Top PDF Modeling Cascading Failures in Complex Networks

Modeling Cascading Failures in Complex Networks

Modeling Cascading Failures in Complex Networks

5. CONCLUSION ∗ We introduced a load-based cascade model to study the vulnerability of complex net- works under random single-node attacks, where the ER random graph with finite size was used to represent the network. We assumed that the capacity of a node is proportional to its initial load and the load of a failed node is redistributed to its neighbors according to their capacity. The average failure ratio at each step was used to quantify the damage ex- perienced by the network. A step-by-step estimation of the average failure ratio has been provided. The accuracy of such estimations was validated by numerical results. Our anal- ysis for finite-size networks revealed a phase transition phenomenon in network reactions to single-node attacks, where the average value of the failure ratio drops quickly within a short interval of the load margin. We characterized this interval by finding the critical value of the tolerance parameter at which the failure ratio takes its median value and is most sensitive to the variation of the tolerance parameter. We also derived the threshold interval within which this phase transition occurs. Our findings shed light on how to set the load margin for both robustness and efficient use of resources in designing networks resilient to random single-node attacks.
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Modeling Cascading Failures in Power Systems in the Presence of Uncertain Wind Generation

Modeling Cascading Failures in Power Systems in the Presence of Uncertain Wind Generation

Development of efficient synthetic power system models requires that their size, complexity, and electrical and topological characteristics match with those of real power grids. Power networks are complex infrastructures with various components. In addition to topological characteristics of power networks, they include several components with different electrical characteristics such as different types of transformers, switched shunt reactive power compensation, remote tap changing bus voltage regulation, etc. Development of synthetic power networks with the same complexity that can simulate the exact behavior of actual grids needs a comprehensive study of different components from both electrical and topological perspectives. For example, authors in [123] used historical data and probabilistic methods for reliability assessment of the distribution system. Also, the increasing level of renewable generation in power systems has introduced an unprecedented level of uncertainty into grids [124]. In the literature, many studies are dedicated for characterizing actual power networks mainly from topological perspectives such as ring-structured power grid developed in [125] and tree-structured power grid model to address the power system robustness [26], [126]. Small world approach described in [127] served as a reference for the works of [28], [128], [129] to develop an approach for generating truly synthetic transmission line topologies. A random topology power network model, called RT-nestedSmallWorld, is proposed in [129] based on comprehensive studies on the electrical topology of some real-world power grids. The impacts of different bus type assignments in synthetic power networks on grid vulnerability to cascading failures are investigated in [130].
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Epidemic and Cascading Survivability of Complex Networks

Epidemic and Cascading Survivability of Complex Networks

Abstract—Our society nowadays is governed by complex networks, examples being the power grids, telecommunication networks, biological networks, and social networks. It has become of paramount importance to understand and characterize the dynamic events (e.g. failures) that might happen in these complex networks. For this reason, in this paper, we propose two measures to evaluate the vulnerability of complex networks in two different dynamic multiple failure scenarios: epidemic-like and cascading failures. Firstly, we present epidemic survivability (ES), a new network measure that describes the vulnerability of each node of a network under a specific epidemic intensity. Secondly, we propose cascading survivability (CS), which characterizes how potentially injurious a node is according to a cascading failure scenario. Then, we show that by using the distribution of values obtained from ES and CS it is possible to describe the vulnerability of a given network. We consider a set of 17 different complex networks to illustrate the suitability of our proposals. Lastly, results reveal that distinct types of complex networks might react differently under the same multiple failure scenario.
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Analysis of Cascading Failures of Interdependent Networks under Random Attacks

Analysis of Cascading Failures of Interdependent Networks under Random Attacks

Abstract. With continuous deepening of researches on complex network problems, the cascading failure problem of interdependent networks becomes one of the hot research topics in this field at present. This paper establishes a cascading failure model of interdependent networks based on a load-capacity model. And then we focus on the robustness problem of interdependent networks under random attacks based on four different network combinations. At last, an interdependent coupling network constituted of two networks with the node quantity of ten is taken as the example to verify conclusions obtained in this paper. Research results show that under random attacks, robustness of the asymmetric interdependent networks is similar with the previous research results, namely, robustness of the networks decreases with the increase of removal proportion and increases with the increase of tolerance coefficient. In addition, BA-BA interdependent networks and disassortative link networks perform better than other network combinations and coupling manners.
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Attack induced cascading breakdown in complex networks

Attack induced cascading breakdown in complex networks

Another example of cascading failure is the Internet, where the load represents data packets a node (router) is requested to transmit and overloading corresponds to con- gestion [20]. The rerouting of data packets from a con- gested router to another may spread the congestion to a large fraction of the network. With the possibility of cas- cading failures, a realistic concern is attacks on complex networks. In particular, for a scale-free network, major- ity of the nodes deal with small amount of load, so the probability for a node with a large amount of load to fail randomly is small. This, of course, will not be the case of intentional attacks that usually target one or a few of the most heavily linked nodes. The work by Albert et al. [21] demonstrated that scale-free networks possess the robust-yet-fragile property, in the sense that they are robust against random failures of nodes but fragile to in- tentional attacks. Cohen et. al. [22, 23] studied inter- net breakdown by random failure and intentional attack. In their works, a scale-free network can become disinte- grated under attacks on a small but still appreciable set of nodes that include a substantial fraction of links in the network. Attack on a single or very few nodes will in general not bring down the network. This result was ac- tually obtained based purely on the scale-free architecture of the network. In other words, dynamics in the network, i.e., how information or load is distributed in the network, was not taken into account.
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A Survey on - Vulnerability Analysis on Cascading Failure in Power Grid Complex Network

A Survey on - Vulnerability Analysis on Cascading Failure in Power Grid Complex Network

One of many threats posed to complex network systems due to the large scale inter-connectivity is the cascading failure. Cascading failures are common in most of the complex communication and transportation networks that are the basic components of our lives and industry. Cascading failures take place in electrical power grids. In fact, when for any reason a line goes down, its power is automatically shifted to the neighboring lines, which in most of the cases are able to handle the extra load. Sometimes, however, these lines are also overloaded and must redistribute their increased load to their neighbors. This eventually leads to a cascade of failures: a large number of transmission lines are overloaded and malfunction at the same time. This is exactly what happened on 10 August 1996 when a 1300-mw electrical line in southern Oregon sagged in the summer heat, initiating a chain reaction that cut power to more than 4 million people in 11 Western States. And probably this is also what happened on 14 August 2003 when an initial disturbance in Ohio triggered the largest blackout in the U.S.’s history in which millions of people remained without electricity for as long as 15 h [3].
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Modeling, Simulation, and Analysis of Cascading Outages in Power Systems

Modeling, Simulation, and Analysis of Cascading Outages in Power Systems

Cascading phenomena are complicated because of the diversity of failures and the many different mechanisms by which failures can interact. There are varying modeling requirements and timescales (milliseconds for electromechanical effects and tens of minutes for voltage support and thermal heating). Combinations of several of types of failures and interactions can typically occur in large blackouts, including cascading overloads, failures of protection equipment, transient instability, forced or unforced initiating outages, reactive power problems and voltage collapse, software, communication, and operational errors. Therefore it is very difficult to analyze it through conventional power system analysis approaches and models. Many models and approaches have been proposed to try to consider those mechanisms [11-94]. Some models and approaches are utilizing complex network theory to investigate the relationship between the propagation of cascading outages and topological structure. Some are using stochastic approaches to consider the uncertainties in a cascading outage. Some are modeling dynamics of system to involve machine, voltage and frequency issues. High-level statistical models have also been proposed to estimate the average cascading outage propagation and blackout distribution sizes, which can provide useful suggestions for power system long- term planning. Interdependent infrastructures are modeled to analyze the interactions between power grids and cyber networks and study the propagation of cascading outage between different networks.
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Topological analysis and mitigation strategies for cascading failures in power grid networks

Topological analysis and mitigation strategies for cascading failures in power grid networks

