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Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field. [ABSTRACT FROM AUTHOR]

With rising numbers of students, shorter hospital stays and increasing workload of academic teachers, teaching time have become a scarce resource. Thus, optimal preparation by teachers is key for good clinical teaching. Though a lot of teaching duties are performed by residents in Germany, they are not likely to have been educated in didactic techniques. We developed and evaluated a mobile teaching application using the "Learning Toolbox (LTB)" platform (Raycom BV, Utrecht, Netherlands) that was offered to academic teachers for preparation and support during curricular courses at our medical school. Courses were part of the curricula in Anaesthesiology, Emergency Medicine, and Orthopaedic and Trauma Surgery, and course length could vary up to 40 h per week. Information provided by the novel platform included logistic information, learning objectives, and curricula for the individual courses. A basic tutorial on didactic techniques, suggestions for providing feedback and for enhancing students' participation was also part of the platform. After one semester, interviews with teachers indicated an increase in overall satisfaction. Residents appreciated didactic aids and content provided for preparation, leading to higher motivation and self-confidence. The more experienced teachers were particularly satisfied with easy access to scheduling, teaching assignments and daily planning. The app increased teachers' satisfaction with their performance and enabled better integration of teaching in the daily schedule. [ABSTRACT FROM AUTHOR]

Background: Pollen allergies affect a significant proportion of the population globally. At present, Web-based tools such as pollen diaries and mobile apps allow for easy and fast documentation of allergic symptoms via the internet.Objective: This study aimed to characterize the users of the Patient's Hayfever Diary (PHD), a Web-based platform and mobile app, to apply different symptom score calculations for comparison, and to evaluate the contribution of organs and medications to the total score for the first time.Methods: The PHD users were filtered with regard to their location in Austria and Germany, significant positive correlation to the respective pollen type (birch/grass), and at least 15 entries in the respective season. Furthermore, 4 different symptom score calculation methods were applied to the datasets from 2009 until 2018, of which 2 were raw symptom scores and 2 were symptom load index (normalized) calculations. Pearson correlation coefficients were calculated pairwise for these 4 symptom score calculations.Results: Users were mostly male and belonged to the age groups of 21 to 40 years or >40 years. User numbers have increased in the last 5 years, especially when mobile apps were made available. The Pearson correlation coefficients showed a significant linear relationship above 0.9 among the 4 symptom score datasets and thus indicated no significant difference between the different methods of symptom score calculation. The nose contributed the most to the symptom score and determined about 40% of the score.Conclusions: The exact method of calculation of the symptom score is not critical. All computation methods show the same behavior (increase/decrease during the season). Therefore, the symptom load index is a useful computation method in all fields exploring pollen allergy, and Web-based diaries are a globally applicable tool to monitor the effect of pollen on human health via electronically generated symptom data. [ABSTRACT FROM AUTHOR]