304 research outputs found
Invariant Manifolds in Parametric turbulent Models
The article is devoted to examining the so-called local-equilibrium approximations used while modeling turbulent flows. The dynamics of a far plane turbulent wake are investigated as an example. In this article, we analyze these approximations by using the method of differential constraints. We show that some algebraic models based on using the local-equilibrium approximation can be interpreted as equations of invariant manifolds generated by the models under consideration. Reduction of the models on the corresponding invariant manifolds made it possible to find self similar solutions and to separate explicit solutions. Moreover, some empirical constants may be calculated and their obtained values are close to the recommended quantities.El artΓculo trata de examinar el llamado equilibrio local de aproximaciones usado cuando se modelan fluidos turbulentos. La dinΓ‘mica de una estela turbulenta plana es estudiada como un ejemplo. Analizamos estas aproximaciones usando el mΓ©todo de restricciones diferenciales. Mostramos que algunos modelos algebraicos basados en el uso de la aproximaciΓ³n de equilibrio local pueden ser interpretadas como ecuaciones de variedades invariantes generadas por los modelos bajo consideraciΓ³n. La reducciΓ³n de los modelos a las variedades correspondientes hicieron posible encontrar soluciones auto-similares y separar soluciones explΓcitas. Es mΓ‘s, algunas constantes empΓricas pueden ser calculadas y los valores obtenidos son cercanos a las cantidades recomendadas
ΠΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ Π°Π»ΡΡΠ°ΡΠΈΡΠ½ΠΈΡ Π°Π»ΡΠ΄Π΅Π³ΡΠ΄ΡΠ² Ρ ΡΠΈΠ½ΡΠ΅Π·Ρ Π½ΠΎΠ²ΠΈΡ 1H-2,1-Π±Π΅Π½Π·ΠΎΡΡΠ°Π·ΠΈΠ½-4-ΠΎΠ½ 2,2-Π΄ΡΠΎΠΊΡΠΈΠ΄ΡΠ², ΠΊΠΎΠ½Π΄Π΅Π½ΡΠΎΠ²Π°Π½ΠΈΡ Π· ΠΏΡΡΠ°Π½ΠΎΠ²ΠΈΠΌ ΡΠ΄ΡΠΎΠΌ Π·Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΎΡ Π΄ΠΎΠΌΡΠ½ΠΎ-Π²Π·Π°ΡΠΌΠΎΠ΄ΡΠΉ. ΠΠ½ΡΠΈΠΌΡΠΊΡΠΎΠ±Π½Π° Π°ΠΊΡΠΈΠ²Π½ΡΡΡΡ ΡΠΈΠ½ΡΠ΅Π·ΠΎΠ²Π°Π½ΠΈΡ ΡΠΏΠΎΠ»ΡΠΊ
Domino-type Knoevenagel-Michael-hetero-Thorpe-Ziegler and Knoevenagel-hetero-Diels-Alder interactions using 1-ethyl-1H-2,1-benzothiazin-4(3H)-one 2,2-dioxide and aliphatic aldehydes as initial compounds have been studied. These reactions have led to 2-amino-3-cyano-4H-pyran and 2H-3,4-dihydropyran derivatives, respectively. It has been shown that the three-component one-pot interaction of 1-ethyl-1H-2,1-benzothiazin-4(3H)one 2,2-dioxide with saturated aliphatic aldehydes and malononitrile proceeds under rather mild conditions and results in formation of 2-amino-6-ethyl-4-alkyl-4,6-dihydropyrano[3,2-c][2,1]benzothiazin-3-carbonitrile 5,5-dioxides with moderate and high yields. At the same time, the yields of target products decrease with