• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.7
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• pp.1018-1027
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• 2005

The purpose of this study was to investigate nutrients intake and health-related behaviors in elderly people residing in Gyeongsan-si, Gyeongbuk who have no problem in daily living. Information on general characteristics of the elderly, health-related behaviors and dietary habits were obtained by interview based on questionnare. Dietary nutrients intake data were obtained through the 24 recall method. The subject group of this study was composed of 113 males and 112 females, the average age being $73.1\pm6.06$ years old. In health related factors, $76.9\%$ of subjects exercised regularly. The rates of alcohol drinking and smoking showed to be $38.2\%\;and\;22.2\%$, respectively. There were many elderly with neuralgia, hypertension, and gastrointestinal disease, especially female were worse. Average heights of the subjects were lower than the standard established in the Korean Recommended Dietary Allowances, and average weights were similar to the standards. The mean BMI and WHR were 24.8 (male 23.7, female 25.7) and 0.92 (male 0.92, female 0.89), respectively. Most of the subjects had a regular meal pattern comsuming three meals a day, and many elderly, especially more than $79.5\%$ of female, prepared the meals for themselves. Mean daily energy intakes and RDA percentage of energy intakes of the male and female subjects were estimated as 1426.9kcal $(79.3\%)$ and 1381.3 kcal $(86.3\%)$, respectively. Mean daily intakes of nutrients were estimatied as 48.1g for protein, 411.3mg for calcium, 8.05mg for iron, 541.8 R.E. for vitamin A, 0.84mg for vitamin $B_1$, and 0.79mg for vitamin $B_2$. Most nutrients except protein, clacium, iron and vitamin $B_2$ were consumed over $75\%$ of the RDA. Female elderly showed significant lower intakes (p<0.05) for most of the nutrients except calcium, phosphorus and vitamin ethan the elderly male.

• 한국사회복지학회:학술대회논문집
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• 2003.10a
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• pp.185-216
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• 2003

This study was aimed at investigating important factors influencing career success among regular workers. The current researcher scrutinized the degree to which variables and factors affect the career success and occupational turnover rates of the research participants. At the same tune, two hypothetical path models established by the researcher were examined using linear multiple regression methods and the LISREL. After examining the differences among the factors of career success, a comparison was made between the disabled worker group and the non-disabled worker group. A questionnaire using the 5-point Likert scale was distributed to a group of 374 workers with disabilities and 463 workers without disabilities. For the data analysis purpose, the structural equation model, factor analysis, correlation analysis, and multiple regression analysis were carried out. The results of this study ran be summarized as follows. First, the results of factor analysis showed important categories of conceptual themes of career success. The initial conceptual factor model did not accord with the empirical one. A three-factorial model revealed categories of personal, family, and organizational factor respectively. The personal factor was composed of the self-esteem and self-efficiency. The family factor was consisted of the multi-roles stress and the number of children. Finally, the organizational factor was composed of the capacity for utilizing resources, networking, and the frequency of mentoring. In addition, the total 10 sub areas of career success were divided by two important aspects; the subjective career success and the objective career success. Second, both research participant groups seemed to be influenced by their occupational types. However, all predictive variables excluding the wage rate and the average length of work years had significant impact on job success for the disabled work group, while all the variables excluding the frequency of advice and length of working years had significant impact on job success for the non-disabled worker group. Third, the turnover rate was significantly influenced by the age and the experience of turnover of the research participants. However, the number of co-workers was the strongest predictive variable for the worker group with disabilities, but the occupation choice variable for the worker group without disabilities. For the disabled worker group, the turnover rate was differently influenced by the type of occupation, the length of working years, while multi-role stress and the average working years at the time of turnover for the worker group without disabilities. Fifth, as a result of verifying the hypothetical path model, it showed that the first model was somewhat proper and could predict the career success on both research participant groups. In the second model, the Chi-square, the degree of freedom (($x^2=64.950$, df=61, P=0.341), and the adjusted Goodness of Fit Index (AGFI) were .964, and the Comparative Fit Index (CFI) were .997, and the Root Mean Squared Residual (RMR) was respectively. .038. The model was best fitted and could predict the career success more highly because the goodness of fit index in the whole models was within the allowed range. In conclusion, the following research implications can be suggested. First, the occupational type of research participants was one of the most important variables to predict the career success for both research participant groups. It means that people with disabilities require human development services including education. They need to improve themselves in this knowledge-based society. Furthermore, for maintaining the career success, people with disabilities should be approached by considering the subjective career success aspects including wages and the promotion opportunities than the objective career success aspects.

