Knowledge graphs offer a structured framework for organizing accident causation knowledge and supporting maritime safety decision-making. However, existing construction methods based on deep learning rely heavily on large-scale annotated corpora, resulting in high costs and limited scalability. This study proposes a novel knowledge graph construction method based on ontology-constrained prompt engineering for large language models, eliminating the need for model fine-tuning. A ship collision accident ontology comprising 10 entity types and 8 relation types is established to define semantic boundaries, and a two-stage extraction strategy is designed for entity-attribute identification and triplet construction. By embedding the ontology into structured prompts, the framework achieves high-precision conversion from unstructured accident reports to structured knowledge using few-shot prompting with lightweight human verification. Experiments on 318 official Chinese investigation reports demonstrate that entity recognition attains an F1 score of 94.7%, while most relation types exceed 96% F1. Ablation studies confirm that ontology constraints yield a 105.8% improvement over the baseline, primarily by anchoring extraction to the predefined ontology schema and improving coverage and standardization. The resulting knowledge graph reveals characteristic patterns including elevated accident frequency during early morning hours, human factor dominance, and a strong association between management deficiencies and severe accident outcomes. These findings provide a low-cost, scalable knowledge foundation for maritime accident causation analysis and safety governance.
The blockchain and artificial intelligence (AI) utilized in electric vehicle (EV) industry have received increasing attention in recent decades. However, few studies have examined the impact of AI capability dynamics under a learning curve theory combined with blockchain, on emission reduction in a closed-loop EV supply chain. Therefore, this study builds a differential game model with the learning-curve-based dynamic AI intelligence upgrading, to explore three strategies, i.e., the AI with non-blockchain strategy, the AI with passive-cooperation blockchain strategy (reverse traceability only), and the AI with active-cooperation blockchain strategy (forward–reverse traceability). The analytic outcomes reveal that AI adoption for emission reduction is prioritized over blockchain traceability, and the higher AI conversion efficiency promote the adoption of blockchain. Additionally, we also find when the unit blockchain cost is smaller and the conversion efficiency is relatively higher, the supply chain prefers to the AI with active-cooperation blockchain strategy, but when the unit blockchain cost is higher, no matter what is the conversion efficiency, the supply chain turns to the AI with passive-cooperation blockchain strategy for emission reduction. Finally, we also address the cap-and-trade policy, the retail distribution model, two competitive supply chains and the nonlinear emission reduction impact in the extensions, which further proves the robustness of model.
Hexagonal boron nitride (h-BN) is an environmentally friendly lubricant that shows great potential in lubrication applications. However, there is currently a lack of research on the synergistic mechanisms of carboxylic acid-modified h-BN at different friction interfaces. In this study, the tribological mechanism of carboxylic acid-modified h-BN across various contact interfaces was investigated through experimental and molecular dynamics simulations. At the steel-steel contact, wear reductions relative to the base oil were 21.74% for unmodified h-BN, 58.94% for oleic acid (OA)-modified h-BN, and 55.80% for stearic acid (SA)-modified h-BN. At the alumina-steel contact, the corresponding wear reductions were 8.50%, 25.30%, and 23.54%, respectively. Additionally, at the alumina-steel contact, OA-modified and SA-modified h-BN reduced the coefficients of friction by 35.18% and 23.32%, respectively, compared with the base oil. Simulations revealed the adsorption mechanism of carboxylic acids: the carboxyl group anchors to the metal or oxide surfaces, while the alkyl end is compatible with the base oil. Among these, the conformational flexibility of the C=C bond in the OA molecule enables the synergistic anti-wear effect between OA and h-BN. This research outcome can guide the development of high-performance nano-lubricants suitable for multiple interfaces.
Nitrogen oxides (NOx) are among the major atmospheric pollutants, and selective catalytic reduction with ammonia (NH3-SCR) represents the state-of-the-art technology for NOx abatement. However, its reaction mechanism-particularly the existence of gas-phase radical intermediates and high-temperature side reaction pathways-remains debated. In this study, synchrotron-based vacuum ultraviolet photoionization mass spectrometry (SVUV-PIMS), combined with XPS and XRD techniques, was employed to systematically investigate the NH3-SCR reaction mechanisms over Cu-SSZ-13 and Fe-BEA catalysts. The results indicate that no gas-phase radical intermediates (e.g., NH2, HONO) were detected under any tested conditions (150-400 degrees C), confirming that the reaction is a surface-confined process. Over Cu-SSZ-13, the deposition of surface ammonium nitrate (NH4NO3) at low temperatures was observed to block pore channels, leading to a decrease in NO conversion. In contrast, Fe-BEA exhibited a pronounced N2O formation peak below 300 degrees C under Fast SCR conditions, with notable side reactions at elevated temperatures. By directly capturing the "absence" of transient species and the evolution of byproducts, this study elucidates the competitive mechanisms among active sites in zeolite catalysts with different topological structures.
Industry-university-research cooperation has emerged as the primary pathway for achieving green innovation breakthroughs in the new energy vehicle sector, particularly amid environmental uncertainty. However, research on how industry-university-research cooperation can resolve the “connected but not integrated” dilemma remains inadequate, particularly lacking an in-depth exploration of the paradoxical effects of meso-level motif structures and the nonlinear impacts of network capabilities. Based on network organization theory and knowledge-based theory, this study constructs a “Motif Network Structure-Capability-Performance” analytical framework. Using patent data from 803 Chinese listed new energy vehicle companies, the study combines colored network motif analysis with regression models to examine the underlying impact mechanisms through which the motif network structures and capabilities affect green innovation performance. The results indicate that: (1) motif structures exert paradoxical effects. Motifs centered on enterprises and characterized by star-like or chain-like connections positively promote green innovation performance by reducing coordination costs, whereas motifs characterized by homogeneous actor composition and arrow-shaped structures inhibit green innovation performance because of resource redundancy and elevated coordination costs; (2) the three network capabilities (knowledge acquisition, integration, and reconfiguration) exhibit U-shaped nonlinear effects characterized by a threshold effect and progressive threshold breakthrough. This research bridges the macro-micro divide in inter-organizational network studies through a meso-level motif perspective, extending network organization and knowledge-based theories to the context of industry-university-research cooperation. The conclusions provide both theoretical and practical guidance for enterprises and governments to optimize industry-university-research cooperation structures, enhance knowledge management capabilities, and develop differentiated collaborative strategies to advance green innovation.
