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Driven by the development of the global digital economy, knowledge management in industrial enterprises offers more possibilities for green innovation. Based on the perspective of external knowledge sources, this study aims to construct a panel regression model to explore the relationship between digital economy and industrial green innovation efficiency.

Panel data from 30 regions in China from 2011 to 2020 were selected as research samples. All data are obtained from national and provincial statistical yearbooks. Coupling coordination degree analysis, entropy method, panel regression analysis, robustness test and threshold effect test by Stata 16.0 were used to test the hypotheses.

The empirical results demonstrate the hypotheses and reveal the following findings: the digital economy is positively related to industrial green innovation efficiency and external knowledge sources, and external knowledge sources mediate the relationship between them. Moreover, based on the threshold test results, the digital economy has a double-threshold effect on industrial green innovation efficiency.

Based on the perspective of external knowledge sources, the proposed mediating mechanism between the digital economy and industrial green innovation efficiency has not been established previously, further enriching the research on the antecedents and outcomes of external knowledge sources. Moreover, this study estimated the direct influence mechanism and double-threshold effect of the digital economy on industrial green innovation efficiency from theoretical and empirical analysis, thus responding to the call of scholars and adding to existing research on how the digital economy affects the green transformation of industrial enterprises.

Due to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot.

This paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving.

Experiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods.

The proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

Despite extensive research on personality and leader emergence, very little is known about the process by which employees become or emerge as leaders based on their performance. Integrating functional leadership theory and a behavior perspective, the authors aim to explore the parallel multiple behavioral mediators in the conscientiousness–leader emergence link.

By integrating a field survey study and two experimental studies, the authors use parallel multiple mediation analysis to explore the mechanisms by which conscientiousness leads to high levels of leader emergence.

Conscientiousness is positively associated with employee leader emergence. Employee functional behaviors are positively associated with leader emergence. The authors consistently found that the effect of conscientiousness on leader emergence is primarily explained by increases in task- and change-oriented behaviors but not relations-oriented behaviors.

Organizations can design relevant training programs to cultivate and enhance employees' functional behavior, as the study findings suggest that an effective way to translate employees' conscientiousness into their leader emergence is to improve their task- and change-oriented behaviors.

This research highlights the consistent and important role of employees' functional behaviors in the form of task- and change-oriented behaviors linking conscientiousness to leader emergence.

The purpose of this paper is to provide the extensive review on dynamic monitoring of forestry area in China.

Countermeasure and suggestions were proposed for three aspects including the establishment of data sets with unified standards, top-level design of monitoring and assessment and analysis models, and establishment of the decision support platform with multiple scenario simulation.

Finally, the authors proposed key research area in this field, i.e., improving the systematic and optimal forest management through integrating and improving the data, models and simulation platforms and coupling the data integration system, assessment system and decision support system.

The authors explored the limitation of dynamic monitoring and state of the art research on data accumulation, professional model development and the analytical platform.

This paper aims to provide a series of new methods for projecting a three-dimensional (3D) object onto a free-form surface. The projection algorithms presented can be divided into three types, namely, orthogonal, perspective and parallel projection.

For parametric surfaces, the computing strategy of the algorithm is to obtain an approximate solution by using a geometric algorithm, then improve the accuracy of the approximate solution using the Newton–Raphson iteration. For perspective projection and parallel projection on an implicit surface, the strategy replaces Newton–Raphson iteration by multi-segment tracing. The implementation takes two mesh objects as an example of calculating an image projected onto parametric and implicit surfaces. Moreover, a comparison is made for orthogonal projections with Hu’s and Liu’s methods.

The results show that the new method can solve the 3D objects projection problem in an effective manner. For orthogonal projection, the time taken by the new method is substantially less than that required for Hu’s method. The new method is also more accurate and faster than Liu’s approach, particularly when the 3D object has a large number of points.

The algorithms presented in this paper can be applied in many industrial applications such as computer aided design, computer graphics and computer vision.

Airports are booming in China, to enlarge their capacities and stimulate economic development. Large-span spatial steel structures are commonly used in the terminal buildings of airport projects. Their advantages include prefabrication, strength, usability, adaptability and aesthetic quality. To manage large-span spatial steel structure projects, building information modeling (BIM) is recommended. Although there are plenty of studies on BIM application in steel structure projects, it is still rare to apply BIM to optimize the schedule and cost of steel structures, especially for airport projects.

This paper aims to develop a framework in which BIM and a time-cost optimization model are integrated to optimize construction costs and the duration of large-span spatial steel structure projects. A real case study was conducted to verify the feasibility of the BIM-based time-cost optimization model in an airport terminal building, which was built with a large-span spatial steel structure.

The results preliminarily support the reliability of the proposed BIM-based time-cost optimization model. The BIM-based time-cost optimization model will benefit construction planning for professionals and enrich relevant research on the application of BIM in large-span spatial steel structure projects.

