In this study, we comparatively analyzed the efficiency of conventional image recognition methods and propose a digital information provisioning method for autonomous vehicle traffic safety facility recognition. We evaluated the practicality of both approaches from the perspective of autonomous vehicles' capabilities of processing regulatory information and the distribution of legal responsibility. Comprehensive field experiments were conducted at 9 major intersections in the Pangyo Techno Valley area of Hwaseong City over a 10- day period from July 12-23, 2021. Three test vehicles equipped with in-vehicle terminals and video cameras collected data through 300 driving scenarios, including 240 during peak hours and 60 during off-peak periods. The proposed digital information provision method exhibited superior performance, achieving a 100.0 % recognition success rate across all test scenarios and road conditions. In contrast, the conventional image recognition method exhibited significant variability in performance, ranging from 56.9 % in underpass conditions to 95.9 % in areas with communication interference, with an overall average of 70.8 %. The digital information provision method demonstrated superior performance compared to conventional image recognition approaches for autonomous vehicle regulatory compliance. The proposed approach delivered consistent and reliable information regardless of physical obstacles or environmental conditions. This method ensures complete comprehension of regulatory information, which is essential for establishing clear legal responsibility frameworks in autonomous driving environments.

In the Autonomous Mobility Living Lab, traffic situations with both autonomous vehicles (AVs) and ordinary vehicles driven by humans (HDVs) are explored. Research on countermeasures and efficient transportation management plans has emerged from this context. In this study, we analyzed the effect of AVs with different speeds on signal intersections and road networks to derive efficient traffic operation plans for roads on which various AVs and HDVs with different driving behaviors are mixed in Living Lab cities. To that end, we conducted a simulation-based analysis of the effects of AV mixing rates on continuous signal intersections and the road network in traffic situations where AVs and HDVs were mixed at peak and non-peak driving hours. The simulation scenario was designed by classifying the traffic volume levels at peak and non-peak times and defining various AV mixing rates; we also set the driver behaviors of the AVs as either conservative or aggressive. By performing a small-scale traffic simulation, the average control delay, average stopped delay, average queue length, and average travel time of the signal intersection for each scenario were derived, and the impact of the AV mixing rate on traffic operation was analyzed. The results of the analysis show that higher AV mixing rates were associated with lower measurements of the effectiveness of signal intersections, which had a positive effect on traffic operation. This resulted in a stable and efficient improvement of the traffic flow at intersections as more vehicles passed through at the time of the allocated signal, as the AVs in the simulation could be driven at short intervehicle intervals by receiving real-time traffic information. In the traffic operation on the network, we found that the higher the AV mixing rate, the lower the average travel time, resulting in a greater effect of facilitating the traffic flow of the urban network. These simulated results indicate that higher AV mixing rates were associated with positive outcomes in terms of signal intersections and network traffic operation. We expect that this simulation can be used to establish real traffic operation plans in traffic situations where AVs are mixed at each stage of autonomous driving technology in the future.

In this study, we investigated the characteristics of meteorological factors influencing odor emissions based on odor complaint records and meteorological observation data collected from 2014 to 2021 in Seoul, a megacity with a high population density and no regulated facilities or management zones apart from public environmental infrastructure and neighborhood living facilities. A total of 134,976 odor complaints were recorded over eight years (2,922 days), with a daily peak of 946 cases. From 2018 onward, daily complaints consistently exceeded 200 cases, reaching their highest level in 2019. Complaints were regularly and intensively concentrated during specific periods of the year. Among meteorological factors, dry tide time and dominant wind direction exhibited bimodal distributions within the observation range, while the other 11 meteorological factors were concentrated at a single value. The average meteorological conditions during the period with the highest number of odor complaints and observation days were: temperature 24.2oC, surface temperature 26.7oC, local pressure 1,000.4 hPa, sea-level pressure 1,010.8 hPa, vapor pressure 4.3 hPa, solar irradiance 10.5 MJ/m2, precipitation 0.6 mm, relative humidity 61.5%, wind speed 2.2 m/ s, and dominant wind direction 57.9o. These factors corresponded with the periods of highest odor complaints and observation days; however, dew point temperatures (15.3oC and 19.1oC), dry tide times (14.7 hours and 9.7 hours), and sunshine times (15.1 hours and 9.0 hours) did not coincide with those periods. The meteorological factors with the highest odor complaint emission rates (SROCE, cases/day) across 13 sections, ranked in descending order, were: dry tide time (73.5), surface temperature (67.7), solar irradiance (65.1), sea-level pressure (64.3), temperature (62.7), local pressure (62.7), dew point temperature (60.3), vapor pressure (58.7), sunshine times (54.1), relative humidity (53.1), wind speed (51.2), dominant wind direction (48.7), and precipitation (46.3). The intensity of odor complaint emission (IOCE, cases/day), calculated across the entire meteorological observation range, was highest in the following order: solar irradiance (63.8), dry tide time (60.5), sunshine times (60.3), local pressure (57.2), surface temperature (57.1), sea-level pressure (57.0), temperature (56.4), vapor pressure (55.8), dew point temperature (55.7), relative humidity (49.4), dominant wind direction (49.2), wind speed (49.0), and precipitation (46.5). The IOCE for solar irradiance was 37.2% higher than that for precipitation, which had the lowest IOCE. Additionally, the average IOCE of sunlightrelated meteorological factors such as dry tide time, sunshine times, and solar irradiance was 61.5 cases/day, which is 29.5% higher than that of moisture-related factors, including precipitation and relative humidity (47.5 cases/day). These results suggest that sunlight-related factors significantly contribute to odor complaints. This study evaluated the characteristics of odor emissions associated with complaints within the meteorological observation range, identified meteorological factors corresponding to the highest observed odor emissions, and assessed the contribution of these factors to odor emissions based on the IOCE metric. Based on these results, we anticipate being able to predict odor emission levels using weather forecast data.

