“…Pertinent new findings and results are presented on these topics:(i) Origins and multiple layers of intersubjectivity and empathy(ii) Neurosocial support of (pre)verbal intersubjectivity, participant perception, and simulation of mind(iii) From preverbal sharing and early speech perception to meaning acquisition and verbal intersubjectivity(iv) New windows on other-centred movements and moments of meeting in therapy and intervention. (Series B)…”

“…An IARC Monographs Working Group reviewed evidence from cancer bioassays in experimental animals and mechanistic studies to assess the carcinogenic hazard to humans of exposure to these agents and concluded that: Gentian violet, leucomalachite green, and CI Direct Blue 218 are possibly carcinogenic to humans (Group 2B) Leucogentian violet and malachite green are not classifiable as to their carcinogenicity in humans (Group 3)…”

“…New theories and technologies for the construction of roads, railroads, subways, airports, bridges, tunnels and other infrastructure; b. Advanced theories and technologies for the construction of ports, dams, reservoirs, sluices, hydraulic tunnels, canals and other infrastructure; c. …”

“…Through each pillar, Kanungo illustrates smart takeaways with the eye-opening strategies of some the world's greatest disruptors and most unlikely "change agents"--The first female pharaoh to the rapper Cardi B. Far from being one-in-a-million David-and-Goliath triumphs, Kanungo puts these individual stories together to create a map, showing you how you can replicate their tactics and emulate their strategies to create your own path forward…”

Tabla de Contenidos: “….) -- TABLA 3.1 DATOS BOMBA (B) TABLA 3.2 DATOS (...) -- TABLA 3.3 DATOS DE LA BOMBA COMO TURBINA -- TABLA 3.4 DATOS DEL MOTOR ASÍNCRONO EN (...) -- TABLA 3.5 CONDICIONES DE ARRANQUE DEL SGEH -- TABLA 3.6 VARIABLES Y UNIDADES BÁSICAS (...)…”

Tabla de Contenidos: “…9.10.4.1 Decreasing Impact on the Grid -- 9.10.4.2 Decreasing the Impact on the Switches -- 9.10.5 Bidirectional Battery Chargers -- 9.10.6 Other Charger Topologies -- 9.10.7 Contactless Charging -- 9.10.8 Wireless Charging -- 9.11 Modeling and Simulation of HEV Power Electronics -- 9.11.1 Device-Level Simulation -- 9.11.2 System-Level Model -- 9.12 Emerging Power Electronics Devices -- 9.13 Circuit Packaging -- 9.14 Thermal Management of HEV Power Electronics -- 9.15 Conclusions -- References -- 10 Electric Machines and Drives in HEVs -- 10.1 Introduction -- 10.2 Induction Motor Drives -- 10.2.1 Principle of Induction Motors -- 10.2.2 Equivalent Circuit of Induction Motor -- 10.2.3 Speed Control of Induction Machine -- 10.2.4 Variable Frequency, Variable Voltage Control of Induction Motors -- 10.2.5 Efficiency and Losses of Induction Machine -- 10.2.6 Additional Loss in Induction Motors Due to PWM Supply -- 10.2.7 Field-Oriented Control of Induction Machine -- 10.3 Permanent Magnet Motor Drives -- 10.3.1 Basic Configuration of PM Motors -- 10.3.2 Basic Principle and Operation of PM Motors -- 10.3.3 Magnetic Circuit Analysis of IPM Motors -- 10.3.3.1 Unsaturated Motor -- 10.3.3.2 Saturated Motor -- 10.3.3.3 Operation Under Load -- 10.3.3.4 Flux Concentration -- 10.3.4 Sizing of Magnets in PM Motors -- 10.3.4.1 Input Power -- 10.3.4.2 Direct-Axis Armature Reaction Factor -- 10.3.4.3 Magnetic Usage Ratio and Flux Leakage Coefficient -- 10.3.4.4 Maximum Armature Current -- 10.3.4.5 Inner Power Angle -- 10.3.5 Eddy Current Losses in the Magnets of PM Machines -- 10.4 Switched Reluctance Motors -- 10.5 Doubly Salient Permanent Magnet Machines -- 10.6 Design and Sizing of Traction Motors -- 10.6.1 Selection of A and B -- 10.6.2 Speed Rating of the Traction Motor -- 10.6.3 Determination of the Inner Power…”

