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This study aims to investigate the effects of graphite-MoS₂ composite solid lubricant on the tribological properties of copper-based bearing materials under dry conditions.

The mixture of Graphite-MoS₂ was inlaid in ZQSn6-6–3 tin bronze and ZQAl9-4 aluminum bronze matrix. These copper-embedded self-lubricating bearing materials were considered in friction pairs with 2Cr13 stainless steel, and their tribological properties were studied by using an MM200 wear test machine.

The results show that the friction coefficients and wear rates of copper-embedded self-lubricating bearing materials are lower than those of the ordinary copper-based bearing materials. The wear performance of the tin bronze inlaid self-lubricating bearing material is better than that of the aluminum bronze inlaid self-lubricating bearing material. The wear mechanism of the tin bronze bearing material is mainly adhesive wear, and that of the aluminum bronze bearing material is mainly grinding wear, oxidation wear and adhesive wear. The copper-embedded self-lubricating bearing materials had no obvious abrasion, whereas the aluminum bronze inlaid self-lubricating bearing material exhibited deep furrows and obvious abrasion under high loads.

These results are helpful for the application of copper-embedded self-lubricating bearing materials.

Under the background of coexistence of information overload and information fragmentation, it is of great significance to identify influencing factors and reveal the evolution logic of public opinion for public opinion governance.

Taking 24 hot social events as research cases, firstly, the evolution process of public opinion was divided into initial stage and response stage. Secondly, eight antecedent variables were extracted for qualitative comparative analysis of fuzzy sets. Finally, the configuration path of public opinion evolution results was summarized.

The research showed that compared with the initial stage, the influencing factors in the reaction stage played a key role in the continuous evolution of public opinion. The influencing factors in the initial stage and response stage played an indispensable role in promoting the evolution of public opinion to calm down.

This research can provide reference for regulators to timely grasp the initiative, discourse power and leadership of public opinion development.

Research on the two-stage configuration path of public opinion evolution is helpful to clarify the key factors affecting the evolution trend of online public opinion of hot events.

The purpose of this paper is to summarize the current applications of BIM, the integration of related technologies and the tendencies and challenges systematically.

Using quantitative and qualitative bibliometric statistical methods, the current mode of interaction between BIM and other related technologies is summarized.

This paper identified 24 different BIM applications in the life cycle. From two perspectives, the implementation status of BIM applications and integrated technologies are respectively studied. The future industry development framework is drawn comprehensively. We summarized the challenges of BIM applications from the perspectives of management, technology and promotion, and confirmed that most of the challenges come from the two driving factors of promotion and management.

The technical challenges reviewed in this paper are from the collected literature we have extracted, which is only a part of the practical challenges and not comprehensive enough.

We summarized the current mode of interactive use of BIM and sorted out the challenges faced by BIM applications to provide reference for the risks and challenges faced by the future industry.

There is little literature to integrate BIM applications and to establish BIM related challenges and risk frameworks. In this paper, we provide a review of the current implementation level of BIM and the risks and challenges of stakeholders through three aspects of management, technology and promotion.

This paper aims to explore the citation aging pattern of Library and Information Science (LIS) and further investigate the impact of interdisciplinary citation on the aging of scientific literature.

The study examines LIS journal articles published between 2016 and 2020. Articles were retrieved from the Web of Science (WoS) and were organized using Scopus's discipline classification system. Citation aging patterns within LIS are described using literature aging indicators. The study examines the effect of interdisciplinary citations on the literature aging pattern by comparing the half-life of LIS literature and the median age of interdisciplinary citations.

The study results show that the citation aging rate of LIS in the last five years has been slow, and the rate of slowing down is decreasing. Interdisciplinary citations are sourced from various disciplines, focusing on computer science, social sciences and business. The proportion of self-citations is declining. The Reference Diversity Index (RDI) increases from 0.690 to 0.724 between 2016 and 2020. Currently, the median age of interdisciplinary citations is higher than the LIS's half-life. It has a diminishing effect on the citation aging rate. But the median age of interdisciplinary citations is decreasing. The interdisciplinary citation may contribute to the literature aging rate in the future. The effect of interdisciplinary citation on literature aging needs to be judged dialectically.

