“…Also included is information on how producers can now more beneficially connect with their audiences, project earnings, distribution methods, and funding, in addition to coverage on new opportunities created by the explosive growth of VR, AR, 360, gaming, and the rapid conversion to OTT.Additional features include:Updated production financing worksheets '" an essential tool for producers;Expanded information for low-budget independent producers, internationally-based producers, producers using government funding, and film school students alike;Coverage of China's changing entertainment landscape, their entertainment consumption, commitment to produce content for the big global territories, and more;New, full-color illustrations and symbols, visually simplifying complex topics."…”

“…In the food and beverage industries, implementing novel methods using digital technologies such as artificial intelligence (AI), sensors, robotics, computer vision, machine learning (ML), and sensory analysis using augmented reality (AR) has become critical to maintaining and increasing the products’ quality traits and international competitiveness, especially within the past five years. …”

Tabla de Contenidos: “…El indefinido débil; 11.4.1.1. Verbos en -ar; 11.4.1.2. Verbos en -ir; 11.4.1.3. Verbos en -er; 11.4.2. …”

Tabla de Contenidos: “….) -- CARACTERÍSTICAS DE LOS LODOS Y LOS BIOSÓLIDOS -- PRODUCCIÓN DE LODO EN LAS INSTALACIONES DE SANEAMIENTO DE AGUAS RESIDUALES -- CANTIDADES PRODUCIDAS DE LODO -- REMOCIÓN DE LOS LODOS EN LAS UNIDADES DE TRATAMIENTO -- COMPOSICIÓN DEL LODO DE AGUA RESIDUAL -- CONTAMINANTES CONTENIDOS EN LOS LODOS GENERADOS EN EL SANEAMIENTO DE AR -- LODOS GENERADOS EN TANQUES SÉPTICOS -- MATERIAL GRUESO (DESECHOS) RESIDUOS DE LAS REJILLAS -- RESIDUOS DE LOS DESARENADORES -- MATERIAL FLOTANTE…”

Tabla de Contenidos: “…Intro -- Introduction -- Acknowledgments -- 1 Stationary Processes and Time Series -- 1.1 Introduction -- 1.2 The Prediction Problem -- 1.3 Random Variable -- 1.4 Random Vector -- 1.5 Stationary Process -- 1.6 White Process -- 1.7 MA Process -- 1.8 AR Process -- 1.9 Yule-Walker Equations -- 1.10 ARMA Process -- 1.11 Spectrum of a Stationary Process -- 1.12 ARMA Model: Stability Test and Variance Computation -- 1.13 Fundamental Theorem of Spectral Analysis -- 1.14 Spectrum Drawing -- 1.15 Proof of the Fundamental Theorem of Spectral Analysis -- 1.16 Representations of a Stationary Process -- 2 Estimation of Process Characteristics -- 2.1 Introduction -- 2.2 General Properties of the Covariance Function -- 2.3 Covariance Function of ARMA Processes -- 2.4 Estimation of the Mean -- 2.5 Estimation of the Covariance Function -- 2.6 Estimation of the Spectrum -- 2.7 Whiteness Test -- 3 Prediction -- 3.1 Introduction -- 3.2 Fake Predictor -- 3.3 Spectral Factorization -- 3.4 Whitening Filter -- 3.5 Optimal Predictor from Data -- 3.6 Prediction of an ARMA Process -- 3.7 ARMAX Process -- 3.8 Prediction of an ARMAX Process -- 4 Model Identification -- 4.1 Introduction -- 4.2 Setting the Identification Problem -- 4.3 Static Modeling -- 4.4 Dynamic Modeling -- 4.5 External Representation Models -- 4.6 Internal Representation Models -- 4.7 The Model Identification Process -- 4.8 The Predictive Approach -- 4.9 Models in Predictive Form -- 5 Identification of Input-Output Models -- 5.1 Introduction -- 5.2 Estimating AR and ARX Models: The Least Squares Method -- 5.3 Identifiability -- 5.4 Estimating ARMA and ARMAX Models -- 5.5 Asymptotic Analysis -- 5.6 Recursive Identification -- 5.7 Robustness of Identification Methods -- 5.8 Parameter Tracking -- 6 Model Complexity Selection -- 6.1 Introduction -- 6.2 Cross‐validation -- 6.3 FPE Criterion…”

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