지난 수십 년간 발생한 강진으로 인해 철근의 내부 구속력이 부족한 철근 콘크리트(RC) 기둥에 수많은 심각한 피해가 발생하였다. 다양한 보수공법 중 형상기억합금(SMA)을 이용한 능동구속 기법은 지진에 취약한 RC기둥의 휨 연성을 높이는 데 유의미한 결과를 보였다. SMA로 구속된 RC기둥의 동적거동에 관한 연구는 선행 연구자들에 의해 실험적으로 수행되었지만 구조적인 모델 개발은 거의 수행되지 않았다. 따라서 본 연구에서는 다양한 조건에서 능동구속된 RC기둥의 지진 시 거동을 확보하기 위해 SMA와이어로 보강된 RC 기둥의 진동대 실험 결과를 바탕으로 한 능동구속된 RC 기둥의 해석 모델을 제안한다. 제안된 해석모델은 양방향 지진력에 의한 SMA- RC기둥의 동적 거동 및 손상 정도를 확인할 수 있는 것으로 나타났다.

Prediction of the behavior of heavy metals over time is important to evaluate the heavy metal toxicity in algae species. Various modeling studies have been well established, but there is a need for an improved model for predicting the chronic effects of metals on algae species to combine the metal kinetics and biological response of algal cells. In this study, a kinetic dynamics model was developed to predict the copper behavior (5 μg L-1, 10 μg L-1, and 15 μg L-1) for two freshwater algae (Pseudokirchneriella subcapitata and Chlorella vulgaris) in the chronic exposure experiments (8 d and 21 d). In the experimental observations, the rapid change in copper mass between the solutions, extracellular and intracellular sites occurred within initial exposure periods, and then it was slower although the algal density changed with time. Our model showed a good agreement with the measured copper mass in each part for all tested conditions with an elapsed time (R 2 for P. subcapitata: 0.928, R 2 for C. vulgaris: 0.943). This study provides a novel kinetic dynamics model that is compromised between practical simplicity and realistic complexity, and it can be used to investigate the chronic effects of heavy metals on the algal population.

As interest in the sustainable fashion industry continues to increase along with climate issues, it is necessary to identify research trends in sustainable fashion and seek new development directions. Therefore, this study aims to analyze research trends on sustainable fashion. For this purpose, related papers were collected from the KCI (Korean Citation Index) and Scopus, and 340 articles were used for the study. The collected data went through data transformation, data preprocessing, topic modeling analysis, core topic derivation, and visualization through a Python algorithm. A total of eight topics were obtained from the comprehensive analysis: consumer clothing consumption behavior and environment, upcycle product development, product types by environmental approach, ESG business activities, materials and material development, process-based approach, lifestyle and consumer experience, and brand strategy. Topics were related to consumption, production, and education of sustainable fashion, respectively. KCI analysis results and Scopus analysis results derived eight topics but showed differences from the comprehensive analysis results. This study provides primary data for exploring various themes of sustainable fashion. It is significant in that the data were analyzed based on probability using a research method that excluded the subjective value of the researcher. It is recommended that follow-up studies be conducted to examine social trends.