Tabla de Contenidos: “…Monitoring MongoDB -- Monitoring Memory Usage -- Introduction to Computer Memory -- Tracking Memory Usage -- Tracking Page Faults -- Minimizing Btree Misses -- IO Wait -- Tracking Background Flush Averages -- Calculating the Working Set -- Some Working Set Examples -- Tracking Performance -- Tracking Free Space -- Monitoring Replication -- Chapter 22. …”

Tabla de Contenidos: “…Abstimmverfahren unter Klassifikatoren -- Bagging und Pasting -- Bagging und Pasting in Scikit-Learn -- Out-of-Bag-Evaluation -- Zufällige Patches und Subräume -- Random Forests -- Extra-Trees -- Wichtigkeit von Merkmalen -- Boosting -- AdaBoost -- Gradient Boosting -- Stacking -- Übungen -- Kapitel 8: Dimensionsreduktion -- Der Fluch der Dimensionalität -- Die wichtigsten Ansätze zur Dimensionsreduktion -- Projektion -- Manifold Learning -- Hauptkomponentenzerlegung (PCA) -- Erhalten der Varianz -- Hauptkomponenten -- Die Projektion auf d Dimensionen -- Verwenden von Scikit-Learn -- Der Anteil erklärter Varianz -- Auswählen der richtigen Anzahl Dimensionen -- PCA als Komprimierungsverfahren -- Randomisierte PCA -- Inkrementelle PCA -- Kernel-PCA -- Auswahl eines Kernels und Optimierung der Hyperparameter -- LLE -- Weitere Techniken zur Dimensionsreduktion -- Übungen -- Kapitel 9: Techniken des unüberwachten Lernens -- Clustering -- K-Means -- Grenzen von K-Means -- Bildsegmentierung per Clustering -- Vorverarbeitung per Clustering -- Clustering für teilüberwachtes Lernen einsetzen -- DBSCAN -- Andere Clustering-Algorithmen -- Gaußsche Mischverteilung -- Anomalieerkennung mit gaußschen Mischverteilungsmodellen -- Die Anzahl an Clustern auswählen -- Bayessche gaußsche Mischverteilungsmodelle -- Andere Algorithmen zur Anomalie- und Novelty-Erkennung -- Übungen -- Teil II: Neuronale Netze und Deep Learning -- Kapitel 10: Einführung in künstliche neuronale Netze mit Keras -- Von biologischen zu künstlichen Neuronen -- Biologische Neuronen -- Logische Berechnungen mit Neuronen -- Das Perzeptron -- Mehrschichtiges Perzeptron und Backpropagation -- Regressions-MLPs -- Klassifikations-MLPs -- MLPs mit Keras implementieren -- TensorFlow 2 installieren -- Einen Bildklassifikator mit der Sequential API erstellen…”

Tabla de Contenidos: “…EXTRACTIVE SYSTEMS -- CHAPTER 7 FLOW RATE MONITORS -- REFERENCE METHODS FOR VOLUMETRIC FLOW -- Type-S Pitot Tube (Method 2) -- Three-Dimensional Pitot Tubes (Method 2F) -- Yaw-Nulling (Method 2G) -- Wall-Effect Measurements (Method 2H and CTM-041) -- Vane Anemometers -- Tracer Gas Dilution -- CONTINUOUS FLOW MONITORING METHODS -- Single Type-S Pitot Tube -- Averaging Differential Pressure Probes -- Thermal Mass Flowmeters -- Ultrasonic Flow Monitors -- Time-of-Flight Methods -- OTHER METHODS -- The Stack Venturi -- Physical Models -- Computational Fluid Dynamics -- Determination of Flow Rates from Engineering Calculations -- MONITORING FLOW IN PIPES -- CALIBRATING FLOW MONITORING SYSTEMS -- The "Pre-RATA" and the K Factor -- Problems in Calibrating Against a Reference Method -- PERFORMANCE SPECIFICATIONS FOR FLOW MONITOR CERTIFICATION -- Part 60 Appendix B Performance Specification 6 -- Part 75 Appendix A Specifications. -- Environment Canada Specifications -- EN-ISO 16911 Specifications -- FLOW MONITOR QUALITY ASSURANCE -- Daily Calibration Checks -- Daily Interference Checks -- Quarterly Leak Checks -- Flow-to-Load Ratio Test -- Semiannual/Annual RATA -- CHAPTER 8 OPACITY MONITORS -- BASIC COMPONENTS OF TRANSMISSOMETERS -- DESIGN SPECIFICATIONS AND MANUFACTURER'S PERFORMANCE SPECIFICATIONS FOR OPACITY MONITORS -- Opacity Monitoring Specific Design Criteria -- Manufacturers' Performance Specification Tests…”

