52 research outputs found

    Spatial differences in dissolved silicon utilisation in Lake Baikal, Siberia: examining the impact of high diatom biomass events and eutrophication

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    Recent research has highlighted how Lake Baikal, Siberia, has responded to the direct and indirect effects of climate change (e.g., ice-cover duration), nutrient loading, and pollution, manifesting as changes in phytoplankton/zooplankton populations, community structure, and seasonal succession. Here, we combine and compare= analyses of chlorophyll a (an estimate of total algal biomass), carotenoid pigments (biomarkers of algal groups), and lake water silicon isotope geochemistry (d30SiDSi) to differentiate spatial patterns in dissolved silicon (DSi) uptake at Lake Baikal. A total of 15 sites across the three basins (south, central, and north) of Lake Baikal were sampled in August 2013 along a depth gradient of 0–180 m. Strong, significant correlations were found between vertical profiles of photic zone DSi concentrations and d30SiDSi compositions (r 5 20.81, p < 0.001), although these are strongest in the central basin aphotic zone (r 5 20.98, p < 0.001). Data refute the hypothesis of DSi uptake by picocyanobacteria. Algal biomass profiles and high surface d30SiDSi compositions suggest greater productivity in the south basin and more oligotrophic conditions in the north basin. d30SiDSi signatures are highest at depth (20 m) in central basin sites, indicating greater (10–40%) DSi utilization at deep chlorophyll maxima. DSi limitation occurs in the pelagic central basin, probably reflecting a high diatom biomass bloom event (Aulacoseira baicalensis). Meanwhile in the more hydrologically restricted, shallow Maloe More region (central basin), both high d30SiDSi compositions and picocyanobacteria (zeaxanthin) concentrations, respectively point to the legacy of an β€œAulacoseira bloom year” and continuous nutrient supply in summer months (e.g., localized eutrophication)

    Sinking properties of some phytoplankton shapes and the relation of form resistance to morphological diversity of plankton – an experimental study

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    Form resistance (Phi) is a dimensionless number expressing how much slower or faster a particle of any form sinks in a fluid medium than the sphere of equivalent volume. Form resistance factors of PVC models of phytoplankton sinking in glycerin were measured in a large aquarium (0.6 x 0.6 x 0.95 m). For cylindrical forms, a positive relationship was found between Phi and length/ width ratio. Coiling decreased Phi in filamentous forms. Form resistance of Asterionella colonies increased from single cells up to 6-celled colonies than remained nearly constant. For Fragilaria crotonensis chains, no such upper limit to Phi was observed in chains of up to 20 cells ( longer ones were not measured). The effect of symmetry on Phi was tested in 1 - 6-celled Asterionella colonies, having variable angles between the cells, and in Tetrastrum staurogeniaeforme coenobia, having different spine arrangements. In all cases, symmetric forms had considerably higher form resistance than asymmetric ones. However, for Pediastrum coenobia with symmetric/asymmetric fenestration, no difference was observed with respect to symmetry. Increasing number and length of spines on Tetrastrum coenobia substantially increased Phi. For a series of Staurastrum forms, a significant positive correlation was found between arm-length/cell-width ratio and Phi: protuberances increased form resistance. Flagellates (Rhodomonas, Gymnodinium) had a Phi 1. The highest value ( Phi = 8.1) was established for a 20-celled Fragilaria crotonensis chain. Possible origin of the so-called 'vital component' ( a factor that shows how much slower viable populations sink than morphologically similar senescent or dead ones) is discussed, as is the role of form resistance in evolution of high diversity of plankton morphologies

    Modeling the Interaction of the Regions of Russia and the Republic of Belarus in the Sphere of the Processing Industry

