Tabla de Contenidos: “…JAVIER MELGOSA Proyecciones del turismo rural en Europa KLAUS EHRLICH Evolución del alojamiento de turismo rural en España. …”
Enlace del recurso

“…In the second part, we analyse the relationship between rural producers and large companies and sectors of the agro-industrial complex. …”

“…However, these forces, sometimes labeled 'animal spirits' or 'irrational exuberance, have remained elusive - until now. …”

“…Next, we will see how to store data using linked lists, arrays, stacks, and queues. Then, we will learn how to implement different sorting algorithms, such as quick sort and heap sort. …”

“…The book starts with the fundamentals of the language, including expressions, variables, functions, and definitions and then covers the most common C++ features including Organizing data with Arrays Standard Algorithms and Containers Using Libraries within C++ programs Going beyond Arrays with Structures Using Pointers Manipulating data using Operators The second half of the book covers OOP, including Classes, Inheritance, Encapsulation, and more. …”

“…Additionally, we will explore the concept of arrays, including multidimensional arrays and utilizing the Split function to work with strings as arrays. …”

“…Devoted to economic problems of the agri-food sector and rural development, mainly in the international aspect…”

Tabla de Contenidos: “…Front Cover -- Wills' Mineral Processing Technology -- Copyright Page -- Contents -- Preface -- Acknowledgments -- 1 Introduction -- 1.1 Minerals -- 1.2 Abundance of Minerals -- 1.3 Deposits And Ores -- 1.4 Metallic and Nonmetallic Ores -- 1.5 The Need for Mineral Processing -- 1.6 Liberation -- 1.7 Concentration -- 1.8 Representing Mineral Processing Systems: The Flowsheet -- 1.9 Measures of Separation -- 1.9.1 Grade -- 1.9.2 Recovery -- 1.9.3 Grade-Recovery Relationship -- 1.9.4 A Measure of Technical Separation Efficiency -- 1.10 Economic Considerations -- 1.10.1 Contained Value -- 1.10.2 Processing Costs -- 1.10.3 Milling Costs -- 1.10.4 Tailings Reprocessing and Recycling -- 1.10.5 Net Smelter Return and Economic Efficiency -- 1.10.6 Case Study: Economics of Tin Processing -- 1.10.7 Case Study: Economics of Copper Processing -- 1.10.8 Economic Efficiency -- 1.11 Sustainability -- References -- 2 Ore Handling -- 2.1 Introduction -- 2.2 The Removal of Harmful Materials -- 2.3 Ore Transportation -- 2.4 Ore Storage -- 2.5 Feeding -- 2.6 Self-Heating of Sulfide Minerals -- References -- 3 Sampling, Control, and Mass Balancing -- 3.1 Introduction -- 3.2 Sampling -- 3.2.1 Sampling Basics: What the Metallurgist Needs to Know -- Composition Variance -- Distribution Variance -- Preparation Variance and Analysis Variance -- 3.2.2 Gy's Equation and Its Use to Estimate the Minimum Sample Size -- 3.2.3 Sampling Surveys -- Rules of Thumb -- 3.2.4 Sampling Equipment -- Probabilistic Samplers -- Non-Probabilistic Samplers -- 3.3 On-line Analysis -- 3.3.1 On-line Element Analysis -- On-stream Analysis -- On-belt Analysis -- 3.3.2 On-stream Mineral Phase Analysis -- 3.3.3 On-stream Ash Analysis -- 3.3.4 On-line Particle Size Analysis -- 3.3.5 Weighing the Ore -- 3.3.6 Mass Flowrate -- Magnetic Flowmeter -- Ultrasonic Flowmeters -- Array-based Flowmeters…”

Tabla de Contenidos: “…Case Study: A Field Programmable Gate Array Prototype for the Validation of Real-Time Algorithms -- 3. …”

