Recent studies in data mining have proposed a new classification approach, called associative classification, which, according to several reports, such as [6, 7, 8, 9], achieves higher classification accuracy than traditional classification approaches such as C4.5 [11] . However, the approach also suffers from one major deficiency: a training data set often generates a huge set of rules. It is challenging to store, retrieve, prune and sort a large number of rules efficiently for classification, especially on dense databases.In this study, we propose a new associative classification method, ACCF(Associative Classification Based on Closed Frequent Itemsets). The method extends an efficient closed frequent pattern mining method, Charm to mine all frequent closed itemsets (CFIs) and their tidsets, which would help to generate the Class Association Rules (CARs) [6] . And we also adopt a new way to classify an unseen case correspondingly. Our extensive experiments on 18 databases from UCI machine learning database repository [10] show that ACCF is consistent, highly effective at classification of various kinds of databases and has better average classification accuracy in comparison with CBA [6] . Moreover, our performance study shows that the method helps to solve a number of problems that exist in the current classification systems.

Dissipative particle dynamics (DPD) simulation was used to investigate the self-assembling dynamics process of poly(styrene-b-ethylene oxide) (PS-b-PEO) block copolymer and quantum dots (QDs) in an aqueous solution. The effects of molecular weight (MW) and segment construction of a PS−PEO block copolymer on the structure and size of the self-assembled micelles were discussed. The structural properties of micelles were characterized by a radial distribution function. The simulation results are qualitatively consistent with those of previous experiments and show that there are only small QD clusters. The hydrophobic PS chains form the micelle core, while the hydrophilic PEO chains form the shell. The size of the self-assembled PS−PEO/QDs micelle increases with the MW of PS-b-PEO block copolymer and the lengths of PEO and PS segments. The simulation results indicate that the assembling process includes four sequential transient stages: (1) the random distribution of all components in aqueous solution;(2) formation of small clusters with polymer chains and QDs; (3) crashing together of small spheres and the formation of larger aggregates; (4) stabilization of assembled micelles. The simulation reveals the physical insights of the QD loading mechanism of the PEG micelle at the mesoscopic scale, indicating the DPD simulation can be used as an adjunct to provide other valuable information for experiments.

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