Son Nguyen

ObjectiveThe goal of the study was to explore the relationship between parent–children relationships related to using the internet among kids and potentially associated factors.Materials and MethodsA sample of 1.216 Vietnamese students between the ages of 12 and 18 agreed to participate in the cross-sectional online survey. Data collected included socioeconomic characteristics and internet use status of participants, their perceived changes in relationship and communication between parents and children since using the internet, and parental control toward the child’s internet use. An Ordered Logistic Regression was carried out to determine factors associated with parent–children relationship since using the internet.ResultsThe characteristics of the relationship between children and their parents since using the Internet were divided into three levels: deterioration, stability, and improvement. The topics that children most often communicate with their parents include learning, housework, and future directions. Two-way interactive activities, such as supporting parents to use the Internet, have a positive impact on the parent–child relationship. Stubborn parental control, such as establishing rules about contact or allowing Internet access and setting up global positioning system to track negatively affecting parent–child relationships.ConclusionFindings indicated that changes in the quality of the parent–child relationship were self-assessed by participants regard to kids’ internet use, especially in the COVID-19 epidemic context. Educational campaigns and programs to raise awareness of parents as to the dangers and negative influences that their children may encounter online, psychology of children’s behaviors and effects of different responding strategies are recommended.

The ongoing coronavirus 2019 pandemic caused by the severe acute respiratory syndrome coronavirus 2 has resulted in a severe ramification on the global healthcare system, principally because of its easy transmission and the extended period of the virus survival on contaminated surfaces. With the advances in computer-aided diagnosis and artificial intelligence, this paper presents the application of deep learning and adversarial network for the automatic identification of COVID-19 pneumonia in computed tomography scans of the lungs. The complexity and time limitation of the reverse transcription-polymerase chain reaction swab test makes it disadvantageous to depend solely on as COVID-19’s central diagnostic mechanism. Since CT imaging systems are of low cost and widely available, we demonstrate that the drawback of the RT-PCR can be alleviated with a faster, automated, and reduced contact diagnostic process via the use of a neural network model for the classification of infected and noninfected CT scans. In our proposed model, we explore the benefit of transfer learning as a means of resolving the problem of inadequate dataset and the importance of semisupervised generative adversarial network for the extraction of well-mapped features and generation of image data. Our experimental evaluation indicates that the proposed semisupervised model achieves reliable classification, taking advantage of the reflective loss distance between the real data sample space and the generated data.