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Finite element analysis of underwater transducers typically requires a high level of expertise, and the iterative process of testing various sizes, material parameters and other factors is often inefficient. To address this challenge, this paper aims to introduce underwater transducer parametric simulation (UTPS) software to streamline the design and optimization process.

The design methodology integrates the strengths of ANSYS Parametric Design Language (APDL) for parametric design with the Qt Creator framework for developing a visual interface. C++ is used to encapsulate complex, hard-to-master APDL macros and interact with ANSYS software to execute the relevant APDL macros, performing finite element analysis on the underwater transducer in the background. The results are then processed and displayed on the visual interface.

UTPS enables parametric modeling, modal analysis, harmonic response analysis and directivity analysis of underwater transducers. Users only need to input parameters into the software interface to obtain the transducer’s performance, significantly improving work efficiency and lowering the professional threshold. A prototype transducer was fabricated and tested based on UTPS results, which confirmed the accuracy of the software.

This paper presents an innovative parametric simulation tool for underwater transducers, combining finite element analysis and APDL to simplify and expedite the design process. UTPS reduces the need for specialized knowledge, cutting down on training costs, while its parametric design capabilities accelerate the design process, saving resources.

This study aims to investigate the intentions of Muslim cryptocurrency (CC) holders to fulfil their zakat obligations on digital assets, exploring the unique motivations and barriers within this emerging financial landscape.

The research uses a quantitative approach and a cross-sectional research design through online surveys, using purposive sampling to gather data from Muslim CC holders. The integrated model, known as the theory of planned behaviour and social cognitive theory (TPB-SCT) model, is used to comprehensively analyse the key factors influencing intentions to pay zakat on cryptocurrencies (CCs).

The study reveals that attitude towards zakat on CCs and perceived behavioural control regarding zakat on CCs have a significant and positive effect on the intention to pay. In contrast, subjective norms show no significant influence. CCs-related financial risk exerts a negative impact on intention. Moreover, CCs-related zakat knowledge and adherence to Shariah compliance are strongly associated with intention. These findings provide insights into the intricate dynamics of religious compliance within the evolving realm of digital assets.

Outcomes offer profound indications to stakeholders, including financial institutions, zakat agencies, policymakers and the community, on how to integrate zakat into this new and rapidly evolving financial paradigm like CC.

A pioneering effort was made in this study by exploring the intentions of Muslim CC holders to fulfil zakat obligations, bridging a significant gap in the existing literature. Developing and validating an integrated model of TPB-SCT in the realm of zakat on CC enriches the literature with a novel theoretical framework.

Open-source communities are platforms that promote knowledge sharing. The mitigation of open-source risks is crucial to these communities. Therefore, this article explores the governance mechanisms of knowledge sharing in open-source communities.

To answer the core research question – “What are the governance mechanisms of knowledge sharing in open-source communities?” – we conducted an in-depth case study analysis of two open-source communities based in China.

Two types of open-source communities were found: technology-driven communities and enterprise ecosystem-oriented communities. Hence, their governance mechanisms differed. For the former type, it was important to integrate social and commercial value to encourage knowledge exchange and enhance business scenarios through community-user experience. For the latter type, mutual collaboration and knowledge sharing could be fostered through differentiated layouts and the distributed collaboration of developers around data-driven innovation scenarios. This required the integration of individual and ecosystem value through value exchange.

This study advances our understanding of the coordinated development between founding firms and digital technology-based open-source communities. The findings offer important guidance to business practitioners seeking to manage knowledge-sharing activities during digital transformations.

This paper aims to regulate the oil retention rate and tribological properties of pored polytetrafluoroethylene (PPTFE) using polyvinyl alcohol (PVA)-based oil gel.

PPTFE was first prepared by using citric acid (CA) as an efficient pore-making agent. Subsequently, PVA and chitosan solution was introduced into the pores and experienced a freezing-thawing process, forming PVA-based gels inside the pores. Then, the PPTFE/PVA composite was impregnated with polyethylene glycol 200 (PEG200), yielding an oil-impregnated PPTFE/PVA/PEG200 composite.

It was found that the oil-impregnated PPTFE/PVA/PEG200 composite exhibited advanced tribological properties than neat PTFE with reductions of 53% and 70% in coefficient of friction and wear rate, respectively.

This study shows an efficient strategy to regulate the tribological property of PTFE using a PVA-based oil-containing gel.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0432/

This paper aims to investigate the crucial roles of textured surfaces on oil-impregnated polytetrafluoroethylene (PTFE) created by a facile tattoo strategy in improving tribological properties.

Pored PTFE (PPTFE) was prepared by mixing powder PTFE and citric acid and experienced a cold-press sintering molding process. Subsequently, textured surfaces were obtained with using a tattoo strategy. Surface-textured PPTFE was thus impregnated with polyethylene glycol 200, yielding oil-impregnated and pore-connected PPTFE.

This study found that oil-impregnated and surface-textured PPTFE exhibited excellent tribological performances with an 82% reduction in coefficient of friction and a 72.5% lowering in wear rate comparing to PPTFE.

This study shows an efficient strategy to improve the tribological property of PTFE using a tattoo-inspired surface texturing method.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0378/

This paper aims to explore the possible forms and characteristics of an artificial intelligence (AI) leader and discuss the potential applications of AI in political leadership and governance.

A categorization system consisting of three categories – the level of responsibility, the voting system and the bindingness of the AI’s decisions – was developed to better understand the various types of AI leaders. Additionally, to identify the main characteristics of an AI leader, a comprehensive literature review was conducted. The themes from the literature were then categorized and supplemented with additional discussions.

