Grey Correlation Degree Analysis Research Articles

Exploring the coupling mode of water and fertilizer for improving growth, fruit quality, and yield of the pear in the arid region.

Considering the pear in the arid region as the research object, single-factor testing and water-fertilizer coupling testing were conducted. The response of pear tree growth to water, nitrogen, and phosphorus was explored and provided a theoretical basis for efficient water and fertilizer management. Among them, the single-factor test set water, nitrogen, and phosphorus as the three factors, and five levels were set. Screening out W3, W4, N3, N4, P3, and P4 promoted plant nutrient uptake and fruit quality. Eight treatments were set up in the water and fertilizer coupling test: Treatment 1 (T1, W3N3P3), Treatment 2 (T2, W3N3P4), Treatment 3 (T3, W3N4P3), Treatment 4 (T4, W3N4P4), Treatment 5 (T5, W4N3P3), Treatment 6 (T6, W4N3P4), Treatment 7 (T7, W4N4P3), and Treatment 8 (T8, W4N4P4). The results showed that the leaf area index of the T1, T2, T3, and T4 treatments was significantly higher than that of the other treatments at maturity. The yield, single fruit weight, and primary fruit rate were the highest under T3 treatment. The gray correlation degree analysis of fruit quality showed that the T3 treatment had the highest degree of correlation and ranking of each fruit quality index, indicating that the T3 treatment had the highest fruit quality. The yield model showed that irrigation with 6510.06 m3 hm-2, nitrogen fertilizer with 337.5 kg N hm-2, and phosphate fertilizer with 262.5 kg P hm-2 had the best yield. A detailed investigation of pear tree growth and fruit quality showed that the T3 treatment had the best fruit growth and development performance, and the pear fruit quality was the best.

Evaluation of Green Suppliers Based on a Hybrid Weighting Approach and GRA Method

To align with the national dual carbon strategy and facilitate low-carbon production for enterprises, this paper incorporates the concept of green supply chain management and develops a scientifically sound evaluation index system for green suppliers by comprehensively considering five factors: quality, availability, cost, environmental management performance, and technology. The analytic hierarchy process and entropy weight method are employed to assign subjective and objective weights to each evaluation index while the grey correlation degree analysis method is utilized to rank and sort suppliers in order to select the optimal green supplier. Finally, an empirical example is presented to verify the reliability of our model.

Thermal properties and grey correlation degree analysis of tar-rich coal under cryogenic and pyrolysis condition

The thermal properties of coal are important factors that influence the heat and mass transfer during the pyrolysis process. Therefore, thermal properties were investigated for two tar-rich coal under cryogenic temperature and pyrolysis condition using laser flash analyzer. Gray correlation analysis and chemical composition analysis are combined to analyze the impact of chemical properties on thermal properties. With decreasing temperature, the thermal diffusivity of coal experiences a significant increase by 108.9–121.4 %, while the specific heat capacity decreases by 57.9–65.9 %. The volatile matter and fixed carbon content in tar-rich coal have the highest correlation with thermal properties (∼0.9), followed by moisture content (∼0.62), while the ash content has the lowest correlation with thermal properties (∼0.5). The thermal diffusivity of tar-rich coal increases with increasing volatile matter content. Furthermore, within the range of −100 °C to 300 °C, the thermal diffusivity of tar-rich coal decreases with increasing carbon content, while the specific heat capacity increases with increasing carbon content. The results indicate that under low-temperature cryogenic conditions, the internal heat transfer capability of tar-rich coal is significantly improved.

