• Journal of agriculture & life science
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• v.50 no.5
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• pp.173-182
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• 2016

This study was aimed to identify the phenotypic relationships between calving interval and lactation curve parameters in Korean Holstein cow. The data of 36,505 lactation records was obtained from the Dairy Herd Improvement program run by Dairy Cattle Improvemnet Center of National Agricultural Federation of Korea. All lactation records were collectied from the multiparous cows calving between 2011 to 2013. The estimated lactation curves were drawn using Wood model based on actual milk yield records, and NLIN Procedure of SAS program (ver. 9.2). General linear multivariate models for calving interval, 305-d milk yield, lactation parameters(A, b, c), persistency, peak day, and peak yield included fixed effects of calving year-season (spring, summer, fall and winter) and parity(2, 3 and 4). For calving interval, 305-d milk yield, lactation parameters(A, b, c), persistency, peak day and peak yield, all two fixed effect(calving year-season, parity) were significant(p<0.05). The estimated lactation functions using Wood model for 2, 3, and 4 parity were yt=24.66t0.175e-0.00302t, yt=24.69t0.192e-0.00334t, and yt=24.22t0.200e-0.00341t, respectively. Phenotypic correlation (partial residual correlation) between calving interval and 305-d milk yield, A, b, c, persistency, peak day, and peak yield were 0.093, -0.014, 0.028, -0.046, 0.099, 0.085, and 0.052, respectively. To conclude, if calving interval increase then ascent to peak, persistency, peak day and peak yield are increase, and descent after peak is decrease. So, total 305-d milk yield is increase.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.1-11
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• 2016

With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm's Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products' accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ('7, 5, 9, 3' vs. '1'). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.311-330
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• 2023

Development of Carbon Capture and Storage (CCS) technique is becoming increasingly important as a method to mitigate the strengthening effects of global warming, generated from the unprecedented increase in released anthropogenic CO₂. In the recent years, the characteristics of basaltic rocks (i.e., large volume, high reactivity and surplus of cation components) have been recognized to be potentially favorable in facilitation of CCS; based on this, research on utilization of basaltic formations for underground CO₂ storage is currently ongoing in various fields. This study investigated the feasibility of underground storage of CO₂ in basalt, based on the examination of the CO₂ storage mechanisms in subsurface, assessment of basalt characteristics, and review of the global research on basaltic CO₂ storage. The global research examined were classified into experimental/modeling/field demonstration, based on the methods utilized. Experimental conditions used in research demonstrated temperatures ranging from 20 to 250 ℃, pressure ranging from 0.1 to 30 MPa, and the rock-fluid reaction time ranging from several hours to four years. Modeling research on basalt involved construction of models similar to the potential storage sites, with examination of changes in fluid dynamics and geochemical factors before and after CO₂-fluid injection. The investigation demonstrated that basalt has large potential for CO₂ storage, along with capacity for rapid mineralization reactions; these factors lessens the environmental constraints (i.e., temperature, pressure, and geological structures) generally required for CO₂ storage. The success of major field demonstration projects, the CarbFix project and the Wallula project, indicate that basalt is promising geological formation to facilitate CCS. However, usage of basalt as storage formation requires additional conditions which must be carefully considered - mineralization mechanism can vary significantly depending on factors such as the basalt composition and injection zone properties: for instance, precipitation of carbonate and silicate minerals can reduce the injectivity into the formation. In addition, there is a risk of polluting the subsurface environment due to the combination of pressure increase and induced rock-CO₂-fluid reactions upon injection. As dissolution of CO₂ into fluids is required prior to injection, monitoring techniques different from conventional methods are needed. Hence, in order to facilitate efficient and stable underground storage of CO₂ in basalt, it is necessary to select a suitable storage formation, accumulate various database of the field, and conduct systematic research utilizing experiments/modeling/field studies to develop comprehensive understanding of the potential storage site.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.421-431
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• 2023

Sewer deterioration models can offer important information on prediction of future condition of the asset to decision makers in their implementing sewer pipe networks management program. In this study, Markov chain model was used to estimate sewer deterioration trend based on the historical structural condition assessment data obtained by CCTV inspection. The data used in this study were limited to Hume pipe with diameter of 450 mm and 600 mm in three sub-catchment areas in city A, which were collected by CCTV inspection projects performed in 1998-1999 and 2010-2011. As a result, it was found that sewers in sub-catchment area EM have deteriorated faster than those in other two sub-catchments. Various main defects were to generate in 29% of 450 mm sewers and 38% of 600 mm in 35 years after the installation, while serious failure in 62% of 450 mm sewers and 74% of 600 mm in 100 years after the installation in sub-catchment area EM. In sub-catchment area SN, main defects were to generate in 26% of 450 mm sewers and 35% of 600 mm in 35 years after the installation, while in sub-catchment area HK main defects were to generate in 27% of 450 mm sewers and 37% of 600 mm in 35 years after the installation. Larger sewer pipes of 600 mm were found to deteriorate faster than smaller sewer pipes of 450 mm by about 12 years. Assuming that the percentage of main defects generation could be set as 40% to estimate the life expectancy of the sewers, it was estimated as 60 years in sub-catchment area SN, 42 years in sub-catchment area EM, 59 years in sub-catchment area HK for 450 mm sewer pipes, respectively. For 600 mm sewer pipes, on the other hand, it was estimated as 43 years, 34 years, 39 years in sub-catchment areas SN, EM, and HK, respectively.

