이 연구는 유튜브의 사용과 사용시간에 따라 정치참여 동학이 어떻게 나타나는지를 증명하고자 한다. 연구는 유튜브의 정치 참여적인 특성에 관한 선행 연구를 비판적으로 검토하여 변인과 연구가설을 설정하였다. 연구는 설문조사를 활용한 계량적인 연구방법론을 적용했다. 분석을 과 학적으로 시행하기 위해 연구에서는 유튜브 비사용자 집단, 라이트유저 집단, 헤비유저 집단 등으로 구분했다. 분석 결과, 첫째, 유튜브 사용에 따른 온라인과 오프라인 참여의 차이는 나타났다. 둘째, 유튜브 사용유형 에 따른 정치참여 유형별 차이 분석 결과는 온라인 관찰적 참여, 온라인 상호작용적 참여, 오프라인 비관습적 참여는 헤비유저 집단 > 라이트유 저 집단 > 비사용자 집단 순으로 참여 지향성이 강한 것으로 나타났다. 셋째, 시민 정치참여 유형별 회귀분석 결과는 오프라인 관습적 참여를 제외하고 유튜브 사용시간 변인이 중요한 변인임을 확인했다. 요컨대, 유 튜브 사용은 정치참여에 정(+)의 인과성이 있으며, 사용자 집단 중에서도 사용시간이 길수록 더욱 강한 정(+)의 인과관계가 있는 것으로 확인되었 다.

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.

This study proposes a methodology for predicting the physical properties such as the density of polymer composites, including asphalt binders, and evaluates its feasibility by identifying the quantitative relationship between the structure and properties of individual polymers. To this end, features are constructed using molecular dynamics (MD) simulation results and descriptor calculation tools. This study investigates the changes in the calculated density depending on the characteristics of the training dataset and analyzes the feature characteristics across datasets to identify key features. In this study, 2,415 hydrocarbon and binder-derived polymer molecules were analyzed using MD simulations and 2,790 chemical descriptors generated using alvaDesc. The features were pre-processed using correlation filtering, PCA, and recursive feature elimination. The XGBoost models were trained using k-fold cross-validation and Optuna optimization. SHAP analysis was used to interpret feature contributions. The variables influencing the density prediction differed between the hydrocarbon and binder groups. However, the hydrogen atom count (H), van der Waals energy, and descriptors such as SpMAD_EA_LboR consistently had a strong impact. The trained models achieved high accuracy (R² > 0.99) across different datasets, and the SHAP results revealed that the edge adjacency, topological, and 3D geometrical descriptors were critical. In terms of predictive accuracy and interpretability, the integrated MDQSPR framework demonstrated high reliability for estimating the properties of individual binder polymers. This approach contributed to a molecular-level understanding and facilitated the development of ecofriendly and efficient modifiers for asphalt binders.

This study proposes a methodology for predicting properties such as the density of polymer composites, including asphalt binders, and evaluates its feasibility by identifying the quantitative relationship between the structure and properties of individual polymers. To this end, this study investigates the variations in molecular dynamics (MD) results with molecular structural complexity and assesses the independence and correlation of variables that influence density. In this study, MD simulations were performed on hydrocarbon-based and individual asphalt binder molecules. The effects of various temperatures, molecular conditions, and structural features on the density were analyzed. MD-related variables influencing the calculated density were evaluated and compared with experimentally measured densities. The MD-calculated densities were used as target variables in a subsequent study, in which a machine learning model was applied to perform quantitative structure–property relationship analysis.The MD-calculated densities showed a strong correlation with experimental measurements, achieving a coefficient of determination of R2 > 0.95. Potential energy exhibited a tendency to cluster into 4–6 groups depending on the molecular structure. In addition, increasing molecular weight and decreasing temperature led to higher density and viscosity. Torsional energy and other individual energy components were identified as significant factors influencing both potential energy and density. This study provided foundational data for the property prediction of asphalt binders by quantitatively analyzing the relationship between the molecular structure and properties using MD simulations. Key features that could be used in the construction of polymer structure databases and AI-based material design were also proposed. In particular, the integration of MD-based simulation and machine learning was confirmed to be a practical alternative for predicting the properties of complex polymer composite systems.