ABSTRACT
We use many kinds of digital technologies in our daily life and they lead to radical changes. These technologies are recently being adopted by agriculture and food industry and their use in various applications is tested. The results of the studies conducted with the use of these technologies, especially IoT (internet of things)-based systems, are quite promising for the solution of the chronic problems of agriculture and food industry such as food-borne diseases, mycotoxin contaminations, pesticide residues, increasing waste, etc. Under extraordinary conditions, just like the ones we have recently experienced due to COVID-19 pandemic, IoT-based systems are crucial to ensure the sustainability of agriculture and food supply chain. In this review, the fundamentals of IoT-based systems and recent developments in their use in agriculture and food supply chain are explained. Based on the literature, examples of successful applications of IoT-based systems for irrigation efficiency, pesticide treatments, supply chain management etc. are given. Nowadays, there is a great demand for the integration of IoT-based systems into the present agricultural practices and supply chains and it seems to increase exponentially. Experts in electronics and computer sciences have achieved noteworthy success in the simulations. On the other hand, only a few studies have been conducted in real agricultural and food systems. However, IoT-based systems should be tested on-site and their success in practical applications should be proved. It is obvious that new era will be one in which IoT-based technologies and their tools will be more commonly used in agriculture and food supply. © 2023 Elsevier Ltd
ABSTRACT
The World Health Organization (WHO) defines a pandemic as the worldwide spread of a new disease, which stands for coronavirus disease 2019, Covid-19. Since December 2019, the Covid-19 causes infection of over 130 million people and over 3 million deaths by March 3, 2021. In this study, we use fuzzy stochastic differential equations (FSDEs) to describe population dynamics including fuzzyfied stochastic Richards growth model. Due to the lack of information or misleading information about the number of reported cases of Covid-19, we could think that these reported numbers given by authorities might not be very accurate and reliable. In this study, to solve FSDE, the Euler-Maruyama (EM) method which is one of the simplest numerical approximations between other methods for the FSDE is used. Also, fuzzy time series forecasting models have a great scope in the present area especially in the case of epidemic diseases projection. For this reason, Fuzzy ARIMA models are used for forecasting the future growth of Covid-19 in Turkey. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
This study was conducted to determine the relationship between Turkish nursing students’ intolerance of uncertainty and their depression, anxiety and stress levels during the COVID-19 outbreak. This descriptive and cross-sectional study was conducted between 18 and 29 May 2020, with the participation of 506 students. The data were collected using a Personal Information Form, the Intolerance of Uncertainty Scale (IUS-12) and the Depression, Anxiety and Stress Scale (DASS-21). It was found that the participants had a mean total IUS-12 score of 37.18±10.10. When the subscales of DASS-21 were examined, the participants’ mean scores were found to be 13.20±5.87 for the ‘depression’ sub-scale, 10.76±3.94 for the ‘anxiety’ sub-scale and 14.21±5.90 for the ‘stress’ sub-scale. It was ascertained that there were statistically significant differences in the mean scores obtained by the participants in the overall IUS-12 and DASS-21 sub-scales on the basis of the variables of ‘the nursing students’ academic achievements’ and ‘how the nursing students felt during the COVID-19 pandemic’ (p<0.05). It was discerned that there was a weak positive statistically significant relationship between the mean scores obtained in the overall IUS-12 and those obtained in the DASS-21 sub-scales (p<0.01). As the nursing students’ intolerance of uncertainty increased, their depression, anxiety and stress levels also went up. Psychological counseling should be offered to nursing students to prevent them from having mental disorders, to identify their mental issues and to help them cope with these mental problems. © 2021 The Author(s).