186 research outputs found
ΠΠ΅ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΈΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π² ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΌ Π°Π½Π°Π»ΠΈΠ·Π΅ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΡΡΠ½ΠΊΠ° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ
This article presents results of non-quantitative observations and their processing methods, which significantly widen the analytical capabilities of the statistical measurement of the Russian IT market. There is a need to expand statistical tools to reflect current and future sectoral development trends in the IT sphere promptly and broadly, due to the rapid penetration of these services into the Russian market. With the help of business climate indicators and different homogeneous behavior models the author analyzed business trends in the financial and economic activities of IT organizations, highlighting their operating characteristics within the various cyclical episode of 2010-2017.Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π΅ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠΉ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΈΡ
ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠ΅ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎ ΠΏΠΎΠ²ΡΡΠΈΡΡ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΡΡΠ½ΠΊΠ° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ (ΠΠ’). ΠΡΠ³ΡΠΌΠ΅Π½ΡΠΈΡΡΠ΅ΡΡΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΡ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π³ΠΎ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎ ΠΈ Ρ ΠΎΡ
Π²Π°ΡΠΎΠΌ ΠΎΡΡΠ°ΠΆΠ°ΡΡ ΡΠ΅ΠΊΡΡΠΈΠ΅ ΠΈ Π±ΡΠ΄ΡΡΠΈΠ΅ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΠΈ ΠΎΡΡΠ°ΡΠ»Π΅Π²ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΠ’-ΡΡΠ΅ΡΡ Π² ΡΠΈΠ»Ρ Π²ΡΡΠΎΠΊΠΎΠΉ ΡΠΊΠΎΡΠΎΡΡΠΈ ΠΏΡΠΎΠ½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ Π΄Π°Π½Π½ΡΡ
ΡΡΠ»ΡΠ³ Π½Π° ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΠΉ ΡΡΠ½ΠΎΠΊ. Π‘ ΠΏΠΎΠΌΠΎΡΡΡ ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠΎΠ² Π΄Π΅Π»ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠ»ΠΈΠΌΠ°ΡΠ°, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΡΡ Π°Π½Π°Π»ΠΈΠ· Π΄Π΅Π»ΠΎΠ²ΡΡ
ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΠΉ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΠ’-ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ, ΠΏΠΎΠ΄ΡΠ΅ΡΠΊΠΈΠ²Π°ΡΡΠΈΡ
ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΡ ΠΈΡ
ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΏΠΈΠ·ΠΎΠ΄ΠΎΠ² 2010-2017 Π³Π³.
Renormalization group evolution of neutrino masses and mixing in seesaw models: A review
We consider different extensions of the standard model which can give rise to
the small active neutrino masses through seesaw mechanisms, and their mixing.
These tiny neutrino masses are generated at some high energy scale by the heavy
seesaw fields which then get sequentially decoupled to give an effective
dimension-5 operator. The renormalization group evolution of the masses and the
mixing parameters of the three active neutrinos in the high energy as well as
the low energy effective theory is reviewed in this article.Comment: 54 pages. Invited review submitted to IJMP
Modern methods of stimulation of angiogenesis in patients with critical limb ischemia (review)
The term "critical ischemia of extremities" (critical limb ischemia) was first introduced by P.R.F. Bell in 1982 to refer to a group of diseases accompanied by pain in the legs at rest, trophic ulcers and distal necroses of the lower limbs. Critical ischemia of the lower limbs is a condition of almost complete cessation of arterial blood flow to the tissues of the lower limbs. If the arterial blood supply is not improved, limb amputation becomes inevitable for all patients. Treatment of ischemia of the lower extremities should be complex and differentiated depending on the stage and features of the course of the disease. Modern approaches to the therapy of lower limb ischemia include conservative and surgical methods of treatment, all of which are aimed at improving blood flow in the affected limbs. In the article modern methods of stimulating angiogenesis in patients with lower limb ischemia and results are shown
ΠΡΡΠ΅ΠΊΡΡ Π²Π»ΠΈΡΠ½ΠΈΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΎ-ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΠ’-ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ° Π½Π° ΡΠΈΡΡΠΎΠ²ΡΡ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΡ ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ
This paper presents the results of measuring intersectoral economic and technological effects, allowing to determine the degree of dependence between the segments that produce digital technologies and implement them. The basis for empirical calculations was the survey data of leaders among Russian IT companies and retail organizations on the current state of digital and business activity.The purpose of the work is to identify the presence and establish the strength of the relationship between these segments in terms of existing localized industry effects, expressed in the transfer of technology from the IT segment to retail. The authors of the work identified and tested several specific hypotheses, the general meaning of which was to suggest that retail trade in the current stage of economic development in Russia is susceptible to emerging trends in the rapidly changing IT services sector that can quickly and efficiently respond to the growth of the IT companies digital activity by increasing investments in digital