Botanical extracts are employed in management of aphids. Extracts from Tanacetum cineariaiaefolium, Derris elliptica, and Sophora flavescens are widely used to control various insects. In this study, we determined concentrations of active insecticidal compounds (Ais) in commercial botanical extracts of these plants, and we investigated the time and concentration for lethal results with the green peach aphid, Myzus persicae. The concentrations of Ais, pyrethrins from T. cineariaiaefolium, rotenone from D. elliptica, and matrine and oxymatrine from S. flavescens, were determined after their fractionation by liquid chromatography followed by mass analysis and comparison with standard compounds. The extracts were tested for lethality in a bioassay with green peach aphids. Sprays at defined doses were applied to tobacco leaves infested with aphid nymphs. The lethal concentrations (LC50) were 20.4 ppm for pyrethrins, 34.1 ppm for rotenone, and 29.6 ppm for matrine at 48 h after treatments. At 100 ppm application levels, the lethal time LT50 was 13.4 h for pyrethrin, 15.1 h for rotenone, and 14.4 h for matrine. Kaplan - Meier analysis indicated the lethal times for the three botanical extracts at 100 ppm were significantly faster than application of a chemical insecticide, Sulfoxaflor, applied at the recommended level. These results provide baselines to develop and formulate single or mixed preparations containing botanical extracts to control green peach aphids on commercial crops.

Leaf-spray in vitro bioassays appraise new aphicidal formulations for managing deleterious plant-feeding aphids. The formulation may utilize alternative and integrated strategies. However, leaf spraying even under controlled conditions may affect aphid reproduction and mortality. This study examines leaf spray applications for optimum and reproducible aphicidal results using tobacco leaves overlaid on cotton fabric or water agar surfaces. Infestation of the undersides of tobacco leaves with nymphs of green peach aphids was used in the assays. Spray distance and volume were optimized using water-sensitive paper to ascertain the best surface coverage. Overlays of the leaves on water agar caused less mortality and greater reproduction than the use of cotton fabric. The relative humidity of the insect-rearing chambers changed with the watering regime for the insect - rearing chambers with cotton fabric; 60% relative humidity was optimal. Relative humidity was not affected by the concentration of agar in the water agar chambers. Applications of the chemical aphicidal standard, Sulfoxaflor, under the optimized conditions exhibited similar times for lethality although the rate was faster with leaves on the cotton fabric than on water agar. These studies establish reproducible and sensitive techniques for assessing the lethality and effects on reproduction of potential aphicidal products.

아메리카동애등에(H. illucens)는 음식물 폐기물 등 유기성 폐자원을 효율적으로 처리할 수 있는 능력을 가지 고 있어 전세계적으로 주목받고 있는 환경정화 곤충이다. 하지만 유기성 폐자원을 처리 시 가장 큰 문제는 아메리 카동애등에가 먹이인 유기성 폐자원을 소화시킬 때 발생되는 악취이다. 국내에서 현재 아메리카동애등에를 사육하고 있는 농가는 223호로 조사되고 있지만 이중 악취발생 저감장치 등을 설치한 농가는 10%가 안되는 것으 로 생각된다. 따라서 국내에서 동애등에 먹이로 가장 많이 사용되는 습식사료를 먹이로 사용하였을 때 농가 사육 장 안에서 발생되는 복합악취와 지정악취 22종에 대하여 분석하였다. 그 결과, 복합악취는 249배였으며, 지정악 취는 22종 중 7종(암모니아, 메틸메르캅탄, 트라이메틸아민, 아세트알데하이드, 프로피온알데하이드, 뷰틸알 데하이드, i-발레르알데하이드)가 검출되었다. 이중 가장 높은 농도를 나타낸 악취물질은 암모니아로 98.4ppm 이 분석되었다. 또한, 아메리카동애등에를 사육 시 가장 많이 발생되는 암모니아의 발생시기는 사육초기인 1~4 령보다 5령 이후 전생육기 중의 대부분을 발생시키는 것으로 조사되었다. 이러한 결과는 암모니아 저감을 위한 적정시기를 설정하는데 도움이 될 것으로 생각된다.

