• Animal Bioscience
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• v.37 no.4
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• pp.622-630
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• 2024

Objective: Pig breeders cannot obtain phenotypic information at the time of selection for sow lifetime productivity (SLP). They would benefit from obtaining genetic information of candidate sows. Genomic data interpreted using deep learning (DL) techniques could contribute to the genetic improvement of SLP to maximize farm profitability because DL models capture nonlinear genetic effects such as dominance and epistasis more efficiently than conventional genomic prediction methods based on linear models. This study aimed to investigate the usefulness of DL for the genomic prediction of two SLP-related traits; lifetime number of litters (LNL) and lifetime pig production (LPP). Methods: Two bivariate DL models, convolutional neural network (CNN) and local convolutional neural network (LCNN), were compared with conventional bivariate linear models (i.e., genomic best linear unbiased prediction, Bayesian ridge regression, Bayes A, and Bayes B). Phenotype and pedigree data were collected from 40,011 sows that had husbandry records. Among these, 3,652 pigs were genotyped using the PorcineSNP60K BeadChip. Results: The best predictive correlation for LNL was obtained with CNN (0.28), followed by LCNN (0.26) and conventional linear models (approximately 0.21). For LPP, the best predictive correlation was also obtained with CNN (0.29), followed by LCNN (0.27) and conventional linear models (approximately 0.25). A similar trend was observed with the mean squared error of prediction for the SLP traits. Conclusion: This study provides an example of a CNN that can outperform against the linear model-based genomic prediction approaches when the nonlinear interaction components are important because LNL and LPP exhibited strong epistatic interaction components. Additionally, our results suggest that applying bivariate DL models could also contribute to the prediction accuracy by utilizing the genetic correlation between LNL and LPP.

• International Area Studies Review
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• v.21 no.4
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• pp.173-194
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• 2017

The research study conducted logistics regression analysis through HLM (Hierarchical Linear Modeling) and presented the value of the outcome in order to investigate characteristic factors of demanders which influence the intention to move into public rental housing. (1) The results of the regression analysis of characteristic factors of household and housing were analyzed as having the significant effect on the intention to move in public rental housing, according to residents moving in monthly rent housing, residents' occupations, rental housing, the number of household, the location of surrounding public rental housing, monthly average income, children's educational level, the number of children, the types of housing and one's own house, in order, out of the types of housing tenure. (2) The results of the regression analysis of characteristic factors of the conditions of location were analyzed that out of the conditions of location of the top five areas in public rental rates, what influences significant effects on the intention to move in public rental housing is the location of surrounding rental housing, income, the number of household and children, children's educational level, job state, housing types, ones' own house, rent housing, monthly rent housing, in order. (3) In case of Seoul, Expanding public rental housing is inevitable in order to stabilize ordinary people's housing stability, owing to the high and rental prices of private housing. Nevertheless, an accurate analysis of the intention to move in public rental housing has not been conducted. Eventually, the research was, thus, conducted, based on the fact that the preference on public housing is low. According to the analytic results of the study, it is required for the government institutions and agencies should consider individual and local characteristics and provide an alternative that meets the real situation, in order to help ordinary citizens with low incomes stabilize housing.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.1
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• pp.34-41
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• 2008

Future impact of anthropogenic climate-induced change on ecological regime has been an issue and information on water temperature is required for estimating coastal aquatic environment. One way to induce water temperature is to relate water temperature to air temperature and GCM is able to provide future air temperature data to do this. However, GCM data of low spatial resolution doesn't incorporate local or sitespecific air temperature in need of application, and downscaling processes are essential. In this study, a linear regression is used to relate nationally averaged air temperature to local area for the time period of 2000-2005. The RMSE for calibration (2000-2005) is 1.584, while the RMSE for validation is 1.675 for the year 2006 and 1.448 for the year 2007. The NSC for calibration (2000-2005) is 0.962, while the NSC for validation is 0.955 for the year 2006 and 0.963 for the year 2007. The results show that the linear regression is a good tool to relate local air temperature to nationally averaged air temperature with $1.0{\sim}2.0^{\circ}C$ of RMSE. The study will contribute to estimate future impact of climate-induced change on aquatic environment in Korean coastal zone.

