Abstract: A liquid injection device and a liquid injection method are provided. The liquid injection device injects a liquid into a liquid storage assembly. The liquid storage assembly includes a liquid storage tube and a liquid storage element in the liquid storage tube. The liquid injection device includes a receiving portion with a receiving groove. The receiving groove receives an atomizable base liquid. The liquid injection device further includes communication holes communicating with the receiving groove. The receiving portion is mounted at an end of the liquid storage tube, thereby causing the atomizable base liquid in the receiving portion to be injected into the liquid storage element through the communication holes.

Abstract: An atomizer assembly, an atomizer, and an inhaler are provided. The atomizer assembly includes a core shell and a liquid guiding member. The core shell defines a receiving cavity and also a first groove communicated with the receiving cavity. The liquid guiding member comprises a first liquid guiding portion and a second liquid guiding portion. The first liquid guiding portion is disposed in the receiving cavity. The second liquid guiding portion is integrally connected to the first liquid guiding portion, extends out of the receiving cavity through the first groove, and is used to connect a liquid storage member.

Abstract: An atomizer, producing an inhalable vapor, includes a liquid storage chamber, an air duct, and an atomizing core in the liquid storage chamber. The atomizing core includes a core shell and a liquid guiding cotton. The core shell includes a base and a housing. A sealing member is disposed between the air duct and the core shell, the sealing member includes a sealing body and a first connecting pipe extending from the sealing body facing the air duct. An elastic part is disposed on an outer wall of the first connecting pipe, the air duct abuts the sealing body, a bottom of the sealing body abuts the liquid guiding cotton, the first connecting pipe is located in the air duct, and the elastic part presses tightly against an inner wall of the air duct to provide sealing of liquid in all circumstances. An inhalation device is also disclosed.

Abstract: A sealing member for sealing an atomizer against undesired leakage of liquid includes a base body and a resisting portion. The base body includes a first surface, a second surface opposite to the first surface, and an outer wall connecting the first surface with the second surface. A vent passes through the first and second surfaces. The resisting portion is disposed on the outer wall and is configured to be compressed against an inner wall of a liquid storage pipe to provide sealing and avoid leakage. An atomizer and an inhalation device are also disclosed.

Abstract: An atomizer control circuit and an electronic cigarette are disclosed. The atomizer control circuit includes a first atomization module, a second atomization module, a detection module, a main control module, and a touch module. The first atomization module is configured to control a first atomizer The second atomization module is configured to control a second atomizer The detection module is configured to turn on the first atomizer or the second atomizer according to an operation action of a user and output a turn-on signal. The main control module is configured to output a detection signal according to the turn-on signal. The detection module is further configured to detect the first atomizer or the second atomizer according to the detection signal. The touch module is configured to generate a trigger signal.

Abstract: Disclosed is an electronic atomizer, which includes a housing internally provided with an E-liquid storage cavity and an atomization gas channel, the E-liquid storage cavity is communicated with the atomization gas channel, and the housing is provided with a gas inlet and a smoking port; an atomizing assembly arranged in the housing and communicated with the atomization gas channel, the atomizing assembly is used for atomizing E-liquid in the E-liquid storage cavity; a power supply assembly arranged in the housing, the power supply assembly is electrically connected with the atomizing assembly; and a first E-liquid adsorbing member arranged between the power supply assembly and an inner wall of the housing, the first E-liquid adsorbing member is provided with an air channel; and the gas inlet, the air channel, the atomization gas channel and the smoking port are sequentially communicated to form at least a part of smoking flow channel.

Abstract: An atomization assembly and an electronic atomizer are provided. The atomization assembly includes: a first housing, a base, a separator, an e-liquid storage cotton and an atomization core; the base is connected to the first housing to form an accommodating cavity inside the first housing; the separator is configured to separate the accommodating cavity into a first e-liquid storage cavity and a second e-liquid storage cavity, where the first e-liquid storage cavity is configured to store e-liquid; the separator has at least one e-liquid passage hole which is communicated with the first e-liquid storage cavity and the second e-liquid storage cavity; the e-liquid storage cotton is arranged in the second e-liquid storage cavity; and the e-liquid storage cotton is in contact with a periphery of the atomization core.

