• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.182-182
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• 2015

우리나라에서는 강우관측시스템의 지역적 불균형으로 상대적으로 소규모 저수지의 경우 미계측유역의 특성을 가지며, 신뢰성 있는 강우량, 유출량, 증발량 자료가 매우 부족한 실정이다. 다목적댐 유역과 같은 계측유역의 경우 상류유역의 유입량 자료의 확보가 용이하지만 대부분의 유역의 경우 계측장비가 부족하여 신뢰성이 확보된 유입량 자료를 얻는데 많은 어려움이 있다. 본 연구에서는 미계측유역의 유입량 산정을 위하여 계측유역을 대상으로 강우-유출 모형의 매개변수를 산정하였으며, 산정된 매개변수를 유역특성인자와의 상관성을 토대로 다중선형회귀분석기법(multiple linear regression, MLR)을 적용하여 지역화(regionalization)를 위한 회귀식을 도출하였다. 이를 위해 양질의 유량자료가 확보된 K-water 17개 댐 유역을 대상으로 매개변수를 산정하였으며 이 중 2개의 댐 유역을 미계측유역으로 간주하여 개발된 모형을 검증하였다. 대부분의 통계 지표에서 우수한 모의능력을 확인하였으며, 본 연구를 통하여 개발된 지역화 기법을 미계측유역에 활용한다면 보다 정량적이고 효율적인 수자원 계획이 가능할 것으로 판단된다. 향후 연구로는 불확실성을 고려한 Bayesian GLM 모형을 이용한 지역화기법을 개발하여 매개변수의 불확실성까지 고려할 수 있는 방안을 모색하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.13-13
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• 2018

우리나라의 하천 홍수량 자료는 대부분 댐 상류나 홍수위험 지역 등 유역 내 하천관리가 필요한 주요 지점에서만 측정되고 있다. 그러나 매년 관측되는 강우량 자료에 비해 유출량 자료는 유역의 크기가 작아질수록 매우 제한적이며, 신뢰성 있는 홍수량자료의 구축이 어려운 실정이다. 이에 본 연구에서는 유역특성인자(유역면적, 유역경사)를 매개변수로 활용하여 권역별 설계홍수량 자료에 대한 지역화 분석을 수행하였으며, 미계측 유역에서 홍수량 추정이 가능하도록 모형을 개발 하였다. 모형에서 발생하는 불확실성을 고려하기 위하여 Bayesian GLM(generalized linear method)기법을 활용하였으며, 최종적으로 모형의 매개변수와 산정되는 홍수량 결과에 대한 불확실성 구간을 정량적으로 제시하였다. 제안된 모형을 통해 일부 유역을 미계측 유역으로 가정하여 홍수량을 추정하였으며, 통계적 지표를 활용하여 기수립된 설계홍수량 자료와의 비교를 통해 모형의 적합성을 평가하였다. 본 연구를 통해 제안된 모형은 검증과정과 도출된 결과를 통해 유역특성에 따른 재현기간별 홍수량을 효과적으로 재현하는데 유리할 뿐만 아니라, Bayesian 기법을 도입하여 매개변수와 도출된 결과에 대한 불확실성의 정량적인 평가가 가능한 장점을 확인하였다.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.41-55
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• 2021

We examine the effects of the sustainability report (SURE) and investment decision on share price (SPRC). Explore whether the sustainability report changes the value-relevance of financial accounting variables indirectly. It is evident that the number of banks is only 12, which are all banks in Saudi Arabia, and we have included all of them in the final sample. Moreover, the same number of banks applied for the analysis concerning the accounting variables. This article utilizes a panel dataset from a sample of Saudis registered banks from the first quarter of 2014 to the last quarter of 2018. We utilize a balanced sample that contains all banks listed in Tadawul, 240 observations. Run GLM regression to tests the relationships. Findings exhibit that investors value the complementary disclosure of accounting information provided in SURE, and this disclosure produces a positive effect on SPRC. The SURE figure is robustly significant, suggesting that the market assigns a positive-significant correlation to the further information in the SURE. The indirect effects show that BPS×SURE is a positive-significant effect on SPRC, whereas EPS×SURE is positively-insignificant. The analysis shows that SURE's value relevance conforms through Saudis Banks, consistent with the hypothesis that diverse institutional perspectives probably influence the value-relevance of SURE.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.167-178
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• 2022

