Grey Relational Degree Analysis Research Articles

Discovery of antitussive material basis and mechanisms in Citri Sarcodactylis Fructus by coupling UHPLC-Q/Orbitrap HRMS combined spectrum-effect relationship and metabolomics analyses

Citri Sarcodactylis Fructus (CSF) is widely used as food raw material and traditional Chinese medicine. Fingerprints of different fractions of CSF were established for spectrum-effect relationship analysis, and the main compounds were identified by UHPLC Quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q/Orbitrap HRMS). The antitussive effect was evaluated using a classical mouse model of cough induced by ammonia water. One-way ANOVA was used to determine differences in efficacy. The potential active compounds were screened by spectrum-effect relationship with grey relational degree analysis (GRA), Pearson bivariate correlation analysis (Pearson's), and partial least squares analysis (PLS) analyses. Differential metabolites associated with cough in serum were screened and identified using orthogonal partial least squares-discriminant analysis, HMDB database, and UHPLC-Q/Orbitrap HRMS. Metabolic pathway analysis was performed using MetaboAnalyst 5.0. Results indicate that 70 % ethanol elution fraction (70 % EF) is the major active fraction, and 8 components were identified to possess antitussive effects. Metabolomic analysis showed that 19 metabolites are potential biomarkers related to cough, and 70 % EF can remarkable restore 13 of them to normal levels (P < 0.05). These biomarkers are mainly involved in glycerophospholipid metabolism and sphingolipid metabolism. This study aims to reveal the main pharmacodynamic active sites and potential active ingredients of CSF's antitussive effect. In addition, metabolomics was used to preliminarily elucidate the in-vivo regulatory mechanism of the antitussive effect of the 70 % EF of CSF.

Online Ride-hailing Demand Prediction Model Based on GRU & LSTM

While online car-hailing provides fast and convenient services, it has the same problems in the process of ‘seeking customers’ as traditional taxis. With the research goal of the demand for online ride-hailing, an online ride-hailing demand forecast model based on G & LSTM is proposed. Firstly, grey relational degree analysis and other methods are used to analyze which and how the relevant factors affect the demand for online car-hailing. Secondly, the demand prediction model of online car-hailing based on GRU&LSTM is trained an adjusted. Through the adjustment and comparison experiment, the values of each parameter in the model are properly adjusted. Finally, based on the running data of Chengdu, the model proposed is verified and evaluated. The experimental results show that the GRU&LSTM prediction model has good prediction effect. By comparing the prediction effect of weighting data and unweighting data, it can be seen that weighted data has better prediction effect.

Camellia oleifera seeds cake: Polyphenol profile and in vitro antioxidant activities as determined by different harvest periods

Camellia oleifera seed oil is widely recognized for its high nutritional value. And a large amount of seed cake is produced as a by-product of oil extraction. Studies have shown that the polyphenol content of oil obtained from materials at different harvesting stages varies, but no study has examined changes in the polyphenols of seed cake (CP). In our study, the composition profiles of 44 polyphenols contained in CP were identified and 8 of them were quantified by UPLC-QTOF-MS/MS and HPLC-QQQ-MS/MS, respectively. The antioxidant activity of CP was evaluated by ABTS and FRAP methods in combination with the H2O2-induced oxidative damage model of RAW264.7 cells. The results showed that although the content of CPs in the four stages showed different trends, they all had good ABTS+ scavenging and FRAP-reducing abilities. Moreover, CP attenuated oxidative damage in cells by enhancing cell viability, increasing SOD activity, decreasing the contents of MDA and ROS, decreasing apoptosis, and inhibiting the activation of the NF-κB signaling pathway. Correlation results from Grey relational degree analysis and Pearson analysis showed that chlorogenic acid, 5, 7-dimethoxy-4'-hydroxyflavanone, and kaempferol-3-O-rutinoside contributed significantly to antioxidant activity. The results would provide a theoretical basis for the development and utilization of the added value of Camellia oleifera seed cake.