Research on future trends of electricity consumption based on conditional generative adversarial network considering dual‐carbon target

AbstractThe emergence of novel factors, such as the energy Internet and electricity supply‐side reform within the context of the dual‐carbon target (carbon peaking and carbon neutrality), has heightened the uncertainty surrounding electricity consumption (EC). This increased uncertainty poses challenges for accurate long‐term EC forecasting. Due to the complexities of feature extraction and the absence of labelled data, conventional supervised learning‐based forecasting methods, such as support vector machines (SVM) and long short‐term memory networks (LSTM), struggle to predict EC with precision in situations of heightened uncertainty resulting from the interplay of multiple factors. To address this issue, a novel method based on a conditional generative adversarial network (CGAN) is proposed. Initially, the dominant factors influencing future electricity consumption trends through grey correlation degree analysis and the K‐L information method are identified. Subsequently, an EC forecast model is introduced based on CGAN, adept at capturing essential factors and the non‐linear relationship between EC and exogenous factors. This approach effectively models the uncertainty of EC, accurately approximating the true distribution with only a small dataset. Finally, the proposed method by forecasting China's EC from 2015 to 2020 is validated. The results demonstrate that the authors’ method achieves lower root mean square error and mean absolute percentage error values, specifically 0.177% and 2.39%, respectively, outperforming established advanced methods such as SVM and LSTM.

The Multi-Objective Optimization Design and Hydrothermal Performance Evaluation of Anhydrous Calcium Sulfate Whisker and Polyester Fiber Compound Modified Asphalt Mixture in Hot-Humid Areas.

In hot and humid climates, asphalt pavements frequently encounter environmental factors such as elevated temperatures and rainfall, leading to rutting deformations and potholes, which can affect pavement performance. The primary objective of this study was to enhance the hydrothermal characteristics of asphalt mixtures through an investigation into the impact of anhydrous calcium sulfate whisker (ACSW) and polyester fiber (PF) on the hydrothermal properties of asphalt mixtures. In this paper, a central composite concatenation design (CCC) was employed to determine the optimal combination of ACSW and PF contents, as well as the asphalt aggregate ratio (AAR). Each influencing factor was assigned three levels for analysis. The evaluation indexes included dynamic stability, retained Marshall stability, and tensile strength ratio. Using the analysis methods of variance and gray correlation degree analysis, the hydrothermal properties of the asphalt mixture were examined in relation to the contents of ACSW, PF, and AAR based on the CCC results. Consequently, the optimal mix design parameters for composite modified asphalt mixture incorporating ACSW and PF were determined. The results indicated that the asphalt mixtures with hydrothermal qualities exhibited optimal performance in terms of 4.1% ARR, 11.84% ACSW, and 0.4% PF. The interaction between AAR and ACSW content had a greater effect on the dynamic stability and tensile strength ratio of the asphalt mixture, whereas the incorporation of ACSW and PF had a greater effect on the retained Marshall stability of the asphalt mixture. Among the three contributing factors, AAR exhibited the strongest relationship with the hydrothermal characteristics of the asphalt mixture, followed by the ACSW content; the correlation of PF content was the lowest. Therefore, to enhance the hydrothermal characteristics of the asphalt mixture, it is important to conduct a full evaluation of the constituents of ACSW and PF, along with the AAR in hot-humid regions.

Genetic diversity and salt tolerance assessment of 51 alfalfa (Medicago sativa) varieties under saline soil conditions

IntroductionSalt stress significantly impacts plant growth worldwide. Although alfalfa exhibits some tolerance to salt-alkali soils, the specific salt tolerance levels across different alfalfa varieties remain inadequately understood. For this purpose, we conducted a study to investigate the diversity of agronomic traits of alfalfa (Medicago sativa) under different soil environments in order to elucidate the salt tolerance of 51 alfalfa varieties.MethodsWe chose three different soil conditions for our study: normal conditions in Yantai as control, saline conditions in Yantai with soil salt concentrations ranging from 0.25 to 0.33%, and saline-alkali conditions in Dongying with soil salt concentrations ranging from 0.28 to 0.32%. We chose 51 alfalfa varieties from different sources as our experimental materials. Planting was conducted following a completely randomized block design with three replicates for each variety. Harvesting was performed when the plants reached 50% flowering, cutting them at a height of 5 cm above the ground. Comprehensive assessments encompassed 10 parameters, namely fresh weight, dry weight, stem-leaf ratio, branch count, plant height, crude protein content, crude fat content, acid detergent fiber, neutral detergent fiber, and ash content.ResultsOur findings revealed that the 51 studied alfalfa varieties displayed significant generalized heritability and coefficient of variation, indicating a high level of genetic diversity. Using principal component and cluster analyses, we categorized the varieties into three distinct clusters based on their agronomic traits. Grey correlation degree analysis revealed the commendable performance of Ying st, PI 672734, and Mei zuo across diverse environments. Genotype emerged as a predominant determinant of all parameters, except crude fat, acid detergent fiber, and neutral detergent fiber. Through the application of membership function analysis, PI 672768 emerged as a variety that demonstrated strong tolerance in both saline soil contexts.DiscussionDespite extensive previous studies indicating Zhong Mu No. 1 as a salt-tolerant alfalfa variety, its performance in this experiment did not distinguish itself. The findings of this study provide a fundamental basis for improving the management of saline-alkali lands and advancing alfalfa cultivation practices.