Tabla de Contenidos: “…2.32 Conclusions - predictions about storage systems -- 2.32.1 Resurgence in shared storage, but Fibre-Channel fades -- 3 Disk drive data placement and scheduling -- 3.1 The organization of Hard Disk Drives - HDDs -- 3.2 Internal organization of files in UNIX -- 3.3 Review of disk arm scheduling -- 3.3.1 Implementation of SATF -- 3.3.2 Disk performance studies by Windsor Hsu and Alan Jay Smith at IBM ARC -- 3.3.3 Linux support for disk scheduling -- 3.4 Disk scheduling for mixed workloads -- 3.5 Real time disk scheduling for multimedia -- 3.6 Storage virtualization -- 3.7 File placement on disk -- 3.7.1 Anticipatory disk arm placement -- 3.8 Disks with Shingled Magnetic Recording - SMR -- 3.9 Review of analyses of disk scheduling methods -- 3.10 Analytic studies of disk storage -- 3.11 Analysis of a zoned disk with the FCFS scheduling -- 3.11.1 Disk service time in zoned disks with FCFS scheduling -- 3.12 Performance analysis of the SCAN policy -- 3.13 Analysis of the SATF policy -- 3.13.1 Preliminary investigation of SATF -- 3.13.2 First method for the analysis of SATF -- 3.13.3 Second method for the analysis of SATF -- 3.14 Conclusions -- 4 Mirrored &amp -- hybrid arrays -- 4.1 Introduction to mirrored and hybrid disk arrays -- 4.2 Mirrored and hybrid disk array organizations -- 4.2.1 Basic Mirroring - BM -- 4.2.2 Group Rotate Declustering - GRD -- 4.2.3 Interleaved Declustering - ID -- 4.2.4 Chained Declustering - CD -- 4.2.5 Dual striping -- 4.2.6 Logical volume and automatic storage management and GPFS -- 4.2.7 LSI Logic RAID -- 4.2.8 Adaptive disk arrays -- 4.2.9 SSPiRAL (Survivable Storage using Parity in Redundant Array Layout) -- 4.2.10 B-code -- 4.2.11 Weaver codes -- 4.2.12 Robust, Efficient, Scalable, Autonomous, Reliable - RESAR -- 4.2.13 Multiway placement -- 4.2.14 Classification of mirrored and hybrid disk arrays…”

Tabla de Contenidos: “…Composantes de la croissance de la population totale dans quelques pays de l'OCDE et dans l'Union européenne, 1960-2001 -- B. Les immigrés et le marché du travail…”

Tabla de Contenidos: “…Tarifas de IRPF: ingresos laborales (panel A) e ingresos por actividades empresariales independientes (panel B) -- Costa Rica sufre también una importante elusión de impuestos en el ámbito de las profesiones liberales -- Figura 3.9. …”

Tabla de Contenidos: “…Salmelin -- 2.1 Introduction 3 -- 2.2 LTE Backhaul Planes 5 -- 2.2.1 3GPP Planes and Protocol Stacks 5 -- 2.2.2 Synchronization Plane 7 -- 2.2.3 Management Plane 9 -- 2.2.4 Active Monitoring Plane 9 -- 2.2.5 Security Control Plane 10 -- 2.2.6 Control and User Plane of Additional Proprietary Applications 10 -- 2.3 Radio Features of LTE and LTE‐A 11 -- 2.3.1 LTE 11 -- 2.3.2 LTE‐A 12 -- 2.4 R equirements for LTE Backhaul (SLAs) 17 -- 2.4.1 Capacity 17 -- 2.4.2 Latency and Loss 18 -- 2.4.3 QoS Capabilities 21 -- 2.4.4 Synchronization 21 -- 2.4.5 Availability 22 -- 2.4.6 Security 22 -- 2.4.7 Examples 23 -- 2.5 Transport Services 26 -- 2.6 Planning Problems 27 -- 2.7 LTE Backhaul Technologies 29 -- 2.7.1 Access 30 -- 2.7.2 Aggregation and Backbone Network 34 -- 2.8 Small Cell Backhaul 34 -- 2.9 Future Radio Features Affecting Backhaul 35 -- 2.9.1 Inter NodeB CoMP (eCoMP) 35 -- 2.9.2 Dual Connectivity 36 -- 2.9.3 Dynamic eICIC 38 -- 2.10 R elated Standards and Industry Forums 39 -- 2.10.1 3GPP 39 -- 2.10.2 ITU‐T SG15 40 -- 2.10.3 IEEE 802 40 -- 2.10.4 IETF 40 -- 2.10.5 MEF 40 -- 2.10.6 NGMN 41 -- 2.10.7 BBF 41 -- 2.10.8 SCF 41 -- 2.11 Operator Example 42 -- References 42 -- 3 Economic Modeling and Strategic Input for Lte Backhaul 45 / Gabriel Waller and Esa Markus Metsälä -- 3.1 Introduction 45 -- 3.1.1 Role of Backhaul Within Lte 46 -- 3.1.2 Why and What to Model 48 -- 3.2 Strategic Input for Planning 49 -- 3.2.1 Physical infrastructure 49 -- 3.2.2 Transmission media 50 -- 3.2.3 Capacity and interfaces 50 -- 3.2.4 Network technologies 51 -- 3.2.5 Network topology 51 -- 3.2.6 Make or buy 51 -- 3.2.7 Backhaul security aspects 52 -- 3.3 Quantifying benefits 53.…”

