Background. To date, investigating respiratory disease patients visiting the emergency departments related with fined dust is limited. This study aimed to analyze the effects of two variable-weather and air pollution on respiratory disease patients who visited emergency departments. Methods. This study utilized the National Emergency Department Information System (NEDIS) database. The meteorological data were obtained from the National Climate Data Service. Each weather factor reflected the accumulated data of 4 days: a patients visit day and 3 days before the visit day. We utilized the RandomForestRegressor of scikit-learn for data analysis. Result. The study included 525,579 participants. This study found that multiple variables of weather and air pollution influenced the respiratory diseases of patients who visited emergency departments. Most of the respiratory disease patients had acute upper respiratory infections [J00J06], influenza [J09J11], and pneumonia [J12J18], on which PM₁₀ following temperature and steam pressure was the most influential. As the top three leading causes of admission to the emergency department, pneumonia [J12J18], acute upper respiratory infections [J00J06], and chronic lower respiratory diseases [J40J47] were highly influenced by PM₁₀. Conclusion. Most of the respiratory patients visiting EDs were diagnosed with acute upper respiratory infections, influenza, and pneumonia. Following temperature, steam pressure and PM₁₀ had influential relations with these diseases. It is expected that the number of respiratory disease patients visiting the emergency departments will increase by day 3 when the steam pressure and temperature values are low, and the variables of air pollution are high. The number of respiratory disease patients visiting the emergency departments will increase by day 3 when the steam pressure and temperature values are low, and the variables of air pollution are high.

Identifying agricultural fields that grow cabbage in the highlands of South Korea is critical for accurate crop yield estimation. Only grown for a limited time during the summer, highland cabbage accounts for a significant proportion of South Korea’s annual cabbage production. Thus, it has a profound effect on the formation of cabbage prices. Traditionally, labor-extensive and time-consuming field surveys are manually carried out to derive agricultural field maps of the highlands. Recently, high-resolution overhead images of the highlands have become readily available with the rapid development of unmanned aerial vehicles (UAV) and remote sensing technology. In addition, deep learning-based semantic segmentation models have quickly advanced by recent improvements in algorithms and computational resources. In this study, we propose a semantic segmentation framework based on state-of-the-art deep learning techniques to automate the process of identifying cabbage cultivation fields. We operated UAVs and collected 2010 multispectral images under different spatiotemporal conditions to measure how well semantic segmentation models generalize. Next, we manually labeled these images at a pixel-level to obtain ground truth labels for training. Our results demonstrate that our framework performs well in detecting cabbage fields not only in areas included in the training data but also in unseen areas not included in the training data. Moreover, we analyzed the effects of infrared wavelengths on the performance of identifying cabbage fields. Based on the results of our framework, we expect agricultural officials to reduce time and manpower when identifying information about highlands cabbage fields by replacing field surveys.

Semi-supervised learning (SSL) methods have shown promising results in solving many practical problems when only a few labels are available. The existing methods assume that the class distributions of labeled and unlabeled data are equal; however, their performances are significantly degraded in class distribution mismatch scenarios where out-of-distribution (OOD) data exist in the unlabeled data. Previous safe SSL studies have addressed this problem by making OOD data less likely to affect training based on labeled data. However, even if the studies effectively filter out the unnecessary OOD data, they can lose the basic information that all data share regardless of class. To this end, we propose to apply a self-supervised contrastive learning (SSCL) approach to fully exploit a large amount of unlabeled data. We also propose a contrastive loss function with a coefficient schedule to aggregate as an anchor the labeled negative examples of the same class into positive examples. To evaluate the performance of the proposed method, we conduct experiments on image classification datasets–CIFAR-10, CIFAR-100, Tiny ImageNet, and CIFAR-100+Tiny ImageNet—under various mismatch ratios. The results show that SSCL significantly improves classification accuracy, and our proposed loss function further enhances the performance, collectively outperforming existing methods by 2~9% across various benchmark datasets. The performance gains become more pronounced as dataset complexity increases and remain robust even in challenging cross-dataset scenarios.

Large language models (LLMs) increasingly serve as decision-support systems across linguistically diverse populations, yet whether they reason consistently across languages remains underexplored. We investigate whether LLMs exhibit language-dependent preferences in distributive justice scenarios and whether domain persona prompting can reduce cross-linguistic inconsistencies. Using six behavioral economics scenarios adapted from canonical social preferences research, we evaluate Gemini 2.0 Flash across English and Korean in both baseline and persona-injected conditions, yielding 1,201,200 observations across ten professional domains. Results reveal substantial baseline cross-linguistic divergence: five of six scenarios exhibit significant language effects (9–56 percentage point gaps), including complete preference reversals. Domain persona injection reduces these gaps by 62.7% on average, with normative disciplines (sociology, economics, law, philosophy, and history) demonstrating greater effectiveness than technical domains. Systematic boundary conditions emerge: scenarios presenting isolated ethical conflict resist intervention. These findings parallel human foreign-language effects in moral psychology while demonstrating that computational agents are more amenable to alignment interventions. We propose a compensatory integration framework explaining when professional framing succeeds or fails, providing practical guidance for multilingual LLM deployment, and establishing cross-linguistic consistency as a critical alignment metric.