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Problem definition: As related party transactions (RPTs) increase in global supply chains, understanding the impact of corporate governance on such transactions becomes crucial for businesses. RPTs often lead to operational diversion due to power disparities between parent and its subsidiaries. In this study, we explore how operational diversion in RPTs within multinational firms is affected by the roles of foreign subsidiaries and corporate governance mechanisms. Methodology/results: Using a unique data set on RPTs of Korean multinational firms from 2006 to 2013, we compare the performance of multinational firms engaging in RPTs with two types of foreign subsidiaries: vertical and horizontal. We conduct our empirical analysis based on the adoption of International Financial Reporting Standards (IFRS) in Korea in 2011, which acts as a policy shock affecting corporate governance and deterring operational diversion. Our results show that the improvement in operational performance of a multinational firm following the IFRS adoption is more significant when the parent firm engages in transactions with vertical subsidiaries compared with horizontal ones. We further show that strong corporate governance mechanisms, such as internal governance, institutional ownership, and large shareholders, play a crucial role in restraining operational diversion in RPTs involving vertical subsidiaries. Managerial implications: The implications of our study extend to shareholders and auditors, highlighting the importance of prioritizing monitoring efforts concerning a parent firm's RPTs with vertical subsidiaries, especially when corporate governance mechanisms are weak. In contrast, RPTs with horizontal subsidiaries are relatively robust against operational diversion, making them a natural deterrent to such malpractices. Funding: This work was supported by the Institute of Management Research at Seoul National University, the Hankuk University of Foreign Studies Research Fund [2023], and the Research Fellowship Fund of the Sangnam Institute of Management, Yonsei University [2020-22-0007]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0372. [ABSTRACT FROM AUTHOR]

It is important to explore the characteristics and rules of atmospheric aerosol in the East Asian Sea for monitoring and evaluating atmospheric environmental quality. Based on Aerosol Robot Network (AERONET), Visible Infrared Imaging Radiometer (VIIRS), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the temporal and spatial variation characteristics and differences of aerosol parameters and types in the East Asian Sea were studied by using figure classification method (FIGCM), aerosol optical depth (AOD)440-Angstrom exponent (AE)440–870 method (AA1M), and AOD550-AE490-670 method (AA2M). The results show that the seasonal variation trend of aerosol characteristics and types is obvious in East Asia Sea. AOD, volume concentration (Cv), and aerosol effective radius (reff) in the Bohai-Yellow Sea and the Sea of Japan in autumn are lower than those in other seasons, and the occurrence frequency of ocean-type aerosols is high. Different from the Bohai-Yellow Sea and Sea of Japan, human activities in winter, summer, and autumn seriously affect the air quality in the East China Sea and South China Sea. Especially at the Taipei CWB site, from aerosol parameters and high biomass burning/urban industrial (BB/UI) aerosol, human activity is an important factor for high pollution at the Taipei CWB site. Aerosol types of AA1M, FIGCM, AA2M, and CALIPSO were compared at Anmyon and Yonsei University sites in the Bohai-Yellow Sea in March 2020. The results show that aerosol types based on threshold classification methods generally have higher mixed aerosol results, and the marine (MA) results of AA1M, FIGCM, and AA2M are close to the clean marine aerosol results of CALIPSO. Comparing the results of AA 2 M and CALIPSO on a spatial scale, it is found that the clean marine aerosol proportion identified by CALIPSO (0.38, 0.48, 0.82) is consistent with the MA proportion identified by AA 2 M (0.43, 0.46, 0.97) in the East China Sea, South China Sea, and Western Pacific Ocean. [ABSTRACT FROM AUTHOR]

