Tabla de Contenidos: “…Common Emitter Transistor Configurations -- Common Base Transistor Configuration -- Common Collector Transistor Configuration -- Basic Operation -- N-P-N Transistor Working -- The Current Components through the Transistor -- Base width Modulation and Early Effect -- 3.2 Transistor Characteristics -- Input Characteristics -- Output Characteristics -- Interpretation of Output Characteristics -- 3.3 Small Signal Low Frequency Amplifier, h-Parameters -- 3.3 Common Base Mode Transistor -- Input Characteristics of CB Transistor -- 3.4 High Frequency Linear Models -- Hybrid-p or Giacoletto Model -- Determination of Hybrid-p Parameters -- Validity of Hybrid-p Model -- 3.5 Field Effect Transistor -- 3.6 FET Characteristics -- Discussion on the Output Charcterristics of FET -- 3.7 N-channel FET as an Amplifier -- Comparison between Field Effect Transistor (FET) and Transistor (BJT) -- 3.8 Metal Oxide Semiconductor FET (MOSFET) -- Manufacturing Process of MOSFET -- 3.9 Unijunction Transistor -- UJT Circuit with Biasing Voltages -- UJT Equivalent Circuit -- Principle of Working of the UJT Device -- 3.10 Silicon Controlled Rectifier (Thyristor) -- SCR Conceptas Two Back-to-Back Connected Transistors -- Questions for Practice -- Points to Remember -- Chapter 4: Amplifiers Using Bipolar Junction Transistors or FET -- 4.1 BJT and FET More Often used in Amplifiers -- 4.2 Transistor Biasing Methods -- Fixed Bias Circuit -- Collector to Base Bias Circuit -- Potential Divider Bias or Self-Bias Circuit -- 4.3 Various Bias Compensation Circuits and their Working -- 4.4 Thermistor Compensation -- Thermal Runaway and Thermal Stability -- 4.5 Small Signal Low Frequency Amplifier -- Analys is of Amplifiers using H-Parameter Models -- Small Signal Low Frequency Model for a Common Emitter Tranisstor Amplifier -- High frequency equivalent circuit -- 4.6 Emitter Follower…”

Tabla de Contenidos: “…Intro -- Near Extensions and Alignment of Data in R -- Contents -- Preface -- Overview -- Structure -- 1 Variants 1-2 -- 1.1 The Whitney Extension Problem -- 1.2 Variants (1-2) -- 1.3 Variant 2 -- 1.4 Visual Object Recognition and an Equivalence Problem in R -- 1.5 Procrustes: The Rigid Alignment Problem -- 1.6 Non-rigid Alignment -- 2 Building -distortions: Slow Twists, Slides -- 2.1 c-distorted Diffeomorphisms -- 2.2 Slow Twists -- 2.3 Slides -- 2.4 Slow Twists: Action -- 2.5 Fast Twists -- 2.6 Iterated Slow Twists -- 2.7 Slides: Action -- 2.8 Slides at Different Distances -- 2.9 3D Motions -- 2.10 3D Slides -- 2.11 Slow Twists and Slides: Theorem 2.1 -- 2.12 Theorem 2.2 -- 3 Counterexample to Theorem 2.2 (part (1)) for card (E )&gt -- d -- 3.1 Theorem 2.2 (part (1)), Counterexample: k&gt -- d -- 3.2 Removing the Barrier k&gt -- d in Theorem 2.2 (part (1)) -- 4 Manifold Learning, Near-isometric Embeddings, Compressed Sensing, Johnson-Lindenstrauss and Some Applications Related to the near Whitney extension problem -- 4.1 Manifold and Deep Learning Via c-distorted Diffeomorphisms -- 4.2 Near Isometric Embeddings, Compressive Sensing, Johnson-Lindenstrauss and Applications Related to c-distorted Diffeomorphisms -- 4.3 Restricted Isometry -- 5 Clusters and Partitions -- 5.1 Clusters and Partitions -- 5.2 Similarity Kernels and Group Invariance -- 5.3 Continuum Limits of Shortest Paths Through Random Points and Shortest Path Clustering -- 5.3.1 Continuum Limits of Shortest Paths Through Random Points: The Observation -- 5.3.2 Continuum Limits of Shortest Paths Through Random Points: The Set Up -- 5.4 Theorem 5.6 -- 5.5 p-powerWeighted Shortest Path Distance and Longest-leg Path Distance -- 5.6 p-wspm,Well Separation Algorithm Fusion -- 5.7 Hierarchical Clustering in Rd -- 6 The Proof of Theorem 2.3 -- 6.1 Proof of Theorem 2.3 (part(2))…”

