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The purpose of this paper is to present a scheduling model, scheduling algorithms, and formal model and analysis techniques for concurrency transaction in grid database environment.

Classical transaction models and scheduling algorithms developed for homogeneous distributed architecture will not work in the grid architecture and should be revisited for this new and evolving architecture. The conventional model is improved by three‐level transaction scheduling model and the scheduling algorithms for concurrency transaction is improved by considering transmission time of a transaction, user's priority, and the number of database sites accessed by the transaction as a priority of the transaction. Aiming at the problems of analysis and modeling of the transaction scheduling in grid database, colored dynamic time Petri nets (CDTPN) model are proposed. Then the reachability of the transaction scheduling model is analyzed.

The three‐level transaction scheduling model not only supports the autonomy of grid but also lightens the pressure of communication. Compared with classical transaction scheduling algorithms, the algorithms not only support the correctness of the data but also improve the effectiveness of the system. The CDTPN model is convenient for modeling and analyzing dynamic performance of grid transaction. Some important results such as abort‐ratio and turnover‐time are gotten by analyzing reachability of CDTPN.

The three‐level transaction scheduling model and improved scheduling algorithms with more complex priority are presented in the paper. The paper gives a CDTPN model for modeling transaction scheduling in grid database. In CDTPN model, the time interval of a transition is a function of tokens in input places of the transition.

Given that women around the world face more exclusion in terms of limited access to economic and innovation activities than men do, it is understandable that some female-led start-ups in weak institutional environments engage in bribery to help reduce the difficulties they encounter in the face of intense business competition. However, the link between bribery and product innovation performance is unclear. The purpose of this study is to investigate the relationship between bribery and product innovation performance for female-led start-ups, as well as the roles of institutional support and self-control in this link.

This empirical study evaluates survey data from female-led manufacturing start-ups in China's Yangtze River Delta region to investigate the relationship between bribery and product innovation performance.

This research shows that bribery has an inverted U-shaped impact on product innovation performance in female-led manufacturing start-ups, meaning that the product innovation performance of these firms initially increases but then decreases as the bribery intensity (i.e. the frequency and amount of bribes) increases. The authors also focus on the roles of institutional support and self-control in this link, where the authors find that this relationship is steeper for firms with strong institutional support, as well as for individual female entrepreneurs who have high levels of self-control.

The findings of this study indicate that policymakers should undertake efforts to improve institutional quality (e.g. increasing clarity around decisions, providing more institutional support, etc.) and to guide female entrepreneurs to cultivate higher levels of self-control, as such efforts would reduce the appeal of, and the opportunity for, bribery.

To date, very few studies focus specifically on female-led enterprises in the field of bribery research. The research findings presented here on the effect of bribery in female-led start-ups on firm product innovation performance are useful to researchers, policymakers and businesspeople, as they provide a better understanding of bribery in female-led start-ups in China, which can also be extrapolated to encompass other transition economy contexts.

The stress state near the notch affects fatigue damage directly, but quantifying the stress field is difficult. The purpose of this study is to provide a mathematical description method of the stress field near the notch to achieve a reliable assessment of the fatigue life of notched specimens.

Firstly, the stress distribution of notched specimens of different materials and shapes under different stress levels is investigated, and a method for calculating the stress gradient impact factor is presented. Then, the newly defined stress gradient impact factor is used to describe the stress field near the notch, and an expression for the stress at any point along a specified path is developed. Furthermore, by combining the mathematical expressions for the stress field near the notch, a multiaxial fatigue life prediction model for notched shaft specimens is established based on the damage mechanics theory and closed solution method.

The stress gradient factor for notched specimens with higher stress concentration factors (V60-notch, V90-notch) varies to a certain extent when the external load and material change, but for notched specimens with relatively lower stress concentration factors (C-notch, U-notch, stepped shaft), the stress gradient factor hardly varies with the change in load and material, indicating that the shape of the notch has a greater influence on the stress gradient. It is also found that the effect of size on the stress gradient factor is not obvious for notched specimens with different shapes, there is an obvious positive correlation between the normal stress gradient factor and the normal stress concentration factor compared with the relationship between the shear stress gradient factor and the stress concentration factor. Moreover, the predicted results of the proposed model are in better agreement with the experimental results of five kinds of materials compared with the FS model, the SWT model, and the Manson–Coffin equation.

In this paper, a new stress gradient factor is defined based on the stress distribution of a smooth specimen. Then, a mathematical description of the stress field near the notch is provided, which contains the nominal stress, notch size, and stress concentration factor which is calculated by the finite element method (FEM). In addition, a multiaxial fatigue life prediction model for shaft specimens with different notch shapes is established with the newly established expressions based on the theory of damage mechanics and the closed solution method.

The purpose of this paper is to examine the influences of positive-framed and negative-framed green advertising on pro-environmental WTP. This study also explores the impacts of regulatory focus, environmental concern and pleasant level on green advertising effectiveness.

