Methods of Seawater Analysis

Provides the basic skills and information required to prepare an environmental sample for analysis. Divided into two sections, i.e. Inorganic Analysis and Organic Analysis, this book covers selected techniques, principally atomic spectroscopy and chromatography. Using flow diagrams to augment the experimental information, it highlights the most appropriate methods and the likely results. Detailed experimental information provided in an easy-to-follow style with illustrations Describes the specific sample preparation approaches necessary to analyse a particular sample type Discussion of selected literature sources highlights the most appropriate methods and the likely results obtained

This exceptional book reveals the results of twelve years of extensive thermoanalytical investigations into petroleum and its products with the aid of 236 tables, 284 diagrams and 159 references. Firstly, the methods employed in obtaining thermoanalytic data, in particular thermogravimetry, differential thermal analysis and differential scanning calorimetry, are presented, and the underpinning theory described. Next, the data obtained from model substances, i.e. pure hydrocarbons, is displayed; it is then explained how multicomponent hydrocarbon systems may be characterized by comparison of their data with this. Research into petroleum and its products using these methods is outlined. The reactions central to various refinery processes, tertiary oil recovery, lubricant stability testing and oil shale retorting, to name but a few examples, are investigated as are relevant pyrolysis and oxidation reactions. Finally, readers are brought up-to-date with recent developments in instrumentation, are recommended hardware and software and are provided with a list of suppliers. Scientists, engineers and technicians working on research, product characterization, process development or quality control in the oil recovery, oil refining, petrochemical, lubricant and asphalt industries will find the advice and information in this book to be of great value.

Reliability and survival analysis are important applications of stochastic mathematics (probability, statistics and stochastic processes) that are usually covered separately in spite of the similarity of the involved mathematical theory. This title aims to redress this situation: it includes 21 chapters divided into four parts: Survival analysis, Reliability, Quality of life, and Related topics. Many of these chapters were presented at the European Seminar on Mathematical Methods for Survival Analysis, Reliability and Quality of Life in 2006.

Written and edited by a group of renowned specialists in the field, this outstanding new volume addresses primary computational techniques for developing new technologies in soft computing. It also highlights the security, privacy, artificial intelligence, and practical approaches needed by engineers and scientists in all fields of science and technology. It highlights the current research, which is intended to advance not only mathematics but all areas of science, research, and development, and where these disciplines intersect. As the book is focused on emerging concepts in machine learning and artificial intelligence algorithmic approaches and soft computing techniques, it is an invaluable tool for researchers, academicians, data scientists, and technology developers. The newest and most comprehensive volume in the area of mathematical methods for use in real-time engineering, this groundbreaking new work is a must-have for any engineer or scientist’s library. Also useful as a textbook for the student, it is a valuable contribution to the advancement of the science, both a working handbook for the new hire or student, and a reference for the veteran engineer.

Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts «.an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models.» –Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis–with all data sets real. Topics include: multi-response parameter estimation; models defined by systems of differential equations; and improved methods for presenting inferential results of nonlinear analysis. 1988 (0-471-81643-4) 365 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild «.[a] comprehensive and scholarly work.impressively thorough with attention given to every aspect of the modeling process.» –Short Book Reviews of the International Statistical Institute In this introduction to nonlinear modeling, the authors examine a wide range of estimation techniques including least squares, quasi-likelihood, and Bayesian methods, and discuss some of the problems associated with estimation. The book presents new and important material relating to the concept of curvature and its growing role in statistical inference. It also covers three useful classes of models –growth, compartmental, and multiphase –and emphasizes the limitations involved in fitting these models. Packed with examples and graphs, it offers statisticians, statistical consultants, and statistically oriented research scientists up-to-date access to their fields. 1989 (0-471-61760-1) 768 pp. Mathematical Programming in Statistics T. S. Arthanari and Yadolah Dodge «The authors have achieved their stated intention.in an outstanding and useful manner for both students and researchers.Contains a superb synthesis of references linked to the special topics and formulations by a succinct set of bibliographical notes.Should be in the hands of all system analysts and computer system architects.» –Computing Reviews This unique book brings together most of the available results on applications of mathematical programming in statistics, and also develops the necessary statistical and programming theory and methods. 1981 (0-471-08073-X) 413 pp.

This book is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used. From the contents: * Understanding and Preprocessing Microarray Data * Clustering of Microarray Data * Reconstruction of the Yeast Cell Cycle by Partial Correlations of Higher Order * Bilayer Verification Algorithm * Probabilistic Boolean Networks as Models for Gene Regulation * Estimating Transcriptional Regulatory Networks by a Bayesian Network * Analysis of Therapeutic Compound Effects * Statistical Methods for Inference of Genetic Networks and Regulatory Modules * Identification of Genetic Networks by Structural Equations * Predicting Functional Modules Using Microarray and Protein Interaction Data * Integrating Results from Literature Mining and Microarray Experiments to Infer Gene Networks The book is for both, scientists using the technique as well as those developing new analysis techniques.

The analysis of variance is presented as an exploratory component of data analysis, while retaining the customary least squares fitting methods. Balanced data layouts are used to reveal key ideas and techniques for exploration. The approach emphasizes both the individual observations and the separate parts that the analysis produces. Most chapters include exercises and the appendices give selected percentage points of the Gaussian, t, F chi-squared and studentized range distributions.

A key component of the overall quality of a pharmaceutical is control of impurities, as their presence, even in small amounts, may affect drug safety and efficacy. The identification and quantification of impurities to acceptable standards presents a significant challenge to the analytical chemist. Analytical science is developing rapidly and provides increasing opportunity to identify the structure, and therefore the origin and safety implications of these impurities, and the challenges of their measurement drives the development of modern quantitative methods. Written for both practicing and student analytical chemists, Analysis of Drug Impurities provides a detailed overview of the challenges and the techniques available to permit accurate identification and quantification of drug impurities.

Layer of protection analysis (LOPA) is a recently developed, simplified method of risk assessment that provides the much-needed middle ground between a qualitative process hazard analysis and a traditional, expensive quantitative risk analysis. Beginning with an identified accident scenario, LOPA uses simplifying rules to evaluate initiating event frequency, independent layers of protection, and consequences to provide an order-of-magnitude estimate of risk. LOPA has also proven an excellent approach for determining the safety integrity level necessary for an instrumented safety system, an approach endorsed in instrument standards, such as ISA S84 and IEC 61511. Written by industry experts in LOPA, this pioneering book provides all the necessary information to undertake and complete a Layer of Protection Analysis during any stage in a processes' life cycle. Loaded with tables, charts, and examples, this book is invaluable to technical experts involved with ensuring the safety of a process. Because of its simplified, quicker risk assessment approach, LOPA is destined to become a widely used technique. Join other major companies and start your LOPA efforts now by purchasing this book.