ABSTRACT
Wearing a mask during the COVID-19 epidemic can effectively prevent the spread of the virus. In view of the problems of small target size, crowd blocking each other and dense arrangement of targets in crowded places, a target detection algorithm based on the improved YOLOv5m model is proposed to achieve efficient detection of whether a mask is worn or not. This paper introduces four attention mechanisms in the feature extraction network based on the YOLOv5m model to suppress irrelevant information, enhance the information representation of the feature map, and improve the detection capability of the model for small-scale targets. The experimental results showed that the introduction of the SE module increased the mAP value of the original network by 9.3 percentage points, the most significant increase among the four attention mechanisms. And then a dual-scale feature fusion network is used in the Neck layer, giving different weights to the feature layers to convey more effective feature information. In the image pre-processing, the Mosaic method was used for data enhancement, and the CIoU loss function was used for coordinate frame positioning in the prediction layer. Experiments on the improved YOLOv5m algorithm demonstrate that the mean recognition accuracy of the method improves by 10.7 percentage points over the original method while maintaining the original model size and detection speed, and better solves the problems of small scale, dense arrangement and mutual occlusion of targets in mask wearing detection tasks in crowded places. © 2023 IEEE.
ABSTRACT
This paper analyzes the employment trajectories of engineering workers-both workers in occupations formally classified as engineering and workers in occupations not formally classified as engineering but where engineering knowledge is important-during the COVID-19 pandemic. We find that the employment rate of workers in engineering occupations fell by 6.6 percentage-points at the onset of the pandemic compared to a 13.1 percentage-point drop among workers in non-engineering jobs, and that workers in jobs where engineering knowledge is important were less likely to suffer employment loss during the pandemic, regardless of whether their occupation is formally classified as a STEM engineering occupation. This suggests that engineering knowledge is beneficial in reducing a worker's unemployment risk during recessions. We also find that industries with the highest share of engineers as workers tended to experience smaller percentage declines in employment during the pandemic compared to overall US employment, although employment in aerospace and motor vehicle manufacturing industries remained over 10% below pre-recession employment as of 2021Q4. © American Society for Engineering Education, 2022.
ABSTRACT
The article discusses the impact of the first wave of Covid-19 on the trend of ICT Professionals’ wages in the Czech Republic. The first goal of the article is to analyze the impact of the first wave of Covid-19 on the trend of ICT Professionals’ wages in the Czech Republic. We analyzed also gender pay gap also based on it as the second goal of the article. For analyzing the wage trend, we used data from Trexima, a.s. for the years 2016-2020. We used standard MS Excel tools to analyze the obtained time series data. The results show that the impact of the first wave of Covid-19 on the nominal gross wages of ICT Professionals was minimal. We have also found out that the gender gap pay still exists. However, the positive information is that the gender pay gap decreased during the analyzed time period and that the trend lines also indicate its decrease in the future - the average wage by 3.7 percentage points and the median wage by 3.7 percentage points as well. © Proceedings of the 14th International Conference on Strategic Management and its Support by Information Systems 2021, SMSIS 2021.