145 research outputs found
Tunguska explosion and the Earth's magnetic field
For at least a century, the geophysicists have been wondering about the future of the Earth's magnetic field and whether it is going to flip, while the astrophysicists have been wondering what kind of celestial body exploded on June 30, 1908 by the river of Podkamennaya Tunguska. Using the data recently made available by NOAA, we demonstrate an intimate relationship between the two, previously thought completely unrelated, phenomena
ΠΡΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΎ Π°ΠΊΡΠΈΠ²Π½Ρ ΡΠΏΠΎΠ»ΡΠΊΠΈ ΠΊΠΎΡΠ΅Π½Π΅Π²ΠΈΡ Iris hungarica
Species of Iris genus (Iridaceae) have a long history of traditional medicinal use in different countries as alternative aperient, tonic, cathartic, diuretic, gall bladder diseases, liver complaints, dropsy, purification of blood, venereal infections, fever, bilious infections and for a variety of heart diseases. The rhizomes of Iris are the rich source of the secondary metabolites, in which flavonoids predominate. The clinical studies of substances from irises gave positive results in the treatment of cancer, bacterial and viral infections. Continuing the search of new biologically active compounds from the plants of Iridaceae family for the first time three isoflavones that are new for this species β irigenin, iristectorigenin B and its glucoside iristectorin B have been isolated from the ethanolic extract of the rhizomes of Iris hungarica Waldst. et Kit., which is widespread in Ukraine. The structure of the compounds is described as 5,7,3β-trihydroxy-6,4β,5β-trimethoxyisoflavone, 5,7,4β-trihydroxy-6,3β-dimethoxyisoflavone and iristectorigenin B-7-O-Ξ²-D-glucoside, respectively. The compounds were obtained from the ethyl acetate fraction of the iris rhizomes by column chromatography on silica gel with sequential elution of the chloroform β ethanol solvent with different concentrations. The structure of the compounds has been determined by chemical and spectral methods and in comparison with the literature data.Π Π°ΡΡΠ΅Π½ΠΈΡ ΡΠΎΠ΄Π° Iris (Iridaceae) ΠΈΠΌΠ΅ΡΡ Π΄Π°Π²Π½ΡΡ ΠΈΡΡΠΎΡΠΈΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π² ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Π΅ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΡΡΠ°Π½ ΠΊΠ°ΠΊ Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Π½ΠΎΠ΅ ΡΠ»Π°Π±ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅, ΡΠΎΠ½ΠΈΠ·ΠΈΡΡΡΡΠ΅Π΅, ΠΎΡΡ
Π°ΡΠΊΠΈΠ²Π°ΡΡΠ΅Π΅, ΠΌΠΎΡΠ΅Π³ΠΎΠ½Π½ΠΎΠ΅ ΡΡΠ΅Π΄ΡΡΠ²ΠΎ, Π΄Π»Ρ Π»Π΅ΡΠ΅Π½ΠΈΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΆΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ·ΡΡΡ, ΠΏΠ΅ΡΠ΅Π½ΠΈ, Π²ΠΎΠ΄ΡΠ½ΠΊΠΈ, Π΄Π»Ρ ΠΎΡΠΈΡΠ΅Π½ΠΈΡ ΠΊΡΠΎΠ²ΠΈ, Π»Π΅ΡΠ΅Π½ΠΈΡ Π²Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΉ, Π»ΠΈΡ
ΠΎΡΠ°Π΄ΠΊΠΈ, ΠΆΠ΅Π»ΡΠ½ΡΡ
ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΉ ΠΈ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΡΠ΅ΡΠ΄ΡΠ°. ΠΠΎΡΠ½Π΅Π²ΠΈΡΠ° ΠΈΡΠΈΡΠΎΠ² ΡΠ²Π»ΡΡΡΡΡ Π±ΠΎΠ³Π°ΡΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ Π²ΡΠΎΡΠΈΡΠ½ΡΡ
ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠΎΠ², ΡΡΠ΅Π΄ΠΈ ΠΊΠΎΡΠΎΡΡΡ
ΠΏΡΠ΅ΠΎΠ±Π»Π°Π΄Π°ΡΡ ΡΠ»Π°Π²ΠΎΠ½ΠΎΠΈΠ΄Ρ. ΠΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π²Π΅ΡΠ΅ΡΡΠ² ΠΈΠ· ΠΈΡΠΈΡΠΎΠ² Π΄Π°Π»ΠΈ ΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΡΠΈ Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΡΠ°ΠΊΠ°, Π±Π°ΠΊΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΈ Π²ΠΈΡΡΡΠ½ΡΡ
ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΉ. ΠΡΠΎΠ΄ΠΎΠ»ΠΆΠ°Ρ ΠΏΠΎΠΈΡΠΊ Π½ΠΎΠ²ΡΡ
Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈ Π°ΠΊΡΠΈΠ²Π½ΡΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ ΠΈΠ· ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΡΠ΅ΠΌΠ΅ΠΉΡΡΠ²Π° ΠΈΡΠΈΡΠΎΠ²ΡΠ΅ β Iridaceae ΠΈΠ· ΡΡΠ°Π½ΠΎΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΠΊΡΡΡΠ°ΠΊΡΠ° ΠΊΠΎΡΠ½Π΅Π²ΠΈΡ ΠΈΡΠΈΡΠ° Π²Π΅Π½Π³Π΅ΡΡΠΊΠΎΠ³ΠΎ β Iris hungarica Waldst. Et Kit., ΠΊΠΎΡΠΎΡΡΠΉ ΡΠΈΡΠΎΠΊΠΎ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ Π½Π° ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ Π£ΠΊΡΠ°ΠΈΠ½Ρ, Π²ΠΏΠ΅ΡΠ²ΡΠ΅ Π²ΡΠ΄Π΅Π»Π΅Π½Ρ ΡΡΠΈ Π½ΠΎΠ²ΡΡ
Π΄Π»Ρ Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π²ΠΈΠ΄Π° ΠΈΠ·ΠΎΡΠ»Π°Π²ΠΎΠ½ΠΎΠΈΠ΄Π°: ΠΈΡΠΈΠ³Π΅Π½ΠΈΠ½, ΠΈΡΠΈΡΡΠ΅ΠΊΡΠΎΡΠΈΠ³Π΅Π½ΠΈΠ½ Π ΠΈ Π΅Π³ΠΎ Π³Π»ΡΠΊΠΎΠ·ΠΈΠ΄ ΠΈΡΠΈΡΡΠ΅ΠΊΡΠΎΡΠΈΠ½ Π. Π‘ΡΡΡΠΊΡΡΡΠ° Π²Π΅ΡΠ΅ΡΡΠ² ΠΎΡ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π° ΠΊΠ°ΠΊ 5,7,3β-ΡΡΠΈΠ³ΠΈΠ΄ΡΠΎΠΊΡΠΈ-6,4β,5β-ΡΡΠΈΠΌΠ΅ΡΠΎΠΊΡΠΈΠΈΠ·ΠΎΡΠ»Π°Π²ΠΎΠ½, 5,7,4β-ΡΡΠΈΠ³ΠΈΠ΄ΡΠΎΠΊΡΠΈ- 6,3β-Π΄ΠΈΠΌΠ΅ΡΠΎΠΊΡΠΈΠΈΠ·ΠΎΡΠ»Π°Π²ΠΎΠ½ ΠΈ ΠΈΡΠΈΡΡΠ΅ΠΊΡΠΎΡΠΈΠ³Π΅Π½ΠΈΠ½ Π-7-O-Ξ²-D-Π³Π»ΡΠΊΠΎΠΏΠΈΡΠ°Π½ΠΎΠ·ΠΈΠ΄, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ. ΠΠ΅ΡΠ΅ΡΡΠ²Π° Π±ΡΠ»ΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΊΠΎΠ»ΠΎΠ½ΠΎΡΠ½ΠΎΠΉ Ρ
ΡΠΎΠΌΠ°ΡΠΎΠ³ΡΠ°ΡΠΈΠΈ Π½Π° ΡΠΈΠ»ΠΈΠΊΠ°Π³Π΅Π»Π΅ ΠΈΠ· ΡΡΠΈΠ»Π°ΡΠ΅ΡΠ°ΡΠ½ΠΎΠΉ ΡΡΠ°ΠΊΡΠΈΠΈ ΠΊΠΎΡΠ½Π΅Π²ΠΈΡ ΠΈΡΠΈΡΠ° ΠΏΡΠΈ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΠΌ ΡΠ»ΡΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΡΠ°ΡΡΠ²ΠΎΡΠΈΡΠ΅Π»Π΅ΠΌ Ρ
Π»ΠΎΡΠΎΡΠΎΡΠΌ β ΡΡΠ°Π½ΠΎΠ» ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠΉ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ. Π‘ΡΡΡΠΊΡΡΡΠ° Π²Π΅ΡΠ΅ΡΡΠ² ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Π° Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΈ ΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΠΈ Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ½ΡΠΌΠΈ Π΄Π°Π½Π½ΡΠΌΠΈ.Π ΠΎΡΠ»ΠΈΠ½ΠΈ ΡΠΎΠ΄Ρ Iris (Iridaceae) ΠΌΠ°ΡΡΡ Π΄Π°Π²Π½Ρ ΡΡΡΠΎΡΡΡ Π·Π°ΡΡΠΎΡΡΠ²Π°Π½Π½Ρ Ρ ΡΡΠ°Π΄ΠΈΡΡΠΉΠ½ΡΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ ΡΡΠ·Π½ΠΈΡ
ΠΊΡΠ°ΡΠ½ ΡΠΊ Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Π½ΠΈΠΉ ΠΏΡΠΎΠ½ΠΎΡΠ½ΠΈΠΉ, ΡΠΎΠ½ΡΠ·ΡΡΡΠΈΠΉ, Π²ΡΠ΄Ρ
Π°ΡΠΊΡΠ²Π°Π»ΡΠ½ΠΈΠΉ, ΡΠ΅ΡΠΎΠ³ΡΠ½Π½ΠΈΠΉ Π·Π°ΡΡΠ±, Π΄Π»Ρ Π»ΡΠΊΡΠ²Π°Π½Π½Ρ Π·Π°Ρ
Π²ΠΎΡΡΠ²Π°Π½Ρ ΠΆΠΎΠ²ΡΠ½ΠΎΠ³ΠΎ ΠΌΡΡ
ΡΡΠ°, ΠΏΠ΅ΡΡΠ½ΠΊΠΈ, Π²ΠΎΠ΄ΡΠ½ΠΊΠΈ, Π΄Π»Ρ ΠΎΡΠΈΡΠ΅Π½Π½Ρ ΠΊΡΠΎΠ²Ρ, Π²Π΅Π½Π΅ΡΠΈΡΠ½ΠΈΡ
ΡΠ½ΡΠ΅ΠΊΡΡΠΉ, Π»ΠΈΡ
ΠΎΠΌΠ°Π½ΠΊΠΈ, ΠΆΠΎΠ²ΡΠ½ΠΈΡ
