Network isolators inhibit failure spreading in complex networks

Resilience of transportation infrastructure networks to road failures

Anthropogenic climate change drives extreme weather events, leading to significant consequences for both society and the environment. This includes damage to road infrastructure, causing disruptions in transportation, obstructing access to emergency services, and hindering humanitarian organizations after natural disasters. In this study, we develop a novel method for analyzing the impacts of natural hazards on transportation networks rooted in the gravity model of travel, offering a fresh perspective to assess the repercussions of natural hazards on transportation network stability. Applying this approach to the Ahr valley flood of 2021, we discovered that the destruction of bridges and roads caused major bottlenecks, affecting areas considerably distant from the flood's epicenter. Furthermore, the flood-induced damage to the infrastructure also increased the response time of emergency vehicles, severely impeding the accessibility of emergency services. Our findings highlight the need for targeted road repair and reinforcement, with a focus on maintaining traffic flow for emergency responses. This research provides a new perspective that can aid in prioritizing transportation network resilience measures to reduce the economic and social costs of future extreme weather events.

Predicting dynamic stability from static features in power grid models using machine learning

A reliable supply with electric power is vital for our society. Transmission line failures are among the biggest threats for power grid stability as they may lead to a splitting of the grid into mutual asynchronous fragments. New conceptual methods are needed to assess system stability that complement existing simulation models. In this article, we propose a combination of network science metrics and machine learning models to predict the risk of desynchronization events. Network science provides metrics for essential properties of transmission lines such as their redundancy or centrality. Machine learning models perform inherent feature selection and, thus, reveal key factors that determine network robustness and vulnerability. As a case study, we train and test such models on simulated data from several synthetic test grids. We find that the integrated models are capable of predicting desynchronization events after line failures with an average precision greater than 0.996 when averaging over all datasets. Learning transfer between different datasets is generally possible, at a slight loss of prediction performance. Our results suggest that power grid desynchronization is essentially governed by only a few network metrics that quantify the networks' ability to reroute the flow without creating exceedingly high static line loadings.

Cascading Robustness Analysis of Wireless Sensor Networks with Varying Multisink Placement

In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement affects its cascading robustness is essential but still largely missing in the field of complex networks. To this end, this paper puts forward an actual cascading model for WSNs based on the multisink-oriented load distribution characteristics, in which two load redistribution mechanisms (i.e., global routing and local routing) are designed to imitate the most commonly used routing schemes. On this basis, a number of topological parameters are considered to quantify the sinks' locations, and then, the relationship between these quantities with network robustness is investigated on two typical WSN topologies. Moreover, by employing the simulated annealing approach, we find the optimal multisink placement for maximizing network robustness and compare the topological quantities before and after the optimization to validate our findings. The results indicate that for the sake of enhancing the cascading robustness of a WSN, it is better to place its sinks as hubs and decentralize these sinks, which is independent of network structure and routing scheme.

Impact of policy response on health protection and economic recovery in OECD and BRIICS countries during the early stages of the COVID-19 pandemic

OBJECTIVES

During the early stages of the COVID-19 pandemic, the full reopening of the economy typically accelerated viral transmission. This study aims to determine whether policy response could contribute to the dual objective of both reducing the spread of the epidemic and revitalising economic activities.

STUDY DESIGN

This is a longitudinal study of Organization for Economic Cooperation and Development (OECD) and Brazil, Russia, India, Indonesia, China, and South Africa (BRIICS) from the first quarter (Q1) of 2020 to the same period of 2021.

METHODS

From a health-economic perspective, this study established a framework to illustrate the following outcomes: suppression-prosperity, outbreak-stagnancy, outbreak-prosperity and suppression-stagnancy scenarios. Multinomial logistic models were used to analyse the associations between policy response with both the pandemic and the economy. The study further examined two subtypes of policy response, stringency/health measures and economic support measures, separately. The probabilities of the different scenarios were estimated.

