Tabla de Contenidos: “…Champ d'application possible de la clause de sauvegarde spéciale par pays de l'OCDE et par p... -- I.5. Champ couvert par la clause de sauvegarde spéciale pour l'agriculture 139 -- I.6. …”

Tabla de Contenidos: “…-- 7.1.2 Traf c and Performance Measures -- 7.1.3 Characterizing Traf c -- 7.1.4 Average Delay in a Single Link System -- 7.1.5 Nonstationarity of Traf c -- 7.2 Applications' View -- 7.2.1 TCP Throughput and Possible Bottlenecks -- 7.2.2 Bandwidth-Delay Product -- 7.2.3 Router Buffer Size -- 7.3 Traf c Engineering: An Architectural Framework -- 7.4 Traf c Engineering: A Four-Node Illustration -- 7.4.1 Network Flow Optimization -- 7.4.2 Shortest Path Routing and Network Flow -- 7.5 IGP Metric (Link Weight) Determination Problem for the Load Balancing Objective: Preliminary Discussion -- 7.6 Determining IGP Link Weights via duality of MCNF Problems -- 7.6.1 Illustration of Duality Through a Three-Node Network for Minimum Cost Routing -- 7.6.2 Minimum Cost Routing, Duality, and Link Weights -- 7.6.3 Illustration of Duality Through a Three-Node Network for the Load Balancing Objective -- 7.6.4 Load Balancing Problem, duality, and Link Weights -- 7.6.5 A Composite Objective Function, duality, and Link Weights -- 7.6.6 Minimization of Average Delay, duality, and Link Weights -- 7.7 Illustration of Link Weight Determination through Duality -- 7.7.1 Case Study: I -- 7.7.2 Case Study: II -- 7.8 Link Weight Determination: Large Networks -- 7.9 IP Traf c Engineering of PoP-to-DataCenter Networks -- 7.10 Summary -- Further Lookup -- Exercises -- 8 Multicast Routing -- 8.1 Multicast IP Addressing -- 8.2 Internet Group Management Protocol (IGMP) -- 8.3 Multicast Listener Discovery Protocol (MLD) -- 8.4 Reverse Path Forwarding (RPF) -- 8.5 Distance Vector Multicast Routing Protocol (DVMRP) -- 8.6 Multicast OSPF -- 8.7 Core Based Trees…”

Tabla de Contenidos: “…3.2.2 Indirect Method Sensors -- 3.2.3 Dynamometer -- 3.2.4 Accelerometer -- 3.2.5 Acoustic Emission Sensor -- 3.2.6 Current Sensors -- 3.3 Other Sensors -- 3.3.1 Temperature Sensors -- 3.3.2 Optical Sensors -- 3.4 Interaction of Sensors During Machining Operation -- 3.4.1 Milling Machining -- 3.4.2 Turning Machining -- 3.4.3 Drilling Machining Operation -- 3.5 Sensor Fusion Technique -- 3.6 Interaction of Internet of Things -- 3.6.1 Identification -- 3.6.2 Sensing -- 3.6.3 Communication -- 3.6.4 Computation -- 3.6.5 Services -- 3.6.6 Semantics -- 3.7 IoT Technologies in Manufacturing Process -- 3.7.1 IoT Challenges -- 3.7.2 IoT-Based Energy Monitoring System -- 3.8 Industrial Application -- 3.8.1 Integrated Structure -- 3.8.2 Monitoring the System Related to Service Based on Internet of Things -- 3.9 Decision Making Methods -- 3.9.1 Artificial Neural Network -- 3.9.2 Fuzzy Inference System -- 3.9.3 Support Vector Mechanism -- 3.9.4 Decision Trees and Random Forest -- 3.9.5 Convolutional Neural Network -- 3.10 Conclusion -- References -- Chapter 4 Application of Internet of Things (IoT) in the Automotive Industry -- 4.1 Introduction -- 4.2 Need For IoT in Automobile Field -- 4.3 Fault Diagnosis in Automobile -- 4.4 Automobile Security and Surveillance System in IoT-Based -- 4.5 A Vehicle Communications -- 4.6 The Smart Vehicle -- 4.7 Connected Vehicles -- 4.7.1 Vehicle-to-Vehicle (V2V) Communications -- 4.7.2 Vehicle-to-Infrastructure (V2I) Communications -- 4.7.3 Vehicle-to-Pedestrian (V2P) Communications -- 4.7.4 Vehicle to Network (V2N) Communication -- 4.7.5 Vehicle to Cloud (V2C) Communication -- 4.7.6 Vehicle to Device (V2D) Communication -- 4.7.7 Vehicle to Grid (V2G) Communications -- 4.8 Conclusion -- References -- Chapter 5 IoT for Food and Beverage Manufacturing -- 5.1 Introduction -- 5.2 The Influence of IoT in a Food Industry…”

