LEADER 05646nam 22011415 450 001 996571858703316 005 20240516125543.0 010 $a0-8147-7297-8 024 7 $a10.18574/9780814772973 035 $a(CKB)1000000000522493 035 $a(EBL)865885 035 $a(OCoLC)784884474 035 $a(SSID)ssj0000164453 035 $a(PQKBManifestationID)11164753 035 $a(PQKBTitleCode)TC0000164453 035 $a(PQKBWorkID)10124310 035 $a(PQKB)11138236 035 $a(DE-B1597)546846 035 $a(DE-B1597)9780814772973 035 $a(MiAaPQ)EBC865885 035 $a(EXLCZ)991000000000522493 100 $a20200723h20062006 fg 0 101 0 $aeng 135 $aurun#---|u||u 181 $ctxt 182 $cc 183 $acr 200 04$aThe good fight continues $eWorld War II letters from the Abraham Lincoln Brigade /$fPeter N. Carroll, Michael Nash, Melvin Small 205 $a1st ed. 210 1$aNew York, NY :$cNew York University Press,$d[2006] 210 4$dİ2006 215 $a1 online resource (305 p.) 300 $aDescription based upon print version of record. 311 0 $a0-8147-1659-8 327 $tFront matter --$tContents --$tIllustrations --$tPreface --$tAcknowledgments --$tChapter 1: Before Pearl Harbor --$tChapter 2: At War with the Army --$tChapter 3: Problems in Red and Black --$tChapter 4: In the Combat Theaters --$tChapter 5: Premature Antifascists and the Postwar World --$tAppendix : Biographical Index of Letter Writers --$tBibliography --$tIndex --$tAbout the Editors 330 $aWritten with passion and intelligence, the letters of the Abraham Lincoln Brigade in World War II express the raw idealism of anti-fascist soldiers who experienced the war in boot camps, cockpits, and foxholes, but never lost sight of the great global issues at stake. When the United States entered World War II on December 7, 1941, only one group of American soldiers had already confronted the fascist enemy on the battlefield: the U.S. veterans of the Lincoln Brigade, a volunteer army of about 2,800 men and women who had enlisted to defend the Spanish Republic from military rebels during the Spanish Civil War (1936-1939). They fought on the losing side. After Pearl Harbor, Lincoln Brigade veterans enthusiastically joined the U.S. Army, welcoming this second chance to fight against fascism. However, the Lincoln recruits soon encountered suspicious military leaders who questioned their patriotism and denied them promotions and overseas assignments, foreshadowing the political persecution of the postwar Red Scare. African American veterans who fought in fully integrated units in Spain, faced second-class treatment in America's Jim Crow army. Nevertheless, the Lincolns served with distinction in every theater of the war and won a disproportionate number of medals for courage, dedication, and sacrifice. The 154 letters in this volume, selected from thousands held in the Abraham Lincoln Brigade Archives at NYU?s Tamiment Library, provide a new and unique perspective on aspects of World War II. 606 $aAnti-fascist movements$xUnited States$zHistory$z20th century 606 $aSoldiers$xUnited States$zCorrespondence 606 $aWorld War, 1939-1945$xParticipation, African American 606 $aWorld War, 1939-1945$vPersonal narratives, American 606 $aWorld War, 1939-1945$xPublic opinion 606 $aWorld War, 1939-1945$zUnited States 606 $aWorld War, 1939-1945$xPublic opinion$y20th century$zUnited States$vPersonal narratives, American 606 $aWorld War, 1939-1945$xParticipation, African American$zUnited States$vCorrespondence 606 $aWorld War, 1939-1945$xHistory$zUnited States 606 $aWorld War, 1939-1945 606 $aAnti-fascist movements 606 $aSoldiers 607 $aSpain$xHistory$xCivil War, 1936-1939$zVeterans$xCorrespondence 607 $aUnited States$xForeign relations$y1933-1945 610 $aAbraham. 610 $aBrigade. 