Tabla de Contenidos: “…WebSphere BI Message Broker as a Web services router -- 4.1 Introducing the WebSphere BI Message Broker product -- 4.1.1 WebSphere Business Integration reference architecture -- 4.1.2 Components of WebSphere BI Message Broker -- 4.1.3 HTTP transport nodes -- 4.2 Why Web services for a message broker -- 4.3 Building a development and runtime environment -- 4.3.1 Installation of the Message Broker product -- 4.3.2 Creating the broker and configuration manager -- 4.3.3 Connecting the Toolkit to the broker domain -- 4.4 Overview of implemented scenarios -- 4.5 Scenario 1: Routing a Web service through a message flow -- 4.5.1 Implementation steps -- 4.5.2 Define the message flow -- 4.5.3 Create the broker test environment -- 4.5.4 Run the Web client -- 4.6 Scenario 2: Invoke a Web service in a message flow -- 4.6.1 Implementation steps -- 4.6.2 MQ message triggers a Web service invocation -- 4.6.3 Populating Web service invocation with MQ message elements -- 4.6.4 Populating an MQ message with the results of a Web service -- 4.7 Scenario 3: Publishing a message flow as a Web service -- 4.7.1 Implementation steps -- 4.7.2 Generate WSDL for a message definition -- 4.7.3 Update message flow -- 4.7.4 Create a bar file and deploy to broker -- 4.7.5 Build a Web service client and run it -- 4.8 Summary -- Chapter 5. …”

