Abstract: A vehicle corner system for a dual-axle wheels assembly may include: a sub-frame; a first wheel interface having a first spinning axis about which a first wheel spins when connected to the first wheel interface; a first suspension linkage connected to and being transverse to the first wheel interface and to the sub-frame; a second wheel interface having a second spinning axis about which a second wheel spins when connected to the second wheel interface; a second suspension linkage connected to and being transverse to the second wheel interface and to the sub-frame; and a motion restrainer comprising a spring interconnecting the first suspension linkage and the second suspension linkage.

Abstract: A system includes at least one electrode configured for transcutaneous stimulation of a pudendal nerve or surrounding perianal tissue or both. The at least one electrode is coupled to a substrate that includes a rectal portion and a buttocks support portion. The rectal portion includes a fecal blocking element. A control module is coupled to the at least one electrode, and includes a microprocessor for controlling operational parameters of the system.

Abstract: Methods and systems for managing loyalty programs using a decentralized blockchain are provided. Transactions between users and a merchant are monitored via an application programming interface (API) of a service provider. Based on the monitoring, it may be determined that at least one of the users has satisfied reward criteria for a loyalty program associated with the merchant. The loyalty program includes a plurality of tokens to be distributed to users who satisfy the reward criteria configured by the merchant via a merchant interface. At least one token from among the plurality of tokens may be selected for the user, based on at least one smart contract associated with the merchant. The smart contract is stored on the blockchain in association with a unique identifier for the merchant. A transaction is broadcasted to the decentralized blockchain for transferring the at least one token to a digital wallet of the user.

Abstract: Methods and systems for managing loyalty programs using a decentralized blockchain are provided. Transactions between users and a merchant are monitored via an application programming interface (API) of a service provider. Based on the monitoring, it may be determined that at least one of the users has satisfied reward criteria for a loyalty program associated with the merchant. The loyalty program includes a plurality of tokens to be distributed to users who satisfy the reward criteria configured by the merchant via a merchant interface. At least one token from among the plurality of tokens may be selected for the user, based on at least one smart contract associated with the merchant. The smart contract is stored on the blockchain in association with a unique identifier for the merchant. A transaction is broadcasted to the decentralized blockchain for transferring the at least one token to a digital wallet of the user.

Abstract: Root cause analysis in a communication network includes eliminating candidate events using a computer-implemented method, comprising creating a virtual network model that describes nodes and links of the network at a plurality of time periods; receiving from the network a first event that indicates a problem in the network; receiving a set of second events comprising candidates for a root cause of the first event; determining a network topological indicator from the first event; determining a network implication of the first event based on the topological indicator; based on the network implication and a time value of the first event, retrieving data from the virtual network model that indicates a path between nodes associated with the first event at a time at which the first event occurred; removing, from the set of second events, all events that are not on the path between devices associated with the first event.

Abstract: Root cause analysis in a communication network may be provided in a network management system in accordance with an embodiment of the present invention. The system receives a set of events the network. One such received event may indicate a network problem. The system, through time windowing and/or filtering, identifies a sub-set of events, in the first set, as candidate events for a root cause of the problem. The system generates a score value for each candidate event in the sub-set, indicating how likely each such candidate event is the root cause of the problem. The score value is based in part on a hop distance between an entity that generated the event indicating the problem and another entity that generated the candidate event. The system then selects one of the candidate events, which has a minimum score value, as the root cause event for the problem reported.

Abstract: Root cause analysis in a communication network may be provided in a network management system in accordance with an embodiment of the present invention. The system receives a set of events the network. One such received event may indicate a network problem. The system, through time windowing and/or filtering, identifies a sub-set of events, in the first set, as candidate events for a root cause of the problem. The system generates a score value for each candidate event in the sub-set, indicating how likely each such candidate event is the root cause of the problem. The score value is based in part on a hop distance between an entity that generated the event indicating the problem and another entity that generated the candidate event. The system then selects one of the candidate events, which has a minimum score value, as the root cause event for the problem reported.

Abstract: Root cause analysis in a communication network includes eliminating candidate events using a computer-implemented method, comprising creating a virtual network model that describes nodes and links of the network at a plurality of time periods; receiving from the network a first event that indicates a problem in the network; receiving a set of second events comprising candidates for a root cause of the first event; determining a network topological indicator from the first event; determining a network implication of the first event based on the topological indicator; based on the network implication and a time value of the first event, retrieving data from the virtual network model that indicates a path between nodes associated with the first event at a time at which the first event occurred; removing, from the set of second events, all events that are not on the path between devices associated with the first event.