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In the context of fixed-wing aircraft wing assembly, there is a need for a rapid and precise measurement technique to determine the center distance between two double-hole components. This paper aims to propose an optical-based spatial point distance measurement technique using the spatial triangulation method. The purpose of this paper is to design a specialized measurement system, specifically a spherically mounted retroreflector nest (SMR nest), equipped with two laser displacement sensors and a rotary encoder as the core to achieve accurate distance measurements between the double holes.

To develop an efficient and accurate measurement system, the paper uses a combination of laser displacement sensors and a rotary encoder within the SMR nest. The system is designed, implemented and tested to meet the requirements of precise distance measurement. Software and hardware components have been developed and integrated for validation.

The optical-based distance measurement system achieves high precision at 0.04 mm and repeatability at 0.02 mm within a range of 412.084 mm to 1,590.591 mm. These results validate its suitability for efficient assembly processes, eliminating repetitive errors in aircraft wing assembly.

This paper proposes an optical-based spatial point distance measurement technique, as well as a unique design of a SMR nest and the introduction of two novel calibration techniques, all of which are validated by the developed software and hardware platform.

At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications.

Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications.

This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development.

To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

Despite the detrimental effects of supervisor knowledge hiding on employees and organizations, little research has focused on how subordinates cope with it. Drawing on the impression management theory, this study proposes a mediated moderation model to explain who and how cope with supervisor knowledge hiding.

Using a sample of 340 full-time participants in various organizations and industries in China, the proposed model was tested using ordinary least squares regression with the PROCESS 3.5.

The results support the mediated moderation model, indicating that proactive subordinates are motivated to manage their impression towards supervisors and engage in upward ingratiation to cope with supervisor knowledge hiding. In contrast, subordinates with low level of proactive personality trait have less impression management motive and engage in fewer upward ingratiatory behaviors.

Based on the impression management theory, the current paper contributes to the literature on supervisor knowledge hiding by expanding the consequences of supervisor knowledge hiding, identifying a boundary condition of supervisor knowledge hiding on subordinate’s subsequent behaviors and enriching the mechanism underlying the effect of supervisor knowledge hiding with proactive personality.

With the development of the digital economy, digital entrepreneurship has become increasingly popular. For college students preparing for digital entrepreneurship, it is necessary to cope with the uncertainty of the start-up process through meaningful managing learning and continuous entrepreneurship education. The purpose of this study is to examine the relationship between Chinese college students' digital entrepreneurship intention and digital entrepreneurship behavior, as well as the role of managing learning and entrepreneurship education in this relationship.

Based on the existing literature, this study established the digital entrepreneurship hypothesis model and investigated the digital entrepreneurship behavior of college students.

The results showed that managing learning and entrepreneurship education can promote the transformation of the digital entrepreneurship intention to digital entrepreneurship behavior. Managing learning and entrepreneurship education played a driving role in the transformation of the digital entrepreneurship intention to digital entrepreneurship behavior.

This study explored the complex mechanism of the relationship between digital entrepreneurship intention and digital entrepreneurship behavior among Chinese college students. Based on survey data from 235 college students in China, the empirical results supported theoretical research hypotheses on the relationship between college students and digital entrepreneurship intention, digital entrepreneurship behavior, managing learning and entrepreneurship education.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.