Modeling the Voltage Produced by Ultrasound in Seawater by Stochastic and Artificial Intelligence Methods.

Alina Bărbulescu, Cristian Ștefan Dumitriu

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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.

Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation.Materials (Basel, Switzerland) · 2022
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

2 authors.

Alina BărbulescuDepartment of Civil Engineering, Transylvania University of Brașov, 5 Turnului Str., 900152 Brasov, Romania.ORCID 0000-0001-9840-2443

Cristian Ștefan DumitriuDepartment of Installations for Constructions, Transylvania University of Brașov, 5 Turnului Str., 900152 Brasov, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Experiments have proved that an electrical signal appears in the ultrasonic cavitation field; its properties are influenced by the ultrasound frequency, the liquid type, and liquid characteristics such as density, viscosity, and surface tension. Still, the features of the signals are not entirely known. Therefore, we present the results on modeling the voltage collected in seawater, in ultrasound cavitation produced by a 20 kHz frequency generator, working at 80 W. Comparisons of the Box-Jenkins approaches, with artificial intelligence methods (GRNN) and hybrid (Wavelet-ARIMA and Wavelet-ANN) are provided, using different goodness of fit indicators. It is shown that the last approach gave the best model.

Indexed as

Artificial IntelligenceSeawaterIncidenceUltrasonographyautoregressive integrated moving average (ARIMA)cavitationGeneralized Regression Neural Network (GRNN)voltageWavelet-ARIMAwavelet-artificial neural network (ANN)

Identifiers

PMID35161834

DOI10.3390/s22031089

PMCPMC8839338

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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.