Springtails (class Collembola) play a crucial role in soil ecosystems. They are commonly used as standard species in soil toxicity assessments. According to the ISO 11267 guidelines established by the International Organization for Standardization (ISO), Allonychiurus kimi uses adult survival and juvenile production as toxicity assessment endpoint. Conventional toxicity assessment methods require manually counting adults and larvae under a microscope after experiments, which is time-consuming and laborintensive. To overcome these limitations, this study developed a model using YOLOv8 to detect and count both adults and juveniles of A. kimi. An AI model was trained using a training dataset and evaluated using a validation dataset. Both training and validation datasets used for AI model were created by picturing plate images that included adults and larvae. Statistical comparison of validation dataset showed no significant difference between manual and automatic counts. Additionally, the model achieved high accuracies (Precision=1.0, Recall=0.95 for adults; Precision=0.95, Recall=0.83 for juveniles). This indicates that the model can successfully detect objects. Additionally, the system can automatically measure body areas of individuals, enabling more detailed assessments related to growth and development. Therefore, this study establishes that AI-based counting methods in toxicity assessments with offer high levels of accuracy and efficiency can effectively replace traditional manual counting methods. This method significantly enhances the efficiency of large-scale toxicity evaluations while reducing researcher workload.

감마-오리자놀(γ-oryzanol)은 phytochemical의 한 종류로 항산화, 항염증, 항암, 항당뇨, 콜레스테롤 감소 등의 효능이 알려져 있다. 본 연구에서는 현미를 백미로 도정할 때 부산물로 발생하는 미강으로부터 생리활성물질 중의 하나인 γ-oryzanol 함량이 높은 추출조건에서의 분말을 제조하고, 이를 쌀국수에 첨가하여 항산화성이 개선된 국수를 제조하고자 하였다. 미강을 에탄올 농도 0, 20, 40, 80%의 에탄올로 추출한 결과 80% 에탄올로 추출한 추출분말에서 γ-oryzanol 함량이 가장 높았으며, DPPH 소거능과 ABTS 소거능이 가장 높았다. 미강추출분말의 γ-oryzanol의 함량을 높이기 위하여 Saccharomyces cerevisiae로 미강을 고상발효시킨 후 80% 에탄올로 추출하여 동결건조시킨 분말의 γ-oryzanol의 함량은 발효시키지 않은 미강을 추출분말보다 약 2.18배 증가하였으며, DPPH 소거능 및 ABTS 소거능, 단백질분해효능이 증가하였다. 오리자놀 함량이 높은 미강추출 물을 쌀가루에 첨가하여 쌀국수를 제조한 결과 쌀국수의 경도, 탄력성, 씸힘성, 점착성 등이 증가하였으며, 쌀국수의 γ-oryzanol 함량, DPPH 소거능, ABTS 소거능 등이 증가하여 항산화 활성이 개선되었음을 확인하였다.

Purpose This study aimed to develop and evaluate a simulation-based autotransfusion device training program to enhance the clinical performance, performance confidence, and educational satisfaction of post-anesthesia care unit (PACU) nurses. Methods: A single-group pretestposttest study was conducted with 30 PACU nurses. The program, based on the ADDIE model, included orientation, simulation training, and debriefing. Data were collected using validated tools before and after the program and analyzed using the Wilcoxon signed-rank test. Results: Clinical performance improved from a median of 30.00 to 43.00 (Z =−4.78, p < .001). Performance confidence increased from 31.00 to 47.50 (Z =−4.71, p < .001), while educational satisfaction rose from 26.00 to 40.00 (Z =−4.73, p < .001). Conclusions: The simulation-based program effectively enhanced the clinical performance, performance confidence, and education satisfaction of PACU nurses. These findings underscore the value of simulation-based training for enhancing nurses’ competence in using complex, high-risk medical devices.

The aim of this study was to produce a fermented rice bran extract with enhanced ferulic acid γ-oryzanol contents and high antioxidant activities. The ferulic acid content in the freeze-dried extract of rice bran treated with plantase PT enzyme, increased by 4.1-fold compared to that of untreated sample, the DPPH radical scavenging activity also increased by 1.5-fold and 1.2-fold, respectively. The γ-oryzanol content of the dried powder prepared by inoculating Apergillus oryzae BOT1869 onto steamed rice bran for solid-state fermentation followed by extraction with 80% ethanol, increased 2.3-fold compared to that in an 80% ethanol extract powder of raw materials. The ABTS scavenging activity also increased 1.5-fold. When the ferulic acid content-enhanced extract and the γ-oryzanol content-enhanced extract of rice bran were mixed and subjected to liquid fermentation with Lactiplantibacillus pentosus BOT406 and then freeze-dried, the ferulic acid content of the extract powder increased about 3.0 times compared to that of original extract powder. In addition, its γ-oryzanol content increased about 1.5 times, the DPPH radical scavenging activity increased 1.4 times, and the ABTS radical scavenging activity increased 1.6 times.

Reinforcement learning (RL) is successfully applied to various engineering fields. RL is generally used for structural control cases to develop the control algorithms. On the other hand, a machine learning (ML) is adopted in various research to make automated structural design model for reinforced concrete (RC) beam members. In this case, ML models are developed to produce results that are as similar to those of training data as possible. The ML model developed in this way is difficult to produce better results than the training data. However, in reinforcement learning, an agent learns to make decisions by interacting with an environment. Therefore, the RL agent can find better design solution than the training data. In the structural design process (environment), the action of RL agent represent design variables of RC beam. Because the number of design variables of RC beam section is many, multi-agent DQN (Deep Q-Network) was used in this study to effectively find the optimal design solution. Among various versions of DQN, Double Q-Learning (DDQN) that not only improves accuracy in estimating the action-values but also improves the policy learned was used in this study. American Concrete Institute (318) was selected as the design codes for optimal structural design of RC beam and it was used to train the RL model without any hand-labeled dataset. Six agents of DDQN provides actions for beam with, beam depth, bottom rebar size, number of bottom rebar, top rebar size, and shear stirrup size, respectively. Six agents of DDQN were trained for 5,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases that is not used for training. Based on this study, it can be seen that the multi-agent RL algorithm can provide successfully structural design results of doubly reinforced beam.

