Stationary Distribution of Molecules in NanoCommunication via Microtubules
Bibliographic record
Abstract
Nanocommunications are communication techniques used in nanonetworks. Molecular communication is a type of nanocommunication which uses molecules to encode messages. In this paper, we consider a hybrid of molecular communication using microtubules and free diffusion in a bounded channel and show that in the proposed scenario the molecules can be kept in a desired region. This is a property needed in several applications. We use a Markov model to analyze molecular propagation in the channel. The Markov model is an approximation for Brownian motion and jump diffusion process, the two processes which explain molecular movement in the considered scenario. The scenario in this paper considers a two dimensional channel. Future work can generalize the model to three dimensions. We solve the Markov model using a matrix analytic method, and find the stationary probability distribution for final position of molecules. We will show that the probability distribution of final position of molecules is mostly concentrated in the region desired to keep the molecules in. Finally, the model is used to investigate performance of the system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".