LEADER 05603nam 2201525z- 450 001 9910595072203321 005 20231214133050.0 035 $a(CKB)5680000000080804 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/92170 035 $a(EXLCZ)995680000000080804 100 $a20202209d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNon-destructive Testing in Civil Engineering 210 $aBasel$cMDPI Books$d2022 215 $a1 electronic resource (320 p.) 311 $a3-0365-5131-X 311 $a3-0365-5132-8 330 $aThis Special Issue, entitled ?Non-Destructive Testing in Civil Engineering?, aims to present to interested researchers and engineers the latest achievements in the field of new research methods, as well as the original results of scientific research carried out with their use?not only in laboratory conditions but also in selected case studies. The articles published in this Special Issue are theoretical?experimental and experimental, and also show the practical nature of the research. They are grouped by topic, and the main content of each article is briefly discussed for your convenience. These articles extend the knowledge in the field of non-destructive testing in civil engineering with regard to new and improved non-destructive testing (NDT) methods, their complementary application, and also the analysis of their results?including the use of sophisticated mathematical algorithms and artificial intelligence, as well as the diagnostics of materials, components, structures, entire buildings, and interesting case studies. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $aconveyor belt 610 $aclimatic factors 610 $athermal shocks 610 $amechanical properties 610 $aapplied geophysics 610 $aurban geophysics 610 $aEleftheria Square 610 $aERT 610 $aprobability-based ERT inversion (PERTI) method 610 $aGPR 610 $aEMI 610 $aimpact-echo 610 $atooth gear impactor 610 $acontact duration 610 $adelamination 610 $arapid scanning 610 $anon-destructive testing 610 $aconcrete slab 610 $abuilding defects 610 $abuilding diagnosis 610 $abuilding envelope 610 $abuilding inspection system 610 $aurgency of repair 610 $ahistoric buildings 610 $abrick walls 610 $anondestructive testing 610 $aartificial neural networks 610 $aNearly Zero Energy Buildings (NZEB) 610 $apassive house (PH) 610 $aheat flux meter (HFM) 610 $aquantitative infrared thermography (QIRT) 610 $abuilding thermal performance 610 $aU-value 610 $aMediterranean climate 610 $aelectrical resistivity 610 $aelectrical impedance 610 $aconcrete 610 $amortar 610 $ameasurement accuracy 610 $aultrasonic elastography 610 $aunderground detection 610 $asoil inspection 610 $aunderwater acoustics 610 $anon-destructive test 610 $amonitoring 610 $ahousing 610 $abuildings 610 $afac?ade 610 $athermal transmittance 610 $aHFM method 610 $ainfrared thermography 610 $asolar loading thermography 610 $alock-in thermography 610 $apassive thermography 610 $athermal thickness 610 $athermal effusivity 610 $ainfrastructure 610 $aNDT 610 $aonsite X-ray bridge inspection 610 $a950 keV/3.95 MeV X-ray sources 610 $aPC bridge 610 $aunfilled grout 610 $aquantitative evaluation of stage of unfilled grout 610 $astep-heating thermography 610 $aactive thermography 610 $alinear effusivity fit 610 $aground-penetrating radar 610 $asignal features 610 $amaterial moisture 610 $aclassification 610 $amachine learning 610 $amoisture measurements 610 $abuilding floors 610 $acivil engineering 610 $acrack measurement 610 $aimage processing 610 $ahigh resolution 610 $aworking distance 610 $acrack width 610 $avisual inspection 610 $astructure state assessment 610 $aground sample distance (GSD) 610 $asonic resonant method 610 $aimpulse excitation technique 610 $aresonant frequency 610 $asteel fiber reinforced concrete 610 $afiber orientation 610 $amicro-computed tomography 610 $aultrasound 610 $aspectral induced polarization 610 $asmall force 610 $aload 610 $ainstrumental signal 610 $atwo-point bending test 610 $astrain gauge 610 $acalibration equation 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 700 $aHo?a$b Jerzy$4edt$01319567 702 $aSadowski$b ?ukasz$4edt 702 $aHo?a$b Jerzy$4oth 702 $aSadowski$b ?ukasz$4oth 906 $aBOOK 912 $a9910595072203321 996 $aNon-destructive Testing in Civil Engineering$93033974 997 $aUNINA LEADER 05759nam 2200781Ia 450 001 9910811555203321 005 20200520144314.0 010 $a9786613909411 010 $a9783527645015 010 $a3527645012 010 $a9781283596961 010 $a1283596962 010 $a9783527645022 010 $a3527645020 010 $a9783527645121 010 $a3527645128 035 $a(CKB)3460000000080887 035 $a(EBL)1021397 035 $a(OCoLC)818862492 035 $a(SSID)ssj0000700878 035 $a(PQKBManifestationID)11427608 035 $a(PQKBTitleCode)TC0000700878 035 $a(PQKBWorkID)10672676 035 $a(PQKB)10806093 035 $a(MiAaPQ)EBC1021397 035 $a(Au-PeEL)EBL1021397 035 $a(CaPaEBR)ebr10598733 035 $a(CaONFJC)MIL390941 035 $a(Perlego)1011726 035 $a(EXLCZ)993460000000080887 100 $a20111205d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aStatistical modelling of molecular descriptors in QSAR/QSPR /$fedited by Matthias Dehmer, Kurt Varmuza, and Danail Bonchev 205 $a2nd ed. 210 $aWeinheim $cWiley-VCH ;$a[Chichester $cJohn Wiley, distributor]$dc2012 215 $a1 online resource (458 p.) 225 0 $aQuantitative and network biology ;$vv. 2 300 $aDescription based upon print version of record. 311 08$a9783527324347 311 08$a3527324348 320 $aIncludes bibliographical references and index. 327 $aStatistical Modelling of Molecular Descriptors in QSAR/QSPR; Contents; Preface; List of Contributors; 1 Current Modeling Methods Used in QSAR/QSPR; 1.1 Introduction; 1.2 Modeling Methods; 1.2.1 Methods for Regression Problems; 1.2.1.1 Multiple Linear Regression; 1.2.1.2 Partial Least Squares; 1.2.1.3 Feedforward Backpropagation Neural Network; 1.2.1.4 General Regression Neural Network; 1.2.1.5 Gaussian Processes; 1.2.2 Methods for Classification Problems; 1.2.2.1 Logistic Regression; 1.2.2.2 Linear Discriminant Analysis; 1.2.2.3 Decision Tree and Random Forest; 1.2.2.4 k-Nearest Neighbor 327 $a1.2.2.5 Probabilistic Neural Network1.2.2.6 Support Vector Machine; 1.3 Software for QSAR Development; 1.3.1 Structure Drawing or File Conversion; 1.3.2 3D Structure Generation; 1.3.3 Descriptor Calculation; 1.3.4 Modeling; 1.3.5 General purpose; 1.4 Conclusion; References; 2 Developing Best Practices for Descriptor-Based Property Prediction: Appropriate Matching of Datasets, Descriptors, Methods, and Expectations; 2.1 Introduction; 2.1.1 Posing the Question; 2.1.2 Validating the Models; 2.1.3 Interpreting the Models; 2.2 Leveraging Experimental Data and Understanding their Limitations 327 $a2.3 Descriptors: The Lexicon of QSARs2.3.1 Classical QSAR Descriptors and Uses; 2.3.2 Experimentally Derived Descriptors; 2.3.2.1 Biodescriptors; 2.3.2.2 Descriptors from Spectroscopy/Spectrometry and Microscopy; 2.3.3 0D, 1D and 2D Computational Descriptors; 2.3.4 3D Descriptors and Beyond; 2.3.5 Local Molecular Surface Property Descriptors; 2.3.6 Quantum Chemical Descriptors; 2.4 Machine Learning Methods: The Grammar of QSARs; 2.4.1 Principal Component Analysis; 2.4.2 Factor Analysis 327 $a2.4.3 Multidimensional Scaling, Stochastic Proximity Embedding, and Other Nonlinear Dimensionality Reduction Methods2.4.4 Clustering; 2.4.5 Partial Least Squares (PLS); 2.4.6 k-Nearest Neighbors (kNN); 2.4.7 Neural Networks; 2.4.8 Ensemble Models; 2.4.9 Decision Trees and Random Forests; 2.4.10 Kernel Methods; 2.4.11 Ranking Methods; 2.5 Defining Modeling Strategies: Putting It All Together; 2.6 Conclusions; References; 3 Mold2 Molecular Descriptors for QSAR; 3.1 Background; 3.1.1 History of QSAR; 3.1.2 Introduction to QSAR; 3.1.3 Molecular Descriptors: Bridge for QSAR 327 $a3.1.3.1 Molecular Descriptors3.1.3.2 Role of Molecular Descriptors; 3.1.3.3 Types of Molecular Descriptors; 3.1.3.4 Calculation of Molecular Descriptors (Software Packages); 3.2 Mold2 Molecular Descriptors; 3.2.1 Description of Mold2 Descriptors; 3.2.1.1 Topological Descriptors; 3.2.1.2 Constitutional Descriptors; 3.2.1.3 Information Content-based Descriptors; 