Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and lowlight conditions, respectively, with annotated objects/faces. We launched the UG 2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.Index Terms-Poor visibility environment, object detection, face detection, haze, rain, low-light conditions *The first two authors Wenhan Yang and Ye Yuan contributed equally. Ye Yuan and Wenhan Yang helped prepare the dataset proposed for the UG2+ Challenges, and were the main responsible members for UG2+ Challenge 2019 (Track 2) platform setup and technical support. Wenqi Ren, Jiaying Liu, Walter J. Scheirer, and Zhangyang Wang were the main organizers of the challenge and helped prepare the dataset, raise sponsors, set up evaluation environment, and improve the technical submission. Other authors are the group members of winner teams in UG2+ challenge Track 2 contributing to the winning methods.

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Background This study aimed to provide insight into the training load of newly recruited nurses in grade-A tertiary hospitals in Shanghai, China. The lack of nurses in hospitals across China has resulted in newly recruited nurses in grade-A tertiary hospitals in Shanghai having to integrate into the work environment and meet the needs of the job quickly; thus, they undergo several training programs. However, an increase in the number of training programs increases the training load of these nurses, impacting the effectiveness of training. The extent of the training load that newly recruited nurses have to bear in grade-A tertiary hospitals in China remains unknown. Methods This qualitative study was conducted across three hospitals in Shanghai, including one general hospital and two specialized hospitals, in 2020. There were 15 newly recruited nurses who were invited to participate in semi-structured in-depth interviews with the purpose sampling method. A thematic analysis approach was used to analyze the data. The COREQ checklist was used to assess the overall study. Results Three themes emerged: external cognitive overload, internal cognitive overload, and physical and mental overload. Conclusion Through qualitative interviews, this study found that the training of newly recruited nurses in Shanghai’s grade-A tertiary hospitals is in a state of overload, which mainly includes external cognitive overload, internal cognitive overload, physical and mental overload, as reflected in the form of training overload, the time and frequency of training overload, the content capacity of training overload, the content difficulty of training overload, physiological load overload, and psychological load overload. The intensity and form of the training need to be reasonably adjusted. Newly recruited nurses need to not only improve their internal self-ability, but also learn to reduce internal and external load. Simultaneously, an external social support system needs to be established to alleviate their training burden and prevent burnout.

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Background Obstetric critical illness is an important factor that leads to an increase in maternal mortality. Early warning assessment can effectively reduce maternal and neonatal mortality and morbidity. However, there are multiple early warning systems, and the effect and applicability of each system in China still need to be explored. Objectives To elaborate on the application, effectiveness and challenges of the existing early warning systems for high‐risk obstetric women in China and to provide a reference for clinical practice. Design A scoping review guided by the Arksey and O'Malley framework and reported using the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis for scoping review (PRISMA‐ScR) guidelines. Eligibility criteria We included original studies related to early warning and excluded those that were guidelines, consensus and reviews. The included studies were published in Chinese or English by Chinese scholars as of June 2021. Data sources CNKI, Wanfang, VIP, Cochrane, CINAHL, Embase, PubMed and Web of Science databases were searched systematically, and the reference sections of the included papers were snowballed. Results In total, 598 articles were identified. These articles were further refined using keyword searches and exclusion criteria, and 17 articles met the inclusion criteria. We extracted data related to each study's population, methods and results. Early warning tools, outcome indices, effects and challenges are discussed. Conclusions Although all studies have shown that early warning systems have good application effects, the use of early warning systems in China is still limited, with poor regional management and poor sensitivity for specific obstetric women. Future research needs to develop more targeted early warning tools for high‐risk obstetric women and address the current challenges in clinical applications.