Data are collected from a within-participant between-group online experiment in China. The generalized estimating equation (GEE) is employed to investigate the impact of green advertising on WTP. Grouped regression and mediation analyses are conducted to explore the influences of regulatory focus, environmental concern and pleasure on advertising efficacy.

The experimental outcomes indicate that green advertising significantly increases participants’ pro-environmental WTP, and negative-framed advertising is more effective than its positive-framed counterpart. Prevention focus heightens receptivity to green advertising, and the relation of environmental concern to advertising effectiveness is inverted U-shaped. Pleasure mediates the effect of green advertising on the WTP, and this mediating role is influenced by emotional intensity when advertising is negatively framed.

Evidence suggests that green advertising may propel pro-environmental WTP by raising environmental awareness, but such a relationship remains severely understudied. As such, this study pioneers in exploring the impact of different-framed green advertising on pro-environmental WTP, extending the concept of green advertising to environmental management. By considering the influences of regulatory focus, environmental concern and pleasure, this study raises practical implications for designing green advertisements, such as increasing the usage of visual elements.

The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

This paper aims to deal with the grey relational problem of panel data with an attribute value of interval numbers. The grey relational model of interval number for panel data is constructed in this paper.

First, three kinds of interval grey relational operators for the behavior sequence of a dimensionless system are proposed. At the same time, the positive treatment method of interval numbers for cost-type and moderate-type indicators is put forward. On this basis, the correlation between the three-dimensional interval numbers of panel data is converted into the correlation between the two-dimensional interval numbers in time series and cross-sectional dimensions. The grey correlation coefficients of each scheme and the ideal scheme matrix are calculated in the two dimensions, respectively. Finally, the correlation degree of panel interval number and scheme ordering are obtained by arithmetic mean.

This paper proves that the grey relational model of the panel interval number still has the properties of normalization, uniqueness and proximity. It also avoids the problem that the results are not unique due to the different orders of objects in the panel data.

The effectiveness and practicability of the model is verified by taking supplier selection as an example. In fact, this model can also be widely used in agriculture, industry, society and other fields.

The accuracy of the relational results is higher and more accurate compared with the previous studies.

The traditional grey relational models directly describe the behavioural characteristics of the systems based on the sample point connections. Few grey relational models can measure the dynamic periodic fluctuation rules of the objects, and most of these models do not have affinities, which results in instabilities of the relational results because of sequence translation. The paper aims to discuss these issues.

Fourier transform functions are used to fit the system behaviour curves, redefine the area difference between the curves and construct a grey relational model based on discrete Fourier transform (DFTGRA).

To verify its validity, feasibility and superiority, DFTGRA is applied to research on the correlation between macroeconomic growth and marine economic growth in China coastal areas. It is proved that DFTGRA has the superior properties of affinity, symmetry, uniqueness, etc., and wide applicability.

DFTGRA can not only be applied to equidistant and equal time sequences but also be adopted for non-equidistant and unequal time sequences. DFTGRA can measure both the global relational degree and the dynamic correlation of the variable cyclical fluctuation between sequences.

Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey systems in marine economics is gaining importance. The purpose of this paper is to summarize and review literature on grey models, providing new directions in their application in the marine economy.

This paper organized seminal studies on grey systems published by Chinese core journal database – CNKI, Web of Science and Elsevier from 1982 to 2018. After searching the aforementioned database for the said duration, the authors used the CiteSpace visualization tools to analyze them.

The authors sorted the studies according to their countries/regions, institutions, keywords and categories using the CiteSpace tool; analyzed current research characteristics on grey models; and discussed their possible applications in marine businesses, economy, scientific research and education, marine environment and disasters. Finally, the authors pointed out the development trend of grey models.

Although researches are combining grey theory with fractals, neural networks, fuzzy theory and other methods, the applications, in terms of scope, have still not met the demand. With the increasingly in-depth research in marine economics and management, international marine economic research has entered a new period of development. Grey theory will certainly attract scholars’ attention, and its role in marine economy and management will gain considerable significance.

A key finding in the extant literature on adopting information systems has been the importance of management support and a champion. Further research has indicated that business managers need to have appropriate IT knowledge and skills to lead adoption adequately. In the context of small and medium enterprises (SMEs), this role is usually assumed by the owner/manager. This research aims to synthesise these two tenets by identifying and understanding the type of business intelligence and analytics (BI&A) leadership skills that owners/managers need to develop during the adoption of BI&A.

Five BI&A knowledge areas are identified and connected to different types of BI&A leadership skills through qualitative in-depth case studies of fourteen Australian SMEs.

The case studies reveal that several BI&A leadership skills need to be developed to bring SMEs to higher stages of BI&A adoption.

This study proposes a BI&A leadership skills development framework that allows practitioners to develop progressive BI&A skills concerning managing data, analytical skills, business processes, social and cultural change, and investment decisions to achieve sustainable operational, management and strategic goals.

The paper takes a unique approach that links five knowledge areas to BI&A leadership skills that owners/managers need to ensure for effective adoption and orchestration of BI&A in their organisations. The BI&A leadership framework includes a developmental approach that relates to the iterative and complex nature of BI&A adoption.