• Tuberculosis and Respiratory Diseases
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• v.60 no.6
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• pp.663-672
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• 2006

Background: $PM_{10}$(Particulate matter with a diameter ($<10{\mu}m$), which is characterized by different environmental conditions, is a complex mixture of organic and inorganic compounds. The Asian dust event caused by meteorological phenomena can also produce unique particulate matter in affected areas. This study investigated the cytokine produced by A549 epithelial cells exposed to particles collected during both the Asian dust pfenomenon and ambient air particles in a non-dusty period. Method: Air samples were collected using a high volume air sampler(Sibata Model HV500F) with an air flow at $500{\ell}/min$ for at least 6 hours. The cytokine messenger RNA(mRNA) was measured using a reverse transcriptase polymerase chain reaction(RT-PCR). The A549 cells were exposed to 10 to $500{\mu}g/m{\ell}$ of a suspension containing $PM_{10}$ for 24 hours. Each was compared with those in the non-exposed control cells. Result: The mRNA levels of interleukin(IL)-$1{\alpha}$, $IL-I{\beta}$, IL-8, and the granulocyte macrophage colony stimulating factor(GM-CSF) increased after veing exposed to $PM_{10}$ in the ambient air particles, compared with those in the non-exposed control cells. The increase in $IL-1{\alpha}$ and IL-8 were dose dependent at a $PM_{10}$ concentration between $100{\mu}g/m{\ell}$ and $500{\mu}g/m{\ell}$. The mRNA level of IL-8 in the A549 epithelial cells was higher during the in the Asian dust period($500{\mu}g/m{\ell}$) than during the non dust period. Conclusion: A549 cells exposed to the $PM_{10}$ collected during the Asian dust period produce more proinflammatory cytokine than during non-dusty period. This cytokine enhances the local inflammatory response in the airways and can also contribute to the systemic component of this inflammatory process.

• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.1
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• pp.60-64
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• 2017

Purpose Westgard multi-rules application based on test quality improvement and commercialized international standard has been widely used in quality control. However, it is difficult to applicate the Westgard multi-rules in nuclear medicine in vitro tests due to the larger sample sizes and the simultaneous measurement of quality control material and patient sample. This study investigated the usefulness of Westgard multi-rules application in nuclear medicine in vitro tests. Materials and Methods A total of 282 systematic error multi-rules (22s, 101s) recorded in the samsung medical center computer system from January 2013 to June 2016 along with 117 cases of corrective measure record was analyzed. The Quality control implementation is recorded in Hospital information system were divided into 4 high-level areas including quality control material error, experimental procedural error, Kit lot number management error, and others. To prevent quality control material error, the existing method that each staff used their own method was changed. The staff who in charge of managing the quality control material was designated and daily consumption amount of every test was strictly controlled by one person. To prevent other errors, every test step was standardized so that the entire test procedures are identically implemented. Results The total quality control implementation was 117 cases; As a result, 62 quality control material errors were 62 cases, experimental process errors were 24 cases, Kit lot number control errors were 18 cases, and other errors were 13 cases. The quality control material error was corrected and could be used fresh materials within 2 days after thawing. The cases of systemic error were decreased to causes as quality control material error. The quality control materials were reduced above 10 vials to a monthly average. In addition, these errors of experimental processing and Kit lot number were improved by test standardization. Consequently, the cases of 101s and 22s in systematic error rules decreased at least 2 cases to a monthly average. Conclusion To confirm of systematic error through multi-rules application quickly, it is necessary to base on management of the QC material, target values and standard deviation. Moreover, in the event of a systematic error, it was found important to record measures based on test cause analysis. The experiment results are expected to contribute to internal quality control improvement and prompt and accurate result reporting through error recording and causal analysis based on Westgard multi-rules analysis.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2014.10a
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• pp.1-24
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• 2014

