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Underrepresentation of minorities is present in the field of engineering, both in education and practice. As in every profession, diversity and inclusion needs to be incorporated in order to provide the same opportunities for all people. Strategies for Increasing Diversity in Engineering Majors and Careers is an essential reference work for the latest research on the need for diversity and inclusion within the engineering workforce and provides approaches to restructure engineering education to achieve this goal. Featuring expansive coverage on a broad range of topics including minority recruitment, experiential education systems, and study abroad programs, this book is ideally designed for students, professionals, academic advisors, and recruitment officers seeking current research on ways to diversify engineering education and careers.
Measurement techniques form the basis of scientific, engineering, and industrial innovations. The methods and instruments of measurement for different fields are constantly improving, and it’s necessary to address not only their significance but also the challenges and issues associated with them. Strategic Applications of Measurement Technologies and Instrumentation is a collection of innovative research on the methods and applications of measurement techniques in medical and scientific discoveries, as well as modern industrial applications. The book is divided into two sections with the first focusing on the significance of measurement strategies in physics and biomedical applications and the second examining measurement strategies in industrial applications. Highlighting a range of topics including material assessment, measurement strategies, and nanoscale materials, this book is ideally designed for engineers, academicians, researchers, scientists, software developers, graduate students, and industry professionals.
In the present era, technological developments are increasing the efficiency and potential of each stakeholder in a business. Robotic process automation is one of the key areas that can be applied in business organizations and corporate sectors to enhance productivity and show a path to success. Application and Adoption of Robotic Process Automation for Smart Cities provides relevant theoretical frameworks and various developments in the area of robotic process automation. Covering topics such as banking and financial services, public engagement, and smart cities, this premier reference source is a valuable resource for business leaders, IT managers, government officials, engineers, students and educators of higher education, researchers, and academicians.
Experts continue to struggle with developing methods to effectively navigate the intricate landscape of the Internet of Things (IoT). As the IoT landscape continues to expand and influence various industries, from healthcare to smart cities and beyond, scholars often find themselves facing an absence of comprehensive guidance in navigating this evolving technological landscape. The challenges are multifaceted and include the need for intelligent modeling techniques, the intricacies of managing IoT applications, and the relentless pace of technological advancements. This issue of staying well-informed and equipped to address these challenges demands an insightful solution. To tackle these challenges, Scalable Modeling and Efficient Management of IoT Applications emerges as a valuable resource, offering a multitude of effective solutions to address these concerns. This is a book that was meticulously crafted to empower scholars with the knowledge and tools they need. By tackling the scarcity of guidance on intelligent modeling techniques, the book equips readers with a profound understanding of the fundamental concepts, algorithms, and methodologies crucial for designing and managing intelligent IoT systems.
In the age of immediate technical expansion, our world faces a multifaceted challenge: ensuring the sustainability of our digital transformation. Governments and organizations have wholeheartedly embraced innovative technologies such as artificial intelligence, blockchain, and e-governance, but in doing so, they have encountered a complex web of issues. These range from cybersecurity concerns in an increasingly digitalized world to the need for intelligent systems capable of managing automation infrastructure and interconnected environments. Sustainable Development in AI, Blockchain, and E-Governance Applications offers a forward-thinking approach that harnesses the synergy between intelligent systems, machine learning, deep learning, and blockchain methods. It explores data-driven decision-making, automation infrastructure, autonomous transportation, and the creation of connected buildings, all aimed at crafting a sustainable digital future. By delving into topics like machine learning for smart parking, disease classification through neural networks, and the Internet of Things (IoT) for smarter cities, this book equips academic scholars with the tools they need to navigate the complex terrain of technology and governance. Academic scholars and researchers in technology, governance, and sustainability will find this book to be an indispensable resource. It caters to those seeking a comprehensive understanding of current and future trends in the integration of intelligent systems with cybersecurity applications.
The world is confronting the reality of climate change, which transcends geographical boundaries, necessitating a multidisciplinary approach. Effective mitigation strategies for climate change demand diversity, inclusion, equity, and collaboration as paramount considerations in the formulation and execution of mitigation strategies. Global Perspectives on Climate Change, Social Resilience, and Social Inclusion illuminates the interplay between climate shifts and society's complex tapestry. Authored by leading sociologists and interdisciplinary scholars, this researched volume offers an exploration of climate change through the lens of sociology, unveiling its implications for our global community. Moreover, the book spotlights the proactive voices of voluntary associations and activists dedicated to championing climate change victims' rights. Their collective message resonates throughout: mitigating climate change risks must be integrated into a broader framework of resilient development, elevating the economic standing and quality of life for disadvantaged groups while fostering equity across all strata of society. This book is ideal for scholars, policymakers, and activists seeking a nuanced understanding of the intricate interplay between climate change and the social fabric of our world.
