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This paper applied grey wave forecasting in a decomposition–ensemble forecasting method for modelling the complex and non-linear features in time series data. This application aims to test the advantages of grey wave forecasting method in predicting time series with periodic fluctuations.

The decomposition–ensemble method combines empirical mode decomposition (EMD), component reconstruction technology and grey wave forecasting. More specifically, EMD is used to decompose time series data into different intrinsic mode function (IMF) components in the first step. Permutation entropy and the average of each IMF are checked for component reconstruction. Then the grey wave forecasting model or ARMA is used to predict each IMF according to the characters of each IMF.

In the empirical analysis, the China container freight index (CCFI) is applied in checking prediction performance. Using two different time periods, the results show that the proposed method performs better than random walk and ARMA in multi-step-ahead prediction.

The decomposition–ensemble method based on EMD and grey wave forecasting model expands the application area of the grey system theory and graphic forecasting method. Grey wave forecasting performs better for data set with periodic fluctuations. Forecasting CCFI assists practitioners in the shipping industry in decision-making.

The purpose of this study is to provide effective and environmental-friendly corrosion inhibitors derived from graphene oxide for Q235 steel.

Nontoxic and environment-friendly 4-aminobenzoic acid was used to functionalize graphene oxide via amidation and diazotization. The obtained amidation 4-aminobenzoic acid functionalized graphene oxide (PAGO) and diazotization 4-aminobenzoic acid functionalized graphene oxide (PDGO) were characterized by FTIR, Raman and TEM, while the inhibition efficiencies were analyzed by electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP). Furthermore, theoretical inhibition efficiencies were investigated by density functional theory (DFT) approach.

At a concentration of 40 ppm, the maximum inhibition efficiency of PAGO and PDGO were 97.90% and 96.72% in EIS measurement, respectively, which were in accordance with PDP data. Moreover, experimental results were supported by DFT-based quantum chemical calculation.

Environmental-friendly PAGO and PDGO were synthesized successfully. The synthetic inhibitors exhibited excellent inhibition efficiencies in EIS and PDP measurements. Furthermore, a computational study using DFT supported the trend that PAGO was better inhibitor than PDGO.

This study aims to investigate the effects of graphite-MoS₂ composite solid lubricant on the tribological properties of copper-based bearing materials under dry conditions.

The mixture of Graphite-MoS₂ was inlaid in ZQSn6-6–3 tin bronze and ZQAl9-4 aluminum bronze matrix. These copper-embedded self-lubricating bearing materials were considered in friction pairs with 2Cr13 stainless steel, and their tribological properties were studied by using an MM200 wear test machine.

The results show that the friction coefficients and wear rates of copper-embedded self-lubricating bearing materials are lower than those of the ordinary copper-based bearing materials. The wear performance of the tin bronze inlaid self-lubricating bearing material is better than that of the aluminum bronze inlaid self-lubricating bearing material. The wear mechanism of the tin bronze bearing material is mainly adhesive wear, and that of the aluminum bronze bearing material is mainly grinding wear, oxidation wear and adhesive wear. The copper-embedded self-lubricating bearing materials had no obvious abrasion, whereas the aluminum bronze inlaid self-lubricating bearing material exhibited deep furrows and obvious abrasion under high loads.

These results are helpful for the application of copper-embedded self-lubricating bearing materials.

Parallel factor analysis (PARAFAC) coupled with support-vector machine (SVM) was carried out to identify and discriminate between the fluorescence spectroscopies of coconut water brands.

PARAFAC was applied to reduce three-dimensional data of excitation emission matrix (EEM) to two-dimensional data. SVM was applied to discriminate between six commercial coconut water brands in this study. The three largest variation data from fluorescence spectroscopy were extracted using the PARAFAC method as the input data of SVM classifiers.

The discrimination results of the six commercial coconut water brands were achieved by three SVM methods (Ga-SVM, PSO-SVM and Grid-SVM). The best classification accuracies were 100.00%, 96.43% and 94.64% for the training set, test set and CV accuracy.

