A content-aware corpus-based model for analysis of marine accidents

Could vehicles analyze driving risks using human fuzzy semantic logic? A data-knowledge-driven new perspective

Accurate risk identification is crucial for ensuring the safe operation of Host vehicles (HoVs) in environments shared with Neighboring vehicles (NeVs). Traditional risk identification mechanisms typically rely on large amounts of precise numerical data, making it difficult to comprehensively and accurately identify traffic crash risks under conditions of imperfect data associated with fuzzy information. However, human drivers rely on knowledge-driven, subjective assessments using fuzzy descriptors like distance and speed semantics to evaluate driving risk. These insights provide significant value for addressing the limitations of precise data-driven methods. This study proposes a novel traffic crash risk analysis framework called Token Tree Generation and Parsing (TTGP). It integrates knowledge-driven insights from human drivers with data-driven methods. TTGP includes the Token Tree Generation Module (Module 1) and the Token Tree Parsing Module (Module 2). In Module 1, we apply the token-tree-of-thoughts method to transform natural language traffic regulations and vehicles' traffic behaviors and attribute parameters into token tree based on semantic rules. This module simulates the generation of human fuzzy semantics in traffic scenarios. In Module 2, we integrate three encoders and decoders to extract traffic crash risk semantic features and identify traffic crash risk level from the digitized token tree. Experiments in the highway and urban expressway interweaving areas demonstrate that TTGP can accurately analyze risk using imprecise data. The TTGP performs better than traditional methods such as Tree, Naïve Bayes, RUSBoost and Efficient Logistic Regression models. This study significantly enhances the flexibility, generalization, and reliability of risk assessment. It bridges the gap in how HoVs handle fuzzy information in risk analysis.

An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models

Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data. The system employs a multi-layer architecture implemented through multiple LLMs, enabling not only rapid and effective processing of multi-source sensor data but also enhanced environmental perception through physical interactions. By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. This study established a comprehensive experimental framework integrating numerical simulation, scenario simulation, and real-world testing to evaluate the system through embodied intelligence. Experimental results demonstrate that the system effectively processes sensor data and exhibits rapid, efficient safety assessment capabilities during embodied interactions, offering an innovative solution for coal mine safety.

Digitalization and innovation in green ports: A review of current issues, contributions and the way forward in promoting sustainable ports and maritime logistics

As a fundamental transportation mode, maritime logistics has become an indispensable component on a global scale. However, there are multiple drawbacks associated with ports operating in traditional ways, such as higher cost, lower efficiency and generating more environmental pollution. Digital technologies have been researched and implemented gradually in green ports, especially in data collection and real-time monitoring, and these advances help to promote higher work efficiency and reduce detrimental environmental impacts. It was found that green ports (e.g. ports of Raffina, Los Angeles, and Long Beach) generally perform better in energy conservation and pollutant emission reduction. However, considering the variability in the level of digitalization, there are challenges in achieving effective communications between individual ports. Therefore, to optimize and update green port practices, a systematic review is necessary to comprehensively analyze the beneficial contributions of green ports. This review adopted bibliometric analysis to examine the shipping framework focusing on green ports digitalization and innovation. After that, with regards to the bibliometric results, five aspects were analyzed, including environment, performance, policy, technology, and management. Besides, intelligent life-cycle management was systematically discussed to improve green ports and maritime logistics performance and sustainability in three aspects, namely waste discharge, shipping management system and green ports management. The findings revealed that green ports and maritime logistics require digital cooperation, transformation, and management to achieve sustainable development goals, including route selection and control of ships' numbers, weather prediction, and navigational effluent monitoring, albeit with some obstacles.