• Animal Bioscience
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• v.34 no.2
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• pp.163-171
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• 2021

Objective: Considering the importance of dairy farming and the negative effects of heat stress, more tolerant genotypes need to be identified. The objective of this study was to investigate the effect of heat stress via temperature-humidity index (THI) and diurnal temperature variation (DTV) in the genetic evaluations for daily milk yield of Holstein dairy cattle, using random regression models. Methods: The data comprised 94,549 test-day records of 11,294 first parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (THI and DTV) from 18 weather stations. Least square linear regression models were used to determine the THI and DTV thresholds for milk yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were combined in various ways and tested for different days, totaling 41 models. Results: The THI and DTV thresholds for milk yield losses was THI = 74 (-0.106 kg/d/THI) and DTV = 13 (-0.045 kg/d/DTV). The model that included THI and DTV as fixed effects, considering the two-day average, presented better fit (-2logL, Akaike information criterion, and Bayesian information criterion). The estimated breeding values (EBVs) and the reliabilities of the EBVs improved when using this model. Conclusion: Sires are re-ranking when heat stress indicators are included in the model. Genetic evaluation using the mean of two days of THI and DTV as fixed effect, improved EBVs and EBVs reliability.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Journal of Animal Science and Technology
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• v.45 no.5
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• pp.739-748
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• 2003

The objective of this study was to estimate genetic parameters for test-day milk production and somatic cell score using field data collected by dairy herd improvement program in Korea. Random regression animal models were applied to estimate genetic variances for milk production and somatic cell score. Heritabilities for milk yields, fat percentage, protein percentage, solid-not-fat percentage, and somatic cell score from test day records of 5,796 first lactation Holstein cows were estimated by REML algorithm in single trait random regression test-day animal models. For these analyses, Legendre polynomial covariate function was applied to model the fixed effect of age-season, the additive genetic effect and the permanent environment effect as random. Homogeneous residual variance was assumed to be equal throughout lactation. Heritabilities as a function of time were calculated from the estimated curve parameters from univariate analyses. Heritability estimates for milk yields were in range of 0.13 to 0.29 throughout first lactation. Heritability estimates for fat percentage, protein percentage and solid-not-fat percentage were within 0.09 to 0.11, 0.12 to 0.19 and 0.17 to 0.23, respectively. For somatic cell score, heritabilities were within 0.02 to 0.04. Heritabilities for milk productions and somatic cell score were fluctuated by days in milk with comparing 305d milk production.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.12
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• pp.1696-1702
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• 2015

The objective of this study was to compare the effects of body weight growth adjustment methods on genetic parameters of body growth and tissue among three pig breeds. Data collected on 101,820 Landrace, 281,411 Yorkshire, and 78,068 Duroc pigs, born in Korean swine breeder farms since 2000, were analyzed. Records included body weights on test day and amplitude (A)-mode ultrasound carcass measures of backfat thickness (BF), eye muscle area (EMA), and retail cut percentage (RCP). Days to 90 kg body weight (DAYS90), through an adjustment of the age based on the body weight at the test day, were obtained. Ultrasound measures were also pre-adjusted (ABF, EMA, AEMA, ARCP) based on their test day measures. The (co)variance components were obtained with 3 multi-trait animal models using the REMLF90 software package. Model I included DAYS90 and ultrasound traits, whereas model II and III accounted DAYS90 and pre-adjusted ultrasound traits. Fixed factors were sex (sex) and contemporary groups (herd-year-month of birth) for all traits among the models. Additionally, model I and II considered a linear covariate of final weight on the ultrasound measure traits. Heritability ($h^2$) estimates for DAYS90, BF, EMA, and RCP ranged from 0.36 to 0.42, 0.34 to 0.43, 0.20 to 0.22, and 0.39 to 0.45, respectively, among the models. The $h^2$ estimates of DAYS90 from model II and III were also somewhat similar. The $h^2$ for ABF, AEMA, and ARCP were 0.35 to 0.44, 0.20 to 0.25, and 0.41 to 0.46, respectively. Our heritability estimates varied mostly among the breeds. The genetic correlations ($r_G$) were moderately negative between DAYS90 and BF (-0.29 to -0.38), and between DAYS90 and EMA (-0.16 to -0.26). BF had strong $r_G$ with RCP (-0.87 to -0.93). Moderately positive $r_G$ existed between DAYS90 and RCP (0.20 to 0.28) and between EMA and RCP (0.35 to 0.44) among the breeds. For DAYS90, model II and III, its correlations with ABF, AEMA, and ARCP were mostly low or negligible except the $r_G$ between DAYS90 and AEMA from model III (0.27 to 0.30). The $r_G$ between AEMA and ABF and between AEMA and ARCP were moderate but with negative and positive signs, respectively; also reflected influence of pre-adjustments. However, the $r_G$ between BF and RCP remained non-influential to trait pre-adjustments or covariable fits. Therefore, we conclude that ultrasound measures taken at a body weight of about 90 kg as the test final should be adjusted for body weight growth. Our adjustment formulas, particularly those for BF and EMA, should be revised further to accommodate the added variation due to different performance testing endpoints with regard to differential growth in body composition.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 1999.10a
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• pp.77-80
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• 1999

In this study, to evaluate the performance of leachate collection and removal system, the clogging of geotextile filter was tested and simulated for HELP. As a result of clogging test, the all permeability of geotextiles were decreased rapidly in several day. Also, $Ca^{2+}$ and CODcr concentrations were decreased during test. In model simulation for HELP, Daily discharge volume was shown insignificant change with the filter clogging. however, this result may effect on increasing of the hydraulic gradient in waste layer.r.