This study developed an early warning model for Korea’s salmon import supply chain, which relies heavily on a single country. A supply chain crisis is defined as a significant change in the CIF import price beyond a stable range, with potential impacts on domestic prices. The crisis index, using January 2010 as the base point, combines the relative price level and its year-on-year growth rate. The threshold was set based on earlier agricultural early warning studies. Monthly and quarterly data were used to select explanatory variables including supply factors and demand or macroeconomic factors. Variables with high 3~5 month lagged correlations were chosen using a stepwise method. Ordinary Least Squares (OLS) regression and Probit models were applied for both all crises and continuous crises, and predictive accuracy was evaluated using MAE and RMSE. Results show that the Probit model with a five-month lag for continuous crises provided the highest accuracy.

This study determined the appropriate pressure for high pressure shucking of oysters (Magallana gigas) according to size categories. One-year and two-year-old oysters harvested from Yeosu and Tongyeong coastal areas were subjected to high pressure treatments with pressure ranging from 180 to 240 MPa and holding times of 0 to 420 seconds. Results demonstrated that 100% shucking rates were achieved at 220 MPa with 120-180 seconds holding time regardless of harvest time and production area. K-means cluster analysis was used to classify oyster specimens into small, medium, and large size groups with mean shell heights of 69.0 mm, 86.6 mm, and 106.7 mm, respectively. The analysis revealed that shucking efficiency improved directly proportional to oyster size. However, the appropriate shucking pressures converged at approximately 220 MPa for all size groups, with small, medium, and large groups showing 218.9 MPa, 221.7 MPa, and 220.6 MPa, respectively, and no significant differences among groups. These findings suggest that the appropriate pressure for high pressure shucking processes be approximately 220 MPa regardless of harvest time and production area, providing foundational data for potential automation of oyster shucking processes.

This study investigates the effects of canopy fabric material and vent diameter on the deployment performance of sea anchors for fishing operations through field experiments. Three canopy configurations were tested: polyamide (PA), polyester (PES), and a PA – PES alternating combination. Vent diameters of 80 cm and 40 cm were applied to each fabric as controlled structural factors. Deployment performance was evaluated from entrance diameter computed using four evenly spaced water level loggers, and maximum towing tension was recorded with a load cell. Key findings were discovered as follows: PES and the PA – PES mix achieved deployment diameter/ratio and maximum tension comparable to the current PA standard, indicating practical substitutability. PES also exhibited superior stability, showing reduced variability even with the smaller vent (a change in standard deviation ΔSD-1.75 cm). The mixed canopy maintained performance similar to PA. As expected, vent diameter systematically affected geometry and load (80 cm: 187 – 198 kgf vs. 40 cm: 249 – 268 kgf), underscoring the need to pair material selection with appropriate vent sizing. In conclusion, PES and mixed fabrics are viable alternatives to PA, and co-optimization of fabric choice and vent diameter can enhance sea-anchor performance, durability, and operational reliability for fishing operations.

This study assessed the processing suitability and functional potential of sweet potato paste by comparing quality characteristics across different cultivars and heat treatment methods (steaming and baking). Generally, moisture content was higher after steaming, with the ‘Bodami’ and ‘Pungwonmi’ cultivars retaining more moisture, while ‘Jinyulmi’ and ‘Danjami’ had lower moisture levels. Purple-fleshed cultivars displayed negative a* and b* values, indicating bluish hues, whereas yellow-fleshed cultivars maintained stable b* values after heating. Both °Brix and free sugar levels increased after treatment, with baking significantly elevating maltose levels and enhancing sweetness. Apparent viscosity was higher in ‘Danjami’, ‘Jinyulmi’, and ‘Bodami’, while ‘Hogammi’, ‘Hopungmi’, and ‘Sodammi’ exhibited lower viscosity. Additionally, ‘Bodami’ and ‘Danjami’ demonstrated the highest levels of polyphenols, flavonoids, and antioxidant activities, confirming their potential as valuable functional ingredients. These findings underscore the importance of selecting appropriate cultivars and heat treatments to optimize the physicochemical and functional qualities of sweet potato paste.

Conventional fixed-time traffic signal operations at urban intersections are typically based on prescheduled plans that presume stable and recurring traffic patterns, particularly during peak commuting hours. However, recent societal changes—including flexible work schedules, telecommuting, and evolving workweek structures—have introduced greater variability in traffic demand, thereby diminishing the effectiveness of traditional peak-hour-focused control strategies. This study investigated the performance of an AI-based adaptive traffic signal control system that operated independently of predefined time plans. A field demonstration was conducted in Jeju City, South Korea, where the system was deployed in both the cyclic and acyclic operation modes. By leveraging real-time traffic data obtained from AI-enabled video detectors, the system adjusted the signal timings on a per-second basis in response to dynamic traffic conditions. The performance was evaluated against the conventional time-of-day (TOD) control method under diverse traffic scenarios, including typical weekdays, weekends, and local event days. The AI-based system achieved substantial reductions in intersection delays—24.3% on weekdays, 22.2% on weekends, and 17.1% on event days—compared with the TOD baseline. Moreover, it preserved a comparable level of traffic progression (measured by the proportion of non-stop vehicle flows) even during acyclic operations. The greatest efficiency gains were observed during the nighttime and low-traffic periods, underscoring the capacity of the system to minimize unnecessary delays under variable conditions. These results highlighted the potential of AI-based adaptive signal control as a viable alternative to conventional fixed-time operations, offering enhanced responsiveness and operational flexibility in increasingly complex urban traffic environments. Future research will focus on scaling the system to larger networks and developing integrated optimization strategies across multiple intersections.

본 논문은 아프리카 마다가스카르의 의료 인력 부족과 기본 외과 수술 접근성의 제약 문제를 해결하기 위해 시행된 지구병원 기본 외과 의사 양성 프로그램을 소개한다. 2017년부터 기획되어 2023~2024년 실행된 본 사업은 한국의 정부 및 비정부 기관 (KOICA, AFF, WELL International, 한국누가회 등)과 마다가스카르 보건부 및 국립보건원 (INSPC)의 협력으로 추진되었다. 마다가스카르 정부 소속 현지 공무원 의사 20명을 선발해 2년간 7가지 핵심 외과술기 중심의 훈련을 시행하고, 이후 5년간 비수도권 지구병원에 배치함으로써 약 500만 명의 주민에게 외과 진료 접근성을 제공하였다. 마다가스카르 정부의 급여 지원과 제도화 의지는 이 프로그램의 효과 및 지속 가능성을 보여주는 성과이다. 본 프로그램은 선교적 가치에 기반한 현지인 역량 강화형 의료선교 모델로, 향후 사업의 확대와 유사한 환경의 저개발국에서 적용 가능한 보건의료 인력개발 전략의 하나로서 그 가능성과 의의를 가진다.

본 연구는 병원성 박테리아 Agrobacterium rhizogenes에 대해 저압 수은 UV-C 소독 시스템의 살균 성능을 정량적으로 평가하고, 램프 소비전력과 유량의 조합에 따른 소독 효율에 미치는 영향을 분석하였다. 농업용수는 경상남도 사천시 소 재 세 농가(A, B, C)와 함안군 군북면의 한 농가(D)에서 채취 하였으며, 목표 병원균을 인위적으로 접종한 뒤 실험을 수행 하였다. 실험은 1차와 2차로 구분되며, 1차 실험은 소비전력 40, 75, 105W와 유량 10, 15, 30L/min의 3×3 조합으로 구성 되었고, 2차 실험은 소비전력 75W 조건 하에서 고유량(60, 120L/min) 조건을 적용하였다. 대부분의 실험 조건에서 박테 리아가 완전히 사멸되었으며, 유일하게 생균이 검출된 조건 (#33, 75W－60L/min)에서만 생균이 검출되었다. 그러나 해 당 조건에서도 생균수는 UV 비처리 대조군(#37)에 비해 약 4.9log 수준 감소하여 유의미한 소독 효과가 확인되었다. 이 러한 결과는 고유량 조건에서도 충분한 UV 조사량이 확보된 다면 안정적인 소독이 가능함을 시사한다. 본 연구는 저압 UV-C 시스템의 운전 변수에 따라 살균 효율이 어떻게 달라지 는지를 실험적으로 입증하였으며, 농업용수 처리에 있어 해 당 기술의 실용적 적용 가능성을 제시하였다.

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.