Bibliographic record
Abstract
The modern challenge faced with the global energy situation is the growing energy demand and the strong dependence on unsustainable fossil fuels.Another concurrent issue is the adverse health and socio-economic implications of adult obesity.Human Power Generation, which uses metabolized human energy to generate electrical power, could potentially address both these challenges.This paper discusses design and development of a method of exercising on a bicycle to convert the mechanical rotational power of human peddling to useable electrical energy using a dynamo integrated charge controller that charges a battery.Also, during exercising the health parameters such as body temperature and heart rate are reliably monitored using wearable sensors and wireless embedded processer to analyze the fitness level of a person and issue health alarm to prevent mishap during exercising.Further, the data is wirelessly transmitted to remote user interface for monitoring, logging and analyzing the electrical power generation capabilities of a person along with health condition.Experimental proven results indicate that system is capable to generate electrical energy during cycling by young humans with typical average efficiency of 17% or more.The system model is a smart power-generating exercising mechanism that is attractive and effective to be used in homes, gyms, health clubs and other extraterrestrial environments as a source of low cost sustainable green energy cum fitness analyzer.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".