Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.

Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.

Abstract: A method for predicting a stick-slip event includes measuring one or more surface properties using a sensor at the surface. The method also includes measuring one or more downhole properties using a downhole tool in a wellbore. The method also includes determining that the one or more surface properties and the one or more downhole properties match a distribution. The distribution occurs before two or more previously-detected stick-slip events. The method also includes determining a likelihood that a stick-slip event will occur based at least partially upon the distribution that the one or more surface properties and the one or more downhole properties match.

Abstract: Bayesian recursive estimation is used to analyze performance parameters of a membrane separation system based on historical operational data of a membrane system. Bayesian estimation considers historical data over prior time intervals to predict future membrane separation performance to avoid unexpected downtime and unanticipated maintenance. A set of state variables used for modeling performance is used with a degradation model of to anticipate performance changes and maintenance based on measured properties of permeate, non-permeate, and feed flows.

Abstract: A method including generating, by a navigation service, a route for navigating from a route origin to a route destination using a private roads repository. The method includes identifying a ghost origin and a ghost destination of a ghost road along the route. The method includes sending, using an application programming interface of a base roads engine, a first request for a route from the ghost origin to the ghost destination. The method includes receiving, from the base roads engine in response to the first request, a replacement section from the ghost origin to the ghost destination. The method includes replacing, in the route, the ghost road with the replacement section to create an updated route including segments. The method includes generating an estimated travel time from the route origin to the route destination over the segments of the updated route. The method includes presenting the estimated travel time.

Abstract: Bayesian recursive estimation is used to analyze performance parameters of a membrane separation system based on historical operational data of a membrane system. Bayesian estimation considers historical data over prior time intervals to predict future membrane separation performance to avoid unexpected downtime and unanticipated maintenance. A set of state variables used for modeling performance is used with a degradation model of to anticipate performance changes and maintenance based on measured properties of permeate, non-permeate, and feed flows.

Abstract: The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation.

Abstract: A visual interactive multi-criteria decision-making method and computer-based apparatus for portfolio management. The method/apparatus supports partitioning of a portfolio of physical or other assets into two mutually exclusive categories, such as assets recommended for sale and assets recommended for retention. The method/apparatus utilizes one or more coupled 2-D projections of the portfolio in criteria space. The user interacts with the projections to express and record preferences.

Abstract: A visual interactive multi-criteria decision-making method and computer-based apparatus for portfolio management. The method/apparatus supports partitioning of a portfolio of physical or other assets into two mutually exclusive categories, such as assets recommended for sale and assets recommended for retention. The method/apparatus utilizes one or more coupled 2-D projections of the portfolio in criteria space. The user interacts with the projections to express and record preferences.

Abstract: A visual interactive multi-criteria decision-making method and computer-based apparatus for portfolio management. The method/apparatus supports partitioning of a portfolio of physical or other assets into two mutually exclusive categories, such as assets recommended for sale and assets recommended for retention. The method/apparatus utilizes one or more coupled 2-D projections of the portfolio in criteria space. The user interacts with the projections to express and record preferences.