Tabla de Contenidos: “…The Importance of Planning -- Resolving Overloads -- Automated Planning -- Planning Example -- Characteristics of a Good Production Plan -- Managing Inventory Targets and Constraints -- Summary -- 22 Workforce Engagement -- Selling the Idea -- Designing the New Process -- Executing the New Process -- 23 Changeover Reduction - SMED -- SMED and Its Origins -- SMED Concepts -- Process Industry Changeovers -- Automotive Fluids Packaging -- Diaper Manufacturing -- SMED Beyond Product Changes -- A Non-Manufacturing Example -- SMED Applied to Blue Lakes Packaging -- Summary -- 24 Production Stability -- Total Productive Maintenance -- TPM Relevance in Process Industries -- TPM Saves Money -- Overall Equipment Effectiveness (OEE) -- Availability -- Performance -- Quality -- Calculation of OEE -- VSM Data Boxes: OEE -- Non-Standard OEE Metrics -- Summary -- 25 Cellular Manufacturing -- Typical Process Plant Equipment Configurations -- Cellular Manufacturing Applied to Process Lines -- Synthetic Sheet Manufacturing Example -- Virtual Cell Implementation in a Synthetic Rubber Production Facility -- Would Cellular Flow Apply to the Salad Dressing Operation? …”

Tabla de Contenidos: “…Molecular Shape Is Related to Molecular Function -- Hydrogen Ions in Solution Can Alter Molecular Shape -- Protein Interactions -- Proteins Are Selective about the Molecules They Bind -- Protein‐Binding Reactions Are Reversible -- Binding Reactions Obey the Law of Mass Action -- The Dissociation Constant Indicates Affinity -- Multiple Factors Alter Protein Binding -- The Body Regulates the Amount of Protein in Cells -- Reaction Rate Can Reach a Maximum -- CHAPTER SUMMARY -- REVIEW QUESTIONS -- Chapter 3: Compartmentation: Cells and Tissues -- RUNNING PROBLEM Pap Tests Save Lives -- Functional Compartments of the Body -- The Lumens of Some Organs Are Outside the Body -- Functionally, the Body Has Three Fluid Compartments -- Biological Membranes -- The Cell Membrane Separates Cell from Environment -- Membranes Are Mostly Lipid and Protein -- Membrane Lipids Create a Hydrophobic Barrier -- Membrane Proteins May Be Loosely or Tightly Bound to the Membrane -- Membrane Carbohydrates Attach to Both Lipids and Proteins -- Intracellular Compartments -- Cells Are Divided into Compartments -- The Cytoplasm Includes Cytosol, Inclusions, Fibers, and Organelles -- Inclusions Are in Direct Contact with the Cytosol -- Cytoplasmic Protein Fibers Come in Three Sizes -- Microtubules Form Centrioles, Cilia, and Flagella -- Emerging Concepts Single Cilia Are Sensors -- The Cytoskeleton Is a Changeable Scaffold -- Motor Proteins Create Movement -- Organelles Create Compartments for Specialized Functions -- The Nucleus Is the Cell's Control Center -- Tissues of the Body -- Extracellular Matrix Has Many Functions -- Cell Junctions Hold Cells Together to Form Tissues -- Epithelia Provide Protection and Regulate Exchange -- Connective Tissues Provide Support and Barriers -- Muscle and Neural Tissues Are Excitable -- Tissue Remodeling -- Apoptosis Is a Tidy Form of Cell Death…”

