A densely connected causal convolutional network separating past and future data for filling missing PM

Peng Yuan, Yiwen Jiao, Jiaxue Li, Yan Xia

Open access · goldAbstract read

In one paragraph

Article in Heliyon, 2024. 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

0.2field-weighted citation impact, top 56% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

4 authors at 1 institution in 1 country.

Peng YuanCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China.

Yiwen JiaoCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China.

Jiaxue LiCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China.

Yan XiaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China.

Hunan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Air pollution poses a significant threat to human health and the environment globally. Precise analysis and prediction of pollutant concentrations are essential for monitoring and managing air quality. However, reliable analysis and prediction require comprehensive and high-quality data, which is often compromised due to missing data during collection. Unfortunately, conventional methods for addressing missing data fall short of providing adequate solutions. The missing data for air quality indicators are commonly systematic, with all data points missing for extended periods. This makes it difficult to establish correlations and populate the missing data accurately. To address this problem, we propose a Densely Connected Causal Convolutional Network Separating Past and Future Data (DCCN-SPF), a deep learning-based model that fills in continuous missing PM

Indexed as

Air qualityDeep learningDensely connected causal convolutional networkMissing data fillingPM2.5

Identifiers

PMID38312707

DOI10.1016/j.heliyon.2024.e24738

PMCPMC10835227

OpenAlexW4390948557

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.