Tabla de Contenidos: “…2.5.1 Carbon capture and storage -- 2.5.2 Lignite pre-drying -- 2.6 Summary -- Sources of further information -- References -- 3 - Biomass and agricultural residues for energy generation -- 3.1 Introduction -- 3.2 Biomass resources and supply chains -- 3.2.1 European and global woody biomass resources and supply chains -- 3.2.2 Herbaceous and fruit biomass resources and supply chains -- 3.3 Biomass properties and measurement of properties -- 3.3.1 Properties of biomass -- 3.3.2 Sampling and sample reduction -- 3.3.3 Measurement of main properties and applied standards -- 3.3.3.1 Proximate and ultimate analysis -- 3.3.3.2 Calorific value -- 3.3.3.3 Moisture content -- 3.3.3.4 Particle-size determination -- 3.3.3.5 Bulk density -- 3.4 Future trends -- Symbols and abbreviations -- Terminology -- References -- Two - Fuel preparation, handling and transport -- 4 - Biomass fuel transport and handling -- 4.1 Introduction -- 4.1.1 The critical importance of fuel handling to cost-effective biomass fuel valorisation -- 4.1.2 The special features of biomass as a fuel -- 4.1.3 The underlying causes of handling problems with biomass -- 4.2 The challenges of biomass handling -- 4.2.1 Effect of low and variable volumetric energy density -- 4.2.2 Effect of variation of volumetric energy density on feeder, conveyor and store design -- 4.2.3 Moisture effects -- 4.3 Sources and types of biomass, and classifications according to handling properties -- 4.3.1 Identifying the class to which a biomass belongs -- 4.3.2 Typical common biomass materials and their classifications -- 4.3.3 Selection of handling equipment for different biomass materials, and compatibility between different fuels in common systems -- 4.4 Other considerations for compatibility of different fuels with a handling system -- 4.4.1 Need for stock rotation and limitation of storage time…”

