• KOREAN JOURNAL OF CROP SCIENCE
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• v.27 no.1
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• pp.84-86
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• 1982

The experiment was conducted with three varieties (Hicks, Burley 21, and Sohyang) and cultivation type (Improved mulching, general mulching, and non mulching) of NC 2326 to model to curve of tabacco growth against time. The basic growth data were obtained by harvest method at intervals of ten days from transplanting at 7-8 times and analyzed by polynomial regression, orthogonal polynomial, and logarithmic transformation. It is shown that the C model of growth curve: T = A +$\sqrt{(1.4 AK + K)}$2K provides an excellent fit.

• Geomechanics and Engineering
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• v.16 no.5
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• pp.539-545
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• 2018

Longitudinal Displacement Profile (LDP) is an appropriate tool for determination of the displacement magnitude of the tunnel walls as a function of the distance to the tunnel face. Some useful formulations for calculation of LDP have been developed based on the monitoring data on site or by 3D numerical simulations. However, the presented equations are only based on the tunnel dimensions and for different quality of rock masses proposed a unique LDP. In the present study, it is tried to present a new formulation, for calculation of LDP, on the basis of Rock mass quality. For this purpose, a comprehensive numerical simulation program was developed to investigate the effect of rock mass quality on the LDP. Results of the numerical modelling were analyzed and the least square technique was used for fitting an appropriate curve on the derived data from the numerical simulations. The proposed formulation in the present study, is a logistic function and the constants of the logistic function were predicted by rock mass quality index (GSI). Results of this study revealed that, the LDP curves of the tunnel surrounded by rock masses with high quality (GSI>60) match together; because the rock mass deformation varies over an elastic range.

• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.339-350
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• 2021

In medical research, the risk factors associated with human diseases need to be identified to predict the incidence rate and determine the treatment plan. Logistic regression analysis is primarily used in order to select risk factors. However, individuals who are unfamiliar with statistics outcomes have trouble using these methods. In this study, we develop a nomogram that graphically represents the numerical association between the disease and risk factors in order to identify the risk factors for delirium and to interpret and use the results more effectively. By using the logistic regression model, we identify risk factors related to delirium, construct a nomogram and predict incidence rates. Additionally, we verify the developed nomogram using a receiver operation characteristics (ROC) curve and calibration plot. Nursing home, stroke/epilepsy, metabolic abnormality, hemodynamic instability, and analgesics were selected as risk factors. The validation results of the nomogram, built with the factors of training set and the test set of the AUC showed a statistically significant determination of 0.893 and 0.717, respectively. As a result of drawing the calibration plot, the coefficient of determination was 0.820. By using the nomogram developed in this paper, health professionals can easily predict the incidence rate of delirium for individual patients. Based on this information, the nomogram could be used as a useful tool to establish an individual's treatment plan.

• Journal of Korean Society of Forest Science
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• v.99 no.2
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• pp.163-171
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• 2010

Forest fires have potentials to change the structure and function of forest ecosystems and significantly influence on atmosphere and biogeochemical cycles. Forest fire also affects the quality of public benefits such as carbon sequestration, soil fertility, grazing value, biodiversity, or tourism. The prediction of fire occurrence and its spread is critical to the forest managers for allocating resources and developing the forest fire danger rating system. Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behaviors and its spread. Thus, meteorological factors as well as social factors were considered in the fire danger rating systems. A total of 298 forest fires occurred during the fall season from 2002 to 2006 in South Korea were considered for developing a logistic model of forest fire occurrence. The results of statistical analysis show that only effective humidity and temperature significantly affected the logistic models (p<0.05). The results of ROC curve analysis showed that the probability of randomly selected fires ranges from 0.739 to 0.876, which represent a relatively high accuracy of the developed model. These findings would be necessary for the policy makers in South Korea for the prevention of forest fires.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.2
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• pp.111-118
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• 2020

