Note: Intro -- Supervisor's Foreword -- Parts of this doctoral thesis have been published in the following journal articles:Wang, W., Liu, S.W., Santosh, M., Wang, G.H., Bai, X., Guo, R.R., 2015a. Neoarchean intra-oceanic arc system in the Western Liaoning Province: Implications for Early Precambrian crustal evolution in the Eastern Block of the North China Craton. Earth-Science Reviews 150, 329-364. (Reproduced with permission)Wang, W., Liu, S.W., Santosh, M., et al. 2015b. Late Paleoproterozoic geodynamics of the North China Craton: -- Acknowledgements -- Contents -- About the Author -- 1 Introduction -- Abstract -- 1.1 Research Background -- 1.1.1 Archean Greenstone Belt and Crust-Mantle Interactions -- 1.1.2 Archean Plate Tectonics -- 1.2 Research Progress and Key Issues of Precambrian Geology of North China Craton -- 1.2.1 Summary of Precambrian Research of North China Craton -- 1.2.2 Key Scientific Issues -- 1.3 Objectives of this Thesis -- 1.4 Research Contents of this Thesis -- 1.5 Methodology -- 1.5.1 Whole-Rock Major Element Analyses -- 1.5.2 Whole-Rock Trace Element Analyses -- 1.5.3 Whole-Rock Rb-Sr Isotope Analyses -- 1.5.4 LA-ICPMS Zircon U-Pb Isotope Analyses -- 1.5.5 SHRIMP Zircon U-Pb Isotope Analyses -- 1.5.6 Zircon Lu-Hf Isotope Analyses -- References -- 2 Geological Background -- Abstract -- 2.1 Tectonic Framework of the Crystalline Basement in the North China Craton -- 2.2 Geological Background of the Western Liaoning-Northeastern Hebei Provinces -- 2.2.1 Precambrian Crystalline Basement -- 2.2.2 Yanliao Rift and Late Paleoproterozoic Magmatism -- 2.2.3 Mesoproterozoic Mafic Dykes -- References -- 3 Neoarchean Basement Rock Assemblage, Crustal Evolution and Crust-Mantle Interactions of Western Liaoning Province -- Abstract -- 3.1 Metavolcanic Rocks in the Fuxin-Yixian Greenstone Belt. , 3.1.1 Geological and Petrographic Features and Sampling -- 3.1.2 Zircon U-Pb and Lu-Hf Isotopes -- 3.1.2.1 Sample FX09-1 (Metaandesitic Rocks) -- 3.1.2.2 Sample 12FX08-5 (Metabasaltic Rocks) -- 3.1.2.3 Sample 12FX28-2 (Metabasaltic Rocks) -- 3.1.2.4 Sample 12FX25-6 (Metabasaltic Rocks) -- 3.1.2.5 Sample 12FX28-3 (Metaandesitic Rocks) -- 3.1.3 Whole-Rock Geochemistry and Classification -- 3.1.4 Discussion -- 3.1.4.1 Assessment of Element Mobility -- 3.1.4.2 Petrogenesis -- 3.1.4.3 Geodynamic Setting -- 3.2 Neoarchean Dioritic and TTG Gneisses in the Western Liaoning Province -- 3.2.1 Geological and Petrographic Characteristics -- 3.2.1.1 Granitoid Gneisses in the North Chaoyang-Fuxin-Yixian Granite-Greenstone Belt -- 3.2.1.2 Granitoid Gneisses in the High Grade Jianping Gneissic Terrane -- 3.2.2 Geochemical Features -- 3.2.2.1 Major Element Compositions -- 3.2.2.2 Rare Earth Element Compositions -- 3.2.2.3 Other Trace Element Compositions -- 3.2.3 Zircon U-Pb and Lu-Hf Isotopes -- 3.2.3.1 Granitoid Gneisses in the NCFY-GGB -- 3.2.3.2 Granitoid Gneisses in the JPGT -- 3.2.4 Petrogenesis -- 3.2.4.1 Information from the Inherited/Xenocrystic Zircon Grains and Lu-Hf Isotopes -- 3.2.4.2 Information from Whole-Rock Geochemistry -- 3.2.5 Tectonic Implications -- 3.3 Late Neoarchean Potassium-Rich Granitoid Gneisses -- 3.3.1 Geological and Petrographic Features -- 3.3.2 Geochemical Features -- 3.3.3 Zircon U-Pb and Lu-Hf Isotopes -- 3.3.4 Petrogenesis -- 3.3.5 Tectonic Setting -- 3.4 Neoarchean Crustal Evolution and Crust-Mantle Geodynamics of the Western Liaoning Province -- 3.4.1 Neoarchean Sequences of Geological Events and Crustal Evolution -- 3.4.2 Late Neoarchean (sim2.6-2.5 Ga) Crustal Growth -- 3.4.3 Late Neoarchean Crust-Mantle Geodynamics -- References -- 4 Paleo- to Mesoproterozoic Magmatic Rock Assemblage and Crust-Mantle Geodynamic Processes. , Abstract -- 4.1 Late Paleoproterozoic Jianping Diorite-Monzonite-Syenite Suite in the Western Liaoning Province -- 4.1.1 Geological and Petrographic Characteristics -- 4.1.2 Geochemical Characteristics -- 4.1.2.1 Whole-Rock Geochemistry -- 4.1.2.2 Whole-Rock Rb-Sr Isotopes -- 4.1.3 Zircon U-Pb and Lu-Hf Isotopes -- 4.1.3.1 The Hatonggou Pluton and Jianchanggou Stock -- 4.1.3.2 The Xiaozhangzi Pluton -- 4.1.3.3 The Shinao Pluton -- 4.1.4 Petrogenesis -- 4.1.4.1 Genetic Links Among Different Lithologies -- 4.1.4.2 Nature of the Magma Sources -- 4.1.4.3 Assessment of Crustal Contamination and Fractional Crystallization -- 4.1.5 Summary -- 4.2 Late Paleoproterozoic Pinggu K-Rich Volcanic Rocks -- 4.2.1 Geological and Petrographic Characteristics -- 4.2.2 Zircon U-Pb and Lu-Hf Isotopes -- 4.2.2.1 The Tuanshanzi Formation -- 4.2.2.2 The Dahongyu Formation -- 4.2.3 Whole-Rock Geochemical Data -- 4.2.3.1 Major Element Compositions -- 4.2.3.2 Rare Earth Elements (REEs) -- 4.2.3.3 Other Trace Elements -- 4.2.4 Petrogenetic Discussion -- 4.2.4.1 Assessment of Element Mobility -- 4.2.4.2 Genetic Relationships Among Different Lithologies -- 4.2.4.3 Nature of the Mantle Source and Melting Conditions -- 4.2.4.4 Lithosphere Signature Preserved in the Alkaline Volcanic Rocks -- 4.2.4.5 Late Paleoproterozoic Asthenospheric Mantle Upwelling -- 4.2.5 Summary -- 4.3 Mesoproterozoic (sim1.23 Ga) Mafic Dykes Along the Northern Margin of North China Craton -- 4.3.1 Geological and Petrographic Features -- 4.3.2 Zircon U-Pb and Lu-Hf Isotopes -- 4.3.3 Whole-Rock Geochemistry -- 4.3.4 Discussion -- 4.3.4.1 sim1.23 Ga Mafic Dykes Identified in the North China Craton -- 4.3.4.2 Nature of the Mantle Sources and Petrogenesis -- 4.3.5 Summary -- 4.4 Paleo- to Mesoproterozoic Sequence of Geological Events and Crust-Mantle Geodynamics. , 4.4.1 Paleo- to Mesoproterozoic Geological Events of Western Liaoning-Northeastern Hebei Provinces -- 4.4.2 Late Paleoproterozoic Geodynamic Settings -- 4.4.3 Mesoproterozoic Geodynamic Setting -- References -- 5 Precambrian Crustal Evolution, Lithospheric Mantle Evolution and Crust-Mantle Geodynamics of Western Liaoning-Northeastern Hebei Provinces -- Abstract -- 5.1 Precambiran Crustal Evolution History in Western Liaoning-Northeastern Hebei -- 5.2 Precambiran Lithospheric Mantle Evolution and Crust-Mantle Geodynamic Processes of the Western Liaoning-Northeastern Hebei Provinces -- 5.2.1 Formation Regime of Late Archean Lithospheric Mantle and Crust-Mantle Geodynamics -- 5.2.1.1 Formation of Late Paleoproterozoic Enriched Lithospheric Mantle and Crust-Mantle Geodynamic Processes -- 5.2.2 Late Paleoproterozoic to Mesoproterozoic (sim1670-1230 Ma) Lithospheric Mantle Evolution and Crust-Mantle Geodynamics -- References -- 6 Concluding Remarks -- Abstract.

