作者：
FROSCH, RAPresidentAmerican Association of Engineering Societies
Inc Dr. Robert A. Frosch born in New York City on 22 May 1928
attended Columbia University from which he received his B.A. degree in 1947 his M.A. degree in 1949 and his Ph.D. degree in 1952 all in the field of Theoretical Physics. While completing his studies for his doctorate he joined Columbia's Hudson Laboratories in 1951 and worked on naval research projects as a Research Scientist until 1958 when he became the Director Hudson Laboratories a post he held until 1963. From 1965 to 1966
he was Deputy Director Advanced Research Projects Agency (APRA) Department of Defense (DOD) having first joined ARPA in 1963 as the Director for Nuclear Test Detection the position he held until 1965. Since 1969 he also has served as the DOD member of the Committee for Policy Review National Council of Marine Resources and Engineering Development and in 1967 and 1970 as the Chairman of the U.S. Delegation to the Intergovernmental Oceanographic Commission meetings at UNESCO in Paris. In addition he was the Assistant Secretary of the Navy for Research & Development from 1966 to 1973 Assistant Executive Director of the United Nations Environment Program
with the rank of Assistant Secretary General of the United Nations from 1973 to 1975 and Assistant Director for Applied Oceanography at the Woods Hole Oceanographic Institution from 1975 until mid-1977.In June 1977
he became the Administrator of the National Aeronautics and Space Agency (NASA) the position he held prior to joining the American Association of Engineering Societies (AAES) Incorporated. On 20 January 1981 he was elected to his present post as President AAES. Additionally he was the Sea Grant Lecturer for the Massachusetts Institute of Technology in 1974 and currently is a National Lecturer for Sigma Xi. During his distinguished career
Dr. Frosch has been the recipient of numerous awards among which are the Arthur S. Flemming Award in 1966 the Navy Distinguished Public Service Award in 1
A random access machine model that has parallel processing and string manipulation is introduced. It is shown that NP is equal to the class of sets accepted by this model in nondeterministic time 0(log n), and this re...
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IT is the purpose of this letter first to point out that several independent lines of evidence seem to indicate that the ‘intercloud medium’ does not exist in its usually quoted state with density≳0.1 cm−3and temper...
IT is the purpose of this letter first to point out that several independent lines of evidence seem to indicate that the ‘intercloud medium’ does not exist in its usually quoted state with density≳0.1 cm−3and temperatureT∼104K. Rather, a hot, tenuous medium (n≲10−2cm−3,T∼106K) seems more consistent with observations in the neighbourhood of the Sun. That such might be the situation has been previously suggested (see refs 1 and 2). A mechanism for producing such a medium has been proposed by Cox and Smith3. Second, this letter points out several implications of such a “missing intercloud medium” on the large-scale structure of spiral galaxies.
We report a measurement of the missing mass (MM) spectra from the reaction π−+p→(MM)−+p at 11, 13.4, and 16 GeV, in the region of the S(1930), T(2200), and U(2375) mesons. Our mass spectra exhibit no narrow enhancem...
We report a measurement of the missing mass (MM) spectra from the reaction π−+p→(MM)−+p at 11, 13.4, and 16 GeV, in the region of the S(1930), T(2200), and U(2375) mesons. Our mass spectra exhibit no narrow enhancements in this region with 〈dσdt〉≳10 μb/GeV2.
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and s...
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.
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