The analysis of the power grid network carried out in this thesis, and the above results lead us to some interesting conclusions. The topology of the power grid network greatly contributes to its robustness. It determines the connectivity of the network and hence gives an idea about which connections may impart more strength to the network. However, the feasibility of modeling the real power grid networks according to the presented analysis must be investigated. The generated networks are better connected than the standard networks because of shorter characteristic path length and shorter diameter. They show more resistance to cascading failures because of their topology.
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Cascading failures analysis considering extreme virus propagation of cyber-physical systems in smart grids

Cascading failures analysis considering extreme virus propagation of cyber-physical systems in smart grids

Copyright © 2019 Tao Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Communication networks as smart infrastructure systems play an important role in smart girds to monitor, control, and manage the operation of electrical networks. However, due to the interdependencies between communication networks and electrical networks, once communication networks fail (or are attacked), the faults can be easily propagated to electrical networks which even lead to cascading blackout; therefore it is crucial to investigate the impacts of failures of communication networks on the operation of electrical networks. This paper focuses on cascading failures in interdependent systems from the perspective of cyber- physical security. In the interdependent fault propagation model, the complex network-based virus propagation model is used to describe virus infection in the scale-free and small-world topologically structured communication networks. Meanwhile, in the electrical network, dynamic power flow is employed to reproduce the behaviors of the electrical networks after a fault. In addition, two time windows, i.e., the virus infection cycle and the tripping time of overloaded branches, are considered to analyze the fault characteristics of both electrical branches and communication nodes along time under virus propagation. The proposed model is applied to the IEEE 118-bus system and the French grid coupled with different communication network structures. The results show that the scale-free communication network is more vulnerable to virus propagation in smart cyber-physical grids.
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Mitigation of cascade failures in complex networks: theory and application

Mitigation of cascade failures in complex networks: theory and application

The electric power system as a pillar of the economy is a complex infrastructure. Delivery of electric power across long distances is made possible via its inter-connectivity. But, at the same time, it could be turned into power system flaw since the disturbances can be propagated through the same network. In other words, the occurrence of blackouts raised by cascading failures is the direct result of electrical and physical properties of power systems. Cascading failure in power systems as a complex dynamical event is usually triggered by ever-rising demand, increasingly renewables penetration, power system component failures sudden load redistribution in transmission lines, complicated interconnections which sometimes gives rise to inevitable control systems malfunction. Even if this event is managed by protection relays’ immediate reaction by disconnecting the faulty line, it can make other lines overloaded, or busbars work higher than their standard limitations. Dwivedi et al. (Dwivedi and Yu, 2013) proposed a new centrality measure for ranking the power lines in a power system based on the maximum flow algorithm (Ford and Fulkerson, 1956a). They showed that in power systems, there are some power lines more vulnerable to attacks which if removed, there would be a significant drop in power system functionality by shifting their loads to adjacent components.
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Modeling and Analysis of Cascading Failure in Complex Power Networks

Modeling and Analysis of Cascading Failure in Complex Power Networks

The Potential Cascading Models (PCM) is an integrated function of Physical and Operational Margins (POM) [103]. POM is an AC power flow analysis application which utilizes the full Newton-Raphson method to solve a nonlinear power flow. PCM, integrated in POM, aims at predicting potential cascading failures. It allows the identification of initiating events and cascading chains, the ranking and visualizing of cascading outages. It utilizes the “cluster” approach to get the initial N-1 or N-2 contingencies. The cascading failure analysis was performed using US 2007 Eastern Interconnection model summer peak case consisting of approximate 50,000 buses and 65,000 branches. Most results were consistent with prior manually analysis, while some unidentified potentially cascading-initiating possibilities were found. Later, the model was used to test remedial actions with the same data [104]. Optimal remedial actions were applied at each stage during the cascading until the cascading has been fully stopped. These remedial actions included MW dispatch, MVAr dispatch, transformer tap change, phase-shifter adjustment, capacitor and reactor switching, emergency load curtailment, line switching in and out and optimal capacitor placement (new sources of reactive power) [105]. It showed that all identified potential cascading failure can be prevented using the proposed remedial actions. The paper also studied the minimum amount of load shedding needed to mitigate the cascading.
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Model of Cascading Failures for Communication Networks

