Tabla de Contenidos: “…5.1.3 Gather and analyse comparable evidence -- 5.1.4 Establish basis of value -- 5.1.5 Make assumptions and special assumptions as appropriate -- 5.1.6 Select valuation approach(es) and method(s) and undertake the valuation -- 5.1.7 Produce a valuation report -- 5.2 Valuation governance -- 5.2.1 Standards of conduct -- 5.2.2 Valuation process standards -- 5.2.3 International valuation standards (IVS) -- 5.2.4 National valuation standards -- 5.3 Valuation systems -- 5.3.1 Information systems -- 5.3.2 Valuation capacity -- 5.3.3 Professional valuers associations -- 5.4 Conclusion -- Notes -- References -- Section B Valuation Approaches and Methods -- Chapter 6 Market Approach -- 6.1 Introduction -- 6.2 The comparison method -- 6.2.1 Collect comparable evidence of market transactions -- 6.2.2 Identification of value-significant characteristics -- 6.2.3 Adjustment of value-significant characteristics -- 6.3 Hedonic regression method -- 6.3.1 Simple linear regression -- 6.3.2 Multiple linear regression -- Notes -- References -- Chapter 7 Income Approach -- 7.1 Introduction -- 7.2 Income capitalisation method -- 7.2.1 Perpetual annuities (freeholds) -- 7.2.2 Annuities with a term certain (leaseholds) -- 7.3 Discounted cash-flow method -- 7.3.1 A discounted Cash-Flow valuation model -- 7.3.2 Perpetual annuities -- 7.3.3 Annuities with a term certain -- 7.4 Profits method -- 7.4.1 Method -- Notes -- References -- Chapter 8 Cost Approach -- 8.1 Introduction -- 8.2 Replacement cost method -- 8.2.1 Replacement cost -- 8.2.2 Depreciation -- 8.2.3 Land value -- 8.2.4 Application of the replacement cost method -- 8.2.5 Issues arising from the application of the replacement cost method -- 8.3 Residual method -- 8.3.1 Basic residual technique -- 8.3.2 Basic residual profit appraisal -- 8.3.3 Discounted cash-flow technique -- Notes -- References…”

Tabla de Contenidos: “…La croissance dans la zone de l'OCDE a été inégale -- Graphique I.1. PIB par habitant : d'importants écarts -- Graphique I.2. …”

Tabla de Contenidos: “…Fast Exchange -- 2.3.3 Determination of the Underlying Equilibria -- 2.3.4 Software for Non‐linear Regression Fitting -- 2.3.5 Common Issues -- 2.4 Experimental Techniques: UV-Vis Spectroscopy -- 2.4.1 When to Use…”

Tabla de Contenidos: “…Autonomous systems versus linear systems -- 4.2.1. Linear systems -- 4.2.2. …”

Tabla de Contenidos: “…Image formation model -- 11.5.1. Partially linearly polarized incident illumination -- 11.5.2. …”

Tabla de Contenidos: “…Concept of a Struct as a Structure Type -- Difference Between Struct and Class -- Struct with a Default Constructor Only -- Struct with a User Constructor -- Struct Instantiation Without the New Keyword -- Struct Instantiation with the New Keyword -- Creating a Constructor -- Creating Member Properties (Get and Set Accessors) -- Encapsulation -- Readonly Struct -- Creating a Readonly Struct -- C# 8 readonly Members -- C# 8 Nullable Reference Types -- Chapter Summary -- Chapter 20: Enumerations -- Concept of Enumerations -- Defining an Enumeration -- Enumeration Examples -- Enumerated Values: Use and Scope -- Enumeration Methods -- Using the foreach Iteration -- Enumeration Values: GetValues() -- Assigning Our Own Values to the Enumeration -- Use the GetName() and GetValues() Methods -- Sample Application Using Enumerations -- Chapter Summary -- Chapter 21: Delegates -- Concept of Delegates -- Single Delegate -- Declare the Delegate with Its Return Type and Method Signature -- Instantiate the Delegate and Set Its Target Method -- Invoke the Delegate -- Multicast Delegates -- Instantiate the Delegate Again and Set the New Instances' Target Method -- Chain the Delegates -- Invoke the Multicast Delegate -- More Complex Example -- Chapter Summary -- Chapter 22: Events -- Concept of Events -- Publisher and Subscriber -- Declare an Event -- Raise an Event -- Handle an Event -- Add a Method to an Event Using += -- Refer the Event to a Second Method Using += -- Refer the Event to a Third Method Using += -- Remove a Method from an Event Using -= -- Chapter Summary -- Chapter 23: Generics -- Concept of Generics -- Generic Class, Generic Method, Generic Parameters -- Generic Class, Generic Method, Mixed Parameter Types -- Generic Method Only -- Chapter Summary -- Chapter 24: Common Routines -- Common Programming Routines with C# -- Linear Search…”