Surrogate models aim to approximate the performance function with an active-learning design of experiments (DoE) to obtain a sufficiently accurate prediction of the performance function's sign for an inexpensive computational demand in reliability analysis. Nevertheless, many existing active-learning methods are limited to the Kriging model, while the uncertainties of the Kriging itself affect the reliability analysis results. Moreover, the existing general active-learning methods may not achieve a fully satisfactory balance between accuracy and efficiency. Therefore, a novel active-learning method GLM-CM is constructed to yield the issues, which conciliates several merits of existing methods. To demonstrate the performance of the proposed method, four examples, concerning both mathematical and engineering problems, were selected. By benchmarking obtained results with literature findings, various surrogate models combined with the proposed method not only provide an accurate reliability evaluation while highly alleviating the computational burden, but also provides a satisfactory balance between accuracy and efficiency compared to the other reliability methods.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.273-273
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• 2022

The increasing number of Westernized eating patterns based on wheat flour in Korea has led to an increase in the rate of diseases such as obesity and diabetes, which has become a social problem. Wheat consumption is increasing due to changes in eating habits, but domestic wheat has low price competitiveness and has stagnated recently, so it is necessary to secure new resources to differentiate from imported wheat. Resistant starch, a newly created resource in domestic wheat, can act as a prebiotic similar to dietary fiber in the body, inducing microbial changes in the gut and having beneficial effects on metabolic syndrome. Wheat research on resistant starch was carried out based on the breeding of high amylose. A genome-wide association study (GWAS) was used to perform SNP identification and expression analysis related to wheat amylose through phenotype and genotype. 561 wheat core collection gene sources were investigated for amylose content in wheat, and related genes were extracted and analyzed. In the GWAS analysis, the model formulas BLIMK, FarmCPU, GLM, MLM, and MLMM were used to derive results such as QQ plots and Manhattan plots through phenotypic data. Among these models, BLAST was conducted to find the association between the SNPs identified using FarmCPU and genes related to starch, and 15 were found. Using the identified markers, it becomes easier to develop and browse related wheat cultivars according to their amylose content.

• Computers and Concrete
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• v.33 no.2
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• pp.137-145
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• 2024

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.

• Korean Journal of Childcare and Education
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• v.20 no.3
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• pp.67-83
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• 2024

Objective: This study aimed to determine whether preschoolers' self-compassion mediates the effects of mothers' emotional expression and self-compassion on preschoolers' emotional regulation abilities in children aged 4 to 6 years. Methods: The study included 305 mothers residing in City J who were raising preschoolers aged 4 to 6. Data collected were analyzed for the significance of the mediating effects among the main variables using the GLM Mediation Model in the JAMM package of the Jamovi 2.3.16 statistical program. Results: Firstly, statistically significant positive and negative correlations were found among the main variables. Secondly, preschoolers' positive self-compassion was found to partially mediate the relationship between mothers' positive emotional expression and preschoolers' emotional regulation abilities. Furthermore, preschoolers' negative self-compassion partially mediated the relationship between mothers' negative emotional expressions and preschoolers' emotional regulation abilities. Conclusion/Implications: This study identified parental and child variables influencing preschoolers' emotional regulation abilities and discussed intervention strategies for education and counseling to enhance these abilities.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.1
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• pp.119-123
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• 2007