610 $aLincoln. 610 $aWorld. 610 $aWritten. 610 $aanti-fascist. 610 $aboot. 610 $acamps. 610 $acockpits. 610 $aexperienced. 610 $aexpress. 610 $afoxholes. 610 $aglobal. 610 $agreat. 610 $aidealism. 610 $aintelligence. 610 $aissues. 610 $aletters. 610 $alost. 610 $anever. 610 $apassion. 610 $asight. 610 $asoldiers. 610 $astake. 610 $awith. 615 0$aAnti-fascist movements$xUnited States 615 0$aSoldiers$xUnited States 615 0$aWorld War, 1939-1945$xParticipation, African American. 615 0$aWorld War, 1939-1945 615 0$aWorld War, 1939-1945$xPublic opinion. 615 0$aWorld War, 1939-1945 615 0$aWorld War, 1939-1945$xPublic opinion 615 0$aWorld War, 1939-1945$xParticipation, African American 615 0$aWorld War, 1939-1945$xHistory 615 0$aWorld War, 1939-1945 615 0$aAnti-fascist movements 615 0$aSoldiers 676 $a940.540973 702 $aCarroll$b Peter N.$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNash$b Michael$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSmall$b Melvin$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 02$aAbraham Lincoln Brigade Archives 801 0$bDE-B1597 801 1$bDE-B1597 906 $aBOOK 912 $a996571858703316 996 $aThe good fight continues$93670653 997 $aUNISA LEADER 10556nam 22005533 450 001 9911042410503321 005 20251031080323.0 010 $a1-394-29029-2 010 $a1-394-29030-6 010 $a1-394-29028-4 035 $a(CKB)41826618100041 035 $a(MiAaPQ)EBC32379056 035 $a(Au-PeEL)EBL32379056 035 $a(OCoLC)1547906433 035 $a(EXLCZ)9941826618100041 100 $a20251031d2025 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData-Driven Energy Management and Tariff Optimization in Power Systems $eShaping the Future of Electricity Distribution Through Analytics 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2025. 210 4$dİ2026. 215 $a1 online resource (320 pages) 311 08$a1-394-29027-6 327 $aCover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Chapter 1 Fundamentals of Power System Data and Analytics -- 1.1 Introduction -- 1.2 Background -- 1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems -- 1.2.2 Transformation in the Power Industry -- 1.2.3 Drivers and Barriers -- 1.3 Data?rich Power Systems -- 1.3.1 Data Sources and Types -- 1.3.2 Data Structure -- 1.4 Data Analytics in Power Systems -- 1.4.1 What Is Data Analytics? -- 1.4.2 Analytics Techniques -- 1.5 Data Analytics?Based Decision?Making in Future Power Systems -- 1.5.1 Decision Framework -- 1.5.1.1 Uncertainty Issues -- 1.5.1.2 Behavioral Analytics -- 1.5.1.3 Policy Mechanisms -- 1.5.2 Computational Aspects -- 1.6 Conclusion -- 1.7 Future Trends and Challenges -- References -- Chapter 2 Advanced Predictive Modeling for Energy Consumption and Demand -- 2.1 The Role of Load Forecasting in Power System Planning -- 2.2 Need for Short?Term Demand Forecasting -- 2.3 Components of Power Demand and Factors Affecting Demand Growth -- 2.3.1 Electricity Demand from the Consumer Type Perspective -- 2.3.2 Electricity Demand from the Supply Perspective -- 2.4 Electricity Demand in Networks with High Renewable Energy Sources -- 2.5 Machine Learning and Its Applications in Demand Forecast -- 2.5.1 Application of Clustering in Load Forecasting -- 2.6 The Impact of Macro?decisions on Long?term Load Forecasting -- 2.6.1 Natural Gas as a Primary Energy Carrier for Heating Demand -- 2.7 Conclusion -- References -- Chapter 3 Demand Response and Customer?Centric Energy Management -- 3.1 Introduction -- 3.2 Background -- 3.3 Future Power Systems Aspects, Trends, and Challenges -- 3.4 Transforming to Customer?Centric Era -- 3.4.1 Differences Between Customer?Centric DR Solution and Other Ways in the Future Power System. 