“…In Benjamin Ginsberg and Jennifer Bachner's simple experiments, in which the authors measured the impact of different stories their subjects heard about the past, these "history lessons" moved contemporary policy preferences by an average of 16 percentage points; forecasts of the future moved contemporary policy preferences by an average of 12 percentage points; the two together moved preferences an average of 21 percentage points. …”

Tabla de Contenidos: “…Cumulative Hazard Function 5.1.6. Average Failure Rate 5.1.7. Failure Rate Units 5.1.8. …”

Tabla de Contenidos: “…-- 20.2 Reading a P&amp -- L statement -- 20.2.1 Revenue -- 20.2.2 Expenses -- 20.3 Averages -- 20.4 OpEx and CapEx -- 20.4.1 Understanding the two types of expenses -- 20.4.2 Driving business decisions -- 20.5 Business architecture -- 20.6 Further reading -- 20.7 Action items -- 21 Tools for the modern job hunt -- 21.1 Job hunt tasks to do now -- 21.2 Review your brand -- 21.2.1 Your professional brand and the job hunt -- 21.2.2 Reviewing your public footprint -- 21.2.3 How would others describe your brand? …”

Tabla de Contenidos: “…6.4 Evaluation of recent reforms -- Conclusion -- References -- Germany -- Abstract -- Table of Contents -- 1 The context -- Life expectancy increases, but at a slower pace than the OECD average -- Germany's population is projected to age and to decrease -- 2 Long-term care insurance -- Germany introduced the LTCI as a "fifth pillar "of the social security system in 1995 -- Employers and employees contribute to the social LTCI with a share of their salary -- The rate of beneficiaries has increased over the past three decades -- An ageing population and the expansion in beneficiaries and benefits lead to increasing expenditures -- 3 Beneficiaries at a glance -- Four out of five beneficiaries receive home care -- 3.1 Eligibility for LTCI benefits -- 3.2 Entitlements for LTCI benefits -- The system favours home care over residential care -- Most beneficiaries opt for cash benefits for home care, but expenditures are highest for residential -- Benefits have been expanded since 1995, most notably in 2015/2017 -- 4 Providers of home and residential care -- 4.1 Home care providers -- 4.2 Residential care providers -- 4.3 LTC workforce in home and residential care -- 5 Choice and service pricing -- 5.1 Price-setting in home care -- Co-payment rates are unclear in LTC -- 5.2 Price-setting in residential care -- Co-payments of beneficiaries have increased over the past decades -- References -- Japan -- Abstract -- Table of Contents -- Introduction -- 1 Development of health services -- 1.1 Historical background -- 1.2 Regulating the number and function of hospital beds -- 2 Development of social services -- 3 LTCI -- 3.1 Basic design -- 3.2 Development of LTCI -- 4 Health Insurance Fee Schedule -- 4.1 Basic structure -- 4.2 Revision process -- 4.3 Outpatient care and home care -- 4.4 Inpatient care -- 5 LTCI Fee Schedule -- 5.1 Basic structure…”

Tabla de Contenidos: “…-- Introducing Yourself to Your Computer -- Writing Prompts for Input -- A Moment of Silence, Please (Pause) -- Working with User Input -- Converting Fahrenheit to Celsius -- Try It Out 6-1 -- Averaging Numbers -- Reading Text -- Try It Out 6-2 -- Programming Challenges -- Chapter 7: Empowering Programs with Math -- Exponent Methods -- SquareRoot() and Good Old Pythagoras -- Powerful Powers -- Try It Out 7-1 -- Rounding Methods -- Traditional Rounding -- Rounding to the Nearest Hundredth -- Try It Out 7-2 -- Abs(), Min(), and Max() Methods -- Try It Out 7-3 -- The Remainder() Method -- Try It Out 7-4 -- Random Numbers -- Try It Out 7-5 -- Trigonometric Methods -- Try It Out 7-6 -- Programming Challenges -- Chapter 8: Making Decisions with If Statements…”

Tabla de Contenidos: “…MSCI ESG data -- 7.3.2. Data coverage and average rating -- 7.3.3. An overview of MSCI ESG rating methodology -- 7.3.4. …”

Tabla de Contenidos: “…Tail Events Can Greatly Affect the Average Risk -- 4.7.3. Tail Events Can Be Identified From Frequency Distributions -- 4.7.4. …”

