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It was divided into different modes according to the level of set point temperature (SPT): SPT ≥ 26 °C and SPT < 26 °C. To study behavioral adaptation of different set point temperature modes in office buildings with split air conditioners, a summer field survey was conducted in Zhengzhou and Jiaozuo, China. The results showed that the clothing insulation in high SPT mode was primarily affected by age, whereas the clothing insulation in low SPT mode was influenced by age and indoor temperature. The clothing insulation in high SPT mode was significantly lower than that in low SPT mode, and the air velocity and air movement sensation of subjects in H mode were significantly higher than those in L mode. 1 °C rise in Top could lead to 0.026 m/s increase in air velocity in high SPT mode, but the air velocity in low SPT mode had no relationship with indoor temperature. People’s behavioral adaptation enthusiasm in high SPT mode was greater than that in low SPT mode. The energy consumption can be saved by about 14.8% after considering the adjustment of people’s clothing.

In order to understand the changes in human thermal sensation in different seasons in cold regions. The data on skin temperature and indoor environmental parameters(of the head, back, chest, upper arm, forearm, hand, thigh, calf, and ankle)were obtained by field test and questionnaire survey in four seasons. The results showed that the parts which had the greatest influence on thermal sensation were different in different seasons. Spring forearms have the greatest most significant on overall thermal sensation. The palm has the greatest influence on the overall thermal sensation in summer. In autumn, the calf has the greatest influence on the overall thermal sensation. In winter, the back has the greatest influence on the overall thermal sensation. The local thermal sensation varies in different seasons under the influence of physiological and environmental factors, so careful consideration should be given to improving people’s thermal sensation in different seasons. In different seasons, affected by individual differences, the thermal comfort of the whole body is lower than that of all parts of the body. This study can provide a theoretical basis for people to obtain higher thermal sensations in different seasons.

Perceived control is considered one of the influencing factors that affect the thermal satisfaction and comfort of indoor occupants. In order to explore the relationship between human gender in thermal response and perceived control and conduct in-depth research, field research was conducted on 1186 occupants in a college dormitory in Jiaozuo (the cold region of China). The perceived control vote was divided into four scales from “ no control ” to “ complete control ” . Through collation and analysis of the data obtained, we found that with improved perceived control, females ’ discomfort is significantly reduced, which is lower than males’. Therefore, there are significant gender differences in the impact of perceived control on the thermal response, which can be explained by individual differences in physiological regulation. Similarly, there are body type differences in perceived control. This research can provide support for the adaptive model and provide a new perspective for further research on individual differences in adaptive psychological adjustment.

This paper analyzed and compared the forecast effect of three machine learning algorithms (multiple linear regression, random forest and LSTM network) in stock price forecast using the closing price data of NASDAQ ETF and data of statistical factors. The test results show that the prediction effect of the closing price data is better than that of statistical factors, but the difference is not significant. Multiple linear regression is most suitable for stock price forecast. The second is random forest, which is prone to overfitting. The forecast effect of LSTM network is the worst and the values of RMSE and MAPE were the highest. The forecast effect of future stock price using closing price of NASDAQ ETF is better than that using statistical factors, but the difference is not significant.