Tabla de Contenidos: “…8.2 Tropospheric Refraction and Delay -- 8.2.1 Zenith Delay Functions -- 8.2.2 Mapping Functions -- 8.2.3 Precipitable Water Vapor -- 8.3 Troposphere Absorption -- 8.3.1 The Radiative Transfer Equation -- 8.3.2 Absorption Line Profiles -- 8.3.3 General Statistical Retrieval -- 8.3.4 Calibration of WVR -- 8.4 Ionospheric Refraction -- 8.4.1 Index of Ionospheric Refraction -- 8.4.2 Ionospheric Function and Cycle Slips -- 8.4.3 Single-Layer Ionospheric Mapping Function -- 8.4.4 VTEC from Ground Observations -- 8.4.5 Global Ionospheric Maps -- 8.4.5.1 IGS GIMs -- 8.4.5.2 International Reference Ionosphere -- 8.4.5.3 GPS Broadcast Ionospheric Model -- 8.4.5.4 NeQuick Model -- 8.4.5.5 Transmission to the User -- Chapter 9 GNSS Receiver Antennas -- 9.1 Elements of Electromagnetic Fields and Electromagnetic Waves -- 9.1.1 Electromagnetic Field -- 9.1.2 Plane Electromagnetic Wave -- 9.1.3 Complex Notations and Plane Wave in Lossy Media -- 9.1.4 Radiation and Spherical Waves -- 9.1.5 Receiving Mode -- 9.1.6 Polarization of Electromagnetic Waves -- 9.1.7 The dB Scale -- 9.2 Antenna Pattern and Gain -- 9.2.1 Receiving GNSS Antenna Pattern and Reference Station and Rover Antennas -- 9.2.2 Directivity -- 9.2.3 Polarization Properties of the Receiving GNSS Antenna -- 9.2.4 Antenna Gain -- 9.2.5 Antenna Effective Area -- 9.3 Phase Center -- 9.3.1 Antenna Phase Pattern -- 9.3.2 Phase Center Offset and Variations -- 9.3.3 Antenna Calibrations -- 9.3.4 Group Delay Pattern -- 9.4 Diffraction and Multipath -- 9.4.1 Diffraction Phenomena -- 9.4.2 General Characterization of Carrier Phase Multipath -- 9.4.3 Specular Reflections -- 9.4.4 Antenna Down-Up Ratio -- 9.4.5 PCV and PCO Errors Due to Ground Multipath -- 9.5 Transmission Lines -- 9.5.1 Transmission Line Basics -- 9.5.2 Antenna Frequency Response -- 9.5.3 Cable Losses -- 9.6 Signal-to-Noise Ratio…”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Land Use and Land Cover Mapping of Remotely Sensed Data Using Fuzzy Set Theory-Related Algorithm -- 1.1 Introduction -- 1.1.1 Overview on Landsat 8 -- 1.2 Image Classification -- 1.3 Unsupervised Classification -- 1.4 Supervised Classification -- 1.5 Overview of Fuzzy Sets -- 1.5.1 Fuzzy C-Means Clustering -- 1.5.2 Algorithm of Fuzzy C-Means -- 1.6 Methodology -- 1.6.1 Modified Fuzzy C-Means Technique -- 1.6.2 Construction of a Fuzzy Inference System -- 1.6.3 K-Means Algorithm -- 1.7 Results and Discussion -- 1.7.1 FCM Technique Results -- 1.7.2 Modified FCM Technique Results -- 1.7.3 K-Means Technique Results -- 1.8 Conclusion -- References -- Chapter 2 Role of AI in Mortality Prediction in Intensive Care Unit Patients -- 2.1 Introduction -- 2.2 Background -- 2.3 Objectives -- 2.4 Machine Learning and Mortality Prediction -- 2.4.1 Model Selection -- 2.4.2 Mortality Prediction for ICU Patients -- 2.4.3 Datasets Generation and Preprocessing -- 2.4.3.1 A &gt -- Inclusion Criteria -- 2.4.3.2 B &gt -- Exclusion Criteria -- 2.4.4 Structure of Datasets -- 2.5 Discussions -- 2.6 Conclusion -- 2.7 Future Work -- 2.8 Acknowledgments -- 2.9 Funding -- 2.10 Competing Interest -- References -- Chapter 3 A Survey on Malware Detection Using Machine Learning -- 3.1 Background -- 3.2 Introduction -- 3.3 Literature Survey -- 3.4 Discussion -- 3.5 Conclusion -- References -- Chapter 4 EEG Data Analysis for IQ Test Using Machine Learning Approaches: A Survey -- Introduction -- 4.1 Related Work -- 4.1.1 Signal Pre-Processing, Filtering, and Feature Extraction -- 4.2 Equations -- 4.2.1 Alternating a Diffusion Map-Based Combination of Two FCN Datasets -- 4.2.2 Information Examination -- 4.2.3 Gaussian Kernel Function -- 4.3 Classification -- 4.4 Data Set -- 4.4.1 Pre-Preparing…”