327 $a3.4.2 Drivers and Enablers -- 3.5 Customer?Centric Power System Structure -- 3.5.1 Physical Layer -- 3.5.1.1 Physical Resources -- 3.5.1.2 Physical Constraints of the System -- 3.5.2 Cyber?Social Layers -- 3.5.2.1 Centralized Approach (Traditional) -- 3.5.2.2 Decentralized Approach (Future) -- 3.6 Conclusion and Future Trends -- References -- Chapter 4 Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights -- 4.1 Introduction -- 4.1.1 Data Mining: Concepts, Procedures, and Tools -- 4.1.2 Energy Management and the Role of Data Mining -- 4.1.3 Aims and Scope -- 4.2 Investigating Industrial Load Data: Analysis Through Various Indexes -- 4.3 Classification of Industries -- 4.4 Discussion and Conclusions -- References -- Chapter 5 Data?Driven Tariff Design for Equitable Energy Distribution -- 5.1 Introduction -- 5.1.1 Literature Review and Contributions -- 5.1.2 Chapter Organization -- 5.2 Proposed Approach and Formulations -- 5.3 Describing the Case Study -- 5.4 Simulation Results -- 5.5 Conclusions and Future Works -- References -- Chapter 6 Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems -- 6.1 Introduction -- 6.2 Machine Learning Techniques -- 6.2.1 Artificial Neural Network and Deep Neural Network -- 6.2.2 Convolutional Neural Network -- 6.2.3 Recurrent Neural Network -- 6.2.4 Long Short?Term Memory -- 6.3 General View of ML/DL Methods for RES Integration -- 6.3.1 Data Preprocessing -- 6.3.1.1 Normalization -- 6.3.1.2 Wrong/Missing Values and Outliers -- 6.3.1.3 Data Resolution -- 6.3.1.4 Inactive Time Data -- 6.3.1.5 Data Augmentation -- 6.3.1.6 Correlation -- 6.3.1.7 Data Clustering -- 6.3.2 Deterministic/Probabilistic Forecasting Methods -- 6.3.2.1 Deterministic Methods -- 6.3.2.2 Probabilistic Forecasting Methods -- 6.3.3 Evaluation Measures. 327 $a6.4 ML/DL Application for Integration of RES -- 6.4.1 Renewable Resources Data Prediction/Planning -- 6.4.2 RES Power Generation Prediction/Operation -- 6.4.3 Electric Load and Demand Forecasting -- 6.4.4 Stability Analysis -- 6.4.4.1 Security Assessment -- 6.4.4.2 Stability Assessment -- 6.5 Integrated Machine Learning and Optimization Approach -- 6.6 Conclusion -- References -- Chapter 7 Machine Learning?Based Solutions for Renewable Energy Integration -- 7.1 Introduction -- 7.2 Machine Learning Importance in RESs Sector -- 7.2.1 AI?Based Algorithms in RESs -- 7.2.2 ML Algorithms Application in RESs -- 7.3 Role of ML in Optimizing Renewable Energy Generation -- 7.3.1 Different Programming Models in RES Optimization -- 7.3.2 Optimization Objectives in RESs -- 7.3.3 ML Applications in Optimizing Renewable Energy Generation -- 7.4 Ensuring Grid Stability Through ML?Based Forecasting -- 7.4.1 Grid Stability Forecasting -- 7.4.2 Grid Stability Through ML?Based Forecasting -- 7.5 Challenges and Future Direction in ML?Based Approaches to RESs -- 7.5.1 Challenges in ML?Based Approaches to RESs -- 7.5.2 Future Directions in ML?Based Approaches to RESs -- 7.6 Conclusion -- References -- Chapter 8 Application of Artificial Neural Networks in Solar Photovoltaic Power Forecasting -- 8.1 RES Share in World Energy Transition -- 8.2 Applications of PV Panels in Energy Systems -- 8.3 Disadvantages of PV Panels -- 8.4 Importance of PV Power Forecasting -- 8.5 Proposed Algorithm for PV Power Prediction -- 8.6 Numerical Results and Discussions -- 8.7 Concluding Remarks -- References -- Chapter 9 Power System Resilience Evaluation Data Challenges and Solutions -- 9.1 Introduction -- 9.2 A Review of Power System Resilience Metrics -- 9.3 The General Framework for the Resilience Assessment of the Power System -- 9.4 Data Required for Power System Resilience Studies. 