the increase of the length of the aliphatic aldehyde carbon chain. In this regard, the use of citronellal allowed us to obtain the product of the three-component interaction with a low yield. To date, there is no information in the literature about the possible application of aliphatic dialdehydes in such three-component interactions. It has been found that the use of glutaric aldehyde results in the synthesis of a new class of bis-derivatives of 2-amino-4H-pyran, in which two fragments are linked by the polymethylene bridge. The use of Ξ±,Ξ²-unsaturated aldehydes in the three-component interaction with 1-ethyl-1H-2,1-benzothiazin-4(3H)-one 2,2-dioxide and malononitrile was accompanied by decrease in the process efficiency compared to saturated aliphatic aldehydes. The target fused 2-amino-3-cyano-4H-pyran was obtained only when Ξ±-methylcinnamic aldehyde was used in the reaction. A two-component interaction of 1-ethyl-1H-2,1-benzothiazin-4(3H)-one 2,2-dioxide with citronellal has been also studied. It has been shown that this reaction is stereospecific. It proceeds through domino Knoevenagel-heteroDiels-Alder sequence resulting in a new heterocyclic system β 2,2a,3,4,5,6,6a,8-octahydroisochromeno[4,3-c] [2,1]benzothiazine 7,7-dioxide. The study of the antimicrobial activity of the compounds synthesized has allowed finding compounds with a moderate activity against P. aeruginosa Ρ C. albicans.ΠΠ·ΡΡΠ΅Π½Ρ Π΄ΠΎΠΌΠΈΠ½ΠΎ-Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΠ½Π΅Π²Π΅Π½Π°Π³Π΅Π»Ρ-ΠΠΈΡ
Π°ΡΠ»Ρ-Π³Π΅ΡΠ΅ΡΠΎ-Π’ΠΎΡΠΏΠ°-Π¦ΠΈΠ³Π»Π΅ΡΠ° ΠΈ ΠΠ½Π΅Π²Π΅Π½Π°Π³Π΅Π»Ρ-Π³Π΅ΡΠ΅ΡΠΎ-ΠΠΈΠ»ΡΡΠ°-ΠΠ»ΡΠ΄Π΅ΡΠ° Ρ ΡΡΠ°ΡΡΠΈΠ΅ΠΌ 1-ΡΡΠΈΠ»-2,1-Π±Π΅Π½Π·ΠΎΡΠΈΠ°Π·ΠΈΠ½-4(3Π)-ΠΎΠ½ 2,2-Π΄ΠΈΠΎΠΊΡΠΈΠ΄Π° ΠΈ Π°Π»ΠΈΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π°Π»ΡΠ΄Π΅Π³ΠΈΠ΄ΠΎΠ², ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡΠΈΡ
ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ ΠΊ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΡΡ
2-Π°ΠΌΠΈΠ½ΠΎ-3-ΡΠΈΠ°Π½ΠΎ-4Π-ΠΏΠΈΡΠ°Π½Π° ΠΈ 2Π-3,4-Π΄ΠΈΠ³ΠΈΠ΄ΡΠΎΠΏΠΈΡΠ°Π½Π°. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΡΡΠ΅Ρ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ½ΠΎΠ΅ ΠΎΠ΄Π½ΠΎΡΡΠ°Π΄ΠΈΠΉΠ½ΠΎΠ΅ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ 1-ΡΡΠΈΠ»-2,1-Π±Π΅Π½Π·ΠΎΡΠΈΠ°Π·ΠΈΠ½-4(3Π)-ΠΎΠ½ 2,2-Π΄ΠΈΠΎΠΊΡΠΈΠ΄Π° Ρ Π½Π°ΡΡΡΠ΅Π½Π½ΡΠΌΠΈ Π°Π»ΠΈΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ Π°Π»ΡΠ΄Π΅Π³ΠΈΠ΄Π°ΠΌΠΈ ΠΈ ΠΌΠ°Π»ΠΎΠ½ΠΎΠ΄ΠΈΠ½ΠΈΡΡΠΈΠ»ΠΎΠΌ ΠΏΡΠΎΡΠ΅ΠΊΠ°Π΅Ρ Π² ΠΎΡΠ΅Π½Ρ ΠΌΡΠ³ΠΊΠΈΡ
ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΈ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ 2-Π°ΠΌΠΈΠ½ΠΎ-6-ΡΡΠΈΠ»-4-Π°Π»ΠΊΠΈΠ»-4,6-Π΄ΠΈΠ³ΠΈΠ΄ΡΠΎΠΏΠΈΡΠ°Π½ΠΎ[3,2-c][2,1]Π±Π΅Π½Π·ΠΎΡΠΈΠ°Π·ΠΈΠ½-3-ΠΊΠ°ΡΠ±ΠΎΠ½ΠΈΡΡΠΈΠ» 5,5-Π΄ΠΈΠΎΠΊΡΠΈΠ΄ΠΎΠ² Ρ Π²ΡΡΠΎΠΊΠΈΠΌΠΈ ΠΈ ΡΠΌΠ΅ΡΠ΅Π½Π½ΡΠΌΠΈ Π²ΡΡ
