Abstract: Approaches are provided for a prediction model determining a prediction indicative of the authorized user being associated with at least one anomaly event. In one example, a computer system may receive data from different sources. For example, the system may receive data associated with use of one or more monitored units of an automated storage and retrieval location. The system may also receive request data associated with a request for execution of the monitored controlled unit by an authorized user to a target user. The prediction model of the system may utilize the received data to determine the prediction that the authorized user is associated with at least one anomaly event associated with a diversion of a monitored unit away from the target user. The system may then provide the prediction for presentation.

Abstract: Approaches are provided for a prediction model determining a prediction indicative of the authorized user being associated with at least one anomaly event. In one example, a computer system may receive data from different sources. For example, the system may receive data associated with use of one or more monitored units of an automated storage and retrieval location. The system may also receive request data associated with a request for execution of the monitored controlled unit by an authorized user to a target user. The prediction model of the system may utilize the received data to determine the prediction that the authorized user is associated with at least one anomaly event associated with a diversion of a monitored unit away from the target user. The system may then provide the prediction for presentation.

Abstract: Approaches are provided for a prediction model determining a prediction indicative of the authorized user being associated with at least one anomaly event. In one example, a computer system may receive data from different sources. For example, the system may receive data associated with use of one or more monitored units of an automated storage and retrieval location. The system may also receive request data associated with a request for execution of the monitored controlled unit by an authorized user to a target user. The prediction model of the system may utilize the received data to determine the prediction that the authorized user is associated with at least one anomaly event associated with a diversion of a monitored unit away from the target user. The system may then provide the prediction for presentation.

Abstract: In some examples, there may be provided systems, devices, and methods for using data from disparate sources to determine whether diversion events have occurred. A diversion event may be when a monitored unit intended for a target user is diverted away from the target user.

Abstract: In some examples, there may be provided systems, devices, and methods for using data from disparate sources to determine whether diversion events have occurred. A diversion event may be when a monitored unit intended for a target user is diverted away from the target user.

Abstract: As will be described in more detail hereinafter, there is disclosed herein a titanium nitride diffusion barrier layer and associated method for use in non-silicon semiconductor technologies. In one aspect of the invention, a semiconductor device includes a non-silicon active surface. The improvement comprises an ohmic contact serving to form an external electrical connection to the non-silicon active surface in which the ohmic contact includes at least one layer consisting essentially of titanium nitride. In another aspect of the invention, a semiconductor ridge waveguide laser is disclosed which includes a semiconductor substrate and an active layer disposed on the substrate. A cladding layer is supported partially on the substrate and partially on the active layer. The cladding layer includes a ridge portion disposed in a confronting relationship with the active region.