This two‐part study presents a comprehensive analysis of (1) the vortex structure, (2) the inner core planetary boundary layer (PBL) wind profile, and (3) the overland surface winds of Typhoon Faxai(2019) during landfall observationally (Part I) and in high‐resolution Weather Research and Forecasting Model (WRF) simulations (Part II). Part II presents a framework for evaluating the wind field in the WRF simulations of Typhoon Faxai (2019) by comparing them with the observations presented in Part I. First, WRF simulations with two different surface roughness z0$$ {z}_0 $$ tables are presented. The simulation with the default z0$$ {z}_0 $$ table in WRF is shown to largely overestimate the surface wind speed. The overestimation is largely mitigated by using larger z0$$ {z}_0 $$ based on the climatology for urban and forest areas in Japan. Next, using the modified z0$$ {z}_0 $$ table, WRF simulations with three different PBL parametrizations, namely, Mellor–Yamada–Janjić, Yonsei University, and Mellor–Yamada–Nakanishi–Niino schemes are presented, and their impacts on the simulated vortex structure, inner core PBL wind profile, and overland surface winds are evaluated. In particular, the simulated surface winds are in good agreement with the observations outside the inner core of Typhoon Faxai. However, during the passage of the eyewall, the maximum surface winds are underestimated in the simulations. We show that the underestimation of the inertial stability, associated with the excessively large inner core of Typhoon Faxai in the simulations, led to the overestimation of the eyewall PBL jet height. This, in turn, caused the underestimation of the peak surface winds, as based on the empirical model of the wind reduction factor F2 km$$ {F}_{2\;\mathrm{km}} $$ proposed in Part I. [ABSTRACT FROM AUTHOR]

Lightning discharge from thunderstorms is a major weather hazard that leads to substantial loss of lives and properties in Bangladesh, particularly in the pre-monsoon season (March–May) due to frequent lightning activity. In this study, numerical simulations in predicting the lightning flashes using diagnostic and explicit lightning parameterization options in WRF (Weather Research and Forecasting) model are performed for three selected pre-monsoon lightning events (01 April 2019, 26 May 2020 and 20 May 2021) over Bangladesh. Additionally, this study investigates the WRF model sensitivity to five microphysics and three planetary boundary layer schemes. The combination of Morrison and YSU (Yonsei University scheme) is found to be the best configuration by comparing the RMSE (root mean square error) of hourly area averaged rainfall. The lightning flash counts are estimated by using four diagnostic methods based on (1) maximum updraft intensity ( w max ), (2) 20 dBZ cloud top, (3) level of neutral buoyancy, (4) Lightning Potential Index (LPI) conditioned to cloud hydrometeors and updraft, and (5) an explicit physics-based method from cloud electrification referred to as WRF-Elec. The WWLLN (World Wide Lightning Location Network) and NASA LIS (Lightning Imaging Sensor) observations are used to compare the simulated lightning flashes for the selected events. The study also analyzes 24-h (hour) accumulated rainfall that shows a good consistency with the observations from NASA GPM datasets. An assessment based on Fraction Skill Score (FSS) and performance diagrams is carried out to achieve deeper insights into the performance of model simulation in predicting rainfall. In a qualitative assessment framework, the spatial patterns of lightning flashes derived from WRF-Elec simulations, used for predicting the primary regions of lightning events, exhibit good agreement with observations. [ABSTRACT FROM AUTHOR]

OBJECTIVES: The limitations of current early warning scores have prompted the development of deep learning-based systems, such as deep learning-based cardiac arrest risk management systems (DeepCARS). Unfortunately, in South Korea, only two institutions operate 24-hour Rapid Response System (RRS), whereas most hospitals have part-time or no RRS coverage at all. This study validated the predictive performance of DeepCARS during RRS operation and nonoperation periods and explored its potential beyond RRS operating hours. DESIGN: Retrospective cohort study. SETTING: In this 1-year retrospective study conducted at Yonsei University Health System Severance Hospital in South Korea, DeepCARS was compared with conventional early warning systems for predicting in-hospital cardiac arrest (IHCA). The study focused on adult patients admitted to the general ward, with the primary outcome being IHCA-prediction performance within 24 hours of the alarm. PATIENTS: We analyzed the data records of adult patients admitted to a general ward from September 1, 2019, to August 31, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Performance evaluation was conducted separately for the operational and nonoperational periods of the RRS, using the area under the receiver operating characteristic curve (AUROC) as the metric. DeepCARS demonstrated a superior AUROC as compared with the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS), both during RRS operating and nonoperating hours. Although the MEWS and NEWS exhibited varying performance across the two periods, DeepCARS showed consistent performance. CONCLUSIONS: The accuracy and efficiency for predicting IHCA of DeepCARS were superior to that of conventional methods, regardless of whether the RRS was in operation. These findings emphasize that DeepCARS is an effective screening tool suitable for hospitals with full-time RRS, part-time RRS, and even those without any RRS. [ABSTRACT FROM AUTHOR]