This study still has some limitations. Due to the wide variety of citation journals in LIS, there is no database to cover all journals, so it is impossible to match all citation journals with disciplines. Therefore, it is still feasible to analyze interdisciplinary citations based on the two-eight principle for large-scale data. This approach necessarily sacrifices some of the precision of the study. However, the results of this paper can still be helpful for the development of the discipline. In addition, LIS is a discipline with solid cross-cutting properties, and this paper concludes only with this interdisciplinary discipline in mind. It is necessary to test the applicability of the findings to other disciplines.

The study explores the impact of interdisciplinary citation on literature aging from a professional communication perspective. The results reveal underlying reasons for the aging of scientific literature. These findings further enrich the study of the effect of interdisciplinary communication.

This study explores whether interdisciplinary components' citation intensity (ICCI) affects papers' scientific impact. In this study, the term “interdisciplinary components” refers to the disciplines that are different from the discipline to which the target research belongs. The citation intensity is the degree of density or sparseness of the paper citation network for a discipline. Previous studies have shown that the scientific impact of interdisciplinary research is influenced by interdisciplinarity and its properties, namely, variety, balance and disparity. However, the effect of ICCI on scientific impact has not been comprehensively explored.

This study is based on the entire publication database of the Web of Science for the year 2000, where the authors provide an indicator to measure the ICCI of each publication. A tobit regression model is used to examine the effect of ICCI on scientific impact, controlling for a range of variables associated with the characteristics of the publications studied.

The results show that ICCI has a positive effect on scientific impact. The authors’ results further point out that ICCI displays a curvilinear inverted U-shape relationship with scientific impact. It means that including more citation-intensive interdisciplinary components can increase the scientific impact of interdisciplinary research. However, excessive use of citation-intensive interdisciplinary components may reduce the scientific impact of interdisciplinary research.

This study shows that, in addition to interdisciplinarity, the scientific impact of interdisciplinary research is also affected by the citation characteristics of interdisciplinary components, namely ICCI.

This paper focuses on the differences in domain intellectual structure discovery between author bibliographic coupling analysis (ABCA) and author co-citation analysis (ACA) considering all authors. The purpose of this study is to examine whether and in what ways these two all-author network approaches yield different results.

The sample was collected from the database of Web of Science, including all articles published in Scientometrics and Journal of Informetrics from 2011 to 2020. First, 100 representative authors were selected from each set, and ABCA matrices and ACA matrices were constructed. Second, factor analysis was carried out on the matrices, to detect the intellectual structure of scientometrics and informetrics.

The intellectual structures identified by ABCA and ACA are similar overall, but the results differ somewhat when it comes to specific structures. The ABCA is more sensitive to some highly collaborative research teams and presents a clearer picture of current intellectual structures and trends while ACA seems to have some advantages in representing the more traditional and proven research topics in the field. The combined use of ABCA and ACA allows for a more comprehensive and specific intellectual structure of research fields.

This paper compares the performance of ABCA and ACA detecting the intellectual structure of the domain from the perspective of all authors, revealing the intellectual structure of scientometrics and informetrics comprehensively.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2020-0540.

This study aims to tackle the critical issue of detecting stock market manipulation, which undermines the integrity and stability of financial markets globally. Even enhanced with machine learning, traditional statistical methods often struggle to analyze high-frequency trading data effectively due to inherent noise and the limited availability of publicly known manipulation cases. This leads to poor model generalization and a tendency toward over-fitting. Focusing on China's securities market, our study introduces an innovative approach that employs deep learning-based high-frequency jump tests to overcome these challenges and to develop a more effective method for identifying manipulative activities.

We employed the “Jump Variation – Time-of-Day” (JV-TOD) non-parametric technique for jump tests on high-frequency data, coupled with the synthetic minority over-sampling technique (SMOTE) algorithm for re-balancing sample data. Our approach trains a deep neural network (DNN) on refined data to enhance its ability to identify manipulation patterns accurately.

Our results show that the deep neural network model, calibrated with high-frequency price jump data, identifies manipulation behavior more specifically and accurately than traditional models. The model achieved an accuracy rate of 94.64%, an F1-score of 95.26% and a recall rate of 95.88%, significantly outperforming traditional models. These results demonstrate the effectiveness of our approach in mitigating over-fitting and improving the robustness of market manipulation detection.