기후변화 위기에 대응하기 위하여 대한민국은 2030년까지 신재생연료 혼합비율(Renewable Fuel Standard, RFS)을 3.5%에서 8%까지 상향하기로 결정하였다. 국내에서 제조되는 신재생연료의 원료가 되는 바이오매스의 60%이상이 해외에서 수입하는 실정이며, 2030 탄소중립 및 RE100과 같은 바이오연료 사용증가 정책의 확대로 인하여 국내에서 제조되어 활용가능한 바이오매스의 확보가 절실하게 필요하다. 곤충은 높은 비율의 단백질과 지질을 체내에 저장하는 특징을 가지고 있으며, 곤충의 대량사육을 통하여 지질의 대량생산이 가능할 것으로 판단된다. 본 연구에서는 곤충의 대량사육환경에서 제조되는 지질을 원료로 하여 기후변화에 대응할 수 있는 바이오연료로의 전환 제조가능성을 확인하고자 하였으며, 이를 위하여 촉매공정을 통하여 디젤과 혼합하여 사용할 수 있는 수첨바이오디젤을 제조하여 그 특성을 분석하였다. 그 결과 곤충지질을 활용하여 수첨바이오디 젤로 전환가능함을 확인하였다.

Buckwheat (Fagopyrum esculentum), which is a traditional Korean crop, has been known as a health food due to its rich nutrition. This study was conducted to evaluate the change in flavonoid content of flowers and seeds during post-flowering growth of Korean tartary buckwheat variety ‘Hwanggeummiso’, with the aim of providing basic data for the development of functional food and feed additive. Tartary buckwheat took 69 and 99 days from the sowing date to reach the flowering and maturity stages, respectively. As a result of examining the flavonoid components of each part of tartary buckwheat, chlorogenic acid, rutin, and isoquercitrin of flowers increased from the flowering period on 22 May (0 days after flowering) to 42 days after flowering, while quercetin increased until 21 days after flowering and then decreased thereafter. In seeds, chlorogenic acid, rutin, and isoquercitrin were most abundant at the time of seed-bearing on 14 days after flowering, and showed a decreasing tendency thereafter. On the other hand, quercetin showed a tendency to increase until 21 days after flowering and then decrease. Overall, the flavonoid content was higher in flowers than in seeds, with rutin being particularly prominent. Based on this, the possibility as food materials and feed additives was confirmed using buckwheat produced in Korea.

The decommissioning of Korea Research Reactor Units 1 and 2 (KRR-1&2), the first research reactors in South Korea, began in 1997. Approximately 5,000 tons of waste will be generated when the contaminated buildings are demolished. Various types of radioactive waste are generated in large quantities during the operation and decommissioning of nuclear facilities, and in order to dispose of them in a disposal facility, it is necessary to physico-chemically characterize the radioactive waste. The need to transparently and clearly conduct and manage radioactive waste characterization methods and results in accordance with relevant laws, regulations, acceptance standards is emerging. For radioactive waste characterization information, all information must be provided to the disposal facility by measuring and testing the physical, chemical, and radiological characteristics and inputting related documents. At this time, field workers have the inconvenience of performing computerized work after manually inputting radioactive waste characterization information, and there is always a possibility that human errors may occur during manual input. Furthermore, when disposing of radioactive waste, the production of the documents necessary for disposal is also done manually, resulting in the aforementioned human error and very low production efficiency of numerous documents. In addition, as quality control is applied to the entire process from generation to treatment and disposal of radioactive waste, it is necessary to physically protect data and investigate data quality in order to manage the history information of radioactive waste produced in computerized work. In this study, we develop a system that can directly compute the radioactive waste characterization information at the field site where the test and measurement are performed, protect the stored radioactive waste characterization data, and provide a system that can secure reliability.

