Helmet liner evaluation to mitigate head response from primary blast exposure
Bibliographic record
Abstract
Head injury resulting from blast loading, including mild traumatic brain injury, has been identified as an important blast-related injury in modern conflict zones. A study was undertaken to investigate potential protective ballistic helmet liner materials to mitigate primary blast injury using a detailed sagittal plane head finite element model, developed and validated against previous studies of head kinematics resulting from blast exposure. Five measures reflecting the potential for brain injury that were investigated included intracranial pressure, brain tissue strain, head acceleration (linear and rotational) and the head injury criterion. In simulations, these measures provided consistent predictions for typical blast loading scenarios. Considering mitigation, various characteristics of foam material response were investigated and a factor analysis was performed which showed that the four most significant were the interaction effects between modulus and hysteretic response, stress-strain response, damping factor and density. Candidate materials were then identified using the predicted optimal material values. Polymeric foam was found to meet the density and modulus requirements; however, for all significant parameters, higher strength foams, such as aluminum foam, were found to provide the highest reduction in the potential for injury when compared against the unprotected head.
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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 | 0.001 | 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) | 0.002 | 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".