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Каталог книг и продолжающихся изданий библиотеки Института биофизики СО РАН (7)

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Developing the control criterion for a continuous culture of microorganisms / V. V. Adamovich, D. Yu. Rogozin, A. G. Degermendzhi // Mikrobiologiya. - 2005. - Vol. 74, Is. 1. - С. 5-16 . - ISSN 0026-3656
Кл.слова (ненормированные):
Chemostat -- Control criterion -- Control factor -- Microorganism population -- Sensitivity coefficients -- algorithm -- bacterium -- biological model -- biomass -- culture medium -- ecosystem -- growth, development and aging -- methodology -- microbiological examination -- review -- Algorithms -- Bacteria -- Bacteriological Techniques -- Biomass -- Culture Media -- Ecosystem -- Models, Biological
Аннотация: A short survey and critical analysis of previously proposed criteria for growth control of populations of microorganisms in the chemostat are presented. Based on the analysis of a mathematical model of the steady-state of a microbial population in the chemostat, an adequate control criterion is suggested, along with a method to identify the corresponding regulating factors. The new control criterion is expressed as a product of the factor transformation coefficient and the biomass sensitivity coefficient (SC) with respect to the change of the factor at the chemostat inlet (referred to in the sequel as the biomass SC). The control criterion determines the strength of the control exerted by this or that factor. The method of determination of the regulating factors consists in experimental determination of the real SCs for factors and the biomass and in calculating on this basis the corresponding ideal SCs for constant factor transformation coefficients. The ideal SCs are shown to add up to an integer value, a constraint that we call "quantization" relationships. Such relationships are used to test the completeness of the drawn list of control factors. The proposed method was applied to our own and literature data.

Scopus
Держатели документа:
Institute of Biophysics, Siberian Division, Russian Academy of Sciences, Krasnoyarsk, 660036, Russian Federation : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Adamovich, V.V.; Rogozin, D.Yu.; Degermendzhi, A.G.

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Challenges and opportunities for integrating lake ecosystem modelling approaches / W. M. Mooij [et al.] // Aquatic Ecology. - 2010. - Vol. 44, Is. 3. - P633-667, DOI 10.1007/s10452-010-9339-3 . - ISSN 1386-2588
Кл.слова (ненормированные):
Adaptive processes -- Analysis -- Aquatic -- Bifurcation -- Biodiversity -- Climate warming -- Community -- Eutrophication -- Fisheries -- Food web dynamics -- Freshwater -- Global change -- Hydrology -- Lake -- Management -- Marine -- Mitigation -- Model integration -- Model limitations -- Non-linear dynamics -- Nutrients -- Plankton -- Population -- Prediction -- Spatial -- Understanding -- adaptive management -- algorithm -- aquatic community -- biodiversity -- ecosystem modeling -- eutrophication -- fishery production -- food web -- fuzzy mathematics -- global warming -- hydrology -- lake ecosystem -- mitigation -- model test -- numerical model -- nutrient availability -- plankton -- prediction -- saline lake -- spatial analysis
Аннотация: A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models. В© 2010 The Author(s).

