This book presents problems and solutions of the mathematical theories of thermoelasticity and magnetothermoelasticity. The classical, coupled and generalized theories are solved using the eigenvalue methodology. Different methods of numerical inversion of the Laplace transform are presented and their direct applications are illustrated. The book is very useful to those interested in continuum mechanics.

Celebrating Sorrow explores the medieval Japanese fascination with grief in tributes to The Tale of Sagoromo, the classic story of a young man whose unrequited love for his foster sister leads him into a succession of romantic tragedies as he rises to the imperial throne. Charo B. D'Etcheverry translates a selection of Sagoromo-themed works, highlighting the diversity of medieval Japanese creative practice and the persistent and varied influence of a beloved court tale. Medieval Japanese readers, fascinated by Sagoromo's sorrows and success, were inspired to retell his tale in stories, songs, poetry, and drama. By recontextualizing the tale's poems and writing new libretti, stories, and commentaries about the tale, these medieval aristocrats, warriors, and commoners expressed their competing concerns and ambitions during a chaotic period in Japanese history, as well as their shifting understandings of the tale itself. By translating these creative responses from an era of uncertainty and turmoil, Celebrating Sorrow shows the richness and enduring relevance of Japanese classical and medieval literature.

This book presents problems and solutions of the mathematical theories of thermoelasticity and magnetothermoelasticity. The classical, coupled and generalized theories are solved using the eigenvalue methodology. Different methods of numerical inversion of the Laplace transform are presented and their direct applications are illustrated. The book is very useful to those interested in continuum mechanics.

This monograph covers the topic of Wireless for Machine Learning (ML). Although the general intersection of ML and wireless communications is currently a prolific field of research that has already generated multiple publications, there is little review work on Wireless for ML. As data generation increasingly takes place on devices without a wired connection, ML related traffic will be ubiquitous in wireless networks. Research has shown that traditional wireless protocols are highly inefficient or unsustainable to support ML, which creates the need for new wireless communication methods. This monograph gives an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. Currently, there are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. A comprehensive introduction to these methods is presented, reviews are made of the most important works, open problems are highlighted and application scenarios are discussed.