Tabla de Contenidos: “…Dificultades y aspectos a mejorar -- 3.3.4. Caso Endeavour Mining y mineros artesanales de Burkina Faso, Costa de Marfil y Senegal (África occidental) -- 3.3.4.1. …”

Tabla de Contenidos: “…Machine generated contents note: List of Contributors Preface Part One The Contributions of Intelligent Techniques in Multicriteria Decision Aiding 1 Computational Intelligence Techniques for Multicriteria Decision Aiding: An Overview 1.1 Introduction 1.2 The MCDA Paradigm 1.2.1 Modeling Process 1.2.2 Methodological Approaches 1.3 Computational Intelligence in MCDA 1.3.1 Statistical Learning and Data Mining 1.3.2 Fuzzy Modeling 1.3.3 Metaheuristics 1.4 Conclusions References 2 Intelligent Decision Support Systems 2.1 Introduction 2.2 Fundamentals of Human Decision Making 2.3 Decision Support System 2.4 Intelligent Decision Support Systems 2.4.1 Artificial Neural Networks for Intelligent Decision Support 2.4.2 Fuzzy Logic for Intelligent Decision Support 2.4.3 Expert Systems for Intelligent Decision Support 2.4.4 Evolutionary Computing for Intelligent Decision Support 2.4.5 Intelligent Agents for Intelligent Decision Support 2.5 Evaluating Intelligent Decision Support Systems 2.5.1 Determining Evaluation Criteria 2.5.2 Multi-Criteria Model for IDSS Assessment 2.6 Summary and Future Trends References Part Two Intelligent Technologies for Decision Support and Preference Modeling 3 Designing Distributed Multi-Criteria Decision Support Systems for Complex and Uncertain Situations 3.1 Introduction 3.2 Example Applications 3.3 Key Challenges 3.4 Making Trade-offs: Multi-criteria Decision Analysis 3.4.1 Multi-attribute Decision Support 3.4.2 Making Trade-offs Under Uncertainty 3.5 Exploring the Future: Scenario-based Reasoning 3.6 Making Robust Decisions: Combining MCDA and SBR 3.6.1 Decisions Under Uncertainty: The Concept of Robustness 3.6.2 Combining Scenarios and MCDA 3.6.3 Collecting, Sharing and Processing Information: A Distributed Approach 3.6.4 Keeping Track of Future Developments: Constructing Comparable Scenarios 3.6.5 Respecting Constraints and Requirements: Scenario Management 3.6.6 Assisting Evaluation: Assessing Large Numbers of Scenarios 3.7 Discussion 3.8 Conclusion References 4 Preference Representation with Ontologies 4.1 Introduction 4.1.1 Structure of the Chapter 4.2 Ontology-based Preference Models 4.3 Maintaining the User's Profile up to Date 4.4 Decision Making Methods Exploiting the Preference Information Stored in Ontologies 4.4.1 Recommendation Based on Aggregation 4.4.2 Recommendation Based on Similarities 4.4.3 Recommendation Based on Rules 4.5 Discussion and Open Questions References Part Three Decision Models 5 Neural Networks in Multicriteria Decision Support 5.1 Introduction 5.2 Basic Concepts of Neural Networks 5.2.1 Neural Networks for Intelligent Decision Support 5.3 Basics in Multicriteria Decision Aid 5.3.1 MCDM Problems 5.3.2 Solutions of MCDM Problems 5.4 Neural Networks and Multicriteria Decision Support 5.4.1 Review of Neural Network Applications to MCDM Problems 5.4.2 Discussion 5.5 Summary and Conclusions References 6 Rule-Based Approach to Multicriteria Ranking 6.1 Introduction 6.2 Problem Setting 6.3 Pairwise Comparison Table (PCT) 6.4 Rough Approximation of Outranking and Non-outranking Relations 6.5 Induction and Application of Decision Rules 6.6 Exploitation of Preference Graphs 6.7 Illustrative Example 6.8 Summary and Conclusions References 7 About the Application of Evidence Theory in MultiCriteria Decision Aid 7.1 Introduction 7.2 Evidence Theory: Some Concepts 7.2.1 Knowledge Model 7.2.2 Combination 7.2.3 Decision Making 7.3 New Concepts in Evidence Theory for MCDA 7.3.1 First Belief Dominance 7.3.2 RBBD Concept 7.4 Multicriteria Methods modeled by Evidence Theory 7.4.1 Evidential Reasoning Approach 7.4.2 DS/AHP 7.4.3 DISSET 7.4.4 A Choice Model Inspired by ELECTRE I 7.4.5 A Ranking Model Inspired by Xu et al.'…”

