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109081
Fractals in Engineering: Theoretical Aspects and Numerical Approximations
Cham : Springer International Publishing : Imprint: Springer, 2021
1st ed. 2021.Format: Electronic eBookFull text (Wentworth users only)
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109082
Models for discrete data
Oxford : Oxford University Press, 2006
Rev. ed.Format: Electronic eBookFull text (Emerson users only)
Full text (Emmanuel users only)
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109083
Unbalanced : a view from the vestibule : schizophrenia and hyperattention
Singapore ; Hackensack, NJ : World Scientific, 2011Format: Electronic eBookFull text (Emerson users only)
Full text (Emmanuel users only)
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109084
Handbook of single molecule fluorescence spectroscopy
Oxford : Oxford University Press, 2006Format: Electronic eBookFull text (Emerson users only)
Full text (Emmanuel users only)
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109085
Free the Mind.
[San Francisco, California, USA] : Kanopy Streaming, 2016Format: Electronic VideoStreaming video (Wentworth users only)
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109086
La jetée
[San Francisco, California, USA] : Kanopy Streaming, 2014Format: Electronic VideoStreaming video (Emerson users only)
Streaming video (Wentworth users only)
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109087
Battle of the Ages
[Place of publication not identified] : National Geographic Television & Film, 2014Format: Electronic VideoStreaming video (Emerson users only)
Streaming video (Wentworth users only)
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109088
Quantum Causality Conceptual Issues in the Causal Theory of Quantum Mechanics
Dordrecht : Springer Netherlands, 2009
1.Format: Electronic eBookFull text (Wentworth users only).
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109089
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109090
MOVE TO THE EDGE, DECLARE IT CENTER
[Place of publication not identified] : JOHN WILEY & SONS, 2022Format: Electronic eBookFull text (Wentworth users only)
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109091
Brain games. Season 3, Episode 10, Follow the leader
District of Columbia : National Geographic, 2014Format: Electronic VideoFull text (Emerson users only)
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109092
Advances in biometrics for secure human authentication and recognition
Boca Raton : Taylor & Francis, 2014Format: Electronic eBookAccess E-Book
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109093
Applied statistics : theory and problem solutions with R
Hoboken, NJ, USA : Wiley, 2020Format: Electronic eBookFull text (Wentworth users only)
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109094
IoT, machine learning and blockchain technologies for renewable energy and modern hybrid power systems
[United States] : River Publishers, 2022Table of Contents: “…93 5.1.1 Phrases in Machine Learning 94 5.1.2 Steps Involved in Machine Learning Practices 94 5.1.3 Properties of Data 94 5.1.4 Real-World Applications of Machine Learning 95 5.2 Classification of Machine Learning Techniques 96 5.2.1 Supervised Learning 96 5.2.1.1 Classification 97 5.2.1.2 Regression 98 5.2.2 Unsupervised Learning 99 5.2.2.1 Clustering 99 5.2.2.2 Association 100 5.2.3 Reinforcement Learning 100 5.2.3.1 Crucial terms in reinforcement learning 101 5.2.3.2 Salient features of reinforcement learning 102 5.2.3.3 Types of reinforcement learning 102 5.2.3.4 Reinforcement learning algorithms 103 5.3 Some Crucial Algorithmic Mathematical Models in Machine Learning 104 5.3.1 Logistic Regression 104 