본 연구는 챗GPT를 대학 교양영어 수업의 학습 도구로 활용하는 효과 적인 수업구성을 하는 것을 목표로 하고 있다. 구체적인 논의는 다음의 네 가지이다. 첫째, 교육계에서 챗GPT 사용에 관해 찬반 논쟁이 있음에 도 불구하고 생성형 AI가 교육계에 큰 변화를 가져올 것이라는 점에는 거의 이견이 없다. 둘째, 챗GPT를 사용함에 있어서 발생 가능한 문제점 을 팩트 체크의 부재, 지적 재산권의 문제, 문해력 저하로 정리하였다. 학습에서 챗GPT를 도구로 사용하기에 앞서서 이러한 문제점들을 학습자 들에게 반드시 인식시키는 교과과정을 만들어야 할 것이다. 셋째, 교육현 장에서 챗GPT를 사용하기 위해서 학습자에게 선제적으로 인식시켜야 할 점은 생성형 AI가 학습의 도구로서의 역할을 하며, 학습자가 인공지능에 의존하는 것이 아니라 인공지능과 협업을 한다는 것이다. 넷째, 학습능력 이 매우 상이한 학생집단을 대상으로 챗GPT를 학습도구로 사용하기 위 해서는 그룹별 협업을 통한 활동이 중요하다. 그룹원들의 다양한 질문을 인공지능에 주입하여 다채로운 결괏값을 얻어 지식을 창조적으로 (재)생 산할 수 있기 때문이다. 본 연구의 의의는 연구에서 제시하는 수업구성 을 실제 수업에 적용하여 또 다른 연구물을 도출할 수 있다는 점이다.

This study was conducted with the aim of confirming the impact and relative contribution of extreme weather to dry matter yield (DMY) of silage corn in the central inland region of Korea. The corn data (n=1,812) were obtained from various reports on the new variety of adaptability experiments conducted by the Rural Development Administration from 1978 to 2017. As for the weather variables, mean aerial temperature, accumulated precipitation, maximum wind speed, and sunshine duration, were collected from the Korean Meteorological Administration. The extreme weather was detected by the box plot, the DMY comparison was carried out by the t-test with a 5% significance level, and the relative contribution was estimated by R2 change in multiple regression modeling. The DMY of silage corn was reduced predominantly during the monsoon in summer and autumn, with DMY damage measuring 1,500-2,500 kg/ha and 1,800 kg/ha, respectively. Moreover, the relative contribution of the damage during the monsoons in summer and autumn was 40% and 60%, respectively. Therefore, the impact of autumn monsoon season should be taken into consideration when harvesting silage corn after late August. This study evaluated the effect of extreme weather on the yield damage of silage corn in Korea and estimated the relative contribution of this damage for the first time.

본 연구는 썹 시술이 여성의 이미지 변화에 어떤 영향을 미치는지 그리고 속눈썹 시술 이 여성의 뷰티 행동에 어떤 변화를 가져오는지 살펴보고자 하였다 또한 여성의 속눈썹 시술과 뷰티행동에 대한 실태조사를 통해 속눈썹 시술이 여성의 이미지변화에 미치는 영향 과 뷰티행동에 미치는 영향을 분석하고 그 결과를 바탕으로 여성의 뷰티행동에 대한 시사 점을 제시함으로써 여성의 뷰티행동에 대한 이해를 높이고 여성의 뷰티행동을 개선하는 데 기여하고 속눈썹 시술의 시장성 및 발전 방향을 제시를 연구의 목적으로 하였다 본 연 구를 위하여 서울지역 속눈썹 시술 전문 샵 방문자를 대상으로 설문조사를 통해 자료를 수 집하여 대상자의 일반적 특성과 속눈썹 시술 이용행태 시술 관련 인식 현황을 살펴보기 위해 빈도분석 을 실시하였다 또한 속눈썹 시술경험이 있는 조사대상 자를 대상으로 조사대상자의 특성과 속눈썹 시술 이용행태에 따른 시술 비용 수준 인식 차 이 만족도 차이 시술 후 미적 주관적 자신감 증대 차이 지속적 속눈썹 시술 의향 차이를 살펴보기 위해 카이제곱 검정을 실시하였다 본 연구결과 속눈썹 시술이 여성 의 외모와 심리적 상태에 긍정적인 영향을 미칠 수 있고 여성의 외모를 변화시켜 자신감과 자존감을 높이는 데 도움이 될 수 있고 이는 속눈썹 시술이 여성의 외모와 심리적 상 태에 긍정적인 영향을 미칠 수 있음을 시사하는 것으로 나타났다

