ABSTRACT
This study's primary intent was to investigate the effect of extreme conditions, specifically the COVID-19 pandemic, by examining nurses' perceptions of authentic leadership, meaningful work, and job meaning, and to compare this with the nurses' perceptions from before the outbreak. In the study, 458 responses for both periods were analyzed and compared statistically by using the Mann–Whitney U test. The findings showed that nurses' perception of line managers' authenticity decreased during the outbreak. Therefore, in extreme conditions, leadership behaviors can be affected negatively by the context. During the outbreak, nurses attributed more meaning to their work. They felt more self-worth because of working for the greater good, and found greater meaning through the work during the COVID-19 outbreak compared to before the pandemic. The findings suggest that extreme conditions in a challenging environment can help nurses to find more meaning at work. For nurses, during the COVID-19 outbreak the purpose and meaning of their jobs remained the same as before the pandemic. Nursing requires different skills, talents, and opportunities for self-development, and it is challenging in nature. © 2023 by the authors.
ABSTRACT
Mask detection has become a hot topic since the COVID-19 pandemic began in recent years. However, most scholars only focus on the speed and accuracy of detection, and fail to pay attention to the fact that mask detection is not suitable for people living under extreme conditions due to the degraded image quality. In this work, a denoising convolutional auto-encoder, a multitask cascaded convolutional networks (MTCNN) and a MobileNet were used to solve the problem of mask detection for COVID-19 under extreme environments. First of all, a network based on AlexNet is designed for the auto-encoder. This study found that the two-layer max pooling layers in AlexNet could not accurately extract image features but damage the quality of restored image. Therefore, they were deleted, and other parameters such as channel number were also modified to fit the new net, and finally trained using cosine distance. In addition, for MTCNN, this study changed the output condition of ONet from thresholding to maximum return, and lowered the thresholds of PNet and RNet to solve the problem that faces might not be found in low-quality images with mask and other covers. Furthermore, MobileNet was trained using categorical cross entropy loss function with adam optimizer. In the end, the accuracy of system for the photos captured under extreme conditions enhance from 50 % to 85% in test images. © 2022 IEEE.
ABSTRACT
Modern digital technologies allow potentially to explore Cultural Heritage sites in immersive virtual environments. This is surely an advantage for the users that can better experiment and understand a specific site, also before a real visit. This specific approach has gained increasing attention during the extreme conditions of the recent COVID-19 pandemic. In this work, we present the processes that lead to the implementation of an immersive app for different kinds of low and high-cost devices, which have been attained in the context of the 3dLab-Sicilia project. 3dLab-Sicilia’s main objective is to sponsor the creation, development, and validation of a sustainable infrastructure that interconnects three main Sicilian centres specialized in augmented and virtual reality. The project gives great importance to the cultural heritage, as well as to the tourism-related areas. Despite the presentation of the case study of the Santa Maria La Vetere church, the process of the final app implementation guided by the general pipeline here presented is general and can be applied to other cultural heritage sites. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.