Tabla de Contenidos: “…Khan, Soummya Kar and Jos A A Moura) -- 17.1 Introduction -- 17.2 Preliminaries -- 17.3 Distributed Detection -- 17.4 Consensus Algorithms -- 17.5 Zero-Dimension (Average) Consensus -- 17.6 Consensus in Higher Dimensions -- 17.7 Leader-Follower (Type) Algorithms -- 17.8 Localization in Sensor Networks -- 17.9 Linear System of Equations: Distributed Algorithm -- 17.10 Conclusions -- References. -- 18. …”

Tabla de Contenidos: “…Appendix B List of Symbols -- References -- 3 Endogenous Firm Dynamics and Labor Flows via Heterogeneous Agents -- 1 Introduction -- 2 Dynamics of Team Production -- 2.1 Equilibrium of the Team Production Game -- Singleton Firms -- Nash Equilibrium -- Homogeneous Teams -- 2.2 Stability of Nash Equilibrium, Dependence on Team Size -- Unstable Equilibria and Pattern Formation Far from Agent Level Equilibria -- 3 From One Team to Six Million Firms, Computationally -- 3.1 Set-up of the Computational Model Using Agents -- 3.2 A Typical Realization of the Model: Agents Form Firms -- 3.3 An Aggregate Steady-State Emerges: Properties -- Number of Firms, Entrance and Exit, and Average Firm Size -- Typical Effort and Utility Levels -- Labor Flows -- 3.4 The Steady-State Population of Firms: Sizes, Productivities, Ages, Survival Rates, Lifetimes, and Growth Rates -- Firm Sizes (by Employees and Output) -- Labor Productivity -- Firm Ages, Survival Rates, and Lifetimes -- Joint Distribution of Firms by Size and Age -- Firm Growth Rates -- 3.5 The Steady-State Population of Agents: Wages Earned, Job Tenure, and Employment as a Function of Firm Size and Age -- Wage Distribution -- Job Tenure Distribution -- Employment as a Function of Firm Size and Age -- 3.6 Steady-State Job-to-Job Flows: The Labor Flow Network -- 3.7 Steady-State Agent Welfare -- 4 Model Variations: Sensitivity and Robustness -- 5 Summary and Conclusions -- 5.1 Emergence of Firms, Out of Microeconomic Equilibrium -- 5.2 From Theories of the Firm to a Theory of Firms -- 5.3 Economics of Computation and Computational Economics -- Appendix A Generalized Preference Speci cations -- Appendix B Generalized Compensation and Nash Stability -- Appendix C Sensitivity to `Sticky' Effort Adjustment -- Appendix D Extension: Stabilizing Effect of Agent Loyalty -- Appendix E Extension: Hiring…”

Tabla de Contenidos: “…6.1.2 Managing permissions between GitHub and CircleCI -- 6.1.3 Signing commits and tags with Git -- 6.2 Access control for container storage -- 6.2.1 Managing permissions between Docker Hub and CircleCI -- 6.2.2 Signing containers with Docker Content Trust -- 6.3 Access control for infrastructure management -- 6.3.1 Managing permissions using AWS roles and policies -- 6.3.2 Distributing secrets to production systems -- Summary -- Part 2: Watching for anomalies and protecting services against attacks -- Chapter 7: Collecting and storing logs -- 7.1 Collecting logs from systems and applications -- 7.1.1 Collecting logs from systems -- 7.1.2 Collecting application logs -- 7.1.3 Infrastructure logging -- 7.1.4 Collecting logs from GitHub -- 7.2 Streaming log events through message brokers -- 7.3 Processing events in log consumers -- 7.4 Storing and archiving logs -- 7.5 Accessing logs -- Summary -- Chapter 8: Analyzing logs for fraud and attacks -- 8.1 Architecture of a log-analysis layer -- 8.2 Detecting attacks using string signatures -- 8.3 Statistical models for fraud detection -- 8.3.1 Sliding windows and circular buffers -- 8.3.2 Moving averages -- 8.4 Using geographic data to find abuses -- 8.4.1 Geoprofiling users -- 8.4.2 Calculating distances -- 8.4.3 Finding a user's normal connection area -- 8.5 Detecting anomalies in known patterns -- 8.5.1 User-agent signature -- 8.5.2 Anomalous browser -- 8.5.3 Interaction patterns -- 8.6 Raising alerts to operators and end users -- 8.6.1 Escalating security events to operators -- 8.6.2 How and when to notify end users -- Summary -- Chapter 9: Detecting intrusions -- 9.1 The seven phases of an intrusion: the kill chain -- 9.2 What are indicators of compromise? …”