The rapid expansion of bridge and tunnel infrastructure has resulted in a growing incidence of wind-induced traffic accidents occurring at bridge approaches and tunnel portals. These accidents not only inflict direct damage on vehicles but also lead to substantial social and economic losses, stemming from roadway infrastructure repair and maintenance costs, as well as elevated logistics expenses due to traffic delays and congestion. In this study, a theoretical expression for the lateral displacement of vehicles as a function of wind speed was derived. Subsequently, lateral displacement and lateral wind force were analyzed and compared across vehicle types, considering both straight and curved roadway sections. An analysis of prevailing wind directions at each site revealed that, for passenger cars, the maximum lateral force and displacement on straight sections occurred at a wind incidence angle of 45°, whereas on curved sections with a pier curvature of 90°, the critical wind direction ranged from 0° to 120°. These results demonstrate that vehicle stability can be significantly compromised during high-speed travel under crosswind conditions. Based on departure trajectories of vehicles under varying wind speeds, a risk-assessment scale for wind-induced accidents was developed. In addition, design guidelines were proposed for the strategic placement of windbreak barriers to enhance driving safety under strong wind conditions.

최근 한반도 주변 해역에서 해상사고로 인한 인명피해가 증가함에 따라 실종자 수색 및 구조 활동을 지원하기 위한 수중환경 정보 산출 기술 개발의 필요성이 대두되고 있다. 끊임없이 변화하는 해양환경 속에서 수중시야를 예측하는 것은 매우 중요한 과제이다. 본 연구에서는 인공위성 영상과 현장실험 자료를 활용하여 탁도와 수중시야 간의 상관 성을 분석하고, 한반도 서해 연안에 적합한 수중시야 산출 알고리즘을 제시하고자 한다. 이를 위해 대천항 인근 해역의 두 정점을 선정하여 탁도 센서와 세키디스크를 이용해 깊이에 따른 탁도 및 수중시야를 측정하였으며, 동시에 고해상도 인공위성 영상을 수집하여 표층 원격반사도 자료를 획득하였다. 또한 서해 연안에서의 탁도 변화에 따른 수중시야의 공 간 분포를 비교하기 위해 Sentinel-2 위성의 560 nm 반사도를 활용하여 연구 해역의 탁도를 산출하고, 실측 탁도와 수 중시야 간의 상관성을 분석하였다. 연구 결과, 서해 연안에서는 2 .0- 3.0NTU 범위의 탁도가 분포하였으며, 수중거리는 1.5-3.5 m 사이의 값을 나타냈다. 특히, 만 지형이나 섬 주변에서는 상대적으로 낮은 수중시야가 관측된 반면, 대천해수 욕장 해안선을 따라서는 약 3.0m 이상의 높은 수중시야를 보였다. 본 연구를 통해 실측 및 원격탐사 자료를 활용한 수중시야 산출 가능성을 확인하였으며, 위 결과는 향후 해상 실종자 수색 지원을 위한 기초 자료로 활용될 것으로 기 대된다.

Autonomous vehicle technology is targeted for commercialization in 2027. However, a mixed traffic environment of conventional vehicles and autonomous vehicles is expected to be inevitable. In mixed traffic, conventional vehicles drive at reduced speeds due to limited visibility, while autonomous vehicles can drive at normal speeds using sensors. The difference in driving speeds between the two vehicles creates a mismatch in traffic flow, and the risk of congestion and accidents is likely to increase. It is necessary to analyze the impact of the interaction between autonomous vehicles and regular vehicles on traffic safety in advance and develop management measures to mitigate it. In this study, we aim to analyze the effect of reducing the speed deviation between general vehicles and autonomous vehicles by providing the driving speed deceleration level information to autonomous vehicles in the event of fog to induce the same traffic flow and improve the safety level accordingly. We examined the method of delivering the driving speed deceleration level information to autonomous vehicles. When providing speed limit information to autonomous vehicles through systems such as VMS, each country has different ways of recognizing regulatory symbols. Due to these differences, it may not be easy to provide regulatory information to overseas vehicles through external systems such as VMS in Korea. For this reason, there is a possibility that autonomous vehicles may violate laws and regulations by not recognizing them properly, and there are still limitations in defining the responsibility for applying laws and regulations between countries. Therefore, we adopted an information provision approach that encourages autonomous vehicles to maintain a harmonious traffic flow with regular vehicles by sharing safe driving speed information to be encouraged at the public center level. To analyze the effectiveness of these safe driving speed management measures, we used a quantitative indicator, the number of observable conflicts, to distinguish the mixing ratio of regular vehicles and autonomous vehicles. The analysis was divided into early (30%), mid (50%), and late (80%) periods of autonomous vehicle introduction. As a result of giving autonomous vehicles the same traffic flow as regular vehicles, the number of collisions decreased by 128 collisions/hour in the early period, 393 collisions/hour in the mid period, and 337 collisions/hour in the late period. This indicates that the interaction between autonomous vehicles and conventional vehicles becomes more complex as the mixing ratio increases, and the effectiveness of the safe speed management measures proposed in this study increases accordingly. These results can be used as an important basis for transportation policy and design.