아메리카동애등에 성충은 음식물 폐자원 등 유기물이 있는 곳에 알을 낳는 습성이 있다. 대부분의 농가는 음식 물폐자원을 가공한 단미사료(습식사료)를 유인배지로 활용하여 그 위에 플로랄폼(오아시스)를 놓고 알을 받는 다. 그러나 플로랄폼은 재사용이 불가하고 생분해되지 않는 환경폐기물로서 처리가 곤란하며 포름알데하이드, 카본 블랙 등의 발암물질을 함유한 것으로도 알려져 있다. 이에 본 연구는 먹이원 자체를 활용하여 폐기물이 발생하지 않는 친환경 산란받이를 개발하였으며 일회용으로 사용되는 플로랄폼을 대체하였다. 먹이원으로 활 용할 수 있는 습식사료와 건식사료를 주재료로 하여 제작하며, 습식사료(수분60~80%)와 건식사료(1~10%)를 1:0.5~1 비율로 혼합한 사료 혼합물과 보조첨가제와 물을 포함하여 제작한다. 친환경 산란받이는 기존 플로랄폼 대비 산란율이 34% 증가하였으며 구매비용 또한 75% 절감하였다.

아메리카동애등에 유충은 유기성폐기물을 먹이원으로 하며 그 분해산물인 동애등에분은 비료원료로 활용 가능하다. 그러나 농가에서 나오는 분변토는 염분함량이 높아 단독으로 사용하면 토양에 염류집적의 우려가 있다. 이에 산업곤충인 동애등에 분변토의 염분을 낮춰 퇴비로 활용하고자 옥수수(미백2호)에 5처리(무처리, 동애등에분, 동애등에분:흰점박이꽃무지분(2:8), 동애등에분:퇴비(2:8), 퇴비)로 비료를시용하였다. 옥수수 생 육은 초장, 간장, 웅수장, 착수고를 조사하였고 종실은 이삭중, 이삭장, 착립이삭장, 이삭폭 등을 조사하였다. 처리구별 옥수수 수량(kg/10a)은 무처리구 702.8kg, 동애등에분처리구 835.6kg. 동애등에분:흰점박이꽃무지분 (2:8) 처리구 723.7kg, 동애등에분:퇴비(2:8) 처리구 862.3kg, 퇴비 처리구 803.7kg으로 조사되었다. 동애등에 분변 토를 시판퇴비와 혼합하여 퇴비로 활용하면 옥수수 생산을 증진시키는데 효과적이나 장기적인 실험을 통해 토양과 작물에 미치는 영향을 모니터링해야 될 것으로 판단된다.

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.

As the decommissioning of nuclear power plants progresses, interest in the inevitably generated radioactive waste is also increasing. Especially, because the containers of ILW packages are significantly more expensive than the containers of LLW packages, the special attention should be focused on minimizing the number of the containers of ILW packages. The radiation dose limit for packaging of ILW shall not exceed 2 mSv/h and 0.1 mSv/h on contact and at 2 m, respectively in South Korea. Meanwhile, The DEMplus provides various environmental geometry and all properties such as materials, absorptions, and reflections and the estimation of the radiation dose rates is based on the radiation interactions of the designed 3D geometry model. With the consideration of the radiation dose rate by using DEMplus and its strategy of packaging and cutting plan, the number of containers for ILW packages generated from decommissioning of Reactor Vessel Internal (RVI) of a nuclear power plant that has been in operation for decades was optimized in this paper. The modular shielded containers (MSC) with shielding inserted were used for radioactive wastes that require shielded packaging. In order to verify the accuracy of the estimated radiation dose rate by using DEMplus, the estimated results were compared with those obtained using MicroShield. The trends of the estimated radiation dose rates using DEMplus and the estimation of MicroShield were similar to each other. The results of this study demonstrated the feasibility of using DEMplus as a means of estimating the radiation dose limit in packaging plan of the radioactive waste.

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

A 19-year-old male Siberian tiger was presented with inappetence and paralysis of hind limbs. In a computed tomography (CT) scan, intervertebral disk disease at L3–L4 was detected. Cardiac arrest occurred during the surgery. At autopsy, myxomatous mitral valve degeneration (MMVD) and eccentric hypertrophy of the left heart were noted. The diagnosis was congestive heart failure caused by MMVD. Microscopically, myocardial and pulmonary fibrosis were observed in addition to the disintegration of the fibrosa layer and accumulation of glycosaminoglycans and proteoglycans in the spongiosa layer of the mitral valve. This is the first case of congestive heart failure with MMVD in a Siberian tiger.

Smart factories can be defined as intelligent factories that produce products through IoT-based data. In order to build and operate a smart factory, various new technologies such as CPS, IoT, Big Data, and AI are to be introduced and utilized, while the implementation of a MES system that accurately and quickly collects equipment data and production performance is as important as those new technologies. First of all, it is very essential to build a smart factory appropriate to the current status of the company. In this study, what are the essential prerequisite factors for successfully implementing a smart factory was investigated. A case study has been carried out to illustrate the effect of implementing ERP and MES, and to examine the extensibilities into a smart factory. ERP and MES as an integrated manufacturing information system do not imply a smart factory, however, it has been confirmed that ERP and MES are necessary conditions among many factors for developing into a smart factory. Therefore, the stepwise implementation of intelligent MES through the expansion of MES function was suggested. An intelligent MES that is capable of making various decisions has been investigated as a prototyping system by applying data mining techniques and big data analysis. In the end, in order for small and medium enterprises to implement a low-cost, high-efficiency smart factory, the level and goal of the smart factory must be clearly defined, and the transition to ERP and MES-based intelligent factories could be a potential alternative.

When considering military operations that require rapid response time, forward supply operation of various type of ammunition is essential. Also, t is necessary to supply ammunition in a timely manner before an ammunition shortage situation occurs. In this study, we propose a mathematical model for allocation of ammunition to ammunition storehouse at the Ammunition Supply Post (ASP). The model has several objectives. First, it ensures that the frequent used ammunition is stored in a distributed manner at a high workability ammunition storehouses. Second, infrequent used ammunition is required to be stored intensively at a single storehouse as much as possible. Third, capacity of the storehouse and compatible storage restriction required to be obeyed. Lastly, criticality of ammunition should be considered to ensure safety distance. We propose an algorithm to find the pareto-based optimal solution using the mathematical model in a reasonable computation time. The computational results show that the suggested model and algorithm can solve the real operational scale of the allocation problem.