Hallym Polytechnic University is a college located in Chuncheon, South Korea..
RSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization. This approach automates estimation of leaf area index, aboveground dry matter, and grain yield without extensive ground-based calibration. The system couples a Python data interface with a high-performance C simulation engine, enabling efficient regional-scale processing. Validation using independent datasets for rice, wheat, and maize demonstrated robust performance: yield Model Efficiency reached 0.99, with a minimum ME of 0.67 for wheat. The Bayesian prior regularization constrained parameter estimates while maintaining predictive accuracy. Regional applications in South Korea, North Korea, and the U.S. Corn Belt captured spatial yield gradients and inter-annual variability across millions of pixels. RSCM provides a computationally efficient tool bridging process-based modeling and remote sensing for precision agriculture and food security monitoring.
Depression is a major mental health concern among college students. However, limited research has examined Otaku status as a psychosocial factor associated with depression. This cross-sectional correlational survey study included 206 Korean college students recruited from online communities and content-related exhibitions. Depression was measured using the Patient Health Questionnaire-9 (PHQ-9), and Otaku status was quantitatively assessed using the Otaku Syndrome Scale. Participants were classified into the Otaku group and the Non-otaku group based on objective cut-off scores. Hierarchical multiple regression analyses were conducted to examine differences in depression after adjusting for self-esteem, college adjustment, academic stress, employment stress, and economic status. The Otaku group reported significantly lower levels of depression than the Non-otaku group (t = 2.05, p = 0.041). After adjusting for covariates, Otaku status remained a significant predictor of lower depression (B = -1.63, p = 0.004). Self-esteem and college adjustment showed the strongest associations with depression. Otaku status may be associated with lower depression levels among college students and should be considered a meaningful psychosocial factor in mental health promotion strategies.
Operating room nurses involved in DBD organ retrieval surgery face profound ethical dilemmas, emotional distress, and professional challenges in a practice area that remains comparatively under-examined. This study focused on donation after brain death (DBD) and explored operating room nurses' experiences during DBD organ retrieval surgery. This study aimed to explore the lived experiences of Korean operating room nurses involved in these procedures, with a focus on the meanings and impacts derived from their participation. Using a descriptive phenomenological approach, in-depth individual interviews were conducted with 10 nurses from two university hospitals in South Korea. Data were analyzed through Colaizzi's method, and four central themes emerged: confronting a sudden surge of disorientation; experiencing emotional detachment when confronted by stark reality; reflection on a dignified death; developing professionalism and ethics through inner growth. The findings underscore the need for nursing-focused education and support systems to sustain perioperative nurses' moral resilience and well-being.
Background/Objectives: Effective strategies to prevent inpatient falls are essential for reducing fall-related injuries, mortality, length of hospital stay, and healthcare costs. Although advanced technologies have increasingly been adopted for fall prevention, evidence regarding effectiveness in real-world clinical settings remains limited. This study evaluated the effect of implementing an artificial intelligence-assisted video monitoring system on the incidence of inpatient falls and fall-related injuries. Methods: This retrospective matched cohort study used electronic medical record data from a tertiary hospital in C city, South Korea. The system was implemented in January 2022. Patients admitted between 2020 and 2021 comprised the non-exposed group, whereas those admitted between 2023 and 2024 comprised the exposed group. Nearest neighbor propensity score matching based on age, sex, the number of diagnoses, and the number of ward days was performed to create comparable groups. Fall incidence rates per 1000 patient-days were calculated, and Firth’s penalized likelihood logistic regression and Cox proportional hazards regression with robust errors were conducted. Results: Propensity score matching yielded a 1:1 matched sample of 3002 cases per group. The fall incidence rate was 1.017 per 1000 patient-days in the exposed group, lower than 1.286 in the non-exposed group. However, penalized likelihood logistic regression and Cox proportional hazards regression revealed no statistically significant effect of artificial intelligence-assisted video monitoring on fall reduction. Conclusions: Artificial intelligence-assisted video monitoring was associated with a lower fall incidence, but no statistically significant effect was identified. These findings highlight the potential and limitations of artificial intelligence-assisted video monitoring for inpatient fall prevention and underscore the need for further research to enhance its clinical utility.
The risk of developing atherosclerotic cardiovascular disease (ASCVD) varies among individuals and is related to a variety of lifestyle factors in addition to the presence of chronic diseases. We aimed to assess the predictive accuracy of machine learning (ML) models incorporating lifestyle risk behaviors for ASCVD risk using Korean nationwide database. Utilizing data from the Korea National Health and Nutrition Examination Survey, five ML algorithms were employed for the prediction of high ASCVD risk: logistic regression, support vector machine, random forest, extreme gradient boosting (XGB), and light gradient boosting (LGB) models. ASCVD risk was assessed using the Pooled Cohort Equations, with a high-risk threshold of ≥7.5% over 10 years. Among the 8,573 participants aged 40–79 years, propensity score matching (PSM) was used to adjust for demographic confounders. We divided the dataset into a training and a test dataset in an 8:2 ratio. We also used bootstrapping to train the ML model with the area under the receiver operating characteristics curve (AUROC) score. Shapley additive explanations was used to identify the models’ important variables in assessing high ASCVD risks. In sensitivity analysis, we additionally performed binary logistic regression analysis, in which the ML model’s results were consistent with conventional statistical model. Of 8,573 participants, 41.7% had high ASCVD risk. Before PSM, age and sex differed significantly between groups. PSM (1:1) yielded 1,976 patients with balanced demographics. After PSM, the high ASCVD risk group had higher alcohol/tobacco use, lower omega-3 intake, higher BMI, less physical activity, and spent less time sitting. In 5 ML models, XGB model showed high AUROC values, with LGB model outperforming in accuracy, recall, and F1 score. Variable importance analysis using Shapley additive explanations identified smoking and age as the strongest predictors, while BMI, sodium or omega-3 intake, and LDL cholesterol also had significant variables. Sensitivity analysis using multivariable LR analysis also confirmed these findings, showing that smoking, BMI, and LDL cholesterol increased ASCVD risk, whereas omega-3 intake and physical activity were associated with lower risk. Analyzing lifestyle behavioral factors in ASCVD risk with ML improves predictive performance compared to traditional models. Personalized prevention strategies tailored to an individual’s lifestyle can effectively reduce ASCVD risk.
