Advancing model-based systems engineering in biomedical and aerospace research:
Bibliographic record
Abstract
Model-Based Systems Engineering (MBSE) represents a modern methodology for developing complex systems using models, prioritizing alignment with customer preferences through comprehensive systems based modeling. Using PRISMA guidelines, data was gathered from peer-reviewed journals, systematic reviews, case studies, and computational studies from databases such as PubMed and Google Scholar, from the past 24 years. The study provides a comprehensive view of the current state of MBSE applications in healthcare and engineering addressing the practical challenges they face, offering strategic suggestions to improve future outcomes. This research introduces the Dynamic Risk Management Framework (DRMF), designed to leverage real-time data and predictive analytics to bolster system reliability and performance. The reviewed articles illuminate the essential role of MBSE in creating sophisticated systems and emphasize the need for improved modeling language integration, standardized processes, and increased interoperability. Further studies are required to validate its effectiveness and overcome its current limitations. As an emergent discipline within systems engineering, MBSE holds significant promise for future development, positioning itself as a critical tool for optimizing diverse fields of application. Further investigations are essential to validate MBSE's effectiveness and address its existing limitations.
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 | 0.049 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".