Evidence map›Paper›PMID 37609400›Full record

ArticleHeliyon2023

ResNet14Attention network for identifying the titration end-point of potassium dichromate.

Siwen Liang, Linfei Yin, Dashui Zhang, Dongwei Su, Hui-Ying Qu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 59% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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 at 1 institution in 1 country.

Siwen LiangGuangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning, Guangxi, 530004, China.
Linfei YinGuangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning, Guangxi, 530004, China.
Dashui ZhangSchool of Chemistry and Chemical Engineering, Nanning University, Nanning, Guangxi, 530004, China.
Dongwei SuGuangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning, Guangxi, 530004, China.
Hui-Ying QuSchool of Chemistry and Chemical Engineering, Nanning University, Nanning, Guangxi, 530004, China.
Guangxi University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of industry, the increasing discharge of sewage causes the detection of water quality to be of increasing importance. Potassium dichromate titration is one of the most important testing methods in water quality detection; the ability to accurately identify the titration end-point of potassium dichromate is currently a research challenge. To identify titration end-point quickly and accurately, this study proposes a ResNet14Attention network, which utilizes residual modules that focus on original image information and an attention mechanism that focuses highly on classification targets. The proposed ResNet14Attention network is compared with 12 convolutional neural networks such as ResNet series networks, VGG, and GoogLeNet. The results of comparison experiments reveal that only the proposed ResNet14Attention network has the highest training and testing accuracy of 100% among all convolutional neural networks in the comparison experiment; the proposed ResNet14Attention network has the highest training speed compared to all the networks that over 90% accuracy.

Indexed as

AttentionDeep learningPotassium dichromate methodResNetTitration end-point

Identifiers

PMID37609400
PMCPMC10440524
OpenAlexW4385613162

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.