Tabla de Contenidos: “…Special Measures for Controlling STDs and HIV/AIDS -- Recommended Actions -- Women's Interaction with Other Stakeholders -- Possible Areas of Women's Involvement -- Women as Managers in the NGOs -- Women as Members of the Consultants' Teams -- Notes -- Chapter 10: Compliance and Reconciliation in Linear Projects -- Introduction -- Encroachment, Acquisition and Displacement in Linear Projects: APSHP -- R&R Policy and Systems in APSHP -- Policy Provisions -- R&R Systems -- R&R Implementation -- Positive and Best Practices -- Positive Attitude and Fast Decision-Making Process of Top Management -- R&R Systems and Procedures -- Innovative, Flexible and Adaptive Practicesby Field Personnel -- Active and Intimate Association of Implementers with PAPs -- Areas of Concern and Suggestions for Improvement -- Periodic Orientation on R&R -- Continuity of the Key Project Staff -- Constitution of Committee Members -- Construction of Free Houses Through RBD -- Land Acquisition -- Inter Departmental Coordination -- Involving Professional NGOs -- Grievance Redressal Mechanism -- Management Information System -- Concerns of Women -- Limited Employment Opportunities -- Conclusion -- Notes -- Section III: Relocation and Social Disarticulation -- Chapter 11: Transhumant Pastoralists to Sedentary Farmers -- Introduction -- Relocation in the Conservation Scenario -- General Pattern -- The Study Area -- A Profile of the Gujjar Community -- Livestock Population Demography -- Income Patterns -- Migration Patterns -- The Gujjar Relocation Experience -- Conclusion -- Notes -- References -- Chapter 12: Impact of Displacement on Forest Dwellers -- Introduction -- Desired Role of Government for R&R -- 'Good Governance' Versus 'Governmentality' -- Articulating Rights of the Displaced Persons for Better R&R -- Policy-Making for R&R -- Research Methodology: Related Issues…”

Tabla de Contenidos: “…-- 6.2.2 Calculating correlations with Dask DataFrames -- 6.3 Custom aggregate functions -- 6.3.1 Testing categorical variables with the t-test -- 6.3.2 Using custom aggregates to implement the Brown-Forsythe test -- 6.4 Rolling (window) functions -- 6.4.1 Preparing data for a rolling function -- 6.4.2 Using the rolling method to apply a window function -- Summary -- Chapter 7: Visualizing DataFrames with Seaborn -- 7.1 The prepare-reduce-collect-plot pattern -- 7.2 Visualizing continuous relationships with scatterplot and regplot -- 7.2.1 Creating a scatterplot with Dask and Seaborn -- 7.2.2 Adding a linear regression line to the scatterplot -- 7.2.3 Adding a nonlinear regression line to a scatterplot -- 7.3 Visualizing categorical relationships with violinplot -- 7.3.1 Creating a violinplot with Dask and Seaborn…”

Tabla de Contenidos: “…Fundamentals of Sequential System Monitoring and Prognostics Methods -- 3.1 Fundamentals -- 3.1.1 Prognostics and SHM -- 3.1.2 Damage response modelling -- 3.1.3 Interpreting uncertainty for prognostics -- 3.1.4 Prognostic performance metrics -- 3.2 Bayesian tracking methods -- 3.2.1 Linear Bayesian Processor: The Kalman Filter -- 3.2.2 Unscented Transformation and Sigma Points: The Unscented Kalman Filter -- 3.2.3 Sequential Monte Carlo methods: Particle Filters -- 3.2.3.1 Sequential importance sampling -- 3.2.3.2 Resampling -- 3.3 Calculation of EOL and RUL -- 3.3.1 The failure prognosis problem -- 3.3.2 Future state prediction -- 3.4 Summary…”

Tabla de Contenidos: “…The power of resize() -- Enter reserve() -- Optimizing with shrink_to_fit() -- Real-world relevance -- Custom allocator basics -- The role and responsibility of an allocator -- Under the hood - the allocator interface -- Trade-offs and the need for custom allocators -- Choosing std::allocator over new, delete, and managed pointers -- Creating a custom allocator -- Custom allocators - the heart of memory flexibility -- Understanding the motivation behind custom allocators -- Memory pools - a popular custom allocator strategy -- Unlocking the potential of custom allocators -- Allocators and container performance -- Why allocators matter in performance -- The performance characteristics of std::allocator -- When to consider alternative allocators -- Profiling - the key to making informed decisions -- Summary -- Chapter 4: Mastering Algorithms with std::vector -- Technical requirements -- Sorting a vector -- Getting started with std::sort -- The engine under the hood - introsort -- Efficiency unparalleled - O(n log n) -- Sorting in descending order -- Sorting custom data types -- Pitfalls and precautions -- Searching elements -- Linear search with std::find -- Binary search techniques -- Using std::lower_bound and std::upper_bound -- Binary search versus linear search - efficiency and versatility -- Manipulating vectors -- Transforming with std::copy -- Reversing elements with std::reverse -- Rotating vectors with std::rotate -- Filling a vector with std::fill -- Putting manipulation to use -- Considerations in manipulation -- Custom comparators and predicates -- Understanding comparators -- The power of predicates -- Crafting effective comparators and predicates -- User-defined structs and classes -- Understanding container invariants and iterator invalidation -- Understanding iterator invalidation -- Strategies to counteract invalidation…”

Tabla de Contenidos: “…The Copy Command -- The Rotate Command -- The Array Command -- Radial Array -- Linear Array -- The Mirror Command -- The Align Tool -- The Split Element Command -- You Can Also Add a Gap -- Revit Wants It Clean! …”

Tabla de Contenidos: “…16 Extensions of Smooth Small Distortions: Picking Motions -- Lemma 16.1 -- Lemma 16.2 -- 17 Extensions of Smooth Small Distortions: Unity Partitions -- 18 Extensions of Smooth Small Distortions: Function Extension -- Lemma 18.1 -- Lemma 18.2 -- 19 Equidistribution: Extremal Newtonian-like Configurations, Group Invariant Discrepancy, Finite Fields, Combinatorial Designs, Linear Independent Vectors, Matroids and the Maximum Distance Separable Conjecture -- 19.1 s-extremal Configurations and Newtonian s-energy -- 19.2 [−1, 1] -- 19.2.1 Critical Transition -- 19.2.2 Distribution of s-extremal Configurations -- 19.2.3 Equally Spaced Points for Interpolation -- 19.3 The n-dimensional Sphere, Sn Embedded in Rn +1 -- 19.3.1 Critical Transition -- 19.4 Torus -- 19.5 Separation Radius and Mesh Norm for s-extremal Configurations -- 19.5.1 Separation Radius of s&gt -- n-extremal Configurations on a Set Yn -- 19.5.2 Separation Radius of s&lt -- n − 1-extremal Configurations on Sn -- 19.5.3 Mesh Norm of s-extremal Configurations on a Set Yn -- 19.6 Discrepancy of Measures, Group Invariance -- 19.7 Finite Field Algorithm -- 19.7.1 Examples -- 19.7.2 Spherical ̂t-designs -- 19.7.3 Extension to Finite Fields of Odd Prime Powers -- 19.8 Combinatorial Designs, Linearly Independent Vectors, MDS Conjecture -- 19.8.1 The Case q=2 -- 19.8.2 The General Case -- 19.8.3 The Maximum Distance Separable Conjecture -- 20 Covering of SU(2) and Quantum Lattices -- 20.1 Structure of SU(2) -- 20.2 Universal Sets -- 20.3 Covering Exponent -- 20.4 An Efficient Universal Set in PSU(2) -- 21 The Unlabeled Correspondence Configuration Problem and Optimal Transport -- 21.1 Unlabeled Correspondence Configuration Problem -- 21.1.1 Non-reconstructible Configurations -- 21.1.2 Example -- 21.1.3 Partition Into Polygons -- 21.1.4 Considering Areas of Triangles-10-step Algorithm…”

