Tabla de Contenidos: “…Shope and Ji-Hyung Park Multiple Time-Scale Monitoring to Address Dynamic Seasonality and Storm Pulses of Stream Water Quality in Mountainous Watersheds Reprinted from: Water 2015, 7(11), 6117-6138 http://www.mdpi.com/2073-4441/7/11/6117180 -- Haoran Wang, Yongcan Chen, Zhaowei Liu and Dejun Zhu Effects of the "Run-of-River" Hydro Scheme on Macroinvertebrate Communities and Habitat Conditions in a Mountain River of Northeastern China Reprinted from: Water 2016, 8(1), 31 http://www.mdpi.com/2073-4441/8/1/31203 -- Dong-Hwan Kim, Tae-Soo Chon, Gyu-Suk Kwak, Sang-Bin Lee and Young-Seuk Park Effects of Land Use Types on Community Structure Patterns of Benthic Macroinvertebrates in Streams of Urban Areas in the South of the Korea Peninsula Reprinted from: Water 2016, 8(5), 187 http://www.mdpi.com/2073-4441/8/5/187225 -- Sun-Ah Hwang, Soon-Jin Hwang, Se-Rin Park and Sang-Woo Lee Examining the Relationships between Watershed Urban Land Use and Stream Water Quality Using Linear and Generalized Additive Models Reprinted from: Water 2016, 8(4), 155 http://www.mdpi.com/2073-4441/8/4/155248 -- Young-Jin Yun and Kwang-Guk An Roles of N:P Ratios on Trophic Structures and Ecological Stream Health in Lotic Ecosystems Reprinted from: Water 2016, 8(1), 22 http://www.mdpi.com/2073-4441/8/1/22269 -- Kyoung-Jin An, Sang-Woo Lee, Soon-Jin Hwang, Se-Rin Park and Sun-Ah Hwang Exploring the Non-Stationary Effects of Forests and Developed Land within Watersheds on Biological Indicators of Streams Using Geographically-Weighted Regression Reprinted from: Water 2016, 8(4), 120 http://www.mdpi.com/2073-4441/8/4/120295 -- Ji Yoon Kim and Kwang-Guk An Integrated Ecological River Health Assessments, Based on Water Chemistry, Physical Habitat Quality and Biological Integrity Reprinted from: Water 2015, 7(11), 6378-6403 http://www.mdpi.com/2073-4441/7/11/6378324 -- Katarzyna Glińska-Lewczuk, Paweł Burandt, Roman Kujawa, Szymon Kobus, Krystian Obolewski, Julita Dunalska, Magdalena Grabowska, Sylwia Lew and Jarosław Chormański Environmental Factors Structuring Fish Communities in Floodplain Lakes of the Undisturbed System of the Biebrza River Reprinted from: Water 2016, 8(4), 146 http://www.mdpi.com/2073-4441/8/4/146353 -- Jeong-Hui Kim, Ju-Duk Yoon, Seung-Ho Baek, Sang-Hyeon Park, Jin-Woong Lee, Jae-An Lee and Min-Ho Jang An Efficiency Analysis of a Nature-Like Fishway for Freshwater Fish Ascending a Large Korean River Reprinted from: Water 2016, 8(1), 3 http://www.mdpi.com/2073-4441/8/1/3382 -- Darren Drapper and Andy Hornbuckle Field Evaluation of a Stormwater Treatment Train with Pit Baskets and Filter Media Cartridges in Southeast Queensland Reprinted from: Water 2015, 7(8), 4496-4510 http://www.mdpi.com/2073-4441/7/8/4496 404.…”

Tabla de Contenidos: “…PVC Detection Using a Convolutional Autoencoder and Random Forest Classifier -- 1. Introduction -- 2. Methods -- 2.1. …”

Tabla de Contenidos: “…1.13.6 Applying Cognition to Develop Health and Wellness -- 1.13.7 Welltok -- 1.13.8 CaféWell Concierge in Action -- 1.14 Advantages of Cognitive Computing -- 1.15 Features of Cognitive Computing -- 1.16 Limitations of Cognitive Computing -- 1.17 Conclusion -- References -- 2 Machine Learning and Big Data in Cyber-Physical System: Methods, Applications and Challenges -- 2.1 Introduction -- 2.2 Cyber-Physical System Architecture -- 2.3 Human-in-the-Loop Cyber-Physical Systems (HiLCPS) -- 2.4 Machine Learning Applications in CPS -- 2.4.1 K-Nearest Neighbors (K-NN) in CPS -- 2.4.2 Support Vector Machine (SVM) in CPS -- 2.4.3 Random Forest (RF) in CPS -- 2.4.4 Decision Trees (DT) in CPS -- 2.4.5 Linear Regression (LR) in CPS -- 2.4.6 Multi-Layer Perceptron (MLP) in CPS -- 2.4.7 Naive Bayes (NB) in CPS -- 2.5 Use of IoT in CPS -- 2.6 Use of Big Data in CPS -- 2.7 Critical Analysis -- 2.8 Conclusion -- References -- 3 HemoSmart: A Non-Invasive Device and Mobile App for Anemia Detection -- 3.1 Introduction -- 3.1.1 Background -- 3.1.2 Research Objectives -- 3.1.3 Research Approach -- 3.1.4 Limitations -- 3.2 Literature Review -- 3.3 Methodology -- 3.3.1 Methodological Approach -- 3.3.2 Methods of Analysis -- 3.4 Results -- 3.4.1 Impact of Project Outcomes -- 3.4.2 Results Obtained During the Methodology -- 3.5 Discussion -- 3.6 Originality and Innovativeness of the Research -- 3.6.1 Validation and Quality Control of Methods -- 3.6.2 Cost-Effectiveness of the Research -- 3.7 Conclusion -- References -- 4 Advanced Cognitive Models and Algorithms -- 4.1 Introduction -- 4.2 Microsoft Azure Cognitive Model -- 4.2.1 AI Services Broaden in Microsoft Azure -- 4.3 IBM Watson Cognitive Analytics -- 4.3.1 Cognitive Computing -- 4.3.2 Defining Cognitive Computing via IBM Watson Interface -- 4.3.3 IBM Watson Analytics -- 4.4 Natural Language Modeling…”

