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The purpose of this paper is to propose an assembly coordination modelling approach based on measured data for assembly quality control of multi-constrained objects in aircraft assembly. This approach aims to establish a high-precision digital mirror of physical assembly system in the virtual environment, with which the assembly process in the virtual environment can be performed synchronously with that in the physical world.

This paper presents a realistic geometrical representation model based on measured point cloud, as well as the multiple constraints modelling methods for local and global constraints with the proposed representation model. For the assembly target optimization, a novel optimization method based on the evaluation of multi-dimensional tolerance zone is proposed, where the particle swarm optimization and simulated annealing algorithm are combined to calculate the optimal solutions.

As shown in the validation results, the minimum easiness value for easiness model in global optimization is 3.01, while the best value for weighting model by adjusting weights for more than 10 times is 1.94. The results verify that the proposed coordination modelling approach is effective to the assembly of multi-constrained objects, and the optimization model has an obvious advantage over the traditional weighting method.

This paper provides a new idea for the fine control of assembly quality of non-ideal components by introducing the measured data into the on-line assembly process. Besides, a novel optimization method based on the evaluation of multi-dimensional tolerance zone is proposed, which overcomes the problem of traditional weighting model wherein the weightings are difficult to determine.

The purpose of this paper is to propose an on-line iterative compensation method combining with a feed-forward compensation method to enhance the assembly accuracy of a metrology-integrated robot system (MIRS).

By the integration of a six degrees of freedom (6DoF) measurement system (T-Mac), the robot’ movement can be tracked with real-time measurement. With the on-line measured data, the proposed iterative compensation for absolute positioning and the feed-forward compensation for relative linear motion are integrated into the assembly process to improve the assembly accuracy.

It is found that the MIRS exhibits good performance in both accuracy and efficiency with the application of the proposed compensation method. With the proposed assembly process, a component can be automatically aligned to the target in seconds, and the assembly error can be decreased to 0.021 mm for position and 0.008° for orientation on average.

This paper presents a 6DoF MIRS for high-precision assembly. Based on the system, a novel on-line compensation method is proposed to enhance the assembly accuracy. In this paper, the assembly accuracy and the corresponding distance parameter are given by a series of experiments as reference for assembly applications.

This study aims to build on equity theory to assess the effect of chief executive officer (CEO) underpayment on the accumulation of firm-specific knowledge, accounting for the moderating effects of the CEO compensation gap and the clarity of the board’s informal hierarchy.

This study starts with all firms listed in the Execucomp database for the period 1992 to 2006. Then, all data sources are merged and entries with missing information are excluded. The final data set used for model estimations includes 1,152 firm-year observations. The command xtreg in Stata 12 with the fixed-effect option (fe) is used to estimate the relationship between CEO underpayment and firm-specific knowledge.

This study proposed and examined the role of CEO underpayment in discouraging CEO willingness to invest firm-specific human capital and, accordingly, to adopt a strategy of accumulating lower levels of firm-specific knowledge assets. The empirical analyses strongly support this argument. Moreover, CEO compensation gaps and the informal hierarchy of boards negatively moderated this relationship. That is, CEO underpayment had a weaker negative effect on firm-specific knowledge when the CEO compensation gap and the clarity of the board’s informal hierarchy were high.

Prior studies from the knowledge-based perspective have focused on the importance of firm-specific knowledge in enabling a firm to achieve superior financial performance. However, relatively little attention has been paid to CEOs’ willingness to accumulate firm-specific knowledge. The present study contributes to the knowledge-based view of the firm. This study integrates equity theory with the knowledge-based view of the firm by highlighting how unfair compensation of CEOs may discourage them to fully realize a firm’s potential to generate specific knowledge. By incorporating the fairness issue of CEO compensation into the knowledge-based view, this study contributes to a deeper understanding of the origins of firm-specific knowledge.

In the field of steel defect detection, the existing detection algorithms struggle to achieve a satisfactory balance between detection accuracy, computational cost and inference speed due to the interference from complex background information, the variety of defect types and significant variations in defect morphology. To solve this problem, this paper aims to propose an efficient detector based on multi-scale information extraction (MSI-YOLO), which uses YOLOv8s as the baseline model.

First, the authors introduce an efficient multi-scale convolution with different-sized convolution kernels, which enables the feature extraction network to accommodate significant variations in defect morphology. Furthermore, the authors introduce the channel prior convolutional attention mechanism, which allows the network to focus on defect areas and ignore complex background interference. Considering the lightweight design and accuracy improvement, the authors introduce a more lightweight feature fusion network (Slim-neck) to improve the fusion effect of feature maps.