The proposed model provides regulatory entities and financial institutions with a more efficient tool to monitor and counteract market manipulation, thereby improving market fairness and investor protection.

By integrating the JV-TOD jump test with deep learning, this study proposed a new approach to market manipulation detection. The innovation is in its capacity to detect subtle manipulation signals that traditional methods typically overlook. Our model, which is trained on jump test data enhanced by the SMOTE algorithm, excels at learning complex manipulation patterns. This enhances both detection accuracy and robustness. In contrast to existing methods that are challenged by the noisy and intricate nature of high-frequency data, our approach shows enhanced performance in identifying nuanced market manipulations, offering a more effective and reliable method for detecting market manipulation.

This paper aims to examine the day-to-day within-person associations between employees' flow experience and organizational identification within the rarely studied context of construction engineering project organizations.

In this daily diary design, the authors surveyed 204 (M_age = 28.3, SD = 5.69) employees of a state-owned construction engineering project organization in southwest China via the online questionnaires comprising flow and organizational identification scales once daily on each workday for three consecutive weeks, which yielded 3,060 data entries. The authors then tested the temporal directionality between flow and organizational identification with multilevel time-series cross-lagged path analysis using Mplus 8.3.

Daily flow experience was linked positively with same-day organizational identification. Importantly, flow experience on the previous day predicted organizational identification on a subsequent day, but not vice versa.

This study suggests that construction engineering project managers should implement interventions fostering the employees' flow experience to promote organizational identification, with important implications for organizations aiming at flourishing workforces by facilitating organizational identification through implementing flow strategies.

There is a dearth of diary studies on flow and organizational identification specific to construction engineering project employees. The authors’ findings provide concrete evidence of the fluctuant nature of daily flow experience and organizational identification as well as their dynamic predictive pathway relationship.

The information used for supervision by regulatory departments in public-private partnership (PPP) projects is primarily transmitted and processed by the PPP implementation department, which negatively impacts the information quality, leading to information asymmetry and undermining the overall effectiveness of supervision. This study aims to explore how to use blockchain to anchor the information used for supervision in PPP projects to the original information, to strengthen the oversight.

This paper adopts the principles of design science research (DSR) to design a conceptual framework that systematically organizes information along the information dissemination chain, ensuring the reliable anchoring of original information. Two-stage interviews involving experts from academia and industry are conducted, serving as formative and summative evaluations to guide the design.

The framework establishes a weak-centralized information organizing mode, including the design of governance community and on-chain and off-chain governance mechanisms. Feedback from experts is collected via interviews and the designed framework is thought to improve information used for supervision. Constructive suggestions are also collected and analyzed for further development.

This paper provides a novel example exploring the inspirations blockchain can bring to project governance, like exercising caution regarding the disorderly expansion of public sector authority in addressing information disadvantages and how to leverage blockchain to achieve this. Technical details conveyed by the framework deepen understanding of how blockchain benefits and the challenges faced in successful implementation for practitioners and policymakers. The targeted evaluation serves as rigorous validation, guiding experts to provide reliable feedback and richer insights by offering them a more cognitively convenient scenario.

Library science and information science, two subdisciplines of library and information science (LIS), are developed independently but interconnectedly. In this information age, LIS is in a special period of transformation and development, which has caused some changes in both library science and information science. By accurately capturing these changes and analyzing them, the authors can effectively map the development of LIS in the new century, thus providing a reference for the evolution and development of the field. The purposes of this paper are to explore the mainstream research fields and frontiers of library science and information science, respectively, since the new century, and to make a comparative analysis of the two subdisciplines.

By using CiteSpace to visualize LIS journals, this study draws knowledge maps of the two subdisciplines of LIS through the co-occurrence descriptors network. Using burst detection algorithm, this study detects words of high frequency variation by investigating the time frequency distribution.

The results show that the research focus of library science has experienced a change from traditional to digital library while information science has moved from information to data focus. This study also finds the similarities and differences between mainstream areas of library science and information science.

This study focuses on the evolution of library science and information science, and explores their mainstream research fields and frontiers in the 21st century. These findings will promote the transformation and development of LIS as well as provide research directions for scholars in the field.