Tabla de Contenidos: “…Introducing Tivoli Data Warehouse 1.2 -- 1.1 Data warehousing basics -- 1.1.1 Data warehouse -- 1.1.2 Data mart -- 1.1.3 Business intelligence -- 1.1.4 Data mining -- 1.2 Tivoli Data Warehouse -- 1.3 What is new in Tivoli Data Warehouse 1.2 -- 1.3.1 Crystal EnterpriseTM -- 1.3.2 IBM DB2 UDB for OS/390 and z/OS support -- 1.3.3 Flexible and extended configuration support -- 1.3.4 Installation enhancements -- 1.3.5 Serviceability and scalability improvements -- 1.4 Tivoli Data Warehouse architecture -- 1.4.1 Tivoli Data Warehouse control center server -- 1.4.2 Source databases -- 1.4.3 Central data warehouse -- 1.4.4 Data marts -- 1.4.5 Warehouse agents and agent sites -- 1.4.6 Crystal Enterprise Server -- 1.5 Benefits of using Tivoli Data Warehouse -- Chapter 2. …”

Tabla de Contenidos: “…Creative destruction in Korea's manufacturing and mining sectors -- Policies to improve SME financing -- The government role is expanding, while problems remain in private-sector lending -- Table 1.10. …”

Tabla de Contenidos: “…3.3 Grainsize Characterization -- 4 Conclusions -- References -- High Precision Fabrication of an Innovative Fiber-Optic Displacement Sensor -- 1 Introduction -- 2 Sensor Principle -- 3 High Precision Machining Technique -- 3.1 The High Precision Turning Machine -- 3.2 The Machining Strategy of the Cones Assembled Grating -- 4 Conclusion -- References -- 3D Printing of Hydrogel-Based Seed Planter for In-Space Seed Nursery -- 1 Introduction -- 2 Materials and Method -- 2.1 Materials -- 2.2 Hydrogel Printing -- 2.3 Seed Planting -- 3 Results and Discussion -- 3.1 Hydrogel Printing Quality Control and Mass Evaluation -- 3.2 Volume Evaluation -- 3.3 Soybean Density Evaluation -- 4 Conclusions -- References -- Modelling and Simulation of Automated Hydraulic Press Brake -- 1 Introduction -- 2 Methodology -- 2.1 Computer Aided Modelling and Simulation -- 2.2 Model Control of Triplet Cylinder -- 2.3 The Operational Model of the Triplet Cylinder -- 3 Results and Discussion -- 4 Conclusion -- References -- Assessment of Reconfigurable Vibrating Screen Technology for the Mining Industries -- 1 Introduction -- 2 Methodology -- 3 Results and Discussion -- 4 Conclusion -- References -- Manufacturing Systems -- Deep Anomaly Detection for Endoscopic Inspection of Cast Iron Parts -- 1 Introduction -- 2 Related Work -- 2.1 One-Class Classification (OCC) for Visual Anomaly Detection -- 2.2 One-Class Classification Anomaly Detection for Industrial Inspection -- 3 Use Case 'Endoscopic Cavity Inspection of Cast Iron Part' -- 4 Proposed Approach -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Threshold Selection and Comparison with Supervised Learning -- 4.4 Discussion -- 5 Conclusion and Outlook -- References -- Classification and Detection of Malicious Attacks in Industrial IoT Devices via Machine Learning -- 1 Introduction -- 2 Background…”