ΡΠ½ΡΠ΅ΠΊΡΡΠΉ Ρ Π΄Π»Ρ Π»ΡΠΊΡΠ²Π°Π½Π½Ρ Π·Π°Ρ
Π²ΠΎΡΡΠ²Π°Π½Ρ ΡΠ΅ΡΡΡ. ΠΠΎΡΠ΅Π½Π΅Π²ΠΈΡΠ° ΡΡΠΈΡΡΠ² Ρ Π±Π°Π³Π°ΡΠΈΠΌ Π΄ΠΆΠ΅ΡΠ΅Π»ΠΎΠΌ Π²ΡΠΎΡΠΈΠ½Π½ΠΈΡ
ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΡΡΡΠ², ΡΠ΅ΡΠ΅Π΄ ΡΠΊΠΈΡ
ΠΏΠ΅ΡΠ΅Π²Π°ΠΆΠ°ΡΡΡ ΡΠ»Π°Π²ΠΎΠ½ΠΎΡΠ΄ΠΈ. ΠΠ»ΡΠ½ΡΡΠ½Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΡΠ΅ΡΠΎΠ²ΠΈΠ½ ΡΠ· ΡΡΠΈΡΡΠ² Π΄Π°Π»ΠΈ ΠΏΠΎΠ·ΠΈΡΠΈΠ²Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ ΠΏΡΠΈ Π»ΡΠΊΡΠ²Π°Π½Π½Ρ ΡΠ°ΠΊΡ, Π±Π°ΠΊΡΠ΅ΡΡΠ°Π»ΡΠ½ΠΈΡ
Ρ Π²ΡΡΡΡΠ½ΠΈΡ
ΡΠ½ΡΠ΅ΠΊΡΡΠΉ. ΠΡΠΎΠ΄ΠΎΠ²ΠΆΡΡΡΠΈ ΠΏΠΎΡΡΠΊ Π½ΠΎΠ²ΠΈΡ
Π±ΡΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΎ Π°ΠΊΡΠΈΠ²Π½ΠΈΡ
ΡΠΏΠΎΠ»ΡΠΊ Π· ΡΠΎΡΠ»ΠΈΠ½ ΡΠΎΠ΄ΠΈΠ½ΠΈ ΡΡΠΈΡΠΎΠ²Ρ β Iridaceae Π· Π΅ΡΠ°Π½ΠΎΠ»ΡΠ½ΠΎΠ³ΠΎ Π΅ΠΊΡΡΡΠ°ΠΊΡΡ ΠΊΠΎΡΠ΅Π½Π΅Π²ΠΈΡ ΡΡΠΈΡΡ ΡΠ³ΠΎΡΡΡΠΊΠΎΠ³ΠΎ β Iris hungarica Waldst. et Kit., ΠΏΠΎΡΠΈΡΠ΅Π½ΠΎΠ³ΠΎ Π½Π° ΡΠ΅ΡΠΈΡΠΎΡΡΡ Π£ΠΊΡΠ°ΡΠ½ΠΈ, Π²ΠΏΠ΅ΡΡΠ΅ Π²ΠΈΠ΄ΡΠ»Π΅Π½ΠΎ ΡΡΠΈ Π½ΠΎΠ²Ρ Π΄Π»Ρ Π΄Π°Π½ΠΎΠ³ΠΎ Π²ΠΈΠ΄Ρ ΡΠ·ΠΎΡΠ»Π°Π²ΠΎΠ½ΠΎΡΠ΄ΠΈ: ΡΡΠΈΠ³Π΅Π½ΡΠ½, ΡΡΠΈΡΡΠ΅ΠΊΡΠΎΡΠΈΠ³Π΅Π½ΡΠ½ Π Ρ ΠΉΠΎΠ³ΠΎ Π³Π»ΡΠΊΠΎΠ·ΠΈΠ΄ ΡΡΠΈΡΡΠ΅ΠΊΡΠΎΡΠΈΠ½ Π. Π‘ΡΡΡΠΊΡΡΡΠ° ΡΠ΅ΡΠΎΠ²ΠΈΠ½ ΠΎΡ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π° ΡΠΊ 5,7,3β-ΡΡΠΈΠ³ΡΠ΄ΡΠΎΠΊΡΠΈ-6,4β,5β-ΡΡΠΈΠΌΠ΅ΡΠΎΠΊΡΡΡΠ·ΠΎΡΠ»Π°Π²ΠΎΠ½, 5,7,4β-ΡΡΠΈΠ³ΡΠ΄ΡΠΎΠΊΡΠΈ-6,3β- Π΄ΠΈΠΌΠ΅ΡΠΎΠΊΡΡΡΠ·ΠΎΡΠ»Π°Π²ΠΎΠ½ ΡΠ° ΡΡΠΈΡΡΠ΅ΠΊΡΠΎΡΠΈΠ³Π΅Π½ΡΠ½ Π-7-O-Ξ²-D-Π³Π»ΡΠΊΠΎΠΏΡΡΠ°Π½ΠΎΠ·ΠΈΠ΄, Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ. Π Π΅ΡΠΎΠ²ΠΈΠ½ΠΈ Π±ΡΠ»ΠΈ ΠΎΡΡΠΈΠΌΠ°Π½Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΊΠΎΠ»ΠΎΠ½ΠΊΠΎΠ²ΠΎΡ Ρ
ΡΠΎΠΌΠ°ΡΠΎΠ³ΡΠ°ΡΡΡ Π½Π° ΡΠΈΠ»ΡΠΊΠ°Π³Π΅Π»Ρ Π· Π΅ΡΠΈΠ»Π°ΡΠ΅ΡΠ°ΡΠ½ΠΎΡ ΡΡΠ°ΠΊΡΡΡ ΠΊΠΎΡΠ΅Π½Π΅Π²ΠΈΡ ΡΡΠΈΡΡ ΠΏΡΠΈ ΠΏΠΎΡΠ»ΡΠ΄ΠΎΠ²Π½ΠΎΠΌΡ Π΅Π»ΡΡΠ²Π°Π½Π½Ρ ΡΠΎΠ·ΡΠΈΠ½Π½ΠΈΠΊΠΎΠΌ Ρ
Π»ΠΎΡΠΎΡΠΎΡΠΌ β Π΅ΡΠ°Π½ΠΎΠ» ΡΡΠ·Π½ΠΎΡ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΡΡ. Π‘ΡΡΡΠΊΡΡΡΠ° ΡΠ΅ΡΠΎΠ²ΠΈΠ½ Π²ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Π° Ρ
ΡΠΌΡΡΠ½ΠΈΠΌΠΈ Ρ ΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΠΈΠΌΠΈ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΡΠ° Ρ ΠΏΠΎΡΡΠ²Π½ΡΠ½Π½Ρ Π· Π»ΡΡΠ΅ΡΠ°ΡΡΡΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ
Dispersion and collapse of wave maps
We study numerically the Cauchy problem for equivariant wave maps from 3+1
Minkowski spacetime into the 3-sphere. On the basis of numerical evidence
combined with stability analysis of self-similar solutions we formulate two
conjectures. The first conjecture states that singularities which are produced
in the evolution of sufficiently large initial data are approached in a
universal manner given by the profile of a stable self-similar solution. The
second conjecture states that the codimension-one stable manifold of a
self-similar solution with exactly one instability determines the threshold of
singularity formation for a large class of initial data. Our results can be
considered as a toy-model for some aspects of the critical behavior in
formation of black holes.Comment: 14 pages, Latex, 9 eps figures included, typos correcte