본 연구는 우리나라에서 수수-수단그라스 교잡종 (sorghum bicolor L.: SSH)에 대해 극단기상과 정상기상 간 생산량을 비교할 목적으로 수행하였다. SSH 데이터 (n=1,025)는 농촌진흥청의 신품종 적응성 실험보고서(1979 ―2019)로부터 수집하였다. 기상자료는 기상청으로부터 평균기온, 최저기온, 최고기온, 최대 강수량, 누적 강수량, 최대풍속, 평균풍속 및 일조시간을 10일 기준으로 계산하 여 수집하였다. 극단기상과 정상기상 간 구별을 위해 상 자 그림을 이용하여 탐색하였다. 극단기상과 정상기상 간 생산량 차이는 5% 유의수준 하에서 t-검정 및 ANOVA를 통해 확인하였다. 그 결과, 극단기상은 극단적으로 강한 바람을 동반한 봄 가뭄, 극단적으로 높은 강우량을 기록 하는 여름장마와 가을장마가 두드러졌다. 예측 생산량 피 해(kg/ha)는 각각 1,961―6,541, 2,161―4,526 및 508― 5,582로 나타났다. 본 연구는 우리나라의 SSH에 대한 취 약성 및 피해 산정에 도움이 되는 기초자료로서 극단기상 과 정상기상 사이의 생산량 차이를 확인하는 데 의의가 있다.

This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMYnormal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.

This study was designed to evaluate the objective meat qualities of Hanwoo longissimus lumborum muscle after a period of long-term storage (40 days) in which conditions similar to those under which the meat would be stored for export to the Hong Kong beef market were simulated. Twelve LL muscles were sampled from animals slaughtered the previous day at a commercial beef export abattoir and assigned to one of three groups. Each group was subjected to a different packaging condition; Shrink film packed (SFP), vacuum packed (VP), or modified atmosphere packed (MAP)(O2/60%, CO2/40%). Objective meat qualities were assessed at day 1, 7, 21 and 40 of storage. Different Packaging conditions had no noticeable influence on cooking loss significantly. However, the moisture content in both the SFP and MAP groups tended to decline in a linear manner with storage periods throughout the 40 days period. Drip loss of MAP (5.68%) group was much higher in the SFP (3.18%) and VP (2.64%) groups at storage day 40. Redness (CIE a*) of meat color responded in a significant and completely different manner to each packaging method. Redness significantly (p<0.05) and continuously increased 17.51 at day 1 to 20.41 at day 40 in VP group, while MAP linearly dropped and ultimately reached 10.6 after 40 days of aging (p<0.05), at which point the meat had a brown color. Tests of Warner-Bratzler shear force (WBSF) indicated that the tenderness levels of the ready-for-export Hanwoo LL muscles were acceptable 7 days postmortem in the SFP and VP groups, however there was no significant difference between each group. Our gathered data suggests that the packaging method selected for export determines how well the objective qualities of the beef hold up, and indicate that VP is likely the most reliable method.

This study aimed to confirm the importance ratio of climate and management variables on production of orchardgrass in Korea (1982―2014). For the climate, the mean temperature in January (MTJ, ℃), lowest temperature in January (LTJ, ℃), growing days 0 to 5 (GD 1, day), growing days 5 to 25 (GD 2, day), Summer depression days (SSD, day), rainfall days (RD, day), accumulated rainfall (AR, mm), and sunshine duration (SD, hr) were considered. For the management, the establishment period (EP, 0―6 years) and number of cutting (NC, 2nd―5th) were measured. The importance ratio on production of orchardgrass was estimated using the neural network model with the perceptron method. It was performed by SPSS 26.0 (IBM Corp., Chicago). As a result, EP was the most important variable (100%), followed by RD (82.0%), AR (79.1%), NC (69.2%), LTJ (66.2%), GD 2 (63.3%), GD 1 (61.6%), SD (58.1%), SSD (50.8%) and MTJ (41.8%). It implies that EP, RD, AR, and NC were more important than others. Since the annual rainfall in Korea is exceed the required amount for the growth and development of orchardgrass, the damage caused by heavy rainfall exceeding the appropriate level could be reduced through drainage management. It means that, when cultivating orchardgrass, factors that can be controlled were relatively important. Although it is difficult to interpret the specific effect of climates on production due to neural networking modeling, in the future, this study is expected to be useful in production prediction and damage estimation by climate change by selecting major factors.

