LEADER 03221nam 2200649 450 001 9910460483303321 005 20200903223051.0 010 $a90-04-28081-2 024 7 $a10.1163/9789004280816 035 $a(CKB)3710000000342929 035 $a(EBL)1936119 035 $a(SSID)ssj0001420777 035 $a(PQKBManifestationID)11787853 035 $a(PQKBTitleCode)TC0001420777 035 $a(PQKBWorkID)11404081 035 $a(PQKB)10172176 035 $a(MiAaPQ)EBC1936119 035 $a(OCoLC)893452189 035 $a(nllekb)BRILL9789004280816 035 $a(Au-PeEL)EBL1936119 035 $a(CaPaEBR)ebr11014943 035 $a(CaONFJC)MIL718564 035 $a(OCoLC)902674238 035 $a(EXLCZ)993710000000342929 100 $a20150210h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAvi Sagi $eexistentialism, pluralism, and identity /$fedited by Hava Tirosh-Samuelson and Aaron W. Hughes 210 1$aLeiden, The Netherlands :$cKoninklijke Brill,$d2015. 210 4$d©2015 215 $a1 online resource (209 p.) 225 1 $aLibrary of Contemporary Jewish Philosophers,$x2213-6010 ;$vVolume 10 300 $aDescription based upon print version of record. 311 $a90-04-28080-4 311 $a1-322-87282-1 320 $aIncludes bibliographical references. 327 $tPreliminary Material -- $tAvi Sagi: An Intellectual Portrait /$rHava Tirosh-Samuelson -- $tThe Punishment of Amalek in Jewish Tradition: Coping with the Moral Problem /$rAvi Sagi -- $tNatural Law and Halakhah: A Critical Analysis /$rAvi Sagi -- $tTikkun Olam: Between Utopian Idea and Socio-Historical Process /$rAvi Sagi -- $tJustifying Interreligious Pluralism /$rAvi Sagi -- $tInterview with Avi Sagi /$rHava Tirosh-Samuelson -- $tSelect Bibliography. 330 $aAvi Sagi is Professor of Philosophy at Bar Ilan University in Ramat Gan, Israel, and Senior Fellow at the Shalom Hartman Institute in Jerusalem, Israel. A philosopher, literary critic, scholar of cultural studies, historian and philosopher of halakhah, public intellectual, social critic, and educator, Sagi has written most lucidly on the challenges that face humanity, Judaism, and Israeli society today. As an intertextual thinker, Sagi integrates numerous strands within contemporary philosophy, while critically engaging Jewish and non-Jewish philosophers. Offering an insightful defense of pluralism and multiculturalism, his numerous writings integrate philosophy, religion, theology, jurisprudence, psychology, art, literature, and politics, charting a new path for Jewish thought in the twenty-first century. 410 0$aLibrary of contemporary Jewish philosophers ;$vVolume 10. 606 $aJewish philosophy 606 $aPhilosophy$y21st century 608 $aElectronic books. 615 0$aJewish philosophy. 615 0$aPhilosophy 676 $a181/.06 702 $aTirosh-Samuelson$b Hava 702 $aHughes$b Aaron W.$f1968- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910460483303321 996 $aAvi Sagi$92285191 997 $aUNINA LEADER 04747nam 22006975 450 001 996466367803316 005 20200704065306.0 010 $a3-540-30137-2 024 7 $a10.1007/b104035 035 $a(CKB)1000000000212648 035 $a(SSID)ssj0000204702 035 $a(PQKBManifestationID)11172535 035 $a(PQKBTitleCode)TC0000204702 035 $a(PQKBWorkID)10188717 035 $a(PQKB)10671856 035 $a(DE-He213)978-3-540-30137-0 035 $a(MiAaPQ)EBC3068434 035 $a(PPN)13412359X 035 $a(EXLCZ)991000000000212648 100 $a20101024d2005 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aModular Algorithms in Symbolic Summation and Symbolic Integration$b[electronic resource] /$fby Jürgen Gerhard 205 $a1st ed. 2005. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2005. 215 $a1 online resource (XVI, 228 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v3218 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-24061-6 320 $aIncludes bibliographical references (p. [207]-216) and index. 327 $a1. Introduction -- 2. Overview -- 3. Technical Prerequisites -- 4. Change of Basis -- 5. Modular Squarefree and Greatest Factorial Factorization -- 6. Modular Hermite Integration -- 7. Computing All Integral Roots of the Resultant -- 8. Modular Algorithms for the Gosper-Petkov?ek Form -- 9. Polynomial Solutions of Linear First Order Equations -- 10. Modular Gosper and Almkvist & Zeilberger Algorithms. 