Scopus
Держатели документа:
Netherlands Institute of Ecology (NIOO-KNAW), Department of Aquatic Ecology, Rijksstraatweg 6, 3631 AC Nieuwersluis, Netherlands
Aarhus University, National Environmental Research Institute, Department of Freshwater Ecology, 8600 Silkeborg, Denmark
Greenland Climate Research Centre (GCRC), Greenland Institute of Natural Resources, Kivioq 2, P.O. Box 570, 3900 Nuuk, Greenland
University of Toronto, Department of Physical and Environmental Sciences, Toronto, ON M1C 1A4, Canada
Institute of Computational Modelling (SB-RAS), Siberian Federal University, 660036 Krasnoyarsk, Russian Federation
Tanzania Fisheries Research Institute (TAFIRI), Mwanza Centre, P.O. Box 475, Mwanza, Tanzania
Institute of Biophysics (SB-RAS), Akademgorodok, 660036 Krasnoyarsk, Russian Federation
University of Miami, Florida Integrated Science Centre, USGS, Coral Gables, FL 33124, United States
Wageningen University, Department of Aquatic Ecology and Water Quality, P.O. Box 47, 6700 AA Wageningen, Netherlands
Centre for Ecology and Hydrology, Lancaster Environment Centre, Lake Ecosystem Group, Algal Modelling Unit, Bailrigg, Lancaster LA1 4AP England, United Kingdom
Federal University of Alagoas, Centre for Technology, Campus A.C. Simoes, 57072-970 Maceio-AL, Brazil
Institute of Biochemistry and Biology, Department of Ecology and Ecosystem Modelling, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany
Swedish University of Agricultural Sciences, Department of Aquatic Sciences and Assessment, P.O. Box 7050, 75007 Uppsala, Sweden
University of Waikato, Centre for Biodiversity and Ecology Research, Private Bag 3105, Hamilton, New Zealand
University of Western Australia, School of Earth and Environment, Crawley, WA 6009, Australia
Technische Universitat Dresden, Institute of Hydrobiology, 01062 Dresden, Germany
Technische Universitat Dresden, Neunzehnhain Ecological Station, Neunzehnhainer Str. 14, 09514 Lengefeld, Germany
Deltares, P.O. Box 177, 2600 MH Delft, Netherlands
Technion-Israel Institute of Technology, Faculty of Civil and Environmental Engineering, Technicon City, Haifa 32000, Israel
Helmholtz Centre for Environmental Research, Department of Lake Research, Brueckstrasse 3a, 39114 Magdeburg, Germany
Witteveen and Bos, P.O. Box 233, 7400 AV Deventer, Netherlands
University of Oslo, Department of Biology, P.O. Box 1066, Blindern, 0316 Oslo, Norway
UNESCO-IHE Institute of Water Education, 2601 DA Delft, Netherlands
Portland State University, Department of Civil and Environmental Engineering, Portland, OR 97207, United States
Netherlands Environmental Assessment Agency (PBL), P.O. Box 303, 3720 AH Bilthoven, Netherlands : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Mooij, W.M.; Trolle, D.; Jeppesen, E.; Arhonditsis, G.; Belolipetsky, P.V.; Chitamwebwa, D.B.R.; Degermendzhy, A.G.; DeAngelis, D.L.; De Senerpont Domis, L.N.; Downing, A.S.; Elliott, J.A.; Fragoso Jr., C.R.; Gaedke, U.; Genova, S.N.; Gulati, R.D.; Hakanson, L.; Hamilton, D.P.; Hipsey, M.R.; 't Hoen, J.; Hulsmann, S.; Los, F.H.; Makler-Pick, V.; Petzoldt, T.; Prokopkin, I.G.; Rinke, K.; Schep, S.A.; Tominaga, K.; van Dam, A.A.; van Nes, E.H.; Wells, S.A.; Janse, J.H.

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The "quantization" of sensitivity coefficients is preserved in microbial populations heterogeneous with respect to growth rate and age / V. M. Nekrasov, A. V. Chernychev, A. G. Degermendzhy // Doklady Biological Sciences. - 2006. - Vol. 406, Is. 1-6. - P91-93, DOI 10.1134/S0012496606010261 . - ISSN 0012-4966
Кл.слова (ненормированные):
algorithm -- article -- bacterium -- biodiversity -- growth, development and aging -- population dynamics -- theoretical model -- time -- Algorithms -- Bacteria -- Biodiversity -- Models, Theoretical -- Population Dynamics -- Time Factors

Scopus
Держатели документа:
Institute of Chemical Kinetics and Combustion, Siberian Division, Russian Academy of Sciences, Institutskaya ul. 3, Novosibirsk, 630090, Russian Federation
Institute of Biophysics, Siberian Division, Russian Academy of Sciences, Akademgorodok, Krasnoyarsk, 660036, Russian Federation : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Nekrasov, V.M.; Chernychev, A.V.; Degermendzhy, A.G.

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Mathematical model of seasonal agrophytocenosis productivity based on terrestrial and satellite monitoring / T. I. Pisman [et al.] // Doklady Biological Sciences. - 2009. - Vol. 428, Is. 1. - P467-470, DOI 10.1134/S0012496609050226 . - ISSN 0012-4966
Кл.слова (ненормированные):
agriculture -- algorithm -- article -- biological model -- biomass -- computer simulation -- crop -- growth, development and aging -- methodology -- season -- space flight -- wheat -- Agriculture -- Algorithms -- Biomass -- Computer Simulation -- Crops, Agricultural -- Models, Biological -- Seasons -- Spacecraft -- Triticum

Scopus
Держатели документа:
Institute of Biophysics, Siberian Branch, Russian Academy of Sciences, Akademgorodok 50.50, Krasnoyarsk 660036, Russian Federation
Institute of Natural Sciences and Mathematics, Khakass State University, pr. Lenina 90, Abakan, 655000 Khakassia, Russian Federation
Institute of Space and Information Technologies, Siberian Federal University, ul. Kirenskogo 26, Krasnoyarsk 660074, Russian Federation : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Pisman, T.I.; Pugacheva, I.Y.; Jukova, E.Y.; Shevyrnogov, A.P.

