Abstract: A computer-implemented method, a computer system and a computer program product boost efficiency through energy-aware workload placement. The method includes obtaining an energy profile for a plurality of computer servers and power consumption data for each computer server in the plurality of computer servers. The method also includes determining an optimal temperature for each computer server in the plurality of computer servers based on the energy profile. The method further includes determining a target processor utilization for each computer server in the plurality of computer servers based on the optimal temperature. In addition, the method includes calculating an efficiency rank for each computer server in the plurality of computer servers based on the target processor utilization and the power consumption data. Lastly, the method includes deploying a workload on a computer server with a highest efficiency rank.

Abstract: A hardware processor may be coupled to a communication network and receive charging requests and discharging requests from a plurality of prosumer facilities via the communication network. One or more energy storage systems may be coupled to an energy grid and able to charge from and discharge to the energy grid, and may communicate with the hardware processor via the communication network. Based on the charging requests and discharging requests, an energy schedule may be generated. The energy schedule may include a first set of the prosumer facilities from which charge requests are accepted, and a second set of prosumer facilities from which discharge requests are accepted. One or several energy storage systems may be controlled or triggered to charge or discharge repeatedly via the energy grid according to an updated energy schedule (e.g., regularly updated).

Abstract: A priority seating management method, system, and computer program product, includes obtaining user trip data including a current location and a trip destination for a trip of a user, retrieving potential route data from a transportation database for each transportation vehicle traveling a route between the current location and the trip destination, retrieving real-time priority seating occupancy information for each transportation vehicle traveling the route including a location of a priority seat and an occupancy type of the priority seat, and compiling the user trip data, the potential route data, and the real-time priority seating occupancy information to determine a status availability of the priority seat for the user.

Abstract: A mobile energy storage system and a method are provided for supplying power to entities at disparate locations. The system includes a set of vehicles. Each of the vehicles in the set includes a battery set having one or more batteries for storing power. Each of the vehicles in the set further includes a power connector having an end connected to the battery set and another end for connecting to a power transfer switch at the entities. The system also includes a central server for scheduling vehicles in the set to provide power to the entities responsive to energy charging requests issued by the entities.

Abstract: Color images of food a user consumes, text information associated with the food and audio information associated with the food may be received. Color images are converted into hyperspectral images. A machine learning model classifies the hyperspectral images into features comprising at least taste, nutrient content and chemical composition. A database of food consumption pattern associated with the user is created based on classification features associated with the hyperspectral images, the text information and the audio information. A color image of local food may be received and converted into a hyperspectral image. The machine learning model is run with the hyperspectral image as input, and outputs classification features associated with the local food. Based on whether the classification features associated with the local food satisfies the food consumption pattern associated with the user, the local food may be recommended.

Abstract: Various embodiments provide a systems and method to manage creation of pen-tests in conjunction with scoring on multiple vertices to establish a consistent measure of level of difficulty associated with the pen-test. The system may manage execution of the pen-test with a sample group selected from a test target to establish a baseline score that other users of the broader target can be expected to meet or can be graded against. According to various embodiments, tailored scoring (e.g., specific to an entity) yields a more accurate measure for evaluating vulnerability relative to conventional approaches. Users having high risk scores can be required to complete targeted training exercises and/or repeat pen-test evaluations until their scores improve. In some embodiments, user permissions, rights, and/or roles on their computer systems can be automatically adjusted to increase security. Any adjustments may be automatically reversed responsive to completing training and/or improving a risk score.

Abstract: A method of operating a battery pack includes identifying a plurality of batteries in the battery pack, estimating a state of charge of each of the plurality of the batteries, obtaining a plurality of parameters of each of the plurality of the batteries, and determining, selectively, a charge current or a discharge current for each of the plurality of batteries using the state of charge and the plurality of parameters of each of the plurality of the batteries.

Abstract: A cognitive IoT gateway may receive real-time data associated with a user from sensor devices that are detecting user data in real-time. The real-time data is analyzed to determine whether the real-time data indicates a deviation from a predicted user behavior data predicted based on historical data. The real-time data is transformed into collated data that removes protected and sensitive information from the real-time data, and the collated data is transmitted to a server computer, wherein the protected and sensitive information received from the sensor devices remain local to the cognitive IoT gateway. Updated information computed by the server computer based on the collated data is received and a real-time alert is sent to a user device notifying the user of the updated information.

Abstract: A system for verifying an energy generation source, includes a key forming device that forms a key in a DC voltage signal generated by the energy generation source, and a verifying device that verifies the energy generation source based on the key in the DC voltage signal.

Abstract: A computer-implemented method for determining routes includes receiving a user's location and health measures, for a plurality of users, analyzing the plurality of users and their corresponding locations to determine health behaviors at a given location and time, receiving a request from a user for a route, developing a route to the user's destination from a current location to obtain a target health behavior within a given threshold or constraint based on the plurality of user's healthy behaviors, and presenting the a route on a computing device.

Abstract: A system for verifying an energy generation source, includes a key forming device that forms a key in a DC voltage signal generated by the energy generation source, and a verifying device that verifies the energy generation source based on the key in the DC voltage signal.

