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The purpose of this study is to prepare ZnFe₂O₄ nanospheres, sheet MoS₂ and three ZnFe₂O₄@MoS₂ core-shell composites with various shell thicknesses, and add them to the base oil for friction and wear tests to simulate the wear conditions of hybrid bearings.

Through the characterization and analysis of the morphology of wear scars and the elemental composition of friction films, the tribological behavior and wear mechanism of sample materials as lubricant additives were investigated and the effects of shell thickness and sample concentration on the tribological properties of core–shell composite lubricant additives were discussed.

The findings demonstrate that each of the five sample materials can, to varying degrees, enhance the lubricating qualities of the base oil and that the core–shell nanocomposite sample lubricant additive has superior lubricating properties to those of ZnFe₂O₄ and MoS₂ alone, among them ZnFe₂O₄@MoS₂-2 core–shell composites with moderate shell thickness performed most ideally. In addition, the optimal concentration of the ZnFe₂O₄@MoS₂ lubricant additive was 0.5 Wt.%, and a concentration that was too high led to particle deposition and affected the friction effect.

In this work, ZnFe₂O₄@MoS₂ core–shell composites were synthesized for the first time using ZnFe₂O₄ as the carrier and the lubrication mechanism of core–shell composites and single materials were compared and studied, which illustrated the advantages of core–shell composite lubricant additives. At the same time, the influence of different shell thicknesses on the lubricant additives of core–shell composites was studied.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2022-0367/

The purpose of this article is to prepare graphene/polyimide composite materials for use as bearing cage materials, improving the friction and wear performance of bearing cages.

The oil absorption and discharge tests were conducted to evaluate the oil content properties of the materials, while the mechanical properties were analyzed through cross-sectional morphology examination. Investigation into the tribological behavior and wear mechanisms encompassed characterization and analysis of wear trace morphology in PPI-based materials. Consequently, the influence of varied graphene nanoplatelets (GN) concentrations on the oil content, mechanical and tribological properties of PPI-based materials was elucidated.

The composites exhibit excellent oil-containing properties due to the increased porosity of PPI-GN composites. The robust formation of covalent bonds between GN and PPI amplifies the adhesive potency of the PPI-GN composites, thereby inducing a substantial enhancement in impact strength. Notably, the PPI-GN composites showed enhanced lubrication properties compared to PPI, which was particularly evident at a GN content of 0.5 Wt.%, as evidenced by the minimization of the average coefficient of friction and the width of the abrasion marks.

This paper includes implications for elucidating the wear mechanism of the polyimide composites under frictional wear conditions and then to guide the optimization of oil content and tribological properties of polyimide bearing cage materials.

In this paper, homogeneously dispersed PPI-GN composites were effectively synthesized by introducing GN into a polyimide matrix through in situ polymerization, and the lubrication mechanism of the PPI composites was compared with that of the PPI-GN composites to illustrate the composites’ superiority.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0415

The purpose of this paper is to put forward the lubrication model of oil bearing and enrich the design theory under the condition of mixed lubrication.

A mixed lubrication model of bilayer porous bearing is established. The effects of the working conditions on the lubrication performance and seepage behavior were analyzed.

Results show that the oil film pressure mainly occurs in the bearing convergence zone and contact pressure mainly occurs near the minimum film thickness. The oil infiltrates into the porous matrix in the contact area and precipitates out to the friction surface at the inlet of the contact area. The oil seepage velocity and dynamic pressure effect at the friction interface can be improved by reasonably matching the load and speed. With the decrease of the external load or increase of the rotating speed, the lubrication performance becomes well.

This study provides a reference for the design and application of oil bearing under harsh working conditions.

As China's economy begins to transform into a high-quality development, and under the national “carbon peak and carbon neutral” target, all sectors of society and industries need to transform to green development to varying degrees, coupled with the catalyst of epidemics and other factors, new development requirements are put forward for enterprises to better fulfill their climate risk disclosure behaviors. Thus, it is clear that improving corporate climate risk disclosure is of far-reaching significance to both countries and enterprises.