Tabla de Contenidos: “…A Simple and General Model for Power Analysis 2.1 The General Linear Model, the F Statistic, and Effect Size 2.1.1 Effect Size 2.2 Understanding Linear Models 2.3 The F Distribution and Power 2.3.1 Confidence Intervals for PV and d 2.4 Using the Noncentral F Distribution to Assess Power 2.5 Translating Common Statistics and ES Measures into F 2.5.1 Worked Example -- Hierarchical Regression 2.5.2 Worked Examples Using the d Statistic 2.6 Defining Large, Medium and Small Effects 2.7 Nonparametric and Robust Statistics 2.8 From F to Power Analysis 2.9 Analytic and Tabular Methods of Power Analysis 2.10 Using the One-Stop F Table 2.11 Simple and General Software for Power Analysis 2.12 R code for Power Analysis for Traditional and Modern Hypothesis Tests 2.13 Summary 3. …”

Tabla de Contenidos: “…Extensibility -- Platform Implementation -- Component Implementation -- Application Builders -- Distributed Operation -- Prototype -- Evaluation -- The Linear Road Benchmark -- Experiment Setup -- Porting MXQuery and Linear Road -- Overhead of the Exoengine Architecture -- Replacing an Slet at Runtime -- Distributed Deployment -- Developing with the Exoengine -- Heterogeneity -- Related Work -- Discussion and Outlook -- References -- Replication and Caching -- Leader Election for Replicated Services Using Application Scores -- Introduction -- Application Scores -- Background: Replicated Coordination Services -- Request Latency -- Recovery Time -- The POLE Algorithm -- Failure Detection -- Leader Selection Algorithm -- Oracles -- Experimental Evaluation -- Experimental Setup and Methodology -- Request Latency and Random Request Distributions -- Request Latency and Uneven Request Distributions -- Recovery Time -- Extensions -- Related Work -- Conclusion -- References -- PolyCert: Polymorphic Self-optimizing Replication for In-Memory Transactional Grids -- Introduction -- Motivations -- System Architecture -- The PolyCert Replication Protocol -- Replication Protocol Selection Oracle -- Experimental Evaluation -- Related Work -- Conclusions -- References -- A Trigger-Based Middleware Cache for ORMs -- Introduction -- Background and Related Work -- Design -- Caching Abstractions -- Database Triggers -- Consistency Guarantees -- Implementation -- Evaluation -- Experimental Setup -- Programmer Effort -- Microbenchmarks -- Social Networking Workload -- Conclusion -- References -- Security and Interoperability -- Deploy, Adjust and Readjust: Supporting Dynamic Reconfiguration of Policy Enforcement -- Introduction -- Background and Example -- The Reference Enforcement Model -- Illustrative Example -- Attribute Configuration -- Attribute Retrieval…”

