Background Health care workers, especially frontline nurses, faced great challenges during the coronavirus disease 2019 (COVID-19) outbreak. Aims To assess the magnitude of the psychological status and associated risk factors among nurses in the pandemic center in Wuhan, China. Methods In this study, we enrolled nurses from Renmin Hospital of Wuhan University. The questionnaire was designed to obtain basic information of the participants, and included four psychological assessment scales. We issued the questionnaires at two different points of time. We conducted the first survey on January 29 to February 2 (outbreak period) with 709 eligible responses, and the second survey on February 26 to February 28 (stable period) with 621 eligible responses. The nurses from Wuchang Fangcang shelter hospital were also enrolled in the second survey. Results During the pandemic, over one-third of nurses suffered from depression, anxiety, and insomnia. In the outbreak period, the nurses showed significantly higher risks for depression, anxiety, and posttraumatic stress disorder (PTSD) symptoms than those in the stable period ( P < 0.01). Notably, the nurses from the Fangcang shelter hospitals were more likely to present psychological problems than those from other frontline or non-frontline (all P < 0.001) units, especially for insomnia (38.3% with severe insomnia). The nurses from the frontline, with worse physical condition and uncertain concerns about this pandemic as compared to the others, were more likely to bear psychological problems. Thus, online psychological information and sufficient protection conditions were effective interventions to help mitigate psychological distress. The nurses from Fangcang shelter hospitals suffered a significantly higher risk of psychological problems than those from other units. Conclusion The psychological status of nurses needs more attention during the COVID-19 pandemic, especially for those who fought in the frontline during the peak of the outbreak.

Background: The COVID-19 epidemic has been outbreak and even spread to the global. The whole medical system in the world is facing great challenges. As one of the main forces, nursing staff are at the highest risk. Their negative emotions and job burnout are worthy of attention. This study aims to investigate the status of burnout and anxiety among nurses during COVID-19 epidemic and analyze the influencing factors of burnout.Methods: A cross-sectional survey was conducted from February 19 to February 25, 2020. A total of 1011 nurses from Wuhan tertiary hospitals were distributed with the basic information questionnaire, Maslach Burnout Inventory - General Survey (MBI-GS) and State-Trait anxiety inventory (STAI). The number of final valid questionnaire was 885. The effective recovery rate was 87.5%. Linear regression analysis was performed to explore the influencing factors.Results: The mean score of three dimensions of MBI-GS was 11.50, 6.02, 24.47, respectively. The mean score of state anxiety was 45.52 and trait anxiety, 43.78. Anxiety was positively related to emotional exhaustion and cynicism, and negatively to personal accomplishment. The positive factors of burnout were personnel agency, 5 years or less work experience, living in hospital dormitory, Wuhan medical team, working more than 9 hours, and the best level of knowledge of COVID-19. And having no siblings, intermediate title, working in isolation wards, 3 and more night shifts per week, living in hotels, and having confirmed or suspected medical staff around were negative factors. Conclusions: From this study, the anxiety level among nurses during the COVID-19 is serious, however, the level of burnout is mild to medium. Managers should continue to pay attention to the negative emotions of nurses and related factors, and take interventions timely to stabilize the nursing team.

Background The coronavirus disease 2019 (COVID-19) epidemic has broken out and even spread globally. The healthcare system worldwide faces enormous challenges, and nurses are at the highest risk as one of the leading forces. It's worth paying attention to nurses' anxiety and job burnout. This study aimed to investigate nurses' levels of burnout and anxiety during the epidemic of COVID-19 and to analyze influencing factors of burnout. Methods A cross-sectional survey was conducted from 19 to 25 February 2020. Questionnaires such as the basic information questionnaire, Maslach Burnout Inventory-General Survey (MBI-GS), and State-Trait Anxiety Inventory (STAI) were used among 1011 nurses in Wuhan tertiary hospitals via the online survey. The final number of valid questionnaires was 885. The effective response rate was 87.5%. Results The average score of MBI-GS was 11.50, 6.02, 24.47, respectively. The average score for state anxiety was 45.52 and trait anxiety, 43.78. Anxiety was positively associated with emotional exhaustion and cynicism, and negatively related to personal accomplishment. The protective factors of burnout were personnel agency, five years or less work experience, living in hospital dormitory, Wuhan medical team, working time exceeding 9 h, and the best knowledge of COVID-19. The absence of siblings, median job title, working in isolation wards, three or more night shifts per week, living in hotels, and being surrounded by confirmed or suspected medical staff were all negative factors. Conclusions Nurses had high anxiety levels during the COVID-19 period, but the level of burnout was mild to moderate. Managers should continue to pay attention to nurses' psychological state and related factors and intervene to stabilize the nursing team.

Hyperspectral images, which contain not only spatial information but also rich spectral information, have been extensively applied to the fields of agriculture, urban planning and so on. However, it is difficult for a single image to cover a large area. Therefore, it requires to take photos of various parts and apply image stitching technology to obtain a panoramic hyperspectral image. When the viewpoint of the scene changes a lot, the ghost issue will occur with traditional methods. In order to get the high-precision resultant panoramas, this paper proposes an automatic image stitching algorithm for hyperspectral images using robust feature matching and elastic warp. Our method contains two stages. The first stage is to choose one band as reference band and obtain the panorama in a single band. In particular, we extract feature points by SIFT. Then we propose an efficient algorithm called multiscale Top K Rank Preservation algorithm (mTopKRP), for establishing robust point correspondences between two sets of points. Next, we adopt robust elastic warp to obtain the panorama of each band. The second stage is to stitch all remaining bands based on the transformation obtained in the first stage and fuse the information of all bands together to get the final panoramic hyperspectral image. Extensive experiments have demonstrated the effectiveness of our proposed method.