• International Journal of Highway Engineering
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• v.19 no.5
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• pp.49-58
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• 2017

PURPOSES : The objective of this study is to develop a simple regression model in designing the asphalt concrete (AC) overlay thickness using the Mechanistic-empirical pavement design guide (MEPDG) program. METHODS : To establish the AC overlay design equation, multiple regression analyses were performed based on the synthetic database for AC thickness design, which was generated using the MEPDG program. The climate in Seoul city, a modified Hirsh model for determining dynamic modulus of asphalt material, and a new damaged master curve approach were used in this study. Meanwhile, the proposed rutting model developed in Seoul city was then used to calibrate the rutting model in the MEPDG program. The AC overlay design equation is a function of the total AC thickness, the ratio of AC overlay thickness and existing AC thickness, the ratio of existing AC modulus and AC overlay modulus, the subgrade condition, and the annual average daily truck traffic (AADTT). RESULTS : The regression model was verified by comparing the predicted AC thickness, the AADTT from the model and the MEPDG. The regression model shows a correlation coefficient of 0.98 in determining the AC thickness and 0.97 in determining AADTT. In addition, the data in Seoul city was used to validate the regression model. The result shows that correlation coefficient between the predicted and measured AADTT is 0.64. This indicates that the current model is more accuracy than the previous study which showed a correlation coefficient of 0.427. CONCLUSIONS:The high correlation coefficient values indicate that the regression equations can predict the AC thickness accurately.

• Computers and Concrete
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• v.29 no.1
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• pp.15-29
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• 2022

Concrete-filled steel tubes (CFSTs) are increasingly used as composite sections in structures owing to their excellent load bearing capacity. Therefore, predicting the mechanical behavior of CFST sections under axial compression loading is vital for design purposes. This paper presents the first study on the nonlinear analysis of heated CFSTs with high-strength concrete core containing steel fiber and waste tire rubber under axial compression loading. CFSTs had steel fibers with 0, 1, and 1.5% volume fractions and 0, 5, and 10% rubber particles as sand alternative material. They were subjected to 20, 250, 500, and 750℃ temperatures. Using flow rule and analytical analysis, a model is developed to predict the load bearing capacity of steel tube, and hoop strain-axial strain relationship, and axial stress-volumetric strain relationship of CFSTs. An elastic-plastic analysis method is applied to determine the axial and hoop stresses of the steel tube, considering elastic, yield, and strain hardening stages of steel in its stress-strain curve. The axial stress in the concrete core is determined as the difference between the total experimental axial stress and the axial stress of steel tube obtained from modeling. The results show that steel tube in CFSTs under 750℃ exhibits a higher load bearing contribution compared to those under 20, 250, and 500℃. It is also found that the ratio of load bearing capacity of steel tube at peak point to the load bearing capacity of CFST at peak load is noticeable such that this ratio is in the ranges of 0.21-0.33 and 0.31-0.38 for the CFST specimens with a steel tube thickness of 2 and 3.5 mm, respectively. In addition, after the steel tube yielding, the load bearing capacity of the tube decreases due to the reduction of its axial stiffness and the increase of hoop strain rate, which is in the range of about 20 to 40%.

• Journal of the Korean Society of Radiology
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• v.11 no.3
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• pp.131-137
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• 2017

This study investigated the risk factor of Gallbladder stone in Busan and Kyungnam area. The subjects of the experiment was performed with patients by abdominal ultrasonography among the patients who came to the P hospital from June 2016 to December 2016. Among them, risk factors were analyzed on 353 people at the same time when abdominal ultrasonography and hematological test. The statistical analysis of risk factors related to the Gallbladder stone was performed by independent t-test and chi-square test. In consider of difference verification result for calculations odds ratio about independent variables, multiple logistic regression analysis to conduct verify adequacy by calculating forecasting model from variable. As a result, Gallbladder stone risk factors have relevance to age ${\gamma}GTP$ with probability model and values to calculated. Age was showed sensitivity 49.7%, specificity 82.2%, receiver operating characteristic area under curve 0.724. Forecasting probability sensitivity 69.3%, specificity 62.4%, receiver operating characteristic area under curve 0.699 showed, ${\gamma}GTP$ confirmed validity of forecasting model.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.6
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• pp.188-197
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• 2010

This paper presents research result to study the change of structural capacity of reinforced concrete beams with various shear reinforcement ratios after damage by fire load. In addition, fundamental data are given in order to predict the strength variation of RC member due to fire damage by evaluating the previous calculation method codified in codes. Nine RC beam specimens were made and exposed to the fire controled by the standard fire curve. And the structural capacity was evaluated through a failure test under simple support condition. Previous code formula, ACI code and Eurocode were reviewed and used for the calculation of the strength of specimens damaged by fire. From the test, RC beam specimens exhibited very brittle failure when it exposed to fire controled by standard fire curve during more than one hour. And this failure pattern tended to be more serious when shear reinforcement ratio decreased or fire loading duration increased. From the evaluation of the calculation process in code, the change of strength due to fire can be properly predicted if the damage of materials is well defined.

