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1. |
Record Nr. |
UNINA9910958079803321 |
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Autore |
Niiniluoto Ilkka |
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Titolo |
Critical scientific realism / / Ilkka Niiniluoto |
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Pubbl/distr/stampa |
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Oxford ; ; New York, : Oxford University Press, 1999 |
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ISBN |
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Descrizione fisica |
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Collana |
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Clarendon library of logic and philosophy |
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Disciplina |
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Soggetti |
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Realism |
Science - Philosophy |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and indexes. |
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Nota di contenuto |
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Intro -- Contents -- 1. The Varieties of Realism -- 1.1 The problems of realism -- 1.2 Science and other belief systems -- 1.3 Critical scientific realism and its rivals -- 1.4 Realism and the method of philosophy -- 2. Realism in Ontology -- 2.1 Materialism, dualism, and idealism -- 2.2 Popper's three worlds -- 2.3 Existence, mind-independence, and reality -- 2.4 The world and its furniture -- 2.5 Arguments for ontological realism -- 3. Realism in Semantics -- 3.1 Language as representation -- 3.2 Logical, analytic, and factual truth -- 3.3 How semantics is effable: model theory -- 3.4 Truth as correspondence: Tarski's definition -- 3.5 Truthlikeness -- 4. Realism in Epistemology -- 4.1 Certainty, scepticism, and fallibilism -- 4.2 Knowledge of the external world -- 4.3 Kant's 'Copernican revolution' -- 4.4 Critical epistemological realism -- 4.5 Epistemic probability and verisimilitude -- 4.6 Epistemic theories of truth -- 5. Realism in Theory Construction -- 5.1 Descriptivism, instrumentalism, and realism -- 5.2 Meaning variance, reference, and theoretical terms -- 5.3 Laws, truthlikeness, and idealization -- 5.4 Examples of the realism debate -- 6. Realism in Methodology -- 6.1 Measuring the success of science -- 6.2 Axiology and methodological rules -- 6.3 Theory-choice, underdetermination, and simplicity -- 6.4 From empirical success to truthlikeness -- 6.5 Explaining the success of science -- 6.6 Rationality and progress in science -- 7. Internal Realism -- 7.1 Ways of worldmaking -- 7.2 |
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Putnam on internal realism -- 7.3 World-versions and identified objects -- 8. Relativism -- 8.1 Varieties of relativism -- 8.2 Moral relativism -- 8.3 Cognitive relativism -- 8.4 Feminist philosophy of science -- 9. Social Constructivism -- 9.1 The Edinburgh programme: strong or wrong? -- 9.2 Finitism -- 9.3 Life in laboratory. |
10. Realism, Science, and Society -- 10.1 Social reasons for realism and anti-realism -- 10.2 Science as a cultural value -- 10.3 Science in a free society -- References -- Index of Names -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Z -- Index of Subjects -- A -- B -- C -- D -- E -- F -- G -- H -- I -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W. |
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Sommario/riassunto |
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Ilkka Niiniluoto comes to the rescue of realism in the philosophy of science. Philosophical realism holds that the aim of a particular discourse is to make true statements about its subject-matter. Niiniluoto surveys different kinds of realism in various areas of philosophy, then sets out his own critical realist philosophy of science, characterizing scientific progress in terms of increasing truthlikeness, and defends this theory against its rivals. |
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2. |
Record Nr. |
UNINA9911021978703321 |
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Autore |
Chaturvedi Sanjay K |
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Titolo |
