Cascading Phenomena Research Articles

Analysis of Topological Properties and Robustness of Urban Public Transport Networks

With the acceleration of urbanization, public transport networks are an important part of urban transport systems, and their robustness is critical for city operation. The objective of this study is to analyze the topological properties and robustness of an urban public transport network (UPTN) with a view to enhancing the sustainability of urbanization. In order to present the topological structure of the UPTN, the L-Space complex network modeling method is used to construct a model. Topological characteristics of the network are calculated. Based on single evaluation indices of station significance, a comprehensive evaluation index is proposed as the basis for selecting critical stations. The UPTN cascading failure model is established. Using the proportion of the maximum connected subgraph as the evaluation index, the robustness of the UPTN is analyzed using different station significance indices and deliberate attack strategies. The public transport network of Xuzhou city is selected for instance analysis. The results show that the UPTN in Xuzhou city has small-world effects and scale-free characteristics. Although the network has poor connectivity, it is a convenient means to travel for residents with many independent communities. The network’s dynamic robustness is demonstrably inferior to its static robustness due to the prevalence of cascading failure phenomena. Specifically, the failure of important stations has a wider impact on the network performance. Improving their load capacity and distributing the routes via them will help bolster the network resistance against contingencies. This study provides a scientific basis and strategic recommendations for urban planners and public transport managers to achieve a more sustainable public transport system.

Influence maximization based on threshold models in hypergraphs.

Influence maximization problem has received significant attention in recent years due to its application in various domains, such as product recommendation, public opinion dissemination, and disease propagation. This paper proposes a theoretical analysis framework for collective influence in hypergraphs, focusing on identifying a set of seeds that maximize influence in threshold models. First, we extend the message passing method from pairwise networks to hypergraphs to accurately describe the activation process in threshold models. Then, we introduce the concept of hypergraph collective influence (HCI) to measure the influence of nodes. Subsequently, we design an algorithm, HCI-TM, to select the influence maximization set, taking into account both node and hyperedge activation. Numerical simulations demonstrate that HCI-TM outperforms several competing algorithms in synthetic and real-world hypergraphs. Furthermore, we find that HCI can be used as a tool to predict the occurrence of cascading phenomena. Notably, we find that the HCI-TM algorithm works better for larger average hyperdegrees in Erdös-Rényi hypergraphs and smaller power-law exponents in scale-free hypergraphs.

Coronavirus-like all-angle all-polarization broadband scatterer

Creeping waves traveling around a volumetric electromagnetic scatterer provide a significant contribution to its radar cross-section. While quite a few efforts were devoted to suppressing creeping waves as a part of radar countermeasures, here we utilize specially engineered creeping waves to our advantage to create broadband, all-angle, and polarization scatterers. Metalized spherical surfaces, patterned with corona virus-like spikes are designed to provide a broadband constructive interference between the specular reflection and creeping waves, elevating the scattering cross-section. The demonstrated miniature corona scatterers utilize both resonant cascading phenomena and traveling wave interference to tailor electromagnetic interactions, outperforming a resonant dipole in terms of amplitude and bandwidth quite significantly. Our experimental samples are fabricated with an additive manufacturing technique, where a 3D-printed plastic skeleton is subsequently metalized. Micron-thick layers allow governing electromagnetic interactions as if the entire object was made of solid metal. Lightweight, all-angle, all-polarization, and broadband compact scatterers such as these, reported here, have numerous applications, including radar deception, electromagnetic beckoning, and many others.

A Preventive Control Approach for Power System Vulnerability Assessment and Predictive Stability Evaluation

Early detection of cascading failures phenomena is a vital process for the sustainable operation of power systems. Within the scope of this work, a preventive control approach implementing an algorithm for selecting critical contingencies by a dynamic vulnerability analysis and predictive stability evaluation is presented. The analysis was carried out using a decision tree with a multi-parameter knowledge base. After the occurrence of an initial contingency, probable future contingencies are foreseen according to several vulnerability perspectives created by an adaptive vulnerability search module. Then, for cases identified as critical, a secure operational system state is proposed through a vulnerability-based, security-constrained, optimal power flow algorithm. The modular structure of the proposed algorithm enables the evaluation of possible vulnerable scenarios and proposes a strategy to alleviate the technical and economic impacts due to prospective cascading failures. The presented optimization methodology was tested using the IEEE-39 bus test network and a benchmark was performed between the proposed approach and a time domain analysis software model (EMTP). The obtained results indicate the potential of analysis approach in evaluating low-risk but high-impact vulnerabilities in power systems.