• Tuberculosis and Respiratory Diseases
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• v.40 no.2
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• pp.135-146
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• 1993

Background: In acute pulmonary embolism it has been postulated that the constriction of bronchi and pulmonary artery secondary to neurohumoral response plays an important role in cardiopulmonary dysfunction in addition to the mechanical obstruction of pulmonary artery. Serotonin is considered as the most important mediator. Positive end expiratory pressure (PEEP) stimulates $PGI_2$ secretion from the vascular endothelium, but its role in acute pulmonary embolism is still in controversy. Methods: To study the cardiopulmonary effect and therapeutic role of Ketanserin, selective antagonist of 5-HT2 receptor, and PEEP in acute pulmonary embolism experimental acute pulmonary embolism was induced in dogs with autologous blood clot. The experimental animals were divided into 3 groups, that is control group, Ketanserin injection group and PEEP application group. Results: Thirty minutes after embolization, mean pulmonary arterial pressure and pulmonary vascular resistance increased and cardiac output decreased. $PaO_2,\;P\bar{v}O_2$ and oxygen transport decreased and physiological shunt and $PaCO_2$ increased. After injection of Ketanserin, comparing with control group, mean pulmonary arterial pressure, pulmonary vascular resistance and physiological shunt decreased, while cardiac output, $PaO_2$ and oxygen transport increased. All these changes sustained till 4 hours after embolization. After PEEP application pulmonary vascular resistance, $PaO_2$ and $PaCO_2$ increased, while physiological shunt, cardiac output and oxygen transport decreased. After discontinuation of PEEP, mean pulmonary arterial pressure and pulmonary vascular resistance decreased and were lower than control group, while $PaO_2$ and cardiac output increased and higher than control group. $PaCO_2$ decreased but showed no significant difference comparing with control group. Conclusion: It can be concluded that Ketanserin is effective for the treatment of acute pulmonary embolism. With PEEP hemodynamic status deteriorated, but improved better than control group after discontinuation of PEEP. Thus PEEP may be applied carefully for short period in acute pulmonary embolism if the hemodynamic status is tolerable.

• Journal of the korean academy of Pediatric Dentistry
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• v.31 no.4
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• pp.569-578
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• 2004

The purpose of this study was to clarify the palatal arch length, width and height in the primary and permanent dentition. Samples were consisted of normal occlusions both in the primary dentition(50 males and 50 females) and in the permanent dentition(50 males and 50 females). With their upper plaster casts were used and through 3-dimensional laser scanning(3D Scanner, DS4060, LDI, U.S.A.), cloud data, polygonization, section curve and loft surface, fit and horizontal plane were based to measure the palatal arch length, width and height(Surfacer 10.0, Imageware, U.S.A.). T-tests were applied for the statistical analyze of the data. The results were as follows : 1. In the measurement values, the values of the male were higher than those of the female except primary anterior palatal height. There were not only statistically significant differences in anterior palatal width(p<0.05) and posterior palatal width(p<0.01) in primary dentition but palatal width(p<0.05), anterior palatal length(p<0.01), middle and posterior palatal length(p<0.05) in permanent dentition between male and female. 2. In the indices of palate, there were statistically significant differences in height-length index(p<0.05) and width-length index(p<0.01) between male and female in primary dentition. In permanent dentition, there was statistically difference between male and female. 3. In the measurement values, posterior palatal width was increased most greatly. Posterior palatal height, anterior palatal width and anterior palatal length were followed by descending order. On the other hand, anterior palatal height and posterior palatal length were decreased. 4. In the indices of palate, the height-length index, the width-length index and posterior height-width index were increased, but the others were decreased.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of the Korean Geographical Society
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• v.29 no.2
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• pp.166-182
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• 1994

