• Animal Bioscience
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• v.34 no.1
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• pp.26-35
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• 2021

Objective: This study was conducted to assess the level of pregnancy-associated glycoprotein (PAG) in whole and skim milk samples, and its suitability for early pregnancy diagnosis in goats. Methods: A two-step sandwich enzyme-linked immunosorbent assay (ELISA) system for estimation of milk PAG was developed and validated, which employed caprine-PAG specific polyclonal antisera. Whole and skim milk samples (n = 210 each) from fifteen multiparous goats were collected on alternate days from d 10 to d 30, and thereafter weekly till d 51 post-mating. PAG levels in milk samples were estimated by ELISA and the pregnancies were confirmed at d40 post-mating by transrectal ultrasonography (TRUS). Results: The level of PAG in whole and skim milk samples of both pregnant and nonpregnant goats remained below the threshold values until d 24 after mating. Thereafter, PAG concentration in whole and skim milk increased steadily in pregnant goats, whereas it continued below the threshold in non-pregnant does. The PAG profiles in whole and skim milk of pregnant goats were almost similar and exhibited strong positive relationship (r = 0.891; p<0.001). Day 26 post-mating was identified as the first time-point for significantly (p<0.05) higher milk PAG concentration in pregnant goats than to non-pregnant goats. When compared to TRUS examination for pregnancy diagnosis, the accuracy and specificity of PAG ELISA using whole and skim milk samples were 94.5% and 95.4%; and 95.3% and 100%, respectively. The high values of area-under-curve (0.904 [whole milk] and 0.922 [skim milk]), demonstrate outstanding discrimination ability of the milk assays. Among the sampling dates chosen, d 37 post-mating was identified as the best suitable time point for collection of milk samples to detect pregnancy in goats. Conclusion: The PAG concentration in whole and skim milk of goats collected between days 26 and 51 post-breeding can be used for the accurate prediction of pregnancy and may be useful for assisting management decisions in goat flocks.

• Journal of the Korean Institute of Gas
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• v.25 no.1
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• pp.20-29
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• 2021

During multi-stage fracturing in a low permeable shale formation, stress interference occurs between the stages which is called the "stress shadow effect(SSE)". The effect may alter the fracture propagation direction and induce ununiform geometry. In this study, the stress shadow effect on the hydraulic fracture geometry and the well productivity were investigated by the commercial full-3D fracture model, GOHFER. In a homogeneous reservoir model, a multi-stage fracturing process was performed with or without the SSE. In addition, the fracturing was performed on two shale reservoirs with different geomechanical properties(Young's modulus and Poisson's ratio) to analyze the stress shadow effect. In the simulation results, the stress change caused by the fracture created in the previous stage switched the maximum/minimum horizontal stress and the lower productivity L-direction fracture was more dominating over the T-direction fracture. Since the Marcellus shale is more brittle than more dominating over the T-direction fracture. Since the Marcellus shale is more brittle than the relatively ductile Eagle Ford shale, the fracture width in the former was developed thicker, resulting in the larger fracture volume. And the Marcellus shale's Young's modulus is low, the stress effect is less significant than the Eagle Ford shale in the stage 2. The stress shadow effect strongly depends on not only the spacing between fractures but also the geomechanical properties. Therefore, the stress shadow effect needs to be taken into account for more accurate analysis of the fracture geometry and for more reliable prediction of the well productivity.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.373-383
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• 2020

In this study we established an operational storm-surge system for the northwestern pacific ocean, based on the NEMO (Nucleus for European Modeling of the Ocean). The system consists of the tide and the surge models. For more accurate storm surge prediction, it can be completed not only by applying more precise depth data, but also by optimal parameterization at the boundaries of the atmosphere and ocean. To this end, we conducted several sensitivity experiments related to the application of available bathymetry data, ocean bottom friction coefficient, and wind stress and air pressure on the ocean surface during August~September 2018 and the case of typhoon SOULIK. The results of comparison and verification are presented here, and they are compared with POM (Princeton Ocean Model) based Regional Tide Surge forecasting Model (RTSM). The results showed that the RTSM_NEMO model had a 29% and 20% decrease in Bias and RMSE respectively compared to the RTSM_POM model, and that the RTSM_NEMO model had a lower overall error than the RTSM_POM model for the case of typhoon SOULIK.

