LEADER 05575nam 2200661 450 001 9910808856203321 005 20230721011127.0 010 $a3-03813-136-9 035 $a(CKB)2550000001152588 035 $a(EBL)1867711 035 $a(SSID)ssj0000760512 035 $a(PQKBManifestationID)11432526 035 $a(PQKBTitleCode)TC0000760512 035 $a(PQKBWorkID)10714397 035 $a(PQKB)11311902 035 $a(MiAaPQ)EBC1867711 035 $a(Au-PeEL)EBL1867711 035 $a(CaPaEBR)ebr10851520 035 $a(OCoLC)897070019 035 $a(EXLCZ)992550000001152588 100 $a20070322h20072007 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNanocomposites and nanoporous materials $eISNAM7 : proceedings of the 7th International Symposium on Nanocomposites and Nanoporous Materials (ISNAM7) February 15-17, 2006, Gyeongju, Korea /$fedited by Chang Kyu Rhee 210 1$aUetikon-Zuerich :$cTrans Tech Publications Ltd.,$d[2007] 210 4$d©2007 215 $a1 online resource (344 p.) 225 1 $aDiffusion and defect data. Pt. B, Solid state phenomena,$x1012-0394 ;$vvolume 119 300 $aDescription based upon print version of record. 311 $a3-908451-27-2 320 $aIncludes bibliographical references and indexes. 327 $aNanocomposites and Nanoporous Materials VII; Committees; Preface; Table of Contents; Decomposition and Crystallization Induced by High-Energy Ball-Milling; A Gas Control by Metal Nanoclusters-Supported Porous Carbon Nanofibers; Effect of Particle Size on the Oxidation Behavior of Nanophase Tin Synthesized by Inert Gas Condensation; Intimate Heterojunction Structure between Titania and Polythiocyanogen and Its Photovoltaic Effect; Fabrication of Nanoparticles Supported on Metal Oxides by PS-PVP Block Copolymer Encapsulation Method 327 $aSynthesis of CdS Nanocrystallites in Polymer Matrix: Sui-Generis ApproachNanostructured Thin films of Anthracene by Liquid-Liquid Interface Recrystallization Technique; Spark Plasma Sintering of Nanoscale (Ni+Al) Powder Mixture; Comparison of Optical Properties of Pyrazoline Derivative Nanoparticles; Characterization of Nano Scaled Mullite Powders Prepared by Organic-Inorganic Solution Technique; Preparation and Characterization of Anti-Fogging Low Density Polymer Film; Ion Conducting Behaviors of Polymeric Composite Electrolytes Containing Mesoporous Silicates 327 $aPreparation and Characterization of Activated Carbon Nanofiber Webs Containing Multiwalled Carbon Nanotubes by ElectrospinningMicrostructure and Thermal Stability of Carbon Nanotubes Dispersed Alumina Nanocomposites Prepared by Spark Plasma Sintering; Properties of Dispersion Strengthened Cu-TiB2 Nanocomposites Prepared by Spark Plasma Sintering; Preparation of TiO2 Nanorod Arrays by Electrophoretic Deposition of Titania Nanoparticles ; Synthesis and Characterization of Co Nanoparticles by Solventless Thermal Decomposition 327 $aThe Fabrication of Iron Sulfide Powders for Enhancing the Machinability and the Comparison of Machining Behavior at the Sintered SteelSynthesis of SiOx Nanowires through the Thermal Heating of Au-Coated Si Substrates; Comparison Studies on Nitrile-Butadiene Rubber Nanocomposites Depending on the Organically Modified Montmorillonites ; Effects of Certain Variables on the Materials Properties of Nickel Electrodeposits: Current Density and Duty Cycle; Creep Rupture Properties of Type 316LN Stainless Steel Welded by the SAW Process; Radiation Effect on Poly(?-Caprolactone) Nanofibrous Scaffold 327 $aSurface Slip Markings Of Fatigue-Tested Materials Hardened By Precipitates: Dislocation Dynamics ApproachAdsorption of Carboxymethylated Polyethyleneimine (CM-PEI) on a Microporous Activated Carbon; Synthesis and Dilatometric Study of Ca(Sr, La)TiO3 Prepared by Solution Combustion Synthesis (SCS); Phase Analysis of the Precipitates in an Alloy 600/182 Weld; Role of Organic Modifiers of Master Batches and Layered-Silicates in Styrene-Butadiene Rubber Nanocomposites; Ion Conductivity of Polymer Electrolytes Based on PEO Containing Li Salt and Additive Salt 327 $aInfluence of Atmospheric-Pressure