technologies and increasing the intensity of their application in business processes.In particular, hypotheses were tested regarding the impact of business activity in the IT services segment on the growth of electronic commerce turnover, the use of online marketplaces, Big Data technologies, virtual and augmented reality technologies in retail trade organizations, as well as hypotheses suggesting a connection between the development of mobile applications in the IT segment and the use of mobile technologies, expectations regarding the growth of electronic goods turnover in retail organizations.The obtained results confirmed the majority of the hypotheses put forward, thereby supporting the authorsβ general assumption about the existence of specific effects of the development of the IT segment on intersectoral technological transfers, revealed the existing specifics of penetration and spread of modern technological trends in trade, and also showed that the IT is currently important component in the process of digital transformation of Russian retail trade organizations.Π ΡΡΠ°ΡΡΠ΅ Π΅Π΅ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΠΌΠ΅ΠΆΠΎΡΡΠ°ΡΠ»Π΅Π²ΡΡ
ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΎ-ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΡΠ΅ΠΊΡΠΎΠ², ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΡ
ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ ΡΡΠ΅ΠΏΠ΅Π½Ρ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΠΌΠ°ΠΊΡΠΎΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΠΌΠΈ, ΠΏΡΠΎΠ΄ΡΡΠΈΡΡΡΡΠΈΠΌΠΈ ΡΠΈΡΡΠΎΠ²ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ Π²Π½Π΅Π΄ΡΡΡΡΠΈΠΌΠΈ ΠΈΡ
. Π¦Π΅Π»Ρ ΡΠ°Π±ΠΎΡΡ - Π²ΡΡΠ²ΠΈΡΡ Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΈ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡ ΡΠΈΠ»Ρ ΡΠ²ΡΠ·ΠΈ ΠΌΠ΅ΠΆΠ΄Ρ Π΄Π°Π½Π½ΡΠΌΠΈ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΠΌΠΈ Π² ΡΠ°ΡΡΠΈ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
Π»ΠΎΠΊΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΡ
ΠΎΡΡΠ°ΡΠ»Π΅Π²ΡΡ
ΡΡΡΠ΅ΠΊΡΠΎΠ², Π²ΡΡΠ°ΠΆΠ°ΡΡΠΈΡ
ΡΡ Π² ΠΏΠ΅ΡΠ΅Π½ΠΎΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΈΠ· ΠΠ’-ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ° Π² ΡΠΎΠ·Π½ΠΈΡΠ½ΡΡ ΡΠΎΡΠ³ΠΎΠ²Π»Ρ. ΠΡΠ½ΠΎΠ²ΠΎΠΉ Π΄Π»Ρ ΡΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΡΡΠ΅ΡΠΎΠ² Π²ΡΡΡΡΠΏΠΈΠ»ΠΈ Π΄Π°Π½Π½ΡΠ΅ ΠΎΠΏΡΠΎΡΠΎΠ² ΡΡΠΊΠΎΠ²ΠΎΠ΄ΠΈΡΠ΅Π»Π΅ΠΉ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΠΠ’-ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ ΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ ΠΎ ΡΠ»ΠΎΠΆΠΈΠ²ΡΠ΅ΠΌΡΡ ΡΠΎΡΡΠΎΡΠ½ΠΈΠΈ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΈ Π΄Π΅Π»ΠΎΠ²ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ.ΠΠ²ΡΠΎΡΠ°ΠΌΠΈ ΡΠ°Π±ΠΎΡΡ Π±ΡΠ»ΠΈ ΠΎΠ±ΠΎΠ·Π½Π°ΡΠ΅Π½Ρ ΠΈ ΠΏΡΠΎΠ²Π΅ΡΠ΅Π½Ρ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΎ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
Π³ΠΈΠΏΠΎΡΠ΅Π·, ΠΎΠ±ΡΠΈΠΉ ΡΠΌΡΡΠ» ΠΊΠΎΡΠΎΡΡΡ
ΡΠ²ΠΎΠ΄ΠΈΠ»ΡΡ ΠΊ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡ, ΡΡΠΎ ΡΠΎΠ·Π½ΠΈΡΠ½Π°Ρ ΡΠΎΡΠ³ΠΎΠ²Π»Ρ Π² ΡΠ΅ΠΊΡΡΠ΅ΠΉ ΡΠ°Π·Π΅ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π² Π ΠΎΡΡΠΈΠΈ Π΄Π΅ΠΉΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎ Π²ΠΎΡΠΏΡΠΈΠΈΠΌΡΠΈΠ²Π° ΠΊ Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΠΌ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΡΠΌ Π² Π±ΡΡΡΡΠΎ ΠΈΠ·ΠΌΠ΅Π½ΡΡΡΠ΅ΠΌΡΡ ΡΠ΅ΠΊΡΠΎΡΠ΅ ΠΠ’-ΡΡΠ»ΡΠ³, ΡΠΏΠΎΡΠΎΠ±Π½Π° ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎ ΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎ ΡΠ΅Π°Π³ΠΈΡΠΎΠ²Π°ΡΡ Π½Π° ΡΠΎΡΡ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΠ’-ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΉ Π² ΡΠΈΡΡΠΎΠ²ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΈΡ
ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π² Π±ΠΈΠ·Π½Π΅Ρ-ΠΏΡΠΎΡΠ΅ΡΡΠ°Ρ
. Π ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, Π±ΡΠ»ΠΈ Π²ΡΠ΄Π²ΠΈΠ½ΡΡΡ ΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½Ρ Π³ΠΈΠΏΠΎΡΠ΅Π·Ρ, ΠΊΠ°ΡΠ°ΡΡΠΈΠ΅ΡΡ Π²Π»ΠΈΡΠ½ΠΈΡ Π΄Π΅Π»ΠΎΠ²ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π² ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ΅ ΠΠ’-ΡΡΠ»ΡΠ³ Π½Π° ΠΏΡΠΈΡΠΎΡΡ ΠΎΠ±ΠΎΡΠΎΡΠ° ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΠΏΠ»ΠΎΡΠ°Π΄ΠΎΠΊ, ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Big Data, ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΈ Π΄ΠΎΠΏΠΎΠ»Π½Π΅Π½Π½ΠΎΠΉ ΡΠ΅Π°Π»ΡΠ½ΠΎΡΡΠΈ Π² ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΡ
ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ Π³ΠΈΠΏΠΎΡΠ΅Π·Ρ, ΠΏΡΠ΅Π΄ΠΏΠΎΠ»Π°Π³Π°ΡΡΠΈΠ΅ Π½Π°Π»ΠΈΡΠΈΠ΅ ΡΠ²ΡΠ·ΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΎΠΉ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΡΡ
ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ Π² ΠΠ’-ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ΅ ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ, ΠΎΠΆΠΈΠ΄Π°Π½ΠΈΡΠΌΠΈ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΠΎΡΡΠ° ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠΎΠ²Π°ΡΠΎΠΎΠ±ΠΎΡΠΎΡΠ° Π² ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΡ
ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ.ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π΄Π°Π»ΠΈ Π΄ΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠ½ΡΡ Π±Π°Π·Ρ Π΄Π»Ρ Π±ΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²Π° Π²ΡΠ΄Π²ΠΈΠ½ΡΡΡΡ
Π³ΠΈΠΏΠΎΡΠ΅Π·, ΡΠ΅ΠΌ ΡΠ°ΠΌΡΠΌ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠ΄ΠΈΠ² ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ Π°Π²ΡΠΎΡΠΎΠ² ΠΎ ΡΡΡΠ΅ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
