Existing reinforced concrete buildings with seismically deficient columns experience reduced structural capacity and lateral resistance due to increased axial loads from green remodeling or vertical extensions aimed at reducing CO2 emissions. Traditional performance assessment methods face limitations due to their complexity. This study aims to develop a machine learning-based model for rapidly assessing seismic performance in reinforced concrete buildings using simplified structural details and seismic data. For this purpose, simple structural details, gravity loads, failure modes, and construction years were utilized as input variables for a specific reinforced concrete moment frame building. These inputs were applied to a computational model, and through nonlinear time history analysis under seismic load data with a 2% probability of exceedance in 50 years, the seismic performance evaluation results based on dynamic responses were used as output data. Using the input-output dataset constructed through this process, performance measurements for classifiers developed using various machine learning methodologies were compared, and the best-fit model (Ensemble) was proposed to predict seismic performance.

기후 변화로 인한 해양 온도 상승으로 해양생물독소의 발생 빈도가 점점 증가하고 있으며, 이는 식품 안전과 공 중 보건에 중대한 위협을 가하고 있다. 해양생물독소를 검 출하기 위한 기존의 방법인 마우스 생체검사(MBA), 고성 능 액체 크로마토그래피(HPLC), 액체 크로마토그래피-질 량 분석법(LC-MS) 등은 절차가 오래 걸리고 비용이 많이 든다는 한계가 있다. 이러한 문제를 해결하기 위한 대안 으로 바이오센서 기술이 유망한 해결책으로 부상하고 있 다. 이러한 바이오센서는 세포, 항체, 압타머, 펩타이드와 같은 바이오리셉터를 이용해 해양생물독소를 신속하고 정 확하게 검출한다. 본 리뷰에서는 다양한 종류의 바이오리 셉터를 논의하고, 해양생물독소 검출을 위한 바이오센서 기술의 최근 발전을 탐구한다. 또한, 이러한 바이오리셉터 의 장점을 강조하며, 해양생물독소 검출을 위한 바이오센 서 성능 향상을 위한 미래 연구 방향을 고려한다.

Existing reinforced concrete (RC) building frames constructed before the seismic design was applied have seismically deficient structural details, and buildings with such structural details show brittle behavior that is destroyed early due to low shear performance. Various reinforcement systems, such as fiber-reinforced polymer (FRP) jacketing systems, are being studied to reinforce the seismically deficient RC frames. Due to the step-by-step modeling and interpretation process, existing seismic performance assessment and reinforcement design of buildings consume an enormous amount of workforce and time. Various machine learning (ML) models were developed using input and output datasets for seismic loads and reinforcement details built through the finite element (FE) model developed in previous studies to overcome these shortcomings. To assess the performance of the seismic performance prediction models developed in this study, the mean squared error (MSE), R-square (R2), and residual of each model were compared. Overall, the applied ML was found to rapidly and effectively predict the seismic performance of buildings according to changes in load and reinforcement details without overfitting. In addition, the best-fit model for each seismic performance class was selected by analyzing the performance by class of the ML models.

캐비어에 대한 수요가 증가하면서 캐비어 종류의 진위 여부를 확인 할 수 있는 분석법 마련이 필요해졌다. 본 연 구에서는 캐비어 종류을 나누는 철갑상어 종 특이 KASP 마커를 개발하였고 모니터링을 통해 국내 유통중인 불법 캐비어 제품을 확인하고자 하였다. 본 연구에서는 16S ribosomal RNA gene, cytochrome b gene, cytochrome c oxidase subunit I gene, cytochrome c oxidase subunit II gene, NADH dehydrogenase subunit 5 gene에서 철갑상어 종 특이적인 SNP를 선정하고 이를 표적하는 KASP 마커 11종을 개발하였다. 개발한 KASP 마커를 이용하여 한국 의 온라인 마켓에서 유통중인 캐비어 10종을 모니터링 분 석한 결과 2개의 제품에서 제품에 표기된 철갑상어 종과 다른 것으로 확인하였으며 두 제품 모두 실제보다 더 비 싼 캐비어로 둔갑하여 판매한 것으로 확인되었다. 본 연 구를 통해 제작한 KASP 분석방법으로 유통되는 캐비어 종류 분류가 가능하였고 모니터링을 통해 국내 유통되고 있는 불법 캐비어 제품도 확인하였다. 이에 따라 본 연구 에서 개발한 SNP기반 KASP 분석법으로 불법 캐비어 제 품 유통 근절에 기여 할 수 있을 것으로 기대한다.

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

In this study, we developed Rapid Enrichment Broth for Vibrio parahaemolyticus (REB-V), a broth capable enriching V. parahaemolyticus from 100 CFU/mL to 106 CFU/mL within 6 hours, which greatly facilitates the rapid detection of V. parahaemolyticus. Using a modified Gompertz model and response surface methodology, we optimized supplement sources to rapidly enrich V. parahaemolyticus. The addition of 0.003 g/10 mL of D-(+)- mannose, 0.002 g/10 mL of L-valine, and 0.002 g/10 mL of magnesium sulfate to 2% (w/v) NaCl BPW was the most effective combination of V. parahaemolyticus enrichment. Optimal V. parahaemolyticus culture conditions using REB-V were at pH 7.84 and 37oC. To confirm REB-V culture efficiency compared to 2% (w/v) NaCl BPW, we assessed the amount of enrichment achieved in 7 hours in each medium and extracted DNA samples from each culture every hour. Real-time PCR was performed using the extracted DNA to verify the applicability of this REB-V culture method to molecular diagnosis. V. parahaemolyticus was enriched to 5.452±0.151 Log CFU/mL in 2% (w/v) NaCl BPW in 7 hours, while in REB-V, it reached 7.831±0.323 Log CFU/mL. This confirmed that REB-V enriched V. parahaemolyticus to more than 106 CFU/mL within 6 hours. The enrichment rate of REB-V was faster than that of 2% (w/v) NaCl BPW, and the amount of enrichment within the same time was greater than that of 2% (w/v) NaCl BPW, indicating that REB-V exhibits excellent enrichment efficiency.

