Tabla de Contenidos: “…""PARTICIPATION IN SERIES OTHER THAN THE SELECTION SERIES""""6 The Referent Cohort ""; ""7 Exposure Definition and Measurement ""; ""DNA-PROVIDED DOSE ESTIMATES""; ""Individual Doses""; ""Alternative Uses of Dose Data""; ""POTENTIAL SURROGATE MEASURES OF DOSE""; ""DECISIONS FOR THE ANALYSES IN THIS REPORT""; ""FUTURE OPTIONS FOR USE OF DOSIMETRY""; ""8 Mortality Ascertainment ""; ""FACT-OF-DEATH ASCERTAINMENT""; ""FACT-OF-DEATH VALIDATION""; ""DATE OF DEATH""; ""CAUSE-OF-DEATH ACQUISITION""; ""CAUSE-OF-DEATH VALIDATION""; ""9 Analysis Structure ""; ""OVERVIEW""; ""AVAILABLE DATA""…”

Tabla de Contenidos: “…Transforming data into a usable form -- Simple text cleaning -- Removing stop words -- Finding words in text -- Finding and replacing text -- Data imputation -- Subsetting data -- Sorting text -- Data validation -- Validating data types -- Validating dates -- Validating e-mail addresses -- Validating ZIP codes -- Validating names -- Cleaning images -- Changing the contrast of an image -- Smoothing an image -- Brightening an image -- Resizing an image -- Converting images to different formats -- Summary -- Chapter 4: Data Visualization -- Understanding plots and graphs -- Visual analysis goals -- Creating index charts -- Creating bar charts -- Using country as the category -- Using decade as the category -- Creating stacked graphs -- Creating pie charts -- Creating scatter charts -- Creating histograms -- Creating donut charts -- Creating bubble charts -- Summary -- Chapter 5: Statistical Data Analysis Techniques -- Working with mean, mode, and median -- Calculating the mean -- Using simple Java techniques to find mean -- Using Java 8 techniques to find mean -- Using Google Guava to find mean -- Using Apache Commons to find mean -- Calculating the median -- Using simple Java techniques to find median -- Using Apache Commons to find the median -- Calculating the mode -- Using ArrayLists to find multiple modes -- Using a HashMap to find multiple modes -- Using a Apache Commons to find multiple modes -- Standard deviation -- Sample size determination -- Hypothesis testing -- Regression analysis -- Using simple linear regression -- Using multiple regression -- Summary -- Chapter 6: Machine Learning -- Supervised learning techniques -- Decision trees -- Decision tree types -- Decision tree libraries -- Using a decision tree with a book dataset -- Testing the book decision tree -- Support vector machines -- Using an SVM for camping data…”

Tabla de Contenidos: “…-- Development and validation of a screening instrument for cognitive fluctuation in patients with neurocognitive disorder with Lewy bodies (NCDLB): the Mayo Fluctuations Scale-Thai version -- Regional homogeneity of intrinsic brain activity related to the main alexithymia dimensions -- Work-family conflict and job burn-out among Chinese doctors: the mediating role of coping styles -- Microglia activation in the offspring of prenatal Poly I: C exposed rats: a PET imaging and immunohistochemistry study -- Psychosis as an indicator of recurrent non-Hodgkin's lymphoma: a rare presentation -- Switch from previous major depression comorbid with CLIPPERS to mania-like episode following glucocorticosteroid therapy: a case report -- Correction: Regional homogeneity of intrinsic brain activity related to the main alexithymia dimensions…”

Tabla de Contenidos: “…Quality Control and Analysis Algorithms for Tissue Microarrays as Biomarker Validation Tools12. CNV-Interactome-Transcriptome Integration to Detect Driver Genes in Cancerology; 13. …”

Tabla de Contenidos: “…Introduction, Context, and Motivations of a Circular Economy for Composite Materials -- The FiberEUse Demand-Driven, Cross-Sectorial, Circular Economy Approach -- Disassembly of large composite-rich installations -- Smart composite mechanical demanufacturing processes -- Thermal demanufacturing processes for long fibers recovery -- Styrene-free liquid resins for composite reformulation -- Fiber resizing, compounding and validation -- Additive manufacturing of recycled composites -- Composite Finishing for Reuse.…”

