Hollow core negative curvature fiber based refractive index sensor design and investigation for tuberculosis monitoring
Bibliographic record
Abstract
Abstract A myriad of pensile but pertinent issues found in the optical fiber sensors can be sought resolution based on the antiresonant reflecting optical waveguide (ARROW) working principle. Due to its compact structure, the anti-resonance based sensor has several advantages such as high sensitivity response, low confinement loss, and high stability that make the sensor more effective for health monitoring. In this manuscript, an anti-resonance fiber sensor has been proposed for the detection of tuberculosis cells. An analytical structure has been explored to simulate the characteristics of the ARROW. For the suggested structure, the Finite Element Method (FEM) is used to conduct its numerical investigations. The proposed optical sensor working on the ARROW principle was implemented on the Comsol Multiphysics software. From the numerical analysis, it is noted that the designed sensor has reached around 99% sensitivity with negligible confinement loss and single modality due to the excellent light-guiding properties of the anti-resonance fiber. Besides, lots of optical parameters such as effective area, V-Parameter, spot-size along beam divergence have been calculated over the wide wavelength region. The achieved result indicates the various application’s suitability of Antiresonant Hollow-Core Fiber (ARHCF) as a tuberculosis sensor.
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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 | 0.000 |
| 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) | 0.000 | 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".