Informative, entertaining and upbeat, this book continues Grazier and Cass's exploration of how technology, science, and scientists are portrayed in Hollywood productions. Both big and small-screen productions are featured and their science content illuminated—first by the authors and subsequently by a range of experts from science and the film world. Starring roles in this volume are played by, among other things, computers (human and mechanical), artificial intelligences, robots, and spacecraft. Interviews with writers, producers, and directors of acclaimed science-themed films stand side by side with the perspectives of scientists, science fiction authors, and science advisors. The result is a stimulating and informative reading experience for the layperson and professional scientist or engineer alike. The book begins with a foreword by Zack Stentz, who co-wrote X-Men: First Class and Thor, and is currently a writer/producer on CW’s The Flash.

The random-cluster model has emerged as a key tool in the mathematical study of ferromagnetism. It may be viewed as an extension of percolation to include Ising and Potts models, and its analysis is a mix of arguments from probability and geometry. The Random-Cluster Model contains accounts of the subcritical and supercritical phases, together with clear statements of important open problems. The book includes treatment of the first-order (discontinuous) phase transition.

An introductory overview of spatial analysis and statistics through GIS, including worked examples and critical analysis of results.

Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences provides readers with an understanding and appreciation for the design and analysis of mixed models for non-normally distributed data. It is the only publication of its kind directed specifically toward the agricultural and natural resources sciences audience. Readers will especially benefit from the numerous worked examples based on actual experimental data and the discussion of pitfalls associated with incorrect analyses.