Based on early experiences and current conditions, a future time perspective influences college students’ behaviors, while psychological violence critically threatens college students’ health. This study explored the relationship between a future time perspective and the psychological violence of perpetrators based on an online investigation of 1424 college students aged 17 to 31 in China. The results showed that a future time perspective is significantly positively associated with psychological violence. Positive future orientation is negatively associated with psychological violence. Negative and confused (...) future orientations are positively associated with psychological violence. These findings support the need to introduce an intervention regarding a future time perspective to reduce psychological violence among college students. (shrink)

The formation control issue for a group of underactuated unmanned surface vehicles is discussed in the paper, and a staged finite-time control strategy for the USVs is proposed. Firstly, we try to steer each USV to its own starting point in the formation for a limited time, under the initial condition that each of these vehicles is parked at random. To deal with the nonholonomic behavior of the system, the dynamics of the USV is transformed into cascade systems. Then, the (...) finite-time controller is designed for each vehicle based on homogeneity theory. After each USV reaches its own starting point with desired orientation, the model of the vehicle is decomposed into two subsystems under the Serret-Frenet frame. In order to maintain the formation pattern, two finite-time distributed controllers are developed for the surge subsystem and the yaw subsystem, respectively. The settling time for the staged control strategy is limited. Numerical simulations are carried out to illustrate the effectiveness of the proposed formation control strategy. (shrink)

Clustering algorithm is one of the important research topics in the field of machine learning. Neutrosophic clustering is the generalization of fuzzy clustering and has been applied to many fields. This paper presents a new neutrosophic clustering algorithm with the help of regularization. Firstly, the regularization term is introduced into the FC-PFS algorithm to generate sparsity, which can reduce the complexity of the algorithm on large data sets. Secondly, we propose a method to simplify the process of determining regularization parameters. (...) Finally, experiments show that the clustering results of this algorithm on artificial data sets and real data sets are mostly better than other clustering algorithms. Our clustering algorithm is effective in most cases. (shrink)