본 연구에서는 높은 이산화탄소 투과성과 선택성을 가지는 미세다공성 고분자 PIM-1을 합성하고, 나노미터 수준 에서 두께를 정밀하게 조절할 수 있는 water casting 기법을 적용하여 박막복합막을 제조하였다. 제조된 분리막의 성능을 평 가하기 위해 FTIR-ATR, BET, GPC, XRD, TEM-EDS 등의 분석을 수행하였으며, 기체 투과 시험을 통해 CO2/N2 선택성과 투과도를 측정하였다. 연구 결과, 본 연구에서 제조된 박막복합막은 2700 GPU 이상의 CO2 투과도와 약 25의 CO2/N2 선택도 를 나타내며, 기존의 PIM-1 기반 분리막보다 우수한 성능을 보였다. 이를 통해 water casting 기법을 이용한 PIM-1 기반 분리 막이 경제적이고 효율적인 이산화탄소 분리 기술로 활용될 가능성을 제시하였다.

This paper presents a novel methodology for assessing the vulnerabilities of autonomous vehicles (AVs) across diverse operational design domains (ODDs) related to road transportation infrastructure, categorized by the level of service (LOS). Unlike previous studies that primarily focused on the technical performance of AVs, this study addressed the gap in understanding the impact of dynamic ODDs on driving safety under real-world traffic conditions. To overcome these limitations, we conducted a microscopic traffic simulation experiment on the Sangam autonomous mobility testbed in Seoul. This study systematically evaluated the driving vulnerability of AVs under various traffic conditions (LOSs A–E) across multiple ODD types, including signalized intersections, unsignalized intersections, roundabouts, and pedestrian crossings. A multivariate analysis of variance (MANOVA) was employed to quantify the discriminatory power of the evaluation indicators as the traffic volume was changed by ODD. Furthermore, an autonomous driving vulnerability score (ADVS) was proposed to conduct sensitivity analyses of the vulnerability of each ODD to autonomous driving. The findings indicate that different ODDs exhibit varying levels of sensitivity to autonomous driving vulnerabilities owing to changes in traffic volume. As the LOS deteriorates, driving vulnerability significantly increases for AV–bicycle interactions and AV right turns at both signalized and unsignalized intersections. These results are expected to be valuable for developing scenarios and evaluation systems to assess the driving capabilities of AVs.

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.

This study aimed to establish a marker compound in roasted Astragalus membranaceus (AM) water extract and to validate its analytical methods. The roasting process significantly enhanced the isoflavone content in AM. Among the four isoflavones analyzed, calycosin 7-glucoside (C7G) emerged as the most abundant, with a concentration of 847.88 μg/g in the AM extract. Due to its concentration and representativeness, C7G was designated as the marker compound, and its analytical method was thoroughly validated. Specificity was confirmed by the consistent retention time of the C7G peak at 15.2 minutes across both the sample and the standard compound. High absorbance was recorded at UV wavelengths of 220, 250, and 260 nm. The method exhibited excellent linearity, with a correlation coefficient (R2) of 0.9999 across a concentration range of 0.2 to 50.0 μg/mL. The limits of detection and quantification were determined to be 0.029 μg/mL and 0.088 μg/mL, respectively. Precision assessments revealed intra-day and inter-day variations of 0.812% and 1.650%, respectively. Recovery tests yielded values ranging from 99.419% to 104.861%, with relative standard deviations between 1.152% and 2.215%. These results affirm that the analytical method for C7G is highly specific, linear, accurate, and precise. This validated method may serve as a valuable tool for the standardization of roasted AM water extract.