• Tuberculosis and Respiratory Diseases
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• v.46 no.2
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• pp.204-214
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• 1999

Background: Resting pulmonary function tests(PFTs) are routinely used in the evaluation of pulmonary impairment/disability. But the significance of the cardiopulmonary exercise test(CPX) in the evaluation of pulmonary impairment is controvertible. Many experts believe that dyspnea, though a necessary part of the assessment, is not a reliable predictor of impairment. Nevertheless, oxygen requirements of an organism at rest are different from at activity or exercising, and a clear relationship between resting PFTs and exercise tolerance has not been established in patients with chronic pulmonary disease. As well, the relationship between resting PFTs and dyspnea is complex. To investigate the relationship of dyspnea, resting PFTs, and CPX, we evaluated the patients of stabilized chronic pulmonary disease with clinical dyspnea rating(baseline dyspnea index, BDI), resting PFTs, and CPX. Method: The 50 patients were divided into two groups: non-severe and severe group on basis of results of resting PFTs(by criteria of ATS), CPX(by criteria of ATS or Ortega), and dyspnea rating(by focal score of BDI). Groups were compared with respect to pulmonary function, indices of CPX, and dyspnea rating. Results: 1. According to the criteria of pulmonary impairment with resting PFTs, $VO_2$max, and focal score of BDI were significantly low in the severe group(p<0.01). According to the criteria of $VO_2$max(ml/kg/min) and $VO_2$max(%), the parameters of resting PFTs, except $FEV_1$ were not significantly different between non-severe and severe(p>0.05). According to focal score($FEV_1$(%), FVC(%), MW(%), $FEV_1/FVC$, and $VO_2$max were significantly lower in the severe group(p<0.01). However, in the more severe dyspneic group(focal score<5), only $VO_2$max(ml/kg/min) and $VO_2$max(%) were low(p<0.01). $FEV_1$(%) was correlated with $VO_2$max(%)(r=0.52;p<0.01), but not predictive of exercise performance. The focal score had the correlation with max WR(%) (r=0.55;p<0.01). Sensitivity and specificity analysis were utilized to compare the different criteria used to evaluate the severity of pulmonary impairment, revealed that the classification would be different according to the criteria used. And focal score for dyspnea showed similar sensitivity and specificity. Conclusion : According to these result, resting PFTs were not superior to rating of dyspnea in prediction of exercise performance in patients with chronic pulmonary diseases and less correlative with focal score for dyspnea than $VO_2$max and max WR. Therefore, if not contraindicated, CPX would be considered to evaluate the severity of pulmonary impairment in patients with chronic pulmonary diseases, including with severe resting PFTs. Current criteria used to evaluate the severity of impairment were insufficient in considering the degree of dyspnea, so new criteria, including the severity of dyspnea, may be necessary.

• Korean Journal of Cognitive Science
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• v.23 no.2
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• pp.205-224
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• 2012

How would people who buy an insurance policy respond to a low probability risk with a high future cost? Presented with a scenario describing a low probability accident of a chemical plant, participants in four experiments were asked to rate their perception of the risk and also their intention to buy an insurance of a given premium, an insurance, or a ratio insurance. Participants differently responded only to ratio insurance when rating their perception of risk, not to either premium or insurance. The pattern of results in four experiments converged to the conclusion that ratio insurance, an ecologically valid cue, makes people sensitive to the level of risk expressed in low probabilities of an accident. Our results were consistent with the prediction generated by the ecological cue hypothesis which empathizes the importance of frequency over probability in risk perception (Gigerenzer, 2000).

• Applied Chemistry for Engineering
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• v.4 no.4
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• pp.709-714
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• 1993

The cetane number is a measure of ignition quality, specifically ignition delay, of diesel fuel. It is an engine measure of kinetic phenomena. The ignition quality such as kinetic behavior does correlate with the molecular structure, the carbon type structural composition. In fact, we use the group additivity rule to dissect the molecular structures and predict cetane number. In this study, the use of $^{13}C-Nuclear$ Magnetic Resonance spectroscopic measuring the molecular structure and group additivity rule at different diesel fuels, whose cetane numbers were determined on a number of standard cetane rating engines is proposed to predict cetane numbers that relate the carbon type structural composition. The effect of the molecular structures on the cetane numbers has been studied.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

• Journal of Korean Society of Steel Construction
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• v.11 no.2 s.39
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• pp.213-222
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• 1999

