• Journal of Korean Society of Forest Science
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• v.95 no.1
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• pp.82-90
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• 2006

To examine the effects of nitrogen and phosphorus fertilization on soil nitrogen (N) mineralization, we monitored rates of soil nitrogen mineralization and nitrification in 41-year-old pitch pine (Pinus rigida Mill.) and Japanese larch (Larix kaempferi Gordon) stands growing on similar soil condition in central Korea. For this study, we used the buried-bag incubation method. Fertilizers were applied at three levels [control (C), 200 N kg/ha+25 P kg/ha (LNP), and 400 N kg/ha+50 P kg/ha(HNP)] on 5 June, 1996. Mineral soils (0~20 cm) were incubated 6 times with 45-day-interval from 5 June 1996 to 4 June 1997. Initial soil moisture contents were significantly different among sampling dates and between tree species. Initial soil moisture contents were 32% for C, 28% for LNP, and 26% for HNP at the P. rigida stand, and 31% for C, 31% for LNP, and 33% for HNP at the L. kaempferi stand, respectively. Mean daily N mineralization rates were significantly different among sampling dates and treatments. Annual net N mineralization and nitrification were also significantly different between the two tree species. The annual net N mineralization was 10.6 kg/ha/year for C, 23.3 kg/ha/year for LNP and 6.6 kg/ha/year for HNP at the P. rigida stand, and 2.0 kg/ha/year for C, 12.1 kg/ha/year for LNP and 16.7 kg/ha/year for HNP at the L. kaempferi stand. The annual nitrification was 2.8 kg/ha/year for C, 7.6 kg/ha/year for LNP and 4.3 kg/ha/year for HNP at the P. rigida stand, and 4.3 kg/ha/year for C, 14.8 kg/ha/year for LNP and 6.6 kg/ha/year for HNP at the L. kaempferi stand. The ratios of annual net nitrification to annual net N mineralization were 26% for C, 33% for LNP, 65% for HNP at the P. rigida stand, and 100% for C, 100% for LNP, 40% for HNP at the L. kaempferi stand, respectively. This study indicates that N mineralization in forest may be different by the predominant tree species and fertilization even under similar environments. It is likely that the quality of organic matter might control nitrogen mineralization and nitrification in soils.

• Journal of Korean Society of Forest Science
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• v.37 no.1
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• pp.41-56
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• 1978

This experimentation aims to study on the characterics of tree growth by forestry land soil system and the effect of applied fertilizer by kind of tree for obtaining basic data on application of fertilizer to forestry land and to further investigate the restriction factor of tree growth by soil system for promoting more effective application of fertilizer to forestry area. 1. The characteristics of tree growth by soil system showed that tree growth was worst on erosive soil, bad on red or red and yellow soil and generally good on brown soil. 2. With regard to Black Locust, the restriction factor of its growth appeared the content of $P_2O_5$ in the soil and the less the content of $P_2O_5$ is in the soil, the worse its growth was and its growth was bad on clay soil. 3. The growth of pinus seems closely related with the content of $K_2O$ in the soil and the growth of PH was bad on neutral or alkali soil and good on weak-acid soil. Its growth greatly depended upon the content of OM or FN. 4. As Suwon Poplar is a kind of tree requiring for a special soil, its growth required for fertile soil and the content of O. MN $P_2O_5$ $K_2O$ was proportional to its growth. 5. The growth of Black Locust, pinus and Suwon Poplar was good on the soil containing much sand but bad on the soil containing much clay.

• Korean Journal of Psychosomatic Medicine
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• v.27 no.2
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• pp.101-110
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• 2019

Objectives : It is not clear which clinical variables are most closely associated with delirium in the Intensive Care Unit (ICU). By comparing clinical data of ICU delirium and non-delirium patients, we sought to identify variables that most effectively differentiate delirium from non-delirium. Methods : Medical records of 6,386 ICU patients were reviewed. Random Subset Feature Selection and Principal Component Analysis were utilized to select a set of clinical variables with the highest discriminatory capacity. Statistical analyses were employed to determine the separation capacity of two models-one using just the selected few clinical variables and the other using all clinical variables associated with delirium. Results : There was a significant difference between delirium and non-delirium individuals across 32 clinical variables. Richmond Agitation Sedation Scale (RASS), urinary catheterization, vascular catheterization, Hamilton Anxiety Rating Scale (HAM-A), Blood urea nitrogen, and Acute Physiology and Chronic Health Examination II most effectively differentiated delirium from non-delirium. Multivariable logistic regression analysis showed that, with the exception of vascular catheterization, these clinical variables were independent risk factors associated with delirium. Separation capacity of the logistic regression model using just 6 clinical variables was measured with Receiver Operating Characteristic curve, with Area Under the Curve (AUC) of 0.818. Same analyses were performed using all 32 clinical variables;the AUC was 0.881, denoting a very high separation capacity. Conclusions : The six aforementioned variables most effectively separate delirium from non-delirium. This highlights the importance of close monitoring of patients who received invasive medical procedures and were rated with very low RASS and HAM-A scores.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.3
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• pp.679-690
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• 2015

