Tabla de Contenidos: “…2.2.5 Amplitude Vectors2.2.6 Time-Averaged Poynting Vector; 2.2.7 Wavenumbers; 2.2.8 Phase Speed and Characteristic Lengths; 2.2.9 Characteristics of Simple SPP Waves; 2.2.10 Fano Wave; 2.2.11 Zenneck Wave; 2.3 Optical Excitation of Simple SPP Waves; 2.3.1 Turbadar-Kretschmann-Raether Configuration; 2.3.1.1 Boundary-Value Problem; 2.3.1.2 p-Polarized Incident Plane Wave; 2.3.1.3 s-Polarized Incident Plane Wave; 2.3.1.4 Illustrative Results; 2.3.1.5 SPR-Based Prism-Coupled Sensing; 2.3.1.6 Fiber-Optic Coupling; 2.3.2 Turbadar-Otto Configuration; 2.3.3 Sarid Configuration…”

Tabla de Contenidos: “…Chapter 6: Creating a Web Page to Display Sensor Data -- Extending our application -- Serving static files from the Express server -- Building the UI's functionality -- Adding client-side JavaScript -- Fetching sensor readings using XHR -- Visually enhancing the UI -- Changing the structure of our UI -- Adding style to the newly modified structure -- Summary -- Chapter 7: Enhancing Our UI - Using Interactive Charts -- Considerations when implementing complex features -- Introducing Chart.js -- Installing Chart.js -- Creating our first chart -- Making the server response data-friendly -- Modifying the sensor dashboards to consume JSON data -- Integrating sensor data into our charts -- A code overview -- index.js -- public/index.html -- public/script.js -- Summary -- Chapter 8: SQLite - The Fast and Portable Database -- Picking the correct tool for the job -- Installation -- Creating the temperature and humidity tables -- Running CRUD operations -- Create -- Read -- Update -- Delete -- Aggregations -- Advanced aggregations using subqueries -- Summary -- Chapter 9: Integrating SQLite into Our Application -- Getting started: Interfacing SQLite with node -- Running queries with node -- Making our database module -- Adding a new temperature to the database -- Fetching the last -- Fetching readings between a certain time period -- Fetching the average of readings between time periods -- Putting the functions together in a module -- Integrating the database module into our server application -- Upgrading the sensor interface module -- Adding an API to get the latest ten readings -- Consuming the API on the client side -- Adding new features - the ability to view readings from a custom time period -- Adding the required APIs -- Summary -- Chapter 10: Making our Application Real Time with Web Sockets -- Web sockets…”

Tabla de Contenidos: “…5.3.4 Higher-Order Interpolation5.4 Spatial Transformation; 5.4.1 Control-Grid Mapping; 5.5 Applications; 5.5.1 Distortion Removal; 5.5.2 Image Registration; 5.5.3 Stitching; 5.6 Summary of Important Points; References; Chapter 6: Image Enhancement; 6.1 Introduction; 6.2 Spatial Domain Methods; 6.2.1 Contrast Stretching; 6.2.2 Clipping and Thresholding; 6.2.3 Image Subtraction and Averaging; 6.2.4 Histogram Equalization; 6.2.5 Histogram Specification; 6.2.6 Spatial Filtering; 6.2.7 Directional and Steerable Filtering; 6.2.8 Median Filtering; 6.3 Fourier Transform Methods…”

