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: A virtual storage appliance having multiple storage processors is installed in a virtualized execution environment. Each one of the multiple virtual storage processors is a virtual machine that executes in the virtualized execution environment. At the time the virtual storage appliance is installed, properties of virtual resources provided by the virtualized execution environment to the virtual storage processors are obtained. The virtual resources provided by the virtualized execution environment to the virtual storage processors are consumed by the virtual storage processors while processing Input/Output (I/O) requests that are received by the virtual storage processors from at least one consumer application. The virtual storage processors are automatically configured in response to the properties of the virtual resources provided by the virtualized execution environment that are obtained at the time the virtual storage appliance is installed.

Abstract: A virtual storage appliance having multiple storage processors is installed in a virtualized execution environment. Each one of the multiple virtual storage processors is a virtual machine that executes in the virtualized execution environment. At the time the virtual storage appliance is installed, properties of virtual resources provided by the virtualized execution environment to the virtual storage processors are obtained. The virtual resources provided by the virtualized execution environment to the virtual storage processors are consumed by the virtual storage processors while processing Input/Output (I/O) requests that are received by the virtual storage processors from at least one consumer application. The virtual storage processors are automatically configured in response to the properties of the virtual resources provided by the virtualized execution environment that are obtained at the time the virtual storage appliance is installed.

Abstract: A computer executes a predictive information discovery engine in an operating environment that includes execution of an application with which a user interacts to accomplish a task. Trigger signals are received from trigger components that monitor the user's interaction with the application and respond to user actions to generate the trigger signals. The trigger signals include signal-specific metadata obtained from a store of metadata including current context information about the user's use of the application. The trigger signals are automatically responded by (i) querying external data sources for information relevant to a current operating context as reflected in the signal-specific metadata, and (ii) for information returned in response to the querying, presenting the returned information to the user in the current operating context to enable the user to use the presented information in connection with the task.

Abstract: Techniques are directed to a method performed by a computing device. The method includes (a) receiving, from a client via a network connection of the computing device, a first management request to manage a data storage system, the first management request being in a RESTful style, (b) generating, by the computing device, a second management request formatted in a non-RESTful style compliant with a back-end storage management protocol, (c) sending the second management request to a back-end storage management server, (d) receiving a first management response from the back-end storage management server in response to the second management request, the first management response being formatted in the non-RESTful style compliant with the back-end storage management protocol, (e) converting, by the computing device, the first management response into a second management response in the RESTful style, and (f) sending the second management response to the client via the network connection.

Abstract: A cloud black box (CBB) subsystem in a cloud computing infrastructure includes CBB storage and computer processing circuitry executing a CBB application having first and second operating modes. In a depository mode information messages are continually received from hardware computing devices during normal operation and device information from the messages is stored into the CBB storage. The information messages are generated by CBB agents executing on the hardware computing devices, which continually collect the device information and generate the information messages according to a common information transfer protocol. In a retrieval mode, device information in the CBB storage is provided to a requestor such as a data analysis application, which may be part of or external to the CBB subsystem. The CBB subsystem operates independently and remains available upon failure of hardware or software components in the cloud infrastructure, providing a centralized source of information for diagnosis or other analysis.

Abstract: Automated calibration of an automatic transmission design generates upshift scheduling curves in a throttle level/transmission output speed plane based upon laboratory-generated data and user generated drivability data. Downshift scheduling curves and torque converter lock-up and unlock curves are then generated as offsets from breakpoints on corresponding upshift curves, the offsets determined principally from the drivability data.

Abstract: Automated calibration of an automatic transmission design generates upshift scheduling curves in a throttle level/transmission output speed plane based upon laboratory-generated data and user generated drivability data. Downshift scheduling curves and torque converter lock-up and unlock curves are then generated as offsets from breakpoints on corresponding upshift curves, the offsets determined principally from the drivability data.

Abstract: A method for controlling slip in an EMCC equipped torque converter is provided. The method determines whether an EMCC slip rate exceeds a predetermined RPM, determines whether a crankshaft RPM acceleration exceeds a predetermined rate, reduces an output torque of an engine in response to one of said EMCC slip rate exceeding said predetermined RPM and said crankshaft RPM acceleration exceeding said predetermined rate; and disables said output torque of an engine in response to said EMCC slip rate being less than said predetermined RPM and said crankshaft RPM acceleration being less than said predetermined rate.

Abstract: The present invention provides a method for determining fill volumes of a plurality of clutch elements in an automatic transmission wherein each clutch element is applied in a predefined order and the fill volume of each clutch element is obtained by determining when the rotational speed of the engine varies from the rotational speed of the transmission's turbine by a predefined amount. The plurality of clutch elements comprises at least a 2C, 4C, UD, OD, and LR clutch element.

Abstract: A method and apparatus are provided for controlling the hydraulic line pressure in an automatic transmission for a vehicle in response to the measured pressure and torque transmitted through the transmission such that a minimum hydraulic line pressure is provided to the frictional elements to achieve a non-slip condition through the transmission which increases transmission efficiency and fuel economy. Hydraulic line pressure is controlled by activating a solenoid activated valve. Control signals are based on measured hydraulic line pressure, engine speed, turbine speed, vehicle speed, driver selected gear, and torque converter operating condition for the solenoid actuated valve. The present invention further provides a closed-loop adaptive control technology to learn the minimum line pressure to compensate for variability in the clutch return springs, friction characteristics, surface finishes, as well as hydraulic wear, and changes over the life of the transmission.

Abstract: A transmission assembly and method for controlling a torque converter of a transmission assembly in which an electronic controller monitors the load on the vehicle power train in addition to a number of other vehicle dynamics, including the currently engaged gear ratio and the temperature of the transmission fluid. The electronic controller is operable for causing the activation of the converter clutch in response to a number of predetermined operating conditions, one of which includes the operation of the vehicle when the fluid in the torque converter exceeds a predetermined temperature and the vehicle power train is operated under a heavy load for a period of time which exceeds a predetermined time interval. Engagement of the converter clutch during such times inhibits relative rotation between the torque converter turbine and impeller, improving fuel economy and preventing the torque converter from generating a substantial amount of heat.

Abstract: A torque management apparatus for an automatic transmission of an internal combustion engine. The apparatus includes a sensor for sensing shifting of the transmission to a Neutral mode. An engine limiting means is operatively connected to a sensor for limiting the speed of the engine in response to the sensor, which senses the shifting of the transmission into or from the Neutral mode. Also, an engine delimiting means is operatively connected to the sensor and to the engine limiting means for returning the engine to a normal throttle speed in response to shifting of the transmission out of the Neutral mode. The engine limiting means and the engine delimiting means minimize stress on the transmission due to the engagement and disengagement of the transmission components caused by the shifting of the transmission in and out of a Neutral mode.