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Background: Screening for Tuberculosis (TB) is a critical tactic for minimizing the prevalence of illness within schools. Tuberculosis Preventive Therapy (TPT), in turn, effectively staves off the development of TB from latent tuberculosis infection (LTBI). Unfortunately, there is limited research on LTBI and TPT among students. This study aimed to assess LTBI among freshmen in Changping District and advocate for the implementation of TPT. Methods: The prospective study collected data from 12 educational institutions within the Changping District of Beijing. The Kolmogorov − Smirnov test and other statistical methods were used for statistical analysis, x 2 was obtained using the formula x 2 = nΣA2/nRnC-1, df = (C-1) (R-1). We analyzed potential factors impacting the LTBI rate, and scrutinized the possible causes behind the low application of TPT and its efficacy for LTBI treatment, China. Results: Among 19,872 freshmen included in this study, 18 active TB cases (91 per 10,0000) and 2236 LTBI cases (11.6% of 19,223) were identified, respectively. Furthermore, of those with LTBI, 1045 (5.4% of 19,223) showed a strong positive for purified protein derivative (PPD), but only 312 opted for TB preventive treatment. There appeared to be no significant difference in the prevalence of LTBI and TPT rate between male and female students. Concurrently, 11 (71 per 100,000) and 7 (158 per 100,000) cases of active tuberculosis were identified in 6 universities and 6 higher vocational colleges, respectively. Interestingly, almost all freshmen who underwent TPT came from universities, suggesting a statistically significant disparity in TPT rate (χ2 = 139.829, P < 0.001) between these two types of educational institutions. Meanwhile, as for the age-wise distribution of latent infection among 17–20 years old freshmen, the LTBI rate exhibited 10.5%, 11.6%, 12.1% and 13.5%, respectively. Correlation between LTBI rate, the strong positive rate was statistically significant among different ages (χ2 = 34.559, P < 0.001). Over a follow-up period of 2 years, three students were diagnosed with active tuberculosis, one of which was resistant to rifampicin. All three students manifested a strong positive for PPD and declined preventive treatment during TB screening. Conclusions: The data indicates a high rate of LTBI amongst students in areas with a heavy TB burden, potentially leading to cross-regional TB transmission due to the migration of students. Education level might contribute to the limited uptake of TPT. Therefore, improving the implementation of TB preventive treatments is crucial in controlling and preventing TB across schools. [ABSTRACT FROM AUTHOR]

As for the problem that the traditional single depth prediction model has poor strain capacity to the prediction results of time series data when predicting lake eutrophication, this study takes the multi-factor water quality data affecting lake eutrophication as the main research object. A deep reinforcement learning model is proposed, which can realize the mutual conversion of water quality data prediction models at different times, select the optimal prediction strategy of lake eutrophication at the current time according to its own continuous learning, and improve the reinforcement learning algorithm. Firstly, the greedy factor, the fixed parameter of Agent learning training in reinforcement learning, is introduced into an arctangent function and the mean value reward factor is defined. On this basis, three Q estimates are introduced, and the weight parameters are obtained by calculating the realistic value of Q, taking the average value and the minimum value to update the final Q table, so as to get an Improved MIMO-DD-3Q Learning model. The preliminary prediction results of lake eutrophication are obtained, and the errors obtained are used as the secondary input to continue updating the Q table to build the final Improved MIMO-DD-3Q Learning model, so as to achieve the final prediction of water eutrophication. In this study, multi-factor water quality data of Yongding River in Beijing were selected from 0:00 on July 26, 2021 to 0:00 on September 5, 2021. Firstly, data smoothing and principal component analysis were carried out to confirm that there was a certain correlation between all factors in the occurrence of lake eutrophication. Then, the Improved MIMO-DD-3Q Learning prediction model was used for experimental verification. The results show that the Improved MIMO-DD-3Q Learning model has a good effect in the field of lake eutrophication prediction. [ABSTRACT FROM AUTHOR]

Ammonia (NH 3) is a vital precursor of secondary aerosols, which play an important role in urban air pollution. The passive observation of NH 3 in the urban atmosphere showed higher concentrations than conventionally believed; however, the NH 3 dynamics remain limited due to the paucity of active measurements with fast response instruments. In this study, intensive online measurements of ambient NH 3 were conducted using cavity ring-down spectroscopy in late spring and early summer 2020 at the megacities of Beijing and Shanghai in northern and southern China. The results show that the average NH 3 concentration in Beijing (23.1 ± 10.3 ppb) was nearly double that in Shanghai (12.0 ± 5.0 ppb). This spatial difference was confirmed by satellite column observations and bottom-up emission patterns. In addition, there was a significant increase in NH 3 concentrations during the morning in our study. This special diurnal profile was a universal feature, with an occurrence frequency exceeding 50% in both cities. These frequent morning increases were mainly caused by motor vehicle emissions, with an additional contribution from the residual layer breakup for one-quarter of the days in the morning increase. On these days, the NH 3 concentration increased by approximately 20% due to the downward mixing of the residual layer rupture. The unique data observed here could be used to validate the chemistry and transport models at a higher time resolution and improve the understanding of the current status of NH 3 pollution in the urban atmosphere. [Display omitted] • 1 Hz NH 3 observations were conducted at megacities of Beijing and Shanghai. • Atmospheric NH 3 concentrations in urban Beijing were double that in Shanghai. • A morning pulse of NH 3 levels was a common feature at Beijing and Shanghai. • Highly frequent morning increases of NH 3 were mainly caused by vehicle emissions. • Boundary layer rupture further enhanced the rapid increase of NH 3 in the morning. [ABSTRACT FROM AUTHOR]