Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Chapter 1 - Overview of microgrid -- 1.1 - History -- 1.2 - Current situation of microgrid outside China and analysis -- 1.2.1 - USA -- 1.2.2 - Japan -- 1.2.3 - European Union -- 1.3 - Analysis of current status in China -- 1.4 - Prospects -- Chapter 2 - Composition and classification of the microgrid -- 2.1 - Composition -- 2.2 - Structure -- 2.2.1 - Distribution network dispatch layer -- 2.2.2 - Centralized control layer -- 2.2.3 - Local control layer -- 2.3 - Operation modes -- 2.3.1 - Grid-connected operation -- 2.3.2 - Islanded operation -- 2.4 - Control modes -- 2.4.1 - Microgrid control modes -- 2.4.1.1 - Master-slave mode -- 2.4.1.2 - Peer-to-peer mode -- 2.4.1.3 - Combined mode -- 2.4.2 - Inverter control modes -- 2.4.2.1 - P/Q control -- 2.4.2.2 - U/f control -- 2.4.2.3 - Droop control -- 2.5 - Integration voltage class -- 2.6 - Classification -- 2.6.1 - By function demand -- 2.6.1.1 - Simple microgrid -- 2.6.1.2 - Multi-DG microgrid -- 2.6.1.3 - Utility microgrid -- 2.6.2 - By capacity -- 2.6.2.1 - Simple microgrid -- 2.6.2.2 - Corporate microgrid -- 2.6.2.3 - Feeder area microgrid -- 2.6.2.4 - Substation area microgrid -- 2.6.2.5 - Independent microgrid -- 2.6.3 - By AC/DC type -- 2.6.3.1 - DC microgrid -- 2.6.3.2 - AC microgrid -- 2.6.3.3 - AC/DC hybrid microgrid -- Chapter 3 - Microgrid and distributed generation -- 3.1 - Concepts and characteristics -- 3.2 - Photovoltaics -- 3.2.1 - Independent PV power system -- 3.2.2 - Grid-connected PV power system -- 3.3 - Wind power -- 3.3.1 - Independent wind power system -- 3.3.2 - Grid-connected wind power system -- 3.4 - Microturbine -- 3.4.1 - Components of microturbine -- 3.4.2 - High-speed AC generator -- 3.4.3 - Regenerator -- 3.4.4 - Converter system -- 3.4.5 - Control system -- 3.5 - Other types of DGs…”

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Tabla de Contenidos: “…AIoT in societal research and development -- 12 Beyond smart networking -- 12.1 Introduction -- 12.2 The purpose of networking and beyond -- 12.3 Benefits of networking and beyond -- 12.3.1 Access to new opportunities -- 12.3.2 Personal and professional growth -- 12.3.3 Better communication and social skills -- 12.3.4 More visibility and credibility -- 12.3.5 Positive effects on society -- 12.4 Different kinds of networking -- 12.4.1 Networking in person -- 12.4.2 Online networking -- 12.4.3 Professional networking organizations -- 12.4.4 Informal networking -- 12.4.5 Networking through social media -- 12.5 The 3P's of networking -- 12.5.1 Presence -- 12.5.2 Professionalism -- 12.5.3 Be persistent -- 12.6 Four important steps to making networking work well -- 12.6.1 Figure out what you want -- 12.6.2 Build your network -- 12.6.3 Get involved and talk to people -- 12.6.4 Keep and grow your network -- 12.7 Several things that make up a network -- 12.7.1 Interdependence -- 12.7.2 Connectivity -- 12.7.3 Diversity -- 12.7.4 Trust -- 12.7.5 Influence -- 12.7.6 Emergence -- 12.7.7 Resilience -- 12.8 Some tips for building strong networking skills -- 12.8.1 Write down your goals and objectives -- 12.8.2 Build your network -- 12.8.3 Get involved and talk to people -- 12.8.4 Work on your communication skills -- 12.8.5 Build your personal brand -- 12.8.6 Stay up to date -- 12.9 How to develop your networking and beyond? …”

