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The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

Differences in annual publication counts may reflect the dynamic of scientific progress. Declining annual numbers of publications may be interpreted as missing progress in field-specific knowledge.

In this paper, we present empirical results on dynamics of progress in economic fields (defined by Journal of Economic Literature (JEL), codes) based on a methodological approach introduced by Bornmann and Haunschild (2022). We focused on publications that have been published between 2012 and 2021 and identified those fields in economics with the highest dynamics (largest rates of change in paper counts).

We found that the field with the largest paper output across the years is “Economic Development”. The results reveal that the field-specific rates of changes are mostly similar. However, the two fields “Production and Organizations” and “Health” show point estimators which are clearly higher than the estimators for the other fields. We investigated the publications in “Production and Organizations” and “Health” in more detail.

Understanding how a discipline evolves over time is interesting both from a historical and a recent perspective. This study presents results on the dynamics in economic fields using a new methodological approach.

This paper aims to examine the Islamic accounting research. In particular, the paper extensively investigates the literature on Islamic accounting to understand the issues, contexts, methods and theoretical paradigms thereof.

The study has adopted a literature review approach. It has examined the key journal publications for 30 years in accounting discipline and systematically identified the Islamic accounting papers during 1990–2020. In total, 95 papers were identified until June 2020, and they were thoroughly reviewed to identify the relevant issues, contexts, methods and theoretical paradigms.

The study has found that Islamic accounting papers covered issues of both Islamic organizations (e.g. Islamic financial institutions) and of Muslim countries. The key issues include the regulation and regulatory compliance, annual report disclosures, corporate and Islamic governance, accounting profession, gender, accountability matters, management accounting and control, waqf accounting and zakat management. The study has also observed various normative guidelines from the academics on how the teaching of Islam is enacted in accounting, accountability and governance matters to attain the maqasid al-shari'a, i.e. human welfare, social justice and equity.

The study is not empirical. Hence, the limitations of literature review papers are applicable in this case. Moreover, it is possible that this study could not identify some of the important literature on Islamic accounting (such as the papers published in Arabic by the academicians and professionals of Arab world).

The study enables both Islamic accounting academics and practitioners to identify the main Islamic accounting issues and realize the importance of Islamic accounting.

When the author considers Islamic accounting as a social construction and tries to understand the phenomenon through social theories, the author acknowledges the relevance of Islamic accounting in the society in which it operates. It can be noticed from the discussion that Islamic accounting emphasizes on social welfare, balance, equity and providing relevant information to follow the commandments of God.

To the best of the author’s knowledge, this study is the first to provide a synoptic view on the issues, context, methods and theoretical paradigms of Islamic accounting, while covering major accounting journals in 30 years.

This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.

Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.

Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.

The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.

This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.

The purpose of this essay is to explore the opportunities and challenges that early-career researchers (ECRs) face when they seek to contribute to academic knowledge production through research activities “other than” those directly focused on making progress with their own, to-be-published, research papers in a context associated with the “publish or perish” (PoP) mentality.

Drawing broadly on the notion of technologies of humility (Jasanoff, 2003), this reflective essay develops upon the experiences of the authors in organizing and participating in a series of nine workshops undertaken between June 2013 and April 2021, as well as the arduous process of writing this paper itself. Retrospective accounts, workshop materials, email exchanges and surveys of workshop participants provide the key data sources for the analysis presented in the paper.

The paper shows how the organization of the workshops is intertwined with the building of a small community of ECRs and exploration of how to address the perceived limitations of a “gap-spotting” approach to developing research ideas and questions. The analysis foregrounds how the workshops provide a seemingly valuable research experience that is not without contradictions. Workshop participation reveals tensions between engagement in activities “other than” working on papers for publication and institutionalized pressures to produce publication outputs, between the (weak) perceived status of ECRs in the field and the aspiration to make a scholarly contribution, and between the desire to develop a personally satisfying intellectual journey and the pressure to respond to requirements that allow access to a wider community of scholars.

Our analysis contributes to debates about the ways in which seemingly valuable outputs are produced in academia despite a pervasive “publish or perish” mentality. The analysis also shows how reflexive writing can help to better understand the opportunities and challenges of pursuing activities that might be considered “unproductive” because they are not directly related to to-be-published papers.

The purpose of this study is to investigate the effects of a pigment coating on the mechanical properties of fluting paper.

Two different calcium carbonate pigments were used in the preparation of the coated color, precipitated calcium carbonate (PCC) and ground calcium carbonate (GCC). Fluting paper produced by recycling waste paper was used as base paper. The effects of PCC and GCC pigments on mechanical properties were compared. Ring crush test (RCT), corrugating medium test (CMT), corrugating crush test (CCT), tensile and burst strength tests were applied to the coated papers, and the results were compared to the mechanical properties of base paper.

The tensile and burst indices of the coated papers were found to be higher than base papers about 13.9% and 6.05%, respectively. While the coating process positively affected the RCT and CCT values, it did not show a significant impact on the CMT values. GCC, one of the pigments used in coating colors, had a more effective effect on the mechanical properties of fluting papers compared to PCC.

These results suggest that coating of fluting papers has a positive effect on mechanical properties and the use of GCC as a pigment is more effective than PCC.