• Journal of agricultural medicine and community health
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• 제23권2호
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• pp.157-174
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• 1998

A survey was conducted to study the influence of socia-demographic factors on health-related behaviors. from June 1 to July 31, 1996. The study population was 1,903 adults in Kyongju City. A questionnaire method was used to collect data. Health-related behaviors included 24 items for men and 26 items for women. The followings are summaries of findings : The compliance of health promotion activities was higher when the age was older in men, when married, when having no religion and when the education level was higher than the other groups. And it was significantly higher when the income was lower in men and higher in women, in the residents living in apartment, in white collar workers, in the chronic ill people and when the body weight was lower than the other groups. Notable differences were found in the composition of health behavior factors for socio-demographic characteristics. Men used more tobacco, coffee and tea, salt and alcohol than women. However, the practice rates of regular exercise and physical examination were higher in men than women. On the other hand, the practice rates of fruit/vegetable intake, milk drinking and regular tooth brushing were higher in women than men. When the age was old, the amount of fruit/vegetable intake, the frequency of physician visit and health check-up, and regularity of meal were increased. When the income was high, the use rate of seat-belts, the amount of coffee, milk, fruit/vegetable and red meat intake were increased. The frequency of regular exercise. tooth brushing, health check-up, pap test and breast self examination were higher in the rich than the poor. When the education level was high, the frequency of regular exercise and tooth brushing, and the use rate of seat belts were increased, and the amount of alcohol consumption and salt intake were decreased. These findings suggest that socio-demographic factors are significantly associated with the patterns of health behaviors. In conclusion public health programs and individual counseling efforts should be multifaceted and behavior-specific to encourage to practice healthy life-style.

• Journal of Korean Society of Forest Science
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• 제38권1호
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• pp.33-45
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• 1978

The purpose of present study is to analyze the genetic variation of natural stand of Pinus densiflora. In 1975 following after the selection of 1974, twenty trees from each of three natural populations of the species were selected and their open-pollinated seeds were collected, and the locations and conditions of the populations ate presented in table 1, 2 and figure 1. Some morphological traits of the populations were already detailed in our second report of this series, in which Myong-Ju and Ul-Jin populations were regarded to be superior phenotypically to suweon population. The morphological traits of cone, seed and seed-wing, and also the growth performances and needle characters of the seedling were observed in the present study according to the previous methods. The results obtained are summarized as follows; 1. The meteorological data obtained by averaging the records of 30 year period (1931~1960) measured from the nearest meteorological stations to each population are shown in fig.2, 3, 4. The distributional patterns of investigated climate factors are generally considered to be similar among the locations. However, the precipitation density during growing season and the air temperature during dormant season on Suweon area, population 6, were quite different from those of the other areas. 2. The measurements of fresh cone weight, length, diameter and cone index, i.e., length to diameter ratio are presented in table 7. As shown in table 7, all these traits except for cone diameter seem to be highly significant in population differences and family differences within population. 3. The morphological traits of seed and seed-wing are detailed in table 8, 9, and highly significant differences are recognized among the populations and the families within population in seed-wing length, seed-wing index, seed weight, seed-length and seed index but not among the populations in the other observed traits. The values of correlation coefficient between the characters of cone and seed are given in table 10 and the positive significant correlations can be observed in the most parts of the compared traits. 4. Significant statistical differences among populations and families within population are observed in the growth performances of 1-0 and 1-1 seedling height of these progenies. But the differences in root collar diameter are shown only among families within population. As shown in table 13, the most parts of correlations are not significant statistically between the growth performances of seedling and the seed characters. 5. The number of stomata row on both sides of needle and the serration density were measured in the seedlings from each of the families of the three populations. As shown in table 15, statistical differences are considered to be significant among the populations and among the families within population in serration density but not among the populations in stomata row on both sides of the needle. The results differ from those of the third report of this series. Even if one of the reason seems to be the diversity of selected populations, it could not be confirmed definitely. The correlations between progenies and parents are not generally observed in the investigated traits of needle as shown in table 16.