Tabla de Contenidos: “…WiFi HaLow -- 7.5 5 G Advanced Security Model -- 7.5.1 Confidentiality -- 7.5.2 Integrity -- 7.5.3 Accessibility -- 7.5.4 Integrated Safety Rule -- 7.5.5 Visibility -- 7.6 Safety Challenges and Resolution of Three-Tiers Structure of 5G Networks -- 7.6.1 Heterogeneous Access Networks -- 7.6.1.1 Safety Challengers -- 7.6.1.2 Safety Resolutions -- 7.6.2 Backhaul Networks -- 7.6.2.1 Safety Challenges -- 7.6.2.2 Safety Resolutions -- 7.6.3 Core Network -- 7.6.3.1 Safety Challenges -- 7.6.3.2 Safety Resolutions -- 7.7 Conclusion and Future Research Directions -- References -- 8 Blockchain Assisted Secure Data Sharing in Intelligent Transportation Systems Gujkan Madaan, Avinash Kumar, and Bharat Bhushan -- 8.1 Introduction -- 8.2 Intelligent Transport System -- 8.2.1 ITS Overview -- 8.2.2 Issues in ITS -- 8.2.3 ITS Role in IoT -- 8.3 Blockchain Technology -- 8.3.1 Overview -- 8.3.2 Types of Blockchain -- 8.3.2.1 Public Blockchain -- 8.3.2.3 Private Blockchain -- 8.2.3.2 Federated Blockchain -- 8.3.3 Consensus Mechanism -- 8.3.3.1 Proof of Work -- 8.3.3.2 Proof of Stake -- 8.3.3.3 Delegated Proof of Stake -- 8.3.3.4 Practical Byzantine Fault Tolerance -- 8.3.3.5 Casper -- 8.3.3.6 Ripple -- 8.3.3.7 Proof of Activity -- 8.3.4 Cryptography -- 8.3.5 Data Management and Its Structure -- 8.4 Blockchain Assisted Intelligent Transportation System -- 8.4.1 Security and Privacy -- 8.4.2 Blockchain and Its Application foe Improving Security and Privacy -- 8.4.3 ITS Based on Blockchain -- 8.4.4 Recent Advancement -- 8.5 Future Research Perspectives -- 8.5.1 Electric Vehicle Recharging -- 8.5.2 Smart City Enabling and Smart Vehicle Security -- 8.5.3 Deferentially-Privacy Preserving Solutions -- 8.5.4 Distribution of Economic Profits and Incentives -- 8.6 Conclusion -- References -- 9 Utilization of Agro Waste for Energy Engineering Applications: Toward the Manufacturing of Batteries and Super Capacitors S.N. Kumar, S. Akhil, R.P. Nishita, O. Lijo Joseph, Aju Matthew George, and I Christina Jane -- 9.1 Introduction -- 9.2 Super Capacitors and Electrode Materials -- 9.2.1 Energy Density -- 9.3 Related Works in the Utilization of Agro Waste for Energy EngineeringApplications -- 9.4 Inferences from Work Related with Utilization of Coconut. …”

