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The purpose of this study is to develop new biodegradable magnesium alloy. Magnesium possesses similar mechanical properties to natural bone; it is a potential candidate for resorbable implant applications. However, in physiological conditions, the degradation rate of Mg is too high to be used as an implant material.

In this research, Zn, Sr and Ca were chosen as alloying elements; a coating was deposited on the MgZnSrCa alloy surface by means of a biomimetic technique. The corrosion rates of the uncoated and coated specimens were tested in simulated body fluid.

The hydroxyapatite coating formed on the MgZnSrCa alloy surface and the hydroxyapatite layer markedly decreased the corrosion rate of the MgZnSrCa alloy.

A homogenous hydroxyapatite coating was formed on the MgZnSrCa alloy surface by using a biomimetic coating technique. The biomimetic hydroxyapatite coating markedly reduced the corrosion rate of the MgZnSrCa alloy, and the largest decrease in wastage rate was 44 per cent.

The purpose of this study is to investigate the effectiveness of a composite lubrication system combining polytetrafluoroethylene (PTFE) film and oil lubrication in steel–steel friction pairs.

A PTFE layer was sintered on the surface of a steel disk, and a lubricant with additives was applied to the surface of the steel disk. A friction and wear tester was used to evaluate the tribological properties and insulation capacity. Fourier transform infrared spectrometer was used to analyze the changes in the composition of the lubricant, and X-ray photoelectron spectroscopy was used to analyze the chemical composition of the worn surface.

It was found that incorporating the PTFE film with PSAIL 2280 significantly enhanced both the friction reduction and insulation capabilities at the electrical contact interface during sliding. The system consistently achieved ultra-low friction coefficients (COF < 0.01) under loads of 2–4 N and elucidated the underlying lubrication mechanisms.

This work not only confirm the potential of PTFE films in insulating electrical contact lubrication but also offer a viable approach for maintaining efficient and stable low-friction wear conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0222/

The purpose of this study was to synthesize composite nanoparticles (TiO₂@SiO₂) via the chemical deposition method and investigate their efficacy as additives in polytetrafluoroethylene (PTFE) lubricating grease. The focus was on examining the frictional and conductive properties of the TiO₂@SiO₂ grease using a friction tester.

Composite nanoparticles (TiO₂@SiO₂) were synthesized using the chemical deposition method and incorporated into PTFE grease. Frictional and conductive properties were evaluated using a friction tester. Surface morphology and chemical composition of wear tracks were analyzed using scanning electron microscope and X-ray photoelectron spectroscopy, respectively.

Incorporating TiO₂@SiO₂ at a mass fraction of 1 Wt.% led to a significant reduction in friction coefficient and wear width. The wear depth exhibited a remarkable decrease of 260%, while the contact resistance reached its peak value. This improvement in tribological properties could be attributed to the presence of TiO₂@SiO₂, where TiO₂ served as the core and SiO₂ as the shell during the friction process. The high hardness of the SiO₂ shell contributed to enhanced load-bearing capacity. In addition, the exceptional insulation properties of SiO₂ demonstrated excellent electron-capturing capabilities, resulting in improved friction and insulation performance of the TiO₂@SiO₂ lubricating grease.

This study demonstrates the potential of TiO₂@SiO₂ composite nanoparticles as additives in lubricating greases, offering improved friction and insulation performance. The findings provide insights into the design of advanced lubricating materials with enhanced tribological properties and insulation capacity, contributing to the development of more efficient and durable lubrication systems.

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.

A unified model of ODD monitoring is constructed, which consists of three modules: weather condition monitoring for unusual weather conditions, such as rain, snow and fog; vehicle behavior monitoring for abnormal vehicle behavior, such as traffic rule violations; and road condition monitoring for abnormal road conditions, such as road defects, unexpected obstacles and slippery roads. Additionally, the applications of the proposed unified ODD monitoring framework are demonstrated. The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.

First, the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework. Second, the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework. Third, the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.

The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.

This paper aims to investigate the photocatalytic activity of zinc doped MAO-TiO₂ films under the optimum MAO treatment condition.

The coating was prepared by micro arc oxidation, and the influence of doping on the properties of the coating was also investigated.

The results show that the BET surface area is 78.25±0.03m²/g, total pore area is 76.32 ± 0.04m²/g, and the total pore volume is 0.2135 ± 0.0004cm³/g. The degradation ratio of the film electrode with Zn-doped in methyl orange solution is up to 94%. When the react circles is 10 times, the degradation ratio is up to more than 85% and remains steady. With the different reaction conditions, these kinetics of the reactions show some different formulas.

A kinetic equation for photocatalytic activity is established.