Tabla de Contenidos: “…9.2.2 Converting PowerShell arrays to tables -- 9.3 Getting web data -- 9.3.1 API keys -- 9.4 Using external applications -- 9.4.1 Calling an external executable -- 9.4.2 Monitoring execution -- 9.4.3 Getting the output -- 9.4.4 Creating Start-Process wrapper function -- 9.5 Putting it all together -- Summary -- 10 Automation coding best practices -- 10.1 Defining the full automation -- 10.1.1 Structuring your automation -- 10.2 Converting a manual task to an automated one -- 10.3 Updating structured data -- 10.4 Using external tools -- 10.4.1 Finding installed applications -- 10.4.2 Call operators -- 10.5 Defining parameters -- 10.6 Making resumable automations -- 10.6.1 Determining code logic and functions -- 10.7 Waiting for automations -- 10.8 Think of the next person -- 10.8.1 Do not overcomplicate it -- 10.8.2 Comment, comment, comment -- 10.8.3 Include help and examples on all scripts and functions -- 10.8.4 Have a backup plan -- 10.9 Do not forget about the presentation -- Summary -- Part 3. -- 11 End-user scripts and forms -- 11.1 Script frontends -- 11.1.1 SharePoint trial tenant -- 11.2 Creating a request form -- 11.2.1 Gathering data -- 11.2.2 Creating a SharePoint form -- 11.3 Processing requests -- 11.3.1 Permissions -- 11.3.2 Monitoring for new requests -- 11.3.3 Processing the request -- 11.4 Running PowerShell script on end-user devices -- 11.4.1 Custom Git install -- 11.4.2 Running as system versus the user -- 11.4.3 Using Active Setup with PowerShell -- Summary -- 12 Sharing scripts among a team -- 12.1 Sharing a script -- 12.1.1 Creating a gist -- 12.1.2 Editing a gist -- 12.1.3 Sharing a gist -- 12.1.4 Executing a gist -- 12.2 Creating a shared module -- 12.2.1 Uploading the module to a GitHub repository -- 12.2.2 Giving access to the shared module -- 12.2.3 Installing the shared module -- 12.3 Updating a shared module…”

Tabla de Contenidos: “…21.4 Amortized Performance and Average Performance -- 21.5 Two Principles: Reducing Overhead and Managing Memory -- 21.6 Performance Tip: Use Numerical Libraries When Applicable -- 21.7 Performance Tip: Delete Large Structures You Don't Need -- 21.8 Performance Tip: Use Built-In Functions When Possible -- 21.9 Performance Tip: Avoid Superfluous Function Calls -- 21.10 Performance Tip: Avoid Creating Large New Objects -- 21.11 Further Reading -- 21.12 Glossary -- Part III Specialized or Advanced Topics -- Chapter 22 Computer Memory and Data Structures -- 22.1 Virtual Memory, the Stack, and the Heap -- 22.2 Example C Program -- 22.3 Data Types and Arrays in Memory -- 22.4 Structs -- 22.5 Pointers, the Stack, and the Heap -- 22.6 Key Data Structures -- 22.7 Further Reading -- 22.8 Glossary -- Chapter 23 Maximum Likelihood Estimation and Optimization -- 23.1 Maximum Likelihood Estimation -- 23.2 A Simple Example: Fitting a Line -- 23.3 Another Example: Logistic Regression -- 23.4 Optimization -- 23.5 Gradient Descent and Convex Optimization -- 23.6 Convex Optimization -- 23.7 Stochastic Gradient Descent -- 23.8 Further Reading -- 23.9 Glossary -- Chapter 24 Advanced Classifiers -- 24.1 A Note on Libraries -- 24.2 Basic Deep Learning -- 24.3 Convolutional Neural Networks -- 24.4 Different Types of Layers. …”

Tabla de Contenidos: “…Storage management in depth -- 4.1 DB2 UDB logical and physical database objects -- 4.1.1 Tables, indexes, and views -- 4.1.2 Table spaces -- 4.1.3 Table space containers -- 4.2 Working with SMS and DMS table spaces -- 4.2.1 SMS table spaces -- 4.2.2 File based DMS table spaces -- 4.2.3 Device based DMS table spaces -- 4.3 SAP data classes (TABARTs) -- 4.4 Space reclamation strategies -- 4.4.1 Offline table and index reorganization -- 4.4.2 Inplace table and index reorganization -- 4.4.3 Criteria for reorganization -- 4.4.4 High water mark -- 4.5 Best practices for intelligent storage subsystems -- 4.5.1 Database stored on single SSA/SCSI disks -- 4.5.2 Database stored on RAID-5 arrays -- 4.6 Moving tables and indexes using DB6CONV -- 4.6.1 Principle of moving tables and indexes using DB6CONV -- 4.6.2 Example: Moving table PCL2 to improve performance -- Chapter 5. …”