ΡΡΡΠ΅ΠΊΡΠΎΠ² Π²Π»ΠΈΡΠ½ΠΈΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΠ’-ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ° Π½Π° ΠΌΠ΅ΠΆΠΎΡΡΠ°ΡΠ»Π΅Π²ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠ΅ΡΠ΅Π½ΠΎΡΡ, Π²ΡΡΠ²ΠΈΠ»ΠΈ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΡΡ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΡ ΠΏΡΠΎΠ½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ ΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΠ΅ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ΅Π½Π΄ΠΎΠ² Π² ΡΠΎΡΠ³ΠΎΠ²Π»Ρ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΠΠ’-ΡΠ΅Π³ΠΌΠ΅Π½Ρ Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π²ΡΡΡΡΠΏΠ°Π΅Ρ Π²Π°ΠΆΠ½ΡΠΌ ΡΠΎΡΡΠ°Π²Π»ΡΡΡΠΈΠΌ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΠΎΠ·Π½ΠΈΡΠ½ΡΡ
ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ
ΠΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΈΡ ΠΎΡΠ΅Π½ΠΎΠΊ ΠΎΡΡΠ°ΡΠ»Π΅Π²ΡΡ ΡΠΎΠ±ΡΡΠΈΠΉ Π² ΠΌΠ°Π»ΠΎΠΌ ΡΠΎΡΠ³ΠΎΠ²ΠΎΠΌ Π±ΠΈΠ·Π½Π΅ΡΠ΅
The paper presents an analytical aspect of business surveys data processing, which allows highlighting key points in the dynamics of small retail businesses economic development in various phases of business cycle (case study: retail trade). In the introduction in addition to calculating balance characteristics and composite indicator of business conditions the authors substantiate the necessity to implement methodological approach to studying behavioral modals of economic entities that are attributable to small retail enterprises, based on the statistical distribution of respondentsβ answers. In reviewing cluster analysis individual data for clustering is suggested as variables that are an entrepreneurial assesses the actual and expected trends in real time. Features of the application technique of cluster analysis in determining the different Β«behavioral patternsΒ» can be classified as individual responses of economic agents at different stages of the business cycle. A more thorough examination of this information may be useful in analyses of various operational indicators of organizations activity. This aspect is essential for investigating small business aggregate behavior in specific phases of business cycle, when it is necessary to detail business reaction with respect to actual or expected economic events.ΠΠ²ΡΠΎΡΠ°ΠΌΠΈ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°ΡΠΏΠ΅ΠΊΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΡΡ
ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠΉ Π΄Π΅ΡΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΠΎ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ΅ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΌΠ°Π»ΡΡ
ΡΠΈΡΠΌ (Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠΉ ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ). ΠΠΎ Π²Π²Π΅Π΄Π΅Π½ΠΈΠΈ Π°ΡΠ³ΡΠΌΠ΅Π½ΡΠΈΡΡΠ΅ΡΡΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ - Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊ ΡΠ°ΡΡΠ΅ΡΡ Π±Π°Π»Π°Π½ΡΠΎΠ²ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΠΈ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠ½ΡΡ
ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠΎΠ² Π΄Π΅Π»ΠΎΠ²ΠΎΠΉ ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΡ - ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π³ΠΎ ΠΈΠ·ΡΡΠ°ΡΡ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ Ρ
ΠΎΠ·ΡΠΉΡΡΠ²ΡΡΡΠΈΡ
ΡΡΠ±ΡΠ΅ΠΊΡΠΎΠ², ΠΎΡΠ½ΠΎΡΠΈΠΌΡΡ
ΠΊ ΠΌΠ°Π»ΡΠΌ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡΠΌ ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ, Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΉ ΠΎΡΠ²Π΅ΡΠΎΠ² ΡΠ΅ΡΠΏΠΎΠ½Π΄Π΅Π½ΡΠΎΠ². ΠΡΠΈ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΠΈ ΡΠ΅Ρ
Π½ΠΈΠΊΠΈ ΠΊΠ»Π°ΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΡΡ
Π΄Π°Π½Π½ΡΡ
Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠ΅Π΄Π»Π°Π³Π°ΡΡΡΡ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅, ΡΠ²Π»ΡΡΡΠΈΠ΅ΡΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΈΠΌΠΈ ΠΎΡΠ΅Π½ΠΊΠ°ΠΌΠΈ ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΠΎΠΆΠΈΠ΄Π°Π΅ΠΌΡΡ
ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΠΉ Π² ΡΠ΅Π°Π»ΡΠ½ΠΎΠΌ ΠΌΠ°ΡΡΡΠ°Π±Π΅ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ΅Ρ
Π½ΠΈΠΊΠΈ ΠΊΠ»Π°ΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΡΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Β«ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉΒ» ΡΠΎΡΡΠΎΠΈΡ Π² ΡΠΎΠΌ, ΡΡΠΎ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Ρ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΎΡΠ²Π΅ΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
Π°Π³Π΅Π½ΡΠΎΠ² Π½Π° ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΡΠ°ΠΏΠ°Ρ
Π΄Π΅Π»ΠΎΠ²ΠΎΠ³ΠΎ ΡΠΈΠΊΠ»Π°. ΠΠΎΠ»Π΅Π΅ ΡΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΠ°ΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°ΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ. ΠΠ°Π½Π½ΡΠΉ Π°ΡΠΏΠ΅ΠΊΡ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ Π²Π°ΠΆΠ΅Π½ ΠΏΡΠΈ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠΈ ΡΠΎΠ²ΠΎΠΊΡΠΏΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΌΠ°Π»ΠΎΠ³ΠΎ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΡΠ²Π°, ΠΊΠΎΠ³Π΄Π° Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ Π΄Π΅ΡΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΡΠ΅Π°ΠΊΡΠΈΡ Π±ΠΈΠ·Π½Π΅ΡΠ° ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΠ΅Π°Π»ΡΠ½ΡΡ
ΠΈΠ»ΠΈ ΠΎΠΆΠΈΠ΄Π°Π΅ΠΌΡΡ
ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²Π»Π΅Π½ΠΈΠΉ
ΠΡΠ΅Π½ΠΊΠ° ΡΡΠΎΠ²Π½Ρ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ Π ΠΎΡΡΠΈΠΈ