Nurses' work engagement is critical for the service quality of the hospital. Thus, investigation on the influencing factors of nurses' work engagement has become an important issue. This study addresses this issue by exploring the effect of daily family-to-work conflict (FWC) on next-day work engagement among Chinese nurses.

The theoretical model was tested using 555 experience sampling data from 61 nurses collected for 10 workdays in China.

Nurses' daily FWC is associated with their next-day ego depletion. Moreover, increased ego depletion ultimately reduces their next-day work engagement. In addition, a between-individual factor of frequency of perceived patient gratitude mitigates the effect of FWC on ego depletion and the indirect effect on work engagement via ego depletion.

This study is important to the management of health-care organizations as it carries significant implications for theory and practice toward understanding the influence of FWC among nurses. On the one hand, the authors apply the job demands-resources (JD-R) model as the overarching theoretical framework, which contributes to the authors’ understanding of how FWC impairs work engagement. On the other hand, the authors extend extant theoretical models of FWC by identifying the frequency of perceived patient gratitude as an important contextual factor that counteracts the negative effects of FWC among nurses. Moreover, organizations could encourage patients to express their gratitude to nurses by providing more channels, such as thank-you notes, to offer nurses some support for overcoming the destructive effect of FWC.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

The purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.

The traditional grey relational proximity and grey relational similarity degree are integrated into the novel comprehensive grey evaluation framework. The evaluation system of regional green innovation vitality is constructed from three dimensions: economic development vitality, innovative transformation power and environmental protection efficacy. The weights of each indicator are obtained by the entropy weight method. The GIV of 31 provinces in China is measured based on provincial panel data from 2016 to 2020. The ward clustering and K-nearest-neighbor (KNN) algorithms are utilized to explore the regional green innovation discrepancies and promotion paths.

The novel grey evaluation method exhibits stronger ability to capture intrinsic patterns compared with two separate traditional grey relational models. Green innovation vitality shows obvious regional discrepancies. The Matthew effect of China's regional GIV is obvious, showing a basic trend of strong in the eastern but weak in the western areas. The comprehensive innovation vitality of economically developed provinces exhibits steady increasing trend year by year, while the innovation vitality of less developed regions shows an overall steady state of no fluctuation.

The grey entropy comprehensive relational model in this study is applied for the measurement and evaluation of regional GIV, which improves the one-sidedness of traditional grey relational analysis on the proximity or similarity among sequences. In addition, a three-dimensional evaluation system of regional GIV is constructed, which provides the practical guidance for the research of regional development strategic planning as well as promotion paths.

A comprehensive grey entropy relational model based on traditional grey incidence analysis (GIA) in terms of proximity and similarity is proposed. The three-dimensional evaluation system of China's regional GIV is constructed, which provides a new research perspective for regional innovation evaluation and expands the application scope of grey system theory.

Green human resource management (GHRM) is critical to enhancing the ability of the companies' green innovation, but this link is rarely explored or empirically tested in the literature. Drawing upon human capital theory, the study examines a conceptual model that incorporates the effects of green human capital and management environment concern.

Data were collected from 143 firms in China, and the regression analysis and bootstrapping test were used to assess the hypothesis.

Our findings indicate that GHRM can positively influence green innovation, and green human capital mediated the link between GHRM and green innovation. In addition, management environment concern moderates the effect of GHRM on green human capital. The results further explore that the indirect effect of GHRM on green innovation through green human capital is significant for the firms with a high management environment concern, but not for this relationship with a low management environment concern.

The findings further extend the scope of GHRM research, and theoretical and practical implications of GHRM are presented to enhance environment sustainability.

This study aims to identify key contributors, research themes, research gaps, and future directions in hospitality innovation by conducting bibliometric and content analyses of peer-reviewed articles in this field.

A bibliometric analysis was conducted using VOSviewer software on 2,698 peer-reviewed English-language articles retrieved from the Web of Science database, published between 1995 and 2023. Key contributors were identified based on publication volume, citation, and co-citation analysis. Co-occurrence analysis of index keywords and content analysis of influential articles were used to identify research themes.

The study identified four distinct research themes in hospitality innovation: (1) digital technology adoption primarily among customers, (2) innovation management within hospitality firms, focusing on knowledge management and eco-innovation, (3) service innovation primarily among employees, and (4) business model innovation involving multiple stakeholders. Additionally, the study determined key contributors, highlighted research gaps, and provided suggestions for future research directions.

This study contributes to the existing literature by providing a systematic and in-depth review of hospitality innovation research. It identifies key contributors, research themes, and potential gaps for future research, offering valuable insights for both industry practitioners and scholars.