Cascade Model Research Articles

Identifying critical nodes of cyber–physical power systems based on improved adaptive differential evolution

The identification and protection of critical nodes have a significant influence on the reliability and robustness of the cyber–physical power system (CPPS), which can effectively reduce the risk of cascading failures. Firstly, this paper establishes a one-to-one coupled cascading fault model, which considers different topological characteristics of power and cyber networks. Based on the interdependence between the two networks, the cascading fault propagation mechanism after node failures in CPPS is analyzed. Then, a critical node identification method based on improved adaptive differential evolution (JADE) is proposed to identify the sets of critical nodes that can maximize the impact on the system load of CPPS. Combined with the cascading failure characteristics of CPPS, in the population generation strategy, the test vectors that do not meet the conditions after the crossover operation are filtered and replaced to improve the search efficiency of the algorithm. Finally, in the case study, an interdependent CPPS is constructed with an IEEE 39-bus system and a scale-free network of the same nodes. The simulation results verify the effectiveness of the proposed node importance assessment strategy. In addition, it is also shown that evaluating the critical nodes in power systems is important for enhancing the robustness and ensuring the security of the power grid operation.

Topological analysis, endogenous mechanisms, and supply risk propagation in the polycrystalline silicon trade dependency network

High-purity polycrystalline silicon, as a core raw material in the photovoltaic industry, has a trade structure whose robustness affects the supply security of the entire photovoltaic industry. Using social network analysis methods and dependence indicators, this study constructs a Polycrystalline Silicon Trade Dependency Network (PSTDN) from 1995 to 2019, and performs descriptive statistical analysis and trade community divisions on the network. The Temporal Exponential Random Graph Model (TERGM) is used to explore the factors influencing the formation of trade dependencies. Based on the cascading failure model, a Photovoltaic Multilayer Network Supply Risk Propagation Model (PMNSRPM) is constructed to study the risk propagation process of polycrystalline silicon supply interruption as a raw material in the photovoltaic industry. Finally, using the risk propagation range and dependency degree, the study measures the importance of nations comparatively. The results show that (1) the PSTDN exhibits reciprocity, geographical clustering, and weak network convergence; (2) the evolution of the PSTDN's community has gone through three stages: the rising expansion phase from 1995 to 2007, the crisis recession phase from 2008 to 2010, and the competitive contraction phase from 2010 to 2019; (3) the evolution of the PSTDN shows strong reciprocity effects, transitive effects, stabilization effects, and lag effects as endogenous mechanisms; (4) the average risk propagation range is continuously increasing along the supply chain, while there is no significant difference in the risk propagation rate along the supply chain. The risk propagation rate of different risk sources shows a long-tail effect, with the risk propagation rate of leading countries like the United States and China growing rapidly over time. The United States can infect China, but China cannot infect the United States and Western Europe; (5) The risk propagation range and the dependency degree present a positive correlation ranging between 0.5 and 0.7, which is not as highly positive as initially anticipated, and the correlation between them shows a downward trend. This study explores the statistical characteristics of the PSTDN, community evolution, and evolutionary factors, demonstrates the importance of polycrystalline silicon supply security for the production of the photovoltaic industry, quantifies the risk propagation capabilities of different countries, and identifies a group of countries that actually have a high risk propagation range but tend to be underestimated due to their low dependency degree in normal trade network. This can help enhance the early warning capabilities for photovoltaic supply security risks of various countries.

Cell type-specific roles of APOE4 in Alzheimer disease.

The ɛ4 allele of the apolipoprotein E gene (APOE), which translates to the APOE4 isoform, is the strongest genetic risk factor for late-onset Alzheimer disease (AD). Within the CNS, APOE is produced by a variety of cell types under different conditions, posing a challenge for studying its roles in AD pathogenesis. However, through powerful advances in research tools and the use of novel cell culture and animal models, researchers have recently begun to study the roles of APOE4 in AD in a cell type-specific manner and at a deeper and more mechanistic level than ever before. In particular, cutting-edge omics studies have enabled APOE4 to be studied at the single-cell level and have allowed the identification of critical APOE4 effects in AD-vulnerable cellular subtypes. Through these studies, it has become evident that APOE4 produced in various types of CNS cell - including astrocytes, neurons, microglia, oligodendrocytes and vascular cells - has diverse roles in AD pathogenesis. Here, we review these scientific advances and propose a cell type-specific APOE4 cascade model of AD. In this model, neuronal APOE4 emerges as a crucial pathological initiator and driver of AD pathogenesis, instigating glial responses and, ultimately, neurodegeneration. In addition, we provide perspectives on future directions for APOE4 research and related therapeutic developments in the context of AD.

