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1. |
Record Nr. |
UNINA9910455896103321 |
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Titolo |
Opportunities in the nutrition and food sciences [[electronic resource] ] : research challenges and the next generation of investigators / / Committee on Opportunities in the Nutrition and Food Sciences, Food and Nutrition Board, Institute of Medicine ; Paul R. Thomas and Robert Earl, editors |
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Pubbl/distr/stampa |
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Washington, D.C., : National Academy Press, 1994 |
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ISBN |
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1-280-21132-6 |
9786610211326 |
0-309-58541-4 |
0-585-02174-0 |
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Descrizione fisica |
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1 online resource (328 p.) |
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Altri autori (Persone) |
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ThomasPaul R. <1953-> |
EarlRobert O |
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Disciplina |
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Soggetti |
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Nutrition - Research |
Food - Research |
Electronic books. |
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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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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 (p. 269-280) and index. |
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Nota di contenuto |
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""OPPORTUNITIES IN THE NUTRITION AND FOOD SCIENCES""; ""Copyright""; ""Dedication""; ""Foreword""; ""Preface""; ""ORGANIZATION OF THIS REPORT""; ""Acknowledgments""; ""Contents""; ""Summary and Conclusions""; ""A PRESIDENTIAL INITIATIVE FOR THE NUTRITION AND FOOD SCIENCES""; ""Research""; ""Nutrients and Biologically Active Food Constituents in Development, Cell Differentiation, Growth, Maturation, and Aging""; ""Genes, Food, and Chronic Diseases""; ""Determinants of Food Intake""; ""Improving Food and Nutrition Policies""; ""Enhancing the Food Supply""; ""Education and Training"" |
""Undergraduate Education""""Graduate Education""; ""Graduate Education Support""; ""Support""; ""Federal Government""; ""Industry""; ""Private, Nonprofit Organizations""; ""CREATING THE FUTURE""; ""1 Introduction ""; ""WHO WE ARE AND WHAT WE STUDY""; ""THEMES""; ""Theme 1: Nutrients and Biologically Active Food Constituents in |
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Development, Cell Differentiation, Growth, Maturation, and...""; ""Theme 2: Genes, Food, and Chronic Diseases""; ""Theme 3: Determinants of Food Intake""; ""Theme 4: Improving Food and Nutrition Policies""; ""Theme 5: Enhancing the Food Supply"" |
""Crosscutting Examples of Our Themes""""Understanding the Functions of Vitamin D""; ""Combating Atherosclerotic Heart Disease""; ""CONCLUDING REMARKS""; ""2 Accomplishments in the Nutrition and Food Sciences""; ""EXAMPLES OF ACCOMPLISHMENTS AND CHALLENGES""; ""Gene-Nutrient Interactions""; ""Iron""; ""Energy Balance and the Risks of Diabetes and Obesity""; ""Folate and Neural Tube Defects""; ""Oxidative Damage to DNA, Proteins, and Fats""; ""Improving the Food Supply""; ""Sensory Biology and the Development of New Foods""; ""Biotechnology""; ""Preventing Childhood Morbidity and Mortality"" |
""Oral Rehydration Therapy""""Vitamin A""; ""New Concepts of Nutrient Requirements""; ""CONCLUDING REMARKS""; ""3 Understanding Genetic, Molecular, Cellular, and Physiological Processes""; ""ACCOMPLISHMENTS AND RELATED POSSIBILITIES""; ""Brown and Goldstein and Lipid Metabolism""; ""Retinoic Acid""; ""Vitamin D�Receptors and Metabolism""; ""Metabolism from Vitamin to Steroid Hormone""; ""Physiological Actions via Genomic and Nongenomic Pathways""; ""Neurotransmitters�Regulation and Action""; ""Influences on Eating Behaviors""; ""Excitatory and Inhibitory Receptor Systems in the Brain"" |
""Iron Metabolism and Regulation""""Transferrin and the Transferrin Receptor""; ""Regulation of Iron Storage as Ferritin""; ""TECHNOLOGIES CREATING NEW OPPORTUNITIES FOR BASIC RESEARCH IN NUTRITION SCIENCE""; ""Genetics""; ""Manipulation of the Mammalian Genome""; ""Why transgenes are useful Transgenes have two key uses""; ""Approaches to Analysis of Multifactorial Traits""; ""Identification, Isolation, and Tracking of Specific Cell Types""; ""Monoclonal Antibodies""; ""Visualization""; ""Fluorescence-Activated Cell Sorting""; ""Cell and Tissue Culture Systems""; ""Immortalization of Cells"" |
""Complex Culture Systems"" |
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2. |
Record Nr. |
UNINA9910145383903321 |
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Titolo |
2009 Asia Pacific Microwave Conference |
