• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.208-220
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• 2016

Automatic Mountain Meteorology Observation System (AMOS) is an important ingredient for several climatological and forest disaster prediction studies. In this study, we select the optimal sites for AMOS in the mountain areas of Honam and Jeju in order to prevent forest disasters such as forest fires and landslides. So, this study used spatial dataset such as national forest map, forest roads, hiking trails and 30m DEM(Digital Elevation Model) as well as forest risk map(forest fire and landslide), national AWS information to extract optimal site selection of AMOS. Technical methods for optimal site selection of the AMOS was the firstly used multifractal model, IDW interpolation, spatial redundancy for 2.5km AWS buffering analysis, and 200m buffering analysis by using ArcGIS. Secondly, optimal sites selected by spatial analysis were estimated site accessibility, observatory environment of solar power and wireless communication through field survey. The threshold score for the final selection of the sites have to be higher than 70 points in the field assessment. In the result, a total of 159 polygons in national forest map were extracted by the spatial analysis and a total of 64 secondary candidate sites were selected for the ridge and the top of the area using Google Earth. Finally, a total of 26 optimal sites were selected by quantitative assessment based on field survey. Our selection criteria will serve for the establishment of the AMOS network for the best observations of weather conditions in the national forests. The effective observation network may enhance the mountain weather observations, which leads to accurate prediction of forest disasters.

• Journal of Trauma and Injury
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• v.25 no.2
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• pp.49-56
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• 2012

Purpose: For determining the prognosis of critically injured patients, transporting patients to medical facilities capable of providing proper assessment and management, running rapid assessment and making rapid decisions, and providing aggressive resuscitation is vital. Considering the high mortality and morbidity rates in critically injured patients, various studies have been conducted in efforts to reduce those rates. However, studies related to diagnostic factors for predicting severity in critically injured patients are still lacking. Furthermore, patients showing stable vital signs and alert mental status, who are injured via a severe trauma mechanism, may be at a risk of not receiving rapid assessment and management. Thus, this study investigates diagnostic factors, including physical examination and laboratory results, that may help predict severity in trauma patients injured via a severe trauma mechanism, but showing stable vital signs. Methods: From March 2010 to December 2011, all trauma patients who fit into a diagnostic category that activated a major trauma team in CHA Bundang Medical Center were analyzed retrospectively. The retrospective analysis was based on prospective medical records completed at the time of arrival in the emergency department and on sequential laboratory test results. PASW statistics 18(SPSS Inc., Chicago, IL, USA) was used for the statistical analysis. Patients with relatively stable vital signs and alert mental status were selected based on a revised trauma score of more than 7 points. The final diagnosis of major trauma was made based on an injury severity score of greater than 16 points. Diagnostic variables include systolic blood pressure and respiratory rate, glasgow coma scale, initial result from focused abdominal sonography for trauma, and laboratory results from blood tests and urine analyses. To confirm the true significance of the measured values, we applied the Kolmogorov-Smirnov one sample test and the Shapiro-Wilk test. When significance was confirmed, the Student's t-test was used for comparison; when significance was not confirmed, the Mann-Whitney u-test was used. The results of focused abdominal sonography for trauma (FAST) and factors of urine analysis were analyzed using the Chi-square test or Fisher's exact test. Variables with statistical significance were selected as prognostics factors, and they were analyzed using a multivariate logistics regression model. Results: A total of 269 patients activated the major trauma team. Excluding 91 patients who scored a revised trauma score of less than 7 points, 178 patients were subdivided by injury severity score to determine the final major trauma patients. Twenty-one(21) patients from 106 major trauma patients and 9 patients from 72 minor trauma patients were also excluded due to missing medical records or untested blood and urine analysis. The investigated variables with p-values less than 0.05 include the glasgow coma scale, respiratory rate, white blood cell count (WBC), serum AST and ALT, serum creatinine, blood in spot urine, and protein in spot urine. These variables could, thus, be prognostic factors in major trauma patients. A multivariate logistics regression analysis on those 8 variables showed the respiratory rate (p=0.034), WBC (p=0.005) and blood in spot urine (p=0.041) to be independent prognostic factors for predicting the clinical course of major trauma patients. Conclusion: In trauma patients injured via a severe trauma mechanism, but showing stable vital signs and alert mental status, the respiratory rate, WBC count and blood in the urine can be used as predictable factors for severity. Using those laboratory results, rapid assessment of major trauma patients may shorten the time to diagnosis and the time for management.