The paper presents the main results of conjuncture monitoring which characterize the digital activity of retail trade organizations in 2018. The objects of observation are large, medium and small retail companies registered in Russia. The main goal of this work is to evaluate digital activity level of retail sector using the developed non-quantitative indicators that meet international standards and the countryβs digital agenda. The presented set of harmonized indicators made it possible to identify the trends, scope and intensity of the spread of digital technologies in organizations. The level and intensity of digitalization are first determined on the basis of entrepreneurial opinions and intentions regarding the pace and scale of introducing digital technologies, readiness for digital transition, necessary skills to work with digital technologies, the use of specific digital products, as well as the main factors hindering digital transformation.Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΠ΅ ΡΠΈΡΡΠΎΠ²ΡΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ Π² 2018 Π³. ΠΠ±ΡΠ΅ΠΊΡΠ°ΠΌΠΈ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ Π±ΡΠ»ΠΈ ΠΊΡΡΠΏΠ½ΡΠ΅, ΡΡΠ΅Π΄Π½ΠΈΠ΅ ΠΈ ΠΌΠ°Π»ΡΠ΅ ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡ ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ, Π·Π°ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ Π² Π ΠΎΡΡΠΈΠΈ. ΠΡΠ½ΠΎΠ²Π½Π°Ρ ΡΠ΅Π»Ρ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π»Π°ΡΡ Π² ΠΎΡΠ΅Π½ΠΊΠ΅ ΡΡΠΎΠ²Π½Ρ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΡ
Π½Π΅ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠΎΠ², ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΡ
ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΌ ΡΡΠ°Π½Π΄Π°ΡΡΠ°ΠΌ ΠΈ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡΠΊΠ΅ ΡΡΡΠ°Π½Ρ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΠΉ Π½Π°Π±ΠΎΡ Π³Π°ΡΠΌΠΎΠ½ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ» Π²ΡΡΠ²ΠΈΡΡ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΠΈ, ΠΌΠ°ΡΡΡΠ°Π± ΠΈ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π² ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΡ
. Π£ΡΠΎΠ²Π΅Π½Ρ ΠΈ ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΠΈ Π²ΠΏΠ΅ΡΠ²ΡΠ΅ Π±ΡΠ»ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΈΡ
ΠΌΠ½Π΅Π½ΠΈΠΉ ΠΈ Π½Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎΡΠ½ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΠ΅ΠΌΠΏΠΎΠ² ΠΈ ΠΌΠ°ΡΡΡΠ°Π±ΠΎΠ² ΠΎΡΠ²ΠΎΠ΅Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ, Π³ΠΎΡΠΎΠ²Π½ΠΎΡΡΠΈ ΠΊ ΡΠΈΡΡΠΎΠ²ΠΎΠΌΡ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Ρ, ΡΠ»ΠΎΠΆΠΈΠ²ΡΠΈΡ
ΡΡ ΡΠΈΡΡΠΎΠ²ΡΡ
Π½Π°Π²ΡΠΊΠΎΠ², ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΠΏΡΠ΅ΠΏΡΡΡΡΠ²ΡΡΡΠΈΡ
ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΠΈ
U(1) textures and Lepton Flavor Violation
U(1) family symmetries have led to successful predictions of the fermion mass
spectrum and the mixing angles of the hadronic sector. In the context of the
supersymmetric unified theories, they further imply a non-trivial mass
structure for the scalar partners, giving rise to new sources of flavor
violation. In the present work, lepton flavor non-conserving processes are
examined in the context of the minimal supersymmetric standard model augmented
by a U(1)-family symmetry. We calculate the mixing effects on the \mu-> e\gamma
and \tau -> \mu\gamma rare decays. All supersymmetric scalar masses involved in
the processes are determined at low energies using two loop renormalization
group analysis and threshold corrections. Further, various novel effects are
considered and found to have important impact on the branching ratios. Thus, a
rather interesting result is that when the see-saw mechanism is applied in the
(12 X 12)-sneutrino mass matrix, the mixing effects of the Dirac matrix in the
effective light sneutrino sector are canceled at first order. In this class of
models and for the case that soft term mixing is already present at the GUT
scale, tau -> \mu \gamma decays are mostly expected to arise at rates
significantly smaller than the current experimental limits. On the other hand,
the \mu \ra e \gamma rare decays impose important bounds on the model
parameters, particularly on the supersymmetric scalar mass spectrum. In the
absence of soft term mixing at high energies, the predicted branching ratios
for rare decays are, as expected, well below the experimental bounds.Comment: 24p, 10 figures, version to appear in Phys. Rev.
ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»Π΅ΠΉ Π² Π ΠΎΡΡΠΈΠΈ: ΠΏΠΎΠ΄Ρ ΠΎΠ΄, ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΡ, ΠΏΠΈΠ»ΠΎΡΠ½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ
The process of the digitalization of economic activity currently raises many questions of economic and social policy in Russia that need to be solved with relevant statistical and analytical data.In this case, it is important to significantly increase the sources of efficient, accessible and comparable data on sectoral digitalization. The authors believe that the opinions and assessments of direct participants of digital events (economic agents from various kinds of economic activity) should also be taken into account when forming targeted decisions of policymakers aimed at the inclusive growth of the national economy, including through such force as digitalization.Therefore, in 2018 the Centre for Business Tendencies Studies at National Research University Higher School of Economics launched the pilot project focused on measuring the digital activity of Russian enterprises of main types of economic activity. The authors define the main task of the study as the development of such criteria for measuring the industryβs digital market conditions, sample