• Animal Bioscience
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• v.34 no.9
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• pp.1451-1459
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• 2021

Objective: The objectives of the study were to evaluate reproductive performance and selection response through genetic trend of community-based breeding programs (CBBPs) of Bonga sheep. Methods: Reproduction traits data were collected between 2012 and 2018 from Bonga sheep CBBPs. Phenotypic performance was analyzed using the general linear model procedures of Statistical Analysis System. Genetic parameters were estimated by univariate animal model for age at first lambing (AFL) and repeatability models for lambing interval (LI), litter size (LS), and annual reproductive rate (ARR) traits using restricted maximum likelihood method of WOMBAT. For correlations bivariate animal model was used. Best model was chosen based on likelihood ratio test. The genetic trends were estimated by the weighted regression of the average breeding value of the animals on the year of birth/lambing. Results: The overall least squares mean±standard error of AFL, LI, LS, and ARR were 375±12.5, 284±9.9, 1.45±0.010, and 2.31±0.050, respectively. Direct heritability estimates for AFL, LI, LS, and ARR were 0.07±0.190, 0.06±0.120, 0.18±0.070, and 0.25±0.203, respectively. The low heritability for both AFL and LI showed that these traits respond little to selection programs but rather highly depend on animal management options. The annual genetic gains were -0.0281 days, -0.016 days, -0.0002 lambs and 0.0003 lambs for AFL, LI, LS, and ARR, respectively. Conclusion: Implications of the result to future improvement programs were improving management of animals, conservation of prolific flocks and out scaling the CBBP to get better results.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.629-638
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• 2021

Background and objective: This study was conducted to develop diameter growth models for thinned Quercus glauca Thunb. (QGT) stands to inform production goals for treatment and provide the information necessary for the systematic management of this stands. Methods: This study was conducted on QGT stands, of which initial thinning was completed in 2013 to develop a treatment system. To analyze the tree growth and trait response for each thinning treatment, forestry surveys were conducted in 2014 and 2021, and a one-way analysis of variance (ANOVA) was executed. In addition, non-linear least squares regression of the PROC NLIN procedure was used to develop an optimal diameter growth model. Results: Based on growth and trait analyses, the height and height-to-diameter (H/D) ratio were not different according to treatment plot (p > .05). For the diameter of basal height (DBH), the heavy thinning (HT) treatment plot was significantly larger than the control plot (p < .05). As a result of the development of diameter growth models by treatment plot, the mean squared error (MSE) of the Gompertz polymorphic equation (control: 2.2381, light thinning: 0.8478, and heavy thinning: 0.8679) was the lowest in all treatment plots, and the Shapiro-Wilk statistic was found to follow a normal distribution (p > .95), so it was selected as an equation fit for the diameter growth model. Conclusion: The findings of this study provide basic data for the systematic management of Quercus glauca Thunb. stands. It is necessary to construct permanent sample plots (PSP) that consider stand status, location conditions, and climatic environments.

• Journal of Korean Clinical Nursing Research
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• v.28 no.2
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• pp.198-209
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• 2022

Purpose: The purpose of the study was to shorten the KPCS-1 (Korean Patient Classification System-1) for predicting nursing care need level and to explore whether the patients can be clustered by their acuity and dependency. Methods: The participants were inpatients in two surgical wards and two internal medicine wards at a teritory hospital during 14 days investigations. The KPCS-1 was evaluated once a day for all inpatients and 2,082 cases of data from a total of 411 patients were analyzed. Results: The items were reducted from 50 items to 26 items by partial least squares analysis and expert review. Through factor analysis, it was confirmed that hygiene, diet, elimination, and exercise were categorized as dependence factors. Patients were clustered with low acuity/low dependency (average score: 7.68±2.81/1.05±1.33), high acuity/low dependency (average score: 17.20±4.15/1.94±2.40), medium acuity/high dependency (average score: 13.56±5.30/9.66±2.64) through cluster analysis. The total score of the three groups for their nursing care needs was 8.73±3.36, 19.14±5.74, and 23.24±6.31 in order, and the results showed a statistically significant difference (F=1712.12, p<.001). Conclusion: The shortening of the KPCS-1 and the new criteria for categorizing patients according to acuity and dependence will increase clinical utility and be useful for manpower assignment criteria in detail.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.349-371
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• 2022

