Effects of electric and magnetic loadings on bone surface remodeling: a model modification and simulation
Bibliographic record
Abstract
This paper presents a new modification to the previous model of bone surface remodeling under electric and magnetic loadings. For this study, the thermo-electro-magneto-elastic model of bone surface remodeling is used. This model is modified by considering an important phenomenon occurring in living bone through its adaptation to external loadings called desensitization. In fact, bone cells lose their responsiveness and sensitivity to long-term external loadings, i.e., they become desensitized. Therefore, bone cells need a recovery period, during which they become resensitized. In this work, this phenomenon is considered in the original model. The effects of various electric and magnetic loading conditions, including various frequencies, waveforms and pulse duty cycles, are explored on the modified model and compared to the original model. The modified model is also searched for the optimal frequency and duty cycle, to obtain the best bone growth response under electromagnetic fields. The results of this paper show that the modified model is consistent with experimental observations. In addition, it is indicated that this modified model in contrast to the original model, is sensitive to frequency. It is shown that the optimal frequency of loading for the modified model is 1 Hertz (Hz), and the pulse duty cycles up to 50% are sufficient for bone remodeling to reach its maximum value.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".