Tabla de Contenidos: “…6.7.4 Canonical Correlation Forest -- 6.7.5 Extreme Gradient Boosting -- 6.7.6 Light Gradient Boosting Machines -- 6.7.7 Gradient Boosting Machines -- 6.7.8 Categorical Boosting -- 6.7.9 Natural Gradient Boosting -- 6.8 Concluding Remarks -- References -- Chapter 7 Deep Learning -- 7.1 Fundamentals -- 7.1.1 Stochastic Gradient Descent -- 7.1.2 Backpropagation -- 7.1.3 Regularization -- 7.1.3.1 Weight Decay -- 7.1.3.2 Dropout -- 7.1.3.3 Data Augmentation -- 7.1.3.4 Early Stopping -- 7.1.4 Activation Functions -- 7.1.5 Loss Functions -- 7.2 Neural Network Architectures -- 7.2.1 Multilayer Perceptron -- 7.2.2 Convolutional Neural Networks -- 7.2.2.1 Convolutional Layers -- 7.2.2.2 Pooling Layers -- 7.2.2.3 Fully Connected Layers -- 7.2.2.4 Receptive Field and Feature Map -- 7.2.2.5 Training CNNs -- 7.2.2.6 Data Structures in CNNs -- 7.2.2.7 Evolving Trends in CNN Design -- 7.2.3 Recurrent Neural Networks -- 7.2.3.1 Long- and Short-Term Memory -- 7.2.3.2 Gated Recurrent Unit -- 7.2.4 Vision Transformers -- 7.2.5 Deep Multilayer Perceptron -- 7.2.6 Generative Adversarial Networks -- 7.2.7 Deep Autoencoders -- 7.2.7.1 Undercomplete Autoencoders -- 7.2.7.2 Regularized Autoencoders -- 7.2.7.3 Sparse Autoencoders -- 7.2.7.4 Denoising Autoencoders -- 7.2.7.5 Variational Autoencoders -- 7.3 Learning Paradigms -- 7.3.1 Transfer Learning -- 7.3.2 Semi-Supervised Learning -- 7.3.3 Reinforcement Learning -- 7.3.4 Active Learning -- 7.3.5 Multitask Learning -- 7.4 Application of DL in Remote Sensing -- 7.4.1 Semantic Segmentation -- 7.4.2 Object Detection -- 7.4.3 Scene Classification -- 7.4.4 Change Detection -- 7.5 Concluding Remarks -- References -- Chapter 8 Object-B ased Image Analysis -- 8.1 Clustering-Based Segmentation -- 8.1.1 Mean-Shift Algorithm -- 8.1.2 Superpixel Segmentation -- 8.2 Thresholding-Based Segmentation -- 8.3 Edge-Based Segmentation…”