Tabla de Contenidos: “…4.3.7 K-Means Method -- 4.3.8 Watershed Method -- 4.3.9 Comparison of Different Segmentation Techniques Based on the Advantages and Disadvantages -- 4.3.10 Comparison of Different Segmentation Techniques Based on Accuracy -- 4.3.11 Comparison of Region Based and Threshold Based Segmentation Techniques Based on Different Parameters -- 4.4 Machine Learning -- 4.4.1 Supervised Learning -- 4.4.2 Unsupervised Learning -- 4.4.3 Reinforcement Learning -- 4.4.4 K-Nearest Neighbour (KNN) -- 4.4.5 Support Vector Machine (SVM) -- 4.4.6 Random Forest -- 4.5 Deep Learning (DL) -- 4.5.1 Convolutional Neural Networks (CNN) -- 4.5.1.1 Convolution Layer -- 4.5.1.2 Pooling Layer -- 4.5.1.3 Architecture of CNN -- 4.5.1.4 Comparison of Different Variations of CNN Techniques -- 4.5.2 Long Short-Term Memory (LSTM) -- 4.5.3 Artificial Neural Network (ANN) -- 4.5.4 Accuracy of Different Models Discussed Above -- 4.5.5 Accuracy of Other Different Techniques Being Used -- 4.6 Performance Metrics -- 4.6.1 Accuracy -- 4.6.2 Precision -- 4.6.3 Recall -- 4.6.4 Specificity -- 4.6.5 F1-Measure -- 4.7 Method Wise Trend of Using Techniques for Detection of Brain Tumor -- 4.8 Conclusion -- References -- Chapter 5 Advancements in Tumor Detection and Classification -- 5.1 Introduction -- 5.2 Imaging Techniques Used in Tumor Detection and Classification -- 5.2.1 X-Ray -- 5.2.2 CT Scan -- 5.2.3 MRI -- 5.2.4 Ultrasound -- 5.3 Molecular Biology Techniques -- 5.3.1 PCR -- 5.3.2 FISH -- 5.3.3 Next-Generation Sequencing -- 5.3.4 Western Blotting -- 5.4 Machine Learning and Artificial Intelligence -- 5.5 Tumor Classification -- 5.5.1 TNM Staging System -- 5.5.2 Histological Grading -- 5.5.3 Molecular Subtyping -- 5.6 Challenges and Future Directions -- References -- Chapter 6 Classification of Brain Tumor Using Machine Learning Techniques: A Comparative Study -- 6.1 Introduction…”

“…Bats feeding on nectar help to maintain diversity in forests through the dispersal of seeds and pollen, essential to many plant species with high economic, biological, and cultural value. …”

Tabla de Contenidos: “…-- Creating an actor -- Using actors to extend the functionality -- More on actor interaction -- Buffered actors -- Actor with CoroutineContext -- CoroutineStart -- Mutual exclusions -- Understanding mutual exclusions -- Creating mutexes -- Interacting with mutual exclusions -- Volatile variables -- Thread cache -- @Volatile -- Why @Volatile doesn't solve thread-safe counters -- When to use @Volatile -- Atomic data structures -- Actors in action -- Adding the label to the UI -- Creating an actor to use as a counter -- Increasing the counter as results are loaded -- Adding a channel so that the UI reacts to updates -- Sending the updated value through the channel -- Updating the UI on changes -- Testing the implementation -- Extending the actor to allow for resetting the counter -- Resetting the counter upon new searches -- Summary -- Chapter 8: Testing and Debugging Concurrent Code -- Testing concurrent code -- Throwing away assumptions -- Focus on the forest, not the trees -- Writing Functional Tests -- More advice on tests -- Writing the tests -- Creating a flawed UserManager -- Adding the kotlin-test library -- Adding a happy path test -- Testing for an edge case -- Identifying the issue -- Fixing the crash -- Retesting -- Debugging -- Identifying a coroutine in the logs -- Using automatic naming -- Setting a specific name -- Identifying a coroutine in the debugger -- Adding a debugger watch -- Conditional breakpoint -- Resiliency and stability -- Summary -- Chapter 9: The Internals of Concurrency in Kotlin -- Continuation Passing Style -- Continuations -- The suspend modifier…”