This paper identifies several potential AI leaders, including the AI President, the AI Dictator, the AI Minister and the AI Consultant. The key characteristics of an AI leader were also discussed. The primary strengths of AI lie in their intelligence and rationality, which could potentially lead our societies toward a peaceful and prosperous future. However, a significant drawback of AI is that it will always be limited by the capabilities and intentions of its programmer, whether human or AI.

Understanding the forms and characteristics of AI leaders may help policymakers and decision-makers explore the possibilities of integrating AI into political leadership and governance.

This paper contributes to the emerging field of AI in governance by exploring the forms and characteristics of AI leaders and discussing their potential applications in political leadership.

In laser powder bed fusion (L-PBF) additive manufacturing, spatter particles are ejected from the melt pool and can be detrimental to material performance and powder recycling. Quantifying spatter generation with respect to processing conditions is a step toward mitigating spatter and better understanding the phenomenon. This paper reveals process insights of spatter phenomena by automatically annotating spatter particles in high-speed video observations using machine learning.

A high-speed camera was used to observe the L-PBF process while varying laser power, laser scan speed and scan strategy on a constant geometry on an EOSM290 using Ti-6Al-4V powder. Two separate convolutional neural networks were trained to segment and track spatter particles in captured high-speed videos for spatter characterization.

Spatter generation and ejection angle significantly differ between keyhole and conduction mode melting. High laser powers lead to large ejections at the beginning of scan lines. Slow and fast build rates produce more spatter than moderate build rates at constant energy density. Scan strategies with more scan vectors lead to more spatter. The presence of powder significantly increases the amount of spatter generated during the process.

With the ability to automatically annotate a large volume of high-speed video data sets with high accuracy, an experimental design of observed parameter changes reveals quantitively stark changes in spatter morphology that can aid process development to mitigate spatter occurrence and impacts on material performance.

This study aims to investigate the practical utilisation of Artificial Intelligence (AI) techniques in combating credit card fraud (CCF) within the accounting and finance sectors. It will evaluate the efficacy of machine learning (ML), blockchain and fuzzy logic in detecting fraudulent transactions, aiming to provide valuable insights for professionals including fraud examiners, auditors, accountants, bankers and organisations. The research seeks to determine whether AI and ML methods yield beneficial outcomes in the realm of credit card fraud detection (CCFD). It will focus on applying AI and ML techniques in CCFD, incorporating interviews, cross-country questionnaires and a sample size of 403. The study endeavours to contribute to understanding the optimal mechanism for detecting CCF and its potential for widespread application.

The construction industry is notorious for high risks and accident rates, prompting professionals to adopt emerging technologies for improved construction workers’ health and safety (CWHS). Despite the recognized benefits, the practical implementation of these technologies in safety management within the Construction 4.0 era remains nascent. This study aims to investigate the mechanisms influencing the implementation of Construction 4.0 technologies (C4.0TeIm) to enhance CWHS in construction organizations.

Drawing upon integrated institutional theory, the contingency resource-based view of firms and the theory of planned behavior, this study developed and tested an integrated C4.0TeIm-CWHS framework. The framework captures the interactions among key factors driving C4.0TeIm to enhance CWHS within construction organizations. Data were collected via a questionnaire survey among 91 construction organizations and analyzed using partial least squares structural equation modeling to test the hypothesized relationships.

The results reveal that: (1) key C4.0TeIm areas are integrative and centralized around four areas, such as artificial intelligence and 3D printing, Internet of Things and extended reality; and (2) external coercive and normative forces, internal resource and capability, business strategy, technology competency and management (BST), organizational culture and use intention (UI) of C4.0 technologies, collectively influence C4.0TeIm-CWHS. The findings confirm the pivotal roles of BST and UI as mediators fostering positive organizational behaviors related to C4.0TeIm-CWHS.

Practically, it offers actionable insights for policymakers to optimize technology integration in construction firms, promoting industrial advancement while enhancing workforce well-being.

The novel C4.0TeIm-CWHS framework contributes to the theoretical discourses on safety management within the C4.0 paradigm by offering insights into internal strategic deployment and compliance challenges in construction organizations.

The ever-evolving landscape of financial technology (Fintech) has revolutionised payment methods and raised questions about what drives user behaviour in adopting these innovative solutions. This study, using narrative transportation theory as an underpinning theory, aims to investigate into the dynamics of green user behaviour in adopting Fintech payments.

This study used a deductive approach, and with data obtained from 635 respondents through the purposive sampling technique, partial least squares structural equation modelling was employed to yield significant insights.

The study found a positive association between green brand positioning and product differentiation. However, it unexpectedly didn't impact user attitudes towards Fintech payments. Green brand image and perceived performance positively influenced product differentiation. Perceived product differentiation fully mediated the association between green brand positioning and user attitudes. The study introduced fear of missing out's (FOMO) moderating role, enriching eco-conscious marketing insights and user behaviour understanding.

This study reveals crucial implications for marketers, policymakers and user experience (UX) designers operating within the Fintech industry. It emphasises green brand positioning's impact on product differentiation, user attitudes and its mediating role. It advocates for sustainability integration, innovation, strategic messaging and user-centric improvements to optimise user perceptions and competitiveness in the evolving Fintech landscape. The study's cross-sectional design may limit the ability to establish causal relationships over time and overlook temporal changes in green Fintech adoption dynamics; thus, longitudinal studies are warranted to better understand the evolving nature of user attitudes and behaviours towards green Fintech payments.

This study adds novelty to the existing body of literature by introducing the dimension of innovation appeal to green brand positioning and employing narrative transportation theory in the Fintech realm. The findings also add novelty by highlighting the moderating impact of fear of missing out in predicting the association between green brand positioning and product differentiation in the realm of green Fintech and green use behaviour.