• Ecology and Resilient Infrastructure
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• v.10 no.1
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• pp.11-19
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• 2023

Alien species are known to threaten regional biodiversity globally, which has increased global interest regarding introduction of alien species. The Ministry of Environment of Korea designated species that have not yet been introduced into the country with potential threat as alert alien species to prevent damage to the ecosystem. In this study, potential habitats of Esox lucius and Maccullochella peelii, which are predatory and designated as alert alien fish, were predicted on a national basis. Habitat suitability was evaluated using EHSM (Ecological Habitat Suitability Model), and water temperature data were input to calculate Physiological Habitat Suitability (PHS). The prediction results have shown that PHS of the two fishes were mainly controlled by heat or cold stress, which resulted in biased habitat distribution. E. lucius was predicted to prefer the basins at high latitudes (Han and Geum River), while M. peelii preferred metropolitan areas. Through these differences, it was expected that the invasion pattern of each alien fish can be different due to thermal preference. Further studies are required to enhance the model's predictive power, and future predictions under climate change scenarios are required to aid establishing sustainable management plans.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Journal of Industrial Convergence
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• v.21 no.11
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• pp.13-27
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• 2023

This study aims to contribute to the reduction of the failure rate and social costs resulting from business failures by analyzing factors that affect the insolvency of social enterprises, as the role of social enterprises is increasing in our economy. The data used in this study were classified as normal and insolvent companies among social enterprises (including prospective social enterprises) that were established between 2009 and 2018 and received credit guarantees from credit guarantee institutions as of the end of June 2022. Among the collected data, 439 social enterprises with available financial information were targeted; 406 (92.5%) were normal enterprises, and 33 (7.5%) were insolvent enterprises. Through a literature review, eight non-financial factors commonly used for insolvency prediction were selected. The cross-analysis results showed that four of these factors were significant. Logistic regression analysis revealed that two variables, including corporate credit rating and the personal credit rating of the representative, were significant. Financial factors such as debt ratio, sales operating profit rate, and total asset turnover were used as control variables. The empirical analysis confirmed that the two independent variables maintained their influence even after controlling for financial factors. Given that government-led support and development policies have limitations, there is a need to shift policy direction so that various companies aspiring to create social value can enter the social enterprise sector through private and regional initiatives. This would enable the social economy to create an environment where local residents can collaborate to realize social value, and the government should actively support this.

• Journal of Trauma and Injury
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• v.37 no.1
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• pp.37-47
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• 2024

Purpose: This study aimed at developing a novel tube thoracostomy technique using the sternum, a fixed anatomical structure, as an indicator to reduce the possibility of incorrect chest tube positioning and complications in patients with chest trauma. Methods: This retrospective study analyzed the data of 184 patients with chest trauma who were aged ≥18 years, visited a single regional trauma center in Korea between April and June 2022, and underwent chest computed tomography (CT) with their arms down. The conventional gold standard, 5th intercostal space (ICS) method, was compared to the lower 1/2, 1/3, and 1/4 of the sternum method by analyzing CT images. Results: When virtual tube thoracostomy routes were drawn at the mid-axillary line at the 5th ICS level, 150 patients (81.5%) on the right side and 179 patients (97.3%) on the left did not pass the diaphragm. However, at the lower 1/2 of the sternum level, 171 patients (92.9%, P<0.001) on the right and 182 patients (98.9%, P= 0.250) on the left did not pass the diaphragm. At the 5th ICS level, 129 patients (70.1%) on the right and 156 patients (84.8%) on the left were located in the safety zone and did not pass the diaphragm. Alternatively, at the lower 1/2, 1/3, and 1/4 of the sternum level, 139 (75.5%, P=0.185), 49 (26.6%, P<0.001), and 10 (5.4%, P<0.001), respectively, on the right, and 146 (79.3%, P=0.041), 69 (37.5%, P<0.001), and 16 (8.7%, P<0.001) on the left were located in the safety zone and did not pass the diaphragm. Compared to the conventional 5th ICS method, the sternum 1/2 method had a safety zone prediction sensitivity of 90.0% to 90.7%, and 97.3% to 100% sensitivity for not passing the diaphragm. Conclusions: Using the sternum length as a tube thoracostomy indicator might be feasible.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.3
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• pp.209-218
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• 2024