Tabla de Contenidos: “…MDC dimension analyzer -- Overview of the dimension analyzer -- The script evaluate_dimensions.ksh -- How average row size is obtained -- The skeleton SQL file, evaluate_dimensions.sql -- A real example of use -- Create a duplicate of the lineitem table -- First cut -- Second cut, generated columns -- Creating lineitem as MDC table -- Summary of results -- Listing of evaluate_dimensions.ksh…”

Tabla de Contenidos: “…Preface xiii Table of Engineering Applications xvii Part 1 Introduction 1 Mathematica Environment and Basic Syntax 3 1.1 Introduction 3 1.2 Selecting Notebook Characteristics 4 1.3 Notebook Cells 8 1.4 Delimiters 12 1.5 Basic Syntax 12 1.5.1 Introduction 12 1.5.2 Templates: Greek Symbols and Mathematical Notation 15 1.5.3 Variable Names and Global Variables 18 1.6 Mathematical Constants 19 1.7 Complex Numbers 21 1.8 Elementary, Trigonometric, Hyperbolic, and a Few Special Functions 22 1.9 Strings 25 1.9.1 String Creation: StringJoin[] and ToString[] 25 1.9.2 Labeled Output: Print[], NumberForm[], EngineeringForm[], and TraditionalForm[] 26 1.10 Conversions, Relational Operators, and Transformation Rule 28 1.11 Engineering Units and Unit Conversions: Quantity[] and UnitConvert[] 30 1.12 Creation of CDF Documents and Documents in Other Formats 33 1.13 Functions Introduced in Chapter 1 34 Exercises 35 2 List Creation and Manipulation: Vectors and Matrices 39 2.1 Introduction 39 2.2 Creating Lists and Vectors 39 2.2.1 Introduction 39 2.2.2 Creating a List with Table[] 45 2.2.3 Summing Elements of a List: Total[] 46 2.2.4 Selecting Elements of a List 47 2.2.5 Identifying List Elements Matching a Pattern: Position[] 49 2.3 Creating Matrices 51 2.3.1 Introduction 51 2.3.2 Matrix Generation Using Table[] 54 2.3.3 Accessing Elements of Arrays 55 2.4 Matrix Operations on Vectors and Arrays 56 2.4.1 Introduction 56 2.4.2 Matrix Inverse and Determinant: Inverse[] and Det[] 57 2.5 Solution of a Linear System of Equations: LinearSolve[] 58 2.6 Eigenvalues and Eigenvectors: EigenSystem[] 59 2.7 Functions Introduced in Chapter 2 61 References 61 Exercises 61 3 User-Created Functions, Repetitive Operations, and Conditionals 69 3.1 Introduction 69 3.2 Expressions and Procedures as Functions 69 3.2.1 Introduction 69 3.2.2 Pure Function: Function[] 74 3.2.3 Module[] 78 3.3 Find Elements of a List that Meet a Criterion: Select[] 80 3.4 Conditionals 82 3.4.1 If[] 82 3.4.2 Which[] 83 3.5 Repetitive Operations 83 3.5.1 Do[] 83 3.5.2 While[] 83 3.5.3 Nest[] 84 3.5.4 Map[] 84 3.6 Examples of Repetitive Operations and Conditionals 85 3.7 Functions Introduced in Chapter 3 92 Exercises 92 4 Symbolic Operations 95 4.1 Introduction 95 4.2 Assumption Options 101 4.3 Solutions of Equations: Solve[] 101 4.4 Limits: Limit[] 105 4.5 Power Series: Series[], Coefficient[], and CoefficientList[] 108 4.6 Optimization: Maximize[]/Minimize[] 112 4.7 Differentiation: D[] 114 4.8 Integration: Integrate[] 120 4.9 Solutions of Ordinary Differential Equations: DSolve[] 126 4.10 Solutions of Partial Differential Equations: DSolve[] 136 4.11 Laplace Transform: LaplaceTransform[] and InverseLaplaceTransform[] 138 4.12 Functions Introduced in Chapter 4 145 References 145 Exercises 146 5 Numerical Evaluations of Equations 151 5.1 Introduction 151 5.2 Numerical Integration: NIntegrate[] 151 5.3 Numerical Solutions of Differential Equations: NDSolveValue[] and ParametricNDSolveValue[] 154 5.4 Numerical Solutions of Equations: NSolve[] 178 5.5 Roots of Transcendental Equations: FindRoot[] 180 5.6 Minimum and Maximum: FindMinimum[] and FindMaximum[] 182 5.7 Fitting of Data: Interpolation[] and FindFit[] 186 5.8 Discrete Fourier Transforms and Correlation: Fourier[], InverseFourier[], and ListCorrelate[] 189 5.9 Functions Introduced in Chapter 5 194 References 195 Exercises 196 6 Graphics 209 6.1 Introduction 