Evidence map›Paper›PMID 41724746›Full record

ArticleScientific reports2026

Research on the construction and dynamic adaptation algorithm of cognitive graph multimodal knowledge network for enterprise management communication.

Minghui Ma, Yaweng Wang, Wei Sun

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

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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

3 authors.

Minghui MaCollege of Culture and Tourism, Anhui Vocational and Technical College, Hefei, 230011, Anhui, China. kuaikuaihaoqilaiba@163.com.
Yaweng WangCollege of Culture and Tourism, Anhui Vocational and Technical College, Hefei, 230011, Anhui, China.
Wei SunState-Owned Assets Management Office, Anhui Vocational and Technical College, Hefei, 230011, Anhui, China.

Funding

Anhui Provincial Quality Engineering Project for Higher Education Institutions 2023jyxm1345
6 · The paper itself

Abstract

Modern enterprise management faces data from many sources and many types. The data are split, so decision speed and strategic collaboration drop, especially in fast-changing situations like supply chain breaks. Existing methods like fuzzy cognitive maps (FCMs) are static, so they do not handle these cases well, and they have limits in joining multimodal data. This study gives a method to build multimodal management knowledge networks with cognitive maps and a dynamic update algorithm. The method has three steps: first, align multimodal features with an enhanced ELMo model; next, build a quantum embedding model to help cross-domain generalization; then, use an event-driven weight update for real-time change. In a supply chain test, the method raises decision-making efficiency by 74.4%, reaches 92.1% accuracy in predicting chip-shortage risk paths, and raises supplier turnover by 77.1%.

Indexed as

Cognitive mapDecision efficiencyDynamic adaptation algorithmMultimodal fusionQuantum embeddingSupply chain collaboration

Identifiers

PMID41724746
PMCPMC13022080

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.