Microorganisms oxidize glucose through distinct pathways in permeable and cohesive sediments.

In marine sediments, microbial degradation of organic matter under anoxic conditions is generally thought to proceed through fermentation to volatile fatty acids, which are then oxidized to CO2 coupled to the reduction of terminal electron acceptors (e.g. nitrate, iron, manganese, and sulfate). It has been suggested that, in environments with a highly variable oxygen regime, fermentation mediated by facultative anaerobic bacteria (uncoupled to external terminal electron acceptors) becomes the dominant process. Here, we present the first direct evidence for this fermentation using a novel differentially labeled glucose isotopologue assay that distinguishes between CO2 produced from respiration and fermentation. Using this approach, we measured the relative contribution of respiration and fermentation of glucose in a range of permeable (sandy) and cohesive (muddy) sediments, as well as four bacterial isolates. Under anoxia, microbial communities adapted to high-energy sandy or bioturbated sites mediate fermentation via the Embden-Meyerhof-Parnas pathway, in a manner uncoupled from anaerobic respiration. Prolonged anoxic incubation suggests that this uncoupling lasts up to 160h. In contrast, microbial communities in anoxic muddy sediments (smaller median grain size) generally completely oxidized 13C glucose to 13CO2, consistent with the classical redox cascade model. We also unexpectedly observed that fermentation occurred under oxic conditions in permeable sediments. These observations were further confirmed using pure cultures of four bacteria isolated from permeable sediments. Our results suggest that microbial communities adapted to variable oxygen regimes metabolize glucose (and likely other organic molecules) through fermentation uncoupled to respiration during transient anoxic conditions.

Measurement of the neutrino-oxygen neutral-current quasielastic cross section using atmospheric neutrinos in the SK-Gd experiment

We report the first measurement of the atmospheric neutrino-oxygen neutral-current quasielastic (NCQE) cross section in the gadolinium-loaded Super-Kamiokande (SK) water Cherenkov detector. In June 2020, SK began a new experimental phase, named SK-Gd, by loading 0.011% by mass of gadolinium into the ultrapure water of the SK detector. The introduction of gadolinium to ultrapure water has the effect of improving the neutron-tagging efficiency. Using a 552.2 day dataset from August 2020 to June 2022, we measure the NCQE cross section to be 0.74±0.22(stat)−0.15+0.85(syst)×10−38 cm2/oxygen in the energy range from 160 MeV to 10 GeV, which is consistent with the atmospheric neutrino-flux-averaged theoretical NCQE cross section and the measurement in the SK pure-water phase within the uncertainties. Furthermore, we compare the models of the nucleon-nucleus interactions in water and find that the binary cascade model and the Liège intranuclear cascade model provide a somewhat better fit to the observed data than the Bertini cascade model. Since the atmospheric neutrino-oxygen NCQE reactions are one of the main backgrounds in the search for diffuse supernova neutrino background (DSNB), these new results will contribute to future studies—and the potential discovery—of the DSNB in SK. Published by the American Physical Society 2024

Conformity-aware adoption maximization in competitive social networks

Influence maximization (IM) problem is an extensively studied problem in social networks. It aims to find a small set of users in the social network to initiate the diffusion process and maximize the expected influence spread. Existing works on conformity-aware IM focus on the interaction between influence and conformity in a single-influence setting and ignore the role of conformity in a competitive and multiple-influence setting. This paper proposes a conformity-aware independent cascade (C-IC) model that considers the competition among multiple influences as well as the role of conformity in a user’s decision-making. It is proved that the adoption of an influence under the C-IC model is monotone and submodular. Meanwhile, we formulate two adoption maximization (AM) problems, O-AM and S-AM, which are both NP-hard. Because estimating the adoption through diffusion simulations is very time-consuming, we propose a reverse adoption estimation (RAE) method based on a reverse multiple influence sampling (RMIS) technology for the C-IC model and integrate it into the D-SSA-fix (Nguyenet al., 2018) framework, DSSA for short, to compute a solution with approximation guarantee. To further boost the performance, we present a fast one-hop adoption estimation (OAE) method and develop a heuristic algorithm based on OAE, called GOAE. Extensive experiments on eight real-world social networks show that the C-IC model is superior to a non-conformity diffusion model and that RAE+DSSA and GOAE are efficient and effective. In most cases, GOAE finds comparable solutions to RAE+DSSA and CELF with less time and memory overhead. GOAE is five to six orders of magnitude faster than CELF and RAE+DSSA is up to three orders of magnitude faster than CELF on NetHEPT. GOAE runs up to four to five orders of magnitude faster than RAE+DSSA with at most two orders of magnitude less memory usage. GOAE is more scalable than RAE+DSSA in terms of the number of seeds and the size of the social network.

