academic sectors, an atmosphere in which the central importance of technology transfer is clearly understood by the participants in the process.
Technology transfer, computational and mathematical modeling, and education have an importance to economic competitiveness that is very large relative to the recognition given to these activities by the academic mathematical sciences community.
Manpower and technical training are also crucial for economic competitiveness. The mathematical sciences community has a significant responsibility in this area.
The ability of the mathematical sciences community to deliver short-term results with any degree of consistency depends crucially on healthy support for its long-term development. Conversely, the more fundamental areas of the mathematical sciences are continually invigorated by interaction with applications. It is an almost universal experience that once an application succeeds, further progress depends on the development of new, and often fundamental, theories. The Board on Mathematical Sciences endorses the principle that short-term applications and fundamental theories are virtually inseparable. Detailed studies of the health and vitality of the mathematical sciences technology base have been conducted (see, e.g.,  and ). Their primary conclusion was that renewal of the U.S. mathematical sciences was at risk owing to weaknesses in manpower and training, and to a lack of balance in the funding level for the mathematical sciences. Widespread underinvestment in the mathematical sciences was documented in these reports. The recommendations were to increase the priority given to manpower and training and to eliminate the funding deficiencies.
The board endorses the conclusions and recommendations of the reports Renewing U.S. Mathematics: Critical Resource for the Future  and Renewing U.S. Mathematics: A Plan for the 1990s , to assure future manpower availability, correct funding imbalances, and preserve the vitality of fundamental research in the mathematical sciences.
Engineering and manufacturing research and design depend heavily on computational and mathematical modeling. Both the knowledge