453 research outputs found

    Stationary States in Bistable System Driven by L\'evy Noise

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    We study the properties of the probability density function (PDF) of a bistable system driven by heavy tailed white symmetric L\'evy noise. The shape of the stationary PDF is found analytically for the particular case of the L\'evy index \alpha = 1 (Cauchy noise). For an arbitrary L\'evy index we employ numerical methods based on the solution of the stochastic Langevin equation and space fractional kinetic equation. In contrast with the bistable system driven by Gaussian noise, in the L\'evy case the positions of maxima of the stationary PDF do not coincide with the positions of minima of the bistable potential. We provide a detailed study of the distance between the maxima and the minima as a function of the potential's depth and L\'evy noise parameters.Comment: Accepted to EPJS

    Rook placements in G2G_2 and F4F_4 and associated coadjoint orbits

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    Let n\mathfrak{n} be a maximal nilpotent subalgebra of a simple complex Lie algebra with root system Ξ¦\Phi. A subset DD of the set Ξ¦+\Phi^+ of positive roots is called a rook placement if it consists of roots with pairwise non-positive scalar products. To each rook placement DD and each map ΞΎ\xi from DD to the set CΓ—\mathbb{C}^{\times} of nonzero complex numbers one can naturally assign the coadjoint orbit Ξ©D,ΞΎ\Omega_{D,\xi} in the dual space nβˆ—\mathfrak{n}^*. By definition, Ξ©D,ΞΎ\Omega_{D,\xi} is the orbit of fD,ΞΎf_{D,\xi}, where fD,ΞΎf_{D,\xi} is the sum of root covectors eΞ±βˆ—e_{\alpha}^* multiplied by ΞΎ(Ξ±)\xi(\alpha), α∈D\alpha\in D. (In fact, almost all coadjoint orbits studied at the moment have such a form for certain DD and ΞΎ\xi.) It follows from the results of Andr\`e that if ΞΎ1\xi_1 and ΞΎ2\xi_2 are distinct maps from DD to CΓ—\mathbb{C}^{\times} then Ξ©D,ΞΎ1\Omega_{D,\xi_1} and Ξ©D,ΞΎ2\Omega_{D,\xi_2} do not coincide for classical root systems Ξ¦\Phi. We prove that this is true if Ξ¦\Phi is of type G2G_2, or if Ξ¦\Phi is of type F4F_4 and DD is orthogonal.Comment: 16 pages, 4 figure

    A direction finding technique for the ULF electromagnetic source

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    International audienceA technique of direction finding is proposed, which can be applied to the magnetic-dipole type source located in the conductive ground. To distinguish a weak ULF source signal from the natural noise a network of multicomponent magnetometers is supposed to be used. The data obtained by the ground-based stations is processed in such a way that a set of partial derivatives of the magnetic perturbations due to the source are found. Comparing these derivatives with theoretical formulae makes it possible, in principle, to find the ULF source parameters such as the distance and amplitude. Averaging the data and a special procedure are proposed in order to exclude random fluctuations in the magnetic moment orientation and to avoid hydrogeological and other local factors

    azTotMD 2.0: Molecular Dynamics with the Radiative Thermostat and Temperature-Dependent Force Field (CUDA Version)

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    azTotMD 2.0 is a parallel molecular dynamics program which includes both conventional algorithms and a novel β€œradiative” thermostat and a temperature-dependent force field. The radiative thermostat is based on the black-body radiation law and acts like virtual adsorption and radiation of photons. The thermostat algorithm has complexity of O(N), it can accelerate and decelerate atoms and this action over time leads to a Maxwell-like distribution of velocities. The temperature-dependent pair potential includes atomic radii, which are functions of the thermal excitation of atoms. The combination of the radiative thermostat and the potential allows to reproduce many phenomena such as phase transitions, thermal expansion, defect formation, surface tension, vapor saturation, glass formation and devitrification. Β© 2022 The Author(s).The reported study was funded by RFBR, Russia, project number 20-03-00897