Tabla de Contenidos: “…29.5 Token Ring multistation access units (MAUs) -- 29.6 Cabling and connectors -- 29.7 Repeaters -- 29.8 Jitter suppression -- 29.9 Exercise -- FDDI -- 30.1 Introduction -- 30.2 Operation -- 30.3 FDDI layers -- 30.4 SMT protocol -- 30.5 Physical connection management -- 30.6 Fault tolerance method -- 30.7 FDDI token format -- 30.8 FDDI Frame format -- 30.9 MAC protocol -- 30.10 Applications of FDDI networks -- 30.11 FDDI backbone network -- 30.12 FDDI attachments -- 30.13 FDDI media -- 30.14 FDDI specification -- 30.15 FDDI-II -- 30.16 Standards -- 30.17 Practical FDDI network - EaStMAN -- 30.18 Exercises -- ASYNCHRONOUS TRANSFER MODE (ATM) -- 31.1 Introduction -- 31.2 Real-time sampling -- 31.3 PCM-TDM systems and ISDN -- 31.4 Objectives of ATM -- 31.5 ATM versus ISDN and PCM-TDM -- 31.6 Statistical multiplexing -- 31.7 ATM user network interfaces (UNIs) -- 31.8 ATM cells -- 31.9 Routing cell within an ATM network -- 31.10 Virtual channels and virtual paths -- 31.11 ATM and the OSI model -- 31.12 ATM physical layer -- 31.13 AAL service levels -- 31.14 ATM flow control -- 31.15 Practical ATM networks -- 31.16 Tutorial -- INTEGRATED SERVICES DIGITAL NETWORK (ISDN) -- 32.1 Introduction -- 32.2 ISDN channels -- 32.3 ISDN physical layer interfacing -- 32.4 ISDN data link layer -- 32.5 ISDN network layer -- 32.6 Exercises -- MODEMS -- 33.1 Introduction -- 33.2 RS-232 communications -- 33.3 Modem standards -- 33.4 Modem commands -- 33.5 Modem setups -- 33.6 Modem indicators -- 33.7 Digital Modulation -- 33.8 Typical modems -- 33.9 Fax transmission -- 33.10 Exercises -- ASCII CODING -- A.1 International alphabet No. 5 -- A.2 Extended ASCII code -- A.3 RS-232C interface -- A.4 RS-449 interface -- CABLE SPECIFICATIONS -- B.1 Introduction -- RLE PROGRAM -- C.1 RLE program -- SNR FOR PCM -- D.1 SNR -- RFC STANDARDS -- UNIX NETWORK STARTUP FILES…”

Tabla de Contenidos: “…13.1.3 Die optimale Strategie -- 13.2 Die grundlegende Theorie von Deep-Q-Learning-Netzwerken -- 13.2.1 Value-Funktionen -- 13.2.2 Q-Value-Funktionen -- 13.2.3 Einen optimalen Q-Value schätzen -- 13.3 Einen DQN-Agenten definieren -- 13.3.1 Initialisierungsparameter -- 13.3.2 Das neuronale-Netze-Modell des Agenten bauen -- 13.3.3 Sich an das Spiel erinnern -- 13.3.4 Training über Memory Replay -- 13.3.5 Eine Aktion auswählen -- 13.3.6 Speichern und Laden der Modellparameter -- 13.4 Mit einer OpenAI-Gym-Umgebung interagieren -- 13.4.1 Hyperparameter-Optimierung mit SLM Lab -- 13.5 Agenten jenseits von DQN -- 13.5.1 Policy-Gradienten und der REINFORCE-Algorithmus -- 13.5.2 Der Actor-Critic-Algorithmus -- 13.6 Zusammenfassung -- Schlüsselkonzepte -- Teil IV -- KI und Sie -- 14 Mit Ihren eigenen Deep-Learning-Projekten beginnen -- 14.1 Ideen für Deep-Learning-Projekte -- 14.1.1 Machine Vision und GANs -- 14.1.2 Natural Language Processing -- 14.1.3 Deep Reinforcement Learning -- 14.1.4 Ein vorhandenes Machine-Learning-Projekt überführen -- 14.2 Ressourcen für weitere Projekte -- 14.2.1 Gesellschaftlich nützliche Projekte -- 14.3 Der Modellierungsprozess einschließlich der Anpassung der Hyperparameter -- 14.3.1 Automatisierung der Hyperparameter-Suche -- 14.4 Deep-Learning-Bibliotheken -- 14.4.1 Keras und TensorFlow -- 14.4.2 PyTorch -- 14.4.3 MXNet, CNTK, Caffe und so weiter -- 14.5 Software 2.0 -- 14.6 Die kommende Artificial General Intelligence -- 14.7 Zusammenfassung -- Anhang -- A Die formale Notation neuronaler Netze -- B Backpropagation -- C PyTorch -- C.1 PyTorch-Eigenschaften -- C.1.1 Das Autograd System -- C.1.2 Das Define-by-Run-Framework -- C.1.3 PyTorch im Vergleich mit TensorFlow -- C.2 PyTorch in der Praxis -- C.2.1 Die PyTorch-Installation -- C.2.2 Die grundlegenden Bausteine in PyTorch -- Einfache Operationen mit Tensoren…”

