14 research outputs found

    Pan-Eurasian Experiment (PEEX) : towards a holistic understanding of the feedbacks and interactions in the land–atmosphere–ocean–society continuum in the northern Eurasian region

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    Contributors: Hanna K. Lappalainen1,2, Veli-Matti Kerminen1, Tuukka Petäjä1, Theo Kurten3, Aleksander Baklanov4,5, Anatoly Shvidenko6, Jaana Bäck7, Timo Vihma2, Pavel Alekseychik1, Stephen Arnold8, Mikhail Arshinov9, Eija Asmi2, Boris Belan9, Leonid Bobylev10, Sergey Chalov11, Yafang Cheng12, Natalia Chubarova11, Gerrit de Leeuw1,2, Aijun Ding13, Sergey Dobrolyubov11, Sergei Dubtsov14, Egor Dyukarev15, Nikolai Elansky16, Kostas Eleftheriadis17, Igor Esau18, Nikolay Filatov19, Mikhail Flint20, Congbin Fu13, Olga Glezer21, Aleksander Gliko22, Martin Heimann23, Albert A. M. Holtslag24, Urmas Hõrrak25, Juha Janhunen26, Sirkku Juhola27, Leena Järvi1, Heikki Järvinen1, Anna Kanukhina28, Pavel Konstantinov11, Vladimir Kotlyakov29, Antti-Jussi Kieloaho1, Alexander S. Komarov30, Joni Kujansuu1, Ilmo Kukkonen31, Ella Kyrö1, Ari Laaksonen2, Tuomas Laurila2, Heikki Lihavainen2, Alexander Lisitzin32, Aleksander Mahura5, Alexander Makshtas33, Evgeny Mareev34, Stephany Mazon1, Dmitry Matishov35,†, Vladimir Melnikov36, Eugene Mikhailov37, Dmitri Moisseev1, Robert Nigmatulin33, Steffen M. Noe38, Anne Ojala7, Mari Pihlatie1, Olga Popovicheva39, Jukka Pumpanen40, Tatjana Regerand19, Irina Repina16, Aleksei Shcherbinin27, Vladimir Shevchenko33, Mikko Sipilä1, Andrey Skorokhod16, Dominick V. Spracklen8, Hang Su12, Dmitry A. Subetto19, Junying Sun41, Arkady Yu Terzhevik19, Yuri Timofeyev42, Yuliya Troitskaya34, Veli-Pekka Tynkkynen42, Viacheslav I. Kharuk43, Nina Zaytseva22, Jiahua Zhang44, Yrjö Viisanen2, Timo Vesala1, Pertti Hari7, Hans Christen Hansson45, Gennady G. Matvienko9, Nikolai S. Kasimov11, Huadong Guo44, Valery Bondur46, Sergej Zilitinkevich1,2,11,34, and Markku Kulmala1 1Department of Physics, University of Helsinki, 00014 Helsinki, Finland 2Finnish Meteorological Institute, Research and Development, 00101 Helsinki, Finland 3Department of Chemistry, University of Helsinki, 00014 Helsinki, Finland 4World Meteorological Organization, 1211 Genève, Switzerland 5Danish Meteorological Institute, Research and Development Department, 2100, Copenhagen 6International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria 7Department of Forest Sciences, University of Helsinki, 00014 Helsinki, Finland 8Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK 9Institute of Atmospheric Optics, Russian Academy of Sciences, Tomsk 634021, Russia 10Nansen International Environmental and Remote Sensing Center, St. Petersburg, Russia 11Lomonosov Moscow State University, Faculty of Geography, Moscow 119899, Russia 12Max Planck Institute for Chemistry, 55128 Mainz, Germany 13Institute for Climate and Global Change Research & School of Atmospheric Sciences, Nanjing University, 210023 Nanjing, China 14Institute of Chemical Kinetics & Combustion, Russian Academy of Sciences, 630090 Novosibirsk, Russia 15Institute of Monitoring of Climatic & Ecological Systems SB RAS, 634055 Tomsk, Russia 16A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Russia 17National Centre of Scientific Research "DEMOKRITOS", Greece 18Nansen Environmental and Remote Sensing Center/Bjerknes Centre for Climate Research, 5006 Bergen, Norway 19Northern Water Problems Institute, Karelian Research Center, Russian Academy of Sciences,185003 Petrozavodsk, Russia 20P. P. Shirshov, Institute of Oceanology, Russian Academy of Sciences, Russian Academy