Tabla de Contenidos: “…Carbonara). -- Figure 4. 3D model of bronze dagger. (picture by V. Carbonara)…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Table of Contents -- List of Figures -- List of Tables -- Abbreviations and Acronyms -- Glossary -- Acknowledgments -- Dedication -- Online Materials Accompanying this Handbook -- Preface -- Part 1 - Introduction and Overview -- 1 Purpose and Scope -- 1.1 Purpose -- 1.2 Scope of Book and Target Audience -- 1.3 Terms for Laboratories and Pilot Plants -- 1.4 Distinctions between Laboratories and Pilot Plants -- 1.5 Organization of This Handbook -- 2 Managing Risk to Prevent Incidents -- 2.1 Some LAPP Characteristics -- 2.2 Safety in Laboratories and Pilot Plants -- 2.3 Where to Start with a Risk-based Approach in the LAPP -- 2.4 Gain Leadership Support to Implement Risk Based Process Safety -- 2.5 Laboratory Safety Management System Considerations -- 2.6 Resources for Risk Based Process Safety Management System -- 3 Leaks and Spills in the LAPP -- 3.1 Leaks of Hazardous Materials -- 3.2 Spills of Hazardous Materials -- Part 2 - Committing to Process Safety -- 4 LAPP Risk Management Concepts -- 4.1 Occupational Safety and Process Safety -- 4.2 Hierarchy of Controls -- 4.3 Inherently Safer Design (ISD) -- 4.4 Basic Risk Concepts -- 4.5 A Risk Management Program -- 4.6 Anatomy of an Incident -- 4.7 Preventive and Mitigative Safeguards -- 4.8 Applying a Risk-Based Approach in a LAPP -- 5 Process Safety Culture in the LAPP -- 5.1 RBPS Element 1: Process Safety Culture -- 5.2 Leaders' Responsibilities for Positive Safety Culture -- 5.3 Resources and Examples for Process Safety Culture -- 6 Standards for the LAPP -- 6.1 RBPS Element 2: Compliance with Standards -- 6.2 Risk Management Focus -- 6.3 Different Codes and Standards When Scaling Up from Laboratory to Pilot Plant -- 6.4 Jurisdictional Requirements -- 6.5 Resources for Compliance with Standards -- 7 Process Safety Competency and Training in the LAPP…”

Tabla de Contenidos: “…Using Power Analyses 4.1 Estimating the Effect Size 4.2 Using the One-Stop Tables and the R Code/Shiny Web app to Perform Power Analyses 4.2.1 Worked Example: Calculating F-equivalents and Power 4.3 Four Applications of Statistical Power Analysis 4.4 Calculating Power 4.5 Determining Sample Sizes 4.6 A Few Simple Approximations for Determining Sample Size Needed 4.7 Determining the Sensitivity of Studies 4.8 Determining Appropriate Decision Criteria 4.8.1 Finding a Sensible Alpha 4.9 Post-Hoc Power Analysis Should be Avoided 4.10 Summary 5. …”

Tabla de Contenidos: “…Approach -- Sample Output and Templates -- Step 3.12 Data Decay -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 3.13 Usability and Transactability -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 3.14 Other Relevant Data Quality Dimensions -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 3 Summary -- Step 4 Assess Business Impact -- Introduction to Step 4 -- Step 4.1 Anecdotes -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.2 Connect the Dots -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.3 Usage -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.4 Five Whys for Business Impact -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.5 Process Impact -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.6 Risk Analysis -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.7 Perception of Relevance and Trust -- Business Benefit and Context -- Approach -- Sample Output and Templates -- Step 4.8 Benefit vs. …”

Tabla de Contenidos: “…-- 3.8 Summary of Chapter 3 -- 4: Ethics and Religion -- 4.1 Bio- and Ecocentrism -- 4.2 Agency-centric Views -- 4.3 Consciousness without Valence -- 4.4 Rationality-centric Views -- 4.5 Abrahamic Religions and the Stewardship Tradition -- 4.6 Indian Religions and Ahimsa -- 4.7 Summary of Chapter 4.…”