Note: Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 Introduction -- 1.1 Microfluidics and Its Superiority in Controllable Fabrication of Functional Materials -- 1.2 Microfluidic Fabrication of Microspheres and Microcapsules from Microscale Closed Liquid-Liquid Interfaces -- 1.3 Microfluidic Fabrication of Membranes in Microchannels from Microscale Nonclosed Layered Laminar Interfaces -- 1.4 Microfluidic Fabrication of Microfiber Materials from Microscale Nonclosed Annular Laminar Interfaces -- References -- Chapter 2 Shear-Induced Generation of Controllable Multiple Emulsions in Microfluidic Devices -- 2.1 Introduction -- 2.2 Microfluidic Strategy for Shear-Induced Generation of Controllable Emulsion Droplets -- 2.3 Shear-Induced Generation of Controllable Monodisperse Single Emulsions -- 2.4 Shear-Induced Generation of Controllable Multiple Emulsions -- 2.4.1 Shear-Induced Generation of Controllable Double Emulsions -- 2.4.2 Shear-Induced Generation of Controllable Triple Emulsions -- 2.5 Shear-Induced Generation of Controllable Multicomponent Multiple Emulsions -- 2.5.1 Shear-Induced Generation of Controllable Quadruple-Component Double Emulsions -- 2.5.2 Extended Microfluidic Device for Controllable Generation of More Complex Multicomponent Multiple Emulsions -- 2.6 Summary -- References -- Chapter 3 Wetting-Induced Generation of Controllable Multiple Emulsions in Microfluidic Devices -- 3.1 Introduction -- 3.2 Microfluidic Strategy for Wetting-Induced Production of Controllable Emulsions -- 3.2.1 Strategy for Wetting-Induced Production of Controllable Emulsions via Wetting-Induced Spreading -- 3.2.2 Strategy for Wetting-Induced Production of Controllable Emulsions via Wetting-Induced Coalescing -- 3.3 Generation of Controllable Multiple Emulsions via Wetting-Induced Spreading. , 3.3.1 Wetting-Induced Generation of Monodisperse Controllable Double Emulsions -- 3.3.2 Wetting-Induced Generation of Monodisperse Higher Order Multiple Emulsions -- 3.3.3 Wetting-Induced Generation of Monodisperse Multiple Emulsions via Droplet-Triggered Droplet Pairing -- 3.4 Generation of Controllable Multiple Emulsions via Wetting-Induced Droplet Coalescing -- 3.5 Summary -- References -- Chapter 4 Microfluidic Fabrication of Monodisperse Hydrogel Microparticles -- 4.1 Introduction -- 4.2 Microfluidic Fabrication of Monodisperse PNIPAM Hydrogel Microparticles for Sensing Tannic Acid (TA) -- 4.2.1 Microfluidic Fabrication of Monodisperse PNIPAM Hydrogel Microparticles -- 4.2.2 Volume-Phase Transition Behaviors of PNIPAM Microgels Induced by TA -- 4.3 Microfluidic Fabrication of Monodisperse Core-Shell PNIPAM Hydrogel Microparticles for Sensing Ethyl Gallate (EG) -- 4.3.1 Microfluidic Fabrication of Monodisperse Core-Shell PNIPAM Hydrogel Microparticles -- 4.3.2 Thermo-Responsive Phase Transition Behaviors of PNIPAM Microspheres in EG Solution -- 4.3.3 The Intact-to-Broken Transformation Behaviors of Core-Shell PNIPAM Microcapsules in Aqueous Solution with Varying EG Concentrations -- 4.4 Microfluidic Fabrication of Monodisperse Core-Shell Hydrogel Microparticles for the Adsorption and Separation of Pb2+ -- 4.4.1 Microfluidic Fabrication of Monodisperse Core-Shell Microparticles with Magnetic Core and Hydrogel Shell -- 4.4.2 Pb2+ Adsorption Behaviors of Magnetic PNB Core-Shell Microspheres -- 4.5 Summary -- References -- Chapter 5 Microfluidic Fabrication of Monodisperse Porous Microparticles -- 5.1 Introduction -- 5.2 Microfluidic Fabrication of Monodisperse Porous Poly(HEMA-MMA) Microparticles -- 5.2.1 Microfluidic Fabrication Strategy -- 5.2.2 Structures of Poly(HEMA-MMA) Porous Microspheres. , 5.3 Microfluidic Fabrication of Porous PNIPAM Microparticles with Tunable Response Behaviors -- 5.3.1 Microfluidic Fabrication Strategy -- 5.3.2 Tunable Response Behaviors of Porous PNIPAM Microparticles -- 5.4 Microfluidic Fabrication of PNIPAM Microparticles with Open-Celled Porous Structure for Fast Response -- 5.4.1 Microfluidic Fabrication Strategy -- 5.4.2 Morphologies and Microstructures of Porous PNIPAM Microparticles -- 5.4.3 Thermo-Responsive Volume Change Behaviors of PNIPAM Porous Microparticles -- 5.5 Summary -- References -- Chapter 6 Microfluidic Fabrication of Uniform Hierarchical Porous Microparticles -- 6.1 Introduction -- 6.2 Microfluidic Strategy for Fabrication of Uniform Hierarchical Porous Microparticles -- 6.3 Controllable Microfluidic Fabrication of Uniform Hierarchical Porous Microparticles -- 6.3.1 Preparation of Hierarchical Porous Microparticles -- 6.3.2 Hierarchical Porous Microparticles with Micrometer-Sized Pores from Deformed W/O/W Emulsions -- 6.3.3 Integration of Nanometer- and Micrometer-Sized Pores for Creating Hierarchical Porous Microparticles -- 6.4 Hierarchical Porous Microparticles for Oil Removal -- 6.4.1 Concept of the Hierarchical Porous Microparticles for Oil Removal -- 6.4.2 Hierarchical Porous Microparticles for Magnetic-Guided Oil Removal -- 6.5 Hierarchical Porous Microparticles for Protein Adsorption -- 6.5.1 Concept of Hierarchical Porous Microparticles for Protein Adsorption -- 6.5.2 Hierarchical Porous Microparticles for Enhanced Protein Adsorption -- 6.6 Summary -- References -- Chapter 7 Microfluidic Fabrication of Monodisperse Hollow Microcapsules -- 7.1 Introduction -- 7.2 Microfluidic Fabrication of Monodisperse Ethyl Cellulose Hollow Microcapsules -- 7.2.1 Microfluidic Fabrication Strategy -- 7.2.2 Morphologies and Structures of Ethyl Cellulose Hollow Microcapsules. , 7.3 Microfluidic Fabrication of Monodisperse Calcium Alginate Hollow Microcapsules -- 7.3.1 Microfluidic Fabrication Strategy -- 7.3.2 Morphologies and Structures of Calcium Alginate Hollow Microcapsules -- 7.4 Microfluidic Fabrication of Monodisperse Glucose-Responsive Hollow Microcapsules -- 7.4.1 Microfluidic Fabrication Strategy -- 7.4.2 Glucose-Responsive Behaviors of Microcapsules -- 7.4.3 Glucose-Responsive Drug Release Behaviors of Microcapsules -- 7.5 Microfluidic Fabrication of Monodisperse Multi-Stimuli-Responsive Hollow Microcapsules -- 7.5.1 Microfluidic Fabrication Strategy -- 7.5.2 Stimuli-Responsive Behaviors of Microcapsules -- 7.5.3 Controlled-Release Characteristics of Multi-Stimuli-Responsive Microcapsules -- 7.6 Summary -- References -- Chapter 8 Microfluidic Fabrication of Monodisperse Core-Shell Microcapsules -- 8.1 Introduction -- 8.2 Microfluidic Strategy for Fabrication of Monodisperse Core-Shell Microcapsules -- 8.3 Smart Core-Shell Microcapsules for Thermo-Triggered Burst Release -- 8.3.1 Fabrication of Core-Shell Microcapsules for Thermo-Triggered Burst Release of Oil-Soluble Substances -- 8.3.2 Fabrication of Core-Shell Microcapsules for Thermo-Triggered Burst Release of Nanoparticles -- 8.3.3 Fabrication of Core-Shell Microcapsules for Direction-Specific Thermo-Responsive Burst Release -- 8.4 Smart Core-Shell Microcapsules for Alcohol-Responsive Burst Release -- 8.5 Smart Core-Shell Microcapsules for K+-Responsive Burst Release -- 8.6 Smart Core-Shell Microcapsules for pH-Responsive Burst Release -- 8.6.1 Concept of the Core-Shell Microcapsules for pH-Responsive Burst Release -- 8.6.2 Fabrication of the Core-Shell Chitosan Microcapsules -- 8.6.3 Core-Shell Chitosan Microcapsules for pH-Responsive Burst Release -- 8.7 Summary -- References -- Chapter 9 Microfluidic Fabrication of Monodisperse Hole-Shell Microparticles. , 9.1 Introduction -- 9.2 Microfluidic Strategy for Fabrication of Monodisperse Hole-Shell Microparticles -- 9.3 Hole-Shell Microparticles for Thermo-Driven Crawling Movement -- 9.3.1 Concept of the Hole-Shell Microparticles for Thermo-Driven Crawling Movement -- 9.3.2 Fabrication of Hole-Shell Microparticles for Thermo-Driven Crawling Movement -- 9.3.3 Effect of Inner Cavity on the Thermo-Responsive Volume-Phase Transition Behaviors of Hole-Shell Microparticles -- 9.3.4 Hole-Shell Microparticles for Thermo-Driven Crawling Movement -- 9.4 Hole-Shell Microparticles for Pb2+ Sensing and Actuating -- 9.4.1 Fabrication of Hole-Shell Microparticles for Pb2+ Sensing and Actuating -- 9.4.2 Magnetic-Guided Targeting Behavior of Poly(NIPAM-co-B18C6Am) Hole-Shell Microparticles -- 9.4.3 Effects of Pb2+ on the Thermo-Responsive Volume Change Behaviors of Poly(NIPAM-co-B18C6Am) Hole-Shell Microparticles -- 9.4.4 Effects of Hollow Cavity on the Time-Dependent Volume Change Behaviors of Poly(NIPAM-co-B18C6Am) Hole-Shell Microparticles -- 9.4.5 Micromanipulation of Poly(NIPAM-co-B18C6Am) Hole-Shell Microparticles for Preventing Pb2+ Leakage from Microcapillary -- 9.5 Hole-Shell Microparticles for Controlled Capture and Confined Microreaction -- 9.5.1 Microfluidic Fabrication of Hole-Shell Microparticles -- 9.5.2 Precise Control over the Hole-Shell Structure of the Microparticles -- 9.5.3 Precise Control over the Functionality of Hollow Core Surface -- 9.5.4 Hole-Shell Microparticles for Controlled Capture and Confined Microreaction -- 9.6 Summary -- References -- Chapter 10 Microfluidic Fabrication of Controllable Multicompartmental Microparticles -- 10.1 Introduction -- 10.2 Microfluidic Strategy for the Fabrication of Controllable Multicompartmental Microparticles -- 10.3 Multi-core/Shell Microparticles for Co-encapsulation and Synergistic Release. , 10.3.1 Microfluidic Fabrication of Multi-core/Shell Microparticles.

