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Record W4285608046 · doi:10.53730/ijhs.v6ns4.9037

Vibrational characteristic of heart stent using finite element model

2022· article· en· W4285608046 on OpenAlexaff
Mehrshad Alizadeh, Mohammad Choulaei, Majid Roshanfar, Javad Dargahi

Bibliographic record

VenueInternational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsÉcole de Technologie SupérieureConcordia University
Fundersnot available
KeywordsFinite element methodDisplacement (psychology)Spectral densityStentModal analysisHarmonicAccelerationNormal modeKinematicsModalStructural engineeringVibrationAcousticsPhysicsMaterials scienceComputer scienceEngineeringSurgeryClassical mechanicsMedicineComposite material

Abstract

fetched live from OpenAlex

Heart stents are widely implemented for those patients who suffer from chronic heart diseases. The primary failure of this biomedical device is its collapse during the operation. The most common sources of this failure come from the nature of the stent material and surgery conditions. The focus of the paper is on the vibrational behavior of the integrated part of the artery and stent by simulating the operating condition using a finite element model. Modal analysis of the proposed model is performed to determine the natural frequencies and corresponding mode shapes of the system. In addition, harmonic analysis of the model is performed to derive the kinematic characteristics, including displacement, velocity, acceleration, and directional stresses, by considering the effect of blood pressure. Finally, the spectral analysis of the complex is applied to investigate the influence of random vibrational excitations on the system by using power spectral density (PSD) analysis.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.103
GPT teacher head0.412
Teacher spread0.309 · 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
Published2022
Admission routes1
Has abstractyes

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