A multi-criteria decision analysis of implanted biomedical device antenna: electro-thermal simulation, design, and data analysis
Bibliographic record
Abstract
In this article, three elemental (dipole, loop, and meander) designs of an implanted biomedical device (IBD) antenna are studied to provide an enhanced understanding of fundamental (material and geometry) factors which would contribute to the regulation of a steady vital sign, with minimal effect on the surrounding tissue during monitoring and treatment. Dipole and Loop antennas are known to offer similar advantages in that they are both balanced antennas, while the Meander antenna is likely to offer better radiation resistance. The investigation was carried out through the design of experiments and finite element modeling of the temperature field due to the AC level applied (10 or 100mA), under the geometry of each given antenna type and three material options (Titanium, Cobalt Chromium, and Macor). Finally, a multi-criteria decision-making process is applied to select the optimal design under maximum field temperature, thermal conductivity, electrical conductivity, density, and specific heat attributes. The findings suggested that the Meander design made of Titanium would allow for a maximized overall performance with minimal thermal and radiation effects.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".