of Sciences, 117997 Moscow, Russia 21Institute of Geography, Russian Academy of Sciences, Moscow, Russia 22Depart ment of Earth Sciences of the Russian Academy of Sciences, Russian Academy of Sciences, 119991, Moscow, Russia 23Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany 24Wageningen University, 6708 Wageningen, Nederland 25Institute of Physics, University of Tartu, 18 Ülikooli St., 50090 Tartu, Estonia 26University of Helsinki, Department of World Cultures, 00014 Helsinki, Finland 27Department of Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland 28Russian State Hydrometeorological University, 195196 Saint Petersburg, Russia 29Institute of Geography, Russian Academy of Sciences, Moscow, Russia 30Institute of Physico-chemical & Biological Problems in Soil Science, Russian Academy of Sciences, 142290 Institutskaya, Russia 31University of Helsinki, Geophysics and Astronomy, 00014 Helsinki, Finland 32Shirshov Institute of Oceanology, Russian Academy of Sciences, 117997 Moscow, Russia 33Actic & Antarctic Research Institute, Russian Academy of Sciences, St. Petersburg 199397, Russia 34Department of Radiophysics, Nizhny Novgorod State University, Nizhny Novgorod, Russia 35Southern Center of Russian Academy of Sciences, Rostov on Don, Russia 36Tyumen Scientific Center, Siberian Branch, Russian Academy of Science, Russia 37Saint Petersburg State University, 7/9 Universitetskaya nab., St. Petersburg, 199034 Russia 38Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, 51014 Tartu, Estonia 39Skobeltsyn Institute of Nuclear Physics, Moscow State University, Department Microelectronics, Russia 40University of Eastern Finland, Department of Environmental Science, P.O. Box 1627, FI-70211 Kuopio, Finland 41Craduate University of Chinese Academy of Sciences, 100049 Beijing, China 42Aleksanteri Institute and Department of Social Research, 00014 University of Helsinki, Finland 43Sukachev Forest Institute, Russian Academy of Sciences, Krasnoyarsk 660036, Russia 44Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China 45Environmental Science and Analytical Chemistry, Stockholm University, Sweden 46AEROCOSMOS Research Institute for Aerospace Monitoring, 105064, Moscow, Russia †deceased, 20 August 2015The Northern Eurasian regions and Arctic Ocean will very likely undergo substantial changes during the next decades. The arctic-boreal natural environments play a crucial role in the global climate via the albedo change, carbon sources and sinks, as well as atmospheric aerosol production via biogenic volatile organic compounds. Furthermore, it is expected that the global trade activities, demographic movement and use of natural resources will be increasing in the Arctic regions. There is a need for a novel research approach, which not only identifies and tackles the relevant multi-disciplinary research questions, but is also able to make a holistic system analysis of the expected feedbacks. In this paper, we introduce the research agenda of the Pan-Eurasian Experiment (PEEX), a multi-scale, multi-disciplinary and international program started in 2012 (https://www.atm.helsinki.fi/peex/). PEEX is setting a research approach where large-scale research topics are investigated from a system perspective and which aims to fill the key gaps in our understanding of the feedbacks and interactions between the land–atmosphere–aquatic–society continuum in the Northern Eurasian region. We introduce here the state of the art of the key topics in the PEEX research agenda and give the future prospects of the research which we see relevant in this context.Peer reviewe

