• Korean Journal of Radiology
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• v.22 no.12
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• pp.1996-2005
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• 2021

Objective: To investigate the multidetector computed tomography (MDCT) features of fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) with germline or somatic mutations, and compare them with those of papillary type II RCC (pRCC type II). Materials and Methods: A total of 24 patients (mean ± standard deviation, 40.4 ± 14.7 years) with pathologically confirmed FH-deficient RCC (15 with germline and 9 with somatic mutations) and 54 patients (58.6 ± 12.6 years) with pRCC type II were enrolled. The MDCT features were retrospectively reviewed and compared between the two entities and mutation subgroups, and were correlated with the clinicopathological findings. Results: All the lesions were unilateral and single. Compared with pRCC type II, FH-deficient RCC was more prevalent among younger patients (40.4 ± 14.7 vs. 58.6 ± 12.6, p < 0.001) and tended to be larger (8.1 ± 4.1 vs. 5.4 ± 3.2, p = 0.002). Cystic solid patterns were more common in FH-deficient RCC (20/24 vs. 16/54, p < 0.001), with 16 of the 20 (80.0%) cystic solid tumors having showed typical polycystic and thin smooth walls and/or septa, with an eccentric solid component. Lymph node (16/24 vs. 16/54, p = 0.003) and distant (11/24 vs. 3/54, p < 0.001) metastases were more frequent in FH-deficient RCC. FH-deficient RCC and pRCC type II showed similar attenuation in the unenhanced phase. The attenuation in the corticomedullary phase (CMP) (76.3% ± 25.0% vs. 60.2 ± 23.6, p = 0.008) and nephrographic phase (NP) (87.7 ± 20.5, vs. 71.2 ± 23.9, p = 0.004), absolute enhancement in CMP (39.0 ± 24.8 vs. 27.1 ± 22.7, p = 0.001) and NP (50.5 ± 20.5 vs. 38.2 ± 21.9, p = 0.001), and relative enhancement ratio to the renal cortex in CMP (0.35 ± 0.26 vs. 0.24 ± 0.19, p = 0.001) and NP (0.43 ± 0.24 vs. 0.29 ± 0.19, p < 0.001) were significantly higher in FH-deficient RCC. No significant difference was found between the FH germline and somatic mutation subgroups in any of the parameters. Conclusion: The MDCT features of FH-deficient RCC were different from those of pRCC type II, whereas there was no statistical difference between the germline and somatic mutation subgroups. A kidney mass with a cystic solid pattern and metastatic tendency, especially in young patients, should be considered for FH-deficient RCC.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.266-279
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• 2014

For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1259-1268
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• 2021

The 2nd Geostationary Ocean Color Imager (GOCI-II) is the successor to the Geostationary Ocean Color Imager (GOCI), which employs one near-ultraviolet wavelength (380 nm) and eight visible wavelengths(412, 443, 490, 510, 555, 620, 660, 680 nm) and three near-infrared wavelengths(709, 745, 865 nm) to observe the marine environment in Northeast Asia, including the Korean Peninsula. However, the multispectral radiance image observed at satellite altitude includes both the water-leaving radiance and the atmospheric path radiance. Therefore, the atmospheric correction process to estimate the water-leaving radiance without the path radiance is essential for analyzing the ocean environment. This manuscript describes the GOCI-II standard atmospheric correction algorithm and its initial phase validation. The GOCI-II atmospheric correction method is theoretically based on the previous GOCI atmospheric correction, then partially improved for turbid water with the GOCI-II's two additional bands, i.e., 620 and 709 nm. The match-up showed an acceptable result, with the mean absolute percentage errors are fall within 5% in blue bands. It is supposed that part of the deviation over case-II waters arose from a lack of near-infrared vicarious calibration. We expect the GOCI-II atmospheric correction algorithm to be improved and updated regularly to the GOCI-II data processing system through continuous calibration and validation activities.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.417-425
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• 2023

The aim of this study was to derive stem taper equations for Quercus acuta, one of main evergreen broad-leaved tree species found in warm temperate regions, and to prepare a stem volume table using those stem taper equations. A total of 688 individual trees were used in the analysis, which were collected from Jeonnam-do, Gyeongnam-do, and Jeju-do. The stem taper models applied to derive the stem curve pattern were the Max and Burkhart, Kozak, and Lee models. Among the three stem taper models, the best explanation of the stem curve shape of Q. acuta was found to be given by the Kozak model, which showed a fitness index of 0.9583, bias of 0.0352, percentage of estimated standard error of 1.1439, and mean absolute deviation of 0.6751. Thus, the stem taper of Q. acuta was estimated using the Kozak model. Moreover,thestemvolumecalculationwasperforme d by applying the Smalian formula to the diameter and height of each stem interval. In addition, an analysis of variance (ANOVA) was conducted to compare the two existing Q. acuta stem volume tables (2007 and 2010) and the newly created stem volume table (2023). This analysis revealed that the stem volume table constructed in the Wando region in 2007 included about twice as much as the stem volume tables constructed in 2010 and 2023. The stem volume table (2023) developed in this study is not only based on the regional collection range and number of utilized trees but also on a sound scientific basis. Therefore, it can be used at the national level as an official stem volume table for Q. acuta.