Abstract: Provided herein are methods of ex vivo administration of a lipid particle or a payload gene, to a subject. In some embodiments, the methods are in-line methods of administration of a lipid particle or payload gene that are performed in a closed fluid circuit. Also provided are related compositions, containers, and systems in connection with the provided methods.

Abstract: Process control parameters are predicted to fabricate an object using deposition. An input design geometry is provided for the object. A training data set includes past post-build physical inspection data for a plurality of objects that comprise at least one object that is different from the object to be physically fabricated; and training data generated through a repetitive process of randomly choosing values for each of multiple process control parameters and scoring adjustments to the multiple process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments. A machine learning algorithm is trained using the provided training data set and a predicted optimal set of the multiple process control parameters is generated for initiating and performing the deposition process to fabricate the object.

Abstract: A system forms a part in an initial geometry (e.g., a sheet) into a desired geometry. The system includes a robot arm with an end effector, a model and a controller. The model receives an input geometry and an input parameter value indicating an interaction between the part and the end effector. The model determines an output geometry of the part based on the input geometry and the input parameter value. The controller receives the initial and desired geometries; applies the model to the initial geometry and to different input parameter values; based on output geometries of the model, determines a set of parameter values for controlling the robot arm; and controls the robot arm according to the determined set of parameter values to form the part into the desired geometry using the end effector.

Abstract: A system forms a part in an initial geometry (e.g., a sheet) into a desired geometry. The system includes a robot arm with an end effector, a model and a controller. The model receives an input geometry and an input parameter value indicating an interaction between the part and the end effector. The model determines an output geometry of the part based on the input geometry and the input parameter value. The controller receives the initial and desired geometries; applies the model to the initial geometry and to different input parameter values; based on output geometries of the model, determines a set of parameter values for controlling the robot arm; and controls the robot arm according to the determined set of parameter values to form the part into the desired geometry using the end effector.

Abstract: A system includes a frame holding a part, a robot arm adjacent to the frame, a tool rack with a plurality of tools that are interchangeable, and a controller. The controller controls the robotic arm to automatically attach a forming tool from the tool rack to the tool holder; controls the robotic arm with the forming tool to form the part in a first geometry into a second geometry; and controls the robotic arm to automatically return the forming tool to the tool rack and detach the forming tool from the tool holder.

Abstract: A system for treating a part with ultrasonic vibrations. The system includes a robotic arm, an ultrasonic end effector, and a controller. The robotic arm includes an actuator system that controls motion of the robotic arm and a tool holder. The ultrasonic end effector is configured to apply ultrasonic vibrations to a region of the part. The controller executes a program for controlling motion of the robotic arm for the ultrasonic end effector to apply ultrasonic vibrations to the region of the part; and controls the ultrasonic vibrations of the ultrasonic end effector based on a programmed ultrasonic parameter value for the region.

Abstract: Provided herein are systems, methods and computer readable media for facilitating an authentication process validating the location of a first device, for example, using a second device, before authorizing an action An example apparatus may be configured to receive a request to cause an action from a first device, cause the first device to communicate with a second device to verify a proximity of the first device and the second device, receive verification of the proximity, receive a first identifying data string, receive a second identifying data string, and upon confirmation of a match of the first identifying data string and the second identifying data string, authorizing the action.

Abstract: Process control parameters are predicted to fabricate an object using deposition. An input design geometry is provided for the object. A training data set includes past post-build physical inspection data for a plurality of objects that comprise at least one object that is different from the object to be physically fabricated; and training data generated through a repetitive process of randomly choosing values for each of multiple process control parameters and scoring adjustments to the multiple process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments. A machine learning algorithm is trained using the provided training data set and a predicted optimal set of the multiple process control parameters is generated for initiating and performing the deposition process to fabricate the object.

Abstract: Methods for control of post-design free form deposition processes or joining processes are described that utilize machine learning algorithms to improve fabrication outcomes. The machine learning algorithms use real-time object property data from one or more sensors as input, and are trained using training data sets that comprise: i) past process simulation data, past process characterization data, past in-process physical inspection data, or past post-build physical inspection data, for a plurality of objects that comprise at least one object that is different from the object to be fabricated; and ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments.

Abstract: Methods for control of post-design free form deposition processes or joining processes are described that utilize machine learning algorithms to improve fabrication outcomes. The machine learning algorithms use real-time object property data from one or more sensors as input, and are trained using training data sets that comprise: i) past process simulation data, past process characterization data, past in-process physical inspection data, or past post-build physical inspection data, for a plurality of objects that comprise at least one object that is different from the object to be fabricated; and ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments.

Abstract: Methods for control of post-design free form deposition processes or joining processes are described that utilize machine learning algorithms to improve fabrication outcomes. The machine learning algorithms use real-time object property data from one or more sensors as input, and are trained using training data sets that comprise: i) past process simulation data, past process characterization data, past in-process physical inspection data, or past post-build physical inspection data, for a plurality of objects that comprise at least one object that is different from the object to be fabricated; and ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments.

Abstract: Provided herein are embodiments directed to facilitating an authentication process before allowing an action. An example apparatus may be configured to receive a request, via an authentication session established during a log-in process of a second device, to cause the action, send an alert to a first device associated with an account associated with the requested action, cause the first device to communicate, via the short-range wireless communication protocol, with the second device to verify a proximity, receive verification of the proximity, receive, from the second device, a first identifying data originating at the second device, having been used to start the authentication session, receive, from the first device, a second identifying data string originating from a trusted agent, configured as software or hardware, and upon confirmation of a match of the first identifying data string and the second identifying data string, authorize the action.

Abstract: Provided herein are embodiments directed to facilitating an authentication process before allowing an action. An example apparatus may be configured to receive a request, via an authentication session established during a log-in process, to cause the action, send alert to a first device associated with an account associated with the requested action, cause the first device to communicate, via the short range wireless communication protocol, with a second device to verify a proximity, receive verification of the proximity, receive a first identifying data originating in a browser having been used to start the authentication session, receive a second identifying data string originating from a trusted agent, configured as software or hardware, residing on the second device, and upon confirmation of a match of the first identifying data string and the second identifying data string, authorize the action.

Abstract: Disclosed herein are machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.

Abstract: Methods for control of post-design free form deposition processes or joining processes are described that utilize machine learning algorithms to improve fabrication outcomes. The machine learning algorithms use real-time object property data from one or more sensors as input, and are trained using training data sets that comprise: i) past process simulation data, past process characterization data, past in-process physical inspection data, or past post-build physical inspection data, for a plurality of objects that comprise at least one object that is different from the object to be fabricated; and ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments.

Abstract: Disclosed herein are machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of additive manufacturing and/or welding processes.