The steel structure is difficult to control budgets and progress. This paper is expected to be adopted for optimizing the time and cost plans for projects involving steel structures in airport terminal buildings.

The construction industry is experiencing digital transformation, which is also defined as intelligent construction. With the rise of intelligent construction, job characteristics are changing rapidly. Current knowledge about job competencies required by intelligent construction is lacking. Therefore, the aim of this paper is to explore job competencies related to intelligent construction by text mining recruitment information. It is expected to reveal the trend of talent development for the intelligent construction industry.

A total of 375 job advertisements regarding the demanding professionals and industrial workers related to intelligent construction were collected and analyzed to reveal the demands of the current labor market. Different job posts related to intelligent construction were classified into 11 categories. Job competencies were extracted and analyzed using the latent Dirichlet allocation (LDA) model, frequency–inverse document frequency (TF-IDF) algorithm and k-means cluster analysis method. The text mining results identified 10 job competencies.

Currently, there was a high demand for high-tech talents in the labor market related to intelligent construction. Those high-tech job posts, such as software engineers and R&D staff, required digital technology, R&D skills, electrical automation knowledge and programming capability. Current employees demanding for intelligent construction are expected to be capable of both using information technology and having a general knowledge of the construction industry.

Through text mining of current job advertisements, the overall demand for compound talents in the labor market of intelligent construction were explored. The results provide empirical reference for personnel training and talent cultivation in the development of intelligent construction. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in intelligent construction companies, would benefit from the results of our analysis.

The construction industry has experienced an irreversible digital transformation to smart construction. Many countries have published supporting policies to encourage the development of smart construction. However, there is no universally valid approach. This paper thus aims to evaluate smart construction policies issued by 24 pilot cities in China and identify applicable policy tools and their impact.

This paper collected 33 governmental documents on smart construction through the official websites in China. Different policy tools were classified into supply-side, demand-side and environment-side categories. The supporting policies of smart construction development in pilot cities were quantitatively evaluated by using a policy modeling consistency index (PMC-index) model.

Supply-type and environment-type policy instruments were used more frequently than demand-type policies in 24 pilot cities. Most of the 24 pilot cities had an evaluation of PMC-index over 8, realizing the consistency of smart construction policies. Eight pilot cities had an evaluation of PMC-index of 6–7.99, realizing acceptable consistency. Only Foshan City has an evaluation of PMC-index below 4, which may reflect a poor consistency of policy implementation. The paper proposes consistencies of smart construction policies of 24 pilot cities and valid policy instruments, including the presale of commercial residential buildings, additional bonus points in the tendering process and cooperating with multiple departments when promoting smart construction.

This paper contributes to expanding policy evaluation studies in the smart construction field and provides concrete suggestions for policymakers to formulate more effective and specific policies and strategies for the development of smart construction.

The purpose of this paper is to explore the high-quality development (HQD) strategy of Chinese mineral resource enterprises, which is important for Chinese mineral resource enterprises to improve the efficiency and benefit of resource utilization, reduce the intensity of resource and energy consumption and gradually form resource-saving and environment-friendly enterprises.

This study establishes an evaluation index system with four dimensions: economy, environment, society and management innovation. The entropy value method assigns weights to them and then uses the system dynamics (SD) model for case simulation.

The results of the SD simulation conclude that the fulfillment of social responsibility and the implementation of management innovation can accelerate the realization of HQD of mineral resource enterprises; profitability plays a crucial role in economic indicators; the improvement of energy-saving volume has the most significant impact on environmental benefits; the social contribution is the key element to measure social indicators; and the sales rate of core products has the most significant impact on the benefits of management innovation.

Based on the few studies on the evaluation of the development strategy of mineral resource enterprises, this study establishes an evaluation index system that considers the interactions between indicators, combines the entropy value method with SD and uses the SD model to comprehensively and systematically analyze the impact and degree of each factor on the HQD of mineral resource enterprises.

To avoid braking accidents caused by excessive wear of brake pad, this study aims to achieve online prediction of brake pad wear life (BPWL).

A simulated braking test bench for automobile disc brake was used. The correlation and mechanism between the three braking condition parameters of initial braking speed, braking pressure and initial braking temperature and the tribological performance were analyzed. The different artificial neural network (ANN) models of wear loss were discussed. Genetic algorithm was used to optimize the ANN model. The structure scheme of the online prediction system of BPWL was discussed and completed.

The results showed that the braking conditions were positively correlated with the wear loss, but negatively correlated with the friction coefficient. The prediction accuracy of back propagation (BP) ANN model was higher. The model was optimized by genetic algorithm, and the average deviation of prediction results was 4.67%. By constructing the online monitoring system of automobile braking conditions, the online prediction of BPWL based on the ANN model could be realized.

The research results not only have important theoretical significance for the study of BPWL but also have practical value for guiding the maintenance and replacement of automobile brake pads and avoiding the occurrence of braking accidents.