Tabla de Contenidos: “…Product packaging, installation and resource definition enhancements -- 2.1 Product packaging -- 2.2 Resource definition and installation changes -- 2.2.1 Shared SDFSRESL for different configurations and system definitions -- 2.2.2 DBRC keyword ignored in the system definition -- 2.2.3 Removing conditional link-edit for online change modules -- 2.2.4 ETO feature checking -- 2.2.5 Dynamic update of DBRC type 4 SVC -- 2.2.6 Replacing DFSMRCL0 by new resource cleanup services -- 2.3 IMS Application Menu -- 2.4 Installation verification program (IVP) enhancements -- 2.4.1 IVP sub-options with IMS Version 9 -- 2.4.2 The new "E" IVP steps -- 2.4.3 The new "O" IVP steps -- 2.4.4 The new "P" IVP steps -- 2.4.5 DFSIVPEX IVP utility to export and import the variables -- 2.4.6 IVP help text enhancements -- 2.4.7 JES3 improvements in IVP -- 2.4.8 IVP data set allocation enhancements -- 2.4.9 IVP enhanced dump formatter setup -- 2.4.10 Miscellaneous IVP changes -- 2.5 Syntax Checker enhancements -- 2.6 Documentation changes -- 2.6.1 IMS Information Center -- Chapter 3. …”

Tabla de Contenidos: “…8.3.2 Drawbacks of CMOS Power Amplifier -- 8.3.3 Design of CMOS Power Amplifier -- 8.3.3.1 Common Cascode PA Design -- 8.3.3.2 Self-Bias Cascode PA Design -- 8.3.3.3 Differential Cascode PA Design -- 8.3.3.4 Power Combining PA Design -- 8.4 Linearization Principles: Predistortion Technique, Phase-Correcting Feedback, Envelope Elimination and Restoration (EER), Cartesian Feedback -- 8.4.1 Predistortion Linearization Technique -- 8.4.2 Phase Correcting Feedback Technique -- 8.4.3 Cartesian Feedback Technique -- 8.4.4 Envelope Elimination and Restoration Technique -- Acknowledgement -- References -- Chapter 9 RF Oscillators -- 9.1 Introduction -- 9.2 Specifications -- 9.2.1 Frequency and Tuning -- 9.2.2 Tuning Constant and Linearity -- 9.2.3 Power Dissipation -- 9.2.4 Phase to Noise Ratio -- 9.2.5 Reciprocal Mixing -- 9.2.6 Signal to Noise Degradation of FM Signals Spurious Emission -- 9.2.7 Harmonics, I/Q Matching, Technology and Chip Area -- 9.3 LC Oscillators -- 9.3.1 Frequency, Tuning and Phase Noise Frequency Tuning Phase Noise to Carrier Ratio -- 9.3.2 Topologies -- 9.3.3 NMOS Only Cross-Coupled Structure -- 9.3.4 RC Oscillators -- 9.4 Design Examples -- 9.4.1 830 MHz Monolithic LC Oscillator Circuit Design Measurements -- 9.4.2 A 10 GHz I/Q RC Oscillator with Active Inductors -- 9.5 Conclusion -- Acknowledgement -- References -- Part III: RF Circuit Applications -- Chapter 10 mmWave Highly-Linear Broadband Power Amplifiers -- 10.1 Basics of PAs -- 10.1.1 Single Transistor Amplifier -- 10.1.2 Trade-Offs Among Power Amplifier Design Parameters (P0, PAE and Linearity) -- 10.1.3 Harmonic Terminations and Switching Amplifiers -- 10.1.4 Challenges at Millimeter-Wave -- 10.2 Millimeter Wave-Based AB Class PA -- 10.2.1 Efficiency at Power Back-Off -- 10.2.2 Sources of AM-PM Distortion -- 10.2.3 Distortion Cancellation Techniques…”

