Microplastics (MPs) in the water system could easily enter the human body and pose a potential threat, so finding a green and effective solution remains a great challenge. At present, the advanced oxidation technology represented by photocatalysis has been proven to be effective in the removal of organic pollutants, making it a feasible method to solve the problem of MP pollution. In this study, the photocatalytic degradation of typical MP polystyrene (PS) and polyethylene (PE) by a new quaternary layered double hydroxide composite photomaterial CuMgAlTi-R400 was tested under visible light irradiation. After 300 h of visible light irradiation, the average particle size of PS decreased by 54.2% compared with the initial average particle size. The smaller the particle size, the higher the degradation efficiency. The degradation pathway and mechanism of MPs were also studied by GC–MS, which showed that PS and PE produced hydroxyl and carbonyl intermediates in the process of photodegradation. This study demonstrated a green, economical, and effective strategy for the control of MPs in water.

It is very challenging to propose a strong learning algorithm with high prediction accuracy of cross-media retrieval, while finding a weak learning algorithm which is slightly higher than that of random prediction is very easy. Inspired by this idea, we propose an imaginative Bagging based cross-media retrieval algorithm (called BCMR) in this paper. First, we utilize bootstrap sampling to carry out random sampling of the original training set. The amount of the sample abstracted by bootstrap is set to be same as the original dataset. Second, 50 bootstrap replicates are used for training 50 weak classifiers independently. We take advantage of homogenous individual classifiers and integrate eight different baseline methods in our experiments. Finally, we generate the final strong classifier from the 50 weak classifiers by the integration strategy of sample voting. We use collective wisdom to eliminate bad decisions so that the generalization ability of the integrated model could be greatly enhanced. Extensive experiments performed on three datasets show that BCMR can effectively improve the accuracy of cross-media retrieval.

Over the past few years, green small towns have gained a lot of attention, which is a significant trend in the construction industry. Beginning with the life cycle of green small towns, this paper analyses their incremental cost and income using mathematical quantitative models, which can serve as a reference for further evaluating the incremental cost and benefits of green small towns, and proposes countermeasures and suggestions to encourage the green small town growth.

It is very challenging to propose a strong learning algorithm with high prediction accuracy of cross-media retrieval, while finding a weak learning algorithm which is slightly higher than that of random prediction is very easy. Inspired by this idea, we propose an imaginative Bagging based cross-media retrieval algorithm (called BCMR) in this paper. First, we utilize bootstrap sampling to carry out random sampling of the original training set. The amount of the sample abstracted by bootstrap is set to be same as the original dataset. Second, 50 bootstrap replicates are used for training 50 weak classifiers independently. We take advantage of homogenous individual classifiers and integrate eight different baseline methods in our experiments. Finally, we generate the final strong classifier from the 50 weak classifiers by the integration strategy of sample voting. We use collective wisdom to eliminate bad decisions so that the generalization ability of the integrated model could be greatly enhanced. Extensive experiments performed on three datasets show that BCMR can effectively improve the accuracy of cross-media retrieval.

There is an urgent need to screen candidates for CO 2 fixation and biofuels production from more taxonomic species. A Selenastrum capricornutum mutant, whose wild types were not considered as candidates for CO 2 fixation and biofuels production, with genetic stability under high-level CO 2 , here, was screened by 96-well microplates-UV mutagenesis and named as SDEC-2M. To evaluate the potential for CO 2 fixation and biofuels production, SDEC-2M was cultivated under air and 15% CO 2 (v/v), and its wild type (WT) as control. SDEC-2M got better growth performance under high-level CO 2 . It implies that SDEC-2M had high tolerance under highlevel CO 2 . Under high-level CO 2 , not only that, SDEC-2M tended to synthesize energy storage compounds than its wild type, with a total carbohydrate content of up to 37.45%. Meanwhile, the highest overall biomass productivities were obtained in SDEC-2M under 15% CO 2 . Benefiting from these results, the highest productivities of carbohydrate, lipid and protein were obtained, among which carbohydrate productivity of 11.22 mg L À1 d À1 was highest. Based on higher light conversion efficiency (14.81%) and maximal PSII quantum yields (70.14%), the highest photosynthetic efficiency of SDEC-2M was also evaluated under 15% CO 2 . Compared with Selenastrum wild types, the mutant SDEC-2M with excellent CO 2 tolerance and photosynthetic efficiency is better. The results provided a new idea for further enriching the energy microalgal pool.