• Tunnel and Underground Space
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• v.4 no.2
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• pp.144-155
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• 1994

This research is designed to assess current achievement levels for mechanized excavation systems in Korea adn suggest the model predictive of TBM performance using statistical approaches. A test section in the TBM construction sites is selected to measure and analyze TBM performance. The field records including operating data, time allocation into downtime catagories, and machine design are analyzed on a shift basis. There are a total of 240 shifts, with most days operating two shifts per day. Examples of the probability density functions produced from the test section are presented and discussed. Relationships between TBM penetration rate and rock physical properties are investigated and the empirical equations for TBM performance prediction are also assessed with the field data.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.3-42
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• 2021

Conventional global magnetic field maps, such as daily updated synoptic maps, have been constructed by merging together a series of observations from the Earth's viewing direction taken over a 27-day solar rotation period to represent the full surface of the Sun. It has limitations to predict real-time farside magnetic fields, especially for rapid changes in magnetic fields by flux emergence or disappearance. Here, we construct accurate synchronic magnetic field maps using frontside and AI-generated farside data. To generate the farside data, we train and evaluate our deep learning model with frontside SDO observations. We use an improved version of Pix2PixHD with a new objective function and a new configuration of the model input data. We compute correlation coefficients between real magnetograms and AI-generated ones for test data sets. Then we demonstrate that our model better generate magnetic field distributions than before. We compare AI-generated farside data with those predicted by the magnetic flux transport model. Finally, we assimilate our AI-generated farside magnetograms into the flux transport model and show several successive global magnetic field data from our new methodology.

• International Journal of Contents
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• v.8 no.1
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• pp.74-81
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• 2012

The aim is to investigate the analgesic effect of transcranial direct current stimulation(tDCS) on central neuropathic pain(CNP) in spinal cord contusive rat model. Twenty Sprague-Dawley rats($250{\pm}50$ g, male) were used. Thoracic spinal cord(T10) was contused using New York University(NYU) spinal cord impactor. The animals were randomly assigned to two groups; GroupI: Non-treatment after SCI induction(n=10), GroupII: application of tDCS(0.1 mA, 20 min/time, 2 times/day, 5 days/6week) after SCI induction(n=10). Assess the effect of tDCS using the Basso Beattie Bresnahan(BBB) locomotor rating scales, Touch $test^{TM}$ sensory evaluator(TTSE), Plantar test$^{\circledR}$after contusion at the $2^{nd}$, $3^{rd}$, $4^{th}$, $5^{th}$, $6^{th}$ week and the immunohistochemistric response of c-fos in the thalamus, cerebral cortex after contusion at the $3^{rd}$, $6^{th}$ week after SCI. The scores of BBB scales were significantly different from $3^{rd}$week. TTSE were different significantly over time, but there were no differences at each evaluation times on between-measure time effects. Plantar test were different significantly over time and there were difference at the $4^{th}$, $6^{th}$ week after SCI on between-measure time effects. Also, immunohistochemistric response of c-fos was reduced significantly from $3^{rd}$, $6^{th}$ week after SCI in tDCS group compared with control group in thalamus and cortex. These results identified that tDCS of non-invasive therapeutic method may have beneficial analgesic effect on CNP after SCI with behavioral test and immunohistochemical test.

• Korean Journal of Veterinary Service
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• v.26 no.1
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• pp.73-80
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• 2003

In order to obtain information on murine model for foot and mouth disease virus(FMDV) type Asia 1, we studied whether guinea pig was a suitable model for studying FMDV. Apparently healthy 3 months old albino guinea pigs and unweaned 3 days old Swiss albino mice were used for this study. Total of 8 guinea pigs were divided into the infected(n=5) and control(n=3) groups. The incubation period of FMDV in the guinea pigs were roughly 2 days and the viremia persisted for 3 days in the guinea pigs. Mice inoculated with the plasma from control guinea pigs did not show any sign of viremia. The plasma were titrated by virus neutralization test using suckling mice as an indicator host. The mean virus neutralizing antibody titers of infected guinea pig at 3 DPI, 4 DPI and 5 DPI were log$\_$10/2.16, log$\_$10/ 3.39 and log$\_$10/ 3.44, respectively whereas there was no neutralizing antibody titer in control group. The difference between the mortality pattern and mean virus neutralizing antibody titer of infected and that of control group at day 3, 4, 5 were statistically significant(p<0.0l).

• Journal of Korean Academy of Nursing
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• v.34 no.5
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• pp.781-790
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• 2004

Purpose: The purpose of this study was to develop a model of an elderly health promotion center after witnessing the effect of a social support program on dependent variables in older adults at a local community health center. Method: The subjects were 60 female adults over 65 years dwelling in a rural area, and they were divided into experimental and control groups each with 30 people. A social support program was implemented 6 hours a day, 3 times a week, for 4 months in the experimental group. Included was health assessment, health education, counseling, consultation, exercise, physical & occupational therapy, primary care, recreation, lunch & transfer service. Data was collected from May 1stto September 14th, 2002 by questionnaires, and analyzed by $x^2$-test, t-test, Pearson's correlation coefficient and stepwise multiple regression using SAS. Result: The social support program in the elderly was very effective on all dependent variables of physical health (t=4.68, p=.001), health knowledge (t=3.60, p=.001), life satisfaction (t=8.65, p=.001), and health promoting behaviors (t=5.23, p=.001). Conclusion: The Social Support Program at a Community Health Center was effective on health promoting behaviors in the elderly.