Unlike some other reproductions of classic texts (1) We have not used OCR(Optical Character Recognition), as this leads to bad quality books with introduced typos. (2) In books where there are images such as portraits, maps, sketches etc We have endeavoured to keep the quality of these images, so they represent accurately the original artefact. Although occasionally there may be certain imperfections with these old texts, we feel they deserve to be made available for future generations to enjoy.

ELECTRICAL MACHINE DESIGN- THE DESIGN AND SPECIFICATION OF DIRECT AND ALTERNATING CURRENT MACHINERY by ALEXANDER GRAY. Originally published in 1913. PREFACE: The following work was compiled as a course of lectures on Electrical Machine Design delivered at Mo Gill University. Since the design of electrical machinery is as much an art as it is science no list of formula or collection of data is sufficient to enable one to become a successful designer. There is a certain amount of data, however, sifted from the mass of material on the subject, which every designer finds convenient to compile for ready reference. This work contains data that the author found necessary to tabulate during several years of experience as a designer of electrical apparatus. A study of design Is of the utmost importance to all students, because only by such a study can a knowledge of the limitations of machines be acquired. The machines discussed are those which have become more or less standard, namely, direct-cur rent generators and motors, alternating current generators, syn chronous motors, polyphase induction motors, and transformers; other apparatus seldom offers an electrical problem that is not discussed under one or more of the above headings. The principle followed throughout the work is to build up the design for the given rating by the use of a few fundamental formula*, and design constants, the meaning and limits of which are discussed thoroughly, and the same procedure has been followed for the several pieces of apparatus.

Since 1981, the British National Conferences on Databases (BNCOD) have p- vided a forum for database researchers to report the latest progress and explore new ideas. Over the last 28 years, BNCOD has evolved from a predominantly national conference into one that is truly international, attracting research c- tributions from all over the world. This volume contains the proceedings of BNCOD 2008. We received 45 s- missions from 22 countries. Each paper was reviewed by three referees, and 14 full papers and 7 posters were accepted. All the research papers and posters are included in this volume, and they are organized into ?ve sections: data mining and privacy, data integration, stream and event data processing, query proce- ing and optimization, and posters. The keynote was delivered by Monica Marinucci, EMEA Programme Dir- tor for Oracle in R&D. She has been involved in various advanced developments concerning Oracle, and participated in EC-funded projects as an expert, es- cially the CHALLENGERS special support action to propose the future of grid computing. In her keynote presentation,she addressedthe audience on the topic of the power of data, emphasizing that the ability to store, handle, manipulate, distribute and replicate data and information can provide a tremendous asset to organizations. She also explored some of the latest directions and developments in the database ?eld, and described how Oracle contributes to them partnering up with other leading organizations in di?erent sectors.

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest. An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date. Fully revised and expanded Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from astronomical surveys Uses a freely available Python codebase throughout Ideal for graduate students, advanced undergraduates, and working astronomers