5.3.2 Decision Trees 105 5.3.3 Linear Regression 107 5.3.4 K-Nearest Neighbors 108 5.3.5 K-Means Clustering 110 5.4 Pre-Eminent Python Libraries Intended for Machine Learning 112 5.4.1 Human Detection (OpenCV, HoG, SVM with Multi-Threading) 113 5.4.2 Instagram Filters ⁰́₃ (OpenCV, Matplotlib, NumPy) 114 5.5 Machine Learning Techniques in State of Affairs of Power Systems 115 5.6 Conclusion 117 References 118 6 Machine Learning Techniques for Renewable Energy Resources 121 6.1 Introduction 122 6.2 Overview of Machine Learning 126 6.3 Deep Learning Architecture 128 6.4 LSTM Network Based Prediction 132 6.5 Concepts of Solar PV and its MPPT Techniques 134 6.6 Simulation Results and Discussion 135 6.6.1 Modeling and Performance Analysis 135 6.6.2 Prediction or Forecasting Methodology 141 6.6.3 Utilizing Predicted Value in MPPT Technique 143 6.7 Conclusion and Future Directions 145 References 146 7 Application of Optimization Technique in Modern Hybrid Power Systems 149 7.1 Introduction 150 7.2 Modern Power System 151 7.2.1 Deregulated Power System 152 7.2.2 Components of Deregulation 152 7.2.3 Types of Transactions 154 7.2.3.1 Bilateral transactions 154 7.2.3.2 DPM and APF 155 7.2.4 Renewable Energy Sources 156 7.2.4.1 Doubly fed induction generator 156 7.2.4.2 DFIG in deregulated power system 158 7.3 Optimization Techniques and Proposed Technique 161 7.3.1 Controllers 161 7.3.2 PI Controller 161 7.3.3 Artificial Optimization Algorithm for Tuning PI 162 7.3.3.1 Differential evolution 162 7.3.3.2 Flower pollination algorithm 163 7.3.3.3 Hybrid algorithm 164 7.3.3.4 Design of a hybrid DE-FPA algorithm for LFC 165 7.4 Simulation Results and Discussion 165 7.5 Conclusion 167 References 169 8 Application of Machine Learning Techniques in Modern Hybrid Power Systems ⁰́₃ A Case Study 173 8.1 Introduction 174 8.2 Technical Issues in Modern Hybrid Power Systems 176 8.2.1 Power Quality 177 8.2.2 Demand-Supply Management 177 8.2.3 Synchronization and Islanding 177 8.2.4 Protective Devices, Safety, and Environment 177 8.2.5 Human Factor 178 8.3 Application of ML and Optimization Techniques in MHPS 178 8.4 A Prediction Case Study of ML in MHPS 179 8.4.1 Forecasting Irradiance of SPP 182 8.4.2 Metrics for Understanding the Performance of Predictions using ML Methods 184 8.4.3 Model-Based and Model-Free Regression Techniques 185 8.4.4 Prediction Block 186 8.4.5 Forecasting of Solar Irradiance with a Model-Based Regression Approach 187 8.4.6 Forecasting of Solar Irradiance with a Model-Free Regression Approach (ANNs) 190 8.4.7 Normalization, Training, and Testing for Model-Free Regression 191 8.5 Optimization Block in MHPS 193 8.5.1 Optimization-Assisted ML of MHPS 193 8.5.2 Experimental Setup 197 8.5.3 Validation Block 197 8.5.3.1 Thorough comparisons in voltage-magnitudes for the actual test day for model-based and model-free approaches 198 8.6 Conclusion 200 References 201 9 Establishing a Realistic Shunt Capacitor Bank with a Power System using PSO/ACCS 205 9.1 Introduction 206 9.2 Problem Statement 208 9.2.1 Power Flow Equations 209 9.2.2 Mathematical Representation 210 9.2.3 Sensitivity Calculations 211 9.3 Capacitor Bank Operation Strategies 212 9.4 Particle Swarm Optimization 214 9.5 Limitation Treatment 216 9.6 PSO Implementation for Offline Capacitor Study 216 9.7 Simulation System for Optimal Capacitor Allocation 218 9.7.1 Modified System Data 219 9.7.2 Simulation Study 221 9.8 Automatic Capacitor Control Scheme 224 9.8.1 ACCS IED Scope 225 9.8.2 ACCS Operation Logic Steps 225 9.8.3 ACCS Operation Sample 227 9.9 Conclusion 230 References 231 10 Introduction to Blockchain Technologies 235 10.1 Introduction and Classification 236 10.2 Blockchain Technology Characteristics 237 10.2.1 Multi-Centralization 