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Design and Implement an Elastically Suspended Back Frame for Reducing the Burden of Carrier

2021· article· en· W3199809594 on OpenAlexaff
Yuquan Leng, Xin Lin, Ranbao Deng, Jing Chang, Lianxin Yang, Kuangen Zhang, Chenglong Fu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversity of British Columbia
FundersState Key Laboratory of RoboticsNational Natural Science Foundation of China
KeywordsBackpackStiffnessWork (physics)Heavy loadStructural engineeringFrame (networking)Computer scienceMaterials scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Previous work has proved that carrying an elastically suspended load can improve the biomechanics of human body and reduce the burden of human body compared with carrying a rigid load. However, how to design and implement an effective elastically suspended load has been less researched. In this paper, we firstly analyze the relationship between the elastic load force acting on the human body and the parameters (stiffness and damping) of the elastic system to guide the design. Then, our prototype which is an effective elastically suspended back frame and can bind loads of various shapes is realized. The mass of the whole prototype is 2.66 kg. Experiments were carried to test the effect of the system. Results show that an elastically suspended back frame with a load of 25.3 kg could reduce the amplitude of load by 30.2%, reduce maximal load force acting on the carrier by 16.6% compared with the rigid load with backpack. Compared with carrying the rigid load with backpack, the mechanical work is reduced by 56.5% and the maximal mechanical power is also reduced by 66.1%.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.138
GPT teacher head0.476
Teacher spread0.338 · 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.

Study designNot applicable
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

Citations6
Published2021
Admission routes1
Has abstractyes

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