Abstract: A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.

Abstract: A VCN process may receive information associated with a set of value chain network entities. A VCN process may provide the information to a first set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the first set of AI-based learning models is trained to generate a prediction of future demand for an item. A VCN process may provide the information to a second set of AI-based learning models, wherein at least one member of the second set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity. A VCN process may determine a potential risk in the value chain network associated with the at least one value chain network entity based upon, at least in part, an output of the AI-based learning models.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may provide a computer code instruction set to a machine to execute the task to facilitate an improvement in the operation of the value chain network.

Abstract: A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of: a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a procurement action to be taken in the value chain network based upon, at least in part, an output of the set of AI-based learning models. A VCN process may execute the procurement action.

Abstract: A VCN process may receive, by a computing device, information associated with a set of value chain network entities of a value chain network, the information generated by at least one of a set of sensors of the set of value chain network entities, a set of IoT devices configured to collect data relating to the set of value chain network entities, or a set of APIs configured to publish data relating to the set of value chain network entities. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models. A VCN process may determine a potential risk in the value chain based upon, at least in part, an output of the AI-based learning classification. A VCN process may execute an action to mitigate the potential risk in the value chain network.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained on a training data set of a set of value chain network entities operating data to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of at least one value chain entity of the set of value chain network entities. A VCN process may determine a task to be completed for the value chain network based upon, at least in part, on an output of the set of AI-based learning models. A VCN process may execute the task to facilitate an improvement in the value chain network.

Abstract: A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may configure a robotic process automation system to execute the task to facilitate an improvement in the value chain network.

Abstract: Systems and methods for alerting rail workers of an oncoming rail vehicle are described. The system and method provide for a vehicle alert device on a rail vehicle. The vehicle alert device has two radios operating at different frequencies. Further provided is a personal alert device worn by a rail worker. The personal alert device has two radios operating at different frequencies. The frequencies of the radios in the vehicle alert device and of the radios in the personal alert device correspond to one another. One of the radios in the vehicle alert device and one of the radios in the personal alert device operate on the same frequency and perform ranging functions. The other radio in the vehicle alert device and the other radio in the personal alert device operate at the same frequency and provide for data communication between the rail worker and the rail vehicle.

Abstract: Systems and methods for alerting rail workers of an oncoming rail vehicle are described. The system and method provide for a vehicle alert device on a rail vehicle. The vehicle alert device has two radios operating at different frequencies. Further provided is a personal alert device worn by a rail worker. The personal alert device has two radios operating at different frequencies. The frequencies of the radios in the vehicle alert device and of the radios in the personal alert device correspond to one another. One of the radios in the vehicle alert device and one of the radios in the personal alert device operate on the same frequency and perform ranging functions. The other radio in the vehicle alert device and the other radio in the personal alert device operate at the same frequency and provide for data communication between the rail worker and the rail vehicle.

Abstract: A method and apparatus are provided for facilitating access from a control system to the memory of a processor across two buses, one of which acts as a bottleneck to communication between the control system and the processor. A bridge between the two buses acts as an intermediary. The control system issues simple diagnosis and data loading verification commands across a slow bus to the bridge. The bridge then performs the data intensive tasks by communicating with the processor through a faster bus. The bridge writes and reads data to the processor, and generates checksums of the written and read data. The bridge then returns status information to the control system indicative of the comparison of the checksums. In the case of memory diagnosis, the control system need only issue a simple command to the bridge through the slower, which then diagnoses the memory through the fast bus by writing and reading data, and returns a status to the control system through the slow bus.

Abstract: The backpressure mechanism and method described here do not completely shut off the traffic when a queue is experiencing congestion. Instead of completely shutting off the traffic and waiting for the effects, a series of backpressure pulses are sent to the upstream stage for intermittently slowing the traffic between the upstream and downstream stages. These pulses of backpressure effectively slowly down the rate of the ingress traffic to the queue to a rate less than the egress rate. This allows queue utilization to slowly decrease. These pulses continue as long as the queue utilization is above a threshold called “Starving Threshold”. This technique allows much lower queue utilization, thus requiring smaller queues sizes.

Abstract: A method and apparatus are provided for facilitating access from a control system to the memory of a processor across two buses, one of which acts as a bottleneck to communication between the control system and the processor. A bridge between the two buses acts as an intermediary. The control system issues simple diagnosis and data loading verification commands across a slow bus to the bridge. The bridge then performs the data intensive tasks by communicating with the processor through a faster bus. The bridge writes and reads data to the processor, and generates checksums of the written and read data. The bridge then returns status information to the control system indicative of the comparison of the checksums. In the case of memory diagnosis, the control system need only issue a simple command to the bridge through the slower, which then diagnoses the memory through the fast bus by writing and reading data, and returns a status to the control system through the slow bus.

Abstract: A method for synchronizing a data signal to a clock signal in a source-synchronous system, the source-synchronous system having first and second systems linked by an interface, the first system providing the clock signal to the second system, the second system providing the data signal and a return clock signal synchronous to the data signal to the first system, the method comprising: determining a first time delay between the clock signal and the return clock signal and delaying the data signal by the first time delay; after a predetermined period, determining a second time delay between the clock signal and the return clock signal; determining a difference between the first and second time delays; and, further delaying the data signal by at least a portion of the difference to thereby compensate for a temperature change of the source-synchronous system.

Abstract: A method for synchronizing a data signal to a clock signal in a source-synchronous system, the source-synchronous system having first and second systems linked by an interface, the first system providing the clock signal to the second system, the second system providing the data signal and a return clock signal synchronous to the data signal to the first system, the method comprising: determining a first time delay between the clock signal and the return clock signal and delaying the data signal by the first time delay; after a predetermined period, determining a second time delay between the clock signal and the return clock signal; determining a difference between the first and second time delays; and, further delaying the data signal by at least a portion of the difference to thereby compensate for a temperature change of the source-synchronous system.

Abstract: The backpressure mechanism and method described here do not completely shut off the traffic when a queue is experiencing congestion. Instead of completely shutting off the traffic and waiting for the effects, a series of backpressure pulses are sent to the upstream stage for intermittently slowing the traffic between the upstream and downstream stages. These pulses of backpressure effectively slowly down the rate of the ingress traffic to the queue to a rate less than the egress rate. This allows queue utilization to slowly decrease. These pulses continue as long as the queue utilization is above a threshold called “Starving Threshold”. This technique allows much lower queue utilization, thus requiring smaller queues sizes.