Nitrogen Content Inversion of Corn Leaf Data Based on Deep Neural Network Model

To obtain excellent regression results under the condition of small sample hyperspectral data,a deep neural network with simulated annealing(SA-DNN)is proposed.According to the characteristics of data,the attention mechanism was applied to make the network pay more attention to effective features,thereby improving the operating efficiency.By introducing an improved activation function,the data correlation was reduced based on increasing the operation rate,and the problem of over-fitting was alleviated.By introducing simulated annealing,the network chose the optimal learning rate by itself,which avoided falling into the local optimum to the greatest extent.To evaluate the performance of the SA-DNN,the coefficient of determination(R^(2)),root mean square error(RMSE),and other metrics were used to evaluate the model.The results show that the performance of the SA-DNN is significantly better than other traditional methods.

Analysis on the Content Features and Their Correlation of Web Pages for Spam Detection

In the global information era,people acquire more and more information from the Internet,but the quality of the search results is degraded strongly because of the presence of web spam.Web spam is one of the serious problems for search engines,and many methods have been proposed for spam detection.We exploit the content features of non-spam in contrast to those of spam.The content features for non-spam pages always possess lots of statistical regularities; but those for spam pages possess very few statistical regularities,because spam pages are made randomly in order to increase the page rank.In this paper,we summarize the regularities distributions of content features for non-spam pages,and propose the calculating probability formulae of the entropy and independent n-grams respectively.Furthermore,we put forward the calculation formulae of multi features correlation.Among them,the notable content features may be used as auxiliary information for spam detection.

Quantitative detection and comparison of sulfate glycosaminoglycans content in extracellular matrix of in vitro cultured epiphyseal, articular and rib chondrocytes

Objective To establish a method for quantitative detection of the sulfate glycosaminoglycans ( GAG) content in extracellular matrix of in vitro cultured chondrocytes so as to evaluate the biological characteristics of epiphyseal, articular and rib chondrocytes. Methods Sulfate GAG content in extracellular matrix of three chondrocytes was measured by the modified dimethylmethylene blue (DMB) method. The changes of the toluidine blue (TB) stain of chondrocytes were observed by light microscope. Results Primary chondrocytes had the highest content of sulfate GAG in the extracellular matrix, ie, epiphyseal chondrocytes reached ( 70. 12 ± 7. 72 )μg/cm2, articular chondrocytes (92.00 ± 10.15) μg/cm2 and rib chondrocytes (80.61 ± 11. 40) μg/cm2, respectively. On the third pasage chondrocytes, epiphyceal chondrocytes decreased to (53.27 ± 9. 50 ) μg/cm2, articular chondrocytes to (63.88 ± 11.92) μg/cm2 and rib chondrocytes to (58.94 ±8.21) μg/cm2, respectively. The change of TB in every passage

Research Progresses on Extraction and Detection Techniques of Artemisinin

[Objective] The aim was to make a summary on research progresses on extraction and detection techniques of artemisinin. [Method] In the research, the concerning progress was made based on physicochemical property, source, extraction methods and detection technology of artemisinin. [Result] Artemisinin is colorless acicular crystal and easier to be dissolved in organic solvent. The extraction methods of artemisinin include organic solvent extraction, ultrasonic extraction, microwave extraction and supercritical fluid extraction; the detection technologies include ultraviolet spectrophotometry, high performance liquid chromatography and HPLC-evaporative light-scattering. [Conclusion] The research laid foundation for further exploration and global market.