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Frontiers of manufacturing science and measuring technology IV : selected, peer reviewed papers from the 2014 4th International Conference on Frontiers of Manufacturing Science and...

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Microwave and millimeter wave circuits and systems : emerging design, technologies, and applications

Table of Contents: “…1.1.7 MBF Model -- the Memoryless PA Behavioural Model of ChoiceAcknowledgements; References; 2 Artificial Neural Network in Microwave Cavity Filter Tuning; 2.1 Introduction; 2.2 Artificial Neural Networks Filter Tuning; 2.2.1 The Inverse Model of the Filter; 2.2.2 Sequential Method; 2.2.3 Parallel Method; 2.2.4 Discussion on the ANN's Input Data; 2.3 Practical Implementation -- Tuning Experiments; 2.3.1 Sequential Method; 2.3.2 Parallel Method; 2.4 Influence of the Filter Characteristic Domain on Algorithm Efficiency; 2.5 Robots in the Microwave Filter Tuning; 2.6 Conclusions; Acknowledgement…”
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Java Data Mining - Strategy, Standard, and Practice : a Practical Guide for architecture, design, and implementation. by Hornick, Mark F.

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Advanced Analytics with R and Tableau. by Stirrup, Jen

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Integral equations, boundary value problems and related problems : dedicated to Professor Chien-Ke Lu on the occasion of his 90th birthday : Yinchuan, Ningxia, China, 19-23 August...

Table of Contents: “…Dong, X.Y. Yao and C.F. Wang -- Anti-plane problem of two collinear cracks in a functionally graded coating-substance structure / S.H. …”
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The bitcoin big bang : how alternative currencies are about to change the world by Kelly, Brian, 1971-

Table of Contents: “…; 8 Building the Nautiluscoin Economy; Dynamic Proof-of-Stake; Nautiluscoin Gross Domestic Product Target; Algorithmic Monetary Policy.…”
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From complexity in the natural sciences to complexity in operation management systems by Briffaut, Jean-Pierre

Table of Contents: “…A complex system possesses a structure spanning several levels -- C.2.3. A complex system is capable of emerging behavior -- C.2.4. …”
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Pediatric incontinence : evaluation and clinical management

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Listed Volatility and Variance Derivatives : a Python-based Guide. by Hilpisch, Yves

Table of Contents: “…Chapter 2 Introduction to Python2.1 Python Basics; 2.1.1 Data Types; 2.1.2 Data Structures; 2.1.3 Control Structures; 2.1.4 Special Python Idioms; 2.2 NumPy; 2.3 matplotlib; 2.4 pandas; 2.4.1 pandas DataFrame class; 2.4.2 Input-Output Operations; 2.4.3 Financial Analytics Examples; 2.5 Conclusions; Chapter 3 Model-Free Replication of Variance; 3.1 Introduction; 3.2 Spanning with Options; 3.3 Log Contracts; 3.4 Static Replication of Realized Variance and Variance Swaps; 3.5 Constant Dollar Gamma Derivatives and Portfolios; 3.6 Practical Replication of Realized Variance.…”
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Digital wealth : an automatic way to invest successfully by Moore, Simon, 1978-

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6G Wireless Communications and Mobile Networking.

Table of Contents: “…Terminal-positioning and Other Novel Applications -- MASSIVE MIMO ANTENNA FOR MOBILE COMMUNICATIONS -- Massive MIMO Antenna Array Design and Synthesis -- Massive MIMO Antenna Decoupling Technology -- Large-Scale Antenna Beamforming Technology -- Null-notch Beamforming Algorithm Based on LMS Criterion -- DESIGN OF FEED NETWORK AND RF FRONT-END -- Feeding Technology of Base Station Antenna -- Design of RF Front-End for Large-Scale Active Antenna -- ANTENNA SELECTION TECHNOLOGY -- Antenna Selection Criteria and Classification -- Optimal Antenna Selection Algorithm -- Incremental Antenna Selection Algorithm -- Decreasing Antenna Selection Algorithm -- MEASUREMENT TECHNOLOGY OF MASSIVE MIMO ANTENNA -- OTA Testing Requirements for Massive MIMO Antenna -- Near-Field and Far-Field Measurement -- Far-Field Test -- Near-Field Test: -- OTA Testing Process -- SUMMARY -- CONSENT FOR PUBLICATION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES.…”
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Clinical simulation : operations, engineering and management

Table of Contents: “…; 3.5 The Systems Approach to Training; 3.6 Defining the Performance Requirement; 3.7 Cost Versus Value Added; 3.8 Operations Cost; 3.9 Standardization: What is it, and who Wants it?; 3.10 Patients as Training Conditions; 3.11 Equipment as Training Conditions; 3.12 Increase in Training System Cost; 3.13 You as the Leader-Manager; 3.14 Conclusion; Endnotes; Topic II What's In It For Me.…”
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Other geographies : the influences of Michael Watts

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Mechanical and Electronics Engineering : proceedings of the International Conference on ICMEE 2009, Chennai, India, 24-26 July 2009

Table of Contents: “…Shafie -- The investigation of input shaping with different polarities for anti-sway control of a gantry crane system / Mohd. …”
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Integration of swarm intelligence and artificial neural network

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Oracle SOA Suite 11g Performance Cookbook. by Brasier, Matthew

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Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

Table of Contents: “…DISCOVERING KNOWLEDGE IN DATA -- Contents -- Preface -- 1 An Introduction to Data Mining -- 1.1 What is Data Mining? -- 1.2 Wanted: Data Miners -- 1.3 The Need for Human Direction of Data Mining -- 1.4 The Cross-Industry Standard Practice for Data Mining -- 1.4.1 Crisp-DM: The Six Phases -- 1.5 Fallacies of Data Mining -- 1.6 What Tasks Can Data Mining Accomplish? …”
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Building Machine Learning Systems with Python. by Richert, Willi

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Cybersecurity Law, standards and regulations by Schreider, Tari

Table of Contents: “…Authors of original encryption algorithms never really thought that governments would want to have access to their en... -- In an effort to bring sanity to the uncontrolled growth of encryption regulations, two important laws have been introduced. …”
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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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