• Korean Chemical Engineering Research
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• v.57 no.5
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• pp.637-651
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• 2019

In the event of a major accident such as an explosion in a refinery or a petrochemical plant, it has caused a serious loss of life and property and has had a great impact on the insurance market. In the case of catastrophic incidents occurring in process industries such as refinery and petrochemical plants, only the proximate causes of loss have been drawn and studied from inspectors or claims adjustors responsible for claims of property insurers, incident cause investigators, and national forensic service workers. However, it has not been done well for conducting root cause analysis (RCA) and identifying the factors that contributed to the failure and establishing preventive measures before leading to chemical plant's catastrophic incidents. In this study, the criteria of warning signs on CCPS catastrophic incident waning sign self-assessment tool which was derived through the RCA method and the contribution factor analysis method using the swiss cheese model principle has been reviewed first. Secondly, in order to determine the major incident warning signs in an actual chemical plant, 614 recommendations which have been issued during last the 17 years by loss control engineers of global reinsurers were analyzed. Finally, in order to facilitate the assessment index for catastrophic incident warning signs, the criteria for the catastrophic incident warning sign index at chemical plants were grouped by type and classified into upper category and lower category. Then, a catastrophic incident warning sign index for a chemical plant was developed using the weighted values of each category derived by applying the analytic hierarchy process (pairwise comparison method) through a questionnaire answered by relevant experts of the chemical plant. It is expected that the final 'assessment index for catastrophic incident warning signs' can be utilized by the refinery and petrochemical plant's internal as well as external auditors to assess vulnerability levels related to incident warning signs, and identify the elements of incident warning signs that need to be tracked and managed to prevent the occurrence of serious incidents in the future.

• Korean Chemical Engineering Research
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• v.54 no.5
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• pp.621-629
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• 2016

Recently, the researches on quantitative structure activity relationship (QSAR) for describing toxicities or activities of chemicals based on chemical structural characteristics have been widely carried out in order to estimate the toxicity of chemicals in multiuse facilities. Because the toxicity of chemicals are explained by various kinds of molecular descriptors, an important step for QSAR model development is how to select significant molecular descriptors. This research proposes a statistical selection of significant molecular descriptors and a new QSAR model based on partial least square (PLS). The proposed QSAR model is applied to estimate the logarithm of partition coefficients (log P) of 130 polychlorinated biphenyls (PCBs) and lethal concentration ($LC_{50}$) of 14 PCBs, where the prediction accuracies of the proposed QSAR model are compared to a conventional QSAR model provided by OECD QSAR toolbox. For the selection of significant molecular descriptors that have high correlation with molecular descriptors and activity information of the chemicals of interest, correlation coefficient (r) and variable importance of projection (VIP) are applied and then PLS model of the selected molecular descriptors and activity information is used to predict toxicities and activity information of chemicals. In the prediction results of coefficient of regression ($R^2$) and prediction residual error sum of square (PRESS), the proposed QSAR model showed improved prediction performances of log P and $LC_{50}$ by 26% and 91% than the conventional QSAR model, respectively. The proposed QSAR method based on computational toxicology can improve the prediction performance of the toxicities and the activity information of chemicals, which can contribute to the health and environmental risk assessment of toxic chemicals.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of Soil and Groundwater Environment
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• v.16 no.4
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• pp.38-52
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• 2011