Tabla de Contenidos: “…9.2.2.2 Second leader stage -- 9.2.2.3 First leader stage -- 9.2.2.4 First leader learning -- 9.2.2.5 Second leader learning -- 9.2.2.6 Second leader decision -- 9.2.2.7 First leader decision -- 9.2.3 Control parameters in spider monkey optimization -- 9.3 Related work -- 9.3.1 Optimization problems -- 9.3.2 Deep learning -- 9.3.3 Data clustering -- 9.3.4 Big data problems -- 9.3.5 Networking problems -- 9.3.6 Cloud computing -- 9.3.7 Scheduling issues -- 9.3.8 Privacy problems -- 9.3.9 Image processing -- 9.3.10 Software engineering field -- 9.3.11 Other applications -- 9.4 Discussion -- 9.5 Conclusion and future works -- References -- 10 Marine predator's algorithm: a survey of recent applications -- 10.1 Introduction -- 10.2 Marine Predator's Algorithm -- 10.3 Related Works -- 10.3.1 Engineering Problems -- 10.3.2 Image Processing -- 10.3.3 Benchmark Function -- 10.3.4 Feature Selection -- 10.4 Discussion -- 10.5 Conclusion and Future Work -- References -- 11 Quantum approximate optimization algorithm: a review study and problems -- 11.1 Introduction -- 11.2 Methods -- 11.2.1 Fixed p algorithm -- 11.2.2 Concentration -- 11.2.3 The ring of disagrees -- 11.2.4 Maxcut on 3-regular graphs -- 11.2.5 Relation to the quantum adiabatic algorithm -- 11.2.6 A variant of the algorithm -- 11.3 Related works -- 11.4 Result -- 11.5 Discussion -- 11.6 Conclusion -- References -- 12 Crow search algorithm: a survey of novel optimizer and its recent applications -- 12.1 Introduction -- 12.2 Crow search algorithm -- 12.2.1 Inspiration -- 12.2.2 Continuous crow search algorithm -- 12.3 Related work -- 12.4 Conclusion and future work -- References -- 13 A review of Henry gas solubility optimization algorithm: a robust optimizer and applications -- 13.1 Introduction -- 13.2 Henry gas solubility optimization -- 13.2.1 Henry's law -- 13.2.2 Inspiration source…”

Tabla de Contenidos: “…8.3 Simplified Scripts for Frequently Used Analyses -- 8.4 Example: Difference of Biases -- 8.5 Sampling from the Prior Distribution in JAGS -- 8.6 Probability Distributions Available in JAGS -- 8.6.1 Defining new likelihood functions -- 8.7 Faster Sampling with Parallel Processing in RunJAGS -- 8.8 Tips for Expanding JAGS Models -- 8.9 Exercises -- Chapter 9: Hierarchical Models -- 9.1 A Single Coin from a Single Mint -- 9.1.1 Posterior via grid approximation -- 9.2 Multiple Coins from a Single Mint -- 9.2.1 Posterior via grid approximation -- 9.2.2 A realistic model with MCMC -- 9.2.3 Doing it with JAGS -- 9.2.4 Example: Therapeutic touch -- 9.3 Shrinkage in Hierarchical Models -- 9.4 Speeding up JAGS -- 9.5 Extending the Hierarchy: Subjects Within Categories -- 9.5.1 Example: Baseball batting abilities by position -- 9.6 Exercises -- Chapter 10: Model Comparison and Hierarchical Modeling -- 10.1 General Formula and the Bayes Factor -- 10.2 Example: Two Factories of Coins -- 10.2.1 Solution by formal analysis -- 10.2.2 Solution by grid approximation -- 10.3 Solution by MCMC -- 10.3.1 Nonhierarchical MCMC computation of each model'smarginal likelihood -- 10.3.1.1 Implementation with JAGS -- 10.3.2 Hierarchical MCMC computation of relative model probability -- 10.3.2.1 Using pseudo-priors to reduce autocorrelation -- 10.3.3 Models with different "noise" distributions in JAGS -- 10.4 Prediction: Model Averaging -- 10.5 Model Complexity Naturally Accounted for -- 10.5.1 Caveats regarding nested model comparison -- 10.6 Extreme Sensitivity to Prior Distribution -- 10.6.1 Priors of different models should be equally informed -- 10.7 Exercises -- Chapter 11: Null Hypothesis Significance Testing -- 11.1 Paved with Good Intentions -- 11.1.1 Definition of p value -- 11.1.2 With intention to fix N -- 11.1.3 With intention to fix z.…”