    Generalized Eshelby Problem in the Gradient Theory of Elasticity

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    A generalized Eshelby problem of arbitrary order in the gradient elasticity for a multilayer inclusions of spherical shape with a polynomial strain field at infinity is considered. For this problem we propose a constructive method solution in a closed finite form, using generalized Papkovich-Neuber representation and the system of canonical potentials based on harmonic polynomials. We use also the Gauss theorem on the decomposition of an arbitrary homogeneous polynomials. The solutions of the generalized Eshelby problem are applied in the method of asymptotic homogenization of the gradient elasticity to accurately calculation of the effective characteristics of composite materials with scale effects

    Effectiveness analysis of the kulback’s information measure in the technological objects monitoring

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    The technological objects’ state monitoring is focused on the following purposes: to ensure the production processes effective functioning and to improve the quality and competitiveness of the products. The aim of the proposed scientific approach is the improvement of the known statistical methods used for production processes monitoring. It allows to minimize the first and the second kind errors when evaluating the technological objects’ states. The usage of Kulback’s information measure allows to compress large amounts of data without losing their validity. This approach mostly satisfies the requirements for the implementation of known methods of technological objects’ state monitoring. These requirements are the following: processing of large amounts of current information for setting the parameters of the monitoring and control system; real time decision-making using the monitoring results on the production process’ state; timely detection of possible precritical conditions of the technological process, which can be associated with great economic damage or even catastrophic consequences

    Effectiveness analysis of the kulback’s information measure in the technological objects monitoring

    No full text
    The technological objects’ state monitoring is focused on the following purposes: to ensure the production processes effective functioning and to improve the quality and competitiveness of the products. The aim of the proposed scientific approach is the improvement of the known statistical methods used for production processes monitoring. It allows to minimize the first and the second kind errors when evaluating the technological objects’ states. The usage of Kulback’s information measure allows to compress large amounts of data without losing their validity. This approach mostly satisfies the requirements for the implementation of known methods of technological objects’ state monitoring. These requirements are the following: processing of large amounts of current information for setting the parameters of the monitoring and control system; real time decision-making using the monitoring results on the production process’ state; timely detection of possible precritical conditions of the technological process, which can be associated with great economic damage or even catastrophic consequences

    Modelling performing calculations over the data presented in a probabilistic form

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    The article presents the results of comparison of different forms of representation and transformation of information in the form of non-positional probabilistic display. Assessment of the hardware of the compute nodes and the analysis of the relationship between accuracy of representing the information and their performance have been carried out

    Detecting changes simulation of the technological objects’ information states

    No full text
    The purpose of the production processes’ states simulation has two constituents. The first is to ensure the effective operation of technological facilities. The second is to improve the quality and competitiveness of the produced products. The proposed new scientific approach aims to develop the known statistical methods in the following direction. They are to be adopted for monitoring accuracy and stability of production processes: real-time processing of large amount of current data, large volumes of a priori information used for decision-making, adjusting the parameters of the monitoring system, critical technological conditions detection. It can prevent great economic damage or even catastrophic consequences

    Modelling performing calculations over the data presented in a probabilistic form

    No full text
    The article presents the results of comparison of different forms of representation and transformation of information in the form of non-positional probabilistic display. Assessment of the hardware of the compute nodes and the analysis of the relationship between accuracy of representing the information and their performance have been carried out

    Modeling of monitoring processes of structurally heterogeneous technological objects

    No full text
    The tasks of technological information capture, taken from industrial enterprise monitoring systems, and its further transfer to the level of management and decision-making are considered in the article. The problems arising at the stage of integration of isolated automation systems into a single information space of the enterprise have been analyzed. A model of the monitoring system of structurally heterogeneous technological objects has been proposed. It allows to estimate events’ probabilities of the following types: successful information processing, computing equipment downtime

    Detecting changes simulation of the technological objects’ information states

    No full text
    The purpose of the production processes’ states simulation has two constituents. The first is to ensure the effective operation of technological facilities. The second is to improve the quality and competitiveness of the produced products. The proposed new scientific approach aims to develop the known statistical methods in the following direction. They are to be adopted for monitoring accuracy and stability of production processes: real-time processing of large amount of current data, large volumes of a priori information used for decision-making, adjusting the parameters of the monitoring system, critical technological conditions detection. It can prevent great economic damage or even catastrophic consequences

    Modeling of monitoring processes of structurally heterogeneous technological objects

    No full text
    The tasks of technological information capture, taken from industrial enterprise monitoring systems, and its further transfer to the level of management and decision-making are considered in the article. The problems arising at the stage of integration of isolated automation systems into a single information space of the enterprise have been analyzed. A model of the monitoring system of structurally heterogeneous technological objects has been proposed. It allows to estimate events’ probabilities of the following types: successful information processing, computing equipment downtime
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