ΠΎΠ΄Π°ΠΌΠΈ. Π ΡΠΎ ΠΆΠ΅ Π²ΡΠ΅ΠΌΡ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ Π΄Π»ΠΈΠ½Ρ ΡΠ³Π»Π΅ΡΠΎΠ΄Π½ΠΎΠΉ ΡΠ΅ΠΏΠΈ Π°Π»ΠΈΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π°Π»ΡΠ΄Π΅Π³ΠΈΠ΄ΠΎΠ² ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΡ Π²ΡΡ
ΠΎΠ΄Π° ΡΠ΅Π»Π΅Π²ΡΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ². Π’Π°ΠΊ, ΠΏΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ ΡΠΈΡΡΠΎΠ½Π΅Π»Π»Π°Π»Ρ ΠΏΡΠΎΠ΄ΡΠΊΡ ΡΡΠ΅Ρ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ½ΠΎΠ³ΠΎ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΡΠ΄Π°Π»ΠΎΡΡ ΠΏΠΎΠ»ΡΡΠΈΡΡ ΡΠΎΠ»ΡΠΊΠΎ Ρ Π½Π΅Π²ΡΡΠΎΠΊΠΈΠΌ Π²ΡΡ
ΠΎΠ΄ΠΎΠΌ. ΠΠ»ΠΈΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π΄ΠΈΠ°Π»ΡΠ΄Π΅Π³ΠΈΠ΄Ρ Π½Π΅ Π±ΡΠ»ΠΈ ΡΠ°Π½Π΅Π΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ Π² Π΄Π°Π½Π½ΡΡ
Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡΡ
; ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π³Π»ΡΡΠ°ΡΠΎΠ²ΠΎΠ³ΠΎ Π°Π»ΡΠ΄Π΅Π³ΠΈΠ΄Π° ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ Π½ΠΎΠ²ΠΎΠΌΡ ΠΊΠ»Π°ΡΡΡ Π±ΠΈΡ-ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΡΡ
2-Π°ΠΌΠΈΠ½ΠΎ-4Π-ΠΏΠΈΡΠ°Π½Π°, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΡ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½Ρ ΠΏΠΎΠ»ΠΈΠΌΠ΅ΡΠΈΠ»Π΅Π½ΠΎΠ²ΡΠΌ ΠΌΠΎΡΡΠΈΠΊΠΎΠΌ. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Ξ±,Ξ²-Π½Π΅Π½Π°ΡΡΡΠ΅Π½Π½ΡΡ
Π°Π»ΡΠ΄Π΅Π³ΠΈΠ΄ΠΎΠ² Π² ΡΡΠ΅Ρ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ½ΠΎΠΌ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠΈ Ρ 1-ΡΡΠΈΠ»-2,1-Π±Π΅Π½Π·ΠΎΡΠΈΠ°Π·ΠΈΠ½-4(3Π)-ΠΎΠ½ 2,2-Π΄ΠΈΠΎΠΊΡΠΈΠ΄ΠΎΠΌ ΠΈ ΠΌΠ°Π»ΠΎΠ½ΠΎΠ΄ΠΈΠ½ΠΈΡΡΠΈΠ»ΠΎΠΌ ΡΠΎΠΏΡΠΎΠ²ΠΎΠΆΠ΄Π°Π»ΠΎΡΡ ΡΠΌΠ΅Π½ΡΡΠ΅Π½ΠΈΠ΅ΠΌ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Π½Π°ΡΡΡΠ΅Π½Π½ΡΠΌΠΈ Π°Π»ΠΈΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ Π°Π»ΡΠ΄Π΅Π³ΠΈΠ΄Π°ΠΌΠΈ. Π¦Π΅Π»Π΅Π²ΠΎΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΊΠΎΠ½Π΄Π΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉ 2-Π°ΠΌΠΈΠ½ΠΎ-3-ΡΠΈΠ°Π½ΠΎ-4Π-ΠΏΠΈΡΠ°Π½ Π±ΡΠ» ΠΏΠΎΠ»ΡΡΠ΅Π½ ΡΠΎΠ»ΡΠΊΠΎ Π² ΡΠ»ΡΡΠ°Π΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Ξ±-ΠΌΠ΅ΡΠΈΠ»ΠΊΠΎΡΠΈΡΠ½ΠΎΠ³ΠΎ Π°Π»ΡΠ΄Π΅Π³ΠΈΠ΄Π°. ΠΠ·ΡΡΠ΅Π½ΠΎ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ ΠΌΠ΅ΠΆΠ΄Ρ 1-ΡΡΠΈΠ»-2,1-Π±Π΅Π½Π·ΠΎΡΠΈΠ°Π·ΠΈΠ½-4(3Π)-ΠΎΠ½ 2,2-Π΄ΠΈΠΎΠΊΡΠΈΠ΄ΠΎΠΌ ΠΈ ΡΠΈΡΡΠΎΠ½Π΅Π»Π»Π°Π»Π΅ΠΌ; ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ Π΄Π°Π½Π½Π°Ρ ΡΠ΅Π°ΠΊΡΠΈΡ ΠΏΡΠΎΡΠ΅ΠΊΠ°Π΅Ρ ΠΈΡΠΊΠ»ΡΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΊΠ°ΠΊ ΡΡΠ΅ΡΠ΅ΠΎ-ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΎΠ΅ Π΄ΠΎΠΌΠΈΠ½ΠΎ-Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ ΠΠ½Π΅Π²Π΅Π½Π°Π³Π΅Π»Ρ-Π³Π΅ΡΠ΅ΡΠΎ-ΠΠΈΠ»ΡΡΠ°-ΠΠ»ΡΠ΄Π΅ΡΠ° ΠΈ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π½ΠΎΠ²ΠΎΠΉ Π³Π΅ΡΠ΅ΡΠΎΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ β 2,2a,3,4,5,6,6a,8-ΠΎΠΊΡΠ°Π³ΠΈΠ΄ΡΠΎΠΈΠ·ΠΎΡ
ΡΠΎΠΌΠ΅Π½ΠΎ[4,3-c][2,1]Π±Π΅Π½Π·ΠΎΡΠΈΠ°Π·ΠΈΠ½ 7,7-Π΄ΠΈΠΎΠΊΡΠΈΠ΄Π°. ΠΠ·ΡΡΠ΅Π½ΠΈΠ΅ Π°Π½ΡΠΈΠΌΠΈΠΊΡΠΎΠ±Π½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΠΎΠ±Π½Π°ΡΡΠΆΠΈΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΡΠ΅, ΠΏΡΠΎΡΠ²Π»ΡΡΡΠΈΠ΅ ΡΠΌΠ΅ΡΠ΅Π½Π½ΡΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΎΡΠΈΠ² P. aeruginosa ΠΈ C. albicansΠΠΈΠ²ΡΠ΅Π½Ρ Π΄ΠΎΠΌΡΠ½ΠΎ-Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ ΠΠ½ΡΠΎΠ²Π΅Π½Π°Π³Π΅Π»Ρ-ΠΡΡ
Π°Π΅Π»Ρ-Π³Π΅ΡΠ΅ΡΠΎ-Π’ΠΎΡΠΏΠ°-Π¦ΡΠ³Π»Π΅ΡΠ° ΡΠ° ΠΠ½ΡΠΎΠ²Π΅Π½Π°Π³Π΅Π»Ρ-Π³Π΅ΡΠ΅ΡΠΎ-ΠΡΠ»ΡΡΠ°-ΠΠ»ΡΠ΄Π΅ΡΠ° Π·Π° ΡΡΠ°ΡΡΡ 1-Π΅ΡΠΈΠ»-1Π-2,1-Π±Π΅Π½Π·ΠΎΡΡΠ°Π·ΠΈΠ½-4(3Π)-ΠΎΠ½Ρ 2,2-Π΄ΡΠΎΠΊΡΠΈΠ΄Ρ ΡΠ° Π°Π»ΡΡΠ°ΡΠΈΡΠ½ΠΈΡ
Π°Π»ΡΠ΄Π΅Π³ΡΠ΄ΡΠ², ΡΠΎ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡΡ Π΄ΠΎ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ ΠΏΠΎΡ
ΡΠ΄Π½ΠΈΡ
2-Π°ΠΌΡΠ½ΠΎ-3-ΡΡΠ°Π½ΠΎ-4Π-ΠΏΡΡΠ°Π½Ρ ΡΠ° 2Π-3,4-Π΄ΠΈΠ³ΡΠ΄ΡΠΎΠΏΡΡΠ°Π½Ρ. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΠΎ ΡΡΠΈΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ½Π° ΠΎΠ΄Π½ΠΎΡΡΠ°Π΄ΡΠΉΠ½Π° Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ 1-Π΅ΡΠΈΠ»-1Π-2,1-Π±Π΅Π½Π·ΠΎΡΡΠ°Π·ΠΈΠ½-4(3Π)-ΠΎΠ½Ρ 2,2-Π΄ΡΠΎΠΊΡΠΈΠ΄Ρ Π· Π½Π°ΡΠΈΡΠ΅Π½ΠΈΠΌΠΈ Π°Π»ΡΡΠ°ΡΠΈΡΠ½ΠΈΠΌΠΈ Π°Π»ΡΠ΄Π΅Π³ΡΠ΄Π°ΠΌΠΈ Ρ ΠΌΠ°Π»ΠΎΠ½ΠΎΠ΄ΠΈΠ½ΡΡΡΠΈΠ»ΠΎΠΌ ΠΏΠ΅ΡΠ΅Π±ΡΠ³Π°Ρ Ρ Π΄ΡΠΆΠ΅ ΠΌβΡΠΊΠΈΡ
ΡΠΌΠΎΠ²Π°Ρ
Ρ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡΡ Π΄ΠΎ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ 2-Π°ΠΌΡΠ½ΠΎ-6-Π΅ΡΠΈΠ»-4-Π°Π»ΠΊΡΠ»-4,6-Π΄ΠΈΠ³ΡΠ΄ΡΠΎΠΏΡΡΠ°Π½ΠΎ[3,2 c][2,1]Π±Π΅Π½Π·ΠΎΡΡΠ°Π·ΠΈΠ½-3-ΠΊΠ°ΡΠ±ΠΎΠ½ΡΡΡΠΈΠ» 5,5-Π΄ΡΠΎΠΊΡΠΈΠ΄ΡΠ² Π· Π²ΠΈΡΠΎΠΊΠΈΠΌΠΈ ΡΠ° ΠΏΠΎΠΌΡΡΠ½ΠΈΠΌΠΈ Π²ΠΈΡ
ΠΎΠ΄Π°ΠΌΠΈ. Π£ ΡΠΎΠΉ ΠΆΠ΅ ΡΠ°Ρ Π·Π±ΡΠ»ΡΡΠ΅Π½Π½Ρ Π΄ΠΎΠ²ΠΆΠΈΠ½ΠΈ Π²ΡΠ³Π»Π΅ΡΠ΅Π²ΠΎΠ³ΠΎ Π»Π°Π½ΡΡΠ³Π° Π°Π»ΡΡΠ°ΡΠΈΡΠ½ΠΎΠ³ΠΎ Π°Π»ΡΠ΄Π΅Π³ΡΠ΄Ρ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡΡ Π΄ΠΎ Π·ΠΌΠ΅Π½ΡΠ΅Π½Π½Ρ Π²ΠΈΡ
ΠΎΠ΄Ρ ΡΡΠ»ΡΠΎΠ²ΠΈΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ². Π’Π°ΠΊ, ΠΏΡΠΈ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ ΡΠΈΡΡΠΎΠ½Π΅Π»Π°Π»Ρ ΠΏΡΠΎΠ΄ΡΠΊΡ ΡΡΠΈΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ½ΠΎΡ Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ Π²Π΄Π°Π»ΠΎΡΡ ΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈ ΡΡΠ»ΡΠΊΠΈ Π· Π½Π΅Π²ΠΈΡΠΎΠΊΠΈΠΌ Π²ΠΈΡ
ΠΎΠ΄ΠΎΠΌ. ΠΠ»ΡΡΠ°ΡΠΈΡΠ½Ρ Π΄ΡΠ°Π»ΡΠ΄Π΅Π³ΡΠ΄ΠΈ Π½Π΅ Π±ΡΠ»ΠΈ ΡΠ°Π½ΡΡΠ΅ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Ρ Ρ Π΄Π°Π½ΠΈΡ
Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡΡ
; ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΠΎ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ Π³Π»ΡΡΠ°ΡΠΎΠ²ΠΎΠ³ΠΎ Π°Π»ΡΠ΄Π΅Π³ΡΠ΄Ρ Π΄ΠΎΠ·Π²ΠΎΠ»ΡΡ ΠΎΡΡΠΈΠΌΠ°ΡΠΈ Π½ΠΎΠ²ΠΈΠΉ ΠΊΠ»Π°Ρ Π±ΡΡ-ΠΏΠΎΡ
ΡΠ΄Π½ΠΈΡ
