Abstract: The present invention relates to the field of composite membrane and discloses a polyurethane/polyvinylidene fluoride composite membrane for extracting organic sulfide from naphtha. The polyurethane/polyvinylidene fluoride composite membrane includes an active layer and a support layer where the active layer is a polyurethane casting membrane and the support layer is a polyvinylidene fluoride membrane. The polyurethane/polyvinylidene fluoride composite membrane is prepared by coating the active layer onto the support layer. At the same time, a preparation method for the polyurethane/polyvinylidene fluoride composite membrane is disclosed. The present invention has the following beneficial effects: the polyurethane/polyvinylidene fluoride composite membrane prepared in the present invention may be used to extract organic sulfide in naphtha with high separation efficiency.

Abstract: The present invention relates to the field of composite membrane and discloses a polyurethane/polyvinylidene fluoride composite membrane for extracting organic sulfide from naphtha. The polyurethane/polyvinylidene fluoride composite membrane includes an active layer and a support layer where the active layer is a polyurethane casting membrane and the support layer is a polyvinylidene fluoride membrane. The polyurethane/polyvinylidene fluoride composite membrane is prepared by coating the active layer onto the support layer. At the same time, a preparation method for the polyurethane/polyvinylidene fluoride composite membrane is disclosed. The present invention has the following beneficial effects: the polyurethane/polyvinylidene fluoride composite membrane prepared in the present invention may be used to extract organic sulfide in naphtha with high separation efficiency.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an inferred subscription level based at least in part on the type associated with each notification; classifying the notifications based on the inferred subscription of each notification; and sending one or more of the notifications to the user. Each of the sent notifications has an inferred subscription level higher than a pre-determined threshold subscription level.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an interest of each activity to the user based at least in part on the type of each notification; ranking the notifications based at least in part on the calculated interest; and sending one or more of the notifications to the user. Each of the sent notifications has a ranking higher than a pre-determined threshold ranking.

Abstract: In one embodiment, a method includes sending, through a communications network, several volumes of notifications corresponding to a first notification type to multiple users and several volumes of notifications corresponding to a second notification type to multiple users. The method further determines visitation impacts of the volumes of notifications of the first and second notification types and trains a machine-learning model based on the visitation impacts. The machine-learning model generates an assessment of a likelihood of interaction by a recipient user with each of the notifications.

Abstract: In one embodiment, a method includes receiving a number of notifications of one or more activities relevant to a user. Each notification has an associated receipt time and type of notification. The method also includes aggregating one or more of the notifications based on the type of notification; determining a sending time to send the aggregated notifications based at least in part on determining that a pre-determined amount of time that has elapsed from a receipt time of a most recent one of the aggregated notifications; and sending the aggregated notifications to the user based on the sending time.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating a level of interest in content of each notification to the user based at least in part on the type of each notification; modifying the content of one or more of the notifications based at least in part on the calculated level of interest; and sending one or more of the notifications with modified content to the user.

Abstract: In one embodiment, a method includes one or more computing devices accessing a notification to be sent to a user, where the notification has a context. The method also includes one or more computing devices sending a request to a history service for historical notification data associated with the user with respect to the context of the notification and a ranking of the notification where the ranking indicates a probability of the user interacting with the notification. The method also includes one or more computing devices receiving the historical notification data and the ranking from the history service. Moreover, the method also includes one or more computing devices determining a notification policy to apply to the notification based at least in part on the context of the notification, the historical notification data, and the ranking. Furthermore, the method also includes one or more computing devices applying the notification policy to the notification to be sent to the user.

Abstract: In one embodiment, a method includes receiving an indication of an occurrence of a triggering event for one or more notifications relevant to one or more users. The notifications are sent to one or more of the users through one or more of a number of notifications channels, and each notification channel is associated with one or more software applications. The method also includes accessing a number of notification policies associated with the software applications. Each of the software applications has at least one associated notification policy. At least one of the notification policies associated with a first one of the software applications is interrelated with at least one other notification policy associated with a second one of the software applications. The method also includes sending one or more notifications to one or more users through one or more of the number of notification channels.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an estimated click-through rate (CTR) for each notification based at least in part on the type associated with each notification; determining a push threshold value for each notification based at least in part on the estimated CTR for each notification; and sending one or more of the notifications to the user. Each of the sent notifications has a push threshold value higher than a pre-determined push threshold value.

Abstract: In one embodiment, a method includes receiving an indication of an occurrence of a triggering event for one or more notifications relevant to one or more users. The notifications are sent to one or more of the users through one or more of a number of notifications channels, and each notification channel is associated with one or more software applications. The method also includes accessing a number of notification policies associated with the software applications. Each of the software applications has at least one associated notification policy. At least one of the notification policies associated with a first one of the software applications is interrelated with at least one other notification policy associated with a second one of the software applications. The method also includes sending one or more notifications to one or more users through one or more of the number of notification channels.

Abstract: In one embodiment, a method includes sending, through a communications network, several volumes of notifications corresponding to a first notification type to multiple users and several volumes of notifications corresponding to a second notification type to multiple users. The method further determines visitation impacts of the volumes of notifications of the first and second notification types and trains a machine-learning model based on the visitation impacts. The machine-learning model generates an assessment of a likelihood of interaction by a recipient user with each of the notifications.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating a level of interest in content of each notification to the user based at least in part on the type of each notification; modifying the content of one or more of the notifications based at least in part on the calculated level of interest; and sending one or more of the notifications with modified content to the user.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an interest of each activity to the user based at least in part on the type of each notification; ranking the notifications based at least in part on the calculated interest; and sending one or more of the notifications to the user. Each of the sent notifications has a ranking higher than a pre-determined threshold ranking.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an estimated click-through rate (CTR) for each notification based at least in part on the type associated with each notification; determining a push threshold value for each notification based at least in part on the estimated CTR for each notification; and sending one or more of the notifications to the user. Each of the sent notifications has a push threshold value higher than a pre-determined push threshold value.

Abstract: In one embodiment, a method includes receiving a number of notifications of one or more activities relevant to a user. Each notification has an associated receipt time and type of notification. The method also includes aggregating one or more of the notifications based on the type of notification; determining a sending time to send the aggregated notifications based at least in part on determining that a pre-determined amount of time that has elapsed from a receipt time of a most recent one of the aggregated notifications; and sending the aggregated notifications to the user based on the sending time.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an inferred subscription level based at least in part on the type associated with each notification; classifying the notifications based on the inferred subscription of each notification; and sending one or more of the notifications to the user. Each of the sent notifications has an inferred subscription level higher than a pre-determined threshold subscription level.

Abstract: In one embodiment, a method includes one or more computing devices accessing a notification to be sent to a user, where the notification has a context. The method also includes one or more computing devices sending a request to a history service for historical notification data associated with the user with respect to the context of the notification and a ranking of the notification where the ranking indicates a probability of the user interacting with the notification. The method also includes one or more computing devices receiving the historical notification data and the ranking from the history service. Moreover, the method also includes one or more computing devices determining a notification policy to apply to the notification based at least in part on the context of the notification, the historical notification data, and the ranking. Furthermore, the method also includes one or more computing devices applying the notification policy to the notification to be sent to the user.