• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.4
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• pp.211-216
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• 2018

This study was intended to analyze the effects of the selection of excellent design product by the KIDP(Korea Institute of Design Promotion) on the business performance of the company focused on the start-up with relatively low competitiveness. According to the statistics of KIDP in 2013, a design accounted for 27.53% of the factors affecting the sales of products and a design was the most important factor among the factors affecting the sales of products for the small sized company in terms of the scale of company. In addition, looking at the trend by industry in the data collected for this study, the technology-based industry showed a relatively higher growth rate than the retail industry and other industries. In this study, the author surveyed 186 companies of the companies that recognized the importance of design in difficult management conditions and received excellent design product certificate from KIDP(2013, 2014 and 2015) and empirically verified whether there was any difference in business performance between the start-ups with a business history of less than 7 years and the ongoing firms with a business history of more than 7 years. To evaluate the value of design, we used analytical method of measuring related values by comparison between groups. In addition, we also analyzed the difference in business performance(sales) between manufacturing companies, where the role of design was relatively large, and non-manufacturing companies among companies that received the excellent design product certificate. We have established a study hypothesis that the selection of excellent design product by the KIDP would have more positive effect on the business performance(sales) of the start-up compared to the ongoing firm, and conducted an empirical analysis by comparing both the year before and the year after the selected year. As a result, we found that the selection of excellent design product by the KIDP has a positive effect on the business performance of the start-up, and the selection of excellent design product has a significant effect on the difference in business performance between manufacturing and non-manufacturing companies. This study was conducted in the hope that the government actively supports the design-related policies so that the selection of excellent design product become an important indicator of the business performance of the start-up, and thus the design management will be a way to enhance the competitiveness of the start-up.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.163-169
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• 2024

India is a developing nation and has come with comprehensive way in modernizing its reducing poverty, economy and rising living standards for an outsized fragment of its residents. The STEM (Science, Technology, Engineering, and Mathematics) education plays an important role in it. STEM is an educational curriculum that emphasis on the subjects of "science, technology, engineering, and mathematics". In traditional education scenario, these subjects are taught independently, but according to the educational philosophy of STEM that teaches these subjects together in project-based lessons. STEM helps the students in his holistic development. Youth unemployment is the biggest concern due to lack of adequate skills. There is a huge skill gap behind jobless engineers and the question arises how we can prepare engineers for a better tomorrow? Now a day's Industry 4.0 is a new fourth industrial revolution which is an intelligent networking of machines and processes for industry through ICT. It is based upon the usage of cyber-physical systems and Internet of Things (IoT). Industrial revolution does not influence only production but also educational system as well. IoT in academics is a new revolution to the Internet technology, which introduced "Smartness" in the entire IT infrastructure. To improve socio-economic status of the India students must equipped with 21st century digital skills and Universities, colleges must provide individual learning kits to their students which can help them in enhancing their productivity and learning outcomes. The major goal of this paper is to present a low cost, effective learning mechanism for STEM implementation using Raspberry Pi 3+ model (Single board computer) and Node Red open source visual programming tool which is developed by IBM for wiring hardware devices together. These tools are broadly used to provide hands on experience on IoT fundamentals during teaching and learning. This paper elaborates the appropriateness and the practicality of these concepts via an example by implementing a user interface (UI) and Dashboard in Node-RED where dashboard palette is used for demonstration with switch, slider, gauge and Raspberry pi palette is used to connect with GPIO pins present on Raspberry pi board. An LED light is connected with a GPIO pin as an output pin. In this experiment, it is shown that the Node-Red dashboard is accessing on Raspberry pi and via Smartphone as well. In the final step results are shown in an elaborate manner. Conversely, inadequate Programming skills in students are the biggest challenge because without good programming skills there would be no pioneers in engineering, robotics and other areas. Coding plays an important role to increase the level of knowledge on a wide scale and to encourage the interest of students in coding. Today Python language which is Open source and most demanding languages in the industry in order to know data science and algorithms, understanding computer science would not be possible without science, technology, engineering and math. In this paper a small experiment is also done with an LED light via writing source code in python. These tiny experiments are really helpful to encourage the students and give play way to learn these advance technologies. The cost estimation is presented in tabular form for per learning kit provided to the students for Hands on experiments. Some Popular In addition, some Open source tools for experimenting with IoT Technology are described. Students can enrich their knowledge by doing lots of experiments with these freely available software's and this low cost hardware in labs or learning kits provided to them.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Korea Journal of Hospital Management
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• v.9 no.1
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• pp.66-94
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• 2004

