This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies – Bitcoin, Ethereum, Litecoin, and EOS – and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies – AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet – representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning- based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

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.

Broad bean wilt virus 2 (BBWV2) is a species in the genus Fabavirus and family Secoviridae, which is transmitted by aphids and has a wide host range. The BBWV2 genome is composed of two single-stranded, positive-sense RNAs, RNA-1 and RNA-2. The representative symptoms of BBWV2 are mosaic, mottle, vein clearing, wilt, and stunting on leaves, and these symptoms cause economic damage to various crops. In 2019, Perilla fructescens leaves with mosaic and yellowing symptoms were found in Geumsan, South Korea. Reverse-transcription polymerase chain reaction (RTPCR) was performed with specific primers for 10 reported viruses, including BBWV2, to identify the causal virus, and the results were positive for BBWV2. To characterize a BBWV2 isolate (BBWV2-GS-PF) from symptomatic P. fructescens, genetic analysis and pathogenicity tests were performed. The complete genomic sequences of RNA-1 and RNA-2 of BBWV2-GS-PF were phylogenetically distant to the previously reported BBWV2 isolates, with relatively low nucleotide sequence similarities of 76-80%. In the pathogenicity test, unlike most BBWV2 isolates with mild mosaic or mosaic symptoms in peppers, the BBWV2-GS-PF isolate showed typical ring spot symptoms. Considering these results, the BBWV2-GS-PF isolate from P. fructescens could be classified as a new strain of BBWV2.

호두는 경제적으로 중요한 유실수 중 하나이지만, 호두나무 포장 내에서 해충에 의한 피해 특성에 대한 정보는 매우 부족하다. 본 연구에서 는 복숭아명나방과 굴피가는나방에 의한 호두열매와 잎의 피해를 조사하였다. 2017년 충남 부여군에 위치한 포장에서 복숭아명나방에 의한 호두열매 피해율의 변화를 조사하였다. 또한 김천, 부여, 영동 및 화성의 포장 내에서 2종의 나방에 의한 호두열매 및 잎의 피해율을 조사하였다. 부여 포장 내 호두열매의 피해율은 시간이 흐름에 따라 지속적으로 증가하였고, 피해율은 늦여름에 가장 높았다. 호두열매의 최종 피해율은 부여, 김천 및 화성에서 각각 22.1%, 20.5%, 11.7%로 조사되었다. 굴피가는나방에 의한 잎 피해는 성목의 신초 가지(평균 11.2%)에 비해 묘목의 신초 가지(평균 58.5%)에서 더 높은 것으로 조사되었다. 본 연구 결과, 호두나무 포장 내에서 복숭아명나방과 굴피가는나방에 의한 열매와 잎 피해가 심각한 것으로 확인되었으며, 향후 이들에 대한 방제기술 개발이 필요할 것으로 판단된다.

Walnuts are economically important for forestry workers in recent, but there are few available data for insect pestsin walnut orchards. In 2017, walnut fruits and leaves were damaged by Conogethes punctiferalis and Acrocercops transecta,respectively. In late summer, damage rates by C. punctiferalis in Buyeo, Gimcheon, and Hwaseong were 22.1%, 20.5%,and 11.7%, respectively. According to A. transecta, damage rates in seedlings (58.5% in average) were higher than inolder trees (11.2% in average) irrespective of study locations.

본 연구는 Sprague-Dawley 계통의 4주령 랫드에서 MSG의 반복경구투여 독성평가와 행동실험을 통해 어린랫드의 인지능력과 불안감에 미치는 영향을 평가하기 위해서 시행하였다. 시험군은 MSG 대신 distilled water를 투여한 대조군(Control, n = 4), MSG를 3 g/kg을 투여한 군(Low, n =4), MSG를 5 g/kg을 투여한 군(High, n = 4)으로 나누어 4주 동안 주5회 경구투여를 하였다. MSG의 안정성을 확인하기 위해 다음과 같은 관찰 및 검사를 하였다. 검사항목으로는 체중의 변화, 임상증상, 행동실험인 T-maze, Elevatedplus-maze와 혈액학적 검사, 혈청생화학적 검사, 병리조직 학적 검사를 관찰한 결과 모든 투여군 및 대조군에서 특이할 만한 임상증상과 체중변화는 관찰되지 않았으며, 폐사 및 빈사 동물은 시험 전 기간을 통하여 발견되지 않았다. 행동실험인 T-maze 결과 MSG 고용량 투여군에서 움직이는 횟수가 감소하다 증가하는 양상을 보였고, Elevatedplus-maze 실험에서는 MSG 고용량 투여군의 Open arm의 출입빈도가 증가하는 등 유의적인 변화가 나타났다. 혈액학적 검사 및 혈청생화학적 검사에서는 대조군과 비교 시 유의적인 변화가 관찰되었지만 그 수치가 정상범위 안에 포함되기 때문에 이상을 나타내지 않았다. 병리조직검사 또한 약간의 염증발생 소견이 나왔지만 정상범위 안에 포함되기 때문에 이상을 나타내지 않았다고 판단했다. 따라서, 어린랫드를 이용한 MSG의 반복투여 독성시험 결과 특이할만한 신체적인 변화는 나타나지 않았으나 고용량의 MSG 투여는 어린랫드에서 인지능력의 저하 및 행동의 불 안을 유발할 가능성이 있다고 사료된다.