Screening Optimal Oat Varieties for Cultivation in Arid Areas in China: A Comprehensive Evaluation of Agronomic Traits

This study was undertaken to identify oat (Avena sativa L.) varieties optimal for cultivation in the Jiuquan region, China, in 2021. A selection of 27 domestic and international oat varieties were analyzed, considering ten key agronomic traits, including plant height, stem diameter, spike length, leaf width, and yield. Employing methods such as cluster analysis, principal component analysis, and grey correlation degree, a comprehensive evaluation was conducted. The principal component analysis distilled the ten indicators to three core components. The most influential factors in the first principal component were plant height, ear length, and hay yield, while leaf length and leaf area index were the highest contributors to the second component. The stem-to-leaf ratio emerged as the principal indicator in the third component. The cluster analysis resulted in the classification of the 27 oat varieties into 3 categories. Following a comprehensive evaluation through the grey correlation degree and principal component analysis methodologies, we found that the oat varieties Sweety 1, Fuyan 1, Dingyan 2, Baler, Quebec, and Longyan 2 received the highest scores. These varieties, hence, appear to be the most suitable for cultivation and promotion in the Jiuquan region. This study thus provides invaluable insights into oat cultivation practices, offering guidance for farmers, agricultural policymakers, and future research in the field.

Evaluation of Steaming and Drying of Black Sesame Seeds for Nine Cycles Using Grey-Correlation Analysis Based on Variation-Coefficient Weight.

This study aimed to improve the steaming process of black sesame seeds. A comprehensive evaluation was conducted using the grey-correlation method based on the variation-coefficient weight to observe the treatments of normal-pressure (NPS) and high-pressure (HPS) steaming (with/without soaking in water) for nine cycles. Their effects on the contents of water, protein, fat, ash, melanin, sesamin, and sesamolin of black sesame seeds, as well as the sensory score of the black sesame pill, were determined. We found that with varied steaming methods and increased steaming cycles, the contents of the nutritional and functional components of black sesame seeds and the sensory score of the black sesame pill differed. The results of the variation-coefficient method showed that water, protein, fat, ash, melanin, sesamin, sesamolin, and sensory score had different effects on the quality of black sesame seeds with weighting factors of 34.4%, 5.3%, 12.5%, 11.3%, 13.9%, 11.3%, 7.8%, and 3.5%, respectively. The results of two-factor analysis of variance without repeated observations indicated that the grey-correlation degree of HPS was the largest among the different steaming treatments, and the following sequence was HPS after soaking in water (SNPS), NPS, and SNPS. There was no significant difference between NPS and SNPS (p < 0.05). Moreover, with increased cycles, the value of the grey-correlation degree increased. The comprehensive score of the procedure repeated nine times was significantly higher than other cycles (p < 0.05). The results of the grey-correlation degree and grade analysis showed that the best steaming process of black sesame seeds was HPS for nine cycles, followed by HPS for eight cycles and NPS after soaking in water (SNPS) for nine cycles. These findings could provide a scientific basis for replacing SNPS with HPS to simplify steaming and realize the parametric steaming of black sesame seeds, and thus, ensure the quality of black-sesame products.

