COVID - 19

Lung infection is the most dangerous sign of Covid 19. X-ray images are the most effective means of diagnosing this virus. In order to detect this disease, deep learning algorithms and machine vision are widely used by computer scientists. Convolutional neural networks (CNN), DenseNet121, Resnet50, and VGG16 were used in this study for the detection of Covid-19 in X-ray images. In the current study, 1341 chest radiographs from the COVID-19 dataset were used to detect COVID-19 including infected and Healthy classes using a modified pre-trained CNN (train and test accuracy of 99.75% and 99.63%, respectively). The DENSENET121 model has a training accuracy of 43.89% and a test accuracy of 57.89%, respectively. The train and test accuracy of ResNet-50 are, respectively, 89.43% and 90%. Additionally, the CNN model has test and train accuracy of 98.13% and 96.73%, respectively. The suggested model has COVID-19 detection accuracy that is at least 1% higher than all other models.

INTRODUCTION: Lack of a comprehensive classification of crisis and emergency management in Helal houses has caused many losses in the Red Crescent Society of Iran. Therefore, the root of these problems can be identified and investigated with a thorough investigation. METHODS: This applied research was conducted using a hybrid design. In this research, field and library methods were used. The data were collected by using questionnaire and conducting in-depth interviews with managers, specialists, and experts of the country's crisis management organization and professionals until theoretical saturation. In the first stage, 24 interviews were conducted and we reached theoretical saturation in the 20th interview. In the second stage, relying on inferential statistics methods, 422 questionnaires were distributed. The statistical population of the research is the Red Crescent Society of Iran and its time domain is the second half of 2022. FINDINGS: After performing the three stages of open, central, and selective coding, the final research model was obtained in which, 6 general categories, 15 subcategories, and 49 main concepts are mentioned. CONCLUSION: The results demonstrated that the categories of human resources, organizational communication, and government policies are among the effective factors on crisis management in Helal houses in the Iranian Red Crescent Society in the critical condition of Covid-19 and the consequences of these categories have a significant impact. There are other problems and crises in the formation.

With the COVID-19 outbreak, it appears that social research to better understand how people react to epidemics, particularly this one, is a necessity. A lesser-known societal feature of COVID-19 is the trust of Corona patients in modern medical institution. The current study addresses this issue. This study drew on institutional trust theories, most notably Giddens' theory of abstract systems trust. In this study, the qualitative method was applied. Thematic analysis is a qualitative research method which was used. Braun and Clarke's thematic analysis was employed to analyze the data. The statistical population for this study is comprised of Covid-19 patients who have been infected with the virus between the Covid-19 virus outbreak in Iran and September 2020, and 21 of them were chosen through a purposive sampling method. Semi-structured interviews were conducted with these patients. According to the research findings, Corona patients' opinions can be classified into three categories: 'medical science and expertise', 'hospital environment,' and 'medical staff,' with each category containing sources of trust and distrust. The article delves deeper into sub-themes and other details. Among the COVID-19 patients who have been interviewed, there appears to be a boost in public trust in medical personnel. While these patients' trust in medical science and expertise, as well as in the hospital environment, has eroded.

The widespread use of the Internet has led to both beneficial and detrimental impacts on adolescents and young adults worldwide. Understanding the factors that contribute to these effects enables the development of culturally appropriate strategies for addressing them at a global scale. The COVID-19 pandemic has led to a global surge in Internet use, particularly among adolescents. This study aimed to explore the prevalence of Internet addiction among adolescents in Iran during the post-COVID-19 period. In this web-based cross-sectional study, 1358 Tehran-based adolescents aged 13-18 completed the Young's Internet Addiction Questionnaire. Findings revealed a 10.3% prevalence of Internet addiction among the participants following the COVID-19 pandemic. Factors such as age of first exposure to the Internet and time spent online were correlated with addiction, while gender played no significant role. Additionally, Internet addiction was significantly linked to recreational use, gaming, and insufficient parental support, but not to educational usage. By comparing the cultural contexts of Iran and other countries, this study determines Iran's standing in terms of Internet addiction and the influence of culture. The implications of the findings suggest the need for policymakers, educators, and healthcare providers to devise effective strategies to combat this emerging public health concern.