For the engineering and scientific professional, A Physicist's Guide to Mathematica, 2/e provides an updated reference guide based on the 2007 new 6.0 release, providing an organized and integrated desk reference with step by step instructions for the most often used features of the software as it applies to research in physics.
For Professors teaching physics and other science courses using the Mathematica software, A Physicist's Guide to Mathematica, 2/e is the only fully compatible (new software release) Mathematica text that engages students by providing complete topic coverage, new applications, exercises and examples that enable the user to solve a wide range of physics problems.
- Does not require prior knowledge of Mathematica or computer programming
- Can be used as either a primary or supplemental text for upper-division physics majors and an Instructor's Solutions Manual is available
- Provides over 450 end-of-section exercises and end-of-chapter problems
- Serves as a reference suitable for chemists, physical scientists, and engineers
- Compatible with Mathematica Version 6, a recent major release
- Compact disk contains all of the Mathematica input and output in this book

Essential Java serves as an introduction to the programming language, Java, for scientists and engineers, and can also be used by experienced programmers wishing to learn Java as an additional language. The book focuses on how Java, and object-oriented programming, can be used to solve science and engineering problems.
Many examples are included from a number of different scientific and engineering areas, as well as from business and everyday life. Pre-written packages of code are provided to help in such areas as input/output, matrix manipulation and scientific graphing.
Java source code and solutions to selected exercises are available at: www.bh.com/companions/0750659912
A free solutions manual is available to lecturers at: http: //textbooks.elsevier.com
* Takes a 'dive-in' approach, getting the reader writing and running programs immediately
* Teaches object-oriented programming for problem-solving in engineering and science
* Fully supported with online resources and listings to download

Advances in healthcare technologies have offered real-time guidance and technical assistance for diagnosis, monitoring, operation, and interventions. The development of artificial intelligence, machine learning, internet of things technology, and smart computing techniques are crucial in today's healthcare environment as they provide frictionless and transparent financial transactions and improve the overall healthcare experience. This, in turn, has far-reaching effects on economic, psychological, educational, and organizational improvements in the way we work, teach, learn, and provide care. These advances must be studied further in order to ensure they are adapted and utilized appropriately. Mathematical Modeling for Smart Healthcare Systems presents the latest research findings, ideas, innovations, developments, and applications in the field of modeling for healthcare systems. Furthermore, it presents the application of innovative techniques to complex problems in the case of healthcare. Covering a range of topics such as artificial intelligence, deep learning, and personalized healthcare services, this reference work is crucial for engineers, healthcare professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre.