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The Insect Fascicle Morphology Research and Bionic Needle Pierced Mechanical Mechanism Analysis

2010· article· en· W1875036014 on OpenAlexvenueno aff
Xinhua Qi, Yingchun Qi, Yan Li, Qian Cong

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsnot available
Fundersnot available
KeywordsFascicleMaterials scienceDragMicrostructureComposite materialAnatomyBiologyMechanicsPhysics

Abstract

fetched live from OpenAlex

In this paper, mosquito and cicadas two kinds of insects fascicle were studied, and observed the fascicle surface morphology and distribution through scanning electron microscopy, discussed the height of the six parameters of sawtooth and analyzed quantitatively, compared the two types of fascicle in micro-structure size, and shape, the experiment results show that there are obviously different among the two mouth fascicle morphological structure. Triangular sawtooth are all clearly visible in the two kinds of insect fascicle, in which the mosquito has the small microstructure, and the cicada has the larger one; microstructure of mosquitoes tilt to the rear part of the fascicle, while the microstructure of cicada is symmetric on bottom corner. Based on non-smooth surface structure of fascicle obvious principles of drag reduction effect, the model of drag reduction bionic syringe is proposed, Designed a bionic drag painless needles, and simulated needle piercing power is also measured. Bionic needle surface microstructure can help reduce the needle to decrease the contact area, form rolling, friction, and thus reduce the resistance to needle piercing. Bionic needle has been proved that its puncture resistance is less than smooth one consequently has significant drag reduction effects. Keywords: Insects fascicle; surface morphology; bionic needles; drag reduction

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.013
Science and technology studies0.0030.014
Scholarly communication0.0000.004
Open science0.0030.001
Research integrity0.0000.005
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 head0.073
GPT teacher head0.479
Teacher spread0.406 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations4
Published2010
Admission routes1
Has abstractyes

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