Ethical voice involves individuals’ perceptions of what is right and what is wrong. Although prior research has investigated the impacts of individual-level prohibitive ethical expression on personal outcomes, there has been limited examination of ethical voice at the group level. Our study examines how and when different types of ethical voice influence group outcomes. Using data gathered from 363 participants from 61 groups in two Chinese companies, the results reveal that promotive and prohibitive ethical voices at the group level exert (...) opposing influences on group moral transitive motivation. Group moral transitive motivation serves as a mediating factor in the association between the two types of ethical voice and group ethical behaviors (GEB). In addition, group faultlines moderate the impact of promotive ethical voice and prohibitive ethical voice on group moral transitive motivation and on GEB (via group moral transitive motivation). Our study extends the focus of research from a single type of ethical voice at the individual level to multiple types at the group level. The findings contribute to the body of knowledge regarding group ethical voice (GEV) by articulating its mediating mechanism as well as its boundary condition. This research provides valuable insights for researchers and managers by offering a comprehensive understanding about the role of different types of GEV and how to reform GEV. (shrink)

To improve the positioning accuracy of industrial robots and avoid using the coordinates of the end effector, a novel kinematic calibration method based on the distance information is proposed. The kinematic model of an industrial robot is established. The relationship between the moving distance of the end effector and the kinematic parameters is analyzed. Based on the results of the analysis and the kinematic model of the robot, the error model with displacements as the reference is built, which is linearized (...) for the convenience of the following identification. The singular value decomposition is used to eliminate the redundant parameters of the error model. To solve the problem that traditional optimization algorithms are easily affected by data noise in high dimension identification, a novel extended Kalman filter and regularized particle filter hybrid identification method is presented. EKF is used in the preidentification of the linearized error model. With the preidentification results as the initial parameters, RPF is used to identify the kinematic parameters of the linearized error model. Simulations are carried out to validate the effectiveness of the proposed method, which shows that the method can identify the error of the parameters and after compensation the accuracy of the robot is improved. (shrink)

Insufficient stiffness of industrial robots is a significant factor which affects its positioning accuracy. To improve the positioning accuracy, a novel positioning error compensation method based on the stiffness modelling is proposed in this paper. First, the positioning errors considering the end load and gravity of industrial robots due to stiffness are analyzed. Based on the results of analysis, it is found that the positioning errors can be described by two kinds of deformation errors at joints: the axial deformation error (...) and the radial deformation error. Then, the axial deformation error is modelled by the differential relationship of kinematics equations. The model of radial deformation error is deduced through the recurrence method and rotation transformation between joints. Finally, these two models are transformed into a Cartesian coordinate system, and a positioning error compensation method based on these two models is presented. Simulations based on the finite element analysis are implemented to verify the positioning error compensation method. The results show that the suggested method can efficiently predict the positioning error according to the gravity and loads, so that the positioning accuracy of industrial robots can be improved with the proposed method. (shrink)

Purpose: Short track speed skating is a racing sport with racing tactics are equally crucial to speed and technical skating skills. Therefore, to investigate the relationship between starting and finishing positions for elite skaters and subsequently, explore pacing patterns for champions are necessary.Methods: To investigate a pattern of effective tactical positioning strategy, Kendall’s tau-b correlations between starting and finishing position were calculated, with 500 m races having the most positive correlation.Results: Furthermore, starting position distributions of winners in each round, as (...) well as the fluctuations in champion starting positions across rounds were analyzed. Our findings showed that skaters on the first track were inclined to win the rounds in 500, 1,000, and 1,500 m, and the differences between starting and finishing positions for champions were minimized in semi-finals. Meanwhile, the pacing patterns were gaining more fluctuations by the increase of race distances for champions, as the average standard deviation of lap rankings equaled 0.90, 1.15, and 2.21 for 500, 1,000, and 1,500 m races, respectively.Conclusion: In conclusion, elite skaters should adopt flexible tactics at the lowest cost of energy consumption. The overall variability of lap ranking in long-distance races were distinctly higher than it in short distance. (shrink)

With the rapid development of society and the dramatic change of environment, previous career counseling focusing on personal choice has been difficult to meet individuals’ needs. It is very meaningful and valuable to introduce the ideology of Life Design Counseling. In this mini review, we introduce and analyze the theory and methodology of LDC. This review puts forward challenges in the field of LDC, including the lack of attention to clients from multiple backgrounds and professional counselors, the lack of diversified (...) methods in the intervention process, and the lack of diversified research. The theoretical research, practical research, and the integration of theory and practice of LDC still need to be further concerned by researchers. (shrink)

Acid fracturing is the most important stimulation method in the carbonate reservoir. Due to the high cost and high risk of acid fracturing, it is necessary to predict the reservoir productivity before acid fracturing, which can provide support to optimize the parameters of acid fracturing. However, the productivity of a single well is affected by various construction parameters and geological conditions. Overfitting can occur when performing productivity prediction tasks on the high-dimension, small-sized reservoir, and acid fracturing dataset. Therefore, this study (...) developed a stacking heterogeneous ensemble model with a hybrid wrapper-based feature selection strategy to forecast reservoir productivity, resolve the overfitting problem, and improve productivity prediction. Compared to other baseline models, the proposed model was found to have the best predictive performances on the test set and effectively deal with the overfitting. The results proved that the hybrid wrapper-based feature selection strategy introduced in this study reduced data acquisition costs and improved model comprehensibility without reducing model performance. (shrink)