Effects of Irrigation and Fertilization Management on Yield and Quality of Rice and the Establishment of a Quality Evaluation System

Yield and rice quality indicators of crops are a direct reflection of the rational irrigation and fertilizer strategy. However, the effects of controlled irrigation (CI) combined with the split application of fertilization managements (straw returning, organic fertilizer, and conventional fertilizer) on rice quality are not clear in southeast China. This study aims at exploring the effects of three fertilization managements applied under CI or flooding irrigation on rice yield, quality, enzyme activity, and soluble sugar content including 43 indicators, to determine the optimal comprehensive evaluation model, management, and representative indexes. The results showed that compared with CF (CI + conventional fertilizer), CS (CI + straw returning) significantly increased yield (27.65%), irrigation water use efficiency (6.20%), chalky grain rate (9.67%), chalkiness (1.83%), protein content (4.29%), and amylose content (0.33%), indicating that CS improved yield and milling quality but decreased cooking and appearance quality. This was mainly because CS promoted the activities of alpha-amylase, ADPG (ADP-glucose pyrophosphorylase), and GBSS (granule-bound starch synthase) and reduced the soluble sugar content in rice. Grey relational degree analysis (GRD), the entropy method (ETM), and TOPSIS (the technique for order preference by similarity to an ideal solution) were used to comprehensively evaluate the rice quality and determined that CS treatments could synergistically improve yield and rice quality. The five indexes (adhesive strength, HPV, ADPG, soluble sugar (leaf), yield) and TOPSIS model can be used as the best indexes and model to evaluate the rice quality. These results could provide scientific management and evaluate practices for high-yield and high-quality rice cultivation, which may be promising for a cleaner production strategy.

Dynamic variation of groundwater level and its influencing factors in typical oasis irrigated areas in Northwest China

Abstract It is essential to analyze the dynamic characteristics of regional groundwater levels and their driving factors for the rational development of groundwater in irrigated areas. This article explores the spatial distribution characteristics of the amplitude of groundwater level change in the Shihezi irrigated area by using the ArcGIS interpolation method and contrast coefficient variance method and analyzes the influence factors of dynamic change of groundwater levels by integrating the grey relational degree and path analysis methods and obtaining the sensitivity of each influencing factor to changes in groundwater levels and the relative importance of the influencing factors. The following results are obtained: (1) the groundwater level of the Shihezi irrigated area showed an overall increasing trend from 2012 to 2019, with a fluctuation range of 12.26–14.14 m. The groundwater level in the southeast of the irrigated area showed an upward trend, while the groundwater level in the northwest area showed a downward trend. (2) The groundwater level in the irrigated area first increased, then decreased, and then increased again. The variance of the contrast coefficient in the irrigated area ranged from 0.04 to 11.31, and the fluctuation range of the groundwater level in the central area was higher than that in the northern and southern areas. (3) The irrigated area of cultivated land and evaporation are the main factors affecting groundwater level change in the Shihezi irrigated area. The grey relation analysis shows that the irrigated area of cultivated land has the highest grey correlation degree with the evolution of the groundwater level, which is 0.947, and the average grey correlation index is between 0.74 and 0.95. Path analysis showed that the irrigated area of cultivated land, surface water usage, and evaporation were the main factors affecting groundwater levels. Human activities are one of the main driving forces of groundwater level change, and the research results provide a theoretical basis for the rational utilization and sustainable development of groundwater resources in the Shihezi irrigation area.

A Power Load Prediction by LSTM Model Based on the Double Attention Mechanism for Hospital Building

This work proposed a LSTM (long short-term memory) model based on the double attention mechanism for power load prediction, to further improve the energy-saving potential and accurately control the distribution of power load into each department of the hospital. Firstly, the key influencing factors of the power loads were screened based on the grey relational degree analysis. Secondly, in view of the characteristics of the power loads affected by various factors and time series changes, the feature attention mechanism and sequential attention mechanism were introduced on the basis of LSTM network. The former was used to analyze the relationship between the historical information and input variables autonomously to extract important features, and the latter was used to select the historical information at critical moments of LSTM network to improve the stability of long-term prediction effects. In the end, the experimental results from the power loads of Shanxi Eye Hospital show that the LSTM model based on the double attention mechanism has the higher forecasting accuracy and stability than the conventional LSTM, CNN-LSTM and attention-LSTM models.

Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model

Accurate prediction of landslide displacement is an effective way to reduce the risk of landslide disaster. Under the influence of periodic precipitation and reservoir water level, many landslides in the Three Gorges Reservoir area underwent significant displacement deformation, showing a similar step-like deformation curve. Given the nonlinear characteristics of landslide displacement, a prediction model is established in this study according to the variational mode decomposition (VMD) and support vector regression (SVR) optimized by gray wolf optimizer (GWO-SVR). First, the original data are decomposed into trend, periodic and random components by VMD. Then, appropriate influential factors are selected using the grey relational degree analysis (GRDA) method for constructing the input training data set. Finally, the sum of the three displacement components is superimposed as the total displacement of the landslide, and the feasibility of the model is subsequently tested. Taking the Shuizhuyuan landslide in the Three Gorges Reservoir area as an example, the accuracy of the model is verified using the long time-series monitoring data. The results indicate that the newly proposed model achieves a relatively good prediction accuracy with data decomposition and parameter optimization. Therefore, this model can be used for the predict the accuracy of names and affiliations ion of landslide displacement in the Three Gorges Reservoir area.

Optimal configuration method of wind farm hybrid energy storage based on EEMD-EMD and grey relational degree analysis

The large-scale grid connection of new energy wind power generation has caused serious challenges to the power quality of the power system. The hybrid energy storage system (HESS) is an effective means to smooth the fluctuation of wind power and improve the economy of the system. In order to determine the optimal capacity configuration of the hybrid energy storage system, first, a decomposition method which combines ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) is proposed, and a series of intrinsic mode functions are obtained, the grey correlation analysis method is used to analyze the similarity, and the components with similar correlation values are reconstructed to obtain high-frequency and low-frequency components; second, considering the battery life loss of the hybrid energy storage system, with the goal of minimizing the entire life cycle cost, the optimal configuration model of hybrid energy storage capacity is established, and different energy storage schemes are analyzed to obtain the energy storage configuration scheme with the best economy; finally, based on the typical daily historical data of a wind farm, the effectiveness and economy of the proposed method are verified.

Study on Influencing Factors of Ethanol Coupling to Prepare C4 Olefins

In this paper, we study the data analysis and calculation of the factors influencing the preparation of C4 olefins by ethanol coupling. Specifically, by combining each catalyst, the relationship between temperature and ethanol conversion and C4 olefin selectivity was analyzed by regression analysis and correlation analysis, respectively. In addition, the grey relational degree analysis was used to solve the grey relational degree between the influencing factors of the experiment. The nonlinear correspondence between ethanol conversion and C4 olefin selectivity with different catalyst combinations and temperatures was evaluated by means of random forest model and BP neural network model. Finally, we build a support vector machine regression (SVMR) model and optimize the cost and gamma parameters of this model using a genetic algorithm.

Air Conditioning Load Prediction of an Office Building Based on Long Short Term Memory Neural Network

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy but also extend the life of the equipment. Introduction: The neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which helps improving the prediction accuracy. Combined with the grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short-Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with the Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has a better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time, and great application potential.

Effect of different boron levels on yield and nutrient content of wheat based on grey relational degree analysis

Effect of different boron levels on yield and nutrient content of wheat based on grey relational degree analysis

Grey Correlation Analysis of Economic Growth and Cultural Industry Competitiveness

The influence of cultural industry competitiveness on economic growth is analyzed by using grey relational degree method. Then, the influence of cultural industry on the three industries is analyzed and compared in the same way. On this basis, further from the cultural industry, the impacts of core layer, outer layer, and related layer on economic growth were compared and analyzed. Finally, the economic growth model is used to measure the impact of investment, labor, and innovation in cultural industry on economic growth. The results show that cultural industry has a great influence on economic growth. The cultural industry has an obvious driving effect on the tertiary industry. The correlative layer of cultural industry has the greatest influence on economic growth. Cultural industry innovation has a huge pulling effect on economic growth. The SPSS software was used to process the data, and the data indicators were screened. Finally, the grey relational degree model was constructed. Then, the improved diamond model was used to select the influential factors of the development of cultural industry, and the main influencing factors were found out through the grey relational degree analysis. This paper analyzes the factors affecting economic benefits and puts forward countermeasures. The results show that it is of great significance to pay attention to the development of cultural manufacturing industry in cultural industry and promote cultural innovation to promote economic growth.

Spatial differentiation of the NPP and NDVI and its influencing factors vary with grassland type on the Qinghai-Tibet Plateau.

