01752nam a2200433 i 450099100087803970753620020507175103.0990126s1991 us ||| | eng 0821841068b10769936-39ule_instLE01303704ExLDip.to Matematicaeng515.353AMS 34L20AMS 35-06AMS 35J10AMS 35P20AMS 35P30AMS 45A05AMS 47A05AMS 47B99AMS 47G30Birman, M. Sh.535336Estimates and asymptotics for discrete spectra of integral and differential equations /M. Sh. Birman, editorProvidence, R.I. :American Mathematical Society,c1991x, 204 p. ;27 cmAdvances in soviet mathematics, ISSN 10518037 ;7"The papers of this collection contain adapted expositions of a series of talks on spectral theory given to the Leningrad Seminar on Mathematical Physics in the academic year of 1989-90.": editor's preface.Includes bibliographical referencesDifferential equations-asymptotic theoryIntegral equations-asymptotic theorySchrodinger operator-SpectraSpectral theory (Mathematics).b1076993623-02-1728-06-02991000878039707536LE013 35-XX BIR11 (1991)12013000111087le013-E0.00-l- 00000.i1086829x28-06-02Estimates and asymptotics for discrete spectra of integral and differential equations922569UNISALENTOle01301-01-99ma -engus 0101190nam 2200385 450 991016028880332120230808201359.01-909454-48-6(CKB)3710000001023408(MiAaPQ)EBC4782982(EXLCZ)99371000000102340820170126h20162016 uy 0engurcnu||||||||rdacontentrdamediardacarrierFucked by rock ; I have the greatest respect for you, George /Mark MannigLondon, England :Cherry Red Books,2016.©20161 online resource (289 pages)"'Fucked By Rock' first published in Great Britain in 2000"--T.p. verso.1-901447-32-4 Rock musiciansGreat BritainBiographyRock musicians781.66092Manning Mark1958-1080101Manning Mark1958-1080101MiAaPQMiAaPQMiAaPQBOOK9910160288803321Fucked by rock ; I have the greatest respect for you, George2592855UNINA04157nam 2200793z- 450 9910404080803321202102113-03928-211-5(CKB)4100000011302330(oapen)https://directory.doabooks.org/handle/20.500.12854/55528(oapen)doab55528(EXLCZ)99410000001130233020202102d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierOvercoming Data Scarcity in Earth ScienceMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (94 p.)3-03928-210-7 heavily Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models rely on the data collected by direct field observations. However, assembly of a functional and comprehensive dataset for any environmental variable is difficult, mainly because of i) the high cost of the monitoring campaigns and ii) the low reliability of measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response's complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited. Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problems. More recently, machine learning techniques, such as clustering and classification, have been proposed to complete missing data. This book showcases the body of knowledge that is aimed at improving the capacity to exploit the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.History of engineering and technologybicssc3D-Vararthropod vectorattribute reductionclimate extreme indices (CEIs)ClimPACTcore attributedata assimilationdata imputationdata qualitydata scarcityDataset Licensedatabasedecision treesearth-science dataensemble learningenvironmental modelingenvironmental observationsExpert Team on Climate Change Detection and Indices (ETCCDI)Expert Team on Sector-specific Climate Indices (ET-SCI)geophysical monitoringGLDASinvasive speciesk-Nearest Neighborsmachine learningmagnetotelluric monitoringmicrohabitatmissing datamulti-class classificationprocessingremote sensingrough set theoryrule extractionsoil texture calculatorspecies distribution modelingstatistical methodssupport vector machineswater qualityHistory of engineering and technologyEtcheverry Venturini Lorenaauth1325393Chreties Ceriani ChristianauthCastro Casales AlbertoauthGorgoglione AngelaauthBOOK9910404080803321Overcoming Data Scarcity in Earth Science3036840UNINA