MSI-YOLO achieves 79.9% mean average precision on the public data set Northeastern University (NEU)-DET, with a model size of only 19.0 MB and an frames per second of 62.5. Compared with other state-of-the-art detectors, MSI-YOLO greatly improves the recognition accuracy and has significant advantages in computational cost and inference speed. Additionally, the strong generalization ability of MSI-YOLO is verified on the collected industrial site steel data set.

This paper proposes an efficient steel defect detector with high accuracy, low computational cost, excellent detection speed and strong generalization ability, which is more valuable for practical applications in resource-limited industrial production.

Using the Shanghai pilot free trade zone (SPFTZ) as the testing ground for further reform and opening up,the links between global value chain (GVC) and pilot free trade zone (PFTZ) programs are mutually reinforcing. GVC creates opportunities for companies to use PFTZ to reduce their costs and increase their competitiveness, while PFTZ can facilitate the movement of goods within GVC and promote the development of GVC by attracting foreign investment. Overall, in SPFTZ, the industrial structure is promoted due to trade and investment facilitation, innovation promotion, and comprehensive service platform inside SPFTZ.

This study examined industrial upgrading in GVC (IUGVC) using five indicators under three quantitative dimensions: product, process, and skill upgrading. Difference-in-Differences (DID) model is employed for the impact assessment of SPFTZ. Parallel trend analysis and Granger causality analysis are performed to check the reliability of DID outcome. Finally, robustness test using exogenous control variables are carried out.

A positive impact of SPFTZ is found on IUGVC, which is due to promoting effect of SPFTZ on foreign direct investment and technological innovation. Based on the study's findings, policy recommendations are given, such as providing business support to enterprises operating inside a PFTZ.

From a GVC perspective, the impact of theSPFTZ establishment on IUGVC cannot be ignored, and is so far missing in the literature.

In the absence of previous work, this study investigates how the psychological contract (PC) influences the safety performance of construction workers in China.

The literature is first consulted to obtain a set of PC and safety performance measures that fits the specific situation of construction workers, which is then moderated by five construction experts. A questionnaire survey of 206 workers from 4 different construction sites is followed by a descriptive statistical analysis of the nature of the PC and level of the safety performance of the respondents. Finally, a regression analysis is used to ascertain the level of influence of the PS, and an analysis is made of the influence of PC on safety performance.

A set of PC and safety performance measures is identified that fits in the construction workers' specific situation. The PC of the respondents is found to be intact and well-performed, and their safety performance is maintained at a high level. Safety performance is highly influenced by the state of the PC, with the three dimensions of safety performance (safety result, safety compliance and safety participation) positively correlated with the three dimensions of the PC (normative, interpersonal and developmental).

Suggestions are made to improve safety production management and safety performance by providing adequate material and economic conditions, helping the workers establish good interpersonal relationships and realize their personal values.

This paper seeks to answer two questions: (1) where do a country's entrepreneurship policies come from? (2) How do they evolve and shape entrepreneurial activities?

Drawing upon the comparative political economy literature and the institutional perspective, this paper proposes a theoretical model of the origin and evolution of entrepreneurship policies. We use China as a case study to apply the theoretical model and demonstrate the evolution of entrepreneurship policies in three stages during the period 1978 to 2012.

The case analysis of China provides evidence and support for our theoretical model and unpacks the process by which entrepreneurship policies originate and evolve as the result of the interplay among constantly changing policymaking, production, and knowledge regimes.

Because of the research context, findings may lack generalisability. Additional studies on policymaking and production regimes of different kinds and their respective roles in shaping entrepreneurship policies are encouraged to further advance this line of research.

This paper offers important implications concerning entrepreneurship policy and activities for policymakers, practitioners and other stakeholders in emerging economies.

Our study fills a gap in the entrepreneurship literature by expanding scholarly understanding of the origin and evolution of entrepreneurship policies.

In the era of the digital economy, digital platform capability plays an important role in the process of firm digitization. This study aims to investigate the relationships among digital platform capability and innovation performance from the perspective of external knowledge search and resource bricolage.

This study uses the hierarchical regression and bootstrapping methods to test the theoretical framework and research hypotheses. The survey data were collected from 629 Chinese firms.

This study finds that digital platform capability has a significant positive effect on innovation performance; external knowledge search and resource bricolage both play partial mediating roles between digital platform capability and innovation performance. There exist the serial mediation effects of “digital platform capability – external knowledge search – resource bricolage – innovation performance”.

The conclusion of this study can expand the research on digital platform capability, reveal the internal mechanism of digital platform capability in firm innovation and provide ideas and inspiration for firms to improve innovation performance and realise high-quality development.

With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks.

This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students.

The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation.

These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS.

This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.