Evidence map›Paper›PMID 40870264›Full record

ArticleEntropy (Basel, Switzerland)2025

Modelling Cascading Failure in Complex CPSS to Inform Resilient Mission Assurance: An Intelligent Transport System Case Study.

Theresa Sobb, Benjamin Turnbull

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Theresa SobbSchool of Systems and Computing, University of New South Wales at the Australian Defence Force Academy, Campbell, ACT 2612, Australia.ORCID 0000-0001-9373-2179
Benjamin TurnbullSchool of Systems and Computing, University of New South Wales at the Australian Defence Force Academy, Campbell, ACT 2612, Australia.ORCID 0000-0003-0440-5032

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intelligent transport systems are revolutionising all aspects of modern life, increasing the efficiency of commerce, modern living, and international travel. Intelligent transport systems are systems of systems comprised of cyber, physical, and social nodes. They represent unique opportunities but also have potential threats to system operation and correctness. The emergent behaviour in Complex Cyber-Physical-Social Systems (C-CPSSs), caused by events such as cyber-attacks and network outages, have the potential to have devastating effects to critical services across society. It is therefore imperative that the risk of cascading failure is minimised through the fortifying of these systems of systems to achieve resilient mission assurance. This work designs and implements a programmatic model to validate the value of cascading failure simulation and analysis, which is then tested against a C-CPSS intelligent transport system scenario. Results from the model and its implementations highlight the value in identifying both critical nodes and percolation of consequences during a cyber failure, in addition to the importance of including social nodes in models for accurate simulation results. Understanding the relationships between cyber, physical, and social nodes is key to understanding systems' failures that occur because of or that involve cyber systems, in order to achieve cyber and system resilience.

Indexed as

Complex Cyber–Physical–Social Systemcomplex systemscritical infrastructureCyber–Physical–Social SystemCyber–Physical SystemIntelligent Transport SystemMission Assuranceresiliencesmart citysocial phenomena

Identifiers

PMID40870264
PMCPMC12385918

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.