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Nano graphitic-carbon nitride (g-C₃N₄) is an emerging lubrication technology with excellent performance and significant potential for future applications. This study aims to investigate the effect of nano g-C₃N₄ as a lubricant additive on the wear performance of bearing steel disk.

Various mass fractions of g-C₃N₄ were introduced into the base oil. Combining tribological testing, rheological testing and surface analysis methods, the anti-wear properties and lubrication mechanisms were analyzed.

Transmission electron microscopy images revealed that the size of the nanoparticles of g-C₃N₄ ranges from 10 to 100 nm. Phase analysis of the g-C₃N₄ sample was conducted using X-ray diffraction. Further, 1.0% mass fraction of g-C₃N₄ in the base oil provides excellent anti-wear and friction-reducing performance. Compared to the base oil alone, it reduces the average friction coefficient by 63.8% and decreases the wear rate by 43.1%, significantly reducing the depth and width of the wear scar. Energy-dispersive X-ray spectroscopy and scanning electron microscope analysis revealed that the oil sample containing nano g-C₃N₄ can form a lubricating film on the sliding surface of bearing steel after wear, which enhances the lubricating properties of the base oil.

The synergistic effect of the base oil and nanoparticles reduces friction and wear and is expected to extend the service life of bearing steel. These findings suggest that incorporating nano g-C₃N₄ as a lubricant additive offers significant potential for improving the performance of mechanical components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0456/

The purpose of this study is to explore the interfacial adhesion between superhydrophobic coatings FC-X (X = 1%, 2%, 3%, 4% and 5%) and the concrete substrate, along with the impact of FC-X on the water repellency characteristics of the concrete substrate.

One synthetic step was adopted to prepare novel F-SiO₂ NP hybrid fluororesin coating. The impact of varying mass fractions of F-SiO₂ NPs on the superhydrophobicity of FC-X was analyzed and subsequently confirmed through water contact angle (WCA) measurements. Superhydrophobic coatings were simply applied to the concrete substrate using a one-step spraying method. The interfacial adhesion between FC-X and the concrete substrate was analyzed using tape pasting tests and abrasion resistance measurements. The influence of FC-X on the water repellency of the concrete substrate was investigated through measurements of water absorption, impermeability and electric flux.

FC-4% exhibits excellent superhydrophobicity, with a WCA of 157.5° and a sliding angle of 2.3°. Compared to control sample, FC-X exhibits better properties, including chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

This study offers a thorough investigation into the practical implications of enhancing the durability and water repellency of concrete substrates by using superhydrophobic coatings, particularly FC-4%, which demonstrates exceptional superhydrophobicity alongside remarkable chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

Through the examination of the interfacial adhesion between FC-X and the concrete substrate, along with an assessment of FC-X’s impact on the water repellency of the concrete, this paper provides valuable insights into the practical application of superhydrophobic coatings in enhancing the durability and performance of concrete materials.

The application of galvanized steel is widespread across industries due to its protective zinc coating that protects against atmospheric corrosion. However, previous studies have primarily focused on long-term corrosion rates rather than the full-scale corrosion behavior of the zinc. This paper aims to study the full-scale corrosion evolution of galvanic steel under simulated marine atmospheric environment using real-time EIS measurement.

Electrochemical impedance spectroscopy (EIS) provides an advanced method in monitoring such behavior. Therefore, the EIS method has been used to conduct a comprehensive investigation on the corrosion behavior of galvanic steel in a full-time manner.

The results indicate that the corrosion process of galvanic steel can be divided into three stages: an initial stage with an increased corrosion rate, a subsequent stage with a reduced corrosion rate, and finally a third stage with the lowest and constant corrosion rate. The evolution of corrosion resistance is closely related to changes in composition and structure of the patina layer. In the initial stage, galvanized steel undergoes the formation of soluble ZnCl₂ and needle-like Zn₅(OH)₈Cl₂·H₂O, which promotes the generation and maintenance of an electrolyte layer, consequently leading to an increase in corrosion rate. With prolonged corrosion time, there is a continuous accumulation of Zn₅(OH)₈Cl₂·H₂O within the patina layer, which reduces the content of soluble components and promotes the development of a denser inner layer, thus enhancing corrosion resistance.