This study uses geo-spatial crop modeling to quantify the biophysical impact of weather extremes. More specifically, the study analyzes the weather extreme which affected maize production in the USA in 2012; it also estimates the effect of a similar weather extreme in 2050, using future climate scenarios. The secondary impact of the weather extreme on food security in the developing world is also assessed using trend analysis. Many studies have reported on the significant reduction in maize production in the USA due to the extreme weather event (combined heat wave and drought) that occurred in 2012. However, most of these studies focused on yield and did not assess the potential effect of weather extremes on food prices and security. The overall goal of this study was to use geo-spatial crop modeling and trend analysis to quantify the impact of weather extremes on both yield and, followed food security in the developing world. We used historical weather data for severe extreme events that have occurred in the USA. The data were obtained from the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA). In addition we used five climate scenarios: the baseline climate which is typical of the late 20th century (2000s) and four future climate scenarios which involve a combination of two emission scenarios (A1B and B1) and two global circulation models (CSIRO-Mk3.0 and MIROC 3.2). DSSAT 4.5 was combined with GRASS GIS for geo-spatial crop modeling. Simulated maize grain yield across all affected regions in the USA indicates that average grain yield across the USA Corn Belt would decrease by 29% when the weather extremes occur using the baseline climate. If the weather extreme were to occur under the A1B emission scenario in the 2050s, average grain yields would decrease by 38% and 57%, under the CSIRO-Mk3.0 and MIROC 3.2 global climate models, respectively. The weather extremes that occurred in the USA in 2012 resulted in a sharp increase in the world maize price. In addition, it likely played a role in the reduction in world maize consumption and trade in 2012/13, compared to 2011/12. The most vulnerable countries to the weather extremes are poor countries with high maize import dependency ratios including those countries in the Caribbean, northern Africa and western Asia. Other vulnerable countries include low-income countries with low import dependency ratios but which cannot afford highly-priced maize. The study also highlighted the pathways through which a weather extreme would affect food security, were it to occur in 2050 under climate change. Some of the policies which could help vulnerable countries counter the negative effects of weather extremes consist of social protection and safety net programs. Medium- to long-term adaptation strategies include increasing world food reserves to a level where they can be used to cover the production losses brought by weather extremes.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.147-187
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• 2008

Online game researchers focus on the flow and factors influencing flow. Flow is conceptualized as an optimal experience state and useful explaining game experience in online. Many game studies focused on the customer loyalty and flow in playing online game, In showing specific game experience, however, it doesn't examine multidimensional experience process. Flow is not construct which show absorbing process, but construct which show absorbing result. Hence, Flow is not adequate to examine multidimensional experience of games. Online game is included in hedonic consumption. Hedonic consumption is a relatively new field of study in consumer research and it explores the consumption experience as a experiential view(Hirschman and Holbrook 1982). Hedonic consumption explores the consumption experience not as an information processing event but from a phenomenological of experiential view, which is a primarily subjective state. It includes various playful leisure activities, sensory pleasures, daydreams, esthetic enjoyment, and emotional responses. In online game experience, therefore, it is right to access through a experiential view of hedonic consumption. The objective of this paper was to make up for lacks in our understanding of online game experience by developing a framework for better insight into the hedonic experience of online game. We developed this framework by integrating and extending existing research in marketing, online game and hedonic responses. We then discussed several expectations for this framework. We concluded by discussing the results of this study, providing general recommendation and directions for future research. In hedonic response research, Lacher's research(1994)and Jongho lee and Yunhee Jung' research (2005;2006) has served as a fundamental starting point of our research. A common element in this extended research is the repeated identification of the four hedonic responses: sensory response, imaginal response, emotional response, analytic response. The validity of these four constructs finds in research of music(Lacher 1994) and movie(Jongho lee and Yunhee Jung' research 2005;2006). But, previous research on hedonic response didn't show that constructs of hedonic response have cause-effect relation. Also, although hedonic response enable to different by stimulus properties. effects of stimulus properties is not showed. To fill this gap, while largely based on Lacher(1994)' research and Jongho Lee and Yunhee Jung(2005, 2006)' research, we made several important adaptation with the primary goal of bringing the model into online game and compensating lacks of previous research. We maintained the same construct proposed by Lacher et al.(1994), with four constructs of hedonic response:sensory response, imaginal response, emotional response, analytical response. In this study, the sensory response is typified by some physical movement(Yingling 1962), the imaginal response is typified by images, memories, or situations that game evokes(Myers 1914), and the emotional response represents the feelings one experiences when playing game, such as pleasure, arousal, dominance, finally, the analytical response is that game player engaged in cognition seeking while playing game(Myers 1912). However, this paper has several important differences. We attempted to suggest multi-dimensional experience process in online game and cause-effect relation among hedonic responses. Also, We investigated moderate effects of perceived complexity. Previous studies about hedonic responses didn't show influences of stimulus properties. According to Berlyne's theory(1960, 1974) of aesthetic response, perceived complexity is a important construct because it effects pleasure. Pleasure in response to an object will increase with increased complexity, to an optimal level. After that, with increased complexity, pleasure begins with a linearly increasing line for complexity. Therefore, We expected this perceived complexity will influence hedonic response in game experience. We discussed the rationale for these suggested changes, the assumptions of the resulting framework, and developed some expectations based on its application in Online game context. In the first stage of methodology, questions were developed to measure the constructs. We constructed a survey measuring our theoretical constructs based on a combination of sources, including Yingling(1962), Hargreaves(1962), Lacher (1994), Jongho Lee and Yunhee Jung(2005, 2006), Mehrabian and Russell(1974), Pucely et al(1987). Based on comments received in the pretest, we made several revisions to arrive at our final survey. We investigated the proposed framework through a convenience sample, where participation in a self-report survey was solicited from various respondents having different knowledges. All respondents participated to different degrees, in these habitually practiced activities and received no compensation for their participation. Questionnaires were distributed to graduates and we used 381 completed questionnaires to analysis. The sample consisted of more men(n=225) than women(n=156). In measure, the study used multi-item scales based previous study. We analyze the data using structural equation modeling(LISREL-VIII; Joreskog and Sorbom 1993). First, we used the entire sample(n=381) to refine the measures and test their convergent and discriminant validity. The evidence from both the factor analysis and the analysis of reliability provides support that the scales exhibit internal consistency and construct validity. Second, we test the hypothesized structural model. And, we divided the sample into two different complexity group and analyze the hypothesized structural model of each group. The analysis suggest that hedonic response plays different roles from hypothesized in our study. The results indicate that hedonic response-sensory response, imaginal response, emotional response, analytical response- are related positively to respondents' level of game satisfaction. And game satisfaction is related to higher levels of game loyalty. Additionally, we found that perceived complexity is important to online game experience. Our results suggest that importance of each hedonic response different by perceived game complexity. Understanding the role of perceived complexity in hedonic response enables to have a better understanding of underlying mechanisms at game experience. If game has high complexity, analytical response become important response. So game producers or marketers have to consider more cognitive stimulus. Controversy, if game has low complexity, sensorial response respectively become important. Finally, we discussed several limitations of our study and suggested directions for future research. we concluded with a discussion of managerial implications. Our study provides managers with a basis for game strategies.

