• Journal of Life Science
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• v.34 no.8
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• pp.558-566
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• 2024

The potential therapeutic effects of Kaempferia Parviflora ethanol extract (KPE) on osteoarthritis were investigated using the human chondrocyte cell line (CHON-001) to explore its application in functional foods. The CHON-001 cells were pre-treated with either 1 ㎍/ml or 5 ㎍/ml of KPE before exposure to 10 ng/ml of IL-1β to induce osteoarthritis. Results showed that KPE treatment significantly suppressed IL-1β-induced TNF-α production by 66% and 50% at concentrations of 1 ㎍/ml and 5 ㎍/ml KPE, respectively. In addition, COX-2 protein expression was reduced by 26% and 34% compared to control levels. The preservation of chondrocyte-specific matrix proteins, aggrecan, and collagen type II, was notable, with aggrecan and mRNA levels maintained by 5% and 8%, and collagen II levels preserved by 62% and 47% at the same KPE concentrations. This preservation is likely due to the reduced expression of MMP1 and MMP13, enzymes responsible for matrix protein degradation. Overall, the current results suggest that KPE may protect chondrocytes from IL-1β-induced osteoarthritis by suppressing TNF-α production and COX-2 expression while preserving critical matrix proteins like aggrecan and collagen II by suppressing the expressions of their degrading enzymes (MMP-1 and MMP-13). Therefore, KPE holds promise as a candidate for developing functional foods aimed at reducing osteoarthritis.

• Korean Journal of Environment and Ecology
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• v.38 no.4
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• pp.375-387
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• 2024

This study investigated the ecological characteristics of Fraxinus chiisanensis Nakai, one of the endemic and rare plant species in Korea, based on its distribution status, characteristics of the growth environment, and species composition. A vegetation survey that analyzed the correlation between species distribution patterns and environmental variables, along with the traits of the emergent plant species, was performed according to the explanation of environmental growth conditions and phytosociological method for the location where F. chiisanensis is found. A total of 19 dominant locations and 9 non-dominant locations of F. chiisanensis were observed in 28 study sites in 12 regions, and a total of 155 taxa were observed. According to the vegetation climate of Korea, the growth environment of the study site where F. chiisanensis is located is characterized as cold and is primarily situated within the northern temperate deciduous broadleaf forest zone. The average elevation was 859m above sea level, with an average rock exposure of 60.4%, soil exposure of 24.7%, and an average slope of 18.7°. The taxa belonging to the top P-NCD(Percentage of Net Contribution Degree) among the emergent species were mostly designated as the taxa emerging in valley vegetation. The correlation analysis of environmental variables revealed that altitude had the strongest correlation, with rock exposure showing the second highest correlation. The ongoing dynamics of the F. chiisanensis forest are anticipated to persist due to the high P-NCD values exhibited by the F. chiisanensis within the shrub and herbaceous layers among the taxa associated with tree species. Most F. chiisanensis habitats are currently situated within protected regions such as national parks, provincial parks, and county parks, where there are relatively minimal human-induced disturbances. However, there is potential for damage in areas not designated as protected, such as forest tending operation sites or new hiking trails. Concerns about declining habitat quality have prompted suggestions for management strategies such as establishing Forest Genetic Resource Reserves in these locations. In addition, follow-up and further research should be conducted to identify possible sites for distribution and establish candidate conservation areas based on various environmental conditions of F. chiisanensis.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.3
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• pp.235-245
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• 2008

