuring individualization. It includes the establishment of a list of needs, an analysis of the structure and volume of resources, as well as of the ways to satisfy specific needs for achieving the selected goal. For updating a strategy, it is necessary to consider both growing needs and resources to satisfy them. We have concluded that the individualization of city development strategies improves itself through its implementation and focus on satisfying the needs of a specific community, which depend on the characteristics of its social progress. We have proved that the mentality of residents and their social imprinting greatly influence the individualization of cities and their strategies. The scientific, technological and sociocultural progress increase the individuality of a city. The research results can be used by public authorities and local governments while creating and implementing strategic development documents. Β© 2020 Institute of Economics, Ural Branch of the Russian Academy of Sciences. All rights reserved.Π‘ΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ΠΌ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΉ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ Π² XXI Π². Π²ΡΠ΅ Π±ΠΎΠ»Π΅Π΅ ΠΏΠΎΠΏΡΠ»ΡΡΠ½ΡΠΌ Π²ΠΎ Π²ΡΠ΅ΠΌ ΠΌΠΈΡΠ΅. ΠΠ°ΠΈΠ±ΠΎΠ»ΡΡΠΈΠ΅ ΡΡΡΠ΄Π½ΠΎΡΡΠΈ Π² ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡ ΠΏΡΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΠ»Π°Π½ΠΎΠ² ΠΈ ΡΠ΅Π»Π΅ΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΊΡΡΠΏΠ½ΡΡ
Π³ΠΎΡΠΎΠ΄ΡΠΊΠΈΡ
Π°Π³Π»ΠΎΠΌΠ΅ΡΠ°ΡΠΈΠΉ, ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ ΡΠ»ΠΎΠΆΠ½Π°Ρ ΡΡΡΡΠΊΡΡΡΠ° ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ ΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅Ρ ΡΠ°Π·Π½ΠΎΠ½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΡΡΡ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π³ΡΡΠΏΠΏ Π½Π°ΡΠ΅Π»Π΅Π½ΠΈΡ. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ°Π±Π»ΠΎΠ½ΠΎΠ² ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π»Ρ ΡΠ°ΠΊΠΈΡ
ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΉ Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΠΎ, ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΡΡΠ»ΠΎΠ²ΠΈΡ ΠΈ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠΈ ΠΌΠ΅ΡΡΠ½ΡΡ
ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ² ΡΡΠ΅Π±ΡΡΡ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΊΠ°ΠΊ ΡΠ°ΠΌΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΡΠ°ΠΊ ΠΈ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ². Π Π΄Π°Π½Π½ΠΎΠΌ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ Π°Π²ΡΠΎΡΡ ΡΡΠ°ΡΡΠΈ ΠΎΠ±ΡΠ°ΡΠΈΠ»ΠΈΡΡ ΠΊ ΠΈΠ΄Π΅Π΅ ΡΡΠ°Π²Π½ΠΈΡΡ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π΄Π²ΡΡ
Π³ΠΎΡΠΎΠ΄ΠΎΠ², ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΠΠΊΠ°ΡΠ΅ΡΠΈΠ½Π±ΡΡΠ³Π° ΠΈ Π°Π½Π³Π»ΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΠΠΈΡΠΌΠΈΠ½Π³Π΅ΠΌΠ°, Π²ΠΎ-ΠΏΠ΅ΡΠ²ΡΡ
, ΠΏΠΎΡΠΎΠΌΡ ΡΡΠΎ ΠΌΠ½ΠΎΠ³ΠΈΠ΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π΅Π½Π½ΡΠ΅, ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΠΊΡΠ»ΡΡΡΡΠ½ΡΠ΅ ΠΈ Π΄Π΅ΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΡΡΠΈΡ
Π³ΠΎΡΠΎΠ΄ΠΎΠ² Π±Π»ΠΈΠ·ΠΊΠΈ, Π° Π²ΠΎ-Π²ΡΠΎΡΡΡ
, ΠΎΠ±Π° ΡΡΠΈ Π³ΠΎΡΠΎΠ΄Π° ΡΡΡΠ΅ΠΌΡΡΡΡ ΡΠ°Π·Π²ΠΈΠ²Π°ΡΡ ΡΠ²ΠΎΠΈ ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠ»Π°Π½ΠΎΠ², ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π½Π° ΡΡΠΈΠ»Π΅Π½ΠΈΠ΅ ΠΈΡ
ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ. Π ΡΡΠ°ΡΡΠ΅ Π²ΠΏΠ΅ΡΠ²ΡΠ΅ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π³ΠΎΡΠΎΠ΄ΠΎΠ², ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡ Π΅Π΅ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΡΠ²ΠΎΠ΅ΠΎΠ±ΡΠ°Π·ΠΈΠ΅ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΉ ΠΈΡ
ΡΠ°Π·Π²ΠΈΡΠΈΡ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ΅ΡΡΠ°. ΠΡΠΈ ΡΡΠΎΠΌ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΏΡΠΎΠ³ΡΠ΅ΡΡ Π²Π΅Π΄Π΅Ρ ΠΊ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅Π³ΠΎ ΡΡΠΎΠ²Π½Ρ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΠ» Π³ΠΎΡΠΎΠ΄ΠΎΠ², Π° ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΠΉ ΠΏΡΠΎΠ³ΡΠ΅ΡΡ ΡΠ΅ΡΠ΅Π· ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅Π½ΡΠ°Π»ΠΈΡΠ΅ΡΠ°, ΡΠΎΡΠΈΠΎΠΊΡΠ»ΡΡΡΡΠ½ΡΡ
, ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ Π΄ΡΡΠ³ΠΈΡ
ΡΠ΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Π΅Ρ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΉ ΠΈΡ