Tabla de Contenidos: “…Bringing the Full Picture Into Focus: A Consideration of the Internal and External Validity of Charter School Effects -- Chapter 5. Neighborhood Effects, the Life Course, and Educational Outcomes: Four Theoretical Models of Effect Heterogeneity -- Chapter 6. …”

Tabla de Contenidos: “…; 3 Making Ideas Real; Make It Real: At the Birth of an Idea; Make It Real: Co-Creation Makes Momentum; Make It Real: Powering Prototypes and Validating Ideas; Make It Real: Steel in Your Backbone…”

Tabla de Contenidos: “…17 New Products18 Systems; 19 Testing; 20 Data; 21 Reports; 22 Calculation; 23 Mathematical Model and Systems Validation; 24 Regulatory, Legal and Compliance; 25 Business Continuity Planning; PART IV WHAT CAN GO WRONG, THE CREDIT CRISIS; 26 Credit Derivatives and the Crisis of 2007; Appendix: Summary of Risks; Recommended Reading; Index…”

Tabla de Contenidos: “…Assessing The Science Behind Methodologies Being Used To Characterize Food As Addictive -- A Brief History of Food Intake Research -- What Imaging Technologies Reveal About Food Behaviors: Perspective 1 -- What Imaging Technologies Reveal About Food Behaviors: Perspective 2 -- Assessing the Validity of Questionnaires for Food Behaviors and Addiction -- DSM-5: Substance-Related and Addictive Disorders -- Discussion with the Audience -- 4. …”

Tabla de Contenidos: “…Book Cover; Title; Copyright; Contents; Preface; PART I INTRODUCTION AND BASIC CONCEPTS; CHAPTER 1 Introduction; CHAPTER 2 The Architecture of the Information-Processing System; CHAPTER 3 Determinants and Effects of Knowledge Accessibility; PART II COMPREHENSION PROCESSES; CHAPTER 4 The Comprehension and Validation of Information About Familiar People and Events: The Role of Situation Models; CHAPTER 5 The Construction of Episode Models and Generalized Narrative Representations; CHAPTER 6 The Impact of Generalized Event Representations on Comprehension and Memory…”

Tabla de Contenidos: “…Section 8 ETHICAL CHALLENGES IN GERIATRIC NEUROPSYCHOLOGYSection 9 ETHICAL CHALLENGES WITH ETHNICALLY AND CULTURALLY DIVERSE POPULATIONS IN NEUROPSYCHOLOGY; Section 10 ETHICAL CHALLENGES WITH THE USE OF INFORMATION TECHNOLOGY AND TELECOMMUNICATIONS IN NEUROPSYCHOLOGY; Section 11 ETHICAL CHALLENGES IN NEUROPSYCHOLOGICAL RESEARCH; Section 12 ETHICAL CHALLENGES IN NEUROPSYCHOLOGICAL TEST DEVELOPMENT; Section 13 ETHICAL CHALLENGES IN THE DETERMINATION OF RESPONSE VALIDITY IN NEUROPSYCHOLOGY; Appendix A ETHICAL PRINCIPLES OF PSYCHOLOGISTS AND CODE OF CONDUCT…”

Tabla de Contenidos: “…Cover; Contents; Chapter 1 - International Perspectives on Child Witnesses: An Introduction to the Issues; Chapter 2 - Research on Children's Suggestibility: Implications for the Investigative Interview; Chapter 3 - Children's Testimony in the Netherlands: A Study of Statement Validity Analysis; Chapter 4 - Child Sexual Abuse Investigations in Israel: Evaluating Innovative Practices; Chapter 5 - Children and the Courts in Canada; Chapter 6 - The Effect of Child Witness Research on Legislation in Great Britain; Chapter 7 - Children's Evidence: Scottish Research and Law…”

Tabla de Contenidos: “…A Typology of Revolutions from a Morphological Viewpoint 3. Definitions and Validation in the Study of Revolutions 4. Four Metaphors for Analysing Causes of Revolutions 5. …”

