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An ongoing effort is underway at NASA Ames Research Center (ARC) to develop an On-line Project Information System (OPIS) for the Advanced Life Support (ALS) Program. The objective of this three-year project is to develop, test, revise and deploy OPIS to enhance the quality of decision-making metrics and attainment of Program goals through improved knowledge sharing. OPIS will centrally locate detailed project information solicited from investigators on an annual basis and make it readily accessible by the ALS Community via a Web-accessible interface. The data will be stored in an object-oriented relational database (created in MySQL®) located on a secure server at NASA ARC. OPIS will simultaneously serve several functions, including being an research and technology development (R&TD) status information hub that can potentially serve as the primary annual reporting mechanism for ALS-funded projects.

The ALS Metric is the predominant tool for predicting the cost of ALS systems. Metric goals for the ALS Program are daunting, requiring a threefold increase in the ALS Metric by 2010. Compounding the problem is the slow rate new ALS technologies reach the maturity required for consideration in the ALS Metric and the slow rate at which new configurations are developed. This limits the search space and potentially gives the impression of a stalled research and development program. Without significant increases in the state of the art of ALS technology, the ALS goals involving the Metric may remain elusive. A paper previously presented at his meeting entitled, “Managing to the metric: An approach to optimizing life support costs.” A conclusion of that paper was that the largest contributors to the ALS Metric should be targeted by ALS researchers and management for maximum metric reductions.

The handling of uncertain data is demonstrated on an empirical grading function used for the assessment of tomato quality. The grading function studied here is designed to measure the departure of the properties of a tomato or a population of tomatoes from an assumed optimal tomato. Uncertain data are considered using the Taylor Series expansion of the grading function, a function of random variables, which provides the ability to determine the variability of the outcome of the function. Once this variability is quantified, confidence intervals are determined and considered. The degree of confidence in a result has a wide array of ramifications, ranging from providing valuable decision support to assisting in guidance of research activity.

Metric analysis of research activity and technology development has become one of the deciding factors in whether or not the research of potential technologies receives the needed funding or a technology is incorporated into a system. It is difficult to accurately predict the configuration of an ALS system that will transport humans to the surface of Mars and support surface exploration. Determining which ALS research activities will support this effort is a very discretionary process, and there simply is not enough information to accurately make these types of decisions. Requirements change as research develops, and it is very difficult to create a metric that can accurately assess a potential or ongoing research project. The SSM team of the NJ-NSCORT has developed an internet platform to perform the assessment of potential technologies for the purpose of the development of an ALS system. The platform is called IFA and it has completed validation with current NJ-NSCORT projects.

As ALS goals branch out to extended missions to the moon and Mars, concurrent science and engineering projects take center stage in the development of new ALS technology. It is necessary to optimize the interdisciplinary research activities in order to ensure ALS research goals are met in a timely manner, and to guarantee the reliability of future long term missions. The SSM team of the NJ-NSCORT has developed an internet software platform capable of performing a systems level analysis of the ALS research activity. The information produced by the analysis can assist ALS researchers in the streamlining of research activity.