• Journal of Korean Academy of Nursing
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• v.49 no.3
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• pp.307-316
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• 2019

Purpose: This study was conducted to investigate the influence of emotional expressivity, emotional intelligence, affectivity, job autonomy, social support, and emotional labor on clinical nurses' individual well-being and to provide guidelines for interventions and strategies for its improvement. Methods: The sample consisted of 207 nurses recruited from a general hospital in Korea. The participants completed a structured self-report questionnaire comprising measures of emotional expressivity, emotional intelligence, positive affectivity, negative affectivity, job autonomy, supervisor support, coworker support, deep acting, surface acting, emotional exhaustion, and job satisfaction. Data were analyzed using SPSS statistics 22.0 and AMOS 22.0. Results: The final model was a good fit for the data based on the model fit indices. In the path analysis, surface acting, negative affectivity, supervisor support, and coworker support had statistically significant effects on emotional exhaustion, explaining 29.0% of the variance. Deep acting, emotional exhaustion, positive affectivity, and emotional intelligence had statistically significant effects on job satisfaction, explaining 43.0% of the variance. Conclusion: Effective strategies to improve clinical nurses' individual well-being should focus on surface acting, deep acting, affectivity, social support, and emotional intelligence. The results of this study can be utilized as base data to manage emotional labor and improve clinical nurses' individual well-being.

• Journal of Korean society of Dental Hygiene
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• v.23 no.4
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• pp.323-331
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• 2023

Objectives: This study was conducted to confirm the relationship between role conflict, positive psychological capital, and the psychological well-being of dental hygienists and to provide data for improving professionalism by increasing the psychological well-being of dental hygienists. Methods: From May 21 to June 22, 2023, 190 data were collected through an online community of dental hygienists. The collected data were analyzed IBM SPSS program ver. 23.0 and AMOS ver. 26.0. Statistical analysis methods, convergent validity test, structural equation model analysis, and model fit were performed using independent sample t-test, one-way ANOVA, confirmatory factor analysis, structural model fit test, and path analysis. Results: The lower the role conflict of dental hygienists and the higher the positive psychological capital, the higher the psychological well-being. The lower the role conflict, the higher the positive psychological capital. However, there was no mediating effect between positive psychological capital and role conflict, as it was not statistically significant. Analysis of the total effect of role conflict and positive psychological capital on psychological well-being revealed that positive psychological capital had a greater influence on psychological well-being than role conflict. Conclusions: Psychological well-being was related to positive psychological capital and role conflict. Future efforts are needed to improve positive psychological capital and psychological well-being of dental hygienists and reduce role conflict.

• Geomechanics and Engineering
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• v.35 no.5
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• pp.499-509
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• 2023

The objective of this study is to perform outlier analysis to obtain the distribution of groundwater levels through the best model. The groundwater levels are measured in 10, 25 and 30 piezometers in Seoul, Daejeon and Suncheon in South Korea. Fifty-eight empirical distribution functions were applied to determine a suitable fit for the measured groundwater levels. The best fitted models based on the measured values are determined as the Generalized Pareto distribution, the Johnson SB distribution and the Normal distribution for Seoul, Daejeon and Suncheon, respectively; the reliability is estimated through the Anderson-Darling method. In this study, to choose the appropriate confidence interval, the relationship between the amount of outlier data and the confidence level is demonstrated, and then the 95% is selected at a reasonable confidence level. The best model shows a smaller error ratio than the GEV while the Mahalanobis distance and outlier labelling methods results are compared and validated. The outlier labelling and Mahalanobis distance based on median shown higher validated error ratios compared to their mean equivalent suggesting, the methods sensitivity to data structure.

