Evidence map›Paper›PMID 41559286›Full record

ArticleScientific reports2026

Innovative impact of artificial intelligence and management in rural revitalization under fuzzy based decision-making approach.

Minyan Wang, Hua Zhang, Han Zhao

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

3 authors.

Minyan WangDepartment of Intellectual Property, College of Humanities and Social Sciences, Qingdao Agricultural University, Qingdao, 266100, China.
Hua ZhangCollege of Humanities and Social Sciences, Qingdao Agricultural University, Qingdao, 266100, China. zhangh202504@163.com.
Han ZhaoZhaoyuan Municipal Real-Name System Management Service Center for Institutional Establishment, Zhaoyuan, 265400, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of Artificial Intelligence (AI) and modern management strategies plays a transformative role in rural revitalization, helping rural regions shift from traditional, resource-based economies to innovation-driven and sustainable development models. Decision-making plays an efficient role in rural revitalization in the process of planning, evaluating, and implementing development strategies. In this article, we discuss a novel decision-making approach to the measurement of alternatives and ranking according to the compromise solution (MARCOS) method to investigate a reliable ranking of the alternatives under different conflicting criteria. For this purpose, a pythagorean fuzzy set (PyFS) is modified to handle vague type information using some robust mathematical methodologies and operational laws. The discussed decision-making model of the MARCOS method applies to resolve a group of experts’ judgments under the multi-attribute group decision-making (MAGDM) problem. The suggested PyF-MARCOS framework would offer a viable decision-support framework to rural policymakers and planners through a systematic evaluation of strategies of AI-based development in the context of uncertainty and conflicting criteria. The model facilitates the socially inclusive, economically sustainable, and sustainable rural revitalization decisions through the transparent comparison of alternatives and expert hesitation which is incorporated in a systematic process.

Indexed as

Artificial intelligenceData aggregationDecision-making models and the MARCOS methodPythagorean fuzzy information

Identifiers

PMID41559286
PMCPMC12887033

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Registered trials

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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.