Evidence map›Paper›PMID 42086653›Full record

ArticleScientific reports2026

VR-H2Safe: a VR-based hydrogen vehicle safety & efficiency optimization platform with V2X integration.

Mohammad BaniSalman, Mohammad Aljaidi, Ahmad Abdullah Alshammari, Ali Fayez Alkoradees, Samy Abdelwahab Safaan, Mohammad Khishe, Elham Askari

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Mohammad BaniSalmanDepartment of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, 13110, Jordan.
Mohammad AljaidiDepartment of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, 13110, Jordan.
Ahmad Abdullah AlshammariDepartment of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Rafha, 91911, Saudi Arabia.
Ali Fayez AlkoradeesComputer Sciences and Educational Technologies, Applied College, Qassim University, Buraidah, Saudi Arabia.
Samy Abdelwahab SafaanComputer Sciences and Educational Technologies, Applied College, Qassim University, Buraidah, Saudi Arabia.
Mohammad KhisheApplied Science Research Center, Applied Science Private University, Amman, Jordan.
Elham AskariDepartment of Computer Engineering, FSh.C., Islamic Azad University, Fouman, Iran. askary.elham@iau.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hydrogen vehicles are facing significant challenges, including safety risks in the event of leakage, high costs associated with physical testing, and fuel cell inefficiency under dynamic conditions; to address these concerns, we propose VR-H2Safe, a cyber-physical platform that combines high-fidelity simulation, physics-informed reinforcement learning (RL), and secure vehicle-to-everything (V2X) communication. Using ANSYS Fluent Large Eddy Simulation (LES) to train real-time predictive models, the platform's digital twin simulates hydrogen leakage with 92% spatio-temporal correlation compared to high-fidelity LES-CFD benchmarks at key monitoring points. Furthermore, we introduce the H2-LEAK protocol, which extends the SAE J2735 standard with a 128-bit risk vector and blockchain authentication, which provides hazard warnings in less than 2 s with 95% spoofing resistance. In addition, our physics-informed AI system optimizes the fuel cell performance, achieving a 10% increase in energy efficiency and a 58% reduction in thermal violations during hardware verification. The experimental findings of urban, tunnel, and road conditions prove that VR-H2Safe accelerates the process of safety verification and reduces the engineering validation phase from 14 to 10 weeks.

Indexed as

BlockchainCyber-physical systemsDigital twinHydrogen vehicle safetyPhysics-informed machine learningV2X communication

Identifiers

PMID42086653
PMCPMC13333926

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.