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    In connection with the processes of the formation of the Union State of Russia and Belarus, the relevance of conducting a study of economic, infrastructural and institutional factors affecting the change in the level of economic interaction between the regions of the Russian Federation and the Republic of Belarus is increasing. The aim of the work is to carry out spatial modeling of the possible interaction of the regions of the Russian Federation and the Republic of Belarus in the manufacturing industry and to assess the factors affecting this interaction. The main hypothesis of the study is the assumption that the elements of the matrix of interregional interactions are proxy variables that characterize the degree of this interaction. At the first stage, the spatial distribution of the volume of output in the manufacturing sector of the regions of the two countries is investigated in order to assess possibilities of interaction between the regions in this sector. In modeling, the Republic of Belarus is considered as a separate region within the Union State. Calculations of the global and local Moran’s indices have been carried out and possible spatial autocorrelations have been determined, both between the regions of the Russian Federation and between the regions of these two countries. In this study, economic indicators calculated on the basis of inverse values of the difference in interregional gross regional products were selected as elements of the weight matrix. At the second stage, the influence of economic, infrastructural and institutional factors on the indicator characterizing the degree of possible interaction of the regions of the two countries in the manufacturing industry was studied. Using quantile regression, the influence of economic, infrastructural and institutional factors on this investigated indicator was studied. The use of this approach makes it possible to substantiate the priority directions of economic development of the territories within the framework of the Union State and, in particular, to search for centers of attraction of resources and spheres of their influence on the territory. The results of the work can be used in preparation of strategies, programs and schemes for the placement and development of industries, taking into account the potential of a new level of integration of the economies of Russia and Belarus.Π’ связи с процСссами формирования Боюзного государства возрастаСт Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ провСдСния исслСдования экономичСских, инфраструктурных ΠΈ ΠΈΠ½ΡΡ‚ΠΈΡ‚ΡƒΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ уровня экономичСского взаимодСйствия ΡΡƒΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ РСспублики Π‘Π΅Π»Π°Ρ€ΡƒΡΡŒ. ЦСлью Ρ€Π°Π±ΠΎΡ‚Ρ‹ являСтся ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ пространствСнного модСлирования Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΠ³ΠΎ взаимодСйствия Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ РСспублики Π‘Π΅Π»Π°Ρ€ΡƒΡΡŒ Π² сфСрС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ ΠΈ ΠΎΡ†Π΅Π½ΠΊΠ΅ Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° это взаимодСйствиС. Основной Π³ΠΈΠΏΠΎΡ‚Π΅Π·ΠΎΠΉ исслСдования являСтся ΠΏΡ€Π΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΠΎ Ρ‚ΠΎΠΌ, Ρ‡Ρ‚ΠΎ элСмСнты ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Ρ‹ ΠΌΠ΅ΠΆΡ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… взаимовлияний ΡΠ²Π»ΡΡŽΡ‚ΡΡ прокси-ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹ΠΌΠΈ, Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡŽΡ‰ΠΈΠΌΠΈ ΡΡ‚Π΅ΠΏΠ΅Π½ΡŒ этого взаимовлияния. На ΠΏΠ΅Ρ€Π²ΠΎΠΌ этапС исслСдуСтся пространствСнноС распрСдСлСниС объСма выпуска Π² сСкторС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² ΡƒΠΊΠ°Π·Π°Π½Π½Ρ‹Ρ… стран с Ρ†Π΅Π»ΡŒΡŽ ΠΎΡ†Π΅Π½ΠΊΠΈ возмоТностСй взаимодСйствия этих Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² Π² Π΄Π°Π½Π½ΠΎΠΌ сСкторС экономики. ΠŸΡ€ΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠΈ РСспублика Π‘Π΅Π»Π°Ρ€ΡƒΡΡŒ рассматриваСтся ΠΊΠ°ΠΊ ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹ΠΉ Ρ€Π΅Π³ΠΈΠΎΠ½ Π² Ρ€Π°ΠΌΠΊΠ°Ρ… Боюзного государства. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½Ρ‹ расчСты глобального ΠΈ локального индСксов ΠœΠΎΡ€Π°Π½Π° ΠΈ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Ρ‹ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ‹Π΅ пространствСнныС автокоррСляции ΠΊΠ°ΠΊ ΠΌΠ΅ΠΆΠ΄Ρƒ ΡΡƒΠ±ΡŠΠ΅ΠΊΡ‚Π°ΠΌΠΈ Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ, Ρ‚Π°ΠΊ ΠΈ ΠΌΠ΅ΠΆΠ΄Ρƒ Ρ€Π΅Π³ΠΈΠΎΠ½Π°ΠΌΠΈ ΡƒΠΊΠ°Π·Π°Π½Π½Ρ‹Ρ… стран. Π’ ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠΌΠΎΠΌ исслСдовании Π² качСствС элСмСнтов вСсовой ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Ρ‹ Π²Ρ‹Π±Ρ€Π°Π½Ρ‹ экономичСскиС ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ, рассчитанныС Π½Π° основС ΠΎΠ±Ρ€Π°Ρ‚Π½Ρ‹Ρ… Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ Ρ€Π°Π·Π½ΠΈΡ†Ρ‹ ΠΌΠ΅ΠΆΡ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Π²Π°Π»ΠΎΠ²Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ². На Π²Ρ‚ΠΎΡ€ΠΎΠΌ этапС ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΎ влияниС экономичСских, инфраструктурных ΠΈ ΠΈΠ½ΡΡ‚ΠΈΡ‚ΡƒΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² Π½Π° ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ, Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡŽΡ‰ΠΈΠΉ ΡΡ‚Π΅ΠΏΠ΅Π½ΡŒ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΠ³ΠΎ взаимодСйствия Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² ΡƒΠΊΠ°Π·Π°Π½Π½Ρ‹Ρ… стран Π² сфСрС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ. Π‘ ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ ΠΊΠ²Π°Π½Ρ‚ΠΈΠ»ΡŒΠ½ΠΎΠΉ рСгрСссии ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΎ влияниС экономичСских, инфраструктурных ΠΈ ΠΈΠ½ΡΡ‚ΠΈΡ‚ΡƒΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² Π½Π° этот исслСдуСмый ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ. ИспользованиС Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Π° позволяСт ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Ρ‚ΡŒ ΠΏΡ€ΠΈΠΎΡ€ΠΈΡ‚Π΅Ρ‚Π½Ρ‹Π΅ направлСния экономичСского развития Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΉ Π² Ρ€Π°ΠΌΠΊΠ°Ρ… Боюзного государства, Π² частности ΠΎΡΡƒΡ‰Π΅ΡΡ‚Π²ΠΈΡ‚ΡŒ поиск Ρ†Π΅Π½Ρ‚Ρ€ΠΎΠ² притяТСния рСсурсов ΠΈ сфСр ΠΈΡ… влияния Π½Π° Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΈ. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ Ρ€Π°Π±ΠΎΡ‚Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ ΠΏΡ€ΠΈ ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠ΅ стратСгий, ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ ΠΈ схСм размСщСния ΠΈ развития отраслСй с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»Π° Π½ΠΎΠ²ΠΎΠ³ΠΎ уровня ΠΈΠ½Ρ‚Π΅Π³Ρ€Π°Ρ†ΠΈΠΈ экономик России ΠΈ БСлоруссии.Π’ связи с процСссами формирования Боюзного государства возрастаСт Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ провСдСния исслСдования экономичСских, инфраструктурных ΠΈ ΠΈΠ½ΡΡ‚ΠΈΡ‚ΡƒΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ уровня экономичСского взаимодСйствия ΡΡƒΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ² Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ РСспублики Π‘Π΅Π»Π°Ρ€ΡƒΡΡŒ. ЦСлью Ρ€Π°Π±ΠΎΡ‚Ρ‹ являСтся ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ пространствСнного модСлирования Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΠ³ΠΎ взаимодСйствия Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ РСспублики Π‘Π΅Π»Π°Ρ€ΡƒΡΡŒ Π² сфСрС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ ΠΈ ΠΎΡ†Π΅Π½ΠΊΠ΅ Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° это взаимодСйствиС. Основной Π³ΠΈΠΏΠΎΡ‚Π΅Π·ΠΎΠΉ исслСдования являСтся ΠΏΡ€Π΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΠΎ Ρ‚ΠΎΠΌ, Ρ‡Ρ‚ΠΎ элСмСнты ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Ρ‹ ΠΌΠ΅ΠΆΡ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… взаимовлияний ΡΠ²Π»ΡΡŽΡ‚ΡΡ прокси-ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹ΠΌΠΈ, Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡŽΡ‰ΠΈΠΌΠΈ ΡΡ‚Π΅ΠΏΠ΅Π½ΡŒ этого взаимовлияния. На ΠΏΠ΅Ρ€Π²ΠΎΠΌ этапС исслСдуСтся пространствСнноС распрСдСлСниС объСма выпуска Π² сСкторС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² ΡƒΠΊΠ°Π·Π°Π½Π½Ρ‹Ρ… стран с Ρ†Π΅Π»ΡŒΡŽ ΠΎΡ†Π΅Π½ΠΊΠΈ возмоТностСй взаимодСйствия этих Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² Π² Π΄Π°Π½Π½ΠΎΠΌ сСкторС экономики. ΠŸΡ€ΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠΈ РСспублика Π‘Π΅Π»Π°Ρ€ΡƒΡΡŒ рассматриваСтся ΠΊΠ°ΠΊ ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹ΠΉ Ρ€Π΅Π³ΠΈΠΎΠ½ Π² Ρ€Π°ΠΌΠΊΠ°Ρ… Боюзного государства. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½Ρ‹ расчСты глобального ΠΈ локального индСксов ΠœΠΎΡ€Π°Π½Π° ΠΈ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Ρ‹ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ‹Π΅ пространствСнныС автокоррСляции ΠΊΠ°ΠΊ ΠΌΠ΅ΠΆΠ΄Ρƒ ΡΡƒΠ±ΡŠΠ΅ΠΊΡ‚Π°ΠΌΠΈ Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ, Ρ‚Π°ΠΊ ΠΈ ΠΌΠ΅ΠΆΠ΄Ρƒ Ρ€Π΅Π³ΠΈΠΎΠ½Π°ΠΌΠΈ ΡƒΠΊΠ°Π·Π°Π½Π½Ρ‹Ρ… стран. Π’ ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠΌΠΎΠΌ исслСдовании Π² качСствС элСмСнтов вСсовой ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Ρ‹ Π²Ρ‹Π±Ρ€Π°Π½Ρ‹ экономичСскиС ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ, рассчитанныС Π½Π° основС ΠΎΠ±Ρ€Π°Ρ‚Π½Ρ‹Ρ… Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ Ρ€Π°Π·Π½ΠΈΡ†Ρ‹ ΠΌΠ΅ΠΆΡ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Π²Π°Π»ΠΎΠ²Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ². На Π²Ρ‚ΠΎΡ€ΠΎΠΌ этапС ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΎ влияниС экономичСских, инфраструктурных ΠΈ ΠΈΠ½ΡΡ‚ΠΈΡ‚ΡƒΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² Π½Π° ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ, Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡŽΡ‰ΠΈΠΉ ΡΡ‚Π΅ΠΏΠ΅Π½ΡŒ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΠ³ΠΎ взаимодСйствия Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² ΡƒΠΊΠ°Π·Π°Π½Π½Ρ‹Ρ… стран Π² сфСрС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ. Π‘ ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ ΠΊΠ²Π°Π½Ρ‚ΠΈΠ»ΡŒΠ½ΠΎΠΉ рСгрСссии ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΎ влияниС экономичСских, инфраструктурных ΠΈ ΠΈΠ½ΡΡ‚ΠΈΡ‚ΡƒΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² Π½Π° этот исслСдуСмый ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ. ИспользованиС Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Π° позволяСт ΠΎΠ±ΠΎΡΠ½ΠΎΠ²Π°Ρ‚ΡŒ ΠΏΡ€ΠΈΠΎΡ€ΠΈΡ‚Π΅Ρ‚Π½Ρ‹Π΅ направлСния экономичСского развития Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΉ Π² Ρ€Π°ΠΌΠΊΠ°Ρ… Боюзного государства, Π² частности ΠΎΡΡƒΡ‰Π΅ΡΡ‚Π²ΠΈΡ‚ΡŒ поиск Ρ†Π΅Π½Ρ‚Ρ€ΠΎΠ² притяТСния рСсурсов ΠΈ сфСр ΠΈΡ… влияния Π½Π° Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΈ. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ Ρ€Π°Π±ΠΎΡ‚Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ ΠΏΡ€ΠΈ ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠ΅ стратСгий, ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ ΠΈ схСм размСщСния ΠΈ развития отраслСй с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»Π° Π½ΠΎΠ²ΠΎΠ³ΠΎ уровня ΠΈΠ½Ρ‚Π΅Π³Ρ€Π°Ρ†ΠΈΠΈ экономик России ΠΈ БСлоруссии.The study was supported by the RFBR grant 20-510-0002 (Bel_a) Β«Tools for assessing the interaction of the regions of Russia and Belarus in industrial and technological development and substantiating its priorities in the context of deepening integration processes and global challengesΒ».ИсслСдованиС Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΏΡ€ΠΈ ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠ΅ Π³Ρ€Π°Π½Ρ‚Π° РЀЀИ 20-510-0002 (Π‘Π΅Π»_Π°) Β«Π˜Π½ΡΡ‚Ρ€ΡƒΠΌΠ΅Π½Ρ‚Π°Ρ€ΠΈΠΉ ΠΎΡ†Π΅Π½ΠΊΠΈ Π² заимодСйствия Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² России ΠΈ БСларуси Π² ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎ-тСхнологичСском Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΠΈ ΠΈ обоснования Π΅Π³ΠΎ ΠΏΡ€ΠΈΠΎΡ€ΠΈΡ‚Π΅Ρ‚ΠΎΠ² Π² условиях углублСния ΠΈΠ½Ρ‚Π΅Π³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… процСссов ΠΈ Π³Π»ΠΎΠ±Π°Π»ΡŒΠ½Ρ‹Ρ… Π²Ρ‹Π·ΠΎΠ²ΠΎΠ²Β»

    Assessment of Energy Supply to the Yamal Peninsula Based on Fuzzy Multicriteria Analysis

