Evidence map›Paper›PMID 34984622›Full record

ArticleEnvironmental science and pollution research international2022

Optimized neural network to predict the experimental minimum period of coal spontaneous combustion.

Yang Xiao, Yong Cao, Kai-Qi Zhong, Lan Yin, Jun Deng

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2022. 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

5 authors.

Yang XiaoSchool of Safety Science and Engineering, Xi'an University of Science and Technology, 58, Yanta Mid. Rd., Xi'an, Shaanxi, 710054, People's Republic of China. xiaoy@xust.edu.cn.ORCID http://orcid.org/0000-0002-3960-9708
Yong CaoSchool of Safety Science and Engineering, Xi'an University of Science and Technology, 58, Yanta Mid. Rd., Xi'an, Shaanxi, 710054, People's Republic of China.
Kai-Qi ZhongSchool of Safety Science and Engineering, Xi'an University of Science and Technology, 58, Yanta Mid. Rd., Xi'an, Shaanxi, 710054, People's Republic of China.
Lan YinSchool of Safety Science and Engineering, Xi'an University of Science and Technology, 58, Yanta Mid. Rd., Xi'an, Shaanxi, 710054, People's Republic of China.
Jun DengSchool of Safety Science and Engineering, Xi'an University of Science and Technology, 58, Yanta Mid. Rd., Xi'an, Shaanxi, 710054, People's Republic of China.

Funding

National Natural Science Foundation of China 51974233
6 · The paper itself

Abstract

The harmful gases produced from coal spontaneous combustion (CSC) can cause the environmental pollution. Being able to predict the experimental minimum period of CSC (EMPCSC) is essential in controlling CSC and effectively reducing harmful gas emissions. To obtain high prediction accuracy, we used three optimization algorithms, namely the genetic algorithm (GA), ant colony algorithm (ACO), and particle swarm optimization algorithm (PSO), to optimize the backpropagation neural network (BPNN). R

Indexed as

CoalSpontaneous CombustionAlgorithmsChinaNeural Networks, ComputerCoalAnt colony algorithmCoal spontaneous combustionGenetic algorithmHarmful gasParticle swarm optimization

Identifiers

PMID34984622

What OpenQuestion holds

Textmetadata
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.