IntroductionAlbuminuria is a well-known risk factor for end-stage kidney disease, all-cause mortality, and cardiovascular mortality, even when the albumin-to-creatinine ratio is <30 mg/g. However, the association between transiently observed trace albuminuria and these major adverse outcomes has not yet been reported. This study aimed to examine the effect of transient albuminuria on these major adverse outcomes using the National Health Insurance Service data in Korea.Methods and ResultsThe National Health Insurance Service-National Sample Cohort from Korea, followed from 2002 to 2015, consisted of 1,025,340 individuals, accounting for 2.2% of the total Korean population. We analyzed the effect of transient albuminuria on all-cause death, cardiovascular death, and incident chronic kidney disease (CKD) and compared it with the group without albuminuria. Among 1,025,340 individuals, 121,876 and 2,815 had transient albuminuria and no albuminuria, respectively. Adjusted hazard ratios of the transient albuminuria group for cardiovascular death and incident CKD were 1.76 (1.01–3.08) and 1.28 (1.15–1.43), respectively. There were significant differences in all-cause death, cardiovascular death, and incident CKD between the two groups after propensity score matching (p = 0.0037, p = 0.015, and p < 0.0001, respectively). Propensity score matching with bootstrapping showed that the hazard ratios of the transient albuminuria group for all-cause death and cardiovascular death were 1.39 (1.01–1.92) and 2.18 (1.08–5.98), respectively.ConclusionsIn this nationwide, large-scale, retrospective cohort study, transient albuminuria was associated with all-cause death, cardiovascular death, and incident CKD, suggesting that transient albuminuria could be a risk marker for adverse outcomes in the future, and that its own subclinical phenotype could play an important role during the course of CKD.

The Korean film market has been rapidly growing, and the importance of explainable artificial intelligence (XAI) in the film industry is also increasing. In this highly competitive market, where producing a movie incurs substantial costs, it is crucial for film industry professionals to make informed decisions. To assist these professionals, we propose DRECE (short for Dimension REduction, Clustering, and classification for Explainable artificial intelligence), an XAI-powered box office classification and trend analysis model that provides valuable insights and data-driven decision-making opportunities for the Korean film industry. The DRECE framework starts with transforming multi-dimensional data into two dimensions through dimensionality reduction techniques, grouping similar data points through K-means clustering, and classifying movie clusters through machine-learning models. The XAI techniques used in the model make the decision-making process transparent, providing valuable insights for film industry professionals to improve the box office performance and maximize profits. With DRECE, the Korean film market can be understood in new and exciting ways, and decision-makers can make informed decisions to achieve success.

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This study introduces HYTREM, a hybrid tree-based ensemble learning model conceived with the sustainable development of eco-friendly transportation and renewable energy in mind. Designed as a digital model, HYTREM primarily aims to enhance solar power generation systems’ efficiency via accurate solar irradiance forecasting. Its potential application extends to regions such as Jeju Island, which is committed to advancing renewable energy. The model’s development process involved collecting hourly solar irradiance and weather-related data from two distinct regions. After data preprocessing, input variables configuration, and dataset partitioning into training and testing sets, several tree-based ensemble learning models—including extreme gradient boosting, light gradient boosting machine, categorical boosting, and random forest (RF)—were employed to generate prediction values in HYTREM. To improve forecasting accuracy, separate RF models were constructed for each hour. Experimental results validated the superior performance of HYTREM over state-of-the-art models, demonstrating the lowest mean absolute error, root mean square error (RMSE), and normalized RMSE values across both regions. Due to its transparency and efficiency, this approach suits energy providers with limited computational resources. Ultimately, HYTREM is a stepping stone towards developing advanced digital twin systems, highlighting the importance of precise forecasting in managing renewable energy.

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