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This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data.

First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers’ experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward.

The scientific data manufacturing process and data stakeholders’ responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data.

As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders.

The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues.

This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.

Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.

This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.

Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.

The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.

Outlines the history of accounting in China and reviews the literature published in English on the full range of Chinese accounting issues. Summarizes the contents of three books, refers to sections in other books and analyses journal articles by period, journal, research topic and research method. Argues that this accounting research has historical, academic and practical value,believes it will continue to improve and calls for greater use of more rigid research methodologies in this area.

The purpose of this paper is to describe some tribological experiments which were executed to find the influence of braking pressure on tribological performance of non‐asbestos brake shoe used in mine hoister during its emergency braking.

The WSM‐3 non‐asbestos brake shoe, which has been widely used in mine hoister, was selected as experimental material. Some tribological experiments of the brake shoe sliding on 16Mn steel were investigated on the X‐DM friction tester by simulating of emergency braking conditions of mine hoister. Three kinds of tribological indexes: friction coefficient, stability coefficient of friction coefficient, and wear rate were considered to score the tribological performance and the morphology of worn surfaces were observed through the S‐3000N scanning electron microscopy (SEM) to explore the tribological mechanisms.

It was found first, that the instant friction coefficient is not constant during emergency braking. After a short climbing period, it rises gradually to steady value. Second, with the increasing of braking pressure, the mean friction coefficient rises first then falls, while its stability coefficient falls gradually. The wear rate rises continuously with the braking pressure increasing. Also, the rising velocity of wear rate at high pressure is higher than it is at low pressure. Third, the instant surface temperature rises first then falls during braking and the mean surface temperature rises continuously with the braking pressure increasing.

It is found that the increasing of braking pressure within a certain range is helpful for achieving a high friction coefficient and a steady wear rate. But too high pressure will cause contrarily the falling of frictional performance and serious of wear performance. So it is not reliable to rise the braking pressure without limited during emergency braking.

Marketing is all about understanding your consumers and giving them what they want. However, this process becomes more complicated in times of economic crisis and national slowdown. Consumers can become scattered and unpredictable in their behaviour, making it hard to understand what they want or need. At times like these, it is more important than ever to rely on qualitative market research to understand the views of consumers and managers. Thus, this study aims to look at the significant consumer challenges that arise during times of crisis and the marketing strategies that managers prefer to counter the crisis.

The data collected for this study involves an extensive literature review followed by personal interviews with industry experts. This study presents two separate models, indicating hierarchical relationships among consumer challenges during crisis and marketing strategies using the total interpretive structural modelling approach. Further MICMAC analysis (popularly known as cross-impact matrix multiplication) was also performed to assess each variable's driving and dependence power.

“Price sensitiveness” and “adaptive buying” result as driving factors with the highest driving and lowest dependence power, which further gives rise to other consumer behaviour challenges. Likewise, the most critical strategies are “information systems” and the formation of “crisis management teams” during a crisis. At the same time, other strategies have resulted as linkage and dependent factors and none as the autonomous factor.

This paper provides a systematic understanding of how a manager can understand the challenges consumers face during a crisis and suggests a powerful summary of strategies companies can implement to sail through a crisis.