Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS) is a method for stimulating human tissue especially neural tissue. An electric current in the stimulation coil produces a magnetic field, and a changing magnetic field induces a flow of electric current in nearby human tissue. TMS follows this fundamental principle and is well established in physiology, brain mapping and therapeutic applications.The simplest TMS coil is a circle, 5 to 15 cm in outer diameter that includes 5 to 50 turns. TMS coil windings are usually made of insulated copper wire. Rapid TMS with conventional coils quickly results in heat especially for prolonged high speed stimulation. This is due to high frequency current that tends to the outside of the wire bundle and increases the eddy currents inside the coil. This effect reduces the effective cross-sectional area of the coil, increasing the coil resistance, energy loss and heating. Coil cooling systems are generally required in such instances. Cooling system adds substantial weight and bulk. This has led to more elaborate and expensive designs using multi-stranded magnet wires.Previous research on TMS coil investigated coil arrays, and winding methods; these studies show interesting results based on numerical computations. However, the instrumentation challenge is in the details; it is not clear, what type of wire is exactly the best choice for TMS coil, and what type of winding results in a better structure for stimulation.In this paper, effects of wire and winding on TMS coil are studied. Round, square and rectangular magnet wires as well as braided and compacted Litz wires are compared. Horizontal- and vertical-spiral windings are compared. Results of a simulation study using finite element method are presented. Results show coil efficiency could be improved by the choice of wire and winding method. At last, a set of standards for wire, winding and inner diameter is presented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".