Tabla de Contenidos: “…-- 10.2 Insights into Correlation -- Properties of Correlation Coefficient -- 10.3 Basics of Regression -- Need for Regression -- 10.4 Regression Model -- Historical Perspective -- How does Simple Linear Regression work in practice? -- The concept of Coefficient of Determination for Statistical Validity -- Regression with Pleasure from Microsoft Excel -- Explanation on the Output -- Item of interest on the ANOVA output -- Multiple Linear Regression Model -- 10.5 Chapter Summary -- Glossary -- Review Questions -- Answers to Review Questions -- Practice Problems -- 2. …”

Tabla de Contenidos: “…6.5 Exceptions to the Octet Rule: Odd-Electron Species, Incomplete Octets, and Expanded Octets -- Odd-Electron Species -- Incomplete Octets -- Expanded Octets -- 6.6 Bond Energies and Bond Lengths -- Bond Energy -- Bond Length -- 6.7 VSEPR Theory: The Five Basic Shapes -- Two Electron Groups: Linear Geometry -- Three Electron Groups: Trigonal Planar Geometry -- Four Electron Groups: Tetrahedral Geometry -- Five Electron Groups: Trigonal Bipyramidal Geometry -- Six Electron Groups: Octahedral Geometry -- 6.8 VSEPR Theory: The Effect of Lone Pairs -- Four Electron Groups with Lone Pairs -- Five Electron Groups with Lone Pairs -- Six Electron Groups with Lone Pairs -- 6.9 VSEPR Theory: Predicting Molecular Geometries -- Representing Molecular Geometries on Paper -- Predicting the Shapes of Larger Molecules -- 6.10 Molecular Shape and Polarity -- Vector Addition -- REVIEW -- Self-Assessment Quiz -- Key Learning Outcomes -- Key Terms -- Key Concepts -- Key Equations and Relationships -- EXERCISES -- Review Questions -- Problems by Topic -- Cumulative Problems -- Challenge Problems -- Conceptual Problems -- Answers to Conceptual Connections -- 7 Chemical Bonding II: Valence Bond Theory and Molecular Orbital Theory -- 7.1 Oxygen: A Magnetic Liquid -- 7.2 Valence Bond Theory: Orbital Overlap as a Chemical Bond -- 7.3 Valence Bond Theory: Hybridization of Atomic Orbitals -- sp3 Hybridization -- sp2 Hybridization and Double Bonds -- sp Hybridization and Triple Bonds -- sp3d and sp3d2 Hybridization -- Writing Hybridization and Bonding Schemes -- 7.4 Molecular Orbital Theory: Electron Delocalization -- Linear Combination of Atomic Orbitals (LCAO) -- Second-Period Homonuclear Diatomic Molecules -- Second-Period Heteronuclear Diatomic Molecules -- 7.5 Molecular Orbital Theory: Polyatomic Molecules -- 7.6 Bonding in Metals and Semiconductors…”

Tabla de Contenidos: “…De los cuervos e de los búhosLas liebres y la fuente de la luna; La gineta, la liebre y el gato religioso; El religioso y los tres ladrones; La mujer del viejo; El diablo y el ladrón; El carpintero engañado; La rata cambiada en niña; La culebra; Capítulo VII; El asno sin corazón y sin orejas; Capítulo VIII; Del religioso e del can; es el capítulo del home que face las cosas rabiosamente, e a que torna su facienda; El religioso que vertió la miel y manteca sobre su cabeza; Capítulo IX; Del gato e del mur; Capítulo X; Del rey Varamunt e del ave que dicen Catra; Capítulo XI…”

