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CHUNG, NAMIL,SUBERKROPP, KELLER Blackwell Publishing Ltd 2009 Freshwater biology Vol.54 No.11
<P>Summary</P><P> 1. It has been accepted that aquatic hyphomycetes colonising submerged leaves increase the nutritional value of leaf detritus and suggested that fungal biomass plays a greater role in the growth of shredders than leaf tissue itself. However, it is not clear what proportion of the nutritional needs of shredders is met by fungal biomass.</P><P> We fed <I>Pycnopsyche gentilis</I> larvae with tulip poplar (<I>Liriodendron tulipifera</I>) leaf discs colonised by the aquatic hyphomycete, <I>Anguillospora filiformis</I>, which had been radiolabelled to quantify the contribution of fungal carbon to the growth of the shredder at different larval developmental stages. Instantaneous growth rates of larvae on this diet were also estimated.</P><P> When provided with fungal-colonised leaves (14–16% fungal biomass), the third and the fifth instar larvae of <I>P. gentilis</I> grew at the rates of 0.061 and 0.034 day<SUP>−1</SUP>, respectively, but on a diet of sterile leaves, both larval instars lost weight. The incorporation rates of fungal carbon were 31.6 &mgr;g C mg<SUP>−1</SUP> AFDM day<SUP>−1</SUP>, accounting for 100% of the daily growth rate of the third instar larvae and 8.6 &mgr;g C mg<SUP>−1</SUP> AFDM day<SUP>−1</SUP>, accounting for 50% of the daily growth rate of the fifth instar larvae.</P><P> These results suggest that leaf material colonised by <I>A. filiformis</I> is a high quality food resource for <I>P. gentilis</I> larvae, and that fungal biomass can contribute significantly to the growth of these larvae. Differences in feeding behaviour and digestive physiology may explain the significantly greater assimilation of fungal biomass by the earlier instar than the final instar. To satisfy their nutritional needs the fifth instar larvae would have to assimilate detrital mass that may have been modified by fungal exoenzymes.</P>
( Namil Um ),( Jin-mo Yeon ),( Hee-sung Lee ),( Seong-kyeong Jeong ),( Min-young Choi ),( David Chung ),( Tae-wan Jeon ),( Sun-kyoung Shin ) 한국폐기물자원순환학회(구 한국폐기물학회) 2015 한국폐기물자원순환학회 3RINCs초록집 Vol.2015 No.-
This study investigated the dissolution kinetics of insoluble chloride in MSWI bottom ash under physical condition with submerged particle via accelerated carbonation. The water-to-solid ratio was controlled by the condition, 10 dm<sup>3</sup>/kg, and the CO<sub>2</sub> concentration was kept constant at 30%. The reaction temperature was varied from 20℃ to 40℃ for dissolution kinetics. The result of an XRD analysis indicated that insoluble chloride (Friedel’s Salt) in untreated bottom ash could combine with CO<sub>2</sub> to form mainly an amorphous Al-rich material and calcite. In addition, the theoretical model was fitted well to the kinetics data pertaining to the dissolved insoluble chloride as the carbonation process proceeded; in the theoretical model, the product-layer diffusion was predominant. The variation of the rate constant upon dissolution with the temperature obeyed the Arrhenius equation with activation energy of 24.61 kJ/mol.
지영춘(Youngchun Ji),전남일(Namil Jeon),서원진(Wonjin Seo),두민수(Minsoo Doo),정인승(Inseung Chung) 한국자동차공학회 2004 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
Recently automotive company demands supplier to provide goods as a module unit, Automotive company presents some requirements for a modular assembly and supplier delivers modules satisfying those requirements. This trend requires the development of methods to predict the performance of modules in the vehicle environment. The development of assembled analytical models that reflect the interactions between components within a modular assembly is necessary to insure that a module is properly designed. This paper describes the construction of the finite element model to investigate the structural requirements for Damper Spring Module(DSM) as installed in a McPherson strut front suspension. The analytical model is constructed as the 1/4 car. The modeling techniques to construct the components within DSM are presented. Results of the structural analysis under some loading conditions are displayed. Additionally, some aspects to be observed in the assembly model are considered.
Lee, Keun Young,Chung, Namil,Hwang, Suntae Elsevier 2016 Ecological Informatics Vol.36 No.-
<P><B>Abstract</B></P> <P>The mosquito species is one of most important insect vectors of several diseases, namely, malaria, filariasis, Japanese encephalitis, dengue, and so on. In particular, in recent years, as the number of people who enjoy outdoor activities in urban areas continues to increase, information about mosquito activity is in demand. Furthermore, mosquito activity prediction is crucial for managing the safety and the health of humans. However, the estimation of mosquito abundances frequently involves uncertainty because of high spatial and temporal variations, which hinders the accuracy of general mechanistic models of mosquito abundances. For this reason, it is necessary to develop a simpler and lighter mosquito abundance prediction model. In this study, we tested the efficacy of the artificial neural network (ANN), which is a popular empirical model, for mosquito abundance prediction. For comparison, we also developed a multiple linear regression (MLR) model. Both the ANN and the MLR models were applied to estimate mosquito abundances in 2-year observations in Yeongdeungpo-gu, Seoul, conducted using the Digital Mosquito Monitoring System (DMS). As input variables, we used meteorological data, including temperature, wind speed, humidity, and precipitation. The results showed that performances of the ANN model and the MLR model are almost same in terms of <I>R</I> and root mean square error (RMSE). The ANN model was able to predict the high variability as compared to MLR. A sensitivity analysis of the ANN model showed that the relationships between input variables and mosquito abundances were well explained. In conclusion, ANNs have the potential to predict fluctuations in mosquito numbers (especially the extreme values), and can do so better than traditional statistical techniques. But, much more work needs to be conducted to assess meaningful time delays in environmental variables and mosquito numbers.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Performances of the ANN model and the MLR model are almost same for predicting mosquito abundances. </LI> <LI> The ANN model was able to predict the high variability of mosquito abundances as compared to MLR. </LI> <LI> Time lagged weather data influence the performance of the ANN significantly. </LI> <LI> Among weather data, humidity is main factor in mosquito abundances. </LI> </UL> </P>