Tabla de Contenidos: “…Shaofeng Wang, Xin Cai, Jian Zhou, Zhengyang Song and Xiaofeng Li Analytical, Numerical and Big-Data-Based Methods in Deep Rock Mechanics Reprinted from: Mathematics 2022, 10, 3403, doi:10.3390/math10183403 . 1 -- Jianguo Zhang, Peitao Li, Xin Yin, Sheng Wang and Yuanguang Zhu Back Analysis of Surrounding Rock Parameters in Pingdingshan Mine Based on BP Neural Network Integrated Mind Evolutionary Algorithm Reprinted from: Mathematics 2022, 10, 1746, doi:10.3390/math10101746 . 7 -- Chong Li and Zhijun Xu Numerical Modeling and Investigation of Fault-Induced Water Inrush Hazard under Different Mining Advancing Directions Reprinted from: Mathematics 2022, 10, 1561, doi:10.3390/math10091561 . 23 -- Yangchun Wu, Linqi Huang, Xibing Li, Yide Guo, Huilin Liu and Jiajun Wang Effects of Strain Rate and Temperature on Physical Mechanical Properties and Energy Dissipation Features of Granite Reprinted from: Mathematics 2022, 10, 1521, doi:10.3390/math10091521 . 35 -- Laifu Song, Hao Ying, Wei Wang, Ning Fan and Xueming Du Reliability Modelling of Pipeline Failure under the Impact of Submarine Slides-Copula Method Reprinted from: Mathematics 2022, 10, 1382, doi:10.3390/math10091382 . 55 -- Minglei Zhai, Dan Ma and Haibo Bai Diffusion Mechanism of Slurry during Grouting in a Fractured Aquifer: A Case Study in Chensilou Coal Mine, China Reprinted from: Mathematics 2022, 10, 1345, doi:10.3390/math10081345 . 81 -- Yuan Zhao, Guoyan Zhao, Jing Zhou, Xin Cai and Ju Ma Mining Stress Evolution Law of Inclined Backfilled Stopes Considering the Brittle-Ductile Transition in Deep Mining Reprinted from: Mathematics 2022, 10, 1308, doi:10.3390/math10081308 . 95 -- Ying Chen, Shirui Chen, Zhengyu Wu, Bing Dai, Longhua Xv and Guicai Wu Optimization of Genetic Algorithm through Use of Back Propagation Neural Network in Forecasting Smooth Wall Blasting Parameters Reprinted from: Mathematics 2022, 10, 1271, doi:10.3390/math10081271 . 117 -- Baoping Chen, Bin Gong, Shanyong Wang and Chun'an Tang Research on Zonal Disintegration Characteristics and Failure Mechanisms of Deep Tunnel in Jointed Rock Mass with Strength Reduction Method Reprinted from: Mathematics 2022, 10, 922, doi:10.3390/math10060922 139 -- Diyuan Li, Zida Liu, Danial Jahed Armaghani, Peng Xiao and Jian Zhou Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest Reprinted from: Mathematics 2022, 10, 787, doi:10.3390/math10050787 159 -- Kewei Liu, Shaobo Jin, Yichao Rui, Jin Huang and Zhanxing Zhou Effect of Lithology on Mechanical and Damage Behaviors of Concrete in Concrete-Rock Combined Specimen Reprinted from: Mathematics 2022, 10, 727, doi:10.3390/math10050727 183 -- Tong Zhang, Xiaodong Nie, Shuaibing Song, Xianjie Hao and Xin Yang Modeling Uranium Transport in Rough-Walled Fractures with Stress-Dependent Non-Darcy Fluid Flow Reprinted from: Mathematics 2022, 10, 702, doi:10.3390/math10050702 201 -- Shaofeng Wang, Yu Tang, Ruilang Cao, Zilong Zhou and Xin Cai Regressive and Big-Data-Based Analyses of Rock Drillability Based on Drilling Process Monitoring (DPM) Parameters Reprinted from: Mathematics 2022, 10, 628, doi:10.3390/math10040628 223 -- Longjun Dong, Lingyun Zhang, Huini Liu, Kun Du and Xiling Liu Acoustic Emission b Value Characteristics of Granite under True Triaxial Stress Reprinted from: Mathematics 2022, 10, 451, doi:10.3390/math10030451 243 -- Barkat Ullah, Muhammad Kamran and Yichao Rui Predictive Modeling of Short-Term Rockburst for the Stability of Subsurface Structures Using Machine Learning Approaches: t-SNE, K-Means Clustering and XGBoost Reprinted from: Mathematics 2022, 10, 449, doi:10.3390/math10030449 259 -- Min Wang, Qifeng Guo, Yakun Tian and Bing Dai Physical and Mechanical Properties Evolution of Coal Subjected to Salty Solution and a Damage Constitutive Model under Uniaxial Compression Reprinted from: Mathematics 2021, 9, 3264, doi:10.3390/math9243264 279 -- Bo Sun, Zhiyu Zhang, Jiale Meng, Yonghui Huang, Hongchao Li and Jun Wang Research on Deep-Hole Cutting Blasting Efficiency in Blind Shafting with High In-Situ Stress Environment Using the Method of SPH Reprinted from: Mathematics 2021, 9, 3242, doi:10.3390/math9243242 299 -- Zhuo Rong, Xiang Yu, Bin Xu and Xueming Du Reliability Analysis of High Concrete-Face Rockfill Dams and Study of Seismic Performance of Earthquake-Resistant Measures Based on Stochastic Dynamic Analysis Reprinted from: Mathematics 2021, 9, 3124, doi:10.3390/math9233124 315 -- Yuantian Sun, Guichen Li and Sen Yang Rockburst Interpretation by a Data-Driven Approach: A Comparative Study Reprinted from: Mathematics 2021, 9, 2965, doi:10.3390/math9222965 333 -- Jiasen Liang, Shaokun Ma and Xueming Du Diffusion Model of Parallel Plate Crack Grouting Based on Foaming Expansion Characteristics of Polymer Slurry Reprinted from: Mathematics 2021, 9, 2907, doi:10.3390/math9222907 347 -- Shan Yang, Zitong Xu and Kaijun Su Variable Weight Matter-Element Extension Model for the Stability Classification of Slope Rock Mass Reprinted from: Mathematics 2021, 9, 2807, doi:10.3390/math9212807 365 -- Kexin Yin, Lianghui Li and Eugenia Di Filippo A Numerical Investigation to Determine the p-y Curves of Laterally Loaded Piles Reprinted from: Mathematics 2021, 9, 2783, doi:10.3390/math9212783 379 -- Lihai Tan, Ting Ren, Linming Dou, Xiaohan Yang, Gaofeng Wang and Huaide Peng Analytical Stress Solution and Numerical Mechanical Behavior of Rock Mass Containing an Opening under Different Confining Stress Conditions Reprinted from: Mathematics 2021, 9, 2462, doi:10.3390/math9192462 393 -- Daoyuan Sun, Yifan Wu, Longjun Dong and Qiaomu Luo Closed-Form Solutions for Locating Heat-Concentrated Sources Using Temperature Difference Reprinted from: Mathematics 2022, 10, 2843, doi:10.3390/math10162843 . 411.…”