Note: Intro -- Preface -- Organization -- Table of Contents - Part I -- Opinion Mining -- Mining E-Commerce Feedback Comments for Dimension Rating Profiles -- 1 Introduction -- 2 Related Work -- 3 Mining Feedback Comments for Dimension Ratings -- 4 Computing Dimension Weights by Matrix Factorisation -- 4.1 Singular Value Decomposition -- 4.2 Computing Dimension Weights in the Latent Component Space -- 5 Experiments -- 5.1 Accuracy of the DR-Mining Algorithm -- 5.2 Dimension Rating Profiles for Sellers -- 6 Conclusions -- References -- Generating Domain-Specific Sentiment Lexicons for Opinion Mining -- 1 Introduction -- 2 Previous Work -- 3 Domain-Specific Lexicons Generation -- 3.1 Part Of Speech Tagging (POST) -- 3.2 Preprocessing -- 3.3 Determining the Sentiment Score and Polarity of Each Term -- 3.4 Lexicon Generation -- 4 Opinion Mining -- 5 Evaluation -- 5.1 Test Data -- 5.2 Results -- 6 Conclusions -- References -- Effective Comment Sentence Recognition for Feature-Based Opinion Mining -- 1 Introduction -- 2 Related Work -- 3 Feature Triples Extraction Approach -- 3.1 Pattern Matching Method Analysis -- 3.2 Improved Extraction Process -- 4 Effective Comment Sentence Extraction -- 4.1 Attributes Selection of Effective Comment Sentence -- 4.2 Neural Network Classification -- 5 Experiment -- 5.1 Data Set -- 5.2 Experiment Results Analysis -- 6 Conclusion -- References -- Exploiting Co-occurrence Opinion Words for Semi-supervised Sentiment Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 4 Experiments -- 4.1 Experimental Data -- 4.2 Compared Methods -- 4.3 Experimental Results -- 5 Conclusion -- References -- Behavior Mining -- HN-Sim: A Structural Similarity Measure over Object-Behavior Networks -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notation and Definition. , 3.2 Similarity Measure Based on Heterogeneous Neighborhood -- 4 Experiments -- 4.1 Datasets -- 4.2 Case Study -- 4.3 Result and Evaluation -- 4.4 Homo-Info Adjustment Discussion -- 5 Conclusions -- References -- Community Based User Behavior Analysis on Daily Mobile Internet Usage -- 1 Introduction -- 2 Related Work -- 3 Data Set and Initial Findings -- 3.1 Mobile Data -- 3.2 Daily Usage -- 3.3 User Network -- 4 Mobile Communities Identification -- 4.1 Methodology -- 4.2 Clustering Approach -- 5 Global Analysis Results -- 6 Conclusion -- References -- Stream Mining -- Tracking Drift Types in Changing Data Streams -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 RelativeDriftTypeDetector -- 4.1 Slope and Magnitude -- 4.2 Gaussian Distribution -- 4.3 Types of Relative Drift -- 5 Experimental Results -- 5.1 Synthetic Data Generator -- 5.2 Experiments -- 6 Conclusion and Future Work -- References -- Continuously Extracting High-Quality Representative Set from Massive Data Streams* -- 1 Introduction -- 2 Related Work -- 3 Overview -- 4 Methodology -- 4.1 Initial Core Clustering -- 4.2 Extracting Representative Set -- 4.3 Online Adjustment on Clusters -- 5 Experimental Evaluations -- 5.1 Dataset -- 5.2 Effectiveness -- 5.3 Efficiency -- 6 Conclusion -- References -- Change ItemsetMining in Data Streams -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 4 Change Itemset Mining -- 4.1 Types of Changed Itemsets -- 4.2 Our Algorithm -- 5 Experimental Results -- 5.1 Accuracy -- 5.2 Execution Time -- 5.3 RealWorld Datasets -- 6 Contribution -- 7 Conclusions and Future Work -- References -- Sequential Data Mining -- TKS: Efficient Mining of Top-K Sequential Patterns -- 1 Introduction -- 2 Problem Definition and Related Work -- 3 The TKS Algorithm -- 3.1 The Database Representation and Candidate Generation Procedure -- 3.2 The TKS Algorithm. , 4 Experimental Study -- 5 Conclusion -- References -- When Optimization Is Just an Illusion -- 1 Introduction -- 2 On Using the Genetic Algorithms to Combine Similarity Measures of Time Series in a Classification Task -- 3 Legitimate Remarks -- 4 A Counter Example -- 5 What Went Wrong with CSM-GA -- 6 Conclusion -- References -- Accurate and Fast Dynamic Time Warping -- 1 Introduction -- 2 Background and Related Work -- 3 Accurate and Fast Dynamic Time Warping -- 3.1 Backward Strategy -- 3.2 Reduced Scope -- 3.3 Choice of Threshold Value -- 4 Experiments -- 4.1 Different Thresholds Based Comparison -- 4.2 Different Length Based on Comparison -- 4.3 Classification -- 5 Conclusions -- References -- Online Detecting Spreading Events with the Spatio-temporal Relationship in Water Distribution Networks* -- 1 Introduction -- 2 Related Work -- 3 Preliminary Concepts -- 4 Methodology -- 4.1 Model Spatio-temporal Relationship -- 4.2 Event Detection -- 5 Experiments -- 6 Conclusions -- References -- MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns -- 1 Introduction -- 2 Preliminaries -- 3 Related Work -- 4 Mining Multi-Level Sequential Patterns -- 5 The MLSP Algorithm -- 6 Experiments -- 7 Conclusion -- References -- Mining Maximal Sequential Patterns without Candidate Maintenance -- 1 Introduction -- 2 Problem Definition -- 3 The MaxSP Algorithm -- 3.1 Discovering Sequential Patterns by Pattern-Growth -- 3.2 The MaxSP Algorithm -- 3.3 Optimizations -- 4 Experimental Evaluation -- 5 Conclusion -- References -- Web Mining -- Improved Slope One Collaborative Filtering Predictor Using Fuzzy Clustering -- 1 Introduction -- 2 Background -- 2.1 State of the Art in Collaborative Filtering -- 2.2 Principle of Slope One Predictor -- 2.3 Fuzzy Clustering and Its Advantages -- 3 Fuzzy Clustering-Based Slope One Predictor. , 3.1 Philosophy of Proposed Approach -- 3.2 Data Preprocess: Filling Algorithm -- 3.3 Cluster Users through Fuzzy Clustering Technique -- 3.4 Prediction -- 4 Experiments -- 4.1 Dataset, Evaluation Metric and Algorithms -- 4.2 Methodology -- 4.3 Results and Discussions -- 5 Conclusion -- References -- Towards Building Virtual Vocabularies in the Semantic Web -- 1 Introduction -- 2 Overview -- 2.1 Model of Virtual Vocabulary -- 2.2 System Architecture -- 3 Building Concept Relationship Graph(CRG) -- 3.1 Definition -- 3.2 Relationship Detection Rules -- 3.3 Weighting Concept Relationship Graph -- 4 Building Virtual Vocabularies -- 4.1 CRG Clustering by Partitioning -- 4.2 CRG Clustering by Hierarchical Clustering -- 5 Experiments -- 5.1 Dataset -- 5.2 Quality of Virtual Vocabularies -- 5.3 Evaluation on Partitioning -- 5.4 Evaluation on Hierarchical Clustering -- 5.5 Comparison -- 6 Related Works -- 7 Conclusion and Future Works -- References -- Web Mining Accelerated with In-Memory and Column Store Technology -- 1 Introduction -- 2 Related Work -- 3 Blog Intelligence -- 4 Application Areas -- 4.1 Crawling -- 4.2 Analytics -- 4.3 New Application Area -- 5 Evaluation -- 5.1 Data Set -- 5.2 Setup -- 5.3 Crawling -- 5.4 Analytics -- 6 Future Work -- 7 Conclusion -- References -- Image Mining -- Constructing a Novel Pos-neg Manifold for Global-Based Image Classification* -- 1 Introduction -- 2 Related Works -- 3 A Pos-neg Manifold Construction -- 3.1 Motivation -- 3.2 Image Manifold Definition -- 3.3 An Improved GNLLE Algorithm -- 3.4 Classifier Design -- 4 Experiments -- 4.1 Experimental Preparation -- 4.2 Parameter Setting -- 4.3 Results and Analysis -- 5 Conclusions -- References -- 3-D MRI Brain Scan Feature Classification Using an Oct-Tree Representation -- 1 Introduction -- 2 Previous Works -- 3 The Classification Process -- 3.1 Segmentation. , 3.2 Image Decomposition -- 3.3 Feature Extraction, Selection, and Classification -- 4 Experimentation -- 5 The Classification Results -- 6 Conclusions -- References -- Biometric Template Protection Based on Biometric Certificate and Fuzzy Fingerprint Vault -- 1 Introduction -- 2 Biometric Template Protection Based on BC and Fuzzy Fingerprint Vault -- 2.1 Biometric Certificate -- 2.2 Fingerprint-Based Pseudo Random Number Generator -- 2.4 Vault Decoding -- 3 Experiment and Result Analysis -- 4 Conclusion -- References -- A Comparative Study of Three Image Representations for Population EstimationMining Using Remote Sensing Imagery -- 1 Introduction -- 2 Previous Work -- 3 Census Mining Framework -- 4 Image Segmentation -- 5 Image Representation -- 5.1 Colour Histogram -- 5.2 Local Binary Pattern -- 5.3 Graph-Based Structure -- 6 Evaluation -- 6.1 Test Data -- 6.2 Results -- 7 Conclusion -- References -- Mixed-Norm Regression for Visual Classification -- 1 Introduction -- 2 Preliminary -- 3 Approach -- 3.1 Objective Function -- 4 Optimization -- 5 Experiments -- 5.1 Parameters' Sensitivity -- 5.2 Comparison -- 6 Conclusion -- References -- Research on Map Matching Based on Hidden Markov Model -- 1 Introduction -- 2 Related Work -- 3 Map Matching Problems -- 4 HMM for Map Matching -- 4.1 Observation Probability -- 4.2 State Transition Probabilities -- 4.3 Infer of Map Match hing Route -- 4.4 Map-Matching Result and Algorithm Evaluation -- 5 Conclusions and Future Research -- References -- Text Mining -- A Rule-Based Named-Entity Recognition for Malay Articles -- 1 Introduction -- 2 Types of NER -- 3 A Rule-Based Named-Entity Recognition Algorithm for Malay Language -- 3.1 Rules for Identifying a Person-Entity -- 3.2 Location Rule -- 3.3 Organization Rule -- 4 Experimental Setup -- 5 Results and Discussions -- 6 Conclusion -- References. , Small Is Powerful! Towards a Refinedly Enriched Ontology by Careful Pruning and Trimming.

Note: Intro -- Contents -- Inception Based Medical Image Registration -- 1 Introduction -- 2 Inception-Based Image Registration Networks -- 2.1 Inception Structure -- 2.2 U-net Structure -- 2.3 Inception-Based Image Registration Network Structure -- 3 Experimental Results and Analysis -- 4 Conclusion -- References -- Research on Smart Home System Based on Internet of Things -- 1 Introduction -- 2 Design of Smart Home System -- 2.1 Hardware Components -- 2.2 Software Architecture -- 3 System Function -- 3.1 Sensor Node Control -- 3.2 Personalized Scenario Design -- 4 Conclusion -- References -- Autoencoder-Based Baseline Parameterized by Central Limit Theorem for ICS Cybersecurity -- 1 Introduction -- 2 Relative Works -- 3 Background and Structure -- 3.1 ICS Network Introduction -- 3.2 Autoencoder-Baseline Structure and Processes -- 3.3 Training Autoencoder and Parameterizing Baseline -- 4 Result -- 5 Conclusion -- References -- Secrecy Capacity-Approaching Neural Communications for Gaussian Wiretap Channel -- 1 Introduction -- 2 Basis of Gaussian Wiretap Channel -- 3 Dual MINE-Based Neural Secure Communications Model -- 3.1 MINE Block -- 3.2 Encoder Block -- 3.3 Decoder Block -- 4 Numerical Results and Analysis -- 4.1 Reliability and Security -- 4.2 Comparison to Existing Methods -- 5 Conclusions -- References -- Image Compression Based on Mixed Matrix Decomposition of NMF and SVD -- 1 Introduction -- 2 Related Work -- 3 Mixed Matrix Decomposition -- 3.1 NMF -- 3.2 SVD, Singular Value Decomposition -- 4 Image Quality Measures -- 4.1 MSE, Mean Square Error -- 4.2 PSNR, Peak Signal to Noise Ratio -- 4.3 CR, Compression Ratio -- 5 Experiment Result -- 5.1 Performance Analysis of NMF -- 5.2 Performance Analysis of NMF and SVD -- 6 Conclusion -- References -- MocNet: Less Motion Artifacts, More Clean MRI -- 1 Introduction -- 2 Materials and Method. , 2.1 Research Subjects -- 2.2 Motion Artifacts Simulation -- 2.3 MocNet -- 3 Experiment -- 3.1 Data Preprocessing and Augmentation -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- The Application Exploration of Digital Twin in the Space Launch Site -- 1 Digital Twin -- 2 Artificial Intelligence -- 3 Digital Twin Applications And Analysis In Space -- 4 The Design of the Digital Twin System of the Space Launch Site -- 5 Conclusion -- References -- Reverse Attention U-Net for Brain Grey Matter Nuclei Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Overview -- 2.2 Reverse Attention Module -- 2.3 Loss Function -- 3 Experiment Results -- 3.1 Experiment Data and Data Processing -- 3.2 Implementation Settings -- 3.3 Segmentation Results -- 4 Conclusion -- References -- Transmit RRH Selection in User-Centric Cell-Free Massive MIMO Using Discrete Particle Swarm Optimization -- 1 Introduction -- 2 System Model -- 3 Intelligent RRH Selection Algorithm -- 4 Numerical Result -- 5 Conclusion -- References -- Research on Emotion Recognition Based on GA-BP-Adaboost Algorithm -- 1 Introduction -- 2 Emotional Data Acquisition -- 2.1 Experimental Paradigm -- 2.2 Feature Extraction -- 3 Research Method -- 3.1 Optimization of BP Neural Network Based on GA -- 3.2 GA-BP-Adaboost Algorithm -- 4 Results and Discussion -- 4.1 Results -- 4.2 Eigenvalue Analysis -- 5 Conclusion -- References -- Information Extraction of Air-Traffic Control Instructions via Pre-trained Models -- 1 Introduction -- 2 Related Work -- 3 PTMs-CRF Model -- 3.1 BERT Family -- 3.2 CRF Tagging Model -- 4 Experiments -- 4.1 Datasets -- 4.2 Performance -- 4.3 Probing Task -- 5 Conclusions -- References -- Medical Image Segmentation Using Transformer -- 1 Introduction -- 2 Method -- 2.1 TransHarDNet -- 2.2 HarDNet Block (HDB) -- 2.3 Transformer -- 2.4 RFB. , 2.5 Cascaded Partial Decoder -- 2.6 Loss Function -- 3 Experiments -- 3.1 Training and Inference Environment Setting -- 3.2 Polyp Segmentation -- 4 Conclusion -- References -- Satellite Online Scheduling Algorithm Based on Proximal Policy -- 1 Introduction -- 2 Problem Formulation -- 3 Algorithm -- 3.1 Overview of Algorithm -- 3.2 Parameter Description -- 3.3 Constraint of Problem -- 3.4 Time Window Conflict Handling -- 3.5 Proximal Policy Optimization -- 3.6 Network Parameters -- 4 Simulation and Result Analysis -- 4.1 Parameter Setting -- 4.2 Case Study -- 5 Conclusion -- References -- Generation Method of Control Strategy for Aircrafts Based on Hierarchical Reinforcement Learning -- 1 Introduction -- 2 Problem Description -- 3 The Proposed Algorithm -- 4 Experimental Results -- 5 Conclusion -- References -- Finding Significant Influencing Factors of Core Quality and Ability Development of Teachers Based on Improved Genetic Algorithm -- 1 Introduction -- 2 Preliminary Basic Works -- 3 Study Method of the Paper -- 4 Simulation Results -- 5 Conclusion -- References -- A New Augmented Method for Processing Video Datasets Based on Deep Neural Network -- 1 Introduction -- 2 Experiments Details -- 2.1 Datasets Augment -- 2.2 Training Methods -- 3 Results -- 4 Conclusion -- References -- Small-Object Detection with Super Resolution Embedding -- 1 Introduction -- 2 Related Work -- 3 Super Resolution Embedding Augmentation Training -- 3.1 Super Resolution Embedding Network -- 3.2 Adversarial Training -- 3.3 Loss Function -- 3.4 YOLO Detector -- 4 Experiments -- 4.1 Experiment Setup -- 4.2 Results on Benchmark Datasets -- 5 Conclusion -- References -- Graph-Based Anomaly Detection of Wireless Sensor Network -- 1 Introduction -- 2 Theoretical Basis -- 2.1 Graph Theory -- 2.2 Anomaly Detection Theory. , 3 Graph-Based Anomaly Detection of Wireless Sensor Network -- 3.1 Graph Model and Network Anomaly Detection -- 3.2 Optimized Graph Construction Algorithm -- 4 Experimental Results and Analysis -- 4.1 IntelLabData -- 4.2 Experimental Results and Analysis -- 5 Conclusion -- References -- Identifying Important Attributes for Secondary School Student Performance Prediction -- 1 Introduction -- 2 Dataset and Feature Selection Methods -- 2.1 Student Data and Processing Method -- 2.2 Feature Selection Method -- 3 Simulation Results -- 4 Conclusion -- References -- A Cross-Modal Attention and Multi-task Learning Based Approach for Multi-modal Sentiment Analysis -- 1 Introduction -- 2 Related Work -- 2.1 Multi-task Learning -- 2.2 Multi-modal Sentiment Analysis -- 3 Proposed Method -- 3.1 Modality Representation Extraction -- 4 Co-attention -- 5 Experiment -- 5.1 Dataset -- 5.2 Baselines -- 5.3 Results and Discussion -- 6 Conclusion -- References -- Research on Problem Matching Classification Based on Artificial Intelligence Technology -- 1 Introduction -- 2 Problems Encountered by the Problem Matching Classification -- 3 Research of Question Matching Classification -- 3.1 Text Preprocessing -- 3.2 Vectorization -- 3.3 Classification Algorithm -- 4 Experimental Results -- 5 Conclusions -- References -- Research on the Application of Natural Language Processing in the Virtual Comment's Classification -- 1 Introduction -- 2 Significance of Natural Language Processing Research -- 3 Application of Natural Language Processing in the Virtual Comment's Classification -- 3.1 Data Collection and Classification -- 3.2 Text Preprocessing -- 3.3 Build a Model -- 3.4 Training Model -- 4 Conclusions -- References -- Design and Application of Endangered Animal Monitoring System Based on Mobile APP -- 1 Introduction -- 2 Requirements Analysis of Mobile App. , 2.1 Stronger Self-adaptation -- 2.2 Visualization of Data -- 2.3 Maximize Rate -- 2.4 High Demand for Pictures -- 2.5 Mobile Geolocation Services -- 3 Porpoise Monitoring Mobile App Requirements Analysis -- 3.1 Determination of the Time and Location of the Appearance of Porpoises -- 3.2 Status Determination of Porpoise -- 3.3 Aggregation and Storage of Collected Information -- 3.4 Release of Information -- 4 Mobile App Design Ideas -- 4.1 System Architecture -- 4.2 Function Modules -- 4.3 Front-End Design -- 4.4 Back-End Systems -- 4.5 Front-End and Back-End Communication -- 5 Conclusions -- References -- A Rapid Image Semantic Segment Method Based on Deeplab V3+ -- 1 Introduction -- 2 A Rapid Image Semantic Segment Method Based on Deeplab v3+ -- 3 Experiment -- 3.1 Evaluation Index -- 3.2 Environment and Dataset -- 3.3 Training Configuration -- 4 Result -- References -- Analysis and Research on China's New Energy Vehicles Industry Policy Based on Policy Subjects, Tools and Objectives -- 1 Introduction -- 2 Research Design -- 2.1 Policy Analysis Framework Design -- 2.2 Data Sources -- 3 Three Dimensional Analysis Based on Policy Subjects, Policy Tools and Policy Objectives -- 3.1 Policy Subjects Analysis -- 3.2 Policy Tools Analysis -- 3.3 Policy Objectives Analysis -- 4 Conclusions and Suggestions -- References -- Research on Semantic Retrieval System of Scientific Literature Based on Deep Learning -- 1 Introduction -- 2 Related Research at Home and Abroad -- 2.1 Semantic Retrieval System for Scientific Literature Based on Ontology -- 2.2 Semantic Retrieval System of Scientific Literature Based on NLP -- 2.3 Semantic Retrieval System of Scientific Literature Based on Knowledge Graph -- 3 The Overall Architecture of the System -- 3.1 Semantic Annotation Based on Deep Learning -- 3.2 Multidimensional Semantic Index -- 3.3 Semantic Search Interface. , 4 Key Technology Research.