• Journal of Korean Society of Forest Science
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• v.87 no.1
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• pp.82-89
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• 1998

This study was carried out to investigate working time, performance, and to predict performance that related to the factor of forest road in earth work using excavator. It was found that the real working time was 503 minutes in a day. The ratio of real working time and allowance per total working time was approximately 85.7% and 14.3% individually. The rate of soil movement(Sm) to net working time was 38.6%, and earth cutting(Ec) was 32.5%. According to performance analysis, performance of earth work using excavator($0.8m^3$) in straight part was 1.4 times larger than curve part and rock work using excavator($0.8m^3$) which had breaker in straight was 9.1 times larger than earth work using excavator($0.8m^3$) which had bucket. Performance of earth work using excavator($1.0m^3$) was 1.3 times larger than using excavator($0.8m^3$) in straight and curve part. Working performance in earth work using excavator($0.8m^3$) was influenced by the conditions of radius of curve, width of roadway, slope gradient. It is not influenced by diameter and number of root stock.

• Korean Journal of Radiology
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• v.25 no.2
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• pp.189-198
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• 2024

Objective: To investigate the prognostic utility of radiomics features extracted from ¹⁸F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). Materials and Methods: A total of 126 adults with ENKTCL who underwent ¹⁸F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. Results: Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. Conclusion: The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2861-2866
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• 2012

Background: The aim of our study was to assess the practical utility of the palliative prognostic index (PPI) as a prognostic tool used by nurse specialists in a hospice consultation setting in Taiwan. Methods: In total, 623 terminal cancer patients under hospice consultation care from one medical center in northern Taiwan were enrolled between January 1 and June 30, 2011. PPI was assessed by a nurse specialist at first hospice consultation and patients categorized into groups by prognosis (good, intermediate, poor). Patient survival was analyzed retrospectively to determine significance of between-group differences. Results: By PPI sum score, 37.2% of patients were in the good prognosis group, 18% in the intermediate prognosis group and 44.8% in the poor prognosis group. The death rates were 56%, 81.2% and 89.6% and median survivals were 76, 18 and 7 days, respectively. The hazard ratio was 0.19 (95% confidence interval [CI] 0.10-0.24, p<0.001) for the poor versus good prognosis group and 0.54 (95% CI 0.43-0.69, p<0.001) for the poor versus intermediate prognosis group. The sensitivity and specificity for the poor prognosis group was 66% and 71%; the positive predictive value and negative predictive value were 81% and 52%, respectively, to predict patient death within 21 days (area under the curve of the receiver operating characteristic was 0.68). Conclusions: Assessment by PPI can accurately predict survival of terminal cancer patients receiving hospice consultation care. PPI is a simple tool and can be administered by nurse members of hospice consultation teams.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.7
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• pp.747-756
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• 2014

In this paper, we propose a path loss model with the multiple antennas using diversity effect. Currently wireless communication systems use the multiple antennas in order to improve the channel capacity or diversity gain. However, until recently, many researches on path loss model only consider geographical environment between the transmitter and the receiver. There is no study about path loss model considering diversity effect. Nowaday wireless communication use the multiple antennas and we in common find examples using diversity scheme that is method in order to enhance a channel capacity. Moreover we anticipate that it work harder in future researches. But in this communication system, path loss model isn't established that predict strength of received signal. So, in order to predict strength of received signal, we take changing SNR by diversity gain. When exceeding the number of antennas of receiver are 7 in proposed model, diversity effect is saturated. Therefore we consider the number of antenna of receiver until 10. We find RMSE between proposed model and value of calculation is 1. We calculate the diversity gain by conventional BER curve. Proposed model can predict loss of received signal in system using multiple antennas.

• Tuberculosis and Respiratory Diseases
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• v.78 no.4
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• pp.349-355
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• 2015

Background: The tuberculin skin test (TST) is the standard tool to diagnose latent tuberculosis infection (LTBI) in mass screening. The aim of this study is to find an optimal cut-off point of the TST+ rate within tuberculosis (TB) contacts to predict the active TB development among adolescents in school TB outbreaks. Methods: The Korean National Health Insurance Review and Assessment database was used to identify active TB development in relation to the initial TST (cut-off, 10 mm). The 7,475 contacts in 89 schools were divided into two groups: Incident TB group (43 schools) and no incident TB group (46 schools). LTBI treatment was initiated in 607 of the 1,761 TST+ contacts. The association with active TB progression was examined at different cut-off points of the TST+ rate. Results: The mean duration of follow-up was $3.9{\pm}0.9years$. Thirty-three contacts developed active TB during the 4,504 person-years among the TST+ contacts without LTBI treatment (n=1,154). The average TST+ rate for the incident TB group (n=43) and no incident TB group (n=46) were 31.0% and 15.5%, respectively. The TST+ rate per group was related with TB progression (odds ratio [OR], 1.025; 95% confidence interval [CI], 1.001-1.050; p=0.037). Based on the TST+ rate per group, active TB was best predicted at TST+ ${\geq}$ 16% (OR, 3.11; 95% CI, 1.29-7.51; area under curve, 0.64). Conclusion: Sixteen percent of the TST+ rate per group within the same grade students can be suggested as an optimal cut-off to predict active TB development in middle and high schools TB outbreaks.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.