Tabla de Contenidos: “…-- The job of licensed practical nurse in Alberta saw a spike in demand during the pandemic -- Alberta's Welders and Machinists had one of the steepest declines in the average number of OJPS during the peak of the pandemic -- References -- Notes -- 4 Assessing the impact of digitalisation in Alberta through the lens of big data -- The digital transition: Challenges and opportunities for Alberta going forward -- Identifying digital occupations and the volume of job postings available in Alberta -- The geography of Alberta's demand for digital occupations -- Trends in online job postings for group one of the digital professionals -- The demand for data mining analysts and business intelligence analysts has been rapidly growing pre-pandemic -- Data scientists and data engineers are amongst the occupations with the fastest rate of growth across digital occupations -- Demand for Computer Scientists and Cyber Security Professionals has been trending upwards in Alberta -- Skill requirements for occupations in the group of Computer and data analysts / administrators -- Trends in online job postings for group two of the digital professionals -- Software developers, programmers and engineers concentrate a high volume of online job postings in Alberta -- Trends in online job postings for group three of the digital professionals -- The demand for ICT supporting roles has experience more rapid growth in Alberta than in Canada -- Trends in online job postings for group four of the digital professionals -- Demand for managerial roles as well as for HR and marketing professionals has been steadily increasing, and rapidly growing since mid-2020 -- How quickly are digital skill demands permeating Alberta's workplaces?…”

Tabla de Contenidos: “…Interpreting the posterior mean -- Interpreting the posterior predictions -- Generalizing the linear model -- Counting bikes -- Robust regression -- Logistic regression -- The logistic model -- Classification with logistic regression -- Interpreting the coefficients of logistic regression -- Variable variance -- Hierarchical linear regression -- Centered vs. noncentered hierarchical models -- Multiple linear regression -- Summary -- Exercises -- Chapter 5: Comparing Models -- Posterior predictive checks -- The balance between simplicity and accuracy -- Many parameters (may) lead to overfitting -- Too few parameters lead to underfitting -- Measures of predictive accuracy -- Information criteria -- Akaike Information Criterion -- Widely applicable information criteria -- Other information criteria -- Cross-validation -- Approximating cross-validation -- Calculating predictive accuracy with ArviZ -- Model averaging -- Bayes factors -- Some observations -- Calculation of Bayes factors -- Analytically -- Sequential Monte Carlo -- Savage-Dickey ratio -- Bayes factors and inference -- Regularizing priors -- Summary -- Exercises -- Chapter 6: Modeling with Bambi -- One syntax to rule them all -- The bikes model, Bambi's version -- Polynomial regression -- Splines -- Distributional models -- Categorical predictors -- Categorical penguins -- Relation to hierarchical models -- Interactions -- Interpreting models with Bambi -- Variable selection -- Projection predictive inference -- Projection predictive with Kulprit -- Summary -- Exercises -- Chapter 7: Mixture Models -- Understanding mixture models -- Finite mixture models -- The Categorical distribution -- The Dirichlet distribution -- Chemical mixture -- The non-identifiability of mixture models -- How to choose K -- Zero-Inflated and hurdle models -- Zero-Inflated Poisson regression -- Hurdle models…”

Tabla de Contenidos: “…Beetle on a String / Vince Gotera -- Ang Tunay na Lalaki Stalks the Streets of New York / Nick Carbo -- Ang Tunay na Lalaki Realizes He's Below Average / Nick Carbo -- La Reina / Maria Lourdes Antenorcruz -- Password / Noel Abubo Mateo -- Dora Ty in Kansas / Noel Abubo Mateo -- Jeepneyfying / Noel Abubo Mateo -- Cadenza / Jose Wendell P. …”

Tabla de Contenidos: “…Sanderson, Josâe de Sousa e Silva-Jâunior -- Grassland versus non-grassland bird abundance and diversity in managed grasslands : local, landscape, and regional scale effects / Pâeter Batâary, Andrâas Bâaldi, Sarolta Erdîos -- Biogeographic patterns of the East African coastal forest vertebrate fauna / Ermias T. Azeria ... [et al.] -- Sooty shearwater (Puffinus griseus) on Guafo Island : the largest seabird colony in the world? …”

“…L'aération est alors arrêtée, la boue est décantée et le surnageant est retiré. …”