An adaptable in silico ensemble model of the arachidonic acid cascade.

Eicosanoids are a family of bioactive lipids, including derivatives of the ubiquitous fatty acid arachidonic acid (AA). The intimate involvement of eicosanoids in inflammation motivates the development of predictive in silico models for a systems-level exploration of disease mechanisms, drug development and replacement of animal models. Using an ensemble modelling strategy, we developed a computational model of the AA cascade. This approach allows the visualisation of plausible and thermodynamically feasible predictions, overcoming the limitations of fixed-parameter modelling. A quality scoring method was developed to quantify the accuracy of ensemble predictions relative to experimental data, measuring the overall uncertainty of the process. Monte Carlo ensemble modelling was used to quantify the prediction confidence levels. Model applicability was demonstrated using mass spectrometry mediator lipidomics to measure eicosanoids produced by HaCaT epidermal keratinocytes and 46BR.1N dermal fibroblasts, treated with stimuli (calcium ionophore A23187), (ultraviolet radiation, adenosine triphosphate) and a cyclooxygenase inhibitor (indomethacin). Experimentation and predictions were in good qualitative agreement, demonstrating the ability of the model to be adapted to cell types exhibiting differences in AA release and enzyme concentration profiles. The quantitative agreement between experimental and predicted outputs could be improved by expanding network topology to include additional reactions. Overall, our approach generated an adaptable, tuneable ensemble model of the AA cascade that can be tailored to represent different cell types and demonstrated that the integration of in silico and in vitro methods can facilitate a greater understanding of complex biological networks such as the AA cascade.

A cascade training protocol for schoolteachers on oral health: Investigating the influence of primary and secondary mentors.

Oral health holds paramount importance for overall well-being, particularly among school-aged children, where inadequate oral health can cause significant discomfort and impede educational performance. Despite its critical nature, oral health awareness and practices within Indian school communities remain suboptimal. There exists an urgent necessity for efficacious educational initiatives to bridge this gap and foster oral health awareness among both schoolteachers and students. This monitored training introduces a pioneering cascade model training initiative aimed at evaluating its impact on enhancing oral health knowledge among schoolteachers and students in Indian schools. A total of 100 school teachers from 50 Centre government schools will undergo training in two distinct sessions. Following this, two teachers from each school will be selected to further train 24 class monitors from grades 6-9, who will subsequently impart knowledge to approximately 40 students per class. This phase of the training will be ongoing, ensuring training consistency through online supervision and active participation of the expert team in training activities with newly designated master trainers. Pre- and post-training assessments will be conducted at each level to gauge the monitored training's effectiveness. The entire training is projected to conclude within a year. This cascade model monitored training initiative represents a novel approach in promoting oral health awareness in Indian schools, marking a pioneering endeavor in this domain. Through this program, we aim to empower 1200 class monitors as master trainers and reach approximately 24,000 school students across 480 supervised training sessions. The establishment of master trainers through a cascade process, coupled with the engagement of the CDER expert team, ensures accurate dissemination of information at every stage. The comprehensive evaluation facilitated by pre- and post-training assessments at each level further enhances the program's effectiveness, laying a solid foundation for future oral health initiatives within school communities.

Research on Chaotic Characteristics of Cascade Failure in Rail Transit Networks considering Nonlinear Load Fluctuations

The stable and efficient operation of rail transit networks (RTNs) is critical for the integrated development of metropolitan areas. However, numerous studies have indicated that RTNs are prone to large‐scale cascading failures when subjected to disturbances. To address the limitations of traditional cascading failure models, this paper proposes an innovative cascading failure model for metropolitan areas RTNs, which incorporates nonlinear load fluctuations and the bounded rationality of passengers. This model aims to capture the cascading failure characteristics of RTNs with chaotic properties under 12 combination strategies. A single‐ and dual‐parameter coupling analysis of chaotic evolution parameters and prospect theory parameters are conducted. Taking the RTN in the Chengdu metropolitan area as an example, both the static characteristics and cascading failure features of the network are analyzed. The findings reveal the following: (i) the RTN is a assortativity network and lacks small‐world and scale‐free properties. (ii) During network disturbances, a higher level of passenger familiarity with the network increases the likelihood of large‐scale cascading failures. (iii) When passengers tend to avoid risks, stations with higher carrying capacity are more prone to failures. This study holds significant implications for ensuring the stable and reliable operation of rail transit systems within metropolitan areas.

