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Record Nr. |
UNINA9910807945603321 |
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Titolo |
Advanced ceramic materials / / edited by Ashutosh Tiwari, Rosario A. Gerhardt and Magdalena Szutkowska |
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Pubbl/distr/stampa |
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Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , 2016 |
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©2016 |
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ISBN |
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1-119-24272-X |
1-119-24273-8 |
1-119-24259-2 |
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Descrizione fisica |
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1 online resource (445 p.) |
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Collana |
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Disciplina |
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Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Cover; Title Page; Copyright Page; Contents; Preface; Part 1 Design, Processing, and Properties; 1 Development of Epitaxial Oxide Ceramics Nanomaterials Based on Chemical Strategies on Semiconductor Platforms; 1.1 Introduction; 1.2 Integration of Epitaxial Functional Oxides Nanomaterials on Silicon Entirely Performed by Chemical Solution Strategies; 1.2.1 Integration of Piezoelectric Quartz Thin Films on Silicon by Soft Chemistry; 1.2.2 Controllable Textures of Epitaxial Quartz Thin Films; 1.2.3 Integration of Functional Oxides by Quartz Templating; 1.2.4 Highly Textured ZnO Thin Films |
1.3 Integration of Functional Oxides by Combining Soft Chemistry and Physical Techniques1.4 Conclusions; Acknowledgments; References; 2 Biphasic, Triphasic, and Multiphasic Calcium Orthophosphates; 2.1 Introduction; 2.2 General Definitions and Knowledge; 2.3 Various Types of Biphasic, Triphasic, and Multiphasic CaPO4; 2.4 Stability; 2.5 Preparation; 2.6 Properties; 2.7 Biomedical Applications; 2.8 Conclusions; References; 3 An Energy Efficient Processing Route for Advance Ceramic Composites Using Microwaves; 3.1 Introduction; 3.2 Historical Developments in Materials Processing by Microwaves |
3.3 Introduction to Microwave Heating Process3.3.1 Microwave- |
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materials Interaction Theory; 3.3.2 Microwave Heating Mechanisms; 3.4 Heating Methods by Microwaves; 3.4.1 Direct Microwave Heating; 3.4.2 Microwave Hybrid Heating; 3.4.3 Selective Heating; 3.4.4 Microwave-assisted Processing of Materials; 3.5 Advantages/Limitations of Microwave Material Processing; 3.5.1 Highly Energy Efficient Processing Method; 3.5.2 Better Quality of Processed Materials; 3.5.3 Cleaner Energy Processing; 3.5.4 Compact Processing Unit; 3.5.5 Restriction in Processing of All Varieties of Materials |
3.5.6 Restrictions in Processing of Complex Shapes3.5.7 Non-uniformity in Heating; 3.5.8 Human Safety Issues; 3.6 Application of Microwave Heating in Composite Processing; 3.6.1 Recent Review of Work Carried Out in MMC/CMC/ Alloys/Ceramic Processing by Microwaves; 3.6.2 Microwave Melting/Casting of Metals/Metal Matrix Composites; 3.7 Future Prospectives; 3.8 Conclusion; References; Part 2 Ceramic Composites: Fundamental and Frontiers; 4 Continuous Fiber-reinforced Ceramic Matrix Composites; 4.1 Introduction; 4.2 Parts of a CMC; 4.2.1 Fibers; 4.2.2 Interphase; 4.2.3 Matrix |
4.3 Modern Uses of CMCs4.4 History; 4.5 Ceramic Fibers; 4.5.1 Oxide Fibers; 4.5.1.1 Alumina Fibers; 4.5.1.2 Stabilized Alumina Fibers; 4.5.1.3 Alumina Silicate Fibers; 4.5.1.4 Other Oxide Fibers; 4.5.2 Non-oxide Fibers (SiC); 4.5.2.1 Oxidation; 4.5.2.2 Irradiation; 4.5.2.3 Sintering; 4.5.3 Carbon Fibers; 4.5.3.1 Polyacrylonitrile; 4.5.3.2 Pitch; 4.6 Interface/Interphase; 4.6.1 Requirements; 4.6.2 Non-oxide; 4.6.3 Oxide; 4.7 Matrix Materials; 4.7.1 Carbon; 4.7.2 Silicon Carbide; 4.7.3 Oxides; 4.8 Matrix Fabrication Techniques; 4.8.1 Polymer Impregnation and Pyrolysis |
4.8.2 Chemical Vapor Infiltration |
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2. |
Record Nr. |
UNINA9910557353503321 |
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Autore |
Rundo Leonardo |
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Titolo |
Advanced Computational Methods for Oncological Image Analysis |
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Pubbl/distr/stampa |
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
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Descrizione fisica |
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1 online resource (262 p.) |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians' unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations-such as segmentation, co-registration, classification, and dimensionality reduction-and multi-omics data integration.] |
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