1,113 research outputs found
Modeling of "groove" rolling defect on internal surface of pipes at lengthwise rolling
The research has been conducted in pipe forming at rolling off by lengthwise rolling mill with stub mandrel; the patterns of change in dimensionless parameters were determined, which characterize deformation in side angle depending on http://www.multitran.ru/c/m.exe?t=424995_2_1elongation ratio. The model of formation of lengthwise groove on internal surface of pipes has been proposed
ΠΠΠ ΠΠ€ΠΠΠΠ¦ΠΠ― ΠΠΠΠΠΠΠΠ’Π ΠΠ§ΠΠ‘ΠΠΠ ΠΠΠΠΠΠ Π‘ Π£Π§ΠΠ’ΠΠ ΠΠΠ ΠΠΠ ΠΠ«Π₯ ΠΠΠ ΠΠΠΠ§ΠΠΠΠ ΠΠ Π‘Π’Π Π£ΠΠ’Π£Π ΠΠ«Π ΠΠΠ ΠΠΠΠ’Π Π«
The article describes a method for verification of a statistical model, which, firstly, is represented by the time series of original data and, secondly, is linear in the estimated parameters. Experience in statistical calculations on real empirical data shows that the most well-known and conventionally used in the econometric modeling of mathematical-statistical methods (least squares, maximum likelihood method, and similar methods) often do not ensure successful verification of theoretically required forms of econometric models. The developed method which is called an alternative method of linear regression (AMLR) provides an account of a priori restrictions on the absolute values and signs of the parameters identified by the model. The AMLR based on the concept of best linear index, is known in the theory of statistics from the end of the 1950s. Mathematically AMLR it based on the method of principal components. The article analyzes conditions for applying the AMLR in econometric modeling and methods of transformation of the initial statistical information to ensure correct application of the developed evaluation procedures.Special problems of the proposed method are to determine the level of accuracy of approximation of the dependent variable of the model. In this regard, to assess the level of precision of the statistical model verifiable by using the AMLR, was developed an original method of decomposition of the time series on the regular and stochastic components. The author analyzes the properties of the proposed method of decomposition and gave a numerical illustration of its use in econometric calculations.Π ΡΡΠ°ΡΡΠ΅ ΠΎΠΏΠΈΡΠ°Π½ ΠΌΠ΅ΡΠΎΠ΄ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ, ΠΊΠΎΡΠΎΡΠ°Ρ, Π²ΠΎ-ΠΏΠ΅ΡΠ²ΡΡ
, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠΌΠΈ ΡΡΠ΄Π°ΠΌΠΈ ΠΈΡΡ
ΠΎΠ΄Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΈ, Π²ΠΎ-Π²ΡΠΎΡΡΡ
, ΡΠ²Π»ΡΠ΅ΡΡΡ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ ΠΏΠΎ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π΅ΠΌΡΠΌ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌ. ΠΠΏΡΡ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΡΡΠ΅ΡΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠ΅Π°Π»ΡΠ½ΡΡ
ΡΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°Π½Π½ΡΡ
ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΠ΅Ρ ΠΎ ΡΠΎΠΌ, ΡΡΠΎ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΠ΅ ΠΈ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΠΌΡΠ΅ Π² ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅ ΡΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΎ-ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ (ΠΌΠ΅ΡΠΎΠ΄ Π½Π°ΠΈΠΌΠ΅Π½ΡΡΠΈΡ
