Tapasztalatok es motiváltság: magyar középiskolások véleménye az egészségvédő programokról.

INTRODUCTION: Health-related attitudes can be encouraged most effectively at young ages. Young generations would require more interactive methods in programs engaged in health promotion. AIM: The aim of the authors was to get an insight into the attitudes, experience and motivation of youngsters in connection with health promotion programs and the community service work. METHOD: The questionnaires were filled in by high school students studying in Budapest and in the countryside (N = 898). RESULTS: 44.4% of the students did not have lessons or extracurricular activities dealing with health promotion. Concerning health promotion programs, youngsters in Budapest had more positive experience, while female students showed a more adoptive attitude. CONCLUSIONS: It was concluded that in one of the most susceptible life stages, many youngsters either do not participate in programs dealing with health promotion, or participate in programs that are within the framework of school subjects or extracurricular activities building on traditional teaching methods. Orv. Hetil., 2016, 157(2), 65-69

Kortárs egészségfejlesztési programok közvetlen hatása alsó tagozatos gyermekek kézhigiénés tudására és megfelelő kézmosási technikájára

INTRODUCTION AND AIM: In the case of primary school children in Budapest (n = 165), data on their social status and their previous knowledge on hand hygiene were elicited with the help of pre-knowledge questionnaires issued by students of higher education. The aim of the research was introducing a novel pedagogical procedure - application and optimization of peer education in the development of proper hand hygiene among primary school students. METHOD: The knowledge-based survey was conducted after four (n = 85) and eight hours of teaching (n = 36). In addition, the effectiveness of hand washing was tested immediately before (n = 166) and after the four (n = 74) and eight hours of teaching (n = 35) with Semmelweis Scanner after rubbing the hand with fluorescent cream. RESULTS: Prior knowledge of hand hygiene significantly increased after the four-hour and eight-hour trainings. In the case of smaller children, the effect of the eight-hour training was more pronounced. Similar results were obtained with regards to the changes in the number of areas missed while rubbing the surface of the hand as a result of the teaching. CONCLUSION: Sociological surveys on hand hygiene knowledge and direct physical measurements indicate that training with appropriate pedagogical procedures is effective and contributes to the environmentally conscious hygiene culture of children aged 6 to 10. Orv Hetil. 2018; 159(12): 485-490

Image dataset for the creation of an automatic system for meteor fall detection

Image dataset with sky photos showing the occurrence or non-occurrence of falling meteors. The database comprises 7,000 images in JPEG format -- 3,850 (55%) images show the event of falling meteors, and 3,150 (45%) images show no meteors. Different instruments captured the photos from 2014 to 2023. We used the images to train a deep-learning neural network for an automatic falling meteor detector.The primary image data sources were the Brazilian Meteor Observation Network (BRAMON -- http://www.bramonmeteor.org), UK Meteor Network (UKMON -- https://ukmeteornetwork.co.uk), and Base des Observateurs Amateurs de Météores (BOAM -- http://boam.fr) repositories.Folders StructureWe divided the folder structure into two levels. In the first level, we have two folders: RawImages, which holds images with captions stored in the repositories; and CroppedImages, which contains images without the captions (we cropped a band of 24 pixels in the lower part of the image).In the second level, in each of the previous folders, we have another two folders: meteor, which has images with meteors; and non-meteors, with images without occurrences of meteors.Naming pattern for the filesThe naming pattern in the meteor folder follows the format <source>_<date>_<id>.jpg where: <source> is one of the 3 data sources: bramon, ukmon, or boam. <date> is the date-time the instrument captured the image in the format yyyymmdd_hhnnss (y:year, m:month, d:day, h:hours, n:minutes, s:seconds). <id> is an identifier from a specific source to avoid date-time conflicts: BRAMON: radiant identifier. UKMON: station identifier. BOAM: station identifier.For the non-meteor folder, the naming pattern is <source>_<date>_nonmeteor.jpg to avoid homonyms (with the same date-time) and to identify that they are images of non-meteors

Image dataset for the creation of an automatic system for meteor fall detection

Image dataset with sky photos showing the occurrence or non-occurrence of falling meteors. The database comprises 7,000 images in JPEG format -- 3,850 (55%) images show the event of falling meteors, and 3,150 (45%) images show no meteors. Different instruments captured the photos from 2014 to 2023. We used the images to train a deep-learning neural network for an automatic falling meteor detector.The primary image data sources were the Brazilian Meteor Observation Network (BRAMON -- http://www.bramonmeteor.org), UK Meteor Network (UKMON -- https://ukmeteornetwork.co.uk), and Base des Observateurs Amateurs de Météores (BOAM -- http://boam.fr) repositories.Folders StructureWe divided the folder structure into two levels. In the first level, we have two folders: RawImages, which holds images with captions stored in the repositories; and CroppedImages, which contains images without the captions (we cropped a band of 24 pixels in the lower part of the image).In the second level, in each of the previous folders, we have another two folders: meteor, which has images with meteors; and non-meteors, with images without occurrences of meteors.Naming pattern for the filesThe naming pattern in the meteor folder follows the format <source>_<date>_<id>.jpg where: <source> is one of the 3 data sources: bramon, ukmon, or boam. <date> is the date-time the instrument captured the image in the format yyyymmdd_hhnnss (y:year, m:month, d:day, h:hours, n:minutes, s:seconds). <id> is an identifier from a specific source to avoid date-time conflicts: BRAMON: radiant identifier. UKMON: station identifier. BOAM: station identifier.For the non-meteor folder, the naming pattern is <source>_<date>_nonmeteor.jpg to avoid homonyms (with the same date-time) and to identify that they are images of non-meteors.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV