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For the measurement of large-scale components in aircraft assembly, the evaluation of coordinate transformation parameters between the coordinate frames of individual measurement systems to the assembly frame is an essential task, which is usually completed by registration of the enhanced reference system (ERS) points. This paper aims to propose an analytical method to evaluate the uncertainties of transformation parameters considering both the measurement error and the deployment error of ERS points.

For each measuring station, the measured coordinates of ERS points are first roughly registered to the assembly coordinate system using the singular value decomposition method. Then, a linear transformation model considering the measurement error and deployment error of ERS points is developed, and the analytical solution of transformation parameters’ uncertainties is derived. Moreover, the covariance matrix of each ERS points in the transformation evaluation is calculated based on a new uncertainty ellipsoid model and variance-covariance propagation law.

For the transformation of both single and multiple measuring stations, the derived uncertainties of transformation parameters by the proposed analytical method are identical to that obtained by the state-of-the-art iterative method, but the solution process is simpler, and the computation expenses are much less.

The proposed uncertainty evaluation method would be useful for in-site measurement and optimization of the configuration of ERS points in the design of fixture and large assembly field. It could also be applied to other registration applications with errors on both sides of registration points.

Purpose: The issue of whether participation in online peer-support communities has positive or negative impacts on the psychological adjustment of cancer patients warrants further explorations from new perspectives. This research investigates the role of personality traits in moderating the impact of online participation on the psychological adjustment of cancer patients in terms of their general psychological well-being and cancer-specific well-being.

Methodology: Study participants consisted of adults diagnosed with leukemia. Questionnaires were collected from 111 participants in two leukemia-related forums in China, Baidu Leukemia Community and Bloodbbs. Information regarding the personality traits, online participation, and psychological adjustment were collected using an online questionnaire. A linear regression model was used to test the moderation effect of personality traits on the relationship between online participation and psychological adjustment.

Findings: The main effect of participation in online support communities on psychological adjustment was not statistically significant. Importantly, two personality traits (i.e., emotional stability and openness to experience) moderated the relationship between online participation and psychological adjustment to cancer. Leukemia patients with high emotional stability and high openness to experience reported better psychological adjustment as they participated more in the online community. However, this was not the case for patients with low stability and low openness, who reported worse psychological adjustment as their participation in the online support community increased.

Value: This study introduces two personality moderators into the discussion of how participation in online support communities influences the lives of cancer patients. The moderation effects help to explain why there have been contradictions in the findings of previous studies. In addition, this study adds to the current literature on online support communities as little research on this topic has been conducted outside of the US and Europe. Practically, this study not only highlights the need to evaluate the personality traits of patients who are recommended to participate in online communities, but also underlines the necessity of intervention in these communities.

This study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.

The authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.

The results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.

The findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

This paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.

The paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.

The paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R²) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.

Machine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R²) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.

Many European businesses are small and medium enterprises (SMEs), contributing significantly to the well-being of local economies and regions. Even so, SMEs face many challenges in fostering innovation and improving performance. Furthermore, the raw material consumption is increasing globally, necessitating the development of strategies that will reduce the number of raw materials extracted and imported while improving the sustainability of small and medium-sized businesses. Consequently, promoting circular economy (CE) strategies, such as industrial symbiosis (IS) partnerships, whereby waste products from other industries serve as a source of raw materials for companies, is critical. Identifying and analysing enablers or drivers that support IS collaborations among SMEs is necessary to achieve this goal. In this regard, the purpose of this study will explore the enablers of IS among SMEs considering sustainability dimensions (environmental, social and economic). As facing a decision-making (DM) problem, the multiple attribute decision-making (MADM) approach was applied in a hesitant fuzzy (HF) environment in this research to answer the research questions. In this regard, in phase 1, IS enablers were identified and extracted using a literature review and experts’ opinions. In phase 2, the hesitant fuzzy Delphi (HFD) method was implemented to screen and finalise the enablers identified. In phase 3, casual relations among final enablers were determined using the hesitant fuzzy ANP (HF-ANP) method. Finally, in phase 4, the relative importance of enablers was calculated using the hesitant fuzzy best–worst method (HF-BWM). Consequently, this study provided potential strategies for IS that can be implemented quickly and used by local authorities to support SMEs in achieving circular waste management.

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.

In aircraft assembly, standard reference points with nominal coordinates are commonly applied for coordinate transformation between multiple measurement stations and the assembly coordinate system. For several reasons in practical application, these points often fail to envelop the key assembly space, which leads to large transformation uncertainty. This paper aims to analyze and further reduce the coordinate transformation uncertainty by introducing a new hybrid reference system (HRS).

Several temporary extension points without known coordinates are added to enhance the tightness between different stations, especially at the weakness area in the network, thus constituting an HRS together with the existing standard reference points. The coordinate transformation model of the HRS-based measurement network is established based on an extend Gauss–Markov model. By using the geometrical differential property and variance-covariance propagation law, the covariance matrixes in the transformation model are calculated, and the analytical solution of the uncertainties of transformation parameters are ultimately derived. The transformation uncertainty of each check points is presented by Helmert error expression.

The proposed analytical solution of transformation uncertainty is verified using the state-of-the-art Monte Carlo simulation method, but the solution process is simpler and the computation expenses are much less.

The HRS with three temporary extension points is practically applied to a tail boom in-site measurement for assembly. The average transformation uncertainty has been reduced by 26 per cent to less than 0.05 mm.

The hybrid coordinate transformation model is proposed for the first time. The HRS method for transformation uncertainty reduction is more economical and practical than increasing the number of standard reference points.

The development of information technologies has fueled the emergence of online self-organizing teams that involve members with diverse backgrounds to work on a shared goal voluntarily. However, the differences in members' attributes give rise to diversity. Therefore, the authors’ research is to figure out how diversity affects team performance in the context of online self-organizing teams and how this effect changes over team tenure.

The authors use a dynamic approach to the diversity-team performance relationship and collect a publicly longitudinal dataset on 3,970 collaborative items from 2,550 online self-organizing teams spanning nine years in an open innovation community of an online game.

The empirical results show that culture separation is negatively related to team performance, and this negative relationship weakens as team tenure increases. While skill variety and contribution disparity are positively related to team performance, and these positive relationships strengthen as team tenure increases.

The study provides a research framework to examine the relationship between diversity and team performance and explore how this relationship varies over team tenure in the context of online self-organizing teams. The results not only demonstrate the double-edged role of diversity in affecting the success of online self-organizing teams but also advance the understanding on the temporal effect of diversity on team performance.