Research on free-living plants and parasitic nematodes in the soil environment, food security, and nematode-plant interactions is increasing in importance. Plant-nematode interactions heavily impact nutrient availability, crop production, and soil health. The scenarios of work with plant and soil nematodes clarify the primary in-vitro and in-vivo techniques with plant-parasitic free-living soil nematodes. Nematode-Plant Interactions and Controlling Infection illustrates the techniques and recent methodologies as well as the interaction between host and nematodes to achieve nematode invasion in plants. It further investigates the role of the plant in confronting nematodes upon penetration, the challenges that face infected plants to resist nematode invasion, and the risk of transmission of nematodes. Covering topics such as biological control, molecular plant pathology, and organic farming systems, this premier reference source is an essential resource for crop producers, agrochemical professionals, agricultural scientists, botanists, plant breeders, biologists, students and academicians of higher education, librarians, researchers, and academicians.
In our data-rich era, extracting meaningful insights from the vast amount of information has become a crucial challenge, especially in government service delivery where informed decisions are paramount. Traditional approaches struggle with the enormity of data, highlighting the need for a new approach that integrates data science and machine learning. The book, Machine Learning and Data Science Techniques for Effective Government Service Delivery, becomes a vital resource in this transformation, offering a deep understanding of these technologies and their applications. Within the complex landscape of modern governance, this book stands as a solution-oriented guide. Recognizing data's value in the 21st century, it navigates the world of data science and machine learning, enhancing the mechanics of government service. By addressing citizens' evolving needs, these advanced methods counter inefficiencies in traditional systems. Tailored for experts across technology, academia, and government, the book bridges theory and practicality. Covering foundational concepts and innovative applications, it explores the potential of data-driven decision-making for a more efficient and citizen-centric government future.
In the swiftly evolving realm of technology, the challenge of classical computing's constraints in handling intricate problems has become pronounced. While classical computers excel in many areas, they struggle with complex issues in cryptography, optimization, and molecular simulation. Addressing these escalating challenges requires a disruptive solution to push the boundaries of computation and innovation. Principles and Applications of Quantum Computing Using Essential Math, authored by A. Daniel, M. Arvindhan, Kiranmai Bellam, and N. Krishnaraj. This guide pioneers the transformative potential of quantum computing by seamlessly blending rigorous mathematics with quantum theory. It equips scholars, researchers, and aspiring technologists with insights to grasp and harness quantum computing's capabilities. By delving into quantum gates, algorithms, and error correction techniques, the book demystifies quantum computing, inviting exploration of quantum machine learning, cryptography, and the dynamic interplay between classical and quantum computing. As the quantum landscape expands, this book acts as a vital companion, navigating readers through the converging realms of industry, academia, and innovation. Principles and Applications of Quantum Computing Using Essential Math arrives as a timely answer to the limitations of classical computing, providing scholars with an essential roadmap to navigate the quantum technology landscape. With its clear explanations, practical applications, and forward-looking perspectives, this book serves as an indispensable tool for unraveling quantum computing's mysteries and driving innovation into uncharted domains.
Meta-learning, or learning to learn, has been gaining popularity in recent years to adapt to new tasks systematically and efficiently in machine learning. In the book, Meta-Learning Frameworks for Imaging Applications, experts from the fields of machine learning and imaging come together to explore the current state of meta-learning and its application to medical imaging and health informatics. The book presents an overview of the meta-learning framework, including common versions such as model-agnostic learning, memory augmentation, prototype networks, and learning to optimize. It also discusses how meta-learning can be applied to address fundamental limitations of deep neural networks, such as high data demand, computationally expensive training, and limited ability for task transfer. One critical topic in imaging is image segmentation, and the book explores how a meta-learning-based framework can help identify the best image segmentation algorithm, which would be particularly beneficial in the healthcare domain. This book is relevant to healthcare institutes, e-commerce companies, and educational institutions, as well as professionals and practitioners in the intelligent system, computational data science, network applications, and biomedical applications fields. It is also useful for domain developers and project managers from diagnostic and pharmacy companies involved in the development of medical expert systems. Additionally, graduate and master students in intelligent systems, big data management, computational intelligent approaches, computer vision, and biomedical science can use this book for their final projects and specific courses.