The present book deals with a mathematical theory of machines that uses differential equations as its base. It covers the mathematical models of electric machines having a circular field and an infinite spectrum of fields in the air gap. Analysis is given of the equations involving non sinusoidal asymmetric supply voltages and nonlinear parameters and also to multiwinding machines and machines with several degrees of freedom. An attempt is made to adapt the achievements in the area of magnetic field converters for use in the analysis of electric field and electromagnetic field converters.

In "Liberalization against Democracy", Stephen J. King argues that, in contrast to prevailing views, pro-market economic reforms in Tunisia did not foster democratization. Instead, state-led economic liberalization facilitated the reorganization of authoritarian rule and contributed to the subversion of democratic tendencies at both the national and local levels. In addition to King's analysis of neo-liberal economic transformation and regime change at the national level, his book offers a rare local-level analysis of these processes, based on the author's extensive fieldwork in the rural community of Tebourba.

This book shows marketing professionals how to position their products to take advantage of the $77.7 billion luxury market. In Let Them Eat Cake: Marketing Luxury to the Masses As Well as the Classes, readers will get the first research-based study of the 15 million truly affluent households that make up the leading edge of the new luxury market. Pamela Danziger notes that the luxury market is changing radically from the conspicuous-consumption consumers of the 1990s. Danziger conducted a two-year research study of luxury consumers with incomes of $75,000 and above and discovered a totally new type of luxury consumer.

Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples.