Urinary and blood porphyrin contents were measured in rats exposed to diazinon in order to examine the usefulness of porphyrin profiles as biomarkers of diazinon (organophosphorus insecticide) exposure. Rats were given diazinon(10, 30 and 90 mg/kg b.w) once per day for 5 weeks by gavage and thereafter were given distilled water for further 5weeks. Among the urinary porphyrins, coproporphyrin and uroporphyrin began to increase from the 3rd week of the administration of diazinon and the tendency of increment continued up to 5 weeks(coproporphyrin) or 3 weeks(uroporphyrin) after the withdrawal of diazinon exposure. Protoporphyrin content showed no significant change compared with that of control throughout the whole period of experiment. Of the blood porphyrins, protoporphyrin content of diazinon-treated group was higher than that of control group from the 5th week of exposure to the end of experiment. Uroporphyrin was not detected in the both of control and treatment group. Coproporphyrin level of the treatment group was not significantly differnt from that of control. The results suggest that urinary and blood porphyrin profiles may contribute as biomarkers for diagnosis of diazinon exposure.
With various Korean domestic soybeans, growth and yields analysis were conducted to select the suitable soybean cultivars for cultivation in paddy field. Distinctive aspects of the soybean growth were observed in paddy field such as retarded growth of top plants and roots, relatively higher T/R ratio followed by overgrowth of top plant. However, growth and yields were significantly different among the cultivars showing 134 ㎏/10a in Pal-dokong and 385 ㎏/10a in Doremikong. At V5 and R2 stage, highly positive correlations (r=0.76**~0.9l**) were observed between leaf area and dry weight of top plant and/or root. T/R ratio was negatively correlated with dry weight of root (r=-0.37*) at V5 stage, while significantly correlated with leaf area (r=0.46**) and dry weight of top plant (r=0.65**) at R2 stage. Among the characters, only 100-seed weight was significantly correlated with yield. Considering the growth characters, 37 cultivars could be included in 3 different groups and genotypic properties such as maturity and growth habit were similar in each group. Nine cultivars in group 1 showed retarded growth from V5 to R2 stage, relatively lower T/R ratio, and good seed ripening. Average yields of the cultivers was 257 ㎏/10a. In group 2, 12 cultivars showed higher T/R ratio due to overgrowth of top plant and lowest average yields (230 ㎏/10a) due to poor seed ripening, Sixteen cultivars in group 3 grew fast from V5 to R2 stage representing late maturity traits, low T/R ratio, and good seed ripening. Average yields of the cultivars was highest among groups showing 270 ㎏/10a. In results, stable self-sufficiency of soybean yields could be expected by selective cultivation with high yielding cultivars ranging from 301 to 385 ㎏/10a, such as Shinpaldalkong 2, Sohokong, Doremikong, Keumkangkong, Bukangkong, Dajangkong, and Geomjeongkong 2, or with cultivars included in group 3.
The task of discovering equivalent entities in knowledge graphs (KGs), so-called KG entity alignment, has drawn much attention to overcome the incompleteness problem of KGs. The majority of existing techniques learns the pointwise representations of entities in the Euclidean space with translation assumption and graph neural network approaches. However, real vectors inherently neglect the complex relation structures and lack the expressiveness of embeddings; hence, they may guide the embeddings to be falsely generated which results in alignment performance degradation. To overcome these problems, we propose a novel KG alignment framework, ComplexGCN, which learns the embeddings of both entities and relations in complex spaces while capturing both semantic and neighborhood information simultaneously. The proposed model ensures richer expressiveness and more accurate embeddings by successfully capturing various relation structures in complex spaces with high-level computation. The model further incorporates relation label and direction information with a low degree of freedom. To compare our proposal against the state-of-the-art baseline techniques, we conducted extensive experiments on real-world datasets. The empirical results show the efficiency and effectiveness of the proposed method.
본 연구에서는 CO₂를 흡수하는 아민용액에서 성능 저하의 원인이 되는 HSS를 제거하기 위해 음이온교환수지를 이용한 HSS의 처리특성을 제시하고자 한다. 최적 HSS 제거효율은 수지 SAR10을 0.05 g/mL 사용할 때 316 K, pH 12에서 96.1%로 나타났다. 또한 최적 수지 재생효율은 NaOH 농도 3 M, 316 K에서 78.8%로 나타났다. HSS의 흡착은 Freundlich 모델에 가장 잘 부합하였으며 흡착 동력 크기를 보았을 때 수지를 이용한 HSS의 제거가 용이한 것으로 나타났다. 흡착 선택계수의 경우 전자가, 원자가가 클수록 높게 나타났고 탈착 선택계수는 이와 반대의 경향을 나타냈다. 연속식 HSS 처리시 오염시료 13.3 BV를 최적으로 처리할 수 있었으며, 5.2 BV가 최적 NaOH 소모량으로 나타났다.
The three-phase induction motor is known as one of the most widely-used machine in the manufacturing industry. Electrical and mechanical fault of this machine is able to cause breaking down the plant facilities that leads significant productivity losses. In this paper, we will propose the fault detection method of induction motor by using deep neural network with measurement data of the electrical current signals to provide supervised classification of 2 types ofmotor fault, rotor broken and stator conductor fault. We also studied the size of data-set in faulty state to train the model for fault detection.
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