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Two new records of agaric macrofungi, Cystoagaricus populinus and Psilocybe papuana, are described from Northeast and Southwest China. Morphological characteristics were examined and a phylogenetic tree was constructed. Cystoagaricus populinus is characterized by orange-brown pileus and pileipellis. Additionally, the significant characteristics of P. papuana are brown with umbo pileus, and basidiospores with germ pores. The phylogenetic analyses based on the ITS region indicated that these two species were most closely related to C. populinus and Psilocybe cf. papuana, respectively. Prior to this study, P. papuana distributed found in Papua New Guinea, while C. populinus found in Italy and Spain. Comprehensive descriptions, illustrations, hand drawings and phylogenetic trees of C. populinus and P. papuana are provided.
To study the influence of the structural parameters of blade tip labyrinth seal (BTLS) on leakage flow characteristics, finite element method was used to calculate the relationship between blade tip leakage rate (BTLR) and four structural parameters such as tooth width, tooth height, tooth pitch and tooth number. With the finite element results as samples, support vector regression (SVR), back propagation (BP) neural network and extreme learning machine (ELM) were used to establish the prediction model of the relationship between BTLR and four structural parameters. The accuracy and applicability of three prediction models were compared and analyzed. The results showed that SVR algorithm has higher prediction accuracy and stability compared with other algorithms for the prediction of BTLR. The mean square error and determination coefficient of its test set are 0.00059637 and 0.99253 respectively. After that, SVR results were taken as samples of genetic algorithm to find the combination of structural parameters with the minimum BTLR. The obtained structural parameters were combined for simulation modeling calculation. Its results showed that the fluid velocity in the blade tip region is significantly reduced and the velocity transition is gentle. The difference between simulation and optimization was 0.01%. This method innovatively applies machine learning algorithm to the prediction of BTLR, and improves the problem of low speed and high cost when only using finite element method. It provides a new way to calculate BTLR. In addition, the structural parameters of BTLS are optimized to reduce BTLR. This idea expands the field of application of machine learning algorithms.
The nutrient cycle and organic matter decomposition are catalyzed by soil enzymes. In this study, enzymatic activities of catalase, dehydrogenase, alkaline phosphatase, and urease are studied in soils amended with compost (C), moss (M), or biochar (B) and irrigated with heavy metal-contaminated wastewater (HM-CW), when Nicotiana alata L. and Petunia hybrida L. was grown in pots. The irrigation of HM-CW reduced the soil enzyme activities. However, combined used of 5% M+C+B, results in the improved soil enzyme activities. In case of N. alata and P. hybrida, catalase activity was 222.03 ± 9.24 and 402.34 ± 10.48 mg KMnO4 g-1 soil h-1, respectively, with M+C+B, which was up to 94% higher than non-amended treatment. Similarly, the M+C+B treatment also showed higher activity for dehydrogenase i.e., 180.24 ± 6.95, and 156.79 ± 8.31 μg TPF g-1 soil h-1 for N. alata and P. hybrida, respectively, that were 73% and 49% higher than non-amended treatment. Alkaline phosphatase production (μg p-nitrophenol g-1 soil h-1) for N. alata with M+C+B was 40.10 ± 1.92 and with C+B was 38.41 ± 2.00, while for P. hybrida with M+C+B was 39.33 ± 2.05, which is significantly higher as compared with the non-amended treatment. Urease activity at M+C+B application in soil with P. hybrida was 83.22 ± 5.54 mg urea g-1 soil h-1, which was much higher than that of N. alata. In general enzyme activity enhanced in the soil with N. alata or P. hybrida along with soil amendments. It shows that application of these organic amendments individually or in combination with N. alata or P. hybrida increased enzyme activities possibly through affecting soil nutrient dynamics.