ΠΊΠ²Π°Π΄ΡΠ°ΡΠΎΠ², ΠΌΠ΅ΡΠΎΠ΄ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ°Π²Π΄ΠΎΠΏΠΎΠ΄ΠΎΠ±ΠΈΡ ΠΈ Π±Π»ΠΈΠ·ΠΊΠΈΠ΅ ΠΊ Π½ΠΈΠΌ ΠΌΠ΅ΡΠΎΠ΄Ρ) ΠΎΡΠ΅Π½Ρ ΡΠ°ΡΡΠΎ Π½Π΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΡΡ ΡΡΠΏΠ΅ΡΠ½ΡΡ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈ ΡΡΠ΅Π±ΡΠ΅ΠΌΡΡ
ΡΠΎΡΠΌ ΡΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄, Π½Π°Π·ΡΠ²Π°Π΅ΠΌΡΠΉ Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Π½ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΠΈ (ΠΠΠΠ ), ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Π΅Ρ ΡΡΠ΅Ρ Π°ΠΏΡΠΈΠΎΡΠ½ΡΡ
ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΠΉ Π°Π±ΡΠΎΠ»ΡΡΠ½ΡΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ ΠΈ Π·Π½Π°ΠΊΠΎΠ² ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΡΠ΅ΠΌΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ. Π ΠΎΡΠ½ΠΎΠ²Π΅ ΠΠΠΠ Π»Π΅ΠΆΠΈΡ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈΡ Π½Π°ΠΈΠ»ΡΡΡΠ΅Π³ΠΎ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ ΠΈΠ½Π΄Π΅ΠΊΡΠ°, ΠΈΠ·Π²Π΅ΡΡΠ½Π°Ρ Π² ΡΠ΅ΠΎΡΠΈΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ Ρ ΠΊΠΎΠ½ΡΠ° 1950-Ρ
Π³ΠΎΠ΄ΠΎΠ². Π ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ ΠΠΠΠ ΠΎΡΠ½ΠΎΠ²ΡΠ²Π°Π΅ΡΡΡ Π½Π° ΠΌΠ΅ΡΠΎΠ΄Π΅ Π³Π»Π°Π²Π½ΡΡ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ ΡΡΠ»ΠΎΠ²ΠΈΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΠΠΠ Π² ΡΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΠ΅ ΠΊΠΎΡΡΠ΅ΠΊΡΠ½ΠΎΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ ΠΎΡΠ΅Π½ΠΈΠ²Π°Π½ΠΈΡ.Π‘ΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π° ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΡΠΎΠ²Π½Ρ ΡΠΎΡΠ½ΠΎΡΡΠΈ Π°ΠΏΠΏΡΠΎΠΊΡΠΈΠΌΠ°ΡΠΈΠΈ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΠΉ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ. Π ΡΠ²ΡΠ·ΠΈ Ρ ΡΡΠΈΠΌ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΡΠΎΠ²Π½Ρ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ, Π²Π΅ΡΠΈΡΠΈΡΠΈΡΡΠ΅ΠΌΠΎΠΉ ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ ΠΠΠΠ , ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½ ΠΎΡΠΈΠ³ΠΈΠ½Π°Π»ΡΠ½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π΅ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΈ Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΡΠ΄Π° Π½Π° ΡΠ΅Π³ΡΠ»ΡΡΠ½ΡΡ ΠΈ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΡΡ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΡ. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ ΡΠ²ΠΎΠΉΡΡΠ²Π° ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π° Π΄Π΅ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΈ ΠΈ Π΄Π°Π½Π° ΡΠΈΡΠ»ΠΎΠ²Π°Ρ ΠΈΠ»Π»ΡΡΡΡΠ°ΡΠΈΡ Π΅Π³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π² ΡΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΡΡΠ΅ΡΠ°Ρ
ΠΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ Π² ΡΠΏΠΎΡ Ρ Β«ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ Π·Π½Π°Π½ΠΈΠΉΒ»
