• Animal Bioscience
• /
• v.36 no.7
• /
• pp.1003-1009
• /
• 2023

Objective: The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Methods: Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. Results: The random contemporary group (CG) effect explained about 50%, 45%, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. Conclusion: The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.85-85
• /
• 2021

CDOM(Colored or Chromophoric Dissolved Organic Matter)은 바다, 호수 및 강에서 담수, 오수, 퇴적물 등으로부터 공급된 유기물질의 일종으로 가시광선에서 빛을 흡수하는 성질을 가지며, 2016년부터 환경부에서 선정한 하천, 호수 등 방류수의 수질오염 표준인 TOC(Total Organic Carbon)를 간접 추정할 수 있는 매개변수가 될 수 있다. 따라서, 본 연구에서는 백제보 상류 23 km 구간을 대상으로 2개년(2016~2017) 중 7일의 초분광영상 자료를 활용하여 내륙지역의 CDOM에 대한 적정 반사도 밴드값(R_rs)과 CDOM을 추정하는 알고리즘을 개발하고자 한다. CDOM은 흡수계수(α_CDOM)를 통해 간접 추정되며, 흡수계수의 기준 파장값(λ)은 연구별로 350 nm, 375 nm, 400 nm, 412 nm 및 440 nm 등 다르게 나타난다. 초분광영상은 AsaFENIX 초분광 센서에서 관측된 380~970 nm까지 4 nm 간격, 127개 대역의 분광해상도와 2 m의 공간해상도를 가진 영상을 활용하였으며, 자료의 연속성을 위해 smoothing 기법을 활용하여 가공하였다. 추정 알고리즘은 Random forest를 활용하였으며, 70%의 trainning과 30%의 test로 구분하여 적용하였다. 산출된 CDOM은 결정계수(R²), Nash-Sutcliffe efficiency(NSE)를 이용하여 실측 CDOM과 비교하였다. 흡수계수별 CDOM의 산정 결과 α_CDOM(350 nm)의 trainning, test에서 각각 R²가 0.71, 0.74, NSE가 0.25, 0.49로 가장 높았으며, 적정 반사도 밴드값은 R_rs(466), R_rs(493), R_rs(548), R_rs(641)를 사용하였을 때 trainning, test에서 각각 R²가 0.93, 0.90, NSE가 0.85, 0.69로 가장 높게 나타났다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.1D
• /
• pp.153-160
• /
• 2009

In relation to digital elevation model(DEM) production using high resolution satellite imagery, existing studies present that DEM accuracy differently show according to land cover property. This study therefore proposes auto-selection method of window size for correlation matching according to land cover property of IKONOS Geo-level stereo image. For this, land cover classified image is obtained by IKONOS color image with four bands. In addition, correlation-coefficients are computed at regular intervals in pixels of the window-search area to shorten of matching time. As the results, DEM by the proposed method showed more accurate than DEM using the fixed window-size matching. We estimate that accuracy of the proposed DEM improved more than DEM by digital map and ERDAS in agricultural land.

• Korea Journal of Hospital Management
• /
• v.28 no.2
• /
• pp.1-8
• /
• 2023

Purpose: This study was conducted to identify the impact of human rights sensitivity and patient rights awareness of first-year students in clinical practice on clinical practice adaptation and to prepare practical and systematic personality development program education alternatives to foster high-quality medical personnel. Method: As for the research method, an online survey of 155 medical and nursing students from two universities in G-do (76 medical students and 79 nursing students) was conducted, and the collected data were T-test, ANOVA, Scheffe test, Pearson's correlation coefficient and step-by-step multiple regression analysis using SPSS WIN/25.0. Findings: The results of the study are as follows. First, as a result of analyzing the differences in each variable according to general characteristics, human rights sensitivity had a significant impact on gender, patient rights recognition on personality type, and clinical practice adaptation had a significant impact on major selection motivation. Second, the factors affecting the adaptation of first-year college students to clinical practice had a significant impact on extroverted personality and patient rights perception among personality types (regression model results F=6.38 (p<).001), 24.2% explanatory power). Conclusion: This study suggests that education and policy efforts are needed to foster accurate awareness of human rights issues by developing flexible and flexible extracurricular activity programs in the operation of the curriculum to strengthen medical and nursing students' ability to adapt to clinical practice and improve awareness of human rights issues.

• Computers and Concrete
• /
• v.31 no.2
• /
• pp.85-95
• /
• 2023

Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO₂ and SiO₂+Al₂O₃+Fe₂O₃ contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R²), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.2A
• /
• pp.371-382
• /
• 2006

A buffeting analysis is utilized for the estimation of aerodynamic vulnerability of a cable-stayed bridge due to upcoming wind turbulences. The buffeting analysis requires several input parameters such as structural parameters, aerodynamic parameters, and aero-elastic parameters. This study is motivated to estimate the sensitivity of these parameters on buffeting responses. The Seohae bridge is selected as an example bridge. The investigated parameters consist of the inclination of lift and drag coefficient of stiffening girder section, exponential decay factors of span-wise distributed wind turbulences, roughness length, spectra of wind velocity fluctuation, and structural damping. The buffeting response showed high dependency on the input parameters. As conclusions, the importance of parameter selection is emphasized. A further study is also proposed for more general conclusions.