Tabla de Contenidos: “…HIGMill® -- 7.3.4 Some Operational Points -- Media -- Media Loading -- Slurry Percent Solids -- 7.4 Other Grinding Mill Types -- Vibratory Mills -- Centrifugal Mills -- Roller Mills -- Table and Roller Mills -- Pendulum Roller Mills -- 7.5 Grinding Circuits -- 7.5.1 Circuit Types -- 7.5.2 Circuit Operation -- 7.5.3 Control of the Grinding Circuit -- Instrumentation -- Control Objectives/strategies -- References -- 8 Industrial Screening -- 8.1 Introduction -- 8.2 Screen Performance -- Efficiency Formulae -- Efficiency and Circulating Load -- Efficiency or Partition Curve -- Separation Efficiency -- 8.3 Factors Affecting Screen Performance -- Particle Size -- Feed Rate -- Screen Angle -- Particle Shape -- Open Area -- Vibration -- Moisture -- 8.4 Mathematical Models of Screens -- Phenomenological Models -- Empirical Models -- Numerical Models -- 8.5 Screen Types -- 8.5.1 Vibrating Screens -- Inclined or Circular Motion Screens -- Grizzly Screens -- Horizontal, Low-Head, or Linear Vibrating Screens -- Resonance Screens -- Dewatering Screens -- Banana or Multislope Screens -- Modular Screens -- Mogensen Sizer -- High-Frequency Screens -- Derrick Stack Sizer® -- 8.5.2 Vibration Modes -- Circular Motion (Single-Shaft) Screens -- Linear-Vibration (Double-Shaft) Screens -- Oval Motion (Triple-Shaft) Screens -- 8.5.3 Other Screen Types -- Static Grizzlies -- Mogensen Divergators -- Trommels -- Rotaspiral -- Bradford Breaker -- Roller Screens -- Flip-Flow Screen -- Circular, Gyratory, or Tumbler Screens -- Sieve Bends -- Linear Screen -- Pansep Screen -- 8.5.4 Screening Surfaces -- Bolt-In Screening Surfaces -- Tensioned Screening Surfaces -- Woven-Wire Cloth -- "Self-Cleaning" Wire -- Tensioned Rubber and Polyurethane Mats -- Modular Wire and Wedge Wire Panels -- References -- 9 Classification -- 9.1 Introduction -- 9.2 Principles of Classification…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Introduction -- A brief outline of this book -- Guide to the reader -- Contributors -- Acknowledgements -- Chapter 1 Desirability -- 1.1 Introduction -- 1.2 Reasoning about and with sets of desirable gambles -- 1.2.1 Rationality criteria -- 1.2.2 Assessments avoiding partial or sure loss -- 1.2.3 Coherent sets of desirable gambles -- 1.2.4 Natural extension -- 1.2.5 Desirability relative to subspaces with arbitrary vector orderings -- 1.3 Deriving and combining sets of desirable gambles -- 1.3.1 Gamble space transformations -- 1.3.2 Derived coherent sets of desirable gambles -- 1.3.3 Conditional sets of desirable gambles -- 1.3.4 Marginal sets of desirable gambles -- 1.3.5 Combining sets of desirable gambles -- 1.4 Partial preference orders -- 1.4.1 Strict preference -- 1.4.2 Nonstrict preference -- 1.4.3 Nonstrict preferences implied by strict ones -- 1.4.4 Strict preferences implied by nonstrict ones -- 1.5 Maximally committal sets of strictly desirable gambles -- 1.6 Relationships with other, nonequivalent models -- 1.6.1 Linear previsions -- 1.6.2 Credal sets -- 1.6.3 To lower and upper previsions -- 1.6.4 Simplified variants of desirability -- 1.6.5 From lower previsions -- 1.6.6 Conditional lower previsions -- 1.7 Further reading -- Acknowledgements -- Chapter 2 Lower previsions -- 2.1 Introduction -- 2.2 Coherent lower previsions -- 2.2.1 Avoiding sure loss and coherence -- 2.2.2 Linear previsions -- 2.2.3 Sets of desirable gambles -- 2.2.4 Natural extension -- 2.3 Conditional lower previsions -- 2.3.1 Coherence of a finite number of conditional lower previsions -- 2.3.2 Natural extension of conditional lower previsions -- 2.3.3 Coherence of an unconditional and a conditional lower prevision -- 2.3.4 Updating with the regular extension -- 2.4 Further reading -- 2.4.1 The work of Williams…”

Tabla de Contenidos:

Tabla de Contenidos: “…-- 12.2 Packet-classification problem -- 12.3 Requirements and metrics -- 12.4 Simple solutions -- 12.4.1 Linear search…”

