Utilizing computational design optimization in cancer nanotherapy and biology system principles in air transportation: a successful demonstration of interdisciplinary research
Bibliographic record
Abstract
This thesis considers the utilization of numerical design optimization principles in biomedical engineering and vice versa to enrich the methodologies of the respective disciplines in order to obtain more efficient and effective problem solutions.Specifically, the first part of the thesis introduces the use of numerical optimization to generate optimal nanoparticle designs for cancer nanotherapy, as opposed to traditional empirical methods that are constrained by financial and temporal limitations. Computational models are used to evaluate treatment therapeutic and toxicological parameters. Since the gradients of the functions evaluated using computational models may be unavailable or cannot be approximated reliable, we employ derivative-free direct search optimization algorithms supported by convergence theory. Results revealed design solutions consisting of nanoparticle size, aspect ratio, and surface ligand density that maximize tumor targeting and minimize tumor diameter at the end of treatment. This part of the thesis shows the feasibility of applying optimization to achieve efficacious cancer treatments, and offers a quantitative paradigm to support clinical decision-making. In the second part of the thesis, a physiological mechanism is used to reduce the numerical complexity of an engineering design optimization problem. In particular, we consider an air transportation system-of-systems design problem formulation that integrates aircraft sizing, fleet allocation, and route network configuration. The numerical complexity of the problem hinders its solution for large unstructured (as opposed to hub-spoke) networks. The bioinspired approach is used to eliminate parts of the optimization problem without loss of optimality, leading to significant numerical simplifications. The new approach is first validated for a 15-node network, which is the largest network size reported in the literature. We then obtain, report, and discuss results for a 20-node network, a size that is representative of the Canadian major airport landscape, for which results could not be obtained previously due to prohibitive computational cost and numerical difficulties. The presented bioinspired design methodology provides a paradigm for reducing complexity in the design of system-of-systems engineering with the potential of being useful in a wide range of applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".