Purpose: The HSV1-tk gene has been extensively studied as a type of reporter gene. In hepatocellular carcinoma (HCC), only a small proportion of patients are eligible for surgical resection and there is limitation in palliative options. Therefore, there is a need for the development of new treatment modalities and gene therapy is a leading candidate. In the present study, we investigated the usefulness of substrate, 2'-fluoro-2'-deoxy-1-${\beta}$-D-arabino-furanosyi-5-[$^{124/125}I$]iodo- uracil ([$I^{124/125}I$]FIAU) as a non-invasive imaging agent for HSV1-tk gene therapy in hepatoma model using small animal PET. Material and Methods: With the Morris hepatoma MCA cell line and MCA-tk cell line which was transduced with the HSV1-tk gene, in vitro uptake and correlation study between [$^{125}I$]FIAU uptake according to increasing numeric count of percentage of MCA-tk cell were performed. The biodistribution data and small animal PET images with [$^{124}I$]FIAU were obtained with Balb/c-nude mice bearing both MCA and MCA-tk tumors. Results:, Specific accumulation of [[$^{125}I$]FIAU was observed in MCA-tk cells but uptake was low in MCA cells. Uptake in MCA-tk cells was 15 times higher than that of MCA cells at 480 min. [$^{125}I$]FIAU uptake was linearly correlated (R2 =0.964, p =0.01) with increasing percentage of MCA-tk numeric cell count. Biodistribution results showed that [$^{125}I$]FIAU was mainly excreted via the renal system in the early phase. Ratios of MCA-tk tumor to blood acting were 10, 41, and 641 at 1 h, 4 h, and 24 h post-injection, respectively. The maximum ratio of MCA-tk to MCA tumor was 192.7 at 24 h. Ratios of MCA-tk tumor to liver were 13.8, 66.8, and 588.3 at 1 h, 4 h, and 24 h, respectively. On small animal PET, [$^{124}I$]FIAU accumulated in substantial higher levels in MCA-tk tumor and liver than MCA tumor. Conclusion: FIAU shows selective accumulation to HSV1-tk expressing hepatoma cell tumors with minimal uptake in normal liver. Therefore, radiolabelled FIAU is expected to be a useful substrate for non-invasive imaging of HSV1-tk gene therapy and therapeutic response monitoring of HCC.

• Journal of the Korean Society of Food Culture
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• v.6 no.4
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• pp.421-432
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• 1991

This study was conducted to investigate the intake of a kind of Gom-Tang (Beef soup made with internal organs and bone), which is the Korean traditional food, and factors affecting the eating behavior of customers. Two hundred male customers of a H Korean Restaurant specialized in Gom-Tang, which is a well-known restaurant in Seoul, were surveyed from June 26 to 29, 1990. The results were shown as follows. (1) ${\ulcorner}$Frequency of intake${\lrcorner}$ and ${\ulcorner}$preference${\lrcorner}$ were very high. Those who took a kind of Gom-Tang ${\ulcorner}$more than once a week${\lrcorner}$ were 66.5% of the subjects. Those who evaluated ${\ulcorner}$good${\lrcorner}$ for the taste and flavor were 86.5% and 59.0% of the subjects, respectively. (2) The reasons why they chose a kind of Gom-Tang from among many Korean traditional foods were ${\ulcorner}$preference${\lrcorner}$ and ${\ulcorner}$phygiological condition${\lrcorner}$ in ${\ulcorner}$high frequency of intake${\lrcorner}$ group. ${\ulcorner}$Phygiological condition${\lrcorner}$ was more critical factor than ${\ulcorner}$preference${\lrcorner}$ in ${\ulcorner}$low frequency of intake${\lrcorner}$ group. (3) The effect of the intake of a kind of Gom-Tang on health was evaluated as ${\ulcorner}$healthy${\lrcorner}$ (80.5%). ${\ulcorner}$No effect${\lrcorner}$ and ${\ulcorner}$harmful${\lrcorner}$ were 30.5% and 6.5%, respectively. (4) ${\ulcorner}$High frequency of intake${\lrcorner}$ group, mainly more than 50 years of age, had a high ${\ulcorner}$preference${\lrcorner}$ and ${\ulcorner}$food knowledge${\lrcorner}$ as well as positive ${\ulcorner}$eating behavior${\lrcorner}$ and ${\ulcorner}$healthy state${\lrcorner}$, ${\ulcorner}$Middle frequency of intake${\lrcorner}$ group, mainly the forties, had a high ${\ulcorner}$preference${\lrcorner}$, but had less positive ${\ulcorner}$eating behavior${\lrcorner}$ than ${\ulcorner}$high frequency of intake${\lrcorner}$ group. ${\ulcorner}$Low frequency of intake${\lrcorner}$ group, mainly the twenties and thirties, had a medial ${\ulcorner}$preference${\lrcorner}$. They took a kind of Gom-Tang for reasons of ${\ulcorner}$on the recommendation of friends${\lrcorner}$ better than ${\ulcorner}$preference${\lrcorner}$. Foregoing results showed that ${\ulcorner}$a kind of Gom-Tang${\lrcorner}$ was a typical food recognized as ${\ulcorner}$healthy${\lrcorner}$ as well as ${\ulcorner}$delicious${\lrcorner}$. This may suggest that ${\ulcorner}$a kind of Gom-Tang${\lrcorner}$ is a candidate for the effective food on nutritional education.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Radiation Oncology Journal
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• v.18 no.4
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• pp.257-264
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• 2000