The need to remove the cause of traffic accidents by improving the engineering system for a vehicle and the road in order to minimize the accident hazard. This is likely to cause traffic accident continue to take a large and significant social cost and time to improve the reliability and efficiency of this generally poor road, thereby generating a lot of damage to the national traffic accident caused by improper environmental factors. In order to minimize damage from traffic accidents, the cause of accidents must be eliminated through technological improvements of vehicles and road systems. Generally, it is highly probable that traffic accident occurs more often on roads that lack safety measures, and can only be improved with tremendous time and costs. In particular, traffic accidents at intersections are on the rise due to inappropriate environmental factors, and are causing great losses for the nation as a whole. This study aims to present safety countermeasures against the cause of accidents by developing an intersection Traffic safety evaluation model. It will also diagnose vulnerable traffic points through BPA (Back -propagation algorithm) among artificial neural networks recently investigated in the area of artificial intelligence. Furthermore, it aims to pursue a more efficient traffic safety improvement project in terms of operating signalized intersections and establishing traffic safety policies. As a result of conducting this study, the mean square error approximate between the predicted values and actual measured values of traffic accidents derived from the BPA is estimated to be 3.89. It appeared that the BPA appeared to have excellent traffic safety evaluating abilities compared to the multiple regression model. In other words, The BPA can be effectively utilized in diagnosing and practical establishing transportation policy in the safety of actual signalized intersections.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.357-367
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• 2018

Recent earthquakes in Gyeongju and Pohang have raised interest in liquefaction in South Korea. Liquefaction, which is a phenomenon that excessive pore pressure is generated and the shear strength of soil is decreased by repeated loads such as earthquakes, causes severe problems such as ground subsidence and overturning of structures. Therefore, it is necessary to identify and prepare for the possibility of liquefaction in advance. In general, the possibility of liquefaction is quantitatively assessed using the Liquefaction Potential Index (LPI), but it takes a lot of time and effort for performing site response analysis which is essential for the liquefaction evaluation. In this study, a simple method to evaluate the liquefaction potential without executing the site response analysis in a downtown area with a lot of borehole data was proposed. In this simple method, the correlation between the average shear wave velocity of the target location ground and the LPI divided by thickness of liquefiable layer was established. And the applicable correlation equation for various rock outcrop accelerations were derived. Using the 104 boreholes information in Seoul, the correlation equation between LPI and the shear wave velocity (ground water level: 0m, 1m, 2m, 3m) is obtained and the possibility of liquefaction occurrence in Seoul and Gyeongju is evaluated. The applicability of the proposed simple method was verified by comparing the LPI values calculated from the correlation equation and the LPI values derived using the existing site response analysis. Finally, the distribution map of LPI calculated from the correlation was drawn using Kriging, a geostatistical technique.

• Journal of Distribution Research
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• v.17 no.3
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• pp.115-132
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• 2012

This study empirically examines the impact of SSM market entry on changes in market shares among retailing types. The data is monthly time-series data spanning over the period from January 2000 to December 2010, and the effect of SSM market entry on market shares of retailing types is analyzed by utilizing several key factors such as the number of new SSM monthly entrants, total number of SSMs, the proportion of new SSM entrant that is smaller than $165m^2$ to total new SSM entrants. According to the Korean Standard Industrial Classification codes, the retailing type is classified into 5 groups: department stores, retail sale in other non-specialized large stores(big marts), supermarkets, convenience stores, and retail sale in other non-specialized stores with food or beverages predominating (others). The market shares of retailing types are calculated by the ratio of each retailing type monthly sales to total monthly retailing sales in which total retailing sales is the sum of each retailing type sales. The empirical model controls for the size effects with the number of monthly employees for each retailing type and the macroeconomic effects with M2. The empirical model employed in this study is as follows; $$MS_i=f(NewSSM,\;CumSSM,\;employ_i,\;under165,\;M2)$$ where $MS_i$ is the market share of each retailing type (department stores, big marts), supermarkets, convenience stores, and others), NewSSM is the number of new SSM monthly entrants, CumSSM is total number of SSMs, $employ_i$ is the number of monthly employees for each retailing type, and under165 is the proportion of new SSM entrant that is smaller than $165m^2$ to total new SSM entrants. The correlation among these variables are reported in