Note: Intro -- Preface -- Organization -- Table of Contents -- Clustering -- Semi-supervised Clustering Ensemble Evolved by Genetic Algorithm for Web Video Categorization -- 1 Introduction -- 2 Related Work -- 2.1 Web Video Categorization -- 2.2 Clustering Ensemble -- 2.3 Semantic Similarity -- 2.4 Genetic Algorithm -- 3 Proposed Framework -- 3.1 System Overview -- 3.2 Semantic Vector Space Model (S-VSM) -- 3.3 Genetic Algorithm -- 3.4 The Algorithm -- 4 Experiments -- 4.1 Datasets and Evaluation Criteria -- 4.2 Results -- 4.3 Results Discussion -- 5 Conclusions -- References -- A Scalable Approach for General Correlation Clustering -- 1 Introduction -- 2 Correlation Clustering -- 3 Pseudo-EM Algorithm -- 3.1 The Basic Pseudo-EM Routine -- 3.2 Discussion -- 4 Experiments -- 4.1 Numerical Comparison -- 4.2 Synthetic Data -- 4.3 Image Data -- 4.4 Social Network Data -- 5 Conclusion -- References -- A Fast Spectral Clustering Method Based on Growing Vector Quantization for Large Data Sets -- 1 Introduction -- 2 Preliminaries -- 2.1 Spectral Clustering -- 2.2 Vector Quantization -- 3 Our Method -- 3.1 Minimization of the Increment of Distortion -- 3.2 Growing Vector Quantization Method -- 4 Experiment -- 4.1 Data Sets -- 4.2 Evaluation Metrics -- 4.3 Results -- 5 Conclusion -- References -- A Novel Deterministic Sampling Technique to Speedup Clustering Algorithms -- 1 Introduction -- 2 Related Works -- 3 Background Information -- 3.1 Agglomerative Hierarchical Clustering -- 3.2 Sampling -- 3.3 Clustering Accuracy -- 4 Our Algorithm -- 5 Analysis -- 5.1 Time Complexity -- 5.2 Accuracy -- 6 Simulation Environment -- 7 Simulation Results -- 7.1 Synthetic Datasets -- 7.2 Benchmark Datasets -- 8 Conclusions -- References -- Software Clustering Using Automated Feature Subset Selection -- 1 Introduction -- 2 Related Work -- 3 Our Approach to Software Clustering. , 3.1 Clustering -- 3.2 Feature Selection Technique -- 3.3 K-Means Algorithm -- 4 Experimental Design -- 4.1 Data Sets -- 4.2 Feature Extraction and Selection -- 4.3 Clustering Algorithm -- 4.4 Assessment -- 5 Experimental Evaluation -- 5.1 Experimental Results -- 5.2 Discussion -- 6 Conclusion -- References -- The Use of Transfer Algorithm for Clustering Categorical Data -- 1 Introduction -- 2 The Proposed Objective Function -- 3 Transfer Algorithm for Clustering Categorical Data -- 4 Experimental Results -- 4.1 Clustering Efficacy -- 4.2 Scalability -- 5 Conclusions -- References -- eDARA: Ensembles DARA -- 1 Introduction -- 2 Related Works -- 3 The Framework of Ensemble DARA (eDARA) -- 3.1 Configuration Phase -- 3.2 Consensus Phase -- 3.3 Characterization Phase -- 4 Experimental Design and Results -- 5 Conclusion -- References -- Efficient Mining Maximal Variant and Low Usage Rate Biclusters without Candidate Maintenance in Real Function-Resource Matrix: The DeCluster Algorithm -- 1 Introduction -- 2 Problem Description -- 3 DeCluster Algorithm -- 3.1 Construct Sample Relational Weighted Graph -- 3.2 Mining Maximal Bicluster -- 4 Experimental Results -- 5 Conclusion -- References -- Association Rule Mining -- MEIT: Memory Efficient Itemset Tree for Targeted Association Rule Mining -- 1 Introduction -- 2 Related Work -- 3 The Memory-Efficient Itemset-Tree -- 4 Experimental Study -- 5 Conclusion -- References -- Pattern Mining -- Mining Frequent Patterns in Print Logs with Semantically Alternative Labels -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 Algorithms -- 4.1 Algorithm Basic -- 4.2 Algorithm PaSAL -- 4.3 Discussion -- 5 Experiment Evaluation -- 5.1 Evaluation on Effectiveness -- 5.2 Evaluation on Efficiency -- 6 Conclusion -- References -- Minimising K-Dominating Set in Arbitrary Network Graphs -- 1 Introduction. , 1.1 Minimal k-Dominating Set Problem -- 1.2 Self-stabilizing Algorithm -- 2 Motivation and Contribution -- 3 Self-stabilizing -- Dominating Set Algorithm -- 3.1 Formal Definition of the Problem -- 3.2 Proposed Algorithm -- 4 The Stabilization Time of Algorithm MKDS -- 5 Related Work and Algorithm Comparison -- 6 Conclusions and Future Work -- References -- Regression -- Logistic Regression Bias Correction for Large Scale Data with Rare Events -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Problem Definition -- 3.2 Preprocessing Examples -- 3.3 Estimate Parameters -- 3.4 Correct Bias -- 4 Experimental Setup -- 4.1 Data Set -- 4.2 Data Preprocessing -- 4.3 Experiment Environment -- 5 Experiment Results -- 5.1 Performance Result -- 5.2 Scalability Result -- 6 Conclusion -- References -- An Automatical Moderating System for FML Using Hashing Regression -- 1 Introduction -- 1.1 Machine Learning and Hashing -- 1.2 About FML -- 1.3 Contribution -- 2 The Automatical Moderating System for FML -- 2.1 Overview -- 2.2 Web Crawler -- 2.3 Dict Generator -- 2.4 Feature Extractor -- 2.5 Hash Coder and Predictor -- 3 Experiment -- 3.1 Prediction Accuracy -- 3.2 Success Rate -- 3.3 Computational Cost -- 4 Conclusion -- References -- Batch-to-Batch Iterative Learning Control Based on Kernel Independent Component Regression Model -- 1 Introduction -- 2 KICR -- 3 ILC Methodology -- 4 Simulation Example -- 5 Conclusions -- References -- Prediction -- Deep Architecture for Traffic Flow Prediction -- 1 Introduction -- 2 Background -- 2.1 Traffic Flow Prediction -- 2.2 Deep Belief Network -- 3 Learning Architecture -- 4 Experiments and Results -- 4.1 Experiment Settings -- 4.2 Structure of Deep Architecture -- 4.3 Results of Deep Learning Architecture -- 5 Conclusions -- References. , Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction -- 1 Introduction -- 2 Preliminaries and Related Work -- 3 The Compact Prediction Tree -- 3.1 Training -- 3.2 Prediction -- 3.3 Optimizations -- 4 Experimental Evaluation -- 4.1 Datasets -- 4.2 Evaluation Framework -- 4.3 Experiments -- 5 Conclusion -- References -- Generalization of Malaria Incidence Prediction Models by Correcting Sample Selection Bias -- 1 Introduction -- 2 Methods -- 2.1 SVMs -- 2.2 Data Collection, Processing and Analyses -- 2.3 Sample Selection Bias Correction for Malaria Prediction -- 2.4 Validation Procedure -- 3 Results -- 3.1 Testing Generalization of Model 1 (Infants) -- 3.2 Generalization Test of Model 2 (all ages) -- 4 Conclusion -- References -- Protein Interaction Hot Spots Prediction Using LS-SVM within the Bayesian Interpretation -- 1 Introduction -- 2 Introduction of LS-SVM -- 3 LS-SVM with Bayesian Evidence Framework -- 3.1 Level 1 Inference -- 3.2 Level 2 Inference -- 3.3 Level 3 Inference -- 4 Data and Method -- 4.1 Data Set -- 4.2 Features Extraction -- 4.3 Feature Selection -- 4.4 Performance Evaluation -- 5 Experimental Results -- 6 Conclusion -- References -- Predicting the Survival Status of Cancer Patientswith Traditional Chinese Medicine SymptomVariation Using Logistic Regression Model -- 1 Introduction -- 2 Experiment and Method -- 2.1 Variable Pretreatment -- 2.2 Experiment Design -- 2.3 Statistical Analysis -- 3 Results -- 3.1 Multivariate Analysis Using Logistic Regression Modeling -- 4 Discussion -- References -- Feature Extraction -- Exploiting Multiple Features for Learning to Rank in Expert Finding -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Features in LREF -- 3.2 The LREF Algorithm -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experiments Results -- 5 Conclusions and Further Work -- References. , Convolution Neural Network for Relation Extraction -- 1 Introduction -- 2 Related Work -- 3 Convolution Network Architecture -- 3.1 Word Embedding -- 3.2 Basic Architecture -- 3.3 General Deep Neural Network Architecture -- 4 Experiments -- 4.1 Date Set -- 4.2 Experiments Setup -- 4.3 Result -- 5 Discussion and Future Work -- References -- Extracting Fuzzy Rules from Hierarchical Heterogeneous Neural Networks for Cardiovascular Diseases Diagnosis -- 1 Introduction -- 2 Methodology -- 2.1 HHFNNs -- 2.2 Process of Providing Explanation to Conclusion of HHFNNs -- 2.3 Fuzzy Rule Extraction Approach Using GA -- 3 Test Results -- 3.1 Dataset -- 3.2 Test -- 4 Conclusion -- References -- kDMI: A Novel Method for Missing Values Imputation Using Two Levels of Horizontal Partitioning in a Data set -- 1 Introduction -- 2 A Novel Imputation Method- -- 2.1 Basic Concept -- 2.2 The First Level Partitioning -- 2.3 The Second Level Partitioning -- 2.4 Imputation -- 3 Experimental Results and Discussion -- 4 Conclusion -- References -- Identification -- Traffic Session Identification Based on Statistical Language Model -- 1 Introduction -- 2 Related Work -- 2.1 Web Session Identification -- 2.2 Route Prediction -- 3 Observations -- 4 Session Identification with Improved Statistical Language Model -- 4.1 N-Gram Statistical Language Model -- 4.2 Time Influence Function -- 4.3 Improved Statistical Language Model -- 5 Experiments -- 5.1 Performance Measurement Metrics -- 5.2 Experimental Results and Discussion -- 5.3 Comparison and Analysis -- 6 Conclusion -- References -- Role Identification Based on the Information Dependency Complexity -- 1 Introduction -- 2 Complexity of Information Dependency -- 2.1 Variability and Sensitivity -- 2.2 Relevancy Evaluation -- 3 DSM-Based Particle Swarm Optimization -- 3.1 DSM Based on Information Dependency. , 3.2 Standard Particle Swarm Optimization.

