Pedestrian exposure to black carbon and PM2.5 emissions in urban hot spots: new findings using mobile measurement techniques and flexible Bayesian regression models

Background Data from extensive mobile measurements (MM) of air pollutants provide spatially resolved information on pedestrians’ exposure to particulate matter (black carbon (BC) and PM2.5 mass concentrations). Objective We present a distributional regression model in a Bayesian framework that estimates the effects of spatiotemporal factors on the pollutant concentrations influencing pedestrian exposure. Methods We modeled the mean and variance of the pollutant concentrations obtained from MM in two cities and extended commonly used lognormal models with a lognormal-normal convolution (logNNC) extension for BC to account for instrument measurement error. Results The logNNC extension significantly improved the BC model. From these model results, we found local sources and, hence, local mitigation efforts to improve air quality, have more impact on the ambient levels of BC mass concentrations than on the regulated PM2.5. Significance Firstly, this model (logNNC in bamlss package available in R) could be used for the statistical analysis of MM data from various study areas and pollutants with the potential for predicting pollutant concentrations in urban areas. Secondly, with respect to pedestrian exposure, it is crucial for BC mass concentration to be monitored and regulated in areas dominated by traffic-related air pollution

Emotional Intelligence in Employee Performance: Healthcare Industry in Finland

This comprehensive assessment of the literature explores the complex relationship between employee performance and emotional intelligence in the setting of Finland's healthcare sector. Our rigorous selection method yielded a final collection of 15 articles that closely complied with our inclusion criteria. It started with an initial screening of 33 articles and ended with an extensive full-text examination. Interestingly, every study that was chosen was published after 2021, giving our review a modern perspective on this significant topic. The review emphasizes how important emotional intelligence is in influencing worker performance in healthcare environments. Together, the results of the chosen studies show a strong link between improved performance across a range of performance metrics and healthcare professionals' elevated emotional intelligence. They include but are not limited to, improved job satisfaction, higher-quality patient care, and the development of cohesive cooperation. Emotional intelligence skills such as empathy, self-awareness, and effective interpersonal communication become critical elements that significantly impact performance results in the healthcare industry. Moreover, this research highlights the practical implications for Finnish healthcare institutions. It emphasizes how urgently these organizations must think about including training and development programs for emotional intelligence into their personnel plans. These kinds of programs could develop a more emotionally intelligent workforce in the healthcare industry, which would lead to improved patient experiences, increased employee engagement, and improved healthcare outcomes. This study adds significantly to the body of knowledge by illuminating the complex interactions between employee performance and emotional intelligence that are unique to the Finnish healthcare industry. It not only deepens our comprehension of the underlying processes but also offers practitioners, legislators, and leaders in the healthcare industry useful information. The review highlights the potential for targeted interventions to improve emotional intelligence competencies among healthcare professionals. These interventions have the potential to significantly improve the well-being of the industry's diverse stakeholders and raise performance standards in Finland's healthcare sector

CeyMo: See More on Roads -- A Novel Benchmark Dataset for Road Marking Detection

In this paper, we introduce a novel road marking benchmark dataset for road marking detection, addressing the limitations in the existing publicly available datasets such as lack of challenging scenarios, prominence given to lane markings, unavailability of an evaluation script, lack of annotation formats and lower resolutions. Our dataset consists of 2887 total images with 4706 road marking instances belonging to 11 classes. The images have a high resolution of 1920 x 1080 and capture a wide range of traffic, lighting and weather conditions. We provide road marking annotations in polygons, bounding boxes and pixel-level segmentation masks to facilitate a diverse range of road marking detection algorithms. The evaluation metrics and the evaluation script we provide, will further promote direct comparison of novel approaches for road marking detection with existing methods. Furthermore, we evaluate the effectiveness of using both instance segmentation and object detection based approaches for the road marking detection task. Speed and accuracy scores for two instance segmentation models and two object detector models are provided as a performance baseline for our benchmark dataset. The dataset and the evaluation script is publicly available at https://github.com/oshadajay/CeyMo.Comment: Accepted to 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2022