Optimal Management of Rechargeable Biosensors in Temperature-Sensitive Environments
Bibliographic record
Abstract
Biological sensors (biosensors, for short) are tiny wireless devices attached or implanted into the body of a human or animal to monitor and detect abnormalities and then relay data to physician or provide therapy on the spot. They are distinguished from conventional sensors by their biologically derived sensing elements and by being temperature constrained. Biosensors generate heat when they transmit their measurements and when they are recharged by electromagnetic energy. The generated heat translates to a temperature increase in the tissues surrounding the biosensors. If the temperature increase exceeds a certain threshold, the tissues might be damaged. In this paper, we discuss the problem of finding an optimal policy for operating a rechargeable biosensor inside a temperature-sensitive environment characterized by a strict maximum temperature increase constraint. This problem can be formulated as a Markov Decision Process (MDP) and solved to obtain the optimal policy which maximizes the average number of samples that can be generated by the biosensor while observing the constraint on the maximum safe temperature level. In order to handle large-size MDP models, it is shown how operating policies can be obtained using Q-learning and heuristics. Numerical and simulation results demonstrating the performance of the different policies are 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".