Tabla de Contenidos: “…-- The evolution of machine learning -- Factors that led to the success of machine learning -- Machine learning, statistics, and AI -- Categories of machine learning -- Supervised and unsupervised machine learning -- Supervised machine learning -- Vehicle Mileage, Number Recognition and other examples -- Unsupervised machine learning -- Subdividing supervised machine learning -- Common terminologies in machine learning -- The core concepts in machine learning -- Data management steps in machine learning -- Pre-processing and feature selection techniques -- Centering and scaling -- The near-zero variance function -- Removing correlated variables -- Other common data transformations -- Data sampling -- Data imputation -- The importance of variables -- The train, test splits, and cross-validation concepts -- Splitting the data into train and test sets -- The cross-validation parameter -- Creating the model -- Leveraging multicore processing in the model -- Summary -- Chapter 8: Machine Learning Deep Dive -- The bias, variance, and regularization properties -- The gradient descent and VC Dimension theories -- Popular machine learning algorithms -- Regression models -- Association rules -- Confidence -- Support -- Lift -- Decision trees -- The Random forest extension -- Boosting algorithms -- Support vector machines -- The K-Means machine learning technique -- The neural networks related algorithms -- Tutorial - associative rules mining with CMS data -- Downloading the data -- Writing the R code for Apriori -- Shiny (R Code) -- Using custom CSS and fonts for the application -- Running the application -- Summary -- Chapter 9: Enterprise Data Science -- Enterprise data science overview…”

Tabla de Contenidos: “…Acknowledgments -- References -- Strategic Categorization -- Strategic Categorization -- Producers -- Analysts -- Empirical Test: The Technology Sector -- Data -- Producers: Software Organizations -- Analysts: Gartner -- Methods -- Hypotheses 1 and 2 -- Dependent Variable -- Independent Variables -- Control Variables -- Hypothesis 3 -- Dependent Variable -- Independent Variables -- Control Variables -- Hypothesis 4 -- Dependent Variables -- Independent Variables -- Control Variables -- Results -- Software Producers: Market Category Entry -- Gartner Analysts: Report on a Producer Market Category -- Gartner Analysts: Issue a Magic Quadrant Report -- Discussion -- Notes -- References -- Hybrid Categories as Political Devices: The Case of Impact Investing in Frontier Markets -- Theoretical Foundation -- Politics, Power, and Categories -- Hybrid Categories and Power Relations -- Methodology -- Research Context -- Impact Investors Look to Africa -- Data Sources and Data Collection -- Documents, Media Reports, and Archives -- Semi-Structured Interviews -- Data Analysis -- Analytical Approach -- Case Study -- Birth of a Hybrid Category: The Emergence of Impact Investing -- Creating New Knowledge and Defining a New Collective Identity -- New Forests Company and the Politics of Identity -- Weak Institutional Setting -- Strategic Use of Hybrid Category Membership for Political Advantage -- Hybrid Category Membership for the Transfer of Legitimacy -- Discussion and Conclusion -- Contributions -- Further Research -- Notes -- References -- The Discursive Perspective of Market Categorization: Interaction, Power, and Context -- Introduction -- The Discourse of Market Categorization -- The Interaction among Market Participants -- Power Dynamics among Market Participants -- Contextualizing Category Development…”

Tabla de Contenidos: “…-- Techniques to detect anomalies -- Anomaly detection with STL decomposition -- Twitter's t-ESD algorithm for anomaly detection -- Isolation forests for anomaly detection -- Forecasting as an anomaly detection tool…”

