Accurate prediction of storm surge is a difficult problem. Most forecast systems produce multiple possible forecasts depending on the variability in weather conditions, possible temperature levels, winds, etc. Ensemble modeling techniques have been developed with the stated purpose of obtaining the best forecast (in some specific sense) from the individual forecasts. In this work a statistical methodology of evaluating the performance of multiple ensemble forecasting models is developed. The methodology is applied to predicting storm surge in the New York Harbor area. Data from three hurricane events collected from multiple locations in the New York Bay area are used. The methodology produces three key findings for the particular test data used. First, it is found that even the simplest possible way of creating an ensemble produces results superior to those of any single forecast. Second, for the data used and the events under study the methodology did not interact with any event at any location studied. Third, based on the methodology results for the data studied selecting the best-performing ensemble models for each specific location may be possible.

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As rule-based systems (RBS) technology gains wider acceptance, the need to create and maintain large knowledge bases will assume greater importance. Demonstrating a rule base to be free from error remains one of the obstacles to the adoption of this technology. In the past several years, a vast body of research has been carried out in developing various graphical techniques such as utilizing Petri Nets to analyze structural errors in rule-based systems, which utilize propositional logic. Four typical errors in rule-based systems are redundancy, circularity, incompleteness, and inconsistency. Recently, a DNA-based computing approach to detect these errors has been proposed. That paper presents algorithms which are able to detect structural errors just for special cases. For a rule base, which contains multiple starting nodes and goal nodes, structural errors are not removed correctly by utilizing the algorithms proposed in that paper and algorithms lack generality. In this study algorithms mainly based on Adleman's operations, which are able to detect structural errors, in any form that they may arise in rule base, are presented. The potential of applying our algorithm is auspicious giving the operational time complexity of O(n * (Max{q, K, z})), in which n is the number of fact clauses; q is the number of rules in the longest inference chain; K is the number of tubes containing antecedents which are comprised of distinct number of starting nodes; and z denotes the maximum number of distinct antecedents comprised of the same number of starting nodes.

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Capacitive Differential Pressure Sensor (CPS) is extremely used in industries. However, CPS accuracy is limited because of the adverse effects of the ambient temperature on their output characteristic. In this paper fuzzy rules are employed to create a look-up table. The ambient temperature has adverse effects on CPS output characteristic, by using this look-up table we have access to compensated value of differential pressure 1P.