330 $aThis work brings together two streams in computer algebra: symbolic integration and summation on the one hand, and fast algorithmics on the other hand. In many algorithmically oriented areas of computer science, theanalysisof- gorithms?placedintothe limelightbyDonKnuth?stalkat the 1970ICM ?provides a crystal-clear criterion for success. The researcher who designs an algorithmthat is faster (asymptotically, in the worst case) than any previous method receives instant grati?cation: her result will be recognized as valuable. Alas, the downside is that such results come along quite infrequently, despite our best efforts. An alternative evaluation method is to run a new algorithm on examples; this has its obvious problems, but is sometimes the best we can do. George Collins, one of the fathers of computer algebra and a great experimenter,wrote in 1969: ?I think this demonstrates again that a simple analysis is often more revealing than a ream of empirical data (although both are important). ? Within computer algebra, some areas have traditionally followed the former methodology, notably some parts of polynomial algebra and linear algebra. Other areas, such as polynomial system solving, have not yet been amenable to this - proach. The usual ?input size? parameters of computer science seem inadequate, and although some natural ?geometric? parameters have been identi?ed (solution dimension, regularity), not all (potential) major progress can be expressed in this framework. Symbolic integration and summation have been in a similar state. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v3218 606 $aAlgorithms 606 $aNumerical analysis 606 $aComputer science?Mathematics 606 $aComputer mathematics 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aNumeric Computing$3https://scigraph.springernature.com/ontologies/product-market-codes/I1701X 606 $aSymbolic and Algebraic Manipulation$3https://scigraph.springernature.com/ontologies/product-market-codes/I17052 606 $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018 606 $aComputational Science and Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/M14026 615 0$aAlgorithms. 615 0$aNumerical analysis. 615 0$aComputer science?Mathematics. 615 0$aComputer mathematics. 615 14$aAlgorithm Analysis and Problem Complexity. 615 24$aNumeric Computing. 615 24$aSymbolic and Algebraic Manipulation. 615 24$aAlgorithms. 615 24$aComputational Science and Engineering. 676 $a005.1 686 $a54.10$2bcl 700 $aGerhard$b Jürgen$4aut$4http://id.loc.gov/vocabulary/relators/aut$0441584 906 $aBOOK 912 $a996466367803316 996 $aModular Algorithms in Symbolic Summation and Symbolic Integration$9772838 997 $aUNISA LEADER 10787nam 2200517 450 001 9910555250203321 005 20220629095813.0 010 $a1-119-71689-6 010 $a1-119-71690-X 010 $a1-119-71688-8 035 $a(CKB)4100000012049791 035 $a(MiAaPQ)EBC6739230 035 $a(Au-PeEL)EBL6739230 035 $a(OCoLC)1273978355 035 $a(PPN)26202246X 035 $a(EXLCZ)994100000012049791 100 $a20220629d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aElectronics in advanced research industries $eindustry 4.0 to industry 5.0 advances /$fAlessandro Massaro 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons, Inc.,$d[2021] 210 4$d©2021 215 $a1 online resource (538 pages) 311 $a1-119-71687-X 327 $aCover -- Title Page -- Copyright Page -- Contents -- Preface -- About the Author -- Chapter 1 State of the Art and Technology Innovation -- 1.1 State of the Art of Flexible Technologies in Industry -- 1.1.1 Sensors and Actuators Layer: I/O Layer -- 1.1.2 Agent/Firmware Layer: User Interface Layer -- 1.1.3 Gateway and Enterprise Service Bus Layer -- 1.1.4 IoT Middleware -- 1.1.5 Processing Layer -- 1.1.6 Application Layer -- 1.1.7 File Transfer Protocols -- 1.2 State of the Art of Scientific Approaches Oriented on Process Control and Automatisms -- 1.2.1 Architectures Integrating AI -- 1.2.2 AI Supervised and Unsupersived Algorithms -- 1.2.3 AI Image Processing -- 1.2.4 Production Process Mapping -- 1.2.5 Technologies of Industry 4.0 and Industry 5.0: Interconnection and Main Limits -- 1.2.6 Infrared Thermography in Monitoring Process -- 1.2.7 Key Parameters in Supply Chain and AI Improving Manufacturing Processes -- 1.3 Intelligent Automatic Systems in Industries -- 1.4 Technological Approaches to Transform the Production in Auto-Adaptive Control and Actuation Systems -- 1.5 Basic Concepts of Artificial Intelligence -- 1.6 Knowledge Upgrading in Industries -- References -- Chapter 2 Information Technology Infrastructures Supporting Industry 5.0 Facilities -- 2.1 Production Process Simulation and Object Design Approaches -- 2.1.1 Object Design of a Data Mining Algorithm: Block Functions and Parameter Setting -- 2.1.2 Example 1: BPM Modeling of Wheat Storage Process for Pasta Production -- 2.1.3 Example 2: Block Diagram Design of a Servo Valve Control and Actuation System -- 2.1.4 Example 3: Block Diagram of a Liquid Production System -- 2.1.5 Example 4: UML Design of a Programmable Logic Controller System -- 2.1.6 Example 5: Electronic Logic Timing Diagram -- 2.1.7 Example 6: AR System in Kitchen Production Process. 327 $a2.1.8 Example 7: Intelligent Canned Food Production Line -- 2.2 Electronic Logic Design Oriented on Information Infrastructure of Industry 5.0 -- 2.3 Predictive Maintenance: Artificial Intelligence Failure Predictions and Information Infrastructure Layout in the Temperature Monitoring Process -- 2.4 Defect Estimation and Prediction by Artificial Neural Network -- 2.4.1 Other Methodologies to Map and Read Production Failures and Defects -- 2.5 Defect Clustering and Classification: Combined Use of the K-Means Algorithm with Infrared Thermography for Predictive Maintenance -- 2.6 Facilities of a Prototype Network Implementing Advanced Technology: Example of an Advanced Platform Suitable for Industry 5.0 Integrating Predictive Maintenance -- 2.7 Predictive Maintenance Approaches -- 2.7.1 Preventive Maintenance and Predictive Maintenance Operations in the Railway Industry -- 2.8 Examples of Advanced Infrastructures Implementing AI -- 2.9 Examples of Telemedicine Platforms Integrating Advanced Facilities -- 2.9.1 Advanced Telecardiology Platform -- 2.9.2 Advanced Teleoncology Platform -- 2.9.3 Multipurpose E-Health Platform -- References -- Chapter 3 Human-Machine Interfaces -- 3.1 Mechatronic Machine Interface Architectures Integrating Sensor Systems -- 3.1.1 Multiple Mechatronic Boards Managing Different Production Stages -- 3.1.2 Mechatronic Boards Managing Component Processing -- 3.2 Machine-to-Machine Interfaces: New Concepts of Industry 5.0 -- 3.3 Production Line Command and Actuation Interfaces in Upgraded Systems -- 3.3.1 PLC, PAC, Industrial PC, and Improvements -- 3.3.2 SCADA Systems for Centralization of Data Production -- 3.4 McCulloch-Pitts Neurons and Logic Port for Automatic Decision-Making Setting Thresholds -- 3.5 Programmable Logic Controller I/O Ports Interfacing with AI Engine. 327 $a3.6 Human-Machine Interface for Data Transfer and AI Data Processing -- 3.7 Example of Interface Configuration of Temperature Control -- 3.8 AI Interfaces Oriented on Cybersecurity Attack Detection -- 3.9 AI Interfaces Oriented on Database Security -- 3.10 Cybersecurity Platform and AI Control Interface -- References -- Chapter 4 Internet of Things Solutions in Industry -- 4.1 Cloud Computing IoT -- 4.1.1 IoT Agent -- 4.1.2 IoT Gateway in Smart Environments -- 4.1.3 Basic Elements of a Smart Industry Environment Controlling Production -- 4.1.4 Augmented Reality Hardware and Cloud Computing Processing -- 4.1.5 Real-Time Control and Actuation -- 4.1.6 Localization Technologies in an Industrial Environment -- 4.1.7 GPU Processing Units -- 4.2 IoT and External Artificial Intelligence Engines -- 4.2.1 Artificial Engines and Server Location: Artificial Intelligence and Adaptive Production -- 4.2.2 IoT Security Systems in the Working Environment and Implementation Aspects -- 4.2.3 Example of Energy Power Control and Actuation: Energy Routing and Priority Load Management for Energy Efficiency -- 4.2.4 Online Configurators: Cloud DSS -- 4.3 Blockchain and IoT Data Storage Systems -- 4.3.1 Blockchain Implementation Rules -- 4.3.2 Blockchain and IoT Production Traceability -- 4.4 Mechatronic Machine Interface Architectures Integrating Sensor Systems -- 4.5 Multiple Mechatronic Boards Managing Different Production Stages -- References -- Chapter 5 Advanced Robotics -- 5.1 Collaborative Robotics in Industry and Protocols -- 5.1.1 Data Protocols -- 5.1.2 Basic Concepts of Robotic Arms and Control Improvement -- 5.1.3 Collaborative Exoskeleton Communication System Protocols -- 5.1.4 Advanced Robotics and Intelligent Automation in Manufacturing: Logic Conditions and PLC Programming -- 5.2 Artificial Intelligence in Advanced Robotics and Auto-Adaptive Movement. 