• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4D
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• pp.491-497
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• 2009

This paper presents the results of accelerated pavement tests (APT) that simulate permanent deformation (rutting) of asphalt concrete pavements under different temperatures and loading courses. Also, finite element (FE) analysis has been conducted to predict the test results. Test section for APT is the same as one of test sections at Korea Expressway Corporation test road and is subjected to a constant moving dual tire wheel load of APT at three different temperatures: 30, 40, $50^{\circ}C$. The moving wheel is applied at different loading courses within a 75cm wide wheel path to account for traffic wandering. Also, the effect of wandering on permanent deformation development is investigated numerically with three wandering schemes. In this study, ABAQUS is adopted to model APT pavement section with plain stain elements and creep strain rate model is used to take into account viscoplastic stain of asphalt concrete mixtures, and elastic layer properties are back-calculated from FWD measurements. Plus, the effect of boundary condition and subgrade on FE permanent deformation predictions is investigated. A full FE model that accounted for subgrade provided more realistic rut depth predictions, indicating subgrade has contributed to surface rutting.

• The Journal of KAIRB
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• v.6 no.1
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• pp.17-31
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• 2024

Purpose: This study aims to develop preliminary items for measuring the perceived service quality of clinical trials among participants and to verify content validity. Methods: This study was designed as a methodological study. A conceptual framework was established based on Brady and Cronin's hierarchical model, and preliminary items were prepared through translation-back-translation, a review of existing instruments, and in-depth interviews with clinical trial participants and clinical research coordinators. The final items were completed through content validity testing by experts and a review of items by clinical trial participants for the prepared preliminary items. Results: Through this study, a set of 58 items across four domains (quality of interaction with researchers, the physical environment, performance procedures, and performance results) and 9 components (information·education·communication, trust, respect for participant preferences, securing facilities and space, accessibility, comfortability, informed consent, coordination of care, subjective understanding of clinical trials) on the service quality of clinical trials were completed. The scale content validity index of all preliminary items was 0.96, meeting the recommended standards. The individual-item content validity index also meets the recommended criteria for most items, excluding four items. Conclusion: This study holds significance in developing items to measure the quality of clinical trial execution from the perspective of participants. By verifying the reliability and validity of these items through subsequent research, it is expected that they can be utilized as a valuable instrument to devise strategies for improving the quality of clinical trials.

• Journal of Animal Science and Technology
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• v.50 no.5
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• pp.613-620
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• 2008

The genetic parameters used in National Hanwoo Genetic Evaluation(NHGE) were needed to be monitored and updated periodically for accounting any possible changes in population parameters due to selection and environmental changes. Genetic parameters were estimated with single and two-trait models with MTDFREML package using 2,791 carcass records of steers collected from Hanwoo Progeny Test Program(HPTP). Single and two-trait models gave similar parameter estimates for all traits. The heritability estimates from single and two-trait models for carcass weight(CW), dressing percentage(DP), eye muscle area(EMA), back fat thickness(BFT) and marbling score(MS) were 0.30, 0.30, 0.37, 0.44 and 0.44, respectively. The heritability estimates for all the traits except BFT were slightly lower than those used in NHGE but seemed to be within the acceptable ranges. However, further monitoring is needed because the data might not have fully reflected the changes such as carcass grading standards in performance testing program. In order to shift statistical model of NHGE from single trait model to multiple-trait model, the genetic correlations between carcass traits were estimated with pairwise two-trait models. The genetic correlation coefficients between CW and DP, between CW and EMA, between CW and BFT and between CW and MS were 0.44, 0.63, 0.17 and 0.06, respectively. Those between DP and EMA, between DP and BFT and between DP and MS were 0.29, 0.40 and 0.20. Those between EMA and BFT and between EMA and MS were －0.24 and 0.15, respectively. The genetic correlation coefficient between BFT and MS was 0.03.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• KDI Journal of Economic Policy
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• v.13 no.2
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• pp.141-161
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• 1991

Resale price maintenance has long been employed in book distribution, perhaps longer than for any other product. Another unusual practice in the book trade that has proven to be quite durable in spite of its substantial cost in real resources is the returns policy. Publishers typically grant the right to return unsold books within a stipulated time for full credit against future orders. This paper investigates the functions and effects of resale price maintenance in the book trade, and argues that resale price maintenance and returns are substitute methods of providing the same economic function. Resale price maintenance can be used to compensate booksellers for initially stocking books with uncertain prospects and for providing a conduit through which manufacturers acquire information about consumer demand (market testing services). Permitting the return of unsold books for full credit places a floor under retail prices and transfers a considerable portion of the cost of introducing a new product line back to the publisher. Both reflect publishers' needs to have their books displayed. In the U.S. returns privileges were first proposed in 1913, roughly coincident with the Macy decision outlawing RPM. Publishers slowly granted return privileges, which become nearly universal by 1970. The decline in margins in recent years has been accompanied by an increase in returns as the return policy served to substitute for lost margins on successful titles as a methods of compensating full-line booksellers. In contrast, returns privileges are unusual in countries where price maintenance in books has been practiced. These observations are consistent with our analysis. In Korea, resale price maintenance of books is practiced under an exception to Korean antitrust law. The availability of effective price maintenance is likely to reduce the use of returns programs. Since consumers prefer to obtain books at outlets where they know the books are likely to be stocked rather than taking a chance on stores that carry a more limited line, it also provides a strong incentive for booksellers to expand. But the privilege of resale price maintenance should be confined to books which publishers want to be price maintained. Resale price maintenance and returns system differ in the transactions costs associated with inventory holding, and publishers' judgement on the comparative advantage of the two schemes should be honored. Publishers should also remain free to authorize sales at discount at any time not to impair the ability of booksellers to dispose of product variants that prove unpopular.