Tabla de Contenidos: “…Es ejemplo del enemigo que muestra humildad y gran amor a su enemigo, y se somete hasta que se apodera dél, y después le mata; Las liebres y la fuente de la Luna; La gineta, la liebre y el gato religioso; El religioso y los tres ladrones; La mujer del viejo; El diablo y el ladrón; El carpintero engañado; La rata cambiada en niña; La culebra y las ranas; Capítulo VII. …”

Tabla de Contenidos: “…4.1.4 Evaluating Classification Models' Predictive Accuracy4.1.5 Evaluating Binary Models' Predictive Accuracy; 4.1.6 ROC Charts; 4.1.7 Lift Chart; 4.2 Principal Component Analysis; 4.2.1 Overview; 4.2.2 Principal Components; 4.2.3 Generating Principal Components; 4.2.4 Interpretation of Principal Components; 4.3 Multiple Linear Regression; 4.3.1 Overview; 4.3.2 Generating Models; 4.3.3 Prediction; 4.3.4 Analysis of Residuals; 4.3.5 Standard Error; 4.3.6 Coefficient of Multiple Determination; 4.3.7 Testing the Model Significance; 4.3.8 Selecting and Transforming Variables…”

Tabla de Contenidos: “…-- Um- und Einbau der Weiche -- Steuern mit Mini-Servo -- Einbau der Weiche -- Umbau mit Linear-Servo -- Der Decoder -- Die Landschaft wird modelliert -- Zusammenfassung -- Tag 5: Signale regeln den Betrieb -- Was wollen wir am Tag 5 erreichen? …”

Tabla de Contenidos: “…....................................................... 5.4.2 Institutionelle Optionen: Wahlmöglichkeiten innerhalb und außerhalb des Hochschulsystems ....... 5.4.3 Institutionelle Barrieren und deren Antizipation ....... 5.5 Zusammenfassung und Hypothesen ....................................... 6 Forschungsdesign ........................................................................... 6.1 Übersetzung der Phasen in analytische Schritte ..................... 6.2 Daten ...................................................................................... 6.2.1 Best Up-Individualdaten ........................................... 6.2.2 Best Up-Rekrutierungsdaten ..................................... 6.2.3 Mikrozensus 6.3 Operationalisierung phasenübergreifender Variablen 6.4 Definition des Ausgangssamples 6.5 Methodisches Vorgehen 6.5.1 Hierarchische Datenstruktur 6.5.2 Modelle für lineare und binäre abhängige Variablen 6.5.3 Anmerkungen zur Bedeutung von Signifikanz 7 Phase 1: die Übersetzung von Studienaspiration in Studienintention 7.1 Methodisches Vorgehen 7.1.1 Die Messung von Studienintentionen 7.1.2 Analyseschritte und Operationalisierungen 7.1.3 Faktorieller Survey zur Modellierung institutioneller Einflussfaktoren 7.1.4 Analysesample und Umgang mit fehlenden Werten 7.2 Ergebnisse I: Herkunftsunterschiede bei der Übersetzung und deren Erklärungen 7.2.1 Leistung, rationale Abwägung und soziale/kulturelle Ressourcen 7.2.2 Institutionelle Opportunitäten und Barrieren 7.3 Ergebnisse II: vertikal und horizontal differenzierte Intentionen 7.4 Zusammenfassung 8 Phasen 2-4: die Umsetzung von Studienaspirationen 8.1 Methodisches Vorgehen 8.1.1 Definition des Ausgangssamples 8.1.2 Analyseschritte und Operationalisierungen 8.1.3 Auswertungsstrategie 8.1.4 Umgang mit fehlenden Werten 8.2 Ergebnisse Phase 2: Herkunftsunterschiede im Bewerbungsverhalten 8.2.1 Leistung, rationale Abwägung und soziale/kulturelle Ressourcen 8.2.2 Zugangshürden 8.2.3 Differenzierte Bewerbungen: Hochschultypen/ Fächer/Orte 8.2.4 Bewerbungsstrategien 8.3 Ergebnisse Phase 3: Herkunftsunterschiede bei der Zulassung 8.4 Ergebnisse Phase 4: Herkunftsunterschiede bei der Studienaufnahme 8.4.1 Studienaufnahme: konditional vs. unkonditional 8.4.2 Differenzierte Übergänge: Hochschulorte, Hochschultypen und Studienfächer 8.5 Zusammenfassung 9 Zusammenfassung und Diskussion Literaturverzeichnis…”