The objective of this study is to examine how to maximize the efficiency of hospital management by minimizing the unit cost of hospital operation. For this purpose, this paper proposes to develop a model of the profit maximization based on the cost minimization dictum using the statistical tools of arriving at the maximum likelihood values. The preliminary survey data are collected from the annual statistics and their analyses published by Korea Health Industry Development Institute and Korean Hospital Association. The maximum likelihood value statistical analyses are conducted from the information on the cost (function) of each of 36 hospitals selected by the random stratified sampling method according to the size and location (urban or rural) of hospitals. We believe that, although the size of sample is relatively small, because of the sampling method used and the high response rate, the power of estimation of the results of the statistical analyses of the sample hospitals is acceptable. The conceptual framework of analyses is adopted from the various models of the determinants of hospital costs used by the previous studies. According to this framework, the study postulates that the unit cost of hospital operation is determined by the size, scope of service, technology (production function) as measured by capacity utilization, labor capital ratio and labor input-mix variables, and by exogeneous variables. The variables to represent the above cost determinants are selected by using the step-wise regression so that only the statistically significant variables may be utilized in analyzing how these variables impact on the hospital unit cost. The results of the analyses show that the models of hospital cost determinants adopted are well chosen. The various models analyzed have the (goodness of fit) overall determination (R2) which all turned out to be significant, regardless of the variables put in to represent the cost determinants. Specifically, the size and scope of service, no matter how it is measured, i. e., number of admissions per bed, number of ambulatory visits per bed, adjusted inpatient days and adjusted outpatients, have overall effects of reducing the hospital unit costs as measured by the cost per admission, per inpatient day, or office visit implying the existence of the economy of scale in the hospital operation. Thirdly, the technology used in operating a hospital has turned out to have its ramifications on the hospital unit cost similar to those postulated in the static theory of the firm. For example, the capacity utilization as represented by the inpatient days per employee tuned out to have statistically significant negative impacts on the unit cost of hospital operation, while payroll expenses per inpatient cost has a positive effect. The input-mix of hospital operation, as represented by the ratio of the number of doctor, nurse or medical staff per general employee, supports the known thesis that the specialized manpower costs more than the general employees. The labor/capital ratio as represented by the employees per 100 beds is shown to have a positive effect on the cost as expected. As for the exogeneous variable's impacts on the cost, when this variable is represented by the percent of urban 100 population at the location where the hospital is located, the regression analysis shows that the hospitals located in the urban area have a higher cost than those in the rural area. Finally, the case study of the sample hospitals offers a specific information to hospital administrators about how they share in terms of the cost they are incurring in comparison to other hospitals. For example, if his/her hospital is of small size and located in a city, he/she can compare the various costs of his/her hospital operation with those of other similar hospitals. Therefore, he/she may be able to find the reasons why the cost of his/her hospital operation has a higher or lower cost than other similar hospitals in what factors of the hospital cost determinants.