Purpose: For early stage non-small-cell lung cancer, surgical resection is the treatment of choice. But when the patients are not able to tolerate it because of medical problem and when refuse surgery, radiation therapy is considered an acceptable alternative. We report on the treatment results and the effect of achieving local control of primary tumors on survival end points, and analyze factors that may influence survival and local control. Materials and Method : We reviewed the medical records of 32 patients with medically inoperable non-small cell lung cancer treated at our institution from June, 1987 through June, 1997. All patients had a pathologic diagnosis of non-small cell lung cancer and were not candidate for surgical resection because of either patients refusal (4), old age (2), lung problem (21), chest wail invasion (3) and heart problems (3). In 8 patients, there were more than 2 problems. The median age of the patients was 68 years (ranging from 60 to 86 years). Histologic cell type included souamous (24), adenocarcinoma (6) and unclassiried squamous cell (2). The clinical stages of the patients were 71 in 5, 72 in 25, 73 in 2 patients. Initial tumor size was 3.0 cm in 11, between 3.0 cm and 5.0 cm in 13 and more than 5.0 cm in 8 patients. Ail patients had taken chest x-rays, chest CT, abdomen USG and bone scan. Radiotherapy was delivered using 6 MV or 10 MV linear accelerators. The doses of primary tumor were the ranging from 54.0 Gy to 68.8 Gy (median; 61.2 Gy). The duration of treatment was from 37 days through 64 days (median; 0.5 days) and there was no treatment interruption except 1 patient due to poor general status. In 12 patients, concomitant boost technique was used. There were no neoadjuvant or adjuvant treatments such as surgery or chemotherapy. The period of follow-up was ranging from 2 months through 93 months (median; 23 months). Survival was measured from the date radiation therapy was initiated. Results : The overall survival rate was 44.6$\%$ at 2 years and 24.5$\%$ at 5 years, with the median survival time of 23 months. of the 25 deaths, 7 patients died of intercurrent illness, and cause-specific survival rate was 61.0$\%$ at 2 years and 33.5$\%$ at 5 years. The disease-free survival rate was 38.9$\%$ at 2 years and 28.3$\%$ at 5 years. The local-relapse-free survival rate was 35.1$\%$, 28.1$\%$, respectively. On univariate analysis, tumor size was significant variable of overall survival (p=0.0015, 95$\%$ C.1.; 1.4814-5.2815), disease-free survival (P=0.0022, 95$\%$ C.1., 1.4707-5.7780) and local-relapse-free survival (p=0.0015, 95$\%$ C.1., 1.2910- 4.1197). 7 stage was significant variable of overall survival (p=0.0395, 95$\%$ C.1.; 1.1084-55.9112) and had borderline significance on disease-free survival (p=0.0649, 95$\%$ C.1.; 0.8888-50.7123) and local-relapse-free survival (p=0.0582, 95$\%$ C,1.; 0.9342-52.7755). On multivariate analysis, tumor size had borderline significance on overall survival (p=0.6919, 955 C.1., 0.9610-5.1277) and local-relapse-free survival ( p=0.0585, 95$\%$ C.1.; 0.9720-4.9657). Tumor size was also significant variable of disease-free survival (p=0.0317, 95% C.1.; 1.1028-8.4968). Conclusion : Radical radiotherapy is an effective treatment for small (71 or f3 cm) tumors and can be offered as alternative to surgery in elderly or infirmed patients. But when the size of tumor is larger than 5 cm, there were few long-term survivors treated with radiotherapy alone. The use of hypefractionated radiotherapy, endobronchial boost, radisensitizer and conformal or IMRT should be consider to improve the local control rate and disease-specific survival rate.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.59-80
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• 2008