requirements for respondents and a system of indicators that together with the results of the pilot surveys would allow the expert community to actually provide effective data and analytical support to entrepreneurs with forming of sectoral digital platforms.The main idea of the study is that the success of the country in promoting digital technologies is largely related to the possibilities of collecting large-scale qualitative data on digital trends, barriers and effects comparable to international counterparts.The review presents the main methodological principles and first results of pilot market surveys among industrial and commercial enterprises in Russia and outlines criteria for selecting indicators for the first pilot sampling. As an example, the section of the article devoted to key results of the 2018 pilot survey of industry uses analysis of digital transformation in medium-size and large manufacturing enterprises.Innovations in the 2019 pilot survey are addressed as an independent direction. In this regard the article notes that the survey program involves measuring ICTs capabilities and potential to increase resource and environmental efficiency in industry, evaluating investment activities related to green growth and aimed at the development and dissemination of green technologies and achieving environmental goals, assessing final consumption (demand) for environmental goods in trade, accounting for the development ICT technologies aimed at greening and resource efficiency by organizations.The article concludes that taking into account the first results of the market survey, the basic requirements for a system of market indicators were determined. These requirements reflect the level, directions, intensity, dividends, and effectiveness of digital transformation of the main kinds of economic activities for a more representative consideration of the digital contribution to GDP growth.ΠΠ΅ΡΠ΅Π²ΠΎΠ΄ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π² ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΡΠΎΡΠΌΠ°Ρ ΠΏΠΎΠ΄Π½ΠΈΠΌΠ°Π΅Ρ Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π² Π ΠΎΡΡΠΈΠΈ ΠΌΠ½ΠΎΠ³ΠΈΠ΅ Π²ΠΎΠΏΡΠΎΡΡ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΡΠ΅ΡΠ°ΡΡ, ΠΈΠΌΠ΅Ρ ΡΠ΅Π»Π΅Π²Π°Π½ΡΠ½ΡΡ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΡΡ ΠΈ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ. ΠΠ»Ρ ΡΡΠΎΠ³ΠΎ Π²Π°ΠΆΠ½ΠΎ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎ ΡΠ°ΡΡΠΈΡΠΈΡΡ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΡΡ
, Π΄ΠΎΡΡΡΠΏΠ½ΡΡ
ΠΈ ΡΠΎΠΏΠΎΡΡΠ°Π²ΠΈΠΌΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΠΈ. ΠΡ ΡΡΠΈΡΠ°Π΅ΠΌ, ΡΡΠΎ ΠΌΠ½Π΅Π½ΠΈΡ ΠΈ ΠΎΡΠ΅Π½ΠΊΠΈ Π½Π΅ΠΏΠΎΡΡΠ΅Π΄ΡΡΠ²Π΅Π½Π½ΡΡ
ΡΡΠ°ΡΡΠ½ΠΈΠΊΠΎΠ² ΡΠΈΡΡΠΎΠ²ΡΡ
ΡΠΎΠ±ΡΡΠΈΠΉ - ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
Π°Π³Π΅Π½ΡΠΎΠ² ΠΈΠ· ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π²ΠΈΠ΄ΠΎΠ² ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΡΠ°ΠΊΠΆΠ΅ Π΄ΠΎΠ»ΠΆΠ½Ρ Π±ΡΡΡ ΡΡΡΠ΅Π½Ρ ΠΏΡΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΡΠ΅Π»Π΅Π²ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π΄ΠΈΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΠΎΡΠ³Π°Π½ΠΎΠ², Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° ΠΈΠ½ΠΊΠ»ΡΠ·ΠΈΠ²Π½ΡΠΉ ΡΠΎΡΡ Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π·Π° ΡΡΠ΅Ρ ΡΠ°ΠΊΠΎΠΉ Π΅Π³ΠΎ ΡΠΈΠ»Ρ, ΠΊΠ°ΠΊ ΡΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΡ.ΠΡΠ½ΠΎΠ²Π½ΡΡ Π·Π°Π΄Π°ΡΡ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ° Π¦Π΅Π½ΡΡΠ° ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ (Π¦ΠΠ) ΠΠΠ£ ΠΠ¨Π, ΡΡΠΎΠΊΡΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π½Π° ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΠΈ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠΉ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
Π²ΠΈΠ΄ΠΎΠ² ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ, Π½Π°ΡΠ°ΡΠΎΠ³ΠΎ Π² 2018 Π³., ΠΌΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΠΌ ΠΊΠ°ΠΊ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠ°ΠΊΠΈΡ
ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π² ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΠΎΡΡΠ°ΡΠ»Π΅Π²ΠΎΠΉ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΡ, ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΠΉ ΠΊ Π²ΡΠ±ΠΎΡΠΎΡΠ½ΡΠΌ ΡΠΎΠ²ΠΎΠΊΡΠΏΠ½ΠΎΡΡΡΠΌ ΡΠ΅ΡΠΏΠΎΠ½Π΄Π΅Π½ΡΠΎΠ² ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ΅ ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠΎΠ², Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠ΅ ΠΊΠΎΡΠΎΡΡΡ
, Π½Π°ΡΡΠ΄Ρ Ρ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠΌΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ ΠΏΠΈΠ»ΠΎΡΠ½ΡΡ
ΠΎΠΏΡΠΎΡΠΎΠ², ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡ ΡΠΊΡΠΏΠ΅ΡΡΠ½ΠΎΠΌΡ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²Ρ Π΄Π΅ΠΉΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎ-Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΡΡ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΠΌ Π² ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΎΡΡΠ°ΡΠ»Π΅Π²ΡΡ
ΡΠΈΡΡΠΎΠ²ΡΡ
ΠΏΠ»Π°ΡΡΠΎΡΠΌ.Π ΡΡΠ°ΡΡΠ΅ ΠΏΠΎΠ΄ΡΠ΅ΡΠΊΠΈΠ²Π°Π΅ΡΡΡ, ΡΡΠΎ ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠΉ ΠΏΠΎΡΡΠ» ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ - ΡΡΠΏΠ΅Ρ
ΡΡΡΠ°Π½Ρ Π² ΠΏΡΠΎΠ΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΈ ΡΠΈΡΡΠΎΠ²ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π²ΠΎ ΠΌΠ½ΠΎΠ³ΠΎΠΌ ΡΠ²ΡΠ·Π°Π½ Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΠΌΠΈ ΡΠ±ΠΎΡΠ° ΠΌΠ°ΡΡΡΠ°Π±Π½ΠΎΠΉ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎ ΡΠΈΡΡΠΎΠ²ΡΡ
ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΡΡ
, Π±Π°ΡΡΠ΅ΡΠ°Ρ
