Objective: To explore network relationships among depression, Internet addiction and campus bullying among adolescents, so as to provide a theoretical basis for the comprehensive prevention and control of adolescents psychological status and risky behaviors. Methods: In September 2020, a stratified random cluster sampling method was adopted to select 5 000 middle school students for investigation. A structural equation model was used to analyze depression, Internet addiction and bullying and their related influencing factors in order to clarify the pathway and magnitude of effects. Results: Depression had a positive effect on Internet addiction with adolescents( β=0.35, P <0.01), school bullying had a positive effect on depression and Internet addiction with adolescents( β=0.23, 0.05, P <0.01). Adolescent depression was found to play a partial mediating role with respect to the influence of sleep duration on Internet addiction, and the indirect effect was -0.01, accounting for 63.6% of the total effect. Depression played a partial mediating role regarding the influence of the frequency of moderate and high intensity exercise on Internet addiction in adolescents; the indirect effect was -0.01, accounting for 21.8% of the total effect. Conclusion Considering the interaction among adolescent depression, Internet addiction, and school bullying, it s important to include associated factors when developing effective prevention and intervention strategies, which can thus promote the physical and mental health of students, and provide scientific and effective protection.

Objective: To explore the performance of machine learning prediction models in forecasting student absenteeism due to respiratory symptoms caused by air pollution in short term, aiming to provide a methodological reference for early warning systems of school diseases. Methods: Utilizing data from shortterm sequences of student absenteeism due to respiratory symptoms in Jiangsu Province from September 2019 to October 2022, the study integrated average concentrations of atmospheric pollutants. A univariate distributed lag nonlinear model was employed to select optimal lag variables for the pollutants. An extreme gradient boosting(XGBoost) algorithm model was developed to predict the frequency of absenteeism due to respiratory symptoms and compared with the seasonal autoregressive integrated moving average with exogenous factors(SARIMAX) model. Results: Between 2019 and 2022, an average of 9 709 students per day in Jiangsu Province were absent due to respiratory symptoms. The daily average air quality index (AQI) was 76.96,with mass concentrations of PM2.5, PM10, NO2, and O3 averaging at 35.75, 61.13, 28.89, 104.81 μg/m3, respectively. Granger causality tests indicated that AQI, PM2.5, PM10, NO2, and O3 were significant predictors of absenteeism frequency due to respirutory symptoms(F=1.46,1.79,1.67,3.41,2.18,P<0.01). The singleday lag effects of PM2.5, PM10, NO2, and O3 reached their peak relative risk (RR) values at lag4, lag0, lag0, lag4 respectively. When integrating these optimal lag variables for the pollutants, the XGBoost model demonstrated superior predictive performance to the SARIMAX model, reducing the mean absolute error (MAE) from 2.251 to 0.475, mean absolute percentage error (MAPE) from 0.429 to 0.080, and root mean square error (RMSE) from 2.582 to 0.713; at the P75 percentile alert threshold, the sensitivity improved from 0.086 to 0.694 and specificity from 0.979 to 0.988, with the Youden index increasing from 0.065 to 0.682. Conclusions The XGBoost model exhibits robust predictive performance and effective early warning capabilities for shortterm sequences of student absenteeism due to respiratory symptoms caused by air pollution. Schools could timely adopt this model to preemptively detect and control disease outbreaks, thereby enhancing school health management.