Grasslands are the dominant ecosystem of the Qinghai-Tibet Plateau (QTP), and they play an important role in climate regulation and represent an important ecological barrier in China. However, the spatial differentiation characteristics of net primary productivity (NPP) and normalized differential vegetation index (NDVI) and the main influencing factors that vary with grassland type on the QTP are not clear. In this study, standardized precipitation evapotranspiration index (SPEI), digital elevation model (DEM), precipitation, temperature, slope, photosynthetically active radiation (PAR) and grazing intensity were considered the driving factors. First, a grey relational degree analysis was performed to test for the quantitative relationships between NPP, NDVI and factors. Then, the geographical detector method was applied to analyze the interaction relationships of the factors. Finally, based on the geographically weighted regression (GWR) model, the influence of factors varied with grassland type on the NPP and NDVI was revealed from the perspective of spatial differentiation. The results were as follows: (1) The NPP and NDVI had roughly the same degrees of correlation with each impact factor by the grey relational degree analysis, each factor was closely related to the NPP and NDVI, and the relational degree between grazing intensity and NPP was greater than that between grazing intensity and NDVI. (2) The interaction relationships between influencing factors and NPP and NDVI varied with the grassland type and presented bivariate enhancement and nonlinear enhancement, and the interaction effects between grazing intensity and any factor on each grassland type had a greater impact on NPP. (3) The main influencing factors of the spatial heterogeneity of NPP were grazing intensity and PAR, which were "high from northeast to southwest, low from northwest to southeast" and "low in the middle and high around". The main influencing factors on the NDVI were precipitation and PAR, which were "low in the middle and high around" and "high in the north, low in the south".

The Evaluation of Effectiveness for the Collaborative Combat of an Unmanned Aerial Vehicle Based on Grey Minimum Entropy

The main points to the evaluation of effectiveness for the collaborative combat of the unmanned aerial vehicle (UAV) lie within the construction of a reasonable indicator system and an accurate contribution model. As for point one, this article introduces a new method combining the Delphi consulting method and the principal component analysis method to avoid the underlying subjective and time-consuming defects of the existing methods. As for another point, a weighting method is adopted combining the subjective and objective parameters to minimize the errors caused by a single entity. Firstly, the modified grey relational degree analysis method is used to obtain the subjective weight, which can reduce the influence of the extreme values and outliers by enhancing the selection process of the reference sequence. Secondly, this paper adopts the weight of minimum entropy weight method to obtain the objective weight; it can avoid the information loss caused by the original method, which only determines the weight based on the frequency of each element present in the effective combination. At last, the principle of minimum relative entropy is adopted to obtain a more reasonable weight distribution coefficient. The simulation experiments established the rationality and effectiveness of the proposed method.

Failure Rate Model of Electric Equipment Based on Meteorological Environment

With the development and progress of equipment status monitoring and fault diagnosis technology, the status maintenance based on equipment status evaluation is gradually popularized and applied. By monitoring the quantity of equipment state and diagnosing the equipment state, a reasonable method of equipment state evaluation can be used to determine whether the equipment needs maintenance or not. This paper proposes a method of power equipment failure rate model based on meteorological environment. This method firstly uses the fuzzy c-means algorithm(FCM) to conduct fuzzy clustering of meteorological environment sequences in the historical sample data and forecast meteorological sequences in the maintenance period. Then, on the basis of clustering analysis, the grey relational degree analysis is carried out on the same kind of meteorological feature sequence, so as to obtain the power equipment failure probability with meteorological conditions in the maintenance period. The results show that the proposed method can more accurately calculate the failure probability of the equipment, and it is of practical significance to reduce the influence of maintenance on the reliability of power grid operation.