209 6.2 2D Graphics 209 6.2.1 Basic Plotting 209 6.2.2 Basic Graph Enhancements 213 6.2.3 Common 2D Shapes: Graphics[] 217 6.2.4 Additional Graph Enhancements 222 6.2.5 Combining Figures: Show[] and GraphicsGrid[] 238 6.2.6 Tooltip[] 241 6.2.7 Exporting Graphics 244 6.3 3D Graphics 244 6.4 Summary of Functions Introduced in Chapter 6 253 References 254 Exercises 254 7 Interactive Graphics 263 7.1 Interactive Graphics: Manipulate[] 263 References 287 Exercises 287 Part 2 Engineering Applications 8 Vibrations of Spring Mass Systems and Thin Beams 293 8.1 Introduction 293 8.2 Single Degree-of-Freedom Systems 294 8.2.1 Periodic Force on a Single Degree-of-Freedom System 294 8.2.2 Squeeze Film Damping and Viscous Fluid Damping 298 8.2.3 Electrostatic Attraction 302 8.2.4 Single Degree-of-Freedom System Energy Harvester 304 8.3 Two Degrees-of-Freedom Systems 307 8.3.1 Governing Equations 307 8.3.2 Response to Harmonic Excitation: Amplitude Response Functions 307 8.3.3 Enhanced Energy Harvester 310 8.4 Thin Beams 315 8.4.1 Natural Frequencies and Mode Shapes of a Cantilever Beam with In-Span Attachments 315 8.4.2 Effects of Electrostatic Force on the Natural Frequency and Stability of a Beam 318 8.4.3 Response of a Cantilever Beam with an In-Span Attachment to an Impulse Force 323 References 326 9 Statistics 327 9.1 Descriptive Statistics 327 9.1.1 Introduction 327 9.1.2 Location Statistics: Mean[], StandardDeviation[], and Quartile[] 327 9.1.3 Continuous Distribution Functions: PDF[] and CDF[] 329 9.1.4 Histograms and Probability Plots: Histogram[] and ProbabilityScalePlot [] 331 9.1.5 Whisker Plot: BoxWhiskerChart[] 332 9.1.6 Creating Data with Specified Distributions: RandomVariate[] 334 9.2 Probability of Continuous Random Variables 334 9.2.1 Probability for Different Distributions: NProbability[] 334 9.2.2 Inverse Cumulative Distribution Function: InverseCDF[] 337 9.2.3 Distribution Parameter Estimation: EstimatedDistribution[] and FindDistributionParameters[] 337 9.2.4 Confidence Intervals: CI[] 340 9.2.5 Hypothesis Testing: LocationTest[] and VarianceTest[] 342 9.3 Regression Analysis: LinearModelFit[] 343 9.3.1 Simple Linear Regression 343 9.3.2 Multiple Linear Regression 347 9.4 Nonlinear Regression Analysis: NonLinearModelFit[] 351 9.5 Analysis of Variance (ANOVA) and Factorial Designs: ANOVA[] 354 9.6 Functions Introduced in Chapter 9 358 10 Control Systems and Signal Processing 359 10.1 Introduction 359 10.2 Model Generation: State-Space and Transfer Function Representation 359 10.2.1 Introduction 359 10.2.2 State-Space Models: StateSpaceModel[] 360 10.2.3 Transfer Function Models: TransferFunctionModel[] 362 10.3 Model Connections Closed-Loop Systems and System Response: SystemsModelFeedbackConnect[] and SystemsModelSeriesConnect[] 363 10.4 Design Methods 369 10.4.1 Root Locus: RootLocusPlot[] 369 10.4.2 Bode Plot: BodePlot[] 371 10.4.3 Nichols Plot: NicholsPlot[] 372 10.5 Signal Processing 374 10.5.1 Filter Models: ButterworthFilterModel[], EllipticFilterModel[], ... 374 10.5.2 Windows: HammingWindow[], HannWindow[], ... 381 10.5.3 Spectrum Averaging 385 10.6 Aliasing 388 10.7 Functions Introduced in Chapter 10 390 Reference 391 11 Heat Transfer and Fluid Mechanics 393 11.1 Introduction 393 11.2 Conduction Heat Transfer 394 11.2.1 One-Dimensional Transient Heat Diffusion in Solids 394 11.2.2 Heat Transfer in Concentric Spheres: Ablation of a Tumor 398 11.2.3 Heat Flow Through Fins 401 11.3 Natural Convection Along Heated Plates 405 11.4 View Factor Between Two Parallel Rectangular Surfaces 408 11.5 Internal Viscous Flow 411 11.5.1 Laminar Flow in Horizontal Cylindrical Pipes 411 11.5.2 Flow in Three Reservoirs 412 11.6 External Flow 416 11.6.1 Pressure Coefficient of a Joukowski Airfoil 416 11.6.2 Surface Profile in Nonuniform Flow in Open Channels 419 References 423 Index 425.…”

