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While it remains true that “entropy is not a localized, microscale phenomenon at which we can point, even in our imaginations, and say, ‘Look! There is entropy’” and that, “if we insist on trying to understand a subject in ways inconsistent with its nature, we will be disappointed,” the eight chapters of this guide have prepared us to give a constructive answer to the question “What is entropy?”
Any short description of entropy will necessarily be figurative. After all, one task of a figure of speech is to transfer a complex meaning from an extended description to a word or short phrase. In fact, we have already considered several figurative descriptions of entropy that are appropriate in special contexts: transformation content, disorder, uncertainty, spread in phase space, and missing information. Transformation content was Clausius’s way of referring to how the entropy function indicates the direction in which an isolated system may evolve. Spread in phase space, while appropriate for statistical systems, depends upon familiarity with the technical concept of phase space.
Disorder has long been a popular synonym for entropy. But recently order and disorder as describing low and high entropy systems have fallen into disfavor. This is because scientists have become fascinated with isolated systems that generate apparent order from apparent disorder. For instance, consider a thoroughly shaken bottle of water and olive oil. When left undisturbed, the water and olive oil begin to separate into distinct layers with the less dense olive oil on top. Yet even in this process the entropy of the oil–water system increases. Thus, while order and disorder are suggestive, they can mislead.
Examining important results and analytical techniques, this graduate-level textbook is a step-by-step presentation of the structure and function of complex networks. Using a range of examples, from the stability of the internet to efficient methods of immunizing populations, and from epidemic spreading to how one might efficiently search for individuals, this textbook explains the theoretical methods that can be used, and the experimental and analytical results obtained in the study and research of complex networks. Giving detailed derivations of many results in complex networks theory, this is an ideal text to be used by graduate students entering the field. End-of-chapter review questions help students monitor their own understanding of the materials presented.
Including topics not traditionally covered in literature, such as (1+1)-dimensional QFT and classical 2D Coulomb gases, this book considers a wide range of models and demonstrates a number of situations to which they can be applied. Beginning with a treatise of nonrelativistic 1D continuum Fermi and Bose quantum gases of identical spinless particles, the book describes the quantum inverse scattering method and the analysis of the related Yang–Baxter equation and integrable quantum Heisenberg models. It also discusses systems within condensed matter physics, the complete solution of the sine-Gordon model and modern trends in the thermodynamic Bethe ansatz. Each chapter concludes with problems and solutions to help consolidate the reader's understanding of the theory and its applications. Basic knowledge of quantum mechanics and equilibrium statistical physics is assumed, making this book suitable for graduate students and researchers in statistical physics, quantum mechanics and mathematical and theoretical physics.