Abstract: Methods for forming a Ge-containing film on a substrate comprise the steps of introducing a vapor of a cyclic Ge(II) silylamido precursor into a reactor having the substrate disposed therein and depositing at least part of the cyclic Ge(II) silylamido precursor onto the substrate to form the Ge-containing film using a vapor deposition method. The cyclic Ge(II) silylamido precursor is [SiMe3-(N—)—SiMe2-(N—)—SiMe3]Ge(II) or [tBu-(N—)—SiMe2-(N—)-tBu]Ge(II).

Abstract: An online system predicts the user's activity on the online system during a future time interval (e.g., the next day). The system collects activity data, such as actions that the user has taken on the system. The system predicts whether the user is likely to be active during the future time interval based on features extracted from the activity data. This system determines selection of notifications and delivery of notifications based on the predicted time when the user is likely to be active on the online system. The system further records the user's past interactions with notifications, such as whether the user viewed the notification, whether the user interacted with a content item associated with the notification, and so on. If system determines a rate of delivery of notifications to the user based on the frequency of past user interactions with notifications.

Abstract: An online system predicts the user's activity on the online system during a future time interval (e.g., the next day). The system collects activity data, such as actions that the user has taken on the system. The system predicts whether the user is likely to be active during the future time interval based on features extracted from the activity data. This system determines selection of notifications and delivery of notifications based on the predicted time when the user is likely to be active on the online system. The system further records the user's past interactions with notifications, such as whether the user viewed the notification, whether the user interacted with a content item associated with the notification, and so on. If system determines a rate of delivery of notifications to the user based on the frequency of past user interactions with notifications.

Abstract: An online system predicts the user's activity on the online system during a future time interval (e.g., the next day). The system collects activity data, such as actions that the user has taken on the system. The system predicts whether the user is likely to be active during the future time interval based on features extracted from the activity data. This system determines selection of notifications and delivery of notifications based on the predicted time when the user is likely to be active on the online system. The system further records the user's past interactions with notifications, such as whether the user viewed the notification, whether the user interacted with a content item associated with the notification, and so on. If system determines a rate of delivery of notifications to the user based on the frequency of past user interactions with notifications.

Abstract: An online system predicts the user's activity on the online system during a future time interval (e.g., the next day). The system collects activity data, such as actions that the user has taken on the system. The system predicts whether the user is likely to be active during the future time interval based on features extracted from the activity data. This system determines selection of notifications and delivery of notifications based on the predicted time when the user is likely to be active on the online system. The system further records the user's past interactions with notifications, such as whether the user viewed the notification, whether the user interacted with a content item associated with the notification, and so on. If system determines a rate of delivery of notifications to the user based on the frequency of past user interactions with notifications.

Abstract: An online system predicts the user's activity on the online system during a future time interval (e.g., the next day). The system collects activity data, such as actions that the user has taken on the system. The system predicts whether the user is likely to be active during the future time interval based on features extracted from the activity data. This system determines selection of notifications and delivery of notifications based on the predicted time when the user is likely to be active on the online system. The system further records the user's past interactions with notifications, such as whether the user viewed the notification, whether the user interacted with a content item associated with the notification, and so on. If system determines a rate of delivery of notifications to the user based on the frequency of past user interactions with notifications.

Abstract: Aerosolized food products can be of sufficient size to deposit in the mouth without easily entering into the respiratory tract and of small enough size so as to allow for suspension in air. An apparatus (110) incorporating an aerosol generating device and food products can allow for the aerosolization of the food products and the delivery thereof in a matter suitable for inhalation or deposition and subsequent ingestion. The food delivery apparatus (110) represents a novel means of delivering food to the mouths of humans and animals. Indeed, the apparatus (110) of the invention is designed to produce, transport, and direct aerosolized food particles of sufficient size to deposit in the mouth without substantial exposure or entry into the respiratory tract and of small enough size so as to allow for suspension in air.