Tabla de Contenidos: “…Austell, Cornwall, England, 1996-2001 / Nicholas Grimshaw - Steel House, Mooball, New South Wales, Australia, 1996-97 / Grose Bradley - Contemporary Arts Center, Cincinnati, Ohio, United States, 1998-2001 / Zaha Hadid - JR Kyoto Railway Station, Kyoto, Japan, 1991-97 / Hiroshi Hara - Miyagi Prefectural Library, Sendai, Miyagi, Japan, 1993-98 / Hiroshi Hara - Niigata Performing Arts Center, Niigata City, Niigata, Japan, 1993-98 / Itsuko Hasegawa - Studio Remy Zaugg, Mulhouse, France, 1995-96 / Herzog & de Meuron - Dominus Winery, Yountville, Napa Valley, California, United States, 1995-98 / Herzog & de Meuron - Tate Gallery of Modern Art, London, England, 1995-99 / Herzog & de Meuron - Pharmacy Restaurant, London, England, 1997-98 / Damien Hirst and Mike Rundell - Kiasma Museum of Contemporary Art, Helsinki, Finland, 1993-98 / Steven Holl - Haishi 'Mirage City', Zhuhai City, Hengqin Island, China, 1996-97 / Arata Isozaki - Nara Centennial Hall, Nara, Japan, 1992-98 / Arata Isozaki - Shizuoka Convention and Arts Center 'Granship', Shizuoka, Japan, 1993-98 / Arata Isozaki - Odate Jukai Dome Park, Odate, Japan, 1995-97 / Toyo Ito - Nagaoka Lyric Hall, Nagaoka, Niigata, Japan, 1993-96 / Toyo Ito - Ota-ku resort complex, Shouken District, Nagano, Japan, 1995-98 / Toyo Ito - Type/Variant house, Northern Wisconsin, United States, 1994-97 / Vincent James - House, Higashi-Osaka, Osaka, Japan, 1995-97 / Waro Kishi - Rodin Pavilion, Seoul, South Korea, 1995-97 / Kohn Pedersen Fox - Educatorium, University of Utrecht, The Netherlands, 1995-97 / Rem Koolhaas - Noh Stage in the Forest, Toyama City, Toyama, Japan, 1996 / Kengo Kuma - Felix Nussbaum Museum, Osnabruck, Germany, 1996-98 / Daniel Libeskind - Jewish Museum, Berlin, Germany, 1989-99 / Daniel Libeskind - Kaze-no-Oka crematorium, Nakatsu, Oita, Japan, 1995-97 / Fumihiko Maki - Makuhari Messe phase II, Nakase, Chiba, Japan, 1996-97 / Fumihiko Maki - Central Library, Delft Technical University, Delft, The Netherlands, 1993-98 / Mecanoo - Church of the Year 2000, Rome, Italy, 1996-2000 / Richard Meier - Issey Miyake Pleats Please Boutique, New York, New York, United States, 1998 / Toshiko Mori and Gwenael Nicolas - Private residence, Mount Wilson, New South Wales, Australia, 1989-94 / Glenn Murcutt - Villa VPRO, Hilversum, The Netherlands, 1993-97 / MVRDV - Stade de France, Saint-Denis, France, 1994-97 / MZRC - Lucerne Culture andh Congress Center, Lucerne, Switzerland, 1992-99 / Jean Nouvel - Water Pavilion H2O Expo, Neeltje Jans, The Netherlands, 1994-97 / NOX - V2_Media Lab, Rotterdam, The Netherlands, 1998 / NOX - Jean-Marie Tjibaou Cultural Center, Noumea, New Caledonia, 1992-98 / Renzo Piano - Debis Tower, Berlin, Germany, 1993-99 / Renzo Piano - Law Courts, Bordeaux, France, 1993-98 / Richard Rogers - Baumschulenweg Crematorium, Berlin, Germany, 1992-98 / Axel Schultes and Charlotte Frank - ZKM Center for Art and Media Technology, Karlsruhe, Germany, 1993-97 / Schweger+Partner - Museum of Islamic Arts, Doha, Qatar, 1997 (project) / SITE - Church and Parish Center, Marco de Canavezes, Portugal, 1990-96 / Alvaro Siza - Vieira de Castro House, Famalicao, Portugal, 1984-98 / Alvaro Siza - Portuguese Pavilion Expo '98, Lisbon, Portugal, 1996-98 / Alvaro Siza - Experimental housing, Freiburg, Germany, 1996-97 / Thomas Spiegelhalter - National School of Decorative Arts, Paris, France, 1995-98 / Philippe Starck and Luc Arsene-Henry - Shoji Ueda Museum of Photography, Kishimoto, Tottori, Japan, 1993-95 / Shin Takamatsu - Meteor Plaza, Mihonoseki, Shimane, Japan, 1994-95 / Shin Takamatsu - Worship Hall Seirei, Kawanishi, Hyogo, Japan, 1996-98 / Shin Takamatsu - Televisa Services Building, Mexico City, Mexico, 1993-95 / Ten Arquitectos - Mura-no Terrace, Ibi-Gun, Gifu, Japan, 1994-95 / Makoto Sei Watanabe - K-Museum, Tokyo, Japan, 1995-96 / Makoto Sei Watanabe - National Diet Building, New Capital, Japan, 1996 (project) / Makoto Sei Watanabe - New York City House, New York, New York, United States, 1995-97 / Williams and Tsien - Gana Art Center, Seoul, South Korea, 1996-98 / Jean-Michel Wilmotte - Guthrie Pavilion, Shah Alam, Selangor, Malaysia, 1995-98 / Ken Yeang - Nagoya Expo 2005 Tower, Seto, Nagoya, Japan, 1997 (project) - Prospecta '92 Toyama Observatory Tower, Kosugi, Toyama, Japan, 1990-92 / Shoei Yoh - Glass station, Oguni, Kumamoto, Japan, 1991-93 / Shoei Yoh - Kanada Children Training House, Kanada, Fukuoka, Japan, 1992-94 / Shoei Yoh…”

“…Nous connaissons l’ampleur de l’action requise et les domaines dans lesquels il faut agir : efficacité énergétique, technologies sobres en carbone, déforestation. Nous avons une idée des technologies à développer. …”