    ОбъСдинСниС экономичСских ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΎΠ² с использованиСм экспСртной ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ

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    Purpose of the study. The aim of this work is to consider the possibility of using expert information when combining forecasts as an additional factor in improving the accuracy of economic forecasting. Using the methodology of combining forecasts is increasingly found in the domestic practice of economic forecasting. Most researchers agree that combining forecasts improves forecasting accuracy by using all available information about the process under study, which is included in individual forecasting methods. Β Today, there are many methods for constructing weighting factors when combining forecasts, but all of them are primarily based on the use of only statistical information about the process under study. But economic forecasting cannot be linear in its dynamics, many external factors constantly affect the forecasted process, and some internal ones may not be affected by the methods used. In this case, it is necessary to attract expert information or external information about the forecast obtained in order to increase its accuracy and adjust the further development of the economic process. This is especially true today, during the period of digitalization of the economy and the increasing influence of social and political factors on the dynamics of economic phenomena. Β Materials and methods. For this purpose, methods of constructing integral indicators based on expert information or directly using such information at the stage of constructing a joint forecast can be directly used to make adjustments to the resulting combined forecast. Some of these approaches are already used in foreign practice of economic forecasting, while in domestic practice they are still little known. One of such approaches may be the use of the pairwise preference method or the application of Fishburn formulas for ranking particular forecasting methods by accuracy. The approaches considered in this work can be used as tools for constructing weight coefficients or as a correction of the obtained forecasting results. Β Results. As a result of this article, attempts have been made to propose possible methods for combining forecasts using expert information, a summary table has been compiled with an assessment of one or another method of combining forecasts, and conclusions are drawn on the appropriateness of their application in practice. Such a table will make it possible to better understand the direction of attracting expert information to combine forecasts and choose the most suitable approach for further use in practice. Β Conclusion. Combining forecasts has long established itself as an effective method for increasing forecast accuracy. This technique cannot degrade the result, in most cases increasing accuracy. The use of expert information in combining forecasts is the next step in improving this technique and requires a separate further practical study of possible tools for attracting expert information to the pool.  