Bibliographic record
Abstract
Nanomachines promise to enable new medical applications, including drug delivery and real time chemical reactions' detection inside the human body. Such complex tasks need cooperation between nanomachines using a communication network. Wireless Ad hoc networks, using molecular or electromagnetic-based communication have been proposed in the literature to create flexible nanonetworks between nanomachines. In this paper, we propose a Wired Ad hoc NanoNETwork (WANNET) model design using actin-based nano-communication. In the proposed model, actin filaments self-assembly and disassembly is used to create flexible nanowires between nanomachines, and electrons are used as carriers of information. We give a general overview of the application layer, Medium Access Control (MAC) layer and a physical layer of the model. We also detail the analytical model of the physical layer using actin nanowire equivalent circuits, and we present an estimation of the circuit component's values. Numerical results of the derived model are provided in terms of attenuation, phase and delay as a function of the frequency and distances between nanomachines. The maximum throughput of the actin-based nanowire is also provided, and a comparison between the maximum throughput of the proposed WANNET, vs other proposed approaches is presented. The obtained results prove that the proposed wired ad hoc nanonetwork can give a very high achievable throughput with a smaller delay compared to other proposed wireless molecular communication networks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".