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Record W4396592713 · doi:10.3390/vibration7020020

The Development of a High-Static Low-Dynamic Cushion for a Seat Containing Large Amounts of Friction

2024· article· en· W4396592713 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueVibration · 2024
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCushionMaterials scienceComposite materialEngineeringStructural engineering

Abstract

fetched live from OpenAlex

Exposure to whole-body vibration (WBV) has been shown to result in lower-back pain, sciatica, and other forms of discomfort for operators of heavy equipment. While WBV is defined to be between 0.5 and 80 Hz, humans are most sensitive to vertical vibrations between 5 and 10 Hz. To reduce WBV exposure, a novel seat cushion is proposed that optimally tunes a High-Static Low-Dynamic (HSLD) stiffness isolator. Experimental and numerical results indicate that the cushion can drastically increase the size of the attenuation region compared to a stock foam cushion. When placed on top of a universal tractor seat, the cushion is capable of mitigating vibrations at frequencies higher than 1.1 Hz. For comparison, the universal tractor seat with a stock foam cushion isolates vibrations between 3.4 and 4.1 Hz, as well as frequencies larger than 4.8 Hz. Friction within the universal seat is accurately modeled using the Force Balance Friction Model (FBFM), and an analysis is conducted to show why friction hinders overall seat performance. Finally, the cushion is shown to be robust against changes in mass, assuming accurate tuning of the preload is possible.

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.

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: none
Teacher disagreement score0.916
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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