Recently, bioenergy research using microalgae, one of the most promising biofuel sources, has attracted much attention. Cell disruption, which can be classified as physical or chemical, is essential to extract functional ingredients from microalgae. In this study, we investigated the cell disruption efficiency of Chlorella sp. using low-frequency non-focused ultrasound (LFNFU). This is a continuously physical method that is superior to chemical methods with respect to environmental friendliness and low processing cost. A flat panel photobioreactor was employed to cultivate Chlorella sp. and its growth curve was fitted both with Logistic and Gompertz models. The temporal change in cell reduction by cell disruption using LFNFU was fitted with a Logistic model. The experimental conditions that were investigated were the initial concentration of microalgal cells, relative amplitude of output ultrasound waves, processing volume of microalgal cells, and initial pH value. The optimal conditions for the most efficient cell disruption were determined through the various tests.

• Journal of Technology Innovation
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• v.16 no.1
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• pp.215-237
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• 2008

KIPRIS patent database was analyzed for identifying the technological status of Daedeok Innopolis Biocluster. It was found that the pattern of activities among various technological areas in Daedeok Biocluster is similar to that of an advanced country rather than those of other cities in Korea. The technological growth in Daedeok Innopolis Biocluster is in the progressive stage, which may be due to the innovative activities rather than the rise in the number of new firms or institutes. The concentration of technology in Daedeok Innopolis Biocluster is a favorable condition for the innovation activities. The trend for the technological concentration was remarkably consistent with the growth curve that a population increases according to the logistic equation. The logistic growth may be represented by the result of competition due to the limited resource allocation and then innovation cluster is corresponding to the ecosystem composed by biological individuals. There is strong competition in Daedeok Innopolis Biocluster in around 2009, so the government might make a policy to encourage the technological diversity for healthy knowledge ecosystem.

• Journal of Environmental Policy
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• v.2 no.1
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• pp.77-86
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• 2003

Particle-size distributions (PSDs) are widely used for the estimation of soil hydraulic properties. The objective of this study was to select the best model among the nine PSD models with different underlying assumptions, by using a variety of Korean soils. The Fredlund model with four parameters, the logistic growth curve, and Weibull distribution model showed the highest performance compared to the other models with the majority of soils studied. It was interesting to find that the logistic growth function with no fitting parameters showed a great fitting performance.

• Psychiatry investigation
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• v.15 no.11
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• pp.1037-1045
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• 2018

Objective The present study aimed to identify risk factors for future SI and to predict individual-level risk for future or persistent SI among college students. Methods Mental health check-up data collected over 3 years were retrospectively analyzed. Students were categorized as suicidal ideators and non-ideators at baseline. Logistic regression analyses were performed separately for each group, and the predicted probability for each student was calculated. Results Students likely to exhibit future SI had higher levels of mental health problems, including depression and anxiety, and significant risk factors for future SI included depression, current SI, social phobia, alcohol problems, being female, low self-esteem, and number of close relationships and concerns. Logistic regression models that included current suicide ideators revealed acceptable area under the curve (AUC) values (0.7-0.8) in both the receiver operating characteristic (ROC) and precision recall (PR) curves for predicting future SI. Predictive models with current suicide non-ideators revealed an acceptable level of AUCs only for ROC curves. Conclusion Several factors such as low self-esteem and a focus on short-term rather than long-term outcomes may enhance the prediction of future SI. Because a certain range of SI clearly necessitates clinical attention, further studies differentiating significant from other types of SI are necessary.

• Journal of Korean Port Research
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• v.15 no.1
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• pp.19-26
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• 2001

The purpose of this study is to improve the strategies for transportation systems of container cargoes in Busan port. Therefore, container cargoes forecasting is done through logistic methods based on past trends. In 2011, container cargoes demand was forecasted 8.791 million TEU(T/S including 12.559 million TEU). In order to improve transportation systems of container cargoes, the conclusions of this study can be summarized as follows ; \circled1 port facilities expansion, \circled2 diversity of container transport modes, \circled3 make up ICD and exclusive container roads, \circled4 the second Seoul-Busan Expressway.

• Korean Journal of Radiology
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• v.23 no.8
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• pp.811-820
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• 2022

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.