Abstract: An electronic atomizer is disclosed which includes: a housing with a transparent side wall, the housing is internally provided with an accommodating space; an atomizing assembly arranged in the accommodating space, the atomizing assembly divides the accommodating space into an upper cavity and a lower cavity, a top wall of the upper cavity is provided with a mounting base, the mounting base is provided with a through gas outlet hole, a bottom portion of the lower cavity is provided with an opening, the atomizing assembly is provided with a gas flow channel, the gas flow channel communicates the lower cavity with the gas outlet hole, a side wall of the atomizing assembly is provided with an E-liquid inlet hole, and the E-liquid inlet hole communicates the upper cavity with the gas flow channel; a light-emitting element arranged in the lower cavity; and a light-diffusing sleeve arranged in the lower cavity.

Abstract: An E-liquid storage assembly, an atomizer and an electronic cigarette are disclosed. The E-liquid storage assembly includes an E-liquid storage bottle and an E-liquid sealing assembly. The E-liquid sealing assembly includes an E-liquid sealing sleeve, an E-liquid sealing pipe and a cartridge support, the E-liquid sealing sleeve is provided with a first E-liquid penetrating hole, the E-liquid sealing pipe is provided with a second E-liquid penetrating hole communicated with an accommodating cavity, and the E-liquid sealing sleeve seals the second E-liquid penetrating hole and is capable of rotating relative to the E-liquid sealing pipe to make the first E-liquid penetrating hole communicate with the second E-liquid penetrating hole; and the E-liquid storage bottle is provided with an E-liquid storage space for storing E-liquid. The atomizer includes the E-liquid storage assembly and an atomizing core assembly which are detachably connected.

Abstract: An e-liquid storage bin assembly, an atomizer and an electronic cigarette are disclosed. The e-liquid storage bin assembly includes an e-liquid storage bottle and an e-liquid separation bin assembly. The e-liquid separation bin assembly includes an e-liquid separation bin and an e-liquid separation bin sealing ring. The e-liquid separation bin is provided with a first e-liquid penetration hole communicated with the first accommodating cavity, and the e-liquid separation bin sealing ring seals the first e-liquid penetration hole. The e-liquid separation bin sealing ring is movable to open the first e-liquid penetration hole. The e-liquid storage bottle is provided with an e-liquid storage space for storing e-liquid.

Abstract: A method includes generating, as executed by a processor on a computer, a plurality of topic-specific user knowledge models for each user of a plurality of users, each topic-specific user knowledge model representing a level of knowledge possessed by a respective user on a single topic from a set of globally defined topics shared among the plurality of users, generating a plurality of topic-specific expert knowledge models, each topic-specific expert knowledge model representing an aggregate level of knowledge possessed by a plurality of expert users on a single topic from a set of globally defined topics shared among the plurality of users, comparing the topic-specific user knowledge model of the first user with the topic-specific expert knowledge model for a respective topic to determine a distance between a user knowledge level and an aggregate expert knowledge level for the topic.

Abstract: A system includes a user model module that generates a plurality of topic-specific user knowledge models for each user of a plurality of users, each topic-specific user knowledge model representing a level of knowledge possessed by a respective user on a single topic from a set of globally defined topics shared among the plurality of users, a expertise model building module that generates a plurality of topic-specific expert knowledge models, each topic-specific expert knowledge model representing an aggregate level of knowledge possessed by a plurality of expert users on a single topic from a set of globally defined topics shared among the plurality of users, and a processor of a computer that executes instructions for comparing the topic-specific user knowledge model of the first user with the topic-specific expert knowledge model for a respective topic to determine a distance between a user knowledge level and an aggregate expert knowledge level for the topic.