This work holds significance in the monitoring of corrosion, understanding the evolution of corrosion and predicting the lifespan of galvanized steel.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

The COVID-19 pandemic and the corresponding control measures have harmed the mental health of professionals working in the construction industry. Existing research has also indicated that demographic characteristics are leading variables causing differences in individual’s perceptions on mental health and psychosocial hazardous factors. Combining these, this research aims to compare the differences and similarities of the perceived mental health outcomes and psychosocial hazards among construction professionals with different demographic characteristics during the pandemic.

Using a questionnaire survey, data were collected from 531 construction professionals working in Chinese construction companies, which were analyzed quantitatively using mean score comparative analysis, Mann–Whitney U test and Kruskal–Wallis H test, as well as Spearman’s correlation analysis.

The results indicate that construction professionals with different ages, years of working experience and positions are exposed to different psychosocial hazards, resulting in different mental health conditions during the pandemic. Age and years of working experience are also strong predictors of the level of depression and anxiety experienced by construction professionals; that is, mental ill health tends to decrease with the increase of age and experience. Male department/unit heads, working in a company office environment for a private company and aged 31–40 years old with 11–20 years of working experience, tend to have the best mental health condition. In contrast, psychosocial hazards are more likely to produce the most serious impact on male site-based construction professionals working for a state-owned company, either with less than one year of working experience or in a senior management position.

Despite the significant contribution of the construction industry to the global economy, the differences and similarities of the mental health outcomes and psychosocial hazards among construction professionals with different demographic characteristics during the pandemic remain unknown. This research, therefore, reveals the mental health outcomes and psychosocial hazard impacts among different types of construction professionals during the pandemic. Specifically, this research unveils the important personal characteristics which are closely associated with poor mental health and the stronger impacts of psychosocial hazards on the mental health of construction professionals during the pandemic. The results are valuable for governments and construction companies to formulate targeted mental health intervention strategies during future public health emergencies.

This study aims to explore the perceptions of young consumers, a rapidly growing market segment in Taiwan’s restaurant industry, and analyze the composition and structure of background music alongside the physical environment in luxury restaurants. This study identifies key factors that significantly influence customer emotions and examine the moderating effect of service encounter pace on customer emotions and satisfaction for offering practitioners practical insights to enhance young consumers’ dining experiences.

This study used partial least squares structural equation modeling (PLS-SEM) for hypothesis testing. The research model was evaluated within the context of luxury restaurants, and moderation analyses were conducted to assess the impact of service encounter pace on customer satisfaction.

The study reveals that luxury restaurants’ physical environment and background music significantly affect pleasure and arousal. Notably, arousal positively influences customer satisfaction, while pleasure does not. The findings also support that service encounter pace creates a fully moderated effect between customer emotion (pleasure and arousal) and customer satisfaction.

The findings assist luxury restaurant management in developing effective servicescapes that evoke positive customer emotions and establish an optimal service encounter pace, thereby enhancing overall customer satisfaction.

This study enriches our understanding of the dining experiences of Taiwanese Gen Y consumers in luxury restaurants. It also sheds light on emerging hospitality trends and explores the perceptions of younger generations, potentially including Generation Z.

Work–life conflict (WLC) is widely recognized as having a significant negative impact on construction management. This research seeks to pinpoint the factors contributing to WLC and review how pandemics impact work–life balance specifically within the construction industry.

A comprehensive literature review was carried out to establish a structured framework and theoretical model addressing WLC in construction. The Hunter–Schmidt meta-analysis paradigm, a robust technique based on the random-effects model, was employed to evaluate this framework due to its efficacy of correcting error.

The results indicate the significant relationships between WLC and several factors, including social support, work demands, job autonomy and schedule control, and role blurring. Conversely, the impact of co-worker support remains ambiguous, as evidenced by the broad confidence intervals.

This study introduces a novel theoretical framework for analyzing WLC within the construction sector, addressing sector-specific challenges such as irregular work hours and high-pressure environments. Moreover, it offers practical recommendations for improving health and safety performance in the construction industry.

This study delves into the intricate relationship between specific positive and negative emotions experienced by tourists during their vacations and the corresponding emotion regulation strategies they employ. Drawing from emotion regulation theory, we examine the nuanced impact of various strategies on tourists' emotional experiences, thereby advancing our understanding of emotion dynamics in the context of tourism.

Data were gathered through an online survey and travel diaries, subsequently analyzed using linear mixed-effects models.

Our findings underscore that emotion regulation strategies exert a significant influence on both positive and negative emotions. Furthermore, we identified that different strategies correlate uniquely with specific emotions. For instance, the deployment of Expressive Suppression, Savoring, and Stimulus Control strategies notably amplifies the intensity of joy.