• Journal of the Korean association of regional geographers
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• v.3 no.1
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• pp.183-198
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• 1997

This case study was conducted by verification the site characteristics based on the questionnaire and interview obtained from the all factories located at No. 1 developing area in Kumi industrial complex. The site characteristics were presumed from the process of location behavior and spatial linkage. Kumi industrial complex was developed to improve export industry at national levels by providing chief land price and benefiting various tax. Kumi industrial complex which enticed many factories is playing an important role in export industry in Korea. At beginning, the detention of large enterprises promoted the establishment of related small to medium sized factories into the complex. Two distinctive industries. textile and electronic, were reflected by the purpose to establish the complex and industrial characteristics of Taegu city. respectively. In Kumi industrial complex, positive responses on traffic and raw material supply and negative reactions on the environmental impact on social community as well as high labor charge were investigated. Especially the higher labor cost prevented to hire laborers effectively. In the linkages of spatial and raw material, most factories in the complex depended on the availability of out side the Kumi city. For the textile factories, the supply of raw material and parts were relied on Taegu and/or other cities, whereas in electronic factories purchased them mainly from other cities and partly from abroad. Although questionnaire and interview suggested it, most of the parts were supplied by a parts maturing companies on the complex to a few large enterprises. In the marketing linkage, textile factories revealed higher relation-ship with the foreign countries and sewing factories in Korea. On the other hand, electronic factories have strong relation-ships in the marketing linkage to the parts supplying companies in the complex or large-scale resembling companies in other cities. In the textile companies, the right for decision on purchasing raw materials and parts is belonging to the owner whereas mother enterprise usually have the right for the marketing. In the case of the electronic factories, all the purchasing activities are related to the sub-contracting companies. In the service linkage, the Quality of the service created spatial distinction. There was high linkages on inside of Kumi complex for the low grade services such as repairing and installing machines, whereas strong linkages on outside of the complex for the high grade services such as management, law, taxation, new product development. and manufacturing technology. In the linkages of activity on the R&D (research and development), electronic factories do not have sufficiently qualified institutes in the complex. Strong regional linkages in the field of textile and electronic industries revealed limitations of the local industrial complex. In the sub-contracting linkage, high linkage ship within Kumi boundary reflected the characteristics of industrial site in the complex. There, most decisions by the companies centered by the mother enterprise.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.29 no.5
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• pp.689-700
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• 1996

Fisheries products have to be produced and maintained by work processes from the environment, sometimes helped by people. In Korean fisheries both environmental production and its economic use are included within the windows of system approach. EMERGY is the sum of all inputs expressed as one form of solar energy required directly and indirectly to make a product. Calculating EMERGY flows into Korean fisheries evaluates the real wealth contributed by environmental production and its economic use. Several indices calculated from EMERGY analysis table and a three-arm diagram give perspective on the type and efficiency of the environmental uses. Net EMERGY yield ratio is a measure of its net contribution to the economy beyond its own operation. For adjacent waters fisheries in Korea, the net contribution to the economy is 11.85 or higher, which is a stimulus to the economy that is able to purchase it. EMERGY investment ratio measures the intensity of the economic development and the loading of the environment. The ratio for Korean fisheries as a whole is 0.50, for the adjacent waters fisheries 0.09 and for the shallow-sea cultures 1.28, which is lower than the same index for the industry of the developed country (7.0). The component of environment drawn into production are large compared to purchased investment in Korean fisheries. Much more EMERGY is contained in fisheries products than in the paid services used to process the products. The EMERGY exchange ratio for Korean fisheries as a whole is 6.98, for the adjacent waters fisheries is 10.69 and for the shallow-sea cultures is 1.25. Using market values to evaluate wealth of environment resources is found to be many times too small. Money is paid only to people for their contribution, and never to the environment for its contribution. Macroeconomic value is the appropriate measure for discussing large-scale considerations of an economy, including environment and human goods & services.

• Journal of Preventive Medicine and Public Health
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• v.30 no.4 s.59
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• pp.741-751
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• 1997

