Evidence map›Paper›PMID 42178373›Full record

ArticleScientific reports2026

Robust graph-transformer soft sensor for radar level estimation in dynamic LPG storage systems.

Songqiao Bai, Shidong Fan, Pengcheng Wu, Bin Wang

Abstract read
PubMed Full text at PMC Publisher
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

4 authors.

Songqiao BaiSchool of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, China.
Shidong FanSchool of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, China.
Pengcheng WuCSSC Seago System Technology Co., Ltd, Shanghai, China.
Bin WangSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China. wang_bin@whut.edu.cn.

Funding

the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation GZC20250183the Special Program of Ministry of industry and Information Technology of the People's Republic of China DZBM2024-31-0022
6 · The paper itself

Abstract

Accurate liquid-level monitoring is vital for the safe operation of Liquefied Petroleum Gas (LPG) carriers. Conventional radar gauges are expensive and vulnerable to performance degradation under dynamic conditions. This paper proposes a Robust Graph Transformer Networks (RGTNs)-based Soft Sensor to enhance reliability and accuracy in radar level estimation. The RGTNs integrates a graph attention network for spatial dependency modeling and a Transformer for temporal feature extraction, enabling effective spatiotemporal fusion. Real-world experiments on an LPG carrier demonstrate that RGTNs achieves superior performance, with an R

Indexed as

Graph neural networksLPG carrierRadar level monitoringSoft sensingTransformer architecture

Identifiers

PMID42178373
PMCPMC13429690

What OpenQuestion holds

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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.