Tabla de Contenidos: “…Machine generated contents note: Preface List of Abbreviations 1 Introduction 1.1 Forward Problem 1.2 Inverse Problem 1.3 Issues in Inverse Problem Solving 1.4 Linear, Nonlinear and Linearized Problems 2 Signal and System as Vectors 2.1 Vector Space 2.1.1 Vector Space and Subspace 2.1.2 Basis, Norm and Inner Product 2.1.3 Hilbert Space 2.2 Vector Calculus 2.2.1 Gradient 2.2.2 Divergence 2.2.3 Curl 2.2.4 Curve 2.2.5 Curvature 2.3 Taylor's Expansion 2.4 Linear System of Equations 2.4.1 Linear System and Transform 2.4.2 Vector Space of Matrix 2.4.3 Least Square Solution 2.4.4 Singular Value Decomposition (SVD) 2.4.5 Pseudo-inverse 2.5 Fourier Transform 2.5.1 Series Expansion 2.5.2 Fourier Transform 2.5.3 Discrete Fourier Transform (DFT) 2.5.4 Fast Fourier Transform (FFT) 2.5.5 Two-dimensional Fourier Transform References 3 Basics for Forward Problem 3.1 Understanding PDE using Images as Examples 3.2 Heat Equation 3.2.1 Formulation of Heat Equation 3.2.2 One-dimensional Heat Equation 3.2.3 Two-dimensional Heat Equation and Isotropic Diffusion 3.2.4 Boundary Conditions 3.3 Wave Equation 3.4 Laplace and Poisson Equations 3.4.1 Boundary Value Problem 3.4.2 Laplace Equation in a Circle 3.4.3 Laplace Equation in Three-dimensional Domain 3.4.4 Representation Formula for Poisson Equation References 4 Analysis for Inverse Problem 4.1 Examples of Inverse Problems in Medical Imaging 4.1.1 Electrical Property Imaging 4.1.2 Mechanical Property Imaging 4.1.3 Image Restoration 4.2 Basic Analysis 4.2.1 Sobolev Space 4.2.2 Some Important Estimates 4.2.3 Helmholtz Decomposition 4.3 Variational Problems 4.3.1 Lax-Milgram Theorem 4.3.2 Ritz Approach 4.3.3 Euler-Lagrange Equations 4.3.4 Regularity Theory and Asymptotic Analysis 4.4 Tikhonov Regularization and Spectral Analysis 4.4.1 Overview of Tikhonov Regularization 4.4.2 Bounded Linear Operators in Banach Space 4.4.3 Regularization in Hilbert Space or Banach Space 4.5 Basics of Real Analysis 4.5.1 Riemann Integrable 4.5.2 Measure Space 4.5.3 Lebesgue Measurable Function 4.5.4 Pointwise, Uniform, Norm Convergence and Convergence in Measure 4.5.5 Differentiation Theory References 5 Numerical Methods 5.1 Iterative Method for Nonlinear Problem 5.2 Numerical Computation of One-dimensional Heat equation 5.2.1 Explicit Scheme 5.2.2 Implicit Scheme 5.2.3 Crank-Nicolson Method 5.3 Numerical Solution of Linear System of Equations 5.3.1 Direct Method using LU Factorization 5.3.2 Iterative Method using Matrix Splitting 5.3.3 Iterative Method using Steepest Descent Minimization 5.3.4 Conjugate Gradient (CG) Method 5.4 Finite Difference Method (FDM) 5.4.1 Poisson Equation 5.4.2 Elliptic Equation 5.5 Finite Element Method (FEM) 5.5.1 One-dimensional Model 5.5.2 Two-dimensional Model 5.5.3 Numerical Examples References 6 CT, MRI and Image Processing Problems 6.1 X-ray CT 6.1.1 Inverse Problem 6.1.2 Basic Principle and Nonlinear Effects 6.1.3 Inverse Radon Transform 6.1.4 Artifacts in CT 6.2 MRI 6.2.1 Basic Principle 6.2.2 K-space Data 6.2.3 Image Reconstruction 6.3 Image Restoration 6.3.1 Role of p in (6.35) 6.3.2 Total Variation Restoration 6.3.3 Anisotropic Edge-preserving Diffusion 6.3.4 Sparse Sensing 6.4 Segmentation 6.4.1 Active Contour Method 6.4.2 Level Set Method 6.4.3 Motion Tracking for Echocardiography References 7 Electrical Impedance Tomography 7.1 Introduction 7.2 Measurement Method and Data 7.2.1 Conductivity and Resistance 7.2.2 Permittivity and Capacitance 7.2.3 Phasor and Impedance 7.2.4 Admittivity and Trans-impedance 7.2.5 Electrode Contact Impedance 7.2.6 EIT System 7.2.7 Data Collection Protocol and Data Set 7.2.8 Linearity between