Arama Sonuçları - The Glass Key

Rebuilding the fire and rescue services : policy delivery and assurance / Yazar: Murphy, Peter, Lakoma, Katarzyna, Eckersley, Peter, Glennon, Russ

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Fast Scanning Calorimetry

İçindekiler: “…Metastability and Reorganization in relation to Crystallization and Melting -- New Insights into Polymer Crystallization by Fast Scanning Chip Calorimetry -- Mesophase Formation in iPP Copolymers -- Industrial Applications of Fast Scanning DSC - New Opportunities for studying Polyolefin Crystallization -- Full-Temperature Range Crystallization Rates of Polyamides by FSC as Key to Processing -- Kinetic Studies of Melting, Crystallization, and Glass Formation -- Nucleation Kinetics Analyses of Deeply Undercooled Metallic Liquids by Fast Scanning Calorimetry -- Fast Scanning Calorimetry of Phase Transitions in Metals -- Precipitation- and Dissolution-Kinetics in Metallic Alloys with Focus on Aluminium Alloys by Calorimetry in a Wide Scanning Rate Range -- Martensitic Transformation of NiMnGa Shape Memory Alloys Thin Films Studied by Flash DSC.…”
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High-Entropy Alloys Fundamentals and Applications /

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Micro and Nano Fabrication Technology

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Fillers for Polymer Applications

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Advances in Manufacturing, Production Management and Process Control Joint proceedings of the AHFE 2018 International Conference on Advanced Production Management and Process Contr...

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The Non-halogenated flame retardant handbook /

İçindekiler: “…Cover; Tittle Page; Copyright Page; Contents; Preface; List of Contributors; 1 The History and Future Trends of Non-halogenated Flame Retarded Polymers; 1.1 Introduction; 1.1.1 Why Non-Halogenated Flame Retardants?; 1.2 Key Flame Retardancy Safety Requirements; 1.3 Geographical Trends; 1.4 Applications for Non-halogenated FRP's; References; 2 Phosphorus-based FRs; 2.1 Introduction; 2.2 Main Classes of Phosphorus-based FRs; 2.3 Polyolefins; 2.4 Polycarbonate and Its Blends; 2.5 Polyphenylene Ether Blends; 2.6 Polyesters and Polyamides.…”
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Interpretable Machine Learning with Python : Build Explainable, Fair, and Robust High-Performance Models with Hands-on, Real-world Examples. Yazar: Mas�is, Serg

İçindekiler: “…Understanding limitations of traditional model interpretation methods -- Studying intrinsically interpretable (white-box) models -- Generalized linear models (GLMs) -- Linear regression -- Ridge regression -- Polynomial regression -- Logistic regression -- Decision trees -- CART decision trees -- RuleFit -- Interpretation and feature importance -- Nearest neighbors -- k-Nearest Neighbors -- Na�ive Bayes -- Gaussian Na�ive Bayes -- Recognizing the trade-off between performance and interpretability -- Special model properties -- The key property: explainability -- The remedial property: regularization -- Assessing performance -- Discovering newer interpretable (glass-box) models -- Explainable Boosting Machine (EBM) -- Global interpretation -- Local interpretation -- Performance -- GAMI-Net -- Global interpretation -- Local interpretation -- Performance -- Mission accomplished -- Summary -- Dataset sources -- Further reading -- Chapter 4: Global Model-Agnostic Interpretation Methods -- Technical requirements -- The mission -- The approach -- The preparations -- Loading the libraries -- Data preparation -- Model training and evaluation -- What is feature importance? …”
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