Abstract: A processor retrieves data associated with a set of driving sessions and generates a training dataset by labeling a first subset of data that corresponds to driving sessions that included a first event and labeling a second subset of the data that corresponds to driving sessions that included an indication of an airbag activation. The processor then trains an artificial intelligence model using the training dataset, such that trained artificial intelligence model predicts a score indicative of a likelihood of a new driving session associated with a new driver being associated with at least the first event or an airbag activation. Once trained, the processor can augment the score using data retrieved after each driving session. The processor can also notify the driver if the driver's actions has caused their score to increase/decrease and provide an underlying reason.

Abstract: The present disclosure related to a vehicle prognostication with a management of data communications between individual vehicles and a network service. The network service can facilitate the management of data communications between a selected vehicle and remote service utilizing pseudonymous identifiers. Illustratively, a pseudonymous identifier can correspond to at least a semi-unique identifier generated by a vehicle or configure on a vehicle. The generation/configuration of the pseudonymous is implemented in a manner that the network service does not associate any additional identifiers, such as VINs, associated with the vehicle, vehicle owner, vehicle user, etc. A network service can receive the vehicle data with a pseudonymous and perform the prognostication of the vehicle. The network service can post vehicle prognostication with directives associated with the pseudonymous identifiers.

Abstract: The present disclosure related to a management of a plurality of charging stations utilizing collected usage data. A network service receives and maintains correlated charging station use data as time series data. The time series data corresponds to a defined time window in which individual time intervals may be characterized as use or non-use of the charging station. The non-use can be identified and compared to thresholds of non-use associated with charging stations that are characterized as operational. If an individual time interval of non-use exceeds a threshold, the individual unit may be considered operational but not available for service. More specifically, the network service determines whether any particular instance of sequential non-use corresponds to a statistically significant deviation of established periods of non-use. If such a deviation occurs, the consecutive period of non-use can be considered to be indicative of an unavailability of the charging station.

Abstract: A window or door system includes a frame adapted to be received within a window or door opening. A window or door unit is disposed on the frame, and a retainer is received by interference fit on either the frame or the window or door unit. The retainer has a portion that extends to engage the other of the frame and window or door unit, and to hold the window or door unit on the frame. The frame can be rectangular, for example, and the retainer may be linear extending along at least one edge of the frame. The frame can include an outwardly opening slot, and the retainer may be received by snap fit in the slot.

Abstract: A window or door system includes a frame adapted to be received within a window or door opening. A window or door unit is disposed on the frame, and a retainer is received by interference fit on either the frame or the window or door unit. The retainer has a portion that extends to engage the other of the frame and window or door unit, and to hold the window or door unit on the frame. The frame can be rectangular, for example, and the retainer may be linear extending along at least one edge of the frame. The frame can include an outwardly opening slot, and the retainer may be received by snap fit in the slot.