Tabla de Contenidos: “…Front Cover -- Advances in Concentrating Solar Thermal Research and Technology -- Related titles -- Advances in Concentrating Solar Thermal Research and Technology -- Copyright -- Contents -- List of contributors -- Editors' biographies -- Acknowledgment -- 1 - Introduction -- 1 - Introduction to concentrating solar thermal (CST) technologies -- 1.1 The sun as an energy source -- 1.2 Defining characteristics of CST technologies -- 1.3 Thermal efficiency and the need for concentration -- 1.4 Limits of concentration -- 1.5 Optimum operating temperature to maximize light-to-work conversion efficiency -- 1.6 Main commercially available solar concentrating technologies -- 1.6.1 Line focus solar concentrators -- 1.6.1.1 Parabolic trough -- 1.6.1.2 Linear Fresnel -- 1.6.2 Point-focus solar concentrators -- 1.6.2.1 Parabolic dish -- 1.6.2.2 Heliostat field-central receiver -- 1.7 Industry and market trends -- 1.7.1 Solar thermal electricity -- 1.7.2 Industrial process heat -- 1.7.3 Solar chemistry and material processing -- 1.8 Research priorities, strategies, and trends -- References -- 2 - Advances in the collection and concentration of sunlight -- 2 - Advanced mirror concepts for concentrating solar thermal systems -- Nomenclature -- 2.1 Introduction -- 2.2 Anti-soiling coatings -- 2.3 High-reflective mirror materials -- 2.4 High-temperature mirrors for secondary concentrators -- 2.5 Low-cost mirrors based on stainless steel -- 2.6 Conclusions -- References -- 3 - Improved design for linear Fresnel reflector systems -- 3.1 Introduction (motivation)1 -- 3.1.1 Low energy cost -- 3.1.2 Concentration -- 3.1.3 Etendue -- 3.1.4 CAP Concentration acceptance product [7] -- 3.1.5 Summary: one recipe for low-cost energy delivery -- 3.2 Advanced linear Fresnel reflector concentrators -- 3.2.1 Conventional LFR -- 3.2.2 Advanced concepts…”

Tabla de Contenidos: “…4.26.7 Variance of Normal Distribution -- 4.26.8 Mode of Normal Distribution -- 4.26.9 Median of the Normal Distribution -- 4.26.10 Moment Generating Function of Normal Distribution With Respect to Origin -- 4.26.11 Mean Deviation of Normal Distribution -- 4.26.11.1 Solved Examples -- 4.26.12 Fitting a Normal Distribution -- 4.26.13 Linear Combination of Independent Normal Variables -- 4.26.14 Fitting a Normal Distribution -- 4.26.15 Normal Approximation to Binomial Distribution -- 4.27 Characteristic Function -- 4.28 Gamma Distribution -- 4.28.1 Mean and Variance of Gamma Distribution -- 4.28.2 Gamma Distribution of Second Kind -- 4.29 Beta Distribution of First Kind -- 4.29.1 Beta Distribution of Second Kind -- 4.30 Weibull Distribution -- 5 Curve Fitting -- 5.1 Introduction -- 5.2 The Method of Least Squares -- 5.3 The Least-Squares Line -- 5.4 Fitting a Parabola by the Method of Least Squares -- 5.5 Fitting the exponential curve of the form y=a ebx -- 6 Correlation and Regression -- 6.1 Introduction -- 6.2 Correlation -- 6.2.1 Types of Correlation -- 6.3 Coefficient of Correlation -- 6.3.1 Properties of Coefficient of Correlation -- 6.4 Methods of Finding Coefficient of Correlation -- 6.5 Scatter Diagram -- 6.6 Direct Method -- 6.7 Spearman's Rank Correlation Coefficient -- 6.7.1 Rank Correlation Coefficient When the Ranks Are Tied -- 6.8 Calculation of r (Correlation Coefficient) (Karl Pearson's Formula) -- 6.9 Regression -- 6.10 Regression Equation -- 6.11 Curve of Regression -- 6.12 Types of Regression -- 6.13 Regression Equations (Linear Fit) -- 6.13.1 Linear Regression Equation of y on x -- 6.13.2 Regression Equation of x and y -- 6.14 Angle between Two Lines of Regression -- 6.15 Coefficient of Determination -- 6.16 Coefficient Nondetermination -- 6.17 Coefficient of Alienation -- 6.17.1 Solved Examples -- 6.18 Multilinear Regression…”

Tabla de Contenidos: “…The Influence of the Linear Leakage Rate on Wellbore Pressure -- 8.5.2. …”

