Gray Forecast of Ecosystem Services Value and Its Driving Forces in Karst Areas of China: A Case Study in Guizhou Province, China

A sound ecosystem is the prerequisite for the sustainable development of human society, and the karst ecosystem is a key component of the global ecosystem, which is essential to human welfare and livelihood. However, there remains a gap in the literature on the changing trend and driving factors of ecosystem services value (ESV) in karst areas. In this study, Guizhou Province, a representative region of karst mountainous areas, was taken as a case to bridge the gap. ESV in the karst areas was predicted, based on the land use change data in 2009–2018, and the driving mechanisms were explored through the gray correlation analysis method. Results show that a total loss of CNY 21.47 billion ESV from 2009 to 2018 is due to the conversion of a total of 22.566% of the land in Guizhou, with forest land as the main cause of ESV change. By 2025 and 2030, the areas of garden land, water area, and construction land in Guizhou Province will continue to increase, whereas the areas of cultivated land, forest land, and garden land will decline. The total ESV shows a downward trend and will decrease to CNY 218.71 billion by 2030. Gray correlation analysis results illuminate that the total population and tertiary industry proportion are the uppermost, among all the driving factors that affect ESV change. The findings in this study have important implications for optimizing and adjusting the land use structure ecological protection and will enrich the literature on ESV in ecologically fragile areas.

Exploration of the profile-effect relationship of Siraitia grosvenorii aqueous extracts related to their laxative effect on the basis of gray correlation analysis

Background Siraitia grosvenorii (binomial name Siraitia grosvenorii (Swingle) C. Jeffrey ex Lu et Z. Y. Zhang), also called Arhat Fruit or Monk’s Fruit, is a dried ripe fruit belonging to the Cucurbitaceae Family. S. grosvenorii has a long history of being used for constipation treatment in folk medicine. However, there are few studies where the laxative effect, related mechanisms, and active constituents of S. grosvenorii were investigated. This research explores the relationship between the common components and the laxative effect of aqueous extracts of S. grosvenorii from different habitats in China. Methods The fingerprints of S. grosvenorii aqueous extracts from different habitats were established by HPLC. The constipation mice model was used to investigate the laxative effect of S. grosvenorii aqueous extracts. The motilin (MTL) level in mice serum, and the water content of the large and small intestines in mice were determined. The profile-effect relationship of S. grosvenorii aqueous extracts was preliminarily clarified using gray correlation analysis. Results Nine common peaks were identified from the fingerprint of aqueous extracts of S. grosvenorii. The aqueous extracts obviously shortened the incubation period of defecation, and significantly increased the number of defecations, and the wet and dry weight of defecation in constipated mice. The profile-effect relationship indicated that seven common peaks were highly correlated with the effect of the incubation period of defecation, the number of defecations, and the wet and dry weight of defecation in mice. Conclusion This work provides a promising method for the fingerprint establishment, pharmacodynamic evaluation, and quality control of S. grosvenorii on the basis of its profile-effect relationship.

Research on gray correlation analysis and situation prediction of network information security

In order to further improve the accuracy and efficiency of network information security situation prediction, this study used the dynamic equal-dimensional method based on gray correlation analysis to improve the GM (1, N) model and carried out an experiment on the designed network security situation prediction (NSSP) model in a simulated network environment. It was found that the predicted result of the improved GM (1, N) model was closer to the actual value. Taking the 11th hour as an example, the predicted value of the improved GM (1, N) model was 28.1524, which was only 0.8983 larger than the actual value; compared with neural network and Markov models, the error of the improved GM (1, N) model was smaller: the average error was only 2.3811, which was 67.88% and 70.31% smaller than the other two models. The improved GM (1, N) model had a time complexity that was 49.99% and 39.53% lower than neural network and Markov models; thus, it had high computational efficiency. The experimental results verify the effectiveness of the improved GM (1, N) model in solving the NSSP problem. The improved GM (1, N) model can be further promoted and applied in practice and deployed in the network of schools and enterprises to achieve network information security.

Research on Quality Markers of Guizhi Fuling Prescription for Endometriosis Treatment Based on Gray Correlation Analysis Strategy

Guizhi Fuling prescription (GFP), a prestigious prescription of traditional Chinese medicine (TCM) recorded in “Jingui Yaolue,” was composed of five Chinese medicines, including Moutan Cortex, Paeoniae Radix Alba, Persicae Semen, Poria Cocos, and Cinnamomi Ramulus. It was used for the treatment of endometriosis, primary dysmenorrhea, and blood stasis for centuries. However, its Quality Markers of treating endometriosis have not been clearly elucidated. In this study, a rapid ultraperformance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF-MS/MS) method was established for Quality Markers investigation on GFP, and a total of 50 potentially bioactive constituents including triterpenoids, paeoniflorin and its derivatives, phenolic acids, and other species were identified based on their retention time, fragmentation pattern, and accurately measured mass value. Furthermore, regularity of recipe composition and gray correlation analysis revealed that all of the characteristic peaks contributed to the treatment of endometriosis. The relative correlation degrees were greater than 0.6. Among them, peaks 1 and 10, which were most closely correlated to the endometriosis, were identified as amygdalin and cinnamic acid. Finally, all of the active ingredients were molecularly docked with proteins associated with endometriosis by Schrodinger method. Among them, amygdalin, cinnamic acid, paeonol, gallic acid, and paeoniflorin had the lower binding energies. It was proposed that these constituents could be directed at Quality Markers for GFP. Thus, the integrated approach describing for revealing Quality Markers of GFP could be expected to provide a method for quality evaluation.