Tabla de Contenidos: “…APPLICATION 3D Printing -- Estimating Cost Curves Versus Introspection -- 7.4 Lower Costs in the Long Run -- Long-Run Average Cost as the Envelope of Short-Run Average Cost Curves -- APPLICATION Choosing an Inkjet or Laser Printer -- Short-Run and Long-Run Expansion Paths -- How Learning by Doing Lowers Costs -- 7.5 Cost of Producing Multiple Goods -- APPLICATION Medical Economies of Scope -- CHALLENGE SOLUTION Technology Choice at Home Versus Abroad -- Summary -- Exercises -- Chapter 8 Competitive Firms and Markets -- CHALLENGE The Rising Cost of Keeping On Truckin' -- 8.1 Perfect Competition -- Price Taking -- Why a Firm's Demand Curve Is Horizontal -- Perfect Competition in the Chicago Commodity Exchange -- Deviations from Perfect Competition -- Derivation of a Competitive Firm's Demand Curve -- Why Perfect Competition Is Important -- Solved Problem 8.1 -- 8.2 Profit Maximization -- Profit -- Two Steps to Maximizing Profit -- 8.3 Competition in the Short Run -- Short-Run Competitive Profit Maximization -- Solved Problem 8.2 -- APPLICATION Fracking and Shutdowns -- Short-Run Firm Supply Curve -- Solved Problem 8.3 -- Short-Run Market Supply Curve -- Short-Run Competitive Equilibrium -- Solved Problem 8.4 -- 8.4 Competition in the Long Run -- Long-Run Competitive Profit Maximization -- Long-Run Firm Supply Curve -- APPLICATION The Size of Ethanol Processing Plants -- Long-Run Market Supply Curve -- APPLICATION Entry and Exit of Solar Power Firms -- APPLICATION Upward-Sloping Long-Run Supply Curve for Cotton -- APPLICATION Reformulated Gasoline Supply Curves -- Solved Problem 8.5 -- Long-Run Competitive Equilibrium -- CHALLENGE SOLUTION The Rising Cost of Keeping On Truckin' -- Summary -- Exercises -- Chapter 9 Properties and Applications of the Competitive Model -- CHALLENGE Liquor Licenses -- 9.1 Zero Profit for Competitive Firms in the Long Run…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: The Splunk Interface -- Logging in to Splunk -- The home app -- The top bar -- The Search &amp -- Reporting app -- Data generator -- The Summary view -- Search -- Actions -- Timeline -- The field picker -- Fields -- Search results -- Options -- Events viewer -- Using the time picker -- Using the field picker -- The settings section -- Splunk Cloud -- Try before you buy -- A quick cloud tour -- The top bar in Splunk Cloud -- Splunk reference app - PAS -- Universal forwarder -- eventgen -- Next steps -- Summary -- Chapter 2: Understanding Search -- Using search terms effectively -- Boolean and grouping operators -- Clicking to modify your search -- Event segmentation -- Field widgets -- Time -- Using fields to search -- Using the field picker -- Using wildcards efficiently -- Supplementing wildcards in fields -- All about time -- How Splunk parses time -- How Splunk stores time -- How Splunk displays time -- How time zones are determined and why it matters -- Different ways to search against time -- Presets -- Relative -- Real-time -- Windowed real-time versus all-time real-time searches -- Date range -- Date and time range -- Advanced -- Specifying time in-line in your search -- _indextime versus _time -- Making searches faster -- Sharing results with others -- The URL -- Save As Report -- Save As Dashboard Panel -- Save As Alert -- Save As Event Type -- Searching job settings -- Saving searches for reuse -- Creating alerts from searches -- Enable Actions -- Action Options -- Sharing -- Event annotations -- An illustration -- Summary -- Chapter 3: Tables, Charts, and Fields -- About the pipe symbol -- Using top to show common field values -- Controlling the output of top -- Using stats to aggregate values…”