Purpose: Compared to the rapid growth rate of the domestic automotive LED industry so far, the predictive analysis method for demand forecasting or market outlook was insufficient. Accordingly, product characteristics are analyzed through the life trend of LEDs for automotive exterior lamps and the relative strengths of p and q using the Bass model. Also, future demands are predicted. Methods: We used sales data of a leading company in domestic market of automotive LEDs. Considering the autocorrelation error term of this data, parameters m, p, and q were estimated through the modified estimation method of OLS and the NLS(Nonlinear Least Squares) method, and the optimal method was selected by comparing prediction error performance such as RMSE. Future annual demands and cumulative demands were predicted through the growth curve obtained from Bass-NLS model. In addition, various nonlinear growth curve models were applied to the data to compare the Bass-NLS model with potential market demand, and an optimal model was derived. Results: From the analysis, the parameter estimation results by Bass-NLS obtained m=1338.13, p=0.0026, q=0.3003. If the current trend continues, domestic automotive LED market is predicted to reach its maximum peak in 2021 and the maximum demand is $102.23M. Potential market demand was $1338.13M. In the nonlinear growth curve model analysis, the Gompertz model was selected as the optimal model, and the potential market size was $2864.018M. Conclusion: It is expected that the Bass-NLS method will be applied to LED sales data for automotive to find out the characteristics of the relative strength of q/p of products and to be used to predict current demand and future cumulative demand.

• Journal of Korean Academy of Nursing
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• v.53 no.4
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• pp.426-438
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• 2023

Purpose: Walking practice is a representative indicator of the level of physical activity of local residents. Although the world health organization addressed reduction in prevalence of insufficient physical activity as a global target, the rate of walking practice in Korea has not improved and there are large regional disparities. Therefore, this study aimed to explore the spatial variations of walking practice and its associated factors in Korea. Methods: A secondary analysis was conducted using Community Health Outcome and Health Determinants Database 1.3 from Korea Centers for Disease Control and Prevention. A total of 229 districts was included in the analysis. We compared the ordinary least squares (OLS) and the geographically weighted regression (GWR) to explore the associated factors of walking practice. MGWR 2.2.1 software was used to explore the spatial distribution of walking practice and modeling the GWR. Results: Walking practice had spatial variations across the country. The results showed that the GWR model had better accommodation of spatial autocorrelation than the OLS model. The GWR results indicated that different predictors of walking practice across regions of Korea. Conclusion: The findings of this study may provide insight to nursing researchers, health professionals, and policy makers in planning health programs to promote walking practices in their respective communities.

• Smart Structures and Systems
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• v.31 no.2
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• pp.101-111
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• 2023

Structural health monitoring (SHM) covers a range of damage detection strategies for buildings. In real-time, SHM provides a basis for rapid decision making to optimise the speed and economic efficiency of post-event response. Previous work introduced an SHM method based on identifying structural nonlinear hysteretic parameters and their evolution from structural force-deformation hysteresis loops in real-time. This research extends and generalises this method to investigate the impact of a wide range of flag-shaped or pinching shape nonlinear hysteretic response and its impact on the SHM accuracy. A particular focus is plastic stiffness (K_p), where accurate identification of this parameter enables accurate identification of net and total plastic deformation and plastic energy dissipated, all of which are directly related to damage and infrequently assessed in SHM. A sensitivity study using a realistic seismic case study with known ground truth values investigates the impact of hysteresis loop shape, as well as added noise, on SHM accuracy using a suite of 20 ground motions from the PEER database. Monte Carlo analysis over 22,000 simulations with different hysteresis loops and added noise resulted in absolute percentage identification error (median, (IQR)) in K_p of 1.88% (0.79, 4.94)%. Errors were larger where five events (Earthquakes #1, 6, 9, 14) have very large errors over 100% for resulted K_p as an almost entirely linear response yielded only negligible plastic response, increasing identification error. The sensitivity analysis shows accuracy is reduces to within 3% when plastic drift is induced. This method shows clear potential to provide accurate, real-time metrics of non-linear stiffness and deformation to assist rapid damage assessment and decision making, utilising algorithms significantly simpler than previous non-linear structural model-based parameter identification SHM methods.

