Get the practical insights and classroom examples you need to incorporate immersive technology into curriculum and create engaging, effective learning experiences for students. Teachers nationwide are adopting immersive technology – devices and software that provide augmented, virtual and mixed reality experiences – to enable students to go on virtual field trips, manipulate 3D objects and augment the world around them. Immersive technology resources can be far less costly than many believe, but there are issues school districts should resolve before making purchases and implementing this technology. Learning Transported helps educators plan and establish goals so that their investment in immersive technology benefits the greatest number of students. The book includes: • Definitions and examples of augmented, virtual and mixed reality. • Comparison of devices and platforms, and tips for selecting the best one. • Lesson plans mapped to standards and content areas. • Ideas for using immersive technology tools in the classroom. Immersive technology has great potential to transform learning and create engaging experiences for students. This book helps educators consider the most important factors in bringing this approach into the classroom: that the instruction addresses student outcomes and standards, and that the mechanism for delivering this learning is safe, affordable and suitable for the situation. The companion jump start guide based on this book is Immerse Yourself: Create Engaging AR/VR Experiences for All Learners.

Introducing Hindi learning series, a picture book about transportation for kids. The subtitles written in Roman Hindi and in the English are highly helpful to learn Hindi for non-native Hindi speakers as well.

Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle challenging transportation problems. This textbookis designed for college or graduate-level students in transportation or closely related fields to study and understand fundamentals in machine learning. Readers will learn how to develop and apply various types of machine learning models to transportation-related problems. Example applications include traffic sensing, data-quality control, traffic prediction, transportation asset management, traffic-system control and operations, and traffic-safety analysis. - Introduces fundamental machine learning theories and methodologies - Presents state-of-the-art machine learning methodologies and their incorporation into transportationdomain knowledge - Includes case studies or examples in each chapter that illustrate the application of methodologies andtechniques for solving transportation problems - Provides practice questions following each chapter to enhance understanding and learning - Includes class projects to practice coding and the use of the methods

This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today’s technology. Road sensors, UAVs, GPS, CCTV and incident reports are sources of massive amount of data which are crucial to make serious traffic decisions. Herewith this substantial volume and velocity of data, it is challenging to build reliable prediction models based on machine learning methods and traditional relational database. Therefore, this book includes recent research works on big data, deep convolution networks and IoT-based smart solutions to limit the vehicle’s speed in a particular region, to support autonomous safe driving and to detect animals on roads for mitigating animal-vehicle accidents. This book serves broad readers including researchers, academicians, students and working professional in vehicles manufacturing, health and transportation departments and networking companies.

Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems examines the combined impact of buildings and transportation systems on energy demand and use. With a strong focus on AI and machine learning approaches, the book comprehensively discusses each part of the energy life cycle, considering source, grid, demand, storage, and usage. Opening with an introduction to smart buildings and intelligent transportation systems, the book presents the fundamentals of AI and its application in renewable energy sources, alongside the latest technological advances. Other topics presented include building occupants' behavior and vehicle driving schedule with demand prediction and analysis, hybrid energy storages in buildings with AI, smart grid with energy digitalization, and prosumer-based P2P energy trading. The book concludes with discussions on blockchain technologies, IoT in smart grid operation, and the application of big data and cloud computing in integrated smart building-transportation energy systems. A smart and flexible energy system is essential for reaching Net Zero whilst keeping energy bills affordable. This title provides critical information to students, researchers and engineers wanting to understand, design, and implement flexible energy systems to meet the rising demand in electricity. - Introduces spatiotemporal energy sharing with new energy vehicles and human-machine interactions - Discusses the potential for electrification and hydrogenation in integrated building-transportation systems for sustainable development - Highlights key topics related to traditional energy consumers, including peer-to-peer energy trading and cost-benefit business models