Tabla de Contenidos: “…-- 5.6 Relais schalten -- 5.7 Akkus zeitgesteuert laden. 5.8 Zeitsteuerung einer Kamera -- 5.9 Die Navigation in BlueJ -- Kapitel 6: Digitale Eingänge und Sensoren -- 6.1 Den Raspberry Pi über einen Taster steuern -- 6.2 Externe Prozesse über einen Taster steuern -- 6.3 Der Bewegungsmelder HC-SR501 -- 6.4 Licht und Musik über den Bewegungsmelder steuern -- 6.5 Abstandsmessung mit dem Ultraschallsensor HC-SR04 -- 6.6 Schmitt-Trigger für analoge Sensoren -- Kapitel 7: Swing lernen -- 7.1 Ein Fenster erzeugen -- 7.2 Swing-Komponenten -- 7.3 Auf Ereignisse reagieren und Text verarbeiten -- 7.4 Auf Ereignisse in einer Auswahlliste reagieren -- 7.5 Auf Ereignisse in Checkboxen und Radiobuttons reagieren -- 7.6 Auf Ereignisse in einer Liste reagieren -- 7.7 Noch etwas über Ereignisse -- Kapitel 8: Analoge Eingänge und Sensoren -- 8.1 Den MCP3002 an den Raspberry Pi anschließen -- 8.2 Temperaturmessung mit einem NTC-Widerstand -- 8.2.1 Kennlinie linearisieren -- 8.2.2 Der passende Verstärker -- 8.2.3 Die Schutzschaltung -- 8.3 LDR-Sensoren -- 8.4 Analoge Distanzsensoren -- Kapitel 9: Messwerte speichern -- 9.1 Auf Dateien und Verzeichnisse zugreifen -- 9.1.1 Dateisystem -- 9.1.2 In eine Datei schreiben -- 9.1.3 Aus einer Datei lesen -- 9.2 Listen, Mengen und assoziative Speicher -- 9.2.1 Mit Listen arbeiten -- 9.2.2 Mit Mengen arbeiten -- 9.2.3 Arbeiten mit assoziativem Speicher -- 9.3 Zeichenketten formatieren -- 9.4 Einen Datenlogger schreiben -- 9.5 Temperatur und Beleuchtungsstärke loggen -- Kapitel 10: Messwertgrafiken mit Swing -- 10.1 Das Prinzip der Komponentenentwicklung -- 10.2 Die Hilfsklasse ComputeDiagram -- 10.3 Die Swing-Komponente JDiagram -- 10.4 Diagramme in einer Swing-Anwendung anzeigen -- 10.5 Geloggte Messwerte anzeigen -- Kapitel 11: Threads und reguläre Ausdrücke -- 11.1 Threads implementieren -- 11.1.1 Threads anlegen und starten -- 11.1.2 Threads beenden -- 11.1.3 Daten teilen. 11.1.4 Auf das Ende warten -- 11.2 Reguläre Ausdrücke -- 11.2.1 Zeichenklassen -- 11.2.2 Vordefinierte Zeichenklassen -- 11.2.3 Häufigkeit -- Kapitel 12: Füllstandsanzeige für eine Zisterne -- 12.1 Die Schaltung -- 12.2 Der HTTP-Server stellt sich vor -- 12.3 Den Webserver bauen -- 12.3.1 Die Klasse RaspiHttpServer -- 12.3.2 Die Klasse HttpHandlerZisterne -- 12.3.3 Die Klasse Scheduler -- 12.3.4 Die Klasse Sensor -- 12.4 Der Client -- 12.4.1 Die Klasse HttpClient -- 12.4.2 Die Swing-Anwendung -- Kapitel 13: Eine Heizungssteuerung -- 13.1 Die Schaltung -- 13.2 Das Programmkonzept -- 13.2.1 Die Klasse RaspiHttpServer -- 13.2.2 Die Klasse HttpHandlerHeizung -- 13.2.3 Die Klasse Scheduler -- 13.2.4 Die Klasse Sensor -- 13.3 Der Client -- 13.3.1 Die Klasse HttpClient -- 13.3.2 Die Klasse JTempPanel -- 13.3.3 Die Klasse Gui -- Kapitel 14: Eine Zeitschaltuhr -- 14.1 Die Schaltung -- 14.2 