LEADER 00949nam0 2200265 450 001 9910167953203321 005 20170414121313.0 100 $a20170414d1865---- km y0itay50 ba 101 0 $afre 102 $aFR 105 $a 001yy 200 1 $aPratique de l'art de construire$emaconnerie,terrasse et platrerie$econnaissances relatives à l'esecution et à l'estimation des travaux de maconnerie, de terrasse et de platrerie, et en particulier de ceux du bàtiment$fpar J. Claudel et L. Laroque 205 $a3 ed. revue et considérablement augmentée 210 $aParis$cDunod$d1865 215 $aXXIX, 727 p.$d23 cm 676 $v22 700 1$aClaudel,$bJoseph$03889 701 1$aLaroque,$bL.$0740249 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910167953203321 952 $a13 AR 33 C 15$b1109 / 2017$fFINBC 959 $aFINBC 996 $aPratique de l'art de construire$91467479 997 $aUNINA LEADER 07988nam 2200625Ia 450 001 9910782119903321 005 20230617010820.0 010 $a1-281-93460-7 010 $a9786611934606 010 $a981-279-471-9 035 $a(CKB)1000000000537777 035 $a(StDuBDS)AH24685125 035 $a(SSID)ssj0000253875 035 $a(PQKBManifestationID)11195530 035 $a(PQKBTitleCode)TC0000253875 035 $a(PQKBWorkID)10206087 035 $a(PQKB)11567464 035 $a(MiAaPQ)EBC1681735 035 $a(WSP)00005589 035 $a(Au-PeEL)EBL1681735 035 $a(CaPaEBR)ebr10255850 035 $a(CaONFJC)MIL193460 035 $a(OCoLC)815752347 035 $a(iGPub)WSPCB0005091 035 $a(EXLCZ)991000000000537777 100 $a20050213d2004 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aSupport vector machine in chemistry$b[electronic resource] /$fNianyi Chen ... [et al.] 210 $aSingapore ;$aHackensack, N.J. $cWorld Scientific$dc2004 215 $a1 online resource (344p.) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a981-238-922-9 320 $aIncludes bibliographical references (p. 319-327) and index. 327 $a1. Introduction. 1.1. Support vector machine: data processing method for problems of small sample size. 1.2. Support vector machine: data processing method for complicated data sets in chemistry. 1.3. Underfitting and overfitting: problems of machine learning. 1.4. Theory of overfitting and underfitting control, ERM and SRM principles of statistical learning theory. 1.5. Concept of large margin - a basic concept of SVM. 1.6. Kernel functions: technique for nonlinear data processing by linear algorithm. 1.7. Support vector regression: regression based on principle of statistical learning theory. 1.8. Other machine learning methods related to statistical learning theory. 1.9. Some comments on the application of SVM in chemistry -- 2. Support Vector Machine. 2.1. Margin and optimal separating plane. 2.2. Interpretation by statistical learning therory. 2.3. Support vector classification. 2.4. Support vector regression. 2.5 V-SVM -- 3. Kernel functions. 3.1. Introduction. 3.2. Mercer kernel. 3.3. Properties of kernel. 3.4. Kernel selection -- 4. Feature selection using support vector machine. 4.1. Significance and difficulty of feature selection in chemical data processing. 4.2. SVM-BFS - application of wrapper method and floating search method. 4.3. SVM-RFE: application of optimal brain damage and recursive feature elimination. 4.4. Multitask learning. 4.5. Computer experiments: feature selection of artificially generated data set -- 5. Principle of atomic or molecular parameter-data processing method. 5.1. Two different strategies for structure-property relationship investigation. 5.2. Number of valence electrons of atoms. 5.3. Ionization potential of atoms. 5.4. Atomic radii and ionic radii. 5.5. Electronegativity. 5.6. Charge-radius ratio. 5.7. Topological parameters of molecules and 3-D molecular descriptors. 5.8. Atomic parameters for ionic systems. 5.9. Atomic parameters for covalent compounds. 5.10. Atomic parameters for metallic systems -- 6. SVM applied to phase diagram assessment and prediction. 6.1. Comprehensive assessment and computerized prediction of phase diagrams. 6.2.Atomic parameter-pattern recognition method for phase diagram prediction. 6.3. Prediction of intermediate compound formation. 6.4. Prediction of formation of extended solid solutions. 6.5. Prediction of melting types of intermediate compounds. 6.6. Modeling of melting points or decomposition temperature of intermediate compounds. 6.7. Prediction of crystal types of intermediate compounds. 6.8. Modeling of liquid-liquid immiscibility of inorganic systems. 6.9. SVM applied to intelligent database of phase diagrams. 327 $a7. SVM applied to thermodynamic property prediction. 7.1. Significance of estimation of thermodynamic properties of chemical substances. 