Tabla de Contenidos: “…4.4.2 How to Implement -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Evaluation -- Step 4: Execution and Interpretation -- 4.4.3 Conclusion -- 4.5 Artificial Neural Networks -- 4.5.1 How It Works -- Step 1: Determine the Topology and Activation Function -- Step 2: Initiation -- Step 3: Calculating Error -- Step 4: Weight Adjustment -- 4.5.2 How to Implement -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Evaluation -- Step 4: Execution and Interpretation -- 4.5.3 Conclusion -- 4.6 Support Vector Machines -- Concept and Terminology -- 4.6.1 How It Works -- 4.6.2 How to Implement -- Implementation 1: Linearly Separable Dataset -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Process Execution and Interpretation -- Example 2: Linearly Non-Separable Dataset -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Execution and Interpretation -- Parameter Settings -- 4.6.3 Conclusion -- 4.7 Ensemble Learners -- Wisdom of the Crowd -- 4.7.1 How It Works -- Achieving the Conditions for Ensemble Modeling -- 4.7.2 How to Implement -- Ensemble by Voting -- Bootstrap Aggregating or Bagging -- Implementation -- Boosting -- AdaBoost -- Implementation -- Random Forest -- Implementation -- 4.7.3 Conclusion -- References -- 5 Regression Methods -- 5.1 Linear Regression -- 5.1.1 How it Works -- 5.1.2 How to Implement -- Step 1: Data Preparation -- Step 2: Model Building -- Step 3: Execution and Interpretation -- Step 4: Application to Unseen Test Data -- 5.1.3 Checkpoints -- 5.2 Logistic Regression -- 5.2.1 How It Works -- How Does Logistic Regression Find the Sigmoid Curve? …”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: Fundamentals of AI-Based Smart Manufacturing -- Chapter 1: Machine Learning Fundamentals -- 1.1 Introduction -- 1.2 Classification -- 1.2.1 Linear Model -- 1.2.1.1 Logistic Regression -- 1.2.1.2 Support Vector Machine -- 1.2.2 Nonlinear Model -- 1.2.2.1 K-Nearest Neighbor -- 1.2.2.2 Naive Bayes -- 1.2.2.3 Decision Tree -- 1.2.2.4 Random Forest -- 1.3 Regression -- 1.3.1 Linear Regression -- 1.3.2 Multiple Linear Regression -- 1.3.3 Logistic Regression -- 1.3.3.1 Three Types of Logistic Regression -- 1.3.4 Polynomial Regression -- 1.3.5 Support Vector Regression -- 1.3.6 Decision Tree Regression -- 1.3.7 Random Forest Regression -- 1.3.7.1 Random Forest Algorithm in Practice -- 1.3.7.2 Random Forest Algorithm Application Examples -- 1.3.8 Lasso Regression -- 1.4 Clustering -- 1.4.1 Clustering Algorithms -- 1.4.2 K-Means Clustering Algorithm -- 1.4.3 Mean Shift Clustering Algorithm -- 1.4.4 DBSCAN Clustering -- 1.4.4.1 Two Elements Required by DBSCAN -- 1.4.4.2 Steps followed to do DBSCAN Algorithm -- 1.5 Conclusion -- References -- Chapter 2: Industry 4.0 in Manufacturing, Communication, Transportation, Healthcare -- 2.1 Introduction -- 2.1.1 The Significance of Industry 4.0 Across Multiple Domains -- 2.2 Industry 4.0 in Manufacturing: Overview -- 2.2.1 Benefits of Industry 4.0 in Manufacturing -- 2.2.2 Examples of Industry 4.0 in Manufacturing -- 2.2.3 Challenges in Implementation of Industry 4.0 in Manufacturing -- 2.3 Industry 4.0 in Communication: Overview -- 2.3.1 Benefits of Industry 4.0 in Communication -- 2.3.2 Examples of Industry 4.0 in Communication -- 2.3.3 Challenges of Implementing Industry 4.0 in Communication -- 2.4 Industry 4.0 in Transportation: Overview -- 2.4.1 Industry 4.0â€TMs Advantages for Transportation…”

Tabla de Contenidos:

Tabla de Contenidos: “…Building deeper networks with dropout and rectified linear units -- 6.6.1. Dropping neurons for regularization -- 6.6.2. …”

Texto completo en Odilo