Abstract: Disclosed is a carbon-dioxide-responsive self-thickening intelligent fluid based on supramolecular self-assembly, which comprises a Gemini surfactant, a single-chain amide molecule having a tertiary amine head group and water, wherein the Gemini surfactant and the single-chain amide molecule are self-assembled to form a vesicle structure dispersed in water, with a hydrophilic head group being located outside and a hydrophobic tail group located inside. The method for preparing the carbon-dioxide-responsive self-thickening intelligent fluid of the present invention is simple, the injection viscosity is low, and the fluid is converted into a gel when encountering carbon dioxide, so that the viscosity is greatly increased.

Abstract: The present teaching relates to dynamically generate a score floor model that provides a dynamically determined threshold metric to be used to select future bids that satisfy a first and second set of metrics. Feedback information is first received that relates to a plurality of bids previously submitted by a bidder to a publisher, wherein the feedback information indicates whether each of the plurality of bids has led to an impression. The first set of metrics specified by the publisher is obtained and the second set of metrics is retrieved. The score floor model with respect to the publisher is dynamically updated based on the feedback information as well as the first and second sets of metrics.

Abstract: Systems, devices, and methods are disclosed for predicting a dynamic floor price for increasing cleared revenue cleared after a winning bid is determined in an online bid auction. The dynamic floor price is predicted from a cascading classifier strategy implemented through a series of cascading machine learning based classifier models that have been trained.

Abstract: Systems, devices, and methods are disclosed for predicting a dynamic floor price for increasing cleared revenue cleared after a winning bid is determined in an online bid auction. The dynamic floor price is predicted from a cascading classifier strategy implemented through a series of cascading machine learning based classifier models that have been trained.

Abstract: The present teaching relates to dynamically generate a score floor model that provides a dynamically determined threshold metric to be used to select future bids that satisfy a first and second set of metrics. Feedback information is first received that relates to a plurality of bids previously submitted by a bidder to a publisher, wherein the feedback information indicates whether each of the plurality of bids has led to an impression. The first set of metrics specified by the publisher is obtained and the second set of metrics is retrieved. The score floor model with respect to the publisher is dynamically updated based on the feedback information as well as the first and second sets of metrics.