The objectives of this study were to characterize the changes occurring in the levels of insulin-like growth factors (IGF-I and IGF-II) in bovine milk during a one-year lactation period, and to determine the parameters affecting IGF content in bovine milk. Milk was collected individually from lactating Holstein cows (n=70), and IGF-I and -II levels were determined via radioimmunoassay, using 125I after acid-ethanol treatment. The proximate compositions of the milk samples were determined using a near-infrared milk analyzer. The data were analyzed by the GLM and CORR procedures using SAS software to determine significant differences (p<0.05) occurring within groups (dairy farms, lactation periods, season, and parity). We noted an approximately six-fold reduction in the IGF-I concentration (from 2,462.7 to 353.0 ng/ml) and a three-fold drop in the IGF-II concentration (from 929.1 to 365.7 ng/ml) in the bovine colostrum, between 6 h after parturition and 18 h after parturition. IGF-I and -II content, measured at the early, middle, and late stages of lactation did not change significantly throughout the entirety of the lactation period. Interestingly, parity did not significantly affect IGF-I content, but did significantly affect IGF-II content between the primiparous and multiparous cows. We also found there were no significant relationships between IGF-I and total protein content or somatic cell counts (p<0.05).

• Korean Journal of Agricultural Science
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• v.45 no.1
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• pp.66-73
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• 2018

Hanwoo is an important livestock resource in Korea. Its genetic improvements of economic traits have mainly focused on the steers in the past. However, there is a great necessity to extend the breed improvement programs to the cows as well. Therefore, the objective of this study was to investigate the effects of various environmental factors (person taking the measurement, region, year of measurement, month of measurement, image interpreter, birth-year and birth-year) on ultrasound measured carcass traits. A total of 27,215 ultrasound measurements of carcass traits were recorded between 2004 and 2012 for 22,620 cows born from 1997 to 2011. The ultrasound measures included backfat thickness (BFT), eye muscle area (EMA), and marbling score (MAR). The mean values for the BFT, EMA and MAR were 4.46 mm, $56.24cm^2$, and 4.12 point, respectively. Seven environmental factors, person taking the measurement, region, year of measurement, month of measurement, image interpreter, birth-year and birth-month, were tested to determine if they had a significant effect on the studied traits using the GLM procedure in SAS. All factors were found to significantly affect all the ultrasound carcass traits in this study. Unlike in previous studies, among the environmental effects, the significant effect of the image interpreter on the ultrasound carcass traits was shown for the first time in this study. These results indicate that future genetic evaluations of ultrasound carcass traits of Hanwoo cows should include all of the above environmental factors as well as the effect from people taking the measurements.

• Journal of Ecology and Environment
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• v.42 no.3
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• pp.128-138
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• 2018

Background: This study investigated the variation of soil organic carbon in four land cover types: natural and mixed forest, cultivated land, Eucalyptus plantation and open bush land. The study was conducted in the Birr watershed of the upper Blue Nile ('Abbay') river basin. Methods: The data was subjected to a two-way of ANOVA analysis using the general linear model (GLM) procedures of SAS. Pairwise comparison method was also used to assess the mean difference of the land uses and depth levels depending on soil properties. Total of 148 soil samples were collected from two depth layers: 0-10 and 10-20 cm. Results: The results showed that overall mean soil organic carbon stock was higher under natural and mixed forest land use compared with other land use types and at all depths ($29.62{\pm}1.95Mg\;C\;ha^{-1}$), which was 36.14, 28.36, and 27.63% more than in cultivated land, open bush land, and Eucalyptus plantation, respectively. This could be due to greater inputs of vegetation and reduced decomposition of organic matter. On the other hand, the lowest soil organic carbon stock under cultivated land could be due to reduced inputs of organic matter and frequent tillage which encouraged oxidation of organic matter. Conclusions: Hence, carbon concentrations and stocks under natural and mixed forest and Eucalyptus plantation were higher than other land use types suggesting that two management strategies for improving soil conditions in the watershed: to maintain and preserve the forest in order to maintain carbon storage in the future and to recover abandoned crop land and degraded lands by establishing tree plantations to avoid overharvesting in natural forests.