Sorption and sequential desorption experiments were conducted for Zn using a natural soil (NS) in background status by aging (1, 30 and 100 days). The sorption isotherm showed that Zn had high sorption capacity but low sorption affinity in NS. Sequential desorption was biphasic with appreciable amount of sorbed Zn residing in the desorption-resistant fraction after several desorption steps. The biphasic desorption behavior of Zn was characterized by a biphasic desorption model that includes a linear term to represent labile or easily-desorbing fraction and a Langmuirian-type term to represent desorption-resistant fraction. The biphasic desorption model indicated that the size of the maximum capacity of desorption-resistant fraction ($q^{irr}_{max}$) increased with aging in NS. Desorption kinetics and desorption-resistance of Zn in the soils collected from mine tailings (MA, MB and MC collected from surface, subsurface soils and mine waste, respectively) were investigated and compared to the bioavailability to earthworm (Eisenia fetida). Desorption kinetic data of Zn were fitted to several desorption kinetic models. The ratio ($q_{e,d}/q_0$) of remaining Zn at desorption equilibrium ($q_{e,d}$) to initial sorbed concentration ($q_0$) was in the range of 0.53~0.90 in the mine tailings which was higher than that in NS, except MA. The sequential desorption from the mine tailings with 0.01M Na$NO_3$ and 0.01M $CaCl_2$ showed that appreciable amounts of Zn are resistant to desorption due to aging or sequestration. The SM&T (Standard Measurements and Testing Programme of European Union) analysis showed that the sum of oxidizable (Step III) and residual (Step IV) fractions of Zn was linearly related with its desorption-resistance ($q^{irr}_{max}$) determined by the sequential desorption with 0.01M Na$NO_3$ ($R^2$= 0.9998) and 0.01M $CaCl_2$ ($R^2$= 0.8580). The earthworm uptake of Zn and the desorbed amount of Zn ($q_{desorbed}$ = $q_0-q_{e,d}$) in MB soil were also linearly related ($R^2$ = 0.899). Our results implicate that the ecological risk assessment of heavy metals would be possible considering the relation between desorption behaviors and bioavailability to earthworm.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.891-906
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• 2014

This study suggests the hedging rule of MIP (Mixed Integer Programing) in counting the risk evaluation criteria of the objective function and constraints in order to provide the optimum operating rule in reservoir system as constraining water shortage as much as possible which may happen in the downstream control point of water supply in the aspect of water system management. The proposed model is applied to the Han-river reservoir system for two testing periods (Case I: Jan. 1993~Dec. 1997, Case II: Jan. 1999~Dec. 2003). The model based on the hedging rule with trigger volume, estimated in this study shows that in Case I, the monthly minimum discharge was $310.6{\times}10^6m^3$ in the single operation, $56.3{\times}10^6m^3$ in the joint operation, and $317.5{\times}10^6m^3$ in the hedging rule and also, in Case II, the monthly minimum discharge was found to be $204.2{\times}10^6m^3$ in the single operation, $111.2{\times}10^6m^3$ in the joint operation, and $243.7{\times}10^6m^3$ in the hedging rule. In conclusion, the hedging rule, proposed in this study can decrease vulnerability while guarantees reliability and resiliency.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.473-481
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• 2017

It is well known that the air quality of 92% of the world is known to exceed the standard of WTO and the death caused by air pollution is almost 6 million per year. The $PM_{2.5}$ concentration in Korea is the second most serious among the OECD countries following Turkey. Since the $PM_{2.5}$ has a direct effect on the respiratory system, it has been actively studied in domestic and foreign countries. But current research on the $PM_{2.5}$ is limited in weather factor or air pollutants. In this paper, we consider the influence of spatial neighbor with weather factor or air pollutants using spatial panel model. We applied the proposed method to 25 borough of Seoul in Korea. The result shows a significant effect of spatial neighbor on the $PM_{2.5}$ concentration fields.

