Larger aggregate formed by self-assembly process of the mixture surfactants enhance the dissolution and oxidative removal of non-aqueous phase liquid contaminants in aquifer.

Surfactants can transfer non-aqueous phase liquid (NAPL) contaminants to the aqueous phase, and enhance the removal of the latter in groundwater. However, the extensive use of surfactants causes secondary contamination and increases the non-target consumption of oxidants. It is pressing to develop a surfactant with high phase transfer efficiency and sound compatibility with oxidants to minimize the use of surfactants for groundwater remediation.

A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets.

In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms. However, most of these methods are insufficient to extract the data features and cannot reflect the nonlinear relationship between different features.