• Journal of Ecology and Environment
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• v.48 no.2
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• pp.196-206
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• 2024

Background: Most of the biomass equations were developed using sample trees collected mainly from pan-tropical and tropical regions that may over- or underestimate biomass. Site-specific models would improve the accuracy of the biomass estimates and enhance the country's measurement, reporting, and verification activities. The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. Data was analyzed using SAS procedures. All relevant statistical tests (normality, multicollinearity, and heteroscedasticity) were performed. Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. The coefficient of determination (R² and adjusted [adj]-R²), the Akaike Information Criterion (AIC) and the Schwarz Bayesian information Criterion was employed to select most appropriate models. Results: For the general total AGB models, adj-R² ranged from 0.71 to 0.85, and model 9 with diameter at stump height at 10 cm (DSH₁₀), ρ and crown width (CW) as predictor variables, performed best according to RMSE and AIC. For the merchantable stem models, adj-R² varied from 0.73 to 0.82, and model 8) with combination of ρ, diameter at breast height and height (H), CW and DSH₁₀ as predictor variables, was best in terms of RMSE and AIC. The results showed that a best-fit model for above-ground biomass of tree components was developed. AGB_Stem = exp {-1.8296 + 0.4814 natural logarithm (Ln) (ρD²H) + 0.1751 Ln (CW) + 0.4059 Ln (DSH₃₀)} AGB_Branch = exp {-131.6 + 15.0013 Ln (ρD²H) + 13.176 Ln (CW) + 21.8506 Ln (DSH₃₀)} AGB_Foliage = exp {-0.9496 + 0.5282 Ln (DSH₃₀) + 2.3492 Ln (ρ) + 0.4286 Ln (CW)} AGB_Total = exp {-1.8245 + 1.4358 Ln (DSH₃₀) + 1.9921 Ln (ρ) + 0.6154 Ln (CW)} Conclusions: The results demonstrated that the development of local models derived from an appropriate sample of representative species can greatly improve the estimation of total AGB.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.345-357
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• 2023

Nuclear forensics has been understood as a mendatory component in the international society for nuclear material control and non-proliferation verification. Radiochronometry of nuclear activities for nuclear forensics are decay series characteristics of nuclear materials and the Bateman equation to estimate when nuclear materials were purified and produced. Radiochronometry values have uncertainty of measurement due to the uncertainty factors in the estimation process. These uncertainties should be calculated using appropriate evaluation methods that are representative of the accuracy and reliability. The IAEA, US, and EU have been researched on radiochronometry and uncertainty of measurement, although the uncertainty calculation method using the Bateman equation is limited by the underestimation of the decay constant and the impossibility of estimating the age of more than one generation, so it is necessary to conduct uncertainty calculation research using computer simulation such as Monte Carlo method. This highlights the need for research using computational simulations, such as the Monte Carlo method, to overcome these limitations. In this study, we have analyzed mathematical models and the LHS (Latin Hypercube Sampling) methods to enhance the reliability of radiochronometry which is to develop an uncertainty algorithm for nuclear material radiochronometry using Bateman Equation. We analyzed the LHS method, which can obtain effective statistical results with a small number of samples, and applied it to algorithms that are Monte Carlo methods for uncertainty calculation by computer simulation. This was implemented through the MATLAB computational software. The uncertainty calculation model using mathematical models demonstrated characteristics based on the relationship between sensitivity coefficients and radiative equilibrium. Computational simulation random sampling showed characteristics dependent on random sampling methods, sampling iteration counts, and the probability distribution of uncertainty factors. For validation, we compared models from various international organizations, mathematical models, and the Monte Carlo method. The developed algorithm was found to perform calculations at an equivalent level of accuracy compared to overseas institutions and mathematical model-based methods. To enhance usability, future research and comparisons·validations need to incorporate more complex decay chains and non-homogeneous conditions. The results of this study can serve as foundational technology in the nuclear forensics field, providing tools for the identification of signature nuclides and aiding in the research, development, comparison, and validation of related technologies.