Current and Voltage 7.3 Representation of Physical Phenomena 7.3.1 Derivation of Elliptic PDE 7.3.2 Elliptic PDE for Four-electrode Method 7.3.3 Elliptic PDE for Two-electrode Method 7.3.4 Min-max Property of Complex Potential 7.4 Forward Problem and Model 7.4.1 Continuous Neumann-to-Dirichlet Data 7.4.2 Discrete Neumann-to-Dirichlet Data 7.4.3 Nonlinearity between Admittivity and Voltage 7.5 Uniqueness Theory and Direct Reconstruction Method 7.5.1 Calderon's Approach 7.5.2 Uniqueness and Three-dimensional Reconstruction: Infinite Measurements 7.5.3 Nachmann's D-bar Method in Two Dimension 7.6 Backprojection Algorithm 7.7 Sensitivity and Sensitivity Matrix 7.7.1 Perturbation and Sensitivity 7.7.2 Sensitivity Matrix 7.7.3 Linearization 7.7.4 Quality of Sensitivity Matrix 7.8 Inverse Problem of EIT 7.8.1 Inverse Problem of RC Circuit 7.8.2 Formulation of EIT Inverse Problem 7.8.3 Ill-posedness of EIT Inverse Problem 7.9 Static Imaging 7.9.1 Iterative Data Fitting Method 7.9.2 Static Imaging using 4-channel EIT System 7.9.3 Regularization 7.9.4 Technical Difficulty of Static Imaging 7.10 Time-difference Imaging 7.10.1 Data Sets for Time-difference Imaging 7.10.2 Equivalent Homogeneous Admittivity 7.10.3 Linear Time-difference Algorithm using Sensitivity Matrix 7.10.4 Interpretation of Time-difference Image 7.11 Frequency-difference Imaging 7.11.1 Data Sets for Frequency-difference Imaging 7.11.2 Simple Difference Ft,ω2− Ft,ω1 7.11.3 Weighted Difference Ft,ω2− [alpha] Ft,ω1 7.11.4 Linear Frequency-difference Algorithm using Sensitivity Matrix 7.11.5 Interpretation of Frequency-difference Image References 8 Anomaly Estimation and Layer Potential Techniques 8.1 Harmonic Analysis and Potential Theory 8.1.1 Layer Potentials and Boundary Value Problems for Laplace Equation 8.1.2 Regularity for Solution of Elliptic Equation along Boundary of Inhomogeneity 8.2 Anomaly Estimation using EIT 8.2.1 Size Estimation Method 8.2.2 Location Search Method 8.3 Anomaly Estimation using Planar Probe 8.3.1 Mathematical Formulation 8.3.2 Representation Formula References 9 Magnetic Resonance Electrical Impedance Tomography 9.1 Data Collection using MRI 9.1.1 Measurement of Bz 9.1.2 Noise in Measured Bz Data 9.1.3 Measurement of B = (Bx,By,Bz) 9.2 Forward Problem and Model Construction 9.2.1 Relation between J , Bz and σ 9.2.2 Three Key Observations 9.2.3 Data Bz Traces σ∇u © e z-directional Change of σ 9.2.4 Mathematical Analysis toward MREIT Model 9.3 Inverse Problem Formulation using B or J 9.4 Inverse Problem Formulation using Bz 9.4.1 Model with Two Linearly Independent Currents 9.4.2 Uniqueness 9.4.3 Defected Bz Data in a Local Region 9.5 Image Reconstruction Algorithm 9.5.1 J-substitution Algorithm 9.5.2 Harmonic Bz Algorithm 9.5.3 Gradient Bz Decomposition and Variational Bz Algorithm 9.5.4 Local Harmonic Bz Algorithm 9.5.5 Sensitivity Matrix Based Algorithm 9.5.6 Anisotropic Conductivity Reconstruction Algorithm 9.5.7 Other Algorithms 9.6 Validation and Interpretation 9.6.1 Image Reconstruction Procedure using Harmonic Bz Algorithm 9.6.2 Conductivity Phantom Imaging 9.6.3 Animal Imaging 9.6.4 Human Imaging 9.7 Applications References 10 Magnetic Resonance Elastography 10.1 Representation of Physical Phenomena 10.1.1 Overview of Hooke's Law 10.1.2 Strain Tensor in Lagrangian Coordinates 10.2 Forward Problem and Model 10.3 Inverse Problem in MRE 10.4 Reconstruction Algorithms 10.4.1 Reconstruction of [mu] with the Assumption of Local Homogeneity 10.4.2 Reconstruction of [mu] without the Assumption of Local Homogeneity 10.4.3 Anisotropic Elastic Moduli Reconstruction 10.5 Technical Issues in MRE References…”

