In this study we focus on the specification and assessment of Stochastic Petri net (SPN) models to evaluate the design of an embedded system for reliability and availability. The system provides dynamic driving regulation (DDR) to improve vehicle derivability (antiskid,-slip and steering assist). A functional SPN abstraction was developed for each of three subsystems that incorporate mechanics, failure modes/effects and model parameters. The models are solved in terms of the subsystem and overall system reliability and availability. Four sets of models were developed. The first three sets include subsystem representations for the TC (Traction Control), AB (Antilock Braking) and ESA (Electronic Steering Assistance) systems. The last set combines these systems into one large model. We summarize the general approach and provide sample Petri net graphs and reliability charts that were used to evaluate the design of the DDR in parts and as a whole. 1.

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Information systems now form the backbone of nearly every government and private system. Increasingly these systems are networked together allowing for distributed operations, sharing of databases, and redundant capability. Ensuring these networks are secure, robust, and reliable is critical for the strategic and economic well being of the Nation. This paper argues in favor of a biologically inspired approach to creating survivable cyber-secure infrastructures (SCI). Our discussion employs the power transmission grid. Keywords Infrastructure Vulnerability,

Over the recent past, stylized facts have not yielded a synthesis regarding the predictability of returns for alternative investment assets such as hedge funds. Recent studies on alternative asset return predictability have added to the ambiguity. These studies suggest that classification prediction methods may dominate more traditional return-level prediction methodology. This paper examines the predictive accuracy of three alternate radial basis function neural networks when applied to the returns of thirteen Credit Swiss First Boston/Tremont (CSFB) hedge fund indices. We provide evidence that the Kajiji-4 RBF neural network dominates within the RBF topology in the prediction of hedge fund returns by both level and classification. The results also show that the Kajiji-4 method is capable of near perfect directional prediction.