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The purpose of this paper is to establish a rapid and effective numerical model of thin film lubrication with clear physical conception, in which viscosity variation along the direction of film thickness was used instead of average viscosity, and continuous Reynolds equation was used in the calculation of thin film lubrication.

Based on rheology and thin film lubrication with point contact and considering features of shear thinning and like-solidification of lubricant oil in the thin film lubrication state, a modified formula with overall average equivalent viscosity was proposed by combining numerical calculation and experiment data.

It is a fast and efficient method for film lubrication state simulation.

Thin film lubrication research on a nanoscale is very popular, and a variety of thin film lubrication models are proposed. Due to the complexity of thin film lubrication, it is still in the stage of revealing law and establishing calculation model.

The key issue is how to obtain the viscosity correction formula derived from engineering practice, also considered the lubricating oil class solidification and shear-thinning properties on thin film lubrication, while based on the system experiment, the viscosity modified formula for the gap, speed changes are proposed to obtain the overall average equivalent viscosity which makes the thin film lubrication micro to macro, so that a clear physical meaning for thin-film lubrication numerical calculation model is established.

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.

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 elevate the accuracy when predicting the gross domestic product (GDP) on research and development (R&D) and to develop the grey delay Lotka-Volterra model.

Considering the lag effects between input in R&D and output in GDP, this paper estimated the delay value via grey delay relation analysis. Taking the delay into original Lotka-Volterra model and combining with the thought of grey theory and grey transform, the authors proposed grey delay Lotka-Volterra model, estimated the parameter of model and gave the discrete time analytic expression.

Collecting the actual data of R&D and GDP in Wuhan China from 1995 until 2008, this paper figure out that the delay between R&D and GDP was 2.625 year and found the dealy time would would gradually be reduced with the economy increasing.

Constructing the grey delay Lotka-Volterra model via above data, this paper shown that the precision was satisfactory when fitting the data of R&D and GDP. Comparing the forecasts with the actual data of GDP in Wuhan from 2009 until 2012, the error was small.

The result shows that R&D and GDP would be both growing fast in future. Wuhan will become a city full of activity.

Considering the lag between R&D and GDP, this work estimated the delay value via a grey delay relation analysis and constructed a novel grey delay Lotka-Volterra model.

The purpose of this paper is to investigate the influence of Cl‐ions on the pitting corrosion of water‐wall tube of a boiler and the principle behind it.

The specimens were immersed for seven hours at 300°C in deaerated water subjected to simulation‐modified equilibrium phosphate treatment, containing Cl‐ions at various concentrations. The effects of Cl‐ions on pitting corrosion were assessed by the rate mass loss, transmission reflection metallurgical microscopy, SEM, EDS, and XRD.

The results indicated that Cl‐ions cause the breakdown of passive films. The corrosion mechanism of Cl‐is proposed to involve an intermediate dissolution stage. The Cl‐ions act as a catalyst of corrosion, by inducing the hydrolysis of Fe2+. The critical susceptive Cl‐concentrations are 0.2 and 0.6 mg·L^‐1 for the passivated specimens and for the unpassivated specimens, respectively.

The paper provides information regarding the relationship between Cl‐concentrations and pitting corrosion, useful for understanding the mechanism of Cl‐induced pitting corrosion, and the research results can provide theoretical guidelines for preventing water‐wall of power plants from corroding.

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.

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.

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.