Das Programmkonzept -- 14.2.1 Die Klasse RaspiHttpServer -- 14.2.2 Die Klasse SchedulerPort -- 14.2.3 Die Klasse HttpHandlerZeitschaltuhr -- 14.3 Der Client -- 14.3.1 Die Klasse HttpClient -- 14.3.2 Die Klasse ZeitschaltuhrProperties -- 14.3.3 Die Klasse GuiUtil -- 14.3.4 Die Klasse Gui -- 14.4 Einen Autostart für den Server einrichten -- 14.5 Hinweise zum Betrieb des Servers -- 14.6 Eine Desktop-Verknüpfung erstellen -- Kapitel 15: E-Mail-Alarm -- 15.1 Das Package raspi.mail -- 15.1.1 Die Klasse MailService -- 15.1.2 Die Klasse EMail -- 15.2 Das Projekt -- 15.2.1 Die Klasse AlarmProperties -- 15.3 Die Klasse AlarmService -- 15.4 Die Klasse Shutdown -- 15.5 Die Klasse Alarm -- 15.6 Autostart -- Kapitel 16: Der I2C-Bus -- 16.1 Den I2C-Bus einrichten -- 16.2 Den I2C-Bus mit Pi4j steuern -- 16.3 Eine Hilfsklasse für I2C-Geräte -- 16.4 Der Temperatursensor MCP9808 -- 16.5 Die Arbeitsweise des MCP9808 -- 16.6 Die Klasse MCP9808 -- 16.7 Den Sensor testen. 16.8 Den Sensor im Komparator-Modus testen -- Kapitel 17: Die serielle Schnittstelle RS232 -- 17.1 Die Schnittstelle konfigurieren -- 17.2 Mit Pi4J die serielle Schnittstelle steuern -- 17.3 Der Ultraschall-Entfernungssensor SRF02 -- 17.4 Die Klasse SRF02 -- 17.5 Entfernungsmessung -- 17.6 Kurze Distanzen -- 17.7 Die Sensoradresse ändern -- Kapitel 18: Textausgabe mit LCD-Displays -- 18.1 Der Befehlssatz des HD 44780 -- 18.1.1 Clear Display -- 18.1.2 Cursor Home -- 18.1.3 Entry Mode Set -- 18.1.4 Display On/Off Control -- 18.1.5 Cursor / Display Shift -- 18.1.6 Function Set -- 18.1.7 RAM Address Set und Data write -- 18.1.8 CG RAM Address Set -- 18.1.9 Und noch zwei Lesebefehle -- 18.2 Die Initialisierung des Displays -- 18.3 Das Programmkonzept -- 18.4 Die Klasse DisplayHD44780 -- 18.5 Die Klasse PinDirect -- 18.6 Das Display testen -- 18.6.1 Die Klasse Test8Bit -- 18.6.2 Die Klasse Test4Bit -- 18.6.3 Die Klasse TestDisplay -- 18.7 Das Display über den MCP23017 ansteuern -- 18.7.1 Die Klasse PinMCP23017 -- 18.7.2 Die Klasse Test8BitMCP23017 -- 18.7.3 Die Klasse Test4BitMCP23017 -- Anhang A: Java -- A.1 Der erste Programmcode -- A.2 Java-Konzepte -- A.2.1 Objekte -- A.2.2 Klassen und Instanzen -- A.2.3 Vererbung -- A.2.4 Interface -- A.2.5 Package -- A.3 Java-Grundlagen -- A.3.1 Variable -- A.3.2 Arrays -- A.3.3 Operatoren -- A.3.4 Programmfluss -- A.4 Java ist objektorientiert -- A.4.1 Klasse -- A.4.2 Variablen -- A.4.3 Methoden -- A.4.4 Konstruktoren -- A.4.5 this -- A.4.6 Zugriffsrechte -- A.4.7 static und final -- A.4.8 Anonyme innere Klassen -- A.4.9 ENUM-Datentypen -- A.4.10 Interfaces -- A.4.11 Vererbung -- A.4.12 Methoden überschreiben -- A.4.13 Super -- A.4.14 Überschreiben unterbinden -- A.4.15 Abstrakte Klassen und Methoden -- A.5 Wrapper-Klassen für numerische Datentypen -- A.6 Zeichenkette (String) -- A.7 Character -- A.8 Boolean. …”