327 $a9.4.1 Data of Natural Origin -- 9.4.2 Basic Data of the Power System -- 9.4.3 Data on Failure and Restoration Rates -- 9.5 Data Analysis and Correction -- 9.6 Disaster Forecasting in Power System Resilience Studies -- 9.7 Modeling the Impact of Disaster on Power System Performance -- 9.8 Static Model in Machine Learning -- 9.9 Spatiotemporal Random Process -- 9.9.1 Dynamic Model for Chain Failures -- 9.9.2 Nonstationary Failure?Recovery?Impact Processes -- 9.10 Lessons Learned and Concluding Remarks -- 9.11 Future Work -- References -- Chapter 10 Nonintrusive Load Monitoring in Smart Grids Using Deep Learning Approach -- 10.1 Introduction -- 10.2 Deep Learning Neural Networks -- 10.2.1 RNN -- 10.2.2 LSTM -- 10.2.3 CNN -- 10.2.4 Convolutional Layer -- 10.2.5 Pooling Layer -- 10.2.6 Fully Connected Layer -- 10.3 The Proposed Method -- 10.3.1 Pre?Processing and Preparing Data -- 10.3.2 Proposed Method Architecture -- 10.3.3 Proposed Method's Parameters -- 10.3.4 Performance Evaluation -- 10.4 Results and Discussion -- 10.5 Challenges and Future Trends -- 10.6 Conclusion -- References -- Chapter 11 Power System Cyber?Physical Security and Resiliency Based on Data?Driven Methods -- 11.1 Introduction -- 11.2 Fundamental Concepts -- 11.2.1 Cyber?Physical Power System (CPPS) -- 11.2.2 Security and Resiliency -- 11.3 Role of Data Analytics -- 11.3.1 Basic Methods -- 11.3.1.1 Supervised Learning (SL) -- 11.3.1.2 Unsupervised Learning (UL) -- 11.3.2 Advanced Techniques -- 11.3.2.1 Dimensionality Reduction (DR) -- 11.3.2.2 Feature Engineering -- 11.3.2.3 Reinforcement Learning -- 11.3.2.4 Integrated Models -- 11.4 Interdependency Modeling -- 11.4.1 Direct Modeling -- 11.4.2 Testbeds -- 11.4.3 Game?Theoretic -- 11.4.4 Machine Learning -- 11.5 Cyber?Physical Threats -- 11.5.1 Physical Attacks -- 11.5.2 Cyberattacks -- 11.5.2.1 Confidentiality. 327 $a11.5.2.2 Availability -- 11.5.2.3 Integrity -- 11.5.3 Coordinated Attacks -- 11.6 Defense Framework -- 11.6.1 Preventive Measures -- 11.6.1.1 Supply Chain Security -- 11.6.1.2 Access Control -- 11.6.1.3 Personnel Training -- 11.6.1.4 Resource Allocation -- 11.6.1.5 Infrastructure Hardening -- 11.6.1.6 Moving Target Defense -- 11.6.2 Mitigation Actions -- 11.6.2.1 Attack Detection -- 11.6.2.2 Data Recovery -- 11.6.2.3 Reconfiguration and Restoration -- 11.6.2.4 Forensic Analysis -- 11.7 Conclusion -- References -- Chapter 12 Application of Artificial Intelligence in Undervoltage Load Shedding in Digitalized Power Systems -- 12.1 Introduction -- 12.2 Load?Shedding Strategies -- 12.2.1 Conventional LS -- 12.2.2 Adaptive LS -- 12.2.3 AI?Based LS -- 12.3 Principles of UVLS -- 12.3.1 Amount of Load Shed -- 12.3.2 Location for LS -- 12.3.3 Application of VSI for UVLS -- 12.4 AI?Based Methods -- 12.5 Case Study -- 12.5.1 Database Generation -- 12.5.2 Offline Training -- 12.5.3 Online Application -- 12.6 Future Challenges and Transfer Learning -- 12.7 Conclusion -- References -- Index -- EULA. 330 $aPresents a comprehensive guide to transforming power systems through data Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. 606 $aElectric power systems$xManagement$7Generated by AI 606 $aEnergy consumption$xForecasting$7Generated by AI 615 0$aElectric power systems$xManagement 615 0$aEnergy consumption$xForecasting 676 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