In manufacturing, entrenched challenges like costly maintenance, operational inefficiencies, and product defects loom large, casting shadows over industry progress. Despite the promise of Industry 4.0 and the proliferation of data-driven technologies, many enterprises need help to effectively harness the transformative power of artificial intelligence (AI). The gap between AI's potential and its practical application persists, hindering manufacturing companies from achieving optimal efficiency, competitiveness, and sustainability. Industry Applications of Thrust Manufacturing: Convergence with Real-Time Data and AI is a groundbreaking book meticulously crafted to address the pressing needs of academic scholars and industry professionals. Offering a nuanced exploration of AI's role in revolutionizing manufacturing, this book serves as a beacon of clarity amidst the complexities of modern industrial landscapes. Whether seeking to optimize operational workflows, mitigate risks, or unlock untapped opportunities, this definitive guide offers invaluable insights and actionable strategies to propel manufacturing enterprises into a future of innovation, efficiency, and sustainable growth.

In the dynamic world of manufacturing, the industry has grappled with ongoing issues such as expensive machine maintenance, operational inefficiencies, and the production of defective products. The need for informed decision-making to maintain quality, meet deadlines, and prevent disruptions is more crucial than ever. Enter Using Real-Time Data and AI for Thrust Manufacturing, a groundbreaking book that addresses these challenges head-on. As Industry 4.0 transforms the manufacturing sector through the integration of the Internet of Things (IoT) and artificial intelligence (AI), this book serves as a beacon for academic scholars and industry professionals alike, offering profound insights into the world of AI-driven industry solutions. The objective of this book is clear—to revolutionize the manufacturing sector by leveraging human expertise and AI-driven data technologies. By delving into the realms of Industry 4.0, IoT, and AI, the book systematically tackles issues such as costly downtime, inefficient processes, and the production of substandard products. With a focus on turning raw data into meaningful insights, the book explores AI applications like machine learning and deep learning, natural language processing, and machine vision. From predictive maintenance to improved demand forecasting, quality assurance, inspection, and warehouse automation, the book positions AI as the linchpin of "Industry 4.0," ensuring not only cost savings but also safety improvements and supply-chain efficiencies.

Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.

In the ever-increasing landscape of industry and technology, companies worldwide face an unprecedented challenge. The relentless march of progress, epitomized by the revolution of Industry 4.0, demands adaptation for survival and competitiveness. The integration of technologies such as the Internet of Things (IoT), blockchain, artificial intelligence, additive manufacturing, and robotics has irrevocably altered manufacturing and supply chain operations. What was initially a quest for augmented quality and production has now become an inexorable pursuit of sustainability. The United Nations Sustainable Development Goals (UNSDG) 2030 have left no room for exemptions, making sustainability an imperative at the heart of every business strategy. The answer to this pressing challenge lies within the pages of the book, Convergence of Industry 4.0 and Supply Chain Sustainability. It serves a meticulously curated collection of research that illuminates the intricacies of implementing Industry 4.0 and the ramifications for sustainable supply chains. Our work focuses on the associated challenges and opportunities encountered by the adoption of Industry 4.0 in supply chain management (SCM).

In contemporary healthcare, Industry 5.0 technologies present a paradoxical challenge and opportunity. The rapid integration of Cyber Physical Systems, Cloud Computing, Internet of Things, Artificial Intelligence, Smart Factories, and Cognitive Computing has ushered in unprecedented transformations, yet it has concurrently given rise to critical vulnerabilities within healthcare systems. As sensitive patient data becomes increasingly digitized, the specter of cybersecurity threats looms larger than ever. The book, titled "Technologies for Sustainable Healthcare Development," undertakes the crucial task of addressing this pressing concern. Focused on Cybersecurity and Data Science Innovations in Industry 5.0 Technologies for Sustainable Healthcare, it serves as an indispensable guide for professionals, researchers, and policymakers aiming to fortify healthcare systems against unauthorized access and cyber threats while unlocking the potential of transformative technologies. The overarching objective of Technologies for Sustainable Healthcare Development is to dissect the challenges posed by the convergence of cybersecurity, data science, and Industry 5.0 in healthcare. This timely publication delves into the evolution of cybersecurity and data science, providing insights into their symbiotic relationship and the implications for healthcare. Through its exploration of cutting-edge research, innovative solutions, and practical applications, the book becomes a beacon for those seeking to navigate the evolving landscape of secure healthcare development. It does not merely dissect problems but endeavors to provide sustainable development strategies, contributing to the advancement of robust and efficient healthcare systems.

In the field of pharmaceutical sciences, the integration of artificial intelligence (AI) has emerged as a groundbreaking force, propelling the field into uncharted territories of discovery and innovation. As traditional approaches in drug discovery and development encounter new challenges, the need for cutting-edge technologies becomes increasingly apparent. AI-Powered Advances in Pharmacology offers an insightful exploration of this critical intersection between AI and pharmacological research. This book delves into how AI technologies are reshaping the understanding of diseases, predicting drug responses, and optimizing therapeutic interventions. It navigates through the relationship between AI algorithms, big data analytics, and traditional pharmacological methodologies, promising to accelerate drug development and usher in a new era of precision medicine. The primary objective of AI-Powered Advances in Pharmacology is to conduct a thorough exploration of the integration of artificial intelligence (AI) into pharmacological research, shedding light on its transformative impact on drug discovery, development, and personalized medicine. This comprehensive overview aims to serve as a valuable resource for researchers, practitioners, and students in the field, bridging the gap between traditional pharmacological approaches and AI methodologies. Through case studies and discussions of emerging trends, the book contributes to the evolving landscape of pharmacology, fostering a deeper understanding of diseases, optimizing therapeutic interventions, and shaping the future of precision medicine. By providing practical insights, it aims to inspire further advancements at the intersection of artificial intelligence and pharmacology.

The aviation industry presents a unique conundrum with the complex relationship between government oversight and business management. While the aviation industry operates in a business-oriented manner, a considerable number of aviation companies and airports worldwide are still government-owned. The COVID-19 pandemic has accentuated the pivotal role of governments in the industry, highlighting the need for a comprehensive understanding of this interaction, given the sector's massive economic influence and the extensive investment required. The need for an in-depth exploration of this liaison has never been more pressing. Strategic Management and Policy in the Global Aviation Industry offers an insightful and comprehensive solution to this enigmatic issue. This book delves into the dynamics of the aviation sector's reliance on government intervention and support, providing invaluable perspectives for academic scholars and industry enthusiasts. It explores the critical role governments play in the development and sustainability of aviation companies, especially in the wake of the pandemic, where governments worldwide stepped in to bolster their national aviation companies. Additionally, the book explores the industry's efforts to align with the United Nations' sustainability targets and the critical influence of government regulations in achieving these goals. It also dissects the digital transformation and the integration of innovative technologies in aviation, setting the stage for a more efficient and innovative industry. Furthermore, it unveils the burgeoning role of artificial intelligence in the quest for cost reduction and enhanced operational efficiency in the fiercely competitive aviation market.