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1. |
Record Nr. |
UNINA9910146087103321 |
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Autore |
Pounder Bruce |
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Titolo |
Convergence guidebook for corporate financial reporting [[electronic resource] /] / Bruce Pounder |
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Pubbl/distr/stampa |
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Hoboken, N.J., : John Wiley, c2009 |
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ISBN |
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0-470-46420-8 |
1-119-19745-7 |
1-282-02806-5 |
9786612028069 |
0-470-46414-3 |
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Descrizione fisica |
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1 online resource (259 p.) |
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Disciplina |
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Soggetti |
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Accounting - Standards |
Corporations - Finance |
Financial statements |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Introduction to the convergence of financial reporting standards -- How convergence will impact the United States -- Prepare for the impact of convergence now -- Conceptual frameworks -- Principles-based standards -- Different standards for different companies? -- Financial statements : a first look -- Fair value and related measurement issues -- Major asset classes -- Other balance sheet items -- Reporting financial performance -- Business combinations, intercompany investments, and segment reporting -- Financial statements : what is ahead -- Overview of U.S. labor markets for financial reporting talent -- Obsolescence of knowledge, skills, and abilities -- Commoditization of talent -- Toward a global labor market for financial reporting talent -- Transformation of the talent supply pipeline -- Overview of the challenges of convergence -- The enterprise challenge : strategies for choosing standards and implementing your choice -- Departmental challenge : tactics for |
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managing talent -- Personal challenge : career choices for a hypercompetitive labor market. |
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Sommario/riassunto |
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As a result of the global convergence of financial reporting standards, U.S. GAAP is changing profoundly. U.S. GAAP is also being abandoned by many public and private companies, and will eventually be replaced by a higher-quality set of global standards. The Convergence Guidebook for Corporate Financial Reporting provides the timely, practical guidance that CFOs, controllers, and other financial managers need in order to prepare for the impact of Convergence on their companies, departments, and careers. Guidebook readers will also learn why they must begin preparing for ""the next big challeng |
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2. |
Record Nr. |
UNINA9910784816603321 |
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Titolo |
Analysis of biological data [[electronic resource] ] : a soft computing approach / / editors, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Jason T.L. Wang |
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Pubbl/distr/stampa |
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Singapore ; ; Hong Kong, : World Scientific, c2007 |
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ISBN |
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1-281-91864-4 |
9786611918644 |
981-270-889-8 |
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Descrizione fisica |
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1 online resource (352 p.) |
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Collana |
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Science, engineering, and biology informatics ; ; v. 3 |
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Altri autori (Persone) |
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BandyopadhyaySanghamitra <1968-> |
MaulikUjjwal |
WangJason T. L |
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Disciplina |
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Soggetti |
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Bioinformatics |
Soft computing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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CONTENTS; Preface; Part I OVERVIEW; Chapter 1 Bioinformatics: Mining the Massive Data from High Throughput Genomics Experiments Haixu Tang and Sun Kim; 1 Introduction; 2 Recent Development of Classical Topics; 2.1 Sequence alignment; 2.2 Genome sequencing and fragment |
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assembly; 2.3 Gene annotation; 2.4 RNA folding; 2.5 Motif finding; 2.6 Protein structure prediction; 3 Emerging Topics from New Genome Technologies; 3.1 Comparative genomics: beyond genome comparison; 3.2 Pathway reconstruction; 3.3 Microarray analysis; 3.4 Proteomics; 3.5 Protein-protein interaction; 4 Conclusion |
AcknowledgementReferences; Chapter 2 An Introduction to Soft Computing Amit Konar and Swagatam Das; 1 Classical AI and its Pitfalls; 2 What is Soft Computing?; 3 Fundamental Components of Soft Computing; 3.1 Fuzzy sets and fuzzy logic; 3.2 Neural networks; 3.3 Genetic algorithms; 3.4 Belief networks; 4 Synergism in Soft Computing; 4.1 Neuro-fuzzy synergism; 4.2 Neuro-GA synergism; 4.3 Fuzzy-GA synergism; 4.4 Neuro-belief network synergism; 4.5 GA-belief network synergism; 4.6 Neuro-fuzzy-GA synergism; 5 Some Emerging Areas of Soft Computing; 5.1 Artificial life |
5.2 Particle swarm optimization (PSO)5.3 Artificial immune system; 5.4 Rough sets and granular computing; 5.5 Chaos theory; 5.6 Ant colony systems (ACS); 6 Summary; References; Part II BIOLOGICAL SEQUENCE AND STRUCTURE ANALYSIS; Chapter 3 Reconstructing Phylogenies with Memetic Algorithms and Branch-and-Bound José E. Gallardo, Carlos Cotta and Antonio J. Fernández; 1 Introduction; 2 A Crash Introduction to Phylogenetic Inference; 3 Evolutionary Algorithms for the Phylogeny Problem; 4 A BnB Algorithm for Phylogenetic Inference; 5 A Memetic Algorithm for Phylogenetic Inference |
6 A Hybrid Algorithm7 Experimental Results; 7.1 Experimental setting; 7.2 Sensitivity analysis on the hybrid algorithm; 7.3 Analysis of results; 8 Conclusions; Acknowledgment; References; Chapter 4 Classification ofRNASequences with Support Vector Machines Jason T. L. Wang and Xiaoming Wu; 1 Introduction; 2 Count Kernels and Marginalized Count Kernels; 2.1 RNA sequences with known secondary structures; 2.2 RNA sequences with unknown secondary structures; 3 Kernel Based on Labeled Dual Graphs; 3.1 Labeled dual graphs; 3.2 Marginalized kernel for labeled dual graphs; 4 A New Kernel |
4.1 Extracting features for global structural information4.2 Extracting features for local structural information; 5 Experiments and Results; 5.1 Data and parameters; 5.2 Results; 6 Conclusion; Acknowledgment; References; Chapter 5 Beyond String Algorithms: Protein Sequence Analysis using Wavelet Transforms Arun Krishnan and Kuo-Bin Li; 1 Introduction; 1.1 String algorithms; 1.2 Sequence analysis; 1.3 Wavelet transform; 2 Motif Searching; 2.1 Introduction; 2.2 Methods; 2.3 Results; 2.4 Allergenicity prediction; 3 Transmembrane Helix Region (HTM) Prediction; 4 Hydrophobic Cores |
5 Protein Repeat Motifs |
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Sommario/riassunto |
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Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, |
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