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    Π’Π°ΠΆΠ½Π΅ΠΉΡˆΠΈΠΌ условиСм ΠΏΠΎΠ»Π½ΠΎΡ†Π΅Π½Π½ΠΎΠ³ΠΎ вовлСчСния вновь осваиваСмой Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΈ являСтся обСспСчСниС Π΅Π΅ ΠΏΠΎΠ»Π΅Π·Π½ΠΎΠΉ энСргиСй. Для Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΉ Π½ΠΎΠ²ΠΎΠ³ΠΎ освоСния, Ρ‚Π°ΠΊΠΈΡ… ΠΊΠ°ΠΊ полуостров Π―ΠΌΠ°Π», ΡΡƒΡ‰Π΅ΡΡ‚Π²ΡƒΡŽΡ‚ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Π΅ энСргСтичСскиС Π°Π»ΡŒΡ‚Π΅Ρ€Π½Π°Ρ‚ΠΈΠ²Ρ‹. Π’ настоящСй ΡΡ‚Π°Ρ‚ΡŒΠ΅ Π½Π° основС извСстных Π±Π°Π·ΠΎΠ²Ρ‹Ρ… ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ² Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π° ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Π° Ρ„ΠΎΡ€ΠΌΠ°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ Π°Π½Π°Π»ΠΈΠ·Π° Π°Π»ΡŒΡ‚Π΅Ρ€Π½Π°Ρ‚ΠΈΠ² Π½Π° основС прСимущСствСнно качСствСнной исходной ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ. На ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΎΠ΅ΠΊΡ‚Π½ΠΎΠΉ стадии приходится Π΄Π΅ΠΉΡΡ‚Π²ΠΎΠ²Π°Ρ‚ΡŒ Π² условиях ΠΎΠ³Ρ€Π°Π½ΠΈΡ‡Π΅Π½Π½ΠΎΠΉ ΠΈ Π½Π΅Ρ‡Π΅Ρ‚ΠΊΠΎΠΉ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ, сконцСнтрированной Π² основном Π² экспСртном ΠΎΠΏΡ‹Ρ‚Π΅. Для Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Ρ‡ΠΈ ΠΎΡ‚ΠΎΠ±Ρ€Π°Π½Ρ‹ ΡˆΠ΅ΡΡ‚ΡŒ ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠ΅Π² для экспСртных ΠΎΡ†Π΅Π½ΠΎΠΊ. Π˜Ρ… ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π° с использованиСм Π½Π΅Ρ‡Π΅Ρ‚ΠΊΠΈΡ… ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ Π²Ρ‹Π±ΠΎΡ€Π° Π½Π° основС Π½Π΅Ρ‡Π΅Ρ‚ΠΊΠΎΠ³ΠΎ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π²Π°Ρ€ΠΈΠ°Π½Ρ‚ΠΎΠ², ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‰ΠΈΡ… Ρ„ΠΎΡ€ΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Ρ‚ΡŒ логичСскиС рассуТдСния с расплывчатыми ΠΈΠ»ΠΈ Π½Π΅Ρ‚ΠΎΡ‡Π½Ρ‹ΠΌΠΈ утвСрТдСниями. ΠŸΠΎΡΠΊΠΎΠ»ΡŒΠΊΡƒ Π² Ρ‚Π°ΠΊΠΈΡ… условиях отсутствуСт достаточный для конструирования Π½Π΅Ρ‡Π΅Ρ‚ΠΊΠΈΡ… мноТСств Π½Π°Π±ΠΎΡ€ ΠΈΠ·ΠΌΠ΅Ρ€ΠΈΠΌΡ‹Ρ… свойств, ΡƒΡΠΏΠ΅ΡˆΠ½ΠΎ Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½Π° формализация лингвистичСских ΠΎΡ†Π΅Π½ΠΎΠΊ ΠΈΠ½Ρ‚ΡƒΠΈΡ‚ΠΈΠ²Π½Ρ‹Ρ… ΠΈΠ»ΠΈ логичСских ΠΏΠΎΠΏΠ°Ρ€Π½Ρ‹Ρ… сравнСний с использованиСм схСмы Π‘Π΅Π»Π»ΠΌΠ°Π½Π° - Π—Π°Π΄Π΅, ΡˆΠΊΠ°Π»Ρ‹ Π‘Π°Π°Ρ‚ΠΈ ΠΈ построСния Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΉ принадлСТности. ИскомоС Π½Π΅Ρ‡Π΅Ρ‚ΠΊΠΎΠ΅ мноТСство ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½ΠΎ Π½Π°Ρ…ΠΎΠΆΠ΄Π΅Π½ΠΈΠ΅ΠΌ собствСнного Π²Π΅ΠΊΡ‚ΠΎΡ€Π° ΠΈ наибольшСго собствСнного числа для ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Ρ‹ ΠΏΠΎΠΏΠ°Ρ€Π½Ρ‹Ρ… сравнСний. Π›ΠΎΠ³ΠΈΠΊΠ° Π²Ρ‹Π±ΠΎΡ€Π° Π²Π°Ρ€ΠΈΠ°Π½Ρ‚Π° строится ΠΏΠΎ максиминному ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΡŽ, Π΄ΠΎΠΏΠΎΠ»Π½Π΅Π½Π½ΠΎΠΌΡƒ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Ρ‹ уступок, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‰ΠΈΠΌ Ρ€Π°Π·Π»ΠΈΡ‡ΠΈΡ‚ΡŒ Π°Π»ΡŒΡ‚Π΅Ρ€Π½Π°Ρ‚ΠΈΠ²Ρ‹ ΠΏΡ€ΠΈ ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½ΠΈΠΈ ΠΎΠ΄ΠΈΠ½Π°ΠΊΠΎΠ²Ρ‹Ρ… ΠΈΠ»ΠΈ Π±Π»ΠΈΠ·ΠΊΠΈΡ… ΠΈΡ… ΠΎΡ†Π΅Π½ΠΎΠΊ. Π’ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π΅ исслСдования выявлСна ΠΏΡ€ΠΈΠΎΡ€ΠΈΡ‚Π΅Ρ‚Π½ΠΎΡΡ‚ΡŒ дивСрсификации ΠΏΠ΅Ρ€Π²ΠΈΡ‡Π½Ρ‹Ρ… энСргоноситСлСй для обСспСчСния ΠΏΠΎΠ»Π΅Π·Π½ΠΎΠΉ энСргиСй ΠΏΠΎΡ‚Ρ€Π΅Π±ΠΈΡ‚Π΅Π»Π΅ΠΉ Π―ΠΌΠ°Π»Π°. Π’Π°ΠΊΠΈΠΌ Π΄ΠΈΠ²Π΅Ρ€ΡΠΈΡ„ΠΈΡ†ΠΈΡ€ΡƒΡŽΡ‰ΠΈΠΌ Π²Ρ‹Π±ΠΎΡ€ΠΎΠΌ энСргоисточника, согласно ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π½ΠΎΠΌΡƒ ΠΌΠΎΠ΄Π΅Π»ΡŒΠ½ΠΎΠΌΡƒ экспСримСнту, оказалось ΠΏΡ€Π΅Π΄ΠΏΠΎΡ‡Ρ‚Π΅Π½ΠΈΠ΅ Π°Ρ‚ΠΎΠΌΠ½ΠΎΠΉ энСргии. На Π²Ρ‚ΠΎΡ€ΠΎΠΌ мСстС ΠΏΠΎ ΠΏΡ€Π΅Π΄ΠΏΠΎΡ‡Ρ‚ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ - Π³Π°Π·ΠΎΡ‚ΡƒΡ€Π±ΠΈΠ½Π½Ρ‹Π΅ Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ, сущСствСнно ΠΎΠΏΠ΅Ρ€Π΅ΠΆΠ°ΡŽΡ‰ΠΈΠ΅ вСтроэнСргСтичСскиС источники. Π—Π°ΠΌΡ‹ΠΊΠ°ΡŽΡ‚ ряд ΠΏΠΎ ΠΏΡ€Π΅Π΄ΠΏΠΎΡ‡Ρ‚ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ дизСль-Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€Π½Ρ‹Π΅ установки. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±Ρ€Π°Π·ΠΎΠΌ, Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ модСлирования Π² Π½Π΅Ρ‡Π΅Ρ‚ΠΊΠΎΠΉ срСдС Ρ…ΠΎΡ€ΠΎΡˆΠΎ ΠΊΠΎΡ€Ρ€Π΅ΡΠΏΠΎΠ½Π΄ΠΈΡ€ΡƒΡŽΡ‚ΡΡ с ΠΎΠ±ΡŠΡΡΠ½ΡΡŽΡ‰ΠΈΠΌΠΈ Ρ„Π°ΠΊΡ‚ΠΎΡ€Π°ΠΌΠΈ Π²Ρ‹Π±ΠΎΡ€Π°. Π’ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅ ΠΌΠ΅Ρ‚ΠΎΠ΄Π° Π½Π΅Ρ‡Π΅Ρ‚ΠΊΠΈΡ… мноТСств с ΠΏΡ€Π΅ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ лингвистичСских ΠΎΡ†Π΅Π½ΠΎΠΊ Π² количСствСнныС Π² модСльно-мСтодичСский Π°ΠΏΠΏΠ°Ρ€Π°Ρ‚ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ Π²Ρ‹Π±ΠΎΡ€Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ ΠΈ Π² ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠΈ ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… Π²Π°Ρ€ΠΈΠ°Π½Ρ‚ΠΎΠ². Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ Ρ€Π°Π±ΠΎΡ‚Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ ΠΏΡ€ΠΈ ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠ΅ комплСксных стратСгий ΠΈ схСм размСщСния отрасли спСциализации ΠΈ Π΅Π΅ энСргСтичСской инфраструктуры для ΡƒΠ΄Π°Π»Π΅Π½Π½Ρ‹Ρ… арктичСских Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΉ.Provision of useful energy is the most important condition for realising the potential of underdeveloped regions. For new development areas, including the Yamal Peninsula, there are various energy alternatives. Based on well-known basic approaches, the article presents a procedure for formalising the analysis of alternatives mostly using qualitative initial data. At the pre-project stage, only limited and fuzzy information (predominantly in the form of expert opinions) is accessible. To achieve the set goal, six criteria for export assessment were selected and further processed using fuzzy multicriteria decision-making models based on fuzzy multicriteria analysis of alternatives in order to formalise logical reasoning with vague or imprecise statements. Due to insufficient measurements for constructing fuzzy sets, linguistic estimates of intuitive or logical pairwise comparisons were formalised using the Bellman-Zadeh model, the Saaty scale and the construction of membership functions. The fuzzy set was obtained by finding the eigenvector and the largest eigenvalue for the pairwise comparison matrix. Implementation of the maximin criterion along with the concession matrix allowed us to distinguish between alternatives when obtaining the same or similar estimates. As a result, the study showed the priority of diversification of primary energy source to provide useful energy to consumers in Yamal. According to the model, the most preferable source is atomic energy. In second place are gas turbine technologies, which are significantly ahead of wind energy sources. Diesel generators are considered the least favourable. Thus, the results of fuzzy modelling correspond with the explanatory factors of choice. The fuzzy set method with the transformation of linguistic estimates into quantitative ones can also be included in the apparatus of multicriteria selection with respect to combined options. The research findings can be used to prepare comprehensive strategies and schemes for location of industries and the energy infrastructure in remote Arctic territories.Π Π°Π±ΠΎΡ‚Π° Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½Π° соотвСтствии с ΠΏΠ»Π°Π½ΠΎΠΌ Π½Π°ΡƒΡ‡Π½Ρ‹Ρ… исслСдований Π˜Π½ΡΡ‚ΠΈΡ‚ΡƒΡ‚Π° экономики Π£Ρ€Πž РАН Π½Π° 2022 Π³ΠΎΠ΄.The article has been prepared in accordance with the plan of the Institute of Economics of the Ural Branch of RAS for 2022

    Siliceous microfossil distribution in the surficial sediments of Lake Baikal

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    Examination of surficial sediments at 16 stations shows minor, but consistent differences in the numbers and kinds of siliceous microfossils deposited in different regions of Lake Baikal. There is a general north-south decreasing trend in total microfossil abundance on a weight basis. Endemic plankton diatom species are the most abundant component of assemblages at all stations. Chrysophyte cysts are present at all stations, but most forms are more abundant at northern stations. Non-endemic plankton diatom species are most abundant at southern stations. Small numbers of benthic diatoms and sponge spicules are found in all samples. Although low numbers are present in offshore sediments, the benthic diatom flora is very diverse. Principal components analysis confirms primary north-south abundance trends and suggests further differentiation by station location and depth.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43071/1/10933_2004_Article_BF00682594.pd

    Modelling the Heterogeneity of the Mutual Influence between Russian Regions in the Manufacturing Industry

