Optimization of electromagnetic wave focusing in heterogeneous biological tissue model
Bibliographic record
Abstract
The paper presents some optimization approaches to the electromagnetic wave focusing in heterogeneous biological tissue model. The model of human arm was used for investigation. The possibility of focusing of electromagnetic wave optimisation is strongly connected with investigation of electromagnetic wave propagation through complicated heterogeneous biological structure. Firstly we concentrated on the influence of particular structures thickness on scattering parameters value which gives us information about transmission and reflection of electromagnetic wave on interfaces of structures which differ by dielectric parameters. The next simulation showed the influence of water bolus placed on the arm model surface on values of SAR and consequently the possibility to avoid the overheating of upper region of arm model in the process of microwave hyperthermia. The next step connected with possible optimisation of electromagnetic wave focusing showed the influence of metamaterial structure placed in the front of microwave patch antenna used in role of microwave hyperthermia applicator on the electromagnetic wave focusing. The simulations were done for various numbers of metamaterial structures and various distances from arm model. Finally we simulated the focusing accuracy in the case when the combination of water bolus with metamaterial structure was used.
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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) | 0.001 | 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) | 0.001 | 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".