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Record W4407919307 · doi:10.1016/j.procs.2025.01.335

Innovative Swing Mechanism for Sustainable Energy Generation: Design, Performance, and IoT Integration

2025· article· en· W4407919307 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcedia Computer Science · 2025
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsNSCAD University
Fundersnot available
KeywordsComputer scienceSwingMechanism (biology)Internet of ThingsEnergy (signal processing)Mechanism designSustainable energyEmbedded systemComputer architectureIndustrial engineeringRenewable energyElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

In this study, the choice of mild steel for the swing mechanism was essential due to its mechanical properties, strength, and environmental resistance. The swing system, incorporating a stand, swing, connecting rod, spur gear, bearings, freewheel, battery, dynamo, and sprocket, was designed for sustainable energy generation without compromising user safety. The design balanced ergonomic needs with performance characteristics, ensuring the structure could endure applied stresses. IoT integration allowed for advanced monitoring, enabling real-time data analysis, performance optimization, and issue detection. The study examined how swinging weight and oscillation frequency impacted the output voltage and current, findings showed that output voltage varied with the swinger’s weight. The swing mechanism, capable of generating 6-8 volts, successfully converted kinetic energy into electrical energy. This study serves as a renewable energy education model with IoT integration, demonstrating innovative energy solutions for parks, schools, playgrounds, and communities.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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