Plasma Treatment on Surface of Polyimide Film 330 $aIn recent years, the use of nanosized powders and porous materials has been expected to lead to basic breakthrough solutions in the form of prospective nanomaterial products having high-performance and multi-functional properties. For this reason, many industrialised nations have financially supported nanostructured materials development, and their use in technical innovation. Unlike previous book series covering nanocomposite materials, this new series aims to bring together researchers working in the fields of nanocomposites, nanoporous materials and environmentally-friendly materials resear 410 0$aDiffusion and defect data.$nPt. B,$pSolid state phenomena ;$vvolume 119. 606 $aNanostructured materials$vCongresses 610 1 $aNanocomposites 610 1 $aNanoporous materials 610 1 $aISNAM 610 2 $aISNAM7 615 0$aNanostructured materials 676 $a620.5 702 $aRhee$b Chang Kyu 712 12$aInternational Symposium on Nanocomposites and Nanoporous Materials 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910808856203321 996 $aNanocomposites and nanoporous materials$94010220 997 $aUNINA LEADER 04592nam 22007455 450 001 9910522999103321 005 20251126130405.0 010 $a3-030-89010-4 024 7 $a10.1007/978-3-030-89010-0 035 $a(CKB)5100000000193939 035 $a(MiAaPQ)EBC6855260 035 $a(Au-PeEL)EBL6855260 035 $a(OCoLC)1294143848 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/78249 035 $a(PPN)260834084 035 $a(ODN)ODN0010072106 035 $a(oapen)doab78249 035 $a(DNLM)9918470283606676 035 $a(DE-He213)978-3-030-89010-0 035 $a(EXLCZ)995100000000193939 100 $a20220113d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultivariate Statistical Machine Learning Methods for Genomic Prediction /$fby Osval Antonio Montesinos López, Abelardo Montesinos López, José Crossa 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (707 pages) 311 08$a3-030-89009-0 327 $aPreface -- Chapter 1 -- General elements of genomic selection and statistical learning -- Chapter. 2 -- Preprocessing tools for data preparation -- Chapter. 3 -- Elements for building supervised statistical machine learning models -- Chapter. 4 -- Overfitting, model tuning and evaluation of prediction performance -- Chapter. 5 -- Linear Mixed Models -- Chapter. 6 -- Bayesian Genomic Linear Regression -- Chapter. 7 -- Bayesian and classical prediction models for categorical and count data -- Chapter. 8 -- Reproducing Kernel Hilbert Spaces Regression and Classification Methods -- Chapter. 9 -- Support vector machines and support vector regression -- Chapter. 10 -- Fundamentals of artificial neural networks and deep learning -- Chapter. 11 -- Artificial neural networks and deep learning for genomic prediction of continuous outcomes -- Chapter. 12 -- Artificial neural networks and deep learning for genomic prediction of binary, ordinal and mixed outcomes -- Chapter. 13 -- Convolutional neural networks -- Chapter. 14 -- Functional regression -- Chapter. 15 -- Random forest for genomic prediction. 330 $aThis book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool. 606 $aAgriculture 606 $aBioinformatics 606 $aPlant genetics 606 $aAgricultural genome mapping 606 $aBiometry 606 $aAgriculture 606 $aBioinformatics 606 $aPlant Genetics 606 $aAgricultural Genetics 606 $aBiostatistics 615 0$aAgriculture. 615 0$aBioinformatics. 615 0$aPlant genetics. 615 0$aAgricultural genome mapping. 615 0$aBiometry. 615 14$aAgriculture. 615 24$aBioinformatics. 615 24$aPlant Genetics. 615 24$aAgricultural Genetics. 615 24$aBiostatistics. 676 $a630 686 $aMED090000$aSCI011000$aSCI070000$aSCI086000$aTEC003000$2bisacsh 700 $aMontesinos Lo?pez$b Osval Antonio$00 701 $aMontesinos Lo?pez$b Abelardo$00 701 $aCrossa$b Jose?$00 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910522999103321 996 $aMultivariate Statistical Machine Learning Methods for Genomic Prediction$92590779 997 $aUNINA