Abstract: A warehouse management system may receive a predictive analytics request associated with one or more warehouses and may, in response, input data associated with the one or more warehouses into a warehouse management model to determine one or more predictive analytics associated with the one or more warehouses, where the warehouse management model is trained via machine learning to determine the predictive analytics. The warehouse management system may perform simulations of operations of the one or more warehouses based on the one or more predictive analytics to determine one or more warehouse actions to meet one or more operational requirements. The warehouse management system may communicate the one or more warehouse actions to one or more devices associated with the one or more warehouses to enable the one or more devices to operate according to the one or more warehouse actions to meet the one or more operational requirements.

Abstract: An attack tree model for an aviation system comprises a plurality of tree nodes organized as a tree. For each tree node of the attack tree model model, the tree node corresponds to a respective event that may befall aviation system. An analysis computing system generates one or more attack tree models for the aviation system, wherein the aviation system includes one or more systems, sub-systems, or components. The analysis computing system further performs an assessment of one or more of the system, sub-systems, or components of the aviation system using the one or more attack tree models, and outputs metrics indicative of the assessment.

Abstract: Disclosed herein are embodiments for managing a virtual reality (VR) training exercise via a management server. The management server outputs a graphical dashboard including one or more skill nodes, and selects one or more software agents associated with the skill nodes. The management server provides the software agents to at least one host computing system communicatively coupled to a near-to-eye display device. The near-to-eye display device is configured to display a virtual three dimensional (3D) training environment including a plurality of interactive 3D virtual objects. The software agents are configured to collect VR observables data while the trainee performs actions within the virtual 3D training environment. Based on the VR observables data collected, the management server determines that one or more skills have been demonstrated during the training exercise, and updates the one or more skill nodes to graphically indicate the one or more skills demonstrated by the trainee.

Abstract: An example method includes receiving one or more cross-platform system maintenance rules that are generated by and sent from a central server computing system, determining a type of software operating system currently being executed by a client computing system, determining, based on the one or more cross-platform system maintenance rules and further based on the type of software operating system, one or more platform-specific system maintenance rules that specify one or more platform-specific system maintenance operations and scheduling information, and determining, based on scheduling information identified by the one or more platform-specific system maintenance rules, a schedule indicating when to perform the one or more platform-specific system maintenance operations. The example method includes initiating performance of the one or more platform-specific system maintenance operations in accordance with the schedule, collecting one or more results, and generating and sending at least one summary report.

Abstract: At least one processor of a computing device may determine relevancy metadata associated with of images stored in an imagery processing system. The at least one processor may determine one or more active retention policies for the images based at least in part on the relevancy metadata, wherein the one or more active retention policies include one or more rulesets that are applied to the relevancy metadata. The at least one processor may determine retention priority values associated with the images stored in the imagery processing system based at least in part on the one or more active retention policies. The at least one processor may manage retention of the images in the imagery processing system based at least in part on the retention priority values associated with the images.

Abstract: An example method includes receiving one or more cross-platform system maintenance rules that are generated by and sent from a central server computing system, determining a type of software operating system currently being executed by a client computing system, determining, based on the one or more cross-platform system maintenance rules and further based on the type of software operating system, one or more platform-specific system maintenance rules that specify one or more platform-specific system maintenance operations and scheduling information, and determining, based on scheduling information identified by the one or more platform-specific system maintenance rules, a schedule indicating when to perform the one or more platform-specific system maintenance operations. The example method includes initiating performance of the one or more platform-specific system maintenance operations in accordance with the schedule, collecting one or more results, and generating and sending at least one summary report.