Tabla de Contenidos: “…ITSO lab hardware descriptions -- WebSphere Voice Server on Intel Servers -- Server 1: IBM ^ xSeries 206: Machine Type/Model 8482-2RU -- Server 2: IBM ^ xSeries 230: Machine Type/Model 8658-61Y -- Server 3: IBM ^ xSeries 345: Machine Type/Model 8670-M1X -- IVR 1: WebSphere Voice Response on IBM ^ pSeries -- IVR 2: Avaya Interactive Response (Avaya IR) V1.3 on a Sun Workstation -- IVR 3: Cisco Internet Operating System (IOS) on a Cisco AS5400 Series Universal Gateway…”

Tabla de Contenidos: “…5.7 Exercises -- References -- Chapter 6 System Reliability Analysis -- 6.1 Important Notes and Assumptions -- 6.2 Reliability Block Diagram Method -- 6.2.1 Series System -- 6.2.2 Parallel Systems -- 6.2.3 k-Out-of-n Redundant Systems -- 6.2.4 Standby Systems -- 6.2.5 Load-Sharing Systems -- 6.3 Complex System Evaluation Methods -- 6.3.1 Decomposition Method -- 6.3.2 Path Tracing Methods (Path Sets, Cut Sets) -- 6.4 Fault Tree and Success Tree Methods -- 6.4.1 Fault Tree Method -- 6.4.2 Evaluation of Fault Trees -- 6.4.2.1 Analysis of Logic Trees Using Boolean Algebra -- 6.4.2.2 Combinatorial (Truth Table) Technique for Evaluation of Logic Trees -- 6.4.2.3 Binary Decision Diagrams -- 6.4.3 Success Tree Method -- 6.5 Event Tree Method -- 6.5.1 Construction of Event Trees -- 6.5.2 Evaluation of Event Trees -- 6.6 Event Sequence Diagram Method -- 6.7 Failure Modes and Effects Analysis -- 6.7.1 Objectives of FMEA -- 6.7.2 FMEA/FMECA Procedure -- 6.7.3 FMEA Implementation -- 6.7.3.1 FMEA Using MIL-STD-1629A -- 6.7.3.2 FMEA Using SAE J1739 -- 6.7.4 FMECA Procedure: Criticality Analysis -- 6.7.4.1 Failure Probability Failure Rate Data Source -- 6.7.4.2 Failure Effect Probability β -- 6.7.4.3 Failure Rate λ[sub(p)] -- 6.7.4.4 Failure Mode Ratio α -- 6.7.4.5 Operating Time T -- 6.7.4.6 Failure Mode Criticality Number C[sub(m)] -- 6.7.4.7 Item Criticality Number C[sub(r)] -- 6.8 Exercises -- References -- Chapter 7 Reliability and Availability of Repairable Components and Systems -- 7.1 Definition of Repairable System and Types of Repairs -- 7.2 Variables of Interest: Availability, ROCOF, MTBF -- 7.3 Repairable System Data -- 7.4 Stochastic Point Processes (Counting Processes) -- 7.4.1 Homogeneous Poisson Process -- 7.4.2 Renewal Process -- 7.4.3 Nonhomogeneous Poisson Process -- 7.4.4 Generalized Renewal Process -- 7.5 Data Analysis for Point Processes…”

