Abstract: A weight and center-of-gravity profile for an aircraft is determined prior to flight. An uncertainty band is determined for each of a plurality of points along a fuel vector for the planned flight. Each of the points along the fuel vector is compared to a predetermined flight limit (e.g., an FAA certified envelope). This comparison is used to decide whether the profile is suitable for flight of the aircraft. If so, an indication is provided to a user (e.g., on a display or by a text message) that the profile is suitable and the aircraft is ready for flight.

Abstract: A weight and center-of-gravity profile for an aircraft is determined prior to flight. An uncertainty band is determined for each of a plurality of points along a fuel vector for the planned flight. Each of the points along the fuel vector is compared to a predetermined flight limit (e.g., an FAA certified envelope). This comparison is used to decide whether the profile is suitable for flight of the aircraft. If so, an indication is provided to a user (e.g., on a display or by a text message) that the profile is suitable and the aircraft is ready for flight.

Abstract: A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnostics prediction can be generated using a database of clinical test data. The diagnostics prediction can also be used to develop screening tests to screen for one or more inapparent diseases. The prediction method can be implemented with Bayesian probability estimation techniques. The system and method permit clinical test data to be analyzed and mined for improved disease diagnosis.

Abstract: A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnostics prediction can be generated using a database of clinical test data. The diagnostics prediction can also be used to develop screening tests to screen for one or more inapparent diseases. The prediction method can be implemented with Bayesian probability estimation techniques. The system and method permit clinical test data to be analyzed and mined for improved disease diagnosis.

Abstract: A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnostics prediction can be generated using a database of clinical test data. The diagnostics prediction can also be used to develop screening tests to screen for one or more inapparent diseases. The prediction method can be implemented with Bayesian probability estimation techniques. The system and method permit clinical test data to be analyzed and mined for improved disease diagnosis.

Abstract: A probabilistic approximation of a data distribution is provided, wherein uncertain measurements in data are fused together to provide an indication of whether a new data item belongs to a given disease model. The probabilistic approximation is provided in accordance with a Bayesian analysis technique that examines the relationship of probability distributions for observable events x and multiple hypotheses H regarding those events.

Abstract: A system and method of diagnosing diseases from biological data is disclosed. A system for automated disease diagnostics prediction can be generated using a database of clinical test data. The diagnostics prediction can also be used to develop screening tests to screen for one or more inapparent diseases. The prediction method can be implemented with Bayesian probability estimation techniques. The system and method permit clinical test data to be analyzed and mined for improved disease diagnosis.

Abstract: A method of simulating multibody dynamics of a molecular system so as to produce output data representative of a time evolution of the molecular system includes providing a set of equations for characterizing multibody dynamics of the molecular system. The set of equations is constrained by a constraining equation. The method further includes providing an integrator for integrating the set of equations. The integrator is tailored for the set of equations so as to satisfy the constraining equation. The method further includes applying the integrator to the set of equations over a time step, so as to produce output data representative of the time evolution of the molecular system relative to the time step.

Abstract: A computer assisted method for generating a representation of a three dimensional structure of a molecule is described. The invention recursively filters an a priori vector of structural parameters, representative of the three dimensional structure of the molecule, based upon experimental observations relating to the structural parameters. The invention produces a refined vector of structural parameters and an associated covariance matrix indicating the accuracy of the vector. The invention includes a set of non-linear functions which implement a model relating the observations to the vector of structural parameters. The recursive steps of the method include a prediction step and an update step. During the prediction step, the non-linear model is analyzed by statistical methods. Only the results of the prediction step are incorporated in the update step, thus isolating the non-linear aspects of the model within the prediction step.

Abstract: A computer assisted method for generating a representation of a three dimensional structure of a molecule is described. The invention recursively filters an a priori vector of structural parameters, representative of the three dimensional structure of the molecule, based upon experimental observations relating to the structural parameters. The invention produces a refined vector of structural parameters and an associated covariance matrix indicating the accuracy of the vector. The invention includes a set of non-linear functions which implement a model relating the observations to the vector of structural parameters. The recursive steps of the method include a prediction step and an update step. During the prediction step, the non-linear model is analyzed by statistical methods. Only the results of the prediction step are incorporated in the update step, thus isolating the non-linear aspects of the model within the prediction step.