Exploration And Analysis of Weathering Factors of Glass Artefacts Based on Cluster Analysis and Grey Correlation Analysis

The aim of this paper is to investigate the relationship between the chemical composition of ancient glass artefacts after weathering and to construct a model for classifying and predicting the class of artefacts based on some of the characteristics of the ancient glass artefacts obtained. Cardinality tests, information entropy, cluster analysis, support vector machines and grey correlation are mainly used to conduct the study. Through chi-square test, the relationship between weathering of glass relics and glass type, glass color and glass pattern is explained. Finally, the relationship between weathering and glass type is closer. Information entropy was used to explain the relationship between the weathering of cultural relics and the content of chemical components contained in cultural relics. Among them, potassium oxide ( O) and calcium oxide (CaO) had the largest information gain. The effective information gain of lead barium is silicon dioxide (Si ), potassium oxide (PbO), copper oxide (CuO) and sodium oxide ( O). By using cluster analysis, the law analysis and corresponding subcategories of high-potassium cultural relics and lead-barium cultural relics are made, and 3-5 subcategories are formed. Support vector machine was used to judge the type of glass relics, and the final result was that A1, A6 and A7 were high potassium. A2, A3, A4, A5, A8 are lead barium; The correlation coefficient matrix was used to analyze the correlation between the chemical components in different kinds of cultural relics, and the differences were explained based on the results of grey correlation degree analysis. Finally, the conclusion was drawn: in the firing process of high-potassium glass, the substance with high potassium content as the co-solvent did not have an essential effect on the formation of high-potassium glass. However, lead barium glass is different. As lead ore (high content of lead oxide and barium oxide) is added as co-solvent in the firing process, lead barium glass produced by firing contains a large amount of lead oxide and barium oxide.

Research on the Sustainable Development of Big Health Industry in Binhai New Area, Tianjin

The big-health industry is an emerging industry with huge market potential, which plays an important role in economic growth and industrial structure adjustment. After years of development, the big-health industry in the Binhai New Area has begun to take shape and become an important force to promote the high-quality development of regional economy. This paper tries to use the SWOT and gray correlation degree analysis method to explore the correlation degree between the big-health industry and its sub-industries, so as to provide a reference for the Binhai New Area to create deep health resources, release the development potential of the big-health industry, and promote the sustainable development of the regional economy and its people. It is estimated that in the next five years, the per-capita GDP of Beijing and Tianjin will exceed USD 15,000, and the life expectancy of the population will exceed 80 years. The Beijing–Tianjin–Hebei region will enter the high level of human development (human development index greater than 0.8). With the improvement of living standards, people’s demand for health will rise sharply, and the market prospects are broad. The Binhai New Area should seize this opportunity to accelerate the development of the big-health industry, to create momentum for regional sustainable development, and to create happiness for its people. The main objective of this study is to ascertain the sustainable development status of the big-health industry in the Tianjin Binhai New Area, as well as the direct relationship between the level of economic development and the development of the big-health industry, to lay a good economic foundation for the vigorous development of the big-health industry.

Fault Location Based on Comprehensive Grey Correlation Degree Analysis for Flexible DC Distribution Network

Flexible DC distribution networks have a strong capacity for new energy consumption and have received extensive attention from scholars in recent years, but fault location for DC distribution lines is a difficult task. To solve this problem, we propose using a comprehensive grey correlation degree analysis to analyze the similarity of aerial mode traveling differential current waveforms at the first and last ends of a DC line, thus achieving fault location by obtaining the optimal time shift. Additionally, we built a six-terminal flexible DC distribution network model in Matlab/Simulink for simulation and validation, showing that the method can complete the rapid and accurate location of all types of faults on a DC line, and that it possesses an anti-transition resistance capability, making it suitable and highly reliable for cases of low sampling frequency.

Molecular Dynamics Study on the Compatibility of Asphalt and Rubber Powder with Different Component Contents.

