Development of a near-field sensor to study the effect of glucose concentration
Bibliographic record
Abstract
In this study a near-field sensor design has been developed to study the effect of glucose concentration in a biological medium.The sensor is a combined emitter in the form of a symmetric dipole and an annular frame.The sensor is powered at the input of a symmetric dipole.This sensor was designed to maximize the amount of bound energy in its near zone.The near field of radiowave radiation should not experience absorption in the conducting medium and significant distortion due to small inhomogeneities because of its nature.Therefore, we studied the possibility of creating a sensor sensitive to changes in glucose concentration based on the near-field effect.A biological medium, such as a person's hand or wrist, is generally a conductive medium.The developed combined sensor showed its consistency in the study of solutions with different glucose contents in a numerical model.The research was based on experimental and theoretical studies, as well as on numerical modeling of the influence of the dielectric constant of biological materials (media) on the reflected signal of the sensor.To begin with, we modeled the sensor and tested its sensitivity on the blood layer with different values of glucose concentrations.Next, we carried out a detailed numerical study of the influence of all layers.The main components of the biological medium are blood, fat, muscles and bones.From the point of view of diagnosing glucose in the blood of a person, the most simple and convenient places for diagnosis are his limbs (arms and legs), in particular the wrist.In these limbs, the following layers can be conditionally distinguished: skin, which in turn is divided into epidermis and dermis (the main component of this layer); hypodermis, which forms the cell space, which includes fatty deposits and blood vessels; the next biological layer is the muscles that occupy most of the space of a human limb; in the center of all layers is a bone.Microwave diagnostics is based on establishing the relationship of changes in the dielectric constant of the medium and the parameters of the probing signal.In theoretical studies, the dielectric constant is usually approximated using the Debye model, or a slightly modified version of it -the Cole-Cole model.It was the latter model that made it possible to calculate the permittivity of the substances listed above in a wide frequency range.We independently calculated the dependences of the real part of the dielectric constant of blood, fat, muscle and bone on a frequency in the range from 10 MHz to 10 GHz.As one would expect, substances containing an aqueous solution (blood, muscles and skin) have a higher dielectric constant in a wide frequency band.The same feature is also characteristic of the imaginary part of the dielectric constant.The features of the near-field interaction of the sensor with various substances of the biological medium were studied by us based on the analysis of the behavior of the real part of the radiation power flux density (Poynting vector).The formation of such a flow is a characteristic feature of the interference interaction of overlapping evanescent fields, regardless of their origin (in this case, the opposing fields of the probe and the field reflected from the substance overlap).The calculation results allowed us to estimate the depth of radiation penetration into the studied sample of the substance.Another important parameter of the sensor is its coordination with the studied sample of biological substance, which means that all radiated energy must penetrate into the sample.Such a parameter is the voltage standing wave coefficient, and the resistance of the near-field source should be consistent with the resistance of human blood, since the change in its dielectric constant is the most important for our consideration.During the simulation, the thickness of the layer of blood
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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 | 0.001 |
| 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".