The Definitive Guide to Arm (R) Cortex (R)-M23 and Cortex-M33 Processors focuses on the Armv8-M architecture and the features that are available in the Cortex-M23 and Cortex- M33 processors. This book covers a range of topics, including the instruction set, the programmer's model, interrupt handling, OS support, and debug features. It demonstrates how to create software for the Cortex-M23 and Cortex-M33 processors by way of a range of examples, which will enable embedded software developers to understand the Armv8-M architecture. This book also covers the TrustZone (R) technology in detail, including how it benefits security in IoT applications, its operations, how the technology affects the processor's hardware (e.g., memory architecture, interrupt handling, etc.), and various other considerations in creating secure software.

The advent of connected, smart technologies for the built environment may promise a significant value that has to be reached to develop digital city models. At the international level, the role of digital twin is strictly related to massive amounts of data that need to be processed, which proposes several challenges in terms of digital technologies capability, computing, interoperability, simulation, calibration, and representation. In these terms, the development of 3D parametric models as digital twins to evaluate energy assessment of private and public buildings is considered one of the main challenges of the last years. The ability to gather, manage, and communicate contents related to energy saving in buildings for the development of smart cities must be considered a specificity in the age of connection to increase citizen awareness of these fields. The Handbook of Research on Developing Smart Cities Based on Digital Twins contains in-depth research focused on the description of methods, processes, and tools that can be adopted to achieve smart city goals. The book presents a valid medium for disseminating innovative data management methods related to smart city topics. While highlighting topics such as data visualization, a web-based ICT platform, and data-sharing methods, this book is ideally intended for researchers in the building industry, energy, and computer science fields; public administrators; building managers; and energy professionals along with practitioners, stakeholders, researchers, academicians, and students interested in the implementation of smart technologies for the built environment.

Advances in digital technologies continue to impact all areas of life, including the business sector. Digital transformation is ascertained to usher in the digitalized economy and involves new concepts and management tools that must be considered in the context of management science and practice. For business leaders to ensure their companies remain competitive and relevant, it is essential for them to utilize these innovative technologies and strategies. The Handbook of Research on Digital Transformation Management and Tools highlights new digital concepts within management, such as digitalization and digital disruption, and addresses the paradigm shift in management science incurred by the digital transformation towards the digitalized economy. Covering a range of important topics such as cultural economy, online consumer behavior, sustainability, and social media, this major reference work is crucial for managers, business owners, researchers, scholars, academicians, practitioners, instructors, and students.

The Internet of Medical Things (IoMT) allows clinicians to monitor patients remotely via a network of wearable or implantable devices. The devices are embedded with software or sensors to enable them to send and receive data via the internet so that healthcare professionals can monitor health data such as vital statistics, metabolic rates or drug delivery regimens, and can provide advice or treatment plans based on this real-world, real-time data. This edited book discusses key IoT technologies that facilitate and enhance this process, such as computer algorithms, network architecture, wireless communications, and network security. Providing a systemic review of trends, challenges and future directions of IoMT technologies, the book examines applications such as breast cancer monitoring systems, patient-centric systems for handling, tracking and monitoring virus variants, and video-based solutions for monitoring babies. The book discusses machine learning techniques for the management of clinical data and includes security issues such as the use of blockchain technology. Written by a range of international researchers, this book is a great resource for computer engineering researchers and practitioners in the fields of data mining, machine learning, artificial intelligence and the IoT in the healthcare sector.

This book presents research on recent developments in collective decision-making. With contributions from leading scholars from a variety of disciplines, it provides an up-to-date overview of applications in social choice theory, welfare economics, and industrial organization. The contributions address, amongst others, topics such as measuring power, the manipulability of collective decisions, and experimental approaches. Applications range from analysis of the complicated institutional rules of the European Union to responsibility-based allocation of cartel damages or the design of webpage rankings. With its interdisciplinary focus, the book seeks to bridge the gap between different disciplinary approaches by pointing to open questions that can only be resolved through collaborative efforts.

Advances in Mathematics for Industry 4.0 examines key tools, techniques, strategies, and methods in engineering applications. By covering the latest knowledge in technology for engineering design and manufacture, chapters provide systematic and comprehensive coverage of key drivers in rapid economic development. Written by leading industry experts, chapter authors explore managing big data in processing information and helping in decision-making, including mathematical and optimization techniques for dealing with large amounts of data in short periods.

With the far-reaching global impact of the COVID-19 pandemic, the demand and the necessity for digital enterprise transformation have accelerated exponentially. Management and strategies for the adoption and wider usage of newer digital technologies for the transformation of an enterprise through digital tools such as real-time video communications have shown that people no longer need to be required to be physically present in the same place; rather, they can be geographically dispersed. Technologies such as artificial intelligence, cloud computing, digital banking, and cloud data have taken over tasks that were initially done by human hands and have increased both the automation and efficiency of tasks and the accessibility of information and services. Inclusion of all these newer technologies has shown the fast pace at which the digital enterprise transformation is rapidly evolving and how new ecosystems are reshaping the digital enterprise model. Disruptive Technology and Digital Transformation for Business and Government presents interesting research on digital enterprise transformation at different stages and across different settings within government and industry, along with key issues and deeper insights on the core problems and developing solutions and recommendations for digital enterprise transformation. The chapters examine the three core leaders of transformation: the people such as managers, employees, and customers; the digital technology such as artificial intelligence and robotics; and the digital enterprise, including the products and services being transformed. They unravel the underlying process for management and strategies to fully incorporate new digital tools and technologies across all aspects of an enterprise undergoing transformation. This book is ideally intended for managers, executives, IT consultants, business professionals, government officials, researchers, students, practitioners, stakeholders, academicians, and anyone else looking to learn about new developments in digital enterprise transformation of business systems from a global perspective.

Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today's digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.

This book serves as a guide to help the reader develop an awareness of security vulnerabilities and attacks, and encourages them to be circumspect when using the various computer resources and tools available today. For experienced users, Computer Science Security presents a wide range of tools to secure legacy software and hardware. Computing has infiltrated all fields nowadays. No one can escape this wave and be immune to security attacks, which continue to evolve, gradually reducing the level of expertise needed by hackers. It is high time for each and every user to acquire basic knowledge of computer security, which would enable them to mitigate the threats they may face both personally and professionally. It is this combined expertise of individuals and organizations that will guarantee a minimum level of security for families, schools, the workplace and society in general.

The digital transformation of companies is both a competitive challenge and a complex step for large groups and industries, and at the same time a tremendous opportunity. This transformation is entering a new dimension with the development of immersive technologies such as virtual reality, mixed reality and augmented reality, which are revolutionizing the way we generate content as well as visualize and interact with models and data. The challenges of innovation and digital transformation within companies are now converging. Research shows the potential that immersive technologies have to accelerate the first steps of the innovation process. The objective of this book is to provide a clear vision of the state of research on immersive technologies for design and to deliver practical recommendations for companies wishing to improve their innovation process.

Pultrusion: State-of-the-Art Process Models with Applications, Second Edition is a detailed guide to pultrusion, providing methodical coverage of process models and computation simulation, governing principles and science, and key challenges to help readers enable process optimization and scale-up. This new edition has been revised and expanded to include the latest advances, state-of-the-art process models, and governing principles. The main challenges in pultrusion, such as the process induced residual stresses, shape distortions, thermal history, species conversion, phase changes, impregnation of the reinforcements and pulling force are described, with related examples are provided. Moreover, strategies for having a reliable and optimized process using probabilistic approaches and optimization algorithms are summarized. Another focus of this book is on the thermo-chemical and mechanical analyses of the pultrusion process for industrial profiles.