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Design of a 5-Gauss line system to warn persons with medical implants from critical EM field

2023· article· en· W4391557979 on OpenAlexaff
Yosra Ben Fadhel, Sana Ktata, Kouala Yefrni, S. Rahmani, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical and Bioimpedance Tomography
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsGaussMagnetic fieldLine (geometry)Field (mathematics)Electromagnetic fieldMagnetic resonance imagingElectrical engineeringComputer sciencePhysicsBiomedical engineeringEngineeringMedicineMathematicsRadiology

Abstract

fetched live from OpenAlex

In some medical magnetic resonant imaging environment with a high magnetic field, there is no protection system that detects the 5-Gauss line. This region presents a serious risk zone for both the patient and the implanted device. Frequently, patients having an active medical implant like a pacemaker can cross these areas without taking into consideration that there is a harmful magnetic field. Consequently, health complications may occur. In order to overcome these issues and prevent people with pacemakers from crossing this space, we have proposed a 5-Gauss magnetic field measuring device. It detects and measures the value of the magnetic field present accompanied by an audible signal of the critical line. In our case it is equal to or greater than 5-Gauss (0.5mT). The system measures the magnetic field detected by the Hall effect SS495A sensor that is controlled by an Arduino Uno card. We performed a validation of the 5-Gauss line detection system in the medical magnetic resonant imaging environment of the military hospital of Tunis. The obtained results are very satisfactory, since the system ensures the required function.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.240
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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