Tabla de Contenidos: “…封 -- 异步社区电子书 -- 版权声明 -- 内容提 -- 序 -- 简介 -- 前 -- 作 简介 -- 审 简介 -- 目录 -- 第1 章Hadoop 分布式文件系统-导入和导出数据 -- 1.1 介绍 -- 1.2 使用Hadoop shell 命令导入和导出数据到HDFS -- 1.3 使用distcp 实现 群 数据复制 -- 1.4 使用Sqoop 从MySQL 数据库导入数据到HDFS -- 1.5 使用Sqoop 从HDFS 导出数据到MySQL -- 1.6 置Sqoop 以支持SQL Server -- 1.7 从HDFS 导出数据到MongoDB -- 1.8 从MongoDB 导入数据到HDFS -- 1.9 使用Pig 从HDFS 导出数据到MongoDB -- 1.10 在Greenplum 外 中使用HDFS -- 1.11 利用Flume 加 数据到HDFS 中 -- 第2 章HDFS -- 2.1 介绍 -- 2.2 写HDFS 数据 -- 2.3 使用LZO 压缩数据 -- 2.4 写序列化文件数据 -- 2.5 使用Avro 序列化数据 -- 2.6 使用Thrift 序列化数据 -- 2.7 使用Protocol Buffers 序列化数据 -- 2.8 置HDFS 备份因子 -- 2.9 置HDFS 块大小 -- 第3 章抽取和 换数据 -- 3.1 介绍 -- 3.2 使用MapReduce 将Apache 日志 换为TSV 格式 -- 3.3 使用Apache Pig 滤网络服务器日志中的爬 -- 3.4 使用Apache Pig 根据时 戳对网络服务器日志数据排序 -- 3.5 使用Apache Pig 对网络服务器日志 会 分析 -- 3.6 Python 扩展Apache Pig 的功 -- 3.7 使用MapReduce 及二次排序 算 -- 3.8 使用Hive 和Python 清洗、 换地理事件数据 -- 3.9 使用Python 和Hadoop Streaming 执 时 序列分析 -- 3.10 在MapReduce中利用MultipleOutputs 出多个文件 -- 3.11 创建用户 定义的Hadoop Writable 及InputFormat 取地理事件数据 -- 第4 章使用Hive、Pig 和MapReduce 处理常 的任务 -- 4.1 介绍 -- 4.2 使用Hive 将HDFS 中的网络日志数据映射为外 -- 4.3 使用Hive 动态地为网络日志查 结果创建Hive -- 4.4 利用Hive 字符串UDF 拼接网络日志数据的各个字段 -- 4.5 使用Hive 截取网络日志的IP 字段并确定其对应的国家 -- 4.6 使用MapReduce 对新 档案数据生成n-gram -- 4.7 MapReduce 使用分布式缓存查找新 档案数据中包含关 的 -- 4.8 使用Pig 加 一个 并执 包含GROUP BY 的SELECT操作 -- 第5 章 级 接操作 -- 5.1 介绍 -- 5.2 使用MapReduce 对数据 接 -- 5.3 使用Apache Pig 对数据 复制 接 -- 5.4 使用Apache Pig 对有序数据 归并 接 -- 5.5 使用Apache Pig 对倾斜数据 倾斜 接 -- 5.6 在Apache Hive 中 map 端 接对地理事件 分析 -- 5.7 在Apache Hive 优化的全外 接分析地理事件数据 -- 5.8 使用外 值存储 Redis 接数据 -- 第6 章大数据分析 -- 6.1 介绍 -- 6.2 使用MapReduce 和Combiner 统 网络日志数据 中的独立IP 数 -- 6.3 用Hive 日期UDF 对地理事件数据 中的时 日期 换与排序 -- 6.4 使用Hive 创建基于地理事件数据的每月死亡报告 -- 6.5 实现Hive 用户 定义UDF 用于确 地理事件数据的来源可 性 -- 6.6 使用Hive 的map/reduce 操作以及Python 标 最 的无暴力发生的时 区 -- 6.7 使用Pig 算Audioscrobbler 数据 中 术家之 的余弦相似度 -- 6.8 使用Pig 以及datafu 剔 Audioscrobbler 数据 中的离群值…”

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