• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.366-373
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• 2021

This study attempted to establish a field data-based write analysis standard by analyzing field observation data, which is non-linear data of southern garlic. Five regions, including Goheung, Namhae, Sinan, Changnyeong, and Haenam, were selected for analysis. Observation values for each observation station were extracted from the temperature data of farmland in the region through inverse distance weighted. Southern-type garlic production and temperature data were collected for 10 years, from 2010 to 2019. Local regression analysis (Kernel) of the obtained data was performed, and growth temperatures were analyzed, such as 0.8 (18.781℃), 0.9 (18.930℃), 1.0 (19.542℃), 1.1 (20.165℃), and 1.2 (21.042℃) depending on the bandwidth. The analyzed optimum temperature and the grown temperature (4℃/25℃) were applied to extract the growth temperature for each temperature by using the temperature response model analysis. Regression analysis and correlation analysis were performed between the analyzed growth temperature and production data. The coefficient of determination(R²) was analyzed as 0.325 to 0.438, and in the correlation analysis, the correlation coefficient of 0.57 to 0.66 was analyzed at the significance probability 0.001 level. Overall, as the bandwidth increased, the coefficient of determination was higher. However, in all analyses except bandwidth 1.0, it was analyzed that all variables were not used due to bias. The purpose of this study is to accommodate all data through non-linear data. It was analyzed that bandwidth 1.0 with a high coefficient of determination while accepting modeling as a whole is the most suitable.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.337-347
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• 2021

This study is a qualitative study conducted to provide basic data for therapists working in long-term care hospitals to improve their countermeasure strategies for new infectious diseases and improvement of the treatment room infection management system, and to help therapists understand their infection management work. Colaizzi's phenomenological research method was applied as the research method. Telephone interviews were conducted with nine occupational therapists and physical therapists working in nursing hospitals. The contents of the interview were recorded with the consent of the study subjects, and additional confirmation was received by email. The recorded content was analyzed after transcription, and the meaning and nature of the experience coping with COVID-19 were described. The content was organized into 6 themes, 17 main meaning and 49 meaning units. In accordance with the COVID-19 situation, the infection control system has been strengthened by reinforcing infection control education in nursing hospitals, practicing infection control, and supervising infection control outside business hours. It was found that the treatment environment was changed due to the restriction of treatment activities by practicing distancing in the treatment room, adjusting rest and meal times during working hours, and strengthening infection control. In addition, the therapist's role has been expanded and the paradigm of treatment has changed, such as considering the untact intervention, and they have experienced cohort quarantine, pre-tested for COVID-19, vaccinations, and side effects from COVID-19. However, due to the infection work, the therapist's work burden is increased, and the person is experiencing fear, depression, and work stress from the spread of COVID-19. They were also aware of the need for nursing hospital care personnel support, such as guaranteeing rest after vaccination and providing infection control tools and equipment. The results of this study are expected to be used as basic data for human and physical support for the development of infectious disease response strategy programs in nursing hospital treatment rooms and for infection control in nursing hospitals.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.