Note: Intro -- Foreword -- Preface -- Contents -- 1 A Framework to Support Manufacturing Digitalization -- Abstract -- 1.1 Introduction -- 1.2 An Overview of the Digitalization State of the Art -- 1.2.1 Digitalization Maturity Model -- 1.2.2 Demand Supply Integration (DSI) -- 1.2.3 Six Sigma Based DMAIC Approach -- 1.2.4 DREAMY Based Transformation Approach -- 1.2.5 acatech Industrie 4.0 Maturity Index -- 1.2.6 Singapore Smart Industry Readiness Index (SIRI) -- 1.2.7 Summary -- 1.3 The Proposed Manufacturing Digitalization Framework -- 1.3.1 Industry Input -- 1.3.2 Industry Interview -- 1.3.3 Study -- 1.3.4 Digitalization Roadmap -- 1.4 Framework Implementation: Case Study and Evaluation -- 1.4.1 Case Study: Reliability of Heavy Machinery -- 1.4.2 Evaluation (Comparison with Other Frameworks) -- 1.4.2.1 Digitalization Framework Inputs -- 1.4.2.2 Digitalization Framework Analysis -- 1.4.2.3 Digitalization Framework Outputs -- 1.4.2.4 Digitalization Framework Application -- 1.5 Conclusion -- References -- 2 Real-Time Asset Tracking for Smart Manufacturing -- Abstract -- 2.1 Introduction -- 2.2 RTLS in Smart Manufacturing -- 2.2.1 Parts Tracking -- 2.2.2 Work-in-Progress (WIP) Tracking -- 2.2.3 Tools Tracking -- 2.2.4 Personnel Tracking -- 2.3 RTLS Components -- 2.3.1 RTLS Technologies -- 2.3.1.1 Radio Frequency Identification (RFID) -- 2.3.1.2 Wi-Fi -- 2.3.1.3 Bluetooth -- 2.3.1.4 Ultra-Wideband (UWB) -- 2.3.1.5 Vision -- 2.3.2 Localization Schemes -- 2.3.2.1 Two-Way Ranging (TWR) -- 2.3.2.2 Time of Arrival (TOA) -- 2.3.2.3 Angle of Arrival (AOA) -- 2.3.2.4 Time Difference of Arrival (TDOA) -- 2.3.2.5 Fingerprinting -- 2.4 Challenges -- 2.4.1 Challenges in Accuracy -- 2.4.2 Challenges in the Industrial Environment -- 2.4.3 Challenges to System Adoption -- 2.5 Innovations and Future Trends -- 2.5.1 Hybridization -- 2.5.2 Filtering. , 2.5.3 Machine Learning -- 2.5.4 Deep Learning -- 2.6 Conclusion -- References -- 3 Unified IIoT Cloud Platform for Smart Factory -- Abstract -- 3.1 Introduction -- 3.2 Related Works -- 3.3 Unified IIoT Cloud Platform (Harmony) -- 3.3.1 Architecture -- 3.3.2 Back-End Framework -- 3.3.2.1 ASP.NET -- 3.3.2.2 Microsoft SQL Server -- 3.3.3 Edge Connector Service -- 3.3.3.1 MQTT (Client) -- 3.3.3.2 OPC UA -- 3.3.3.3 XML -- 3.3.4 Front-End User Interface (UI) -- 3.4 Results -- 3.5 Conclusion and Future Work -- Acknowledgements -- References -- 4 A Perspective into Analysing Tool Wear Condition in Hard-Turning Process-The Key Lessons Learnt -- Abstract -- 4.1 Introduction -- 4.2 Brief State-of-the-Art -- 4.2.1 Health Monitoring with Limited Degradation Data -- 4.2.2 Predictions Across Multi-cutting Conditions -- 4.2.3 RUL Estimation with Deep Learning -- 4.2.4 Feature Engineering Versus Deep Transfer Learning -- 4.2.5 Unified RUL Predictions -- 4.3 Hard Turning Process -- 4.3.1 Design of Experiments (DoE) -- 4.3.1.1 Experiments for Tool Wear Classification (DoE-1) -- 4.3.1.2 Experiments for Tool Wear Prediction and RUL Estimation (DoE-2) -- 4.3.1.3 Experiments for Unified Tool Wear RUL Predictions (DoE-3) -- 4.4 Data Analysis Framework -- 4.4.1 Data Pre-processing -- 4.4.2 Feature Engineering -- 4.4.2.1 Feature Extraction -- 4.4.2.2 Feature Selection -- 4.4.3 Modelling Methods -- 4.4.3.1 Methodology for Tool Wear Classification -- 4.4.3.2 Methodology for Tool Wear Prediction with Ensemble -- 4.4.3.3 Methodology for Tool Wear RUL Estimation with Deep Learning -- 4.4.3.4 Methodology for Tool Wear Detection with Deep Transfer Learning -- 4.4.3.5 Methodology for Unified Tool Wear RUL Predictions -- 4.4.4 Performance Evaluation -- 4.5 Key Results and Discussion -- 4.5.1 Results for Tool Wear Classification -- 4.5.2 Results for Tool Wear Prediction with Ensemble. , 4.5.3 Results for Tool Wear RUL Estimation with Deep Learning -- 4.5.4 Results for Tool Wear Detection with Deep Transfer Learning -- 4.5.5 Results for Unified Tool Wear RUL Predictions -- 4.6 Conclusions -- Acknowledgements -- References -- 5 Condition Monitoring for Predictive Maintenance of Machines and Processes in ARTC Model Factory -- Abstract -- 5.1 Introduction -- 5.1.1 Purpose of Condition Monitoring and Predictive Maintenance -- 5.1.2 Machine Monitoring -- 5.2 Sensorization -- 5.3 Data Acquisition System and Storage -- 5.4 Data Modelling and Machine Learning -- 5.4.1 Feature Extraction -- 5.4.1.1 Time and Statistical Domain Method for Tool Wear Prediction -- 5.4.1.2 Frequency Domain Method for Spindle Failure Analysis -- 5.4.2 Feature Ranking -- 5.5 Machine Learning Application for Modeling and Prediction in Turning -- 5.5.1 Tool Wear Size Modeling and Prediction -- 5.5.2 Modeling and Prediction of Workpiece Quality -- 5.6 Machine Learning Application for Modeling and Prediction of Tool Wear in Milling -- 5.6.1 Tool Wear Detection and Feature Extraction -- 5.7 Machine Learning Application for Process Monitoring and Tool Wear Detection in Deep Cold Rolling (DCR) -- 5.8 Conclusion -- References -- 6 Federated Learning for Advanced Manufacturing Based on Industrial IoT Data Analytics -- Abstract -- 6.1 Introduction -- 6.2 Background and Related Work -- 6.2.1 Industrial IoT for Smart Manufacturing -- 6.2.2 Localized Learning -- 6.2.3 Centralized Training for Smart Manufacturing -- 6.2.4 Federated Learning for Smart Manufacturing -- 6.2.4.1 Federated Learning-Privacy and Security -- 6.2.5 Multi-party Computation -- 6.2.5.1 Multi-party Computation-Challenges -- 6.2.6 Related Work-Federated Learning Framework -- 6.3 Proposed Architecture -- 6.3.1 Two-Phase MPC Enabled Federated Learning -- 6.3.2 System Architecture -- 6.3.2.1 Edge Computing. , 6.3.2.2 IIoT Platform -- 6.3.2.3 Data Analytics System -- 6.3.3 Machine Learning Models -- 6.3.3.1 Artificial Neural Network -- 6.3.3.2 Logistic Regression -- 6.3.3.3 Support Vector Machine -- 6.3.4 Federated Learning -- 6.4 Experimental Evaluation -- 6.4.1 Use Cases -- 6.4.1.1 Use Case 1: Tool Wear Detection in Machining Machine -- 6.4.1.2 Use Case 2: Fault Detection in Electrical Machines -- 6.4.2 Experimental Settings -- 6.4.3 Evaluation Metrics -- 6.4.3.1 Comparative Analysis-Federated Versus Centralized Versus Local -- 6.4.3.2 Performance of ML Algorithms -- 6.4.4 Communication Cost -- 6.4.5 Execution Time -- 6.5 Conclusion and Future Work -- References -- 7 Generalized Anomaly Detection Algorithm Based on Time Series Statistical Features -- Abstract -- 7.1 Introduction -- 7.2 Literature Review on Fault Detection on Rotatory Machine -- 7.3 Experiment Methodology -- 7.3.1 Experiment Setup -- 7.3.2 Data Acquisition System -- 7.3.3 Statistical Features -- 7.4 Problem Formulation -- 7.4.1 Pseudo-Code of the Generic Machine Learning Algorithm -- 7.4.1.1 Features Result from the Analysis -- 7.5 Result in Discussion -- 7.6 Conclusions -- Acknowledgements -- References -- 8 Online Overall Equipment Effectiveness (OEE) Improvement Using Data Analytics Techniques for CNC Machines -- Abstract -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Methodology -- 8.3.1 Data-Driven OEE Modules -- 8.3.2 Data Collection -- 8.3.3 Remaining Useful Life -- 8.3.4 Quality Prediction Model -- 8.3.5 Machine Performance Prediction -- 8.3.6 OEE Calculation and Factor Analysis -- 8.4 System Implementation -- 8.4.1 Software Architecture -- 8.4.2 Data Analytic Service Implementation -- 8.4.3 OEE Before Using ML Prediction Models -- 8.4.4 OEE After Using ML Prediction Models -- 8.5 Conclusions -- Acknowledgements -- References. , 9 A Review of Dynamic Scheduling: Context, Techniques and Prospects -- Abstract -- 9.1 Introduction -- 9.2 Context and Objective -- 9.2.1 Context of Scheduling -- 9.2.2 Objective of Scheduling -- 9.3 Dynamic Scheduling Approaches -- 9.3.1 Completely Reactive Approaches -- 9.3.2 Robust Proactive Approaches -- 9.3.3 Predictive-Reactive Approaches -- 9.4 Dynamic Scheduling Techniques -- 9.4.1 Heuristics -- 9.4.1.1 Schedule Repair Methods -- 9.4.1.2 Dispatching Rules -- 9.4.2 Meta-Heuristics -- 9.4.2.1 Genetic Algorithm -- 9.4.2.2 Particle Swarm Optimization -- 9.4.2.3 Ant Colony Optimization [136] -- 9.4.3 Machine Learning Based Approaches -- 9.4.3.1 Artificial Neural Network -- 9.4.3.2 Reinforcement Learning -- 9.4.4 Multi-agent System Approach -- 9.5 Summary and Research Direction -- References -- 10 Digital Twin Architecture and Development Trends on Manufacturing Topologies -- Abstract -- 10.1 Introduction -- 10.1.1 Background of the Need for Digital Twins -- 10.1.1.1 Digital Twin Evolution -- 10.1.1.2 DT-Enhanced Value Creation -- 10.1.2 Analysis of DT Fundamentals and Other 'Twin' Concepts -- 10.1.2.1 DT Components and Functionalities -- 10.1.2.2 Roadmap for Establishing a DT System -- 10.1.2.3 Variants of DT in Different Domains -- 10.1.3 Significance and Applications of DT -- 10.1.3.1 Technologies to Achieve DT-Enhanced Capabilities -- 10.1.3.2 DT Influence in Manufacturing and Alternate Sectors -- 10.2 Digital Twin-Driven Solutions to Enable Intelligent Manufacturing -- 10.2.1 DT Influence in Manufacturing and Alternate Sectors -- 10.2.2 DT Impact on Key Manufacturing Companies -- 10.3 Digital Twin-Driven Solutions to Enable Intelligent Manufacturing -- 10.3.1 Integrated Technologies in Model Factory -- 10.3.2 Model Factory @ARTC, Singapore -- 10.4 Challenges and Future Trends -- 10.4.1 Key Challenges Hindering Mass DT Adoption. , 10.4.2 Potential Advancements in DT Capabilities.

