• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.504-515
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• 2021

To examine the relationship between environmental factors influencing the habitat of Aconitum austrokoreense Koidz., this study employed the MexEnt model to evaluate 21 environmental factors. Fourteen environmental factors having an AUC of at least 0.6 were found to be the age of stand, growing stock, altitude, topography, topographic wetness index, solar radiation, soil texture, mean temperature in January, mean temperature in April, mean annual temperature, mean rainfall in January, mean rainfall in August, and mean annual rainfall. Based on the response curves of the 14 descriptive factors, Aconitum austrokoreense Koidz. on the Baekun Mountain were deemed more suitable for sites at an altitude of 600 m or lower, and habitats were not significantly affected by the inclination angle. The preferred conditions were high stand density, sites close to valleys, and distribution in the northwestern direction. Under the five-age class system, the species were more likely to be observed for lower classes. The preferred solar radiation in this study was 1.2 MJ/m². The species were less likely to be observed when the topographic wetness index fell below the reference value of 4.5, and were more likely observed above 7.5 (reference of threshold). Soil analysis showed that Aconitum austrokoreense Koidz. was more likely to thrive in sandy loam than clay. Suitable conditions were a mean January temperature of - 4.4℃ to -2.5℃, mean April temperature of 8.8℃-10.0℃, and mean annual temperature of 9.6℃-11.0℃. Aconitum austrokoreense Koidz. was first observed in sites with a mean annual rainfall of 1,670- 1,720 mm, and a mean August rainfall of at least 350 mm. Therefore, sites with increasing rainfall of up to 390 mm were preferred. The area of potential habitats having distributive significance of 75% or higher was 202 ha, or 1.8% of the area covered in this study.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.3
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• pp.158-169
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• 2019

A probabilistic model that can predict the residual useful lifetime of structure is formulated by using the gamma process which is one of the stochastic processes. The formulated stochastic model can take into account both the sampling uncertainty associated with damages measured up to now and the temporal uncertainty of cumulative damage over time. A method estimating several parameters of stochastic model is additionally proposed by introducing of the least square method and the method of moments, so that the age of a structure, the operational environment, and the evolution of damage with time can be considered. Some features related to the residual useful lifetime are firstly investigated into through the sensitivity analysis on parameters under a simple setting of single damage data measured at the current age. The stochastic model are then applied to the rubble-mound breakwater straightforwardly. The parameters of gamma process can be estimated for several experimental data on the damage processes of armor rocks of rubble-mound breakwater. The expected damage levels over time, which are numerically simulated with the estimated parameters, are in very good agreement with those from the flume testing. It has been found from various numerical calculations that the probabilities exceeding the failure limit are converged to the constraint that the model must be satisfied after lasting for a long time from now. Meanwhile, the expected residual useful lifetimes evaluated from the failure probabilities are seen to be different with respect to the behavior of damage history. As the coefficient of variation of cumulative damage is becoming large, in particular, it has been shown that the expected residual useful lifetimes have significant discrepancies from those of the deterministic regression model. This is mainly due to the effect of sampling and temporal uncertainties associated with damage, by which the first time to failure tends to be widely distributed. Therefore, the stochastic model presented in this paper for predicting the residual useful lifetime of structure can properly implement the probabilistic assessment on current damage state of structure as well as take account of the temporal uncertainty of future cumulative damage.

• Journal of Hospice and Palliative Care
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• v.5 no.2
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• pp.111-124
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• 2002