This study incorporates management science, psychology and other related knowledge fields, based on stakeholder theory and media dependency theory, and aims to improve the level of corporate compliance with climate risk disclosure, suggesting the influence of entrepreneurs' visibility on corporate climate risk disclosure; on this basis, the role of entrepreneurs' visibility and media attention on corporate climate risk disclosure is verified through an empirical model; finally, targeted and effective response strategies are proposed to improve corporate climate risk disclosure, set reasonable media attention and increase the effectiveness of entrepreneurs' visibility.

This paper establishes a multiple regression model using A-share listed companies in China from 2016 to 2022 as the research sample, verifies the intrinsic association between entrepreneurial visibility and corporate climate risk climate disclosure through empirical analysis, and further examines the mediating role of media attention in the relationship between the two. The results show that entrepreneurs' visibility is positively related to the level of corporate climate risk disclosure, with media attention playing a part in mediating the relationship between the two. Increasing entrepreneurs' visibility is conducive to increasing the level of corporate climate risk disclosure. Therefore, it contributes to the dual incentive effect of reputation and compensation.

This study incorporates management science, psychology and other related knowledge fields, based on stakeholder theory and media dependency theory, and aims to improve the level of corporate compliance with climate risk disclosure, suggesting the influence of entrepreneurs' visibility on corporate climate risk disclosure; on this basis, the role of entrepreneurs' visibility and media attention on corporate climate risk disclosure is verified through an empirical model; finally, targeted and effective response strategies are proposed to improve corporate climate risk disclosure, set reasonable media attention and increase the effectiveness of entrepreneurs' visibility.

Nitrogen oxides (NO_x) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NO_x hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods.

Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model.

The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data.

SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.

This paper aims to propose a soft actuator that combines a sponge-based actuating structure and a layer-jamming-based stiffness-improving structure in a cavity.

The proposed soft actuator consists of film-constrained sponge units (FCSUs) and jamming layers. The FCSUs in the proposed soft actuator bend under vacuum pressure, causing bending deformation of the entire actuator. The jamming layers are strongly coupled through friction under vacuum pressure, increasing the stiffness of the entire actuator. The performance of the proposed soft actuator was examined by measuring its stiffness, bending deformation and response performance. A four-finger soft robotic gripper was proposed based on the proposed soft actuator.

Through experiments, it was shown that the proposed soft actuator exhibited acceptable bending deformation, stiffness and response. Moreover, the proposed four-finger soft gripper could effectively grasp objects in daily life.

In this study, the authors proposed a novel bending actuator (with a volume of approximately 43.2 cm³) based on FCSUs and jamming layers. To the best of the authors’ knowledge, this is the first study to combine a sponge-based actuating structure and a layer-jamming structure in a cavity to achieve simultaneous change in actuation and stiffness. The soft actuator exhibited good bending deformation and high stiffness simultaneously under vacuum pressure. Consequently, it could be used effectively to fabricate soft grippers.

In order to systematically grasp the changes and matching characteristics of wheel and rail profiles of high speed railway (HSR) in China, 172 rail profile measurement points and 384 wheels of 6 high-speed electric motive unites (EMUs) were selected on 6 typical HSR lines, including Beijing–Shanghai, Wuhan–Guangzhou, Harbin–Dalian, Lanzhou–Xinjiang, Guiyang–Guangzhou and Dandong–Dalian for a two-year field test.

Based on the measured data, the characteristics of rail and wheel wear were analyzed by mathematical statistics method. The equivalent conicity of wheel and rail matching in a wheel reprofiling cycle was analyzed by using the measured rail profile.

Results showed that when the curve radius of HSR was larger than 2,495 m, the wear rate of straight line and curve rail was almost the same. For the line with annual traffic gross weight less than 11 Mt, the vertical wear of rail was less than 0.01 mm. The wear rate of the rail with the curve radius less than 800 m increased obviously. The wheel tread wear of EMUs on Harbin–Dalian line, Lanzhou–Xinjiang line and Dandong–Dalian line was relatively large, and the average wear rate of tread was about 0.05–0.06 mm·(10,000 km)⁻¹, while that of Beijing–Shanghai line, Wuhan–Guangzhou line and Guiyang–Guangzhou line was about 0.03–0.035 mm·(10,000 km)⁻¹. When the wear range was small, the equivalent conicity increased with the increase of wheel tread wear. When the wear range of wheel was wide, the wheel–rail contact points were evenly distributed, and the equivalent conicity did not increase obviously.