• Journal of Christian Education in Korea
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• v.78
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• pp.213-229
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• 2024

Purpose of Study : The purpose of this study is to verify whether self-control has a mediating effect on the influence of grit on college students' self-efficacy, which has a significant impact on academic achievement. Research content and methods : In order to verify the influence of grit and self-control on college students' self-efficacy, this study first selected measurement tools for self-efficacy, grit, and self-control and created an online questionnaire. Next, a survey was conducted on 128 students at University A in Chungcheongnam-do. Descriptive statistical analysis and bivariate correlation analysis were performed on the collected data to verify the normality of the data and multicollinearity between factors. In addition, multiple regression analysis was used to verify the influence of grit and self-control on self-efficacy. Next, the effect of grit on self-efficacy was analyzed using structural equation modeling to verify whether self-control mediates it. As a result of the analysis, overall self-efficacy was influenced by the reliability of self-control and academic passion of grit. Self-confidence, a sub-factor of self-efficacy, was influenced by reliability of self-control and academic passion of grit. Self-regulation efficacy was influenced by the reliability of self-control and academic persistence of grit. Preference for task difficulty was influenced by grit, maintenance of academic interest, and self-control. Next, self-control was found to mediate the effect of grit on self-efficacy. Conclusion and Recommendations : This study explored the effects of grit and self-control on college students' self-efficacy. As a result, grit and self-control had a positive effect on self-efficacy. Additionally, self-control was found to mediate the effect of grit on self-efficacy. This study proposes to support grit and self-control in order to support successful academic achievement of college students.

• Journal of Life Science
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• v.28 no.5
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• pp.577-586
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• 2018

We recently reported the potential probiotic properties of Lactobacillus brevis SBB07 isolated from blueberries. The present study investigates the effect of culture conditions such as temperature, initial pH, culture time, and medium constituent for industrial application. The ingredients of the medium to improve cell growth were selected by Plackett-Burman design (PBD) and central composite design (CCD) within a desirable range. The PBD was applied with 19 factors: seven carbon sources, six nitrogen sources, and six microelements. Protease peptone, corn steep powder (CSP), and yeast extract were found to be significant factors for the growth of SBB07. The CCD was then applied with three variables found from the PBD at five levels, and the optimum values were decided for the three variables: protease peptone, CSP, and yeast extract. In the case of the growth of SBB07, the proposed optimal media contained 2.0% protease peptone, 2.5% CSP, and 2.0% yeast extract, and the maximum dried-cell weight was predicted to be 2.93963 g/l. By the model verification, it was confirmed that the predicted and actual results are similar. Finally, the study investigated the effects of incubation temperature and initial pH at the optimized medium. It was confirmed that the dried-cell weight increased from $2.2933{\pm}0.0601g/l$ to $3.85{\pm}0.0265g/l$ when compared to the basal medium at $37^{\circ}C$ and initial pH 8.0. Establishing the optimal culture condition for SBB07 provides good potential for applications in probiotics and can serve as the foundation for the industrialization of materials.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.3-8
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• 2010

