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The application of three-dimensional (3D) printing technology in construction projects is of increasing interest to researchers and construction practitioners. Although the application of 3D printing technology at various stages of the project lifecycle has been explored, few studies have identified the relative importance of critical success factors (CSFs) for implementing 3D printing technology in construction projects. To address this research gap, this study aims to explore the academics (i.e. researchers) and construction practitioners’ perspectives on CSFs for implementing 3D printing technology in construction projects.

To do this, a questionnaire was administered to participants (i.e. academics and construction practitioners) with knowledge and expertise in 3D printing technology in construction projects. The collected data were analysed using mean score ranking, normalization and rank agreement analysis to identify CSFs and determine the consistency of the ranking of CSFs between academics and construction practitioners. In addition, exploratory factor analysis was used to identify the relationships and underlying constructs of the measured CSFs.

Through a rank agreement analysis of the collected data, 11 CSFs for implementing 3D printing technology were retrieved (i.e. 17% agreement), indicating a diverse agreement in the ranking of the CSFs between academics and construction practitioners. In addition, the results show three key components of CSFs including “production demand enabling CSFs”, “optimize the construction process enabling CSFs” and “optimized design enabling CSFs”.

This study highlights the feasibility of implementing the identified CSFs for 3D printing technology in construction projects, which not only serves as a reference for other researchers but also increases construction practitioners’ awareness of the practical benefits of implementing 3D printing technology in construction projects. Specifically, it would optimize the construction lifecycle processes, enhance digital transformation and promote sustainable construction projects.

The purpose of this paper is to improve the mechanical and tribological properties of Si₃N₄ ceramics and to make the application of Si₃N₄ ceramics as tribological materials more extensive.

Si₃N₄-based composite ceramics (SN-2L) containing nitrogen-doped graphene quantum dots (N-GQDs) were prepared by hot press sintering process through adding 2 Wt.% nanolignin as precursor to the Si₃N₄ matrix, and the dry friction and wear behaviors of Si₃N₄-based composite against TC4 disc were performed at the different loads by using pin-on-disc tester.

The friction coefficients and wear rates of SN-2L composite against TC4 were significantly lower than those of the single-phase Si₃N₄ against TC4 at the load range from 15 to 45 N. At higher load of 45 N, SN-2L/TC4 pair presented the lowest friction coefficient of 0.25, and the wear rates of the pins and discs were as low as 1.76 × 10⁻⁶ and 2.59 × 10⁻⁴mm³/N·m. The low friction and wear behavior could be attributed to the detachment of N-GQDs from the ceramic matrix to the worn surface at the load of 30 N or higher, and then an effective lubricating film containing N-GQDs, SiO₂, TiO₂ and Al₂SiO₅ formed in the worn surface. While, at the same test condition, the friction coefficient of the single-phase Si₃N₄ against TC4 was at a range from 0.45 to 0.58. The spalling and cracking morphology formed on the worn surface of single-phase Si3N4, and the wear mechanism was mainly dominated by adhesive and abrasive wear.

Overall, a high-performance green ceramic composite was prepared, and the composite had a good potential for application in engineering tribology fields (such as aerospace bearings).

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0161/

Social media is widely used to capture citizens' opinions and topics deemed important. The importance or interest social media users attribute to a topic is traditionally measured by tweet frequency. This approach is practical but overlooks other user engagement tools such as retweets, likes, quotes, and replies. As a result, it may lead to a misinterpretation of social media signals. This paper aims to propose a method that considers all user engagement indicators and ranks the topics based on the interest attributed by social media users.

A multi-criteria decision-making framework was proposed, which calculates the relative importance of user engagement tools using objective (information entropy) and subjective (Bayesian Best-Worst Method) methods. The results of the two methods are aggregated with a combinative method. Then, topics are ranked based on their user engagement levels using Multi-Objective Optimization by Ratio Analysis.

The proposed approach was used to determine citizens' priorities in transport policy, and the findings are compared with those obtained solely based on tweet frequency. The results revealed that the proposed multi-criteria decision-making framework generated more comprehensive and robust results.

The proposed method provides a systematic way to interpret social media signals and guide institutions in making better policies, hence ensuring that the demands of users/society are properly addressed.

This study presents a systematic method to prioritize user preferences in social media. It is the first in the literature to discuss the necessity of considering all user engagement indicators and proposes a reliable method that calculates their relative importance.

Knowledge sharing is critical to creating value in platform ecosystems. However, participants refrain from sharing knowledge and even engage in free-riding behavior, thereby causing the value co-destruction of the platform ecosystems. To encourage knowledge sharing among participants, it is essential to analyze the influencing factors and decision-making mechanisms of knowledge sharing in the platform ecosystems.

The study investigated the issue of knowledge sharing among participants in platform ecosystems, based on the stochastic differential game model. Considering the uncertain factors, the Nash non-cooperative game, Stackelberg leader-follower game, and cooperative game models were proposed. By utilizing system dynamics and numerical simulations, the key influencing factors and mechanisms of knowledge sharing were deeply explored, consequently providing game solutions to achieve the Pareto optimality of the ecosystem.

Participants' innovation capability and the marginal benefits of knowledge-sharing positively impact knowledge-sharing decisions, while the environmental knowledge decay rate has a negative influence. The platform subsidy mode enhances the knowledge-sharing effect, and the collaborative cooperation mode can realize the Pareto optimization of the system.

The research findings will provide theoretical support for fostering knowledge innovation and sustainable development of platform ecosystems. Managers should cultivate an innovative environment, establish fair reward mechanisms, and utilize subsidies to promote knowledge sharing, leading to higher value creation.

Utilizing the stochastic differential game model, the study proposed various game-theoretic frameworks to analyze participants' knowledge-sharing strategies. The integration of system dynamics and numerical simulations provides a practical approach to understanding the key influencing factors and decision-making processes.

The purpose of this study is to investigate the physical, ultraviolet (UV), colour appearance and colour fastness properties of selected fabrics dyed with natural dyes from Daboya and Ntonso communities of Ghana. The study further highlights the rich cultural heritage of traditional dyeing from these two communities. Craftsmen in West Africa especially Ghana, have sustained the traditional dyeing methods to produce textile products for consumers.

In this study, two sample fabrics were purchased from craftsmen at Ntonso and Daboya communities in Ghana. These fabrics were analysed at the laboratory under standard test methods for their physical, UV, colour appearance and colour fastness properties.

Results showed that all the sample fabrics have good UV shielding performance (ratings above 50+). Daboya sample fabrics (dyed with indigo dyes) produced more colour stains than the sample fabrics from Ntonso (dyed with black “kuntunkuni” dyes). The K/S_sum value or colour yield reduced after washing but that alternatively increased the calculated ultraviolet protection factor.

Findings from this study exposed the unique UV performance of dyed traditional fabrics (using natural dyes) from Ntonso and Daboya communities in Ghana. This inspires and enforces the need for craftsmen to improve their production cycle to produce these fabrics in different sizes which provides the necessary UV shielding abilities for consumers in the wake of climate changes.

This study demonstrated that the natural dyeing process at the two communities produced relatively good UV and colour fastness properties of the sample fabrics. These eco-friendly dyeing practices have survived over time to maintain and promote the concept of sustainability within the textile and fashion industry in Ghana.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

To incentivize innovation, support competitiveness, lower skill scarcities, and alleviate the housing affordability difficulty, proponents underscore the pertinence of embracing contemporary construction methodologies, with particular emphasis on volumetric modular construction (VMC) as a sustainable paradigm for production and consumption. However, construction industry stakeholders in Australia have encountered profound challenges in adopting VMC, as its adoption remains significantly low. Therefore, this study investigated the constraints that hinder VMC in the Australian construction industry.

The study used qualitative methodology using semi-structured interviews as a core approach to glean professional experts' perspectives and insights, along with Pareto and mean index score analyses.

The study identified 77 reported and validated VMC constraints by professionals, categorizing them into eight categories: cultural, economic, knowledge, market, regulatory, stakeholder, supply chain, and technological. The mean index score analysis reveals stakeholder (µ = 9.67) constraints are the most significant, followed by cultural (µ = 9.62) and regulatory (µ = 9.11) constraints. Pareto analysis revealed 25 of the 77 constraints as ‘vital few” among different categories. This study presented causal relationships and mitigation strategies for VMC constraints, followed by an argument on whether VMC adoption in Australia requires a nudge or mandate.

This study offers guidance for efficient resource allocation, aiding management and government policy formulation. It's also valuable for global audiences, especially countries transitioning to modular construction.

This is one of the first studies to identify VMC constraints and delineate them into different categories in Australia, identify their causal interrelationships, and deliver countermeasures to overcome them.

