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We present an ab initio potential energy surface for the Ã 2Σ+ state of the Ar⋯HS van der Waals molecule. The surface represents a fit to 196 points calculated at the RCCSD(T) level with an aug-cc-pV5Z basis set and encompasses both the Ar⋯SH and Ar⋯HS isomers. We have further calculated vibrational levels on this surface using a discrete variable representation (DVR) approach. The vibrational levels supported within the Ar⋯HS minimum are compared with levels derived from high-resolution laser induced fluorescence spectra. Predictions of vibrational levels for the Ar⋯SH isomer, which have not been observed experimentally, are also presented.

The Institute for Personal and Career Development, the external degree-granting arm of Central Michigan University, has been administering graduate external degree programs for approximately eight years. A survey of the graduates of Institute graduate programs revealed that these graduates are satisfied with the education they received through the Institute's external degree programs, that they have experienced personal and career development which they attribute at least in part to participation in Institute programs, and that they feel the educational goals and objectives of Institute programs have been accomplished.

I describe an approach to the problem of forming hypotheses about hidden mechanisms within devices - the "black box" problem for physical systems. The approach involves enumerating different causal structures for devices, placing an ordering on these hypothesis types, and carefully controlling the construction of hypotheses, as well as enumerating a set of physical and causal constraints to prune hypotheses. I relate in detail the performance of an implemented causal modeling system on the surprisingly puzzling pocket tire gauge. Results from several examples indicate that the ideas presented support capabilities for maintaining manageably sized hypothesis sets and for making fine distinctions among hypotheses.I also describe the use of causal models in the problem of monitoring physical systems. Specifically, I address two issues that arise in the task of detecting anomalous behavior in complex systems with numerous sensor channels: how to adjust alarm thresholds dynamically, within the changing operating context of the system, and how to use sensors selectively, so that nominal operation can be verified reliably without processing a prohibitive amount of sensor data. My approach involves simulation of a causal model of the system, which provides information on expected sensor values, and on dependencies between predicted events, that is useful in assessing the relative importance of events so that sensor resources can be allocated effectively.

The First National Aeronautics and Space Administration (NASA) Workshop on Monitoring and Diagnosis was held in Pasadena, California, from 15 to 17 January 1992. The workshop brought together individuals from NASA centers, academia, and aerospace who have a common interest in AI‐based approaches to monitoring and diagnosis technology. The workshop was intended to promote familiarity, discussion, and collaboration among the research, development, and user communities.