Integrating with learning to rank methods, the authors propose a movie ranking prediction model by mining and analyzing the data from movie media websites, which includes extracting and expanding features related to ranking prediction as well as dividing and aligning ranking labels etc. Experiment results show that the proposed model effectively improves the performance of the movie ranking prediction task, which can benefit the cinemas to arrange the number of screenings properly. The model can also provide high quality recommendations to movies for the fans.

Four lead-tolerant strains were isolated from lead-contaminated soil around the lead-zinc ore deposit. All strains could grow in nutrient solution containing 100?1000 mg/L Pb(II). The removal rates of Pb(II) by Pb-X-1, Pb-Z-3 and Pb-Z-4 were 78.31%, 77.8% and 87% respectively in the following conditions: pH was 6, 25℃, 160 r/min, initial Pb²⁺ concentration was 600 mg/L. The Pb(II) removal rate by Pb-X-2 was higher than 95%, when initial Pb(II)concentration was 100?400 mg/L. The result of 16S rRNA sequencing indicates that Pb-X-2 belonged to Stenotrophomonas maltophilia. The removal mechanism of Pb(II) removal by Pb-X-2 was mainly cellular surface adsorption and intracellular enrichment.