Tabla de Contenidos: “…Exercise 2-1: Revisiting the Business Intelligence Use Case -- Defining the Problem -- Solving the Problem -- Problem 1: Find the Daily Active Users for a Given Day -- Problem 2: Calculate the Daily Average Number of Items Across All User Carts -- Problem 3: Generate the Top Ten Most Added Items Across All User Carts -- Exercise 2-1: Summary -- Summary -- Chapter 3: Working with Data -- Docker -- Containers -- Docker Desktop -- Configuring Docker -- Apache Zeppelin -- Interpreters -- Notebooks -- Preparing Your Zeppelin Environment -- Running Apache Zeppelin with Docker -- Docker Network -- Docker Compose -- Volumes -- Environment -- Ports -- Using Apache Zeppelin -- Binding Interpreters -- Exercise 3-1: Reading Plain Text Files and Transforming DataFrames -- Converting Plain Text Files into DataFrames -- Peeking at the Contents of a DataFrame -- DataFrame Transformation with Pattern Matching -- Exercise 3-1: Summary -- Working with Structured Data -- Exercise 3-2: DataFrames and Semi-Structured Data -- Schema Inference -- Using Inferred Schemas -- Using Declared Schemas -- Steal the Schema Pattern -- Building a Data Definition -- All About the StructType -- StructField -- Spark Data Types -- Adding Metadata to Your Structured Schemas -- Exercise 3-2: Summary -- Using Interpreted Spark SQL -- Exercise 3-3: A Quick Introduction to SparkSQL -- Creating SQL Views -- Using the Spark SQL Zeppelin Interpreter -- Computing Averages -- Exercise 3-3: Summary -- Your First Spark ETL -- Exercise 3-4: An End-to-End Spark ETL -- Writing Structured Data -- Parquet Data -- Reading Parquet Data -- Exercise 3-4: Summary -- Summary -- Chapter 4: Transforming Data with Spark SQL and the DataFrame API -- Data Transformations -- Basic Data Transformations -- Exercise 4-1: Selections and Projections -- Data Generation -- Selection -- Filtering -- Projection…”

Tabla de Contenidos: “….) -- Figure 2.12. Progression of average statutory corporate income tax rates -- Table 2.4. …”

Tabla de Contenidos: “…External financial flows to Africa, average 2004-08, 2009-16 current USD billion and % of GDP -- Figure 2.2. …”

Tabla de Contenidos: “…Forecasting -- 5.1 Types of Forecasting Models -- Qualitative Models -- Causal Models -- Time-Series Models -- 5.2 Components of a Time Series -- 5.3 Measures of Forecast Accuracy -- 5.4 Forecasting Models-Random Variations Only -- Moving Averages -- Weighted Moving Averages -- Exponential Smoothing -- Using Software for Forecasting Time Series -- 5.5 Forecasting Models-Trend and Random Variations -- Exponential Smoothing with Trend -- Trend Projections -- 5.6 Adjusting for Seasonal Variations -- Seasonal Indices -- Calculating Seasonal Indices with No Trend…”

Tabla de Contenidos: “…Generating all the permutations of a string -- 53. Average rating of movies -- 54. Pairwise algorithm -- 55. …”

