Abstract: The present disclosure relates to a method for preparing taurine and a method for recovering a mother liquor thereof. The method for recovering the taurine mother liquor comprises: providing a taurine mother liquor, and mixing the taurine mother liquor and a treating agent to obtain a solid-containing suspension, wherein the treating agent is a water-soluble organic solvent or a mixture of the water-soluble organic solvent and water; filtering the suspension to obtain a first solid product and a first filtrate; and dissolving the first solid product with an ammonia source, and then filtering same to obtain a second solid product and a second filtrate.

Abstract: The present disclosure relates to a method for preparing taurine and a method for recovering a mother liquor thereof. The method for recovering the taurine mother liquor comprises: providing a taurine mother liquor, and mixing the taurine mother liquor and a treating agent to obtain a solid-containing suspension, wherein the treating agent is a water-soluble organic solvent or a mixture of the water-soluble organic solvent and water; filtering the suspension to obtain a first solid product and a first filtrate; and dissolving the first solid product with an ammonia source, and then filtering same to obtain a second solid product and a second filtrate.

Abstract: A method for preparing sodium taurine as a taurine intermediate is provided in the present disclosure. The method comprises the following steps: providing sodium hydroxyethyl sulfonate and an ammonia source; and placing the sodium hydroxyethyl sulfonate and the ammonia source in an aminolysis reactor for an aminolysis reaction to obtain a mixture containing sodium taurine as a taurine intermediate, wherein the molar ratio of ammonia in the ammonia source to the sodium hydroxyethyl sulfonate is greater than or equal to 25:1. A method for preparing taurine is further provided.

Abstract: An adaptive web crawling system generates a first utility measurement based on web page snippets associated with individual search result items by crawling from a collection of web page crawling seeds and according to a specific user web crawling criteria. The system generates a second utility measurement based on features extracted from the full webpages downloaded according to the guidance of the first utility measurement results. A web page utility prediction function is introduced to forecast the second utility measurement based on the first utility measurement. The system adapts its priorities for web crawling based on the web page utility prediction function.

Abstract: A personalized content recommendation system includes a client interface device configured to monitor a user's information data stream. A collaborative filter remote from the client interface device generates automated predictions about the interests of the user. A database server stores personal behavioral profiles and user's preferences based on a plurality of monitored past behaviors and an output of the collaborative user personal interest inference engine. A programmed personal content recommendation server filters items in an incoming information stream with the personal behavioral profile and identifies only those items of the incoming information stream that substantially matches the personal behavioral profile. The identified personally relevant content is then recommended to the user following some priority that may consider the similarity between the personal interest matches, the context of the user information consumption behaviors that may be shown by the user's content consumption mode.

Abstract: A personalized content recommendation system includes a client interface configured to automatically monitor a user's information data stream transmitted on the Internet. A hybrid contextual behavioral and collaborative personal interest inference engine resident to a non-transient media generates automatic predictions about the interests of individual users of the system. A database server retains the user's personal interest profile based on a plurality of monitored information. The system also includes a server programmed to filter items in an incoming information stream with the personal interest profile and is further programmed to identify only those items of the incoming information stream that substantially match the personal interest profile.

Abstract: A personalized human perceptual opinion capturing and modeling system includes a processor and memory. Logic stored in the memory renders a user interface on an electronic display. The user interface includes many active areas radially linking multiple peripheral image objects to a central image object where an image object is an object of an image or a visually represented entity. The multiple peripheral image objects are positioned around a curved path about the central image object. The active areas include impression characteristic objects associated with hyperlinks that render a second user interface. The second user interface displays a selected peripheral image object, the central image object, and a color mapping model or a visual feedback mechanism. A database stores user expressed perceptual opinion data representing the similarity of the selected peripheral image object to the central image object.

Abstract: A personalized content recommendation system includes a client interface configured to automatically monitor a user's information data stream transmitted on the Internet. A hybrid contextual behavioral and collaborative personal interest inference engine resident to a non-transient media generates automatic predictions about the interests of individual users of the system. A database server retains the user's personal interest profile based on a plurality of monitored information. The system also includes a server programmed to filter items in an incoming information stream with the personal interest profile and is further programmed to identify only those items of the incoming information stream that substantially match the personal interest profile.

Abstract: A personalized content recommendation system includes a client interface device configured to monitor a user's information data stream. A collaborative filter remote from the client interface device generates automated predictions about the interests of the user. A database server stores personal behavioral profiles and user's preferences based on a plurality of monitored past behaviors and an output of the collaborative user personal interest inference engine. A programmed personal content recommendation server filters items in an incoming information stream with the personal behavioral profile and identifies only those items of the incoming information stream that substantially matches the personal behavioral profile. The identified personally relevant content is then recommended to the user following some priority that may consider the similarity between the personal interest matches, the context of the user information consumption behaviors that may be shown by the user's content consumption mode.