Design and Manufacturing Practices for Performability Engineering |
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Pubbl/distr/stampa |
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Newark : , : John Wiley & Sons, Incorporated, , 2025 |
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©2025 |
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ISBN |
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1-394-34573-9 |
1-394-34572-0 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (449 pages) |
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Collana |
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Performability Engineering Series |
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Altri autori (Persone) |
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GargamaHeeralal |
RaiRajiv Nandan |
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Disciplina |
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Soggetti |
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Reliability (Engineering) |
Engineering design |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgment -- Chapter 1 Mathematical and Physical Reality of Reliability -- 1.1 Introduction -- 1.2 Experiencing Physical Reality of Reliability -- 1.2.1 Joining the Rallying Community -- 1.2.2 Experiencing First Rallying Reality of Reliability -- 1.2.3 Understanding the First Rallying Reality of Reliability -- 1.2.4 Summary of Experienced Physical Reliability Reality -- 1.3 Mathematical Reality of Reliability -- 1.3.1 Mathematical Reality of Reliability of a Component -- 1.3.2 Mathematical Reality of Reliability of a System -- 1.3.3 Physical Meanings of Mathematical Reality of Reliability -- 1.3.3.1 Mathematical Reality: Probability of Design and Production Error of Components and Systems is Equal to Zero -- 1.3.3.2 Mathematical Reality: Probability of Transportation, Storage and Installation Induced Failures is Equal to Zero -- 1.3.3.3 Mathematical Reality: Probability of Component Dependencies Within a System in Equal to Zero -- 1.3.3.4 Mathematical Reality: Probability of Maintenance Induced Failures (Inspections, Repair, Diagnostics, Cleaning, Etc.) is Equal to Zero -- 1.3.3.5 Mathematical Reality: Probability of Discontinuous Operation of the System and Components is Equal to Zero -- 1.3.3.6 Mathematical Reality: Probability of Variable Operational Scenario (Load, Stress, |
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Temperature, Pressure, Etc.) Impacting Reliability is Equal to Zero -- 1.3.3.7 Mathematical Reality: Probability of a Location in Space (GPS or Stellar Co-Ordinates) Impacting Reliability is Equal to Zero -- 1.3.3.8 Mathematical Reality: Probability of a Human Actions Impacting Reliability is Equal to Zero -- 1.3.3.9 Mathematical Reality: Probability of Calendar Time (Seasons Do Not Exist) Impacting Reliability is Equal to Zero. |
1.3.4 Concluding Remarks Regarding Mathematical Reality of Reliability Function -- 1.4 Studying Physical Reality of Reliability -- 1.4.1 Mathematical Reality: Probability of Design and Production Error of Components and Systems is Equal to Zero -- 1.4.2 Observed Physical Reality of Reliability in 2020 - MIRCE Akademy Functionability Archive -- 1.4.3 Observed Physical Reality of Reliability in 2021 - MIRCE Akademy Functionability Archive -- 1.4.4 Observed Physical Reality of Reliability in 2022 - MIRCE Akademy Functionability Archive -- 1.5 Closing Remarks Regarding Observed Physical Reality of Reliability -- 1.5.1 Comparison Between Mathematical and Physical Reality of Reliability -- 1.6 Closing Questions -- 1.7 Personal Message from the Author -- References -- Appendix 1.1 -- Chapter 2 Models and Solutions for Practical Reliability and Availability Assessment -- 2.1 Introduction -- 2.2 Non-State-Space Methods -- 2.3 State-Space-Based Methods -- 2.4 Multi-Level Models -- 2.5 Conclusions -- References -- Chapter 3 Reliability Prediction of Artificial Hip Joints -- 3.1 Introduction -- 3.2 Archard Law Wear Modeling -- 3.2.1 Wear Factor Estimation -- 3.2.2 Sliding Distance Estimation -- 3.2.3 Contact Pressure Estimation -- 3.2.4 Volumetric Wear Estimation -- 3.2.5 Archard Law-Based Wear Model Validation -- 3.3 Physics-Based Stochastic Wear Degradation Modeling -- 3.3.1 Validation of the Gamma Process for the Physics- Based Stochastic Wear Degradation Modeling -- 3.3.2 Parameter Estimation for the Wear Degradation Model -- 3.3.3 Time to Failure Distribution and Reliability Prediction -- 3.3.4 Physics-Based Wear Degradation Model Validation -- 3.4 Effect of Hip Implant Materials, Geometry and Patient's Characteristics on the Wear Volume -- 3.4.1 Effect of Implant Materials -- 3.4.2 Effect of Hip Implant Geometry -- 3.4.3 Effect of Patient Activity Level. |