Tabla de Contenidos: “…5.6 Refactoring schema definitions to avoid repetition -- 5.7 Documenting API responses -- 5.8 Creating generic responses -- 5.9 Defining the authentication scheme of the API -- Summary -- 6 Building REST APIs with Python -- 6.1 Overview of the orders API -- 6.2 URL query parameters for the orders API -- 6.3 Validating payloads with unknown fields -- 6.4 Overriding FastAPI's dynamically generated specification -- 6.5 Overview of the kitchen API -- 6.6 Introducing flask-smorest -- 6.7 Initializing the web application for the API -- 6.8 Implementing the API endpoints -- 6.9 Implementing payload validation models with marshmallow -- 6.10 Validating URL query parameters -- 6.11 Validating data before serializing the response -- 6.12 Implementing an in-memory list of schedules -- 6.13 Overriding flask-smorest's dynamically generated API specification -- Summary -- 7 Service implementation patterns for microservices -- 7.1 Hexagonal architectures for microservices -- 7.2 Setting up the environment and the project structure -- 7.3 Implementing the database models -- 7.4 Implementing the repository pattern for data access -- 7.4.1 The case for the repository pattern: What is it, and why is it useful? …”

Tabla de Contenidos: “…Multivariate Models -- Key Takeaways -- Chapter 2: Model Evaluation for Forecasting -- Evaluation with an Example Forecast -- Model Quality Metrics -- Metric 1: MSE -- Metric 2: RMSE -- Metric 3: MAE -- Metric 4: MAPE -- Metric 5: R2 -- Model Evaluation Strategies -- Overfit and the Out-of-Sample Error -- Strategy 1: Train-Test Split -- Strategy 2: Train-Validation-Test Split -- Strategy 3: Cross-Validation for Forecasting -- K-Fold Cross-Validation -- Time Series Cross-Validation -- Rolling Time Series Cross-Validation -- Backtesting -- Which Strategy to Use for Safe Forecasts? …”

Tabla de Contenidos: “…Implementing an add article component -- Making components functional -- Rendering component trees -- Feature components and utility components -- Summary -- Chapter 6: The React Component Lifecycle -- Why components need a lifecycle -- Initializing properties and state -- Fetching component data -- Initializing state with properties -- Updating state with properties -- Optimize rendering efficiency -- To render or not to render -- Using metadata to optimize rendering -- Rendering imperative components -- Rendering jQuery UI widgets -- Cleaning up after components -- Cleaning up asynchronous calls -- Summary -- Chapter 7: Validating Component Properties -- Knowing what to expect -- Promoting portable components -- Simple property validators -- Basic type validation -- Requiring values -- Any property value -- Type and value validators -- Things that can be rendered -- Requiring specific types -- Requiring specific values -- Writing custom property validators -- Summary -- Chapter 8: Extending Components -- Component inheritance -- Inheriting state -- Inheriting properties -- Inheriting JSX and event handlers -- Composition with higher-order components -- Conditional component rendering -- Providing data sources -- Summary -- Chapter 9: Handling Navigation with Routes -- Declaring routes -- Hello route -- Decoupling route declarations -- Parent and child routes -- Handling route parameters -- Resource IDs in routes -- Optional parameters -- Using link components -- Basic linking -- URL and query parameters -- Lazy routing -- Summary -- Chapter 10: Server-Side React Components -- What is isomorphic JavaScript? …”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction to Java EE -- Introduction to Java EE -- Java community process -- Java EE APIs -- One standard, multiple implementations -- Java EE, J2EE, and the Spring framework -- Summary -- Chapter 2: JavaServer Faces -- Introducing JSF -- Facelets -- Optional faces-config.xml -- Standard resource locations -- Developing our first JSF application -- Facelets -- Project stages -- Validation -- Grouping components -- Form submission -- Named beans -- Named bean scopes -- Navigation -- Custom data validation -- Creating custom validators -- Validator methods -- Customizing JSF's default messages -- Customizing message styles -- Customizing message text -- Ajax-enabling JSF applications -- JSF HTML5 support -- HTML5-friendly markup -- Pass-through attributes -- JSF 2.2 Faces flows -- Injecting JSF artifacts -- JSF WebSocket support -- Additional JSF component libraries -- Summary -- Chapter 3: Object Relational Mapping with the Java Persistence API -- The Customer database -- The Java Persistence API -- Entity relationships -- One-to-one relationships -- One-to-many relationships -- Many-to-many relationships -- Composite primary keys -- Java Persistence Query Language -- The Criteria API -- Updating data with the Criteria API -- Deleting data with the Criteria API -- Bean Validation support -- Final notes -- Summary -- Chapter 4: Enterprise JavaBeans -- Session beans -- A simple session bean -- Implementing EJB client code -- A more realistic example -- Invoking session beans from web applications -- Singleton session beans -- Asynchronous method calls -- Message-driven beans -- Transactions in enterprise JavaBeans -- Container-managed transactions -- Bean-managed transactions…”

