197 research outputs found
Machining of thermosetting composites by means of milling and drilling
Polymer composites are widely spread in machinery, construction activity, incl. production of wear plates and items of fitting systems. In process of manufacturing items from composites, the products must be machined by tools and cutters. During cutting, with mechanical and thermal loads applied, the destruction of composites takes place to deteriorate quality of products. To increase quality of products, the procedures of cutting modes calculation should be developed with introduction of correction factors considering specific properties of polymers, as well as their optimization with usage of multi-criteria methods. Β© IDOSI Publications, 2013
Influence of particulate fillers on complex of dielectric properties of rigid and plasticized PVC compositions
The article discloses efficiency of modifying the polyvinyl chloride by organic and mineral fillers being the wastes of various industrial productions. Main dielectric properties of rigid and plasticized compositions were examined, for different combinations and ratios of modifying additives. The article displayed how the particulate fillers nature and composition influence on character and intervals of dielectric behavior modification. The results of researches were interpreted with consideration of structural-morphological composition of polyvinyl chloride, of modifying additives' nature, content and ratio. Β© IDOSI Publications, 2014
ΠΠ°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ Π±Π°Π½ΠΊΠΎΠ² Π½Π° ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ Π Π΅ΡΠΏΡΠ±Π»ΠΈΠΊΠΈ Π’Π°ΡΠ°ΡΡΡΠ°Π½
The purpose of this study is to develop tools for assessing the state of regional banks and to justify decisions on the allocation of resources between their business areas by adapting existing methods of banking management to the new economic environment.The authors apply both general scientific methods (induction, deduction, analysis, synthesis) and special methods: systematic and retrospective analysis of existing developments in bank decisions.The results of the application of these methods are the mathematical models describing the functioning of the credit-deposit and transaction of commercial banks of Tatarstan in the past three years from the point of elasticity of actives and liabilitiesβ substitution. In the paper, we systematized the indicators of actives and liabilities of the five largest commercial banks in Tatarstan in 2019β2022, we approved the equations that characterize these actives and liabilitiesβ substitution elasticity in MS Excel.The conclusion is that in most cases, there is a unitary elasticity of their mutual substitution, which leads to the conclusion that the Tatarstan banking system is currently in the growth stage of a new life cycle, which began in 2014, after the Russian economy entered new realities due to sanctions pressure. The recommendations were formulated for the banks of Tatarstan in terms of improving the quality of loan portfolios in new conditions: they should improve the methods of making decisions about the loans for companiesβ business activities which are first created.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ βΒ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
Π±Π°Π½ΠΊΠΎΠ² ΠΈ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΡΠ΅ΡΡΡΡΠΎΠ² ΠΌΠ΅ΠΆΠ΄Ρ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΠΌΠΈ ΠΈΡ
