To effectively respond to the increasing damage caused by extreme rainfall due to climate change, it is crucial to accurately estimate the rainfall threshold that triggers heavy rain advisories and warnings. This study aims to simulate extreme rainfall patterns associated with monsoons and typhoons over the Korean Peninsula by using daily cumulative rainfall data with a 5km spatial resolution. First, a maximum threshold was determined to represent the characteristics of extreme rainfall data best and a Deep Convolutional Generative Adversarial Network (DCGAN) model was proposed to simulate such extreme rainfall events. The quality of the generated rainfall images was evaluated using metrics such as Frechet Inception Distance (FID), reconstruction loss, and Mean Absolute Percentage Error (MAPE). Analysis results indicated that setting the threshold at the top 0.01% of rainfall events achieved the most effective trade-off in preserving information. The DCGAN model proved useful for quantitative rainfall estimation in monsoon and typhoon scenarios, showing superior performance across all metrics, especially for events with rainfall amounts below 200mm/day, compared to conventional models. The generated images effectively captured localized rainfall patterns and are expected to provide valuable information for predicting and preparing for potential natural disasters in specific areas.

To effectively respond to the increasing damage caused by extreme rainfall due to climate change, it is crucial to accurately estimate the rainfall threshold that triggers heavy rain advisories and warnings. This study aims to simulate extreme rainfall patterns associated with monsoons and typhoons over the Korean Peninsula by using daily cumulative rainfall data with a 5km spatial resolution. First, a maximum threshold was determined to represent the characteristics of extreme rainfall data best and a Deep Convolutional Generative Adversarial Network (DCGAN) model was proposed to simulate such extreme rainfall events. The quality of the generated rainfall images was evaluated using metrics such as Frechet Inception Distance (FID), reconstruction loss, and Mean Absolute Percentage Error (MAPE). Analysis results indicated that setting the threshold at the top 0.01% of rainfall events achieved the most effective trade-off in preserving information. The DCGAN model proved useful for quantitative rainfall estimation in monsoon and typhoon scenarios, showing superior performance across all metrics, especially for events with rainfall amounts below 200mm/day, compared to conventional models. The generated images effectively captured localized rainfall patterns and are expected to provide valuable information for predicting and preparing for potential natural disasters in specific areas.

In spite of more awareness about the increased concern of women about their body image and shape, a scale measuring the level of concern about body shape has not been developed in Korea yet. The currently available Body Shape Questionnaire (BSQ) was developed and validated within a Western context. Therefore, this study created the Korean version of the BSQ (K-BSQ) to target young women with concern about body shape. This study aimed to translate the 16-item version of the BSQ and examine the reliability and validity of the resulting scale. Participants included 243 young women aged 19-39 years from South Korea. The reliability and validity were examined using Cronbach's alpha, item analysis, factor analysis, and correlation with the Eating Attitudes Test (EAT). Results showed that the reliability was high, as indicated by a Cronbach's alpha of 0.95. The factor analysis of 16 items revealed two factors. They explained 65.74 percent of the variance in the Korean version of the BSQ. In addition, K-BSQ scores were related to those of the EAT significantly (<i>r</i> = 0.61, <i>p</i> < .001). It was concluded that the K-BSQ may serve as an appropriate instrument to measure the body shape concerns of young Korean women.

The results suggest that chatbots have the potential to be used in nursing education. Further research is needed to clarify the effectiveness of using chatbots in nursing education.

Despite concerns regarding body image in young Korean women, no measurement tool has yet been developed or is available. This study examined the validity and reliability of the Korean version of the Body-Appreciation Scale (K-BAS) to assess body image positivity among young women. For this purpose, convenience sampling was conducted using social network services. The participants were 245 women (N = 245) aged 20 to 40 years. Validity and reliability were examined using item analysis, factor analysis, and correlation with body mass index, body dissatisfaction, and Cronbach’s alpha. Twelve items were selected for the study. Two factors were extracted through factor analysis, explaining 64.82% of the variance and showing a good model fit in the K-BAS. The K-BAS score was negatively correlated with body mass index (r = −0.33, p < 0.001) and body dissatisfaction (r = −0.41, p < 0.001). Reliability was high, as indicated by a Cronbach’s alpha of 0.91. These results indicate that the K-BAS may serve as an appropriate instrument for measuring body image positivity among young Korean women. It may also be useful for identifying women with abnormal body perceptions.

The results of this study suggest that chatbots can be used in violence prevention education for elementary school students.

The purpose of this study was to describe the trends of research on follow-up care after neonatal intensive care unit (NICU) graduation for children born preterm. This scoping review was conducted according to Arksey and O'Malley's guidelines. Reviewed studies were searched in PubMed, CHINAHL, and Web of Science. Fifteen studies were analyzed according to general characteristics, elements of follow-up care after NICU graduation, and characteristics of follow-up care intervention after NICU graduation. Most research was conducted in the medical field (60%), with experimental studies (40%) being the majority, and a few studies focused on families (3%) and parents (3%). The major follow-up care after NICU graduation elements were growth/developmental monitoring and support, continuity of care, parent- and family-centered elements, and a multidisciplinary approach. The intervention methods included home visits, phone calls, video calls, and applications. In addition, the intervention period ranged from two weeks to three years. It is suggested that multidisciplinary research with interactive media for a various age of children over longer periods for further study.

The purpose of this study was to test the effects of a child sexual abuse prevention education program using a hybrid application (CSAPE-H) by examining fifth-grade students' (a) knowledge for sexual abuse prevention and (b) self-protective behaviors against sexual abuse. A quasi-experimental, pretest /posttest design was used. Sixty-eight students from two schools participated. The intervention group (<i>n</i> = 48) received six sessions in CSA prevention education using the hybrid app, whereas the control group (<i>n</i> = 35) was taught using a lecture in a classroom environment. Between the two groups, there were significant differences in self-protective behaviors against CSA (<i>t</i> = 2.26, <i>p</i> = .027). In addition, educational satisfaction was significantly higher in the experimental group than the control group (<i>t</i> = 2.41, <i>p</i> = .019). A CSAPE-H may be effective for elementary school students, especially for self-protective behaviors.

Smartphone application use combined with hands-on practice could be an effective education method for breast self-examination.