Model of Cascading Failures for Communication Networks

Communication networks including different kind of networks can provide an information dissemination service for users at different locations. A communication network system consists of different physical devices and a variety of network protocols. Because the routers responsible for storing and forwarding data packets play a central role in the network, research on the interaction between routers is crucial for maintaining network performance. The substantial development of communication networks with few predictable plans has led to the very large scale of, and extremely complex connections between, routers. Based on empirical research, Goldenberg et al. [12] found small-world and scale-free properties in communication networks, thereby laying a solid foundation for the promotion and application of complex network theory.
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Statistical physics of cascading failures in complex networks

Statistical physics of cascading failures in complex networks

Interdependency modifies many known percolation properties for single networks. As mentioned in the previous section, topology of the network affects the percolation properties. Single networks with broad degree distribution, like scale-free network, is more robust than networks with narrow degree distributions such as Random Regular or Erd˝ os-R´ enyi networks. Interdependency changes these properties. Interdependent networks with broad degree distributions are more robust to random failures than networks with narrow degree distribution [21]. Parshani et al. showed that increasing the interdependency between networks also renders the system more vulnerable to cascading failures. In the percolation theory formalism, this result is manifested into percolation transition changing from second- to first- order as the coupling between layers of networks is increased from no interdependency to full interdependency. As explained before, first-order transition is considered to be a sign of instability in the networks [62].
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Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber-Physical Systems in Smart Grids

Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber-Physical Systems in Smart Grids

Copyright © 2019 Tao Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Communication networks as smart infrastructure systems play an important role in smart girds to monitor, control, and manage the operation of electrical networks. However, due to the interdependencies between communication networks and electrical networks, once communication networks fail (or are attacked), the faults can be easily propagated to electrical networks which even lead to cascading blackout; therefore it is crucial to investigate the impacts of failures of communication networks on the operation of electrical networks. This paper focuses on cascading failures in interdependent systems from the perspective of cyber- physical security. In the interdependent fault propagation model, the complex network-based virus propagation model is used to describe virus infection in the scale-free and small-world topologically structured communication networks. Meanwhile, in the electrical network, dynamic power flow is employed to reproduce the behaviors of the electrical networks after a fault. In addition, two time windows, i.e., the virus infection cycle and the tripping time of overloaded branches, are considered to analyze the fault characteristics of both electrical branches and communication nodes along time under virus propagation. The proposed model is applied to the IEEE 118-bus system and the French grid coupled with different communication network structures. The results show that the scale-free communication network is more vulnerable to virus propagation in smart cyber-physical grids.
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iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

According to a global natural disaster synthesis report (Dilley et al., 2005), over 790 million people are exposed to more than one natural hazard, based on the past 2 decades of historical loss data. Concurrent or time-lagged cascading multi-hazards are worldwide phenomena. In spite of their cascading nature, forecasts and warnings and risk assess- ments for such events conventionally are oriented towards single hazards, treating the cascading events as independent phenomena (Hsu et al., 2011; Wastl et al., 2011). One ex- ample is the severe storm system accompanied by a deadly tornado, heavy rain, and flash flooding that occurred in Ok- lahoma City (OKC) on 31 May 2013, in which more peo- ple were killed unexpectedly by the flash flooding than by the tornado, making it the deadliest flooding event that has ever occurred in OKC. This is partly due to the fact that the storm (accompanied by heavy precipitation and the tornado) and flash flood were forecasted by two separate warning sys- tems and their warnings were issued separately (Uccellini et al., 2014); the public was well aware of the tornado threat but largely unaware of the flood threat in spite of several National Weather Service (NWS) products and outreach ef- forts (Uccellini et al., 2014). Moreover, the public’s attention was mostly drawn to the tornado warnings (not to the flash flooding threat) mainly because this storm occurred only 10 days after the disastrous EF-5 tornado which devastated Moore, OK, and resulted in 24 fatalities and USD 2 billion in property damage. Although several recent studies have investigated multi-hazards and multi-hazard risk assessment (Budimir et al., 2014; Gill and Malamud, 2014; May, 2007; Mignan et al., 2014), these multi-hazard studies are still in the early stages of conceptual development (Gill and Mala- mud, 2014; Kappes et al., 2010). Knowledge gaps and dis- ciplinary barriers in the development of multi-hazard ap- proaches remain formidable. It is essential to understand the cascading effects of multiple natural hazards in an integrated way in order to accurately forecast their occurrence and as- sess their potential risks and societal impacts.
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Cascading polar coding and LT coding for radar and sonar networks

