• Neonatal Medicine
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• v.16 no.2
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• pp.213-220
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• 2009

Purpose: Hearing loss is one of the common birth defects in humans, with a reported prevalence of 1-3 per 1000 newborns. We investigated the incidence of hearing loss and evaluated the use of neonatal hearing screening test in neonatal intensive care unit (NICU) graduates who are at greater risk for hearing loss than normal newborns. Methods: The neonates admitted to the NICU of Asan Medical Center from 1 March, 2003 to 30 March, 2008 who were available for follow-up were included. Those who failed the first auditory brainstem response prior to discharge were retested with the stapedial reflex test, auditory brainstem response and tympanometry in the Otolaryngology department. Results: Of 2,137 neonates, 2,000 (93.5%) neonates were tested prior to discharge. Sixty-seven neonates (3.4%) failed the first newborn hearing screening test. Of 67 infants, 52 infants were retested for a second hearing test. Excluding 10 infants (19.2%) who were lost during follow-up, 16 infants were confirmed to have hearing impairment of which 12 and 4 infants had unilateral and bilateral hearing loss, respectively. Of 16 infants, 5 did not meet the criteria set by the Korean National Health Insurance Corporation. Conclusion: The prevalence of hearing impairment in NICU graduates is about 0.8%, excluding those who were lost for follow up, necessitating a systemic and effective hearing assessment program among these high risk infants and more generous national insurance coverage.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.