• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.1
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• pp.54-65
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• 2022

Species distribution models are a useful tool for predicting future distribution and establishing a preemptive response of invasive species. However, few studies considered the possibility of habitat for the aquatic organism and the number of target sites was relatively small compared to the area. Environmental DNA (eDNA) is the emerging tool as the methodology obtaining the bulk of species presence data with high detectability. Thus, this study applied eDNA survey results of Micropterus salmoides and Lepomis macrochirus to species distribution modeling by seasons in the Anyang stream network. Maximum Entropy (MaxEnt) model evaluated that both species extended potential distribution area in October compared to July from 89.1% (12,110,675 m²) to 99.3% (13,625,525 m²) for M. salmoides and 76.6% (10,407,350 m²) to 100% (13,724,225 m²) for L. macrochirus. The prediction value by streams was varied according to species and seasons. Also, models elucidate the significant environmental variables which affect the distribution by seasons and species. Our results identified the potential of eDNA methodology as a way to retrieve species data effectively and use data for building a model.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.4
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• pp.1-13
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• 2022

Household refrigerator cabinets must undergo cyclic testing at -20 ℃ and 65 ℃ for quality control (QC) after their production is complete. These cabinets were assembled from different materials, including acrylonitrile butadiene styrene (ABS), polyurethane (PU) foam, and steel plates. However, different thermal expansion values could be observed owing to differences in the mechanical properties of the materials. In this study, a technique to predict delamination on a refrigerator wall caused by thermal deformation was developed. The mechanical properties of ABS and PU foams were tested, theload factors causing delamination were analyzed, delamination was observed using a high-speed camera, and comparison and verification in terms of stress and strain were performed using a finite element model (FEM). The results indicated that the delamination phenomenon of a refrigerator wall can be defined in two cases. A method for predicting and evaluating delamination was established and applied in an actual refrigerator. To determine the effect of temperature changes on the refrigerator, strain measurements were performed at the weak point and the stress was calculated. The results showed that the proposed FEM prediction technique can be used as a basis for virtual testing to replace future QC testing, thus saving time and cost.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.9-15
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• 2022

NAND flash memory is used as a medium for various storage devices due to its high data processing speed with low power consumption. However, since the read processing speed of data is about 10 times faster than the write processing speed, various studies are being conducted to improve the speed difference. In particular, flash dedicated buffer management policies have been studied to improve write speed. However, SSD(solid state disks), which has recently been used for various purposes, is more vulnerable to read performance than write performance. In this paper, we find out why read performance is slower than write performance in SSD composed of NAND flash memory and study buffer management policies to improve it. The buffer management policy proposed in this paper proposes a method of improving the speed of a flash-based storage device by analyzing the pattern of read data and applying a policy of pre-reading data to be requested in the future from NAND flash memory. It also proves the effectiveness of the read-ahead policy through simulation.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.375-384
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• 2022

As artificial intelligence(AI) education becomes essential in elementary schools with the revised 2022 curriculum, it is necessary to develop an AI curriculum for elementary school students. In this study, I developed the AI content framework to cultivate AI literacy of elementary school students. AI education areas were largely divided into AI understanding and AI development, and detailed areas were divided into eight categories: using of AI, impact of AI, AI ethics, recognition of AI, data expression, data exploring, learning of AI, and prediction of AI. In addition, twice expert Delphi surveys were conducted to verify the validity of the subject elements and achievement standards for each area. The final draft was finalized after reflecting expert opinions on the AI education content framework. In order for AI education to be expanded in elementary schools in the future, continuous research is needed, such as developing textbooks and teaching tools according based on the AI framework proposed in this study, securing the lesson hours to apply them to schools, and correcting and supplementing the problems of them.

