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The purpose of this study is to design a highly integrated smart glove to enable gesture acquisition and force sensory interactions, and to enhance the realism and immersion of virtual reality interaction experiences.

The smart glove is highly integrated with gesture sensing, force-haptic acquisition and virtual force feedback modules. Gesture sensing realizes the interactive display of hand posture. The force-haptic acquisition and virtual force feedback provide immersive force feedback to enhance the sense of presence and immersion of the virtual reality interaction.

The experimental results show that the average error of the finger bending sensor is only 0.176°, the error of the arm sensor is close to 0 and the maximum error of the force sensing is 2.08 g, which is able to accurately sense the hand posture and force-touch information. In the virtual reality interaction experiments, the force feedback has obvious level distinction, which can enhance the sense of presence and immersion during the interaction.

This paper innovatively proposes a highly integrated smart glove that cleverly integrates gesture acquisition, force-haptic acquisition and virtual force feedback. The glove enhances the sense of presence and immersion of virtual reality interaction through precise force feedback, which has great potential for application in virtual environment interaction in various fields.

This paper aims to undertake an evaluation of the research development and works associated with banking frauds and scams.

The authors have conducted bibliometric analysis of 288 studies on issues of banking frauds and scams, published up to August 4, 2024 using Scopus and the VOS viewer software.

This study disseminates top influential authors, countries, journals, papers, funding institutions and affiliations relating to banking frauds and scams. Generally, although a great deal of work has been accomplished in this area, there are prominent gaps in such findings.

To the best of the authors’ knowledge, this paper is the first comprehensive review of extant research relating to banking frauds and scams. It hence represents an original piece of work in applying bibliometric analysis to this topic area, offering valuable insights for practitioners and academics who seek to understand more about banking frauds and scams risks.

This study aims to focused on conducting a comprehensive assessment of the technology readiness level (TRL) of Iran’s oil field intelligence compared to other countries with similar oil reservoirs. The ultimate objective is to optimize oil extraction from this field by leveraging intelligent technology. Incorporating intelligent technology in oil fields can significantly simplify operations, especially in challenging-to-access areas and increase oil production, thereby generating higher income and profits for the field owner.

This study evaluates the level of maturity of present oil field technologies from the perspective of an intelligent oil field by using criteria for measuring the readiness of technologies. A questionnaire was designed and distributed to 18 competent oil industry professionals. Using weighted criteria, a mean estimate of oil field technical maturity was derived from the responses of respondents. Researchers evaluated the level of technological readiness for Brunei, Kuwait and Saudi Arabia’s oil fields using scientific studies.

None of the respondents believe that the intelligent oil field in Iran is highly developed and has a TRL 9 readiness level. The bulk of experts believed that intelligent technologies in the Iran oil industry have only reached TRL 2 and 1, or are merely in the transfer phase of fundamental and applied research. Clearly, Brunei, Kuwait and Saudi Arabia have the most developed oil fields in the world. In Iran, academics and executive and contracting firms in the field of intelligent oil fields are working to intelligently develop young oil fields.

This study explores the level of maturity of intelligent technology in one of Iran’s oil fields. It compares it to the level of maturity of intelligent technology in several other intelligent oil fields throughout the globe. Increasing intelligent oil fields TRL enables better reservoir management and causes more profit and oil recovery.

Aluminum foam-filled thin-walled unit structures have received much attention for their excellent energy absorption properties. To improve the energy absorption effect of car energy absorption box under axial compression, this paper optimizes the fiber lay-up sequence, fiber angle and aluminum foam density of aluminum foam filled carbon fiber reinforced plastic (CFRP) thin-walled square tubes.

Design of sample points required to construct the proxy model using design of experiments (DOE) method, and the data sample points of different models are obtained through Abaqus simulation and test. A double high-precision proxy model with the maximum specific energy absorption (SEA) and the minimum initial peak crash force (PCF) as the evaluation index is constructed based on the response surface function method. The NSGA-II multi-objective genetic algorithm was used to optimize the design parameters and obtain the optimal solution for the Pareto front, and the results were verified by using the multi-objective optimization toolbox in design-expert.

The results show that the optimal solution to the multi-objective optimization problem with the inclusion of the lay-up sequence is ρ = 0.5g/cm3 for a fiber lay-up angle varying in the range ±15–90° and an aluminum foam density varying in the range 0.2g/cm3-0.5g/cm3, with a lay-up method of [±87°/±16°/±15°/±89°]. The two optimization methods correspond to SEA and PCF errors of 2.109% and 4.1828%, respectively. The optimized SEA value is 18.2 J/g and the PCF value is 18,230 N. The optimized design reduces the peak impact force and increases the specific energy absorption, which improves the energy absorption effect of thin-walled energy-absorbing boxes for automobiles.

