145 research outputs found

    Tunguska explosion and the Earth's magnetic field

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    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

    Π‘Ρ–ΠΎΠ»ΠΎΠ³Ρ–Ρ‡Π½ΠΎ Π°ΠΊΡ‚ΠΈΠ²Π½Ρ– сполуки ΠΊΠΎΡ€Π΅Π½Π΅Π²ΠΈΡ‰ Iris hungarica

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    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 – 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 5,7,3’-trihydroxy-6,4’,5’-trimethoxyisoflavone, 5,7,4’-trihydroxy-6,3’-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 – 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.РастСния Ρ€ΠΎΠ΄Π° Iris (Iridaceae) ΠΈΠΌΠ΅ΡŽΡ‚ давнюю ΠΈΡΡ‚ΠΎΡ€ΠΈΡŽ примСнСния Π² Ρ‚Ρ€Π°Π΄ΠΈΡ†ΠΈΠΎΠ½Π½ΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡ†ΠΈΠ½Π΅ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… стран ΠΊΠ°ΠΊ Π°Π»ΡŒΡ‚Π΅Ρ€Π½Π°Ρ‚ΠΈΠ²Π½ΠΎΠ΅ ΡΠ»Π°Π±ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠ΅, Ρ‚ΠΎΠ½ΠΈΠ·ΠΈΡ€ΡƒΡŽΡ‰Π΅Π΅, ΠΎΡ‚Ρ…Π°Ρ€ΠΊΠΈΠ²Π°ΡŽΡ‰Π΅Π΅, ΠΌΠΎΡ‡Π΅Π³ΠΎΠ½Π½ΠΎΠ΅ срСдство, для лСчСния Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΆΠ΅Π»Ρ‡Π½ΠΎΠ³ΠΎ пузыря, ΠΏΠ΅Ρ‡Π΅Π½ΠΈ, водянки, для очищСния ΠΊΡ€ΠΎΠ²ΠΈ, лСчСния вСнСричСских ΠΈΠ½Ρ„Π΅ΠΊΡ†ΠΈΠΉ, Π»ΠΈΡ…ΠΎΡ€Π°Π΄ΠΊΠΈ, ΠΆΠ΅Π»Ρ‡Π½Ρ‹Ρ… ΠΈΠ½Ρ„Π΅ΠΊΡ†ΠΈΠΉ ΠΈ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ сСрдца. ΠšΠΎΡ€Π½Π΅Π²ΠΈΡ‰Π° ирисов ΡΠ²Π»ΡΡŽΡ‚ΡΡ Π±ΠΎΠ³Π°Ρ‚Ρ‹ΠΌ источником Π²Ρ‚ΠΎΡ€ΠΈΡ‡Π½Ρ‹Ρ… ΠΌΠ΅Ρ‚Π°Π±ΠΎΠ»ΠΈΡ‚ΠΎΠ², срСди ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Ρ… ΠΏΡ€Π΅ΠΎΠ±Π»Π°Π΄Π°ΡŽΡ‚ Ρ„Π»Π°Π²ΠΎΠ½ΠΎΠΈΠ΄Ρ‹. ΠšΠ»ΠΈΠ½ΠΈΡ‡Π΅ΡΠΊΠΈΠ΅ исслСдования вСщСств ΠΈΠ· ирисов Π΄Π°Π»ΠΈ ΠΏΠΎΠ»ΠΎΠΆΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Π΅ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΏΡ€ΠΈ Π»Π΅Ρ‡Π΅Π½ΠΈΠΈ Ρ€Π°ΠΊΠ°, Π±Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΈ вирусных ΠΈΠ½Ρ„Π΅ΠΊΡ†ΠΈΠΉ. ΠŸΡ€ΠΎΠ΄ΠΎΠ»ΠΆΠ°Ρ поиск Π½ΠΎΠ²Ρ‹Ρ… биологичСски Π°ΠΊΡ‚ΠΈΠ²Π½Ρ‹Ρ… соСдинСний ΠΈΠ· растСний сСмСйства ирисовыС – Iridaceae ΠΈΠ· ΡΡ‚Π°Π½ΠΎΠ»ΡŒΠ½ΠΎΠ³ΠΎ экстракта ΠΊΠΎΡ€Π½Π΅Π²ΠΈΡ‰ ириса вСнгСрского – Iris hungarica Waldst. Et Kit., ΠΊΠΎΡ‚ΠΎΡ€Ρ‹ΠΉ ΡˆΠΈΡ€ΠΎΠΊΠΎ распространСн Π½Π° Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΈ Π£ΠΊΡ€Π°ΠΈΠ½Ρ‹, Π²ΠΏΠ΅Ρ€Π²Ρ‹Π΅ Π²Ρ‹Π΄Π΅Π»Π΅Π½Ρ‹ Ρ‚Ρ€ΠΈ Π½ΠΎΠ²Ρ‹Ρ… для Π΄Π°Π½Π½ΠΎΠ³ΠΎ Π²ΠΈΠ΄Π° ΠΈΠ·ΠΎΡ„Π»Π°Π²ΠΎΠ½ΠΎΠΈΠ΄Π°: ΠΈΡ€ΠΈΠ³Π΅Π½ΠΈΠ½, иристСкторигСнин Π’ ΠΈ Π΅Π³ΠΎ глюкозид иристСкторин Π’. Π‘Ρ‚Ρ€ΡƒΠΊΡ‚ΡƒΡ€Π° вСщСств ΠΎΡ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΠΎΠ²Π°Π½Π° ΠΊΠ°ΠΊ 5,7,3’-тригидрокси-6,4’,5’-тримСтоксиизофлавон, 5,7,4’-тригидрокси- 6,3’-димСтоксиизофлавон ΠΈ иристСкторигСнин Π’-7-O-Ξ²-D-Π³Π»ΡŽΠΊΠΎΠΏΠΈΡ€Π°Π½ΠΎΠ·ΠΈΠ΄, соотвСтствСнно. ВСщСства Π±Ρ‹Π»ΠΈ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Ρ‹ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ΠΊΠΎΠ»ΠΎΠ½ΠΎΡ‡Π½ΠΎΠΉ Ρ…Ρ€ΠΎΠΌΠ°Ρ‚ΠΎΠ³Ρ€Π°Ρ„ΠΈΠΈ Π½Π° силикагСлС ΠΈΠ· этилацСтатной