Large eddy simulation of three-dimensional hybrid forced-buoyancy convection in channels with a step

This paper follows and integrates the work started in Inam and Lappa (2021, Int. J. of Heat and Mass Transfer, 173, 121267 and 2022, Int. J. of Heat and Mass Transfer, 194(12), 122963) for unsteady flow in a channel with either a forward facing (FFS) or a backward-facing step (BFS) by removing the constraint of two-dimensionality and allowing the flow to develop along the spanwise direction. As a novel aspect with respect to the existing literature (where buoyancy effects in these geometries have generally been ignored), mixed forced-gravitational convection is examined. The governing equations, formulated according to the Boussinesq approximation, are integrated using an incompressible flow solver. Moreover, as the considered flow regime is turbulent [Ri=O(102) and Ra=O(107) for Pr=1], in order, to reduce the scale of the problem to a level where it is affordable, the analysis is developed in the framework of a large eddy simulation (LES) approach. Part of the study is devoted to a critical evaluation of the parameters required for the implementation of such a model. We show that while in some cases these may result in turbulent stress underestimation, in other cases, unphysical flow re-laminarization occurs due to excessive dissipation occurring on the small scales. The outcomes of the three-dimensional simulations are used to clarify some still poorly known aspects, especially the flow behavior in proximity to (before and after) the step, i.e. the point where the abrupt change in the channel cross-sectional area occurs. It is shown that a strong correlation exists between the regions where the horizontal flow separates and the presence of thermal plumes originating from the bottom wall. Moreover, the quantitative differences between two-dimensional (2D) and three-dimensional (3D) results are not limited to the patterning behavior at the flow macroscopic scale (where energy is injected into the system). The problem dimensionality also affects the cascading energy phenomena developing inside the inertial range of scales. In particular, while the thermal plumes in the FFS display a striking 3D nature, the BFS is characterized by a significant macroscopic component of vorticity along the main flow direction. In this specific case, the portion of the spectrum corresponding to the inertial regime is shifted towards higher or smaller amplitudes (with respect to the equivalent 2D dynamics) depending on the thermal boundary condition considered for the channel floor.

Exploring the Cascading Failure in Taxi Transportation Networks

To explore the ability of taxi transportation service capacity in unexpected conditions, based on the taxi GPS trajectory data, this paper presented a taxi transportation network and explored a cascading failure model with the non-linear function of traffic intensity as the initial load. Moreover, the cascading failure conditions for different initial loads with different parameter settings were derived by combining the complex network theory. We verified the ability of taxi transportation networks to withstand unexpected conditions and analyzed the differences and features of taxi transportation service capacity for different areas of Lanzhou city. Three sets of comparative simulation experiments were implemented. The results show that when the initial load regulation factor α<1/θ, the failure of nodes with smaller initial loads in the network is more likely to cause cascading failure phenomena. When α>1/θ, the failure of nodes with larger initial loads in the network is more likely to cause cascading failure phenomena. Additionally, when α=1/θ, there is no significant correlation between whether cascading failure phenomena occur in the network and node loads. This study can provide a prior basis for decision-making in the management of urban taxi operations under different passenger flow intensities.

Provable Sensor Sets for Epidemic Detection over Networks with Minimum Delay

The efficient detection of outbreaks and other cascading phenomena is a fundamental problem in a number of domains, including disease spread, social networks, and infrastructure networks. In such settings, monitoring and testing a small group of pre-selected nodes from the susceptible population (i.e., a sensor set) is often the preferred testing regime. We study the problem of selecting a sensor set that minimizes the delay in detection---we refer to this as the MinDelSS problem. Prior methods for minimizing the detection time rely on greedy algorithms using submodularity. We show that this approach can sometimes lead to a worse approximation for minimizing the detection time than desired. We also show that MinDelSS is hard to approximate within an O(n^(1-1/g))-factor for any constant g greater than or equal to 2 for a graph with n nodes. This instead motivates seeking a bicriteria approximations. We present the algorithm RoundSensor, which gives a rigorous worst case O(log(n))-factor for the detection time, while violating the budget by a factor of O(log^2(n)). Our algorithm is based on the sample average approximation technique from stochastic optimization, combined with linear programming and rounding. We evaluate our algorithm on several networks, including hospital contact networks, which validates its effectiveness in real settings.

