Disclosed is a method, performed by an electronic device, for assigning items into one or more containers. The method comprises obtaining a plurality of attributes associated with a corresponding item. The method comprises obtaining a set of container parameters associated with a corresponding container. The method comprises obtaining one or more constraints, wherein the one or more constraints limit assigning items in a same container. The method comprises determining an assignment of the items to the one or more containers, based on the attributes, the set of container parameters and the one or more constraints. The method comprises outputting, based on the assignment, an assignment plan of the items into the one or more containers.

Disclosed is a method, performed by an electronic device, for assigning items into one or more containers. The method comprises obtaining a plurality of attributes associated with a corresponding item. The method comprises obtaining a set of container parameters associated with a corresponding container. The method comprises obtaining one or more constraints, wherein the one or more constraints limit assigning items in a same container. The method comprises determining an assignment of the items to the one or more containers, based on the attributes, the set of container parameters and the one or more constraints. The method comprises outputting, based on the assignment, an assignment plan of the items into the one or more containers.

A method and system for managing electric vehicle (EV) distributed energy resource(s) (DER) are disclosed. A DER analytics engine may receive electricity consumption data of a plurality of sites from corresponding electricity meters of the plurality of sites, detect EV charging information based at least in part on the electricity consumption data, obtain EV telematics data of EVs associated with the EV charging information, reconcile the EV charging information and the EV telematics data, and generate, based on the reconciled EV charging information and the EV telematics data, models for at least one of continuous EV load prediction, electrical vehicle supply equipment (EVSE detection), and/or optimization for at least one of aggregated load, load per feeder, or maximum revenue for time-of-use tiers.

A device for acoustic monitoring of a monitoring area includes first and second sensor systems which have first and second acoustic sensors, processors, and transmitter, respectively, and which may be mounted at different locations of the monitoring area. The first and second processors may be configured to classify first and second audio signals detected by the first and second acoustic sensors so as to obtain first and second classification results, respectively. The first and second transmitter may be configured to transmit the first and second classification results to a central evaluator, respectively. In addition, the device may include the central evaluator, which may be configured to receive the first classification result and to receive the second classification result, and to generate a monitoring output for the monitoring area as a function of the first classification result and the second classification result.

Methods, systems, and computer programs are presented for scheduling user notifications to maximize short-term and long-term benefits from sending the notifications. One method includes an operation for identifying features of a state used for reinforcement learning. The state is associated with an action to decide if a notification to a user is to be sent and a reward for sending the notification to the user. Further, the method includes capturing user responses to notifications sent to users to obtain training data and training a machine-learning (ML) algorithm with reinforcement learning based on the features and the training data to obtain an ML model. Additionally, the method includes receiving a request to send a notification to the user, and deciding, by the ML model, whether to send the notification based on a current state. The notification is sent to the user based on the decision.

Methods, systems, and computer programs are presented for scheduling user notifications to maximize short-term and long-term benefits from sending the notifications. One method includes an operation for identifying features of a state used for reinforcement learning. The state is associated with an action to decide if a notification to a user is to be sent and a reward for sending the notification to the user. Further, the method includes capturing user responses to notifications sent to users to obtain training data and training a machine-learning (ML) algorithm with reinforcement learning based on the features and the training data to obtain an ML model. Additionally, the method includes receiving a request to send a notification to the user, and deciding, by the ML model, whether to send the notification based on a current state. The notification is sent to the user based on the decision.

Example embodiments provide systems and methods for simulating a disease outbreak using a relatively simple formula based on a limited number of input parameters. In particular, disease severity is computed based on a relationship between leaf wetness duration and average temperature during a wetness period. The resulting model is a physical, deterministic model that accepts hourly weather data as input and outputs the most significant severity event of disease infection during a specified (e.g., one-day) period. This information can then be used to guide the application of various treatments when they can be most effective (e.g., when predicted disease severity is at its worst).

The present disclosure generally relates to a computer implemented method for operating an autonomous vehicle, specifically in relation to efficient planning of interactions with service providers. The present disclosure also relates to a corresponding control system and computer program product.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.

The present disclosure describes transaction-enabling systems and methods. A system can include a facility including a core task including a customer relevant output and a controller. The controller may include a facility description circuit to interpret a plurality of historical facility parameter values and corresponding facility outcome values and a facility prediction circuit to operate an adaptive learning system, wherein the adaptive learning system is configured to train a facility production predictor in response to the historical facility parameter values and the corresponding outcome values. The facility description circuit also interprets a plurality of present state facility parameter values, wherein the trained facility production predictor determines a customer contact indicator in response to the plurality of present state facility parameter values and a customer notification circuit provides a notification to a customer in response.