• Tuberculosis and Respiratory Diseases
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• v.61 no.2
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• pp.121-128
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• 2006

Background: The "health-related quality of life" (HRQL) for patients with chronic respiratory disease has been emphasized, because chronic respiratory disease (CRD) is chronic and progressive, and it finally causes disability. HRQL instruments may be useful for monitoring patients' progress or for determining the most appropriate choice of treatment. We describe the adapting St George's Respiratory Questionnaire (SGRQ), which is a self-administered questionnaire developed by Jones et al. (1991), into the Korean version for covering three domains of health for the patients suffering with airways disease. Method: We obtained the original SGRQ from the author after gaining permission. For adaptation, we created an expert panel and translated the original questionnaire into Korean language. The translated questionnaire was then back-translated by bilingual experts and we compared it with the original questionnaire. After correction and feasibility testing, 74 patients with chronic respiratory disease (COPD, asthma, destroyed lung) completed the Korean version of the SGRQ. The clinical status of each patients was evaluated concurrently with measurement of their health status. Result: The Korean version of the SGRQ was acceptable and easy to understand. Cronbach's alpha reliability coefficient was 0.92 for the overall scale and 0.63 for the "Symptoms", subscale, 0.87 for the "Activity", subscale, and 0.89 for the "Impacts" subscales. The correlation coefficients between the overall score and the Borg scale score, oxygen saturation, and forced expiratory volume in one second ($FEV_1$) were 0.52, -0.32 and -0.26, respectively. These results support that the Korean SGRQ was correlated with other measurements. Conclusion: The Korean SGRQ was reliable and valid for patients with chronic respiratory disease, such as COPD, asthma, and destroyed lung. The SGRQ score was well correlated with other respiratory measurements as well. Although further studies should complete the adaptation work, our results suggest that the SGRQ may be used in Korea and also for international studies involving Korean CRD patients.

• Korean Journal of Health Education and Promotion
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• v.2 no.1
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• pp.31-61
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• 2000

Introduction. Despite the fact that half of premature deaths are caused by unhealthy lifestyles such as smoking tobacco, sedentary lifestyle, alcohol and drug abuse and poor nutrition, there are no theoretical models which accurately explain these health promotion related behaviors. This study tests a new model of health behavior called the Model of Health Promotion Behavior. This model draws on elements and frameworks suggested by the Health Belief Model, Social Cognitive Theory, the Theory of Planned Action and the Health Promotion Model. This model is intended as a general model of behavior but this first test of the model uses amount of exercise as the outcome behavior. Design. This study utilized a cross sectional mail-out, mail-back survey design to determine the elements within the model that best explained intentions to exercise and those that best explained amount of exercise. A follow-up questionnaire was mailed to all respondents to the first questionnaire about 10 months after the initial survey. A pretest was conducted to refine the questionnaire and a pilot study to test the protocols and assumptions used to calculate the required sample size. Sample. The sample was drawn from 2000 eligible participants at two blue collar (utility company and part of a hospital) and two white collar (bank and pharmaceutical) companies located in Southeastern Michigan. Both white collar site had employee fitness centers and all four sites offered health promotion programs. In the first survey, 982 responses were received (49.1％) after two mailings to non-respondents and one additional mailing to secure answers to missing data, with 845 usable cases for the analyzing current intentions and 918 usable cases for the explaining of amount of current exercise analysis. In the follow-up survey, questionnaires were mailed to the 982 employees who responded to the initial survey. After one follow-up mailing to non-respondents, and one mailing to secure answers to missing data, 697 (71.0％) responses were received, with 627 (63.8％) usable cases to predict intentions and 673 (68.5％) usable cases to predict amount of exercise. Measures. The questionnaire in the initial survey had 15 scales and 134 items; these scales measured each of the variables in the model. Thirteen of the scales were drawn from the literature, all had Cronbach's alpha scores above .74 and all but three had scores above .80. The questionnaire in the second mailing had only 10 items, and measured only outcome variables. Analysis. The analysis included calculation of scale scores, Cronbach's alpha, zero order correlations, and factor analysis, ordinary least square analysis, hierarchical tests of interaction terms and path analysis, and comparisons of results based on a random split of the data and splits based on gender and employer site. The power of the regression analysis was .99 at the .01 significance level for the model as a whole. Results. Self efficacy and Non-Health Benefits emerged as the most powerful predictors of Intentions to exercise, together explaining approximately 19％ of the variance in future Intentions. Intentions, and the interaction of Intentions with Barriers, with Support of Friends, and with Self Efficacy were the most consistent predictors of amount of future exercise, together explaining 38％ of the variance. With the inclusion of Prior Exercise History the model explained 52％ of the variance in amount of exercise 10 months later. There were very few differences in the variables that emerged as important predictors of intentions or exercise in the different employer sites or between males and females. Discussion. This new model is viable in predicting intentions to exercise and amount of exercise, both in absolute terms and when compared to existing models.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.