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This paper aims to prepare an oil-impregnated porous polytetrafluoroethylene (PTFE) composite with advanced tribological properties using citric acid as a novel pore-forming agent.

Citric acid (CA) was used to form pores in PTFE, and then oil-impregnated PTFE composites were prepared. The pore-forming efficiency of CA was evaluated. The possible mechanism of lubrication was proposed according to the tribological properties.

The results show CA is an efficient pore-forming agent and completely removed, and the porosity of the PTFE increases with the increase of the CA content. The oil-impregnated porous PTFE exhibits an excellent tribological performance, an increased wear resistance of 77.29% was realized in comparison with neat PTFE.

This study enhances understanding of the lubrication mechanism of oil-impregnated porous polymers and guides for their tribological applications.

Construction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.

This paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.

The experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.

This paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.

The purpose of this study is to improve the mechanical properties, thermal insulation properties and flame retardant properties of polyethylene terephthalate (PET), the organic montmorillonite (OMMT)/SiO₂ aerogel/PET composites and fibers were prepared, and the effects of the OMMT/SiO₂ aerogel on the structure, thermal conductivity and flame retardance of the OMMT/SiO₂ aerogel/PET composites and their fibers were systematically investigated.

The OMMT/SiO₂ aerogel/PET composites and fibers were prepared by in-situ polymerization and melt spinning using SiO₂ aerogel as thermal insulation filler and OMMT (DK2) as comodified filler.

The experimental results showed that OMMT improved the crystallization properties of PET. Compared with the crystallinity of SiO₂ aerogel/PET composites (34.8%), SiO₂ aerogel/PET composites and their fibers reached 45.1% and 49.2%, respectively. The breaking strength of the OMMT/SiO₂ aerogel/PET composite fibers were gradually increased with the OMMT content. When the content of OMMT was 0.8 wt.%, the fracture strength of the composite fibers reached 4.40 cN/dtex, which was 54% higher than that of the SiO₂ aerogel/PET fiber. In addition, the thermal insulation properties of the composites and their fibers were improved by addition of fillers, and at the same time reached the flame retardant level. The thermal conductivity of the 0.8 wt.% OMMT/SiO₂ aerogel/PET composites was 101.78 mW/(m·K), which was 49.3% and 58.8% lower than that of the SiO₂ aerogel/PET composites and the pure PET, respectively. The thermal conductivity of the fiber fabrics woven from the 0.8 wt.% OMMT/SiO₂ aerogel/PET composites was 28.18 mW/(m·K), which was 29.0% and 44.6% lower than that of the SiO₂ aerogel/PET composite fiber fabrics and PET fiber fabrics. The flame retardancy of the composites was improved, with an limiting oxygen index value of 29.2% for the 0.8 wt.% OMMT/SiO₂ aerogel/PET composites, which was 4.1% higher compared to the SiO₂ aerogel/PET composites, and achieved the flame retardant level.

The SiO₂ aerogel/PET composites and their fibers have good mechanical properties, flame retardant properties and thermal insulation properties, exhibited good potential for application in the field of thermal insulation, such as warm clothing. Nowadays, as the energy crisis is becoming more and more serious, it is very important to improve the thermal insulation properties of PET to reduce energy losses and mitigate the energy crisis.

In this study, PET based composites and their fibers with excellent mechanical properties, thermal insulation properties and flame retardant property were obtained by using three-dimensional network porous silica aerogel with low density and low thermal conductivity as the thermal insulation functional filler and two-dimensional layered OMMT as the synergetic modified filler.

The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.

The sale of second-hand goods in the luxury fashion space continues to soar. However, existing literature on this segment is limited and the factors that draw consumers to this space are not well understood. This study aims to fill this gap and proposes a conceptual model demonstrating the linkage between the motivators and barriers toward re-commerce in the luxury fashion space and actual shopping behaviors.

A survey sample of USA second-hand luxury fashion shoppers was collected. Participants were asked questions about various motivators and barriers toward re-commerce, as well as the participants' attitudes and shopping behavior. The results were analyzed using SmartPLS structural equation modeling (SEM).

Economic reasons, originality and self-extension were found to be statistically significant motivators of attitudes toward re-commerce, while status consumption, nostalgia and ecological motivators were not. Superstitious beliefs were also found to be statistically significant motivators toward attitudes of re-commerce. Attitudes were also found to be a significant predictor of shopping behavior as measured by dollars spent and shopping frequency.