Purpose: $^{11}C$-Acetate PET/CT is useful in detecting lesions that are related to livers in the human body and leads to a sensitivity of 87.3%. On the other hand, $^{18}F$-FDG PET/CT has a sensitivity of 47.3% and it has been reported that if both $^{18}F$-FDG and $^{11}C$-Acetate PET/CT are carried out together, their cumulative sensitivity is around 100%. However, the normal intake of the pancreas and the spleen in $^{11}C$-Acetate PET/CT can influence the $^{18}F$-FDG PET/CT leading to an inaccurate diagnosis. This research was aimed at the verification of the usefulness of how much influence these two radioactive medical supplies can cause on the medical images through comparative analysis between the one-day and two-day protocol. Materials and Methods: This research was carried out based on 46 patients who were diagnosed with liver cancer and have gone through the PET/CT (35 male, 11 female participants, average age: $54{\pm}10.6$ years, age range: 29-69 years). The equipment used for this test was the Biograph TruePoint40 PET/CT (Siemens Medical Systems, USA) and 21 participants who went through the one-day protocol test were first given the $^{11}C$-Acetate PET/CT and the $^{18}F$-FDG PET/CT, the latter exactly after one hour. The other 25 participants who went through the two-day protocol test were given the $^{11}C$-Acetate PET/CT on the first day and the $^{18}F$-FDG PET/CT on the next day. These two groups were then graded comparatively by assigning identical areas of interest of the pancreas and the spleen in the $^{18}F$-FDG images and by measuring the Standard Uptake Value (SUV). SPSS Ver.17 (SPSS Inc., USA) was used for statistical analysis, where statistical significance was found through the unpaired t-test. Results: After analyzing the participants' medical images from each of the two different protocol types, the average${\pm}$standard deviation of the SUV of the pancreas carried out under the two-day protocol were as follows: head $1.62{\pm}0.32$ g/mL, body $1.57{\pm}0.37$ g/mL, tail $1.49{\pm}0.33$ g/mL and the spleen $1.53{\pm}0.28$ g/mL. Whereas, the results for participants carried out under the one-day protocol were as follows: head $1.65{\pm}0.35$ g/mL, body $1.58{\pm}0.27$ g/mL, tail $1.49{\pm}0.28$ g/mL and the spleen $1.66{\pm}0.29$ g/mL. Conclusion: It was found that no statistical significant difference existed between the one-day and two-day protocol SUV in the pancreas and the spleen (p<0.05), and nothing which could be misconceived as false positive were found from the PET/CT medical image analysis. From this research, it was also found that no overestimation of the SUV occurred from the influence of $^{11}C$-Acetate on the $^{18}F$-FDG medical images where those two tests were carried out for one day. This result was supported by the statistical significance of the SUV of measurement. If $^{11}C$-Acetate becomes commercialized in the future, the diagnostic ability of liver diseases can be improved by $^{18}F$-FDG and one-day protocol. It is from this result where tests can be accomplished in one day without the interference phenomenon of the two radioactive medical supplies and furthermore, could reduce the waiting time improving customer satisfaction.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Life Science
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• v.20 no.4
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• pp.543-548
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• 2010

The purpose of this study was to examine the differences in kinetics between 6 months of rehabilitation training and 12 months of rehabilitation training after total hip arthroplasty. 10 unilateral THA participants performed kinetic tests. Three dimensional kinematics and hip flexors and abductors electromyography (EMG) were collected during each trial. T-test was used for statistical analysis (p<0.05). There was no significant difference in EMG data between the two groups, but the mean comparison EMG data was higher in the 12 months rehabilitation training group than the 6 months rehabilitation training group. The moment value was found with motion-dependent interaction analyzing method which was used by Feltner and Dapena. There was no significant difference between moment values of the two groups. There was no significant difference between ground reaction forces of the two groups; however, there were some differences shown in Fz (vertical reaction force) between the two groups ($892{\pm}104\;N$, $820{\pm}87\;N$). The first peak impact force was about 9% lower in the 12 months group compared to the 6 months group. The second peak active force was nearly equal between the two groups. More research is necessary to determine exactly what constitutes optimal rehabilitation training biomechanics for patients with total hip arthroplasty.

