Abstract: A computer-implemented method for risk assessment based on usage of autonomous driving systems. The method can include obtaining one or more data sets collected based on a driver operating one or more vehicles having one or more autonomous driving systems. The method also can include determining usage patterns for the one or more autonomous driving systems based at least on the one or more data sets. The method further can include generating, using a machine-learning model, a risk metric based at least on the usage patterns. The method additionally can include outputting the risk metric. Other embodiments are described.

Abstract: A computer-implemented method including receiving a first set of sensor data associated with a user from one or more first IoT devices of the user related to a first domain. The method also can include receiving a second set of sensor data associated with the user from one or more second IoT devices of the user related to a second domain. The method additionally can include analyzing the first set of sensor data associated with the user from the one or more first IoT devices of the user related to the first domain and the second set of sensor data associated with the user from the one or more second IoT devices of the user related to the second domain to identify one or more risks of the user. The method further can include generating one or more risk scores associated with the user based on the one or more risks of the user.

Abstract: Polymeric charge transfer layer compositions suitable for organic layers of electronic devices that show reduced driving voltage and increased luminous efficiency.