Tabla de Contenidos: “…6.9 An Example of Bayes Factor Computation: Diagonal MNP models -- 6.10 Marketing Decisions and Bayesian Decision Theory -- 6.10.1 Plug‐In vs Full Bayes Approaches -- 6.10.2 Use of Alternative Information Sets -- 6.10.3 Valuation of Disaggregate Information -- 6.11 An Example of Bayesian Decision Theory: Valuing Household Purchase Information -- Chapter 7 Simultaneity -- 7.1 A Bayesian Approach to Instrumental Variables -- 7.2 Structural Models and Endogeneity/Simultaneity -- 7.2.1 Demand Model -- 7.2.2 Supply Model - Profit Maximizing Prices -- 7.2.3 Bayesian Estimation -- 7.3 Non‐Random Marketing Mix Variables -- 7.3.1 A General Framework -- 7.3.2 An Application to Detailing Allocation -- 7.3.3 Conditional Modeling Approach -- 7.3.4 Beyond the Conditional Model -- Chapter 8 A Bayesian Perspective on Machine Learning -- 8.1 Introduction -- 8.2 Regularization -- 8.2.1 The LASSO and Bayes -- 8.2.2 Discussion: Informative Regularizers -- 8.2.3 Bayesian Inference -- 8.3 Bagging -- 8.3.1 Bagging for Regression -- 8.3.2 Bagging, Bayesian Model Averaging and Ensembles -- 8.4 Boosting -- 8.4.1 Boosting as Bayes -- 8.5 Deep Learning -- 8.5.1 A Primer on Deep Learning -- 8.5.2 Bayes and Deep Learning -- 8.6 Applications -- 8.6.1 Bayes/ML for Flexible Heterogeneity -- 8.6.2 The Need for ML -- 8.6.3 Discussion -- Chapter 9 Bayesian Analysis for Text Data -- 9.1 Introduction -- 9.2 Consumer Demand -- 9.2.1 The Latent Dirichlet Allocation (LDA) Model -- 9.2.2 Full Gibbs Sampler -- 9.2.3 Processing Text Data for Analysis -- 9.2.4 Collapsed Gibbs Sampler -- 9.2.5 The Sentence Constrained LDA Model -- 9.2.6 Conjunctions and Punctuation -- 9.3 Integrated Models -- 9.3.1 Text and Conjoint Data -- 9.3.2 R Code for Text and Conjoint Data -- 9.3.3 Text and Product Ratings -- 9.3.4 Text and Scaled Response Data -- 9.4 Discussion…”

“…"The average college student graduates with nearly $30,000 of student loan debt. …”

“…The more time spent in mathematics classes, the better students perform, on average; but giving students more work in class is often not enough to improve learning outcomes…”

“…Start-up businesses create an average of three million new jobs each year, while existing businesses of any size or age shed a net average of about one million jobs annually.Unfortunately, the vital signs of America's job-creating entrepreneurial economy are flashing red alert. …”

“…Between three and one years prior to the onset of a crisis, sovereign default risk countries paid to investment banks on average 1.10 per cent of the amount issued, close to double the average paid by emerging countries overall in the same period (0.56 per cent). …”

“…Analysing an unbalanced panel of 30 OECD economies from 1970 to 2008, we found that downturns have, on average, a significant positive impact on the level of structural unemployment rate. …”