• Korean Journal of Environmental Biology
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• v.35 no.3
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• pp.413-425
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• 2017

This manuscript describes the relationship between climate change and forest growth, forest species, carbon stocks, and tree mortality. 1) In the aspect of forest growth, the growth of major coniferous species, including Pinus densiflora, had a negative correlation with temperature. On the other hand, major deciduous oak species, including Quercus variabilis and Quercus mongolica, had a positive correlation with temperature. 2) When considered in the aspect of the forest species distribution, various models commonly showed a decrease in the distribution of coniferous species and an increase in oak species due to climate change in the medium to long term. 3) From the carbon stock perspective, there was a difference in the estimation according to the status of forest management. Most of Korean forests will mature to become over-matured forest after year 2030 and are estimated to produce approximately 410 million ton forest biomass until 2090 with the current cutting regulations for sustainable forest management announced by the Korean Forest Service. 4) In the forest mortality, the mortality rate of the major coniferous species showed a clear tendency to increase higher temperatures while it decreased for the oak species with no verification of statistical significance. Moreover, the mortality of the subalpine coniferous species was projected to progress rapidly. considering the overall impacts described above, there should be a management strategy for coniferous species that are relatively vulnerable to climate change. Moreover, a sustainable forest plan in the aspect of ecosystem services, carbon sequestration and storage, which is linked to global issues such as Sustainable Development Goals, ecosystem services and negative emission.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.269-282
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• 2017

In this study, the surface broadband emissivity ($3.0-14.0{\mu}m$) was calculated using the multiple linear regression model with narrow bands (channels 29, 30, and 31) emissivity data of the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System Terra satellite. The 307 types of spectral emissivity data (123 soil types, 32 vegetation types, 19 types of water bodies, 43 manmade materials, and 90 rock) with MODIS University of California Santa Barbara emissivity library and Advanced Spaceborne Thermal Emission & Reflection Radiometer spectral library were used as the spectral emissivity data for the derivation and verification of the multiple linear regression model. The derived determination coefficient ($R^2$) of multiple linear regression model had a high value of 0.95 (p<0.001) and the root mean square error between these model calculated and theoretical broadband emissivities was 0.0070. The surface broadband emissivity from our multiple linear regression model was comparable with that by Wang et al. (2005). The root mean square error between surface broadband emissivities calculated by models in this study and by Wang et al. (2005) during January was 0.0054 in Asia, Africa, and Oceania regions. The minimum and maximum differences of surface broadband emissivities between two model results were 0.0027 and 0.0067 respectively. The similar statistical results were also derived for August. The surface broadband emissivities by our multiple linear regression model could thus be acceptable. However, the various regression models according to different land covers need be applied for the more accurate calculation of the surface broadband emissivities.

• Journal of the Korean earth science society
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• v.27 no.4
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• pp.450-463
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• 2006

Seasonal threshold values for fog detection over the ten airport areas within the Korean Peninsula have been derived from the data of polar-orbit Aqua/Terra MODIS and geostationary GOES-9 during a two years. The values are obtained from reflectance at $0.65{\mu}m\;(R_{0.65})$ and the difference in brightness temperature between $3.7{\mu}m\;and\;11{\mu}m\;(T_{3.7-11})$. In order to examine the discrepancy between the threshold values of two kinds of satellites, the following four parameters have been analyzed under the condition of daytime/nighttime and fog/clear-sky, utilizing their simultaneous observations over the Seoul metropolitan area: brightness temperature at $3.7{\mu}m$, the temperature at $11{\mu}m,\;the\;T_{3.7-11}$ for day and night, and the $R_{0.65}$ for daytime. The parameters show significant correlations (r<0.5) in spatial distribution between the two kinds of satellites. The discrepancy between their infrared thresholds is mainly due to the disagreement in their spatial resolutions and spectral bands, particularly at $3.7{\mu}m$. Fog detection from GOES-9 over the nine airport areas except the Cheongju airport has revealed accuracy of 60% in the daytime and 70% in the nighttime, based on statistical verification. The accuracy decreases in foggy cases with twilight, precipitation, short persistence, or the higher cloud above fog. The sensitivity of radiance and reflectance with wavelength has been analyzed in numerical experiments with respect to various meteorological conditions to investigate optical characteristics of the three channels.