3.4.4 Effect of Patient Weight Changes -- 3.4.5 Conclusions -- References -- Chapter 4 Principles and Philosophy for an Integrated and Distributed Approach for Reliability and Extensions to Other Qualities -- 4.1 What is Quality? -- 4.1.1 Principle Centered Quality -- 4.2 Reliability -- 4.3 Other Qualities -- 4.3.1 Maintainability -- 4.3.2 Safety -- 4.3.3 Security -- 4.3.4 Robustness -- 4.3.5 Resilience -- 4.3.6 Integration of Time Oriented Qualities -- 4.4 Advances Beyond Binary States (Success/Failure) -- 4.4.1 Reliability Measures Based on Cumulative Customer Experience with the System -- 4.4.2 Some Comments on Role of Probability and Statistics -- 4.5 From Feedback to Prognostics to Feedforward -- 4.6 Prognostics and Feedforward Control -- References -- Chapter 5 An Analytic Toolbox for Optimizing Condition Based Maintenance (CBM) Decisions -- 5.1 Condition Monitoring: Then and Now -- 5.2 Condition Monitoring: Analogy with Heart Attack -- 5.3 Condition Monitoring ''Classical" Approach Vs Proportional Hazards Model (PHM) -- 5.3.1 Which Measurements -- 5.3.2 Optimal Limits -- 5.3.3 Effect of Age -- 5.3.4 Predictions -- 5.3.5 Consequence of Failure -- 5.4 Another Approach to Overcome these Limitations -- 5.5 Early Work with the Proportional-Hazards Model (PHM) -- 5.6 Estimated Hazard Rate at Failure -- 5.7 EXAKT Optimal Decision - A New "Control Chart" -- 5.8 Optimizing CBM Decisions: EXAKT -- 5.8.1 Hazard -- 5.8.2 Transition Probability Matrix -- 5.8.3 Economics -- 5.9 Some Case Studies -- 5.10 |
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University/Industry Collaboration -- 5.11 Acknowledgement to Companies Who Funded the Research Team Who Developed the CBM Optimization Software -- References -- Chapter 6 Degradation Modeling with Imperfect Maintenance -- 6.1 Introduction. |
6.2 Statistical Inference for a Wiener-Based Degradation Model with Imperfect Maintenance Actions Under Different Observation Schemes -- 6.2.1 Notations and Assumptions -- 6.2.2 The Model -- 6.2.3 Statistical Inference -- 6.2.3.1 First Observation Scheme -- 6.2.3.2 Second Observation Scheme -- 6.2.3.3 Third Observation Scheme -- 6.2.3.4 Fourth Observation Scheme -- 6.2.4 Remarks -- 6.3 Modeling Multivariate Degradation Processes with Time- Variant Covariates and Imperfect Maintenance Effects -- 6.3.1 Features of the Model -- 6.3.2 Piecewise Constant Covariates -- 6.3.3 The Multivariate Degradation Process -- 6.3.4 Imperfect Maintenance -- 6.4 Conclusion -- References -- Chapter 7 Asset Maintenance in Railway: Powered by New Technology and Driven by Sustainability -- 7.1 Introduction and Background -- 7.2 RAMS & -- PHM -- 7.3 New Technology for Railway Maintenance -- 7.4 Automation, Robotics and AI in Railway -- 7.4.1 Automation of Data Acquisition -- 7.4.2 Automation of Information Extraction -- 7.4.3 Automation of Maintenance Task Planning, Scheduling and Execution -- 7.4.4 Digital Twins -- 7.4.5 Maintenance in Metaverse -- 7.5 Some Examples of Industrial Projects -- 7.5.1 AI Factory for Railway -- 7.5.2 Differential Eddy Current Sensor System for Detection of Missing Rail Fasteners -- 7.5.3 Assessment of Track Geometry Condition -- 7.5.4 PHM of Railway Catenary -- 7.5.5 The Digital Railway Switches -- 7.5.6 Fr8 RAIL - Predictive Maintenance for Rolling Stocks -- 7.6 Maintenance and Sustainability -- 7.6.1 Life Extension of Ageing Railway Asset -- 7.6.2 Energy Efficiency -- 7.6.3 Risk Mitigation -- 7.7 Challenges Associated with Application of Emerging Technologies -- 7.8 Concluding Remarks -- References -- Chapter 8 ISO 14001 History and Applications -- 8.1 Need for EMS - Help to Prevent Environmental Disasters. |