Tabla de Contenidos: “…4 Common Causes of Maternal Cardiac Arrest -- 4.1 Introduction -- 4.2 Learning Objectives -- 4.3 BAACC TO LIFE: Bleeding -- 4.4 BAACC TO LIFE: Anesthesia -- 4.5 BAACC TO LIFE: Amniotic Fluid Embolism -- 4.6 BAACC TO LIFE: Cardiovascular -- 4.7 BAACC TO LIFE: Peripartum Cardiomyopathy -- 4.8 BAACC to LIFE: Clot/Cerebrovascular Pulmonary Embolism -- 4.9 BAACC TO LIFE: Trauma -- 4.10 BAACC TO LIFE: Overdose -- 4.11 BAACC TO LIFE: Acute Lung Injury/Acute Respiratory Distress -- 4.12 BAACC TO LIFE: Ions (Glucose, K+) -- 4.13 BAACC TO LIFE: Fever -- 4.14 BAACC TO LIFE: Emergency Hypertension/Eclampsia -- Chapter 4. …”

Tabla de Contenidos: “…8.4 Trident 操作 -- 8.4.1 函数 -- 8.4.2 Map函数and FlatMap函数 -- 8.4.3 peek 函数 -- 8.4.4 滤器 -- 8.4.5 窗口操作 -- 8.4.6 合操作 -- 8.4.7 分组操作 -- 8.4.8 合并和组合操作 -- 8.5 DRPC -- 8.6 小 牛刀 -- 用以绑定spout 和bolt 的拓扑构建器 -- 从事件流中 滤电 号码 -- 从事件流中提取 备信息 -- 8.7 小结 -- 第四 分 使用Spark 实现实时 算 -- 第9章 用Spark引擎 -- 9.1 Spark 概 -- Spark 框架和 度器 -- 9.2 Spark 的独特优势 -- 何时 免使用Spark -- 9.3 Spark 用例 -- 9.4 Spark架构--引擎内 的 模式 -- 9.5 Spark 的 用概念 -- RDD- 如其名 -- 9.6 Spark 2.x--数据框和数据 的出现 -- 9.7 小结 -- 第10章 用Spark操作 -- 10.1 Spark--封 和 API -- 10.2 RDD 用探索 -- 10.2.1 换 -- 10.2.2 动作 -- 10.3 共享变 --广播变 和累加器 -- 10.3.1 广播变 -- 10.3.2 累加器 -- 10.4 小结 -- 第11章 Spark Streaming -- 11.1 Spark Streaming 的概念 -- 11.2 Spark Streaming 的简介和体系结构 -- 11.3 Spark Streaming 的封 结构 -- 11.3.1 Spark Streaming API -- 11.3.2 Spark Streaming操作 -- 11.4 接 Kafka 和 Spark Streaming -- 11.5 小结 -- 第五 分 使用Flink实现实时分析 -- 第12章 用Apache Flink -- 12.1 Flink 体系结构和执 引擎 -- 12.2 Flink 的基本组件和 程 -- 12.3 将源流 成到 Flink -- 12.3.1 和Apache Kafka 成 -- 12.3.2 和RabbitMQ 成 -- 12.4 Flink 处理和 算 -- 12.4.1 Datastream API -- 12.4.2 DataSet API -- 12.5 Flink 持久化 -- 和Cassandra 成 -- 12.6 Flink CEP -- 12.7 Pattern API -- 12.7.1 检测模式 -- 12.7.2 模式 择 -- 12.7.3 示例 -- 12.8 Gelly -- Gelly API -- 12.9 小 牛刀 -- 12.10 小结 -- 第六 分 综合应用 -- 第13章 用例研究 -- 13.1 概 -- 13.2 数据建模 -- 13.3 工具和框架 -- 13.4 建立基础 施 -- 13.5 实现用例 -- 13.5.1 构建数据模拟器 -- 13.5.2 Hazelcast 加 器 -- 13.5.3 构建Storm拓扑 -- 13.6 用例 -- 加 Hazelcast -- 13.7 小结…”

“…Newly, innovative nanometer-sized particles have been dispersed in the base fluid in heat transfer fluids. The fluids containing the solid nanometer-sized particle dispersion are called ""nanofluids."" …”

“…En definitiva, su apacible vida «fluye tranquilamente, sin hacer apenas ruido». Aún así, un día Standish siente el súbito impulso de salir en busca de la aventura y se embarca en el Arabella. …”

“…El ciclo del agua marca los tiempos de la vida en todo el planeta. Fluye formando nubes, lluvias, nieves, ríos o mares y en cada lugar crea paisajes y ecosistemas donde se asientan los seres vivos más variados y sorprendentes…”