RESULTS

Economic prosperity and epidemic suppression were significantly associated with policy response. The effects of policy response on health-economic scenarios took the form of inverse U-shapes with the increase in intensity. 'Leptokurtic', 'bimodal' and 'long-tailed' curves demonstrated the estimated possibilities of suppression-prosperity, outbreak-prosperity and suppression-stagnancy scenarios, respectively. In addition, stringency/health policies followed the inverted U-shaped pattern, whereas economic support policies showed a linear pattern.

CONCLUSIONS

It was possible to achieve the dual objective of economic growth and epidemic control simultaneously, and the effects of policy response were shaped like an inverse U. These findings provide a new perspective for balancing the economy with public health during the early stages of the pandemic.

Dual communities in spatial networks

Both human-made and natural supply systems, such as power grids and leaf venation networks, are built to operate reliably under changing external conditions. Many of these spatial networks exhibit community structures. Here, we show that a relatively strong connectivity between the parts of a network can be used to define a different class of communities: dual communities. We demonstrate that traditional and dual communities emerge naturally as two different phases of optimized network structures that are shaped by fluctuations and that they both suppress failure spreading, which underlines their importance in understanding the shape of real-world supply networks.

A new scenario for Braess's paradox in power grids

We consider several topologies of power grids and analyze how the addition of transmission lines affects their dynamics. The main example we are dealing with is a power grid that has a tree-like three-element motif at the periphery. We establish conditions where the addition of a transmission line in the motif enhances its stability or induces Braess's paradox and reduces stability of the entire grid. By using bifurcation theory and nonlocal stability analysis, we show that two scenarios for Braess's paradox are realized in the grid. The first scenario is well described and is associated with the disappearance of the synchronous mode. The second scenario has not been previously described and is associated with the reduction of nonlocal stability of the synchronous mode due to the appearance of asynchronous modes. The necessary conditions for stable operation of the grid, under the addition of a line, are derived. It is proved that the new scenario for Braess's paradox is realized in the grids with more complex topologies even when several lines are added in their bulks.

The use of PLANS and NetworkX in modeling power grid system failures

The theoretical and practical aspects and results of simulations based on a specialized tool that is used in the energy industry were adressed. The previously discussed cases in the literature by taking into account the worst case and critical states of networks in terms of complex networks were extended. Using the Monte-Carlo method, the vulnerability of the power grid to node failures was investigated, both in terms of the use of specialized software, which is used in the power industry, and a tool for the analysis of complex networks graphs. We present the results obtained and the observed analogy between the results of the analysis performed in specialized software and the complex network graph analysis tool. It has been shown that the results obtained coincide for both software packages, even though their application focuses on slightly different aspects of system operation. Moreover, further possibilities of extending the research in this direction are proposed, taking into account not only the improvement of the method used, but also a significant increase in the size of the tested structure model.

Synchronization dynamics on power grids in Europe and the United States

Dynamical simulation of the cascade failures on the Europe and United States (U.S.) high-voltage power grids has been done via solving the second-order Kuramoto equation. We show that synchronization transition happens by increasing the global coupling parameter K with metasatble states depending on the initial conditions so that hysteresis loops occur. We provide analytic results for the time dependence of frequency spread in the large-K approximation and by comparing it with numerics of d=2,3 lattices, we find agreement in the case of ordered initial conditions. However, different power-law (PL) tails occur, when the fluctuations are strong. After thermalizing the systems we allow a single line cut failure and follow the subsequent overloads with respect to threshold values T. The PDFs p(N_{f}) of the cascade failures exhibit PL tails near the synchronization transition point K_{c}. Near K_{c} the exponents of the PLs for the U.S. power grid vary with T as 1.4≤τ≤2.1, in agreement with the empirical blackout statistics, while on the Europe power grid we find somewhat steeper PLs characterized by 1.4≤τ≤2.4. Below K_{c}, we find signatures of T-dependent PLs, caused by frustrated synchronization, reminiscent of Griffiths effects. Here we also observe stability growth following the blackout cascades, similar to intentional islanding, but for K>K_{c} this does not happen. For T<T_{c}, bumps appear in the PDFs with large mean values, known as "dragon king" blackout events. We also analyze the delaying or stabilizing effects of instantaneous feedback or increased dissipation and show how local synchronization behaves on geographic maps.