Tabla de Contenidos: “…3.8.1 Computing the Eigenvector Centrality -- 3.9 PageRank Centrality -- 3.9.1 Computing the PageRank Centrality -- 3.10 Hub and Authority -- 3.10.1 Computing the Hub and Authority Centralities -- 3.11 Network Centralities Calculation by Group -- 3.11.1 By Group Network Centralities -- 3.12 Summary -- Chapter 4 Network Optimization -- 4.1 Introduction -- 4.1.1 History -- 4.1.2 Network Optimization in SAS Viya -- 4.2 Clique -- 4.2.1 Finding Cliques -- 4.3 Cycle -- 4.3.1 Finding Cycles -- 4.4 Linear Assignment -- 4.4.1 Finding the Minimum Weight Matching in a Worker-Task Problem -- 4.5 Minimum-Cost Network Flow -- 4.5.1 Finding the Minimum-Cost Network Flow in a Demand-Supply Problem -- 4.6 Maximum Network Flow Problem -- 4.6.1 Finding the Maximum Network Flow in a Distribution Problem -- 4.7 Minimum Cut -- 4.7.1 Finding the Minimum Cuts -- 4.8 Minimum Spanning Tree -- 4.8.1 Finding the Minimum Spanning Tree -- 4.9 Path -- 4.9.1 Finding Paths -- 4.10 Shortest Path -- 4.10.1 Finding Shortest Paths -- 4.11 Transitive Closure -- 4.11.1 Finding the Transitive Closure -- 4.12 Traveling Salesman Problem -- 4.12.1 Finding the Optimal Tour -- 4.13 Vehicle Routing Problem -- 4.13.1 Finding the Optimal Vehicle Routes for a Delivery Problem -- 4.14 Topological Sort -- 4.14.1 Finding the Topological Sort in a Directed Graph -- 4.15 Summary -- Chapter 5 Real-World Applications in Network Science -- 5.1 Introduction -- 5.2 An Optimal Tour Considering a Multimodal Transportation System - The Traveling Salesman Problem Example in Paris -- 5.3 An Optimal Beer Kegs Distribution - The Vehicle Routing Problem Example in Asheville -- 5.4 Network Analysis and Supervised Machine Learning Models to Predict COVID-19 Outbreaks -- 5.5 Urban Mobility in Metropolitan Cities…”

Tabla de Contenidos: “…-- Wake locks, wakeup sensors, and the FIFO queue -- Wakeup and non-wakeup sensors -- The sensor's hardware FIFO queue -- Summary -- Chapter 5: The Motion, Position, and Fingerprint Sensors -- Understanding motion-based sensors -- The accelerometer sensor -- The gyroscope sensor -- The gravity sensor -- The linear acceleration sensor -- The significant motion sensor -- Understanding position-based sensors -- The magnetometer sensor -- The orientation sensor -- The fingerprint sensor -- Time for action - shake detection using the accelerometer sensor -- Time for action - the compass using orientation sensor and orientation APIs -- Time for action - using the fingerprint sensor -- What just happened? …”