Introduction: Metabolic dysfunction-associated fatty liver disease (MAFLD) is newly proposed nomenclature, and its diagnosis involves an algorithm that can be complicated and impractical for clinicians in real-world clinical settings. Thus, we investigated the association between MAFLD and modified triglycerideglucose index (TyG) indices to find a more concise, feasible method for predicting MAFLD in everyday clinical care. Methods: Data were obtained from people who voluntarily underwent health check-ups at the Health Promotion Centre of Gangnam Severance Hospital, Yonsei University College of Medicine, from January 2017 to October 2020. Four indices were analyzed: TyG-body to mass index (BMI), TyG-waist circumference (WC), TyG, and the fatty liver index (FLI). The odds ratios for MAFLD according to each index were calculated using multiple logistic regression analyses, and the receiver operating characteristics curve (ROC) and area under the ROC were obtained to find the predictive powers of each index. Results: The final number of study participants was 22,391, 8,246 with MAFLD and 14,145 without MAFLD. The odds ratios (95% confidence intervals) from TyGWC and TyG-BMI after adjusting for confounding variables were 12.484 (9.962-15.644) and 12.494 (9.790-15.946), respectively, for quartile 2, 54.332 (43.131-68.442) and 51.580 (40.495-65.699) for quartile 3, and 165.804 (130.243-211.076) and 128.592 (100.601-164.371) for quartile 4. The area under the ROC curve values for TyG-WC and TyG-BMI were 0.862 (0.857-0.867) and 0.867 (0.862-0.872), respectively. Conclusion: The modified TyG indices are highly reliable markers for predicting MAFLD that clinicians can easily and practically apply in everyday, real-world, clinical care settings. [ABSTRACT FROM AUTHOR]

An accurate wind resource dataset is required for assessing the potential energy yield of floating offshore wind farms that are expected along the California outer continental shelf. The National Renewable Energy Laboratory has developed and disseminated an updated wind resource dataset offshore of California, using the Weather Research and Forecasting Model, referred to as the CA20 dataset. As compared to buoy lidar measurements that have become available recently, the CA20 dataset showed significant positive biases for 100-m wind speeds along Northern California wind energy lease areas. To investigate the meteorological drivers for the model errors, we first consider two 1-yr simulations run with two different planetary boundary layer (PBL) parameterizations: the Mellor–Yamada–Nakanishi–Niino (MYNN) PBL scheme (the chosen configuration in the CA20 dataset) and the Yonsei University PBL scheme (which significantly reduces the bias in modeled winds). By comparing the 1-yr simulations to the concurrent lidar buoy observations, we find that errors are larger with the MYNN PBL scheme in warm seasons. We then dive deeper into the analysis by running simulations for short-term (3-day) case studies to evaluate the sensitivity of initial/boundary condition forcings on model results. By analyzing the short-term simulations, we find that during synoptic-scale northerly flows driven by the North Pacific high and inland thermal low, a coastal warm bias in the MYNN simulation is mainly responsible for the modeled wind speed bias by altering the boundary layer thermodynamics. The results of our analysis will help guide the creation of an updated version of the CA20 dataset. [ABSTRACT FROM AUTHOR]

This study simulated the wind energy density distribution in the Jiaodong Peninsula region using the Weather Research and Forecasting (WRF) Model. The impacts of different boundary-layer and near-surface parameterization schemes on the simulated wind speed and direction were investigated. The results indicate that the Yonsei University (YSU) scheme and the Quasi-Normal Scale Elimination (QNSE) scheme performed optimally for wind speed and wind direction. We also conducted a sensitivity test of the simulation results for atmospheric pressure, air temperature, and relative humidity. The statistical analysis showed that the YSU scheme performed optimally, while the MRF and BL schemes performed poorly. Following this, the wind energy distribution in the coastal hilly areas of the Jiaodong Peninsula was simulated using the YSU boundary-layer parameterization scheme. The modeled wind energy density in the mountainous and hilly areas of the Jiaodong Peninsula were higher than that in other regions. The wind energy density exhibits a seasonal variation, with the highest values in spring and early summer and the lowest in summer. In spring, the wind energy density over the Bohai Sea is higher than over the Yellow Sea, while the opposite trend is modeled in summer. [ABSTRACT FROM AUTHOR]

