Safe Path: Energy Harvesting from Pedestrian Movement in Karbala- Spatial Suitability and Crowd Dynamics Towards Sustainability
Bibliographic record
Abstract
The escalating population corresponds with a rising demand for electricity.A viable alternative to address this challenge involves harnessing human kinetic energy during activities such as walking.As individuals walk, they transmit energy to the ground through impact and vibrations.This energy can be captured and converted into electrical power based on the steps taken by pedestrians.This research focuses on Karbala, a city notable for its high pedestrian density, particularly during religious ceremonies where large crowds from diverse nationalities converge in its squares and corridors.The significant congregation necessitates substantial energy for lighting, escalators, air conditioning, and other operational requirements.This study explores the untapped potential of clean and safe energy generation through kinetic energy produced by human movement.It proposes the development of an energy harvesting system that leverages an advanced understanding of pedestrian density, employing the piezoelectric tile system.The research methodology included data collection and photographing the study area during a field visit, alongside the use of geographic information systems to identify optimal paths for implementing this technology to maximize energy harvesting.The study concludes that the Continue harvesting energy for five consecutive years for the purpose of launching forecasts for the coming years and medium and long-term plans to achieve the goal of sustainable development in renewable energy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".