Global existence for coupled systems of nonlinear wave and Klein-Gordon equations in three space dimensions
We consider the Cauchy problem for coupled systems of wave and Klein-Gordon
equations with quadratic nonlinearity in three space dimensions. We show global
existence of small amplitude solutions under certain condition including the
null condition on self-interactions between wave equations. Our condition is
much weaker than the strong null condition introduced by Georgiev for this kind
of coupled system. Consequently our result is applicable to certain physical
systems, such as the Dirac-Klein-Gordon equations, the Dirac-Proca equations,
and the Klein-Gordon-Zakharov equations.Comment: 31 pages. The final versio
The comparison of solar-powered hydrogen closed-cycle system capacities for selected locations
The exhaustion of fossil fuels causes decarbonized industries to be powered by renewable energy sources and, owing to their intermittent nature, it is important to devise an efficient energy storage method. To make them more sustainable, a storage system is required. Modern electricity storage systems are based on different types of chemical batteries, electromechanical devices, and hydrogen power plants. However, the parameters of power plant components vary from one geographical location to another. The idea of the present research is to compare the composition of a solar-powered hydrogen processing closed-cycle power plant among the selected geographical locations (Russia, India, and Australia), assuming the same power consumption conditions, but different insolation conditions, and thus the hydrogen equipment capacity accordingly. The number of solar modules in an array is different, thus the required hydrogen tank capacity is also different. The comparison of equipment requires building an uninterrupted power supply for the selected geographical locations, which shows that the capacity of the equipment components would be significantly different. These numbers may serve as the base for further economic calculations of energy cost
ΠΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ Π²Π°Π³ΠΎΠ½ΠΎΠ² Π² ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΠΎΠΌ ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΠΎΠΌ ΠΏΠ°ΡΠΊΠ΅
The railway marshalling station occupies a central place in the technological chain of freight transportation processes, since the speed of processing trains at marshalling yards determines the volume and cost of transportation. Therefore, development of automation and computerization of sorting processes results in growing efficiency of freight transportation in general. The objective of the study is to formalize the problem of carsβ monitoring within the railway marshalling yard and to develop a method for solving it with the use of algorithms of recognizing and positioning of dynamic objects through the intelligent data analysis of streaming video. The article presents a new approach to solution of the