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Objectives: To develop an evaluation model for, and identify key factors contributing to, burnout in orthopedic surgeons, providing a reference for the management of burnout among orthopedic surgeons in hospitals.Methods: We developed an analytic hierarchy process (AHP) model with 3 dimensions and 10 sub-criteria based on an extensive literature review and expert assessment. We used expert and purposive sampling and 17 orthopedic surgeons were selected as research subjects. The AHP process was then used to obtain the weights and to prioritize the dimensions and criteria for burnout in orthopedic surgeons.Results: The dimension of C1 (personal/family) was the key factor affecting burnout in orthopedic surgeons, and in the sub-criteria, the top four sub-criteria were C11 (little time for family), C31 (anxiety about clinical competence), C12 (work-family conflict), and C22 (heavy work load).Conclusion: This model was effective in analyzing the key factors contributing to job burnout risk, and the results can inform improved management of the levels of burnout affecting orthopedic surgeons in hospitals.

In modern power systems, analyzing the behaviors of the end users can help to improve the system’s security, stability, and economy. Load classification provides an efficient way to implement awareness of the user’s behaviors. However, due to the development of data collection, transmission, and storage technologies, the volumes of the load data keep increasing. Meanwhile, the structure and knowledge hidden in the data become ever more complicated. Therefore, the parallelized ensemble learning method has been widely employed in recent load classification research. Although the positive performance of ensemble learning has been proven, two critical issues remain: class imbalance and base classifier redundancy. These issues raise challenges of improving the classification accuracy and saving computational resources. Therefore, to solve the issues, this article presents an improved selective ensemble learning approach to enable load classification considering base classifier redundancy and class imbalance. First, a Gaussian SMOTE based on density clustering (GSDC) is introduced to handle the class imbalance, which aims to achieve higher classification accuracy. Second, the classifier pruning strategy and the optimization strategy of the ensemble learning are further introduced to handle the base classifier redundancy. The experimental results indicate that when combined with the popular classifiers, the presented approach shows effectiveness for serving the load classification tasks.

Innovation is the primary driving force behind the development of China as a modern economic power. This study examines the impact of paternalistic leadership on innovation, proposing a theoretical model using the three dimensions of paternalistic leadership (i.e., benevolence, morality, and authoritarianism) as independent variables, constructive deviance as a mediating variable, and innovation performance as the dependent variable. Empirical results showed that benevolent and moral leadership has a positive impact on innovation performance while authoritarian leadership has a negative impact. Constructive deviance by employees has a positive impact on innovation performance. Benevolent and moral leadership has a positive impact on the constructive deviance of employees, while authoritarian leadership has a negative impact on constructive deviance. In addition, benevolent and moral leadership has a positive impact on innovation performance through the constructive deviance of employees, while the impact of authoritarian leadership is negative. In practice, leaders should recognize that constructive deviance is a double-edged sword and guide employees to engage in reasonable constructive deviant behavior, thereby creating sound organizational environments to foster innovation, eliminate barriers, and benefit from the positive impact of the constructive deviance of employees to enhance innovation performance.