arXiv:2608. 14746v1 Announce Type: new Abstract: The aviation industry characterized by its stringent safety standards has seen a growing need for innovative approaches to enhance safety measures.
By Aziida Nanyonga
The study investigates whether a model trained on construction‑sector occupational accident narratives can accurately classify accident‑process roles in other sectors and reporting environments. Using 42,244 factual units from 6,040 construction narratives, the authors compared TF‑IDF, frozen pretrained representations, and task‑adapted pretrained models, achieving up to 85.7% balanced accuracy without retraining. The models performed consistently across metallurgy, chemistry‑plastics, and an independent company corpus, though performance varied more on the latter due to differing reporting practices.
By Aho Yapi, Pierre Latouche, Arnaud Guillin, Yan Bailly
The study evaluates how well accident‑process role classifiers trained on construction‑sector French occupational accident narratives generalize to other sectors. An expert‑annotated corpus classifies factual units into four roles—work situation, unfavourable condition, accident event, and consequence—and the classifiers are tested on unseen metallurgy, chemistry‑plastics, and company corpora without retraining. Task‑specific adaptation consistently outperforms frozen representations, achieving balanced accuracies of 85.6%–85.8% across the target domains.
By Aho Yapi, Pierre Latouche, Arnaud Guillin, Yan Bailly
arXiv:2609.15997v1 Announce Type: cross
Abstract: Improving safety at intersections requires identifying crash mechanisms and recommending appropriate countermeasures. However, this process tradition...
By Abu Saif Md Nasim Uddin, Mohamed Abdel-Aty, Zubayer Islam, Parvez Anowar, Chenzhu Wang
arXiv:2608. 04697v1 Announce Type: new Abstract: Operational hazard analysis of aviation system operations must consider interactions among weather, ATC actions, airspace constraints, aircraft operations, and human factors - distinct from the functional hazard assessment applied at the aircraft-system level.
By Cristian Mascia, Roberto Pietrantuono, Daniel Rodriguez, Stefano Russo
The paper presents a decision‑aware framework for predicting dementia‑related crash severity that emphasizes auditability and selective deferral. Using 4,781 Texas crash records, the authors evaluate several models—including structured, narrative, fusion, calibrated fusion, BERT‑family, and local large‑language‑model baselines—under a stratified 70/15/15 split. The leakage‑controlled Gemma model achieves the highest macro‑F1 of 0.545, while a calibrated fusion model reaches 0.522 macro‑F1 with an expected calibration error of 0.033; selective deferral further improves performance, raising macro‑F1 to 0.573 at 70% coverage and reducing severity cost to 0.577.
By Gaurab Chhetri, Anika Baitullah, Subasish Das