Medical textbooks are full of helpful information, but they usually do not address complex clinical scenarios. For anesthesia providers, problems are often encountered in the operating room where textbooks and medical journals are not readily available. Even when a text is handy, practical decisions often must be made immediately. Residents in training, recent graduates, and even the most senior anesthesia providers, learn by experience. Case conferences and grand rounds are held in almost every hospital so that all staff members can share in their colleague s experiences. This book, presents a variety of interesting cases. The author has a unique talent for describing real clinical dilemmas and their solutions in a concise, interesting, and entertaining manner.

Near Misses in Anesthesia: Lessons Learned

Lewis, John M.

Fontrier, Toinette

Medical Books

Butterworth-Heinemann Medical

Dynamic data assimilation is the assessment, combination and synthesis of observational data, scientific laws and mathematical models to determine the state of a complex physical system, for instance as a preliminary step in making predictions about the system's behaviour. The topic has assumed increasing importance in fields such as numerical weather prediction where conscientious efforts are being made to extend the term of reliable weather forecasts beyond the few days that are presently feasible. This book is designed to be a basic one-stop reference for graduate students and researchers. It is based on graduate courses taught over a decade to mathematicians, scientists, and engineers, and its modular structure accommodates the various audience requirements. Thus Part I is a broad introduction to the history, development and philosophy of data assimilation, illustrated by examples; Part II considers the classical, static approaches, both linear and nonlinear; and Part III describes computational techniques. Parts IV to VII are concerned with how statistical and dynamic ideas can be incorporated into the classical framework. Key themes covered here include estimation theory, stochastic and dynamic models, and sequential filtering. The final part addresses the predictability of dynamical systems. Chapters end with a section that provides pointers to the literature, and a set of exercises with instructive hints.

Dynamic Data Assimilation: A Least Squares Approach (Encyclopedia of Mathematics and its Applications, Series Number 104)

Reference

Lakshmivarahan, S.

Dhall, Sudarshan

Encyclopedias & Subject Guides

Cambridge University Press

Inscribed stones and stone sculpture form the most prolific body of material evidence that survives for early medieval Wales. The inscribed memorial stones in Latin or Old Irish ogam commemmorate the lives the Welsh elite of the time and are crucial to understanding the continutity of early medival Welsh culture with the preceding Roman culture, Irish settlements, and early Welsh kingdoms. 

A Corpus of Early Medieval Inscribed Stones and Stone Sculpture in Wales Volume One: South-East Wales and the English Border

History

Redknap, Mark

Europe

University of Wales Press

Layman's Library of Christian Doctrine: Revelation, Inspiration, Scripture

Religion & Spirituality

Baptist Sunday School Board

This book introduces the reader to a new method of data assimilation with deterministic constraints (exact satisfaction of dynamic constraints)―an optimal assimilation strategy called Forecast Sensitivity Method (FSM), as an alternative to the well-known four-dimensional variational (4D-Var) data assimilation method. 4D-Var works with a forward in time prediction model and a backward in time tangent linear model (TLM). The equivalence of data assimilation via 4D-Var and FSM is proven and problems using low-order dynamics clarify the process of data assimilation by the two methods. The problem of return flow over the Gulf of Mexico that includes upper-air observations and realistic dynamical constraints gives the reader a good idea of how the FSM can be implemented in a real-world situation.

Forecast Error Correction using Dynamic Data Assimilation (Springer Atmospheric Sciences)

Computers & Technology

Lakshmivarahan, Sivaramakrishnan

Jabrzemski, Rafal

Computer Science

Springer

This book introduces the reader to a new method of data assimilation with deterministic constraints (exact satisfaction of dynamic constraints)―an optimal assimilation strategy called Forecast Sensitivity Method (FSM), as an alternative to the well-known four-dimensional variational (4D-Var) data assimilation method. 4D-Var works with a forward in time prediction model and a backward in time tangent linear model (TLM). The equivalence of data assimilation via 4D-Var and FSM is proven and problems using low-order dynamics clarify the process of data assimilation by the two methods. The problem of return flow over the Gulf of Mexico that includes upper-air observations and realistic dynamical constraints gives the reader a good idea of how the FSM can be implemented in a real-world situation.        

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