• Journal of Christian Education in Korea
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• v.72
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• pp.245-271
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• 2022

The purpose of this study is to develop a nursing clinical practice education model that applies case-based learning and co-teaching to nursing students, and to secure the validity of the developed model. To verify the validity of the nursing clinical practice education model, it was applied to the subject of 'Health Response and Nursing VI (Perception/ Cognition) Practice' in the 2nd semester of 2021 at J University in Jeonju, and the instructor's response to the model was evaluated. Surveys and focus group interviews were conducted on confidence in clinical practice and teaching and learning models. After deriving the case-based learning stage and co-teaching elements through a review of precedent literature and case studies, an initial model was devised after expert review, and the devised model was reviewed for internal validity by nursing education experts, and then modified and supplemented. As a result of the learner response evaluation conducted after applying the model to the clinical practice subject for external validation verification, the confidence in clinical performance was 4.22 points and the satisfaction with the teaching-learning model was 4.68 points. Summarizing the results of the focus group interview, the importance of prior learning and the learning of selected cases based on actual cases, learning terminology and professional knowledge, eliminated fear of the practice field, felt familiar, and learned various cases. He said that he was able to think critically through the time to organize the knowledge learned in the practice field. In addition, through co-teaching, it was found that field leaders and advisors taught the theoretical and practical aspects at the same time through examples, thereby experiencing practical education closer to practice. It is expected that the nursing clinical practice education model developed through this study, applying case-based learning and co-teaching, will be an effective teaching and learning model that can reduce the gap between theory and practice and improve the clinical performance of nursing students.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1B
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• pp.11-22
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• 2009

Long-term rainfall-runoff modeling is a key element in the Earth's hydrological cycle, and associated with many different aspects such as dam design, drought management, river management flow, reservoir management for water supply, water right permission or coordinate, water quality prediction. In this regard, hydrologists have used the hydrologic models for design criteria, water resources assessment, planning and management as a main tool. Most of rainfall-runoff studies, however, were not carefully performed in terms of considering reservoir effects. In particular, the downstream where is severely affected by reservoir was poorly dealt in modeling rainfall-runoff process. Moreover, the effects can considerably affect overall the rainfallrunoff process. An objective of this study, thus, is to evaluate the impact of reservoir operation on rainfall-runoff process. The proposed approach is applied to Anseong watershed, where is in a mixed rural/urban setting of the area and in Korea, and has been experienced by flood damage due to heavy rainfall. It has been greatly paid attention to the agricultural reservoirs in terms of flood protection in Korea. To further investigate the reservoir effects, a comprehensive assessment for the results are discussed. Results of simulations that included reservoir in the model showed the effect of storage appeared in spring and autumn when rainfall was not concentrated. In periods of heavy rainfall, however, downstream runoff increased in simulations that do not consider reservoir factor. Flow duration curve showed that changes in streamflow depending upon the presence or absence of reservoir factor were particularly noticeable in ninety-five day flow and low flow.

• Exercise Science
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• v.23 no.1
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• pp.1-11
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• 2014

The purpose of this study was to develop and validate regression models to estimate maximal oxygen uptake (VO₂max) from the 20 m Progressive Shuttle Run Test (20 m PSRT) in Korean middle-school girls aged 13-15 years. The 20 m PSRT and VO₂max were assessed in a sample of 194 participants. The sample was randomly split into validation (n=127) and test-retest reliability (n=99, 32 out of 127 participants also performed validity test) groups. 127 participants performed a graded exercise test (GXT, stationary gas analyser) and the 20 m PSRT (portable gas analyser) once to develop a VO₂max prediction model and to analyze the validity of the modified 20 m PSRT protocol (starting at 7.5 km/h and increasing by 0.5 km/h every 1 min). 99 participants performed the 20 m PSRT twice for test-retest reliability purpose. Mean measured VO₂max (39.2±5.1 ml/kg/min) from the potable gas analyzer was significantly increased from that measured during the GXT from stationary gas analyzer (37.7±5.7 ml/kg/min, p=.001) using the modified 20 m PSRT protocol. But it was a narrow range (1.5 ml/kg/min). The measured VO₂max from the potable and stationary gas analyzers correlated at r=.88(p<.001). Test-retest of the 20 m PSRT yielded comparable results (Laps r=.88 & final speed r=.85). New regression equations were developed from present data to predict VO₂max for middle-school girls: y=.231×Laps-.311×weight(in kg)+46.201 (r=.74, SEE=4.29 ml/kg/min). It is concluded that (a) the modified 20 m PSRT protocol is a valid and reliable test and (b) this equation developed in this study provides valid estimates of VO₂max of Korean middle-school girl aged 13-15 years.

• Korean Journal of Culture and Social Issue
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• v.18 no.4
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• pp.435-462
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• 2012

The predominant concern of the study centers on: (1) the direct effects of the job satisfaction, social support and hope on the individual's quality of life; (2) the direct effect of hope alone on the individual's quality life; (3) the mediating effect of the hope between the job satisfaction/social support and life quality; (4) the moderating effect of the worker's legal status(legal labors Vs. illegal labors) on each causal relationship. Research is based on a survey conducted with 453 Mongolian immigrant workers(333 legal workers, 120 illegal workers) from 10 cities including Seoul. In order for respondents to address research questions, structural equation models are explored. A variety of tests are conducted(metric invariance test, critical ratio for difference test, multi-group analysis, bias-corrected boot-strapping, latent mean analysis including Cohen's effect test). The noticeable findings are as follow: First, both job satisfaction and social support have a positive influence respectively on the individual's hope and the individual's quality of life. Second, we found a partial mediating effect of hope between both job satisfaction/social support and the individual's life quality. Third, we failed to find a moderating effect of the workers' legal status on each causal relationship. Finally, there is no significant difference of the latent means of each latent variable -job satisfaction, social support, hope, and life quality - between the legal group and the illegal group, except the latent mean of workers' quality of life. A range of practical and political implications are discussed based on the study's findings.