ΠΈ ΡΡΡΠ΅ΠΊΡΠ°Ρ
, ΡΠΎΠΏΠΎΡΡΠ°Π²ΠΈΠΌΠΎΠΉ Ρ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΌΠΈ Π°Π½Π°Π»ΠΎΠ³Π°ΠΌΠΈ.ΠΠ²ΡΠΎΡΠ°ΠΌΠΈ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΈΠ½ΡΠΈΠΏΡ ΠΈ ΠΏΠ΅ΡΠ²ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΈΠ»ΠΎΡΠ½ΡΡ
ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΡΡ
ΠΎΠΏΡΠΎΡΠΎΠ² ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»Π΅ΠΉ ΡΡΠ΅Π΄ΠΈ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΡΡ
ΠΈ ΡΠΎΡΠ³ΠΎΠ²ΡΡ
ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠΉ Π² Π ΠΎΡΡΠΈΠΈ. Π‘ΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½Ρ ΠΊΡΠΈΡΠ΅ΡΠΈΠΈ ΠΎΡΠ±ΠΎΡΠ° ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π΄Π»Ρ ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ ΠΏΠΈΠ»ΠΎΡΠ½ΠΎΠ³ΠΎ Π·Π°ΠΌΠ΅ΡΠ°. Π ΡΠ°Π·Π΄Π΅Π»Π΅ ΡΡΠ°ΡΡΠΈ ΠΎΠ± ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°Ρ
ΠΏΠΈΠ»ΠΎΡΠ½ΠΎΠ³ΠΎ ΠΎΠΏΡΠΎΡΠ° Π² 2018 Π³. Π² ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΡΠΈΠΌΠ΅ΡΠ° Π΄Π°Π½ Π°Π½Π°Π»ΠΈΠ· ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ ΡΠΈΡΡΠΎΠ²ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π½Π° ΠΊΡΡΠΏΠ½ΡΡ
ΠΈ ΡΡΠ΅Π΄Π½ΠΈΡ
ΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΡΡ
ΠΎΠ±ΡΠ°Π±Π°ΡΡΠ²Π°ΡΡΠ΅ΠΉ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ.Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΡΠ°ΠΌΠΎΡΡΠΎΡΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π² ΡΡΠ°ΡΡΠ΅ Π΄Π°Π΅ΡΡΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ° Π½ΠΎΠ²ΠΎΠ²Π²Π΅Π΄Π΅Π½ΠΈΠΉ Π² ΠΏΠΈΠ»ΠΎΡΠ½ΠΎΠΌ ΠΎΠΏΡΠΎΡΠ΅ Π² 2019 Π³. Π ΡΡΠΎΠΉ ΡΠ²ΡΠ·ΠΈ ΠΎΡΠΌΠ΅ΡΠ°Π΅ΡΡΡ, ΡΡΠΎ Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ΅ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠ΅Π΄ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ ΠΈ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΠΠΠ’ ΠΏΠΎ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΠ΅ΡΡΡΡΠ½ΠΎΠΉ ΠΈ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π² ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ, ΠΎΡΠ΅Π½ΠΊΠ° ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ, ΡΠ²ΡΠ·Π°Π½Π½ΠΎΠΉ Ρ Β«Π·Π΅Π»Π΅Π½ΡΠΌΒ» ΡΠΎΡΡΠΎΠΌ ΠΈ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΠΉ Π½Π° ΠΎΡΠ²ΠΎΠ΅Π½ΠΈΠ΅ ΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΠ΅ Β«Π·Π΅Π»Π΅Π½ΡΡ
Β» ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΈ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΠ΅ ΠΏΡΠΈΡΠΎΠ΄ΠΎΠΎΡ
ΡΠ°Π½Π½ΡΡ
ΡΠ΅Π»Π΅ΠΉ, ΠΎΡΠ΅Π½ΠΊΠ° ΠΊΠΎΠ½Π΅ΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ (ΡΠΏΡΠΎΡΠ°) Π½Π° ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠΎΠ²Π°ΡΡ Π² ΡΠΎΡΠ³ΠΎΠ²Π»Π΅, ΡΡΠ΅Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΎΠΊ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΠΌΠΈ ΠΠΠ’-ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΠ·Π°ΡΠΈΡ ΠΈ ΡΠ΅ΡΡΡΡΠ½ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ.Π Π·Π°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠΈ ΡΡΠ°ΡΡΠΈ ΠΏΠΎΠ΄ΡΠ΅ΡΠΊΠΈΠ²Π°Π΅ΡΡΡ, ΡΡΠΎ Ρ ΡΡΠ΅ΡΠΎΠΌ ΠΏΠ΅ΡΠ²ΡΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Β ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Ρ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡ ΠΊ ΡΠΈΡΡΠ΅ΠΌΠ΅ ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΡΡ
ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠΎΠ², ΠΎΡΡΠ°ΠΆΠ°ΡΡΠΈΡ
ΡΡΠΎΠ²Π΅Π½Ρ, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ, ΠΈΠ½ΡΠ΅Π½ΡΠΈΠ²Π½ΠΎΡΡΡ, Π΄ΠΈΠ²ΠΈΠ΄Π΅Π½Π΄Ρ ΠΈ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
Π²ΠΈΠ΄ΠΎΠ² ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π΄Π»Ρ Π±ΠΎΠ»Π΅Π΅ ΡΠ΅ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΡΠ΅ΡΠ° ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ Π²ΠΊΠ»Π°Π΄Π° Π² ΡΠΎΡΡ ΠΠΠ
ΠΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΎ-ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΎΡΠ³ΠΎΠ²ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΡΠ΄Π½ΠΈΡΠ΅ΡΡΠ²Π° ΠΌΠ΅ΠΆΠ΄Ρ ΠΠΈΡΠ³ΠΈΠ·ΡΠΊΠΎΠΉ Π Π΅ΡΠΏΡΠ±Π»ΠΈΠΊΠΎΠΉ ΠΈ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠ΅ΠΉ
The authors developed economic and statistical analysis on bilateral economic and trade and investment partnership between the Republic of Kyrgyzstan and the Russian Federation for the past decade and formulated proposals for the further coordination of long-term foreign economic policy.The paper provides an analytical review of intercountry trade and investment cooperation with emphasis on manufacturing. It is based on data from various international organizations (United Nations Conference on Trade and Development, Eurasian Economic Commission) and the National statistical committee of the Kyrgyz Republic. Extensive statistical material supports the thesis on using the benefits of integration for the economy of the Kyrgyz Republic, even though it remains dependent on political fluctuations and has an underdeveloped business environment.The authors examine the implementation of bilateral contracts, projects and agreements from 2008 to 2016, with an emphasis on critical long-term interests of two states, and discuss perspectives for trade and economic cooperation in the medium and long terms. They also cover questions regarding coordination of economic policy in the abovementioned areas. Considerable attention is given to evidence-based reasoning in favor of strengthening the Russian-Kyrgyz partnership in the field of trade and investment. There is also a need for statistical support for the implementation of joint programs to improve the sustainability and competitiveness of national economies, as well as a broad integration cooperation agenda within the Eurasian Economic Union and other associations.ΠΠ²ΡΠΎΡΠ°ΠΌΠΈ ΡΡΠ°ΡΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΎ-ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΎΡΠ³ΠΎΠ²ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΡΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ ΠΌΠ΅ΠΆΠ΄Ρ ΠΠΈΡΠ³ΠΈΠ·ΡΠΊΠΎΠΉ Π Π΅ΡΠΏΡΠ±Π»ΠΈΠΊΠΎΠΉ ΠΈ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠ΅ΠΉ Π·Π° ΠΏΠΎΡΠ»Π΅Π΄Π½Π΅Π΅ Π΄Π΅ΡΡΡΠΈΠ»Π΅ΡΠΈΠ΅ ΠΈ ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½Ρ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΏΠΎ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅ΠΉ ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°ΡΠΈΠΈ Π²Π½Π΅ΡΠ½Π΅ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ Π΄Π²ΡΡ