As the significance of knowledge convergence continues to grow, universities are making efforts to develop methods that promote multidisciplinary learning. To address this educational challenge, our paper applies network theory and text mining techniques to analyze university curricula and introduces a graphical syllabus rendering method. Visualizing the course curriculum provides a macro and structured perspective for individuals seeking alternative educational pathways within the existing system. By visualizing the relationships among courses, students can explore different combinations of courses with comprehensive search support. To illustrate our approach, we conduct a detailed demonstration using the syllabus database of Yonsei University. Through the application of our methods, we create visual course networks that reveal the underlying structure of the university curriculum. Our results yield insights into the interconnectedness of courses across various academic majors at Yonsei University. We present both macro visualizations, covering 18 academic majors, and visualizations for a few selected majors. Our analysis using Yonsei University's database not only showcases the value of our methodology but also serves as a practical example of how our approach can facilitate multidisciplinary learning. [ABSTRACT FROM AUTHOR]

Purpose: Patients with Turner syndrome (TS) have distinct neurocognitive and psychosocial characteristics. However, few clinical studies have reported neuropsychological findings in Korean patients. This study investigated the neurocognitive and psychosocial profiles of Korean children with TS. Methods: This retrospective cross-sectional study analyzed 20 pediatric patients (<18 years) with TS at the Department of Pediatric Endocrinology at Yonsei University Severance Children's Hospital in South Korea from January 2016 to March 2019. We selected 20 age- and sex-matched controls from among those who visited the endocrinology clinic and were confirmed to have no clinical abnormalities. All participants underwent several neuropsychological tests. Results: In the Korean Wechsler Intelligence Scale for Children-IV test, the Full-Scale Intelligence Quotient of the TS group was within the normal range. The Perceptual Reasoning Index, Working Memory Index, and Processing Speed Index scores were significantly lower in the TS group than in the control group. In contrast, the Verbal Comprehension Index did not differ significantly between the groups. The Comprehensive Attention Test results showed that the TS group displayed borderline visual selective attention. The social quotient score was significantly lower in the TS group than in the control group. Conclusion: Pediatric patients with TS in Korea displayed distinct neurocognitive and psychosocial characteristics. Patients in the TS group maintained their verbal function, but their attention, visuospatial function, and social competence were low. Our findings will contribute to the development of education programs for patients with TS to improve their neurocognitive and psychosocial functioning. [ABSTRACT FROM AUTHOR]

Considering the complex topographic forcing and large cryosphere concentration, the present study utilized the polar-optimized WRF model (Polar WRF) to conduct downscaling simulations over the Qinghai-Tibet Plateau (TP) and its surrounding regions. Multi-group experiments with the 10 km horizontal resolution are used to evaluate the modeling of precipitation. Firstly, on the basis of the model ground surface properties upgrade and the optimized Noah-MP, the “betterperforming” configuration suite for modeling precipitation is comprehensively examined. Various model parameters such as nudging options, five cumulus parameterization schemes, two planetary boundary layer schemes, and six microphysics schemes are investigated to further refine the Polar WRF configuration. Moreover, the precipitation simulation for a full calendar year is compared with multiple reanalyses and observations. The simulations demonstrate that the Polar WRF model successfully captures the general features of precipitation over this region and is sensitive to model parameters. Based on the results, it is recommended to use grid nudging with q intensity coefficient of 0.0002, the multi-scale kainfritsch cumulus parameterization, the Yonsei University boundary layer scheme, and the Morrison 2-mom microphysics with reduced default droplet concentration value of 100 cm–3. Overall, the model performance is better than the ERAinterim and TRMM 3b42. It is comparable to, and in some cases slightly better than, the CRA-Land, especially in the prediction for the western part of the plateau where in situ observations are limited, and the cryosphere-atmosphere interaction is more pronounced. [ABSTRACT FROM AUTHOR]