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Applied Sciences
Special Issues
Risk Models, Analysis, and Assessment of Complex Systems
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Risk Models, Analysis, and Assessment of Complex Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1743

Special Issue Editors

Dr. Shenghan Zhou

E-Mail Website
Guest Editor
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Interests: data-driven risk analysis and prediction theory; economic risk analysis; healthcare risk analysis
Dr. Linchao Yang

E-Mail Website
Guest Editor
School of Economics and Management, North China Electric Power University, Beijing 102206, China
Interests: risk management; information management; data mining

Special Issue Information

Dear Colleagues,

Whether in the form of a transportation system, a manufacturing system, or an economic system, complex systems involve numerous intertwined variables and potential uncertainties. Therefore, conducting thorough in-depth analyses of the complex relationships and potential risks within these systems, and then effectively carrying out risk modeling and risk assessment to reduce these risks and the hazards they may cause, has become an urgent research task. Moreover, this research is also important for guaranteeing optimal resource allocation, improving system efficiency, and paving the way for sustainable development. With this in mind, we are pleased to invite you to participate in the exciting research and discussions surrounding complex systems and the modeling, analysis, and assessment of their risks, and welcome you to share your findings.

Dr. Shenghan Zhou
Dr. Linchao Yang
Guest Editors

Manuscript Submission Information

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Keywords

  • industrial systems
  • aerospace engineering
  • transportation science and engineering
  • energy systems
  • artificial intelligence
  • risk modeling
  • risk assessment

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Published Papers (3 papers)

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Research

20 pages, 3524 KB  
Article
UMAP and K-Means++ Based Degradation Condition Identification for Switch Machines
by Xiaochen Hu, Ning Guo and Decun Dong
Appl. Sci. 2026, 16(5), 2261; https://doi.org/10.3390/app16052261 - 26 Feb 2026
Viewed by 189
Abstract
To address the challenges of feature extraction and degradation state identification for railway turnout switch machine power signals over the full life cycle, this paper proposes a multi-dimensional feature-fusion-based degradation state identification method for S700K turnout switch machines. Multi-domain features are first extracted [...] Read more.
To address the challenges of feature extraction and degradation state identification for railway turnout switch machine power signals over the full life cycle, this paper proposes a multi-dimensional feature-fusion-based degradation state identification method for S700K turnout switch machines. Multi-domain features are first extracted from degradation power signals in the time domain, frequency domain, and time-frequency domain. Subsequently, a Uniform Manifold Approximation and Projection (UMAP)-based feature fusion strategy is employed to construct low-dimensional feature representations that effectively characterize the evolution of the equipment’s operating state, and corresponding degradation performance indicators are established. Based on the fused features, the K-means++ clustering algorithm is applied to divide the performance degradation process of the switch machine into different stages. The clustering results are comprehensively evaluated using the silhouette coefficient, Calinski–Harabasz (CH) index, and Davies–Bouldin (DB) index, and are compared with those obtained by the fuzzy C-means algorithm and the conventional K-means algorithm. Experimental results demonstrate that the proposed method achieves superior clustering quality and stability in degradation stage partitioning, enabling refined identification of degradation states and providing reliable theoretical support and technical foundations for condition monitoring and maintenance decision-making in intelligent railway turnout operation and maintenance systems. Full article
(This article belongs to the Special Issue Risk Models, Analysis, and Assessment of Complex Systems)
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31 pages, 1361 KB  
Article
Risk Modeling and Robust Resource Allocation in Complex Aviation Networks: A Wasserstein Distributionally Robust Optimization Approach
by Jingxiao Wen, Yiming Chen, Wenbing Chang, Jiankai Wang and Shenghan Zhou
Appl. Sci. 2026, 16(4), 1959; https://doi.org/10.3390/app16041959 - 16 Feb 2026
Viewed by 208
Abstract
Aircraft routing networks are complex systems vulnerable to cascading delays triggered by weather disruptions and airspace constraints. This paper proposes a Distributionally Robust Aircraft Routing (DRAR) model for systemic risk assessment. Conventional robust or stochastic optimization methods often rely on specific assumptions about [...] Read more.
Aircraft routing networks are complex systems vulnerable to cascading delays triggered by weather disruptions and airspace constraints. This paper proposes a Distributionally Robust Aircraft Routing (DRAR) model for systemic risk assessment. Conventional robust or stochastic optimization methods often rely on specific assumptions about delay distributions (e.g., fixed probability distributions or scenario sets). However, due to the suddenness and multi-source nature of flight delays, their true distribution is difficult to accurately characterize, limiting the effectiveness of these methods in real-world uncertain conditions. By constructing a Wasserstein-metric ambiguity set, the proposed model captures distributional uncertainty without assuming fixed probabilities, thereby handling delay risks more robustly. The study incorporated chance constraints to bound extreme delay probabilities and reformulated the model as a tractable mixed-integer program. Experiments on real airline data demonstrate that DRAR outperforms traditional benchmarks, reducing propagation delays by 4–6%, volatility by 7–9%, and extreme delay risks by up to 15.7%. Thus, the model provides a practical tool for aviation decision-makers: airlines can leverage it to optimize aircraft scheduling and routing, systematically mitigate delay propagation risk, control the probability of extreme delays, and consequently reduce indirect operational costs arising from crew overtime and airport scheduling conflicts, thereby enhancing overall resource efficiency and operational resilience. These results validate DRAR as an effective tool for controlling tail risks and ensuring sustainable operations in uncertain aviation environments. Full article
(This article belongs to the Special Issue Risk Models, Analysis, and Assessment of Complex Systems)
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24 pages, 967 KB  
Article
A Component-Oriented Model for Risk Assessment in the Design of High-Tech Products
by Roman Trishch, Liudmyla Lutai, Eduard Khomiak, Zbigniew Krzysiak, Waldemar Samociuk, Edvin Hevorkian, Paweł Stączek and Miroslaw Rucki
Appl. Sci. 2025, 15(23), 12639; https://doi.org/10.3390/app152312639 - 28 Nov 2025
Viewed by 477
Abstract
This study proposes a component-based model for assessing risks in the design of high-tech products. The model took into account the novelty of components, which affected the risk level in the development process. The risk assessment was based on fuzzy set theory, which [...] Read more.
This study proposes a component-based model for assessing risks in the design of high-tech products. The model took into account the novelty of components, which affected the risk level in the development process. The risk assessment was based on fuzzy set theory, which allowed determination of the degree of importance of risk-generating factors, such as technical, economic, and organizational risks. The components were divided into “old” ones with the possibility of adaptation and “new” ones being implemented for the first time. The structure of the project included adaptation, acquisition, and development of new components. The component-oriented approach allowed for a reduction in the negative impact of risks in the early stages of development while optimizing decision-making on further product development. A case study involving the development of unmanned aerial vehicles (UAVs) was conducted to demonstrate the model’s applicability. The assessed aggregated project risk varied from 0.0992 for projects based primarily on reusable components to 0.1902 for those involving a high proportion of newly developed components. The model’s sensitivity to component novelty made it possible to differentiate between low- and moderate-risk design scenarios. This is especially valuable for early-stage project selection and risk-informed “go/no-go” decisions in the design of complex systems. Full article
(This article belongs to the Special Issue Risk Models, Analysis, and Assessment of Complex Systems)
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