Abstract: A vessel closure device for delivering immediate hemostasis at a puncture site in a wall of a blood vessel includes an intravascular anchor having one or more suture attachment points, an extravascular cap having a lumen, a sealant, and a suture connected to at least one of the one or more suture attachment points of the intravascular anchor and threaded through the lumen of the extravascular cap, wherein each of the intravascular anchor, extravascular cap, sealant, and suture are formed of bioabsorbable materials.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to optimize a target variable in an industrial automation environment. In some examples, a design application generates a control program configured and selects a program tag that represents a target variable in an industrial process. A processing application identifies a set of available program tags that represent independent variables in the industrial process and determines correlations between ones of the independent variables and the target variable. The processing application selects available program tags that represent independent variables correlated with the target variable and generates a recommendation that indicates the selected available program tags. The design application modifies the control program using the selected available program tags to optimize the target variable.

Abstract: A device for closing an opening in a body lumen, the device having a first elongate member with a first lumen, a second elongate member distal the first elongate member, and a needle assembly slidably cooperating with the first elongate member to position a plurality of sutures within the lumen of the first elongate member. The needle assembly includes a needle base and a plurality of needle portions extending from the needle base. Slidable movement of the needle assembly in relation to the first elongate member locates the plurality of sutures selectively mounted to the needle assembly within the first lumen.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to surface data pipelines in an industrial automation environment. In some examples, a design component generates a control program configured for implementation by a programmable logic controller to control an industrial process. The design component adds program tags to the control program and implements the control program through the programmable logic controller. The design component establishes data pipelines that correspond to the program tags in the control program between data sources associated with the program tags and a machine learning system that consumes process data generated by the data sources. A pipeline management component generates a pipeline suggestion that indicates individual ones of the data pipelines and their corresponding program tags.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to surface machine learning systems in a design application of an industrial automation environment. In some examples, a design component generates a control program configured for implementation by a Programmable Logic Controller (PLC). The design component receives a user input that selects a program tag that represents a target variable in an industrial automation process. In response to the user selection, the design component identifies one or more machine learning models associated with the target variable and displays the one or more machine learning models. The design component receives a user input that selects one of the one or more machine learning models and responsively integrates another program tag that represents the selected machine learning model into the control program.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to detect malicious behavior in an industrial automation environment. In some examples, a security component generates feature vectors that represents inputs and outputs to a data pipeline and supplies the feature vectors to a machine learning engine. The security component processes a machine learning output that indicates when anomalous behavior is detected in the operations of the data pipeline. When anomalous behavior is detected in the operations of the data pipeline, the security component generates and transfers an alert that characterizes the anomalous behavior.

Abstract: An intravascular device delivery system has an elongated member, a guidewire receiving member, and a distal cap longitudinally fixed to a guidewire receiving member. The distal cap includes an insert having an elongate member, a rim member radially separated from the elongate member, and a wall member supporting the rim member, the wall member being disposed between the rim member and the elongate member.

Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises an interface component configured to display a graphical representation of a machine learning asset in an industrial automation environment, wherein the graphical representation includes a visual indicator representative of an output from the machine learning asset. The interface component is further configured to adjust the visual indicator based on the output from the machine learning asset.

Abstract: Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to optimize a target variable in an industrial automation environment. In some examples, a design application generates a control program configured and selects a program tag that represents a target variable in an industrial process. A processing application identifies a set of available program tags that represent independent variables in the industrial process and determines correlations between ones of the independent variables and the target variable. The processing application selects available program tags that represent independent variables correlated with the target variable and generates a recommendation that indicates the selected available program tags. The design application modifies the control program using the selected available program tags to optimize the target variable.

Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises: a storage component configured to maintain a set of model control schemes for controlling an industrial process, a control component configured to control the industrial process with a control program running a model control scheme, wherein the model control scheme is configured to optimize a first parameter of the industrial process, and a model management component configured to change the model control scheme to optimize a second parameter of the industrial process that is distinct from the first parameter.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to visualize machine learning model status in an industrial automation environment. In some examples, a machine learning component receives process inputs associated with industrial devices in the industrial automated environment. The machine learning component processes the inputs to generate machine learning outputs and transfers the machine learning outputs to influence one or more functions of the industrial devices. The machine learning component reports operational data characterizing the machine learning outputs. A Human Machine Interface (HMI) component displays a visualization of the machine learning component and receives the operational data from the machine learning component.

Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises: a storage component configured to maintain a set of model control schemes for controlling an industrial process, a control component configured to control the industrial process with a control program running a model control scheme, wherein the model control scheme is configured to optimize a first parameter of the industrial process, and a model management component configured to change the model control scheme to optimize a second parameter of the industrial process that is distinct from the first parameter.

Abstract: Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to train machine learning systems to perform autonomous control in an industrial automation environment. In some examples, a data aggregation component receives operational data from Original Equipment Manufacturer (OEM) devices, identifies a device type for the operational data, and transfers the operational data for the device type to a machine learning interface component. The operational data characterizes the operations of the OEM devices. The interface component receives the operational data for the device type and generates feature vectors based on the operational data configured for ingestion by a machine learning model. The interface component transfers the feature vectors to a machine learning model.

Abstract: Embodiments of the present disclosure relate generally to a method, apparatus and system for the evaporation of produced water and dirty water from oil and gas production and other dirty water sources. The evaporation system may consist of a portable pond embodied in an Above Ground Storage Tank (AST) system and a fluid projection system which may be controlled and employ optimized operating conditions to maximize the evaporation of produced water under varying meteorological and chemical condition.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to detect malicious behavior in an industrial automation environment. In some examples, a security component monitors an integrated design application and generates feature vectors that represent operations of the integrated design application. The security component supplies the feature vectors to a machine learning engine. The security component processes a machine learning output that indicates when anomalous behavior is detected in the operations of the integrated design application. When anomalous behavior is detected in the operations of the integrated design application, the security component generates and transfers an alert that characterizes the anomalous behavior.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to train machine learning systems to perform autonomous control in an industrial automation environment. In some examples, a data aggregation component receives operational data from Original Equipment Manufacturer (OEM) devices, identifies a device type for the operational data, and transfers the operational data for the device type to a machine learning interface component. The operational data characterizes the operations of the OEM devices. The interface component receives the operational data for the device type and generates feature vectors based on the operational data configured for ingestion by a machine learning model. The interface component transfers the feature vectors to a machine learning model.

Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing programs. In an embodiment, a system comprises: a control component configured to run a closed-loop industrial process comprises a first machine learning model; a measurement component configured to measure a gap between outcome data predicted by the first machine learning model and actual outcome data; a determination component configured to determine, based on the gap, that the first machine learning model has degraded; and a management component configured to replace the first machine learning model with a second machine learning model, wherein the second machine learning model is trained based at least in part on the actual outcome data.

Abstract: A vessel closure device for delivering substantially immediate hemostasis at a puncture site in a wall of a blood vessel includes an intravascular anchor having one or more suture attachment points, an extravascular cap having a lumen, a sealant, and a suture connected to at least one of the one or more suture attachment points of the intravascular anchor and threaded through the lumen of the extravascular cap, wherein each of the intravascular anchor, extravascular cap, sealant, and suture are formed of bioabsorbable materials. Delivery systems for delivering such a vessel closure device are also disclosed.