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Application of satellite data for investigation of dynamic processes in inland water bodies: Lake Shira (Khakasia, Siberia), a case study / A. P. Shevyrnogov, A. V. Kartushinsky, G. S. Vysotskaya // Aquatic Ecology. - 2002. - Vol. 36, Is. 2. - P153-163, DOI 10.1023/A:1015658927683 . - ISSN 1386-2588
Кл.слова (ненормированные):
Modelling -- Phytopigments -- Satellite data -- Satellite equipment -- Software -- Temperature -- AVHRR -- hydrodynamics -- lake -- limnology -- remote sensing -- saline lake -- satellite data -- water temperature -- Russian Federation
Аннотация: This work describes avenues to use satellite information to analyse dynamic processes in aquatic ecosystems. Information for this analysis, was retrieved from AVHRR satellite sensor data. This information consisteds of time series of images of radiation temperature and turbidity. We expect this information will be of great value in analysing inland water bodies. Methods to process satellite information using original software and data processing techniques are proposed. For the investigation of the process and analyses of satellite information Shira Lake (Khakasia, Siberia) was used as a case study. To study the variability of the surface temperature and turbidity of the Lake in summer, the satellite and ground-truth data of the lake was applied. This study represents the first evaluation of the dynamic processes for Lake Shira based on satellite, ground-truth and modelling data. We developed algorithms and software to process satellite images to enable the reconstruction of time dependence of temperature and spectral reflectance of water bodies in the visible range, and to make computer-animated films visualising the spatial and temporal dynamics of the study parameters. The analyses of morphometric, meteorological and hydrological characteristics of Lake Shira have provided a realistic opportunity for processing the satellite information and to develop numerical models of variability of the hydrological regime of the lake. The results obtained demonstrate the feasibility of systematically retrieving the spatial information from the satellite data on the dynamics of the surface water temperature and of the suspended matter in the lake.

Scopus
Держатели документа:
Institute of Biophysics of SB RAS, Akademgorodok, Krasnoyarsk 660036, Russian Federation : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Shevyrnogov, A.P.; Kartushinsky, A.V.; Vysotskaya, G.S.

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Typification of natural seasonal dynamics of vegetation to reveal impact of land surface change on environment (by satellite data) / A. Shevyrnogov [et al.] // Advances in Space Research. - 2000. - Vol. 26, Is. 7. - P1169-1172, DOI 10.1016/S0273-1177(99)01142-4 . - ISSN 0273-1177
Кл.слова (ненормированные):
ecological modeling -- ecosystem health -- land surface -- satellite data -- vegetation dynamics
Аннотация: Deep insight into types of vegetation variability provided by AVHRR space scanner images of vegetation index spatial distribution helps reveal impact of land surface changes on environment. The Institute of Computational Modeling SB RAS has developed nonparametric algorithms of automatic to classify and recognize patterns of these images which helped to reveal: (1) major variability types (generally connected); (2) areas belonging to small classes, which can be used to reveal deviations from 'normal' (e.g., forest fires, etc.); (3) deviation from a certain type of dynamics indicative of changes in condition of plants, which can be used to diagnose pathology at early stages; (4) impact of economical activities on vegetation in Norilsk area. The authors provide biological interpretation of the satellite data. Computer-animated dynamics and color maps are presented. Nonparametric algorithms of an automatic classification and pattern recognition were provided by the Institute of Computational Modeling SB RAS. (C) 2000 COSPAR. Published by Elsevier Science Ltd.

Scopus
Держатели документа:
Inst. of Biophys. of Russ. A., Siberian Branch, 660036, Krasnoyarsk, Russian Federation
Inst. of Compl. Modeling of Russ. A., Siberian Branch (SB RAS), 660036, Krasnoyarsk, Russian Federation : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Shevyrnogov, A.; Vysotskaya, G.; Sidko, A.; Dunaev, K.

