The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China,

Sonodynamic therapy (SDT) has attracted widespread attention due to its noninvasiveness and deep tissue penetration. However, the development of efficient sonodynamic nanoplatforms to improve the therapeutic efficiency is still one of the main challenges of current research. In this work, a new type of sonosensitizer prepared by a simple method, manganese carbonate nanoparticles (MnCO 3 NPs), is used for enhanced SDT. MnCO 3 NPs could generate large amounts of 1 O 2 and •OH under ultrasound irradiation. At the same time, CO 2 and Mn ions could be released in a weak acid environment due to the excellent degradability of MnCO 3 NPs. The CO 2 bubbles caused cell necrosis by ultrasonic cavitation and used for ultrasound imaging. And Mn ions activated the mitochondrial cell apoptosis pathway. In vivo experiments proved that this sonosensitizer with mitochondrial regulatory capacity showed high tumor inhibition rates for enhanced sonodynamic tumor therapy.

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Cyberbullying can have a terrible impact on the physical and mental health of those involved. In severe cases, some of those involved develop anxiety, depression, and suicidal tendencies. However, few studies focus on cyberbullying among Chinese college students. We aimed to understand the incidence of cyberbullying in social media and online games and its associated factors among college students in China. A cross-sectional STAR questionnaire survey was conducted for college students from the end of June to the beginning of July 2019. Selected via the method of cluster random sampling, students graded 1–5 (college) from two colleges in Shantou were invited to participate in the survey. Information was collected regarding respondents’ socio-demographic information, cyberbullying in social media and online games, self-esteem, anxiety symptoms, Internet addiction, etc. A binary logistic regression model was employed to use all significant variables tested using χ² test or t-test for estimating the effect of potential factors on cyberbullying among college students. Participants were 20.43 ± 1.513(X ± SD) years old, and the age range was 15 to 25 years old. 64.32% college students reported that they had suffered from cyberbullying, and 25.98% reported bullying others online during the semester. Gender, anxiety symptoms, Internet addiction, game time, and violent elements in games were associated with cyberbullying in social media and online games among college students in China. In conclusion, cyberbullying in social media and online games is prevalent among college students in China. The above data provided insights that targeted and effective measures should be taken to prevent college students from cyberbullying.

To maximise asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. Various statistical models have been proposed for predicting this, but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how (1) data on the condition of assets available from their periodic inspection can be used, (2) failure data from related groups of asset can be combined using judgement from experts and (3) expert knowledge of the deterioration's causes can be combined with statistical data to adjust predictions. A case study of bridges on the rail network in Great Britain (GB) is presented, showing how the model could be used for the maintenance decision problem, given typical data likely to be available in practice.

Coal samples and carbonaceous mudstone were collected from the Huaibei coalfield, China, and experiments investigating the factors influencing the extraction of the sixteen US EPA (Environmental Protection Agency) priority polycyclic aromatic hydrocarbons (PAHs) were carried out. Different extraction times, solvents, and methods were used. Major interest was focused on finding optimum conditions for extracting the PAHs from coal. We conclude that (1) coal composition, including the H/C and O/C ratios, is an important factor for the distribution of PAHs in coals; (2) the total amount of EPA priority PAHs increases with increasing extraction time, 30 min being suitable for ultrasonic-assisted extraction and 24 h for Soxhlet extraction; (3) CS2 is effective in extracting low molecular weight PAHs, while CH2Cl2 is better for extracting high molecular weight PAHs (both are excellent extraction solvents vs hexane); (4) both Soxhlet and ultrasonic extraction showed a similar PAH concentration profile, but the ultrasonic method is less efficient.