Tabla de Contenidos: “…8.3.8 Concentration of particles -- 8.3.9 Membrane material -- 8.4 VARIABLES AND APPLICATIONS OF THE MFI-UF -- 8.4.1 Plant profiling and water quality monitoring -- 8.4.2 Flux rate -- 8.4.3 Predicting rate of fouling of seawater RO systems -- 8.4.4 Comparing fouling indices -- 8.5 REFERENCES -- Part 3: Inorganic fouling and scaling -- Chapter 9: Inorganic Fouling: Characterization Tools and Mitigation -- 9.1 INTRODUCTION -- 9.2 MAIN COMPONENTS OF INORGANIC FOULING -- 9.2.1 Colloidal matter/particulate -- 9.2.2 Metals -- 9.2.3 Scaling -- 9.2.4 OTHER COMPONENTS -- 9.3 METHODS FOR INORGANIC FOULING IDENTIFICATION -- 9.4 METHODS FOR INORGANIC FOULING REMOVAL -- 9.5 REFERENCES -- Chapter 10: Assessing Scaling Potential with Induction Time and a Once-through Laboratory Scale RO System -- 10.1 INTRODUCTION -- 10.2 INDUCTION TIME MEASUREMENTS -- 10.2.1 Experimental setup -- 10.2.1.1 Glass reactor -- 10.2.1.3 pH meter -- 10.2.1.4 Peristaltic pump -- 10.2.1.5 Thermostat -- 10.2.2 Experimental procedure -- 10.2.2.1 Preparation of artificial brackish water -- 10.2.2.2 Induction time measurement -- 10.2.3 Calculation of induction time -- 10.2.4 Cleaning of the reactor -- 10.2.5 Example of application of induction time -- 10.3 ONCE THROUGH LAB-SCALE RO SYSTEM -- 10.3.1 Experimental set-up -- 10.3.2 Experimental protocol -- 10.3.3 Example of application -- 10.4 OUTLOOK AND FINAL COMMENTS -- 10.5 REFERENCES -- Part 4: Organic fouling -- Chapter 11: Practical Considerations of Using LC-OCD for Organic Matter Analysis in Seawater -- 11.1 INTRODUCTION -- 11.2 LC-OCD ANALYSIS -- 11.2.1 Instrumentation and chromatogram integration -- 11.2.2 Effect of salinity on organic characterization and calibration -- 11.2.3 LEVEL OF DETECTION -- 11.2.4 REPRODUCIBILITY OF LC-OCD -- 11.2.5 CHARACTERISATION OF ORGANIC MIXTURES -- 11.2.6 Applications…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Quantum Computing -- 1.1 Quantum Computation -- 1.2 Importance of Quantum Mechanics -- 1.3 Security Options in Quantum Mechanics -- 1.4 Quantum States and Qubits -- 1.5 Quantum Mechanics Interpretation -- 1.6 Quantum Mechanics Implementation -- 1.6.1 Photon Polarization Representation -- 1.7 Quantum Computation -- 1.7.1 Quantum Gates -- 1.8 Comparison of Quantum and Classical Computation -- 1.9 Quantum Cryptography -- 1.10 QKD -- 1.11 Conclusion -- References -- Chapter 2 Fundamentals of Quantum Computing and Significance of Innovation -- 2.1 Quantum Reckoning Mechanism -- 2.2 Significance of Quantum Computing -- 2.3 Security Opportunities in Quantum Computing -- 2.4 Quantum States of Qubit -- 2.5 Quantum Computing Analysis -- 2.6 Quantum Computing Development Mechanism -- 2.7 Representation of Photon Polarization -- 2.8 Theory of Quantum Computing -- 2.9 Quantum Logical Gates -- 2.9.1 I-Qubit GATE -- 2.9.2 Hadamard-GATE -- 2.9.3 NOT_GATE_QUANTUM or Pauli_X-GATE -- 2.9.3.1 Pauli_Y-GATE -- 2.9.3.2 Pauli_Z-GATE -- 2.9.3.3 Pauli_S-Gate -- 2.9.4 Two-Qubit GATE -- 2.9.5 Controlled NOT(C-NOT) -- 2.9.6 The Two-Qubits are Swapped Using SWAP_GATE -- 2.9.7 C-Z-GATE (Controlled Z-GATE) -- 2.9.8 C-P-GATE (Controlled-Phase-GATE) -- 2.9.9 Three-Qubit Quantum GATE -- 2.9.9.1 GATE: Toffoli Gate -- 2.9.10 F-C-S GATE (Fredkin Controlled Swap-GATE) -- 2.10 Quantum Computation and Classical Computation Comparison -- 2.11 Quantum Cryptography -- 2.12 Quantum Key Distribution - QKD -- 2.13 Conclusion -- References -- Chapter 3 Analysis of Design Quantum Multiplexer Using CSWAP and Controlled-R Gates -- 3.1 Introduction -- 3.2 Mathematical Background of Quantum Circuits -- 3.2.1 Hadamard Gate -- 3.2.2 CSWAP Gates -- 3.2.3 Controlled-R Gates…”

