A finite element analysis-based study of the effects of biomechanical characteristics on instructional interventions in sports
Bibliographic record
Abstract
In this paper, finite element analysis is applied to the mechanical characterization of the foot.A finite element simulation model of the foot is constructed and its material properties are defined.Finite element analysis is applied to calculate the stresses on various tissues of the foot under different touchdown modes.Set up controlled experiments to verify the advantages of FEA technology in sports.The material property values of each tissue in the simulation model differed greatly, which was in line with the actual situation of biological tissues.In the 2 touchdown modes, the change curves of flexion and extension angles of the supporting foot were generally similar in the latter 75% of the supporting phase, and the differences were concentrated in the first 25%.The movement of the foot on the coronal plane showed a general tendency toward eversion.There were 2 peaks in the vertical ground reaction force variation in the heel-touch mode and only 1 peak in the non-heel-touch mode.The resistance impulse and power impulse ratios varied widely.The time of occurrence of the maximum contact stress on the talo-heel joint surface varied.P<0.05, the experimental group was better than the control group in terms of skill level, learning interest and initiative of the two groups of students after the experiment.The use of finite element analysis to assist physical education teaching can enhance students' enthusiasm and skill level.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".