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.

The prime objective of this computational study was to develop a highly accurate potential for the use of molecular dynamics (MD) simulations of carbon nanotubes (CNTs). This potential was generated using ab initio MD (AIMD) simulations based on density functional theory (DFT). Subsequently, we constructed machine-learned interatomic potentials (MLIPs) based on moment tensor potential (MTP) descriptors using AIMD trajectories as training data. The performance of the developed MLIPs was evaluated by conducting the MD simulations of the stress–strain responses of single-walled CNTs (SWCNTs) and defected SWCNTs (D-SWCNTs) under tensile loading. Furthermore, this work includes extensive MLIP-based MD simulations to examine the influence of diameter and chirality, temperature, and defect concentration on the fracture characteristics and Young’s modulus of SWCNTs. The findings demonstrate the computational reliability and transferability of the MLIPs in predicting the mechanical properties of SWCNTs through MD simulations performed over a temperature range of 1 K to 2000 K. The observed stiffnesses correspond to Young’s modulus ranging from 1.61–0.53 TPa with a mean value of 0.936 TPa for different SWCNTs with diameters ranging from 1.1–2.89 nm and temperatures spanning from 1 to 2000 K, exhibiting a noticeable dependence on chirality.

Business model(BM) innovation is widely known as a differentiated strategy and strategic framework for companies to secure a sustainable competitive advantage in an uncertain environment. While prior research has studied new business models in accordance with changes in manufacturing trends such as digitalization and servitization, empirical understanding of the dynamic processes of BM innovation is still lacking. This study addresses this gap by proposing an analytical framework of the BM innovation matrix that classifies companies' BM innovation cases into four types according to the degree of BM change and the influential level of the industry/market outcome through a critical literature review on business models and dynamics. Drawing on this framework, we conduct longitudinal case studies of leading global 3D printing firms to examine the dynamic processes and external environmental factors that shape the evolution of BM innovation. Our findings reveal previously underexplored patterns of co-evolution between firms’ business models and their broader industrial and market environments. This study has the significance of constructing a framework for dynamically analyzing BM innovation based on longitudinal case studies of emerging 3D printing companies. We presented implications for companies seeking successful commercialization of emerging technologies, such as the strategic usefulness of the BM innovation framework and the importance of co-evolution with industrial structure and environmental factors in the process of change.

본 연구는 플랫폼 시대에 K-Culture가 한국의 소프트파워를 어떻게 형성하고 확장해 왔는지를 문화사회 학적 관점에서 분석하였다. 나이(Nye)의 소프트파워 이론과 부르디외(Bourdieu)의 문화장 이론을 토대로, K-Pop, 드라마, 디지털 콘텐츠, 팬덤, 국가 정책 간 상호작용을 고찰하였다. 팬덤의 사회운동화, 감정 서사 를 중심으로 한 콘텐츠, 플랫폼 알고리즘과 수용자의 문화 번역 사례를 통해 K-Culture가 감정, 기술, 정체 성 정치가 교차하는 복합적인 문화 실천임을 규명하였다. 아울러 플랫폼 종속, 콘텐츠 획일화, 팬덤 의존, 문화 다양성 부족 등 소프트파워의 구조적 한계를 진단하고, 윤리적 공감과 문화적 포용성에 기반한 지속 가능한 전략을 제안하였다.