Tabla de Contenidos: “…Chapter 3: Creating RESTful Endpoints -- Creating a REST API for a blog in PHP -- Creating a database schema -- Blog user/author table schema -- SQL for users table -- Blog post table schema -- Blog post comments schema -- Creating a RESTful API's endpoint -- Code structure -- Common components -- DB class -- Router class -- Code sync -- Creating blog post endpoints -- REST client -- To do -- Visible flaws -- Validation -- Solution -- Authentication -- Solution -- Proper 404 pages -- Summary -- Chapter 4: Reviewing Design Flaws and Security Threats -- Finding problems in the current code -- Structural and design flaws -- Missing query builder layer -- Incomplete router -- Usage of OOP -- Separate Configurations from Implementation -- Should write tests -- Input validation -- Handling 404 and other errors -- Meta information missing -- DB fields abstraction -- Security -- Securing API endpoints -- What is Auth middleware? …”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Journey from Statistics to Machine Learning -- Statistical terminology for model building and validation -- Machine learning -- Major differences between statistical modeling and machine learning -- Steps in machine learning model development and deployment -- Statistical fundamentals and terminology for model building and validation -- Bias versus variance trade-off -- Train and test data -- Machine learning terminology for model building and validation -- Linear regression versus gradient descent -- Machine learning losses -- When to stop tuning machine learning models -- Train, validation, and test data -- Cross-validation -- Grid search -- Machine learning model overview -- Summary -- Chapter 2: Parallelism of Statistics and Machine Learning -- Comparison between regression and machine learning models -- Compensating factors in machine learning models -- Assumptions of linear regression -- Steps applied in linear regression modeling -- Example of simple linear regression from first principles -- Example of simple linear regression using the wine quality data -- Example of multilinear regression - step-by-step methodology of model building -- Backward and forward selection -- Machine learning models - ridge and lasso regression -- Example of ridge regression machine learning -- Example of lasso regression machine learning model -- Regularization parameters in linear regression and ridge/lasso regression -- Summary -- Chapter 3: Logistic Regression Versus Random Forest -- Maximum likelihood estimation -- Logistic regression - introduction and advantages -- Terminology involved in logistic regression -- Applying steps in logistic regression modeling…”

Tabla de Contenidos: “…Test your knowledge -- Chapter 6: Using Regular Expressions in Power BI -- Technical requirements -- A brief introduction to regexes -- The basics of regexes -- Literal characters -- Special characters in regex -- The ^ and anchors -- OR operators -- Negated character classes -- Shorthand character classes -- Quantifiers -- The dot -- Greedy and lazy matches -- Checking the validity of email addresses -- Checking the validity of dates -- Validating data using regex in Power BI -- Using regex in Power BI to validate emails with Python -- Using regex in Power BI to validate emails with R -- Using regex in Power BI to validate dates with Python -- Using regex in Power BI to validate dates with R -- Loading complex log files using regex in Power BI -- Apache access logs -- Importing Apache access logs in Power BI with Python -- Importing Apache access logs in Power BI with R -- Extracting values from text using regex in Power BI -- One regex to rule them all -- Using regex in Power BI to extract values with Python -- Using regex in Power BI to extract values with R -- Summary -- References -- Test your knowledge -- Chapter 7: Anonymizing and Pseudonymizing Your Data in Power BI -- Technical requirements -- De-identifying data -- De-identification techniques -- Information removal -- Data masking -- Data swapping -- Generalization -- Data perturbation -- Tokenization -- Hashing -- Encryption -- Understanding pseudonymization -- What is anonymization? …”