This study aimed to investigate the impacts of extreme weather on the dry matter yield (DMY) of silage maize in South Korea. The maize data (n=3,041) were collected from various reports of the new variety of adaptability experiments by the Rural Development Administration (1978-2017). Eight weather variables were collected: mean temperature, low temperature, high temperature, maximum precipitation, accumulated precipitation, maximum wind speed, mean wind speed, and sunshine duration. These variables were calculated based on ten days within seeding to harvesting period. The box plot detected an outlier to distinguish extreme weather from normal weather. The difference in DMY between extreme and normal weather was determined using a t-test with a 5% significance level. As a result, outliers of high-extreme precipitation were observed in July and August. Low-extreme mean temperature was remarkable in middle May, middle June, and late July. Moreover, the difference in DMY between extreme and normal weather was greatest (5,597.76 kg/ha) during the maximum precipitation in early July. This indicates that the impact of heavy rainfall during the Korean monsoon season was fatal to the DMY of silage maize. However, in this study, the frequency of extreme weather was too low and should not be generalize. Thus, in the future, we plan to compare DMY with statistical simulations based on extreme distributions.

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMYnormal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 ㎧). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

This study was carried out to compare the DMY (dry matter yield) of IRG (Italian ryegrass) in the southern coastal regions of Korea due to seasonal climate scenarios such as the Kaul-Changma (late monsoon) in autumn, extreme winter cold, and drought in the next spring. The IRG data (n = 203) were collected from various Reports for Collaborative Research Program to Develop New Cultivars of Summer Crops in Jeju, 203 Namwon, and Yeungam from the Rural Development Administration (1993 – 2013). In order to define the seasonal climate scenarios, climate variables including temperature, humidity, wind, sunshine were used by collected from the Korean Meteorological Administration. The discriminant analysis based on 5% significance level was performed to distinguish normal and abnormal climate scenarios. Furthermore, the DMY comparison was simulated based on the information of sample distribution of IRG. As a result, in the southern coastal regions, only the impact of next spring drought on DMY of IRG was critical. Although the severe winter cold was clearly classified from the normal, there was no difference in DMY. Thus, the DMY comparison was simulated only for the next spring drought. Under the yield comparison simulation, DMY (kg/ha) in the normal and drought was 14,743.83 and 12,707.97 respectively. It implies that the expected damage caused by the spring drought was about 2,000 kg/ha. Furthermore, the predicted DMY of spring drought was wider and slower than that of normal, indicating on high variability. This study is meaningful in confirming the predictive DMY damage and its possibility by spring drought for IRG via statistical simulation considering seasonal climate scenarios.

This study aimed to analyze causality of climatic factors that affecting the yield of whole crop barley (WCB) by constructing a network within the natural ecosystem via the structural equation model. The WCB dataset (n=316) consisted of data on the forage information and climatic information. The forage information was collected from numerous experimental reports from New Cultivars of Winter Crops (1993-2012) and included details of fresh and dry matter yield, and the year and location of cultivation. The climatic information included details of the daily mean temperature, precipitation, and sunshine duration from the weather information system of the Korea Meteorological Administration. The variables were growing days, accumulated temperature, precipitation, and sunshine duration in the season for the period of seeding to harvesting. The data was collected over 3 consecutive seasons—autumn, winter, and the following spring. We created a causality network depicting the effect of climatic factors on production by structural equation modeling. The results highlight: (i) the differences in the longitudinal effects between autumn and next spring, (ii) the factors that directly affect WCB production, and (iii) the indirect effects by certain factors, via two or more paths. For instance, the indirect effect of precipitation on WCB production in the following spring season via its effect on temperature was remarkable. Based on absolute values, the importance of WCB production in decreasing order was: the following spring temperature (0.45), autumn temperature (0.35), wintering (-0.16), and following spring precipitation (0.04). Therefore, we conclude that other climatic factors indirectly affect production through the final pathway, temperature and growing days in the next spring, in the climate-production network for WCB including temperature, growing days, precipitation and sunshine duration.

