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.

본 연구는 그림책을 활용한 창의음악동화 모의수업 프로그램을 개발· 적용하여 예비유아교사의 음악교수효능감과 음악활동에 대한 태도 향상 효과를 검증하는 것을 목적으로 하였다. 연구대상은 N시 소재 4년제 대 학 ‘아동음악교육’ 수강 여학생 20명이었다. 본 연구 프로그램은 음악적 개념(리듬, 멜로디, 빠르기, 강약, 화성)과 연계가능한 그림책 20권을 선 정·분류하고, 이를 기반으로 새노래지도, 악기연주, 신체표현, 음악감상 등의 활동이 포함된 음악동화를 창작하여 팀별 모의수업을 기획·실기활 동 및 발표하게 하였다. 교육은 주 1회 3시간씩 총13회 진행되었으며, 사전·사후 동일한 검사 도구를 사용하여 대응표본 t -검증으로 분석하였 다. 그 결과, 프로그램은 음악교수효능감 전체 및 하위영역인 ‘개인교수 효능감’과 ‘교수결과기대’를 유의하게 향상시켰다. 또한, 음악활동에 대한 예비유아교사의 태도에서는 자신감, 편안함, 감정적 판단, 필요성 그리고 전체에 긍정적 변화가 있었다. 결론적으로, 본 프로그램은 예비유아교사 의 음악교수효능감 증진과 음악활동에 예바유아교사의 긍정적 태도에 효 과적인 것을 입증하였다. 후속 연구에서는 유아교육현장에 본 프로그램 을 적용하고 지속적으로 추적 관찰하여, 현장 유아교사의 음악교수효능 감과 음악활동에 대한 교사의 태도 변화에 관한 검증 연구가 필요할 것 으로 사료된다.

Polypropylene waste significantly contributes to environmental pollution due to its low biodegradability. Numerous experiments have shown that laser irradiation of polymers can lead to the conversion of laser-induced graphene (LIG). In this paper, the LIG formation process in polypropylene (PP), polydimethylsiloxane (PDMS), and polypropylene/polydimethylsiloxane (PP/PDMS) systems in a vacuum environment was simulated using molecular dynamics. The LIG yields and carbon network sizes of the systems in oxygen and vacuum environments at different temperatures were analyzed to determine the optimal temperature for upgrading PP to LIG. It was observed in all three systems that the LIG structure was formed. The structure was composed not only of six-membered carbon rings, but also of five-membered and seven-membered rings, resulting in out-of-plane fluctuations and bending. A vacuum environment and high temperature promote LIG formation with high yield, large size, and minimal defects. The current study provides theoretical guidance for optimizing the laser graphene process for PP assisted with PDMS in a vacuum environment and helps to understand the mechanism underlying the conversion from polyolefins to graphene under CO2 laser at the atomic level.

This study evaluates how road profile and speed affect tire loads of a hydrogen tube trailer using MSC Adams/Car multibody dynamics simulation. A tractor and trailer loaded with 64 high-pressure cylinders were modeled, and four representative road profiles flat, pothole, short-wave, and long-wave were applied at 30, 60, and 80 km/h. Vertical tire load time histories were extracted for five wheel positions. Flat roads yielded stable loads matching static distribution. Potholes produced short, high-amplitude impacts (up to 120 kN at 30 km/h) with reduced peaks at higher speeds. Short-wave profiles caused severe asymmetric roll loads (67 kN at 80 km/h), while long-wave inputs generated smoother, moderate increases over longer durations. Load amplification diminished toward trailer axles due to suspension energy dissipation. The results inform structural design of tube trailers and development of speed-control or active load-mitigation strategies for autonomous hydrogen transport vehicles.

본 연구는 팀 기반 시뮬레이션실습에서 교수평가, 동료평가와 자기평가의 점수를 확인하 며, 평가주체별로 점수를 어떻게 주는 경향이 있는지 알아보고 그 관계를 파악하는 것을 목적으로 한다. 이를 위해 ‘성인간호학실습(4)’를 수강한 간호대학생 중 연구 참여에 동의한 135명을 대상으로 학생들이 제출한 동료평가지와 자기평가지의 점수와 교수자 점수를 통계 적으로 분석하였다. 연구결과, 평가주체별 점수는 자기평가, 동료평가, 교수평가 순이었고, 자기평가에서 음의 왜도가 크고 첨도가 높아 스스로에게 높은 점수를 주는 경향이 있음과 집중된 평가 경향을 보이는 것으로 나타났다. 반면에 교수평가는 상대적으로 고르게 분포 된 평가를 하는 것으로 나타났다. 평가주체별 점수간 관계는 동료평가와 교수평가간에 정 적 상관관계가 있었다. 결론적으로, 팀 활동에서의 다면평가는 단일 평가자가 평가할 때 발 생하는 평가자 오류에서 상대적으로 자유롭지만, 단순히 평가자의 수를 늘리는 것만으로는 평가 결과의 신뢰성을 보장할 수 없기 때문에 맹목적으로 신뢰해서는 안 된다. 본 연구는 팀 기반 시뮬레이션실습에서 다면평가를 위한 구체적인 기준을 마련하고, 평가의 신뢰도와 타당도를 제고하기 위한 자료로 활용될 수 있을 것이다.

The emergence and re-emergence of infectious diseases pose ongoing threats to public health. This study aims to develop an agent-based simulation model (ABM) to predict the spread of novel infectious diseases during early outbreak phases and evaluate the effectiveness of control measures, specifically focusing on the impact of interventions such as maskwearing, vaccination, and social distancing on outbreak dynamics and the reduction of symptomatic cases. Using demographic and COVID-19 outbreak data from South Korea, we constructed a detailed contact network model encompassing workplaces, schools, households, and communities. Using demographic and COVID-19 outbreak data from Seoul, South Korea, we constructed a detailed contact network model encompassing workplaces, schools, households, and communities. Key transmission parameters were inferred using Approximate Bayesian Computation. The resulting ABM platform, implemented in a C-based R package, allows for flexible scenario simulation involving 56 adjustable parameters, including mask-wearing, vaccination coverage, and social distancing. Simulation outputs demonstrated the model’s capacity to reproduce observed transmission patterns in workplace and school outbreaks, enabling public health authorities to anticipate outbreak dynamics and assess interventions. This framework provides a valuable decision-support tool for controlling future infectious disease incursions.

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.

This study aimed to evaluate the effect of key operational factors on traffic performance in long underground expressways. This study was motivated by the increasing policy interest in underground expressway infrastructure as a solution to chronic surface-level congestion in dense urban regions. A scenario-based microscopic traffic simulation was conducted using VISSIM considering combinations of traffic volume, proportion of heavy vehicles, and longitudinal slopes. A total of 72 scenarios were simulated, and the weighted average speed and total throughput were analyzed. The simulation results showed that the entry traffic volume and longitudinal gradient significantly affected the average speed, particularly in uphill exit segments. The heavy vehicle ratio also contributed to consistent reductions in speed. However, the overall throughput remained relatively stable despite variations in heavy vehicle proportions, suggesting that speed is more sensitive to flow composition than to volume capacity. Although interaction effects were not statistically tested, the combined scenario trends suggested that steeper slopes and high heavy-vehicle ratios jointly intensify speed reduction. These findings support the early-stage design and traffic planning of underground expressways.

Purpose: This study evaluated the impact of a nursing simulation learning module for caring for patients with chronic obstructive pulmonary disease (COPD) on nursing knowledge, clinical competence, team psychological safety, and learning satisfaction among nursing students. Methods: A non-equivalent control-group pretest–posttest quasi-experimental design was used with 36 students (18 per group) assigned to either a simulation group or a lecture group. Data collected from June 8 to July 13, 2024, were analyzed using SPSS 27.0. Results: Nursing knowledge showed no significant between-group difference (F=1.32, p=.260) but improved over time (F=8.24, p=.007). Clinical competence showed a significant group-by-time interaction (F=58.33, p<.001). Team psychological safety (t=2.70, p=.012) and learning satisfaction (t=2.27, p=.030) were higher in the simulation group. Conclusion: These findings provide foundational data for developing simulation-based educational strategies in nursing curricula. The module may also be applied to the training of novice nurses in clinical settings, thereby contributing to enhanced nursing education and improved clinical practice.

In this study, the effects of a hypothetical autonomous vehicle (AV)-exclusive roadway were estimated through a step-by-step approach using both microscopic and macroscopic simulations. First, the AV-exclusive roadway was classified into four types—entry lanes, mainlines, merging lanes, and intersections—and the C, α, and β values of the Bureau of Public Roads (BPR) function were estimated for each type through a microscopic simulation. These estimated values were then applied to a 3×3 (20 km) network, and a macroscopic simulation was conducted to compare the effectiveness of AVs and conventional vehicles (CVs) in terms of traffic volume and travel time.The analysis showed that for the same travel time, the traffic volume increased by more than 12% with AVs compared to that with CVs. Conversely, for the same traffic volume, the total travel time decreased by 11% for AVs. The estimated capacity of the AV-exclusive roadway, similar to the U-Smartway with a size of 3×3 (20 km), was approximately 400,000 vehicles, which was more than 140% higher than that of CVs. Assuming that each AV carries five passengers, up to two million people can be transported per day, indicating a significant potential benefit. However, these results were based on theoretical analyses using hypothetical networks under various assumptions. Future studies should incorporate more realistic conditions to further refine these estimations.