Evidence map›Paper›PMID 41034369›Full record

ArticleScientific reports2025

Enhanced deep learning model for predicting hydraulic performance in recycled porous pipe irrigation systems.

Mohamed Ahmed Moustafa, Ahmed Amin, Zaharaddeen Aminu Bello, Khaled A M Ali, Hassan A A Sayed, Yasser Kamal Osman, Sherouk Hassan, Muhammad Aurangzaib, Mostafa H Fayed

Abstract read
PubMed Full text at PMC Publisher
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  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

9 authors.

Mohamed Ahmed MoustafaFaculty of Agricultural Engineering, Al-Azhar University, Cairo, 11751, Egypt. mohamedahmed.17@azhar.edu.eg.
Ahmed AminCollege of Engineering, Nanjing Agricultural University, Nanjing, 210031, China.
Zaharaddeen Aminu BelloCollege of Engineering, Nanjing Agricultural University, Nanjing, 210031, China.
Khaled A M AliFaculty of Agricultural Engineering, Al-Azhar University, Cairo, 11751, Egypt.
Hassan A A SayedFaculty of Agricultural Engineering, Al-Azhar University, Cairo, 11751, Egypt.
Yasser Kamal OsmanFaculty of Agricultural Engineering, Al-Azhar University, Cairo, 11751, Egypt.
Sherouk HassanCollege of Economics and Managment, Nanjing Agricultural University, Nanjing, 210095, China.
Muhammad AurangzaibCollege of Engineering, Nanjing Agricultural University, Nanjing, 210031, China.
Mostafa H FayedFaculty of Agricultural Engineering, Al-Azhar University, Cairo, 11751, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluates the hydraulic performance of irrigation systems using recycled porous pipes and the predictive modeling capabilities of deep learning algorithms for discharge rates, comparing Type A (recycled rubber-polyethylene blend) and Type B (recycled rubber only). Laboratory experiments measured discharge rates, coefficient of variation (CV), and emission uniformity (EU) across pressures (20-80 kPa) and pipe lengths (3-9 m). Results showed strong discharge-pressure correlations (R

Indexed as

Deep learningHydraulic evaluationRecycled porous pipesSustainable irrigationWater conservation

Identifiers

PMID41034369
PMCPMC12489072

What OpenQuestion holds

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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.