Tabla de Contenidos: “…6.2.2 Wrapper Method -- 6.2.2.1 Procedure -- 6.2.2.2 Advantages and Disadvantages -- 6.2.2.3 Forward Selection Algorithm -- 6.2.2.4 Backward Selection Algorithm -- 6.2.3 Embedded Method -- 6.2.3.1 Least Absolute Shrinkage and Selection Operator -- 6.2.3.2 Advantages -- 6.2.3.3 Disadvantages -- 6.3 Feature Extraction -- 6.3.1 Principal Component Analysis -- 6.3.1.1 Procedure -- 6.3.1.2 Implementation -- 6.3.1.3 Advantages -- 6.3.1.4 Disadvantages -- 6.3.2 Linear Discriminant Analysis -- 6.3.2.1 Concept -- 6.3.2.2 Implementation -- 6.3.2.3 Advantages -- 6.3.2.4 Disadvantages -- 6.4 Feature Learning -- 6.4.1 Supervised Learning -- 6.4.2 Unsupervised Learning -- 6.4.2.1 Procedure -- 6.4.2.2 Advantages -- 6.4.2.3 Disadvantages -- 6.4.3 Deep Learning -- 6.4.3.1 Neural Network Architecture -- 6.4.3.2 Training Process -- 6.4.3.3 Advantages -- 6.4.3.4 Disadvantages -- 6.4.4 Machine Learning and Deep Learning -- 6.5 Future Research and Development -- 6.6 Future Scope -- 6.7 Conclusion -- References -- Chapter 7 Fusion of Phase and Local Features for CBIR -- 7.1 Introduction -- 7.2 Overview of the Proposed System -- 7.3 Proposed Hybrid-Shape Descriptors -- 7.3.1 Global Feature Extraction Using ZMs -- 7.3.1.1 Recurrence Relation for Radial Polynomials Rpq(r) -- 7.3.1.2 Recurrence Relation for Trigonometric Functions -- 7.3.2 Local Feature Extraction Using Hough Transform -- 7.3.3 Features Dimension -- 7.3.4 Effectiveness of the Proposed Descriptors -- 7.4 Similarity Measurement -- 7.5 Experimental Study and Performance Evaluation -- 7.5.1 Precision and Recall (P - R) -- 7.5.2 Database Construction -- 7.5.3 Experimental Study -- 7.5.3.1 Evaluation of Image Retrieval Performance on Subject Databases -- 7.5.3.2 Evaluation of Image Retrieval Performance on Geometric and Photometric Transformed Databases -- 7.5.3.3 Evaluation of Scalability and Time Complexity…”