In order to study the influence of asphalt from different origins on the compatibility of rubber powder and asphalt, the four most representative 90# asphalts from different origins were selected for the four-component test, namely, Karamay, Shell, Esso, and HuanxiLing, which have differences in their microstructure and molecular structure. First, Materials Studio software was used to build the asphalt molecular model based on the four-component test data, and styrene butadiene rubber and natural rubber were used as representative molecules of rubber powder to build the molecular model. The solubility parameters, molecular potential energy, and radial distribution function were obtained by molecular dynamics simulation of rubber powder and four asphalt molecules. The gray correlation degree analysis of the four components of asphalt is carried out with the gray correlation method. The results show that the solubility parameter difference between Esso asphalt and the rubber molecule is the smallest, and the potential energy between them is the largest; that is, the compatibility between Esso asphalt and rubber powder is the best, followed by Shell, Huanxiling, and Karamay. The analysis of the radial distribution function between two molecules also further confirmed the compatibility of the four modified asphalt. The gray correlation degree between the four components of asphalt and the solubility and molecular potential energy index is as follows: saturates > aromatics > resin > asphaltene. It is concluded that the influence of light components on the compatibility of asphalt is greater than that of heavy components. Therefore, in future industrial production, the rubber powder-modified asphalt made of more light components has better performance and storage stability.

Analysis of the following effect of the natural enemies with Frankliniella intonsa in tea garden

AbstractIn order to reasonably protect and utilize natural enemies for comprehensive control of Frankliniella intonsa, a systematic investigation was conducted on F. intonsa and natural enemies in tea gardens in Hefei, Anhui Province, and the spatial following effect of natural enemies on F. intonsa was studied. The semi‐variogram of natural enemies and thrips was obtained by the geostatistics method, and the correlation degree between them was analyzed by the grey correlation degree analysis method. The higher the correlation degree between natural enemies and F. intonsa, the closer the relationship between natural enemies and F. intonsa was. In this study, we analyzed the relationship between 11 natural enemies of F. intonsa in tea gardens from October 28, 2020 to November 20, 2020 and from August 22, 2021 to November 19, 2021. The top five natural enemies with the largest closeness index to F. intonsa in Huangshan Dayezhong tea garden are Oxyopes sertatus, Theridion octomaculatum, Plexippus setipes, Xysticus ephippiafus and Erigonidium graminicolum. The top five natural enemies with the largest closeness index to F. intonsa in Pingyang Tezao tea garden are Erigonidium graminicolum, Plexippus setipes, Xysticus ephippiafus, Oxyopes sertatus and Clubiona japonicola. Among the top five natural enemies in the two tea gardens, those in common are Oxyopes sertatus, Erigonidium graminicolum, Xysticus ephippiafus and Plexippus setipes. According to the summation of the closeness index and the sum of the serial numbers, the top five natural enemies closely related to the spatial following of F. intonsa in the tea gardens were Oxyopes sertatus, Plexippus setipes, Erigonidium graminicolum, Xysticus ephippiafus and Theridion octomaculatum. One of the factors closely related to the space of F. intonsa in the same tea garden was the ratio of F. intonsa to a certain natural enemy. The smaller the ratio was, the closer the following relationship was. The results of this study provide scientific basis for biological control and natural enemy protection of F. intonsa.

Drought resistance identification and drought resistance indexes screening of Tibetan hulless barley resources at adult stage