• Journal of Korea Water Resources Association
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• v.44 no.4
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• pp.275-283
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• 2011

A local or site-specific water temperature is downscaled from the nation-wide air temperature that represents simulation by General Circulation Model (GCM). Both two-step and one-step method are tested and compared in three sites: Masan Bay, Lake Sihwa, and Nakdong River Estuary. Two-step method uses a linear regression model as the first step that converts nation-wide air temperature into local air temperature, and the corresponding coefficient of determination is in the range of 0.98~0.99. The second step that converts air temperature into water temperature uses a nonlinear curve, so called S-curve, and the corresponding root mean squared error (RMSE) is 2.07 for rising limb in Masan Bay, 1.93 for falling limb in Masan Bay, 2.59 for Lake Sihwa, and 1.58 for Nakdong River Estuary. In a similar way, one-step method is performed to directly convert nation-wade air temperature into local water temperature, and the corresponding RMSE is 2.28 for rising limb in Masan Bay, 1.89 for falling limb in Masan Bay, 2.55 for Lake Sihwa, and 1.52 for Nakdong River Estuary. Consequently both methods show a similar level of performance, and one-step method is recommendable in that it is simple and practical in relative terms.

• Journal of Agricultural Extension & Community Development
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• v.28 no.1
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• pp.41-52
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• 2021

It is important to understand the factors that affect the business performance of insect farm for continuous insect farm management. The purpose of this study is to investigate factors influencing the business performance of insect farm. For this study, 1,577 questionnaires were collected through a telephone survey targeting insect farm owner. As a result of analysis using linear multiple regression analysis, the factors affecting total sales were gender, age, business experience, number of workers, and national and local government support projects. The factors affecting the net profit rate were age, business experience, number of workers, national and local government support projects, and education. When the gender of the business operator is male, it only affected the increase in total sales, and it was found that both the total sales amount and the net profit margin increased with the younger the business operator's age.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.19 no.4
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• pp.474-482
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• 2012

Purpose: The purposes of this study were to identify knowledge, health belief and compliance in patients with hypertension and to identify the most important predictors for compliance of hypertensive patient. Method: The participants in this study were 117 patients who were receiving treatment for hypertension at E. university hospital or one of three local clinics in D-city. Data were collected using a knowledge measurement instrument, health belief scale, and an instrument on compliance. Collected data were analyzed using $X^2$ test, ANOVA, multiple linear regression with PASW statistics 18.0 program. Results: There were statistically significantly positive correlations between knowledge of hypertension and health belief, health belief and compliance. But there was no correlation between knowledge of hypertension and compliance. In the multiple regression analysis, perceived barriers, perceived severity, perceived benefits were significant predictors to explain compliance and accounted for 54.1% of the variance in compliance. Conclusion: The results of the study indicate that health belief and compliance are significantly strongly correlated. Thus it is suggested that nursing interventions to improve compliance should include nursing care plans to increase health belief, perceived severity, perceived benefit and to decrease perceived barrier.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.10
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• pp.1406-1410
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• 2014

Age at first calving is an important trait for achieving earlier reproductive performance. To detect quantitative trait loci (QTL) for reproductive traits, a genome wide association study was conducted on the 96 Hanwoo cows that were born between 2008 and 2010 from 13 sires in a local farm (Juk-Am Hanwoo farm, Suncheon, Korea) and genotyped with the Illumina 50K bovine single nucleotide polymorphism (SNP) chips. Phenotypes were regressed on additive and dominance effects for each SNP using a simple linear regression model after the effects of birth-year-month and polygenes were considered. A forward regression procedure was applied to determine the best set of SNPs for age at first calving. A total of 15 QTL were detected at the comparison-wise 0.001 level. Two QTL with strong statistical evidence were found at 128.9 Mb and 111.1 Mb on bovine chromosomes (BTA) 2 and 7, respectively, each of which accounted for 22% of the phenotypic variance. Also, five significant SNPs were detected on BTAs 10, 16, 20, 26, and 29. Multiple QTL were found on BTAs 1, 2, 7, and 14. The significant QTLs may be applied via marker assisted selection to increase rate of genetic gain for the trait, after validation tests in other Hanwoo cow populations.