Abstract: Providing an activity for a participant may include receiving at least location data specifying a location of the participant. An engagement level of the participant may be predicted based on the location data. Sensor data associated with the participant may be received, the sensor data comprising at least current physiological data associated with the participant. Based at least on the predicted engagement level and the sensor data, an exercise for the participant to perform may be determined. A notification signal may be transmitted to the participant to perform the exercise.

Abstract: A system for optimal aggregation of small-scale energy storage capacity includes a processor operatively coupled to memory. The processor is configured to implement the steps of: generating predicted energy consumption data based on a model of expected energy usage within a given electrical network; generating predicted energy generation data based on a model of expected energy generation for the given electrical network; generating dispatchable energy storage capacity for one or more energy storing devices having a small-scale energy storage capacity for the given electrical network; determining a set of energy storage devices that need to be dispatched for the given electrical network; comparing the predicted energy consumption data with the predicted energy generation data; and dispatching the set of energy storage devices for the given electrical network.

Abstract: A hardware processor may be coupled to a communication network and receive charging requests and discharging requests from a plurality of prosumer facilities via the communication network. One or more energy storage systems may be coupled to an energy grid and able to charge from and discharge to the energy grid, and may communicate with the hardware processor via the communication network. Based on the charging requests and discharging requests, an energy schedule may be generated. The energy schedule may include a first set of the prosumer facilities from which charge requests are accepted, and a second set of prosumer facilities from which discharge requests are accepted. One or several energy storage systems may be controlled or triggered to charge or discharge repeatedly via the energy grid according to an updated energy schedule (e.g., regularly updated).

Abstract: A hardware processor may be coupled to a communication network and receive charging requests and discharging requests from a plurality of prosumer facilities via the communication network. One or more energy storage systems may be coupled to an energy grid and able to charge from and discharge to the energy grid, and may communicate with the hardware processor via the communication network. Based on the charging requests and discharging requests, an energy schedule may be generated. The energy schedule may include a first set of the prosumer facilities from which charge requests are accepted, and a second set of prosumer facilities from which discharge requests are accepted. One or several energy storage systems may be controlled or triggered to charge or discharge repeatedly via the energy grid according to an updated energy schedule (e.g., regularly updated).

Abstract: Color images of food a user consumes, text information associated with the food and audio information associated with the food may be received. Color images are converted into hyperspectral images. A machine learning model classifies the hyperspectral images into features comprising at least taste, nutrient content and chemical composition. A database of food consumption pattern associated with the user is created based on classification features associated with the hyperspectral images, the text information and the audio information. A color image of local food may be received and converted into a hyperspectral image. The machine learning model is run with the hyperspectral image as input, and outputs classification features associated with the local food. Based on whether the classification features associated with the local food satisfies the food consumption pattern associated with the user, the local food may be recommended.

Abstract: Techniques for detailed monitoring and evaluation of individual subsystems within solar photovoltaic power generation systems are provided. In one aspect, a method for monitoring a photovoltaic system having at least one array of photovoltaic panels and at least one inverter system configured to convert output from the panels from DC to AC includes the steps of: obtaining sensor data from the photovoltaic system; computing an efficiency of the panels and an efficiency of the inverter system using the sensor data; computing an aging parameter for the panels using the efficiency of the panels; computing an aging parameter for the inverter system using the efficiency of the inverter system; determining whether the aging parameter for the panels or for the inverter system exceeds a predetermined threshold level; and taking action if either the aging parameter for the array or for the inverter system exceeds the predetermined threshold level.

Abstract: A hardware processor may be coupled to a communication network and receive charging requests and discharging requests from a plurality of prosumer facilities via the communication network. One or more energy storage systems may be coupled to an energy grid and able to charge from and discharge to the energy grid, and may communicate with the hardware processor via the communication network. Based on the charging requests and discharging requests, an energy schedule may be generated. The energy schedule may include a first set of the prosumer facilities from which charge requests are accepted, and a second set of prosumer facilities from which discharge requests are accepted. One or several energy storage systems may be controlled or triggered to charge or discharge repeatedly via the energy grid according to an updated energy schedule (e.g., regularly updated).

Abstract: A system for optimal aggregation of small-scale energy storage capacity includes a processor operatively coupled to memory. The processor is configured to implement the steps of: generating predicted energy consumption data based on a model of expected energy usage within a given electrical network; generating predicted energy generation data based on a model of expected energy generation for the given electrical network; generating dispatchable energy storage capacity for one or more energy storing devices having a small-scale energy storage capacity for the given electrical network; determining a set of energy storage devices that need to be dispatched for the given electrical network; comparing the predicted energy consumption data with the predicted energy generation data; and dispatching the set of energy storage devices for the given electrical network.

Abstract: A computer-implemented method for determining routes includes receiving a user's location and health measures, for a plurality of users, analyzing the plurality of users and their corresponding locations to determine health behaviors at a given location and time, receiving a request from a user for a route, developing a route to the user's destination from a current location to obtain a target health behavior within a given threshold or constraint based on the plurality of user's healthy behaviors, and presenting the a route on a computing device.