Since high-level radioactive wastes contain long-lived nuclides and emit high energy, they should be disposed of permanently through a deep geological disposal system. In Korea, the first (2016.07) and the second (2021.12) basic plans for the management of high-level disposal systems were proposed to select sites for deep geological disposal facilities and to implement business strategies. Leading countries such as Finland, Sweden and France have developed and applied safety cases to verify the safety of deep geological disposal systems. By examining the regulatory status of foreign leading countries, we analyze the safety cases ranging from the site selection stage of the deep geological disposal system to the securing of the permanent disposal system to the investigation, analysis, evaluation, design, construction, operation, and closure. Based on this analysis, we will develop safety case elements for long-term safety of deep geological disposal systems suitable for domestic situation. To systemically analyze data based on safety cases, we have established a database of deep geological disposal system regulations in leading foreign countries. Artificial intelligence text mining and data visualization techniques are used to provide database in dashboard form rather than simple lists of data items, which is a limitation of existing methods. This allows regulatory developers to understand information more quickly and intuitively and provide a convenient interface so that anyone can easily access the analyzed data and create meaningful information. Furthermore, based on the accumulated bigdata, the artificial intelligence learns and analyzes the information in the database through deep learning, and aims to derive a more accurate safety case. Based on these technologies, this study analyzed the legal systems, regulatory standards, and cases of major international leading countries and international organizations such as the United States, Sweden, Finland, Canada, Switzerland, and the IAEA to establish a database management system. To establish a safety regulation base suitable for the domestic deep geological disposal environment, the database is provided as data to refer to and apply systematic information management on regulatory standards and regulatory cases of overseas leading countries, and it is expected that it will play a key role as a forum for understanding and discussing the level of safety of deep geological disposal system among stakeholders.

A sample size calculation algorithm was developed in a prototype version to select inspection samples in domestic bulk handling facilities. This algorithm determines sample sizes of three verification methods satisfying target detection probability for defected items corresponding to one significant quantity (8 kg of plutonium, 75 kg of uranium 235). In addition, instead of using the approximation equation-based algorithm presented in IAEA report, the sample size calculation algorithm based on hypergeometric density function capable of calculating an accurate non-detection probability is adopted. The algorithm based the exact equation evaluates non-detection probability more accurately than the existing algorithm based on the approximation equation, but there is a disadvantage that computation time is considerably longer than the existing algorithm due to the large amount of computational process. It is required to determine sample size within a few hours using laptop-level performance because sample size is generally calculated with an inspector’s portable laptop during inspection activity. Therefore, it is necessary to improve the calculation speed of the algorithm based on the exact equation. In this study, algorithm optimization was conducted to improve computation time. In order to determine optimal sample size, the initial sample size is calculated first, and the next step is to perform an iterative process by changing the sample size to find optimal result. Most of the computation time occurs in sample size optimization process performing iterative computation. First, a non-detection probability calculation algorithm according to the sample sizes of three verification methods was improved in the iterative calculation process for optimizing sample size. A computation time for each step within the algorithm was reviewed in detail, and improvement approaches were derived and applied to some areas that have major effects. In addition, the number of iterative process to find the optimal sample size was greatly reduced by applying the algorithm based on the bisection method. This method finds optimal value using a large interval at the beginning step and reduces the interval size whenever the number of repetitions increases, so the number of iterative process is less than the existing algorithm using unit interval size. Finally, the sample sizes were calculated for 219 example cases presented by the IAEA report to compare computation time. The existing algorithm took about 15 hours, but the improved algorithm took only about 41 minutes using high performance workstation (about 22 times faster). It also took 87 minutes for calculating the cases using a regular laptop. The improved algorithm through this study is expected to be able to apply the sample size determination process, which was performed based on the approximate equation due to the complexity and speed issues of the past calculation process, based on the accurate equation.