Tabla de Contenidos: “…Technical requirements -- Understanding features and descriptors -- FAST corner detection -- Corner detection using the imcorner function -- Comparing performance -- BRIEF - efficient duplicate detection method -- Identifying image duplicates -- Creating a panorama from many images -- ORB, rotation invariant image matching -- BRISK - scale invariant image matching -- FREAK - fastest scale and rotation invariant matching -- Running face recognition -- Summary -- Questions -- Chapter 6: Introduction to Neural Networks -- Technical requirements -- Introduction -- The need for neural networks -- The need for MXNet -- First steps with the MNIST dataset -- Getting the data -- Preparing the data -- Defining a neural network -- Fitting the network -- Improving the network -- Predicting new images -- Putting it all together -- Multiclass classification with the CIFAR-10 dataset -- Getting and previewing the dataset -- Preparing the data -- Starting with the linear classifier -- Reusing the MNIST architecture -- Improving the network -- Accuracy - why at almost 70 -- Putting it all together -- Classifying cats versus dogs -- Getting and previewing the dataset -- Creating an image data iterator -- Training the model -- Putting it all together -- Reusing your models -- Saving the model -- Loading the model -- Summary -- Questions -- Further reading -- Chapter 7: Using Pre-Trained Neural Networks -- Technical requirements -- Introduction to pre-trained networks -- Transfer learning -- MXNet Model Zoo -- Predicting image classes using Inception V3 -- Setting up the Inception V3 environment -- Loading the network -- Preparing the datasets -- Running predictions -- Expected performance -- Putting it all together -- Predicting an image class using MobileNet V2 -- Setting up the environment -- Loading the network -- Preparing the datasets -- Running the predictions…”

Tabla de Contenidos: “…Front Cover -- Temporal Data Mining via Unsupervised Ensemble Learning -- Temporal Data Mining via Unsupervised Ensemble Learning -- Copyright -- Contents -- List of Figures -- List of Tables -- Acknowledgments -- 1 - Introduction -- 1.1 BACKGROUND -- 1.2 PROBLEM STATEMENT -- 1.3 OBJECTIVE OF BOOK -- 1.4 OVERVIEW OF BOOK -- 2 - Temporal Data Mining -- 2.1 INTRODUCTION -- 2.2 REPRESENTATIONS OF TEMPORAL DATA -- 2.2.1 TIME DOMAIN-BASED REPRESENTATIONS -- 2.2.2 TRANSFORMATION-BASED REPRESENTATIONS -- Piecewise Local Statistics -- Piecewise Discrete Wavelet Transforms -- Polynomial Curve Fitting -- Discrete Fourier Transforms -- 2.2.3 GENERATIVE MODEL-BASED REPRESENTATIONS -- 2.3 SIMILARITY MEASURES -- 2.3.1 SIMILARITY IN TIME -- 2.3.2 SIMILARITY IN SHAPE -- 2.3.3 SIMILARITY IN CHANGE -- 2.4 MINING TASKS -- 2.5 SUMMARY -- 3 - Temporal Data Clustering -- 3.1 INTRODUCTION -- 3.2 OVERVIEW OF CLUSTERING ALGORITHMS -- 3.2.1 PARTITIONAL CLUSTERING -- K-means -- Hidden Markov Model-Based K-Models Clustering -- 3.2.2 HIERARCHICAL CLUSTERING -- Single Linkage -- Complete Linkage -- Average Linkage -- HMM-Based Agglomerative Clustering -- HMM-Based Divisive Clustering -- 3.2.3 DENSITY-BASED CLUSTERING -- Density-Based Spatial Clustering of Applications with Noise -- 3.2.4 MODEL-BASED CLUSTERING -- EM Algorithm -- HMM-Based Hybrid Partitional-Hierarchical Clustering -- HMM-Based Hierarchical Metaclustering -- 3.3 CLUSTERING VALIDATION -- 3.3.1 CLASSIFICATION ACCURACY -- 3.3.2 ADJUSTED RAND INDEX -- 3.3.3 JACCARD INDEX -- 3.3.4 MODIFIED HUBERT'S Γ INDEX -- 3.3.5 DUNN'S VALIDITY INDEX -- 3.3.6 DAVIES-BOULDIN VALIDITY INDEX -- 3.3.7 NORMALIZED MUTUAL INFORMATION -- 3.4 SUMMARY -- 4 - Ensemble Learning -- 4.1 INTRODUCTION -- 4.2 ENSEMBLE LEARNING ALGORITHMS -- Bagging -- Boosting -- 4.3 COMBINING METHODS -- Linear Combiner -- Product Combiner…”