<p id="C3">Drought is one of the main factors affecting the production of hulless barley in the Qinghai-Tibet Plateau. Identification of drought resistance of hulless barley germplasm resources at adult stage and screening of reliable drought resistance indexes can provide basis for breeding drought resistant varieties. In the present study, a total of 238 hulless barley germplasms were used as materials, and two treatments of normal irrigation and drought stress in the field conditions were conducted in Zhangye of Gansu Province in 2019 and 2020, respectively. Seven indexes, such as the plant height, spike number per plant, biomass per plant, grain weight per spike, spike length, 1000-grain weight, and yield were determined. The drought resistance comprehensive evaluation value (<italic>D</italic>-value), comprehensive drought resistance coefficient (CDC-value), and weight drought resistance coefficient (WDC-value) were used to identify drought resistance at adult stage of hulless barley germplasms. The results showed that the effects of drought stress on all indexes were extremely significant, and the correlation analysis showed that the yield was significantly positively correlated with plant height and 1000-grain weight at<italic> P </italic>&lt; 0.01, and significantly positively correlated with the grain weight per panicle at <italic>P</italic>&lt;0.05. Frequency analysis showed that the sensitivity of seven indexes to drought stress was plant height, yield, biomass per plant, grain weight per ear, number of ears per plant, ear length and 1000-grain weight, respectively. The ranks of drought resistance of germplasms based on the <italic>D</italic>-value, CDC-value, and WDC-value were similar. Grey correlation degree analysis showed that the correlation degree between DC-value and <italic>D</italic>-value was in the order of yield, biomass per plant, grain weight per spike, plant height, number of spikelets per plant, ear length and 1000-grain weight, which was basically consistent with the close degree between DC-value and WDC-value of each index. Stepwise regression analysis showed that yield, plant height, panicle number per plant, 1000-grain weight and biomass per plant could be used as reliable indexes for drought resistance identification of hulless barley in adult stage. <italic>D</italic>-value was determined as the appropriate method for drought resistance identification. Based on <italic>D</italic>-value, the tested hulless barley germplasms were divided into five drought resistance grades, and 38 germplasms were classified in grade I, such as Ziyanwoqingke, Liulengqingke, Ziqingke, ZDM8125, Tuhuluqingke, etc. These results provided important basic materials and theoretical reference for the breeding of drought-resistant varieties and the study of drought-resistant mechanism of hulless barley.

Optimization method of microwave drying process parameters for rice

In this work, the optimal process to improve the efficiency of microwave drying of rice is studied. The optimal conditions for reducing the crack rate and improving the head rice rate are analyzed according to the principle of the orthogonal experiment. The optimization method of the rice microwave drying process parameters is proposed to simultaneously consider the two indicators, namely, crack and head rice rates, based on the grey system theory. The grey correlation degree analysis is carried out on the process conditions of the microwave drying rice. The result shows that the main factor affecting the integrity rate of the rice grain is the vacuum degree, and the main factor affecting the crack rate is the loading density. The grey correlation degree curve of head rice and crack rate fluctuates greatly, and the correlation between the two indexes is high. According to the grey correlation degree, the optimal parameters are microwave power of 500 W, vacuum degree of 0.02 MPa, and loading density of 1289.89 kg/m3. The head rice and crack rate are both optimized when drying under the parameters. The results provide a guiding method for the study of the correlation degree between drying indexes and provide a method basis for the further selection of the best drying process.

Multi-Objective Nonlinear Programming Model for Reducing Octane Number Loss in Gasoline Refining Process Based on Data Mining Technology

To simultaneously reduce automobile exhaust pollution to the environment and satisfy the demand for high-quality gasoline, the treatment of fluid catalytic cracking (FCC) gasoline is urgently needed to minimize octane number (RON) loss. We presented a new systematic method for determining an optimal operation scheme for minimising RON loss and operational risks. Firstly, many data were collected and preprocessed. Then, grey correlative degree analysis and Pearson correlation analysis were used to reduce the dimensionality, and the major variables with representativeness and independence were selected from the 367 variables. Then, the RON and sulfur (S) content were predicted by multiple nonlinear regression. A multi-objective nonlinear optimization model was established with the maximum reduction in RON loss and minimum operational risk as the objective function. Finally, the optimal operation scheme of the operating variable corresponding to the sample with a RON loss reduction greater than 30% in 325 samples was solved in Python.

