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This paper aims to solve the problems molybdenum disulfide (MoS₂) nanosheets suffer from inadequate dispersion stability and form a weak lubricating film on the friction surface, which severely limits their application as lubricant additives.

MoS₂/C₆₀ nanocomposites were prepared by synthesizing molybdenum disulfide (MoS₂) nanosheets on the surface of hydrochloric acid-activated fullerenes (C₆₀) by in situ hydrothermal method. The composition, structure and morphology of MoS₂/C₆₀ nanocomposites were characterized. Through the high-frequency reciprocating tribology test, its potential as a lubricant additive was evaluated.

MoS₂/C₆₀ nanocomposites that were prepared showed good dispersion in dioctyl sebacate (DOS). When 0.5 Wt.% MoS₂/C₆₀ was added, the friction reduction performance and wear resistance improved by 54.5% and 62.7%, respectively.

MoS₂/C₆₀ composite nanoparticles were prepared by in-situ formation of MoS₂ nanosheets on the surface of C₆₀ activated by HCl through hydrothermal method and were used as potential lubricating oil additives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0321/

Gears are prone to instantaneous failure when operating under extreme conditions, affecting the machinery’s service life. With numerous types of gear meshing and complex operating conditions, this study focuses on the gear–rack mechanism. This study aims to analyze the effects and optimization of biomimetic texture parameters on the line contact tribological behavior of gear–rack mechanisms under starvation lubrication conditions.

Inspired by the microstructure of shark skin surface, a diamond-shaped biomimetic texture was designed to improve the tribological performance of gear–rack mechanism under starved lubrication conditions. The line contact meshing process of gear–rack mechanisms under lubrication-deficient conditions was simulated by using a block-on-ring test. Using the response surface method, this paper analyzed the effects of bionic texture parameters (width, depth and spacing) on the tribological performance (friction coefficient and wear amount) of tested samples under line contact and starved lubrication conditions.

The experimental results show an optimal proportional relationship between the texture parameters, which made the tribological performance of the tested samples the best. The texture parameters were optimized by using the main objective function method, and the preferred combination of parameters was a width of 69 µm, depth of 24 µm and spacing of 1,162 µm.

The research results have practical guiding significance for designing line contact motion pairs surface texture and provide a theoretical basis for optimizing line contact motion pairs tribological performance under extreme working conditions.

This paper aims to investigate the influence of stearate types on the thickening ability, dropping point and fiber structure of greases.

Several greases were prepared from polyolefins and various stearates. The melting point of the stearates and the dropping point of the resultant greases were measured, and the intermolecular binding energies of the thickener and the radial distribution function of the metal–oxygen in the thickener were determined with the aid of molecular simulation. The microstructures of the greases were also analyzed via scanning electron microscopy.

A higher stearate binding energy was found to correlate to a higher dropping point of the resultant greases. The thickening ability of the stearate is related to the group and period of the constituent metal ion. Within a group, greater atomic numbers of the metal were correlated to lower thickening ability. In a period, as the atomic number of the metal increased, the thickening ability was enhanced. The radial distribution functions of metal and oxygen can explain the aggregation of the stearate thickeners in the grease.

This work compared the thickening capacity of several stearates. Guidelines for preparing stearates to tailor the resultant grease are presented.

This paper aims to collect and compile the historical data of Guanfu Salt Farm, officially built by the Song Dynasty (960–1279) within modern Hong Kong territories, to reconstruct its history for the reflection of Hong Kong society of the time.

This paper is largely based on identification and analysis of historical documents, including keyword search on electronic databases and verification with the original sources, with reference to archaeological findings when necessary.

This paper reconstructs the history of Guanfu Salt Farm based on documentary sources with reference to archaeological findings. English translation of Chinese sources is also provided when necessary.

There has been an absence of systematic compilation of historical data of Hong Kong during the Song Dynasty, which are limited in quantity and scattered across different sources. This paper seeks to fill the vacuum of knowledge about pre-colonial Hong Kong, with a more comprehensive reconstruction of the history of Guanfu Salt Farm.

Knowledge sharing (KS) and innovation are generally believed as the antecedents of key outcomes that help firms to attain and sustain competitive advantage in long term. The purpose of this paper is to analyze the mechanism of how interpersonal trust and leader support affect KS and improve firm’s innovation capabilities.

This is a research paper which is built using empirical data collected from 68 manufacturing and service firms in China.

First, the findings show that leader supports moderate the correlation between interpersonal trust and KS. Second, KS serves as mediator in the relationship between interpersonal trust and firm’s innovation capabilities.

KS plays a crucial role in stimulating innovation capabilities for both manufacturing and service firms. Future research should explore the effects of the motivational factors (such as positive psychological state, perceived benefits and costs) on KS and firm’s innovation capabilities.