The National Institute of Agricultural Sciences (NAS) operates in-house long-range climate forecasting system to support the agricultural use of climate forecast data. This system, developed through collaborative research with Pusan National University, is based on the PNU/RDA Coupled General Circulation Model (CGCM) and includes the regional climate model WRF (Weather Research and Forecasting). It generates detailed climate forecast data for periods ranging from 1 to 6 months, covering 20 key variables such as daily maximum, minimum, and average temperatures, precipitation, and agricultural meteorological elements like solar radiation, soil moisture, and ground temperature-factors essential for agricultural forecasting. The data are provided at a daily temporal resolution with a spatial resolution of a 5km grid, which can be used in point form (interpolated) or averaged across administrative regions. The system's seasonal temperature and precipitation forecasts align closely with observed climatological data, accurately reflecting spatial and topographical influences, confirming its reliability. These long-range forecasts from NAS are expected to offer valuable insights for agricultural planning and decision-making. The detailed forecast data can be accessed through the Climate Change Assessment Division of NAS.

• Journal of Trauma and Injury
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• v.37 no.3
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• pp.201-208
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• 2024

Purpose: The number of elderly patients with trauma is increasing; therefore, precise models are necessary to estimate the mortality risk of elderly patients with trauma for informed clinical decision-making. This study aimed to develop machine learning based predictive models that predict 30-day mortality in severely injured elderly patients with trauma and to compare the predictive performance of various machine learning models. Methods: This study targeted patients aged ≥65 years with an Injury Severity Score of ≥15 who visited the regional trauma center at Chungbuk National University Hospital between 2016 and 2022. Four machine learning models-logistic regression, decision tree, random forest, and eXtreme Gradient Boosting (XGBoost)-were developed to predict 30-day mortality. The models' performance was compared using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, specificity, F1 score, as well as Shapley Additive Explanations (SHAP) values and learning curves. Results: The performance evaluation of the machine learning models for predicting mortality in severely injured elderly patients with trauma showed AUC values for logistic regression, decision tree, random forest, and XGBoost of 0.938, 0.863, 0.919, and 0.934, respectively. Among the four models, XGBoost demonstrated superior accuracy, precision, recall, specificity, and F1 score of 0.91, 0.72, 0.86, 0.92, and 0.78, respectively. Analysis of important features of XGBoost using SHAP revealed associations such as a high Glasgow Coma Scale negatively impacting mortality probability, while higher counts of transfused red blood cells were positively correlated with mortality probability. The learning curves indicated increased generalization and robustness as training examples increased. Conclusions: We showed that machine learning models, especially XGBoost, can be used to predict 30-day mortality in severely injured elderly patients with trauma. Prognostic tools utilizing these models are helpful for physicians to evaluate the risk of mortality in elderly patients with severe trauma.

• The Korean Journal of Nuclear Medicine
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• v.32 no.6
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• pp.509-515
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• 1998

Purpose: The aim of this study was to evaluate whether T1-201 reinjection distinguishes viable from non-viable myocardium in patients with reverse redistribution after acute myocardial infarction. Materials and Methods: We studied 42 patients with acute myocardial infarction (age, $55{\pm}12$ years). Eighteen (43%) out of 42 showed reverse redistribution on dipyridamole stress-4 hour redistribution T1-201 single photon emission computed tomography (SPECT). T1-201 reinjection was performed at 24 hours. Reverse redistribution was defined as worsening of perfusion defect at 4 hour delayed scan. All patients underwent follow-up echocardiography in 4 months to assess regional wall motion improvement. T1-201 uptake on reinjection images were analyzed for the prediction of myocardial wall motion improvement. Results: Of 36 segments with reverse redistribution, 17 segments showed normal wall motion on echocardiography, while 19 segments showed wall motion abnormalities. Of 19 the segments with reverse redistribution, 11 (58%) showed enhanced uptake after 24 hour reinjection. Myocardial wall motion was improved in 10 of 11 segments (90%) with enhanced uptake on reinjection. Wall motion improvement was not seen in 5 of 8 segments (63%) without enhanced thallium uptake. When myocardial viability was assessed by the uptake on reinjection image, nine of 10 segments (90%) with normal or mildly decreased uptake showed improved wall motion. Wall motion was not improved in 5 of 9 segments (16%) with severely decreased uptake. Conclusion: In patients with acute myocardial infarction, T1-201 reinjection imaging on myocardial segments with reverse redistribution has a high positive predictive value in the assessment of myocardial viability.