In today’s digital era, talent recruitment can help address the growing shortage of skilled labor in the construction industry and promote sustainable growth. While existing research has explored the association between talent acquisition and local labor productivity or economic progress, the impact on construction growth deserves further study. This study aims to (1) explore the influence of talent recruitment on the growth of the construction industry and (2) analyze whether different regional characteristics shape the differential impact of talent acquisition on construction growth.

This research employs a quantitative approach, focusing on 35 major cities in China. A panel data regression model is utilized to analyze annual data from 2013 to 2018, considering variables like the construction talent recruitment index, value added in construction, gross regional product per capita and others. The study also examines regional heterogeneity and conducts robustness tests to validate the findings.

The results reveal a positive and significant correlation between talent recruitment and construction industry growth. This correlation is more pronounced in economically advanced and infrastructure-rich regions. The study also finds that factors like capital investment, educational attainment and housing prices significantly contribute to industry growth. Talent recruitment not only transforms local labor market dynamics but also drives demand for construction services, promoting industry growth through economies of scale.

This research constructs a new measurement for talent recruitment and provides new insights into the pivotal role of talent recruitment in the sustainable growth of the construction industry. It underscores the need for construction firms to tailor talent acquisition policies to their specific circumstances and regional developmental conditions. The findings offer practical guidance for driving regional growth within the sector, emphasizing the importance of talent recruitment as a key yet previously underappreciated factor in industry development.

In the digital economy era, digital platforms are vital infrastructure for innovation subjects to perform digital innovation activities. Achieving efficient and smooth knowledge transfer between innovation subjects through digital platforms has become a novel research subject. This study aims to examine the knowledge transfer mechanism of digital platforms in the digital innovation ecosystem through modeling and simulation to offer a theoretical basis for digital innovation subjects to acquire digital value through knowledge-sharing and thus augment their competitive advantage.

This study explores the optimal symbiotic interaction rate between different users based on the classic susceptible-infected-removed (SIR) model. Additionally, it constructs a knowledge transfer mechanism model for digital platforms in the digital innovation ecosystem by combining the theories of communication dynamics and symbiosis. Finally, Matrix Laboratory (MATLAB) software is used for the model and numerical simulation.

The results demonstrate that (1) the evolutionary path of the symbiotic model is key to digital platforms' knowledge transfer in the digital innovation ecosystem. In the symbiotic model, the knowledge transfer path of digital platforms is “independent symbiosis—biased symbiosis (user benefit)—reciprocal symbiosis,” aligning with the overall interests of the digital innovation ecosystem. (2) Digital platforms' knowledge transfer effects within the digital innovation ecosystem show significant differences. The most effective knowledge transfer model for digital platforms is reciprocal symbiosis, whereas the least effective is parochial symbiosis (platform benefit). (3) The symbiotic rate has a significant positive impact on the evolutionary dynamics of knowledge transfer on digital platforms, especially in the reciprocal symbiosis model.

This study's results aid digital innovators in achieving efficient knowledge transfer through digital platforms and identify how symbiotic relationships affect the knowledge transfer process across the ecosystem. Accordingly, the authors propose targeted recommendations to promote the efficiency of knowledge transfer on digital platforms.

This study aims to investigate the relationships between the perceived neighborhood walkable environment (PNWE), neighborhood interaction (NI) and residents’ mental health, with a focus on examining differences among residents of different age groups.

Using an electronic survey questionnaire, data on PNWE, NI and mental health were collected from 1,159 residents across 205 communities in Shanghai, China. Our study utilized a structural equation modeling (SEM), employing the maximum likelihood estimation method. The structural equation model was fitted using the MPLUS software.

The mental health of young and middle-aged adults is generally poor, and they are at higher risk of depression than children and older adults. The effects of PNWE and NI on the mental health of residents varied among different age groups. As residents get older, their mental health is more affected by the PNWE. In addition, the influence of the PNWE on children and older adults’ mental health is direct and not mediated by NI. For young and middle-aged adults, the influence of the PNWE on their mental health needs to be mediated by NI.

This study marks the first examination of the relationship between PNWE, NI and mental health among different age groups of residents in China. The findings of this research can assist policymakers in gaining a deeper understanding of the underlying mechanisms by which PNWE affects mental health. Furthermore, it can contribute to the development of more targeted walkable environment designs aimed at enhancing mental health among various age groups.