An EFE Model on Skin-Sleeve Interactions During Arm Rotation
Bibliographic record
Abstract
Skin and garment constitute a dynamic contact system for human body comfort and protection. Although dermatological injuries due to fabric actions during human body movement are common, there is still no general guidance or standard for measuring or evaluating skin/garment contact interactions, especially, during intense sports. A three-dimensional explicit finite element (EFE) model combined with Augmented Lagrange algorithm (ALA) is developed to simulate interactions between skin and fabric during rotation of the arm. Normalized effective shear stresses at the interface between skin and the sleeve during the arm rotation are provided to reflect the severity of the interactions. The effects due to changes in fabric properties, fabric-skin gap, and arm rotation rate are also illustrated. It has been demonstrated from our predictions that factors such as elastic modulus, friction coefficients, density of fabric, and the initial gap between skin and fabric influence significantly the shear stress and thus the discomfort and even injury potential to skin during intensive body movement such as sports and military. Thus this study for the first time confirms quantitatively that poorly chosen fabric with inappropriate garment design renders adverse actions on human skin.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".