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Mathematical model of the interaction of components in a plant-rhizospheric microorganisms system at the higher level of carbon dioxide in atmosphere / T. I. Pis'man, L. A. Somova, N. S. Pechurkin // Biofizika. - 2002. - Vol. 47, Is. 5. - С. 920-925 . - ISSN 0006-3029
Кл.слова (ненормированные):
carbon dioxide -- algorithm -- article -- biological model -- biomass -- ecosystem -- microbiology -- physiology -- plant seed -- Pseudomonas putida -- wheat -- Algorithms -- Biomass -- Carbon Dioxide -- Ecosystem -- Models, Biological -- Pseudomonas putida -- Seeds -- Triticum
Аннотация: A mathematical model describing the interaction of plants and rhizospheric microorganisms on complete mineral medium at a higher CO2 level in the atmosphere was constructed. The positive effect of CO2-enrichment on the system plant--rhizospheric microorganisms was shown. The effect of rhizospheric microorganisms on plant growth at normal and high level of carbon dioxide was demonstrated. It was shown that the biomass of plant in the system is smaller than the biomass of plant growing without microorganisms. It was experimentally demonstrated that a simple ecosystem wheat--Pseudomonas putida--artificial soil develops and functions differently than its individual constituents in the case of a wheat-artificial soil system. With unlimited nutrition and a higher CO2 level (0.06%), plants with roots inoculated with microorganisms have a smaller biomass than plants that were not inoculated with microorganisms.

Scopus
Держатели документа:
Institute of Biophysics, Siberian Branch, Russian Academy of Sciences, Krasnoyarsk, 660036 Russia. : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Pis'man, T.I.; Somova, L.A.; Pechurkin, N.S.

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Algorithms of self-adaptation for atmospheric model designing [Text] / J. P. Lankin, T. F. Baskanova ; ed.: GG Matvienko, o, GG Matvie // TENTH JOINT INTERNATIONAL SYMPOSIUM ON ATMOSPHERIC AND OCEAN OPTICS/ATMOSPHERIC PHYSICS, PT 2: LASER SENSING AND ATMOSPHERIC PHYSICS. Ser. PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) : SPIE-INT SOC OPTICAL ENGINEERING, 2004. - Vol. 5397: 10th Joint International Symposium on Atmospheric and Ocean Optics/Atmospheric Physics (JUN 24-28, 2003, Tomsk, RUSSIA). - P. 260-270, DOI 10.1117/12.548609. - Cited References: 23 . - ISBN 0277-786X. - ISBN 0-8194-5316-1

РУБ Environmental Sciences + Meteorology & Atmospheric Sciences + Optics

Аннотация: The paper describes the principal limitations of the traditional methods used to construct atmospheric models. These limitations would not allow any fundamental improvement of atmospheric modeling. Ways are proposed to overcome the current limitations, based oil the methodology of constructing adaptive models and neuroinformatics. Algorithms of self-adaptation for neural networks intended for the construction of atmospheric models are given. Essentially, the developed algorithms are adaptive shells and can be easily transferred to other models.

WOS
Держатели документа:
RAS, SB, Inst Biophys, Krasnoyarsk 660036, Russia
ИБФ СО РАН : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Lankin, J.P.; Baskanova, T.F.; Matvienko, GG \ed.\; Matvie, o, GG \ed.\

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The Information Content of Spectral Vegetation Indices in the Interpretation of Satellite Images of Cultivated Fields / T. I. Pisman [et al.] // Biophysics. - 2019. - Vol. 64, Is. 4. - P588-592, DOI 10.1134/S0006350919040158 . - ISSN 0006-3509
Кл.слова (ненормированные):
bare fallows -- Keywords: sod fields -- NDSI -- NDVI -- Sentinel-2
Аннотация: Abstract—The results of satellite monitoring of vegetation on unused agricultural lands during the growing season of 2018 are presented. Sod fields of different ages (2, 7, and 20 years) and bare fallows on the land used by the Krasnoyarsk Research Institute of Agriculture were the objects of the study. Satellite data with high spatial resolution (Sentinel-2 Earth remote sensing satellites) at the pre-processing Level-1C (https://earthexplorer.usgs.gov/) were used for the interpretation of sod field and fallow images. These data were used to calculate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Soil Index (NDSI). Algorithms and software for the processing of Sentinel-2 satellite data were developed. The possibility of using NDVI dynamics for assessment and monitoring of the condition of sod fields and bare fallows has been demonstrated. The applicability of the NDSI soil index for assessment of the status of arable land has been demonstrated. © 2019, Pleiades Publishing, Inc.

Scopus,
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Держатели документа:
Institute of Biophysics, Siberian Branch, Russian Academy of Sciences, Akademgorodok, 50/50, Krasnoyarsk, 660036, Russian Federation
Agricultural Research Institute, Svobodnyi pr., 66, Krasnoyarsk, 660041, Russian Federation

Доп.точки доступа:
Pisman, T. I.; Shevyrnogov, A. P.; Larko, A. A.; Botvich, I. Y.; Emelyanov, D. V.; Shpedt, A. A.; Trubnikov, Y. N.

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