Abstract: A medical instrument is disclosed. The medical instrument includes a housing, a control lever rotatably coupled to the housing, at least one electrical contact, a radio frequency (RF) generation circuit coupled to and operated by the battery and operable to generate an RF drive signal and to provide the RF drive signal to the at least one electrical contact and an initialization clip coupled to housing and the control lever to prevent operation of the RF generation circuit and movement of the control lever.

Abstract: A medical instrument includes a housing, an RF generation circuit activation button coupled to an activation button lever supported by the housing. The activation button lever includes a top surface, the activation button lever rotatable about an activation button pivot point. A cutting blade is operably coupled to a sheath. A control lever is supported by the housing and pivotally coupled to a trigger lever including a projection to engage the top surface of the activation button lever and to control the actuation of the cutting blade by actuating the sheath in a distal direction by rotatably moving the control lever proximally about a trigger pivot point. The top surface of the activation button lever and the projection of the trigger lever remain engaged to prevent actuation of the cutting blade until the activation button is fully rotatably moved proximally about the activation button pivot.

Abstract: The health of a tool is predicted based on temporally ordered input data representing parameters indicative of tool health. A sliding time window is used to partition input data into temporally displaced data sets. Non-linear regression models determine, based on the data sets, a set of predictive values relating to tool health at a future time. A tool-health metric is then determined based on one or more of the predictive values.

Abstract: Complex process control and maintenance are performed utilizing a nonlinear regression analysis to determine optimal maintenance activities and process adjustments based on an urgency metric.

Abstract: Failure prediction for complex processes is performed utilizing one or more nonlinear regression models to relate operational variable values measured at two or more times to predicted process metric values and maintenance variable values.

Abstract: The health of a tool is predicted based on temporally ordered input data representing parameters indicative of tool health. A sliding time window is used to partition input data into temporally displaced data sets. Non-linear regression models determine, based on the data sets, a set of predictive values relating to tool health at a future time. A tool-health metric is then determined based on one or more of the predictive values.

Abstract: Failure prediction for complex processes is performed utilizing one or more nonlinear regression models to relate operational variable values measured at two or more times to predicted process metric values and maintenance variable values.

Abstract: Non-linear regression models are configured and trained to operate in connection with data sets having null (unknown) values.

Abstract: Complex process control and maintenance are performed utilizing a nonlinear regression analysis to determine optimal maintenance activities and process adjustments based on an urgency metric.

Abstract: Non-linear regression models of a complex process and methods of modeling a complex process feature a filter based on a function of an input variable, the output of which is a predictor of the output of the complex process.

Abstract: Complex process prediction and optimization are performed utilizing an orthogonal transform to represent time-varying continuous signals as discrete values, which are then subjected to a nonlinear regression analysis to relate between the discrete values and process metrics.