Abstract: Aspects of the subject disclosure may include, for example, obtaining roaming agreement data related to roaming agreements that are between a wireless provider and a respective one of a plurality of wireless roaming providers; obtaining, for each wireless subscriber of the wireless provider, respective roaming usage data, all of the respective roaming usage data comprising collective roaming usage data; training, based upon the collective roaming usage data, a set of one or more models, the one or more models comprising one or more statistical models, one or more machine learning models, or any combination thereof, the one or more models being trained with multiple iterations of feedback loops, and the training resulting in one or more trained models; estimating for each wireless subscriber, based upon the one or more trained models, respective projected location information for a future time, all of the respective projected location information comprising collective projected location information; obtaining,

Abstract: The invention discloses a terminal and an electronic device having the same. The terminal includes a connecting part, a mounting part, and a fixing part along an insertion direction, wherein the connecting part is adapted to be connected to a jump wire of an electronic component, the mounting part has an outer profile matching with at least a portion of an inner profile of an insertion hole in a base, for restricting displacement of the terminal after being inserted into the insertion hole, and the fixing part is adapted to be threadedly connected with the a fixing member so as to connect the fixing part to a circuit board.

Abstract: In another example, a device includes a processor and a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations. The operations include acquiring a plurality of data items from a plurality of data sources, wherein the at least two data sources data sources of the plurality of data sources are maintained by different entities, normalizing attributes of the plurality of data items, using a first machine learning technique, matching at least two data items of the plurality of data items to form a grouping, wherein the matching is based on similarities observed in the attributes of the at least two data items subsequent to the normalizing, and creating a single profile for an individual associated with the at least two data items, based on the grouping, wherein the single profile consolidates the attributes of the at least two data items.