Measurement of blood lead (PbB) and blood zinc protoporphyrin (ZPP) are most common biological indices to identify the individual at risk for excess or the health sequences by lead exposure. Because PbB is known most important and reliable index of lead exposure, PbB is often regarded as a gold standard to detect lead exposure. But in Korea PbB is a secondary test item of detailed health check-up with positive finding of screening test in most occasion. Our lead standard requires all lead workers to take annual heath-check twice a year for investigation of their health effect due to lead exposure. Blood ZPP is one of most important index to detect high lead absorption in lead workers as a screening test. Measurement of blood ZPP is known ,well to correlate with PbB in steady state of exposure in most lead workers and is often used as a primary screening test to detect high lead absorption of lead workers with the advantage of simplicity, easiness, portability and low cost. The current cut-off criteria of blood ZPP for further detailed health check-up is $100{\mu}g/d\ell$ which is supposed to match the level of $40{\mu}g/d\ell$ of PbB according to our standard. Authors tried to investigate the validity of current criteria of cut-off level $(100{\mu}g/d\ell)$ of blood ZPP and possible another better cut-off level of it to detect the lead workers whose PbB level over $40{\mu}g/d\ell$. The subjects in our study were 212 male workers in three small scale storage battery industries. Blood ZPP, PbB and hemoglobin (Hb) were selected as the indices of lead exposure. The results were as follows. 1. The mean of blood ZPP, PbB and Hb in lead workers were $79.5{\pm}46.7{\mu}g/d\ell,\;38.7{\pm}15.1{\mu}g/d\ell,\;and\;14.8{\pm}1.2g/d\ell$, respectively. There were significant differences in blood ZPP, PbB and Hb by industry (P<0.01). 2. The percents of lead workers whose blood ZPP were above $100{\mu}g/d\ell$ in the group of work duration below 1, 1-4, 5-9 and above 10 years were 8.6%, 17.2%, 47.6%, and 50.0%, respectively. The percents of lead workers whose PbB were above $40{\mu}g/d\ell$ in those were 31.4%, 40.4%, 71.4%, and 86.4%, respectively. 3. The percents of lead workers whose PbB were below $40{\mu}g/d\ell$, $40-59{\mu}g/d\ell$ and above $60{\mu}g/d\ell$ were 54.7%, 34.9% and 10.4%, respectively. Those of lead workers whose blood ZPP were below $100{\mu}g/d\ell$, $100-149{\mu}g/d\ell$ and above $150{\mu}g/d\ell$ were 79.2%, 13.7% and 7.1%, respectively. 4. Simple linear regression of PbB on blood ZPP was statistically significant (P<0.01) and as PbB was $40{\mu}g/d\ell$, blood ZPP was $82.1{\mu}g/d\ell$. 5. While the highest sensitivity and specificity of blood ZPP test to detect lead workers with PbB eve. $40{\mu}g/d\ell$ were observed in the cut-off level of $50{\mu}g/d\ell$ and $100{\mu}g/d\ell$ of blood ZPP, respectively, the highest validity (sensitivity+specificity) of blood ZPP to detect lead workers with PbB over $40{\mu}g/d\ell$ was observed in the cut-off level of around $70{\mu}g/d\ell$ of blood ZPP. But even with optimal cut-off level of around $70{\mu}g/d\ell$ of blood ZPP, still 25.0% of false negative and 20.7% false positive lead workers were found. As the result of this study, it was suggested that reconsideration of current blood ZPP cut-off of our lead standard from $100{\mu}g/d\ell$ to somewhat lower level such as around $70{\mu}g/d\ell$ and the inclusion of PbB measurement as a primary screening test for lead workers was highly recommended for the effective prevention of lead workers.

• Journal of Korean Society of societal Security
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• v.4 no.2
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• pp.73-78
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• 2011

