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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.

The purpose of this paper is to uncover the underlying drivers of sustained high performing companies based on a field study of 127 companies in Brazilian, Russian, Indian and Chinese (BRIC) and Association of Southeast Asian Nations (ASEAN) emerging markets. Understanding these companies provides a complementary way of appraising the growth, development and transformation of emerging markets. The authors synthesize the findings in an overarching framework that covers six strategies for building and sustaining legacy that leads to the succession of intergenerational wealth over time: overcoming institutional voids, inclusive markets, deepening localization, nurturing government support, building core competencies and harnessing human capital. The authors relate these strategies to different levels of development using Prahalad and Hart’s BOP framework.

This study examines the underlying drivers of sustained high-performance companies based on field studies from an initial set of 105,260 BRIC companies and close to 500 companies in ASEAN. The methods employed four screening tests to arrive at a selection of the highest-performing firms: 70 firms in the BRIC nations and 58 firms from ASEAN. Following the selection, the authors constructed cases using primary interviews and secondary data, with the assistance of Ernst & Young and with academic colleagues in Manila. These studies were originally conducted in two separate time periods and reported accordingly. This paper synthesizes the findings of these two studies to arrive at an extended integrative framework.

From the cases, the authors examine six strategies for building and sustaining legacy that lead to high performance over time: overcoming institutional voids, creating inclusive markets, deepening localization, nurturing government support, building core competencies and harnessing human capital. To address the evolving state of institutional voids in these countries, the authors employ similar methods to hypothesize the placement of these strategies in the context of the world economic pyramid, initially formulated as the “bottom of the pyramid” framework.

This paper synthesizes and extends the authors’ previous works by proposing the concept of legacy to describe the emergence and succession of local exemplary firms in emerging markets. This study aims to complement extant measures of nation-growth based primarily on GDP. The paper also extends the literature on institutional voids in shifting the focus from the mix of voids to their evolving state. Altogether, the paper provides a complementary narrative on assessing the market potential of emerging markets by adopting several categories of performance.

Given a product or service, the number of its installed user base has a significant positive effect on the existing users’ loyalty and new users’ conversion. This effect is conceptualized as network externalities in economics. Network externalities are supposed to be particularly striking in nowadays online business-to-business (B2B) platforms, but yet the mystery behind their effects on user loyalty to online B2B platforms remains to be delicately unraveled. The purpose of this paper is to discover the factors driving users’ loyalty, especially buyers’ loyalty, to online B2B platforms, by highlighting the impacts of network externalities on loyalty and other mediating factors.

A conceptual model of buyer loyalty under network externalities is elaborated. The reliability and validity of the instruments of the latent model constructs are assessed by confirmatory factor analysis, and the hypothesized causal relationships among the constructs are tested by structural equation modeling, on 710 valid buyer samples collected from a famous online B2B platform in China.

The analysis demonstrates that: perceived value, user satisfaction and switching costs are the major predictors of buyer loyalty to online B2B platforms characterized by network externalities; network externalities positively account for buyer loyalty by contributing to perceived value, user satisfaction and switching costs; and direct network externality (measured by perceived network size and perceived external prestige) has a significant effect on indirect network externality (measured by perceived compatibility and perceived complementarity).

The findings allow the authors to conclude meaningful managerial implications for online B2B service providers to build up loyal user bases through improving users’ perceptions of network externalities, switching costs and value.

This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit environmental constraints, while ensuring the reliability of the robot system.

The authors propose a novel DMP that takes into account environmental constraints to enhance the generality of the robot motion skill learning method. First, based on the real-time state of the robot and environmental constraints, the task space is divided into different regions and different control strategies are used in each region. Second, to ensure the effectiveness of the generalized skills (trajectories), the control barrier function is extended to DMP to enforce constraint conditions. Finally, a skill modeling and learning algorithm flow is proposed that takes into account environmental constraints within DMPs.

By designing numerical simulation and prototype demonstration experiments to study skill learning and generalization under constrained environments. The experimental results demonstrate that the proposed method is capable of generating motion skills that satisfy environmental constraints. It ensures that robots remain in a safe position throughout the execution of generation skills, thereby avoiding any adverse impact on the surrounding environment.

This paper explores further applications of generalized motion skill learning methods on robots, enhancing the efficiency of robot operations in constrained environments, particularly in non-point-constrained environments. The improved methods are applicable to different types of robots.

This study aims to examine the cross-institutional variation in university greenness and analyze its underlying dynamics.

This study constructs a University Greenness Index (UGI) and conducts multivariate regression.

This study finds variation within two dimensions; in the vertical dimension, top-tier universities have significantly higher UGIs than tier-2 universities, and in the horizontal dimension, agricultural and forest, engineering and technology and generalist universities have significantly higher UGIs than other specialist universities. The dynamics underlying the greenness variation lies in different universities’ motivations and resources, which are associated with China’s higher education administrative system, especially the mechanism by which funding is allocated.

The Internet-search-based greenness index has some inherent limitations. First, there exists a gap between green information expression and real green achievement. Second, this research may be difficult to apply to other countries, because of the specific characteristics of China’s higher education system.

Based on the empirical results, two policy implications can be generated. First, for the problem of the vertical dimension variation, related institutional transformation should be launched to promote university greenness. Second, for the problem of the horizon dimension variation, specialist universities can take advantage of an interdisciplinary approach to promote greenness.

This research helps scholars and administrators to better understand the progress being made and the achievements realized with regard to green university initiatives in China.

