https://hdl.handle.net/20.500.12809/6 Other Units 2026-04-06T10:39:44Z https://hdl.handle.net/20.500.12809/10220 The Role of Media Literacy in Online Information: Searching Strategies Tatar, İsmail; Şahin, Yusuf Levent; Doğan, Ezgi Along with the spread of Web 2.0 technologies, individuals' habits such as learning, socializing, and getting information have changed rapidly. A lot of information, the accuracy of which cannot be trusted, is available in the web, and it becomes difficult to choose useful, relevant, and accurate information. This pollution is also present in the media. The abilities to choose messages in the media, to look at these messages critically, and to produce your own messages are considered among the 21st-century skills. These reasons bring media literacy (ML) and online information searching strategies (OISS) to the agenda. The processes of ML and OISS have interrelated features. Therefore, it is important and necessary to examine these concepts together. Based on this necessity, the aim of the study was to determine the role of ML in OISS. To this end, the data were collected from 1809 pre-service teachers using the OISS inventory and the ML level determination scale. Data were analyzed by descriptive statistics, MANOVA, and multiple regression analysis. According to the results, pre-service teachers' ML and OISS levels were above the moderate level. ML and OISS vary significantly according to the type of websites. In conclusion, ML was revealed as a predictor variable that could explain OISS at a rate of 33.2%. © 2022 Research Group Education and Virtual Learning (GREAV) 2022-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/9992 Gürsu, U. Rusça-Türkçe Lügat (Ahmed Sedad), Akademi Titiz Yayınları, İstanbul, 2016, 861 sayfa, ISBN: 978-605-4673-74-2 Eraslan, Ebubekir XIX. yüzyılla birlikte Osmanlı coğrafyasında eğitim, kültür, sanat vb. alanlarda gelişmelerin yoğun bir şekilde arttığı görülmektedir. Özellikle Osmanlı İmparatorluğu’nun yüzünü Tanzimat’la birlikte resmî olarak Batı’ya çevirmesiyle eskiden Arap ve Fars dillerine duyulan ilgi Batı dillerine doğru kaymıştır. 2022-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/9794 Wild mushrooms from Ilgaz Mountain National Park (Western Black Sea, Turkey): element concentrations and their health risk assessment Keskin, Feyyaz; Sarıkürkçü, Cengiz; Demirak, Ahmet; Akata, Ilgaz; Sıhoğlu Tepe, Arzuhan The purpose of this study was to determine Fe, Cd, Cr, Se, P, Cu, Mn, Zn, Al, Ca, Mg, and K contents of some edible (Chlorophyllum rhacodes, Clavariadelphus truncatus, Clitocybe nebularis, Hydnum repandum, Hygrophorus pudorinus, Infundibulicybe gibba, Lactarius deliciosus, L. piperatus, L. salmonicolor, Macrolepiota mastoidea, Russula grata, Suillus granulatus, and Tricholoma imbricatum), inedible (Amanita pantherina, Geastrum triplex, Gloeophyllum sepiarium, Hypholoma fasciculare, Phellinus vorax, Pholiota limonella, Russula anthracina, and Tapinella atrotomentosa), and poisonous mushroom species (Amanita pantherina and Hypholoma fasciculare) collected from Ilgaz Mountain National Park (Western Black Sea, Turkey). The element contents of the mushrooms were determined to be 18.0-1239.1, 0.2-4.6, 0.1-3.4, 0.2-3.2, 1.0-8.9, 3.3-59.9, 3.7-220.4, 21.3-154.1, 6.4-754.3, 15.8-17,473.0, 413.0-5943.0, and 2803.0-24,490.0 mg·kg-1, respectively. In addition to metal contents, the daily intakes of metal (DIM) and Health Risk Index (HRI) values of edible mushrooms were also calculated. Both DIM and HRI values of mushroom species except L. salmanicolor, M. mastoidea, and R. grata were within the legal limits. However, it was determined that the Fe content of L. salmanicolor and M. mastoidea and Cd content of R. grata were above the legal limits. 2022-01-01T00:00:00Z https://hdl.handle.net/20.500.12809/9763 Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models Gupta, Yogesh; Raghuwanshi, Ghanshyam; Ahmadini, Abdullah Ali H.; Göktaş, Pınar Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases (COVID-19). In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model. 2021-01-01T00:00:00Z