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Data quality metadata (DQM) is a set of quality measurements associated with the data. Prior research in data quality has shown that DQM improves decision performance. The same research has also shown that DQM overloads the cognitive capacity of decision-makers. Visualization is a proven technique to reduce cognitive overload in decision-making. This paper aims to describe a prototype decision support system with a visual interface and examine its efficacy in reducing cognitive overload in the context of decision-making with DQM.

The authors describe the salient features of the prototype and following the design science paradigm, this paper evaluates its usefulness using an experimental setting.

The authors find that the interface not only reduced perceived mental demand but also improved decision performance despite added task complexity due to the presence of DQM.

A drawback of this study is the sample size. With a sample size of 51, the power of the model to draw conclusions is weakened.

In today’s decision environments, decision-makers deal with extraordinary volumes of data the quality of which is unknown or not determinable with any certainty. The interface and its evaluation offer insights into the design of decision support systems that reduce the complexity of the data and facilitate the integration of DQM into the decision tasks.

To the best of my knowledge, this is the only research to build and evaluate a decision-support prototype for structured decision-making with DQM.

While the normal wheel–rail contact model cannot be accurately used for light rail transit (LRT) wheel wear analysis with large wheelset lateral displacement and wheelset yaw angle, a modified semi-Hertzian contact model (MSHM) is proposed in the paper.

MSHM was first proposed to consider the wheelset motion with the lateral displacement and the yaw angle. Then, a dynamic model of an LRT was established and the influence of some key factors on wheel wear is analyzed. At last, after operating for a certain mileage, the predicted wheel wear is compared with the tested wheel wear.

Compared with the tested wheel wear, the predicted wheel wear shows a good agreement with the measured result, verifying the accuracy of MSHM.

Considering larger wheelset lateral displacement and yaw angle, MSHM can be used to calculate the wheel wear of the LRT with high accuracy.

The purpose of this paper deals with a capacitated multi-item dynamic lot-sizing problem with the simultaneous sequence-dependent setup scheduling of the parallel resource under complex uncertainty.

An improved chance-constrained method is developed, in which confidence level of uncertain parameters is used to process uncertainty, and based on this, the reliability of the constraints is measured. Then, this study proposes a robust reconstruction method to transform the chance-constrained model into a deterministic model that is easy to solve, in which the robust transformation methods are used to deal with constraints with uncertainty on the right/left. Then, experimental studies using a real-world production data set provided by a gearbox synchronizer factory of an automobile supplier is carried out.

This study has demonstrated the merits of the proposed approach where the inventory of products tends to increase with the increase of confidence level. Due to a larger confidence level may result in a more strict constraint, which means that the decision-maker becomes more conservative, and thus tends to satisfy more external demands at the cost of an increase of production and stocks.

Joint decisions of production lot-sizing and scheduling widely applied in industries can effectively avert the infeasibility of lot-size decisions, caused by capacity of lot-sing alone decision and complex uncertainty such as product demand and production cost. is also challenging.

This study provides more choices for the decision-makers and can also help production planners find bottleneck resources in the production system, thus developing a more feasible and reasonable production plan in a complex uncertain environment.

This paper aims to introduce the history and major achievement of the Chinese private enterprise survey (CPES), which is one of the most enduring large-scale nationwide sample surveys in China, providing important micro firm-level data for understanding and studying the development of Chinese enterprises and entrepreneurs over the past 26 years.

The main body of this paper is based on a bibliometric analysis of all literature using CPES until 2017.

This paper discusses problems that users may encounter during data mining. By doing so, it can assist other researchers to get a better understanding of what has been done (e.g. journals, topics, scholars and institutions) and do their research in a more targeted way.

As members of the survey project team, the authors also take a prospect of the future data design and use, as well as offer some suggestions about how to use the CPES data to improve high-quality development and business environment evaluation in China.

This paper is the first to provide an overall picture of academic papers in China and abroad that have used the CPES data.

The paper aimed to summarize innovative rules of Chinese manufacturing enterprises, and put forward some insights in optimal allocation of innovation resources and enhancing the capability of indigenous innovation, so as to overcome the innovation dilemma.

Having diagnosed the current research of innovation flows, the authors defined the concept and stated characteristics of mainstream and new‐stream innovation. Innovation life cycle theory and technology trajectory theory were used to map two evolution patterns of mainstream and new‐stream innovation. It took weeks to stay in firms to obtain original data and interview managers, engineers, and mechanists. From the case study the authors proposed the improvement projects and opportunities for firms to overcome the “innovation dilemma”.

In the indigenous innovation process, enterprises should breed new‐stream innovation as they strengthen the mainstream innovation, view the new‐stream technology as the innovation direction, and fulfill the constant innovation in the convergent innovation and replace evolution process. Meanwhile, enterprises should take precautions both to expand and develop, and fulfill technology transition and innovation upgrade through convergent innovation. Furthermore, firms' convergent innovation, which will finally achieve the “projects‐talents‐products‐markets” collaborative innovation, should be clear about target market and project‐oriented, take the product as the carrier, and be committed to cultivate creative talents and innovative teams.

This paper enriches the existing theory on indigenous innovation by introducing the new concept of mainstream and new‐stream innovation. The findings would help firms to jump out from the innovation morass.

Blockchain technology, a key feature of the fourth industrial revolution, is receiving widespread attention and exploration around the world. Taking the coronavirus pandemic as an example, the purpose of this study to examine the application of blockchain technology from the perspective of epidemic prevention and control.

Exploring multiple case studies in the Chinese context at various stages of deployment, this study documents a framework about how some of the major challenges associated with COVID-19 can be alleviated by leveraging blockchain technology.

The case studies and framework presented herein show that utilization of blockchain acts as an enabler to facilitate the containment of several COVID-19 challenges. These challenges include the following: complications associated with medical data sharing; breaches of patients' data privacy; absence of real-time monitoring tools; counterfeit medical products and non-credible suppliers; fallacious insurance claims; overly long insurance claim processes; misappropriations of funds; and misinformation, rumors and fake news.

Blockchain is ushering in a new era of innovation that will lay the foundation for a new paradigm in health care. As there are currently insufficient studies pertaining to real-life case studies of blockchain and COVID-19 interaction, this study adds to the literature on the role of blockchain technology in epidemic control and prevention.

Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.