Evidence map›Paper›PMID 41286294›Full record

ArticleScientific reports2025

Evaluating digital technologies for architectural heritage preservation using interval-valued q-rung orthopair fuzzy aggregation operators.

Li Wang

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

1 author.

Li WangSchool of Design, Xi'an Technological University, Xi'an, 710021, Shaanxi, China. wltgzztjk24@163.com.

Funding

Supported by Shaanxi Social Science Foundation Project (Project No: 2019k043) Research on the on the Protection of red architectural Heritage in Yan'an. 2019k043
6 · The paper itself

Abstract

Digital technologies have made their presence in the field of architectural heritage as an essential tool to save most historical structures from wearing out, environmental damage, and urban transformation. The objective of this research is to investigate the effects of digital information technology on the preservation of architectural heritage with a novel integration of interval valued q-rung orthopair fuzzy weighted average (IVq-ROFWA) and interval valued q-rung orthopair fuzzy weighted geometric (IVq-ROFWG) operators, an approach that has not, to the best of current knowledge, been previously applied in this domain. The application of these more advanced fuzzy aggregation methods provides the benefit of incorporating uncertainty and imprecision in expert judgments to account for the indeterminate, implicit relationships between various preservation criteria, including data accuracy, integration with restoration efforts, and long-term data accessibility. Given five alternatives considered in terms of five attributes, the IVq-ROFWA method rated augmented reality as the highest. In contrast, the IVq-ROFWG method rated heritage building information modeling as the highest. The present research highlights another aspect of high relevance to decision-makers, as it provides valuable recommendations on selecting the most effective digital tools to preserve their heritage. The novelty of the study lies in the introduction of new types of IVq-ROFWA and IVq-ROFWG operators in architectural heritage conservation, which provide more robust, precise, and reliable decisions based on uncertainty.

Indexed as

Architectural heritage preservationDigital technologiesFuzzy decision-makingInterval valued q-rung orthopair fuzzyWeighted average and weighted geometric operator

Identifiers

PMID41286294
PMCPMC12644537

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.