A model for cascading failures with the probability of failure described as a logistic function

In most cascading failure models in networks, overloaded nodes are assumed to fail and are removed from the network. However, this is not always the case due to network mitigation measures. Considering the effects of these mitigating measures, we propose a new cascading failure model that describes the probability that an overloaded node fails as a logistic function. By performing numerical simulations of cascading failures on Barabási and Albert (BA) scale-free networks and a real airport network, we compare the results of our model and the established model describing the probability of failure as a linear function. The simulation results show that the difference in the robustness of the two models depends on the initial load distribution and the redistribution of load. We further investigate the conditions of our new model under which the network exhibits the strongest robustness in terms of the load distribution and the network topology. We find the optimal value for the parameter of the load distribution and demonstrate that the robustness of the network improves as the average degree increases. The results regarding the optimal load distribution are verified by theoretical analysis. This work can be used to develop effective mitigation measures and design networks that are robust to cascading failure phenomena.

Natural Hazards Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

AbstractThis article is about the state of ICON principles Goldman et al. (2021), https://doi.org/10.1029/2021EO153180 in natural hazards and a discussion on the opportunities and challenges of adopting them. Natural hazards pose risks to society, infrastructure, and the environment. Hazard interactions and their cascading phenomena in space and time can further intensify the impacts. Natural hazards’ risks are expected to increase in the future due to environmental, demographic, and socioeconomic changes. It is important to quantify and effectively communicate risks to inform the design and implementation of risk mitigation and adaptation strategies. Multihazard multisector risk management poses several nontrivial challenges, including: (a) integrated risk assessment, (b) Earth system data‐model fusion, (c) uncertainty quantification and communication, and (d) crossing traditional disciplinary boundaries. Here, we review these challenges, highlight current research and operational endeavors, and underscore diverse research opportunities. We emphasize the need for integrated approaches, coordinated processes, open science, and networked efforts (ICON) for multihazard multisector risk management.

Critical Component Analysis in Cascading Failures for Power Grids Using Community Structures in Interaction Graphs

Cascading phenomena have been studied extensively in various networks. Particularly, it has been shown that the community structures in networks impact their cascade processes. However, the role of community structures in cascading failures in power grids have not been studied heretofore. In this paper, cascading failures in power grids are studied using interaction graphs. Key evidence has been provided that the community structures in interaction graphs bear critical information about the cascade process and the role of system components in cascading failures in power grids. Furthermore, a centrality measure based on the community structures is proposed to identify critical components of the system, which their protection can help in containing failures within a community and prevent the propagation of failures to large sections of the power grid. Various criticality evaluation techniques, including data driven, epidemic simulation based, power system simulation based, and graph based, have been used to verify the importance of the identified critical components in the cascade process and compare them with those identified by traditional centrality measures. Moreover, it has been shown that the loading level of the power grid impacts the interaction graph and consequently, the community structure and criticality of the components in the cascade process.

WPP Model in Power System Security Assessment

The integration of wind power plants at large scale has an impact on the reliability, the stability and the security of power systems. Recent events such as the blackout of Australia in 2016, a power system with large participation of wind generation, have demonstrated the need for appropriate tools to simulate and assess the impacts of wind energy on the security of power systems. Therefore, this paper proposes the inclusion of wind power plants in the simulation and the analysis of cascading phenomena in the power system, to assess the impact of the high penetration of wind generation in the security assessment of the power system based on the well-known Manchester model. This paper proposes a model of wind power plants based on the scheme of connection to the transmission system in order to determine the response of a wind farm when a disturbance occurs in the power system and to obtain a behavior that resembles a situation real. The proposed methodology is tested in the IEEE RTS-system of 24 nodes, in which the impact on security is evaluated by increasing the participation of wind generation, and it is demonstrated that the security of the energy system is reduced by increasing the participation of this type of technology in the generation dispatch.

Hot deformation behaviour of Ti-6Al-4V alloy with a transformed microstructure: a multimodal characterisation