In order to resolve the problem of change in global climate which is worsening as days go by and to preemptively cope with strengthened restriction on carbon emission, the government enacted 'Framework Act on Low Carbon Green Growth' in 2010 and selected green technology and green industry as new national growth engines. For this reason, the necessity to use the un-utilized waste heat across the whole industrial system has become an issue, and studies on and applications of recycling in the agricultural and fishery fields such as cultivation of tropical crops and flatfishes by utilizing the waste heat and thermal effluent generated by large industrial complexes including power plants are being actively carried out. In this study, we looked into the domestic and overseas examples of having utilized waste heat abandoned in the form of power plant thermal effluent, and carried out economic efficiency evaluation of sturgeon aquaculture utilizing thermal effluent of Yeongwol LNG Combined Cycle Power Plant in Gangwon-do. In this analysis, we analyzed the economic efficiency of a model business plan divided into three steps, starting from a small scale in order to minimize the investment risk and financial burden, which is then gradually expanded. The business operation period was assumed to be 10 years (2012~2021), and the NVP (Net Present Value) and economic efficiency (B/C) for the operation period (10 years) were estimated for different loan size by dividing the size of external loan by stage into 80% and 40% based on the basic statistics secured through a site survey. Through the result of analysis, we can see that reducing the size of the external loan is an important factor in securing greater economic efficiency as, while the B/C is 1.79 in the case the external loan is 80% of the total investment, it is presumed to be improved to 1.81 when the loan is 40%. As the findings of this study showed that the economic efficiency of sturgeon aquaculture utilizing thermal effluent of power plant can be secured, it is presumed that regional development project items with high added value can be derived though this, and, in addition, this study will greatly contribute to reinforcement of the capability of local governments to cope with climate change.

• Korean Journal of Occupational Health Nursing
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• v.8 no.2
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• pp.193-211
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• 1999

This study was carried out to investigate the management and support system affecting to the occupational health nursing services(OHNS) provided in group occupational health agencies(GOHA). Questionnaire was developed and distributed to 82 nurses who were working in GOHA and who agreed to participate in the survey. The results were as follow: 1. OH nurses responded were mostly in the age of twenty to thirties(89%), married(73.7%), technical college graduates(88.9%), worked in hospital(85.4%) and participated more than 1 year in group occupational health services (96.3%). 2. Fifty eight point four percent of the OH nurses worked in number of workplace more than 30 to less than 60 in the OHNS form. The figure of workplaces undertaken by nurses was ranged greatly from 9 to more than 100. Number of employees who cared by nurses were mostly under 5,000 peoples in 93.3%. The types of industry was mostly manufacturing and located in the order of factory complex area, suburban, urban and others. 3. Most OH nurses(87.8%) were fully involved in the OHNS for the SSE. Their working days to visit SSE was 5 days per week(77.8%) and one day in the GOHA at 41.3%. 4. The OH documents using by nurses were found in more than 23 different types. However, they were largely summarized in the types of 'Workplace Health Management Card', 'Personal Health Counselling Card', 'Daily Health Management Report', 'Visiting List of Workplace' and 'Sick Employee List'. 5. The items of laboratory test provided by GOHA were mostly achieved in the purpose of basic health examination. They were used to be the blood pressure check(98.8%), blood sugar test (98.8%), urine sugar and protein(91.4%), SGOT and SGPT(85.3% each), cholesterol (82.9%), hepa vaccine immunization(82.9%), r-GPT(81.7%), hemoglobin(79.3%) and triglyceride(75.5%). 6. The OH nurses(92.7%) followed the work pattern to visit the GOHA before and after small-scale enterprises(SSE) visit by car driven by nurses in 74.3%. They were payed by GOHA for transportation fees in certain amounts. However, nurse is the main person(75.0%) who covers up in case of traffic accident. If the GOHA has no transportation regulation for the formal workplace visit, data showed that nurses had been responsible to take charge(31.7%). 7. The personnel manager who takes in charge for nursing services was 'nurse' in 61.7% and 41.2% worked as the final decision maker related to nursing work. The OH nurses' opinions about factors affecting to the management were classified in the four areas such as 'Nature(Quality) of health professional'. 'Content of OHNS', 'Delivery system of the GOHS', and 'Others'. The factors were indicated highly in 'Authority as health professional', 'Level of perception of director on the OH' and 'Physical work condition for OHNS'. The things that this study suggests in the recommendation would be summarized in such as the management and supporting system working for SSE in the OHNS is necessary to reform thoroughly. The reconsidered aspects might be in the matters of number of workplaces undertaken by nurses, development of effectively practical health documents, preparation for guideline of the laboratory test in the workpleces, establishment of convenient and encouraging support system and cooperation between other health professionals with respect and skill.