Abstract: A deep learning model implements continuous, lifelong machine learning (LML) based on a Bayesian neural network using an inventive framework including wide, deep, and prior components that employ diverse algorithms to leverage available real-world healthcare data differently to improve prediction performance. The outputs from each component of the framework are fed into a wide and shallow neural network and the posterior structure of the final model output may be utilized as a prior structure when the deep learning model is refreshed with new data in a deep learning process. Lifelong learning is implemented by dynamically integrating present learning from the wide and deep learning components with past learning from traditional tree models in the prior component into future predictions. Thus, the present Bayesian deep neural network-based LML model increases accuracy in identifying patient profiles by continuously learning, as new data become available, without forgetting prior knowledge.

Abstract: At switch-on or when recovering from lack of coverage, a UE determines first frequency bands corresponding to an initial MCC of a country in which the UE currently is located (402). The frequency bands include a first set of frequency bands associated with a network carrier to which the UE is subscribed and a second set of frequency bands not associated with the network carrier (404). The UE scans the first set of frequency bands (406). In response to detecting a new MCC and determining that no service is available within the first set of frequency bands (408), the UE scans a third set of frequency bands preferred by the UE within a second plurality of frequency bands corresponding to the new MCC (410). In response to determining that no service is available within the third set of frequency bands, the UE scans the second set of frequency bands (412).

Abstract: At switch-on or when recovering from lack of coverage, a UE determines first frequency bands corresponding to an initial MCC of a country in which the UE currently is located (402). The frequency bands include a first set of frequency bands associated with a network carrier to which the UE is subscribed and a second set of frequency bands not associated with the network carrier (404). The UE scans the first set of frequency bands (406). In response to detecting a new MCC and determining that no service is available within the first set of frequency bands (408), the UE scans a third set of frequency bands preferred by the UE within a second plurality of frequency bands corresponding to the new MCC (410). In response to determining that no service is available within the third set of frequency bands, the UE scans the second set of frequency bands (412).

Abstract: The present invention is a unique sequential process for treating carbon and alloy steels to improve resistance to tribological stresses while reducing conventional processing time required to obtain articles having a similar hardness. This process comprises carburizing or carbonitriding the article, tempering the carburized or carbonitrided article, net-shaping (machining) the tempered article, and plasma (ion) nitriding the tempered article for a significantly reduced period of time versus conventional plasma (ion) nitriding.