• Journal of the Korean earth science society
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• v.24 no.6
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• pp.558-567
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• 2003

The regional dependency of cloud-radiative forcing at the top of atmosphere is studied using ERBE (Earth Radiation Budget Experiment), ISCCP (International Satellite Cloud Climatology Project) and NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data for 60 months from January 1985 to December 1989 over tropical ocean. In the interannual time scale, the dependency of cloud-radiative forcing on the sea surface temperature over the equatorial eastern Pacific ocean is about 7.4Wm$^{-2}$K$^{-1}$ for longwave radiation and about -4.4Wm$^{-2}$K$^{-1}$ for shortwave radiation, respectively. This shows that the net cloud-radiative forcing due to the increase of sea surface temperature over the equatorial eastern Pacific ocean heats the atmosphere. But the dependency is reversed over tropical oceans with -3.4Wm$^{-2}$K$^{-1}$ for longwave and 1.9WmWm$^{-2}$K$^{-1}$ for shortwave radiation, indicating that the net cloud-radiative forcing cools the atmosphere over tropical oceans. In raw data including seasonal cycle, the dependency of cloud-radiative forcing over the equatorial eastern Pacific ocean is very similar to that in interannual time scale in both the magnitude and the sign. But the dependency of cloud-radiative forcing on the sea surface temperature over tropical oceans is about 0.2Wm$^{-2}$K$^{-1}$ for longwave and 2.7Wm$^{-2}$K$^{-1}$ for shortwave radiation, respectively. These results represent that the role of seasonal cycle on the cloud radiative forcing is gradually more important than role of interannual time scale as the ocean area is broadening from the tropical central Pacific to the tropical ocean.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.20 no.3
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• pp.119-130
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• 2015

Impacts of Argo temperature assimilation on the analysis fields in the East Sea is investigated by using DAESROM, the East Sea Regional Ocean Model with a 3-dimensional variational assimilation module (Kim et al., 2009). Namely, we produced analysis fields in 2009, in which temperature profiles, sea surface temperature (SST) and sea surface height (SSH) anomaly were assimilated (Exp. AllDa) and carried out additional experiment by withdrawing Argo temperature data (Exp. NoArgo). When comparing both experimental results using assimilated temperature profiles, Root Mean Square Error (RMSE) of the Exp. AllDa is generally lower than the Exp. NoArgo. In particular, the Argo impacts are large in the subsurface layer, showing the RMSE difference of about $0.5^{\circ}C$. Based on the observations of 14 surface drifters, Argo impacts on the current and temperature fields in the surface layer are investigated. In general, surface currents along the drifter positions are improved in the Exp. AllDa, and large RMSE differences (about 2.0~6.0 cm/s) between both experiments are found in drifters which observed longer period in the southern region where Argo density was high. On the other hand, Argo impacts on the SST fields are negligible, and it is considered that SST assimilation with 1-day interval has dominant effects. Similar to the difference of surface current fields between both experiments, SSH fields also reveal significant difference in the southern East Sea, for example the southwestern Yamato Basin where anticyclonic circulation develops. The comparison of SSH fields implies that SSH assimilation does not correct the SSH difference caused by withdrawing Argo data. Thus Argo assimilation has an important role to reproduce meso-scale circulation features in the East Sea.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.3
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• pp.111-121
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• 2013

Accurate prediction of sea water temperature has been emphasized to make precise local weather forecast and to understand change of ecosystem. The Yellow Sea, which has turbid water and strong tidal current, is an unique shallow marginal sea. It is essential to include the effects of the turbidity and the strong tidal mixing for the realistic simulation of temperature distribution in the Yellow Sea. Evaluation of ocean circulation model response to vertical mixing scheme and turbidity is primary objective of this study. Three-dimensional ocean circulation model(Regional Ocean Modeling System) was used to perform numerical simulations. Mellor- Yamada level 2.5 closure (M-Y) and K-Profile Parameterization (KPP) scheme were selected for vertical mixing parameterization in this study. Effect of Jerlov water type 1, 3 and 5 was also evaluated. The simulated temperature distribution was compared with the observed data by National Fisheries Research and Development Institute to estimate model's response to turbidity and vertical mixing schemes in the Yellow Sea. Simulations with M-Y vertical mixing scheme produced relatively stronger vertical mixing and warmer bottom temperature than the observation. KPP scheme produced weaker vertical mixing and did not well reproduce tidal mixing front along the coast. However, KPP scheme keeps bottom temperature closer to the observation. Consequently, numerical ocean circulation simulations with M-Y vertical mixing scheme tends to produce well mixed vertical temperature structure and that with KPP vertical mixing scheme tends to make stratified vertical temperature structure. When Jerlov water type is higher, sea surface temperature is high and sea bottom temperature is low because downward shortwave radiation is almost absorbed near the sea surface.