This study is among the first to propose a conceptual model depicting the relationship between motivators and barriers to actual shopping behavior in the second-hand luxury fashion space. Many of the motivators and barriers examined in this study are novel and have not been considered in prior research. Superstitious beliefs in particular have not been studied in the context of re-commerce.

Most educational institutions worldwide have shifted to online teaching and learning approaches to mitigate risks imposed by the COVID-19 pandemic. This causes several issues, particularly in delivering the construction management (CM) courses which require site visits, interpreting technical drawings and developing 3D building models. This paper aims to identify the key strategies for online learning and teaching adopted during the COVID-19 pandemic and to investigate the implications for construction management education.

The research approach is twofold. First, the study presents a systematic literature review (SLR) through a synthesis of the existing literature to identify the key strategies and lessons learned about online education during the COVID-19 pandemic in tertiary programs. It also discusses their implications in the context of the construction management (CM) sector in particular. Secondly, the authors shared their hands-on experience as construction management course facilitators – using the autoethnography approach – during the COVID-19 crisis.

In addition to identifying the key strategies such as online course delivery and assessments, the paper critically discusses the barriers to online learning and teaching, including (1) the technological and infrastructure barriers; (2) required online teaching skills and competencies; (3) issues surrounding mental health and wellbeing; (4) lack of consistency in the online delivery of various courses in a given program (5) difficulties around students' engagement and (6) the course characteristics and requirements.

The study offers some implications and recommendations not only for educational institutions and staff but also for vendors of online course delivery software. To prepare educational institutions for future online course delivery, the paper proposes several strategies. These include developing a set of guidelines for online course delivery, incorporating online teaching training modules into the recruitment process for academic staff, applying agile and resilience teaching and learning methods, wellbeing and mental health support and continuously improving course features to adapt to the online environment.

In response to the challenges posed by the conventional manual flange docking method in the LNG (Liquefied Natural Gas) loading process, such as low positioning accuracy, constraints on production efficiency and safety hazards, this study analyzed the LNG five-axis loading arm’s main functions and structural characteristics.

An automated solution for the joints of the LNG loading arm was designed. The forward kinematic model of the LNG loading arm was established using the Denavit–Hartenberg (D-H) parameter method, and its workspace was analyzed. The Newton–Raphson iteration method was employed to solve the inverse kinematics of the LNG loading arm, facilitating trajectory planning. The relationship between the target position and the joint variables was established to verify the stability of the arm’s motion. Flange center identification was achieved using the Hough transform function. Based on the ROS platform, combined with Gazebo and Rviz, an experimental simulation of automatic docking of the LNG loading arm was conducted.

The docking errors in the XYZ directions were all less than 0.8 mm, meeting the required docking accuracy. Moreover, the motion performance of the loading arm during docking was smooth and free of abrupt changes, validating its capability to accomplish the automatic docking task.

The proposed trajectory planning and automatic docking scheme can be used for the rapid filling of LNG filling arms and LNG tankers to improve the efficiency of LNG transportation. In guiding the docking, the proposed automatic docking scheme is an accurate and efficient way to improve safety.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.

This paper aims to explore various cultural and behavioral issues associated with the problem of investment fraud in Indonesia.

By examining multiple cases of investment fraud in Indonesia as well as reviewing publicly available government reports, this study highlights several important cultural and behavioral issues associated with the susceptibility of Indonesian financial services consumers to investment fraud to understand better the dynamics of the victimization process. By using multiple cultural and behavioral theories, this study demonstrates how such issues shape the interactions between investment fraudsters and investment fraud victims.

This study demonstrates that multiple cultural and behavioral factors have created and shaped an environment where fraudsters can exploit people’s behavioral loopholes for their fraudulent schemes. In particular, the high power distance and high collectivism have been identified by this study as contributing to the high level of materialism in the country, which in turn makes people more susceptible to the temptation of get-rich-quick schemes. Investment fraudsters, being students of human behavior, use their behavioral knowledge to devise various means to deceive their victims. They use multiple psychological principles to stimulate target victims “gullibility to make them more vulnerable to fraudulent persuasion. In many cases, even financially literate people are not immune to fraudsters” deceitful messages. This study highlights gullibility production as a foundation for investment fraudsters to devise their means by which victims are manipulated to accept certain beliefs that depart from facts and evidence.

This paper contributes to the innovation in anti-fraud practice by building a better understanding of multiple cultural and behavioral issues associated with investment fraud victimization.

This paper brings a new perspective into the field of anti-fraud to stimulate innovation, in particular in investment fraud prevention.