Abstract: The invention discloses a device for producing biogas with high methane content by utilizing livestock and poultry feces, wherein the interior of a tank body of a biogas fermentation tank is divided by a baffle, so as to form a main reaction chamber and an auxiliary reaction chamber which are communicated in upper portions, so that a reactant flows into the auxiliary reaction chamber only after entering the main reaction chamber via a relatively low feeding hole and then reaching a high position of a liquid level, and extension of fermentation time is realized, meanwhile, scales formed at the top of fermentation broth flow into the auxiliary reaction chamber along with liquid, so that the interior of the main reaction chamber keeps a liquid state all the time, and sealing and reduction of quantity of anaerobic bacteria are avoided.

Abstract: A multi-effect energy-saving apparatus for pollution control and utilization of petrochemical water, has a multi-effect treatment device, a post-treatment device, a first loading head, a second loading head, and a third loading head, by combined assembly of devices, the apparatus is simple to disassemble and easy to clean, meanwhile due to the controllable addition of treating agent by using a multi-effect regulator, a fluid outlet connecting member and other components, it is energy-saving and multi-effective, and has excellent treatment effect.

Abstract: A multi-effect energy-saving apparatus for pollution control and utilization of petrochemical water, has a multi-effect treatment device, a post-treatment device, a first loading head, a second loading head, and a third loading head, by combined assembly of devices, the apparatus is simple to disassemble and easy to clean, meanwhile due to the controllable addition of treating agent by using a multi-effect regulator, a fluid outlet connecting member and other components, it is energy-saving and multi-effective, and has excellent treatment effect.

Abstract: The invention discloses a device for producing biogas with high methane content by utilizing livestock and poultry feces, wherein the interior of a tank body of a biogas fermentation tank is divided by a baffle, so as to form a main reaction chamber and an auxiliary reaction chamber which are communicated in upper portions, so that a reactant flows into the auxiliary reaction chamber only after entering the main reaction chamber via a relatively low feeding hole and then reaching a high position of a liquid level, and extension of fermentation time is realized, meanwhile, scales formed at the top of fermentation broth flow into the auxiliary reaction chamber along with liquid, so that the interior of the main reaction chamber keeps a liquid state all the time, and sealing and reduction of quantity of anaerobic bacteria are avoided.

Abstract: An online system causes a graphical user interface to display at a client device. The graphical user interface includes a story field that displays ephemeral content items which are created within a threshold time period and are automatically removed after the time period. The online system uses a computer learned model to rank the selected ephemeral content items for display. The display of a set of ephemeral content items is associated with a session. The computer learned model is trained with sample sets that use an entire past session that includes a plurality of ephemeral content items. The computer model proposes a ranked order of the content items in the past session. Based on the past user actions performed on the past content items, a normalized discounted cumulative gain is determined for the past session. The computer model is trained to optimize the normalized discounted cumulative gain.

Abstract: An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.

Abstract: A social networking system presents users with a content items and ad requests, which may include targeting criteria specifying a topic. Interactions by users who were presented with an advertisement from an ad request including targeting criteria specifying the topic are stored by the social networking system and used to identify a cluster group of additional users having characteristics similar to characteristics of users who were presented with the advertisement from the ad request including targeting criteria specifying the topic and who interacted with the advertisement. The social networking system determines scores for additional users in the cluster group based on measures of similarity between the additional users and the users who were presented with the advertisement and who interacted with the advertisement. Based on the determined scores, the social networking system associates additional users in the cluster group with the topic.

Abstract: A social networking system presents users with a content items and ad requests, which may include targeting criteria specifying a topic. Interactions by users who were presented with an advertisement from an ad request including targeting criteria specifying the topic are stored by the social networking system and used to identify a cluster group of additional users having characteristics similar to characteristics of users who were presented with the advertisement from the ad request including targeting criteria specifying the topic and who interacted with the advertisement. The social networking system determines scores for additional users in the cluster group based on measures of similarity between the additional users and the users who were presented with the advertisement and who interacted with the advertisement. Based on the determined scores, the social networking system associates additional users in the cluster group with the topic.

Abstract: An online system presents various content items received from a publishing user to various users. The online system captures information identifying users to whom the content items were presented and identifies actions performed by the users presented with the content items after being presented with the content items. After presenting content items from the publishing user, the online system identifies characteristics of users to whom the content items were presented and determines performance metrics describing presentation of the content items to users having different characteristics or combinations of characteristics. The performance metrics are presented to the publishing user and grouped based on various characteristics of users to whom the content items were presented, allowing the publishing user to evaluate performance of presentation of content items to users having different characteristics.

Abstract: An online system suggests targeting criteria to advertisers creating new ads in the online system by generating a seed group of targeting criteria. The seed targeting criteria include targeting criteria already selected (if any), targeting criteria previously used, and targeting criteria extracted from the ad being created (e.g., from ad components) or a page being promoted by the ad. The seed targeting criteria are expanded via collaborative filtering on advertisers, collaborative filtering on targeted users, and determination of relationships within topic hierarchies. The online system selects a subset of the expanded targeting criteria by applying a machine learning model to each targeting criterion to determine a probability of the advertiser selecting the targeting criterion if it were suggested. The targeting criteria are ranked based on the determined probabilities and selected based on the ranking. The suggested targeting criteria may also be ordered in the user interface based on the ranking.

Abstract: An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.

Abstract: An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.

Abstract: A social networking system presents users with a content items and ad requests, which may include targeting criteria specifying a topic. Interactions by users who were presented with an advertisement from an ad request including targeting criteria specifying the topic are stored by the social networking system and used to identify a cluster group of additional users having characteristics similar to characteristics of users who were presented with the advertisement from the ad request including targeting criteria specifying the topic and who interacted with the advertisement. The social networking system determines scores for additional users in the cluster group based on measures of similarity between the additional users and the users who were presented with the advertisement and who interacted with the advertisement. Based on the determined scores, the social networking system associates additional users in the cluster group with the topic.

Abstract: An online system allows content items to be targeted based on interests associated with users. When the online system receives a request to specify targeting criteria associated with a content item, the online system provides an interface to specify targeting criteria. As the online system receives input specifying an interest for inclusion in targeting criteria, the online system retrieves stored interests associated with online system users. Each interest stored by the online system is associated with a type. For example, a type associated with a stored interest indicates whether the interest is from a set of user-generated keywords, from a set of semantic topics mapped from the keywords, or from a set of manually curated broad categories. To avoid confusion from overlap in the types of interests, the online system applies rules to stored interests matching at least a portion of the input to select a set of interests.