Globalization has transformed agri-food markets, creating a single global market with reduced trade barriers. In theory, this should bring increased food security, yet challenges persist. Small farmers often need help integrating into global sourcing networks and meeting stringent food safety regulations. Additionally, there is increasing pressure on businesses and governments to address the environmental and resource consequences of agri-food production. Advanced Computational Methods for Agri-Business Sustainability offers a comprehensive analysis of agricultural sector challenges and provides practical solutions. It identifies potential issues in agri-food management and supply chains, offers mitigation strategies, and highlights opportunities for sustainable development. The book aims to bridge the gap between theory and practice, providing insights for academics, policymakers, and industry professionals.

"This book brings computing solutions to ancient practices and modern concerns, sowing the seeds for a sustainable, constant food supply, utilizing cutting-edge computational techniques"--Provided by publisher.

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 intersection of artificial intelligence (AI) and digital twin technology presents a problem and an unparalleled opportunity for transformation. Businesses grapple with the need for operational excellence, innovation, and a competitive edge, all while navigating the intricate web of data analytics, decision-making, and real-time monitoring. In response to these challenges, Harnessing AI and Digital Twin Technologies in Businesses emerges as an example of insight and guidance, offering a comprehensive exploration of the complementary connection between AI and digital twin technology. In a world where the convergence of these powerful tools transforms business intelligence, enabling initiative-taking decision-making and dynamic simulations. This book serves as a solution for decision-makers, technologists, and researchers seeking to not only understand but harness the potential of AI-powered digital twins to enhance productivity, creativity, and judgment in their operations.

In this address I discuss the potential for the revolution in data infrastructure, data science and computation to support and accelerate the transformation towards a more productive, healthy and sustainable agricultural systems. A theme that emerges from both the agricultural systems science and economic-behavioral sciences is that improved acquisition and use of data is a critical constraint on agricultural research and its successful application, both for on-farm production system management and for technology and policy decision making. This in turn suggests potentially high returns to public investment in the data needed to enable computational agricultural science. I conclude with a prototype private-public scheme for investment in the data needed to support advanced computational methods and models, and discuss the economic, technical, legal and institutional challenges to its implementation.

Showing how the method of sustainability assessment plays a key role in choosing the best agricultural productive mode, this book guides the reader through the process of selecting, from among the various approaches for building farming systems, the method of decision-making that will result in the most appropriate outcome, given the context. Case studies hail from polities as diverse as Portugal and Canada, Argentina and Lebanon. The work thus offers a valuable critical survey of the assessment methods that account for sustainability and economics, and which have developed considerably in the last two decades. The heterogeneous approaches covered here make this volume appropriate for consultation in a wide variety of social, political and geographical contexts.