Tabla de Contenidos: “…6.4 AIC Criterion -- 6.5 MDL Criterion -- 6.6 Durbin-Levinson Algorithm -- 7 Identification of State Space Models -- 7.1 Introduction -- 7.2 Hankel Matrix -- 7.3 Order Determination -- 7.4 Determination of Matrices and -- 7.5 Determination of Matrix -- 7.6 Mid Summary: An Ideal Procedure -- 7.7 Order Determination with SVD -- 7.8 Reliable Identification of a State Space Model -- 8 Predictive Control -- 8.1 Introduction -- 8.2 Minimum Variance Control -- 8.3 Generalized Minimum Variance Control -- 8.4 Model‐Based Predictive Control -- 8.5 Data‐Driven Control Synthesis -- 9 Kalman Filtering and Prediction -- 9.1 Introduction -- 9.2 Kalman Approach to Prediction and Filtering Problems -- 9.3 The Bayes Estimation Problem -- 9.4 One‐step‐ahead Kalman Predictor -- 9.5 Multistep Optimal Predictor -- 9.6 Optimal Filter -- 9.7 Steady‐State Predictor -- 9.8 Innovation Representation -- 9.9 Innovation Representation Versus Canonical Representation -- 9.10 K‐Theory Versus K-W Theory -- 9.11 Extended Kalman Filter - EKF -- 9.12 The Robust Approach to Filtering -- 10 Parameter Identification in a Given Model -- 10.1 Introduction -- 10.2 Kalman Filter‐Based Approaches -- 10.3 Two‐Stage Method -- 11 Case Studies -- 11.1 Introduction -- 11.2 Kobe Earthquake Data Analysis -- 11.3 Estimation of a Sinusoid in Noise -- Appendix A: Linear Dynamical Systems -- A.1 State Space and Input-Output Models -- A.2 Lagrange Formula -- A.3 Stability -- A.4 Impulse Response -- A.5 Frequency Response -- A.6 Multiplicity of State Space Models -- A.7 Reachability and Observability -- A.8 System Decomposition -- A.9 Stabilizability and Detectability -- Appendix B: Matrices -- B.1 Basics -- B.2 Eigenvalues -- B.3 Determinant and Inverse -- B.4 Rank -- B.5 Annihilating Polynomial -- B.6 Algebraic and Geometric Multiplicity -- B.7 Range and Null Space -- B.8 Quadratic Forms…”

Tabla de Contenidos: “…Scale Path Loss Models 34 -- 3.3 Multipath Channels 38 -- 3.4 Empirical Wireless Channel Models 46 -- 3.5 Problems 48 -- References 50 -- 4 Optimum Receiver 51 -- 4.1 Decision Theory 51 -- 4.2 Optimum Receiver for AWGN 55 -- 4.3 Matched Filter Receiver 66 -- 4.4 Coherent and Noncoherent Detection 69 -- 4.5 Problems 73 -- References 74 -- 5 Wireless Channel Impairment Mitigation Techniques 75 -- 5.1 Diversity Techniques 75 -- 5.2 Error Control Coding 82 -- 5.2.1 Linear Block Codes 84 -- 5.2.2 Convolutional Codes 92 -- 5.3 MIMO 99 -- 5.4 Equalization 107 -- 5.5 OFDM 114 -- 5.6 Problems 120 -- References 121 -- Part II Wireless Communications Blocks Design 123 -- 6 Error Correction Codes 125 -- 6.1 Turbo Codes 125 -- 6.1.1 Turbo Encoding and Decoding Algorithm 125 -- 6.1.2 Example of Turbo Encoding and Decoding 133 -- 6.1.3 Hardware Implementation of Turbo Encoding and Decoding 149 -- 6.2 Turbo Product Codes 155 -- 6.2.1 Turbo Product Encoding and Decoding Algorithm 155 -- 6.2.2 Example of Turbo Product Encoding and Decoding 156 -- 6.2.3 Hardware Implementation of Turbo Product Encoding and Decoding 174 -- 6.3 Low?]…”

