Pramod Kumar Srivastava – författare

“Of immense use as the authors highlight the overall ethnography of big pharma in the healthcare sector, with the opportunities and challenges in the outside world. . . . Readers will have a picture of the marketing strategies to be adopted to boost the pharmaceutical business.” —From the Foreword by Dhruv Galgotia, CEO, Galgotias University, Greater Noida, IndiaWith particular emphasis on modeling, methodologies, data sources, and application to real-world dilemmas, this new book provides an overview of the pharmaceutical marketplace, discussing pharmacoeconomics in pharmaceutical manufacturing, pharma policy and management, pharmaceutical marketing strategies, the economics of clinical trials for drugs, the role of patents in economic development, and much more.

This book presents recent advancements in Industry 4.0 and addresses how these can be useful in achieving sustainable solutions in Society 5.0. The book also serves as a reference for developing sustainable engineering solutions to various socio-economic and techno-commercial issues. The book is meticulously structured into two sections: Section I sheds light on fundamentals, nitty-gritties, and principles of technological innovations and advancement in artificial intelligence, cloud computing, industrial Internet of Things (IIOT), and Society 5.0, whereas Section II covers viable engineering solutions developments for revamping Industry 4.0 to Society 5.0. Overall, the authors aim to show how technological advancements can be used to address social issues and improve society.

This book presents recent advancements in Industry 4.0 and addresses how these can be useful in achieving sustainable solutions in Society 5.0. The book also serves as a reference for developing sustainable engineering solutions to various socio-economic and techno-commercial issues. The book is meticulously structured into two sections: Section I sheds light on fundamentals, nitty-gritties, and principles of technological innovations and advancement in artificial intelligence, cloud computing, industrial Internet of Things (IIOT), and Society 5.0, whereas Section II covers viable engineering solutions developments for revamping Industry 4.0 to Society 5.0. Overall, the authors aim to show how technological advancements can be used to address social issues and improve society.

This book presents recent advancements in Industry 4.0 and addresses how these can be useful in achieving sustainable solutions in Society 5.0. The book also serves as a reference for developing sustainable engineering solutions to various socio-economic and techno-commercial issues. The book is meticulously structured into two sections: Section I sheds light on fundamentals, nitty-gritties, and principles of technological innovations and advancement in artificial intelligence, cloud computing, industrial Internet of Things (IIOT), and Society 5.0, whereas Section II covers viable engineering solutions developments for revamping Industry 4.0 to Society 5.0. Overall, the authors aim to show how technological advancements can be used to address social issues and improve society.

This book bridges the gap between conventional materials science and emerging data-driven methodologies, highlighting the integration of AI, machine learning, and deep learning technologies to enhance the design, analysis, and optimization of smart materials. It provides a holistic perspective essential for researchers, engineers, and students exploring the intersection of materials engineering and AI technologies.The book examines the connection between recent advancements in materials science and multiscale machine learning, facilitating predictive and prescriptive modeling for assessing material behavior based on composition, structure, and processing. It includes comprehensive discussions on smart material design, optimization, complexity analysis, and advanced computational methods for synthesizing and characterizing materials. Challenges in multiscale modeling, such as biologically inspired material design and the influence of nanotechnology on current trends, are thoroughly explored.Emphasizing the critical role of multiscale machine learning and nanotechnology in creating sustainable smart materials, the book also addresses the ethical implications of this research. It discusses opportunities and challenges in biomaterials, particularly in healthcare and biomedical applications, and anticipates future trends in machine learning for sustainable materials design. The book provides insights into how predictive and prescriptive modeling through machine learning can accelerate the material discovery process, guiding researchers toward promising candidates for further exploration.Serving as a roadmap for researchers and scientists, this book offers valuable insights into innovative approaches that support the future of materials science.

This book bridges the gap between conventional materials science and emerging data-driven methodologies, highlighting the integration of AI, machine learning, and deep learning technologies to enhance the design, analysis, and optimization of smart materials. It provides a holistic perspective essential for researchers, engineers, and students exploring the intersection of materials engineering and AI technologies.The book examines the connection between recent advancements in materials science and multiscale machine learning, facilitating predictive and prescriptive modeling for assessing material behavior based on composition, structure, and processing. It includes comprehensive discussions on smart material design, optimization, complexity analysis, and advanced computational methods for synthesizing and characterizing materials. Challenges in multiscale modeling, such as biologically inspired material design and the influence of nanotechnology on current trends, are thoroughly explored.Emphasizing the critical role of multiscale machine learning and nanotechnology in creating sustainable smart materials, the book also addresses the ethical implications of this research. It discusses opportunities and challenges in biomaterials, particularly in healthcare and biomedical applications, and anticipates future trends in machine learning for sustainable materials design. The book provides insights into how predictive and prescriptive modeling through machine learning can accelerate the material discovery process, guiding researchers toward promising candidates for further exploration.Serving as a roadmap for researchers and scientists, this book offers valuable insights into innovative approaches that support the future of materials science.