During instances of capacity-demand imbalances, efficient planning and decision-making in air traffic flow management is contingent upon the "goodness" of the capacity distributions that estimate airport capacity over time. Airport capacities are subject to substantial uncertainty as they depend on stochastic weather conditions. In this paper, we develop models that take into consideration the stochastic nature of weather. The main objective of this paper is the development of probabilistic capacity forecasts. To assess the improvements that could be gained by using the capacity probabilistic forecasts, the capacity distributions developed in this paper are input into existing static, stochastic, ground holding models, which uses probabilistic capacity forecasts and determines the amount of ground delay to assign to incoming flights.

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The use of text mining and natural language processing can extend into the realm of knowledge acquisition and management for biomedical applications. In this paper, we describe how we implemented natural language processing and text mining techniques on the transcribed verbal descriptions from retinal experts of biomedical disease features. The feature-attribute pairs generated were then incorporated within a user interface for a collaborative ontology development tool. This tool, IDOCS, is being used in the biomedical domain to help retinal specialists reach a consensus on a common ontology for describing age-related macular degeneration (AMD). We compare the use of traditional text mining and natural language processing techniques with that of a retinal specialist's analysis and discuss how we might integrate these techniques for future biomedical ontology and user interface development.

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With cancer-related fatalities being the second leading cause of death in the USA, understanding the activity of effective chemotherapeutic agents is critical to addressing prostate and other cancers. Celecoxib, an FDA-approved drug for the treatment of colon tumors, has been used successfully as a lead compound in the development of antiproliferative agents. The ability of celecoxib to inhibit the development and progression of tumors has been connected to a number of mechanisms of actions that are both dependent on and independent of its cyclooxygenase-2 activity. A structure-based approach has been employed to develop a model that underscores the structural significance of celecoxib as an antiproliferative agent. By evaluating the structure activity of this library of molecules, we were able to create a QSAR model for predicting the antiproliferative activity of structurally similar molecules. The development of the model will be presented in this paper.