Π±ΠΈΠ·Π½Π΅ΡΠ° ΠΏΠΎΡΡΠ΅Π΄ΡΡΠ²ΠΎΠΌ Π°Π΄Π°ΠΏΡΠ°ΡΠΈΠΈ ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π±Π°Π½ΠΊΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΠ° ΠΊ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡΠΌ Π½ΠΎΠ²ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠ΅Π΄Ρ. ΠΠ²ΡΠΎΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡ ΠΊΠ°ΠΊ ΠΎΠ±ΡΠ΅Π½Π°ΡΡΠ½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ (ΠΈΠ½Π΄ΡΠΊΡΠΈΡ, Π΄Π΅Π΄ΡΠΊΡΠΈΡ, Π°Π½Π°Π»ΠΈΠ·, ΡΠΈΠ½ΡΠ΅Π·), ΡΠ°ΠΊ ΠΈ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΠ΅: ΡΠΈΡΡΠ΅ΠΌΠ½ΡΠΉ ΠΈ ΡΠ΅ΡΡΠΎΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
Π½Π°ΡΠ°Π±ΠΎΡΠΎΠΊ Π² ΡΡΠ΅ΡΠ΅ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π±Π°Π½ΠΊΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΡΠΈΡΠΊ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΠ°. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠΌ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠΊΠ°Π·Π°Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠ²Π»ΡΡΡΡΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ, ΠΎΠΏΠΈΡΡΠ²Π°ΡΡΠΈΠ΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΡΠ΅Π΄ΠΈΡΠ½ΠΎ-Π΄Π΅ΠΏΠΎΠ·ΠΈΡΠ½ΠΎΠ³ΠΎ ΠΈ ΡΡΠ°Π½Π·Π°ΠΊΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π±ΠΈΠ·Π½Π΅ΡΠΎΠ² ΠΊΠΎΠΌΠΌΠ΅ΡΡΠ΅ΡΠΊΠΈΡ
Π±Π°Π½ΠΊΠΎΠ² Π’Π°ΡΠ°ΡΡΡΠ°Π½Π° Π² ΠΏΡΠΎΡΠ΅Π΄ΡΠΈΠ΅ 3 Π³ΠΎΠ΄Π° Ρ ΠΏΠΎΠ·ΠΈΡΠΈΠΈ ΡΠ»Π°ΡΡΠΈΡΠ½ΠΎΡΡΠΈ Π²Π·Π°ΠΈΠΌΠ½ΠΎΠ³ΠΎ Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ°Π·Π½ΡΡ
Π²ΠΈΠ΄ΠΎΠ² Π°ΠΊΡΠΈΠ²ΠΎΠ² ΠΈ ΠΏΠ°ΡΡΠΈΠ²ΠΎΠ². Π Ρ
ΠΎΠ΄Π΅ ΡΠ°Π±ΠΎΡΡ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ Π°ΠΊΡΠΈΠ²ΠΎΠ² ΠΈ ΠΏΠ°ΡΡΠΈΠ²ΠΎΠ² ΠΏΡΡΠΈ ΠΊΡΡΠΏΠ½Π΅ΠΉΡΠΈΡ
ΠΊΠΎΠΌΠΌΠ΅ΡΡΠ΅ΡΠΊΠΈΡ
Π±Π°Π½ΠΊΠΎΠ² Π’Π°ΡΠ°ΡΡΡΠ°Π½Π° Π·Π° 2019β2022 Π³Π³., Π² MS Excel ΠΏΠΎΠ΄ΠΎΠ±ΡΠ°Π½Ρ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ, Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΠ΅ ΡΠ»Π°ΡΡΠΈΡΠ½ΠΎΡΡΡ Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ°Π·Π½ΡΡ
Π²ΠΈΠ΄ΠΎΠ² Π°ΠΊΡΠΈΠ²ΠΎΠ² ΠΈ ΠΏΠ°ΡΡΠΈΠ²ΠΎΠ².Π‘Π΄Π΅Π»Π°Π½ Π²ΡΠ²ΠΎΠ΄, ΡΡΠΎ Π² Π±ΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²Π΅ ΡΠ»ΡΡΠ°Π΅Π² ΠΈΠΌΠ΅Π΅Ρ ΠΌΠ΅ΡΡΠΎ Π΅Π΄ΠΈΠ½ΠΈΡΠ½Π°Ρ ΡΠ»Π°ΡΡΠΈΡΠ½ΠΎΡΡΡ ΠΈΡ
Π²Π·Π°ΠΈΠΌΠ½ΠΎΠ³ΠΎ Π·Π°ΠΌΠ΅ΡΠ΅Π½ΠΈΡ, ΡΡΠΎ ΠΏΠΎΠ΄Π²ΠΎΠ΄ΠΈΡ ΠΊ Π·Π°ΠΊΠ»ΡΡΠ΅Π½ΠΈΡ, ΡΡΠΎ Π±Π°Π½ΠΊΠΎΠ²ΡΠΊΠ°Ρ ΡΠΈΡΡΠ΅ΠΌΠ° Π’Π°ΡΠ°ΡΡΡΠ°Π½Π° Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π½Π°Ρ
ΠΎΠ΄ΠΈΡΡΡ Π½Π° ΡΡΠ°ΠΏΠ΅ ΡΠΎΡΡΠ° Π² Π½ΠΎΠ²ΠΎΠΌ ΠΆΠΈΠ·Π½Π΅Π½Π½ΠΎΠΌ ΡΠΈΠΊΠ»Π΅, Π½Π°ΡΠ°Π²ΡΠ΅ΠΌΡΡ Π² 2014 Π³., ΠΏΠΎΡΠ»Π΅ Π²Ρ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ Π² Π½ΠΎΠ²ΡΠ΅ ΡΠ΅Π°Π»ΠΈΠΈ, ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π½ΡΠ΅ ΡΠ°Π½ΠΊΡΠΈΠΎΠ½Π½ΡΠΌ Π΄Π°Π²Π»Π΅Π½ΠΈΠ΅ΠΌ. Π‘ΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½Ρ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΈ Π΄Π»Ρ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
Π±Π°Π½ΠΊΠΎΠ² Π Π΅ΡΠΏΡΠ±Π»ΠΈΠΊΠΈ Π’Π°ΡΠ°ΡΡΡΠ°Π½ Π² ΡΠ°ΡΡΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΊΡΠ΅Π΄ΠΈΡΠ½ΡΡ
ΠΏΠΎΡΡΡΠ΅Π»Π΅ΠΉ Π² Π½ΠΎΠ²ΡΡ
ΡΡΠ»ΠΎΠ²ΠΈΡΡ
, Π° ΠΈΠΌΠ΅Π½Π½ΠΎ: ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎ ΠΊΡΠ΅Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΡΡ
ΠΊΠ»ΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠΎ Π½ΠΎΠ²ΡΠΌ ΡΠΎΠ·Π΄Π°Π²Π°Π΅ΠΌΡΠΌ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΠΌ ΠΈΡ
Π±ΠΈΠ·Π½Π΅ΡΠ°
Generalizing Binary Solubility Data for Low-Volatile Liquids in Supercritical Fluids
EXISTING GENERALIZATION METHODS The key parameters that govern the feasibility of the separation of a mixture by supercritical fluid extraction are the binary solubilities of the components of the mixture in the extractant over a wide range of state variables. Several approaches to predicting the binary solubilities of liquids in compressed gases are known (1) where y i and y j are the concentrations (mole fractions) of the components; B ii is the second virial coefficient, which accounts for the interaction between identical molecules; B ij is the second virial cross coefficient, which accounts for the interaction between different molecules; and v m and z m are the molar volume and the compressibility of the gas phase, respectively. Within the framework