ЦСль исслСдования. ЦСлью настоящСй Ρ€Π°Π±ΠΎΡ‚Ρ‹ являСтся рас­смотрСниС возмоТности использования экспСртной ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΏΡ€ΠΈ объСдинСнии ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΎΠ² ΠΊΠ°ΠΊ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎ Ρ„Π°ΠΊΡ‚ΠΎΡ€Π° ΠΏΠΎΒ­Π²Ρ‹ΡˆΠ΅Π½ΠΈΡ точности экономичСского прогнозирования. Использо­ваниС ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ объСдинСния ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΎΠ² всС Ρ‡Π°Ρ‰Π΅ встрСчаСтся Π² отСчСствСнной ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ΅ экономичСского прогнозирования. Π‘ΠΎΠ»ΡŒΡˆΠΈΠ½ΡΡ‚Π²ΠΎ исслСдоватСлСй сходятся Π²ΠΎ ΠΌΠ½Π΅Π½ΠΈΠΈ Ρ‡Ρ‚ΠΎ объСди­нСниС ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΎΠ² ΠΏΠΎΠ²Ρ‹ΡˆΠ°Π΅Ρ‚ Ρ‚ΠΎΡ‡Π½ΠΎΡΡ‚ΡŒ прогнозирования Ρ‡Π΅Ρ€Π΅Π· ис­пользованиС всСй доступной ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΎΠ± ΠΈΠ·ΡƒΡ‡Π°Π΅ΠΌΠΎΠΌ процСссС, Π²ΠΊΠ»ΡŽΡ‡Π°Π΅ΠΌΠΎΠΉ Π² ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹Π΅ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ прогнозирования.  На сСгодняшний дСнь сущСствуСт мноТСство ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² постро­Сния вСсовых коэффициСнтов ΠΏΡ€ΠΈ объСдинСнии ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΎΠ², Π½ΠΎ всС ΠΎΠ½ΠΈ Π² ΠΏΠ΅Ρ€Π²ΡƒΡŽ ΠΎΡ‡Π΅Ρ€Π΅Π΄ΡŒ ΠΎΡΠ½ΠΎΠ²Ρ‹Π²Π°ΡŽΡ‚ΡΡ Π½Π° использовании Ρ‚ΠΎΠ»ΡŒΠΊΠΎ статистичСской ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΎΠ± ΠΈΠ·ΡƒΡ‡Π°Π΅ΠΌΠΎΠΌ процСссС. Но эко­номичСскоС ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π΅ ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ Π»ΠΈΠ½Π΅ΠΉΠ½Ρ‹ΠΌ Π² своСй Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ΅, мноТСство Π²Π½Π΅ΡˆΠ½ΠΈΡ… Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² постоянно ΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‚ влияниС Π½Π° ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΈΡ€ΡƒΠ΅ΠΌΡ‹ΠΉ процСсс, Π° Π½Π΅ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ Π²Π½ΡƒΡ‚Ρ€Π΅Π½Π½ΠΈΠ΅ ΠΌΠΎΒ­Π³ΡƒΡ‚ Π½Π΅ Π·Π°Ρ‚Ρ€Π°Π³ΠΈΠ²Π°Ρ‚ΡŒΡΡ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅ΠΌΡ‹ΠΌΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Π°ΠΌΠΈ. Π’ этом случаС Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎ ΠΏΡ€ΠΈΠ²Π»Π΅Ρ‡Π΅Π½ΠΈΠ΅ экспСртной ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΈΠ»ΠΈ внСшнСй ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΎ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½ΠΎΠΌ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π΅ для повСшСния Π΅Π³ΠΎ точности ΠΈ ΠΊΠΎΡ€Ρ€Π΅ΠΊΡ‚ΠΈΡ€ΠΎΠ²ΠΊΠΈ дальнСйшСго развития экономичСского процСсса. Π­Ρ‚ΠΎ особСнно Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎ Π½Π° сСгодняшний дСнь, Π² ΠΏΠ΅Ρ€ΠΈΠΎΠ΄ Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΈΒ­Π·Π°Ρ†ΠΈΠΈ экономики ΠΈ увСличСния влияния ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΈ политичСских Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² Π½Π° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΡƒ экономичСских явлСний. Β ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Ρ‹ ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹. Для этой Ρ†Π΅Π»ΠΈ, нСпосрСдствСнно ΠΌΠΎΠ³ΡƒΡ‚ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒΡΡ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ построСния ΠΈΠ½Ρ‚Π΅Π³Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ Π½Π° основС экспСртной ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΈΠ»ΠΈ ΠΆΠ΅ ΠΏΡ€ΠΈΠ²Π»Π΅Ρ‡Π΅Π½ΠΈΠ΅ Ρ‚Π°ΠΊΠΎΠΉ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ Π½Π° стадии построСния объСдинСнного ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π°, для внСсСния ΠΊΠΎΡ€Ρ€Π΅ΠΊΡ‚ΠΈΡ€ΠΎΠ²ΠΎΠΊ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½ΠΎΠ³ΠΎ объСдинСнного ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π°. НСкоторыС ΠΈΠ· этих ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ² ΡƒΠΆΠ΅ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‚ΡΡ Π² Π·Π°Ρ€ΡƒΠ±Π΅ΠΆΠ½ΠΎΠΉ ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ΅ экономичСского прогнозирования, Π² отСчСствСнной ΠΆΠ΅ ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ΅ ΠΎΠ½ΠΈ ΠΏΠΎΠΊΠ° Ρ‡Ρ‚ΠΎ ΠΌΠ°Π»ΠΎ извСстны. Одним ΠΈΠ· Ρ‚Π°ΠΊΠΈΡ… ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ² ΠΌΠΎΠΆΠ΅Ρ‚ ΡΠ²Π»ΡΡ‚ΡŒΡΡ использованиС ΠΌΠ΅Ρ‚ΠΎΠ΄Π° ΠΏΠΎΠΏΠ°Ρ€Π½Ρ‹Ρ… ΠΏΡ€Π΅Π΄ΠΏΠΎΡ‡Ρ‚Π΅Π½ΠΈΠΉ ΠΈΠ»ΠΈ ΠΆΠ΅ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Ρ„ΠΎΡ€ΠΌΡƒΠ» Π€ΠΈΡˆΠ±Π΅Ρ€Π½Π° для ранТирования частных ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² прогнозирования ΠΏΠΎ точности. РассмотрСнныС Π² Ρ€Π°Π±ΠΎΡ‚Π΅ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒΡΡ Π² качСствС инструмСнтов ΠΏΠΎ ΠΏΠΎΡΡ‚Ρ€ΠΎΠ΅Π½ΠΈΡŽ вСсовых коэффициСнтов ΠΈΠ»ΠΈ ΠΆΠ΅ Π² качСствС ΠΊΠΎΡ€Ρ€Π΅ΠΊΒ­Ρ‚ΠΈΡ€ΠΎΠ²ΠΊΠΈ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Ρ… Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² прогнозирования. Β Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. Π’ качСствС ΠΈΡ‚ΠΎΠ³Π° настоящСй ΡΡ‚Π°Ρ‚ΡŒΠΈ сдСланы ΠΏΠΎΠΏΡ‹Ρ‚ΠΊΠΈ ΠΏΠΎ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡŽ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² объСдинСния ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΎΠ², с использованиСм экспСртной ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ, сфор­мирована сводная Ρ‚Π°Π±Π»ΠΈΡ†Π° с ΠΎΡ†Π΅Π½ΠΊΠΎΠΉ Ρ‚ΠΎΠ³ΠΎ ΠΈΠ»ΠΈ ΠΈΠ½ΠΎΠ³ΠΎ ΠΌΠ΅Ρ‚ΠΎΠ΄Π° объСдинСния ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΎΠ² ΠΈ сдСланы Π²Ρ‹Π²ΠΎΠ΄Ρ‹ ΠΎ цСлСсообразности ΠΈΡ… примСнСния Π½Π° ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ΅. Вакая Ρ‚Π°Π±Π»ΠΈΡ†Π° ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡ‚ Π»ΡƒΡ‡ΡˆΠ΅ Ρ€Π°Π·ΠΎΠ±Ρ€Π°Ρ‚ΡŒΡΡ Π² Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΠΈ привлСчСния экспСртной ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ Π² объСдинСниС ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΎΠ² ΠΈ Π²Ρ‹Π±Ρ€Π°Ρ‚ΡŒ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ подходящий ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ для дальнСйшСго ΠΈΠ³ΠΎ использования Π½Π° ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ΅. Β Π—Π°ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅. ОбъСдинСниС ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΎΠ² ΡƒΠΆΠ΅ Π΄Π°Π²Π½ΠΎ Π·Π°Ρ€Π΅ΠΊΠΎΠΌΠ΅Π½Π΄ΠΎΠ²Π°Π»ΠΎ сСбя ΠΊΠ°ΠΊ эффСктивный ΠΌΠ΅Ρ‚ΠΎΠ΄ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡ точности прогнози­рования. Данная ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠ° Π½Π΅ ΠΌΠΎΠΆΠ΅Ρ‚ ΡƒΡ…ΡƒΠ΄ΡˆΠΈΡ‚ΡŒ ΠΏΠΎΠ»ΡƒΡ‡Π°Π΅ΠΌΡ‹ΠΉ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚, Π² Π±ΠΎΠ»ΡŒΡˆΠΈΠ½ΡΡ‚Π²Π΅ случаСв увСличивая Ρ‚ΠΎΡ‡Π½ΠΎΡΡ‚ΡŒ. ИспользованиС экспСртной ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ Π² объСдинСнии ΠΏΡ€ΠΎΠ³Π½ΠΎΒ­Π·ΠΎΠ² являСтся ΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΠΌ этапом ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠΉ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ ΠΈ Ρ‚Ρ€Π΅Π±ΡƒΠ΅Ρ‚ ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎ дальнСйшСго практичСского исслСдования Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ‹Ρ… инструмСнтов ΠΏΠΎ ΠΏΡ€ΠΈΠ²Π»Π΅Ρ‡Π΅Π½ΠΈΡŽ экс­пСртной ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ Π² объСдинСниС.

