Limiting Factors on Speed and Load of Diesel Engines Vehicle Urban Air Quality Management in Thailand
Bibliographic record
Abstract
Speed and load of diesel engine vehicles are the important factors affected on the fine particulate matter emission in Thailand. This study aimed to study the relation of speed and load of diesel engine vehicles affected on the emission of fine particulate matter in Thailand and also related to the emission of the exhaust from diesel engine vehicles. The experiment was designed into a x b Factorial Experiment in Completely Randomized Design. It was divided into 2 experiments as follows: 1) to study the emission of fine particulate matter in Euro 4 diesel engine vehicles, and 2) to study the emission of fine particulate matter in Euro 3 diesel engine vehicles. Moreover, it was to study 3 levels of speed in the driving form of diesel engine vehicles under Bangkok Driving Cycle, 3 levels of load, and 3 replications by using the experiment in Chassis Dynamometer System, and demonstrating the driving similarly to the actual driving. The samples of fine particulate matter from the exhaust were from the speed and load by using Micro-Orifice Uniform Deposition Impactors; MOUDI which had the size from 0.056-18 microns with the flow rate at 30 liter/ minute. The result from this study was found that the experiment of Euro 4 diesel engine vehicles had the factors of speed and load which affected on the emission of fine particulate matter at the level of 0.05, and F-value was 240.03 and 4.60 respectively. About the experiment of Euro 4 diesel engine vehicles, it had the factors of speed and load which affected on the emission of fine particulate matter at the level of 0.05, and F- value was 796.92 and 18.46 respectively. At the speed of 7.8 km/hr, the loads of empty vehicle and 1,000 kg of Euro 3 diesel engine vehicles were different at the level of 0.05. While the speed of 7.8 km/hr was at empty vehicle and 1,000 kg of Euro 4 diesel engine vehicles, it was not different in statistical significance. Therefore, there should be the regulations to control the load of the Euro 4 diesel engine vehicles lower than standard in the city with high traffic jam.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".