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Record W4323687943 · doi:10.1117/12.2667947

The use of nylon actuators within an active textile for applications in compression therapies

2023· article· en· W4323687943 on OpenAlexaff
Sukhneet Dhillon, Ying Li, Adriana J. Cowan, Magan Chang, Tony Jun Huang, Konstantin Borissov, John F. Madden

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsActuatorMaterials scienceMicrocontrollerCompression (physics)TextileNylon 6VoltageAutomotive engineeringComposite materialMechanical engineeringComputer scienceElectrical engineeringEngineeringPolymerComputer hardwareArtificial intelligence

Abstract

fetched live from OpenAlex

Nylon actuators yield a large reversible strain (5-20%+), are compact (300-µm) and provide a low-cost option for biomedical applications. We propose to develop an active textile composed of cotton, silver-coated nylon, and nylon actuators. We will assess the feasibility of nylon actuators to generate effective cycle rates and compression pressures similar to those of clinically effective pneumatic compression pumps. Our aim is to establish correlations between three nylon actuator configurations (parallel, parallel at 30°, and crisscrossed at 30°), thermal distribution, and compression pressure, as well as between power input and nylon actuator cycle rate. A microcontroller unit (MCU) and a pressure sensor will be developed for the nylon actuators to ensure that the actuators are under constant strain, while monitoring pressure, current, voltage and temperature. The development of an actively contracting textile could have significant benefits for portable compression therapies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.056
GPT teacher head0.283
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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