Kyonggi Province in the late Chosun dynasty was a center of superior government offices including 'Han' River water-road transportation and was located in the middle of an 'X'-shaped arterial road network. Because of these reasons, Kyonggi Province had a faster inflow of commodities, informations and technics compared with the other province. At this period of time, every local 'Eup' (name of administrative district) had not been affected by their above administrative districts and had their own autonomy. For this reason, every 'Eup' could be developed as a town, even if its size was small when it had sufficient internal growing conditions. Moreover, the markets ('Si-Jon') in big towns and periodical markets which were spread over the Kyonggi Province played role of commercial functions of town. And because military bases for the defence of the royal capital in Kyonggi Province also took parts of a non-agricultural city role, Xyonggi Provinc had much more possibilities of growing as a town rather than the other provinces. The towns of the late Chosun Dynasty were, except the capital and superior administrative districts which were governed by the 'You-Su', small towns which had only about 3, 000-5, 000 people. Most of the town dewellers were local officials, nobles, merchants, craftmen and slaves. And the farmers who lived near town became a pseudo-towner through suburb agriculture. Among these people, the merchants were leaders of townization. The downtowns were affected by the landform and traffic roads. The most fundamental function of towns were administrative. The opcial's grade, which was dispatched to the local administrative district ('Kun' or 'Hyun'), was decided by the size of population and agricultural land of each county. Large county which was governed by a high ranking opcial had more possibilities to develop as a large town. Because they supervised other opcials of lower rank and obtained more land and population for the town. The phonomena of farm abandonment after the Japanese Invasion of Korea in 1592-1598 stimulated the development of towns for commercial function. The commercial functions of towns were evident in the Si-Jon or Nan-Jon (names of markets) in the big cities such as Hansung and Kaesung, meanffwhile in the local areas it was emerged in the shape of periodical market networks as allied with near markets (which were called as Jang-Si) or permanent markets which were grown up from periodical markets. These facts of commercial development induced the birth of commercial town. Kyonggi Province showed the weak points of its defense system during both wars (Japanese Invasion in 1592 and Manchu's Invasion in 1636). The government reinforced its defense system by adding 4 'You-Su-Bus' and several military bases. Each local districts ('Eup'), where Geo-Jins were established, were stimulated to be a town while Jin-Kwan system were, adjusted and enforced. Among Dok-Jins(name of solitary military bases), Youngjongjin was grown up as a large garrison town which only played a role of defense. The number of towns that took roles of non-agricultural functions in Kyonggi Province was 52. Among these towns, 29 were developed as big towns which had above 3, 000 people and most of these towns were located on the northwest-southeast axes of 'X'-shaped arterial trafic network in the Chosn Dynasty, This fact points out that the traffic road is one of the important causes of the development of towns. When we make hierarchy of the towns of Kyonggi Province according to its population and how many functions it had, we can make it as 6 grades. The virst grade town 'Hansung' was the biggest central town of administration, commerce and defdnse. The 2nd grade town includes 'Kaesung' which had historical inertia that it had been the capital of the Koryo Dynesty. The 3rd grade towns include some 'You- Su-Bus' such as Soowon, Kanghwa, Kwangju and also include Mapo, Yongsan and from this we can imagine that the commercial development in the late Chosun Dynasty extremely affected the townization. The 4th-6th grade towns had smiliar population but it can be discriminated by how many town functions it had. So the 4th grade towns were the core of administration, commerce and defense function. 5th grade towns had administrative functions and one of commercial and defense functions. 6th grade towns had only one of these functions. When we research and town conditions of each grades as the ratio of non-agricultural population, we can find out that the towns from the 1st grade to 4th grade show difference by degree of townization but from the 4th grade to 6th grade towns do not show big difference in general.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.149-161
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• 2014