• Journal of Korean Society on Water Environment
• /
• v.39 no.5
• /
• pp.377-389
• /
• 2023

Chungcheongnam-do has been measuring the flow rate and water quality of streams in the province once a month since 2011 in order to water environment policies. Based on the results, after evaluating the coefficient of variation and the tendency of the water quality trend by using the Mann-Kendall test and Sen's Slope for each stream, the streams subject to priority introduction of Total Pollution Load Management at Tributaries were selected through the Stream Grouping Method. The water quality trend analysis results for 125 streams using the Mann-Kendall test and Sen's Slope were evaluated as streams showing a tendency of deteriorating water quality Biochemical oxygen demand (BOD): 13 streams, Total Phosphorus (T-P): 16 streams). Streams with deteriorating water quality were classified into A-D groups using the Stream Grouping Method. Group A, which has a high flow rate and high water quality, is a stream that requires priority management, and was selected as a stream for introduction of Total Pollution Load Management at Tributaries. There are 7 streams that need to be introduced into the BOD category, and there are 7 streams that need to be introduced into the T-P category. In this study, based on flow and water quality monitoring data accumulated over a long period of time (2011-2022), statistical techniques are used to select watersheds in which water quality is deteriorating. Accordingly, it is expected that it will be useful in establishing a water quality improvement plan in the future.

• Geomechanics and Engineering
• /
• v.35 no.6
• /
• pp.647-663
• /
• 2023

The investigation of the disc cutter wear prediction has an important guiding role in TBM equipment selection, project planning, and cost forecasting, especially when tunneling in a long-distance rock formations with high strength and high abrasivity. In this study, a comprehensive database of disc cutter wear data, geological properties, and tunneling parameters is obtained from a 1326 m excavated metro tunnel project in leptynite in Shenzhen, China. The failure forms and wear consumption of disc cutters on site are analyzed with emphasis. The results showed that 81% of disc cutters fail due to uniform wear, and other cutters are replaced owing to abnormal wear, especially flat wear of the cutter rings. In addition, it is found that there is a reasonable direct proportional relationship between the uniform wear rate (WR) and the installation radius (R), and the coefficient depends on geological characteristics and tunneling parameters. Thus, a preliminary prediction formula of the uniform wear rate, based on the installation radius of the cutterhead, was established. The correlation between some important geological properties (K_V and UCS) along with some tunneling parameters (F_n and p) and wear rate was discussed using regression analysis methods, and several prediction models for uniform wear rate were developed. Compared with a single variable, the multivariable model shows better prediction ability, and 89% of WR can be accurately estimated. The prediction model has reliability and provides a practical tool for wear prediction of disc cutter under similar hard rock projects with similar geological conditions.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.66 no.1
• /
• pp.1-13
• /
• 2024

This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R² (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

• Fisheries and Aquatic Sciences
• /
• v.27 no.6
• /
• pp.392-409
• /
• 2024

The present study aims to analyze the biological aspects and population dynamics of Indian mackerel in Barru waters. Data was collected in Barru for 11 months, from June 2022 to April 2023. The observed parameters of biological aspects included gonadal maturation stages (GMSs), size at first gonadal maturation, and length-weight relationship. Meanwhile, the aspects of population dynamics encompass age group, growth, mortality rate, and exploitation rate. Data analysis consisted of morphological selection of general maturation stages, Spearman-Kärber method in estimating gonadal first maturation size, Bhattacharya method in identifying age group, von Bertalanffy function through FISAT II to measure growth (L_∞ and K), Pauly Model to estimate mortality rate, Beverton & Holt Model to estimate Y/R, and virtual population analysis (VPA) analysis to estimate stock and fish yield. The results demonstrated that GMS I was observed to be dominant, followed by stages II and III. The initial gonadal maturation was estimated to be 17.98-19.28 cm (FL) for females and 17.98-19.27 cm (FL) for males. The length-weight relationship in male and female Indian mackerels indicated a positive allometric growth. The mode grouping analysis results from the fork length measurement revealed three age groups. It was also identified that the asymptotic length (L_∞) = 29.5 cm (fork length), growth rate coefficient (K) = 0.46 per year, and theoretical age at zero length (t₀) = -0.3576 per year. Total mortality (Z) = 2.67 per year, natural mortality (M) = 1.10 per year, fishing mortality (F) = 1.57 per year, and exploitation rate (E) = 0.59, the actual Y/R = 0.083 gram/recruitment, and optimal Y/R 0.03 gram/recruitment. Fishing mortality is higher than the natural mortality rate, and a high exploitation value (E > 0.5) also reflects over-exploitation. VPA analysis on fish yields and stock estimation reported a highly exploited rate between the 11.5 cm and 14.5 cm length classes and an exceeding current yield of 467.07 tons/year with a recommended yield of 233.53 tons/year to ensure population sustainability.