Synergistic Attention-Guided Cascaded Graph Diffusion Model for Complementarity Determining Region Synthesis.

Complementarity determining region (CDR) is a specific region in antibody molecules that binds to antigens, where a small portion of residues undergoes particularly pronounced variations. Generating CDRs with high affinity and specificity is a pivotal milestone in accelerating drug development for daunting and unresolved diseases. However, existing approaches predominantly center on characterizing the attributes of residues through sequential generation models, thus falling short in effectively modeling the intricate spatial correlations among residues and frequently succumbing to the trap of generating sequences that exhibit a high degree of arbitrariness. In this article, we propose a novel synergistic attention-guided cascaded graph diffusion model, termed GraphCas, which offers a pathway for optimized generation of high-affinity CDRs. Our approach is the first cascaded-based graph diffusion model for CDR synthesis. Specifically, we design a graph propagation algorithm with a relation-aware synergistic attention mechanism, enabling the targeted acquisition of structural insights from diverse protein sequences and bolstering the global information representation of the graph by precisely localizing to long-range key residue sites. We design a cascaded conditional enhanced diffusion approach, providing the capability to incorporate additional control constraints into the input. Experimental results demonstrate that GraphCas can generate photo-realistic CDRs and achieve performance comparable to top-tier approaches. In particular, GraphCas reduces the RMSD by nearly 0.42 units in the H1 region and improves the ERRAT by 9.36% points in the L1 region.

The impact of Home Literacy Environment on Chinese children's character recognition, vocabulary knowledge, and reading comprehension: A developmental cascade model

The impact of Home Literacy Environment on Chinese children's character recognition, vocabulary knowledge, and reading comprehension: A developmental cascade model

Fucoxanthin restructures the gut microbiota and metabolic functions of non-obese individuals in an in vitro fermentation model.

Fucoxanthin, a carotenoid exclusively derived from algae, exerts its bioactivities with the modulation of the gut microbiota in mice. However, mechanisms through which fucoxanthin regulates the gut microbiota and its derived metabolites/metabolism in humans remain unclear. In this study, we investigated the effects of fucoxanthin on the gut microbiota and metabolism of non-obese individuals using an in vitro simulated digestion-fermentation cascade model. The results showed that about half of the fucoxanthin was not absorbed in the intestine, thus reaching the colon. The gut microbiota from fecal samples underwent significant changes after 48 or 72 hours in vitro fermentation. Specifically, fucoxanthin significantly enhanced the relative abundance of Bacteroidota and Parabacteroides, leading to improved functions of the gut microbiota in its development, glycan biosynthesis and metabolism as well as in improving the digestive system, endocrine system and immune system. The recovery of fucoxanthin during fermentation showed a decreasing trend with the slight bio-conversion of fucoxanthinol. Notably, fucoxanthin supplementation significantly altered metabolites, especially bile acids and indoles in the simulated human gut ecosystem. Correlation analysis indicated the involvement of the gut microbiota in the manipulation of these metabolites by fucoxanthin. Moreover, all these altered metabolites revealed the improvement in the capacity of fucoxanthin in manipulating gut metabolism, especially lipid metabolism. Overall, fucoxanthin determinedly reshaped the gut microbiota and metabolism, implying its potential health benefits in non-obese individuals.

On the application of a soft-vane cascade model in rotor-stator interaction noise prediction

On the application of a soft-vane cascade model in rotor-stator interaction noise prediction

A deep learning approach using an ensemble model to autocreate an image-based hip fracture registry.