The export of domestic public services to overseas markets contains many potential obstacles, stemming from different export procedures, the target services, and socio-economic environments. In order to alleviate these problems, the business incubation platform as an open business ecosystem can be a powerful instrument to support the decisions taken by participants and stakeholders. In this paper, we propose an ontology model and its implementation processes for the business incubation platform with an open and pervasive architecture to support public service exports. For the conceptual model of platform ontology, export case studies are used for requirements analysis. The conceptual model shows the basic structure, with vocabulary and its meaning, the relationship between ontologies, and key attributes. For the implementation and test of the ontology model, the logical structure is edited using Prot$\acute{e}$g$\acute{e}$ editor. The core engine of the business incubation platform is the simulator module, where the various contexts of export businesses should be captured, defined, and shared with other modules through ontologies. It is well-known that an ontology, with which concepts and their relationships are represented using a shared vocabulary, is an efficient and effective tool for organizing meta-information to develop structural frameworks in a particular domain. The proposed model consists of five ontologies derived from a requirements survey of major stakeholders and their operational scenarios: service, requirements, environment, enterprise, and county. The service ontology contains several components that can find and categorize public services through a case analysis of the public service export. Key attributes of the service ontology are composed of categories including objective, requirements, activity, and service. The objective category, which has sub-attributes including operational body (organization) and user, acts as a reference to search and classify public services. The requirements category relates to the functional needs at a particular phase of system (service) design or operation. Sub-attributes of requirements are user, application, platform, architecture, and social overhead. The activity category represents business processes during the operation and maintenance phase. The activity category also has sub-attributes including facility, software, and project unit. The service category, with sub-attributes such as target, time, and place, acts as a reference to sort and classify the public services. The requirements ontology is derived from the basic and common components of public services and target countries. The key attributes of the requirements ontology are business, technology, and constraints. Business requirements represent the needs of processes and activities for public service export; technology represents the technological requirements for the operation of public services; and constraints represent the business law, regulations, or cultural characteristics of the target country. The environment ontology is derived from case studies of target countries for public service operation. Key attributes of the environment ontology are user, requirements, and activity. A user includes stakeholders in public services, from citizens to operators and managers; the requirements attribute represents the managerial and physical needs during operation; the activity attribute represents business processes in detail. The enterprise ontology is introduced from a previous study, and its attributes are activity, organization, strategy, marketing, and time. The country ontology is derived from the demographic and geopolitical analysis of the target country, and its key attributes are economy, social infrastructure, law, regulation, customs, population, location, and development strategies. The priority list for target services for a certain country and/or the priority list for target countries for a certain public services are generated by a matching algorithm. These lists are used as input seeds to simulate the consortium partners, and government's policies and programs. In the simulation, the environmental differences between Korea and the target country can be customized through a gap analysis and work-flow optimization process. When the process gap between Korea and the target country is too large for a single corporation to cover, a consortium is considered an alternative choice, and various alternatives are derived from the capability index of enterprises. For financial packages, a mix of various foreign aid funds can be simulated during this stage. It is expected that the proposed ontology model and the business incubation platform can be used by various participants in the public service export market. It could be especially beneficial to small and medium businesses that have relatively fewer resources and experience with public service export. We also expect that the open and pervasive service architecture in a digital business ecosystem will help stakeholders find new opportunities through information sharing and collaboration on business processes.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Chest Surgery
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• v.29 no.11
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• pp.1173-1181
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• 1996