• Women's Health Nursing
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• v.5 no.1
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• pp.133-145
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• 1999

The purposesof the study are to analyze the community nursing center in U.S.A and to develop the model of nursing care system based on nurse-midwifery clinic in community for women's health in Korea. 1. In America nursing center is defined as nurse-anchored system of primary care delivery or neighborhood health center. Nursing centers are identified the following four types: (1) community outreach centers, which are similar to traditional public health clinics: (2) institutional-based centers following the mission of a large institution, such as a hospital or university: (3) wellness/health promotion centers, which offer screening, education, counseling, triage, and health maintenance services: and (4) independent practice. Nursing centers are a concept of services provided by nurses in practice arrangements in a community. Nursing centers offer a variety of services, ranging from primary care provided by advanced practice nurses with medical acute management and nursing care to the more traditional education, health promotion, screening wellness and coordination services. Some services, such as the care provided by advanced practice nurses are reimbursed under various insurance plan in some instances and states, where as others, such as preventive and educational services, are not. Thus, lack of reimbursement has threatened the survival of some centers. Licensing of nursing centers varies by state and program and accreditation of nursing centers is also limited. 52% of centers are affiliated with another facility and 48% are freestanding centers. The number of registered nurse at the nursing centers ranges from just one to 115, with a mean of eight RNs peragency and a median of three. Nursing centers avail ability varies: 14% are open 24 hours, 27% have variable short hours, 23% are open 6-7 days per week, and 36% are open Monday- Friday. As the result of my visiting three health centers in Seattle and San Francisco, the women's primary care nurse practitioners focus on a systematic and comprehensive assessment of the health status of women and diagnosis and management of common physical and psychosocial health concerns of women in ambulatory settings. Therapeutic nursing strategies are directed toward self-care, risk reoduction, health surveillance, stress reduction, healthy nutrition, social support, healthy coping, psychological well-being, and pharmacological therapy. They function as primary care providers for the well ness and illness care of women from adolescence through the older adult years and pregnant families. 2. In Korea a nurse-midwife practices independently for pregnant women's health including childbearing family at her own clinic in community. Her services are reimbursed under national health insurance but they are not paid on a fee-for-service schedule covering items. Analyzing the nursing centers in America, I suggest that nurse-midwifery clinics offer primary care for women and home care for chronic ill patients. The health law and health insurance policy should be reovised in order to expand nurse-midwife's and home care nurse's roles at nurse-midwifery clinic.

• Journal of the Korean GEO-environmental Society
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• v.2 no.1
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• pp.103-114
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• 2001

Sorption studies were conducted to determine if slow sorption fraction is observed in recent1y deposited organic matter by studying wetland soils explicitly. Sorption characteristics of hydrophobic organic compounds (chlorobenzene and phenanthrene) in recently deposited freshwater marsh soils were determined using a batch sorption procedure. Relative indicators of organic matter age were assessed using several techniques including the ratio of elemental oxygen to carbon in the organic matter. Slow sorption characteristics for both surface marsh soil (top 0-2 cm, <5 years old) and deeper marsh soil (below 10-cm, >20 years old) were compared against relatively older PPI (Petro Processors, Inc. Superfund site) and BM (Bayou Manchac) soils to investigate whether soil age can cause differences in sorption of organic compounds in wetland soils. Increases in sorption non-linearity of slow sorption model parameters (increase in KF and decrease in N) explain the existence of slow sorption fraction. The results of slow sorption model indicates the presence of a sizable slow sorption fraction; 25.4 - 26.3% (chlorobenzene) and 1.4 - 1.9% (phenanthrene) of the sorbed mass in wetland soils and 40.0 - 55.93% (chlorobenzene) and 2.9 - 3.19% (phenanthrene) of the sorbed mass in PPI and BM soils, respectively. The slow sorption fraction increased in the order of surface < deeper < PPI < BM soil indicating that size of the slow sorption fraction increases with soil organic matter age.