Tabla de Contenidos: “…Chapter 8: Defect Prediction -- 8.1 Defect Detection Economics -- 8.2 Static Code Defect Prediction -- 8.2.1 Easy to Use -- 8.2.2 Widely Used -- 8.2.3 Useful -- Chapter 9: Effort Estimation -- 9.1 The Estimation Problem -- 9.2 How to Make Estimates -- 9.2.1 Expert-Based Estimation -- 9.2.2 Model-Based Estimation -- 9.2.3 Hybrid Methods -- Chapter 10: Data Mining (Under the Hood) -- 10.1 Data Carving -- 10.2 About the Data -- 10.3 Cohen Pruning -- 10.4 Discretization -- 10.4.1 Other Discretization Methods -- 10.5 Column Pruning -- 10.6 Row Pruning -- 10.7 Cluster Pruning -- 10.7.1 Advantages of Prototypes -- 10.7.2 Advantages of Clustering -- 10.8 Contrast Pruning -- 10.9 Goal Pruning -- 10.10 Extensions for Continuous Classes -- 10.10.1 How RTs Work -- 10.10.2 Creating Splits for Categorical Input Features -- 10.10.3 Splits on Numeric Input Features -- 10.10.4 Termination Condition and Predictions -- 10.10.5 Potential Advantages of RTs for Software Effort Estimation -- 10.10.6 Predictions for Multiple Numeric Goals -- Part III: Sharing Data -- Chapter 11: Sharing Data: Challenges and Methods -- 11.1 Houston, We Have a Problem -- 11.2 Good News, Everyone -- Chapter 12: Learning Contexts -- 12.1 Background -- 12.2 Manual Methods for Contextualization -- 12.3 Automatic Methods -- 12.4 Other Motivation to Find Contexts -- 12.4.1 Variance Reduction -- 12.4.2 Anomaly Detection -- 12.4.3 Certification Envelopes -- 12.4.4 Incremental Learning -- 12.4.5 Compression -- 12.4.6 Optimization -- 12.5 How to Find Local Regions -- 12.5.1 License -- 12.5.2 Installing CHUNK -- 12.5.3 Testing Your Installation -- 12.5.4 Applying CHUNK to Other Models -- 12.6 Inside CHUNK -- 12.6.1 Roadmap to Functions -- 12.6.2 Distance Calculations -- 12.6.2.1 Normalize -- 12.6.2.2 SquaredDifference -- 12.6.3 Dividing the Data -- 12.6.3.1 FastDiv -- 12.6.3.2 TwoDistantPoints…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- About the Authors -- Acknowledgments -- About the Companion Website -- Introduction -- Chapter 1 Statistics and Data Science -- 1.1 Big Data: Predicting Pregnancy -- 1.2 Phantom Protection from Vitamin E -- 1.3 Statistician, Heal Thyself -- 1.4 Identifying Terrorists in Airports -- 1.5 Looking Ahead -- 1.6 Big Data and Statisticians -- 1.6.1 Data Scientists -- Chapter 2 Designing and Carrying Out a Statistical Study -- 2.1 Statistical Science -- 2.2 Big Data -- 2.3 Data Science -- 2.4 Example: Hospital Errors -- 2.5 Experiment -- 2.6 Designing an Experiment -- 2.6.1 A/B Tests -- A Controlled Experiment for the Hospital Plans -- 2.6.2 Randomizing -- 2.6.3 Planning -- 2.6.4 Bias -- 2.6.4.1 Placebo -- 2.6.4.2 Blinding -- 2.6.4.3 Before‐after Pairing -- 2.7 The Data -- 2.7.1 Dataframe Format -- 2.8 Variables and Their Flavors -- 2.8.1 Numeric Variables -- 2.8.2 Categorical Variables -- 2.8.3 Binary Variables -- 2.8.4 Text Data -- 2.8.5 Random Variables -- 2.8.6 Simplified Columnar Format -- 2.9 Python: Data Structures and Operations -- 2.9.1 Primary Data Types -- 2.9.2 Comments -- 2.9.3 Variables -- 2.9.4 Operations on Data -- 2.9.4.1 Converting Data Types -- 2.9.5 Advanced Data Structures -- 2.9.5.1 Classes and Objects -- 2.9.5.2 Data Types and Their Declaration -- 2.10 Are We Sure We Made a Difference? …”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Editor -- Contributors -- Preface -- Chapter 1: Distributed and Parallel Computing -- 1.1 Introduction -- 1.2 Distributed Computing -- 1.2.1 Cluster Computing -- 1.2.1.1 Architecture -- 1.2.1.2 Data and Message Communication -- 1.2.1.3 Task Management and Administration -- 1.2.1.4 Example Geospatial Big Data Project on Cluster -- 1.2.2 Grid Computing -- 1.2.2.1 Architecture -- 1.2.2.2 Types of Grid Architectures -- 1.2.2.3 Topology -- 1.2.2.4 Perspectives -- 1.2.2.4.1 User -- 1.2.2.4.2 Administrator -- 1.2.2.4.3 Application Developer -- 1.2.2.5 Example Geospatial Big Data Project on Grids -- 1.2.3 Cloud Computing -- 1.2.3.1 Taxonomies -- 1.2.3.2 Cloud Service Models -- 1.2.3.3 Cloud Deployment Models -- 1.2.3.3.1 Cloud APIs -- 1.2.3.3.2 Levels of Cloud APIs -- 1.2.3.3.3 Categories of APIs -- 1.2.3.4 Example Geospatial Big Data Project on Clouds -- 1.3 Parallel Computing -- 1.3.1 Classes of Parallel Computing -- 1.3.2 Shared Memory Multiple Processing -- 1.3.3 Distributed Memory Multiple Processing -- 1.3.4 Hybrid Distributed Shared Memory -- 1.3.5 Example Geospatial Big Data Project on Parallel Computers -- 1.4 Supercomputing -- 1.4.1 Supercomputing Worldwide -- 1.4.2 Trend -- 1.4.3 Future Supercomputer Research -- 1.4.4 Example Geospatial Big Data Project on Supercomputers -- 1.5 XSEDE: A Single Virtual System -- 1.5.1 Resources -- 1.5.2 Services -- 1.5.3 Example Big Geospatial Data Project on XSEDE -- 1.6 Choosing Appropriate Computing Environment -- 1.7 Summary -- References -- Chapter 2: GEOSS Clearinghouse Integrating Geospatial Resources to Support the Global Earth Observation System of Systems -- 2.1 Introduction -- 2.2 Catalog and Clearinghouse Research Review -- 2.2.1 Metadata Repository and Standardized Metadata…”

