• Clinical and Experimental Pediatrics
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• v.51 no.11
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• pp.1158-1164
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• 2008

Purpose : To investigate the discriminative ability of pediatric index of mortality 2 (PIM2) and pediatric risk of mortality III (PRISM III) in predicting mortality in children admitted into the intensive care unit (ICU). Methods : We retrospectively analyzed variables of PIM2 and PRISM III based on medical records with children cared for in a single hospital ICU from January 2003 to December 2007. Exclusions were children who died within 2 h of admission into ICU or hopeless discharge. We used Students t test and ANOVA for general characteristics and for correlation between survivors and non-survivors for variables of PIM2 and PRISM III. In addition, we performed multiple logistic regression analysis for Hosmer-Lemeshow goodness-of-fit, receiver operating characteristic curve (ROC) for discrimination, and calculated standardized mortality ratio (SMR) for estimation of prediction. Results : We collected 193 medical records but analyzed 190 events because three children died within 2 h of ICU admission. The variables of PIM2 correlated with survival, except for the presence of post-procedure and low risk. In PRISM III, there was a significant correlation for cardiovascular/neurologic signs, arterial blood gas analysis but not for biochemical and hematologic data. Discriminatory performance by ROC showed an area under the curve 0.858 (95% confidence interval; 0.779-0.938) for PIM2, 0.798 (95% CI; 0.686-0.891) for PRISM III, respectively. Further, SMR was calculated approximately as 1 for the 2 systems, and multiple logistic regression analysis showed ${\chi}^2(13)=14.986$, P=0.308 for PIM2, ${\chi}^2(13)=12.899$, P=0.456 for PRISM III in Hosmer-Lemeshow goodness-of-fit. However, PIM2 was significant for PRISM III in the likelihood ratio test (${\chi}^2(4)=55.3$, P<0.01). Conclusion : We identified two acceptable scoring systems (PRISM III, PIM2) for the prediction of mortality in children admitted into the ICU. PIM2 was more accurate and had a better fit than PRISM III on the model tested.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.6
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• pp.73-83
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• 2016

This study modified and applied the ecological network(Indicator 2) from the City Biodiversity Index(CBI) to be tailored to Korea. It is calculated by utilizing a biotope map and a land cover map. The ecological network of Gyeryong-Si was 13,713,703(33.8%) with the biotope map and 17,686,966(37.9%) with the land cover map. The result of the biotope map was lower than the land cover map. The ecological network of Goyang-Si was 4,961,922(4.9%) with the biotope map and 4,383,207(3.7%) with the land cover map. The result of the land cover map was lower than the biotope map. As a main result of the research, an error was discovered in which, when calculating the ecological network, the types of the military unit facilities were distinguished into a special area on the biotope map and into an urbanization promotion area and a forest area on the land cover map. In the case of a middle-classified, land cover map, the land use in the surroundings of the forest area was not subdivided. An error in the development area expressed as a forest green was discovered. When selecting the natural elements, too, regarding the types of artificially-created rivers, artificial ponds, and artificial grasslands, etc. on a biotope map, the exclusions were necessary. Regarding the natural, bare ground on a land cover map, there was a need to calculate by including the natural elements. It was judged that, in the future, the ecological network in the unit of the entire nation can be analyzed roughly by utilizing a land cover map. It was judged that, in a city having a biotope map, the calculation of the ecological network utilizing a map of the present situation of the urban ecology will be a more accurate diagnosis of the present situation.

• Journal of The Korean Society of Grassland and Forage Science
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• v.31 no.4
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• pp.395-408
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• 2011

The study which it sees exclusions the copper and the zinc which contain in pig sludge, It study pig sludge resources fertilizer production which are rational, pig sludge resources fertilizers after seeding, silage corn it investigates growth characteristics and forage value, the result which investigates pig sludge resources fertilizers effectiveness with afterwards is same. With fertilizer ingredients in pig sludge chemical qualities, the content of the nitrogen and the phosphoric acid comes 4.4% to be 6.29%, pH 7.02 and content of the copper and the zinc which is a heavy metal which contains in pig sludge with 805 mg/kg and 1,704 mg/kg, it is a restrictive standard of the fertilizer, 300 mg/kg and 900 mg/kg it sees to be high, it manufactures citric acid 1 hydrate with the organic acid solution, heavy metals of pig sludge where it is a mixture ratio of the organic acid solution, it divides to 25%, 50%, 75% and 100% 4 kind levels, the result which measures the heavy metal exclusion ratio of the copper and the zinc, the mixture ratio of the organic acid solution to be many exclusion ratio of the copper and the zinc is showing a just interrelation, from organic acid solution 100% level content of pig sludge remains copper and zinc 330.03 mg/kg and 41.28 mg/kg, it shows the exclusion ratio of copper 59% zinc 97%. 'Cheonganok' growth characteristics with citric acid 1 hydrate, Treatment 2 and control growth characteristics etc, it exclusion the copper and the zinc it doesn't appear on significant difference statistically but, treatment 3 after only pig sludge in resources disposal where it seeding, growth characteristics of leaf area etc. is badness, it compares in control and treatment 2 the growth characteristics badness, it is appearing, it is caused by with disease and insects occurrence of $Ostrinia$ $furnacalis$ and brown spot, the damage was many. From forage value, Treatment 2 where it exclusion the heavy metal with the citric acid 1 hydrate with control it compares and there are not significant difference from crude protein and ADF and NDF contents etc., seeding only Pig Sludge in resources disposal treatment 3, it is caused by with $Ostrinia$ $furnacalis$ etc., trunk and aging of the leaf to be high ADF content is low. but from crude protein, the nitrogen ingredient which pig sludge has and interrelation it seemed and high numerical value were confirmed.