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This handbook presents comprehensive guidelines for researchers and practitioners for a coordinated execution of emergency response. A useful tool to mitigate logistical problems that often follow disasters or extreme events, whether inadvertent or deliberate, the core of this guide is the human factors in emergency project management. This involves a systematic structure for communication, cooperation, and coordination. This handbook highlights what must be done and when. It can also help to identify the resources required for each effort. The handbook is divided into four parts: Technical Ch

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Acknowledgments -- Authors -- Chapter 1: Artificial Intelligence within Industrial and Systems Engineering Framework -- 1.1 Introduction -- 1.2 Quantum Potential for AI -- 1.3 Old and New AI Achievements -- 1.4 Industrial Engineering Linkage -- 1.5 Historical Background of AI -- 1.6 Origin of Artificial Intelligence -- 1.7 Human Intelligence versus Machine Intelligence -- 1.8 Natural Language Dichotomies -- 1.9 The First Conference on Artificial Intelligence -- 1.10 Evolution of Smart Programs -- 1.11 Branches of Artificial Intelligence -- 1.12 Neural Networks -- 1.13 Emergence of Expert Systems -- References -- Chapter 2: Mathematics of Cantor Set for AI Searches -- 2.1 Introduction -- 2.2 Intelligent Searches -- 2.3 Backdrop for AI Searches -- 2.4 Mathematics of Cantor Set -- 2.4.1 Set Sectioning Technique of Cantor Set -- 2.4.2 Search of Asymmetrically Distributed Data -- 2.4.3 Derivation of the Mode Estimating Formula in Terms of Inclusive Graphic Skewness -- 2.4.4 Graphical Verification of the mod%-IGS Relationship -- 2.5 Results of Preliminary Research -- 2.6 Cantor and Binary Search Comparison of 1500 Data Point Files -- 2.7 Cantor and Binary Search Comparison of 150 Data Point Files -- 2.8 Comparison of the Binary Search and the Cantor Search for the various Database Sizes -- 2.9 Intelligent 1/n Sectioning of the Search Space -- References -- Chapter 3: Set-theoretic Systems for AI Applications -- 3.1 Set Systems in Problem Domains -- 3.2 Sets and Systems in Innovation -- 3.3 Ordered Pairs on Sets -- 3.4 Set Relations in Innovation Systems -- 3.5 Functions on Sets -- 3.6 Cardinality of Sets -- 3.7 Relationships of Set-to-System and Subset-to-Subsystem -- 3.8 Integration Mapping of Subsets -- 3.9 Model Reduction Approach.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- About the Authors -- Chapter 1: Systems Thinking for Innovation -- Chapter 2: Fundamentals of Innovation: Quantitative and Qualitative Techniques -- Chapter 3: Set-Theoretic Systems for Innovation -- Chapter 4: Half-life of Innovation Using Learning Curves -- Chapter 5: Innovation Economic Analysis -- Chapter 6: Human-Systems Integration for Innovation -- Chapter 7: Innovation through Industrial Engineering Techniques -- Chapter 8: Managing Innovation Projects -- Chapter 9: Utility Modeling for Innovation -- Chapter 10: Innovation Workforce Development and Management -- Appendix: Technical Conversion Factors -- Index.

Distance learning (DL) is undergoing a rapid growth in academia, business, industry, and the military. Recent technological developments in computer‐based training, along with shrinking internal resources, and rising industry interest in sustainable workforce development, provide a strong impetus for DL investments. Due to the multiplicity of efforts involved in planning and executing a DL program, it is imperative that organizations view this nontraditional mode of knowledge transfer from a systems perspective. This paper presents a coordination‐centric systems framework for implementing a DL program using the SIMILAR systems engineering process. The paper also suggests linking SIMILAR to two complementary models, Triple C and D‐E‐J‐I, to enhance the overall systems approach. Implementing DL from a systems view aids in identifying the interrelating components, resource interfaces, and improvements needed in techniques used to deliver educational material at a distance. The proposed framework is effective in assisting DL instructors, students, administrators, and other stakeholders to improve the learning environment and manage the complex interaction of people, technology, and instructional processes. Because of its large and diverse operational base, the military is embracing DL programs aggressively. An example is presented of the DL program for graduate systems engineering program at the Air Force Institute of Technology (AFIT). Abstract © 2011 Wiley Periodicals, Inc.