Volume-12, Issue-3, March 2026

Abstract

In the construction of interdependent networks, the functionality of a group in one layer typically relies on the support of a group in another layer. To investigate the stability of such networks, we propose a framework comprising a bilayer interdependent hypergraph system, where the two layers exhibit mutual dependencies. Our core hypothesis is that the removal of nodes in one layer not only leads to node failures but, more critically, triggers the failure of hyperedges, resulting in iterative cascading failures across layers. Using a bilayer system characterized by a Poisson hyperdegree distribution as an example, we have proven through rigorous analysis how parameter changes affect the robustness of the target network. Overall, our study highlights the critical role of hyperedge interdependence mechanisms and network topological structures in mitigating cascading failures in systems with higher-order interactions, providing valuable insights for the design and optimization of network systems.

Keywords: Interdependent Networks, Hypergraph, Hyperedge Dependency, Cascading Failure.

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Abstract

Preventing and defusing local government debt risks is a core issue in current macroeconomic governance. Against the backdrop of policies that promote the separation of credit between urban investment platforms and local governments, market pricing remains constrained by expectations of implicit guarantees. Using urban investment bonds issued between 2014 and 2024 as the research sample, this paper constructs an issuer‑bond and guarantor‑bond bipartite network, introduces two dynamic indicators: issuer node degree and guarantor node degree, and employs a quasi‑natural experiment based on a series of debt resolution policies to examine the interactive effects of policy regulation and network structure on issuance spreads. The empirical results show that policy effects exhibit significant heterogeneity; the pricing weight of network characteristics is reshaped with the policy environment, where issuer node degree transforms into a market‑oriented signal, while guarantor node degree reverses into a hub for risk transmission. Urban investment bond pricing deviates systematically from traditional financial theories, with variables capturing government credit linkages acting as the core pricing determinants. The findings of this paper indicate that the risk pricing of urban investment bonds is undergoing a transition from relationship dependence to fundamental risk pricing, which provides empirical support for identifying implicit guarantees and preventing risk contagion within guarantee networks.

Keywords: Urban Investment Bonds (UIBs); Issuance Spread; Implicit Guarantee; Network Analysis.

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Abstract

Ceramic Matrix Composites (CMCs) have emerged as essential advanced materials for high-temperature and high-performance applications, offering superior thermal stability, lightweight design, oxidation protection, and enhanced fracture resistance compared to traditional monolithic ceramics. Their growing use in demanding applications, such as aerospace propulsion systems, automotive components, and advanced energy technologies, is driven by their unique ability to meet these stringent requirements. However, significant technical and economic obstacles continue to limit their widespread industrial implementation. This paper presents a critical review of the key barriers to CMC development, including inherent pseudo-ductility limits, complexities in fabrication processes, high production costs, challenges in fibre–matrix interface engineering, susceptibility to environmental degradation, and the lack of standardized design methodologies and material databases. The study analyses recent advancements in processing technologies, interfacial design, and environmental protection strategies that aim to improve CMC performance, reliability, and manufacturability. By establishing the current limitations of CMCs, this work identifies future research opportunities necessary to accelerate their adoption in next-generation engineering systems.

Keywords: Ceramic Matrix Composites, Chemical Vapour Infiltration, Fibre-Matrix Interphase, Environmental Barrier Coatings, Processing Challenges.

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Abstract

Loan approval is a pivotal component of financial risk control. Current research often relies on black-box prediction models and lacks in-depth exploration of multidimensional features and interpretability, resulting in shortcomings in model interpretability and robustness. To address this, this paper proposes an Interpretable Loan Approval Identification Model based on Multidimensional Features (ILA-MDF). The ILA-MDF model is constructed using the CatBoost algorithm and is compared with benchmark models such as Random Forest in experimental evaluations. The results indicate that the proposed model performs optimally across six metrics—Accuracy, Precision, Recall, F1-score, AUC, and MCC—achieving values of 93.33%, 87.19%, 82.03%, 84.53%, 97.77%, and 80.34%, respectively. Furthermore, the SHAP framework is introduced to analyze the key factors influencing model decisions. Interpretability analysis reveals that the probability of loan approval increases significantly when the loan interest rate exceeds 14% or when the previous loan default indicator is 0. The ILA-MDF model proposed in this paper demonstrates significant advantages in both predictive performance and decision interpretability, providing a reference for formulating loan approval and risk prevention strategies.

Keywords: CatBoost, Machine Learning, SHAP Framework, Interpretability

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