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Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

The purpose of this paper is to investigate the impact of buyer-seller interpersonal interactions on the purchase intention of buyers, incorporating swift guanxi as a mediator.

Based on survey data obtained from 336 Taobao Live users, PLS techniques were used to test hypotheses.

Swift guanxi exists in buyer-seller interactions and matters, as it drives buyers' purchase intention in live stream shopping. Perceived expertise, perceived similarity and perceived likeability are found to be the three essential interpersonal interaction factors promoting the formation of swift guanxi. Perceived familiarity is also found to be significant but to a lesser extent. In addition, all these interpersonal interaction factors are found to significantly affect purchase intention through the mediation of swift guanxi.

Swift guanxi has been less explored in live stream shopping. This study takes the lead in empirically examining the mediating role of swift guanxi in the relationship between interpersonal interaction factors and purchase intention and offers a description of key buyer-seller interpersonal interaction factors (perceived expertise, perceived similarity and perceived likeability), thereby helping to extend the swift guanxi literature in social commerce.

The purpose of this study to provide a reference basis for effectively managing the risk of agrometeorological disasters in Henan Province, speeding up the establishment of a scientific and reasonable system of agrometeorological disasters prevention and reduction and guaranteeing grain security.

Firstly, according to the statistical data of areas covered by natural disaster, areas affected by natural disaster, sown area of grain crops and output of grain crops from 1979 to 2018 in Henan Province, China. We have constructed an agrometeorological disaster risk assessment system for Henan province, China, which is composed of indicators such as rate covered by natural disaster, rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability. The variation characteristics of agrometeorological disasters in Henan Province and their effects on agricultural production are analyzed. Secondly, the grey relational analysis method is used to analyze the relation degree between the main agrometeorological disaster factors and the output of grain crops of Henan Province. Based on the grey BP neural network, the rate covered by various natural disaster and the rate affected by various natural disaster are simulated and predicted.

The results show that: (1) the freeze injury in the study period has a greater contingency, the intensity of the disaster is also greater, followed by floods. Droughts, windstorm and hail are Henan Province normal disasters. (2) According to the degree of disaster vulnerability, the ability to resist agricultural disasters in Henan Province is weak. (3) During the study period, drought and flood are the key agrometeorological disasters affecting the grain output of Henan Province, China.

The systematic analysis and evaluation of agrometeorological disasters are conducive to the sustainable development of agriculture, and at the same time, it can provide appropriate and effective measures for the assessment and reduction of economic losses and risks.

By calculating and analyzing the rate covered by natural disaster, the rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability of crops in Henan Province of China and using grey BP neural network simulation projections for the rate covered by various natural disaster and the rate affected by various natural disaster, the risk assessment system of agrometeorological disasters in Henan is constructed, which provides a scientific basis for systematic analysis and evaluation of agrometeorological disasters.

With the massive use of fossil energy polluting the natural environment, clean energy has gradually become the focus of future energy development. The purpose of this article is to propose a new hybrid forecasting model to forecast the production and consumption of clean energy.

Firstly, the memory characteristics of the production and consumption of clean energy were analyzed by the rescaled range analysis (R/S) method. Secondly, the original series was decomposed into several components and residuals with different characteristics by the ensemble empirical mode decomposition (EEMD) algorithm, and the residuals were predicted by the fractional derivative grey Bernoulli model [FDGBM (p, 1)]. The other components were predicted using artificial intelligence (AI) models (least square support vector regression [LSSVR] and artificial neural network [ANN]). Finally, the fitting values of each part were added to get the predicted value of the original series.

This study found that clean energy had memory characteristics. The hybrid models EEMD–FDGBM (p, 1)–LSSVR and EEMD–FDGBM (p, 1)–ANN were significantly higher than other models in the prediction of clean energy production and consumption.

Consider that clean energy has complex nonlinear and memory characteristics. In this paper, the EEMD method combined the FDGBM (P, 1) and AI models to establish hybrid models to predict the consumption and output of clean energy.

With the rapid development of e-commerce in China, the third-party payment system greatly improved the efficiency and volume of the entire trading market. The purpose of this paper is to put forward a suitable prediction model to analyse its development trend.

The authors analyse internet third-party payments in China, taking into account online payment transaction values coupled with an ARMA model and the fractional grey model (FGM). First, the rolling FGM model is applied in order to characterise the trends of the transaction volume. The influence of the initial value change on the FGM model is analysed. The optimisation mean absolute percentage error (MAPE) model is constructed to determine the optimal translational values, the corresponding optimal accumulation order and optimal inverse accumulation order.