Tabla de Contenidos: “…7.2.5 WebSphere Studio Application Developer V5.0.1 Interim Fix 003 -- 7.2.6 Install DB2 UDB V8.1 FixPack 2 -- 7.2.7 Install WebSphere Commerce Studio Toolkit V5.5.0.2 FixPack -- 7.3 WebSphere Commerce Studio configuration -- 7.3.1 Required post-install configuration -- 7.3.2 Optional post-install configuration -- 7.3.3 Configure the default Web browser -- 7.3.4 Set default XML editor to optimize performance -- 7.4 WebSphere Commerce Studio verification -- 7.4.1 Start the WebSphere Commerce Payments Server -- 7.4.2 Start the WebSphere Commerce Server -- 7.4.3 Back up the workspace and databases -- 7.5 Content Manager node implementation -- 7.6 Add WDME to WebSphere Commerce Studio -- 7.6.1 Add WDME database tables to the instance database -- 7.6.2 DB2 client configuration to Content Manager node -- 7.6.3 DB2 Information Integrator for Content (EIP) installation -- 7.6.4 Copy files to Development node -- 7.6.5 Import and update servlet mapping files -- 7.6.6 Import and modify the digitalmedia.properties file -- 7.6.7 Import DigitalMediaCommon.jar -- 7.6.8 Import DigitalMediaEJB.jar -- 7.6.9 Import DigitalMediaUsageEJB.jar -- 7.6.10 Add external JARs to the server class path -- 7.6.11 Start servers in WebSphere Commerce Studio -- 7.6.12 Back up the WDME workspace and instance database -- 7.6.13 Create and package the store archive -- 7.6.14 Deploy the store archive -- 7.6.15 Modify the cm8repository.properties file -- 7.6.16 Modify parms.txt -- 7.6.17 Modify the DMConfig.inc JSP include file -- 7.6.18 Modify dmstoreload.properties -- 7.6.19 Modify dmstoreconfig_&lt -- locale&gt -- .properties -- 7.6.20 Create the item type and load content -- 7.6.21 Modify daemon configuration files -- 7.6.22 Verify the digital media store -- 7.6.23 Enable reload (hot deploy of JSPs) -- 7.7 Troubleshooting the development environment…”

Tabla de Contenidos: “…Integrating Tivoli Infoman -- 2.1 The TWS for z/OS and Infoman configuration -- 2.2 Creating problem records in Infoman -- 2.3 TWS for z/OS set up -- 2.3.1 Creating TWS for z/OS exit 7 -- 2.3.2 Setting up the skeleton JCL -- 2.3.3 Activating EQQUX007 -- 2.3.4 Adding the required DD statements -- 2.4 Infoman set up -- 2.4.1 Creating a privilege class -- 2.4.2 Creating a stored response chain -- 2.5 Solving issues with the interface -- Chapter 3. …”

Tabla de Contenidos: “…Modeling aerodynamic flows in CFD 10.1 Basic definitions 10.2 The major classes of CFD codes and their applications 10.2.1. …”

Tabla de Contenidos: “…6.3.2. écart de revenus du travail selon le niveau d'études et la classe d'âge…”