3.2.2 Calculation of Mold2 Descriptors; 3.2.3 Evaluation of Mold2 Descriptors; 3.2.3.1 Information Content by Shannon Entropy Analysis; 3.2.3.2 Correlations between Descriptors; 3.3 QSAR Using Mold2 Descriptors 327 $a3.3.1 Classification Models based on Mold2 Descriptors 330 $aThis handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics. The topics range from investigating information processing in chemical and biological networks to studying statistical and information-theoretic techniques for analyzing chemical structures to employing data analysis and machine learning techniques for QSAR/QSPR.The high-profile international author and editor team ensures excellent coverage of the topic, making th 410 0$aQuantitative and Network Biology (VCH) 606 $aBioinformatics 606 $aMolecules$xModels$xComputer simulation 615 0$aBioinformatics. 615 0$aMolecules$xModels$xComputer simulation. 676 $a572.80285 701 $aDehmer$b Matthias$f1968-$0860612 701 $aVarmuza$b Kurt$f1942-$01636172 701 $aBonchev$b Danail$020960 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910811555203321 996 $aStatistical modelling of molecular descriptors in QSAR$93977324 997 $aUNINA LEADER 04101nam 2200625zu 450 001 9910133378203321 005 20210807004502.0 010 $a2-84246-180-0 024 7 $a10.4000/books.bibpompidou.1192 035 $a(CKB)3390000000053445 035 $a(SSID)ssj0001538113 035 $a(PQKBManifestationID)11861291 035 $a(PQKBTitleCode)TC0001538113 035 $a(PQKBWorkID)11525632 035 $a(PQKB)10831984 035 $a(WaSeSS)IndRDA00045248 035 $a(FrMaCLE)OB-bibpompidou-1192 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/49815 035 $a(PPN)182835480 035 $a(oapen)doab49815 035 $a(EXLCZ)993390000000053445 100 $a20160829d2010 uy 101 0 $afre 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIdentification et surveillance des individus : quels enjeux pour nos démocraties 210 $cÉditions de la Bibliothèque publique d?information$d2010 210 31$a[Place of publication not identified]$cÉditions de la Bibliothèque publique d'information$d2010 215 $a1 online resource (96 pages) 225 0 $aParoles en re?seau,$x1765-2782 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a2-84246-129-0 330 $a« Big Brother is watching you ! » Cet avertissement placardé sur les murs de la cité imaginaire d'Océania dans le roman 1984 de Georges Orwell peut-il s'appliquer à nos sociétés contemporaines ? Passeport biométrique, fichage informatisé des individus, multiplication des caméras de surveillance, utilisation de puce dans des objets de la vie quotidienne, ou pour suivre les criminels en liberté surveillée, les mesures d'identification, de fichage et de surveillance des individus par des techniques de plus en plus sophistiquées se multiplient. Justifiés par la lutte contre le terrorisme, le crime organisé ou l'immigration clandestine, mais aussi par la volonté de mettre en place de nouveaux services, ces processus suscitent des réactions contrastées, entre enthousiasme face aux avancées technologiques et inquiétude à l'idée des dérives possibles. L'État ou d?autres institutions publiques ou privées ont toujours cherché à identifier et à surveiller les citoyens. Cependant, avec la mondialisation, et le développement des transports et des technologies de l?information et de la communication, on assiste, depuis une trentaine d?années, à des changements très profonds dans ces domaines, accélérés par les attentats de cette dernière décennie dans le monde. Nécessaires du fait de l?ampleur des dangers menaçant les sociétés démocratiques pour certains, ces évolutions apparaissent pour d?autres comme le prélude à l?instauration d?une société totalitaire. Elles appellent en tous cas le débat : Quels sont les enjeux politiques, philosophiques, sociaux, économiques, juridiques, etc. de l?identification et de la surveillance des personnes à l?échelle nationale et internationale ? Quelle est leur efficacité contre les dangers dont elles sont censées nous protéger ? 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