Purpose : Although the average life expectancy has increased due to advances in medicine, mortality due to cancer is on an increasing trend. Consequently, the number of terminally ill cancer patients is also on the rise. Predicting the survival period is an important issue in the treatment of terminally ill cancer patients since the choice of treatment would vary significantly by the patents, their families, and physicians according to the expected survival. Therefore, we investigated the prognostic factors for increased mortality risk in terminally ill cancer patients to help treat these patients by predicting the survival period. Methods : We investigated 31 clinical parameters in 157 terminally ill cancer patients admitted to in the Department of Family Medicine, National Health Insurance Corporation Ilsan Hospital between July 1, 2000 and August 31, 2001. We confirmed the patients' survival as of October 31, 2001 based on medical records and personal data. The survival rates and median survival times were estimated by the Kaplan-Meier method and Log-rank test was used to compare the differences between the survival rates according to each clinical parameter. Cox's proportional hazard model was used to determine the most predictive subset from the prognostic factors among many clinical parameters which affect the risk of death. We predicted the mean, median, the first quartile value and third quartile value of the expected lifetimes by Weibull proportional hazard regression model. Results : Out of 157 patients, 79 were male (50.3%). The mean age was $65.1{\pm}13.0$ years in males and was $64.3{\pm}13.7$ years in females. The most prevalent cancer was gastric cancer (36 patients, 22.9%), followed by lung cancer (27, 17.2%), and cervical cancer (20, 12.7%). The survival time decreased with to the following factors; mental change, anorexia, hypotension, poor performance status, leukocytosis, neutrophilia, elevated serum creatinine level, hypoalbuminemia, hyperbilirubinemia, elevated SGPT, prolonged prothrombin time (PT), prolonged activated partial thromboplastin time (aPTT), hyponatremia, and hyperkalemia. Among these factors, poor performance status, neutrophilia, prolonged PT and aPTT were significant prognostic factors of death risk in these patients according to the results of Cox's proportional hazard model. We predicted that the median life expectancy was 3.0 days when all of the above 4 factors were present, $5.7{\sim}8.2$ days when 3 of these 4 factors were present, $11.4{\sim}20.0$ days when 2 of the 4 were present, and $27.9{\sim}40.0$ when 1 of the 4 was present, and 77 days when none of these 4 factors were present. Conclusions : In terminally ill cancer patients, we found that the prognostic factors related to reduced survival time were poor performance status, neutrophilia, prolonged PT and prolonged am. The four prognostic factors enabled the prediction of life expectancy in terminally ill cancer patients.

• Tuberculosis and Respiratory Diseases
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• v.48 no.1
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• pp.14-23
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• 2000

Background: The evaluation of candidates for successful lung resection is important. Our study was conducted to determine the preoperative predictors of postoperative mortality and morbidity in lung cancer patients with impaired lung function. Method; Between October 1, 1995 and August 31, 1997, 36 lung resection candidates for lung cancer with $FEV_1$ of less than 2L or 60% of predicted value were included prospectively. Age, sex, weight loss, hematocrit, serum albumin, EKG and concomitant illness were considered as systemic potential predictors for successful lung resection. Smoking history, presence of pneumonia, dyspnea scale(l to 4), arterial blood gas analysis with room air breathing, routine pulmonary function test were also included for the analysis. In addition, predicted postoperative(ppo) pulmonary factors such as ppo-$FEV_1$ ppo-diffusing capacity(DLco), predicted postoperative product(PPP) of ppo-$FEV_1%{\times}$ppo-DLco% and ppo-maximal $O_2$ uptake($VO_2$max) were also measured. Results: There were 31 men and 5 women with the median age of 65 years(range, 44 to 82) and a mean $FEV_1$ of $1.78{\pm}0.06L$. Pneumonectomy was performed in 14 patients, bilobectomy in 8, lobectomy in 14. Pulmonary complications developed in 10 patients; cardiac complications in 3, other complications(empyema, air leak, bleeding) in 4. Twelve patients were managed in the intensive care unit for more than 48 hours. Two patients died within 30 days after operation. The ppo-$VO_2$max was less than 10 ml/kg/min in these two patients. MVV was the only predictor for the pulmonary complications. However, there was no predictor for the post operative death in this study. Conclusions: Based on the results, MVV was the useful predictor for postoperative pulmonary complications in lung cancer resection candidates with impaired lung function In addition, ppo-$VO_2$max value less than 10 ml/kg/min was associated with postoperative death, so exercise pulmonary function test could be useful as preoperative test. But further studies are needed to validate this result.