• Journal of agricultural medicine and community health
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• 제26권1호
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• pp.19-35
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• 2001

This study was conducted to investigate the internal changes and the countermeasure for performance improvement by Separation of Prescribing and Dispensing Practice (SPDP) in Health Center. Data were collected from two sources: Performance report before and after SPDP of 25 Health Centers in Kyongsangbuk-do and 6 Health Centers in Daegu-City and self-administerd questionnaire survey of 221 officials at health center. The results of this study were summarized as follows: Twenty-four health centers(77.4%) of 31 health centers took convenience measures for medical treatment of citizens and convenience measures were getting map of pharmacy, improvement of health center interior, introduction of order communication system in order. After the SPDP in health centers, 19.4% of health centers increased doctors and 25.8% decreased pharmacists. 58.1% of health centers showed that number of medical treatments were decreased. 96.4%, 80.6% 80.6% 96.7% of health centers showed that number of prescriptions, total medical treatment expenses, amounts paid by the insureds and the expenses to purchase drugs, respectively, were decreased. More than fifty percent(54.2%) of health centers responded that the relative importance of health works increased compared to medical treatments after the SPDP, and number of patients decreased compared to those in before the SPDP. And there was a drastic reduction in number of prescriptions, total medical treatment expenses, amounts paid by insureds, the expenses to purchase drugs after the SPDP. Above fifty percent(57.6%) of officers at health center responded that the function of medical treatment should be reduced after the SPDP. Fields requested improvement in health centers were 'development of heath works contents'(62.4%), 'rearrangement of health center personnel'(51.6%), 'priority setting for health works'(48.4%), 'restructuring the organization'(36.2%), 'quality impro­vement for medical services'(32.1%), 'replaning the budgets'(23.1%) in order. And to better the image of health centers, health center officers replied that 'health information management'(60.7%), 'public relations for health center'(15.8%), 'kindness of health center officers'(15.3%) were necessary in order. Health center officers suggested that 'vaccination program', 'health promotion', 'maternal and children health', 'communicable disease management', 'community health planning' were relatively important works, in order, performed by health center after SPDP. In the future, medical services in health centers should be cut down with a momentum of the SPDP so that health centers might reestablish their functions and roles as public health organizations, but quality of medical services must be improved. Also health centers should pay attention to residents for improving health through 'vaccination program', 'health promotion', 'mother-children health', 'acute and chronic communicable disease management', 'community health planning', 'oral health', 'chronic degenerative disease management', etc. And there should be a differentiation of relative importance between health promotion services and medical treatment services by character of areas(metropolitan, city, county).

• Journal of Intelligence and Information Systems
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• 제20권3호
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• 제20권1호
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• Journal of Intelligence and Information Systems
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• 제18권2호
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• 제19권2호
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Korean Journal of Soil Science and Fertilizer
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• 제27권1호
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• pp.33-39
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• 1994

This survey was conducted to investigate the factors affecting on salt accumulation and chemical components of soils cultivated with horticulture crops in plastic film houses. The soil samples were taken from 40 sites in the northern central areas of Korea and were analyzed for the chemical properties and soil separates. The data were evaluated with soil texture and years of cultivation as major factors. The results are summarized as follows : 1. The chemical properties of surface soils in plastic film house were pH 5.80, EC $3.59mScm^{-1}$, O.M. 4.20%, Av. $P_2O_5$ 1,178ppm, $NO_3-N$ 180ppm, Av. $SO_4{^{2-}}$ 353ppm, $Cl^-$ 240ppm, Ex. Na 0.40me/100g. 2. Compared to the outside soil of plastic film house, the inside soil had 2.5~3 times higher contents of $NO_3-N$, Av. $SO_4{^{2-}}$ and $Cl^-$, 1.2~1.8 times higher exchangeable base elements, and 2.8 times higher electrical conductivity. But pH value of the inside soil was lower than the outside soil by 0.3 pH unit. 3. Soil texture classification showed that sandy loam, loam and silt loam were 32.5 %, 37.5 %, and 30.0 %, respectively. The contents of $NO_3-N$, Av. $SO_4{^{2-}}$, $NH_4-N$ and EC value were very high in silt loam soils. Av. $P_2O_5$ content and pH value of sandy loam soils were higher than those of silt loam and loam soils. 4. The contents of O.M. and Av. $P_2O_5$ were higher in long term cultivation, but the contents of $NO_3-N$, Av. $SO_4{^{2-}}$, $Cl^-$, Ex. Mg and Ex. Na including EC of the soil with 2~4 years cultivation were higher than those of the soil with above 5 years cultivation. 5. Multiple linear regression analysis showed that contribution degree of soil chemical properties to the EC was high in the order of $NO_3-N$ > Av. $SO_4{^{2-}}$ > Ex. Na > $Cl^-$ > Av. $P_2O_5$ > $NH_4-N$ > Ex. Mg>Ex. Ca. Among the soil chemical properties the contribution of anions was remarkably high. 6. EC value correlated with ${\sum}A$(total content of anions)as $r=0.932^{**}$ and with ${\sum}C$(total content of cations) as $r=0.452^{**}$.