Tabla de Contenidos: “…Punithakumar, T. Kirubarajan and Y. Bar-Shalom) -- 16.1 Introduction -- 16.2 Tracking Filters -- 16.3 Data Association -- 16.4 Out-of-Sequence Measurements -- 16.5 Results with Real Data -- 16.6 Summary -- References. -- 17. …”

Tabla de Contenidos: “…8.1.1 Offline Root CA -- 8.1.2 Online Intermediate CA With Issuing CAs -- 8.1.3 Online Issuing CA -- 8.1.4 Online RA -- 8.1.5 Online CRL Service -- 8.1.6 Online OCSP Service -- 8.1.7 End User's Machine -- 8.2 PKI Incident Attack Actions -- 8.2.1 Private Key Compromise -- 8.2.2 Private Key Access -- 8.2.3 Limited Access to the Private Key -- 8.2.4 Other Attacks -- 8.3 PKI Incident Response Plan -- 8.4 Monitoring the PKI Environment Prior to an Incident -- 8.5 Initial Response to an Incident -- 8.6 Detailed Discovery of an Incident -- 8.7 Collection of Forensic Evidence -- 8.8 Reporting of an Incident -- Notes -- Chapter 9 PKI Governance, Risk, and Compliance -- 9.1 PKI Governance -- 9.1.1 Management Organization -- 9.1.2 Security Organization -- 9.1.3 Audit Organization -- 9.2 PKI Risks -- 9.3 Cryptography Risks -- 9.3.1 Algorithm Issues -- 9.3.2 Protocol Issues -- 9.3.3 Product Issues -- 9.4 Cybersecurity Risks -- 9.5 Operational Risks -- 9.5.1 Monitoring -- 9.5.2 Capacity -- 9.5.3 Continuity -- 9.5.4 Resources -- 9.5.5 Knowledge -- 9.6 PKI Compliance -- 9.6.1 Evaluation Criteria -- 9.6.2 Gap Assessment -- 9.6.3 Audit Process -- 9.7 PKI Risk Assessment -- 9.8 PKI Cloud Assessment -- Notes -- Chapter 10 PKI Industry -- 10.1 ITU-T X.509 -- 10.2 EMV Integrated Circuit Card (ICC) -- 10.3 ASC X9 PKI Standards -- 10.4 Secure Electronic Transactions (SET) -- 10.5 Secure Socket Layer (SSL) -- 10.6 PKI Forum -- 10.7 American Bar Association (ABA) -- 10.8 WebTrust CA -- 10.9 CA Browser Forum -- 10.10 Cloud Security Alliance (CSA) -- 10.11 NIST PQC Program -- 10.12 ASC X9 Financial PKI -- 10.12.1 X9 Financial PKI Origins -- 10.12.2 PKI Architecture Models -- 10.12.3 X9 Financial PKI Program -- Notes -- Bibliography -- B.1 ASC X9 Financial Services -- B.2 European Telecommunication Standards Institute (ETSI) -- B.3 Internet Engineering Task Force (IETF)…”

“…All are answered by Gardner himself, and his pungent replies, coupled with the essays, create a provocative, no-holds-barred debate. Also included are an intellectual autobiography and bibliography…”

“…Embark on a global journey, from the highlands of Scotland to the trendy whisky bars of Tokyo, to discover the world of whisky as it is today. …”

“…Use XAML to represent state-of-the-art user interfaces, even across multiple windows Handle touch, mouse, keyboard, and pen input, including handwriting recognition Use new Windows 8.1 controls for creating hubs, flyouts, better app bars, performing in-app searches, rendering PDFs, and much more Encode, decode, and transcode multimedia content and speech-enable your app Leverage rich XAML vector graphics and animation Interact with built-in functionality such as the Camera app, file picker, the lock screen, new contacts and appointments integration, and more Exploit the Windows 8.1 charms bar Integrate DirectX graphics seamlessly Work with the rich set of available sensors: accelerometer, compass, light sensor, location (with geofencing support), proximity, and more Control devices such as fingerprint readers, image and bar code scanners, magnetic stripe readers, and custom Bluetooth, USB, HID, or Wi-Fi Direct devices…”