The paper provides the evidence for the positive effects of interpersonal trust on KS, which in turn is significantly associated with product innovation and process innovation. It highlights the important role of leader supports in promoting the degree of sharing knowledge among individuals to enhance innovation capabilities for firms.

This study puts the theory of innovation forward based on exploring the key factors that have potential and positive impacts on two specific types of innovation capability, namely, product innovation and process innovation, for both manufacturing and service firms.

Until now, many different varieties of grey relational grade methods had been proposed, and there are also many relevant publications, which include ordinal grey relational grade and cardinal grey relational grade. However, the most original and important formula is Deng’s grey relational grade. After careful study it was found that although it is an ordinal form of grey relational grade, a rational mathematics model can be used to transfer it from ordinal into cardinal. It not only can enhance the essential of Deng’s grey relational grade, but also can let Deng’s grey relational grade be used more widely. The paper aims to discuss these issue.

The paper uses fuzzy set theory to get the rational value of distinguish coefficient in Deng’s grey relational grade, then uses grey entropy method to decide the rational weighting for the analysis sequences in Deng’s grey relational grade.

Through the mathematics derivation, it indeed can transfer the Deng’s grey relational grade from ordinal form into cardinal form.

The paper has deeply enhanced the essential of Deng’s grey relational grade, and made Deng’s grey relational grade more available and more usable in grey system theory.

The paper has transferred the Deng’s grey relational grade from ordinal into cardinal, it can let Deng’s grey relational grade be used in a wider area.

The paper attempts to design an efficient algorithm for bearing track correlation of multi‐sensor on the same platform using grey incidence analysis which is on the basis of the line segment Hausdorff distance.

Starting from the line segment, Hausdorff distance that has been extended to calculate the distance between line segment sets by many scholars has been used for face recognition achieving good results. The degree of grey incidence is defined based on the above distance and properties which include normality, symmetry and closeness, are proved. Furthermore, a grey incidence matrix is built. With only the azimuth information detected by bearing sensors track correlation is difficult to judge, however grey incidence analysis can quickly and accurately determine whether two tracks are from the same target, and so an algorithm is designed to solve this dilemma. In the last part of the paper simulation experiment is conducted.

The results are convincing: not only the algorithm proposed in the paper can solve the problem of track correlation of bearing‐only sensors, but also the algorithm can judge the correlation degree of both tracks even in the case of intensive targets.

The method exposed in the paper can be used to judge correlation degree of tracks detected by different sensors even for less information, and also be used to determine the similarity of two waveforms in the field of engineering.

The paper succeeds in introducing the line segment Hausdorff distance into grey incidence analysis and on the basis of that an algorithm is designed to solve the problem of track correlation.

This paper aims to present a broad review of near-a titanium alloys for high-temperature applications.

Following a brief introduction of titanium (Ti) alloys, this paper considers the near-α group of Ti alloys, which are the most popular high-temperature Ti alloys developed for a high-temperature application, particularly in compressor disc and blades in aero-engines. The paper is relied on literature within the past decade to discuss phase stability and microstructural effect of alloying elements, plastic deformation and reinforcements used in the development of these alloys.

The near-a Ti alloys show high potential for high-temperature applications, and many researchers have explored the incorporation of TiC, TiB SiC, Y₂O₃, La₂O₃ and Al₂O₃ reinforcements for improved mechanical properties. Rolling, extrusion, forging and some severe plastic deformation (SPD) techniques, as well as heat treatment methods, have also been explored extensively. There is, however, a paucity of information on SiC, Y₂O₃ and carbon nanotube reinforcements and their combinations for improved mechanical properties. Information on some SPD techniques such as cyclic extrusion compression, multiaxial compression/forging and repeated corrugation and straightening for this class of alloys is also limited.

This paper provides a topical, technical insight into developments in near-a Ti alloys using literature from within the past decade. It also outlines the future developments of this class of Ti alloys.

With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation.

The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion.

Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput.

This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.

Drawing upon new institutional economics and contracting theory, this chapter extends the concept of headquarter (HQ)–subsidiary relationships to capture unconventional types of subsidiary organizations in transition economies. A conceptual framework is first developed to examine how the interplay between institutions and subsidiaries shapes HQ–subsidiary relations in rapidly changing institutional environments. It is then applied to study contractual joint ventures in China, an important, yet often misunderstood, form of multinational subsidiary operation. The research sheds new light on how parent firms design contract provisions, credible commitments, and contract renegotiation mechanisms for the effective management of joint venture subsidiaries. These findings have important implications for future inquiry into the interplay between institutions and organizations in safeguarding subsidiary operations in transition economies.