Abstract: A device, system and method for managing talkgroups is provided. A computing device determines that a communication device is communicating with a talkgroup via a first communication network, the talkgroup dominated by other communication devices communicating on one or more second communication networks different from the first communication network. The computing device determines a level of activity of the communication device in the talkgroup via the first communication network. When the level of activity meets a threshold condition, the computing device: requests a confirmation from the communication device to cease communication with the talkgroup; and when the confirmation is received, drops the communication device from the talkgroup.

Abstract: Device configurations for a machine-learned model. A device analytics system includes an electronic processor configured to determine, for a machine-learned model configured to detect a plurality of features, a prioritization ranking for a plurality of input parameters provided to the machine-learned model and receive, for each device, a confidence value for each feature included in the plurality of features. The electronic processor is configured to determine, for each device included in the plurality of devices, a performance value for each feature included in the plurality of features based on the prioritization ranking for the plurality of input parameters and the confidence value for each feature for the respective device. The electronic processor is configured to select, based on the performance value for each device included in the plurality of devices for a feature, a device configuration for the machine-learned model, and implement the selected device configuration.

Abstract: Device configurations for a machine-learned model. A device analytics system includes an electronic processor configured to determine, for a machine-learned model configured to detect a plurality of features, a prioritization ranking for a plurality of input parameters provided to the machine-learned model and receive, for each device, a confidence value for each feature included in the plurality of features. The electronic processor is configured to determine, for each device included in the plurality of devices, a performance value for each feature included in the plurality of features based on the prioritization ranking for the plurality of input parameters and the confidence value for each feature for the respective device. The electronic processor is configured to select, based on the performance value for each device included in the plurality of devices for a feature, a device configuration for the machine-learned model, and implement the selected device configuration.

Abstract: A pneumatic tire for use on trucks, the tire comprising: a tread, the tread having a non-skid depth (NSK), the tread including a first and second shoulder rib and two or more inner ribs, and circumferential grooves interposed between the ribs. The tread pattern is asymmetric, wherein the first shoulder rib has a rib width ratio in the range of about 1.2 to about 1.8, and the second shoulder rib has a rib width ratio in the range of about 1.0-1.6.

Abstract: A pneumatic tire for use on trucks, the tire comprising: a tread, the tread having a non-skid depth (NSK), the tread including a first and second shoulder rib and two or more inner ribs, and circumferential grooves interposed between the ribs. The tread pattern is asymmetric, wherein the first shoulder rib has a rib width ratio in the range of about 1.2 to about 1.8, and the second shoulder rib has a rib width ratio in the range of about 1.0-1.6.

Abstract: Various embodiments of the invention provide devices, methods and systems, including without limitation data acquisition systems, that can provide flexible sensing and/or data acquisition solutions. An exemplary sensing device, which may be in communication with one or more computers, such as a server, etc., can include one or more sensor(s), a processor and/or a data store. The sensing device can, perhaps in response to instructions received from the computer, filter and/or otherwise process data acquired by the sensor before transmitting the desired data to the computer. The sensing device may store some or all of the acquired data locally and/or may transmit, replicate, etc. some or all of the stored data to the computer.

Abstract: Various embodiments of the invention provide devices, methods and systems, including without limitation data acquisition systems, that can provide flexible sensing and/or data acquisition solutions. An exemplary sensing device, which may be in communication with one or more computers, such as a server, etc., can include one or more sensor(s), a processor and/or a data store. The sensing device can, perhaps in response to instructions received from the computer, filter and/or otherwise process data acquired by the sensor before transmitting the desired data to the computer. The sensing device may store some or all of the acquired data locally and/or may transmit, replicate, etc. some or all of the stored data to the computer.

Abstract: Various embodiments of the invention provide devices, methods and systems, including without limitation data acquisition systems, that can provide flexible sensing and/or data acquisition solutions. An exemplary sensing device, which may be in communication with one or more computers, such as a server, etc., can include one or more sensor(s), a processor and/or a data store. The sensing device can, perhaps in response to instructions received from the computer, filter and/or otherwise process data acquired by the sensor before transmitting the desired data to the computer. The sensing device may store some or all of the acquired data locally and/or may transmit, replicate, etc. some or all of the stored data to the computer.