• Journal of Forest and Environmental Science
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• v.28 no.4
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• pp.205-211
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• 2012

Yield tables are a frequently used data base for regional timber resource forecasting. A normal yield table is based on two independent variables, age and site (species constant), and applies to fully stocked (or normal) stands while empirical yield tables are based on average rather than fully stocked stands. Normal and empirical yield tables essentially have many limitations. The limitations of normal and empirical yield tables led to the development of variable density yield tables. Mathematical models for estimating timber yields are usually developed by fitting a suitable equation to observed data. The model is then used to predict yields for conditions resembling those of the original data set. It may be accurate for the specific conditions, but of unproven accuracy or even entirely useless in other circumstances. Thus, these models tend to be specific rather than general and require validation before applying to other areas. Dalbergia sissoo forms a major portion of irrigated plantations in the hot desert of India and is an important timber tree species where stem wood is primarily used as timber. Variable density yield model is not available for this species which is very crucial in long-term planning for managing the plantations on a sustained basis. Thus, the objective of this study was to develop variable density yield model based on the data collected from 30 sample plots of D. sissoo laid out in IGNP area of Rajasthan State (India) and measured annually for 5 years. The best approximating model was selected based on the fit statistics among the models tested in the study. The model develop was evaluated based on quantitative and qualitative statistical criteria which showed that the model is statistically sound in prediction. The model can be safely applied on D. sissooo plantations in the study area or areas having similar conditions.

• Korean journal of applied entomology
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• v.26 no.1 s.70
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• pp.31-36
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• 1987

A piecewise linear regression model able to describe disease progress curves with simplicity and flexibility was developed in this study. The model divides whole epidemic into several pieces of simple linear regression based on changes in pattern of disease progress in the epidemic and then incorporates the pieces of linear regression into a single mathematical function using indicator variables. When twelve epidemic data obtained from the field experiments were fitted to the piecewise linear regression model, logistic model and Gompertz model to compare statistical fit, goodness of fit was greatly improved with piecewise linear regression compared to other two models. Simplicity, flexibility, accuracy and ease in parameter estimation of the piece-wise linear regression model were described with examples of real epidemic data. The result in this study suggests that piecewise linear regression model is an useful technique for modeling plant disease epidemic.

• Korean Journal of Clinical Pharmacy
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• v.26 no.4
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• pp.312-317
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• 2016

Objective: Midazolam is mainly metabolized by cytochrome P450 (CYP) 3A. Inhibition or induction of CYP3A can affect the pharmacological activity of midazolam. The aims of this study were to develop a population pharmacokinetic (PK) model and evaluate the effect of CYP3A-mediated interactions among ketoconazole, rifampicin, and midazolam. Methods: Three-treatment, three-period, crossover study was conducted in 24 healthy male subjects. Each subject received 1 mg midazolam (control), 1 mg midazolam after pretreatment with 400 mg ketoconazole once daily for 4 days (CYP3A inhibition phase), and 2.5 mg midazolam after pretreatment with 600 mg rifampicin once daily for 10 days (CYP3A induction phase). The population PK analysis was performed using a nonlinear mixed effect model ($NONMEM^{(R)}$ 7.2) based on plasma midazolam concentrations. The PK model was developed, and the first-order conditional estimation with interaction was applied for the model run. A three-compartment model with first-order elimination described the PK. The influence of ketoconazole and rifampicin, CYP3A5 genotype, and demographic characteristics on PK parameters was examined. Goodness-of-fit (GOF) diagnostics and visual predictive checks, as well as bootstrap were used to evaluate the adequacy of the model fit and predictions. Results: Twenty-four subjects contributed to 900 midazolam concentrations. The final parameter estimates (% relative standard error, RSE) were as follows; clearance (CL), 31.8 L/h (6.0%); inter-compartmental clearance (Q) 2, 36.4 L/h (9.7%); Q3, 7.37 L/h (12.0%), volume of distribution (V) 1, 70.7 L (3.6%), V2, 32.9 L (8.8%); and V3, 44.4 L (6.7%). The midazolam CL decreased and increased to 32.5 and 199.9% in the inhibition and induction phases, respectively, compared to that in control phase. Conclusion: A PK model for midazolam co-treatment with ketoconazole and rifampicin was developed using data of healthy volunteers, and the subject's CYP3A status influenced the midazolam PK parameters. Therefore, a population PK model with enzyme-mediated drug interactions may be useful for quantitatively predicting PK alterations.

• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.98-104
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• 2018

Macroscopic accident analyses have been conducted to incorporate transportation safety into long-term transportation planning. In macro-level accident prediction model, exposure variable(e.g. a settled population) have been used as fundamental explanatory variable under the concept that each trip will be subjected to a probable risk of accident. However, a settled population may be embedded error by exclusion of active population concept. The objective of this research study is to develop macro-level accident prediction model using floating population variable(concept of including a settled population and active population) collected from mobile phone data. The concept of accident prediction models is introduced utilizing exposure variable as explanatory variable in a generalized linear regression with assumption of a negative binomial error structure. The goodness of fit of model using floating population variable is compared with that of the each models using population and the number of household variables. Also, log transformation models are additionally developed to improve the goodness of fit. The results show that the log transformation model using floating population variable is useful for capturing the relationships between accident and exposure variable and generally perform better than the models using other existing exposure variables. The developed model using floating population variable can be used to guide transportation safety policy decision makers to allocate resources more efficiently for the regions(or zones) with higher risk and improve urban transportation safety in transportation planning step.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.143-149
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• 2018

The purpose of this study was to construct and validate a middle - aged exercise adherence model. The model was designed based on self - determination theory. Participants were 215 middle-aged men and women aged 40-60 who had been exercising for more than six months. Data was collected from four big cities of Seoul, Busan, Gwangju and Daejeon in Korea, using a questionnaire consisting of basic psychological needs, intrinsic motivation, social support, and exercise adherence. Data were analyzed with SPSS 19.0 and AMOS 20.0. Social support and exercise adherence of the questionnaire were partially revised and verified by confirmatory factor analysis. The results of the study were as follows. The model's fit indices: GFI = .938, AGFI) = .915, NFI = .912, CFI = .941, and RMSEA = 0.041. The model satisfied the model fit of the structural model equation. This study model based on self - determination theory was confirmed that basic psychological needs, intrinsic motivation, and social support were important factors for the middle - aged's exercise adherence. Basic psychological need and intrinsic motivation had a direct influence on the adherence of exercise, and social support indirectly influenced the exercise adherence through intrinsic motivation. Both basic psychological needs and social support directly affected internal motivation. The most influential factor in the middle - aged's exercise adherence was intrinsic motivation. In conclusion, it was found that intrinsic motivation such as interest and fun is important for the middle - aged to continue the exercise. Also, the basic psychological needs were important for middle aged's exercise adherence. The results of this study will provide basic data for restoring or maintaining health by continuing exercise. Strategies that enhance intrinsic motivation are needed when a chronic ill person needs to continue long-term exercising.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.225-225
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• 2015

Drought events usually evolve slowly in time and their impacts generally span a long period of time. This indicates that the sequence of drought is not completely random. The Hidden Markov Model (HMM) is a probabilistic model used to represent dependences between invisible hidden states which finally result in observations. Drought characteristics are dependent on the underlying generating mechanism, which can be well modelled by the HMM. This study employed a HMM with Gaussian emissions to fit the Standardized Precipitation Index (SPI) series and make multi-step prediction to check the drought characteristics in the future. To estimate the parameters of the HMM, we employed a Bayesian model computed via Markov Chain Monte Carlo (MCMC). Since the true number of hidden states is unknown, we fit the model with varying number of hidden states and used reversible jump to allow for transdimensional moves between models with different numbers of states. We applied the HMM to several stations SPI data in South Korea. The monthly SPI data from January 1973 to December 2012 was divided into two parts, the first 30-year SPI data (January 1973 to December 2002) was used for model calibration and the last 10-year SPI data (January 2003 to December 2012) for model validation. All the SPI data was preprocessed through the wavelet denoising and applied as the visible output in the HMM. Different lead time (T= 1, 3, 6, 12 months) forecasting performances were compared with conventional forecasting techniques (e.g., ANN and ARMA). Based on statistical evaluation performance, the HMM exhibited significant preferable results compared to conventional models with much larger forecasting skill score (about 0.3-0.6) and lower Root Mean Square Error (RMSE) values (about 0.5-0.9).

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1067-1076
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• 2006

Although the proportional hazards model is the most common approach used for studying the relationship of event times and covariates, alternative models are needed for occasions when it does not fit data. In the two-sample case, proportional odds models are useful for fitting data whose hazard rates converge asymptotically. In this thesis, we propose a new estimator of the relative odds ratio of the proportional odds model when two independent random samples are observed under uncensorship. We prove the asymptotic normality and consistency of the estimator by using martingale-representation. The efficiency of the proposed is assessed through a simulation study.