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The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

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.

This paper aims to build a scientific evaluation index system for regional low-carbon circular economic development. Taking Sichuan Province as the empirical research object, the paper evaluates its low-carbon circular economy (LCCE) development level and proposes policy recommendations for climate change improvement based on the evaluation results.

This paper, first, built an evaluation index system with 30 indicators within six subsystems, namely, economic development, social progress, energy consumption, low-carbon emissions, carbon sink capacity and environmental carrying capacity. Second, develop an “entropy weight-grey correlation” evaluation method. Finally, from a practical point of view, measure the development level of LCCE in Sichuan Province, China, from 2008 to 2018.

It was found that Sichuan LCCE development had a general downward trend from 2008 to 2012 and a steady upward trend from 2012 to 2018; however, the overall level was low. The main factors affecting the LCCE development are lagging energy consumption and environmental carrying capacity subsystem developments.

This paper puts forward relevant suggestions for improving the development of a low-carbon economy and climate change for the reference of policymakers.

This paper built an evaluation index system with 30 indicators for regional low carbon circular economic development. The evaluation method of “entropy weight-grey correlation” is used to measure the development level of regional LCCE in Sichuan Province, China.

The paper aims to carry out a comprehensive literature mapping to synthesise and descriptively analyse the research trends of biodiversity accounting, providing implications for managers and policymakers, whilst also outlining a future agenda for scholars.

A bibliometric analysis is carried out by adopting the Preferred Reporting Items for Systematic Review and Meta-Analyses protocol for searching and selecting the scientific contributions to be analysed. Citation analysis is used to map a current research front and a bibliographic coupling is conducted to detect the connection networks in current literature.

Biodiversity accounting is articulated in five thematic clusters (sub-areas), such as “Natural resource management”, “Biodiversity economic evaluation”, “Natural capital accounting”, “Biodiversity accountability” and “Biodiversity disclosure and reporting”. Critical insights emerge from the content analysis of these sub-areas.

The analysis of the thematic evolution of the biodiversity accounting literature provides useful insights to inform both practice and research and infer implications for managers, policymakers and scholars by outlining three main areas of intervention, i.e. adjusting evaluation tools, integrating ecological knowledge and establishing corporate social legitimacy.

Currently, the level of biodiversity reporting is pitifully low. Therefore, organisations should properly manage biodiversity by integrating diverse and sometimes competing forms of knowledge for the stable and resilient flow of ecosystem services for future generations.

This paper not only updates and enriches the current state of the art but also identifies five thematic areas of the biodiversity accounting literature for theoretical and practical considerations.

The feasibility and desirability of reverse logistics in market-motivated contexts are examined in China. Interactions between the major barriers, that hinder or prevent the application of reverse logistics in China are analyzed. Management’s key task is to diagnose barriers to the application of reverse logistics that could be crucial to the organization’s future survival. Simultaneity, a value delivery system exists to create value for customers and environments by supplying needed products and services. Value delivery systems are at the heart of every firm and, more than anything else, determine that, whether the firm survives in the marketplace or disappears into bankruptcy or takeover. The processes and model of market-motivated reverse logistics value delivery system are discussed, and the processes content and model are presented. Simultaneity, based on the advantage of the Third Party Reverse Logistics Providers (3PRLs) and Outsourced Service Providers, an integrated evaluation model is built to select 3PRLs by using the integrated decision-making methods. Reflecting the comprehensive information requirement, the Analytic Hierarchy Process and entropy approaches are applied to calculate the objective weights. A new kind of relative similarity degree is established by combining the Euclidean distance with the grey correlation degree. An example demonstrates the model’s efficiency.

It is the responsibility of the national governments to deliver healthcare services that are both effective and affordable to everyone. There are still gaps in this supply, which is extremely demanding. In this sense, companies are attempting to reach neglected markets and disrupt the marketplace with novel solutions. Although there are still anecdotal examples, a thorough literature evaluation is lacking. This study aims to provide a synthesis of the future of healthcare start-ups.