• Journal of Intelligence and Information Systems
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• 제27권3호
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Tuberculosis and Respiratory Diseases
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• 제41권5호
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• pp.452-461
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• 1994

Background: Tumor necrosis factor(TNF)-$\alpha$ and Interleukin(lL)-$1{\beta}$ are thought to play a major role in the pathogenesis of the septic syndrome, which is frequently associated with adult respiratory distress syndrome(ARDS). In spite of many reports for the role of TNF-$\alpha$ in the pathogenesis of ARDS, including human studies, it has been reported that TNF-$\alpha$ is not sensitive and specific marker for impending ARDS. But there is a possibility that the results were affected by the diversity of pathogenetic mechanisms leading to the ARDS because of various underlying disorders of the study group in the previous reports. The purpose of the present study was to evaluate the roles of TNF-$\alpha$ and IL-$1{\beta}$ as a predictable marker for development of ARDS in the patients with septic syndrome, in which the pathogenesis is believed to be mainly cytokine-mediated. Methods: Thirty-six patients of the septic syndrome hospitalized in the intensive care units of the Asan Medical Center were studied. Sixteens suffered from ARDS, whereas the remaining 20 were at the risk of developing ARDS(acute hypoxemic respiratory failure, AHRF). In all patients venous blood samples were collected in heparin-coated tubes at the time of enrollment, at 24 and 72 h thereafter. TNF-$\alpha$ and IL-$1{\beta}$ was measured by an enzyme-linked immunosorbent assay (ELISA). All data are expressed as median with interquartile range. Results: 1) Plama TNF-$\alpha$ levels: Plasma TNF-$\beta$ levels were less than 10pg/mL, which is lowest detection value of the kit used in this study within the range of the $mean{\pm}2SD$, in all of the normal controls, 8 of 16 subjects of ARDS and in 8 in 20 subjects of AHRF. Plasma TNF-$\alpha$ levels from patients with ARDS were 10.26pg/mL(median; <10-16.99pg/mL, interquartile range) and not different from those of patients at AHRF(10.82, <10-20.38pg/mL). There was also no significant difference between pre-ARDS(<10, <10-15.32pg/mL) and ARDS(<10, <10-10.22pg/mL). TNF-$\alpha$ levels were significantly greater in the patients with shock than the patients without shock(12.53pg/mL vs. <10pg/mL) (p<0.01). There was no statistical significance between survivors(<10, <10-12.92pg/mL) and nonsurvivors(11.80, <10-20.8pg/mL) (P=0.28) in the plasma TNF-$\alpha$ levels. 2) Plasma IL-$1{\beta}$ levels: Plasma IL-$1{\beta}$ levels were less than 0.3ng/mL, which is the lowest detection value of the kit used in this study, in one of each patients group. There was no significant difference in IL-$1{\beta}$ levels of the ARDS(2.22, 1.37-8.01ng/mL) and of the AHRF(2.13, 0.83-5.29ng/mL). There was also no significant difference between pre-ARDS(2.53, <0.3-8.34ngfmL) and ARDS(5.35, 0.66-11.51ng/mL), and between patients with septic shock and patients without shock (2.51, 1.28-8.34 vs 1.46, 0.15-2.13ng/mL). Plasma IL-$1{\beta}$ levels were significantly different between survivors(1.37, 0.4-2.36ng/mL) and nonsurvivors(2.84, 1.46-8.34ng/mL). Conclusion: Plasma TNF-$\alpha$ and IL-$1{\beta}$ level are not a predictable marker for development of ARDS. But TNF-$\alpha$ is a marker for shock in septic syndrome. These result could not exclude a possibility of pathophysiologic roles of TNF-$\alpha$ and IL-$1{\beta}$ in acute lung injury because these cytokine could be locally produced and exert its effects within the lungs.