Tabla de Contenidos: “…Cover -- Contents -- Preface -- Chapter 1: Integrated wastewater management for reuse in agriculture -- 1.1 INTRODUCTION -- 1.1.1 Wastewater and agriculture -- 1.1.1.1 Increasing water scarcity and stress -- 1.1.1.2 Population growth -- 1.1.1.3 Wastewater as a resource -- 1.1.2 The end-of-pipe paradigm for wastewater discharge -- 1.1.2.1 Global wastewater production, treatment, reuse, and discharge -- 1.1.2.2 Water resources and wastewater discharges -- 1.1.2.3 Global discharge of nitrogen and phosphorus -- 1.1.2.4 Energy use in mechanized wastewater treatment -- 1.1.3 The integrated wastewater management paradigm -- 1.1.3.1 Wastewater as a water resource -- 1.1.3.2 Semi-arid climates: irrigation water requirement 1500 mm/yr -- 1.1.3.3 Valorization of nutrients (N and P) in wastewater -- 1.1.3.4 Value as fertilizer, 2021 prices -- 1.1.3.5 Energy saved from fertilizer production -- 1.1.3.6 CO2,equiv emissions saved from not using synthetic fertilizers -- 1.1.3.7 Valorization of energy from anaerobic processes -- 1.2 WASTEWATER REUSE IN AGRICULTURE AND DEVELOPMENT OF END-OF-PIPE PARADIGM -- 1.2.1 Historical use of wastewater in agriculture: 3000 BCE-1915 CE -- 1.2.2 Decline of wastewater reuse with end-of-pipe paradigm: 1915-1990 -- 1.2.3 End-of-pipe paradigm with resource recovery in EU and North America: 2000-2020 -- 1.2.3.1 Secondary treatment with tertiary processes and resource recovery -- 1.2.3.2 Wastewater reuse in agriculture in the EU and the US -- 1.2.4 Wastewater treatment and resource recovery in China: 1980-2020 -- 1.2.4.1 Wastewater treatment and discharge of excess nitrogen to surface waters -- 1.2.4.2 Resource recovery in a Chinese 'concept wastewater treatment plant' -- 1.2.5 End-of-pipe paradigm in resource-limited cities/peri-urban areas: 2000-2020 -- 1.2.5.1 Indirect reuse of wastewater in agriculture…”

Tabla de Contenidos: “…Removal -- 7.1 Introduction -- 7.2 Carbon-Allotropes: Synthesis Methods, Applications and Future Perspectives -- 7.3 Reaffirmations of Heavy Metal Contaminations in Water and Their Toxic Effects -- 7.3.1 Copper -- 7.3.2 Zinc -- 7.3.3 Lead -- 7.3.4 Cadmium -- 7.3.5 Arsenic -- 7.4 Technology is Used to Treat Heavy Ions of Metal -- 7.4.1 Chemical Precipitation -- 7.4.2 Ion-Exchange -- 7.4.3 Adsorption -- 7.4.4 Membrane Filtration -- 7.4.5 Electrodialysis -- 7.4.6 Flotation -- 7.4.7 Electrochemical Treatment -- 7.4.8 Electroflotation -- 7.4.9 Coagulation and Flocculation -- 7.5 Factors Influencing How Heavy Metal Ions Adhere to CNTs -- 7.5.1 pH -- 7.5.2 Ionic Strength -- 7.5.3 CNT Dosage -- 7.5.4 Contact Time -- 7.5.5 Temperature -- 7.5.6 Thermodynamic Variables -- 7.5.7 CNT Regeneration -- 7.5.8 Isotherm Equation -- 7.5.9 Current Issues and the Need for Additional Study -- 7.6 Conclusions -- Acknowledgments -- References -- Chapter 8 Carbon Allotropes in Phenolic Compounds Removal -- 8.1 Introduction -- 8.2 Carbon Materials in Phenol Removal -- 8.2.1 Activated Carbon -- 8.2.2 Graphene -- 8.2.3 Carbon Nanotubes -- 8.2.4 Graphene Oxide and Reduced Graphene Oxide -- 8.2.5 Graphitic Carbon Nitride -- 8.2.6 Carbon Materials in the Biodegradation of Phenols -- 8.3 Conclusions -- References -- Chapter 9 Carbon Allotropes in Carbon Dioxide Capturing -- 9.1 Introduction -- 9.1.1 Importance of Carbon Allotropes in Carbon Dioxide Capturing -- 9.2 Main Part -- 9.2.1 Polymer-Based Carbon Allotropes in Carbon Dioxide Capturing…”