2-Π°ΠΌΡΠ½ΠΎ-4Π-ΠΏΡΡΠ°Π½Ρ, Π² ΡΠΊΠΎΠΌΡ ΡΡΠ°Π³ΠΌΠ΅Π½ΡΠΈ Π·βΡΠ΄Π½Π°Π½Ρ ΠΏΠΎΠ»ΡΠΌΠ΅ΡΠΈΠ»Π΅Π½ΠΎΠ²ΠΈΠΌ ΠΌΡΡΡΠΊΠΎΠΌ. ΠΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ Ξ±,Ξ²-Π½Π΅Π½Π°ΡΠΈΡΠ΅Π½ΠΈΡ
Π°Π»ΡΠ΄Π΅Π³ΡΠ΄ΡΠ² Ρ ΡΡΠΈΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ½ΡΠΉ Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ Π· 1-Π΅ΡΠΈΠ»-1Π-2,1-Π±Π΅Π½Π·ΠΎΡΡΠ°Π·ΠΈΠ½-4(3Π)-ΠΎΠ½Ρ 2,2-Π΄ΡΠΎΠΊΡΠΈΠ΄ΠΎΠΌ Ρ ΠΌΠ°Π»ΠΎΠ½ΠΎΠ΄ΠΈΠ½ΡΡΡΠΈΠ»ΠΎΠΌ ΡΡΠΏΡΠΎΠ²ΠΎΠ΄ΠΆΡΠ²Π°Π»ΠΎΡΡ Π·ΠΌΠ΅Π½ΡΠ΅Π½Π½ΡΠΌ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΎΡΠ΅ΡΡ Π² ΠΏΠΎΡΡΠ²Π½ΡΠ½Π½Ρ Π· Π½Π°ΡΠΈΡΠ΅Π½ΠΈΠΌΠΈ Π°Π»ΡΡΠ°ΡΠΈΡΠ½ΠΈΠΌΠΈ Π°Π»ΡΠ΄Π΅Π³ΡΠ΄Π°ΠΌΠΈ. Π¦ΡΠ»ΡΠΎΠ²ΠΈΠΉ ΠΏΡΠΎΠ΄ΡΠΊΡ Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ ΠΊΠΎΠ½Π΄Π΅Π½ΡΠΎΠ²Π°Π½ΠΈΠΉ 2-Π°ΠΌΡΠ½ΠΎ-3-ΡΡΠ°Π½ΠΎ-4Π-ΠΏΡΡΠ°Π½ Π±ΡΠ² ΠΎΡΡΠΈΠΌΠ°Π½ΠΈΠΉ ΡΡΠ»ΡΠΊΠΈ Ρ Π²ΠΈΠΏΠ°Π΄ΠΊΡ Π·Π°ΡΡΠΎΡΡΠ²Π°Π½Π½Ρ Ξ±-ΠΌΠ΅ΡΠΈΠ»ΠΊΠΎΡΠΈΡΠ½ΠΎΠ³ΠΎ Π°Π»ΡΠ΄Π΅Π³ΡΠ΄Ρ. ΠΠΈΠ²ΡΠ΅Π½Π° Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ ΠΌΡΠΆ 1-Π΅ΡΠΈΠ»-1Π-2,1-Π±Π΅Π½Π·ΠΎΡΡΠ°Π·ΠΈΠ½-4(3Π)-ΠΎΠ½Ρ 2,2-Π΄ΡΠΎΠΊΡΠΈΠ΄ΠΎΠΌ Ρ ΡΠΈΡΡΠΎΠ½Π΅Π»Π°Π»Π΅ΠΌ; ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΠΎ ΡΠ°ΠΊΠ° ΡΠ΅Π°ΠΊΡΡΡ ΠΏΠ΅ΡΠ΅Π±ΡΠ³Π°Ρ Π²ΠΈΠ½ΡΡΠΊΠΎΠ²ΠΎ ΡΠΊ ΡΡΠ΅ΡΠ΅ΠΎΡΠΏΠ΅ΡΠΈΡΡΡΠ½Π° Π΄ΠΎΠΌΡΠ½ΠΎ-Π²Π·Π°ΡΠΌΠΎΠ΄ΡΡ ΠΠ½ΡΠΎΠ²Π΅Π½Π°Π³Π΅Π»Ρ-Π³Π΅ΡΠ΅ΡΠΎ-ΠΡΠ»ΡΡΠ°-ΠΠ»ΡΠ΄Π΅ΡΠ° Ρ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡΡ Π΄ΠΎ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΎΡ Π³Π΅ΡΠ΅ΡΠΎΡΠΈΠΊΠ»ΡΡΠ½ΠΎΡ ΡΠΈΡΡΠ΅ΠΌΠΈ β 2,2a,3,4,5,6,6a,8-ΠΎΠΊΡΠ°Π³ΡΠ΄ΡΠΎΡΠ·ΠΎΡ
ΡΠΎΠΌΠ΅Π½ΠΎ[4,3-c][2,1]Π±Π΅Π½Π·ΠΎΡΡΠ°Π·ΠΈΠ½ 7,7-Π΄ΡΠΎΠΊΡΠΈΠ΄Ρ. ΠΠΈΠ²ΡΠ΅Π½Π½Ρ Π°Π½ΡΠΈΠΌΡΠΊΡΠΎΠ±Π½ΠΎΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠΈΠ½ΡΠ΅Π·ΠΎΠ²Π°Π½ΠΈΡ
ΡΠΏΠΎΠ»ΡΠΊ Π΄ΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ Π²ΠΈΡΠ²ΠΈΡΠΈ ΠΏΠΎΡ
ΡΠ΄Π½Ρ, ΡΠΎ ΠΏΡΠΎΡΠ²Π»ΡΡΡΡ ΠΏΠΎΠΌΡΡΠ½Ρ Π°ΠΊΡΠΈΠ²Π½ΡΡΡΡ ΠΏΡΠΎΡΠΈ P. aeruginosa Ρ C. albicans
OMPEGAS: Optimized Relativistic Code for Multicore Architecture
The paper presents a new hydrodynamical code, OMPEGAS, for the 3D simulation of astrophysical flows on shared memory architectures. It provides a numerical method for solving the three-dimensional equations of the gravitational hydrodynamics based on Godunovβs method for solving the Riemann problem and the piecewise parabolic approximation with a local stencil. It obtains a high order of accuracy and low dissipation of the solution. The code is implemented for multicore processors with vector instructions using the OpenMP technology, Intel SDLT library, and compiler auto-vectorization tools. The model problem of simulating a star explosion was used to study the developed code. The experiments show that the presented code reproduces the behavior of the explosion correctly. Experiments for the model problem with a grid size of (Formula presented.) were