With more than 300,000 patients per year in the United States alone, hip fractures are one of the most common injuries occurring in the elderly. The incidence is predicted to rise to 6 million cases per annum worldwide by 2050. Many fracture registries have been established, serving as tools for quality surveillance and evaluating patient outcomes. Most registries are based on billing and procedural codes, prone to under-reporting of cases. Deep learning (DL) is able to interpret radiographic images and assist in fracture detection; we propose to conduct a DL-based approach intended to autocreate a fracture registry, specifically for the hip fracture population. Conventional radiographs (n = 18,834) from 2919 patients from Massachusetts General Brigham hospitals were extracted (images designated as hip radiographs within the medical record). We designed a cascade model consisting of 3 submodules for image view classification (MI), postoperative implant detection (MII), and proximal femoral fracture detection (MIII), including data augmentation and scaling, and convolutional neural networks for model development. An ensemble model of 10 models (based on ResNet, VGG, DenseNet, and EfficientNet architectures) was created to detect the presence of a fracture. The accuracy of the developed submodules reached 92%-100%; visual explanations of model predictions were generated through gradient-based methods. Time for the automated model-based fracture-labeling was 0.03 seconds/image, compared with an average of 12 seconds/image for human annotation as calculated in our preprocessing stages. This semisupervised DL approach labeled hip fractures with high accuracy. This mitigates the burden of annotations in a large data set, which is time-consuming and prone to under-reporting. The DL approach may prove beneficial for future efforts to autocreate construct registries that outperform current diagnosis and procedural codes. Clinicians and researchers can use the developed DL approach for quality improvement, diagnostic and prognostic research purposes, and building clinical decision support tools.

A cascading model for nudging employees towards energy-efficient behaviour in tertiary buildings.

Energy-related occupant behaviour in the built environment is considered crucial when aiming towards Energy Efficiency (EE), especially given the notion that people are most often unaware and disengaged regarding the impacts of energy-consuming habits. In order to affect such energy-related behaviour, various approaches have been employed, being the most common the provision of recommendations towards more energy-efficient actions. In this work, the authors extend prior research findings in an effort to automatically identify the optimal Persuasion Strategy (PS), out of ten pre-selected by experts, tailored to a user (i.e., the context to trigger a message, allocate a task or providing cues to enact an action). This process aims to successfully influence the employees' decisions about EE in tertiary buildings. The framework presented in this study utilizes cultural traits and socio-economic information. It is based on one of the largest survey datasets on this subject, comprising responses from 743 users collected through an online survey in four countries across Europe (Spain, Greece, Austria and the UK). The resulting framework was designed as a cascade of sequential data-driven prediction models. The first step employs a particular case of matrix factorisation to rank the ten PP in terms of preference for each user, followed by a random forest regression model that uses these rankings as a filtering step to compute scores for each PP and conclude with the best selection for each user. An ex-post assessment of the individual steps and the combined ensemble revealed increased accuracy over baseline non-personalised methods. Furthermore, the analysis also sheds light on important user characteristics to take into account for future interventions related to EE and the most effective persuasion strategies to adopt based on user data. Discussion and implications of the reported results are provided in the text regarding the flourishing field of personalisation to motivate pro-environmental behaviour change in tertiary buildings.

ПОРІВНЯЛЬНИЙ АНАЛІЗ МОДЕЛЕЙ ТА МЕТОДІВ УПРАВЛІННЯ МАРКЕТИНГОВИМИ ПРОЕКТАМИ

The article examines the importance and effectiveness of using the project management methodology to ensure timely and budget-effective implementation of projects in accordance with customer requirements. The author emphasizes that project management is a strategic process that encompasses project initiation, planning, execution, monitoring, control, and closure. Special attention is paid to the role of project management in marketing, where complexity and constant dynamics require careful planning and execution. Using project management techniques in marketing teams can improve planning, improve efficiency and productivity, improve communication and collaboration, provide greater control over budget and resources, and lead to improved campaign results. Various marketing project management methodologies, their characteristics and differences are studied. The waterfall cascade model is defined as a sequential approach to dividing a project into fixed tasks, which can be effective for short and well-defined projects, but not suitable for complex and long-term tasks. Agile flexible model and Scrum are defined as fundamentally different approaches based on collaboration, iterability and speed. Agile emphasizes individuals over processes, while Scrum is defined by short iterations and a team motivation system. The Critical Path Method (CPM) and Critical Chain Marketing Project Management (CCPM) are defined as methodologies aimed at determining and optimizing project completion times. Integrated Marketing Project Management (IPM) is seen as an approach focused on standardizing and sharing processes across the organization, which can be useful for large creative agencies. Methodologies (PRiSM, PRINCE2, PMI PMBOK) expand the range of available approaches to marketing project management. Each of them has its own features and applications depending on the specific needs and characteristics of the project. The article provides a comprehensive overview of various methodologies and their relevance to specific conditions and types of marketing projects.