8.2 India's Governmental Alignments with the ISO -- 8.3 Sustainability Goal -- 8.4 History of ISO & -- Environmental Standards -- 8.5 ISO 14000 -- 8.6 ISO Oversight Process -- 8.7 ISO 14001:2015 - Structure -- Bibliography -- 8.8 ISO 14001:2015 - Requirements - Shall's -- 8.9 ISO 14001:2015 - Risk & -- Opportunities -- 8.10 ISO 14001:2015 - Aspects & -- Impacts -- 8.11 ISO 14001:2015 - Life Cycle -- 8.12 Linkage to Other ISO Management System Standards -- 8.13 Potential Environmental Updates Based on Thoughts for ISO 9001:2025 -- References -- Chapter 9 Process Failure Mode and Effects Analysis (PFMEA) with Fuzzy ANP-MARCOS-Based Approach for Manufacturing Process Quality Assessment -- 9.1 Introduction -- 9.2 Literature Review -- 9.2.1 Quality Metrics for the Manufacturing Process -- 9.2.2 Literature Related to Fuzzy ANP -- 9.2.3 Literature Related to Fuzzy MARCOS -- 9.2.4 Research Deliverables -- 9.3 Methodology -- 9.3.1 Phase 1: PFMEA Method -- 9.3.2 Phase 2: Fuzzy ANP Method -- 9.3.3 Phase 3: Fuzzy MARCOS Technique -- 9.4 Case Study -- 9.4.1 Identification of KQC and its Influencing Factors through PFMEA -- 9.4.2 Estimation of Factors' Weights through Fuzzy ANP -- 9.4.3 Ranking of Cases through Fuzzy MARCOS -- 9.5 Results and Discussions -- 9.5.1 Sensitivity Analysis -- 9.5.2 Comparative Analysis -- 9.6 Summary & -- Conclusion -- References -- Chapter 10 Advanced Neural Networks for Estimation of All-Terminal Network Reliability -- 10.1 CNN-Based Network Reliability Estimation -- 10.1.1 Overview -- 10.1.2 The CNN Proposal -- 10.1.3 CNN Structure -- 10.1.4 The Case Study -- 10.1.4.1 Dataset -- 10.1.4.2 The Hyperparameters -- 10.1.4.3 Chosen CNN -- 10.1.4.4 The Cross- |
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Validation -- 10.1.4.5 Computation Time -- 10.1.5 Discussion. |
10.2 All-Terminal Estimation of Network Reliability Considering Degradation with Bayesian Methods, Monte Carlo, and Deep Neural Networks. |
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Sommario/riassunto |
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"As technology continues to reshape the world, this book stands as a testament to the importance of maintaining the highest standards of performability engineering in the pursuit of progress. I expect that this book will inspire the next generation of innovators and problem solvers to tackle the challenges and opportunities of today and tomorrow, ensuring a future where technology serves humanity with utmost dependability and safety." --Professor Way Kuo in the Foreword to Design and Manufacturing Practices for Performability Engineering There are several aspects involved when evaluating a system's performance, such as reliability, cost, quality, safety, maintainability, risks, and performance-related characteristics. Performability engineering provides a unified framework for integrating these aspects in a quantified manner, enabling informed decisions about a system. However, this field faces the daunting task of unifying diversified disciplines and theories that address issues such as quality, reliability, availability, maintainability, and safety (QRAMS), as well as engineering characteristics, statistical data analysis, multi-criteria decision-making, and applications of deep and machine learning. This book documents the latest ideas presented by world leaders in the QRAMS domain. Through diverse chapters, this volume represents the vitality of QRAMS in performability engineering. Design and Manufacturing Practices for Performability Engineering serves as a useful resource for practicing engineers and researchers pursuing this challenging and relevant area for sustainable development. Readers will find the book: Comprehensively covers a wide range of topics in the area of QRAMs; Provides in-depth explanations of best practices in various elements of Performability Engineering; Explores expert insights and real-world scenarios to demonstrate the many applications of QRAMs. Audience Researchers and educators of reliability engineering, electrical, computer science, electronics, and communication engineering with their associated allied areas. Industry analysts and design engineers of engineering systems will also find this book valuable. |
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