Elastic-plastic methods have been used for the better prediction of the actual behavior of continuous-composite plate girder bridges in the overload and maximum load analysis. The structural evaluation using ALFD(Alternate Load Factor Design) uses the elastic-plastic analysis. The plastic rotations that remain after the load is removed can be occurred by the yielding locations of the maximum moment section. This situation can occur due to the residual stresses even if the moment is below the theoretical yield moment. The local yielding causes positive automoments that assure elastic behavior under subsequent overloads. In this study, the automoments at the piers occurred due to the unit plastic rotations and other locations were calculated by the conjugate-beam method and three-moment equation, using the nine design span with progressively smaller pier sections. The automoments were determined by the developed computer programs in this study in which the moments and plastic rotations from the continuity and moment-inelastic rotation relationships must be equal. And also the ratings of 3-span continuous composite plate girder bridges with non-compact section were carried out according to the Korean Highway Bridge Specification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4892-4911
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• 2015

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However, there possibly exist implicit correlations between users, especially for users with the same role (truster or trustee). In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation. In this method, each user is first represented by the same-role users who are co-occurring with the user. Then, similarities between users with the same role are measured based on obtained user representation. Finally, two variants of our method are proposed to fuse these computed similarities into traditional collaborative filtering for rating prediction. Using two publicly available real-world Epinions and Ciao datasets, we conduct comprehensive experiments to compare the performance of our proposed method with some existing benchmark methods. The results show that CF-TC outperforms other baseline methods in terms of RMSE, MAE, and recall.

• Korean Journal of Biological Psychiatry
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• v.23 no.4
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• pp.140-147
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• 2016

Objectives To determine the relationship between the Alu insertion/deletion (I/D) polymorphism in the tissue-type plasminogen activator (tPA) gene and the clinical outcome of mirtazapine treatment in Korean major depressive disorder (MDD) patients. Methods We enrolled 422 patients in this study. Symptoms were evaluated using the 21-item Hamilton Depression Rating (HAMD-21) Scale. After 1, 2, 4, and 8 weeks of mirtazapine treatment, the association between the Alu I/D polymorphism in the tPA gene and remission/response outcomes were evaluated. Results The proportion of I/I homozygotes in responders was higher than that in non-responders, whereas the proportion of D/D homozygotes in responders was lower than that in non-responders at 8 weeks of treatment (p = 0.032, OR = 1.57). The percentage decline of HAMD-21 scores in I allele carriers was larger than that of D/D homozygotes at 2 and 8 weeks of treatment (p = 0.035 and 0.007, respectively). I allele carriers were associated with remission at 8 weeks of treatment (p = 0.047, OR = 2.2). Conclusions These results show that treatment response and remission to mirtazapine were associated with the Alu I/D polymorphism of the tPA gene. This suggests the Alu I/D polymorphism may be a potential genetic marker for the prediction of therapeutic response to mirtazapine treatment in patients with MDD.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.859-869
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• 2011

Interest has increased in academic research regarding key factors that drive box-office success as well as the ability to predict the box-office success of a movie from a commercial perspective. This study analyzed the relationship between key success factors of a movie and box office records based on movies released in 2010 in Korea. At the pre-production investment decision-making stage, the movie genre, motion picture rating, director power, and actor power were statistically significant. At the stage of distribution decision-making process after movie production, among other factors, the influence of star actors, number of screens, power of distributors, and social media turned out to be statistically significant. We verified movie success factors through the application of a Multinomial Logit Model that used the concept of choice probabilities. The Multinomial Logit Model resulted in a higher level of accuracy in predicting box-office success compared to the Artificial Neural Network and Discriminant Analysis.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.389-395
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• 2007

Collaborative filtering is a popular technique for information filtering to reduce information overload and widely used in application such as recommender system in the E-commerce domain. Collaborative filtering systems collect human ratings and provide Predictions based on the ratings of other people who share the same tastes. The quality of predictions depends on the number of items which are commonly rated by people. Therefore, it is difficult to apply pure collaborative filtering algorithm directly to dynamic collections where items are constantly added or removed. In this paper we suggest a method for managing dynamic collections. It creates taste space for items using a technique called Singular Vector Decomposition (SVD) and maintains clusters of core items on the space to estimate relevance of past and future items. To evaluate the proposed method, we divide database of user ratings into those of old and new items and analyze predicted ratings of the latter. And we experimentally show our method is efficiently applied to dynamic collections.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.57-64
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• 2004

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, the forest fire danger rating system was developed to estimate forest fire risk by means of weather, topography, and forest type. Forest fires occurrence prediction needs to improve continually. Logistic regression and spatial analysis was used in developing the forest fire occurrence probability model. The forest fire danger index in accordance to the probability of forest fire occurrence was used in the classification of forest fire occurrence risk regions.