Papers that included the term “healthcare start-ups,” “health-tech start-ups,” “start-up,” “Artificial intelligence in healthcare,” and “Health tech start-ups in India” were considered for the analysis. The Biblioshiny package under the R programming tool was considered for a detailed analysis of the papers.

A total of 854 documents were related to healthcare start-ups, from which only 14 papers are related to health-tech start-ups and four papers are related to artificial intelligence in healthcare start-ups. It has been found from the past works of literature that the effectiveness of technology for information and communication in healthcare has significantly increased in recent years. Technology has already begun to permeate the healthcare market from other fields and industries. One way that the internet will help the industry evolve is by integrating digital health into daily life.

The study is not using other databases but is limited to Google Scholar and Scopus. A significant constraint of this study is the paucity of relevant literature in reputable publications on health and information systems. Another restriction was that gray literature, such as any journal or newspaper written by members of the health community about health-tech start-ups, was not taken into account.

Healthcare players should exhibit a fundamental openness to novel solutions to facilitate the digitalization of the healthcare system. Developing technology is widely used, and from an innovation perspective, a start-up should focus on innovation by employing technology and offering revolutionary healthcare solutions.

The novelty of this research is based on its presentation of an organized and thorough literature evaluation, which defines the current state of the art concerning green start-ups. To create a sustainable start-up, a thorough study of the information gained in respect of its healthcare start-up is presented.

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.

With the use of the grey incidence analysis (GIA), indicators such as the absolute degree of grey incidence (ε_ij), relative degree of grey incidence (r_ij) or synthetic degree of grey incidence (ρ_ij) are calculated. However, it seems that some assumptions made to calculate them are arguable, which may also have a material impact on the reliability of test results. In this paper, the authors analyse one of the indicators of the GIA, namely the relative degree of grey incidence. The aim of the article was to verify the hypothesis: in determining the relative degree of grey incidence, the method of standardisation of elements in a series significantly affects the test results.

To achieve the purpose of the article, the authors used the numerical simulation method and the logical analysis method (in order to draw conclusions from our tests).

It turned out that the applied method of standardising elements in series when calculating the relative degree of grey incidence significantly affects the test results. Moreover, the manner of standardisation used in the original method (which involves dividing all elements by the first element) is not the best. Much more reliable results are obtained by a standardisation that involves dividing all elements by their arithmetic mean.

Limitations of the conducted evaluation involve in particular the limited scope of inference. This is since the obtained results referred to only one of the indicators classified into the GIA.

In this article, the authors have evaluated the model of GIA in which the relative degree of grey incidence is determined. As a result of the research, the authors have proposed a recommendation regarding a change in the method of standardising variables, which will contribute to obtaining more reliable results in relational tests using the grey system theory.

This study aims to provide a picture of the current state of art in the use of big data for decision-making processes for the management of health-care organizations.

A systematic literature review was carried out. The research uses two analyses: descriptive analysis, describing the evolution of citations; keywords; and the ten most influential papers, and bibliometric analysis, for content evaluation, for which a cluster analysis was performed.

A total of 48 articles were selected for bibliographic coupling out of an initial sample of more than 5,000 papers. Of the 48 articles, 29 are linked on the basis of their bibliography. Clustering the 29 articles on the basis of actual content, four research areas emerged: quality of care, quality of service, crisis management and data management.

Health-care organizations believe strongly that big data can become the most effective tool for correctly influencing the decision-making processes. Thus, more and more organizations continue to invest in big data analytics, and the literature on this topic has expanded rapidly. This study seeks to provide a comprehensive picture of the different streams of literature existing, together with gaps in research and future perspectives. The literature is mature enough for an analysis to be made and provide managers with useful insights on opportunities, criticisms and perspectives on the use of big data for health-care organizations. However, to date, there is no comprehensive literature review on the big data analysis in health care. Furthermore, as big data is a “sexy catchphrase,” more clarity on its usage may be needed. It represents an important tool to be investigated and its great potential is often yet to be discovered. This study thus sheds light on emerging issues and suggests further research that may be needed.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).