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The purpose of this study is to reveal the friction reduction performance and mechanism of granular flow lubrication during the milling of difficult-to-machining materials and provide a high-performance lubrication method for the precision cutting of nickel-based alloys.

The milling tests for Inconel 718 superalloy under dry cutting, flood lubrication and granular flow lubrication were carried out, and the milling force and machined surface quality were used to evaluate their friction reduction effect. Furthermore, based on the energy dispersive spectrometer (EDS) spectrums and the topographical features of machined surface, the lubrication mechanism of different granular mediums was explored during granular flow lubrication.

Compared with flood lubrication, the granular flow lubrication had a significant force reduction effect, and the maximum milling force was reduced by about 30%. At the same time, the granular flow lubrication was more conducive to reducing the tool trace size, repressing surface damage and thus achieving better surface quality. The soft particles had better friction reduction performance than the hard particles with the same particle size, and the friction reduction performance of nanoscale hard particles was superior to that of microscale hard particles. The friction reduction mechanism of MoS₂ and WS₂ soft particles is the mending effect and adsorption film effect, whereas that of SiO₂ and Al₂O₃ hard particles is mainly manifested as the rolling and polishing effect.

Granular flow lubrication was applied in the precision milling of Inconel 718 superalloy, and a comparative study was conducted on the friction reduction performance of soft particles (MoS₂, WS₂) and hard particles (SiO₂, Al₂O₃). Based on the EDS spectrums and topographical features of machined surface, the friction reduction mechanism of soft and hard particles was explored.

This study aims to analyse the geographical distribution of global research activities and to investigate the knowledge diffusion embodied in scientific papers.

The geographical summary of Frontiers articles displays the number of visits and categorizes where the visitors hail from. This study uses the records of 23,798 articles published in 16 Frontiers journals from 2007 to 2015 to analyse the geographical distribution of article visits at both country and city levels. The process of knowledge diffusion is investigated on the basis of the different visiting patterns of new and old papers.

Most article visits are concentrated around major metropolitan areas and some high-tech clusters. The top “visiting countries” include both developed countries and developing countries, and the USA and China are two major players. Publishing cities dominate article visits for new papers; as time passes, there is diffusion from the publishing cities to a broader area.

The data on visiting for open access articles may be generated from various repositories besides the publishers’ websites; these data are ignored, as they are not significant enough to have much influence. There is also a lack of a basic theory in the data processing of outliers in the data set. In addition, only static results are given in this paper, as the data were collected on one day, for one time. A longer time period is necessary to track the dynamic diffusion process of the observations.

Introduction of usage data will propose a novel way to analyse research activities and track knowledge diffusion.

The visiting data of articles offer a new way to investigate research activities at the city level in a detailed and timely manner, for the geographical distribution of research activities and the research resource allocation of a specific country to be explored.

This study measured the research activities of scientific papers by examining the usage data. Compared with previous studies that focused on the geographical distribution of scientific activities using publication data, citation data and even altmetrics data, usage data are at the forefront of this research. Therefore, usage data offer a fresh perspective on methodology, providing more detailed and real-time information.

With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

The purpose of this study is to empirically examine the cause–effect relationships between workplace friendship and knowledge-sharing behavior, from a static perspective. Furthermore, it investigates the bi-directional relationship between the increase in both workplace friendship and knowledge-sharing behavior over same time periods, and also endeavors to identify whether there is a significant negative lagged effect of the increase in both workplace friendship on knowledge-sharing behavior, and vice versa, across time from a dynamic perspective.

The study conducts a three-wave questionnaire survey to test the research model. A latent change score approach was used to test the direct relationship between changes in workplace friendship and changes in knowledge-sharing behavior.

The findings reveal that knowledge-sharing behavior fosters workplace friendship and workplace friendship promotes the emergence of knowledge-sharing behavior. An increase in workplace friendship promotes an increase in knowledge-sharing behavior over same time periods. However, an increase in workplace friendship will lead to a lagged decrease of knowledge-sharing behavior across time, and vice versa.

The time interval in this study is a little short to capture the full changes in workplace friendship. Some important control factors and mediating mechanisms are not included in the research model.

This study guides managers to focus on various motivators to better strengthen workplace friendship and knowledge-sharing behavior and to consider and effectively respond to the negative side of workplace friendship and knowledge-sharing behavior across time.

This study emphasizes the predictivity of one important interaction patterns, namely, knowledge-sharing behavior on friendship at the workplace, from a static perspective. This study also shows the benefits of an increase in workplace friendship for the development of knowledge-sharing behavior in the same time period. Furthermore, the study presents a counterintuitive finding when taking the lag effect into consideration in exploring the relationship between changes both in workplace friendship and knowledge-sharing behavior, and identifies a negative side of both when viewed over longer periods.