Rapid and accurate detection of pathogenic bacteria is crucial for various applications, including public health and food safety. However, existing bacteria detection techniques have several drawbacks as they are inconvenient and require time-consuming procedures and complex machinery. Recently, the precision and versatility of CRISPR/Cas system has been leveraged to design biosensors that offer a more efficient and accurate approach to bacterial detection compared to the existing techniques. Significant research has been focused on developing biosensors based on the CRISPR/Cas system which has shown promise in efficiently detecting pathogenic bacteria or virus. In this review, we present a biosensor based on the CRISPR/Cas system that has been specifically developed to overcome these limitations and detect different pathogenic bacteria effectively including Vibrio parahaemolyticus, Salmonella, E. coli O157:H7, and Listeria monocytogenes. This biosensor takes advantage of the CRISPR/Cas system's precision and versatility for more efficiently accurately detecting bacteria compared to the previous techniques. The biosensor has potential to enhance public health and ensure food safety as the biosensor’s design can revolutionize method of detecting pathogenic bacteria. It provides a rapid and reliable method for identifying harmful bacteria and it can aid in early intervention and preventive measures, mitigating the risk of bacterial outbreaks and their associated consequences. Further research and development in this area will lead to development of even more advanced biosensors capable of detecting an even broader range of bacterial pathogens, thereby significantly benefiting various industries and helping in safeguard human health

After detection of red imported fire ant (Solenopsis invicta) at Gamman port in Busan in September of 2017, Animal and Plant Quarantine Agency has surveilled invasive ants in the area with a high invasion risk of ants. However, existing surveillance traps have several limitations such as captured ants could escape easily or it is very hard to set up the trap on a hard ground like concrete or asphalt. To solve these problems, we developed a new trap using multiple narrow tubes to attract ants to the inside of the trap and make it hard for ants to escape. The new trap can be easily set up under various conditions. The new trap has more than four times ant capturing efficacy compared to conventional pitfall traps. Our results confirmed that the new trap could prevent captured ants from escaping. We hope that this newly developed trap would contribute to the prevention of invasive ants.

The official analytical method for the analysis of harmful heavy metals in Meju, distributed in Korea, employs a strong acid to decompose the organic components. This analysis is time consuming and harmful to the users and/or the environment. This study aimed to develop a new pre-treatment technology using laser ablation, to rapidly analyze harmful heavy metals without using strong acids. The results obtained from this method were validated by the National Institute of Food and Drug Safety Evaluation guideline (NIFDS, 2016). Moreover, a comparison of the two methods showed that the analytical time for 55 Meju samples was shortened by 96% or more in the new method. The results showed no significant difference in the recovery ranging from 90–120%. The proposed method proved suitable for detecting harmful heavy metals in Meju.

국내 유통되는 반려동물 사료의 살모넬라 분석시 증균 배양법, 효소면역기법에 의한 분석, 종 특이 primer를 활 용한 PCR 방법을 활용하여 비교 평가하였다. 시료 175점 Salmonella spp. 검출 결과 증균배양법 및 종 특이 primer를 활용한 PCR 방법에 의한 검출 방법에서 2점의 시료(육포, 옥수수 글루텐)가 양성으로 확인되었고, 효소면역기법에 의한 검출방법에서는 1점의 시료(옥수수 글루텐)가 양성 으로 확인되었다. 증균배양법 및 효소면역기법에 의한 검 출방법에 비해 종 특이 primer를 활용한 PCR 방법을 적 용 할 경우 시료에서 분리된 균주의 종(species) 판별이 가 능하였다.

박테리오신은 다양한 식품에서 천연 보존제로 그리고 항생제 대체제로 잠재력을 가지고 있다. 그러나 박테리오신의 다단계 정제 공정은 높은 생산 비용을 야기하여 상업적 이용 등 소비자 접근성에 장애요인이 되고있다. 식품 등 일부 산업 분야에서 활용하기 위한 박테리오신의 순도는 그리 높지 않아도 되며, 이에 따라 정제 공정을 간소화하여 생산 비용을 낮추고 공정 효율성을 강화할 수 있다. 이러한 관점에서 박테리오신 등에 적용할 수 있는 수성- 이상계 시스템(ATPS)은 높은 정제 수율과 빠른 처리 시간으로 인해 산업분야에서 하부 공정 처리 기술로 적절한 대안이 될 수 있으며, 고분자 수용액이 70~90% 물로 이루어진 친환경적 기술로서 환경보호에도 도움을 줄 것으로 전망된다.

The emergency diesel generator of a nuclear power plant is a emergency AC power source that starts up within 10 seconds when a LOOP(Loss Of Off-site Power) occurs and supplies power to essential safety facilities. In this study, factors affecting start signal input time, engine rotation start time, 30% of engine rated speed, 80% of engine rated speed were studied to secure starting reliability. As a result, it was found that the section before the 30% of engine rated speed was affected by the mechanical management status from the start signal to the fuel oil linkage system. After the 30% of engine rated speed section, it was the maximum fuel supply section, and the time reduction effect through management improvement was insignificant.