The causes of degenerative changes in allograft cardiac valves are not well known to this day. Today's preserved allografts possess highly viable endothelial cells and degeneration of allografts can be facilitated by immune reaction which may be mediated by these viable cells. To test the antigenicity of endothelial cells, pieces from aortic wall were obtained from fresh and cryo-preserved rat allograft. Timings of sampling were prior to sterilization, after sterilization, after 1, 2, 7, 14 days of fresh preservation and cryopreservation. Endothelial cells were tested by immunohistochemical methods using monoclonal antibodies to MHC class I(MRC OX-18), class II(MRC OX-6) and ICAM-1 antigens. After transplantation of each group of aortic allograft at the subcutaneous layers of rats, population of CD4$^{+}$ T cell and CD8$^{+}$ T cell were analyzed with monoclonal antibodies after 1, 2, 3, 4, 6 and 8 weeks. MHC class I expression was 23.95% before preservation and increased to 35.53~48.08% after preservation(p=0.0183). MHC Class II expression was 9.72% before preservation and 10.13~13.39% after preservation(P=0.1599). ICAM-1 expression was 15.02% before preservation and increased to 19.85~35.33% after preservation(P=0.001). The proportion of CD4$^{+}$ T-cell was 42.13% before transplantation. And this was 49.23~36.8% after transplantation in No treat group (p=0.955), decreased to 29.56~32.80% in other group(p=0.0001~0.008). In all the groups, the proportion of CD8$^{+}$ T-cell increased from 25.57% before transplantation to 42.32~58.92% after transplantation(p=0.000l~0.0002). The CD4$^{+}$/CD8$^{+}$ ratio decreased from 1.22~2.28 at first week to 0.47~0.95 at eighth week(p=0.0001). The results revealed that the expression of MHC class I and ICAM-1 in aortic allograft endothelium were increased but that of MHC class II were not changed, despite the different method of preservation. During 8 weeks after transplantation of aortic allograft, the subpopulations of CD4$^{+}$ T cell were not changed or only slightly decreased but those of CD8$^{+}$ T cell were progressively increased.ely increased.

• Journal of Preventive Medicine and Public Health
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• v.29 no.4 s.55
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• pp.785-799
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• 1996

We investigated the 1,004 workers who worked in a automobile factory to study the epidemiologic characterists of dermatoses due to cutting oils. Among the workers, 667(66.4%) answered the questionaire. They are belong to 5 departments of the factory-the Engine-Work(258 workers), Gasoline engine Assembly(210), Diesel engine Assembly(96), Power train Work(86), Power train Assembly(17). We measured the oil mist concentration in air of the departments and examined the workers who had dermatologic symptoms. The results were follows; 1) Oil mist concentration ; Of all measured points(52),9 points(17.2%) exeeded $5mg/m^3$- the time-weighed PEL-and one department had a upper confidence limit(95%) higher than $5mg/m^3$. 2) Dermatologists examined 213 workers. 172 of them complained any skin symptoms at that time - itching(32.5%), papule(21.6%), scale(15.7%), vesicle(12.5%) in order. The abnormal skin site found by dermatologist were palm(29.3%), finger & nail(24.6%), forearm(16.2%), back of hand(8.4%) in order. 3) As the result of physical examination, we found that 160 workers had skin diseases. Contact dermatitis was the most common; 69 workers had contact dermatitis alone(43.1%), 11 had contact dermatitis with acne(6.9%), 10 had contact dermatitis with folliculitis(6.3%), 1 had contact dermatitis with acne & folliculitis, and 1 had contact dermatitis with abnormal pigmentation. Others were folliculitis(9 workers, 5.6%), acne(8, 5.0%), folliculitis & acne (2, 1.2%), keratosis(1, 0.6%), abnormal pigmentation (1, 0.6%), and non-specific hand eczema (47, 29.3%). 4) The prevalence of any skin diseases was 34.0 pet 100 in cutting oil users, and 13.3 per 100 in non- users. Especially, the prevalence of contact dermatitis was 23.0 per 100 in cutting oil users and 23.0 per 100 in non-users. 5) We tried patch test(standard serise, oil serise, organic solvents) on 49 patients to differentiate allergic contact dermatitis from irritant contact dermatitis and found 20 were positive. 6) In a multivariate analysis(independant=age, tenure, kinds of cutting oil), the risk of skin diseases was higher in the water-based cutting oil user and both oil user than non-user or neat oil user(odds ratio were 2.16 and 2.78, respectively). And the risk of contact dermatitis was much higher at the same groups(odds ratio were 5.16 and 6.82, respectively).