Ρ„Ρ€Π°ΠΊΡ†ΠΈΠΈ ΠΊΠΎΡ€Π½Π΅Π²ΠΈΡ‰ ириса ΠΏΡ€ΠΈ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠΌ ΡΠ»ΡŽΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠΈ растворитСлСм Ρ…Π»ΠΎΡ€ΠΎΡ„ΠΎΡ€ΠΌ – этанол Ρ€Π°Π·Π»ΠΈΡ‡Π½ΠΎΠΉ ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ. Π‘Ρ‚Ρ€ΡƒΠΊΡ‚ΡƒΡ€Π° вСщСств установлСна химичСскими ΠΈ ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Π°ΠΌΠΈ ΠΈ Π² сравнСнии с Π»ΠΈΡ‚Π΅Ρ€Π°Ρ‚ΡƒΡ€Π½Ρ‹ΠΌΠΈ Π΄Π°Π½Π½Ρ‹ΠΌΠΈ.Рослини Ρ€ΠΎΠ΄Ρƒ Iris (Iridaceae) ΠΌΠ°ΡŽΡ‚ΡŒ давню Ρ–ΡΡ‚ΠΎΡ€Ρ–ΡŽ застосування Ρƒ Ρ‚Ρ€Π°Π΄ΠΈΡ†Ρ–ΠΉΠ½Ρ–ΠΉ ΠΌΠ΅Π΄ΠΈΡ†ΠΈΠ½Ρ– Ρ€Ρ–Π·Π½ΠΈΡ… ΠΊΡ€Π°Ρ—Π½ як Π°Π»ΡŒΡ‚Π΅Ρ€Π½Π°Ρ‚ΠΈΠ²Π½ΠΈΠΉ проносний, Ρ‚ΠΎΠ½Ρ–Π·ΡƒΡŽΡ‡ΠΈΠΉ, Π²Ρ–Π΄Ρ…Π°Ρ€ΠΊΡƒΠ²Π°Π»ΡŒΠ½ΠΈΠΉ, сСчогінний засіб, для лікування Π·Π°Ρ…Π²ΠΎΡ€ΡŽΠ²Π°Π½ΡŒ ΠΆΠΎΠ²Ρ‡Π½ΠΎΠ³ΠΎ ΠΌΡ–Ρ…ΡƒΡ€Π°, ΠΏΠ΅Ρ‡Ρ–Π½ΠΊΠΈ, водянки, для очищСння ΠΊΡ€ΠΎΠ²Ρ–, Π²Π΅Π½Π΅Ρ€ΠΈΡ‡Π½ΠΈΡ… Ρ–Π½Ρ„Π΅ΠΊΡ†Ρ–ΠΉ, Π»ΠΈΡ…ΠΎΠΌΠ°Π½ΠΊΠΈ, ΠΆΠΎΠ²Ρ‡Π½ΠΈΡ… Ρ–Π½Ρ„Π΅ΠΊΡ†Ρ–ΠΉ Ρ– для лікування Π·Π°Ρ…Π²ΠΎΡ€ΡŽΠ²Π°Π½ΡŒ сСрця. ΠšΠΎΡ€Π΅Π½Π΅Π²ΠΈΡ‰Π° ірисів Ρ” Π±Π°Π³Π°Ρ‚ΠΈΠΌ Π΄ΠΆΠ΅Ρ€Π΅Π»ΠΎΠΌ Π²Ρ‚ΠΎΡ€ΠΈΠ½Π½ΠΈΡ… ΠΌΠ΅Ρ‚Π°Π±ΠΎΠ»Ρ–Ρ‚Ρ–Π², сСрСд яких ΠΏΠ΅Ρ€Π΅Π²Π°ΠΆΠ°ΡŽΡ‚ΡŒ Ρ„Π»Π°Π²ΠΎΠ½ΠΎΡ—Π΄ΠΈ. ΠšΠ»Ρ–Π½Ρ–Ρ‡Π½Ρ– дослідТСння Ρ€Π΅Ρ‡ΠΎΠ²ΠΈΠ½ Ρ–Π· ірисів Π΄Π°Π»ΠΈ ΠΏΠΎΠ·ΠΈΡ‚ΠΈΠ²Π½Ρ– Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΈ ΠΏΡ€ΠΈ Π»Ρ–ΠΊΡƒΠ²Π°Π½Π½Ρ– Ρ€Π°ΠΊΡƒ, Π±Π°ΠΊΡ‚Π΅Ρ€Ρ–Π°Π»ΡŒΠ½ΠΈΡ… Ρ– вірусних Ρ–Π½Ρ„Π΅ΠΊΡ†Ρ–ΠΉ. ΠŸΡ€ΠΎΠ΄ΠΎΠ²ΠΆΡƒΡŽΡ‡ΠΈ ΠΏΠΎΡˆΡƒΠΊ Π½ΠΎΠ²ΠΈΡ… Π±Ρ–ΠΎΠ»ΠΎΠ³Ρ–Ρ‡Π½ΠΎ Π°ΠΊΡ‚ΠΈΠ²Π½ΠΈΡ… сполук Π· рослин Ρ€ΠΎΠ΄ΠΈΠ½ΠΈ ірисові – Iridaceae Π· Π΅Ρ‚Π°Π½ΠΎΠ»ΡŒΠ½ΠΎΠ³ΠΎ Скстракту ΠΊΠΎΡ€Π΅Π½Π΅Π²ΠΈΡ‰ ірису ΡƒΠ³ΠΎΡ€ΡΡŒΠΊΠΎΠ³ΠΎ – Iris hungarica Waldst. et Kit., ΠΏΠΎΡˆΠΈΡ€Π΅Π½ΠΎΠ³ΠΎ Π½Π° Ρ‚Π΅Ρ€ΠΈΡ‚ΠΎΡ€Ρ–Ρ— Π£ΠΊΡ€Π°Ρ—Π½ΠΈ, Π²ΠΏΠ΅Ρ€ΡˆΠ΅ Π²ΠΈΠ΄Ρ–Π»Π΅Π½ΠΎ Ρ‚Ρ€ΠΈ Π½ΠΎΠ²Ρ– для Π΄Π°Π½ΠΎΠ³ΠΎ Π²ΠΈΠ΄Ρƒ Ρ–Π·ΠΎΡ„Π»Π°Π²ΠΎΠ½ΠΎΡ—Π΄ΠΈ: Ρ–Ρ€ΠΈΠ³Π΅Π½Ρ–Π½, іристСкторигСнін Π’ Ρ– ΠΉΠΎΠ³ΠΎ глюкозид іристСкторин Π’. Π‘Ρ‚Ρ€ΡƒΠΊΡ‚ΡƒΡ€Π° Ρ€Π΅Ρ‡ΠΎΠ²ΠΈΠ½ ΠΎΡ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΠΎΠ²Π°Π½Π° як 5,7,3’-тригідрокси-6,4’,5’-тримСтоксіізофлавон, 5,7,4’-тригідрокси-6,3’- димСтоксіізофлавон Ρ‚Π° іристСкторигСнін Π’-7-O-Ξ²-D-Π³Π»ΡŽΠΊΠΎΠΏΡ–Ρ€Π°Π½ΠΎΠ·ΠΈΠ΄, Π²Ρ–Π΄ΠΏΠΎΠ²Ρ–Π΄Π½ΠΎ. Π Π΅Ρ‡ΠΎΠ²ΠΈΠ½ΠΈ Π±ΡƒΠ»ΠΈ ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π½Ρ– ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ΠΊΠΎΠ»ΠΎΠ½ΠΊΠΎΠ²ΠΎΡ— Ρ…Ρ€ΠΎΠΌΠ°Ρ‚ΠΎΠ³Ρ€Π°Ρ„Ρ–Ρ— Π½Π° силікагСлі Π· Π΅Ρ‚ΠΈΠ»Π°Ρ†Π΅Ρ‚Π°Ρ‚Π½ΠΎΡ— Ρ„Ρ€Π°ΠΊΡ†Ρ–Ρ— ΠΊΠΎΡ€Π΅Π½Π΅Π²ΠΈΡ‰ ірису ΠΏΡ€ΠΈ послідовному Π΅Π»ΡŽΡŽΠ²Π°Π½Π½Ρ– Ρ€ΠΎΠ·Ρ‡ΠΈΠ½Π½ΠΈΠΊΠΎΠΌ Ρ…Π»ΠΎΡ€ΠΎΡ„ΠΎΡ€ΠΌ – Π΅Ρ‚Π°Π½ΠΎΠ» Ρ€Ρ–Π·Π½ΠΎΡ— ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†Ρ–Ρ—. Π‘Ρ‚Ρ€ΡƒΠΊΡ‚ΡƒΡ€Π° Ρ€Π΅Ρ‡ΠΎΠ²ΠΈΠ½ встановлСна Ρ…Ρ–ΠΌΡ–Ρ‡Π½ΠΈΠΌΠΈ Ρ– ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½ΠΈΠΌΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Π°ΠΌΠΈ Ρ‚Π° Ρƒ порівнянні Π· Π»Ρ–Ρ‚Π΅Ρ€Π°Ρ‚ΡƒΡ€Π½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ

    Dispersion and collapse of wave maps

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    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

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    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

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    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

    Π˜Π½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΠΈΠ·Π°Ρ†ΠΈΡ контроля Π²Π°Π³ΠΎΠ½ΠΎΠ² Π² ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠΌ сортировочном ΠΏΠ°Ρ€ΠΊΠ΅

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    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 cars’ 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.ЖСлСзнодороТная сортировочная станция Π·Π°Π½ΠΈΠΌΠ°Π΅Ρ‚ Ρ†Π΅Π½Ρ‚Ρ€Π°Π»ΡŒΠ½ΠΎΠ΅ мСсто Π² тСхнологичСской Ρ†Π΅ΠΏΠΎΡ‡ΠΊΠ΅ Π³Ρ€ΡƒΠ·ΠΎΠ²Ρ‹Ρ… ΠΏΠ΅Ρ€Π΅Π²ΠΎΠ·ΠΎΡ‡Π½Ρ‹Ρ… процСссов, ΠΏΠΎΡΠΊΠΎΠ»ΡŒΠΊΡƒ ΡΠΊΠΎΡ€ΠΎΡΡ‚ΡŒ ΠΏΠ΅Ρ€Π΅Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½Ρ‹Ρ… составов Π½Π° Π½Π΅ΠΉ опрСдСляСт ΠΎΠ±ΡŠΡ‘ΠΌ ΠΈ ΡΡ‚ΠΎΠΈΠΌΠΎΡΡ‚ΡŒ ΠΏΠ΅Ρ€Π΅Π²ΠΎΠ·ΠΎΠΊ. ΠŸΠΎΡΡ‚ΠΎΠΌΡƒ Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΠ΅ срСдств Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·Π°Ρ†ΠΈΠΈ ΠΈ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ·Π°Ρ†ΠΈΠΈ сортировочных процСссов Π²Π΅Π΄Ρ‘Ρ‚ ΠΊ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡŽ эффСктивности Π³Ρ€ΡƒΠ·ΠΎΠ²Ρ‹Ρ… ΠΏΠ΅Ρ€Π΅Π²ΠΎΠ·ΠΎΠΊ Π² Ρ†Π΅Π»ΠΎΠΌ. ЦСлью Ρ€Π°Π±ΠΎΡ‚Ρ‹ являСтся формализация Π·Π°Π΄Π°Ρ‡ΠΈ контроля Π²Π°Π³ΠΎΠ½ΠΎΠ² Π² ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠΌ сортировочном ΠΏΠ°Ρ€ΠΊΠ΅ ΠΈΡ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° ΠΌΠ΅Ρ‚ΠΎΠ΄Π° Π΅Ρ‘ Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ, основанного Π½Π° использовании Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠΎΠ² распознавания ΠΈ позиционирования динамичСских ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² ΠΏΡƒΡ‚Ρ‘ΠΌ ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½Ρ‹Ρ… ΠΏΠΎΡ‚ΠΎΠΊΠΎΠ²ΠΎΠ³ΠΎ Π²ΠΈΠ΄Π΅ΠΎ. Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ прСдставлСн Π½ΠΎΠ²Ρ‹ΠΉ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ ΠΊ Ρ€Π΅ΡˆΠ΅Π½ΠΈΡŽ Π·Π°Π΄Π°Ρ‡ΠΈ контроля ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½Ρ‹Ρ… Π΅Π΄ΠΈΠ½ΠΈΡ† Π² ΠΏΠΎΠ΄Π³ΠΎΡ€ΠΎΡ‡Π½ΠΎΠΌ (сортировочном) ΠΏΠ°Ρ€ΠΊΠ΅ ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½Ρ‹Ρ… сортировочных станций. ΠŸΡ€ΠΈΠ²ΠΎΠ΄ΡΡ‚ΡΡ основныС ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠΈ опрСдСлСния скорости двиТСния ΠΈ позиционирования Π³Ρ€ΡƒΠΏΠΏ Π²Π°Π³ΠΎΠ½ΠΎΠ² ΠΏΡ€ΠΈ ΠΈΡ… Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΈ послС расформирования Π½Π° сортировочной Π³ΠΎΡ€ΠΊΠ΅. ΠžΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΎ, Ρ‡Ρ‚ΠΎ ΠΊΠΎΠ½Ρ‚Ρ€ΠΎΠ»ΡŒ ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½Ρ‹Ρ… Π΅Π΄ΠΈΠ½ΠΈΡ† Π² сортировочном ΠΏΠ°Ρ€ΠΊΠ΅ являСтся ΠΌΠ΅Π½Π΅Π΅ Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹ΠΌ процСссом ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с ΠΊΠΎΠ½Ρ‚Ρ€ΠΎΠ»Π΅ΠΌ Π½Π° сортировочной Π³ΠΎΡ€ΠΊΠ΅. Для Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ поставлСнной Π·Π°Π΄Π°Ρ‡ΠΈ Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·Π°Ρ†ΠΈΠΈ контроля ΠΏΠΎΠ΄Π²ΠΈΠΆΠ½Ρ‹Ρ… Π΅Π΄ΠΈΠ½ΠΈΡ† Π² сортировочном ΠΏΠ°Ρ€ΠΊΠ΅ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌ Π½Π° Π±Π°Π·Π΅ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π²ΠΈΠ΄Π΅ΠΎΠ΄Π°Π½Π½Ρ‹Ρ… – ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½ΠΎΠ³ΠΎ зрСния – ΠΈ прСдставлСна Π΅Π³ΠΎ модСль Ρ€Π΅Π°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ Π½Π° ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠΌ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚Π΅. ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹ Ρ€Π°Π±ΠΎΡ‚Ρ‹ основаны Π½Π° Ρ‚Π΅ΠΎΡ€ΠΈΠΈ ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½ΠΎΠ³ΠΎ зрСния ΠΈ Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½Ρ‹ Π½Π° распознаваниС ΠΊΠ»ΡŽΡ‡Π΅Π²Ρ‹Ρ… динамичСских ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² Π½Π° ΠΏΠΎΡ‚ΠΎΠΊΠΎΠ²ΠΎΠΌ Π²ΠΈΠ΄Π΅ΠΎ с ΠΈΡ… ΠΏΠΎΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΠΌ ΠΏΠΎΠ·ΠΈΡ†ΠΈΠΎΠ½ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠΌ ΠΏΡ€ΠΎΠ²Π΅Π΄Ρ‘Π½Π½ΠΎΠΉ Ρ€Π°Π±ΠΎΡ‚Ρ‹ являСтся обоснованиС Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ использования ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½ΠΎΠ³ΠΎ зрСния Π² процСссС расформирования-формирования ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½Ρ‹Ρ… составов. Π’ дальнСйшСм планируСтся ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΠ΅ прСдставлСнных Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΎΠΊ для ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠΈ Π³ΠΎΡ‚ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ½ΠΎΠ³ΠΎ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚Π°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‰Π΅Π³ΠΎ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΈΠ²ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΡŽ ΠΎ сортировочном ΠΏΠ°Ρ€ΠΊΠ΅ для ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡ эффСктивности ΠΏΡ€ΠΈΡ†Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎ тормоТСния Π½Π° сортировочной Π³ΠΎΡ€ΠΊΠ΅

    Batch Scheduling of Deteriorating Products

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    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 job’s 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 βˆ’ (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

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    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

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    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

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    A dynamic pricing approach for hotel revenue management is suggested. It aims at increasing revenue over the baseline
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