Study of double combination evaluation of urban comprehensive disaster risk

Aiming at solving the difficult problem of inconsistent single-method evaluation results in comprehensive disaster risk assessment, this paper presents an evaluation method based on a combination of the ranking value and the evaluation value. First, the seven methods of the fuzzy comprehensive evaluation (FCE), the set pair analysis (SPA), the improved grey target analysis (IGT), the fuzzy stochastic simulation (FSS), the grey relational degree analysis (GRA), the extension matter element evaluation (EME), and the technique for order preference by similarity to an ideal solution (TOPSIS) are used to conduct urban comprehensive disaster risk assessment. Second, to ensure the degree of correlation between individual evaluation methods and make full use of more information to achieve the purpose of complementary advantages, the Kendall synergy coefficient, the Spearman rank correlation coefficient, and the comprehensive support coefficient are used as the single-method compatibility consistency test standards. The combined evaluation methods of the cyclic correction model and support model are used to combine the single evaluation results, and the highly coordinated comprehensive disaster risk ranking value and evaluation value are obtained through repeated tests, thus constructing a highly scientific and reasonable dual combination evaluation model. Finally, through analysis and comparison of the final combined ranking and evaluation values of 31 provinces, municipalities directly under the central government and autonomous regions in China under the dual combination mode, it is verified that the dual combination idea can obtain evaluation results with high convergence and credibility, solve the problem of inconsistent evaluation results of multiple methods, provide a new research idea and method for measuring the comprehensive disaster risk of cities, and provide a scientific basis for efficiently serving comprehensive risk prevention management and various urban planning and construction work.

Influencing factors and forecast of carbon emissions from transportation - Taking Shandong province as an example

Under the severe situation of global warming, reducing carbon emissions is an important trend in today’s society. Based on the 2003-2015 Shandong Province transportation energy consumption data, this paper uses the top-down method to calculate the carbon emissions of transportation in Shandong Province.On this basis, the grey relational degree analysis method is used to study the impact of seven factors, namely per capita GDP, total population, energy consumption per unit GDP, private car ownership, tourism income, freight turnover and passenger turnover, on the carbon emissions of transportation in Shandong Province. According to the analysis results, the above influencing factors are closely related to the carbon emissions of transportation in Shandong Province. The impact degree is from large to small: passenger turnover > freight turnover > per capita GDP > total population > energy consumption per unit > tourism income> private car ownership. At the same time, using the gray prediction GM(1,1) model to predict the carbon emissions of transportation in Shandong Province from 2016 to 2021, it is concluded that the carbon emissions of transportation in Shandong Province will continue to grow in the next few years.

基于灰色关联度分析对合金收得率的研究

基于灰色关联度分析对合金收得率的研究

Empirical Research of Local Food Industry Expenditure and Regional Food Economic Growth Based Grey Relational Degree Analysis

In order to promote the development of food economics, the grey relational degree analysis is applied in the relationship analysis between the local food industry expenditure and regional food economic growth. Firstly, the real situation of Chinese food industry economic growth is analyzed. Secondly, the theory model of relational analysis between local food industry expenditure and regional food economic growth is established and the algorithm procedure is designed. Thirdly, the analysis varities are defined based on the analysis requirement, finally the relational analysis is carried out and results show that the grey relational degree analysis is an effective method for carrying out the relational analysis for local food industry expenditure and regional food economic growth.

Spectrum-effect relationship on anti-hepatic fibrosis effect of Radix Hedysari

The spectrum-effect relationship on anti-hepatic fibrosis effect of Radix Hedysari was explored based on high performance liquid chromatographic technique. Hepatic fibrosis was induced in mice by administering a subcutaneous injection of carbon tetrachloride-peanut oil (4:6, v/v) continuously for 35 d at a interval of 5 d (0.1 mL/10 g). And at the same time of modeling, the different extracts of Radix Hedysari were administered orally once daily at a dose of 10 g/kg. The ethanol extract of Radix Hedysari was specified to be most effective on anti-hepatic fibrosis by determining the levels of alanine aminotransferase, aspartate transami- nase, total-protein, albumin, albumin/globulin (ALT, AST, TP, ALB, and A/G) in serum and relative liver weight. Subsequently, the grey relational degree analysis and partial least squares analysis were employed to reveal the correlation between chromatographic fingerprint of ethanol extract of Radix Hedysari from 10 different geographical origins and its anti-hepatic fibrosis efficacy. The results suggest that most chemical constituents of Radix Hedysari have a high correlation with the effect of anti-hepatic fibrosis (> 0.8), which indicates that the effect is related to the various components in Radix Hedysari. Adenosine, calycosin and ononin in ethanol extract of Radix Hedysari have been identified separately among which adenosine and calycosin made the great contribution to the anti-hepatic fibrosis effect.