Deep operational normality modeling: an unsupervised framework with potential applicability to supply chain resilience.

Huang Y, Li W

DOI: 10.1038/s41598-026-60109-5
Published: 2026 Jul 2
Container: Scientific reports
Publisher: Not recorded
Open access: no

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-026-60109-5,
  title = {Deep operational normality modeling: an unsupervised framework with potential applicability to supply chain resilience.},
  author = {Huang Y and Li W},
  year = {2026},
  journal = {Scientific reports},
  doi = {10.1038/s41598-026-60109-5},
  url = {https://doi.org/10.1038/s41598-026-60109-5}
}

RIS

TY  - JOUR
TI  - Deep operational normality modeling: an unsupervised framework with potential applicability to supply chain resilience.
AU  - Huang Y
AU  - Li W
PY  - 2026
JO  - Scientific reports
DO  - 10.1038/s41598-026-60109-5
UR  - https://doi.org/10.1038/s41598-026-60109-5
ER  -

APA

Y, H., & W, L. (2026). Deep operational normality modeling: an unsupervised framework with potential applicability to supply chain resilience.. Scientific reports. https://doi.org/10.1038/s41598-026-60109-5

Source records

pubmed · retrieved 2026-09-25T10:56:34.338Z
europe-pmc · retrieved 2026-09-25T10:56:34.323Z