Abstract: An embodiment for selectively transferring inventory on a temporary basis is provided. The embodiment may include receiving real-time and historical data relating to a plurality of products in a storage facility. The embodiment may also include identifying one or more high priority products during a selected timeframe. The embodiment may further include predicting a required amount of space in the storage facility to store the one or more high priority products. The embodiment may also include in response to determining the storage facility does not have the required amount of space, assigning a level of priority to each remaining product. The embodiment may further include identifying a transportation vehicle and a transit location of the transportation vehicle to temporarily store at least one first remaining product with a medium priority level. The embodiment may also include autonomously loading the transportation vehicle with the at least one first remaining product.

Abstract: According to an aspect, a computer-implemented method includes identifying applications in a network environment that perform periodic data extraction and data transmission operations and identifying a frequency and a data transmission load for each of the operations. Aspects also include predicting an available bandwidth in the network environment based at least in part on the periodic data extraction and data transmission operations and based on a determination that the available bandwidth in the network environment will fall below a threshold value during a time interval instructing the applications to modify the one or more of the periodic data extraction and data transmission operations to increase the frequency associated with the one or more of the periodic data extraction and data transmission operations.

Abstract: Computer-implemented methods for wireless power transfer among multiple vehicles are provided. Aspects include identifying a group of vehicles, from a plurality of vehicles, that require charging and identifying a wireless charging capability of each vehicle of the group of vehicles. Aspects also include deploying wireless power chargers to the vehicles of the group of vehicles that do not have wireless charging capability and charging each vehicle of the group of vehicles using a wireless power transfer module. The charging of the group of vehicles is performed while the vehicles are in motion.

Abstract: A method and system provide computer system security for a focus computer system (FCS). The method comprises creating a security digital twin (SDT) for the FCS with an associated security ontology for the FCS. A potential threat analyzer receives a potential threat object (PTO), and maps it to an enterprise attack vector pattern. The method further comprises searching, on another computer system (OCS) for a predicted attack pattern having a similar pattern to the enterprise attack vector pattern. Conditioned upon finding the predicted attack pattern, and using a potential threat handler locating an action mitigation plan (AMP) related to the predicted attack pattern in the data fabric associated with the OCS, the method further comprises copying the OCS predicted attack pattern to an FCS predicted attack pattern store, copying the OCS AMP to an FCS AMP store, and defending the FCS from the PTO using the AMP.

Abstract: A method comprising establishing a local communication network among and between a plurality of onboard vehicle computers respectively located in a plurality of vehicles, polling the vicinity of each vehicle to determine a set of qualified mobile device(s) that meet the following criteria: (i) the qualified mobile device is located within one of the vehicles of the plurality of vehicles, (ii) the qualified mobile device is configured and equipped to join the local network, and (iii) the qualified mobile device consents to join the local network, and adding the set of qualified mobile device(s) to the local network.

Abstract: At a mirrored operator, a current operand snapshot is received from a source operator. The current operand snapshot comprises configuration data of a set of source operands managed by the source operator, and the set of source operands comprises at least one source operand. A set of mirrored operands is configured by the mirrored operator according to the current operand snapshot, resulting in a first configuration of the set of mirrored operands. At the mirrored operator, a change request is received from the source operator. The change request comprises a request to modify a configuration of the set of source operands. The first configuration of the set of mirrored operands is modified by the mirrored operator according to the change request.

Abstract: A computer-implemented method according to one embodiment includes establishing a handshake between a security and compliance center (SCC) and a first operator of a first cluster via a manager component therebetween. An operand mapping of the SCC is updated, and the established handshake is used to cause the update to be communicated to the first operator of the first cluster. A computer program product according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method. A system according to another embodiment includes a processor, and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.

Abstract: Technology for machine logic (that is, computer software, computer hardware) to perform a process that: (i) receives historical data relating to historical operations of a supply chain; (ii) applies a machine learning algorithm to detect that an antipattern exists inherent in the way the supply chain is being operated; (iii) calculates a bias influence score based on the existing antipattern; (iv) re-applies the machine learning algorithm to identify a discovered anomaly that exists in historical data relating to operation of the supply chain; and (v) sends out a communication identifying the discovered anomaly.

Abstract: A machine learning system for dynamically segmenting inventory includes a memory having instructions therein, and at least one processor configured to execute the instructions to, using a reinforcement learning agent (“RLA”): determine, based at least in part on first inventory data corresponding to at least a portion of a first prediction time window for the RLA, a second prediction time window for the RLA; segment a first supply of inventory, based at least in part on the second prediction time window and at least a portion of the first inventory data; determine, based at least in part on second inventory data corresponding to at least a portion of the second prediction time window, a third prediction time window for the RLA; and segment a second supply of inventory, based at least in part on the third prediction time window and at least a portion of the second inventory data.

Abstract: A machine learning system for dynamically segmenting inventory includes a memory having instructions therein, and at least one processor configured to execute the instructions to, using a reinforcement learning agent (“RLA”): determine, based at least in part on first inventory data corresponding to at least a portion of a first prediction time window for the RLA, a second prediction time window for the RLA; segment a first supply of inventory, based at least in part on the second prediction time window and at least a portion of the first inventory data; determine, based at least in part on second inventory data corresponding to at least a portion of the second prediction time window, a third prediction time window for the RLA; and segment a second supply of inventory, based at least in part on the third prediction time window and at least a portion of the second inventory data.