ΡΡΡΠ°Π½.ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°Π½Π½ΡΡ
ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΡ
ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ (ΠΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ ΠΠΠ ΠΏΠΎ ΡΠΎΡΠ³ΠΎΠ²Π»Π΅ ΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ, ΠΠ²ΡΠ°Π·ΠΈΠΉΡΠΊΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠΌΠΈΡΡΠΈΠΈ) ΠΈ ΠΠ°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΈΡΠ΅ΡΠ° ΠΠΈΡΠ³ΠΈΠ·ΡΠΊΠΎΠΉ Π Π΅ΡΠΏΡΠ±Π»ΠΈΠΊΠΈ Π΄Π°Π½ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΎΠ±Π·ΠΎΡ ΠΌΠ΅ΠΆΡΡΡΠ°Π½ΠΎΠ²ΠΎΠ³ΠΎ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠΎΡΠ³ΠΎΠ²ΠΎΠΉ ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ, ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎ ΠΏΠΎ ΠΎΠ±ΡΠ°Π±Π°ΡΡΠ²Π°ΡΡΠ΅ΠΉ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ. Π‘ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π±ΠΎΠ»ΡΡΠΎΠ³ΠΎ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π° ΡΠ°ΡΠΊΡΡΠ²Π°Π΅ΡΡΡ ΡΠ΅Π·ΠΈΡ ΠΎΠ± ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ² ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ΅ ΠΠΈΡΠ³ΠΈΠ·ΡΠΊΠΎΠΉ Π Π΅ΡΠΏΡΠ±Π»ΠΈΠΊΠΈ, Π½Π΅ΡΠΌΠΎΡΡΡ Π½Π° ΡΠΎ, ΡΡΠΎ ΠΎΠ½Π° ΠΏΠΎ-ΠΏΡΠ΅ΠΆΠ½Π΅ΠΌΡ ΠΎΡΡΠ°Π΅ΡΡΡ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΠΉ ΠΎΡ ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠ»Π΅Π±Π°Π½ΠΈΠΉ ΠΈ ΠΈΠΌΠ΅Π΅Ρ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ ΡΠ°Π·Π²ΠΈΡΡΡ Π±ΠΈΠ·Π½Π΅Ρ-ΡΡΠ΅Π΄Ρ.ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½ Ρ
ΠΎΠ΄ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ Π΄Π²ΡΡΡΠΎΡΠΎΠ½Π½ΠΈΡ
Π΄ΠΎΠ³ΠΎΠ²ΠΎΡΠΎΠ², ΠΏΡΠΎΠ΅ΠΊΡΠΎΠ² ΠΈ ΡΠΎΠ³Π»Π°ΡΠ΅Π½ΠΈΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄Π° 2008-2016 Π³Π³., Π°ΠΊΡΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½ΠΎ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π½Π° ΠΊΠ»ΡΡΠ΅Π²ΡΡ
Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΡΡ
ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ°Ρ
Π΄Π²ΡΡ
Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ² ΠΈ ΠΎΠ±ΠΎΠ·Π½Π°ΡΠ΅Π½Ρ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ Π² ΡΠ°Π·Π²ΠΈΡΠΈΠΈ Π΄Π²ΡΡΡΠΎΡΠΎΠ½Π½ΠΈΡ
ΡΠΎΡΠ³ΠΎΠ²ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ Π² ΡΡΠ΅Π΄Π½Π΅ΠΉ ΠΈ Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΠΎΠΉ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π΅. ΠΡΠ³ΡΠΌΠ΅Π½ΡΠΈΡΡΠ΅ΡΡΡ Π°Π²ΡΠΎΡΡΠΊΠ°Ρ ΠΏΠΎΠ·ΠΈΡΠΈΡ ΠΏΠΎ Π²ΠΎΠΏΡΠΎΡΠ°ΠΌ ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°ΡΠΈΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ Π² ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΡ
ΡΡΠ΅ΡΠ°Ρ
Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ. ΠΠ½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ΄Π΅Π»Π΅Π½ΠΎ ΡΠ°ΠΊΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΌΡ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΡ ΡΠΊΡΠ΅ΠΏΠ»Π΅Π½ΠΈΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎ-ΠΊΠΈΡΠ³ΠΈΠ·ΡΠΊΠΎΠ³ΠΎ ΠΏΠ°ΡΡΠ½Π΅ΡΡΡΠ²Π° Π² ΡΡΠ΅ΡΠ΅ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΉ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΠΏΡΠΎΠ²ΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠΎΠ²ΠΌΠ΅ΡΡΠ½ΡΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ ΠΏΠΎ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΠΈ ΠΈ ΠΊΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΠΈ Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΈΡΠΎΠΊΠΎΠΉ ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡΠΊΠΈ ΡΠΎΡΡΡΠ΄Π½ΠΈΡΠ΅ΡΡΠ²Π° Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΠ²ΡΠ°Π·ΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΠ·Π° ΠΈ Π΄ΡΡΠ³ΠΈΡ
ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ
ΠΠΎΡΠ΅Π½ΡΠΈΠ°Π» ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΡΡ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΡΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ Π·Π°Π½ΡΡΠΎΡΡΠΈ Π² ΠΌΠ°Π»ΠΎΠΌ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΡΠ²Π΅ Π ΠΎΡΡΠΈΠΈ
The article presents results of analysis of the predictive potential of short-term forecast estimates of employment level in the small business segment by four sectors of the Russian economy: manufacturing, construction, wholesale and retail trade.From the authorsβ point of view, one of the promising sources of data for such estimates can be found in market observations of entrepreneurial activity, which now are a common source of economic information in national as well as international practice. These surveys play an important role in measuring the dynamics of employment in countries and industries, being a supplementary statistical tool.The objective of the work was to prove the existence of a stable statistically significant relationship between the predicted estimates of employment based on business (market) surveys and the dynamics of the corresponding statistical macro-aggregates in various sectors, and applicability of predictive models of employment change based on results of business (market) surveys.The novelty of the presented results (authorsβ contribution) resides in the fact that for the first time, using an expanded sample (over 14 thousand respondents), were studied the possibilities of predicting labour market indicators in small businesses based on leading data from business surveys, examining separately retail trade, wholesale trade, construction, and manufacturing. According to the results obtained based on the Granger causality and pseudo-out-of-sample analysis, in all the industries under consideration, entrepreneurial assessments and expectations are effective predictive indicators for forecasting employment dynamics in the short term (two to four months) and identifying turning points in employment growth in the small business segment. The most sensitive predictive estimates were found in the retail and wholesale sectors, with the best results obtained for wholesale trade. For this reason, the authors recommend using the employment expectations indicator primarily in these sectors to monitor the level of employment and unemployment.Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΡΠΎΠ³Π½ΠΎΠ·Π½ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»Π° ΠΊΡΠ°ΡΠΊΠΎΡΡΠΎΡΠ½ΡΡ
ΠΏΡΠΎΠ³Π½ΠΎΠ·Π½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ ΡΡΠΎΠ²Π½Ρ Π·Π°Π½ΡΡΠΎΡΡΠΈ Π² ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ΅ ΠΌΠ°Π»ΠΎΠ³ΠΎ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΡΠ²Π° Π² ΡΠ΅ΡΡΡΠ΅Ρ
Π²ΠΈΠ΄Π°Ρ
Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ: ΠΎΠ±ΡΠ°Π±Π°ΡΡΠ²Π°ΡΡΠ΅ΠΉ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ, ΡΡΡΠΎΠΈΡΠ΅Π»ΡΡΡΠ²Π΅, ΠΎΠΏΡΠΎΠ²ΠΎΠΉ ΠΈ ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»Π΅.Π‘ ΡΠΎΡΠΊΠΈ Π·ΡΠ΅Π½ΠΈΡ Π°Π²ΡΠΎΡΠΎΠ², ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ ΠΌΠΎΠ³ΡΡ ΡΠ»ΡΠΆΠΈΡΡ ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΡΠ΅ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ, ΡΠ²Π»ΡΡΡΠΈΠ΅ΡΡ Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΊΠ°ΠΊ Π² Π½Π°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ, ΡΠ°ΠΊ ΠΈ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅ ΠΈ ΠΈΠ³ΡΠ°ΡΡΠΈΠ΅ Π²Π°ΠΆΠ½ΡΡ ΡΠΎΠ»Ρ Π² ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ Π·Π°Π½ΡΡΠΎΡΡΠΈ Π² ΡΡΡΠ°Π½Π°Ρ
ΠΈ ΠΎΡΡΠ°ΡΠ»ΡΡ
, Π²ΡΡΡΡΠΏΠ°Ρ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°.Π¦Π΅Π»ΡΡ ΡΠ°Π±ΠΎΡΡ Π±ΡΠ»ΠΎ Π΄ΠΎΠΊΠ°Π·Π°ΡΡ Π½Π°Π»ΠΈΡΠΈΠ΅ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΠΎΠΉ ΡΠ²ΡΠ·ΠΈ ΠΏΡΠ΅Π΄ΠΈΠΊΡ-ΠΎΡΠ΅Π½ΠΎΠΊ Π·Π°Π½ΡΡΠΎΡΡΠΈ, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π½Π° Π±Π°Π·Π΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΡΡ
ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, Ρ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΎΠΉ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΡ
ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠ°ΠΊΡΠΎΠ°Π³ΡΠ΅Π³Π°ΡΠΎΠ² Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ΅ΠΊΡΠΎΡΠ°Ρ
, Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π½ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π·Π°Π½ΡΡΠΎΡΡΠΈ, ΠΏΠΎΡΡΡΠΎΠ΅Π½Π½ΡΡ
Π½Π° Π±Π°Π·Π΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΊΠΎΠ½ΡΡΠ½ΠΊΡΡΡΠ½ΡΡ
ΠΎΠΏΡΠΎΡΠΎΠ².ΠΠΎΠ²ΠΈΠ·Π½Π° ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² (Π²ΠΊΠ»Π°Π΄Π° Π°Π²ΡΠΎΡΠΎΠ²) ΡΠΎΡΡΠΎΠΈΡ Π² ΡΠΎΠΌ, ΡΡΠΎ Π²ΠΏΠ΅ΡΠ²ΡΠ΅ Π±ΡΠ»ΠΈ ΠΈΠ·ΡΡΠ΅Π½Ρ Π½Π° ΡΠ°ΡΡΠΈΡΠ΅Π½Π½ΠΎΠΉ Π²ΡΠ±ΠΎΡΠΊΠ΅ (Π±ΠΎΠ»Π΅Π΅ 14 ΡΡΡ. ΡΠ΅ΡΠΏΠΎΠ½Π΄Π΅Π½ΡΠΎΠ²) Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΡΡΠ½ΠΊΠ° ΡΡΡΠ΄Π° Π² ΠΌΠ°Π»ΠΎΠΌ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΡΠ²Π΅ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΎΠΏΠ΅ΡΠ΅ΠΆΠ°ΡΡΠΈΡ
Π΄Π°Π½Π½ΡΡ
Π±ΠΈΠ·Π½Π΅Ρ-ΠΎΠΏΡΠΎΡΠΎΠ², ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π² ΠΎΡΠ΄Π΅Π»ΡΠ½ΠΎ ΡΠΎΠ·Π½ΠΈΡΠ½ΡΡ ΡΠΎΡΠ³ΠΎΠ²Π»Ρ, ΠΎΠΏΡΠΎΠ²ΡΡ ΡΠΎΡΠ³ΠΎΠ²Π»Ρ, ΡΡΡΠΎΠΈΡΠ΅Π»ΡΡΡΠ²ΠΎ ΠΈ ΠΎΠ±ΡΠ°Π±Π°ΡΡΠ²Π°ΡΡΡΡ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΡ. Π‘ΠΎΠ³Π»Π°ΡΠ½ΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠΌ Π½Π° Π±Π°Π·Π΅ Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΡΠΈΡΠΈΠ½Π½ΠΎΡΡΠΈ ΠΏΠΎ ΠΡΠ΅ΠΉΠ½Π΄ΠΆΠ΅ΡΡ ΠΈ ΠΏΡΠ΅Π²Π΄ΠΎ Π²Π½Π΅Π²ΡΠ±ΠΎΡΠΎΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°, Π²ΠΎ Π²ΡΠ΅Ρ
ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΡ
ΠΎΡΡΠ°ΡΠ»ΡΡ
ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΠΊΠΈΠ΅ ΠΎΡΠ΅Π½ΠΊΠΈ ΠΈ ΠΎΠΆΠΈΠ΄Π°Π½ΠΈΡ Π²ΡΡΡΡΠΏΠ°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠΌΠΈ ΠΏΡΠ΅Π΄ΠΈΠΊΡ-ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠ°ΠΌΠΈ Π΄Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ Π·Π°Π½ΡΡΠΎΡΡΠΈ Π½Π° Π±Π»ΠΈΠΆΠ°ΠΉΡΡΡ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Ρ (Π΄Π²Π°-ΡΠ΅ΡΡΡΠ΅ ΠΌΠ΅ΡΡΡΠ°) ΠΈ Π΄Π°ΡΠΈΡΠΎΠ²ΠΊΠΈ ΠΏΠΎΠ²ΠΎΡΠΎΡΠ½ΡΡ
ΡΠΎΡΠ΅ΠΊ Π² ΡΠΎΡΡΠ΅ Π·Π°Π½ΡΡΠΎΡΡΠΈ Π² ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ΅ ΠΌΠ°Π»ΠΎΠ³ΠΎ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΠΈΠΌΠ°ΡΠ΅Π»ΡΡΡΠ²Π°. ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΡΠΌΠΈ ΠΏΡΠ΅Π΄ΠΈΠΊΡ-ΠΎΡΠ΅Π½ΠΊΠΈ ΠΎΠΊΠ°Π·Π°Π»ΠΈΡΡ Π² ΡΠ΅ΠΊΡΠΎΡΠ°Ρ
ΡΠΎΠ·Π½ΠΈΡΠ½ΠΎΠΉ ΠΈ ΠΎΠΏΡΠΎΠ²ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ, ΠΏΡΠΈ ΡΡΠΎΠΌ Π΄Π»Ρ ΠΎΠΏΡΠΎΠ²ΠΎΠΉ ΡΠΎΡΠ³ΠΎΠ²Π»ΠΈ Π±ΡΠ»ΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½Ρ Π½Π°ΠΈΠ»ΡΡΡΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠΎ ΡΡΠΎΠΉ ΠΏΡΠΈΡΠΈΠ½Π΅ Π°Π²ΡΠΎΡΡ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΎΡΠ° ΠΎΠΆΠΈΠ΄Π°Π½ΠΈΠΉ Π·Π°Π½ΡΡΠΎΡΡΠΈ Π² ΡΡΠΈΡ
Π²ΠΈΠ΄Π°Ρ
Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π΄Π»Ρ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΡΠΎΠ²Π½Ρ Π·Π°Π½ΡΡΠΎΡΡΠΈ ΠΈ Π±Π΅Π·ΡΠ°Π±ΠΎΡΠΈΡΡ Π² ΠΏΠ΅ΡΠ²ΡΡ ΠΎΡΠ΅ΡΠ΅Π΄Ρ
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