• Korean Journal of Plant Resources
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• v.35 no.2
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• pp.169-182
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• 2022

This study was conducted to predict the distribution of rare·endemic plants (Sophora koreensis Nakai) in the border forests where wildfire damage occurred and to quantify the damage. For this purpose, we tried to derive more accurate results through forest area damage (NBR) according to the Burn severity of wildfires, damage by tree species type (Vegetation map), and MaxEnt model. For Burn severity analysis, satellite imagery (Landsat-8) was used to analyze Burn severity (ΔNBR2016-2015) and to derive the extent of damage. To prepare the Vegetation map, the land cover map prepared by the Ministry of Environment, the Vegetation map prepared by the Korea Forest Service, and the vegetation survey conducted by itself were conducted to prepare the clinical map before and after the forest fire. Lastly, for MaxEnt model analysis, the AUC value was derived by using the habitat coordinates of Sophora koreensis Nakai based on the related literature and self-report data. As a result of combining the Maxent model analysis data with the Burn severity data, it was confirmed that 45.9% of the 44,760 m² of habitat (predicted) area of Sophora koreensis Nakai in the wildfire damaged area or 20,552 m², was damaged.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1037-1051
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• 2021

Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.1
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• pp.57-78
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• 2022

The disc cutter and cutterbit, which are the most important factors to increase the excavation efficiency of TBM, are key factors in the design and construction of the cutter head. The arrangement, spacing, number, size, and material of disc cutters suitable for the ground conditions determine the success or failure of TBM construction. The disc cutter, which is a representative consumable part in TBM construction, can cause enormous disruption to the construction cost as well as the construction cost unless accurate prediction of wear and replacement cycle is accompanied. Therefore, in this study, the method of calculating the replacement cycle of the disc cutter calculated at the time of design for the slurry shield TBM field, and the depth of wear and replacement location of the disc cutter that occurred during actual construction were compared by analyzing the field data. For a quantitative comparison, weathered soil/weathered rock, soft rock, and hard rock were classified according to the ground in the section showing constant excavation data, and the trajectory of circle was different depending on the location of the disc cutter, so it was compared and analyzed.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.495-507
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• 2021

With the recent industrial development, accidental pollution in riverine environments has frequently occurred. It is thus necessary to simulate pollutant transport and dispersion using water quality models for predicting pollutant residence times. In this study, we conducted a field experiment in a meandering reach of the Sum River, South Korea, to validate the field applicability and prediction accuracy of RAMS+ (River Analysis and Modeling System+), which is a two-dimensional (2D) stream flow/water quality analysis program. As a result of the simulation, the flow analysis model HDM-2Di and the water quality analysis model CTM-2D-TX accurately simulated the 2D flow characteristics, and transport and mixing behaviors of the pollutant tracer, respectively. In particular, CTM-2D-TX adequately reproduced the elongation of the pollutant cloud, caused by the storage effect associated with local low-velocity zones. Furthermore, the transport model effectively simulated the secondary flow-driven lateral mixing at the meander bend via 2D dispersion coefficients. We calculated the residence time for the critical concentration, and it was elucidated that the calculated residence times are spatially heterogeneous, even in the channel-width direction. The findings of this study suggest that the 2D water quality model could be the accidental pollution analysis tool more efficient and accurate than one-dimensional models, which cannot produce the 2D information such as the 2D residence time distribution.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.218-227
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• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Korean Journal of Plant Taxonomy
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• v.52 no.3
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• pp.184-195
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• 2022

The invasive alien species Brassica tournefortii Gouan (Brassicaceae) is herein reported for the first time in Korea, from Gunsan-si, Gochang-gun, and Jeju-si. Brassica tournefortii can easily be distinguished from B. juncea and B. napus by its dense stiff hairs at the base of the stem and leaves, basally and distally branched stems, partially dehiscent fruits, and seeds that become mucilaginous in the presence of moisture. Although some taxonomists have classified this species as belonging to Coincya Rouy based on its fruit and seed characteristics, the existence of one vein on the fruit valves and our maximum likelihood analysis using internal transcribed spacer sequences placed it in Brassica. Distribution data, photographs, and a description of B. tournefortii are presented herein. Moreover, potential changes in the distribution of B. tournefortii were predicted under different climate scenarios, but our analysis showed that the probability of the spreading of this species is low. Nevertheless, continuous monitoring is necessary for an accurate assessment. The results of the present study can be used to conduct an invasion risk assessment and can assist with the effective management of this invasive alien species.