Impact of pattern lines and technological features on the behaviourof vamp-over-quarter footwear type
Bibliographic record
Abstract
This article aims to demonstrate how the positioning of the seam line between the vamp and quarter and the number of stitches affect the joint strength and overall performance of the product during walking. The configuration of analysis conditions and constraints was conducted using ANSYSTM. The gait biomechanics were considered to establish the load model, including the distribution of forces, their magnitudes, and constraints. The analysis focused on the three phases of gait: heel strike, mid-stance, and push-off, evaluating three key parameters: directional displacement, Von Mises stress, and elastic deformation. The study emphasises how directional displacement, stress distribution, and elastic deformation change based on the gait phase and the materials used in the construction of the selected footwear type. Using a two-stitch seam to join the components promotes directional displacements and reduces stress/tension compared to a single-stitch seam. Positioning the seam line between the vamp and quarters along the toe line increases stress/tension in the front section of the shoe. The analysis was validated by comparing simulation results with average plantar pressures obtained from a biomechanical assessment of plantar pressure distribution.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".