The Influence of Impactor Mass on the Dynamic Response of the Hybrid III Headform and Brain Tissue Deformation
Bibliographic record
Abstract
When determining head injury risks through event reconstruction, it is important to understand how individual impact characteristics influence the dynamic response of the head and its internal structures. The effect of impactor mass has not yet been analyzed in the literature. The purpose of this study was to determine the effects of impactor mass on the dynamic impact response and brain tissue deformation. A 50th-percentile Hybrid III adult male head form was impacted using a simple pendulum system. Impacts to a centric and a non-centric impact location were performed with six varied impactor masses at a velocity of 4.0 m/s. The peak linear and peak angular accelerations were measured. A finite element model (University College Dublin Brain Trauma Model) was used to determine brain deformation, namely, peak maximum principal strain and peak von Mises stress. Impactor mass produced significant differences for peak linear acceleration for centric (F5,24 = 217.55, p = 0.0005) and non-centric (F5,24 = 161.98, p = 0.0005) impact locations, and for peak angular acceleration for centric (F5,24 = 52.51, p = 0.0005) and non-centric (F5,24 = 4.18, p = 0.007) impact locations. A change in impactor mass also had a significant effect on the peak maximum principal strain for centric (F5,24 = 11.04, p = 0.0005) and non-centric (F5,24 = 5.87, p = 0.001) impact locations, and for peak von Mises stress for centric (F5,24 = 24.01, p = 0.0005) and non-centric (F5,24 = 4.62, p = 0.004) impact locations. These results confirm that the impactor mass of an impact should be considered when determining risks and prevention of head and brain injury.
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 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 | 0.001 | 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".