• Microbiology and Biotechnology Letters
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• v.50 no.4
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• pp.563-573
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• 2022

Xanthomonas arboricola pv. juglandis (Xaj) is a globally important bacterial pathogen of walnut trees that causes substantial economic losses in commercial walnut production. Although prophages are common in bacterial plant pathogens and play important roles in bacterial diversity and pathogenicity, there has been limited investigation into the distribution and function of prophages in Xaj. In this study, we identified and characterized 13 predicted prophages from the genomes of 12 Xaj isolates from around the globe. These prophages ranged in length from 11.8 kb to 51.9 kb, with between 11-75 genes and 57.82-64.15% GC content. The closest relatives of these prophages belong to the Myoviridae and Siphoviridae families of the Caudovirales order. The phylogenetic analysis allowed the classification of the prophages into five groups. The gene constitution of these predicted prophages was revealed via Roary analysis. Amongst 126 total protein groups, the most prevalent group was only present in nine prophages, and 22 protein groups were present in only one prophage (singletons). Also, bioinformatic analysis of the 13 identified prophages revealed the presence of 431 genes with an average length of 389.7 bp. Prokka annotation of these prophages identified 466 hypothetical proteins, 24 proteins with known function, and six tRNA genes. The proteins with known function mainly comprised prophage integrase IntA, replicative DNA helicase, tyrosine recombinase XerC, and IS3 family transposase. There was no detectable insertion site specificity for these prophages in the Xaj genomes. The identified Xaj prophage genes, particularly those of unknown function, merit future investigation.

• Journal of radiological science and technology
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• v.45 no.6
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• pp.491-502
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• 2022

The purpose of this study was to retrospectively investigate the upper and lower control limits of treatment planning parameters using EBT film based delivery quality assurance (DQA) results and to analyze the results of statistical process control (SPC) in helical tomotherapy (HT). A total of 152 patients who passed or failed DQA results were retrospectively included in this study. Prostate (n = 66), rectal (n = 51), and large-field cancer patients, including lymph nodes (n = 35), were randomly selected. The absolute point dose difference (DD) and global gamma passing rate (GPR) were analyzed for all patients. Control charts were used to evaluate the upper and lower control limits (UCL and LCL) for all the assessed treatment planning parameters. Treatment planning parameters such as gantry period, leaf open time (LOT), pitch, field width, actual and planning modulation factor, treatment time, couch speed, and couch travel were analyzed to provide the optimal range using the DQA results. The classification and regression tree (CART) was used to predict the relative importance of variables in the DQA results from various treatment planning parameters. We confirmed that the proportion of patients with an LOT below 100 ms in the failure group was relatively higher than that in the passing group. SPC can detect QA failure prior to over dosimetric QA tolerance levels. The acceptable tolerance range of each planning parameter may assist in the prediction of DQA failures using the SPC tool in the future.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.103-132
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• 2023

This study developed a risk society education program for undergraduate students to help them understand the epistemological uncertainty of risk caused by COVID-19. And it was applied to science-related classes of undergraduate students, and the purpose was to examine the degree of understanding and thoughts of undergraduate students about the risk society through science writing. As a result, it was found that the degree of understanding of the risk society was very high in all participating students regardless of their majors in science, engineering, humanities and social sciences. In addition, it was analyzed that the risk society education program helped undergraduate students to resolve the epistemological uncertainty of the risk of COVID-19 and to have an attitude to overcome the the difficult mind due to the COVID-19 distancing. The results of this study suggest that risk society education is necessary for future generations living in an era of risk of climate change and pandemic that exceeds the prediction range of science and technology in science education.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.193-193
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• 2023