Tabla de Contenidos: “…Example scripts -- Tivoli Storage Resource Manager for Databases scripts -- Backup and restore scripts -- Appendix B. Additional material…”

Tabla de Contenidos: “…Removing Lotus Enterprise Integrator (LEI) -- Considerations prior to removing LEI -- Access to data -- LEI clustered environment -- Domino Enterprise Connection Services (DECS) -- Process of removing LEI -- Appendix B. Troubleshooting -- Using the LEI log (leilog.nsf) -- Using the Domino log (log.nsf) -- Enabling log conflicts in replication activities -- Logging SQL statements -- Using the trace metaconnector -- Using Notes Server Diagnostic (NSD) -- Appendix C. …”

Tabla de Contenidos: “…Calculating memory needed for an image -- Introduction -- Calculate memory usage for applet viewer -- Calculate memory usage for mid-tier conversion -- Appendix B. Additional material -- Locating the Web material -- Using the Web material -- System requirements for downloading the Web material -- How to use the Web material -- Glossary -- Abbreviations and acronyms -- Related publications -- IBM Redbooks -- Other publications -- Online resources -- How to get IBM Redbooks…”

Tabla de Contenidos: “…Prerequisite steps -- Configuring the Service Tools adapter on the iSeries server -- Setting up Management Central -- Appendix B. Planning checklists -- Independent disk pool checklist -- Disk planning worksheet -- PCI card placement planning worksheet -- Appendix C. …”

Tabla de Contenidos: “…6.2.4.1 Thermal Dosimetry for the Homogeneous Human Brain Model -- 6.2.4.2 Temperature Increase in the Human Eye -- 6.2.4.3 Temperature Rise in Compound and Extracted Eye Models -- 6.2.4.4 Thermal Response of the Human Body -- References -- 7 Biomedical Applications of Electromagnetic Fields -- 7.1 Transcranial Magnetic Stimulation (TMS) Treatment -- 7.1.1 Modeling TMS -- 7.1.1.1 Surface Integral Equation Based Formulation for TMS -- 7.1.2 Human Brain Models -- 7.1.2.1 TMS for Pediatric Population -- 7.1.2.2 Brain Tissue Parameters -- 7.1.3 Numerical Results -- 7.1.3.1 Electric Field Due to Various TMS Coils -- 7.1.3.2 Current Density -- 7.1.3.3 Magnetic Flux Density -- 7.1.3.4 Pediatric Models Using Adult Brain Parameters -- 7.1.3.5 Pediatric Models Using Age-Dependent Brain Parameters -- 7.2 Nerve Fiber Excitation -- 7.2.1 Nerve Fiber Models -- 7.2.1.1 Nerve Fiber Antenna Model -- 7.2.1.2 Numerical Solution -- 7.2.2 Passive Nerve Fiber -- 7.2.3 Active Nerve Fiber -- 7.2.4 Computational Examples -- 7.2.4.1 Numerical Results for Passive Nerve Fiber -- 7.2.4.2 Numerical Results for Active Nerve Fiber -- 7.3 Laser Radiation -- 7.3.1 Laser-Eye Interaction -- 7.3.2 Tissue Optical Parameters -- 7.3.3 Model of the Human Eye -- 7.3.4 Laser Source Modeling -- 7.3.5 Heat Transfer in the Human Eye -- 7.3.6 Numerical Solution of the Heat Transfer -- 7.3.7 Numerical Results -- 7.3.7.1 Steady-State Temperature Distribution -- 7.3.7.2 Nd:YAG 1064 nm Laser -- 7.3.7.3 Ho:YAG 2090 nm Laser -- 7.3.7.4 694.3 nm Ruby Laser -- 7.3.7.5 ArF 193 nm Excimer Laser -- 7.3.7.6 Nd:YLF 1053 nm Laser -- References -- A The Generalized Symmetric Form of Maxwell's Equations -- B A Note on Integral Equations -- C Scalar Green's Function and the Solution to Helmholtz Equation -- C.1 Scalar Green's Function -- C.2 Scalar Helmholtz Equation Solution -- References…”