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In this paper, the concentrations of polycyclic aromatic hydrocarbons (PAHs) were measured in biota (reed, grass, mussel, fish, and red-crowned crane) and sediments collected from seven locations in the Zha Long Wetland. PAHs were recovered from the sediments and biota by ultrasonic extraction and then analyzed by means of gas chromatography-mass spectrometry. The total PAH concentrations were 244-713 ng/g dw in sediments, 82.8-415 ng/g dw in plants and 207-4,780 ng/g dw in animals. The total sediment PAH concentrations were categorized as lower to moderate contamination compared with other regions of China and the world. In the plant samples, the accumulation abilities of reed roots and stems for PAHs were higher than those of grass roots. In addition, the concentration of individual PAHs in mussel muscles was the highest in all of the animal samples, followed by fish, feeding crane fetuses, and wild crane fetuses. Compositional analysis suggests that the PAHs in the sediments from the Zha Long Wetland were derived from incomplete biomass combustion. Risk assessment shows that the levels of PAHs in sediments are mostly lower than the effects range mean value (effects range mean), whereas only naphthalene in all sample sites was higher than the effects range low value. It is worthwhile to note that benzo(b)fluoranthene, benzo(k)fluoranthene, indeno(1,2,3-cd)pyrene and benzo(ghi)perylene were detected in crane fetal, which have potential carcinogenicity for organisms from the Zha Long Wetland.

As a complex hot problem in the financial field, stock trend forecasting uses a large amount of data and many related indicators; hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis. Researchers in the field of machine learning have proved that random forest can form better judgements on this kind of problem, and it has an auxiliary role in the prediction of stock trend. This study uses historical trading data of four listed companies in the USA stock market, and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend prediction. This study applies the exponential smoothing method to process the initial data, calculates the relevant technical indicators as the characteristics to be selected, and proposes the D-RF-RS method to optimize random forest. As the random forest is an ensemble learning model and is closely related to decision tree, D-RF-RS method uses a decision tree to screen the importance of features, and obtains the effective strong feature set of the model as input. Then, the parameter combination of the model is optimized through random parameter search. The experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization, which is 0.18 higher than the average accuracy of light gradient boosting machine model. Combined with the performance of the ROC curve and Precision-Recall curve, the stability of the model is also guaranteed, which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

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