Tabla de Contenidos: “…10.6.2 Data Flow Diagrams -- 10.6.2.1 Doctors -- 10.6.2.2 Patient -- 10.6.2.3 Transaction Flow -- 10.7 Performance Evaluation -- 10.7.1 Performance of the Proposed Model -- 10.7.2 Performance Comparison -- 10.8 Conclusions and Future Caveats -- References -- Chapter 11: AI-Aided Secured ECG Live Edge Monitoring System with a Practical Use-Case -- 11.1 Introduction -- 11.1.1 Background -- 11.1.2 Problem Statement -- 11.1.3 Objective and Scope -- 11.2 Related Work -- 11.3 Proposed AI-Based System Architecture -- 11.3.1 Block Diagram -- 11.3.2 Data Collection and Pre-Processing Steps -- 11.3.3 Detecting Heart Abnormalities Using AI-Aided Techniques -- 11.4 Considered Smart ECG Monitoring System -- 11.4.1 Edge Hardware Components -- 11.4.1.1 System-on-a-Chip (SoC) Model -- 11.4.1.2 IoT Sensor for Heart Rate Data Acquisition -- 11.4.1.3 Microprocessor and Analog to Digital Converter -- 11.4.2 AI-Logic Component -- 11.4.2.1 Decision Tree -- 11.4.2.2 Random Forest -- 11.4.2.3 ANN -- 11.4.2.4 CNN -- 11.5 Bio-Authentication Application of the Considered ECG Monitoring System for Specific Use-Cases -- 11.6 Performance Evaluation -- 11.6.1 Supraventricular Arrhythmia Classification -- 11.6.2 Authorized User Classification for Bio-Authentication System -- 11.7 Challenges Involved with the Proposed System -- Limitations -- 11.8 Conclusion and Future Scope -- References -- Section III -- Chapter 12: Application of Unmanned Aerial Vehicles in Wireless Networks: Mobile Edge Computing and Caching -- 12.1 Introduction -- 12.1.1 Chapter Roadmap -- 12.2 Literature Review -- 12.3 Description of Caching and Mobile Edge Computing -- 12.3.1 Overview of Caching -- 12.3.1.1 Advantages -- 12.3.1.2 Disadvantages -- 12.3.2 Overview of Mobile Edge Computing -- 12.3.2.1 Advantages -- 12.3.2.2 Disadvantages -- 12.4 Layering of UAV-Based MEC Architecture…”

Tabla de Contenidos: “…3.4 Machine Learning Algorithms -- 3.4.1 Linear Regression -- 3.4.2 Logistic Regression -- 3.4.3 K-NN or K Nearest Neighbor -- 3.4.4 Decision Tree -- 3.4.5 Random Forest -- 3.5 Analysis and Prediction of COVID-19 Data -- 3.5.1 Methodology Adopted -- 3.6 Analysis Using Machine Learning Models -- 3.6.1 Splitting of Data into Training and Testing Data Set -- 3.6.2 Training of Machine Learning Models -- 3.6.3 Calculating the Score -- 3.7 Conclusion &amp -- Future Scope -- References -- 4 Rapid Forecasting of Pandemic Outbreak Using Machine Learning -- 4.1 Introduction -- 4.2 Effect of COVID-19 on Different Sections of Society -- 4.2.1 Effect of COVID-19 on Mental Health of Elder People -- 4.2.2 Effect of COVID-19 on our Environment -- 4.2.3 Effect of COVID-19 on International Allies and Healthcare -- 4.2.4 Therapeutic Approaches Adopted by Different Countries to Combat COVID-19 -- 4.2.5 Effect of COVID-19 on Labor Migrants -- 4.2.6 Impact of COVID-19 on our Economy -- 4.3 Definition and Types of Machine Learning -- 4.3.1 Machine Learning &amp -- Its Types -- 4.3.2 Applications of Machine Learning -- 4.4 Machine Learning Approaches for COVID-19 -- 4.4.1 Enabling Organizations to Regulate and Scale -- 4.4.2 Understanding About COVID-19 Infections -- 4.4.3 Gearing Up Study and Finding Treatments -- 4.4.4 Predicting Treatment and Healing Outcomes -- 4.4.5 Testing Patients and Diagnosing COVID-19 -- References -- 5 Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID-19 -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Suggested Methodology -- 5.4 Models in Epidemiology -- 5.4.1 Bayesian Inference Models -- 5.5 Particle Filtering Algorithm -- 5.6 MCM Model Implementation -- 5.6.1 Reproduction Number -- 5.7 Diagnosis of COVID-19 -- 5.7.1 Predicting Outbreaks Through Social Media Analysis -- 5.8 Conclusion -- References…”

Tabla de Contenidos: “…2.3.3.1 Ground object monitoring in the transmission line corridor -- 2.3.3.2 Geological disaster monitoring for the transmission line corridor -- 2.3.3.3 Monitoring the meteorological disasters of transmission lines corridors -- 2.3.3.4 Transmission line corridor forest fire monitoring -- 2.3.4 Wide Area Transmission Lines Monitoring Prospect Based on Satellite Remote Sensing Technology -- References -- 3 Tour inspection technology of transmission lines -- 3.1 Conventional Tour Inspection and its Classification -- 3.1.1 Regular Tour Inspection of Lines -- 3.1.2 Special tour inspection of lines -- 3.1.3 Fault Tour Inspection of Lines -- 3.1.4 On-the-Tower Tour Inspection of Lines -- 3.2 Main Contents of Tour Inspection of Lines -- 3.2.1 Environments Along the Lines -- 3.2.2 Towers, Guy Wires, and Foundations -- 3.2.3 Conductors and Ground Wires -- 3.2.4 Insulators and Fittings -- 3.2.5 Lightning Protection Facilities and Grounding Devices -- 3.2.6 Accessories and Other Facilities -- 3.3 Helicopter Tour Inspection Technology -- 3.3.1 Model of Line Inspection Helicopter -- 3.3.2 Line Inspection Heliborne Equipment -- 3.3.2.1 Infrared thermal imaging equipment -- 3.3.2.2 Ultraviolet (UV) imaging equipment -- 3.3.2.3 Heliborne laser radar -- 3.3.3 Tour Inspection Items -- 3.3.3.1 Regular inspection -- 3.3.3.2 Equipment fault detection -- 3.3.3.3 Vegetation management near transmission lines -- 3.3.3.4 The monitoring and management of the lines' operational state -- 3.3.3.5 Assessment of the electromagnetic environment of transmission lines -- 3.3.3.6 Equipment management of transmission lines -- 3.3.4 Helicopter Tour Inspection Process -- 3.3.4.1 Preparation for patrol -- 3.3.4.2 The patrol process and main defects found -- 3.4 Robot Tour Inspection Technology -- 3.4.1 Characteristics of Robot Tour Inspection…”