This research proposes the distribution range of the equivalent conicity in one reprofiling cycle of various EMU trains, which provides guidance for the condition-based wheel reprofiling.

This paper aims to overcome the problem that the single structure of the driving term of the grey prediction model is not adapted to the complexity and diversity of the actual modeling objects, which leads to poor modeling results.

Firstly, the nonlinear law between the raw data and time point is fully mined by expanding the nonlinear term and the range of order. Secondly, through the synchronous optimization of model structure and parameter, the dynamic adjustment of the model with the change of the modeled object is realized. Finally, the objective optimization of nonlinear driving term and cumulative order of the model is realized by particle swarm optimization PSO algorithm.

The model can achieve strong compatibility with multiple existing models through parameter transformation. The synchronous optimization of model structure and parameter has a significant improvement over the single optimization method. The new model has a wide range of applications and strong modeling capabilities.

A novel grey prediction model with structure variability and optimizing parameter synchronization is proposed.

The highlights of the paper are as follows:

1. A new grey prediction model with a unified nonlinear structure is proposed.

2. The new model can be fully compatible with multiple traditional grey models.

3. The new model solves the defect of poor adaptability of the traditional grey models.

4. The parameters of the new model are optimized by PSO algorithm.

5. Cases verify that the new model outperforms other models significantly.

A new grey prediction model with a unified nonlinear structure is proposed.

The new model can be fully compatible with multiple traditional grey models.

The new model solves the defect of poor adaptability of the traditional grey models.

The parameters of the new model are optimized by PSO algorithm.

Cases verify that the new model outperforms other models significantly.

Hand, foot and mouth disease (HFMD) is a common infectious disease in infants and children. HFMD has caused millions of cases and a large epidemic worldwide. A number of studies have shown that the incidence of HFMD is closely related to various factors such as meteorological factors, environmental air pollution factors and socio-economic factors. However, there are few studies that systematically consider the impact of various factors on the incidence of HFMD. The paper aims to discuss these issues.

This study used grey correlation analysis and principal component analysis (PCA) method to systematically analyse the impact of meteorological factors, health resource factors, socio-economic factors and environmental air pollution factors on the incidence of HFMD in Shenzhen.

The incidence of HFMD in Shenzhen was affected by multiple factors. Grey correlation analysis found eight influencing factors which are as follows: volume of industrial waste gas emission; the days of air quality equal to or above grade; the volume of industrial nitrogen oxide emission; precipitation; the mean air temperature; the gross domestic product; the expenditure for medical and health care; and the gross domestic product per capita. PCA found that the gross domestic product, the volume of industrial soot emission, the relative humidity, and the days of air quality equal to or above grade have a higher load value.

This study is the one of the first studies that apply the grey correlation analysis to analyse the influencing factors of HFMD in the English literature, which to some extent fills up the blank in this field.

The relationship between farm size and greenhouse gas (GHG) emissions has not been clearly defined. This paper aims to assess and compare the impact of farm size on greenhouse gas (GHG) emissions derived from wheat and maize production in the North China Plain (NCP), one of the most important agricultural regions in China.

A field survey through face-to-face interviews was conducted to collect the primary data, and life cycle assessment method, a worldwide comparable framework, was then adopted to characterize the farm-size effect on greenhouse gas (GHG) wheat and maize production in NCP.

It was confirmed that GHG emissions from N fertilizer production and use were the primary contributor to total carbon footprint (CF). As farm size increased, maize yield increased but wheat yield barely changed, while area-scaled and yield-scaled CF declined for both crops. These results were supposed to relate to utilize the inputs more efficiently resulting from increased application of modern agriculture methods on larger operations. It was also found maize not only had higher grain yields, but possessed much smaller CFs. More notably, the reduction of CF with farm size seemed to be more sensitive for maize as compared to wheat. To further mitigate GHG emissions, farm size should better be larger for wheat than for maize.

This study provides useful information guide for Chinese agriculture in increasing crop production, raising farm income and relieving environmental burdens caused by the misuse of agricultural resources.