237 10.2.2 Tamper-Proof, Traceable, and Transparent 237 10.2.3 High Reliability 238 10.3 Blockchain Technology Graph 238 10.3.1 Core Technology Overview 239 10.3.2 Expansion Technology Overview 248 10.3.3 Supporting Technology Overview 251 10.4 Conclusion 253 References 254 11 Blockchain Technologies for Renewable Energy Resources with Case Study: SHA⁰́₃256, 384, and 512 257 11.1 Introduction 258 11.2 Local Energy Trading and Consensus Algorithms 258 11.3 Simulation 260 11.3.1 Energy Trading Model and Case Study 260 11.3.2 Performance Result and Evaluation of the Models at Different Hash Algorithms 262 11.4 Conclusion and Recommendations 266 References 268 Index 271 About the Editors 273.…”
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109095
Use of hydrocolloids to control food appearance, flavor, texture, and nutrition
Hoboken, NJ, USA : Wiley, 2023Table of Contents:Format: Electronic eBookFull text (Wentworth users only)
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109096
The selection process of biomass materials for the production of bio-fuels and co-firing
Hoboken, New Jersey : IEEE/Wiley, 2014Table of Contents: “…8.3.3 Methodology Standard Deviation for S & T / 249 -- 8.3.4 Methodology Standard Deviation for BF / 250 -- 8.3.5 Methodology Standard Deviation / 251 -- 8.4 Analysis / 251 -- 8.5 Conclusion / 255 -- References / 257 -- 9 Results: Part 2 259 -- 9.1 Data and Methodology Application / 259 -- 9.1.1 Introduction / 259 -- 9.2 Tests / 260 -- 9.2.1 Experimental Tests / 260 -- 9.3 S & T Samples Data and Reports (Results) / 265 -- 9.3.1 Fossil Fuel / 265 -- 9.3.2 Biomass Materials / 266 -- 9.4 BF Samples Reports Examples (Results) / 277 -- 9.4.1 Coal BF Data (Altawell, GSTF, 2012) / 277 -- 9.4.2 Rapeseed BF Report / 278 -- 9.4.3 Black Sunfl ower Seed BF Report / 278 -- 9.4.4 Niger Seed BF Report / 279 -- 9.4.5 Apple Pruning BF Report / 280 -- 9.4.6 Striped Sunfl ower Seed BF Report / 281 -- 9.5 The Final Biomass Samples / 282 -- 9.5.1 S & T Results / 282 -- 9.5.2 BF Results / 284 -- 9.6 Samples Final Fitness / 285 -- 9.7 Discussion and Analysis / 289 -- 9.8 Conclusion / 294 -- References / 296 -- 10 Economic Factors 297 -- 10.1 Biomass Fuel Economic Factors and SFS / 297 -- 10.1.1 Introduction / 297 -- 10.2 Economic Factors / 298 -- 10.3 Biomass Business / 300 -- 10.3.1 Step 1 / 300 -- 10.3.2 Step 2 / 301 -- 10.3.3 Step 3 / 302 -- 10.3.4 Step 4 / 304 -- 10.4 Biomass Fuel Supply Chain / 305 -- 10.5 The Demand for a New Biomass Fuel / 306 -- 10.6 The SFS Economic Value Scenario / 307 -- 10.7 Discussion / 308 -- 10.8 Conclusion / 310 -- References / 312 -- 11 Conclusion 315 -- 11.1 General Conclusion / 315 -- 11.2 Methodology (REA1) and Applications / 316 -- 11.3 Why Biomass? …”
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109097
Mining goes digital : proceedings of the 39th International Symposium 'Application of Computers and Operations Research in the Mineral Industry' (APCOM 2019), June 4-6, 2019, Wrocl...
Leiden, Netherlands : CRC Press, 2019Table of Contents: “…adysiewicz Comprehensive, experimental verification of the effects of the lock-up function implementation in LHD haul trucks in the deep underground mine T. …”
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109098
Drug design using machine learning