Tabla de Contenidos: “…DB2 - WebSphere performance aspects -- 10.1 Recommended hardware and software configuration -- 10.2 WebSphere Application Server connection pooling -- 10.2.1 What is new in Version 5 - What is in WebSphere Version 4 -- 10.2.2 How connection pooling works - How connection objects are structured -- 10.2.3 WebSphere data sources -- 10.2.4 Connection pooling - Best practices -- 10.3 DB2 and JDBC -- 10.3.1 Adjusting the number of DB2 threads and connections -- 10.3.2 Enabling DB2 dynamic statement cache -- 10.3.3 Choosing static SQL over dynamic SQL -- 10.3.4 Usage guidelines for JCC type 2 and type 4 connectivity -- 10.3.5 Enterprise JavaBeans (EJBs) and DB2 access -- 10.4 WLM classification of WAS and its impact on DB2 -- 10.4.1 How DB2 and WebSphere Servers are structured on z/OS -- 10.4.2 Enabling WLM dynamic application environments with WAS V5 -- 10.4.3 WebSphere classification and its impact on DB2 -- 10.5 Tuning the storage for z/OS and the Java Virtual Machine -- 10.6 Universal Driver tracing -- 10.6.1 Universal Driver tracing -- 10.6.2 DB2SystemMonitor class -- Chapter 11. Sample application -- 11.1 Introduction -- 11.1.1 Sample table description -- 11.1.2 Importing the tables into WSAD -- 11.2 MVC model with entity Beans and a session Bean -- 11.2.1 Creating entity CMP Beans using JDBC from the imported tables -- 11.2.2 Container-managed persistence entity Beans with SQLJ…”

Tabla de Contenidos: “…Group and role management -- 12.1 Groups -- 12.1.1 Static groups -- 12.1.2 Dynamic groups -- 12.1.3 Nested groups -- 12.1.4 Hybrid groups -- 12.1.5 Determining group membership -- 12.1.6 Group object classes -- 12.1.7 Group attribute types -- 12.2 Roles -- 12.3 Summary -- Chapter 13. …”

Tabla de Contenidos: “…4.7 Core Network Optimisation -- 4.7.1 Basics of the Optimisation Process -- 4.7.2 Data Collection and Analysis -- 4.7.3 Core Network Optimization Plan -- 4.8 GPRS Core Network Planning -- 4.8.1 Packet Core Network Planning -- 4.8.2 Basics of IP Addressing -- 4.8.3 IP Routing -- 4.9 EDGE Core Network Planning -- Part II: 3G UMTS Network Planning and Optimisation -- Chapter 5 3G Radio Network Planning and Optimisation -- 5.1 Basics of Radio Network Planning -- 5.1.1 Scope of Radio Network Planning -- 5.1.2 3G System Requirements -- 5.1.3 WCDMA Radio Fundamentals -- 5.1.4 Service Classes in UMTS -- 5.1.5 Element in a WCDMA Radio Network -- 5.2 Radio Interface Protocol Architecture -- 5.2.1 Protocol Structure for Universal Terrestrial Radio Access Network (UTRAN) -- 5.2.2 Channel Configuration in WCDMA Radio Network -- 5.3 Spreading Phenomenon -- 5.3.1 Scrambling Code -- 5.3.2 Channelisation Code -- 5.4 Multipath Propagation -- 5.5 Radio Network Planning Process -- 5.5.1 Pre-Planning Phase -- 5.5.2 Structure and Performance of the Physical Layer -- 5.5.3 Uplink and Downlink Modulation -- 5.5.4 Uplink and Downlink Spreading -- 5.5.5 Code Planning -- 5.5.6 Power Control in WCDMA Radio Networks -- 5.5.7 Handover in WCDMA Radio Networks -- 5.5.8 Coverage Planning -- 5.5.9 Capacity Planning -- 5.5.10 AMR -- 5.6 Detailed Planning -- 5.6.1 Coverage and Capacity -- 5.6.2 Radio Resource Management -- 5.7 High Speed Packet Access -- 5.7.1 HSDPA -- 5.7.2 HSUPA (High Speed Uplink Packer Access) -- 5.8 WCDMA Radio Network Optimisation -- 5.8.1 Key Performance Indicators -- 5.8.2 Network Performance Monitoring -- 5.8.3 Coverage, Capacity and Quality Enhancements -- 5.8.4 Parameter Tuning -- Chapter 6 3G Transmission Network Planning and Optimisation -- 6.1 Basics of 3G (WCDMA) Transmission Network Planning -- 6.1.1 Scope of Transmission Network Planning…”