Features of modern economy as "economy of knowledge" are defined. The changes happening in education in the conditions of strengthening of a role of knowledge in economic life of society are considered. Tasks of modern universities in production of knowledge are characterized, connection of the state, universities and business is shown. The main directions of development of modern education in the conditions of emergence of new educational technologies and pressure of market forces are proved. Proposals of rectors of higher education institutions about revival of engineering education in the Russian Federation are systematized.ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ ΠΊΠ°ΠΊ Β«ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ Π·Π½Π°Π½ΠΈΠΉΒ». Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ, ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΡΡΠΈΠ΅ Π² ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠΈ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΡΡΠΈΠ»Π΅Π½ΠΈΡ ΡΠΎΠ»ΠΈ Π·Π½Π°Π½ΠΈΠΉ Π² ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΆΠΈΠ·Π½ΠΈ ΠΎΠ±ΡΠ΅ΡΡΠ²Π°. ΠΡ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Ρ Π·Π°Π΄Π°ΡΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠΎΠ² Π² ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π΅ Π·Π½Π°Π½ΠΈΠΉ, ΠΏΠΎΠΊΠ°Π·Π°Π½Π° ΡΠ²ΡΠ·Ρ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π°, ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠΎΠ² ΠΈ Π±ΠΈΠ·Π½Π΅ΡΠ°. ΠΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½Ρ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΠΎΡΠ²Π»Π΅Π½ΠΈΡ Π½ΠΎΠ²ΡΡ
ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΈ Π΄Π°Π²Π»Π΅Π½ΠΈΡ ΡΡΠ½ΠΎΡΠ½ΡΡ
ΡΠΈΠ». Π‘ΠΈΡΡΠ΅ΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡ ΡΠ΅ΠΊΡΠΎΡΠΎΠ² Π²ΡΠ·ΠΎΠ² ΠΎ Π²ΠΎΠ·ΡΠΎΠΆΠ΄Π΅Π½ΠΈΠΈ ΠΈΠ½ΠΆΠ΅Π½Π΅ΡΠ½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π² Π Π€
ΠΠΊΠΎΠ½ΠΎΠΌΡΡΠ½Π° Π±Π΅Π·ΠΏΠ΅ΠΊΠ° ΡΡΠ±βΡΠΊΡΡΠ² Π³ΠΎΡΠΏΠΎΠ΄Π°ΡΡΠ²Π°Π½Π½Ρ Π² ΡΠΌΠΎΠ²Π°Ρ ΠΏΠ°Π½Π΄Π΅ΠΌΡΡ COVID-19: Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ° Π·Π°Π³ΡΠΎΠ·
Π‘ΡΠ²ΠΎΡΠΎΠ² Π. Π. ΠΠΊΠΎΠ½ΠΎΠΌΡΡΠ½Π° Π±Π΅Π·ΠΏΠ΅ΠΊΠ° ΡΡΠ±βΡΠΊΡΡΠ² Π³ΠΎΡΠΏΠΎΠ΄Π°ΡΡΠ²Π°Π½Π½Ρ Π² ΡΠΌΠΎΠ²Π°Ρ
ΠΏΠ°Π½Π΄Π΅ΠΌΡΡ COVID-19: Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ° Π·Π°Π³ΡΠΎΠ· / Π. Π. Π‘ΡΠ²ΠΎΡΠΎΠ² // Π‘ΡΡΠ°ΡΠ½Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΈ ΠΏΡΠ°Π²ΠΎΠ²ΠΎΠ³ΠΎ, Π΅ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΠΎΠ³ΠΎ ΡΠ° ΡΠΎΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΎΠ·Π²ΠΈΡΠΊΡ Π΄Π΅ΡΠΆΠ°Π²ΠΈ : ΡΠ΅Π·ΠΈ Π΄ΠΎΠΏ. ΠΡΠΆΠ½Π°Ρ. Π½Π°ΡΠΊ.-ΠΏΡΠ°ΠΊΡ. ΠΊΠΎΠ½Ρ. (ΠΌ. Π₯Π°ΡΠΊΡΠ², 27 Π»ΠΈΡΡΠΎΠΏ. 2020 Ρ.). β Π₯Π°ΡΠΊΡΠ², 2020. β Π‘. 233 - 234.Π ΡΡΠ°ΡΡΡ Π·Π°Π·Π½Π°ΡΠ°ΡΡΡΡΡ, ΡΠΎ Π·Π°Π³ΡΠΎΠ·ΠΈ Π΅ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΡΠΉ Π±Π΅Π·ΠΏΠ΅ΡΡ ΡΡΠ±βΡΠΊΡΡΠ² Π³ΠΎΡΠΏΠΎΠ΄Π°ΡΡΠ²Π°Π½Π½Ρ, ΡΠΊΡ ΡΠΏΡΠΈΡΠΈΠ½Π΅Π½Ρ
Π²Π²Π΅Π΄Π΅Π½Π½ΡΠΌ ΠΏΡΠΎΡΠΈΠ΅ΠΏΡΠ΄Π΅ΠΌΡΡΠ½ΠΎΠ³ΠΎ ΠΊΠ°ΡΠ°Π½ΡΠΈΠ½Ρ, ΠΌΠ°ΡΡΡ ΠΎΠ±βΡΠΊΡΠΈΠ²Π½ΠΈΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅Ρ, ΠΏΠΎΠ²βΡΠ·Π°Π½Ρ Π·
ΡΠΎΡΡ-ΠΌΠ°ΠΆΠΎΡΠ½ΠΈΠΌΠΈ ΠΎΠ±ΡΡΠ°Π²ΠΈΠ½Π°ΠΌΠΈ, Π²ΠΈΠ½ΠΈΠΊΠ»ΠΈ ΡΠ°ΠΏΡΠΎΠ²ΠΎ Ρ Π½Π΅ ΠΌΠΎΠ³Π»ΠΈ Π±ΡΡΠΈ ΠΏΠ΅ΡΠ΅Π΄Π±Π°ΡΠ΅Π½Ρ. ΠΡΠΊΡΠ»ΡΠΊΠΈ
ΠΊΠ°ΡΠ°Π½ΡΠΈΠ½ Ρ Π°Π΄ΠΌΡΠ½ΡΡΡΡΠ°ΡΠΈΠ²Π½ΠΈΠΌ Π·Π°Ρ
ΠΎΠ΄ΠΎΠΌ Π΄Π΅ΡΠΆΠ°Π²Π½ΠΎΠ³ΠΎ ΡΠ΅Π³ΡΠ»ΡΠ²Π°Π½Π½Ρ, Π΄Π΅ΡΠΆΠ°Π²Π° ΠΌΠ°Ρ ΡΠΎΠ·Π΄ΡΠ»ΠΈΡΠΈ
Π· ΡΡΠ±βΡΠΊΡΠ°ΠΌΠΈ Π³ΠΎΡΠΏΠΎΠ΄Π°ΡΡΠ²Π°Π½Π½Ρ ΡΡ Π²ΡΡΠ°ΡΠΈ, ΡΠΎ Π²ΠΎΠ½ΠΈ ΠΏΠΎΠ½Π΅ΡΠ»ΠΈ Ρ Π·Π²βΡΠ·ΠΊΡ Π· ΠΉΠΎΠ³ΠΎ Π²Π²Π΅Π΄Π΅Π½Π½ΡΠΌ.
