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Record W2336220156 · doi:10.5539/mas.v10n3p201

Rotation of Scanner’s Mirror in MEMS Dimensions with the Use of Lorents Law

2016· article· en· W2336220156 on OpenAlexvenueno aff
Said Farahat, Aria Nouri Jangi

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicExperimental and Theoretical Physics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRotation (mathematics)ScannerElectric fieldPhysicsMagnetic fieldElectromagnetic coilOpticsNuclear magnetic resonanceElectrical engineeringMaterials scienceGeometryMathematicsQuantum mechanicsEngineering

Abstract

fetched live from OpenAlex

<p>In this project we study a kind of scanner in micro dimensions in which it includes three frames also the third<br />frame is considered as fixed surface and the two others are including two separate coils that electric current<br />enters it and when we expose this electric current in a magnetic field under a specific conditions then with the<br />use of Lorents Force law we can cause the rotation of the frame and mirror’s surface accordingly.<br />Discussed field in this project has a magnitude of between 0.2-4 Tesla in which this magnetic field has no<br />destructive effect on human body also the electric current has a magnitude of between 0.001-0.14 Amper in<br />which the maximum obtained rotation angle is equal to ±54.49724 that in every half cycle with the use of two<br />frames it can be deviated 54.49724 degree from the mirror. This plan with the lower number in coil with a design<br />in smaller dimensions (micrometer) is simulated with a more different design and the result was acceptable so it<br />has been much favored for its medical capabilities.<br />Frames are linked together with Su-8 polymer in which this polymer has been used because of its appropriate<br />softness and bending.</p>

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.467

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 studies0.0000.001
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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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 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
Published2016
Admission routes1
Has abstractyes

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