Tabla de Contenidos: “…Learn By Doing-Adding a Second Fact Table to the Manufacturing Data Mart -- Learn By Doing-Populating the Fact Tables in the Manufacturing Data Mart -- Putting Integration Services Packages into Production -- Parameters -- Associating Properties with Parameters -- The Integration Services Catalog -- Deploying SSIS Projects from SQL Server Data Tools -- Importing Packages into the SSIS Catalog -- Learn By Doing-Deploying the Packages to a Server -- Managing Packages in the Integration Services Catalog -- Utilizing Environments -- Setting Permissions -- Executing Packages from the SSIS Catalog -- Execution Dashboard Reports -- Meanwhile, Back at the BI Semantic Model -- Part III: Working with a Multidimensional BI Semantic Model -- Chapter 9: Cubism-Measures and Dimensions -- Building a Multidimensional BI Semantic Model -- Creating a Cube -- Learn By Doing-Building a Multidimensional BI Semantic Model for the Manufacturing Data Mart -- Measures -- Measure Groups -- Measure Groups and Dimensions -- Granularity -- Made-up Facts-Calculated Measures -- Creating Calculated Measures -- It Doesn't Add Up-Measure Aggregates Other Than Sum -- Semiadditive Measures -- Aggregate Functions -- Learn By Doing-Working with Measures and Measure Groups -- Dimensions -- Managing Dimensions -- Learn By Doing-Cleaning Up the Dimensions in the Max Min Manufacturing DM Cube -- Relating Dimensions to Measure Groups -- Learn By Doing-Relating Dimensions in the Max Min Manufacturing DM Cube -- Types of Dimensions -- Fact Dimensions -- Parent-Child Dimensions -- Role Playing Dimensions -- Reference Dimensions -- Data Mining Dimensions -- Many-to-Many Dimensions -- Slowly Changing Dimensions -- Type 1 Slowly Changing Dimensions -- Type 2 Slowly Changing Dimensions -- Type 3 Slowly Changing Dimensions -- Loading the Max Min Sales DM Data Mart -- You Are Special…”

Tabla de Contenidos: “…Scott Plutchak -- Data Expeditions: Mining Data for Effective Decision-Making / Ann Michael, Ivy Anderson, Gwen Evans -- Navigating Access to Knowledge: Copyright, Fake News, Fair Use, and Libraries / Ruth Okediji -- The Long Arm of the Law / Kenneth D. …”

Tabla de Contenidos: “…Asset impairments or losses on sales of discontinued operations -- Accounts receivable -- Accounts payable -- Inventories -- Other -- Net cash provided by (used in) operating activities -- Considering a company's cash from investments -- Getting into a company's cash from financing activities -- How Investors May Be Fooled by Earnings, But Not by Cash Flow -- Understanding the Fundamentals of Free Cash Flow -- Calculating free cash flow -- Measuring a company's cash-burn rate -- Chapter 8 Using Financial Ratios to Pinpoint Investments -- Using Financial Ratios to Find Out What's Really Going on at a Company -- Which financial ratios you should know and how to use them -- Using ratios to grade management -- Return on equity -- Return on invested capital -- Checking up on a company's efficiency -- Accounts receivable turnover -- Inventory turnover -- Accounts payable turnover -- Evaluating companies' financial condition -- Debt to equity -- Quick ratio -- Interest coverage ratio -- Getting a handle on a company's valuation -- Price-to-book ratio -- Dividend yield -- Dividend payout ratio -- Earnings yield -- Getting Familiar with the Price-to-Earnings Ratio -- How to calculate the P-E -- What a P-E tells you about a stock -- Putting the P-E into Perspective -- Taking the P-E to the next level: the PEG -- Evaluating the P-E of the entire market -- Chapter 9 Mining the Proxy Statement for Investment Clues -- Getting up to Speed with What the Proxy Statement Is -- Uncovering info in the proxy statement -- Getting your hands on the proxy -- Expanding Fundamental Analysis Beyond the Numbers -- Appreciating corporate governance -- Getting to know the board -- Stepping Through the Proxy -- Getting to know the board of directors -- Analyzing the independence of board members -- Delving into the board's committees…”

Tabla de Contenidos: “…Rock.AR, a software for the mining industry -- 3.6.1 Bugs detected with the framework -- First error found -- Second error found -- 3.6.2 Bugs detected with Generotron -- 3.6.3 Bug detection on visual representations -- 3.7 Conclusions &amp -- future works -- Acknowledgment -- References -- 4 Stepping stone to smarter supervision: a human-centered multidisciplinary framework -- DSS type, their positive effects, and those more questionable -- Understanding of DSSs' undesired effects -- Towards a Human-Centered Design (HCD) multidisciplinary framework for DSS -- Phase 1. …”