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Record W2976906606 · doi:10.21595/vp.2019.20981

Cell dynamics and natural frequencies: scaling and shape matters

2019· article· en· W2976906606 on OpenAlexaff
C. V. Chandrashekara, Ayman Ahmed Mubeen, Arun Barathwaj R, Amith Basavaraju, M C Srividyadhare Kateel

Bibliographic record

VenueVibroengineering PROCEDIA · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsScalingNatural frequencyFinite element methodStatistical physicsNormal modeFrequency scalingMode (computer interface)Field (mathematics)MechanicsDynamics (music)PhysicsMathematicsComputer scienceStructural engineeringEngineeringGeometryAcousticsVibration

Abstract

fetched live from OpenAlex

Cell dynamics is one of the most investigated areas under the field of biomechanics. Scaling plays a crucial role in determining the dynamic characteristics of biological systems. In the present investigation, cuboidal and cylindrical models of different scales are analysed to depict the dependence of natural frequency on the scaling factors. Three approaches, namely lumped mass system, finite element model (FEM) and numerical approach using Ansys are used to determine the fundamental natural frequencies. Different scaling factors, namely nano, micro (original size), millimeter and meter are considered for the analysis. The results indicate that the natural frequency varies inversely with the size of the cell by the same factor. A simple mathematical relation is derived to support this claim. Results are illustrated with numerical examples and corresponding mode shapes are reported.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.002
GPT teacher head0.174
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2019
Admission routes1
Has abstractyes

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