• The korean journal of orthodontics
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• v.53 no.3
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• pp.185-193
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• 2023

Objective: The growth and development of the mandible strongly depend on modeling changes occurring at its ramus. Here, we investigated covariance patterns between the morphology of the ramus and the rest of the face. Methods: Lateral cephalograms of 159 adults (55 males and 104 females) with no history of orthodontic treatment were collected. Geometric morphometrics with sliding semi-landmarks was used. The covariance between the ramus and face was investigated using a two-block partial least squares analysis (PLS). Sexual dimorphism and allometry were also assessed. Results: Differences in the divergence of the face and anteroposterior relationship of the jaws accounted for 24.1% and 21.6% of shape variation in the sample, respectively. Shape variation was greater in the sagittal plane for males than for females (30.7% vs. 17.4%), whereas variation in the vertical plane was similar for both sexes (23.7% for males and 25.4% for females). Size-related allometric differences between the sexes accounted for the shape variation to a maximum of 6% regarding the face. Regarding the covariation between the shapes of the ramus and the rest of the face, wider and shorter rami were associated with a decreased lower anterior facial height as well as a prognathic mandible and maxilla (PLS 1, 45.5% of the covariance). Additionally, a more posteriorly inclined ramus in the lower region was correlated with a Class II pattern and flat mandibular plane. Conclusions: The width, height, and inclination of the ramus were correlated with facial shape changes in the vertical and sagittal planes.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.501-514
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• 2023

When analyzing high dimensional data such as text data, if we input all the variables as explanatory variables, statistical learning procedures may suffer from over-fitting problems. Furthermore, computational efficiency can deteriorate with a large number of variables. Dimensionality reduction techniques such as feature selection or feature extraction are useful for dealing with these problems. The sparse principal component analysis (SPCA) is one of the regularized least squares methods which employs an elastic net-type objective function. The SPCA can be used to remove insignificant principal components and identify important variables from noisy observations. In this study, we propose a dimension reduction procedure for text data based on the SPCA. Applying the proposed procedure to real data, we find that the reduced feature set maintains sufficient information in text data while the size of the feature set is reduced by removing redundant variables. As a result, the proposed procedure can improve classification accuracy and computational efficiency, especially for some classifiers such as the k-nearest neighbors algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.147-152
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• 2023

This paper is a study of a linear algebra course taught in a high school on-demand course. Compared to the regular course, flipped learning was added to the course, and applications to signal processing related problems were covered in consideration of students' career aspirations. Overall, the class was a mixture of traditional lectures and flipped learning. Flipped learning was implemented twice. The flipped class consisted of pre-class, in-class and post-class. To verify the effectiveness of the course, a survey was conducted and most of the evaluation items were above 4. The topics of the flipped learning were Markov chains and least squares problem, which are very important in the field of signal processing.

• Journal of Radiation Protection and Research
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• v.48 no.3
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• pp.153-161
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• 2023

Background: Fukushima Medical University (FMU) is located 57 km northwest of the Fukushima Daiichi Nuclear Power Plant. Our laboratory has been conducting environmental radiation measurements continuously before and after the nuclear accident. We aimed to report the observed behavior of radiation originating from the released radioactive materials due to the accident, predict future trends, and disseminate the results to the local residents. Materials and Methods: Measurements of the counting rate by a diameter of 76 mm and a length of 76 mm thallium-doped sodium iodide (NaI[Tl]) scintillation detector (S-1211-T; Teledyne Brown Engineering Environmental Services) in the central part of the laboratory, and the dose rate outward at the window by NaI(Tl) scintillation detector and digital processor (EMF211; EMF Japan Co. Ltd.) were conducted. Results and Discussion: Measurements by Teledyne S-1211-T showed that in the early stages, radiation from radioactive isotopes with short half-lives was dominant, while radiation from radioactive isotopes with longer half-lives became dominant as the measurement period became longer. Through nonlinear least squares regression, both short and long half-lives were successfully determined. It was also possible to predict how the radiation dose would decrease. The environmental radiation trends around FMU were measured by the EMF211. Both measurements were affected by rainfall and snow accumulation. Decontamination work on the FMU campus impacted measurements by the EMF211 especially. Conclusion: The results of two types of measurements, one at the center and the other at the window side of the laboratory, were presented. By applying a simplified model, radiation from radioactive isotopes with short and long half-lives was identified. Based on these results, future trends were predicted, and the information was used for public communication with the local residents.