• Sleep Medicine and Psychophysiology
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• v.24 no.1
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• pp.38-45
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• 2017

Objectives: Sleep disturbance is a very rapidly growing disease with aging. The purpose of this study was to investigate the prevalence of sleep disturbances and its predictive factors in a three-year cohort study of people aged 60 years and over in Korea. Methods: In 2012 and 2014, we obtained data from a survey of the Korean Social Life, Health, and Aging Project. We asked participants if they had been diagnosed with stroke, myocardial infarction, angina pectoris, arthritis, pulmonary tuberculosis, asthma, cataract, glaucoma, hepatitis B, urinary incontinence, prostate hypertrophy, cancer, osteoporosis, hypertension, diabetes, hyperlipidemia, or metabolic syndrome. Cognitive function was assessed using the Mini-Mental State Examination for dementia screening in 2012, and depression was assessed using the Center for Epidemiologic Studies Depression Scale in 2012 and 2014. In 2015, a structured clinical interview for Axis I psychiatric disorders was administered to 235 people, and sleep disturbance was assessed using the Pittsburgh Sleep Quality Index. The perceived stress scale and the State-trait Anger Expression Inventory were also administered. Logistic regression analysis was used to predict sleep disturbance by gender, age, education, depression score, number of coexisting diseases in 2012 and 2014, current anger score, and perceived stress score. Results: Twenty-seven percent of the participants had sleep disturbances. Logistic regression analysis showed that the number of medical diseases three years ago, the depression score one year ago, and the current perceived stress significantly predicted sleep disturbances. Conclusion: Comorbid medical disease three years previous and depressive symptoms evaluated one year previous were predictive of current sleep disturbances. Further studies are needed to determine whether treatment of medical disease and depressive symptoms can improve sleep disturbances.

• The Korean Journal of Nuclear Medicine
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• v.32 no.6
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• pp.509-515
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• 1998

Purpose: The aim of this study was to evaluate whether T1-201 reinjection distinguishes viable from non-viable myocardium in patients with reverse redistribution after acute myocardial infarction. Materials and Methods: We studied 42 patients with acute myocardial infarction (age, $55{\pm}12$ years). Eighteen (43%) out of 42 showed reverse redistribution on dipyridamole stress-4 hour redistribution T1-201 single photon emission computed tomography (SPECT). T1-201 reinjection was performed at 24 hours. Reverse redistribution was defined as worsening of perfusion defect at 4 hour delayed scan. All patients underwent follow-up echocardiography in 4 months to assess regional wall motion improvement. T1-201 uptake on reinjection images were analyzed for the prediction of myocardial wall motion improvement. Results: Of 36 segments with reverse redistribution, 17 segments showed normal wall motion on echocardiography, while 19 segments showed wall motion abnormalities. Of 19 the segments with reverse redistribution, 11 (58%) showed enhanced uptake after 24 hour reinjection. Myocardial wall motion was improved in 10 of 11 segments (90%) with enhanced uptake on reinjection. Wall motion improvement was not seen in 5 of 8 segments (63%) without enhanced thallium uptake. When myocardial viability was assessed by the uptake on reinjection image, nine of 10 segments (90%) with normal or mildly decreased uptake showed improved wall motion. Wall motion was not improved in 5 of 9 segments (16%) with severely decreased uptake. Conclusion: In patients with acute myocardial infarction, T1-201 reinjection imaging on myocardial segments with reverse redistribution has a high positive predictive value in the assessment of myocardial viability.

• The Korean Journal of Nuclear Medicine
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• v.32 no.6
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• pp.497-508
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• 1998