Tabla de Contenidos: “…(d) Libraries -- (e) Museums -- (f) Health Care Institutions -- (g) Other Health Care Provider Organizations -- (h) Membership Organizations -- (i) Small Solicitations -- (j) Solicitations for Specified Individuals -- (k) Political Organizations -- (l) Veterans' Organizations -- (m) Named Organizations -- (n) Other Categories of Exempted Organizations -- 3.6 Regulation of Professional Fundraisers -- 3.7 Regulation of Professional Solicitors -- 3.8 Regulation of Commercial Coventurers -- 3.9 Limitations on Fundraising Costs -- 3.10 Availability of Records -- 3.11 Contracts -- 3.12 Registered Agent Requirements -- 3.13 Prohibited Acts -- 3.14 Regulatory Prohibitions -- 3.15 Disclosure Statements and Legends -- 3.16 Reciprocal Agreements -- 3.17 Solicitation Notice Requirements -- 3.18 Fiduciary Relationships -- 3.19 Powers of Attorney General -- 3.20 Miscellaneous Provisions -- 3.21 Sanctions -- 3.22 Unified Registration -- 3.23 Other Laws -- 3.24 Prospect of Law Changes -- Chapter 4 State Regulation of Fundraising -- 4.1 Regulation of Fundraising Costs -- (a) The Disclosure Dilemma -- (b) The Fundraising Cost Percentage Approach -- (c) Fundraising Cost Line Item Approach -- (d) Floating Average Approach -- (e) Pluralization Approach -- (f) Average Gift Size Factor -- (g) Regulated Disclosure Approach -- (h) Factors Affecting Fundraising Costs -- (i) Joint Cost Allocations -- 4.2 Police Power -- 4.3 Registration and Licensing Requirements -- 4.4 Charitable Purposes Revisited -- 4.5 Fundraising as Free Speech -- (a) State of Law before 1980 Supreme Court Decision -- (b) Free Speech Principles in Fundraising Context -- (c) State of Law Subsequent to Supreme Court Decisions -- (d) Airport Terminal Solicitations -- (e) Door‐to‐Door Advocacy -- (f) Outer Boundaries of Doctrine…”

Tabla de Contenidos: “…Case Study- Launch or not to Launch -- Questions -- Chapter 12: Forecasting -- Learning Objectives -- Introduction -- Chapter Outline -- 12.1 Forecasting-Basics -- Forecasting Methods in Practice -- 12.2 Qualitative Methods of Forecasting -- Methods Used in Qualitative Forecasting -- 12.3 Quantitative Methods of Forecasting -- Time Series Analysis -- Moving Average -- Moving Average using Microsoft Excel -- Exponential Smoothing -- New Forecast = (0.3)(50) + (1-0.3)(55) = 53.5. -- Spreadsheet Showing Basic Calculations -- Smooth Exponential Smoothing Using Microsoft -- Trend Projection -- Forecasting Using Multiple Regression Model -- Case Example-Sales Forecasting -- A Brief Note on Accuracy of Forecast…”

Tabla de Contenidos: “…Linear and Quadratic Functions -- 3.1 Properties of Linear Functions and Linear Models -- Graph Linear Functions -- Use Average Rate of Change to Identify Linear Functions -- Determine Whether a Linear Function Is Increasing, Decreasing, or Constant -- Build Linear Models from Verbal Descriptions -- 3.2 Building Linear Models from Data -- Draw and Interpret Scatter Plots -- Distinguish between Linear and Nonlinear Relations -- Use a Graphing Utility to Find the Line of Best Fit -- 3.3 Quadratic Functions and Their Properties -- Graph a Quadratic Function Using Transformations -- Identify the Vertex and Axis of Symmetry of a Parabola -- Graph a Quadratic Function Using Its Vertex, Axis, and Intercepts…”

Tabla de Contenidos: “…3.3 Agglomerative Hierarchical Clustering3.3.1 Overview; 3.3.2 Single Linkage; 3.3.3 Complete Linkage; 3.3.4 Average Linkage; 3.3.5 Other Methods; 3.3.6 Selecting Groups; 3.4 Partitioned-Based Clustering; 3.4.1 Overview; 3.4.2 k-Means; 3.4.3 Worked Example; 3.4.4 Miscellaneous Partitioned-Based Clustering; 3.5 Fuzzy Clustering; 3.5.1 Overview; 3.5.2 Fuzzy k-Means; 3.5.3 Worked Examples; 3.6 Summary; 3.7 Further Reading; 4 PREDICTIVE ANALYTICS; 4.1 Overview; 4.1.1 Predictive Modeling; 4.1.2 Testing Model Accuracy; 4.1.3 Evaluating Regression Models' Predictive Accuracy…”

Tabla de Contenidos: “…1.- Carta de Ignacio Romañá a la Sra.D. Pilar Azemar.Paris.Burgos,11-4-1938…”