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Record W2172192196 · doi:10.1145/2619955.2619970

Stationary Distribution of Molecules in NanoCommunication via Microtubules

2014· article· en· W2172192196 on OpenAlexaff
Kamal Darchini, Attahiru Sule Alfa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMolecular Communication and Nanonetworks
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMolecular communicationMarkov chainStationary distributionMarkov processChannel (broadcasting)Computer scienceBrownian motionStatistical physicsProbability distributionPosition (finance)Jump diffusionTopology (electrical circuits)JumpPhysicsMathematicsTelecommunicationsTransmitterQuantum mechanicsMachine learning

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.193
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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