In this paper, the impact resistance of CFRP thin-walled square tubes filled with aluminum foam is optimized. Based on numerical simulations and experiments to obtain the sample point data for constructing the dual-agent model, we investigate the effect of filling with different densities of aluminum foam under the simultaneous change of fiber lay-up angle and order on its mechanical properties in this process, and carry out the multi-objective optimization design with NSGA-II algorithm.

The purpose of this study is to examine the effect of collaborative innovation networks on new product development (NPD) performance in small and medium-sized enterprises (SMEs). It also investigates the mediating role of business model innovation and moderating role of collaboration experience and external information technology (IT) capability in the above relationship.

To test the research hypotheses about the relationships above, survey data were collected from 209 Chinese manufacturing SMEs. Multiple hierarchical regressions analysis was used to examine the hypotheses.

Results reveal that collaborative innovation networks have positive impacts on NPD performance in SMEs. Moreover, business model innovation mediates and collaboration experience and external IT capability positively moderate the relationship between collaborative innovation networks and NPD performance in SMEs.

This study paints a more complete picture of the relationship between collaborative innovation networks and NPD performance in SMEs and advances the understanding of how and when SMEs enhance their NPD performance through collaborative innovation networks.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

How national culture induces entrepreneurship and business growth remains elusive in research. Questions remain, for example, how can we determine whether a given national culture is good or bad for entrepreneurial activities? What are those pro-entrepreneurship national culture dimensions that could be promoted across nations? These questions are yet open for discussion. The purpose of the study seeks to address these questions and unveil how various national cultural dimensions affect entrepreneurship in different national contexts.

The systematic literature review (SLR) method is meticulously applied. Key terms related to Hofstede’s national culture dimensions are traced alongside entrepreneurial aspects associated with entrepreneurial actions and orientations. By developing series of search queries from these terms, studies within the Web of Science and EBSCO databases are explored.

The review reveals that individualism, long-term orientation, low power distance, feminism, indulgence and low uncertainty avoidance dimensions of culture enable and foster entrepreneurial activities across countries. This study proposes that they be considered Hofstede’s pro-entrepreneurship cultural dimensions. The research suggests that countries endowed with more of these cultural factors tend to create favorable conditions for entrepreneurship. The authors argue that the bundling of these cultural dimensions makes a difference in entrepreneurial performance, not the isolated effect of individual dimensions.

The study reveals the intricate relationship between national culture and entrepreneurship, a relationship that is particularly crucial in today’s globalized work environment and cross-cultural entrepreneurship. The findings underscore the significant role of national culture in shaping the entrepreneurial activities of nations. To enhance the effectiveness of entrepreneurial practices, it is essential to consider the cultural context of societies. While the review does not identify a specific national culture dimension that distinguishes developing countries from developed ones in terms of entrepreneurial performance, it does suggest that promoting pro-entrepreneurship national cultural dimensions, rather than individual dimensions in isolation, can create a fertile ground for entrepreneurship to thrive.

This study significantly advances the understanding of the relationship between national culture and entrepreneurship, considering Hofstede’s six national cultural dimensions and their respective and concurrent influences. This research provides a clearer framework for understanding and promoting cultures that support entrepreneurship, particularly by focusing on how cultural “bundling” rather than isolated traits can drive success in entrepreneurship across different countries. The study also offers practical suggestions to stakeholders on how to promote a pro-entrepreneurship national culture. The use of the SLR methodology enhances the reliability of the findings, shedding light on the most critical national cultural dimensions that must be configured to achieve the maximum returns from entrepreneurial endeavors.

Over the past years, non-fungible tokens (NFTs) have sparked growing interest in the sport industry. NFTs are unique digital assets verified using blockchain technology. Each NFT has a distinct identifier that sets it apart from other tokens, documenting its uniqueness and ownership. NFTs promise innovative growth opportunities by generating revenue via novel products such as digital collectibles which can be owned and traded on dedicated platforms. Despite this promising outlook, it currently seems unclear how sports NFTs should be designed and which features they should offer to align with consumer values, effectively meet their needs and ultimately drive Purchase Intention. This study will therefore attempt to answer the following research question: Which consumer values and consumer needs have a positive impact on PI of sports NFTs? Based on the results, the study seeks to offer advice on concrete characteristics sports NFTs should possess in order to foster mainstream adoption.

