The decommissioning of nuclear power plants will generate a lot of low and intermediate-level radioactive waste (LILW), and preliminary radioactive evaluation for these wastes should be carried out before decommissioning work. Mainly, Concrete, Carbon Steel, Stainless Steel-304 (SS304) and Inconel are used in many parts of nuclear power plants and considered as main resource of nuclear wastes. Depending on the material location, the number of neutrons irradiated to material varies, which can range from self-disposal waste to LILW. In this paper, activation analysis was performed to compare the radiation dose according to the presence or absence of impurity elements present in SS304. For the calculation, SS304 composition and impurity elements were used as described in the report of NUREG-3474. This report lists 41 impurity elements for SS304 and other materials. Calculation code is used ORIGEN-S module in SCALE 6.1 code. Neutron flux is used as arbitrary value that around 1E+11 level and irradiation time is set as 30 year with 10-year cooling time. In the ORIGEN-S calculation, 1g of SS304 is used for easy calculation of specific activity. The ORIGEN-S calculation results are as follows. All impurity elements contained case calculated 9.32E+07 Bq activity. In the absence of all impurity elements case and most cases shows that total Becquerel value after 10-year cooling time around 9.11E+07 Bq, and Co impurity case had larger result. The calculation was performed again by increasing the amount of impurity substances by 100 times to perform the sensitivity evaluation more reliably. Representatively, Li, N, Co, and Ba impurity elements cases were calculated to have a particularly high Becquerel. Especially Co impurity element case, a total Becquerel of 3.03E+08 was calculated. Accordingly, evaluation of impurities mixed in SS304 must be considered, and in particular, the inclusion rate for Co must be considered.

Self-Powered Neutron Detector (SPND) is one of devices for in-core fluxes detecting without external electricity source. SPND consisted with emitter, insulator and collector. When neutrons reacted with emitter material, it generates electrons and these electrons cross insulator area to make electric signal in collector area. For calculating sensitivity of SPND with Monte-Carlo code such as MCNP, many physical components must be considered. Cobalt shows that prompt signal and relatively low signal comparing with other delayed signal SPNDs. Initial sensitivity was calculated as 4.28×10−22 A/nv-cm for one electron. Due to Cobalt’s complex decay chain and maintaining high efficiency of SPND, it is necessary to analysis the effect of activation of emitter. Therefore, the DPA (Displacements Per Atom) assessment and activation analysis of the detector components have been evaluated with MCNP 6.2 and ORIGEN-S. With these activation analysis results, that is expected to be used to determine the shielding thickness of the storage system.

Lilium dauricum is a rare and endangered species belonging to the family Liliaceae. The species contains several bioactive compounds used as functional foods and medicinal agents in Northeast Asia. This study aimed (1) to establish an in vitro bulblet culture using an air-lift bioreactor and callus culture for the conservation of L. dauricum and obtaining its bioactive compounds; (2) investigate the plant phenolic compounds from both cultures system. The highest bulblet production with 12.5-fold increase in growth rate was obtained using MS medium supplemented with 0.5 g L-1 BA and 3% sucrose. Addition of 7% sucrose facilitated bulblet enlargement, with approximate 2.5- and 7-fold increases in diameter and fresh weight, respectively. The highest rate of callus (100%) was obtained using a combination of 1.0 mg L-1 picloram and 0.5 mg L-1 Kinetin. The callus proliferation occurred on MS medium supplemented with 1.0 mg L-1 picloram, 0.25 mg L-1 kinetin, and 0.25 g L-1 casein hydrolysate. There was a significant difference in the total phenolic compound content of callus, which was 1.5-fold higher than that in the bulblets. These findings indicate a suitable system for optimizing both bulblet and callus culture of L. dauricum, therefore, providing useful bio-materials for industrial purposes and contributing to the conservation of this species.

In this paper, a mathematical model of regionalization based on graph theory to investigate the patterns induced by movements of livestock vehicles in cities under outbreaks of highly pathogenic avian influenza (HPAI) is proposed. We then compare the results of simulation from the regionalization model to actual HPAI outbreaks in 2016/2017 to evaluate the validity of the model. Specifically, we (1) configured a complex network structure with analytic tools and properties in graph theory to abstract the paths among farms and livestock facilities; (2) employed statistical methods to estimate the possibility of propagation between two clusters; (3) applied the developed method to an actual HPAI outbreak in Korea in 2016 and conducted a simulation to determine if the proposed modeling for regionalization is an effective prediction measure. The clustered regions proposed by the simulation correctly reflected the regional clustering of actual cases, while simultaneously contain the cities exposed to potential damage when separated. Based on these findings, we conclude that our proposed regionalization model is suitable for making policy judgments to establish a preemptive biosecurity system.

This study was conducted to evaluate a visual surveillance system. The advancement of recording technology and network service make it easy to record and transfer the videos. Moreover, progressed recognition technology help to make a distinction each other. Cows show distinguishing behaviors during their estrus period. The mounting is one of the behaviors. The result was different depending on the breed of the cows and the size of the farm. In the case of Korean native cattle, the estrus detection rate was 71.15%, however, dairy cows, the estrus detection rate was 39.38%. At the farms having below 6 modules, the estrus detection rate was 87.41%. On the other hand, at the farms having over 6 modules, the estrus detection rate was 77.78%. With the proper progress, the visual surveillance system can be used to detect heat detection.