Recently, e-sports are growing with potentiality as a new industry with conspicuous profit model. But studies that dealing with e-sports are not enough. Hence, proposes of this paper are both to establish basic model that is for the design of e-sport marketing strategy and to contribute toward future studies which are related to e-sports. Recently, the researches to explain sports-sponsorship through the identification theory have been discovered. Many researches say that somewhat proper identification is a requirement for most sponsors to improve the their images which is essential to sponsorship activity. Consequently, the research for sponsorship associated with identification in the e-sports, not in the physical sports is the core sector of this study. We extracted the variables from online's major characteristics and existing sport sponsorship researches. First, because e-sports mean the tournaments or leagues in the use of online game, the main event of the game is likely to call it online game. Online media's attributes are distinguished from those of offline. Especially, interactivity, anonymity, and expandibility as a e-sport game attributes are able to be mentioned. So, these inherent online attributes are examined on the relationship with flow. Second, in physical sports games, Fisher(1998) revealed that team similarity and team attractivity were positively related to team identification. Wann(1996) said that the result of former game influenced the evaluation of the next game, then in turn has an effect on the identification of team supporters. Considering these results in the e-sports side, e-sports gamer' attractivity, similarity, and match result seem to be important precedent variables of the identification with a gamer. So, these e-sport gamer attributes are examined on the relationship with both flow and identification with a gamer. Csikszentmihalyi(1988) defined the term flow as feeling status for him to be making current positive experience optimally. Hoffman and Novak(1996) also said that if a user experienced the flow he would visit a website without any reward. Therefore flow might be positively associated with user's identification with a gamer. And, Swanson(2003) disclosed that team identification influenced the positive results of sponsorship, which included attitude toward sponsors, sponsor patronage, and satisfaction with sponsors. That is, identification with a gamer expect to be connected with corporation identification significantly. According to the above, we can design the following research model. All variables used in this study(interactivity, anonymity, expandibility, attractivity, similarity, match result, flow, identification with a gamer, and identification with a sponsor) definitely were defined operationally underlying precedent researches. Sample collection was carried out to the person who has an experience to have enjoyed e-sports during June 2006. Much portion of samples is men because much more men than women enjoy e-sports in general. Two-step approach was used to test the hypotheses. First, confirmatory factor analysis was committed to guarantee the validity and reliability of variables. The results showed that all variables had not only intensive and discriminant validity, but also reliability. Then, research model was examined with fully structural equation using LISREL 8.3 version. The fitness of the suggested model mostly was at the acceptable level. Shortly speaking about the results, first of all, in e-sports game attributes, only interactivity which is called a basic feature in online situation affected flow positively. Secondly, in e-sports gamer's attributes, similarity with a gamer and match result influenced flow positively, but there was no significant effect in the relationship between the attractivity of a gamer and flow. And as expected, similarity had an effect on identification with a gamer significantly. But unexpectedly attractivity and match result did not influence identification with a gamer significantly. Just the same as the fact verified in the many precedent researches, flow greatly influenced identification with a gamer, and identification with a gamer continually had an influence on the identification with a sponsor significantly. There are some implications in these results. If the sponsor of e-sports supports the pro-game player who absolutely should have the superior ability to others and is similar to the user enjoying e-sports, many amateur gamers will feel much of the flow and identification with a pro-gamer, and then after all, feel the identification with a sponsor. Such identification with a sponsor leads people enjoying e-sports to have purchasing intention for products produced by the sponsor and to make a positive word-of-mouth for those products or the sponsor. For the future studies, we recommend a few ideas. Based on the results of this study, it is necessary to find new variables relating to the e-sports, which is not mentioned in this study. For this work to be possible, qualitative research seems to be needed to consider the inherent e-sport attributes. Finally, to generalize the results related to e-sports, a wide range of generations not a specific generation should be researched.