ΡΠ°Π·Π²ΠΈΡΠΈΡ. ΠΡΠ΄Π²ΠΈΠ½ΡΡΠ° Π³ΠΈΠΏΠΎΡΠ΅Π·Π°, ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π³ΠΎΡΠΎΠ΄ΠΎΠ² Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ΅ΡΡΠ°, Π° Π±Π°Π·ΠΎΠΉ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π³ΠΎΡΠΎΠ΄ΠΎΠ² ΡΠ²Π»ΡΡΡΡΡ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠΈ ΠΈΡ
ΠΆΠΈΡΠ΅Π»Π΅ΠΉ. ΠΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΠ΅ Π½ΠΎΠ²ΡΡ
ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠ΅ΠΉ Π²Π΅Π΄Π΅Ρ ΠΊ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ Π°ΠΊΡΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΉ, ΡΡΠΎ Π²ΡΡΠ°ΠΆΠ°Π΅ΡΡΡ Π² ΠΏΡΠΈΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΡΠ΅ΡΡΡΡΠΎΠ² Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠ΅ Ρ Π½ΠΎΠ²ΡΠΌΠΈ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΡΠΌΠΈ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΠΈ ΡΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎΠΏΡΡΠ° ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π²ΡΡ
Π³ΠΎΡΠΎΠ΄ΠΎΠ² ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Ρ Π°Π²ΡΠΎΡΡΠΊΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ ΠΊ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΈ Π°ΠΊΡΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π³ΠΎΡΠΎΠ΄ΠΎΠ². ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π° ΡΠΈΡΡΠ΅ΠΌΠ° ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΠΉ ΠΏΠΎ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π²ΠΎΠΏΡΠΎΡΠΎΠ² ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠΈΡ
ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ ΠΏΠ΅ΡΠ΅ΡΠ½Ρ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠ΅ΠΉ, Π°Π½Π°Π»ΠΈΠ· ΡΡΡΡΠΊΡΡΡΡ ΠΈ ΠΎΠ±ΡΠ΅ΠΌΠΎΠ² ΡΠ΅ΡΡΡΡΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΏΠΎΡΠΎΠ±ΠΎΠ² ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠ΅ΠΉ Π΄Π»Ρ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡ ΠΈΠ·Π±ΡΠ°Π½Π½ΠΎΠΉ ΡΠ΅Π»ΠΈ. ΠΠ±ΠΎΠ·Π½Π°ΡΠ΅Π½Ρ ΡΡΠ»ΠΎΠ²ΠΈΡ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΠ΅ ΠΏΠΎ Π°ΠΊΡΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΈ, ΡΠΎΡΡΠΎΡΡΠΈΠ΅ Π² ΡΡΠ΅ΡΠ΅ ΠΊΠ°ΠΊ Π½Π°ΡΠ°ΡΡΠ°ΡΡΠΈΡ
ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠ΅ΠΉ, ΡΠ°ΠΊ ΠΈ ΡΠ΅ΡΡΡΡΠ½ΡΡ
Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ Π΄Π»Ρ ΠΈΡ
ΡΠ΄ΠΎΠ²Π»Π΅ΡΠ²ΠΎΡΠ΅Π½ΠΈΡ. Π ΡΠ°Π±ΠΎΡΠ΅ Π΄Π΅Π»Π°Π΅ΡΡΡ Π²ΡΠ²ΠΎΠ΄, ΡΡΠΎ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΉ ΡΠ°ΠΌΠΎΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΡΠ΅ΡΡΡ ΡΠ΅ΡΠ΅Π· ΡΠ²ΠΎΡ Π°ΠΊΡΡΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΈ Π²ΡΡΠ°ΠΆΠ°Π΅ΡΡΡ Π² ΡΠ΄ΠΎΠ²Π»Π΅ΡΠ²ΠΎΡΠ΅Π½ΠΈΠΈ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΡ
Π΄Π»Ρ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²Π° ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠ΅ΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅ Π·Π°Π²ΠΈΡΡΡ ΠΎΡ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ΅ΡΡΠ°. ΠΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΎ, ΡΡΠΎ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΡΡΡ Π³ΠΎΡΠΎΠ΄ΠΎΠ² ΠΈ ΠΈΡ
ΡΡΡΠ°ΡΠ΅Π³ΠΈΠΉ Π±Π°Π·ΠΈΡΡΠ΅ΡΡΡ Π½Π° ΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΎΡΡΠΈ ΠΆΠΈΡΠ΅Π»Π΅ΠΉ ΠΈ ΠΈΡ
ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΌ ΠΈΠΌΠΏΡΠΈΠ½ΡΠΈΠ½Π³Π΅. ΠΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΡΡΠΈ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ Π½Π°ΡΡΠ½ΠΎ-ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΠΊΡΠ»ΡΡΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ΅ΡΡΠ°. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ ΠΎΡΠ³Π°Π½Π°ΠΌΠΈ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π²Π»Π°ΡΡΠΈ ΠΈ ΠΌΠ΅ΡΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΌΠΎΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ² ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΈ ΠΈΡ
Π°ΠΊΡΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ.The article has been prepared in accordance with the state task for Institute of Economics of the Ural Branch of RAS for 2020β2022.ΠΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΡ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²Π»Π΅Π½Π° Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΡΠΌ Π·Π°Π΄Π°Π½ΠΈΠ΅ΠΌ Π΄Π»Ρ Π€ΠΠΠ£Π ΠΠ½ΡΡΠΈΡΡΡΠ° ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ Π£ΡΠ Π ΠΠ Π½Π° 2020β2022 Π³Π³