Computational Nuclear Engineering and Radiological Science Using Python provides the necessary knowledge users need to embed more modern computing techniques into current practices, while also helping practitioners replace Fortran-based implementations with higher level languages. The book is especially unique in the market with its implementation of Python into nuclear engineering methods, seeking to do so by first teaching the basics of Python, then going through different techniques to solve systems of equations, and finally applying that knowledge to solve problems specific to nuclear engineering. Along with examples of code and end-of-chapter problems, the book is an asset to novice programmers in nuclear engineering and radiological sciences, teaching them how to analyze complex systems using modern computational techniques. For decades, the paradigm in engineering education, in particular, nuclear engineering, has been to teach Fortran along with numerical methods for solving engineering problems. This has been slowly changing as new codes have been written utilizing modern languages, such as Python, thus resulting in a greater need for the development of more modern computational skills and techniques in nuclear engineering. - Offers numerical methods as a tool to solve specific problems in nuclear engineering - Provides examples on how to simulate different problems and produce graphs using Python - Supplies accompanying codes and data on a companion website, along with solutions to end-of-chapter problems

This book addresses the fundamental technologies, architectures, application domains, and future research directions of the Internet of Things (IoT). It also discusses how to create your own IoT system according to applications requirements, and it presents a broader view of recent trends in the IoT domain and open research issues. This book encompasses various research areas such as wireless networking, advanced signal processing, IoT, and ubiquitous computing. Internet of Things: Theory to Practice discusses the basics and fundamentals of IoT and real-time applications, as well as the associated challenges and open research issues. The book includes several case studies about the use of IoT in day-to-day life. The authors review various advanced computing technologies—such as cloud computing, fog computing, edge computing, and Big Data analytics—that will play crucial roles in future IoT-based services. The book provides a detailed role of blockchain technology, Narrowband IoT (NB-IoT), wireless body area network (WBAN), LoRa (a longrange low power platform), and Industrial IoT (IIoT) in the 5G world. This book is intended for university/college students, as well as amateur electronic hobbyists and industry professionals who are looking to stay current in the IoT domain.

This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.

All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers’ ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.

Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed. Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms. Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments. All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Radiation is all around us: in the rocks under the ground, naturally in food, and raining down from outer space. Most of this radiation is natural, and some of it we use to make our lives better. This book teaches elementary age students the basics of the atom and radioactivity and shows where radiation can be found and how it can be used to improve our lives. A glossary gives definitions of key terms in the radiation and nuclear sciences. This book will teach children that: - Atoms are the building blocks of nature. - Radiation comes to us from natural and artificial sources. - Medicine and dentistry use radiation. - Too much radiation can be dangerous. - Radiation is used to give us energy, learn about dinosaurs, and understand how people lived thousands of years ago.