The number of significant issues on many welding processes are often connected to high productivity and manufacturability at low costs. The research on welding processes in the literature has reported several research activities, but there is still scope for improvement in most industrial settings. The primary goal of this research is to determine the best super-TIG welding settings to use for groove welding. First, in order to determine the quality characteristics and risks associated with them, concepts and frameworks of quality by design (QbD) which is a new standard in pharmaceutical area in order to improve drug qualities were integrated into this process optimization. Second, stepwise experimental design approaches including a factorial design as well as a response surface methodology (RSM) were customized and performed for this specific automated super-TIG welding process. Third, based on experimental design results, the optimal operating conditions with both design space (i.e., acceptable range of operating conditions) and safe operating space (i.e., safe range of operating conditions) were obtained. Finally, a case study including QbD steps, stepwise experimental design approaches, design and operating spaces, the optimal factor settings, and their association validation results was conducted for verification purposes.

본 연구는 국내에서 아직 식품색소로 지정되지 않은 zeaxanthin과 citranaxanthin에 고성능 액체 크로마토그래피 (HPLC)를 적용하여 최적화된 동시 분석법을 개발하고, 이 분석법을 검증하기 위해 수행되었다. 최적의 분석법을 확립하기 위해, 기존에 보고된 다양한 국외 문헌 시험법의 HPLC 분석법을 비교 검토하였다. 그리고 분석 조건 선정 시 선형성, 검출한계(LOD), 정량한계(LOQ), 정확성, 반복 성, 실험실 간 검증 등의 주요 검증 항목을 고려하여 다 양한 식품에 적용 가능한 최적 전처리법을 개발하였다. 개 발된 분석법은 우수한 선형성(R20.999)을 나타내었으며, 식품 매트릭스 내에서 zeaxanthin과 citranaxanthin에 대한 검 출한계(LOD)는 각각 0.11-0.32 mg/kg 및 0.14-0.20 mg/kg, 정량한계(LOQ)는 각각 0.33-0.96 mg/kg 및 0.42-0.60 mg/kg 으로 확인되었다. 기존 연구에 보고된 전처리 방법은 다 양한 식품군에 적용하는 데에 한계를 보였으나, 본 연구 에서 개발된 분석법은 카로티노이드 착색료가 사용되는 주요 식품군에서 모두 적용이 가능하였다. 특히 이 방법 은 적용성 평가에서 zeaxanthin과 citranaxanthin에 대해 모 든 식품군에서 90% 이상의 우수한 동시 회수율을 나타내 었다. 또한, 새로운 HPLC 분석법을 적용한 두 물질의 정 확도(회수율) 및 정밀도(%RSD)는 식품의약품안전처(MFDS) 가이드라인에서 제시한 기준 범위에 부합하였다. 따라서 본 연구에서 개발된 동시 분석법은 식품 중 zeaxanthin과 citranaxanthin을 신뢰성 있고 정확하게 분석할 수 있는 방 법으로 판단되었다.

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.

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.

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.

공력천칭법은 고층건물의 구조풍공학 분야에서 널리 활용되는 수단으로, 풍력실험과 풍하중 해석단계를 명확히 분리 및 연 계하는 체계적인 틀을 제공한다. 기존 방법은 주로 변위응답에 중점을 두었으나, 고층구조물 전반에 걸쳐 중요한 역할을 하는 공력모 멘트의 거동을 간과하는 경향이 있다. 본 연구에서는 이러한 한계를 극복하기 위해 개선된 모멘트 기반 공력천칭법을 소개한다. 제안 된 방법론은 정교하게 도출된 모드형상 보정계수를 반영하고, 모달스펙트럼해석과 확률론적 진동이론을 연계함으로써 측정된 공력모 멘트응답과 확률적 풍하중 효과 간의 세밀한 관계를 정립한다. 더 나아가, 본 연구에서는 건물 높이에 따라 평균, 비공진 및 공진성분 의 공력모멘트응답을 등가정적풍하중 및 풍응답으로 효과적으로 분배하기 위한 유효참여계수의 도출과정을 개념적·수학적으로 상세 히 다룬다. 이런 포괄적 접근법은 풍하중 산정의 정확도를 향상할 뿐만 아니라, 사용성 설계에 중요한 요소인 변위 및 가속도응답의 예측 개선에도 기여한다. 궁극적으로, 풍공학전문가와 구조설계자에게 더욱 안전하고 회복력 있는 고층건물설계를 위한 고도의 평가 수단을 제공할 것이다.

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.