ABSTRACT Hot deformation behaviour of Ti-6Al-4V with transformed microstructure was investigated for very high deformation. Hot compression tests were carried out up to 80% height reduction at 950°C and strain rate of 0.1 s−1. The true stress–strain curve exhibits an anomalous flow behaviour with distinctly different characteristic zones. The flow stress reaches a peak in ‘zone 1’ followed by softening in ‘zone 2’ and further anomalously increases in ‘zone 3’ after a true strain of 0.9. The flow behaviour was further investigated by conducting interrupted hot compression tests of 20% and 50% height reduction in zone 2. Multimodal characterisations such as OM, EBSD, XRD, TKD and TEM analyses were employed for this study. The TKD and TEM analysis reveals that the α-laths structure (under compressive loading) kinks, bends and fragments, resulting in flow stress softening in the transformed microstructure. The bending and breaking coexist and lead to the cascading effect phenomena resulting in globular microstructure. Further, an increase of flow stress at very high strain in zone 3 is attributed to the formation of lamellar morphology. The apparent lamellar morphology holds the Burger orientation relationship where and and it deviates as lamellar morphology weakens. Interestingly, at very high deformation, dynamically recrystallised transformed microstructure shows nano-twinning, unlike the static recrystallised transformed microstructure. These outcomes are only possible when the multimodal characterisation technique is applied, since the volume fraction of β-phase is very low and it is distributed as a very thin layer between α-laths.

Theoretical model for cascading effects analyses

In case of exceptional events of natural or anthropogenic type, the elements at risk (people, buildings, infrastructures, economy, etc.) are often hit by sequences of ‘cascading events’, function of time and space, caused by the triggering event (earthquake, landslide, volcanic eruption, fire, electric failure, etc.).Generally, sequences of events can involve the same element at risk, and the combined effects of cascading phenomena can strongly amplify the impact caused by single events in terms of extension of the affected area and damage level. The final impact on the territory can be significant and require to be carefully assessed in terms of emergency planning and management.This paper discusses from a theoretical point of view the modelling needs and the main issues to be taken into account in the development of simulation tools aiming to include cascading effects analyses to effectively support decision-makers in their preparedness and disaster mitigation strategies in the framework of emergency planning at local, national and international level.The model aims at developing cascading effects scenarios at different level of detail, depending on the availability of inventory/exposure data for the different categories of elements at risk and hazard/impact models for the various hazard sources.It has been developed within EU-FP7 SNOWBALL project (Lower the impact of aggravating factors in crisis situations thanks to adaptive foresight and decision-support tools, 2015–2017).

Digital morphogenesis via Schelling segregation

Schelling’s model of segregation looks to explain the way in which particles or agents of two types may come to arrange themselves spatially into configurations consisting of large homogeneous clusters, i.e. connected regions consisting of only one type. As one of the earliest agent based models studied by economists and perhaps the most famous model of self-organising behaviour, it also has direct links to areas at the interface between computer science and statistical mechanics, such as the Ising model and the study of contagion and cascading phenomena in networks.While the model has been extensively studied it has largely resisted rigorous analysis, prior results from the literature generally pertaining to variants of the model which are tweaked so as to be amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory. In Brandt et al (2012 Proc. 44th Annual ACM Symp. on Theory of Computing) provided the first rigorous analysis of the unperturbed model, for a specific set of input parameters. Here we provide a rigorous analysis of the model’s behaviour much more generally and establish some surprising forms of threshold behaviour, notably the existence of situations where an increased level of intolerance for neighbouring agents of opposite type leads almost certainly to decreased segregation.

Threshold driven contagion on weighted networks

Weighted networks capture the structure of complex systems where interaction strength is meaningful. This information is essential to a large number of processes, such as threshold dynamics, where link weights reflect the amount of influence that neighbours have in determining a node's behaviour. Despite describing numerous cascading phenomena, such as neural firing or social contagion, the modelling of threshold dynamics on weighted networks has been largely overlooked. We fill this gap by studying a dynamical threshold model over synthetic and real weighted networks with numerical and analytical tools. We show that the time of cascade emergence depends non-monotonously on weight heterogeneities, which accelerate or decelerate the dynamics, and lead to non-trivial parameter spaces for various networks and weight distributions. Our methodology applies to arbitrary binary state processes and link properties, and may prove instrumental in understanding the role of edge heterogeneities in various natural and social phenomena.

Robustness of scale-free networks with various parameters against cascading failures

Many crucial real-world networks could be modeled as scale-free networks, which play an important role in the human society. Once these functional network systems suffer from cascading failures, they may lead to the malfunction of the rest part of networks. In recent years, the researches on cascading failures of scale-free networks have drawn great attention, and many studies focused on modeling the cascading phenomena and studying how to improve the robustness of networks against failures. However, the scale-free networks used in most existing studies are with fixed network parameters including scaling exponent and assortativity, which is segmentary for depicting the functionality of networked systems comprehensively. Therefore, in this paper, a series of generated scale-free networks with a certain range of parameters is adopted to evaluate the robustness against cascading failures. In addition, to make an accurate description of the ability of scale-free networks against cascading failures, we propose a link-based robustness index. The results show that influenced by the network structure, the enlargement of assortativity makes the networks weaker to resist node-based cascading failures, yet the impact on promoting link-based robustness is not clear enough. With higher scaling exponents, the tolerance of scale-free networks against link-based cascading failures decreases, however, it does not show obvious relation to node-based robustness.

