Internet use and cyberbullying: Impacts on psychosocial and psychosomatic wellbeing among Chinese adolescents

The use of the internet for entertainment has increased hugely over the last decade among Chinese adolescents, but the psychosocial impacts remain unclear. The aims of this study are to explore the associations between internet use, cyberbullying and psychosocial wellbeing among Chinese adolescents. Questionnaires were completed in the classroom setting by 3378 middle school students aged 11–16 years old (M = 13.58, SD = 0.87) in three provinces representing eastern, central and western China. Key findings included: 1) Internet use of over 3 h per day was associated with higher prevalence of anxiety [OR = 1.6, 95% CI (1.1, 2.2), p = 0.006], depression [OR = 2.1, 95% CI (1.7, 2.6), p < 0.001] and psychosomatic health problems, such as abdominal pain [OR = 2.4, 95% CI (1.8, 3.3), p < 0.001]. 2) Boys were much more likely to play online games. 3) Moderate time of gaming was overall beneficial to well-being. 4) Cyberbullying was common, with 37.5% admitting involvement. 5) Bully-victims were most vulnerable to mental and psychosomatic health problems, and only-bullies were the least vulnerable group. Our findings suggest moderate internet use for entertainment is not detrimental to mental health, but excessive use is. Schools should promote adolescents’ responsible use of the internet and incorporate anti-cyberbullying programs into the curriculum

Self-Harm, Suicidal Ideation and Attempts among School-Attending Adolescents in Bamako, Mali

Suicide and self-harm are major public health concerns for adolescents globally, but there is a dearth of related research in West Africa. This study aims to examine the prevalence and associated factors for self-harm, suicidal ideation and suicide attempts among adolescents in the West African country of Mali. A questionnaire survey was conducted among adolescents attending school or university in August 2019 in Bamako, the capital of Mali. Logistical constraints necessitated convenience sampling. Outcome measures were self-harm and suicide ideation and attempts. Predictor variables included sociodemographic characteristics, bullying and mental health problems. There were 606 respondents who completed questionnaires; their mean age was 16.1 (SD = 2.4); 318 (52.5%) were identified as male; and 44.4% reported self-harm at some point in their life, with 21% reporting suicide ideation and 9.7% actual suicide attempts. For all three outcomes, older age, knowing somebody personally who had experienced self-harm or taken their own life, moderate to severe depression or anxiety, and being a victim of bullying were highly significant risk factors for self-harm, suicidal ideation and suicide attempts in these adolescents, while high self-esteem decreased the risk. The study suggests that self-harm and suicidal behaviour are relatively common in Malian adolescents who are still in education. However, much more research is needed to better understand this phenomenon

Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks

With the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population’s demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent “social nodes”, and a large number of social nodes are combined to form a new “opportunistic social network”. In this network, a large amount of data will be transmitted and the efficiency of data transmission is low. At the same time, the existence of “malicious nodes” in the opportunistic social network will cause problems of unstable data transmission and leakage of user privacy. In the information society, these problems will have a great impact on data transmission and data security; therefore, in order to solve the above problems, this paper first divides the nodes into “community divisions”, and then proposes a more effective node selection algorithm, i.e., the FL node selection algorithm based on Distributed Proximal Policy Optimization in IoT (FABD) algorithm, based on Federated Learning (FL). The algorithm is mainly divided into two processes: multi-threaded interaction and a global network update. The device node selection problem in federated learning is constructed as a Markov decision process. It takes into account the training quality and efficiency of heterogeneous nodes and optimizes it according to the distributed near-end strategy. At the same time, malicious nodes are screened to ensure the reliability of data, prevent data loss, and alleviate the problem of user privacy leakage. Through experimental simulation, compared with other algorithms, the FABD algorithm has a higher delivery rate and lower data transmission delay and significantly improves the reliability of data transmission

Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks

With the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population&rsquo;s demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent &ldquo;social nodes&rdquo;, and a large number of social nodes are combined to form a new &ldquo;opportunistic social network&rdquo;. In this network, a large amount of data will be transmitted and the efficiency of data transmission is low. At the same time, the existence of &ldquo;malicious nodes&rdquo; in the opportunistic social network will cause problems of unstable data transmission and leakage of user privacy. In the information society, these problems will have a great impact on data transmission and data security; therefore, in order to solve the above problems, this paper first divides the nodes into &ldquo;community divisions&rdquo;, and then proposes a more effective node selection algorithm, i.e., the FL node selection algorithm based on Distributed Proximal Policy Optimization in IoT (FABD) algorithm, based on Federated Learning (FL). The algorithm is mainly divided into two processes: multi-threaded interaction and a global network update. The device node selection problem in federated learning is constructed as a Markov decision process. It takes into account the training quality and efficiency of heterogeneous nodes and optimizes it according to the distributed near-end strategy. At the same time, malicious nodes are screened to ensure the reliability of data, prevent data loss, and alleviate the problem of user privacy leakage. Through experimental simulation, compared with other algorithms, the FABD algorithm has a higher delivery rate and lower data transmission delay and significantly improves the reliability of data transmission

Teachers’ perceptions of student mental health in eastern China: A qualitative study

Paul I. Kadetz - ORCID: 0000-0002-2824-1856 https://orcid.org/0000-0002-2824-1856Item not available in this repository.In China, primary and secondary school teachers, known as ban zhu ren, have pastoral responsibility for the students in their class. The aim of this preliminary study is to identify how ban zhu ren perceive the mental health of their students, and how they have acted on these perceptions. Content analysis was used to organize the data and distinguish categories or themes derived from in-depth semi-structured interviews conducted with 27 ban zhu ren from Zhejiang and Anhui provinces. Frequencies of informant responses were used to identify the areas of agreement and disagreement across identified categories and themes among the informants. The results illustrate that the informants consider issues, such as not paying attention in class (n = 14), not getting along well with classmates (n = 12), and excessive gaming (n = 11) to be indicative of mental illness, although these would commonly be considered normal adolescent behaviors. Fifteen informants admitted that they found it difficult to work with student mental health issues, and 18 felt they had inadequate or non-existent training. However, all informants stated that they had intervened with what they perceived to be students’ mental health issues, although only 9 informants had referred students for professional help. The informants reported that they were reluctant to provide referrals, due to the stigmatization they believed students would experience if given a diagnosis of mental illness. We conclude that among our informants there is a lack of agreement on what behavioral and mental health issues are, and that informants may be confusing what are, in actuality, non-conformist or non-compliant (yet often normal), adolescent behaviors with mental illness due to insufficient mental health training.This research was funded by the Global Health Center of Zhejiang University (188020-193810101/129).https://doi.org/10.3390/ijerph1814727118pubpub1

Civets Are Equally Susceptible to Experimental Infection by Two Different Severe Acute Respiratory Syndrome Coronavirus Isolates

Severe acute respiratory syndrome (SARS) was caused by a novel virus now known as SARS coronavirus (SARS-CoV). The discovery of SARS-CoV-like viruses in masked palm civets (Paguma larvata) raises the possibility that civets play a role in SARS-CoV transmission. To test the susceptibility of civets to experimental infection by different SARS-CoV isolates, 10 civets were inoculated with two human isolates of SARS-CoV, BJ01 (with a 29-nucleotide deletion) and GZ01 (without the 29-nucleotide deletion). All inoculated animals displayed clinical symptoms, such as fever, lethargy, and loss of aggressiveness, and the infection was confirmed by virus isolation, detection of viral genomic RNA, and serum-neutralizing antibodies. Our data show that civets were equally susceptible to SARS-CoV isolates GZ01 and BJ01