problem of monitoring moving units in the hump (sorting) yard of marshalling stations. The article suggests core criteria for identifying speed and positioning of the railway wagons when they are running after been separated at the hump. The article specifies that monitoring of moving units at hump yard is less automated in comparison with the monitoring at the hump itself, and that confirms the relevance of the research. To get the problem of the automation monitoring of moving units in the hump yard solved, the authors have suggested an algorithm that is based on the image data intelligent analysis, that is on computer vision, and have described the model of its implementation at a station. The methods used are based on the theory of computer vision and are aimed at recognizing key dynamic objects in streaming video and at their subsequent positioning. The study has resulted in substantiation of acceptability of the use of computer vision in the process of separation and formation of trains. It is planned to proceed with further improvement of the presented approach to develop a software product allowing to objectify information about hump yard in order to increase the efficiency of targeted braking at the hump.ΠΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½Π°Ρ ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½Π°Ρ ΡΡΠ°Π½ΡΠΈΡ Π·Π°Π½ΠΈΠΌΠ°Π΅Ρ ΡΠ΅Π½ΡΡΠ°Π»ΡΠ½ΠΎΠ΅ ΠΌΠ΅ΡΡΠΎ Π² ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅ΠΏΠΎΡΠΊΠ΅ Π³ΡΡΠ·ΠΎΠ²ΡΡ
ΠΏΠ΅ΡΠ΅Π²ΠΎΠ·ΠΎΡΠ½ΡΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ², ΠΏΠΎΡΠΊΠΎΠ»ΡΠΊΡ ΡΠΊΠΎΡΠΎΡΡΡ ΠΏΠ΅ΡΠ΅ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΡΡ
ΡΠΎΡΡΠ°Π²ΠΎΠ² Π½Π° Π½Π΅ΠΉ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅Ρ ΠΎΠ±ΡΡΠΌ ΠΈ ΡΡΠΎΠΈΠΌΠΎΡΡΡ ΠΏΠ΅ΡΠ΅Π²ΠΎΠ·ΠΎΠΊ. ΠΠΎΡΡΠΎΠΌΡ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ ΡΡΠ΅Π΄ΡΡΠ² Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΡΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² Π²Π΅Π΄ΡΡ ΠΊ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π³ΡΡΠ·ΠΎΠ²ΡΡ
ΠΏΠ΅ΡΠ΅Π²ΠΎΠ·ΠΎΠΊ Π² ΡΠ΅Π»ΠΎΠΌ. Π¦Π΅Π»ΡΡ ΡΠ°Π±ΠΎΡΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΡ Π·Π°Π΄Π°ΡΠΈ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ Π²Π°Π³ΠΎΠ½ΠΎΠ² Π² ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΠΎΠΌ ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΠΎΠΌ ΠΏΠ°ΡΠΊΠ΅ ΠΈΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΌΠ΅ΡΠΎΠ΄Π° Π΅Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΈ ΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΏΡΡΡΠΌ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
ΠΏΠΎΡΠΎΠΊΠΎΠ²ΠΎΠ³ΠΎ Π²ΠΈΠ΄Π΅ΠΎ. Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ Π½ΠΎΠ²ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°ΡΠΈ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½ΡΡ
Π΅Π΄ΠΈΠ½ΠΈΡ Π² ΠΏΠΎΠ΄Π³ΠΎΡΠΎΡΠ½ΠΎΠΌ (ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΠΎΠΌ) ΠΏΠ°ΡΠΊΠ΅ ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΡΡ
ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΡΡ
ΡΡΠ°Π½ΡΠΈΠΉ. ΠΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΠΊΡΠΈΡΠ΅ΡΠΈΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠΊΠΎΡΠΎΡΡΠΈ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΈ ΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π³ΡΡΠΏΠΏ Π²Π°Π³ΠΎΠ½ΠΎΠ² ΠΏΡΠΈ ΠΈΡ
Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΈ ΠΏΠΎΡΠ»Π΅ ΡΠ°ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π° ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΠΎΠΉ Π³ΠΎΡΠΊΠ΅. ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΎ, ΡΡΠΎ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½ΡΡ
Π΅Π΄ΠΈΠ½ΠΈΡ Π² ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΠΎΠΌ ΠΏΠ°ΡΠΊΠ΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΌΠ΅Π½Π΅Π΅ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠΌ ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΠΊΠΎΠ½ΡΡΠΎΠ»Π΅ΠΌ Π½Π° ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΠΎΠΉ Π³ΠΎΡΠΊΠ΅. ΠΠ»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎΡΡΠ°Π²Π»Π΅Π½Π½ΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½ΡΡ
Π΅Π΄ΠΈΠ½ΠΈΡ Π² ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΠΎΠΌ ΠΏΠ°ΡΠΊΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π½Π° Π±Π°Π·Π΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π²ΠΈΠ΄Π΅ΠΎΠ΄Π°Π½Π½ΡΡ
β ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·ΡΠ΅Π½ΠΈΡ β ΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° Π΅Π³ΠΎ ΠΌΠΎΠ΄Π΅Π»Ρ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π½Π° ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠΌ ΠΎΠ±ΡΠ΅ΠΊΡΠ΅. ΠΠ΅ΡΠΎΠ΄Ρ ΡΠ°Π±ΠΎΡΡ ΠΎΡΠ½ΠΎΠ²Π°Π½Ρ Π½Π° ΡΠ΅ΠΎΡΠΈΠΈ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·ΡΠ΅Π½ΠΈΡ ΠΈ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Ρ Π½Π° ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ ΠΊΠ»ΡΡΠ΅Π²ΡΡ
Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² Π½Π° ΠΏΠΎΡΠΎΠΊΠΎΠ²ΠΎΠΌ Π²ΠΈΠ΄Π΅ΠΎ Ρ ΠΈΡ
ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠΈΠΌ ΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠΌ ΠΏΡΠΎΠ²Π΅Π΄ΡΠ½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠ΅ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·ΡΠ΅Π½ΠΈΡ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΡΠ°ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ-ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΡΡ
ΡΠΎΡΡΠ°Π²ΠΎΠ². Π Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅ΠΌ ΠΏΠ»Π°Π½ΠΈΡΡΠ΅ΡΡΡ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΡ
ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΎΠΊ Π΄Π»Ρ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠΈ Π³ΠΎΡΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ΄ΡΠΊΡΠ°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π³ΠΎ ΠΎΠ±ΡΠ΅ΠΊΡΠΈΠ²ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΎ ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΠΎΠΌ ΠΏΠ°ΡΠΊΠ΅ Π΄Π»Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΏΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΎΡΠΌΠΎΠΆΠ΅Π½ΠΈΡ Π½Π° ΡΠΎΡΡΠΈΡΠΎΠ²ΠΎΡΠ½ΠΎΠΉ Π³ΠΎΡΠΊΠ΅
