The Influence of Class Teachers’ Leadership Behavior and Personal Characteristics on Student Academic Performance: A Study of Fuzzy Set Qualitative Comparative Analysis Citing H Middle School as a Case Study

Leadership behavior of classroom teachers has a significant impact on students’ academic success. However, little empirical research on this subject has been undertaken in China. By developing truth tables and doing necessary computations, this study examines the influence of differential combinations of parameters connected to class teachers’ qualities and leadership behavior on student academic performance. It has been discovered that the democratic leadership behavior displayed by class teachers is the most popular among students and is helpful to student academic achievement; the numerous combinations of linked characteristics have varying effects on student academic performance. The study compensates for a dearth of empirical research on the subject and sheds light on the process by which class teacher leadership influences student academic progress

Agency problem and mean field system of agents with moral hazard, synergistic effects and accidents

We investigate the existence of an optimal policy to monitor a mean field systems of agents managing a risky project under moral hazard with accidents modeled by L\'evy processes magnified by the law of the project. We provide a general method to find both a mean field equilibrium for the agents and the optimal compensation policy under general, sufficient and necessary assumptions on all the parameters. We formalize the problem as a bilevel optimization with the probabilistic version of a mean field games which can be reduced to a controlled McKean-Vlasov SDE with jumps. We apply our results to an optimal energy demand-response problem with a crowd of consumers subjected to power cut/shortage when the variability of the energy consumption is too high under endogenous or exogenous strains. In this example, we get explicit solution to the mean field game and to the McKean-Vlasov equation with jumpsComment: 2

A new Speech Feature Fusion method with cross gate parallel CNN for Speaker Recognition

In this paper, a new speech feature fusion method is proposed for speaker recognition on the basis of the cross gate parallel convolutional neural network (CG-PCNN). The Mel filter bank features (MFBFs) of different frequency resolutions can be extracted from each speech frame of a speaker's speech by several Mel filter banks, where the numbers of the triangular filters in the Mel filter banks are different. Due to the frequency resolutions of these MFBFs are different, there are some complementaries for these MFBFs. The CG-PCNN is utilized to extract the deep features from these MFBFs, which applies a cross gate mechanism to capture the complementaries for improving the performance of the speaker recognition system. Then, the fusion feature can be obtained by concatenating these deep features for speaker recognition. The experimental results show that the speaker recognition system with the proposed speech feature fusion method is effective, and marginally outperforms the existing state-of-the-art systems