To address the current gap in the literature and provide an answer to the research question, this paper uses structural equation modelling exploring the impact of consumer values and consumer needs or wants on purchase intention regarding sports NFTs.

The results of this study indicate that social needs or wants (SNW) have the strongest impact on purchase intention, as well as on experiential and functional needs or wants. NFTs should therefore possess characteristics that foster community, interaction and connection with other team or athlete supporters while enhancing the overall consumer experience. Incorporating these elements into future NFTs can help sports organizations tap into the social SNW of consumers by providing opportunities for connection, interaction and collective experiences within supporter communities.

Due to the low response rate of Baby Boomers, the results of the study cannot be applied to this cohort. Additional research, potentially using physical in-stadium surveys and targeted specifically at the BB cohort may shed light on their particular values, needs or wants and impact on sports NFT purchase intention. Moreover, Generation Z respondents may statistically be underrepresented in the sample due to the fact that only respondents aged 18 and older were included in the study. Hence, the part of Generation Z, which was born after March 2006 and had not yet come of age at the time of this research, was explicitly excluded from the survey. Results should be applied carefully to the population of sports team or athlete supporters due to the method of data collection which was based on convenience sampling and may therefore not be representative. Since the survey was exclusively administered online, people with no Internet access are not represented in this research.

Sports organizations and marketers can leverage the strong impact of SNW identified in this study to position their NFT portfolio accordingly. Using athletes themselves or other influencers as product ambassadors may trigger purchase intention of consumers. Additionally, it is crucial that socializing agents, such as family, friends, colleagues and other team supporters with a strong influence on consumers own or promote NFTs. Marketers can support this adoption process by encouraging testimonials, reviews and user-generated content that showcase how NFTs have positively impacted others. Reaching a critical mass of adoption among supporters as a first step will ultimately impact consumers’ desire to satisfy ENW and FNW as well. Consumers may then recognize the benefits of using NFTs to enhance their overall consumer experience and to make their lives easier, for instance by using NFTs as season tickets or to collect loyalty points they can redeem later.

This study is the first attempt to determine the relationship between consumer values, consumers’ needs or wants and their impact on purchase intention regarding sports NFTs.

Artificial Intelligence (AI) has revolutionized teaching and learning methods in higher education, especially in English language teaching and learning. This chapter contributes to the existing knowledge by exploring how AI has developed within the framework of teaching and learning of English, highlighting the challenges, dangers, and moral issues associated with its application. The typical classroom environment has significantly changed because of the integration of AI-powered tools and platforms in English instruction. Chatbots, automated grading systems, and language learning apps driven by AI have streamlined language education, increasing its effectiveness and accessibility. But these benefits accompany a variety of challenges and worries. Ethical concerns about data privacy, algorithmic biases, and the depersonalization of education arise as AI becomes more deeply ingrained in educational methods. Reliance on AI may inadvertently exacerbate educational disparities as long as learners' access to technology and its advantages remain unequal. In addition, significant thought must be given to the ethical ramifications of AI-generated content as well as the possible loss of human connection in language learning settings. This chapter examines these dangers and challenges and makes the case for a well-rounded strategy that maximizes AI's benefits while minimizing any potential downsides. Together, educators and legislators need to create moral guidelines that balance the potential of AI with human-centered learning experiences. To ensure responsible and fair AI integration and promote an inclusive learning environment that prioritizes students' holistic development while exploiting technology breakthroughs, comprehensive assessment of the associated obstacles, risks, and ethical issues is necessary.

To address the shortcomings of the traditional back propagation (BP) neural network agent model, such as insufficient fitting accuracy and low computational efficiency, an improved method is proposed.

In this study, an improved sparrow search algorithm (ISSA) is developed to optimize the reliability calculation of the BP neural network (ISSA-BP) using an enhanced BP neural network model. The traditional sparrow search algorithm is enhanced by incorporating a golden sine strategy to improve its position-updating mechanism, thereby overcoming its tendency to converge prematurely to local optima. Additionally, an opposition-based learning strategy is integrated to explore the reverse solution around the optimal solution of the sparrow search algorithm, mitigating the risk of local optima.

The results of the test function demonstrate that the proposed method significantly enhances fitting accuracy while maintaining computational efficiency. Finally, by applying this approach to the metro bogie frame as a case study, the structural reliability of the bogie frame is evaluated using the Monte Carlo method, providing valuable insights for subsequent analysis and structural optimization.

The use of the surrogate model approach for structural reliability analysis significantly improves solution efficiency. Furthermore, the integration of ISSA with the BP neural network enhances both fitting accuracy and computational efficiency, demonstrating the superiority and practicality of the proposed method.