• Tuberculosis and Respiratory Diseases
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• v.45 no.2
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• pp.380-387
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• 1998

Background: Minimal pressure support(PSmin) is a level of pressure support which offset the imposed work of breathing(WOBimp) developed by endotracheal tube and ventilator circuits in pressure support ventilation While the lower applied level of pressure support compared to PSmin could induce respiratory muscle fatigue, the higher level than PSmin could keep respiratory muscle rest resulting in prolongation of weaning period during weaning from mechanical ventilation PSmin has been usually applied in the level of 5~10 cm$H_2O$, but the accurate level of PSmin is difficult to be determinated in individual cases. PSmin is known to be calculated by using the equation of "PSmin = peak inspiratory flow rate during spontaneus ventilation$\times$total ventilatory system resistance", but correlation of calculated PSmin and measured PSmin has not been known. The objects of this study were firstly to assess whether customarily applied pressure support level of 5~10 cm$H_2O$ would be appropriate to offset the imposed work of breathing among the patients under weaning process, and secondly to estimate the correlation between the measured PSmin and calculated PSmin. Method : 1) Measurement of PSmin : Intratracheal pressure changes were measured through Hi-Lo jet tracheal tube (8mm in diameter, Mallinckroft, USA) by using pulmonary monitor(CP-100 pulmonary monitor, Bicore, USA), and then pressure support level of mechanical ventilator were increased until WOBimp was reached to 0.01 J/L or less. Measured PSmin was defined as the lowest pressure to make WOBimp 0.01 J/L or less. 2) Calculation of PSmin : Peak airway pressure(Ppeak), plateau airway pressure(Pplat) and mean inspiratory flow rate of the subjects were measured on volume control mode of mechanical ventilation after sedation. Spontaneous peak inspiratory flow rates were measured on CPAP mode(O cm$H_2O$). Thereafter PSmin was calculated by using the equation "PSmin = peak inspiratory flow rate$\times$R, R = (Ppeak-Pplat)/mean inspiratory flow rate during volume control mode on mechanical ventilation". Results: Sixteen patients who were considered as the candidate for weaning from mechanical ventilation were included in the study. Mean age was 64(${\pm}14$) years, and the mean of total ventilation times was 9(${\pm}4$) days. All patients except one were males. The measured PSmin of the subjects ranged 4.0~12.5cm$H_2O$ in 14 patients. The mean level of PSmin was 7.6(${\pm}2.5\;cmH_2O$) in measured PSmin, 8.6 (${\pm}3.25\;cmH_2O$) in calculated PSmin Correlation between the measured PSmin and the calculated PSmin is significantly high(n=9, r=0.88, p=0.002). The calculated PSmin show a tendancy to be higher than the corresponding measured PSmin in 8 out of 9 subjects(p=0.09). The ratio of measured PSmin/calculated PSmin was 0.81(${\pm}0.05$). Conclusion: Minimal pressure support levels were different in individual cases in the range from 4 to 12.5 cm$H_2O$. Because the equation-driven calculated PSmin showed a good correlation with measured PSmin, the application of equation-driven PSmin would be then appropriate compared with conventional application of 5~10 cm$H_2O$ in patients under difficult weaning process with pressure support ventilation.