Evidence map›Paper›PMID 39922878›Full record

ArticleScientific reports2025

Performance evaluation and prediction of optimal operational conditions for a compact date seeds milling unit using feedforward neural networks.

Khaled Abdeen Mousa Ali, Changyou Li, Wang Han, Sali Issa, Mohamed Hamdy Eid, Samy F Mahmoud, Marwa Abd-Elnaby Mohammed

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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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

7 authors.

Khaled Abdeen Mousa AliCollege of Engineering, South China Agricultural University, Guangzhou, 510642, China.
Changyou LiCollege of Engineering, South China Agricultural University, Guangzhou, 510642, China. lichyx@scau.edu.cn.
Wang HanCollege of Engineering, South China Agricultural University, Guangzhou, 510642, China.
Sali IssaElectrical Information of Science and Technology, Hubei University of Education, Wuhan, China.
Mohamed Hamdy EidInstitute of Environmental Management, Faculty of Earth Science, University of Miskolc, Miskolc- Egyetemváros, 3515, Hungary. Mohamed.hemida@uni-miskolc.hu.
Samy F MahmoudDepartment of Biotechnology, College of Science, Taif University, Taif City, Saudi Arabia.
Marwa Abd-Elnaby MohammedAgricultural Engineering Research Institute (AENRI), Dokki, Giza, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Date seed grinding remains a significant challenge limiting the utilization of this valuable agricultural by-product." In this study, a compact date seeds grinding unit was designed, tested, and evaluated. The machine has two primary: a pair of toothed cylinders and a hammer mill. The machine's performance was assessed in terms of throughput, specific energy consumption, and mean particle size of the product. First, the cylindrical section was tested under various conditions, including cylinder rotational speed (150, 250, 350, and 450 rpm), feed gate opening size (30, 37.5, and 45 cm

Indexed as

Agricultural waste valorizationDate seed processingFeedforward neural networkMilling machine designSustainable resource utilization

Identifiers

PMID39922878
PMCPMC11807147

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Registered trials

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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.