of the thermodynamic similarity method, B ij is computed by various relations of the form where P cr and v cr are, respectively, the critical pressure and the critical molar volume of the component; is the characteristic temperature; and Ο ij is the characteristic acentricity factor. There is a comprehensive review [2] of methods for calculating the characteristic cross parameters of mixtures, including higher order virial coefficients. Ο ij is usually defined as the arithmetic mean of the acentricity factors of the mixture components. It was suggested [1] to calculate the characteristic temperature with the use of the empirical binary intermolecular interaction parameter k ij : ). = A generalized formula for k ij has been derived [1] for the interaction of organic liquids with a compressed gaseous solvent. The formula gives k ij as a function of the number of carbon atoms in the molecule of the dissolved liquid: where n is the number of carbon atoms in the molecules of dissolved n -paraffins, ketones, alcohols, and aromatic hydrocarbons. The interactions of nonpolar molecules with one another and of nonpolar molecules with polar molecules have been considered. The generalized formula is empirical, and the discrepancy between the parameters k ij obtained by experimental data processing and the generalized curve considerably exceeds the experimental error. The binary solubilities of low-volatile substances, including solids, have been represented [3] as a function of the solvent density: where y is the binary solubility (mole fraction), P is the pressure in the system, P 0 is the standard pressure, Ο is the solvent density, and Ο 0 is the standard solvent density. The parameters A and B are interrelated b
ΠΡΠ΅Π΄ΠΈΡΠ½ΡΠ΅ ΡΠΈΡΠΊΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ ΠΊΠΎΠΌΠΌΠ΅ΡΡΠ΅ΡΠΊΠΈΡ Π±Π°Π½ΠΊΠΎΠ²: Π½ΠΎΠ²ΡΠ΅ ΠΏΠΎΠ΄Ρ ΠΎΠ΄Ρ ΠΊ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ
In the realities of the modern domestic economy, the process of risk management of commercial banks associated to credit corporate customers, acquires new content. The assessment of what place in the companyβs activity has a work that contributes to solving the most pressing problems of our time: environment, social and general corporate governance comes to the fore. As a result, the focus is on a group of lending risks known as ESG. Since the areas of work of clients β legal entities, with which these risks are associated, and described mainly by qualitative, non-formalized characteristics, a difficult task for modern bank risk-management becomes normalizing the process of their evaluation when making specific decisions on the loan. This explains the interest and relevance of this research, the object of which is the risk management subsystem for lending to corporate clients by commercial banks, the subject is the consideration of ESG factors in this process. The purpose of the paper is to develop the basics of decision-making tools in the management of bank credit risks, with this group of factors. The authors apply methods of both general scientific (induction, deduction, analysis, synthesis) and special: system and retrospective analysis of existing developments in the field of justification of decisions of bank risk management. The theoretical significance of the research results consists in a complex analysis of the role and place of ESG-risks in the overall risk landscape and the integration of environmental, social and managerial factors into credit risk assessment. Basic principles of construction of phenomenological model, used to support credit decisions by banks of corporate clients taking into account ESG-factors that influence their activity, have been developed.Π ΡΠ΅Π°Π»ΠΈΡΡ
ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΎΡΠ΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ ΠΏΡΠΎΡΠ΅ΡΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΡΠΊΠ°ΠΌΠΈ ΠΊΠΎΠΌΠΌΠ΅ΡΡΠ΅ΡΠΊΠΈΡ
Π±Π°Π½ΠΊΠΎΠ², ΡΠ²ΡΠ·Π°Π½Π½ΡΠΌΠΈ Ρ ΠΊΡΠ΅Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΡΡ
ΠΊΠ»ΠΈΠ΅Π½ΡΠΎΠ², ΠΎΠ±ΡΠ΅ΡΠ°Π΅Ρ Π½ΠΎΠ²ΠΎΠ΅ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅. ΠΠ° ΠΏΠ΅ΡΠ²ΡΠΉ ΠΏΠ»Π°Π½ Π²ΡΡ
ΠΎΠ΄ΠΈΡ ΠΎΡΠ΅Π½ΠΊΠ° ΡΠΎΠ³ΠΎ, ΠΊΠ°ΠΊΠΎΠ΅ ΠΌΠ΅ΡΡΠΎ Π² ΠΆΠΈΠ·Π½ΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈ Π·Π°Π½ΠΈΠΌΠ°Π΅Ρ ΡΠ°Π±ΠΎΡΠ°, ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΡΡΠ°Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΏΡΠΎΠ±Π»Π΅ΠΌ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΡΡΠΈ: ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
(environment), ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
(social) ΠΈ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ (governance). Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ Π² ΡΠ΅Π½ΡΡΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΡ ΠΎΠΊΠ°Π·ΡΠ²Π°Π΅ΡΡΡ Π³ΡΡΠΏΠΏΠ° ΡΠΈΡΠΊΠΎΠ² ΠΊΡΠ΅Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΈΠ·Π²Π΅ΡΡΠ½Π°Ρ ΠΊΠ°ΠΊ ESG. ΠΠΎΡΠΊΠΎΠ»ΡΠΊΡ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠ°Π±ΠΎΡΡ ΠΊΠ»ΠΈΠ΅Π½ΡΠΎΠ² β ΡΡΠΈΠ΄ΠΈΡΠ΅ΡΠΊΠΈΡ
Π»ΠΈΡ, Ρ ΠΊΠΎΡΠΎΡΡΠΌΠΈ ΡΠ²ΡΠ·Π°Π½Ρ ΡΡΠΈ ΡΠΈΡΠΊΠΈ, ΠΎΠΏΠΈΡΡΠ²Π°ΡΡΡΡ Π² ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠΌ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΌΠΈ, Π½Π΅ΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΠΌΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌΠΈ, ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ Π·Π°Π΄Π°ΡΠ΅ΠΉ Π΄Π»Ρ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ Π±Π°Π½ΠΊΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΡΠΈΡΠΊ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΠ° ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ ΡΠΏΠΎΡΡΠ΄ΠΎΡΠΈΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΈΡ
ΠΎΡΠ΅Π½ΠΊΠΈ ΠΏΡΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΠΈ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎ Π²ΡΠ΄Π°ΡΠ΅ ΠΊΡΠ΅Π΄ΠΈΡΠ°. ΠΡΠΈΠΌ ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π° Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ, ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠΌ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠ΄ΡΠΈΡΡΠ΅ΠΌΠ° ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΡΠΊΠ°ΠΌΠΈ ΠΊΡΠ΅Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΡΡ
ΠΊΠ»ΠΈΠ΅Π½ΡΠΎΠ² ΠΊΠΎΠΌΠΌΠ΅ΡΡΠ΅ΡΠΊΠΈΠΌΠΈ Π±Π°Π½ΠΊΠ°ΠΌΠΈ, ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠΎΠΌ β ΡΡΠ΅Ρ ESG-ΡΠ°ΠΊΡΠΎΡΠΎΠ² Π² Π΄Π°Π½Π½ΠΎΠΌ ΠΏΡΠΎΡΠ΅ΡΡΠ΅. Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ β ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΎΡΠ½ΠΎΠ² ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π² ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΈ Π±Π°Π½ΠΊΠΎΠ²ΡΠΊΠΈΠΌΠΈ ΠΊΡΠ΅Π΄ΠΈΡΠ½ΡΠΌΠΈ ΡΠΈΡΠΊΠ°ΠΌΠΈ Ρ ΡΡΠ΅ΡΠΎΠΌ Π΄Π°Π½Π½ΠΎΠΉ Π³ΡΡΠΏΠΏΡ ΡΠ°ΠΊΡΠΎΡΠΎΠ². ΠΠ²ΡΠΎΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΊΠ°ΠΊ ΠΎΠ±ΡΠ΅Π½Π°ΡΡΠ½ΡΠ΅ (ΠΈΠ½Π΄ΡΠΊΡΠΈΡ, Π΄Π΅Π΄ΡΠΊΡΠΈΡ, Π°Π½Π°Π»ΠΈΠ·, ΡΠΈΠ½ΡΠ΅Π·), ΡΠ°ΠΊ ΠΈ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΠ΅: ΡΠΈΡΡΠ΅ΠΌΠ½ΡΠΉ ΠΈ ΡΠ΅ΡΡΠΎΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΡΡΠ΅ΡΡΠ²ΡΡΡΠΈΡ
Π½Π°ΡΠ°Π±ΠΎΡΠΎΠΊ Π² ΡΡΠ΅ΡΠ΅ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Π±Π°Π½ΠΊΠΎΠ²ΡΠΊΠΎΠ³ΠΎ ΡΠΈΡΠΊ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΠ°. Π’Π΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠΌ Π°Π½Π°Π»ΠΈΠ·Π΅ ΡΠΎΠ»ΠΈ ΠΈ ΠΌΠ΅ΡΡΠ° ESG-ΡΠΈΡΠΊΠΎΠ² Π² ΠΎΠ±ΡΠ΅ΠΌ Π»Π°Π½Π΄ΡΠ°ΡΡΠ΅ ΡΠΈΡΠΊΠΎΠ² ΠΈ ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΠΈ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
, ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² Π² ΠΎΡΠ΅Π½ΠΊΡ ΠΊΡΠ΅Π΄ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΈΡΠΊΠ°. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Ρ Π±Π°Π·ΠΎΠ²ΡΠ΅ ΠΏΡΠΈΠ½ΡΠΈΠΏΡ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΡΠ΅Π½ΠΎΠΌΠ΅Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΠΎΠΉ Π΄Π»Ρ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΎ ΠΊΡΠ΅Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ Π±Π°Π½ΠΊΠ°ΠΌΠΈ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΡΡ
ΠΊΠ»ΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΡΠ΅ΡΠΎΠΌ ESG-ΡΠ°ΠΊΡΠΎΡΠΎΠ², Π²Π»ΠΈΡΡΡΠΈΡ
Π½Π° ΠΈΡ
Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΡ
Parameterization of microstructures in material science and material technology
The article deals with possible applications of multi-fractal parameterization of microstructures (MFP) fields of material science and welding. MFP is successfully used for fine selection of microstructures, specification of thermal treatment modes and others. Experimental data were recorded for samples manufactured from steels of austenitic class. The research also comprises data on dependence of grain size upon multi-fractal parameters of uniformity and orderliness got by other authors. The study suggests algorithm of predicting Vickers hardness on basis of multi-fractal parameterization of metallographic specimen by means of calculating the parameters of uniformity and orderliness. Program created on basis of algorithm allows analyze microstructure to determine the grain size. Β© IDOSI Publications, 2013
Composites with improved absorption of noise and vibration
Β© 2017, Allerton Press, Inc. The development of composites based on thermosetting resins with improved vibration and noise absorption is considered. Mathematical and topological models are proposed to describe the influence of the composition on the vibration and noise absorption and elastic properties of the composites. The composition of filled epoxy, polyester, and polyurethane vibration-absorbing composites is optimized in terms of their loading, dynamic, and economic characteristics
Special aspects of preparation of microstructure images for parametrization of welding joints
The paper discusses the implementation areas of multifractal parameterization of microstructures for metallurgy and welding purposes. The objects of investigation are welds made by argon arc welding in the austenitic steel parts. Radiographic, spectral, microstructure analysis, parameterization of microstructures and Vickers hardness test were used. The design data were acquired with MFRDrom software on the basis of the analysis of microstructure images of welding joints made from austenitic steel. It was determined how the preparation defects of thin sections (grind and polish marks) and welding defects such as pores, cracks, influence the results of parameterization of welding joint microstructures. The relations between hardness and multifractal parameters of uniformity and orderliness were established. Β© IDOSI Publications, 2014
The influence of mineral fillers on mechanical properties of polyvinyl chloride composites
The paper reports the investigation results of tensile stress-strain properties of filled PVC composite during static and low cycle testing. The distinctive features of composite mechanical behavior depending on the content of dispersed mineral fillers which are basically industrial waste are established. It is revealed that small filler additives have a strong influence on the structural behavior that manifest itself as their abnormal change depending on the filler content. The experimental data obtained are explained based on the modern ideas about structural morphological model of base polymer structure. Β© IDOSI Publications, 2013
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