327 $a5.2.1 General Technological Aspects about Auto-Adaptive Motion in Advanced Robotics -- 5.2.1.1 Main Aspects of Electrostatic Actuators -- 5.2.1.2 Microelectromechanical System Electrostatic Actuators -- 5.2.1.3 Piezoelectric Actuators -- 5.2.1.4 DC Motor Actuation -- 5.2.1.5 Intelligent Control Integrating AI: Speed Regulation -- 5.2.2 Improvement of Collaborative Exoskeletons by Auto-Adaptive Solutions Implementing Artificial Intelligence -- 5.3 Human-Robot Self-Learning Collaboration in Industrial Applications and Electronic Aspects -- 5.3.1 DC-DC Converter -- 5.3.2 Voltage Source Inverter -- 5.3.3 Current-Source Inverter -- 5.3.4 DC Voltage Source -- 5.3.5 Capacitor and Reactor Effects on Signal Control -- 5.3.6 Human-Robot System and Learning Approaches -- 5.3.6.1 Example of PID Implementation of Self-AdaptingGains -- 5.3.7 Unsupervised Learning Approaches -- 5.3.8 Soft Robotics for Intelligent Collaborative Robotics -- 5.4 Robotics in Additive Manufacturing -- 5.4.1 Additive Manufacturing in Industrial Production and Spray Technique -- 5.4.2 Artificial Intelligence Applications in Additive Manufacturing -- 5.4.3 Advanced Electronic for Design-to-Product Transformation: Laser Texturing Manufacturing and Artificial Intelligence -- References -- Chapter 6 Advanced Optoelectronic and Micro-/Nanosensors -- 6.1 Nanotechnology Laboratories in Industries -- 6.1.1 Facilities for Micro-/Nanosensor Fabrication and Characterization -- 6.2 Micro- and Nanosensors as Preliminary Prototypes for Industry Research -- 6.2.1 Nanocomposite Optoelectronic Sensors and Optoelectronic Circuits for Pressure Sensors -- 6.2.1.1 Optical Fiber Nanocomposite Tip -- 6.2.2 Plasmonic Probes -- 6.2.3 Nanocomposite Pressure Sensor -- 6.2.4 Nanocomposite Sensor for Liquid Detection Systems and Fluid Loss Systems. 327 $a6.2.4.1 Nanocomposite Sensor for Liquid Detection Systems Based on a Pillar-Type Layout -- 6.2.4.2 Micro- and Nanosensors in the Monitoring of Production Processes: Leakage Monitoring -- 6.2.5 Examples of Digital MEMS/NEMS Sensors: Technological Aspects and Applications -- 6.2.5.1 Thin Film MEMS -- 6.2.5.2 Nanoprobes for Medical Imaging -- 6.2.5.3 Diamond Thin Film Devices: Sensing Improvements -- 6.3 Multisensor Systems and Big Data Synchronization of Micro-/Nanoprobes -- References -- Chapter 7 Image Vision Advances -- 7.1 Defect Classification by Artificial Intelligence and Data Processor Units -- 7.1.1 Artificial Intelligence Algorithms and Automatism for Defect Classification: Case Study of Tire Production -- 7.1.2 Welding Classification and Nondestructive Testing Suitable for the Quality Check -- 7.1.2.1 Watershed Image Segmentation and Automatic Welding Defect Classification -- 7.1.3 Encoding and Decoding Circuits in Artificial Intelligence Data Processing -- 7.1.4 Electronic Logic Port Implementations: Pixel Matrix Logic Condition -- 7.2 Image Vision Architectures and Electronic Design -- 7.2.1 Infrared Thermography Monitoring Industrial Processes -- 7.2.1.1 Welding Image Vision Processing and Architecture Design: Radiometric Post Processing -- 7.2.2 Electronic and Firmware for Inline Image Monitoring Systems: Hole Precision in Milling Quality Processes -- 7.2.3 Image Vision and Predictive Maintenance by Artificial Intelligence -- 7.2.3.1 Profilometer for Image Vision -- 7.2.3.2 In-Line 3D Image Vision AI System Integrating Profilometer and Image Processing -- 7.2.4 Augmented Reality Systems and Artificial Neural Networks: Image Vision Supporting Production Processes -- 7.2.5 Infrared Thermography Circuit Design and Automated System -- 7.3 Image Segmentation and Image Clustering. 327 $a7.3.1 Electronic and Firmware for In-Line Monitoring Systems: Camera Connection. 606 $aIndustry 4.0 606 $aElectronics$xSafety measures 615 0$aIndustry 4.0. 615 0$aElectronics$xSafety measures. 676 $a658.4038028563 700 $aMassaro$b Alessandro$f1974-$01246732 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910555250203321 996 $aElectronics in advanced research industries$92890523 997 $aUNINA