performed on an 16-core Intel Core i9-12900K CPU to study the efficiency and performance of the developed code. By using the autovectorization, we achieved a 3.3-fold increase in speed in comparison with the non-vectorized program on the processor with AVX2 support. By using multithreading with OpenMP, we achieved an increase in speed of 2.6 times on a 16-core processor in comparison with the vectorized single-threaded program. The total increase in speed was up to ninefold. Β© 2022 by the authors.Russian Science Foundation,Β RSF: 18-11-00044The work of the third author (I.M.K.) and fourth author (I.G.C.) was supported by the Russian Science Foundation (project no. 18-11-00044). The first author (E.N.A.) and second author (V.E.M.) received no external funding
Comprehensive Treatment of Noninfectious Uveitis Accompanied by Macular Edema with the Use of Autologous Platelet-Rich Plasma
Background.Β A common cause of visual impairment in patients with non-infectious uveitis is macular edema, developing in 38β84 % of cases. Plasma enriched with platelets is widely used in various branches of medicine, the effectiveness of its use in the treatment of non-infectious uveitis, accompanied by macular edema, has not been sufficiently investigated.Β Aim:Β To evaluate the effectiveness of autologous platelet-rich plasma in the complex treatment of non-infectious uveitis accompanied by macular edema.Material and methods.Β The study was conducted on the basis of the academician S.N. Fyodorov Eye Microsurgery Federal State Institution in the period from 2016 to 2018, which included 123 people (176 eyes) from 18 to 50 years with non-infectious uveitis, accompanied by macular edema: 46 men, 77 women. Patients were divided into 2 groups. The main group consisted of patients receiving autologous platelet-rich plasma and anti-inflammatory treatment; the comparison group consisted of patients receiving anti-inflammatory treatment. The results of visual acuity, intraocular pressure, biomicroophthalmoscopy, optical coherence tomography of the macular zone, microperimetry, ultrasound examination on the side of the affected eye were evaluated. Statistical processing of the data was carried out in the program Statistica 10.Results.Β Maintenance of autologous platelet-rich plasma contributes to a statistically significant improvement in visual acuity on the 10thΒ day of treatment by 64.2 %, a decrease in the thickness of the retina in fovea by 36.3 % and an increase in retinal photosensitivity by 34.6 % compared to the group of patients receiving only anti-inflammatory treatment.Β Conclusions.Β The use of autologous platelet-rich plasma in the complex treatment of non-infectious uveitis allows to accelerate the natural mechanisms of tissue regeneration, contributing to the reduction of macular edema, and improve visual performance
Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex
The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which
measures the radio emission of the cosmic-ray air-showers in the frequency band
of 30-80 MHz. Tunka-Rex is co-located with TAIGA experiment in Siberia and
consists of 63 antennas, 57 of them are in a densely instrumented area of about
1 km\textsuperscript{2}. In the present work we discuss the improvements of the
signal reconstruction applied for the Tunka-Rex. At the first stage we
implemented matched filtering using averaged signals as template. The
simulation study has shown that matched filtering allows one to decrease the
threshold of signal detection and increase its purity. However, the maximum
performance of matched filtering is achievable only in case of white noise,
while in reality the noise is not fully random due to different reasons. To
recognize hidden features of the noise and treat them, we decided to use
convolutional neural network with autoencoder architecture. Taking the recorded
trace as an input, the autoencoder returns denoised trace, i.e. removes all
signal-unrelated amplitudes. We present the comparison between standard method
of signal reconstruction, matched filtering and autoencoder, and discuss the
prospects of application of neural networks for lowering the threshold of
digital antenna arrays for cosmic-ray detection.Comment: ARENA2018 proceeding
Current Status and New Challenges of The Tunka Radio Extension
The Tunka Radio Extension (Tunka-Rex) is an antenna array spread over an area
of about 1~km. The array is placed at the Tunka Advanced Instrument for
cosmic rays and Gamma Astronomy (TAIGA) and detects the radio emission of air
showers in the band of 30 to 80~MHz. During the last years it was shown that a
sparse array such as Tunka-Rex is capable of reconstructing the parameters of
the primary particle as accurate as the modern instruments. Based on these
results we continue developing our data analysis. Our next goal is the
reconstruction of cosmic-ray energy spectrum observed only by a radio
instrument. Taking a step towards it, we develop a model of aperture of our
instrument and test it against hybrid TAIGA observations and Monte-Carlo
simulations. In the present work we give an overview of the current status and
results for the last five years of operation of Tunka-Rex and discuss prospects
of the cosmic-ray energy estimation with sparse radio arrays.Comment: Proceedings of E+CRS 201
Improved measurements of the energy and shower maximum of cosmic rays with Tunka-Rex
