No abstract

Mining causes serious destruction of the surface morphology and soil structure of lands, and vegetation restoration on post-mining lands provides an effective way for soil and water conservation. To determine the influence of mining and vegetation restoration on soil properties in the eastern margin of the Qinghai-Tibet Plateau, four land sites, including two vegetation restoration sites (restorated by Elymus nutans and Picea crassifolia, respectively), one non-vegetated mining site and one native grassland site, were selected. Fifty-two topsoil (0–10) samples were collected from these four sites, and then soil properties, trace metals and soil enzyme activities were analyzed. The results showed that there was an increase in soil pH (>8.0) after mining, while vegetation restoration decreased the soil pH compared with native grassland; the soil organic matter and total nitrogen in the site restored with E. nutans increased by 48.8% and 25.17%, respectively, compared with the site restored with P. crassifolia. The soil enzyme activities decreased after mining, and there were no significant increases in urease, phosphatase, β-glucosidase and β-1,4-N-acetylglucosaminidase activities after five years of restoration. In addition, the contents of soil trace metals (cadmium, chromium, mercury, lead and zinc) after mining were lower than the Chinese threshold (GB 15618/2018), but the content of arsenic in non-vegetated soil and P. crassifolia-restored soil exceeded the threshold by 22.61 times and 22.86 times, respectively. Therefore, As-contaminated land areas should be accurately determined and treated in a timely way to prevent arsenic from spreading, and plant species with tolerance to alkaline soil should be selected for vegetation restoration on post-mining lands.

No abstract

With the development of market economy in China, the problem of bad debt becomes increasingly serious in enterprises. In this paper, a bad-debt-risk evaluation model is established based on LS-SVM classifier, using a new set of index system which combines financial factors with non-financial factors on the basis of the 5C system evaluation method. The bad debt rating is separated into four classes-normality, attention, doubt and loss through analyzing accounts payable. Then the LS-SVM classifier is trained with 220 samples which are stochastically extracted from listed companies of China in industry, and the four classes are identified by the trained classifier using 80 samples. Then, BP neural network is also used to assess the same data. The experiment results show that LS-SVM has an excellent performance on training accuracy and reliability in credit risk assessment and achieves better performance than BP neural network.