A two-day virtual regional consultation titled “Engaging with Academia and Research Institutions (ARIs) to Support Family Farmers and Food System Transformation During and Post COVID-19 Pandemic in Asia” was held by the Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA) and the Asian Farmers’ Association for Sustainable Rural Development (AFA) from 8–9 December 2021. The consultation was organized in collaboration with United Nations Educational, Scientific and Cultural Organization (UNESCO), International Cooperation Centre of Agricultural Research for Development (CIRAD), Group For Research and Technology Exchanges (GRET), and with technical assistance from FAO Regional Office for Asia and the Pacific (RAP). It was attended by 157 international participants and 51 speakers coming from different academic and research institutions (ARIs), non-governmental organizations (NGOs) and civil society organizations (CSOs), government agencies, and development partners. The regional consultation highlighted the importance of collaboration among ARIs, family farmers’ organizations, government agencies, and development partners in Asia in enhancing the livelihoods of family farmers and developing their capacities to cope with the COVID-19 pandemic through agroecology. This publication compiles the proceedings of the two-day virtual regional consultation and marks an important milestone in initiating a stocktaking of existing initiatives and collaborations between ARIs, inter-government agencies and family farmers’ organizations in the region with a specific focus on agroecology and sustainable food systems.
The future security, economic growth, and competitiveness of the United States depend on its capacity to innovate. Major sources of innovative capacity are the new knowledge and trained students generated by U.S. research universities. However, many of the complex technical and societal problems the United States faces cannot be addressed by the traditional model of individual university research groups headed by a single principal investigator. Instead, they can only be solved if researchers from multiple institutions and with diverse expertise combine their efforts. The National Science Foundation (NSF), among other federal agencies, began to explore the potential of such center-scale research programs in the 1970s and 1980s; in many ways, the NSF Engineering Research Center (ERC) program is its flagship program in this regard. The ERCs are "interdisciplinary, multi-institutional centers that join academia, industry, and government in partnership to produce transformational engineered systems and engineering graduates who are adept at innovation and primed for leadership in the global economy. To ensure that the ERCs continue to be a source of innovation, economic development, and educational excellence, A New Vision for Center-Based Engineering Research explores the future of center-based engineering research, the skills needed for effective center leadership, and opportunities to enhance engineering education through the centers.
The satisfiability (SAT) problem is central in mathematical logic, computing theory, and many industrial applications. There has been a strong relationship between the theory, the algorithms and the applications of the SAT problem. This book aims to bring together work by the best theorists, algorithmists, and practitioners working on the sat problem and on industrial applications, as well as to enhance the interaction between the three research groups. The book features the applications of theoretical/algorithmic results to practical problems and presents practical examples for theoretical/algorithmic study. Major topics covered in the book include practical and industial SAT problems and benchmarks, significant case studies and applications of the SAT problem and SAT algorithms, new algorithms and improved techniques for satisfiability testing, specific data structures and implementation details of the SAT algorithms, and the theoretical study of the SAT problem and SAT algorithms.
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