Tabla de Contenidos: “…5.5 PyTorch's autograd: Backpropagating all things -- 5.5.1 Computing the gradient automatically -- 5.5.2 Optimizers a la carte -- 5.5.3 Training, validation, and overfitting -- 5.5.4 Autograd nits and switching it off -- 5.6 Conclusion -- 5.7 Exercise -- 5.8 Summary -- 6 Using a neural network to fit the data -- 6.1 Artificial neurons -- 6.1.1 Composing a multilayer network -- 6.1.2 Understanding the error function -- 6.1.3 All we need is activation -- 6.1.4 More activation functions -- 6.1.5 Choosing the best activation function -- 6.1.6 What learning means for a neural network -- 6.2 The PyTorch nn module -- 6.2.1 Using __call__ rather than forward -- 6.2.2 Returning to the linear model -- 6.3 Finally a neural network -- 6.3.1 Replacing the linear model -- 6.3.2 Inspecting the parameters -- 6.3.3 Comparing to the linear model -- 6.4 Conclusion -- 6.5 Exercises -- 6.6 Summary -- 7 Telling birds from airplanes: Learning from images -- 7.1 A dataset of tiny images -- 7.1.1 Downloading CIFAR-10 -- 7.1.2 The Dataset class -- 7.1.3 Dataset transforms -- 7.1.4 Normalizing data -- 7.2 Distinguishing birds from airplanes -- 7.2.1 Building the dataset -- 7.2.2 A fully connected model -- 7.2.3 Output of a classifier -- 7.2.4 Representing the output as probabilities -- 7.2.5 A loss for classifying -- 7.2.6 Training the classifier -- 7.2.7 The limits of going fully connected -- 7.3 Conclusion -- 7.4 Exercises -- 7.5 Summary -- 8 Using convolutions to generalize -- 8.1 The case for convolutions -- 8.1.1 What convolutions do -- 8.2 Convolutions in action -- 8.2.1 Padding the boundary -- 8.2.2 Detecting features with convolutions -- 8.2.3 Looking further with depth and pooling -- 8.2.4 Putting it all together for our network -- 8.3 Subclassing nn.Module -- 8.3.1 Our network as an nn.Module -- 8.3.2 How PyTorch keeps track of parameters and submodules…”

Tabla de Contenidos: “…-- 2.2 Design of a smart contract -- 2.2.1 A use case diagram for the counter -- 2.2.2 Data assets, peer participants, roles, rules, and transactions -- 2.2.3 From class diagram to contract diagram -- 2.3 Development of a smart contract code -- 2.3.1 Solidity language -- 2.3.2 Smart contract code for Counter -- 2.4 Deploying and testing the smart contract -- 2.4.1 The Remix IDE -- 2.4.2 Deployment and testing -- 2.4.3 Key takeaways -- 2.5 What makes a blockchain contract smart? …”

Tabla de Contenidos: “…1.16 Tools -- Summary -- Exercises -- Chapter 2: Preliminaries:Mathematical -- 2.1 Predicates and Boolean Expressions -- 2.2 Control Flow Graph -- 2.2.1 Basic blocks -- 2.2.2 Flow graphs -- 2.2.3 Paths -- 2.2.4 Basis paths -- 2.2.5 Path conditions and domains -- 2.2.6 Domain and computation errors -- 2.2.7 Static code analysis tools and static testing -- 2.3 Execution History -- 2.4 Dominators and Post-Dominators -- 2.5 Program Dependence Graph -- 2.5.1 Data dependence -- 2.5.2 Control dependence -- 2.5.3 Call graph -- 2.6 Strings, Languages, and Regular Expressions -- 2.7 Tools -- Summary -- Exercises -- Part II: Test Generation -- Chapter 3: Domain Partitioning -- 3.1 Introduction -- 3.2 The Test Selection Problem -- 3.3 Equivalence Partitioning -- 3.3.1 Faults targeted -- 3.3.2 Relations -- 3.3.3 Equivalence classes for variables -- 3.3.4 Unidimensional partitioning versus multidimensional partitioning -- 3.3.5 A systematic procedure -- 3.3.6 Test selection -- 3.3.7 Impact of GUI design -- 3.4 Boundary Value Analysis -- 3.5 Category-Partition Method -- 3.5.1 Steps in the category-partition method -- Summary -- Exercises -- Chapter 4: Predicate Analysis -- 4.1 Introduction -- 4.2 Domain Testing -- 4.2.1 Domain errors -- 4.2.2 Border shifts -- 4.2.3 ON-OFF points -- 4.2.4 Undetected errors -- 4.2.5 Coincidental correctness -- 4.2.6 Paths to be tested -- 4.3 Cause-Effect Graphing -- 4.3.1 Notation used in cause-effect graphing -- 4.3.2 Creating cause-effect graphs -- 4.3.3 Decision table from cause-effect graph -- 4.3.4 Heuristics to avoid combinatorial explosion -- 4.3.5 Test generation from a decision table -- 4.4 Tests Using Predicate Syntax -- 4.4.1 A fault model -- 4.4.2 Missing or extra Boolean variable faults -- 4.4.3 Predicate constraints -- 4.4.4 Predicate testing criteria -- 4.4.5 BOR, BRO, and BRE adequate tests…”