7.2. Modeling of enthalpy of formation of compounds. 7.3. Modeling of free energy of mixing of liquid alloy systems. 7.4 Prediction of activity coefficient of concentrated electrolyte solutions. 7.5. Regularity of the solubility of C[symbol] in organic solvents -- 8. SVM applied to molecular and materials design. 8.1. concepts of molecular design and materials design. 8.2. SVM applied to new compound synthesis problems. 8.3. SVM applied to the computerized prediction of properties of materials. 8.4. SVM applied to process design for materials preparation -- 9. SVM applied to structure-activity relationships. 9.1. Concept of Structure-Activity Relationships (SAR). 9.2. Brief Introduction to some of chemometric methods used in SAR. 9.3. Brief introduction to molecular descriptors used in SAR. 9.4 SAR of N-(3-Oxo-3,4-dihydro-2H-benzo[l,4]oxazine-6-carbonyl) guanidines. 9.5. SAR of triazole-derivatives. 9.6. SAR of the 5-hydroxytryptamine receptor antagonists. 9.7. QSAR of N-phenylacetamides as herbicides -- 10. SVM applied to data of trace element analysis. 10.1. Trace element science and chemical data processing. 10.2. SVM applied to trace element analysis of human hair. 10.3. SVM applied to trace elements analysis of cigarettes. 10.4. SVM applied to trace element analysis of tea -- 11. SVM applied to archeological chemistry of ancient ceramics. 11.1. SVM applied to archeological data processing. 11.2. Identification of Jun Wares of Song Dynasty. 11.3. Modeling of official Ru Wares. 11.4. Modeling of composition of Yue Wares. 11.5. Modeling of composition of blue and white porcelain samples. 11.6. Archeological research of ancient porcelain kilns. 11.7. Period discrimination of ancient samples -- 12. SVM applied to cancer research. 12.1. SVM applied to cancer epidemiology. 12.2. Carcinogenic and environmental behaviors of polycyclic aromatic hydrocarbons. 12.3. SVM applied to cancer diagnosis -- 13. SVM applied to some topics of chemical analysis. 13.1. Multivariate calibration in chemical analysis. 13.2. Retention indices estimation in chromatography. 13.3. Detection of hidden explosives -- 14. SVM applied to chemical and metallurgical technology. 14.1. Physico-chemical basis of modeling of chemical processes. 14.2. Characteristics of data processing for industrial process modeling. 14.3. Optimal zone: strategy of large margin search. 14.4. Application of strategy of large margin search. 14.5. Optimal control for target maximization or minimization. 14.6. Optimal control for problem of restricted response. 14.7. Materials properties estimation for production process. 14.8. Comprehensive strategy for industrial optimization. 330 $bIn recent years, the support vector machine (SVM), a new data processing method, has been applied to many fields of chemistry and chemical technology. Compared with some other data processing methods, SVM is especially suitable for solving problems of small sample size, with superior prediction performance. SVM is fast becoming a powerful tool of chemometrics. This book provides a systematic approach to the principles and algorithms of SVM, and demonstrates the application examples of SVM in QSAR/QSPR work, materials and experimental design, phase diagram prediction, modeling for the optimal control of chemical industry, and other branches in chemistry and chemical technology. 606 $aChemistry$xData processing 606 $aChemistry, Technical$xData processing 606 $aMachine learning 615 0$aChemistry$xData processing. 615 0$aChemistry, Technical$xData processing. 615 0$aMachine learning. 676 $a540.285631 701 $aChen$b Nianyi$01531940 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782119903321 996 $aSupport vector machine in chemistry$93777918 997 $aUNINA LEADER 05682nam 2200673 450 001 9910810320503321 005 20230125185200.0 010 $a1-63157-534-1 035 $a(CKB)3710000000746105 035 $a(BEP)4586442 035 $a(OCoLC)953642572 035 $a(CaBNVSL)swl00406730 035 $a(Au-PeEL)EBL4586442 035 $a(CaPaEBR)ebr11235058 035 $a(CaONFJC)MIL938169 035 $a(OCoLC)953971805 035 $a(CaSebORM)9781631575341 035 $a(MiAaPQ)EBC4586442 035 $a(EXLCZ)993710000000746105 100 $a20160716d2016 fy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aAudit committee formation in the aftermath of 2007-2009 global financial crisis$hVolume III$iEmerging issues /$fZabihollah Rezaee 205 $aFirst edition. 210 1$aNew York, New York (222 East 46th Street, New York, NY 10017) :$cBusiness Expert Press,$d2016. 