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Record W4402477406 · doi:10.11159/icceia24.121

Enhancing Nonlinear Solitary Wave Propagation Device using PLA Plate

2024· article· en· W4402477406 on OpenAlexvenueno aff
Bernardo Caicedo, Marı́a José Torres, Juan P. Villacreses, Fabricio Yèpez

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2024
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsNonlinear systemComputer scienceAcousticsMaterials sciencePhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

The following research complements the challenge of reducing plastic deformation when measuring the Young's modulus with the nonlinear solitary wave propagation device.Enhancing the device performance of measuring on soft soils was possible by adding plastic polylactide (PLA) plate in between the last sphere and surveyed medium, designed to dissipate the stresses generated in the contact point.The methodology employed involves a finite element model that represents in an axis-symmetric format the contact between the last sphere of the device and the study surface.Adding to this interaction the plate with different materials and thickness to identify the optimal plate configuration that maximizes stress dispersion without compromising the device's measurement capabilities.Upon selecting the PLA plate, extensive simulations were driven to evaluate the influence of the plate in the interaction.The outcomes for different soil configurations were unified into a coefficient, modifying the contact equation to represent the presence of the plate without adding extensive complexity to it.Results suggest that the alternative of incorporating a plate can significantly enhance the device ability to measure in soft soils, and reduce plastic deformations.The representation of the plate effect into a single coefficient within the contact equation allows this approach to further implemented in numerical and experimental validation on future works.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.731

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 head0.029
GPT teacher head0.249
Teacher spread0.221 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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