Note: Intro -- Contents -- Research on Improved K-Means Clustering Algorithm for Smart Energy Meter Based on Climatic Features -- 1 Introduction -- 2 Selection of Climatic Features -- 3 The Clustering Method of Smart Energy Meter Based on Improved K-Means Algorithm -- 4 Simulation and Result Analysis -- 5 Conclusion -- References -- A Method to Solve the Measurement Error of Track Safety Control Based on Weighted Fusion -- 1 Introduction -- 2 Calculation Model of Ground Safety Control Track -- 3 Calculation Method of Weight Measurement Error -- 3.1 Calculation of Correlation Number -- 3.2 Weight Factor Calculation -- 3.3 Weight Measurement Error Calculation -- 4 Calculation Example -- 4.1 Calculation of Correlation Number -- 4.2 Weight Measurement Errors Calculation -- 4.3 Track Safety Control Calculation -- 5 Conclusion -- References -- Improved Spectral Efficiency Based on Double Spatially Sparse in Millimeter Wave MIMO System -- 1 Introduction -- 2 System Model -- 2.1 System Model -- 2.2 Sparse Array Model -- 3 Simulation Results -- 4 Conclusion -- References -- Electromagnetic Compatibility Test Analysis of Space Launch Field Based on Wavelet Analysis -- 1 Introduction -- 2 Necessity of Electromagnetic Compatibility Test of Space Launch Field -- 3 Multi-resolution Characteristics of Wavelet Analysis -- 3.1 Wavelet and Wavelet Transform -- 3.2 Multi-resolution Characteristics of Wavelet Analysis -- 4 Electromagnetic Compatibility Test Analysis of Space Launch Field Based on Wavelet Analysis -- 4.1 Transmit Field Electromagnetic Compatibility Test -- 4.2 Wavelet Analysis of Typical Electromagnetic Interference in Electromagnetic Compatibility Test -- 5 Concluding Remarks -- References -- Design of a Biometric Access Control System Based on Fingerprint Identification Technology -- 1 Introduction -- 2 Hardware Design of the System. , 2.1 MCU Chip C8051F020 -- 2.2 Fingerprint Sensor FPS200 -- 2.3 Gate Array Logic (GAL) Device and External SRAM Circuit -- 2.4 Other Module Circuits -- 3 Software Design of the System -- 4 Debugging and Implementation of the System -- 5 Conclusion -- References -- Aeronautical Meteorological Decision Supporting Technology Based on 4D Trajectory Prediction -- 1 Introduction -- 2 Overview of Aviation Meteorology -- 3 A New Generation of Aviation Meteorological Technology Research -- 3.1 Meteorological Information Sharing Technology -- 3.2 Meteorological Fusion Display Technology -- 3.3 Five-Dimensional Visualization Meteorological Information Technology of Routes -- 4 Aeronautical Meteorological Assistant Decision-Making Technology Based on 4D Trajectory Prediction -- 5 Conclusion -- References -- Evaluation Index System and Case Study for Smart ATM Based on ASBU -- 1 Introduction -- 2 Research Status -- 3 Construction of Index System -- 3.1 Target and Principle -- 3.2 Analysis of Hierarchy of ASBU -- 3.3 Index System -- 4 Case Study -- 5 Conclusion -- References -- Research on the Development Trend of Artificial Intelligence Based on Papers and Patent Analysis-Data Comparison Between China and the United States -- 1 Introduction -- 2 Paper Analysis -- 2.1 Retrieval Ideas and Strategies -- 2.2 Annual Trends and Analysis of Publication Volume -- 2.3 Analysis of Highly Cited Papers -- 3 Patent Analysis -- 3.1 Basic Trends of Patents -- 3.2 Patent Data Analysis -- 4 Summary and Prospect -- References -- Analysis of 5G and Beyond: Opportunities and Challenges -- 1 Introduction -- 2 Typical Advantages of 5G -- 2.1 EMBB -- 2.2 URLLC -- 2.3 MMTC -- 3 Opportunities and Challenges -- 3.1 Opportunities -- 3.2 Challenges -- 4 Conclusion -- References -- Prediction of PM2.5 Concentration Based on Support Vector Machine and Ridge. , 1 Research Background and Significance -- 2 Related Work -- 3 Experiment -- 4 Ridge -- 5 SVM -- 6 Conclusion -- References -- Helmet Detection Based on an Enhanced YOLO Method -- 1 Introduction -- 2 Yolo Models -- 3 Data Engineering -- 3.1 Data Set -- 3.2 Data Annotation -- 3.3 Data Enhancement -- 4 Experiment Evaluation -- 4.1 Evaluation Setup -- 5 Result -- 6 Conclusion -- References -- Feature Extraction and Classification of Unknown Types of Communication Emitter -- 1 Introduction -- 2 Feature Extraction and Classification -- 2.1 Feature Extraction with Convolutional Neural Network -- 2.2 Clustering with K-Means -- 3 Experiments and Results -- 3.1 Data Collection and Experimental Setup -- 3.2 Feature Extractor Performance -- 3.3 The Performance of Classification -- 4 Conclusion -- References -- Research on Escape Strategy Based on Intelligent Firefighting Internet of Things Virtual Simulation System -- 1 Introduction -- 2 Design of Virtual Simulation System -- 2.1 Perception Layer Design -- 2.2 Network Layer Design -- 2.3 Application Layer Design -- 3 Realization of Escape Strategy Decision -- 3.1 Assemble Sensor Nodes -- 3.2 Design Wireless Networking Mode -- 3.3 Simulate the Real Fire -- 4 Conclusion -- References -- Weather Identification-Based Multi-level Visual Feature Combination -- 1 Introduction -- 2 SCRFS Dataset -- 3 Methodology -- 3.1 Resized and Cropped Images -- 3.2 Convolutional Neural Networks -- 3.3 Support Vector Machine -- 3.4 Image Description-Feature Extraction -- 4 Results -- 5 Conclusion -- References -- Railway Tracks Defects Detection Based on Deep Convolution Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Our Proposed Method -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Dataset Description -- 4.3 Results and Discussion -- 5 Conclusions -- References. , Efficiency Evaluation of Deep Model for Person Re-identification -- 1 Introduction -- 2 Method -- 2.1 Baseline -- 2.2 PCB -- 3 Experiments -- 3.1 Market-1501 -- 3.2 Experiment Settings -- 3.3 Efficiency Evaluation -- 4 Conclusion -- References -- Cloud Recognition Using Multimodal Information: A Review -- 1 Introduction -- 2 Approach -- 2.1 Separate Fusion -- 2.2 End-to-End Fusion -- 3 Experiments -- 3.1 Datasets -- 3.2 Experiment Results -- 4 Conclusion -- References -- Graph Convolution Network for Person Re-identification -- 1 Introduction -- 2 Approach -- 2.1 GCN for Global Feature -- 2.2 GCN for Local Feature -- 3 Experiments -- 3.1 Dataset -- 3.2 Comparison with Other Approaches -- 4 Conclusion -- References -- Cross-Domain Person Re-identification: A Review -- 1 Introduction -- 2 Cross-Domain Person Re-identification -- 2.1 The GAN-Based Methods -- 2.2 The Non GAN-Based Methods -- 3 Experiments -- 4 Conclusion -- References -- A Weighted Least Square Support Vector Regression Method with MPP-GGP Based Sequential Sampling for Efficient Reliability Analysis -- 1 Introduction -- 2 Methods -- 2.1 Sequential Sampling Method -- 2.2 Weighting Function -- 2.3 Procedure -- 3 Application Examples -- 3.1 Complex Nonlinear Function -- 3.2 Flutter Analysis of Composite Wing -- 4 Conclusions -- References -- Design of Quadrotor Automatic Tracking UAV Based on OpenMV -- 1 Introduction -- 2 Hardware System Design -- 2.1 Tm4c123gmicrocontroller -- 2.2 OpenMV Machine Vision Module -- 2.3 Other Modules -- 3 The Realization of Tracking Function -- 3.1 Dynamic Model Establishing -- 3.2 PID Attitude Regulation -- 3.3 Function Realization -- 4 Conclusion -- References -- A Reliability Evaluation Model of Intelligent Energy Meter in Typical Environment -- 1 Introduction -- 2 Establishment of Evaluation Model -- 2.1 The Construction Process of Evaluation Model. , 2.2 Establishment of Index System -- 2.3 Improved Grey Relation Analysis Method -- 3 Case Study -- 4 Conclusion -- References -- Analysis and Prediction of the Resettlement for Climate Refugees in the Maldives -- 1 Introduction -- 2 Prediction of Sea Level Rise and EDPs Population -- 2.1 Land Loss -- 2.2 EDPs Population -- References -- Smart Electricity Meters Test Data Management Service System -- 1 Introduction -- 2 System Design -- 2.1 Android Phone APP Module -- 2.2 Smart Electricity Meters Test management Service System Based on B/S Architecture -- 3 Database Design -- 4 Back-End Technology -- 4.1 Cloud Server -- 4.2 Django Framework -- 5 System Implementation -- 5.1 Android Phone Module -- 5.2 B/S Architecture-Based Intelligent Energy Meter Test Management Service System -- 6 System Test -- 7 Conclusion -- References -- Power Equipment Identification Based on Single Shot Detector -- 1 Introduction -- 2 Related Work -- 3 SSD-Based Power Equipment Identification -- 3.1 Image Preprocessing -- 3.2 Feature Extraction -- 3.3 SSD Model Based on Deep Learning Network Framework -- 4 Experiment and Analysis -- 4.1 Introduction to the Dataset -- 4.2 Introduction to the Experimental Platform -- 4.3 Experimental Results and Analysis -- 5 Conclusion -- References -- Power Equipment Defect Detection Algorithm Based on Deep Learning -- 1 Introduction -- 1.1 Research Background -- 1.2 Research Status at Home and Abroad -- 2 Research on Improvement of Power Equipment Defect Detection Algorithm -- 2.1 Extraction of Multi-Scale Features -- 2.2 For Detecting Defects of Different Shapes -- 2.3 Optimized Learning Strategies -- 3 Experimental Results -- 3.1 Experimental Results -- 4 Summary and Outlook -- References -- Research on Active Learning Method Based on Domain Adaptation and Collaborative Training -- 1 Introduction -- 1.1 Research Background and Significance. , 2 Active Learning Model.

Note: Cover -- Title -- Copyright -- Contents -- Preface for the English version -- Preface for the Chinese version -- 1 Hempel's scientific explanation models and their problems -- 2 Six decades of scientific explanation -- 3 The very nature of laws of nature -- 4 The conceptions of scientific explanation and approaches to laws of nature -- 5 Causal mechanism and lawful explanation -- 6 Is there such a thing as a ceteris paribus law? -- 7 Explanation and reduction -- 8 Scientific explanation and historical interpretation -- 9 Synthesis: explanation, laws, and causation -- Bibliography -- Index.

