Determination of the risk propagation path of cascading faults in chemical material networks based on complex networks
Bibliographic record
Abstract
Abstract In a chemical material network, a small fault is easy to spread and cause serious consequences, which is the embodiment of a complex network cascading fault. In order to ensure the safety and stability of chemical production, it is necessary to study the cascading fault propagation of a chemical material network. Firstly, the chemical material network model is constructed, and the SR algorithm is used to identify the important nodes in the network, which is the basis for selecting attack nodes. Secondly, the fault propagation strength is defined by the fault propagation probability and material hazard degree. The fault propagation probability is calculated by constructing the load cascading fault model of nodes. Based on the rough set theory and analytic hierarchy process (AHP), the index weights of flammability and explosiveness, toxicity, and corrosiveness are calculated to get the material hazard degree. Finally, based on the fault propagation strength, the risk propagation path of cascading fault is obtained by the ant colony algorithm. The case analysis shows that the proposed method can find the risk propagation path of cascading faults in material networks in the chemical production process, which provides certain basis for cascading fault prevention, selection of key monitoring variables, and risk assessment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".