{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T10:45:40Z","timestamp":1767869140938,"version":"3.49.0"},"reference-count":85,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Government","award":["FSEG-2023-0008"],"award-info":[{"award-number":["FSEG-2023-0008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Managing risk in drifting complex systems is hindered by the weak integration of unstructured incident narratives into quantitative, decision-ready models. We present a phenomena-centric semantic factor framework that closes the data\u2013model\u2013decision gap by transforming free-text incident reports into transparent, traceable drivers of risk and actionable interventions. The pipeline normalizes and encodes narratives, extracts domain-invariant phenomena, couples them to risk outcomes through calibrated partial least squares factors, and applies scenario optimization to recommend portfolios of measures aligned with EAM\/CMMS taxonomies. Applied to a large corpus of incident notifications, the method yields stable, interpretable phenomena, improves out-of-sample risk estimation against strong text-only baselines, and delivers prescriptive recommendations whose composition and cost\u2013risk trade-offs remain robust under concept drift. Sensitivity and ablation analyses identify semantic factorization and PLS coupling as the principal contributors to performance and explainability. The resulting end-to-end process is traceable\u2014from tokens through phenomena and factors to actions\u2014supporting auditability and operational adoption in critical infrastructure. Overall, the study demonstrates that phenomenological semantic factorization combined with scenario optimization provides an effective and transferable solution for integrating incident text into the proactive risk management of complex, drifting systems.<\/jats:p>","DOI":"10.3390\/bdcc10010021","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:49:46Z","timestamp":1767707386000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Phenomenological Semantic Factor Method for Risk Management of Complex Systems in Drifting"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1254-0464","authenticated-orcid":false,"given":"Dmitry","family":"Rodionov","sequence":"first","affiliation":[{"name":"Graduate School of Economics and Engineering, St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1362-6283","authenticated-orcid":false,"given":"Prohor","family":"Polyakov","sequence":"additional","affiliation":[{"name":"Research Laboratory \u201cPolytech-Invest\u201d, St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4685-8569","authenticated-orcid":false,"given":"Evgeniy","family":"Konnikov","sequence":"additional","affiliation":[{"name":"Graduate School of Economics and Engineering, St. Petersburg Polytechnic University, St. Petersburg 195251, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"ref_1","unstructured":"(2025, October 24). 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