Research based on the factors affecting the preparation of olefin

The demand for olefin is increasing in countries. At present, prince's principle is mainly used for catalytic coupling ethanol to prepare olefin. This method is cheaper and greener, but there are many problems in the process of preparing olefin. The influence of temperature on ethanol conversion and C4 olefins selectivity was studied by nonlinear regression simulation [1]. Then, the grey correlation degree analysis was used to establish the grey prediction model, and the ranking of the influence of each factor on ethanol conversion was temperature, ethanol concentration, etc., and the ranking of the influence of each factor on C4 olefins selectivity was mass[2], HAP total mass, ethanol flow rate, etc. Finally, the optimum temperature and catalyst combination of olefin preparation were analyzed.

Kinetic analysis on water adsorption of thermally upgraded lignite

An in-situ water adsorption analyzer was designed and set up for obtaining water dynamic adsorption process of thermally upgraded lignite under relative humidity (RH) of 55%–85% at 20 °C and 30 °C. Four upgraded samples with different pore structures and oxygen functional groups were prepared at 120, 160, 240 and 320 °C. Grey correlation degree analysis method was used to study the correlation between water adsorption capacity and its influencing factors. The results showed that upgrading temperature was the dominant factor influencing water adsorption of upgraded coals compared to RH and environmental temperature. The dynamic adsorption process of water on coal could be well described by Intra-particle diffusion model, and it could be divided into surface adsorption stage, pore diffusion stage, and dynamic equilibrium stage. The rate-controlling factors of surface adsorption stage were oxygen functional groups and specific surface area, while those of pore diffusion stage were pore volume and pore size. During adsorption under RH 55%, water was firstly adsorbed on coal surface through oxygen functional groups with a relatively fast rate, which was orderly followed by formation of water clusters and capillary condensation of water in pores with a relatively slow rate.

Utilizing comprehensive decision analysis methods to determine an optimal planting pattern and nitrogen application for winter oilseed rape

Oilseed rape is one of the most important oil crops globally. Attaining the appropriate cultivation method (planting pattern and nitrogen level) is necessary to achieve high yield, quality and resource utilization efficiency. However, the optimal method for oilseed rape varies across countries and regions. The objective of the present study was to determine an appropriate cultivation method, including planting pattern and nitrogen application, for winter oilseed rape in northwestern China. Two planting patterns: ridge film mulching and furrow planting (RFMF) and flat planting (FP), and six nitrogen (N) amounts: 0 (N0), 60 (N60), 120 (N120), 180 (N180), 240 (N240), and 300 (N300) kg N ha−1 were applied across three growing seasons (2014–2017). Three comprehensive decision analysis methods: principal component analysis, grey correlation degree analysis and the combined entropy weight and dynamic technique for order preference by similarity to ideal solution method were used to evaluate the growth and physiological indicators, nutrient uptake, yield, quality, evapotranspiration, and water use efficiency of winter oilseed rape. Planting pattern, nitrogen amount and their interaction significantly affected the indicators aforementioned. The RFMF pattern significantly increased all indicators over the FP pattern. Application of N also markedly increased all the indicators except for seed oil content, but the yield, oil production and water use efficiency were decreased when N fertilizer exceeded 180 kg N ha−1 under FP and 240 kg N ha−1 under RFFM. The evaluation results of the three comprehensive decision analysis methods indicated that RFMF planting pattern with 240 kg N ha−1 is an appropriate cultivation method for winter oilseed rape in northwestern China. These findings are of vital significance to maximize yield, optimize quality and improve resource use efficiencies of winter oilseed rape.

Research on the Evaluation of Information Awareness of Teachers in Contemporary Colleges and Universities Based on Grey Relation Degree Analysis

The information awareness of college teachers directly affects their speed and ability to obtain, judge and use information, and then affects the effectiveness of information teaching. In view of this, on the basis of constructing a scientific and systematic Information awareness evaluation index system for university teachers in the information age, eight experts were invited to empower the first-level indicators of the constructed indicator system; The evaluation model of information consciousness of college teachers in the information age was constructed by using grey correlation degree analysis method. Ten teachers of university in Changsha were selected to make an empirical evaluation of their information awareness, and the evaluation results were analyzed briefly.