Π ΡΡΠ°ΡΡΠ΅ ΠΎΡΠΌΠ΅ΡΠ°Π΅ΡΡΡ, ΡΡΠΎ ΡΠ³ΡΠΎΠ·Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ ΡΡΠ±ΡΠ΅ΠΊΡΠΎΠ² Ρ
ΠΎΠ·ΡΠΉΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ, Π²ΡΠ·Π²Π°Π½Π½ΡΠ΅
Π²Π²Π΅Π΄Π΅Π½ΠΈΠ΅ΠΌ ΠΏΡΠΎΡΠΈΠ²ΠΎΡΠΏΠΈΠ΄Π΅ΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ°ΡΠ°Π½ΡΠΈΠ½Π°, ΠΈΠΌΠ΅ΡΡΠΈΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠΉ Ρ
Π°ΡΠ°ΠΊΡΠ΅Ρ, ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ Ρ
ΡΠΎΡΡ-ΠΌΠ°ΠΆΠΎΡΠ½ΡΠΌΠΈ ΠΎΠ±ΡΡΠΎΡΡΠ΅Π»ΡΡΡΠ²Π°ΠΌΠΈ Π²ΠΎΠ·Π½ΠΈΠΊΠ»ΠΈ Π²Π½Π΅Π·Π°ΠΏΠ½ΠΎ ΠΈ Π½Π΅ ΠΌΠΎΠ³Π»ΠΈ Π±ΡΡΡ ΠΏΡΠ΅Π΄ΡΡΠΌΠΎΡΡΠ΅Π½Ρ. ΠΠΎΡΠΊΠΎΠ»ΡΠΊΡ
ΠΊΠ°ΡΠ°Π½ΡΠΈΠ½ ΡΠ²Π»ΡΠ΅ΡΡΡ Π°Π΄ΠΌΠΈΠ½ΠΈΡΡΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠ΅ΡΠΎΠΉ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠ΅Π³ΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²ΠΎ Π΄ΠΎΠ»ΠΆΠ½ΠΎ ΡΠ°Π·Π΄Π΅Π»ΠΈΡΡ
Ρ ΡΡΠ±ΡΠ΅ΠΊΡΠ°ΠΌΠΈ Ρ
ΠΎΠ·ΡΠΉΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΡΠ΅ΡΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΠ½ΠΈ ΠΏΠΎΠ½Π΅ΡΠ»ΠΈ Π² ΡΠ²ΡΠ·ΠΈ Ρ Π΅Π³ΠΎ Π²Π²Π΅Π΄Π΅Π½ΠΈΠ΅ΠΌ.
The article states that the threats to the economic security of economic entities that are caused
introduction of anti-epidemic quarantine, are objective in nature, related to
force majeure, arose suddenly and could not be foreseen. Because
quarantine is an administrative measure of state regulation, the state must divide
with business entities the losses they have incurred in connection with its introduction
Cement-free binders for radioactive waste produced from blast-furnace slag using vortex layer activation technology
The paper addresses the issue of recycling granulated blast-furnace slag (gBFS) as a source for production of cement-free binder materials for further usage in rare-earth metals production for radioactive waste disposal. The use of the vortex layer activator was provided as main technique allowing to produce high-dispersed chemically activated binders. The paper examines the effect of processing conditions on the physical-chemical and mechanical properties of the resulting BFS-based cement-free materials and gBFS-based concretes
Basic principles of descriptive statistics in medical research
Descriptive statistics provides tools to explore, summarize and illustrate the research data. In this tutorial we discuss two main types of data β qualitative and quantitative variables, and the most common approaches to characterize data distribution numerically and graphically. This article presents two important sets of parameters β measures of the central tendency (mean, median and mode) and variation (standard deviation, quantiles) and suggests the most suitable conditions for their application. We explain the difference between the general population and random samples, that are usually analyzed in studies. The parameters which characterize the sample (for example, measures of the central tendency) are point estimates, that can differ from the respective parameters of the general population. We introduce the concept of confidence interval β the range of values, which likely includes the true value of the parameter for the general population. All concepts and definitions are illustrated with examples, which simulate the research data
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