Tabla de Contenidos: “…Notes Chapter 4 Digital Technologies The Cloud Big Data Mobile The Internet of Things Social Notes Part II: Information Excellence Chapter 5 Processes, Resources, Operations and Information Processes Process Advantage Process Optimization Asset Optimization The Business Value of Information The Role of Information Technology Caveats Notes Chapter 6 The Discipline of Information Excellence From People to Machines From Physical to Virtual From Virtual to Digical From Processes to Experiences From Operations to Improvement From Static Design to Dynamic Optimization From Mass Production to Mass Personalization From Cost Reduction to Revenue Generation From Direct to Indirect Monetization From Touchpoints to Integration From Firms to Networks From Data to Actionable Insight From Answers to Exploration Notes Chapter 7 Burberry--Weaving IT into the Fabric of the Company Operational Excellence and Product Leadership From Operational Excellence to Information Excellence From Physical to Virtual From Virtual to Digical From Processes to Experiences From Mass Production to Mass Personalization From Cost Reduction to Revenue Generation From Touchpoints to Integration From Firms to Networks Notes Part III: Solution Leadership Chapter 8 Products, Services, and Solutions Competitive Strategy Product Elements The Experience Economy Pricing and Business Models Notes Chapter 9 The Discipline of Solution Leadership From Products and Services to Solutions From Generic and Expected to Augmented and Potential From Transactions to Relationships From Sales Results to Customer Outcomes From Standard Products to Custom Solutions From Products and Services to Experiences and Transformations From Standalone to Social From Product to Platform From Engineered to Ecosystem Notes Chapter 10 Nike--A Track Record of Success From Products to Solutions From Generic and Expected to Augmented and Potential From Transactions to Relationships From Sales Results to Customer Outcomes From Standard Products to Custom Solutions From Products to Experiences and Transformations From Standalone to Social From Engineered to Ecosystem Nike and the Other Digital Disciplines Notes Part IV: Collective Intimacy Chapter 11 Customer Experience and Relationships Customer Intimacy A Broad Spectrum of Relationships Dimensions of Interaction Collective and Collaborative Notes Chapter 12 The Discipline of Collective Intimacy From Transactions to Relationships From Relationships to Intimacy From Physical to Virtual From Virtual to Digical From Company to Community From People to Algorithms From Individual to Collective Notes Chapter 13 Netflix - Entertaining Disruption Information Excellence Accelerated Innovation Solution Leadership From Relationships to Intimacy From Physical to Virtual From Virtual to Digical From Company to Community From People to Algorithms From Individual to Collective Notes Part V: Accelerated Innovation Chapter 14 Innovation and Transformation Successful Commercial Innovation The Innovation Process Innovation Principles Innovation of Products, Processes, Relationships, and Innovation Business Model Innovation and Corporate Transformation Notes Chapter 15 The Discipline of Accelerated Innovation From Solitary to Collaborative From Internal to External From Closed to Open From Inside-Out to Outside-In From Products to Platforms From Linear to Agile From Employees to Crowds From Salaries to Prizes From Theoretical to Data-Driven From Human to Machine From Incremental to Transformational Notes Chapter 16 Procter & Gamble Cleans Up From Solitary to Collaborative From Internal to External From Closed to Open From Inside-Out to Outside-In From Employees to Crowds From Incremental to Transformational Notes Part VI: Successful Execution Chapter 17 General Electric--Flying High Digital Disciplines at GE Software at GE Information Excellence Solution Leadership Collective Intimacy Accelerated Innovation Notes Chapter 18 Human Behavior and Gamification Human Behavior Gamification Gamifying Information Excellence Gamifying Solution Leadership Gamifying Collective Intimacy Gamifying Accelerated Innovation Gamification Across Disciplines Notes Chapter 19 Opower--The Power of the Human Mind Human Behavior and Energy Consumption Opower, Information, and Intimacy Notes Chapter 20 Digital Disasters Strategic Errors Cyberattacks Software Design and Development Challenges Operational Issues Unintended Consequences Erratic Algorithms Politics and Pushback Digital Disappointments Notes Part VII: What's Next? …”