This study recommends that tourism managers design experiences that evoke positive emotions through curated sensory cues, storytelling, and stress-free service offerings. Tourism managers should prioritize stress-free services, guide tourists in expressing themselves, and train service providers to manage emotions effectively, thus promoting positive emotional interactions and improving overall customer satisfaction.

Theoretically, this research enriches the emotion regulation literature by contextualizing it within the tourism domain, highlighting the differential effects of regulation strategies on diverse emotional experiences. From a practical standpoint, these insights can guide practitioners in crafting targeted marketing strategies and empower tourists with knowledge to select optimal strategies for enhancing their emotional well-being during vacations.

An S-curve is an essential project-management tool. However, it is difficult to adjust S-curve to deal with a force majeure event. The present study develops four valuable adjustment approaches, designed to achieve a compromise between the views of the client and contractor. These can be used to control projects after a force majeure event.

The present study develops four adjustment approaches, which can be used to achieve a compromise between the views of the client and those of the contractor when controlling projects after a force majeure. To determine the S-curves during a force majeure event, two approaches can be selected: BCWS (budgeted cost of scheduled work)-base approach, or BCWP (budgeted cost of work performed)-base approach. To determine the rest of S-curves after a force majeure event, two approaches can be considered: maintaining the original curve of the remaining BCWS, or allocating the original curve of the remaining BCWS. Based on the validation of three empirical cases, drawn from a professional project-management website, this study confirms the feasibility of four proposed empirical approaches and a selection procedure for S-curve adjustment.

The S-curve-adjustment approaches presented here can be used to deal with cases that are ahead of, on and behind schedule. Using the proposed approaches and selection procedure, contractors can easily revise S-curves and control projects more effectively. To deal with a force majeure event, such as COVID-19, they are strongly advised to adopt the approaches labeled SA-A1 (to adjust the S-curve based on the extension ratio multiplied by the difference in progress during the force majeure) and SA-B1 (to maintain the original curve of the remaining BCWS) for the A/E and E/F curves, respectively.

The proposed approaches can be used in cases of continuous construction during force majeure events. If construction work is totally suspended during such an event, it will be necessary to fine-tune the proposed approaches.

Previous studies have used case-oriented or mathematical-simulation approaches to forecast S-curves. The present study proposes simple approaches that allow the client and contractor to adjust the S-curve easily after a force majeure event. These approaches can be used to adjust work and project-completion targets within an extended duration. Selecting the right S-curve adjustment approach can help to control the remainder of the project, reducing the possibility of delay claims.

This study investigates the relationship between digitalization and agricultural carbon intensity from 2006 to 2021.

Utilizing panel data from 30 provinces in China from 2006 to 2021, this study employs a threshold and a spatial Durbin model to investigate the relationship between digitalization and agricultural carbon intensity. In addition, a heterogeneity analysis was conducted to understand variations across regions.

The study used threshold models and spatial Durbin models to reveal that in agricultural production, digitalization can significantly reduce the carbon emission intensity of planting and livestock production. Through the threshold effect, it was found that the effectiveness of digitization in curbing emissions varies by region and is influenced by the level of urbanization, with the inhibitory effect being: western > central > eastern. In addition, through spatial analysis, it was found that the impact of digitalization on carbon emission intensity has significant spatial effects, presenting a “high-high” and “low-low” clustering pattern. Moreover, through the Durbin model, digitization exhibits a significant negative spatial impact on the planting industry, and the development of the local digitalization can significantly reduce the carbon emission intensity of the planting industry in neighboring areas. There is a significant positive spatial effect on livestock production, and the development of local digitalization will, to some extent, increase the carbon emission intensity of livestock production in neighboring areas. This study underscores the critical importance of digitalization in reducing agricultural carbon emissions and highlights the necessity for tailored digital strategies that consider regional characteristics and urbanization levels.

This study shows the critical importance of digitalization in reducing agricultural carbon emissions and highlights the necessity for tailored digital strategies that consider regional characteristics and urbanization levels.

This paper pioneers the investigation of the spatial impact of digitalization on agricultural carbon emissions using provincial-level panel data and classifies agriculture into planting and livestock production. This study contributes to the literature by filling the research gap and enhancing our understanding of the relationship between digitalization and environmental sustainability in rural areas.