Purpose: Using rest T1-201/dipyridamole stress gated Tc-99m-MIBI/ 24 hour delay T1-201 SPECT, we investigated the predictive values of the markers of the stress-rest reversibility (Rev), T1-201 rest perfusion (Rest), T1-201 24 hour redistribution (Del) and Tc-99m-MIBI gated systolic thickening (Thk) for wall motion improvement after coronary artery bypass surgery. Materials and Methods: In 39 patients (M;F= 34:5, age $58{\pm}8$), preoperative and postoperative (3 months) SPECT were compared. 24 hour delayed SPECT was done in 16 patients having perfusion defects at rest. Perfusion or wall motion was scored from 0 to 3 (0: normal to 3: defect or dyskinesia). Wall motion was abnormal in 142 segments among 585 segments of 99 artery territories which were surgically revascularized. Results: After bypass surgery, ejection fraction increased from $37.8{\pm}9.0%$ to $45.5{\pm}12.3%$ in 22 patients who had decreased ejection fraction preoperatively. Wall motion improved in 103 (72.5%) segments among 142 dysfunctional segments. Positive predictive values (PPV) of Rev, Rest, Del, and Thk were 83%, 76%, 43%, and 69% respectively. Negative predictive values (NPV) of Rev, Rest, Del, and Thk were 48%, 44%, 58%, and 21%, respectively. Rest/gated stress/delay SPECT had PPV of 74% and NPV of 46%. Though univariate logistic regression analysis revealed Rev (p=0.0008) and Rest (p=0.024) as significant predictors, stepwise multivariate test found Rev as the only good predictor (p=0.0008). Conclusion: Among independent predictors obtained by rest T1-201/ stress gated Tc-99m-MIBI/ delayed T1-201 myocardial SPECT for wall motion improvement after bypass surgery, stress-rest reversibility was the single most useful predictor.

• Journal of radiological science and technology
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• v.31 no.2
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• pp.161-169
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• 2008

Since measuring the size of kidney with sonography becomes an important index for diagnosis, treatment, and prognostic prediction in kidney disease, the accurate measurement and evaluation on this are clinically very important. Accordingly, the purpose of this study was to increase reproducibility and objectivity in measuring the size of kidney by enumerating factors that have an impact for measurement. It targeted 44 adults in Korea at the age of 21-27. It measured in order for both kidneys to be seen most largely while changing a subject-examiner's position in a state of fasting for 8 hours and a transducer's approaching direction. It compared a size of kidney by measuring, respectively, with the same method in 30 minutes and in 1 hour after drinking water in 700-1,000cc. In case of the lateral approach scan in decubitus position, the average length of the kidney both to the right and the left and the deviation of measurement to be the largest. In NPO(None Per Oral) state, the average length in the right kidney was 10.19cm, and the average length in the left kidney was 10.33cm. In 60 minutes after taking moisture, the average length in the right kidney was 10.94cm, and the average length in the left kidney was 11.13cm. In comparing the average length of the kidney in NPO state and its average length in 60 minutes after taking moisture, the size swelled by 7.3% for the length in the right kidney and by 7.7% in the left, thereby having been indicated to be statistically significant(P<0.003). The measurement in a size of kidney by using ultrasound may be measured differently depending on a patient's state of taking moisture and a transducer's approaching direction. It is thought that when the measurement in a size of kidney is especially important clinically, the intake and intake time in moisture need to be considered and that measuring with the posterior approach in prone position is a good method aiming to increase reproducibility in measuring length of the kidney.

• Journal of Nutrition and Health
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• v.53 no.5
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• pp.476-487
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• 2020

Purpose: This study evaluated the accuracy of the 24-hour diet recall method for estimating energy intakes in elderly women using the doubly labeled water (DLW) method. Methods: The subjects were 23 elderly women with a mean age of 70.3 ± 3.3 years and body mass index (BMI) of 23.9 ± 2.8 kg/㎡. The total energy expenditure (TEE_DLW) was determined by using the DLW and used to validate the 24-hour diet recall method. The total energy intake (TEI) was calculated from the 24-hour diet recall method for three days. Results: TEI (1,489.6 ± 211.1 kcal/day) was significantly lower than TEE_DLW (2,023.5 ± 234.9 kcal/day) and was largely under-reported by -533.9 ± 228.0 kcal/day (-25.9%). The accurate prediction rate of elderly women in this study was 8.7%. The Bland-Altman plot, which was used to evaluate the TEI and the TEE_DLW, showed that the agreement between them was negatively skewed, ranging from -980.8 kcal/day to -86.9 kcal/day. Conclusion: This study showed that the energy intake of elderly women was underreported. Strategies to increase the accuracy of the 24-hour diet recall methods in the elderly women should be studied through analysis of factors that affect underreporting rate. Further studies will be needed to assess the validity of the 24-hour diet recall method in other population groups.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.