• Steel and Composite Structures
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• v.43 no.4
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• pp.501-509
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• 2022

Cracks are common defects in concrete structures. Thus far, crack inspection has been manually performed using the contact inspection method. This manpower-dependent method inevitably increases the cost and work hours. Various non-contact studies have been conducted to overcome such difficulties. However, previous studies have focused on developing a methodology for non-contact inspection or local quantitative detection of crack width or length on concrete surfaces. However, crack depth can affect the safety of concrete structures. In particular, although macrocrack depth is structurally fatal, it is difficult to find it with the existing method. Therefore, an experimental investigation based on non-contact infrared thermography and multivariate machine learning was performed in this study to estimate the hidden macrocrack depth. To consider practical applications for inspection, an experiment was conducted that considered the simulated piloting of an unmanned aerial vehicle equipped with infrared thermography equipment. The crack depths (10-60 mm) were comparatively evaluated using linear regression, gradient boosting, and random forest (AI regression methods).

• Journal of Medicine and Life Science
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• v.20 no.3
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• pp.107-114
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• 2023

Epidemiological control of coronavirus disease 2019 (COVID-19) is needed to estimate the infection period of confirmed cases and identify potential cases. The present study, targeting confirmed cases for which the time of COVID-19 symptom onset was disclosed, aimed to investigate the relationship between intervals (day) from symptom onset to testing the cycle threshold (CT) values of real-time reverse transcription-polymerase chain reaction. Of the COVID-19 confirmed cases, those for which the date of suspected symptom onset in the epidemiological investigation was specifically disclosed were included in this study. Interval was defined as the number of days from symptom onset (as disclosed by the patient) to specimen collection for testing. A locally weighted regression smoothing (LOWESS) curve was applied, with intervals as explanatory variables and CT values (CTR for RdRp gene and CTE for E gene) as outcome variables. After finding its non-linear relationship, a polynomial regression model was applied to estimate the 95% confidence interval values of CTR and CTE by interval. The application of LOWESS in 331 patients identified a U-shaped curve relationship between the CTR and CTE values according to the number of interval days, and both CTR and CTE satisfied the quadratic model for interval days. Active application of these results to epidemiological investigations would minimize the chance of failing to identify individuals who are in contact with COVID-19 confirmed cases, thereby reducing the potential transmission of the virus to local communities.

• Proceedings of the KSRS Conference
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• v.1
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• pp.305-307
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• 2006

A multi-spectral photometer was set up as an NASA/AERONET site at Kinki University campus in Higashi-Osaka in 2002 for measuring urban aerosols. In addition, the SPM-613D (Kimoto Electric) commenced measurement of suspended particles matter (SPM) as $PM_{10}$ and $PM_{2.5}$ on March 15, 2004 at the same AERONET site. The obtained results revealed that the poor air quality of the Higashi-Osaka site is due not only to anthropogenic particles from local emissions, such as diesel vehicles and chemical industries, but also to dust particles brought from continental desert areas by large scale climatic conditions. To understand the characteristics of background atmosphere over Higashi-Osaka, we examined the relationship between $PM_{2.5}$ concentration and aerosol optical thickness (AOT) at a wavelength of 0.87 μm based on AERONET data for background atmosphere (AOT<0.2). We obtained a linear regression line between AOT and $PM_{2.5}$ concentration. Using the linear relationships between AOT and $PM_{2.5}$, we show ground-level concentrations of $PM_{2.5}$ of background atmosphere from Terra/MODIS satellite measurements.