Batch Scheduling of Deteriorating Products
In this paper we consider the problem of scheduling N jobs on a single machine, where the jobs are processed in batches and the processing time of each job is a simple linear increasing function depending on jobβs waiting time, which is the time between the start of the processing of the batch to which the job belongs and the start of the processing of the job. Each batch starts from the setup time S. Jobs which are assigned to the batch are being prepared for the processing during time S0 < S. After this preparation they are ready to be processed one by one. The non-negative number bi is associated with job i. The processing time of the i-th job is equal to bi(si β (sib + S0)), whereΒ sib andΒ si are the starting time of the b-th batch to which the i-th job belongs and the starting time of this job, respectively. The objective is to minimize the completion time of the last job. We show that the problem is NP-hard. After that we present an O(N) time algorithm solving the problem optimally for the caseΒ bi = b. We further present an O(N2) time approximation algorithm with a performance guarantee 2
Dynamic pricing with demand disaggregation for hotel revenue management
In this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments
with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic
Spinors, Inflation, and Non-Singular Cyclic Cosmologies
We consider toy cosmological models in which a classical, homogeneous, spinor
field provides a dominant or sub-dominant contribution to the energy-momentum
tensor of a flat Friedmann-Robertson-Walker universe. We find that, if such a
field were to exist, appropriate choices of the spinor self-interaction would
generate a rich variety of behaviors, quite different from their widely studied
scalar field counterparts. We first discuss solutions that incorporate a stage
of cosmic inflation and estimate the primordial spectrum of density
perturbations seeded during such a stage. Inflation driven by a spinor field
turns out to be unappealing as it leads to a blue spectrum of perturbations and
requires considerable fine-tuning of parameters. We next find that, for simple,
quartic spinor self-interactions, non-singular cyclic cosmologies exist with
reasonable parameter choices. These solutions might eventually be incorporated
into a successful past- and future-eternal cosmological model free of
singularities. In an Appendix, we discuss the classical treatment of spinors
and argue that certain quantum systems might be approximated in terms of such
fields.Comment: 12 two-column pages, 3 figures; uses RevTeX
Experimentation with a dynamic pricing approach for hotel industry
A dynamic pricing approach for hotel revenue management is suggested. It aims at increasing revenue over the baseline
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