Tabla de Contenidos: “…Merge Variables -- 4.2.3. Restructure Arrays -- 4.2.4. Reorder Variables -- 4.2.5. Dataflow Flattening -- 4.2.6. …”

Tabla de Contenidos: “…Object Discovery Agent -- 14.1 Developing the ODA -- 14.1.1 Setting up the development environment -- 14.1.2 Naming conventions for the ODA -- 14.2 Implementing the ODA -- 14.2.1 Extending the ODA base class -- 14.2.2 Obtaining the handle to the ODKUtility object -- 14.2.3 Initializing the configuration-property array -- 14.2.4 Initializing ODA metadata -- 14.2.5 Initializing the ODA startup -- 14.2.6 Choosing the ODA content protocol -- 14.2.7 Generating source nodes -- 14.2.8 Generating business object definitions…”

Tabla de Contenidos: “…7.1.1 Purpose of Battery Modeling -- 7.1.2 Battery Modeling Requirement of BMS -- 7.2 Common Battery Models and Their Deficiencies -- 7.2.1 Non-circuit Models -- 7.2.2 Equivalent Circuit Models -- 7.3 External Characteristics of the Li-Ion Power Battery and Their Analysis -- 7.3.1 Electromotive Force Characteristic of the Li-Ion Battery -- 7.3.2 Over-potential Characteristics of the Li-Ion Battery -- 7.4 A Power Battery Model Based on a Three-Order RC Network -- 7.4.1 Establishment of a New Power Battery Model -- 7.4.2 Estimation of Model Parameters -- 7.5 Model Parameterization and Its Online Identification -- 7.5.1 Offline Extension Method of Model Parameters -- 7.5.2 Online Identification Method of Model Parameters -- 7.6 Battery Cell Simulation Model -- 7.6.1 Realization of Battery Cell Simulation Model Based on Matlab/Simulink -- 7.6.2 Model Validation -- References -- Part III Functions of BMS -- 8 Battery Monitoring -- 8.1 Discussion on Real Time and Synchronization -- 8.1.1 Factors Causing Delay -- 8.1.2 Synchronization -- 8.1.3 Negative Impact of Non-real-time and Non-synchronous Problems -- 8.1.4 Proposal on Solution -- 8.2 Battery Voltage Monitoring -- 8.2.1 Voltage Monitoring Based on a Photocoupler Relay Switch Array (PhotoMOS) -- 8.2.2 Voltage Monitoring Based on a Differential Operational Amplifier -- 8.2.3 Voltage Monitoring Based on a Special Integrated Chip -- 8.2.4 Comparison of Various Voltage Monitoring Schemes -- 8.2.5 Significance of Accurate Voltage Monitoring for Effective Capacity Utilization of the Battery Pack -- 8.3 Battery Current Monitoring -- 8.3.1 Accuracy -- 8.3.2 Current Monitoring Based on Series Resistance -- 8.3.3 Current Monitoring Based on a Hall Sensor -- 8.3.4 A Compromised Method -- 8.4 Temperature Monitoring -- 8.4.1 Importance of Temperature Monitoring -- 8.4.2 Common Implementation Schemes…”

Tabla de Contenidos: “…Maternidad y sexualidad en la filmografía fordiana: la influencia de Barbara Curran -- 3. Los primeros retratos maternos…”

Tabla de Contenidos: “…2.3 Deep learning applications in brain cancer -- 2.3.1 Tumor grading -- 2.3.2 Survival analysis -- 2.3.3 Radiogenomics -- 2.3.3.1 1p/19q -- 2.3.3.2 Isocitrate dehydrogenase -- 2.3.3.3 6-methylguanine-DNA methyltransferase -- 2.3.4 Pseudoprogression -- 2.4 Deep learning applications in breast cancer -- 2.4.1 Increasing accuracy in breast cancer risk assessment -- 2.4.2 Reproducible breast density assessment for improved breast cancer risk prediction -- 2.4.3 Improving performance in breast cancer diagnosis -- 2.4.4 Enhancing efficacy in breast cancer clinical practice -- 2.5 Conclusion -- Acknowledgments -- References -- 3 Deep neural networks and advanced computer vision algorithms in the early diagnosis of skin diseases -- 3.1 Introduction and motivation for the early diagnosis of melanoma -- 3.2 Artificial intelligence and computer vision in melanoma diagnosis -- 3.3 Medical diagnostic procedures for screening of skin diseases -- 3.4 State-of-the-art survey on skin mole segmentation methods -- 3.4.1 Comparison of the state of the art -- 3.4.2 Summary -- 3.5 Improved local and global patterns detection algorithms by deep learning algorithms -- 3.6 Early classification of skin melanomas in dermoscopy -- 3.6.1 Diagnostic algorithms -- 3.6.2 Approaches to detect the diagnostic criteria -- 3.6.3 Approaches to directly classify skin conditions -- 3.6.3.1 Classifiers utilizing the convolutional neural networks as a feature extractor -- 3.6.3.2 Classifiers using end-to-end learning convolutional neural networks model training with transfer learning -- 3.6.3.3 Convolutional neural networks model training from scratch -- 3.6.3.4 Ensembles of convolutional neural networks models -- 3.7 Conclusions -- 3.8 How to speed up the classification process with field-programmable gate arrays? -- 3.9 Challenges and future directions -- 3.10 Teledermatology…”

“…Eis o cenário de pesquisa vislumbrado nesta obra para o estudo do rural na contemporaneidade…”

“…In the late colonial period, the rural area of Central Mexico was characterized by haciendas, by Spanish-mestic small towns and by indigenous communities, which were defined and composed ethnically according to Spanish law. …”