Tabla de Contenidos: “…6.2 The Effect of Offering a Choice of Modes -- 6.3 Getting People to Respond Online -- 6.4 Sequencing Different Modes of Data Collection -- 6.5 Separating the Effects of Mode on Selection and Reporting -- 6.5.1 Conceptualizing Mode Effects -- 6.5.2 Separating Observation from Nonobservation Error -- 6.5.2.1 Direct Assessment of Measurement Errors -- 6.5.2.2 Statistical Adjustments -- 6.5.2.3 Modeling Measurement Error -- 6.6 Maximizing Comparability Versus Minimizing Error -- 6.7 Conclusions -- References -- Chapter 7 Mobile Web Surveys: A Total Survey Error Perspective -- 7.1 Introduction -- 7.2 Coverage -- 7.3 Nonresponse -- 7.3.1 Unit Nonresponse -- 7.3.2 Breakoffs -- 7.3.3 Completion Times -- 7.3.4 Compliance with Special Requests -- 7.4 Measurement Error -- 7.4.1 Grouping of Questions -- 7.4.1.1 Question-Order Effects -- 7.4.1.2 Number of Items on a Page -- 7.4.1.3 Grids versus Item-By-Item -- 7.4.2 Effects of Question Type -- 7.4.2.1 Socially Undesirable Questions -- 7.4.2.2 Open-Ended Questions -- 7.4.3 Response and Scale Effects -- 7.4.3.1 Primacy Effects -- 7.4.3.2 Slider Bars and Drop-Down Questions -- 7.4.3.3 Scale Orientation -- 7.4.4 Item Missing Data -- 7.5 Links Between Different Error Sources -- 7.6 The Future of Mobile web Surveys -- References -- Chapter 8 The Effects of a Mid-Data Collection Change in Financial Incentives on Total Survey Error in the National Survey of Famil... -- 8.1 Introduction -- 8.2 Literature Review: Incentives in Face-to-Face Surveys -- 8.2.1 Nonresponse Rates -- 8.2.2 Nonresponse Bias -- 8.2.3 Measurement Error -- 8.2.4 Survey Costs -- 8.2.5 Summary -- 8.3 Data and Methods -- 8.3.1 NSFG Design: Overview -- 8.3.2 Design of Incentive Experiment -- 8.3.3 Variables -- 8.3.4 Statistical Analysis -- 8.4 Results -- 8.4.1 Nonresponse Error -- 8.4.2 Sampling Error and Costs -- 8.4.3 Measurement Error…”

Tabla de Contenidos: “…-- 3.2 A Survey of Programming Languages for Data Science -- 3.3 Python Crash Course -- 3.4 Strings -- 3.5 Defining Functions -- 3.6 Python's Technical Libraries -- 3.7 Other Python Resources -- 3.8 Further Reading -- 3.9 Glossary -- Interlude: My Personal Toolkit -- Chapter 4 Data Munging: String Manipulation, Regular Expressions, and Data Cleaning -- 4.1 The Worst Dataset in the World -- 4.2 How to Identify Pathologies -- 4.3 Problems with Data Content -- 4.4 Formatting Issues -- 4.5 Example Formatting Script -- 4.6 Regular Expressions -- 4.7 Life in the Trenches -- 4.8 Glossary -- Chapter 5 Visualizations and Simple Metrics -- 5.1 A Note on Python's Visualization Tools -- 5.2 Example Code -- 5.3 Pie Charts -- 5.4 Bar Charts -- 5.5 Histograms -- 5.6 Means, Standard Deviations, Medians, and Quantiles -- 5.7 Boxplots -- 5.8 Scatterplots -- 5.9 Scatterplots with Logarithmic Axes -- 5.10 Scatter Matrices -- 5.11 Heatmaps -- 5.12 Correlations -- 5.13 Anscombe's Quartet and the Limits of Numbers -- 5.14 Time Series -- 5.15 Further Reading -- 5.16 Glossary…”

Tabla de Contenidos: “…Implementing highly available brokers -- 7.1 Scenario overview -- 7.1.1 Using brokers with WebSphere MQ clustering for high availability -- 7.1.2 Using hardware failover for high-availability in brokers -- 7.2 Introduction to WebSphere BI Message Broker -- 7.2.1 WebSphere BI Message Broker architecture -- 7.2.2 The broker -- 7.2.3 Introducing message flows -- 7.2.4 Introduction to message modeling and message sets -- 7.2.5 Message Brokers Toolkit for WebSphere Studio -- 7.2.6 WebSphere BI Message Broker configuration manager -- 7.2.7 User name server -- 7.3 Installing and configuring the broker -- 7.3.1 Installing and configuring WebSphere MQ -- 7.3.2 Installing and configuring DB2 -- 7.3.3 Installing and configuring WebSphere BI Message Broker -- 7.4 Implementation steps -- 7.5 Creating databases and tables -- 7.5.1 Creating databases on the WebSphere Application Server -- 7.5.2 Database setup on Windows win6336 -- 7.5.3 DB2 client setup on hosts brk1 and brk2 -- 7.6 Design and development of the message set -- 7.6.1 Designing the message set -- 7.6.2 Developing the message set -- 7.6.3 Creating the message definition file -- 7.6.4 Creating the logical structure of the message -- 7.7 Design and development of message flow -- 7.7.1 Message flow design -- 7.7.2 Message flow development -- 7.8 Configuring the broker domain -- 7.8.1 Queue manager configuration -- 7.8.2 Creation and configuration of configuration manager -- 7.8.3 Broker creation -- 7.8.4 Connecting brokers to the configuration manager -- 7.9 Deploying the message flow and message set -- 7.9.1 Creating a message broker archive -- 7.9.2 Deploying a bar file -- 7.10 WebSphere Application Server configuration -- 7.11 Message generation application -- 7.12 Message retrieval application -- 7.12.1 The servlet MonitorStatusServlet.java -- 7.12.2 The class -- 7.13 Application in action…”

