The Development of a High-Static Low-Dynamic Cushion for a Seat Containing Large Amounts of Friction
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".