본 연구는 기계학습을 통한 수량예측모델을 이용하여 이상기상에 따른 WCM의 DMY 피해량을 산출하기 위한 목적으로 수행하였다. 수량예측모델은 WCM 데이터 및 기상 데이터를 수집 후 가공하여 8가지 기계학습을 통해 제작하였으며 실험지역은 경기도로 선정하였다. 수량예측모델은 기계학습 기법 중 정확성이 가장 높은 DeepCrossing (R2=0.5442, RMSE=0.1769) 기법을 통해 제작하였다. 피해량은 정상기상 및 이상기상의 DMY 예측값 간 차이로 산출하였다. 정상기상에서 WCM의 DMY 예측값은 지역에 따라 차이가 있으나 15,003~17,517 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 DMY 예측 값은 지역 및 각 이상기상 수준에 따라 차이가 있었으며 각각 14,947~17,571 kg/ha, 14,986~17,525 kg/ha 및 14,920~17,557 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 피해량은 각각 –68~89 kg/ha, -17~17 kg/ha 및 – 112~121 kg/ha 범위로 피해로 판단할 수 없는 수준이었다. WCM의 정확한 피해량을 산출하기 위해서는 수량예측모델에 이용하는 이상기상 데이터 수의 증가가 필요하다.

This study aimed to determine the trend in dry matter yield (DMY) of a new sorghum-sudangrass hybrid (SSH) in the central inland regions of Korea. The metadata (n=388) were collected from various reports of the experiments examining the adaptability of this new variety conducted by the Rural Development Administration (1988–2013). To determine the trend, the parameters of autoregressive (AR) and moving average (MA) were estimated from correlogram of Autocorrelation function (ACF) and partial ACF (PACF) using time series modeling. The results showed that the trend increased slightly year by year. Furthermore, ARIMA (1, 1, 0) was found to be the optimal model to describe the historical trend. This means that the trend in the DMY of the SSH was associated with changes over the past two years but not with changes from three years ago. Although climatic variables, such as temperature, precipitation, and sunshine were also considered as environmental factors for the annual trends, no clear association was observed between DMY and climates. Therefore, more precise processing and detailed definition of climate considering specific growth stages are required to validate this association. In particular, research on the impact of heavy rainfall and typhoons, which are expected to cause damage in the short term, on DMY trends is ongoing, and the model confirmed in this study is expected to play an important role in studying this aspect. Furthermore, we plan to add the environmental factors such as soil and cultivation management as well as climate to our future studies.

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

본 연구는 실습 교육기간에 따른 실습생의 STCW 협약에 의거한 해기능력 향상 정도를 파악하기 위해 H대학교 실습선에 승선 한 항해계열 학생들을 대상으로 50개의 평가항목에 대하여 자기평가 방법의 설문조사를 수행하였다. 이를 통해 해기기능과 해기능력의 기본적인 이해도를 평가하고, 승선 기간의 경과에 따라 이러한 이해도가 어떻게 변화하는지 심층분석을 실시하였다. 해기기능과 해기능력의 평가시기에 따른 이해도 변화 분석 결과, 해기기능의 경우 1학기와 2학기 승선그룹 모두에서 3차 평가로 갈수록 이해도가 대체로 향 상되는 것으로 나타났다. 또한, 각 학기 승선그룹별로 이해도가 낮은 분야가 도출되었으며, 실습선 교육 과정에 저조한 해기능력에 대한 추가적인 학습 계획이 필요할 것으로 판단된다. 본 연구로 도출된 실습선 교육 기간에 따른 해기능력 향상 정도에 대한 분석 결과는 추후 실습선 및 해사대학 교육 과정 개편에 활용될 수 있을 것으로 사료된다.