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Nuclear Data for Science and Technology

Nuclear Data for Science and Technology
Author: Syed M. Qaim
Publisher: Springer Science & Business Media
Total Pages: 1041
Release: 2012-12-06
Genre: Science
ISBN: 3642581137
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This book describes the Proceedings of the International Conference on Nuclear Data for Science and Technology held at Jillich in May 1991. The conference was in a series of application oriented nuclear data conferences organized in the past under the auspices of the Nuclear Energy Agency-Nuclear Data Committee (NEANDC) and with the support of the Nuclear Energy Agency-Committee on Reactor Physics (NEACRP). It was the fIrst international conference on nuclear data held in Germany, with the scientific responsibility entrusted to the Institute of Nuclear Chemistry of the Research Centre Jillich. The scientific programme was established by the International Programme Committee in consultation with the International Advisers, and the NEA and IAEA cooperated in the organization. A total of 328 persons from 37 countries and fIve international organizations participated. The scope of these Proceedings extends to a wide range of interdisciplinary topics dealing with measu rement, calculation, evaluation and application of nuclear data, with a major emphasis on numerical data. Both energy and non-energy related applications are considered and due attention is given to some fundamental aspects relevant to the understanding of nuclear data.

Data Fusion Methodology and Applications

Data Fusion Methodology and Applications
Author: Marina Cocchi
Publisher: Elsevier
Total Pages: 396
Release: 2019-05-11
Genre: Science
ISBN: 0444639853
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Data Fusion Methodology and Applications explores the data-driven discovery paradigm in science and the need to handle large amounts of diverse data. Drivers of this change include the increased availability and accessibility of hyphenated analytical platforms, imaging techniques, the explosion of omics data, and the development of information technology. As data-driven research deals with an inductive attitude that aims to extract information and build models capable of inferring the underlying phenomena from the data itself, this book explores the challenges and methodologies used to integrate data from multiple sources, analytical platforms, different modalities, and varying timescales. Presents the first comprehensive textbook on data fusion, focusing on all aspects of data-driven discovery Includes comprehensible, theoretical chapters written for large and diverse audiences Provides a wealth of selected application to the topics included

Experimental Design

Experimental Design
Author: S.N. Deming
Publisher: Elsevier
Total Pages: 301
Release: 1987-01-01
Genre: Science
ISBN: 0080868304
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Now available in a paperback edition is a book which has been described as ``...an exceptionally lucid, easy-to-read presentation... would be an excellent addition to the collection of every analytical chemist. I recommend it with great enthusiasm.'' (Analytical Chemistry). Unlike most current textbooks, it approaches experimental design from the point of view of the experimenter, rather than that of the statistician. As the reviewer in `Analytical Chemistry' went on to say: ``Deming and Morgan should be given high praise for bringing the principles of experimental design to the level of the practicing analytical chemist.''.The book first introduces the reader to the fundamentals of experimental design. Systems theory, response surface concepts, and basic statistics serve as a basis for the further development of matrix least squares and hypothesis testing. The effects of different experimental designs and different models on the variance-covariance matrix and on the analysis of variance (ANOVA) are extensively discussed. Applications and advanced topics (such as confidence bands, rotatability, and confounding) complete the text. Numerous worked examples are presented.The clear and practical approach adopted by the authors makes the book applicable to a wide audience. It will appeal particularly to those with a practical need (scientists, engineers, managers, research workers) who have completed their formal education but who still need to know efficient ways of carrying out experiments. It will also be an ideal text for advanced undergraduate and graduate students following courses in chemometrics, data acquisition and treatment, and design of experiments.

Multivariate Analysis of Data in Sensory Science

Multivariate Analysis of Data in Sensory Science
Author: T. Naes
Publisher: Elsevier
Total Pages: 365
Release: 1996-02-01
Genre: Education
ISBN: 0080537162
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The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis. The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques. Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.