Note: Intro -- Preface -- Contents -- Part I Thermo-responsive Hydrogel Functional Materials -- Chapter 1: Structure-Function Relationship of Thermo-responsive Hydrogels -- 1.1 Introduction -- 1.2 Effect of Internal Microstructure on the Equilibrium Thermo-responsive Phase Transition -- 1.3 Effect of Internal Microstructure on the Dynamic Thermo-responsive Phase Transition -- 1.4 Effect of Internal Microstructureon the Thermo-responsive Controlled-Release Characteristics -- 1.5 Effect of Internal Microstructure on the Mechanical Strength of Thermo-responsive Hydrogels -- 1.6 Summary -- References -- Chapter 2: Preparation and Properties of Monodisperse Thermo-responsive Microgels -- 2.1 Introduction -- 2.2 Submicron-Sized Monodisperse Thermo-responsive Core-Shell Hydrogel Microspheres Fabricated via Surfactant-Free Emulsion Polymerization -- 2.2.1 Preparation of P(NIPAM-co-St) Seeds -- 2.2.2 Preparation of Core-Shell Microspheres with PNIPAM Shell Layers -- 2.2.3 Monodispersity of Core-Shell Microspheres with P(NIPAM-co-St) Cores and PNIPAM Shell Layers -- 2.2.4 Thermo-responsive Characteristics of the Core-Shell Microspheres with PNIPAM Shell Layers -- 2.3 Positively Thermo-responsive Submicron-Sized Monodisperse Core-Shell Hydrogel Microspheres -- 2.3.1 Preparation of Positively Thermo-responsive Submicron-Sized Monodisperse Core-Shell Hydrogel Microspheres -- 2.3.2 Morphological Analyses of the Microspheres -- 2.3.3 Positively Thermosensitive Swelling Characteristics -- 2.4 Monodisperse Thermo-responsive Hydrogel Microspheres and Microcapsules Prepared via Membrane Emulsification -- 2.4.1 Strategies for Preparation of Monodisperse PNIPAM Microspheres and Microcapsules via Membrane Emulsification -- 2.4.2 Morphology of Prepared Monodisperse PNIPAM Microspheres -- 2.4.3 Morphology of Prepared Monodisperse PNIPAM Microcapsules. , 2.4.4 Effect of Freeze-Drying and Rehydrating Treatment on the Thermo-responsive Characteristics of PNIPAM Microspheres -- 2.5 Monodisperse Thermo-responsive Hydrogel Microspheres and Microcapsules Fabricated with Microfluidics -- 2.5.1 Microfluidic Fabrication of Monodisperse Thermo-responsive Microgels with Tunable Volume-Phase Transition Kinetics -- 2.5.2 Fabrication of MonodisperseThermo-responsive Microgels in a Microfluidic Chip -- 2.5.3 Fabrication of Monodisperse Microspheres with PNIPAM Core and Poly(2-Hydroxyethyl Methacrylate) (PHEMA) Shell -- 2.6 Summary -- References -- Chapter 3: Flow and Aggregation Characteristicsof Thermo-responsive Microgels During Phase Transition -- 3.1 Introduction -- 3.2 Flow and Aggregation Characteristicsof Thermo-responsive Spheres During the Phase Transition -- 3.2.1 Preparation of Monodisperse PNIPAM Hydrogel Spheres -- 3.2.2 Thermo-responsive Volume-Phase Transition Characteristics of PNIPAM Hydrogel Spheres -- 3.2.3 Flow Characteristics of PNIPAM Hydrogel Spheres During the Phase Transition in a Transparent Glass Pipe -- 3.3 Flow Characteristics of Thermo-responsive Microspheres in Microchannel During the Phase Transition -- 3.3.1 Synthesis of Microspheres in a Simple Microfluidic Device -- 3.3.2 Flow Characteristics of PNIPAM Microspheres in Horizontal Microchannel at Low Reynolds Number of Fluid -- 3.3.3 Effect of the Diameter Ratio of PNIPAM Microsphere to Microchannel on the Flow Characteristics -- 3.4 Effects of Microchannel Surface Property on Flow Behaviors of Thermo-responsive Microspheres During the Phase Transition -- 3.4.1 Modification of Inner Surface of Glass Microchannel -- 3.4.2 Characterization of Wettability and Roughness of Modified Glass Microchannels -- 3.4.3 Effects of Surface Wettability and Roughness of Microchannel on the Average Velocity of Fluid in Microchannel. , 3.4.4 Effect of Surface Wettability and Roughness of Microchannel on Aggregation Behaviors of PNIPAM Microspheres During the Phase Transition -- 3.4.5 Effect of Surface Wettability of Microchannel on Flow Characteristics of PNIPAM Microspheres -- 3.4.6 Effect of Surface Roughness of Microchannel on Flow Characteristics of PNIPAM Microspheres -- 3.4.7 Flow Behaviors of PNIPAM Microspheres in Microchannel with Hydrophobic and Rough Surface During the Phase Transition -- 3.5 Summary -- References -- Chapter 4: Polyphenol-Induced Phase Transition of Thermo-responsive Hydrogels -- 4.1 Introduction -- 4.2 Phase Transition Behaviors of PNIPAM Microgels Induced by Tannic Acid -- 4.2.1 Preparation of Monodisperse PNIPAM Microgels -- 4.2.2 Dynamic Isothermal Volume-Phase Transition of PNIPAM Microgels Induced by TA -- 4.2.3 Equilibrium Isothermal Volume-Phase Transition of PNIPAM Microgels Induced by TA -- 4.2.4 Thermosensitive Phase Transition of PNIPAM Microgels in TA Solutions -- 4.3 Phase Transition Behaviors of PNIPAM Microgels Induced by Ethyl Gallate -- 4.3.1 Preparation of PNIPAM Microspheres and Core-Shell PNIPAM Microcapsules -- 4.3.2 Thermo-responsive Phase Transition Behaviors of PNIPAM Microspheres in EG Solution -- 4.3.3 The Intact-to-Broken Transformation Behaviors of Core-Shell PNIPAM Microcapsules in Aqueous Solution with Varying EG Concentration -- 4.4 Summary -- References -- Chapter 5: Functional Membranes with Thermo-responsive Hydrogel Gates -- 5.1 Introduction -- 5.2 Functional Membranes with Thermo-responsive Hydrogel Gates Fabricated by Plasma-Induced Pore-Filling Graft Polymerization -- 5.2.1 Regulation of Response Temperature of Thermo-responsive Membranes -- 5.2.2 Effect of Grafting Degreeon the Thermo-responsive GatingCharacteristics. , 5.2.3 Gating Characteristics of Thermo-responsive Membranes with Grafted Linear and Cross-linked Hydrogel Gates -- 5.2.4 Membranes with NegativelyThermo-responsive Hydrogel Gates -- 5.2.5 Composite Thermo-responsive Membrane System -- 5.2.6 Thermo-responsive Affinity Membrane -- 5.3 Functional Membranes with Thermo-responsive Hydrogel Gates Fabricated by Atom-Transfer Radical Polymerization -- 5.4 Functional Membranes with Thermo-responsive Hydrogel Gates Fabricated by Free-Radical Polymerization -- 5.5 Summary -- References -- Chapter 6: Functional Microcapsules with Thermo-responsive Hydrogel Shells -- 6.1 Introduction -- 6.2 Functional Microcapsules with GraftedThermo-responsive Hydrogel Chains in the Porous Membranes as Gates -- 6.3 Functional Microcapsules with Thermo-responsive Microgels in the Membranes as Gates -- 6.4 Functional Microcapsules with Thermo-responsive Cross-linked Hydrogels as Membranes -- 6.5 Summary -- References -- Part II pH-Responsive Hydrogel Functional Materials -- Chapter 7: Preparation and Properties of Monodisperse pH-Responsive Microgels -- 7.1 Introduction -- 7.2 Monodisperse pH-Responsive Chitosan Microgels -- 7.3 Monodisperse Cationic pH-Responsive Microgels -- 7.4 Monodisperse Cationic pH-Responsive Hydrogel Capsules -- 7.5 Summary -- References -- Chapter 8: pH-Responsive Membranes and Microcapsules for Controlled Release -- 8.1 Introduction -- 8.2 pH-Responsive Gating Membrane System with Pumping Effect for Improved Controlled Release -- 8.3 pH-Responsive Microcapsules for Burst Release of Hydrophobic Drugs -- 8.4 Monodisperse Core/Shell Microcapsulesfor pH-Responsive Controlled Release -- 8.5 Summary -- References -- Part III Thermo-/pH-Dual-Responsive Hydrogel Functional Materials -- Chapter 9: Thermo-/pH-Dual-Responsive Hydrogels with Rapid Response Properties -- 9.1 Introduction. , 9.2 Thermo-/pH-Dual-Responsive Hydrogels with Rapid Response -- 9.2.1 Fabrication of Comb-Type Grafted P(NIPAM-co-AAc) Hydrogels -- 9.2.1.1 Synthesis of Macromonomers -- 9.2.1.2 Synthesis of Comb-Type Grafted P(NIPAM-co-AAc) Hydrogels -- 9.2.2 Deswelling Kinetics of Hydrogelsin Various Conditions -- 9.2.2.1 Deswelling Behavior of Hydrogels in Pure Water with Temperature Changes -- 9.2.2.2 Deswelling Behavior of Hydrogels in pH Buffers at Room Temperature with pH Changes -- 9.2.2.3 Deswelling Behavior of Hydrogels in pH 7.4 Buffer Undergoing Temperature Changes -- 9.2.2.4 Deswelling Behavior of Hydrogels in pH 2.0 Buffer with Temperature Changes -- 9.2.2.5 Deswelling Behavior of Hydrogel in Buffer with Dual Thermo-change and pH Change -- 9.3 Graft-Type Microgels with Rapid Thermo-responsive and pH-Responsive Properties -- 9.3.1 Fabrication of Graft-Type Microgels -- 9.3.1.1 Synthesis of Poly(NIPAM-co-AAc) Macromonomers -- 9.3.1.2 Synthesis of Graft-Type Poly(NIPAM-co-AAc) Microgels -- 9.3.2 Temperature Dependence of Swelling/Deswelling Degree of Microgels in Water -- 9.3.3 Equilibrium Swelling/Deswelling Degree of Microgels in pH Buffers -- 9.3.4 Deswelling Kinetics of Microgels in Ultrapure Water -- 9.3.5 Deswelling Kinetics of Microgels in pH Buffers -- 9.4 Rapid pH-/Temperature-Responsive Cationic Hydrogels with Grafted Side Chains -- 9.4.1 Fabrication of Cationic Hydrogels with Grafted Side Chains -- 9.4.1.1 Synthesis of Macromonomers -- 9.4.1.2 Synthesis of Comb-Type Grafted Poly(NIPAM-co-DMAEMA) Hydrogels -- 9.4.2 Effects of pH and Temperature on the Equilibrium Swelling Ratio (SR) -- 9.4.3 Dynamic Swelling/Deswelling Behaviors of Hydrogels in pH Buffer Solutions at Fixed Temperature -- 9.4.4 Dynamic Deswelling Behaviors of Hydrogels in Fixed pH Buffer Solutions with Temperature Stimuli. , 9.4.5 Dynamic Deswelling Behaviors of Hydrogels in Buffer Solutions with Both pH and Temperature Stimuli.