• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.203-215
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• 2023

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.82-90
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• 2010

The ecological characteristics of the Korean Aucha perch, Coreoperca herzi, were determined in order to estimate stock of the mid-upper system of the Seomjin River. The age was determined by counting the otolith annuli. The oldest fish observed in this study was 5 years old. Relationships between body length (BL) and body weight (BW) were $BW=0.0195BL^{3.08}$ ($R^2=0.966$) (p<0.01). Relationships between the otolith radius (R) and body length (BL) were BL=3.882R+1.66 ($R^2=0.944$). The von Bertalanffy growth parameters estimated from a non-linear regression method were $L_{\infty}=19.68\;cm$, $W_{\infty}=188.64\;g$, $K=0.17\;year^{-1}$ and $t_0=-1.46$ year. Therefore, growth in length of the fish was expressed by the von Bertalanffy's growth equation as $L_t=19.68$ ($1-e^{-0.17(t+1.46)}$) ($R^2=0.997$). The annual survival rate (S) was estimated to be $0.666\;year^{-1}$. The instantaneous coefficient of natural mortality (M) of estimated from the Zhang and Megrey method was $0.346\;year^{-1}$, and instantaneous coefficient of fishing mortality (F) was calculated $0.061\;year^{-1}$. From the estimates of survival rate (S), the instantaneous coefficient of total mortality(Z) was estimated to be $0.407\;year^{-1}$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.478-485
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• 2019

This is an exploratory study to examine factors affecting South Korean companies' intentions to use big data technology and services based on whether the companies use public data or not. This study, using R, conducted chi-squared tests and logistic regression analysis. As a result of the logistic regression analysis, cost reduction had a positive effect on the big data-use intentions in companies that use public data, whereas with companies that do not use public data, customer satisfaction had a positive impact, and support for decision-making had a negative impact on the intention to use big data. Recently, the South Korean government has focused on improving the utilization of public data and big data. However, as a result of this study, the use of public data and big data in South Korea is still insufficient. Yet, considering that the data utilized for this study was created in 2017, additional study using public data and big data is also required.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.573-581
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• 2020

Using CCTV images and weather parameters, a method for estimating PM (Particulate Matter) index was proposed, and an experiment was conducted. For CCTV images, we proposed a method of estimating the PM index by applying a deep learning technique based on a CNN (Convolutional Neural Network) with ROI(Region Of Interest) image including a specific spot and an full area image. In addition, after combining the predicted result values by deep learning with the two weather parameters of humidity and wind speed, a post-processing experiment was also conducted to calculate the modified PM index using the learned regression model. As a result of the experiment, the estimated value of the PM index from the CCTV image was R2(R-Squared) 0.58~0.89, and the result of learning the ROI image and the full area image with the measuring device was the best. The result of post-processing using weather parameters did not always show improvement in accuracy in all cases in the experimental area.

• Phonetics and Speech Sciences
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• v.14 no.2
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• pp.67-76
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• 2022

This study aimed to examine Korean listeners' mode of perceiving sound duration as prolongation, whether dichotomous or continuous. Thirty-five Korean participants (17 men and 18 women) listened to the Korean segment /s/, which was lengthened by 0-980ms in 20-ms increments. Then, the participants were asked to rate each version of the sound based on a rating of one to 100 (the closer to 100, the more disfluent). To examine whether listeners perceived durational variations for the fricative segment dichotomously or continuously, a curve was estimated using the best-fitting regression model for the observed data with the highest adjusted R-squared value. The mode of perceiving durational variations for the segment was continuous (or gradient) rather than discontinuous (or dichotomous). No gender difference was found in the mode of perceiving prolongation. However, there was a significant gender difference in that men rated the most disfluent sounds higher than women. The findings of this study were further discussed in relation to the existing literature, and clinical implications for the assessment of stuttering were presented.

• Animal Bioscience
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• v.35 no.5
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• pp.648-658
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• 2022

Objective: The identification of nonlinear mixed models that describe the growth trajectory of New Zealand rabbits was performed based on weight records and carcass measures obtained using ultrasonography. Methods: Phenotypic records of body weight (BW) and loin eye area (LEA) were collected from 66 animals raised in a didactic-productive module of cuniculture located in the southern Piaui state, Brazil. The following nonlinear models were tested considering fixed parameters: Brody, Gompertz, Logistic, Richards, Meloun 1, modified Michaelis-Menten, Santana, and von Bertalanffy. The coefficient of determination (R²), mean squared error, percentage of convergence of each model (%C), mean absolute deviation of residuals, Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to determine the best model. The model that best described the growth trajectory for each trait was also used under the context of mixed models, considering two parameters that admit biological interpretation (A and k) with random effects. Results: The von Bertalanffy model was the best fitting model for BW according to the highest value of R² (0.98) and lowest values of AIC (6,675.30) and BIC (6,691.90). For LEA, the Logistic model was the most appropriate due to the results of R² (0.52), AIC (783.90), and BIC (798.40) obtained using this model. The absolute growth rates estimated using the von Bertalanffy and Logistic models for BW and LEA were 21.51g/d and 3.16 cm², respectively. The relative growth rates at the inflection point were 0.028 for BW (von Bertalanffy) and 0.014 for LEA (Logistic). Conclusion: The von Bertalanffy and Logistic models with random effect at the asymptotic weight are recommended for analysis of ponderal and carcass growth trajectories in New Zealand rabbits. The inclusion of random effects in the asymptotic weight and maturity rate improves the quality of fit in comparison to fixed models.

• Imaging Science in Dentistry
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• v.44 no.3
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• pp.229-236
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• 2014

Purpose: To investigate the prevalence of bifid and trifid mandibular canals using cone-beam computed tomography (CBCT) images, and to measure their length, diameter, and angle. Materials and Methods: CBCT images of 500 patients, involving 755 hemi-mandibles, were used for this study. The presence and type of bifid mandibular canal was evaluated according to a modified classification of Naitoh et al. Prevalence rates were determined according to age group, gender, and type. Further, their diameter, length, and angles were measured using PACSPLUS Viewer and ImageJ 1.46r. Statistical analysis with chi-squared and analysis of variance (ANOVA) tests was performed. Results: Bifid and trifid mandibular canals were found in 22.6% of the 500 patients and 16.2% of the 755 sides. There was no significant difference between genders and among age groups. The retromolar canal type accounted for 71.3% of the identified canals; the dental canal type, 18.8%; the forward canal type, 4.1%; and the trifid canal type, 5.8%. Interestingly, seven cases of the trifid canal type, which has been rarely reported, were observed. The mean diameter of the bifid and trifid mandibular canals was 2.2 mm and that of the main mandibular canal was 4.3 mm. Their mean length was 16.9 mm; the mean superior angle was $149.2^{\circ}$, and the mean inferior angle was $37.7^{\circ}$. Conclusion: Bifid and trifid mandibular canals in the Korean population were observed at a relatively high rate through a CBCT evaluation, and the most common type was the retromolar canal. CBCT is suggested for a detailed evaluation of bifid and trifid mandibular canals before mandibular surgery.

• Ocean Systems Engineering
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• v.4 no.4
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• pp.327-342
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• 2014

The tension leg platform (TLP) is one of the compliant structures which are generally used for deep water oil exploration. With respect to the horizontal degrees of freedom, it behaves like a floating structure moored by vertical tethers which are pretension due to the excess buoyancy of the platform, whereas with respect to the vertical degrees of freedom, it is stiff and resembles a fixed structure and is not allowed to float freely. In the current study, a numerical study for square TLP using modified Morison equation was carried out in the time domain with water particle kinematics using Airy's linear wave theory to investigate the effect of changing the tether tension force on the stiffness matrix of TLP's, the dynamic behavior of TLP's; and on the fatigue stresses in the cables. The effect was investigated for different parameters of the hydrodynamic forces such as wave periods, and wave heights. The numerical study takes into consideration the effect of coupling between various degrees of freedom. The stiffness of the TLP was derived from a combination of hydrostatic restoring forces and restoring forces due to cables. Nonlinear equation was solved using Newmark's beta integration method. Only uni-directional waves in the surge direction was considered in the analysis. It was found that for short wave periods (i.e., 10 sec.), the surge response consisted of small amplitude oscillations about a displaced position that is significantly dependent on tether tension force, wave height; whereas for longer wave periods, the surge response showed high amplitude oscillations that is significantly dependent on wave height, and that special attention should be given to tethers fatigue because of their high tensile static and dynamic stress.

• Computers and Concrete
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• v.26 no.4
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• pp.327-342
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• 2020

The objective of this study is to manufacture environmentally-friendly synthetic lightweight aggregates that may be used in the structural lightweight concrete production. The cold-bonding pelletization process has been used in the agglomeration of the pozzolanic materials to achieve these synthetic lightweight aggregates. In this context, it was aimed to recycle the waste fly ash by employing it in the manufacturing process as the major cementitious component. According to the well-known facts reported in the literature, it is stated that the main disadvantage of the synthetic lightweight aggregate produced by applying the cold-bonding pelletization technique to the pozzolanic materials is that it has a lower strength in comparison with the natural aggregate. Therefore, in this study, the metakaolin made of high purity kaolin and calcined kaolin obtained from impure kaolin have been employed at particular contents in the synthetic lightweight aggregate manufacturing as a cementitious material to enhance the particle crushing strength. Additionally, to propose a curing condition for practical attempts, different curing conditions were designated and their influences on the characteristics of the synthetic lightweight aggregates were investigated. Three substantial features of the aggregates, specific gravity, water absorption capacity, and particle crushing strength, were measured at the end of 28-day adopted curing conditions. Observed that the incorporation of thermally treated kaolin significantly influenced the crushing strength and water absorption of the aggregates. The statistical evaluation indicated that the investigated properties of the synthetic lightweight aggregate were affected by the thermally treated kaolin content more than the kaoline type and curing regime. Utilizing the thermally treated kaolin in the synthetic aggregate manufacturing lead to a more than 40% increase in the crushing strength of the pellets in all curing regimes. Moreover, two numerical formulations having high estimation capacity have been developed to predict the crushing strength of such types of aggregates by using soft-computing techniques: gene expression programming and artificial neural networks. The R-squared values, indicating the estimation performance of the models, of approximately 0.97 and 0.98 were achieved for the numerical formulations generated by using gene expression programming and artificial neural networks techniques, respectively.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.