Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external data sources beyond their training sets and querying predefined knowledge bases to generate accurate, context-rich responses. Most RAG implementations use vector similarity searches, but the effectiveness of this approach and the representation of knowledge bases remain underexplored. Emerging research suggests knowledge graphs as a promising solution. Therefore, this paper presents StructuGraphRAG, which leverages document structures to inform the extraction process and constructs knowledge graphs to enhance RAG for social science research, specifically using NSDUH datasets. Our method parses document structures to extract entities and relationships, constructing comprehensive and relevant knowledge graphs. Experimental results show that StructuGraphRAG outperforms traditional RAG methods in accuracy, comprehensiveness, and contextual relevance. This approach provides a robust tool for social science researchers, facilitating precise analysis of social determinants of health and justice, and underscores the potential of structured document-informed knowledge graph construction in AI and social science research.

Vehicular Fog Computing is a promising paradigm to exploit the computational resources on vehicles for providing computing services. Meanwhile, the unique mobility patterns of vehicles at the urban signalized intersection can be used to improve the performance of Vehicular Fog. In this paper, we present an offloading scheme tailored to the signalized intersection, by taking the vehicles' mobility pattern at the intersection. Our offloading scheme addresses the conditions of deadlines and the cost of replications. Predicting mobility is a key factor in further calculating the connection time and the link bandwidth for finding appropriate service vehicles at intersections. The scheme achieves maximum offloading success rate under given deadline constraints and budgets. Our evaluation uses realistic traffic data generated by traffic simulator SUMO. The results are compared with a few benchmarks given multiple parameters. The proposed scheme can achieve a satisfactory offloading success rate compared with the best-case offloading scenario.

The paper introduces a deep reinforcement learning model for a special scenario in future smart transportation. The scenario describes a mobile edge computing platform hosted by a group of self-organized connected vehicles for sharing computation resources. The presented DQN model is to solve the trade-offs between the computing capability and the traffic state. Results show the existence of the trade-off and the need for future research in a few areas.

Flash floods are increasingly frequent and severe, yet standard risk communication messages are often too generic and lack actionable guidance, causing them to be ignored. This research aims to enhance flood risk communication by first, developing a robust flood ontology using a novel semi-automated approach, and second, demonstrating its potential as a semantic foundation for translating complex data into clear, personalized public alerts. We introduce a semi-automated, human-in-the-loop ontology engineering strategy that integrates expert-defined schemas with Large Language Model (LLM)-driven expansion and refinement from authoritative sources. Evaluation results are twofold: (1) Technical metrics confirm our LLM-constructed ontology achieves superior relationship richness and expressiveness compared with existing disaster ontologies. (2) A proof-of-concept case study demonstrates the ontology’s potential by showing how its specific classes and relations (e.g., ‘neededForElderly’ relation linking the class ‘SpecialConsideration’ to ‘ElderlyCommunityMember’) can be used to generate targeted advice like “check on elderly neighbors”, transforming a generic alert into a clear and actionable message. Consequently, this research delivers two key contributions: a replicable and domain-adaptable methodology for semi-automated ontology construction and a practical demonstration of how such an ontology can bridge the critical gap between flood data and public understanding, empowering communities to respond more effectively.