Tabla de Contenidos: “…3.3.2 Composition and Mechanical Behavior of Spongy Bone -- 3.4 Teeth -- 3.4.1 Tooth Anatomy and Evolution -- 3.4.2 Plaque, Organic Acids, Alter pH and Demineralize Tooth Surfaces -- 3.4.3 Dentin Exposure Through the Gum-line: Periodontal Disease -- 3.4.4 Tooth Statics and Dynamics: The Origins of Orthodontia -- 3.4.5 Endodonics: Resolving the Dying Internal Structure of a Tooth -- 3.4.6 Sealants as a Preventive Procedure to Fight Tooth Decay -- 3.4.7 Oral Surgery, Bone Implants, and Fracture Fixation -- 3.5 Conclusions -- 3.6 Problems -- References -- 4 Connective and Soft Tissues -- 4.1 Introduction -- 4.2 Protein Structure and Composition in the Circulatory System -- 4.3 Protein Structure of Valvular Tissue and Leaflets -- 4.3.1 Valve and Leaflet Defects -- 4.3.2 Aneurysms and Fistulae -- 4.3.3 Aortic Dissection -- 4.4 Dermal Tissues, Including Hair and Nerves -- 4.4.1 The Skin -- 4.4.2 The Subcutaneous or Adipose Tissues -- 4.4.3 The Dermis -- 4.4.4 The Stratum Corneum and Epidermis -- 4.4.5 Skin Care as a Business -- 4.5 Hair -- 4.5.1 Hair Morphology -- 4.5.2 Features and Attributes of Hair -- 4.5.3 Hair as a Business -- 4.6 Nails -- 4.7 Muscle Tissues -- 4.8 Looking Ahead -- 4.9 Conclusions -- 4.10 Problems -- References -- 5 Property Assessments of Tissues -- 5.1 Introduction -- 5.2 Mechanical Properties -- 5.2.1 Uniaxial Extension and Compression -- 5.3 How Much Does the Humerus Bone Length Shrink Upon Loading With the Bar? -- 5.3.1 The Tensile Test -- 5.3.2 Hookes Law and Hookean Behavior -- 5.4 Strength -- 5.4.1 Yield Strength -- 5.5 Bending -- 5.6 Torsion -- 5.7 Cyclic Loading and Fatigue Resistance -- 5.8 Relationship to Natural Materials -- 5.9 Viscoelasticity -- 5.9.1 Maxwell Model -- 5.9.1.1 Voigt model: retarded behavior -- 5.10 Time-Dependent Stress-Strain Behavior -- 5.11 Physical Property Determinations -- 5.11.1 Density…”

“…The author's presentation is no-holds-barred, using interviews, archival documentation, and unobtrusive observations as a participant where the research institution was at times noncompliant with the new federal guidelines. …”

“…Many other organizations employ Lean and Six Sigma methodologies to eliminate waste and reduce process variation only to find they are not moving the big bars that measure success for the organization. Driving Strategy to Execution Using Lean Six Sigma: A Framework for Creating High Performance Organizations details a framework for leveraging an organization's Lean Six Sigma capability in a manner that drives their strategic plans to execution by linking their strategy deployment into the fabric of their day-to-day ope…”