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.

Sea fog can seriously affect schedules and safety by reducing visibility during marine transportation. Therefore, the forecasting of sea fog is an important issue in preventing accidents. Recently, in order to forecast sea fog, several deep learning methods have been applied to time series data consisting of meteorological and oceanographic observations or image data to predict fog. However, these methods only use a single image without considering meteorological and temporal characteristics. In this study, we propose a multi-modal learning method to improve the forecasting accuracy of sea fog using convolutional neural network (CNN) and gated recurrent unit (GRU) models. CNN and GRU extract useful features from closed-circuit television (CCTV) images and multivariate time series data, respectively. CCTV images and time series data collected at Daesan Port in South Korea from 1 March 2018 to 14 February 2021 by Korea Hydrographic and Oceanographic Agency (KHOA) were used to evaluate the proposed method. We compare the proposed method with deep learning methods that only consider temporal information or spatial information. The results indicate that the proposed method using both temporal and spatial information at the same time shows superior accuracy.

Skeleton-based human action recognition (HAR) plays an important role in video analytics and recognition systems, with the goal of accurately identifying human actions in videos. However, large-scale action annotation is costly, which has led to the growing interest in HAR research using self-supervised learning (SSL). While existing SSL studies have focused on extracting global information from skeleton sequences, they often overlook local information that captures the relationships between joints and their subtle movements over time. In this study, we propose an SSL-based HAR framework called coarse-to-fine spatiotemporal representation masking (CFSEM) that effectively learns global, local, and temporal information within skeletal. CFSEM captures not only global information in the skeleton using body- and part-level masking but also fine-grained movements using hand masking. In addition, temporal-axis shuffling is introduced into the proposed framework to account for temporal patterns inherent in skeleton sequences. To further enhance the learning process, the loss function is redefined using a cross-correlation matrix, introducing a non-contrastive SSL approach. Experiments on various datasets were conducted to evaluate the proposed framework against baseline methods. Experimental results showed the superior performance of CFSEM and highlighted the possibility of training HAR models using less labeled data, offering the potential to effectively develop HAR models for various industries.