Tabla de Contenidos: “…Intro -- Foreword -- Acknowledgments -- Disclaimer -- Contents -- 1 Assessing the State of Smoke Science -- 1.1 Recent Trends -- 1.2 Environmental and Social Context -- 1.3 Overview of This Assessment -- References -- 2 Fuels and Consumption -- 2.1 Introduction -- 2.1.1 Understanding How Fuels Contribute to Smoke -- 2.2 Wildland Fuels -- 2.2.1 Fuel Characteristics -- 2.2.2 Traditional Methods to Estimate Wildland Fuel Loadings -- 2.2.3 Emerging Technologies and Methods -- 2.3 Fuel Consumption -- 2.3.1 Indirect Estimates of Fuel Consumption -- 2.3.2 Direct Measures of Fuel Consumption -- 2.4 Gaps in Wildland Fuels Characterization -- 2.4.1 Scaling from Fine-Scale to Coarse-Scale Fuel Characterization -- 2.4.2 Challenges in Forest Floor Characterization -- 2.4.3 Modeling Spatial and Temporal Dynamics of Wildland Fuels -- 2.5 Vision for Improving Fuel Science in Support of Smoke Science -- 2.6 Science Delivery to Managers -- 2.7 Research Needs -- 2.8 Conclusions -- References -- 3 Fire Behavior and Heat Release as Source Conditions for Smoke Modeling -- 3.1 Introduction -- 3.2 Current State of Science -- 3.2.1 Representing Fire in Smoke Models -- 3.2.2 Remote Sensing -- 3.2.3 Effects of Management Actions -- 3.3 Gaps in Understanding the Link Between Fire Behavior and Plume Dynamics -- 3.3.1 Heat Release -- 3.3.2 Fire Spread -- 3.3.3 Plume Cores -- 3.4 Vision for Improving Smoke Science -- 3.5 Emerging Issues and Challenges -- 3.5.1 Magnitude of Fire and Smoke Impacts -- 3.5.2 Managing Fuels to Minimize Air Quality Impacts -- 3.5.3 Need for Dispersion Climatologies -- 3.5.4 When and Where is Coupled Fire-Atmosphere Modeling Needed? …”

Tabla de Contenidos: “…13.5.4.1 Artificial Neural Networks (ANNs) -- 13.5.4.2 Decision Trees -- 13.5.4.3 Support Vector Machines (SVMs) -- 13.5.4.4 Random Forest -- 13.5.4.5 Gaussian Process -- 13.6 Case Studies of ML-Based BMS Applications in Industry -- 13.6.1 Machine Learning Approach to Predict SOH of Li-Ion Batteries -- 13.6.2 Anomaly Detection in Battery Management System Using Machine Learning -- 13.6.3 Optimization of Battery Life Cycle Using Machine Learning -- 13.6.4 Prediction of Remaining Useful Life Using Machine Learning -- 13.6.5 Fault Diagnosis of Battery Management System Using Machine Learning -- 13.6.6 Battery Parameter Estimation Using Machine Learning -- 13.6.7 Optimization of Battery Charging Using Machine Learning -- 13.6.8 ML Approach to Estimate State of Charge -- 13.6.9 Battery Capacity Estimation Using ML Approach -- 13.6.10 Anomaly Detection in Batteries Using Machine Learning -- 13.6.11 ML-Based BMS for Li-Ion Batteries -- 13.6.12 Battery Management System Based on Deep Learning for Electric Vehicles -- 13.6.13 A Review of ML Approaches for BMS -- 13.6.14 Battery Management Systems Using Machine Learning Techniques -- 13.6.15 Machine Learning for Lithium-Ion Battery Management: Challenges and Opportunities -- 13.6.16 An ML-Based BMS for Hybrid EVs -- 13.6.17 Battery Management System for EVs Using ML Techniques -- 13.6.18 A Hybrid BMS Using Machine Learning Techniques -- 13.7 Challenges -- 13.8 Conclusion -- References -- Chapter 14 ML Applications in Healthcare -- 14.1 Introduction -- 14.1.1 Supervised Learning -- 14.1.2 Unsupervised Learning -- 14.1.3 Semi-Supervised Learning -- 14.1.4 Reinforcement Learning -- 14.2 Applications of Machine Learning in Health Sciences -- 14.2.1 Diagnosis and Prediction of Disease -- 14.2.1.1 Predicting Thyroid Disease -- 14.2.1.2 Predicting Cardiovascular Disease -- 14.2.1.3 Predicting Cancer…”

“…We understand the basic economic policy incentives in favour of reducing emissions, stopping deforestation and promoting more sensible use of our natural environment and its resources. …”