• The Korean Journal of Air & Space Law and Policy
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• v.29 no.2
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• pp.109-134
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• 2014

Regulation (EC) No 261/2004 of the European Parliament and of the Council of 11 February 2004 establishing common rules on compensation of assistance to passengers in the event of denied boarding and of cancellation or long delay of flights (Regulation No 261/2004) provides extra protection to air passengers in circumstances of denied boarding, cancellation and long-delay. The Regulation intends to provide a high level of protection to air passengers by imposing obligations on air carriers and, at the same time, offering extensive rights to air passengers. If denied boarding, cancellation and long-delay are caused by reasons other than extraordinary circumstances, passengers are entitled for compensation under Article 7 of Regulation No 261/2004. In Wallentin-Hermann v Alitalia-Linee Aeree Italiane SpA(Case C-549/07, [2008] ECR I-11061), the Court did, however, emphasize that this does not mean that it is never possible for technical problems to constitute extraordinary circumstances. It cited specific examples of where: an aircraft manufacturer or competent authority revealed that there was a hidden manufacturing defect on an aircraft which impacts on safety; or damage was caused to an aircraft as a result of an act of sabotage or terrorism. Such events are not inherent in the normal exercise of the activity of the air carrier concerned and is beyond the actual control of that carrier on account of its nature or origin. One further point arising out of the court's decision is worth mentioning. It is not just necessary to satisfy the extraordinary circumstances test for the airline to be excused from paying compensation. It must also show that the circumstances could not have been avoided even if all reasonable measures had been taken. It is clear from the language of the Court's decision that this is a tough test to meet: the airline will have to establish that, even if it had deployed all its resources in terms of staff or equipment and the financial means at its disposal, it would clearly not have been able - unless it had made intolerable sacrifices in the light of the capacities of its undertaking at the relevant time - to prevent the extraordinary circumstances with which it was confronted from leading to the cancellation of the flight.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.