Given the rising threat of terror attacks and the increasing frequency of natural disasters attributed to climate change, enhancing evacuation capacities in various spaces has become crucial for saving lives and accelerating recovery processes. This study investigates the influence of altruistic behavior on evacuation efficiency by developing a social force model that categorizes individuals into three demographic groups: youth, middle-aged, and seniors. Simulation experiments based on the model were conducted to evaluate the impact of altruistic behavior on evacuation efficiency under different conditions, such as evacuation capacity, reliability, and recovery time. The simulation results show that a higher probability of falling leads to longer evacuation times. While an increase in the probability of altruistic behavior improves evacuation efficiency, excessive altruistic behavior causes evacuation times to vary in a zigzag pattern. When the help range exceeds 0.7m, evacuation efficiency fluctuates without a clear trend of improvement.

In this paper, an improved mathematical model for flashover behavior of polluted insulators is proposed based on experimental tests. In order to determine the flashover model of polluted insulators, the relationship between conductivity and salinity of solution pollution layer of the insulator is measured. Then, the leakage of current amplitude of four common insulators versus axial, thermal conductivity and arc constants temperature was determined. The experimental tests show that top leakage distance (TLd) to bottom leakage distance (BLd) ratio of insulators has a significant effect on critical voltage and current. Therefore, critical voltage and current were modeled by TLd to BLd ratio Index (M). Also, salinity of solution pollution layer of the insulators has been applied to this model by resistance pollution parameter. On the other hand, arc constants of each insulator in new model have been identified based on experimental results. Finally, a mathematical model is intended for critical voltage against salinity of solution pollution layer of different insulators. This model depends on insulator profile. There is a good agreement between the experimental tests of pollution insulators obtained in the laboratory and values calculated from the mathematical models developed in the present study.

The rare earth mining area in South China is the main production base of ionic rare earth in the world, which has brought inestimable economic value to the local area and even the whole nation. However, due to the lack of mining technology and excessive pursuit for economic profits, a series of environmental problems have arisen, which is a great threat to the ecosystem of the mining area. Taking Lingbei rare earth mining area in Ganzhou as an example, this paper discriminated and analyzed such aspects as the ecological source, ecological corridor and ecological nodes of the mining area based on the landscape ecological security pattern theory and the minimum cumulative resistance model (MCR) method, and constructed a landscape ecological security pattern of the mining area during the 2009, 2013 and 2018. The results show that: i) The patch area of the ecological source of rare earth mining area is small, mainly concentrated in the east and west sides of the mining area. ii) During the selected year, the ecological source area, ecological corridors, radiation channels and the number of ecological nodes in the rare earth mining area are increasing, indicating that the landscape ecological security of the rare earth mining area has been improved to some extent, but it remains necessary for relevant departments to make a optimized planning to further reconstruct the ecological security pattern of the rare earth mining area.

This study aimed to analyze government policies in education during the Covid-19 pandemic and how teachers exercised discretion in dealing with limitations in policy implementation. This research work used the desk review method to obtain data on government policies in the field of education during the Covid-19 pandemic. In addition, interviews were conducted to determine the discretion taken in implementing the learning-from-home policy. There were three learning models during the pandemic: face-to-face learning in turns (shifts), online learning, and home visits. Online learning policies did not work well at the pandemic’s beginning due to limited infrastructure and human resources. To overcome various limitations, the government provided internet quota assistance and curriculum adjustments and improved online learning infrastructure. The discretion taken by the teachers in implementing the learning-from-home policy was very dependent on the student’s condition and the availability of the internet network. The practical implication of this research is that street-level bureaucrats need to pay attention to discretionary standards when deciding to provide satisfaction to the people they serve.

Research has shown that understanding the fundamental of public support for carbon emission reduction policies may undermine policy formulation and implementation, yet the direction of influence and the transmission mechanism remain unclear. Using data from using data from 1482 questionnaires conducted in Hangzhou, China, this paper has examined a comprehensive model of the factors and paths influencing public support for carbon emission reduction policies, and evaluated the determinants and predictors of policy support regarding individual psychological perceptions, social-contextual perceptions, and perceptions of policy features. The results show that the variables in both the individual psychological perception and social contextual perception dimensions have no significant effect on carbon tax, however, be important constructure in carbon trading; in the policy characteristics perception dimension, both variables have a significant positive effect on both carbon tax and carbon trading, and are also the strongest predictors of policy support for carbon policies. Further evidence suggests that future policies could be more acceptable to residents by strengthening their environmental values, social norms can further arouse residents’ social responsibility to care about climate, and whether the policy is effective or fair to help residents realize the importance of the policy as well as the need for their participation and willingness to dedicate themselves to the mitigation of climate change.

The purpose of this paper is to explore the performance of ridge regression and the random forest model improved by genetic algorithm in predicting the Boston house price data set and conduct a comparative analysis. To achieve it, the data is divided into training set and test set according to the ratio of 70-30. The RidgeCV library is used to select the best regularization parameter for the Ridge regression model, and for the random forest model, the genetic algorithm is used to optimize the model's hyperparameters. The result shows that compared with ridge regression, the random forest model improved by genetic algorithm can perform better in the regression problem of Boston house prices.