Critical arcs detection in influence networks

The influence class of network problems models the propagation of influence (an abstraction of cascading beliefs, behaviors, or physical phenomena) in a network. Such problems have applications in social networks, electrical networks, computer networks, viral spreading, and so on. These types of networks have also been studied through the lens of critical arcs detection; that is, which arcs (edges) are the most important for maintaining some property of the network (e.g., connectivity). We introduce a new class of problems at the intersection of these two models. Specifically, given a set of seed nodes and the linear threshold influence propagation model, our work proposes to determine which arcs (e.g., relationships in a social network or communication pathways in a telecommunication network) are most critical to the influence propagation process. We prove NP‐hardness of the problem. Time‐dependent and time‐independent mixed‐integer programming (MIP) models are introduced. Insights gleaned from MIP solutions leads to the development of an improved MIP‐based exact algorithm rooted in the idea of diffusion expansion. A heuristic based upon a new centrality measure is also proposed, and computational results are presented. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 71(4), 412–431 2018

Using reasoned imagination to learn about cascading hazards: a pilot study

Purpose – The purpose of this paper is to present the results of a pilot study involving high school teachers in natural sciences. The aim was to foster critical thinking about cascading hazards via the use of reasoned imagination. Cascading phenomena can lead to extreme catastrophes and are thus a challenge for disaster prevention and management. Design/methodology/approach – Following a presentation listing some known cascading phenomena, the participants completed a questionnaire consisting of a blank hazard correlation matrix (HCM) and some open-ended questions. The HCM qualitatively described possible interactions between 16 different perils selected from a large spectrum of natural, technological and socio-economic hazards. Findings – Most participants were able to describe cascading phenomena within the HCM by reducing them into sets of one-to-one interactions. Based on their experience and imagination, the participants foresaw additional interactions that were not discussed, never observed but are scientifically plausible. The majority of the respondents reported that they learnt something new and wanted to learn more about cascading hazards. Originality/value – The HCM is especially effective in translating complex hazard scenarios into basic interactions and vice versa. Being imaginative (here via the use of reasoned imagination) and accessible, the HCM could be used as basis for transformative learning in the education of the public and of practitioners on the role of cascading hazards in catastrophes.

Implications of cascading effects for the EU Floods Directive

ABSTRACTThe adoption of the European Floods Directive (2007/60/EC) represented a crucial improvement in the management of watercourses and coastlines. However, the beginning of a new phase of implementation requires the assessment of which emerging topics may be included in the review process. The aim of our research is to understand the existence of any legislative gaps that could limit the preparedness to cascading events and critical infrastructures breakdowns. First, we provide a review of the Floods Directive, the cascading phenomena and the vulnerability of critical infrastructures in the European legislation. Secondly, we analyse some case studies to test the present approach and to improve the work of decision makers. Our results suggest that the Floods Directive tends to focus on localized flood impacts at smaller time scale and it could be ineffective to address the cross-scale impact of cascading events. Although some of the corrective actions may not be of competence of the Directive, we argue that their inclusion could limit uncertainties in the attribution of responsibilities and the coordination among different institutional levels.

Critical infrastructure, panarchies and the vulnerability paths of cascading disasters

Cascading effects and cascading disasters are emerging fields of scientific research. The widespread diffusion of functional networks increases the complexity of interdependent systems and their vulnerability to large-scale disruptions. Although in recent years studies of interconnections and chain effects have improved significantly, cascading phenomena are often associated with the “toppling domino metaphor”, or with high-impact, low-probability events. This paper aimed to support a paradigm shift in the state of the art by proposing a new theoretical approach to cascading events in terms of their root causes and lack of predictability. By means of interdisciplinary theory building, we demonstrate how cascades reflect the ways in which panarchies collapse. We suggest that the vulnerability of critical infrastructure may orientate the progress of events in relation to society’s feedback loops, rather than merely being an effect of natural triggers. Our conclusions point to a paradigm shift in the preparedness phase that could include escalation points and social nodes, but that also reveals a brand new field of research for disaster scholars.