여러 기후변화 시나리오에 의하면 기상재해의 발생빈도 및 강도가 증가할 것으로 예상된다. 그중 가뭄은 강수량 부족, 하천유량 감소, 토양 함수량 감소, 용수 수요량 증가 등의 다양한 요인으로 인해 발생하며, 한 가지 형태뿐만 아니라 복합적인 형태로 발생할 수 있다. 또한, 우리나라는 지역마다 기후 특성의 편차가 있어 기후변화에 따른 가뭄 취약성과 대응 능력이 지역마다 다르게 나타난다. 따라서 가뭄에 대응하기 위해서는 다양한 요인을 고려한 통합가뭄지수를 활용해야 하며, 미래의 기후변화를 고려하여 종합적으로 가뭄을 평가해야 한다. 본 연구에서는 동적 베이지안 분류기(DNBC) 기반의 통합가뭄지수를 활용하여 우리나라 전국에 대해 수문학적 위험도를 분석하고 미래 가뭄을 전망하였다. 기상학적, 수문학적, 농업적 및 사회경제적 요인을 고려한 통합가뭄지수를 산정하기 위하여 DNBC 분류기의 인자로 기후변화 시나리오 기반의 기상학적 가뭄지수 SPI, 수문학적 가뭄지수 SDI, 농업적 가뭄지수 ESI와 사회경제적 가뭄지수 WSCI를 활용하였다. 산정된 통합가뭄지수의 시계열을 기반으로 심도와 지속기간을 추출하고, 코플라 함수를 활용한 이변량 가뭄빈도분석을 수행하였다. 이후, 이변량 가뭄빈도분석에 의해 산정된 재현기간을 활용하여 수문학적 위험도를 산정하였다. 그 결과, P1(2021~2040) 기간이 수문학적 위험도 R=0.588로 가장 높은 위험도를 나타냈으며, 이후 P2(2041~2070) 기간까지 감소하였다가 P3(2071~2099) 기간에 다시 증가하는 추세를 보였다. P1(2021~2040) 기간과 P3(2071~2099) 기간은 영산강 유역이 각각 R=0.625(P1), R=0.550(P3)으로 가장 높은 위험도를 나타냈으나, P2(2041~2070) 기간은 금강 유역이 수문학적 위험도 R=0.482로 가장 높게 나타났다. 본 연구결과를 통해 향후 미래 가뭄에 대한 가뭄계획 수립 시에 기초자료로서 활용성이 높을 것으로 기대된다.

• Atmosphere
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• v.33 no.4
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• pp.331-341
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• 2023

A numerical forecasting models usually predict future states by performing time integration considering fixed static time-steps. A time-step that is too long can cause model instability and failure of forecast simulation, and a time-step that is too short can cause unnecessary time integration calculations. Thus, in numerical models, the time-step size can be determined by the CFL (Courant-Friedrichs-Lewy)-condition, and this condition acts as a necessary condition for finding a numerical solution. A static time-step is defined as using the same fixed time-step for time integration. On the other hand, applying a different time-step for each integration while guaranteeing the stability of the solution in time advancement is called an adaptive time-step. The adaptive time-step algorithm is a method of presenting the maximum usable time-step suitable for each integration based on the CFL-condition for the adaptive time-step. In this paper, the adaptive time-step algorithm is applied for the Korean Integrated Model (KIM) to determine suitable parameters used for the adaptive time-step algorithm through the monthly verifications of 10-day simulations (during January and July 2017) at about 12 km resolution. By comparing the numerical results obtained by applying the 25 second static time-step to KIM in Supercomputer 5 (Nurion), it shows similar results in terms of forecast quality, presents the maximum available time-step for each integration, and improves the calculation efficiency by reducing the number of total time integrations by 19%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.276-278
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• 2022

All drugs have a specific action in the body, and in many cases, drugs are combinated due to complications or new symptoms during existing drug treatment. In this case, unexpected interactions may occur within the body. Therefore, predicting drug-drug interactions is a very important task for safe drug use. In this study, we propose a deep learning-based predictive model that learns using drug information documents to predict drug interactions that may occur when using multiple drugs. The drug information document was created by combining several properties such as the drug's mechanism of action, toxicity, and target using DrugBank data. And drug information document is pair with another drug documents and used as an input to a deep learning-based predictive model, and the model outputs the interaction between the two drugs. This study can be used to predict future interactions between new drug pairs by analyzing the differences in experimental results according to changes in various conditions.