Tabla de Contenidos: “…Vol. 1: Projects featured: Museum of Contemporary Art, Denver (Adjaye Associates) - Active Nature, Saclay, France (Agence Babylone) - Chichu Art Museum, Naoshima, Japan (Tadao Ando) - Toku Tokoyo Line Shibuya Station, Tokyo (Tadao Ando) - Papertainer Museum, Seoul Olympic Park, South Korea (Shigeru Ban) - Svalbard Global Seed Vault, Norway (Barlindhaug Consult AS) - King Abdullah International Gardens, Riyadh (Barton Willmore) - CaixaForum Vertical Garden, Madrid (Patrick Blanc) - Anti-Smog, Paris (Vincent Callebaut Architectures) - Perfumed Jungle, Hong Kong (Vincent Callebaut Architectures) - Westraven office complex, Utrecht, Netherlands (Cepezed) - Phantom House, Southwestern USA (Diller Scofidio + Renfro) - Samir Kassir Square, Beirut (Vladimir Djurovic) - Tree Tower, Los Angeles (Dennis Dollens) - Ecoboulevard of Vallecas, Madrid (Ecosistemo Urbano) - Jinji Lake, Suzhou, China (Edaw) - Shark Alley Retreat, Great Barrier Island, Auckland, New Zealand (Fearon Hay) - Petronas University of Technology, Seri Iskandar, Malaysia (Foster + Partners) - Little Ajax affordable housing, Aspen, Colorado (Peter Gluck) - Schweiger House, Sistrans, Austria (Monika Gogl) - E.ON Energy Research Department, RWTH Aachen, Germany (Zaha Hadid) - Linsdale Beach House, Great Barrier Island, Auckland, New Zealand (Herbst Architects) - Handmade School, Rudrapur, Bangladesh (Anna Heringer and Eike Roswang) - 474 Wing House, Santa Monica Mountains, Malibu, California (David Hertz) - Nk'Mip Desert Cultural Centre, Osoyoos, British Columbia (Hotson Bakker Boniface Haden) - Vertical Farm, Harlem, New York (Chris Jacobs) - Hedge Building, Rostock, Germany (Kempe Thill) - Sidwell Middle Friends School, Washington DC (KieranTimberlake) - Telefonica District C, Las Tablas, Madrid (Rafael De La-Hoz) - Water+Life Museums Campus, Hemet, California (Michael B Lehrer) - Environmental education centre, Hoorn, Netherlands (MIII Architects) - Wood house, Ville-d'Avray, France (Marchi Architectes) – San Francisco Federal Building (Morphosis) - BIP Computer Office & Shop, Providencia, Santiago de Chile (Alberto Moro) - Putrajaya Precinct 4 waterfront development, Kuala Lumpur (Manfredi Nicoletti) - Helios House, Los Angeles (Office DA) - Macallen Building, Boston, Massachusetts (Office DA) - Zhong-Xiao Boulevard Urban Ecological Corridor, Taipei (Sergio Palleroni) - Renovation and expansion of the California Academy of Sciences, San Francisco (Renzo Piano) - Heifer International World Headquarters, Little Rock, Arkansas (Polk Stanley Rowland Curzon Porter) - Digestible Gulf Stream, 11th Venice Architecture Biennale (Philippe Rahm) - World Wide Fund for Nature, Zeist, Netherlands (RAU) - Hybrid Earthship, Tao, New Mexico (Michael Reynolds) - Westend Grun, Berlin (Roswag & Jankowski) - Christine's House, Mason's Bend, Alabama (Rural Studio) - Inujima Art Project Seirensho, Inujima, Japan (Hiroshi Sambuichi) - Fondazione Pierto Rossini Pavilion, Briosco, Italy (Site) - Streetscape in a new world, Beijing (Site) - Masdar Headquarters, Abu Dhabi (Adrian Smith + Gordon Gill Architecture) - Orange County Great Park, Irvine, California (Ken Smith Landscape Architect) - H16, Tieringen, Germany (Werner Sobek) - Hotel Remota, Puerto Natales, Patagonia, Chile (German del Sol) - Salam Center for Cardia Surgery, Soba, Sudan (Studio Tamassociati) - Forest House, Titirangi, Auckland, New Zealand (Chris Tate) - Vigilius Mountain Resort, Lana, Italy (Matteo Thun) - Mosaic House, Meguro-ku, Tokyo, Japan (TNA) - IBG and Tax Offices, Groningen, Netherlands (UNStudio) - Post Rotterdam, Netherlands (UNStudio) - ASLA Green Roof, Washington DC (Michael van Valkenburgh) - Olympic Sculpture Park, Seattle (Weiss/Manfredi) - Grand Rapids Art Museum, Michigan (Why Architecture) - Mewah Oils Headquarters, Pulau Indah Park, Port Klang, Salangor, Malaysia (Ken Yeang) - The New National Library Board Building, Singapore (Ken Yeang) - Rafflesia Zero Energy House, Kuala Lumpur (Zoka Zola) - Solar Tower, Chicago (Zoka Zola)…”

Tabla de Contenidos: “…Governance of the "Big Five" -- Summary -- Governance of the Big Five Organizations -- Key Financiers of the United Nations System and International Organizations -- The World Bank Group -- The Food and Agriculture Organization of the United Nations -- The World Food Programme -- The International Fund for Agricultural Development -- CGIAR -- Collaboration among Rome-Based Agencies -- Partnerships between the World Bank Group and United Nations Agencies -- A Fractionated Record of Donor Evaluations of International Organizations: The Need to Separate the Forest from the Trees -- Evaluating the Governance Processes of the Big Five -- 7. …”