A second action is identified by the edged device. The second action is associated with a second networked device of the plurality of networked devices in the environment. The second networked device is unrelated to the first networked device. A relationship is detected between the first networked device and the second networked device by the edge device. A network device bubble is generated by the edge device. The network device bubble is based on the relationship that is detected.

An imaging device includes a plurality of electronic components, a phase change material, and a heat transfer structure. The plurality of electronic components is configured to collect data and have a predetermined temperature parameter. The plurality of electronic components is disposed within the phase change material. The phase change material has a first material phase and a second material phase. The phase change material has a first material phase and a second material phase. The phase change material is configured to absorb heat through changing from the first material phase to the second material phase. The heat transfer structure is disposed within the phase change material. The heat transfer structure is configured to conduct heat within the phase change material. The phase change material and the heat transfer structure are further configured to regulate a temperature of the electronic components below the predetermined temperature parameter.

A heating, ventilation, or air conditioning (HVAC) system for one or more building zones includes airside HVAC equipment operable to provide clean air to the one or more building zones and a controller. The controller is configured to obtain a dynamic temperature model and a dynamic infectious quanta model for the one or more building zones, determine an infection probability, and generate control decisions for the airside HVAC equipment using the dynamic temperature model, the dynamic infectious quanta model, and the infection probability.

A method of analyzing events for an electrical system includes: receiving event stream(s) of events occurring in the electrical system, the events being identified from captured energy-related signals in the system; analyzing, an event stream(s) of the events to identify different actionable triggers therefrom, the different triggers including a scenario in which a group of events satisfies one or more predetermined triggering conditions; analyzing, over time, the different actionable triggers to identify a combination of occurring and/or non-occurring actionable triggers which satisfies a predefined trigger combination condition and an analysis time constraint; and in response to the observation of the combination, taking one or more actions to address the events. The analysis time constraint can be a time period duration and/or sequence within which time-stamped data of events in the event stream(s) and the associated actionable triggers are considered or not considered in the analysis to identify the combination.

A method of analyzing events for an electrical system includes: receiving event stream(s) of events occurring in the electrical system, the events being identified from captured energy-related signals in the system; analyzing, an event stream(s) of the events to identify different actionable triggers therefrom, the different triggers including a scenario in which a group of events satisfies one or more predetermined triggering conditions; analyzing, over time, the different actionable triggers to identify a combination of occurring and/or non-occurring actionable triggers which satisfies a predefined trigger combination condition and an analysis time constraint; and in response to the observation of the combination, taking one or more actions to address the events. The analysis time constraint can be a time period duration and/or sequence within which time-stamped data of events in the event stream(s) and the associated actionable triggers are considered or not considered in the analysis to identify the combination.

A video encoder determines scaled transform coefficients, wherein determining the scaled transform coefficients comprises scaling transform coefficients of a block of the video data according to a given quantization step. The video encoder determines scalar quantized coefficients, wherein determining the scalar quantized coefficients comprises applying scalar quantization to the scaled transform coefficients of the block. Additionally, the video encoder applies a neural network that determines a respective set of probabilities for each respective transform coefficient of the block. The respective set of probabilities for the respective transform coefficient includes a respective probability value for each possible adjustment value in a plurality of possible adjustment values. Inputs to the neural network include the scaled transform coefficients and the scalar quantized coefficients. The video encoder determines, based on the set of probabilities for a particular transform coefficient of the block, a quantization level for the particular transform coefficient.

An optimization device includes: a state hold circuit that holds values of state variables included in an evaluation function that represents energy; an objective function calculation circuit that calculates an energy change value in an objective function included in the evaluation function for each of state transitions when a state transition occurs in response to a change in any of the values of the state variables; a constraint term calculation circuit that calculates a constraint term evaluation value, which is an evaluation value of a constraint term included in the evaluation function, for each of the state transitions; a temperature control circuit that controls a temperature value that indicates a temperature; and a transition control circuit that determines stochastically whether to accept any of the state transitions based on the temperature value, a random number value, and a sum of the change value and the constraint term evaluation value.

A target value progression for a process parameter functioning as a setting variable is established so that an actual value progression for a selected variable has a desired property, or the desired actual value progression itself ensues. The actual value progression occurring in relation to the first configuration is predetermined as a reference value progression for the selected variable, and a target value progression for the process parameter functioning as the setting variable is established by a computer so that the reference value progression as the actual value progression with the desired property or the desired actual value progression itself ensues when the shaping machine in the second configuration operates in a production cycle in accordance with the selected target value progression for the at least one process parameter functioning as the setting variable.

A target value progression for a process parameter functioning as a setting variable is established so that an actual value progression for a selected variable has a desired property, or the desired actual value progression itself ensues. The actual value progression occurring in relation to the first configuration is predetermined as a reference value progression for the selected variable, and a target value progression for the process parameter functioning as the setting variable is established by a computer so that the reference value progression as the actual value progression with the desired property or the desired actual value progression itself ensues when the shaping machine in the second configuration operates in a production cycle in accordance with the selected target value progression for the at least one process parameter functioning as the setting variable.

A method for managing air quality may include, at one or more processors, receiving sensor data comprising a plurality of air quality parameters for an environment, wherein the sensor data is generated by one or more environment quality monitoring devices located in the environment, predicting an adverse air quality event based on the sensor data, and automatically controlling one or more devices to mitigate the adverse air quality event. An environment quality monitoring device may include a housing, a plurality of sensors in the housing and configured to generate sensor data comprising a plurality of environment quality parameters, a network communication device configured to communicate the sensor data over a network, and an alert configured to indicate an environment quality score of the ambient environment, where the environment quality score is based on at least a portion of the sensor data.