The above results indicate that fluorescence spectroscopy combined with PARAFAC and SVM methods proved to be a simple and rapid detection method for coconut water and perhaps other beverages.

This study explores whether interdisciplinary components' citation intensity (ICCI) affects papers' scientific impact. In this study, the term “interdisciplinary components” refers to the disciplines that are different from the discipline to which the target research belongs. The citation intensity is the degree of density or sparseness of the paper citation network for a discipline. Previous studies have shown that the scientific impact of interdisciplinary research is influenced by interdisciplinarity and its properties, namely, variety, balance and disparity. However, the effect of ICCI on scientific impact has not been comprehensively explored.

This study is based on the entire publication database of the Web of Science for the year 2000, where the authors provide an indicator to measure the ICCI of each publication. A tobit regression model is used to examine the effect of ICCI on scientific impact, controlling for a range of variables associated with the characteristics of the publications studied.

The results show that ICCI has a positive effect on scientific impact. The authors’ results further point out that ICCI displays a curvilinear inverted U-shape relationship with scientific impact. It means that including more citation-intensive interdisciplinary components can increase the scientific impact of interdisciplinary research. However, excessive use of citation-intensive interdisciplinary components may reduce the scientific impact of interdisciplinary research.

This study shows that, in addition to interdisciplinarity, the scientific impact of interdisciplinary research is also affected by the citation characteristics of interdisciplinary components, namely ICCI.

This paper aims to explore the citation aging pattern of Library and Information Science (LIS) and further investigate the impact of interdisciplinary citation on the aging of scientific literature.

The study examines LIS journal articles published between 2016 and 2020. Articles were retrieved from the Web of Science (WoS) and were organized using Scopus's discipline classification system. Citation aging patterns within LIS are described using literature aging indicators. The study examines the effect of interdisciplinary citations on the literature aging pattern by comparing the half-life of LIS literature and the median age of interdisciplinary citations.

The study results show that the citation aging rate of LIS in the last five years has been slow, and the rate of slowing down is decreasing. Interdisciplinary citations are sourced from various disciplines, focusing on computer science, social sciences and business. The proportion of self-citations is declining. The Reference Diversity Index (RDI) increases from 0.690 to 0.724 between 2016 and 2020. Currently, the median age of interdisciplinary citations is higher than the LIS's half-life. It has a diminishing effect on the citation aging rate. But the median age of interdisciplinary citations is decreasing. The interdisciplinary citation may contribute to the literature aging rate in the future. The effect of interdisciplinary citation on literature aging needs to be judged dialectically.

This study still has some limitations. Due to the wide variety of citation journals in LIS, there is no database to cover all journals, so it is impossible to match all citation journals with disciplines. Therefore, it is still feasible to analyze interdisciplinary citations based on the two-eight principle for large-scale data. This approach necessarily sacrifices some of the precision of the study. However, the results of this paper can still be helpful for the development of the discipline. In addition, LIS is a discipline with solid cross-cutting properties, and this paper concludes only with this interdisciplinary discipline in mind. It is necessary to test the applicability of the findings to other disciplines.

The study explores the impact of interdisciplinary citation on literature aging from a professional communication perspective. The results reveal underlying reasons for the aging of scientific literature. These findings further enrich the study of the effect of interdisciplinary communication.

This paper focuses on the differences in domain intellectual structure discovery between author bibliographic coupling analysis (ABCA) and author co-citation analysis (ACA) considering all authors. The purpose of this study is to examine whether and in what ways these two all-author network approaches yield different results.

The sample was collected from the database of Web of Science, including all articles published in Scientometrics and Journal of Informetrics from 2011 to 2020. First, 100 representative authors were selected from each set, and ABCA matrices and ACA matrices were constructed. Second, factor analysis was carried out on the matrices, to detect the intellectual structure of scientometrics and informetrics.