    Oxygen isotope composition of dissolved sulphate in deep-sea sediments: Eastern Equatorial Pacific Ocean

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    High-resolution analyses of the oxygen isotope ratio (18O/16O) of dissolved sulfate in pore waters have been made to depths of >400 meters below seafloor (mbsf) at open-ocean and upwelling sites in the eastern equatorial Pacific Ocean. 18O values of dissolved sulfate (18O-SO4) at the organic-poor open-ocean Site 1231 gave compositions close to modern seawater (+9.5 vs. Vienna-standard mean ocean water, providing no chemical or isotopic evidence for microbial sulfate reduction (MSR). In contrast, the maximum 18O values at Sites 1225 and 1226, which contain higher organic matter contents, are +20 and +28, respectively. Depth-correlative trends of increasing 18O-SO4, alkalinity, and ammonium and the presence of sulfide indicate significant oxidation of sedimentary organic matter by sulfate-reducing microbial populations at these sites. Although sulfate concentration profiles at Sites 1225 and 1231 both show similarly flat trends without significant net MSR, 18O-SO4 values at Site 1225 reveal the presence of significant microbial sulfur-cycling activity, which contrasts to Site 1231. This activity may include contributions from several processes, including enzyme-catalyzed equilibration between oxygen in sulfate and water superimposed upon bacterial sulfate reduction, which would tend to shift 18O-SO4 toward higher values than MSR alone, and sulfide oxidation, possibly coupled to reduction of Fe and Mn oxides and/or bacterial disproportionation of sulfur intermediates. Large isotope enrichment factors observed at Sites 1225 and 1226 ( values between 42 and 79) likely reflect concurrent processes of kinetic isotope fractionation, equilibrium fractionation between sulfate and water, and sulfide oxidation at low rates of sulfate reduction. The oxygen isotope ratio of dissolved pore water sulfate is a powerful tool for tracing microbial activity and sulfur cycling by the deep biosphere of deep-sea sediments

    Π’Π·Π»Π΅Ρ‚Ρ‹ ΠΈ падСния Π΄Π΅Π»ΠΎΠ²ΠΎΠΉ активности Π½Π° Π²ΠΎΠ»Π½Π°Ρ… кризисов, ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ коронавируса ΠΈ бСспрСцСдСнтных Π·Π°ΠΏΠ°Π΄Π½Ρ‹Ρ… санкций

