Rolling optimization for multi-ship coordinated collision avoidance remains challenging under dynamic risk coupling. To address this problem, a rolling optimization mechanism for multi-ship coordinated collision avoidance driven by dynamic integrated risk is proposed in this paper. First, a stage-adaptive DDV/TDV risk model quantifies spatio-temporal emergency to construct risk matrices, thereby isolating local conflict clusters through spectral clustering. Within each cluster, collision-avoidance priorities are uniquely determined by synthesizing COLREGs-based responsibilities, individual risk contributions, and temporal urgency. On this basis, differential evolution is embedded in a rolling decision-making loop, where risk relations are recalculated, conflict clusters are re-identified, and maneuvering plans are updated until no triggered ship pair remains. Simulation results demonstrate that the proposed rolling optimization mechanism effectively decomposes coupled conflicts, dynamically updates residual risk relations, and supports coordinated decision-making with reduced risk exposure and heading alterations.
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.
Building on our previous work in [1], this paper establishes the local well-posedness of the Schr & ouml;dinger-KdV system in H-3/16 & times; H-3/4 for the resonant case. Combining this result with those in [1], we obtain local well-posedness for the parameter range max{-3/4, s1-3} <= s2 <= min{4s1, s1 + 2}, and this range is sharp in the sense of the contraction mapping argument.
Plant extracts are commonly incorporated into the processing of agricultural products to exert preservative effects. However, the molecular mechanisms underlying their decay-inhibiting effects remain poorly understood. Identifying plant extracts with potent anti-spoilage activity is especially crucial for mung bean sprouts (Vigna radiata), which are highly susceptible to rapid decay after harvest. In this study, we comparatively evaluated the efficacy of five extracts (mango seed MS, loquat leaf LL, rosemary R, horsetail H, mate tea MT) on preserving sprouts quality. LL extract achieved optimal quality retention, whereas R accelerated spoilage; MS, H, and MT produced intermediate effects. Integrated transcriptomic and metabolomic analyses revealed that LL extract maintained quality by: (i) reinforcing membrane integrity through the activation of antioxidant system and the suppression of lipid degradation; (ii) enhancing biosynthesis of phenylpropanoids and alkaloids, particularly methyl 4-hydroxycinnamate, naringenin and securinine, which inhibited enzymatic browning and microbial proliferation; and (iii) promoting fructose accumulation while downregulating genes associated with cell wall degradation, thereby delaying tissue softening and water loss. In contrast, R extract depleted these protective metabolites, exacerbated oxidative damage and browning. Notably, securinine, an endogenous metabolite sustained by LL, suppressed the growth of spoilage microorganisms and delayed sprout deterioration. Our work provides insights into plant extract-mediated postharvest quality maintenance and highlights securinine as a promising candidate for developing effective strategies to suppress decay in highly perishable vegetables.
Abstract Accurate identification of ship maneuvering model parameters plays a critical role in ship motion control, navigation safety, and intelligent decision-making. However, the nonlinear and strongly coupled characteristics of ship dynamics render traditional identification methods difficult to apply under limited or noisy data conditions. To address these challenges, a self-adaptive physics-informed neural network (PINN) framework is proposed in this study, in which the governing equations of ship motion are coupled to formulate a multidimensional loss function and a scalable hyperparameter is embedded into the activation function to dynamically adjust the loss function landscape during training, thereby enhancing the prediction accuracy and convergence speed of ship maneuvering performance. The method is validated through parameter identification experiments using ± 20 ° zigzag maneuver simulation data of a Mariner-class vessel. The proposed adaptive-activation PINN method significantly reduces the identification errors of several key maneuvering parameters under limited sampling and noise-contaminated conditions, demonstrating higher accuracy and robustness. This study can provide dynamic support for intelligent navigation decision-making and optimal control, which is conducive to achieving high-precision control and decision optimization for autonomous ships in complex sea conditions.
