Search

[Objective] To solve the problem of difficult valuation of agricultural scientific and technological achievements, a method for dynamically evaluating the transaction price of agricultural scientific and technological achievements using computers was studied, providing data support and pricing basis for the transformation, transaction or transfer of agricultural scientific and technological achievements. [Method] A five-layer Fuzzy neural network model FNN (Fuzzy neural network) was constructed by combining fuzzy theory and neural network, which Abstract: [Objective] To solve the problem of difficult valuation of agricultural scientific and technological achievements, a method for dynamically evaluating the transaction price of agricultural scientific and technological achievements using computers was studied, providing dalearned and stored rule knowledge from the historical transactions data of agricultural scientific and technological achievements and used it to predict the transaction price of the achievements. The accuracy rate of FNN was only 80% after the actual business data test. In order to improve the evaluation accuracy, adapt to the changes caused by increasing transaction samples, reduce the resource consumption of retraining all sample data, so introduce incremental learning, an improved fuzzy neural network model IFNN (Improved fuzzy neural network) and an improved valuation process were proposed. [Result] In the actual business data processing, by applying the improved valuation process and completing the training of incremental data, the IFNN's valuation accuracy rate reached 86.7%. Fifteen rapeseed varieties were selected to compare the results of FNN and IFNN model. The fuzzy membership degrees of FNN and IFNN were different, and the comparison showed that the accuracy of IFNN was higher than FNN. The IFNN model algorithmically met the requirement that the estimation accuracy increased with incremental learning. However, in order to further improve the accuracy of the estimation, more complete training data was needed. [Conclusion] The IFNN can dynamically estimate agricultural scientific and technological achievements based on objective data in practical business, and can adapt to the constantly changing and developing society, and has high practical application value. [ABSTRACT FROM AUTHOR]

【Objective】The study aimed to understand the temporal and spatial distribution of planktonic viruses and abnormal bacteria between different functional divisions in Zhoushan sea area. 【Method】In the winter of 2017, spring of 2018, summer and autumn, six typical stations were selected in different functional areas of the coastal waters of Zhoushan island to collect surface seawater and obtain 24 samples. Flow cytometry was used to detect the abundance of planktonic viruses and abnormal bacteria, and the related environmental factors and their temporal and spatial distribution characteristics were analyzed. 【Result】The seasonal distribution of the Virus（Viruses, Vir） was: Summer>autumn>winter>spring, and the seasonal distribution of Heterotrophic Bacteria（HB） was: Summer>spring>autumn>winter. Correlation analysis showed that there was a significantly positive correlation between planktonic virus and abnormal bacteria and pH in spring, and a significant negative correlation with COD. There was a significantly positive correlation between planktonic virus and VBR. In summer, the abnormal bacteria showed a positive correlation with Chl-a. COD showed a significant negative correlation; In autumn, the floating virus was positively correlated with Chl-a and negatively correlated with COD. The abnormal bacteria were positively correlated with pH and negatively correlated with SiO32- and COD. In winter, planktonic virus and Chl-a positive correlation was observed, and the abnormal bacteria showed a positive correlation with pH and a significant negative correlation with COD. 【Conclusion】It was found that the abundance of planktonic viruses and abnormal bacteria in the coastal waters of Zhoushan can directly or indirectly reflect the pollution of the sea area, and can be used as one of the indicators for detecting water pollution. The above results provided valuable reference materials for the protection of the ecological environment in the different functional areas of Zhoushan, and provided basic information for a more comprehensive study of the ecological environment of the coastal waters of Zhoushan. [ABSTRACT FROM AUTHOR]

[Objective] The present paper aimed to investigate the characteristics and effective factors of phytoplankton’s community structure in Dongji sea area of Zhoushan. [Method] Based on the continuous survey in four seasons on phytoplankton which were carried out from February, 2017 to October, 2017. The species composition, dominant species, abundance of phytoplankton, die diversity index and their relationships with environmental factors were analyzed. [Result]A total of 142 phytoplankton species were identified, which belong to 8 phyla, 70Taxa, including Bacillariophyta (47 genera and 104 species) , Pvrrophyta (12 genera and 27 species) , Gryptophyta (4 genera and 4 species) , Chiysophyta and Cyanophyta (2 genera and 2 species) , Chlorophyta, Xanthophyta and Euglenophyta (1 genera and 1 species) were found. Bacillariophyta and Pvrrophyta were the main phytoplankton, and there were totally 20 kinds of dominant species. The mean abun-dance of phytoplankton was 0.46 x 103 cells,abundance in summer and winter was obviously higher than in spring and autumn. The mean of phytoplankton’s Shannon-wiener diversity index was 3.07, the mean of Margalef richness index was 4.59, and the mean of Pielou evenness index was 0. 65. Three indexes show that the changing tendency was higher in autumn and winter and lower in spring and summer. [Conclusion] RDA analysis analysis indicated that the distributions of phytoplankton were related to water temperate, salinity , dissolved oxygen and nutrient. In addition, it may be affected by geographical location, climate change and anthropological activity. [ABSTRACT FROM AUTHOR]

Copyright of Southwest China Journal of Agricultural Sciences is the property of Editorial Department of Southwest China Journal of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Southwest China Journal of Agricultural Sciences is the property of Editorial Department of Southwest China Journal of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)