Abstract: Detecting propensity profile for a person may comprise receiving artifacts associated with the person; detecting profile characteristics for the person based on the artifacts; receiving a plurality of predefined profiles comprising a plurality of characteristics and relationships between the characteristics over time, each of the plurality of predefined profiles specifying an indication of propensity; matching the profile characteristics for the person with one or more of the plurality of predefined profiles; and outputting one or more propensity indicators based on the matching, the propensity indicators comprising at least an expressed strength of a given propensity in the person at a given time.

Abstract: Automated contextual information retrieval techniques are provided based on multi-tiered user modeling and a dynamic retrieval strategy. Content relevant to a current message is presented by initially obtaining a multi-tiered user model containing a multi-tiered representation of interactions of a first user with each contact, wherein the multi-tiered representation includes a plurality of topic models each corresponding to interactions between the first user and one contact. The topic models contain a set of topics, each containing topic keywords. Context information is extracted based on content of the current message, a sender and/or a recipient of the current message, and the multi-tiered user model. A retrieval strategy is determined based on the extracted context information. Contextual queries are generated to search the information repositories selected based on the determined retrieval strategy.

Abstract: Detecting propensity profile for a person may comprise receiving artifacts associated with the person; detecting profile characteristics for the person based on the artifacts; receiving a plurality of predefined profiles comprising a plurality of characteristics and relationships between the characteristics over time, each of the plurality of predefined profiles specifying an indication of propensity; matching the profile characteristics for the person with one or more of the plurality of predefined profiles; and outputting one or more propensity indicators based on the matching, the propensity indicators comprising at least an expressed strength of a given propensity in the person at a given time.

Abstract: A system includes a user model module that generates a plurality of topic-specific user knowledge models for each user of a plurality of users, each topic-specific user knowledge model representing a level of knowledge possessed by a respective user on a single topic from a set of globally defined topics shared among the plurality of users, a expertise model building module that generates a plurality of topic-specific expert knowledge models, each topic-specific expert knowledge model representing an aggregate level of knowledge possessed by a plurality of expert users on a single topic from a set of globally defined topics shared among the plurality of users, and a processor of a computer that executes instructions for comparing the topic-specific user knowledge model of the first user with the topic-specific expert knowledge model for a respective topic to determine a distance between a user knowledge level and an aggregate expert knowledge level for the topic.

Abstract: A method includes generating, as executed by a processor on a computer, a plurality of topic-specific user knowledge models for each user of a plurality of users, each topic-specific user knowledge model representing a level of knowledge possessed by a respective user on a single topic from a set of globally defined topics shared among the plurality of users, generating a plurality of topic-specific expert knowledge models, each topic-specific expert knowledge model representing an aggregate level of knowledge possessed by a plurality of expert users on a single topic from a set of globally defined topics shared among the plurality of users, comparing the topic-specific user knowledge model of the first user with the topic-specific expert knowledge model for a respective topic to determine a distance between a user knowledge level and an aggregate expert knowledge level for the topic.

Abstract: Detecting propensity profile for a person may comprise receiving artifacts associated with the person; detecting profile characteristics for the person based on the artifacts; receiving a plurality of predefined profiles comprising a plurality of characteristics and relationships between the characteristics over time, each of the plurality of predefined profiles specifying an indication of propensity; matching the profile characteristics for the person with one or more of the plurality of predefined profiles; and outputting one or more propensity indicators based on the matching, the propensity indicators comprising at least an expressed strength of a given propensity in the person at a given time.

Abstract: A system including a global topic model building module that builds a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, a user model building module that builds a plurality of user models, each user model being built based on an activity of a respective user, an expertise model building module that builds an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, a processor to compare a user model of the first user with the expertise model for the topic; and an expertise assessment and learning recommendation module that recommends an activity associated with the set of data to the first user based on the comparison.

Abstract: A method including generating a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, the global topic model including a topic representation for a topic, generating a plurality of user models, each user model being generated based on the activity of a respective user, generating an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, comparing a user model of the first user with the expertise model for the topic, the comparing being performed by a processor of a computer system, and recommending an activity associated with the set of data to the first user based on the comparison.