This paper uses China’s recent third-party online payment data to quantify its development trend. The authors find the coupling model suitable for the development trend of third-party online payment transaction. The results show that the model is suitable to quantify its development trend of China’s recent third-party online payment.

Considering the complex influence factors that lead to the third-party online payment volume data of time-varying grey feature, this paper combines the FGM with ARMA model to describe the development of third-party payment mode.

The purpose of this paper is to research the morphologies of the oxide films formed on the internal surfaces of water wall tubes in a 600 MW furnace at 300° while using CPT, CT, AVT(R) and AVT(O) water chemistry. In these water chemistry conditions, a layer of oxide film spontaneously forms in the furnace wall which could prevent corrosions in boiler water directly contact with the inner tube and reduce the probability of tube perforation.

The different morphologies, specific functions and distribution in the oxide film were identified by electrochemical workstation, XRD, SEM and EDAX.

It is concluded that metal surface was rugged and had deep corrosion in CPT. Ions penetrated into the oxides of large particles with gaps and intergranular corrosion occurred in CT conditions. In AVT(R), the oxide film uniformly covered on the metal surface played a protective role, but could be easily washed away by solution. The oxide film formed in AVT(O) was similar to AVT(R), but the difference is that large solid particles of Fe₂O₃ cover the outermost oxide film, which prevents the oxide film from being taken away by the flowing solution. In consequence, the degree of corrosion sustained by the tube walls is lowest in the case of AVT(O).

The results can provide reference for reducing the high temperature corrosion of metal in the actual operation.

This paper critically examines the effectiveness of male anchor in cross-gender endorsements and questions whether it can truly deliver positive outcomes for advertisers in the context of live streaming. It explores the underlying mechanisms of this effect by examining the mediation effect of perceived gender-identity incongruence and the moderation effect of anchor presence.

Three experiments are conducted to examine the effect of cross-gender endorsement on purchase intention.

The findings from three experiments provide empirical evidence that the endorsement of female-gendered products by male anchors leads to a significant decrease in the evaluation of these products among female consumers. This negative effect is mediated by a sense of gender-identity incongruence experienced by female consumers. Furthermore, the study demonstrates that female customers exhibit higher purchase intent for female-gendered products endorsed by male virtual anchors compared to real anchors; however, the same pattern was not observed in the case of female anchors.

This paper empirically examines the possible negative effects of the male anchor endorsement in the live streaming context. It reveals the underlying mechanism of this negative effect, and how the virtual “presence” take a role in this underlying mechanism.

Given the effects of natural and social factors, data on both the supply and demand sides of electricity will produce obvious seasonal fluctuations. The purpose of this article is to propose a new dynamic seasonal grey model based on PSO-SVR to forecast the production and consumption of electric energy.

In the model design, firstly, the parameters of the SVR are initially optimized by the PSO algorithm for the estimation of the dynamic seasonal operator. Then, the seasonal fluctuations in the electricity demand data are eliminated using the dynamic seasonal operator. After that, the time series after eliminating of the seasonal fluctuations are used as the training set of the DSGM(1, 1) model, and the corresponding fitted, and predicted values are calculated. Finally, the seasonal reduction is performed to obtain the final prediction results.

This study found that the electricity supply and demand data have obvious seasonal and nonlinear characteristics. The dynamic seasonal grey model based on PSO-SVR performs significantly better than the comparative model for hourly and monthly data as well as for different time durations, indicating that the model is more accurate and robust in seasonal electricity forecasting.

Considering the seasonal and nonlinear fluctuation characteristics of electricity data. In this paper, a dynamic seasonal grey model based on PSO-SVR is established to predict the consumption and production of electric energy.

This study compares the informativeness and the appeals used in magazine advertising in China with those used in France and in the United States. It provides international marketers with a snapshot of current magazine advertising tactics and provides scholars with an assessment of how consistently expectations based on seminal cross-cultural research predict the informativeness and the appeals used in magazine advertising in each country. Surprisingly, the expectations based on the literature were most often inconsistent with the observations. These findings should serve as a reminder to international marketing scholars that the seminal cross-cultural works, as useful as they are in providing cultural insights, were never intended to apply equally to all subgroups (e.g., the Little Emperors) within a culture.