We review and synthesize the existing research on directors' and officers’ (D&O) liability insurance. Our objectives are (1) to examine the institutional forces and regulatory requirements that have influenced the development of D&O liability insurance; (2) to identify the factors that influence firms to purchase D&O liability insurance and explore the consequences associated with its usage and (3) to identify gaps in the current literature and provide recommendations for future research on D&O liability insurance.

We perform a systematic literature review (SLR) using the Preferred Reporting Items for a Systematic Review of Meta-Analysis (PRISMA) guidelines to examine archival studies that investigate the determinants and consequences of D&O liability insurance. Using a Boolean search strategy on the “Web of Science” (WoS) and PRISMA selection criteria, we review 64 published archival research articles and three working papers from 1987 to October 2023.

Our review reveals that disclosing detailed information regarding D&O liability insurance, such as total insurance premiums and coverage limit, is predominantly voluntary, except in Taiwan. Our findings suggest that the decision to purchase D&O liability insurance is influenced by litigation risk, which is determined by factors such as firm size, complexity and corporate governance variables. We also find that D&O liability insurance has implications for financial reporting, audit outcomes, investment behavior and capital market performance.

In the post-COVID era, where firms face pressure due to financial constraints, our research emphasizes the practical importance of carefully considering and understanding the impact of D&O liability insurance, particularly as it concerns the demand for such insurance.

To the best of our knowledge, this study represents the first systematic review of previous research on D&O liability insurance. Our review highlights some research gaps, particularly in relation to the implications for financial reporting practices, auditing outcomes, firm investment behavior and capital market consequences.

This study aims to reduce the redundant weight of the anti-roll torsion bar brought by the traditional empirical design and improving its strength and stiffness.

Based on the finite element approach coupled with the improved beluga whale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the design of the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar were defined as random variables, and the torsion bar's mass and strength were investigated using finite elements. Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whale optimization (BWO) algorithm and run case studies.

The findings demonstrate that the IBWO has superior solution set distribution uniformity, convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimize the anti-roll torsion bar design. The error between the optimization and finite element simulation results was less than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress was reduced by 35% and the stiffness was increased by 1.9%.

The study provides a methodological reference for the simulation optimization process of the lateral anti-roll torsion bar.

The purpose of this paper is to obtain more design freedom and realize fast fabrication of mechanism which is the core subsystem of many machines and always consists of several parts with assigned relative motion.

The mechanism is digitally assembled and later directly fabricated by selective laser melting (SLM) without post‐assembly. The joint is re‐designed to facilitate powdered material removal; the displays and the corresponding support additions are discussed to avoid too many supports within the clearances. Then, a series of universal joint are directly fabricated using SLM machine and a minimum clearance of 0.1 mm is obtained; a crank rocker mechanism is also fabricated and it can achieve the required performances.

The digitally assembled mechanism can be successfully fabricated by SLM technique using metal powdered material.

It is well known that the components fabricated by SLM have good mechanical properties. Therefore, it can be expected that more mechanisms with more design freedom will be developed and be used in some practical fields with improvement of fabrication quality.

The purpose of this paper is to examine green university initiatives in the context of China, using Tsinghua University, which is China’s green university pioneer, as a case study.

The research method used for this paper is a case study based on participant observation and document analysis. The approach to data collection includes the examination of archive records, yearbooks and statistical information regarding Tsinghua University.

This paper finds that Tsinghua’s green university initiative is a response to Tsinghua’s strategy of establishing a word-class university, with a goal of bearing more responsibility in promoting a sustainable society. Tsinghua employs one principle (green university) and three dimensions (green education, green research and green campus) to frame its green university initiative. Tsinghua’s green university initiative has earned many achievements, but it has also faced many challenges, such as ignoring social justice, fragmented coordination efforts and the lack of effective communication and assessment mechanisms.

As a leading university and the pioneering green university in the country, Tsinghua University is very influential with regard to the development of green universities in China. Many other universities have designed their own programs based on Tsinghua’s experiences in the green university initiative. As such, Tsinghua’s experiences provide reference values to other universities in China.

This paper comprehensively examines the evolution, framework, achievements and challenges of the green university initiative of Tsinghua University. It helps the audience to know how China’s universities understand and practice education for sustainable development.

This study aims to propose a more precise method for robust design optimization of mechanical structures with black-box problems, while also considering the efficiency of uncertainty analysis.

The method first introduces a dual adaptive chaotic flower pollination algorithm (DACFPA) to overcome the shortcomings of the original flower pollination algorithm (FPA), such as its susceptibility to poor accuracy and convergence efficiency when dealing with complex optimization problems. Furthermore, a DACFPA-Kriging model is developed by optimizing the relevant parameter of Kriging model via DACFPA. Finally, the dual Kriging model is constructed to improve the efficiency of uncertainty analysis, and a robust design optimization method based on DACFPA-Dual-Kriging is proposed.

The DACFPA outperforms the FPA, particle swarm optimization and gray wolf optimization algorithms in terms of solution accuracy, convergence speed and capacity to avoid local optimal solutions. Additionally, the DACFPA-Kriging model exhibits superior prediction accuracy and robustness contrasted with the original Kriging and FPA-Kriging. The proposed method for robust design optimization based on DACFPA-Dual-Kriging is applied to the motor hanger of the electric multiple units as an engineering case study, and the results confirm a significant reduction in the fluctuation of the maximum equivalent stress.

This study represents the initial attempt to enhance the prediction accuracy of the Kriging model using the improved FPA and to combine the dual Kriging model for uncertainty analysis, providing an idea for the robust optimization design of mechanical structure with black-box problem.