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    Π’Π°ΠΆΠ½Ρ‹ΠΌ аспСктом эффСктивного экономичСского развития Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² являСтся Π°Π½Π°Π»ΠΈΠ· Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° ΠΌΠ΅ΠΆΡ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Π΅ взаимодСйствия. Π’ связи с этим ΠΏΡ€ΠΈΠΎΠ±Ρ€Π΅Ρ‚Π°Π΅Ρ‚ Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° инструмСнтария ΠΎΡ†Π΅Π½ΠΊΠΈ этого влияния. Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Ρ‹ мСтодологичСский ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ ΠΈ ΠΎΡ€ΠΈΠ³ΠΈΠ½Π°Π»ΡŒΠ½Ρ‹ΠΉ инструмСнтарий для исслСдования Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², Π²Π»ΠΈΡΡŽΡ‰ΠΈΡ… Π½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ‹Π΅ ΠΌΠ΅ΠΆΡ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Π΅ взаимодСйствия БвСрдловской области с ΠΎΡΡ‚Π°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΡΡƒΠ±ΡŠΠ΅ΠΊΡ‚Π°ΠΌΠΈ Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ Π² сфСрС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ. Основной Π³ΠΈΠΏΠΎΡ‚Π΅Π·ΠΎΠΉ исслСдования являСтся ΠΏΡ€Π΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅, Ρ‡Ρ‚ΠΎ элСмСнты ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Ρ‹ ΠΌΠ΅ΠΆΡ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… взаимовлияний ΡΠ²Π»ΡΡŽΡ‚ΡΡ прокси-ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹ΠΌΠΈ, Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡŽΡ‰ΠΈΠΌΠΈ ΡΡ‚Π΅ΠΏΠ΅Π½ΡŒ этого взаимовлияния. ΠžΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΠΎΡΡ‚ΡŒ Π΄Π°Π½Π½ΠΎΠΉ Π³ΠΈΠΏΠΎΡ‚Π΅Π·Ρ‹ ΠΏΠΎΠ΄Ρ‚Π²Π΅Ρ€ΠΆΠ΄Π΅Π½Π° ΡΠΎΠΎΡ‚Π²Π΅Ρ‚ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΠΌ экономичСским Π°Π½Π°Π»ΠΈΠ·ΠΎΠΌ наличия взаимосвязСй ΠΈ производствСнных Ρ†Π΅ΠΏΠΎΡ‡Π΅ΠΊ ΠΌΠ΅ΠΆΠ΄Ρƒ БвСрдловской ΠΎΠ±Π»Π°ΡΡ‚ΡŒΡŽ ΠΈ Ρ€Π΅Π³ΠΈΠΎΠ½Π°ΠΌΠΈ Π Π€. На ΠΏΠ΅Ρ€Π²ΠΎΠΌ этапС исслСдуСтся пространствСнноС распрСдСлСниС объСма выпуска Π² сСкторС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ БвСрдловской области ΠΈ ΠΎΡΡ‚Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² Π Π€ с Ρ†Π΅Π»ΡŒΡŽ опрСдСлСния Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ показатСля силы взаимовлияния ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹Ρ… Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΉ Π² сфСрС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ. На Π²Ρ‚ΠΎΡ€ΠΎΠΌ этапС с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ ΠΊΠ²Π°Π½Ρ‚ΠΈΠ»ΡŒΠ½ΠΎΠΉ рСгрСссии ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΎ влияниС экономичСских, инфраструктурных ΠΈ ΠΈΠ½ΡΡ‚ΠΈΡ‚ΡƒΡ†ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² Π½Π° ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ, ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹ΠΉ Π½Π° ΠΏΠ΅Ρ€Π²ΠΎΠΌ этапС, Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΡƒΡŽΡ‰ΠΈΠΉ ΡΡ‚Π΅ΠΏΠ΅Π½ΡŒ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΠ³ΠΎ взаимодСйствия БвСрдловской области ΠΈ ΠΎΡΡ‚Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² Π Π€ Π² сфСрС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ. Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ доказываСтся ΠΏΡ€Π°Π²ΠΎΠΌΠ΅Ρ€Π½ΠΎΡΡ‚ΡŒ примСнСния инструмСнтария ΠΊΠ²Π°Π½Ρ‚ΠΈΠ»ΡŒΠ½ΠΎΠΉ рСгрСссии, Ρ‚Π°ΠΊ ΠΊΠ°ΠΊ классичСская рСгрСссия МНК Π΄Π°Π΅Ρ‚ Π½Π΅ΠΊΠΎΡ€Ρ€Π΅ΠΊΡ‚Π½Ρ‹Π΅ ΠΎΡ†Π΅Π½ΠΊΠΈ зависимостСй ΠΌΠ΅ΠΆΠ΄Ρƒ исслСдуСмыми ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹ΠΌΠΈ. Π­Ρ‚ΠΎ выраТаСтся Π² Ρ‚ΠΎΠΌ, Ρ‡Ρ‚ΠΎ коэффициСнты рСгрСссии зависят ΠΎΡ‚ уровня q-квантиля зависимой ΠΏΠ΅Ρ€Π΅ΠΌΠ΅Π½Π½ΠΎΠΉ. ВыявлСно, Ρ‡Ρ‚ΠΎ ΡƒΡ€ΠΎΠ²Π΅Π½ΡŒ Ρ†Π΅Π½ Π² Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ… Π½Π΅ ΠΎΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚ влияния Π½Π° ΠΈΡ… Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΠ΅ взаимодСйствиС со БвСрдловской ΠΎΠ±Π»Π°ΡΡ‚ΡŒΡŽ. Π’Π°ΠΊΠΆΠ΅ слСдуСт ΠΎΡ‚ΠΌΠ΅Ρ‚ΠΈΡ‚ΡŒ, Ρ‡Ρ‚ΠΎ распространСниС Π·Π½Π°Π½ΠΈΠΉ являСтся Π΄Ρ€Π°ΠΉΠ²Π΅Ρ€ΠΎΠΌ взаимодСйствия Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² Π² сфСрС ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‰Π΅ΠΉ ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ Ρ€Π°Π±ΠΎΡ‚Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ ΠΏΡ€ΠΈ ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠ΅ стратСгий, ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ ΠΈ схСм размСщСния ΠΈ развития отраслСй с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»Π° развития БвСрдловской области с ΠΎΡΡ‚Π°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΡΡƒΠ±ΡŠΠ΅ΠΊΡ‚Π°ΠΌΠΈ Π Π€.As factors affecting interregional interactions play an important role in regional economic development. Thus, developing a methodology for assessing these interactions is becoming urgent. The article proposes a methodological approach to analyse the factors influencing possible interactions between Sverdlovsk oblast and other constituent entities of the Russian Federation in the manufacturing industry. It is hypothesised that the elements of an interregional interaction matrix are proxy variables characterising the degree of this interaction. An economic analysis of relations and production chains between Sverdlovsk oblast and other constituent entitles confirmed this hypothesis. First, based on the spatial distribution of manufacturing output in the examined regions, values of an indicator showing the strength of their mutual influence were determined. Second, the impact of economic, infrastructural and institutional factors on the obtained indicator, characterising the interaction between Sverdlovsk oblast and other regions, was assessed using quantile regression. In this case, such a technique was chosen instead of the classical ordinary least squares (OLS) regression that incorrectly estimates the dependencies between the studied variables. This is expressed in the fact that the regression coefficients depend on q-quantile of the dependent variable. We have revealed that price levels of the examined regions do not affect their possible interactions with Sverdlovsk oblast. Simultaneously, the dissemination of knowledge acts a driver of interaction between the considered regional manufacturing industries. The research findings can be used to prepare strategies, programmes and schemes for the placement and development of industries, considering the potential of Sverdlovsk oblast and other Russian regions.Π‘Ρ‚Π°Ρ‚ΡŒΡ ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²Π»Π΅Π½Π° Π² соотвСтствии с ΡƒΡ‚Π²Π΅Ρ€ΠΆΠ΄Π΅Π½Π½Ρ‹ΠΌ ΠΏΠ»Π°Π½ΠΎΠΌ НИР Π˜Π½ΡΡ‚ΠΈΡ‚ΡƒΡ‚Π° экономики Π£Ρ€Πž РАН.The article has been prepared in accordance with the plan of Institute of Economics of the Ural Branch of RAS for 2021–2023

    The β€œMelosira years” of Lake Baikal: Winter environmental conditions at ice onset predict under‐ice algal blooms in spring

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    Winter primary production in seasonally ice‐covered lakes historically has not been well studied, but it is increasingly recognized as an important component of lake metabolism. Lake Baikal in Siberia is not only the World's oldest, deepest, and most biologically diverse lake, but also where large under‐ice blooms of the diatom Aulacoseira baicalensis (formerly Melosira) occur in some years. The phenomenon of β€œMelosira years” is noteworthy both for the intensity of the diatom blooms, in which total under‐ice production can be a majority of total annual production, and for the enigmatic regularity of their occurrence every 3–4 yr. The degree to which these episodic blooms might be controlled by external forcing and endogenous lake processes has been debated for decades. We used a 50‐yr time series of phytoplankton observations to statistically model the occurrence of Aulacoseira blooms as a function of meteorological and climatological predictor variables. The results support the hypothesis that a confluence of meteorological conditions in the preceding fall season, which favor clear ice formation with minimal snow cover, also favor Aulacoseira blooms in the following spring. Further, we observe that this confluence of factors is related to relatively strong states of the Siberian High which, while not strictly periodic, do explain a significant fraction of the interannual bloom pattern. Finally, our analyses show that the timing of the peak abundance of A. baicalensis shifted 1.6 months later across the 50‐yr time series, corresponding with the delay in ice‐on timing that has been associated with climate change
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