Tabla de Contenidos: “…Cover -- Titel -- Impressum -- Inhaltsverzeichnis -- Zum Geleit -- Danksagung -- Inventar der Quellen des BLHA zur brandenburgischen Militärgeschichte 1914-1945 -- Benutzungshinweise -- Rep. 1 Oberpräsident der Provinz Brandenburg -- Rep. 1A Staatliche Verteilungsstelle für Groß- Berlin -- Rep. 1B Provinzial- Fleischstelle für die Provinz Brandenburg -- Rep. 2A Regierung Potsdam I Hb -- Rep. 2A Regierung Potsdam I HG -- Rep. 2A Regierung Potsdam I Kat -- Rep. 2A Regierung Potsdam I Kom -- Rep. 2A Regierung Potsdam I LW -- Rep. 2A Regierung Potsdam I Med -- Rep. 2A Regierung Potsdam I P -- Rep. 2A Regierung Potsdam I Pol -- Rep. 2A Regierung Potsdam I RHK -- Rep. 2A Regierung Potsdam I S -- Rep. 2A Regierung Potsdam I SW -- Rep. 2A Regierung Potsdam I V -- Rep. 2A Regierung Potsdam II Gen -- Rep. 2A Regierung Potsdam II J -- Rep. 2A Regierung Potsdam II OH -- Rep. 2A Regierung Potsdam II Pdm -- Rep. 2A Regierung Potsdam II T -- Rep. 2A Regierung Potsdam II Tp -- Rep. 2A Regierung Potsdam III D -- Rep. 2A Regierung Potsdam III F -- Rep. 2A Regierung Potsdam Karten -- Rep. 3B Regierung Frankfurt (Oder) I E -- Rep. 3B Regierung Frankfurt (Oder) I Hb -- Rep. 3B Regierung Frankfurt (Oder) I HG -- Rep. 3B Regierung Frankfurt (Oder) I Kom -- Rep. 3B Regierung Frankfurt (Oder) I KR -- Rep. 3B Regierung Frankfurt (Oder) I Med -- Rep. 3B Regierung Frankfurt (Oder) I Mil -- Rep. 3B Regierung Frankfurt (Oder) I Pol -- Rep. 3B Regierung Frankfurt (Oder) I Präs. -- Rep. 3B Regierung Frankfurt (Oder) I S -- Rep. 3B Regierung Frankfurt (Oder) I SW -- Rep. 3B Regierung Frankfurt (Oder) I V -- Rep. 3B Regierung Frankfurt (Oder) Abt. …”

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Tabla de Contenidos: “…ITITO and ITPM general overview -- 2.1 Product overview and Web clusters -- 2.1.1 Web clusters -- 2.2 Architecture overview and terminology -- 2.2.1 Terminology -- 2.2.2 Architecture -- 2.3 Integration with other IBM products -- 2.3.1 Tivoli Management Agents -- 2.3.2 IBM Tivoli Configuration Manager -- 2.3.3 zSeries Linux -- 2.3.4 iSeriesTM Linux -- 2.3.5 pSeries -- Part 2 Preparation and installation -- Chapter 3. …”

Tabla de Contenidos: “…8.4.3 Setting of the Temperature Sensor -- 8.4.4 Accuracy -- References -- 9 SoC Estimation of a Battery -- 9.1 Different Understandings of the SoC Definition -- 9.1.1 Difference on the Understanding of SoC -- 9.1.2 Difference and Relation Between SoC and SoP as Well as SoE -- 9.2 Classical Estimation Methods -- 9.2.1 Coulomb Counting Method -- 9.2.2 Open Circuit Voltage Method -- 9.2.3 A Compromised Method -- 9.2.4 Estimation Methods Not Applicable for the Lithium-Ion Battery -- 9.3 Difficulty in an SoC Estimation -- 9.3.1 Difficulty in an Estimation Resulting from Inaccurate Battery State Monitoring -- 9.3.2 Difficulty in an Estimation Resulting from Battery Difference -- 9.3.3 Difficulty in an Estimation Resulting from an Uncertain Future Working Condition -- 9.3.4 Difficulty in an Estimation Resulting from an Uncertain Battery Usage History -- 9.4 Actual Problems to Be Considered During an SoC Estimation -- 9.4.1 Safety of the Electric Vehicle -- 9.4.2 Feasibility -- 9.4.3 Actual Requirements of Drivers -- 9.5 Estimation Method Based on the Battery Model and the Extended Kalman Filter -- 9.5.1 Common Complicated Estimation Method -- 9.5.2 Advantages of a Kalman Filter in an SoC Estimation -- 9.5.3 Combination of an EKF and a Lithium-Ion Battery Model -- 9.5.4 Implementation Rule of the EKF Algorithm -- 9.5.5 Experimental Verification -- 9.6 Error Spectrum of the SoC Estimation Based on the EKF -- 9.6.1 Estimation Error Caused by the Inaccurate Battery Model -- 9.6.2 Estimation Error Resulting from a Measurement Error of the Sensor -- 9.6.3 Factors Affecting SoC Estimation Accuracy -- References -- 10 Charge Control -- 10.1 Introduction -- 10.2 Charging Power Categories -- 10.3 Charge Control Methods -- 10.3.1 Semi-constant Current -- 10.3.2 Constant Current (CC) -- 10.3.3 Constant Voltage (CV) -- 10.3.4 Constant Power (CP)…”

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? …”