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Hanhong Bae,Lakshmi Narayanan Ponpandian,Soon Ok Rim,Gnanendra Shanmugam,Junhyun Jeon,Buyng Su Hwang,Junheon Kim,Seon Keun Lee,Sang-Tae Seo,Sang-Hyun Koh 한국응용곤충학회 2018 한국응용곤충학회 학술대회논문집 Vol.2018 No.10
Pine trees are ecologically important in Korea. They are seriously imperiled by Pine wilt disease (PWD), by pine wood nematode (PWN, Bursaphelenchus xylophilus). Here, we isolated and characterized bacterial endophytes (BEs) from pine trees in Korea for biological control of PWN using BE metabolites. Using culture-dependent approach BE isolates were extracted from three tissues (needles, stems, and roots) of four pine species across 18 sampling sites in Korea. Bacterial isolates were characterized into 389 distinct isolates based on 16S rDNA sequencing. Ethyl acetate crude extracts (CEs) of bacterial liquid cultures were prepared using ethyl acetate and screened for nematicidal activity against PWN. BEs (1,622 isolates) were isolated; their taxonomic binning resulted in 215 operational taxonomic units (OTUs). Analysis of species richness and Shannon’s diversity of the three tissues revealed that BEs colonized the needles more than the stem and root tissues. Furthermore, based on nematicidal activity screening of 389 isolates, 44 BEs were identified, with two isolates exhibiting a significant inhibitory activity against PWN. Taken together, these data revealed numerous nematicidal BEs in pine trees, providing new insights that can serve as an effective and promising alternative approach to combat PWD.
Partial Fault Detection of an Air - conditioning System by using a Moving Average Neural Network
Doyoung Han,Hanhong Lee 대한설비공학회 2003 International Journal Of Air-Conditioning and Refr Vol.11 No.3
The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this paper, two fault detection methods were considered. One is a generic neural network, and the other is an moving average neural network. In order to compare the performance of fault detection results from these methods, two different types of faults in an air-conditioning system were applied. These are the condenser 30% fouling and the evaporator fan 25% slowdown. Test results showed that the moving average neural network was more effective for the detection of partial faults in the air-conditioning system.