The Tunka Radio Extension (Tunka-Rex) is an array of 63 antennas located in
the Tunka Valley, Siberia. It detects radio pulses in the 30-80 MHz band
produced during the air-shower development. As shown by Tunka-Rex, a sparse
radio array with about 200 m spacing is able to reconstruct the energy and the
depth of the shower maximum with satisfactory precision using simple methods
based on parameters of the lateral distribution of amplitudes. The LOFAR
experiment has shown that a sophisticated treatment of all individually
measured amplitudes of a dense antenna array can make the precision comparable
with the resolution of existing optical techniques. We develop these ideas
further and present a method based on the treatment of time series of measured
signals, i.e. each antenna station provides several points (trace) instead of a
single one (amplitude or power). We use the measured shower axis and energy as
input for CoREAS simulations: for each measured event we simulate a set of
air-showers with proton, helium, nitrogen and iron as primary particle (each
primary is simulated about ten times to cover fluctuations in the shower
maximum due to the first interaction). Simulated radio pulses are processed
with the Tunka-Rex detector response and convoluted with the measured signals.
A likelihood fit determines how well the simulated event fits to the measured
one. The positions of the shower maxima are defined from the distribution of
chi-square values of these fits. When using this improved method instead of the
standard one, firstly, the shower maximum of more events can be reconstructed,
secondly, the resolution is increased. The performance of the method is
demonstrated on the data acquired by the Tunka-Rex detector in 2012-2014.Comment: Proceedings of the 35th ICRC 2017, Busan, Kore
Consistent alpha-cluster description of the 12C (0^+_2) resonance
The near-threshold 12C (0^+_2) resonance provides unique possibility for fast
helium burning in stars, as predicted by Hoyle to explain the observed
abundance of elements in the Universe. Properties of this resonance are
calculated within the framework of the alpha-cluster model whose two-body and
three-body effective potentials are tuned to describe the alpha - alpha
scattering data, the energies of the 0^+_1 and 0^+_2 states, and the
0^+_1-state root-mean-square radius. The extremely small width of the 0^+_2
state, the 0_2^+ to 0_1^+ monopole transition matrix element, and transition
radius are found in remarkable agreement with the experimental data. The
0^+_2-state structure is described as a system of three alpha-particles
oscillating between the ground-state-like configuration and the elongated chain
configuration whose probability exceeds 0.9
- β¦