New Systematic Solution for Resolving Nonlinear Dynamics Using System Analytical Theory Based on Engineering Science
Bibliographic record
Abstract
There are many nonlinear dynamics in field of non-physical sciences, such as the food chain, economic systems, or engineering systems with the characteristics of closed or open-loop systems. The problems arising from this have been resolved by the outdated chaos theory in statistical physics based on the paradigm of logical thinking. However, it was founded by classical physicists, and it is imperfect and vague, moreover, very difficult for others. Therefore, we require a perfect systematic solution based on systems thinking, such as systems analytical methods in engineering science. Surprisingly, in 2021, a non-physicist, on behalf of a physicist, has successfully resolved the problems and achieved a new solution based on systems thinking through interdisciplinary research; moreover, it has been published. Unfortunately, most physicists do not welcome it because they have no experience and it is disadvantageous to them like the Copernican theory. In addition, they have no ability to evaluate the new solution because they do not know the analytic method. Nevertheless, non-physicists are greatly welcome it, thus, there is no problem in it. Hence, non-physicists will verify it using MATLAB or simulator and apply it to all science, on behalf of physicists. If so, non-physicists will have both a logical solution and a systematic solution for resolving nonlinear dynamics.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".