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    Subject of article - the dynamics of the integrated Business Activity Index of the Institute of Economics of the Russian Academy of Sciences in 10 main areas of the national economy and the Index of output of goods and services by basic types of economic activity of Rosstat (Rosstat Index) from 2018 to July 2022 inclusive. Growth factors and a list of key macro indicators that determine the level of business activity in the relevant sectors of the economy, as well as the results of calculating the weights of these sectors, are considered.The aim of the article is to substantiate the advantages of the methodology for constructing the IE RAS Index, which includes development indicators of 10 areas of the national economy, in comparison with the Rosstat Index. Theoretical studies are based on practical calculations performed on the basis of official statistical reporting, and a comparative analysis of the results with the dynamics of the Rosstat Index. Research period: post-crisis 2018–2019, pandemic and post-pandemic 2020–2021 and initial stage of the mobilization period for the economy - January-July 2022. To calculate the IE RAS Index, the method of construction of integral estimates of macroeconomic dynamics, correlation analysis, as well as a matrix of coefficients of pair correlation for determination of index weights are used, which is a convincing justification of scientific novelty of the proposed methodology of construction and practical use of the IE RAS Index. Based on a comparative analysis of the dynamics of the indices, it was found that the maximum drop in the IE RAS Index and the Rosstat Index was observed in 2020, and the maximum growth was observed in the post-pandemic 2021. Moreover, according to the IE RAS methodology, larger parameters and earlier dates for the start of decline and growth of business activity in comparison with the Rosstat Index were recorded. As a result, new convincing evidence of the advantages of the IE RAS Index was obtained, the main of which is a more reliable and accurate determination of the critical moments of a change in the business activity trend and, accordingly, the timing of the onset and overcoming of crisis processes in socio-economic development. The authors conclude that, in the new geopolitical reality, it is necessary to include the IE RAS Index as a target indicator for the country’s ability to secure state sovereignty.ΠŸΡ€Π΅Π΄ΠΌΠ΅Ρ‚ исслСдования - Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ° ΠΈΠ½Ρ‚Π΅Π³Ρ€Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ ИндСкса Π΄Π΅Π»ΠΎΠ²ΠΎΠΉ активности Π˜Π½ΡΡ‚ΠΈΡ‚ΡƒΡ‚Π° экономики Российской Π°ΠΊΠ°Π΄Π΅ΠΌΠΈΠΈ Π½Π°ΡƒΠΊ (ИндСкс ИЭ РАН) ΠΏΠΎ дСсяти основным сфСрам Π½Π°Ρ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ хозяйства ΠΈ ИндСкса выпуска Ρ‚ΠΎΠ²Π°Ρ€ΠΎΠ² ΠΈ услуг ΠΏΠΎ Π±Π°Π·ΠΎΠ²Ρ‹ΠΌ Π²ΠΈΠ΄Π°ΠΌ экономичСской Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ Росстата (ИндСкс Росстата) с 2018 ΠΏΠΎ июль 2022 Π³. Π²ΠΊΠ»ΡŽΡ‡ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ. ЦСль Ρ€Π°Π±ΠΎΡ‚Ρ‹ - ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Ρ‚ΡŒ прСимущСства ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ построСния ИндСкса ИЭ РАН ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с ИндСксом Росстата. ВСорСтичСскиС исслСдования основаны Π½Π° практичСских расчСтах, Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½Π½Ρ‹Ρ… Π½Π° Π±Π°Π·Π΅ ΠΎΡ„ΠΈΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎΠΉ статистичСской отчСтности, ΠΈ ΡΡ€Π°Π²Π½ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΌ Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Ρ… Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² с Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΎΠΉ ИндСкса Росстата. ΠŸΠ΅Ρ€ΠΈΠΎΠ΄ исслСдования: посткризисныС 2018–2019 Π³Π³., ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΉΠ½Ρ‹Π΅ ΠΈ постпандСмийныС 2020–2021 Π³Π³. ΠΈ Π½Π°Ρ‡Π°Π»ΡŒΠ½Ρ‹ΠΉ этап ΠΌΠΎΠ±ΠΈΠ»ΠΈΠ·Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ для экономики ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π° β€” ΡΠ½Π²Π°Ρ€ΡŒ-июль 2022 Π³. Для расчСта ИндСкса ИЭ РАН использован ΠΌΠ΅Ρ‚ΠΎΠ΄ построСния ΠΈΠ½Ρ‚Π΅Π³Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΎΡ†Π΅Π½ΠΎΠΊ макроэкономичСской Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ, коррСляционного Π°Π½Π°Π»ΠΈΠ·Π°, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Π° коэффициСнтов ΠΏΠ°Ρ€Π½ΠΎΠΉ коррСляции для опрСдСлСния вСсовых коэффициСнтов ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ индСкса, Ρ‡Ρ‚ΠΎ являСтся ΡƒΠ±Π΅Π΄ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹ΠΌ обоснованиСм Π½Π°ΡƒΡ‡Π½ΠΎΠΉ Π½ΠΎΠ²ΠΈΠ·Π½Ρ‹ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠΉ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΠΈ построСния ΠΈ практичСского использования ИндСкса ИЭ РАН. На основС ΡΡ€Π°Π²Π½ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊ индСксов установлСно, Ρ‡Ρ‚ΠΎ максимальноС ΠΏΠ°Π΄Π΅Π½ΠΈΠ΅ ИндСкса ИЭ РАН ΠΈ ИндСкса Росстата наблюдалось Π² 2020 Π³., Π° ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΉ рост - Π² постпандСмийном 2021 Π³. ΠŸΡ€ΠΈΡ‡Π΅ΠΌ ΠΏΠΎ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠ΅ ИЭ РАН зафиксированы Π±ΠΎΠ»Π΅Π΅ ΠΌΠ°ΡΡˆΡ‚Π°Π±Π½Ρ‹Π΅ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Ρ‹ ΠΈ Π±ΠΎΠ»Π΅Π΅ Ρ€Π°Π½Π½ΠΈΠ΅ сроки Π½Π°Ρ‡Π°Π»Π° падСния ΠΈ роста Π΄Π΅Π»ΠΎΠ²ΠΎΠΉ активности ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ ИндСксом Росстата. Π’ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π΅ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Ρ‹ Π½ΠΎΠ²Ρ‹Π΅ Π΄ΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Π° прСимущСств ИндСкса ИЭ РАН, Π³Π»Π°Π²Π½Ρ‹ΠΌΠΈ ΠΈΠ· ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Ρ… ΡΠ²Π»ΡΡŽΡ‚ΡΡ Π±ΠΎΠ»Π΅Π΅ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΠ΅ ΠΈ Ρ‚ΠΎΡ‡Π½ΠΎΠ΅ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ критичСских ΠΌΠΎΠΌΠ΅Π½Ρ‚ΠΎΠ² измСнСния Ρ‚Ρ€Π΅Π½Π΄Π° Π΄Π΅Π»ΠΎΠ²ΠΎΠΉ активности ΠΈ, соотвСтствСнно, сроков наступлСния ΠΈ прСодолСния кризисных процСссов Π² ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎ-экономичСском Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΠΈ. Авторы Π΄Π΅Π»Π°ΡŽΡ‚ Π²Ρ‹Π²ΠΎΠ΄, Ρ‡Ρ‚ΠΎ Π² условиях Π½ΠΎΠ²ΠΎΠΉ гСополитичСской Ρ€Π΅Π°Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎ Π²ΠΊΠ»ΡŽΡ‡ΠΈΡ‚ΡŒ ИндСкс ИЭ РАН Π² состав Ρ†Π΅Π»Π΅Π²Ρ‹Ρ… ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ, ΠΎΠΏΡ€Π΅Π΄Π΅Π»ΡΡŽΡ‰ΠΈΡ… ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡ‚ΡŒ страны ΠΊ ΠΎΠ±Π΅ΡΠΏΠ΅Ρ‡Π΅Π½ΠΈΡŽ государствСнного сувСрСнитСта

    Measurements of the composition of aerosol component of Venusian atmosphere with Vega 1 lander, preliminary data

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    Preliminary investigation of mass spectra of gaseous products of pyrolyzed Venusian cloud particles collected and analyzed by the complex device of mass-spectrometer and collector pyrolyzer on board Vega 1 lander revealed the presence of heavy particles in the upper cloud layer. Based on 64 amu peak (SO2+), an estimate of the lower limit of the sulfuric acid aerosol content at the 62 to 54 km heights of approximately 2.0 mg/cu m is obtained. A chlorine line (35 and 37 amu) is also present in the mass spectrum with a lower limit of the chlorine concentration of approximately 0.3 mg/ cu m
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