Numerical Simulation Study of Temperature Distribution in Bio-tissue Generated by Different Power Pulse Laser
Bibliographic record
Abstract
Laser is widely applied in military and medicine fields because of its excellent capability. In order to effectively defend excess damage by laser, the thermal processing theory of skin tissue generated by laser should be carried out. The mathematics model of bio-tissue heat transfer that is irradiated by laser is analyzed. And boundary conditions of bio-tissue are discussed. Three layer FEM grid model of bio-tissue is established. The solving flow of temperature in tissue is given by adopting ANSYS software. The temperature distribution characteristic of skin tissue generated by different power pulse laser is numerical simulated. The different times temperature change trend in different radial length and axis depth skin tissue is educed. The results show that the biggest temperature ascend in bio-tissue is increasing geometry doubly with the laser power increase geometry doubly. When the conditions are the same, the area of temperature ascend in bio-tissue doesn't change with laser power changing. The acuity degree of temperature change on the surface layer bio-tissue increase with laser power increasing. But the acuity degree of temperature change inner bio-tissue is less. The temperature in bio-tissue assume linearity trend with axis depth increasing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".