Study on the Dissemination Mode and Influence of Literature on Rural Revitalization Topics in the New Media Environment

Abstract The literature on rural revitalization serves as both a record and a witness to the development of rural revitalization in China. This paper outlines rural revitalization literature’s communication mode in the new media environment and proposes a model for its We propose IEOC, an important node group mining algorithm based on information entropy and overlap coefficient, to construct the communication influence model. We also introduce information entropy calculation nodes, measure the overlap of influence using overlap coefficients, and propose a floating probability cascade model, TFIC, to construct the communication influence model of rural revitalization literature combined with the model for analyzing the communication influence of rural revitalization literature, it reveals a moderately low total word frequency of high-frequency words (2332). Out of the 12 rural revitalization literature works under study, only Moon Beach has a communication influence index of more than 1. of more than 1. Compared to other mainstream literature, rural revitalization literature has a communication power of only 1785 and 18369, with fewer high-communication works and high-influence contents. The number of high-impact works and contents is relatively small. Overseas dissemination is characterized by a higher number of Chinese editions than foreign language editions, and there are also shortcomings in translation.

Project management of the lifecycle of students' research projects in higher pharmaceutical education

Management of students' research activities in recent years has been following the principles of project management, which involves the inclusion of project lifecycle management models. The aim is to determine the possibilities of using project lifecycle management models based on the Project Management Institute Project Management Body of Knowledge-6 methods in the research projects made by students receiving higher pharmaceutical education. The models under study are rigid (cascade, iterative, V-model) and flexible models (Agile models, AZ model). The assessment was carried out by 19 experts in the fields of knowledge of the Six-Pointed Star Project Management Body of Knowledge model according to the Likert scale. It has been established that rigid and flexible models can be selectively applied in the research of junior and senior students, depending on the priority of individual areas of knowledge of the Six-Pointed Star Project Management Body of Knowledge model and the scale of work. Among rigid models, the iterative model and the V-model were better than the cascade model in some areas of knowledge and at a certain scale of the research project. Among flexible models, Agile models outperformed the AZ model in terms of time and cost, and the AZ model was better than Agile models in terms of quality and risk. The preferential opportunities for the application of project lifecycle management models in the students' research projects in each of the fields of knowledge of the Six-Pointed Star Project Management Body of Knowledge model have been shown. For a more detailed study of the process of choosing a project lifecycle management model for students' research projects, it is necessary to create an algorithm for this process.

Cascading failure resilience analysis and recovery of automotive manufacturing supply chain networks considering enterprise roles

With the rapid development of global trade, the member enterprises in the automotive manufacturing supply chain are widely spread around the world. As a result, the scale of the automotive manufacturing supply chain network has become increasingly large, and the structure has become more complex. At the same time, the connection between enterprises is becoming closer. On one hand, the close business relationships between enterprises and the vast network structure accelerate the operation efficiency of the automotive manufacturing supply chain network. On the other hand, it also exposes the entire network system to high risks of cascading failures when facing disruptions due to enterprise production interruptions. Therefore, this work aims to establish a realistic cascading failure model for the automotive manufacturing supply chain network to explore its cascading failure characteristics. Based on this model, a two-stage recovery strategy is proposed to enhance the network’s cascading failure resilience. Experimental results demonstrate the following findings: (1) underload nodes that emerge after load redistribution are the main cause of cascading failure resilience degradation in automotive manufacturing supply chain networks; (2) cascading failure resilience is negatively correlated with the load-exponential coefficient, the capacity lower-limit coefficient, and the attenuation-tolerance coefficient; (3) among four different types of attacks, cascading failures caused by the high eigenvector centrality attack strategy and the high degree attack strategy impose the most severe damage on the automotive manufacturing supply chain network; (4) the proposed two-stage recovery strategy can significantly improve the cascading failure resilience of the automotive manufacturing supply chain network.

Limit Theorems for Default Contagion and Systemic Risk

We consider a general tractable model for default contagion and systemic risk in a heterogeneous financial network subjected to an exogenous macroeconomic shock. We show that under certain regularity assumptions, the default cascade model can be transformed into a death process problem represented by a balls-and-bins model. We state various limit theorems regarding the final size of default cascades. Under appropriate assumptions on the degree and threshold distributions, we prove that the final sizes of default cascades have asymptotically Gaussian fluctuations. We next state limit theorems for different system-wide wealth aggregation functions, which enable us to provide systemic risk measures in relation to the structure and heterogeneity of the financial network. Lastly, we demonstrate how these results can be utilized by a social planner to optimally target interventions during a financial crisis given a budget constraint and under partial information of the financial network.