Tabla de Contenidos: “…4.2.5 Fifth-generation Languages -- 4.2.5.1 Advantages of Fifth-generation Languages -- 4.2.5.2 Drawbacks of Fifth-generation Languages -- 4.3 Programming Languages: Characteristics -- 4.4 Programming Languages: Categorization -- Summary -- Key Words -- Questions -- Chapter 5: Introduction to Programming -- 5.1 Introduction -- 5.2 Program Development Life Cycle -- 5.3 Programming Paradigms -- 5.4 Structured Programming -- 5.4.1 Procedure-o riented P rogramming -- 5.4.2 Modular Programming -- 5.5 Object-oriented Programming (OOP) -- 5.6 Features of Object-oriented Programming -- 5.6.1 Classes -- 5.6.2 Objects -- 5.6.3 Data Abstraction and Encapsulation -- 5.6.4 Inheritance -- 5.6.5 Polymorphism -- 5.6.6 Dynamic Binding -- 5.7 Merits of Object-oriented Programming -- Summary -- Key Words -- Questions -- Unit III: Basics of C++ Programming -- Chapter 6: C++ Programming -- 6.1 Introduction -- 6.2 Features -- 6.3 C++ Program Structure -- 6.3.1 A Simple C++ Program -- 6.3.2 Explanation -- 6.3.3 Compiling a C++ Program -- 6.3.4 Working of C++ Compilation -- 6.4 Tokens -- 6.5 Variables -- 6.5.1 Fundamental Data Types -- 6.5.2 User-defined Data Types -- 6.5.3 Derived Data Types -- 6.5.4 Declaration of Variables -- 6.5.5 Initialization of Variables -- 6.6 Constants -- 6.7 Operators -- 6.8 Expressions -- 6.9 I/O Operations -- 6.10 Control Structures -- 6.10.1 Conditional Structures [if and if-else] -- 6.10.2 Iteration Structures -- 6.10.3 Jump Statements -- Summary -- Key Words -- Questions -- Chapter 7: Functions in C++ -- 7.1 Introduction -- 7.2 A Simple Function -- 7.2.1 Examples -- 7.2.3 Declaring and Using Functions in Programs -- 7.3 The main() Function -- 7.4 Functions with No Arguments: Use of Void -- 7.5 Arguments Passed by Value and Passed by Reference -- 7.6 Default Parameters -- 7.7 Function Overloading -- 7.8 Inline Functions…”

“…Resultados: Se encontró una correlación negativa estadísticamente significativa entre la edad y el desempeño en el Test del Reloj (TR) tanto en la modalidad de la orden (TRO) como en la de la copia (TRC), con coeficientes de correlación de r=-0,60 y r=-0,57 respectivamente (p=0) para ambos. De igual manera aparecieron diferencias significativas entre el sexo y la ejecución en las pruebas TRO y TRC, con valores de p<0,00 y r=-0,43, respectivamente. …”
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“…RESULTS: In our population, the risk of suffering AMD increases with genotypes GG in CFH rs 1410996 polimorphysm (OR: 2,176), TT in ARMS 2 rs 1040924 polymorphism (OR: 6,369) , GG in HTRA1 -625 polymorphism (OR:3,324) and GG in VEGFR 2 rs 2071559 polymorphism (OR:2,010) Genotypes in CFH rs 1410996 , VEGF A rs 833061 and VEGFR 2 rs 2071559 are associated with differences in the response to intravitrea ranibizumab injections treatment (p<0,05). CONCLUSIONS: Allelic variants of CFH, ARMS 2, HTRA 1 and VEGFR 2 genes modify the risk of suffering AMD Response to intravitreal ranibizumab injections is moderately influenced by CFH, VEGF A and VEGFR 2 polymorphisms Age-related macular degeneration (ARMD) is one of the most important ocular diseases due to its prevalence and the visual loss it produces…”
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“…Doctor en Teología Moral por la Universidad P. Comillas, fue el primer moralista -y probablemente el único, hasta hoy- post Concilio Vaticano II que ofreció una visión global de la Teología Moral, desde una perspectiva latinoamericana (Manuales Moral de Discernimiento, San Pablo, 20025). …”
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“…On the other hand, this protein showed specific bactoagglutination for E. coli (CMB equal to 200 μg/ml), as well as insecticidal activity against Symmetrischema plaesiosema larvae (1000 ppm of CqLEC, P < 0.05). Complete amino acid analysis revealed that CqLEC is an acidic lectin (70.54% hydrophilic residues and 29.46% hydrophobic residues), with glutamic acid (33%) prevailing, with a molecular mass of 12.859 kDa. …”

“…“Los tiempos cambian y necesitamos actualizar y adaptar los contenidos a las nuevas realidades”, dice en el prólogo el P. Alejandro Moral Antón, prior general de los agustinos. …”

“…Una vez realizada esta fase, se llevó a cabo el estudio microscópico de la colección arqueológica de la Peña de Estebanvela, procedentes de sus seis niveles, fechados entre el 14.000 y el 11.000 B.P (niveles I y II atribuido al Magdaleniense Final, III y IV al Magdaleniense Superior y V y VI al Magdaleniense Medio). …”
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