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Pubbl/distr/stampa |
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[Place of publication not identified], : I E E E, 2009 |
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Descrizione fisica |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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3. |
Record Nr. |
UNINA9910878054203321 |
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Autore |
Garikipati Krishna |
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Titolo |
Data-driven Modelling and Scientific Machine Learning in Continuum Physics / / by Krishna Garikipati |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 |
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ISBN |
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9783031620294 |
9783031620287 |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (233 pages) |
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Collana |
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Interdisciplinary Applied Mathematics, , 2196-9973 ; ; 60 |
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Disciplina |
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Soggetti |
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Mathematical models |
Artificial intelligence - Data processing |
Machine learning |
Mathematical Modeling and Industrial Mathematics |
Data Science |
Machine Learning |
Aprenentatge automàtic |
Teoria de camps (Física) |
Xarxes neuronals (Informàtica) |
Llibres electrònics |
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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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Part I. Introduction and Background in Continuum Materials Physics -- Introduction -- Nonlinear Elasticity -- Phase Field Methods -- Part II. Solving Partial Differential Equations -- Finite Element Methods -- Part III. Data-driven Modelling and Scientific Machine Learning -- Reduced Order Models: Numerical Homogenization for the Elastic Response of Material Microstructures -- Surrogate Optimization -- Graph Theoretic Methods -- Scale Bridging -- Inverse Modeling and System Inference from Data -- Machine Learning Solvers of Partial Differential Equations -- An Outlook on Scientific Machine Learning in Continuum Physics -- References. |
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Sommario/riassunto |
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This monograph takes the reader through recent advances in data-driven methods and machine learning for problems in science—specifically in continuum physics. It develops the foundations and details a number of scientific machine learning approaches to enrich current computational models of continuum physics, or to use the data generated by these models to infer more information on these problems. The perspective presented here is drawn from recent research by the author and collaborators. Applications drawn from the physics of materials or from biophysics illustrate each topic. Some elements of the theoretical background in continuum physics that are essential to address these applications are developed first. These chapters focus on nonlinear elasticity and mass transport, with particular attention directed at descriptions of phase separation. This is followed by a brief treatment of the finite element method, since it is the most widely used approach to solve coupled partial differential equations in continuum physics. With these foundations established, the treatment proceeds to a number of recent developments in data-driven methods and scientific machine learning in the context of the continuum physics of materials and biosystems. This part of the monograph begins by addressing numerical homogenization of microstructural response using feed-forward as well as convolutional neural networks. Next is surrogate optimization using multifidelity learning for problems of phase evolution. Graph theory bears many equivalences to partial differential equations in its properties of representation and avenues for analysis as well as reduced-order descriptions--all ideas that offer fruitful opportunities for exploration. Neural networks, by their capacity for representation of high-dimensional functions, are powerful for scale bridging in physics--an idea on which we present a particular perspective in the context of alloys. One of the most compelling ideas in scientific machine learning is the identification of governing equations from dynamical data--another topic that we explore from the viewpoint of partial differential equations encoding mechanisms. This is followed by an examination of approaches to replace traditional, discretization-based solvers of partial differential equations with deterministic and probabilistic neural networks that generalize across boundary value problems. The monograph closes with a brief outlook on current emerging ideas in scientific machine learning. |
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