215 $a1 online resource (xii, 98 pages) 225 1 $aFinancial accounting and auditing collection,$x2151-2817 311 $a1-63157-533-3 320 $aIncludes bibliographical references and index. 327 $a1. Audit committee education, evaluation, accountability, and reporting -- 2. Global perspectives of audit committees -- 3. Audit committees of private companies, not-for-profit organizations, and governmental entities -- 4. Audit committees' interaction with corporate gatekeepers and those in charge of governance -- 5. Contemporary issues of audit committees -- Index. 330 3 $aThe audit committee, as an integral component of corporate governance, has gained considerable attention in the aftermath of 2007-2009 global financial crisis. The audit committee's role has evolved from a voluntary liaison between management and external auditors to the standing committee of the board of directors in overseeing all aspects of corporate governance, financial reporting, internal controls, risk assessment, and audit activities. This book addresses the determinants of audit committee oversight effectiveness, including their composition, independence, authority, resources, diligence, and activities. Today, audit committees operate in an environment of ever-increasing corporate governance reforms established to protect investors and the public from receiving misleading financial statements and related audit reports. Audit committees, in complying with emerging corporate governance reforms, are striving to improve their oversight effectiveness to discharge their oversight responsibilities. This book is organized into three separate volumes, and each volume can be utilized separately or in an integrated form. The first volume addresses the formation of the audit committee, its relevance, sources, structure and roles; the second volume focuses on the oversight functions of the audit committee; and the third volume presents the emerging issues of audit committees. The first volume consists of five chapters that examine the relevance and fundamentals of the audit committees as well as the determinants of audit committee effectiveness. The second volume consists of nine chapters on financial, auditing, internal control, risk management, ethics and compliance, antifraud, and other oversight functions of the audit committee. The third volume consists of several chapters on the emerging issues of audit committees pertaining to evaluation, education, reporting, and accountability as well as audit committees of private companies, governmental entities, and not-for-profit organizations. The three volumes of this book present the essential and fundamental aspects and functions of audit committees, with a keen focus on their working relationship with other corporate governance participants including the board of directors, executives, internal auditors, external auditors, legal counsel, financial analysts, investment bankers, governing bodies, standard setters, and other stakeholders. Anyone who is involved with corporate governance, the financial reporting process, and audit functions should be interested in this book. Specifically, corporations and their executives, the boards of directors and audit committees, internal and external auditors, accountants, governing bodies, users of financial statements (investors, creditors, pensioners), business schools, and other professionals (attorneys, financial analysts, bankers) will benefit from this book. The three volumes of the book focus on up-to-date corporate governance measures and best practices in the aftermath of the global financial crisis and their impacts on audit committee effectiveness. 410 0$aFinancial accounting and auditing collection.$x2151-2817 606 $aAudit committees 606 $aGlobal Financial Crisis, 2008-2009$xAuditing 610 $aAudit Committee 610 $aCorporate Governance 610 $aOversight Effectiveness 610 $aFinancial Reports 610 $aAudit Functions 610 $aRisk Assessment 610 $aInternal Controls 610 $aBusiness Ethics 610 $aAudit Committee Structure 610 $aComposition 610 $aResponsibilities and Accountability 615 0$aAudit committees. 615 0$aGlobal Financial Crisis, 2008-2009$xAuditing. 676 $a657.458 700 $aRezaee$b Zabihollah$f1953-,$0857053 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910810320503321 996 $aAudit committee formation in the aftermath of 2007-2009 global financial crisis$94035954 997 $aUNINA