The intellectual structures identified by ABCA and ACA are similar overall, but the results differ somewhat when it comes to specific structures. The ABCA is more sensitive to some highly collaborative research teams and presents a clearer picture of current intellectual structures and trends while ACA seems to have some advantages in representing the more traditional and proven research topics in the field. The combined use of ABCA and ACA allows for a more comprehensive and specific intellectual structure of research fields.

This paper compares the performance of ABCA and ACA detecting the intellectual structure of the domain from the perspective of all authors, revealing the intellectual structure of scientometrics and informetrics comprehensively.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2020-0540.

Under the background of coexistence of information overload and information fragmentation, it is of great significance to identify influencing factors and reveal the evolution logic of public opinion for public opinion governance.

Taking 24 hot social events as research cases, firstly, the evolution process of public opinion was divided into initial stage and response stage. Secondly, eight antecedent variables were extracted for qualitative comparative analysis of fuzzy sets. Finally, the configuration path of public opinion evolution results was summarized.

The research showed that compared with the initial stage, the influencing factors in the reaction stage played a key role in the continuous evolution of public opinion. The influencing factors in the initial stage and response stage played an indispensable role in promoting the evolution of public opinion to calm down.

This research can provide reference for regulators to timely grasp the initiative, discourse power and leadership of public opinion development.

Research on the two-stage configuration path of public opinion evolution is helpful to clarify the key factors affecting the evolution trend of online public opinion of hot events.

Which venture capital is more beneficial in the product innovation of entrepreneurial ventures? The authors study the drawbacks and different effects of corporate venture capital (CVC) and independent venture capital (IVC) on the effectiveness and efficiency of product innovation in entrepreneurial ventures to answer this question.

This study uses a panel dataset of 502 high-tech ventures and runs the Heckman model to correct potential endogeneity issues.

The authors find that CVC increases the product innovation effectiveness of entrepreneurial ventures, but decreases their efficiency. IVC reduces innovation effectiveness and enhances efficiency. However, CVC performs less positively, while IVC performs more positively in terms of innovation effectiveness and efficiency in the B2B market than in the B2C market.

This study provides insights into how to leverage venture capital to develop new products effectively and efficiently.

This study moves beyond the current understanding of the finance-marketing interface. It delineates the two faces of venture capital and reveals the joint effects of equity stakes and market stakes between different types of venture capital and transaction markets in product innovation.

Legislation plays a vital role in solving carbon emissions reduction and climate change issues. China began to implement a low-carbon economic policy in 2010, but the effect of the policy needs to be evaluated. Accordingly, this paper aims to discuss China’s low-carbon policy through exploring the following two questions, namely, whether the policy effect reaches the expected goal and whether the policy effects will balance economic development and emission reduction. Then, the paper puts forward suggestions for the improvement of China’s low-carbon policy.

This paper is organized around three distinct aspects of policy effect evaluation. This paper uses the synthetic control method to construct a policy effect evaluation model and conducts a quasi-natural experiment. The paper selects annual panel data from 2003 to 2015, which is selected from 33 provinces. A comparative analysis of carbon dioxide emissions, energy consumption and economic development between Hubei Province and Liaoning Province.

The results reveal that the implementation of the low-carbon pilot province policy in 2010 has a significant impact on the emission reduction effect of Liaoning Province, but the impact on the emission reduction effect of Hubei Province is not significant. The carbon emission trading system implemented in 2012 has reduced the emission reductions in Hubei Province and Liaoning Province has achieved better emission reduction effects after the implementation of this policy. After the implementation of the policy, the economic development of Hubei Province has been improved, but it has not brought help to the economic development of Liaoning Province. These findings provide new insights into the use of an emissions trading system for improving economic development and ultimately facilitate the attainment of the broader goal of sustainability.

This paper proposes an innovative policy effect evaluation method by considering the status of unit gross domestic product, fixed asset investment in the energy industry, energy consumption, emission reduction technology innovation and other evaluation indicators. This paper contributes to broadening current methods of policy effect evaluation in China.