Abstract: A mortise lock and motor-assisted method of operating. The mortise lock comprises a case, a lever arm, a latch bolt having a linkage, a PCB having a controller and a microprocessor, and a motor assembly. An actuating element including a projection is disposed outside the motor assembly to move the latch bolt from the extended to retracted positions. The microprocessor is configured to detect a stall signal during the motor assembly's initial cycle operation indicating failure of a latch bolt retraction parameter. Upon detection of the stall signal, the controller causes the motor assembly to perform a second cycle operation to move the latch bolt from the extended position to the retracted position.

Abstract: Methods and systems for training a machine learning model using synthetic defect images are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a graphical user interface (GUI) configured for displaying one or more images for a specimen and image editing tools to a user and for receiving input from the user that includes one or more alterations to at least one of the images using one or more of the image editing tools. The component(s) also include an image processing module configured for applying the alteration(s) to the at least one image thereby generating at least one modified image and storing the at least one modified image in a training set. The computer subsystem(s) are configured for training a machine learning model with the training set in which the at least one modified image is stored.

Abstract: Methods and systems for training a machine learning model using synthetic defect images are provided. One system includes one or more components executed by one or more computer subsystems. The one or more components include a graphical user interface (GUI) configured for displaying one or more images for a specimen and image editing tools to a user and for receiving input from the user that includes one or more alterations to at least one of the images using one or more of the image editing tools. The component(s) also include an image processing module configured for applying the alteration(s) to the at least one image thereby generating at least one modified image and storing the at least one modified image in a training set. The computer subsystem(s) are configured for training a machine learning model with the training set in which the at least one modified image is stored.

Abstract: Oral composition comprising from 1 to 60% by weight of the total composition fine ground natural chalk (FGNC), characterised in that the FGNC comprises particulate matter of weight-based median particle size ranging from 1 to 15 &mgr;m and BET surface area ranging from 0.5 to 3 m2/g.