Tabla de Contenidos: “…Profil d'abattage des bœufs en pourcentage de nombre de restes de dents. Fréquence des classes d'âges corrigées. a) au BVSG ancien -- b) au BVSG moyen. -- fig.33 - Tinqueux «la Haubette». …”

Tabla de Contenidos: “…-- 2.3.2.1 Digital Twin and Simulation -- 2.3.2.2 Digital Twin and Cyber-Physical Systems -- 2.4 The Evolution of Digital Twin -- 2.5 Data Twin and the Physical World -- 2.6 Data Twin and the Digital World -- 2.7 Useful Terms and Classifications -- 2.7.1 Prototypes, Instances, and Aggregates -- 2.7.2 Digital Twin Classes -- 2.7.3 Digital Twin Categories -- 2.8 Level of Integration -- 2.8.1 Digital Model…”

“…Master the fundamentals of C++ and object-oriented programming Understand how C++11 features help you write compact and efficient code using concepts such as lambda expressions, move constructors, and assignment operators Learn the Standard Template Library, including containers and algorithms used in most real-world C++ applications Test your knowledge and expertise using exercises at the end of every lesson Learn on your own time, at your own pace: No previous programming experience required Learn C++11, object-oriented programming, and analysis Write fast and powerful C++ programs, compile the source code with a gcc compiler, and create executable files Use the Standard Template Library’s (STL) algorithms and containers to write feature-rich yet stable C++ applications Develop sophisticated programming techniques using lambda expressions, smart pointers, and move constructors Learn to expand your program’s power with inheritance and polymorphism Master the features of C++ by learning from programming experts Learn C++11 features that allow you to program compact and high-performance C++ applications TABLE OF CONTENTS PART I: THE BASICS LESSON 1: Getting Started with C++11 LESSON 2: The Anatomy of a C++ Program LESSON 3: Using Variables, Declaring Constants LESSON 4: Managing Arrays and Strings LESSON 5: Working with Expressions, Statements, and Operators LESSON 6: Controlling Program Flow LESSON 7: Organizing Code with Functions LESSON 8: Pointers and References Explained PART II: FUNDAMENTALS OF OBJECT-ORIENTED C++ PROGRAMMING LESSON 9: Classes and Objects LESSON 10: Implementing Inheritance LESSON 11: Polymorphism LESSON 12: Operator Types and Operator Overloading LESSON 13: Casting Operators LESSON 14: An Introduction to Macros and Templates PART III: LEARNING THE STANDARD TEMPLATE LIBRARY (STL) LESSON 15: An Introduction to the Standard Template Library LESSON 16: The STL String Class LESSON 17: STL Dynamic Array Classes LESSON 18: STL list and forward_list LESSON 19: STL Set Classes LESSON 20: STL Map Classes PART IV: MORE STL LESSON 21: Understanding Function Objects LESSON 22: C++11 Lambda Expressions LESSON 23: STL Algorithms LESSON 24: Adaptive Containers: Stack and..…”

Acceso restringido con credenciales UPSA