Statistical Design - Chemometrics

Statistical Design - Chemometrics
Author: Roy E Bruns
Publisher: Elsevier
Total Pages: 423
Release: 2006-01-27
Genre: Education
ISBN: 0080462138
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Statistical Design-Chemometrics is applicable to researchers and professionals who wish to perform experiments in chemometrics and carry out analysis of the data in the most efficient way possible. The language is clear, direct and oriented towards real applications. The book provides 106 exercises with answers to accompany the study of theoretical principles. Forty two cases studies with real data are presented showing designs and the complete statistical analyses for problems in the areas chromatography, electroanalytical and electrochemistry, calibration, polymers, gas adsorption, semiconductors, food technology, biotechnology, photochemistry, catalysis, detergents and ceramics. These studies serve as a guide that the reader can use to perform correct data analyses. -Provides 42 case studies containing step-by-step descriptions of calculational procedures that can be applied to most real optimization problems -Contains 106 theoretical exercises to test individual learning and to provide classroom exercises and material for written tests and exams -Written in a language that facilitates learning for physical and biological scientists and engineers -Takes a practical approach for those involved in industrial optimization problems

Data-Driven Science and Engineering

Data-Driven Science and Engineering
Author: Steven L. Brunton
Publisher: Cambridge University Press
Total Pages: 615
Release: 2022-05-05
Genre: Computers
ISBN: 1009098489
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A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Practical Data Analysis in Chemistry

Practical Data Analysis in Chemistry
Author: Marcel Maeder
Publisher: Elsevier
Total Pages: 341
Release: 2007-08-10
Genre: Mathematics
ISBN: 0080548830
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The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exemplifies every aspect of theory applicable to data analysis using a short program in a Matlab or Excel spreadsheet, enabling the reader to study the programs, play with them and observe what happens. Suitable data are generated for each example in short routines, this ensuring a clear understanding of the data structure. Chapter 2 includes a brief introduction to matrix algebra and its implementation in Matlab and Excel while Chapter 3 covers the theory required for the modelling of chemical processes. This is followed by an introduction to linear and non-linear least-squares fitting, each demonstrated with typical applications. Finally Chapter 5 comprises a collection of several methods for model-free data analyses. * Includes a solid introduction to the simulation of equilibrium processes and the simulation of complex kinetic processes.* Provides examples of routines that are easily adapted to the processes investigated by the reader* 'Model-based' analysis (linear and non-linear regression) and 'model-free' analysis are covered

Data Science

Data Science
Author: John D. Kelleher
Publisher: MIT Press
Total Pages: 282
Release: 2018-04-13
Genre: Computers
ISBN: 0262535432
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A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

Robustness of Analytical Chemical Methods and Pharmaceutical Technological Products

Robustness of Analytical Chemical Methods and Pharmaceutical Technological Products
Author: M.M.W.B. Hendriks
Publisher: Elsevier
Total Pages: 361
Release: 1996-12-11
Genre: Science
ISBN: 0080540635
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In analytical chemistry and pharmaceutical technology attention is increasingly focussed on improving the quality of methods and products. This book aims at fostering the awareness of the potential of existing mathematical and statistical methods to improve this quality. It provides procedures and ideas on how to make a product or a method less sensitive to small variations in influencing factors. Major issues covered are robustness and stability improvement and ruggedness testing. General strategies and a theoretical introduction to these methods are described, and thorough overviews of methods used in both application areas and descriptions of practical applications are given. Features of this book: • Gives a good overview of mathematical and statistical methods used in two application areas, i.e. pharmaceutical technology and analytical chemistry • Illustrates the different approaches available to attain robustness • Gives ideas on how to use methods in practical situations. The book is intended for those who develop and optimize, and are responsible for the overall quality of, analytical methods and pharmaceutical technological products and procedures.

Hyperspectral Imaging

Hyperspectral Imaging
Author:
Publisher: Elsevier
Total Pages: 800
Release: 2019-09-29
Genre: Science
ISBN: 0444639780
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Hyperspectral Imaging, Volume 32, presents a comprehensive exploration of the different analytical methodologies applied on hyperspectral imaging and a state-of-the-art analysis of applications in different scientific and industrial areas. This book presents, for the first time, a comprehensive collection of the main multivariate algorithms used for hyperspectral image analysis in different fields of application. The benefits, drawbacks and suitability of each are fully discussed, along with examples of their application. Users will find state-of-the art information on the machinery for hyperspectral image acquisition, along with a critical assessment of the usage of hyperspectral imaging in diverse scientific fields. Provides a comprehensive roadmap of hyperspectral image analysis, with benefits and considerations for each method discussed Covers state-of-the-art applications in different scientific fields Discusses the implementation of hyperspectral devices in different environments