Hoboken, NJ : John Wiley & Sons, Inc., 2022Table of Contents: “…6.7 Challenges and Limitations for Machine Learning in Drug Discovery -- 6.8 Conclusion and Future Perspectives -- References -- 7 Loading of Drugs in Biodegradable Polymers Using Supercritical Fluid Technology -- 7.1 Introduction -- 7.2 Supercritical Fluid Technology -- 7.2.1 Supercritical Fluids -- 7.2.2 Physicochemical Properties -- 7.2.3 Carbon Dioxide -- 7.3 Biodegradable Polymers -- 7.3.1 Main Biologically-Derived Polymers Used With SCF Technologies -- 7.3.1.1 Cellulose -- 7.3.1.2 Chitosan -- 7.3.1.3 Alginate -- 7.3.1.4 Collagen -- 7.3.2 Main Synthetic Polymers Used With SCF Technologies -- 7.3.2.1 Polylactic Acid (PLA) -- 7.3.2.2 Poly (Lactic-co-Glycolic Acid) (PLGA) -- 7.3.2.3 Polycaprolactone (PCL) -- 7.3.2.4 Poly (Vinyl Alcohol) (PVA) -- 7.4 Drug Delivery -- 7.4.1 Types of Drugs -- 7.4.2 Influence of Experimental Conditions on the Drug Loading -- 7.5 Conclusion -- Acknowledgments -- References -- 8 Neural Network for Screening Active Sites on Proteins -- 8.1 Introduction -- 8.2 Structural Proteomics -- 8.2.1 PPIs -- 8.2.2 Active Sites in Proteins -- 8.3 Gist Techniques to Study the Active Sites on Proteins -- 8.3.1 In Vitro -- 8.3.1.1 Affinity Purification -- 8.3.1.2 Affinity Chromatography -- 8.3.1.3 Coimmunoprecipitation -- 8.3.1.4 Protein Arrays -- 8.3.1.5 Protein Fragment Complementation -- 8.3.1.6 Phage Display -- 8.3.1.7 X-Ray Crystallography -- 8.3.1.8 Nuclear Magnetic Resonance Spectroscopy (NMR) -- 8.3.2 In Vivo -- 8.3.2.1 In-Silico Two-Hybrid -- 8.3.3 In-Silico and Neural Network -- 8.3.3.1 Data Base -- 8.3.3.2 Sequence-Based Approaches -- 8.3.3.3 Structure-Based Approaches -- 8.3.3.4 Phylogenetic Tree -- 8.3.3.5 Gene Fusion -- 8.4 Neural Networking Algorithms to Study Active Sites on Proteins -- 8.4.1 PDBSiteScan Program -- 8.4.2 Patterns in Nonhomologous Tertiary Structures (PINTS) -- 8.4.3 Genetic Active Site Search (GASS).…”
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109099
Contemporary issues in systems science and engineering
Hoboken, New Jersey : John Wiley & Sons Inc., 2015Table of Contents: “…15.4 E-Cargo Model 590 -- 15.5 A Case Study with RBC and E-Cargo 592 -- 15.6 Conclusions 595 -- IV CLOUD AND SERVICE-ORIENTED COMPUTING 599 -- 16 CONTROL-BASED APPROACHES TO DYNAMIC RESOURCE MANAGEMENT IN CLOUD COMPUTING 601 /Pengcheng Xiong, Calton Pu, Zhikui Wang, and Gueyoung Jung -- 16.1 Introduction 601 -- 16.2 Experimental Setup and Application Models 603 -- 16.2.1 Test Bed and Control Architecture for a Multi-Tier Application 604 -- 16.3 Dynamic Resource Allocation Through Utilization Control 607 -- 16.4 Performance Guarantee Through Dynamic Resource Allocation 612 -- 16.5 Conclusions 614 -- 17 A PETRI NET SOLUTION TO PROTOCOL-LEVEL MISMATCHES IN SERVICE COMPOSITION 619 /Pengcheng Xiong, Mengchu Zhou, Calton Pu, and Yushun Fan -- 17.1 Introduction 619 -- 17.2 Modeling Service Interaction with Petri Nets 624 -- 17.3 Protocol-Level Mismatch Analysis 630 -- 17.4 Illustrating Examples 636 -- 17.5 Conclusions 638 -- 18 SERVICE-ORIENTED WORKFLOW SYSTEMS 645 /Wei Tan and Mengchu Zhou -- 18.1 Introduction 645 -- 18.2 Workflow in SOC: State of the Art 647 -- 18.3 Open Issues 652 -- 18.4 Conclusions 656 -- V SENSING, NETWORKING, AND OPTIMIZATION IN ROBOTICS AND MANUFACTURING 661 -- 19 REHABILITATION ROBOTIC PROSTHESES FOR UPPER EXTREMITY 663 /Han-Pang Huang, Yi-Hung Liu, Wei-Chen Lee, Jiun-Yih Kuan, and Tzu-Hao Huang -- 19.1 Introduction 663 -- 19.2 Rehabilitation Robot Arm and Control 664 -- 19.3 Rehabilitation Robot Hand 678 -- 19.4 Stability of Neuroprosthesis 683 -- 19.5 Conclusions 691 -- 20 ACCELEROMETER-BASED BODY SENSOR NETWORK (BSN) FOR MEDICAL DIAGNOSIS ASSESSMENT AND TRAINING 699 /Ming-Yih Lee, Kin Fong Lei, Wen-Yen Lin, Wann-Yun Shieh, Wen-Wei Tsai, Simon H. …”
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109100
Tight oil reservoirs : characterization, modeling, and field development
Cambridge, MA : Gulf Professional Publishing, an imprint of Elsevier, 2023Table of Contents: “…Parametric validation -- 6.5.2. Experimental data validation -- 6.5.3. Field data validation -- 6.6. …”
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