Cascading polar coding and LT coding for radar and sonar networks

In radar and sonar networks, heavy clutter and noise have generated strong impairments to information trans- mission and processing [1]. Information processing in radar and sonar networks is critical in target detection and recognition [2]. For example, in radar sensor net- works, waveform diversity is the technology that allows one or more sensors on board a platform to automati- cally change operating parameters, e.g., frequency, gain pattern, and pulse repetition frequency (PRF), to meet the varying environments. It has long been recognized that judicious use of properly designed waveforms, coupled with advanced receiver strategies, is fundamental to fully utilize the capacity of the electromagnetic spectrum [3, 4]. As a result, there are emerging and compelling changes in system requirements such as more efficient spectrum
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STATISTICAL MODELLING OF PIPE FAILURES IN WATER NETWORKS

STATISTICAL MODELLING OF PIPE FAILURES IN WATER NETWORKS

The challenge is to develop reliable models for predicting the future renewal requirements for each individual pipe in the water network. Experience has shown that a significant number of network repairs are performed on an unscheduled basis. This reactive maintenance has the disadvantage that damage has to occur before measures are taken (i.e. “putting out fires”). Using this maintenance strategy, the rehabilitated pipes are selected according to emergency criteria, such as the number of breaks on the actual pipe. An alternative to the reactive strategy is a proactive strategy (Sægrov et al., 1999). In a proactive strategy the service determines the maintenance requirements by taking into account the state of the pipes and forecasting their degradation. Pipe failures cause considerable cost and inconvenience. Since it is not practical or economically possible to rehabilitate the entire length of the network, a targeting of rehabilitation resources is required. With limited resources, the ability to avoid damage and to optimise the use of available funds for preventative maintenance by employing predictive models is a preferred option for water network management. The proactive strategy requires a good knowledge of the network characteristics including the deterioration factors and the failure record. This means the installation of a computerised database, preferably in the form of a geographical information system. To this date, the benefits of a proactive over a reactive approach have not been demonstrated. However, this might be as a result of the inadequate evaluation models used to date.
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Making Networks Robust to Component Failures

Making Networks Robust to Component Failures

To address (a), we design link-failure detection and reporting mechanisms that use OpenFlow to detect link failures when and where they occur inside the network. For part (b), we formulate a new problem, Multicast Recycling, that aims to pre-compute backup multicast trees that minimize control plane signaling overhead. We prove Multicast Recycling is at least NP-hard and present a corresponding approximation algorithm. Lastly, two control plane algorithms are proposed that signal data plane switches to install pre-computed backup trees. An optimized version of each installation algorithm is designed that finds a near minimum set of forwarding rules by sharing forwarding rules across multicast groups. This optimization reduces backup tree install time and control state. We implement these algorithms using the POX open-source OpenFlow controller [57] and evaluate them using the Mininet emulator [50], quantifying control plane signaling and installation time.
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Complex Networks as a Unified Framework for Descriptive Analysis and Predictive Modeling in Climate

Complex Networks as a Unified Framework for Descriptive Analysis and Predictive Modeling in Climate

A closer look reveals that the clusters reflect many known relationships among climate variables, but also a few that are not as obvious and hence may be of interest to climate scientists. For example, cluster 5 in panel 3(a) seems to capture a well known teleconnection between the El Ni˜ no Southern Oscillation and the Indian Ocean [3, 17]. Moreover, panels 3(a), 3(d), 3(e), and 3(f) all look remarkably similar. The close correspondence between sea surface temperature and precipitable water is explained by the Clausius-Clapeyron relation [24], which describes the increased water-holding capacity of air with increasing temperature. Relative humidity in turn is a function of temperature and atmospheric water content, explaining its relationship with both sea surface temperature and precipitable water. Lastly, the connection of wind speed with this group is not apparent, but a search of domain literature revealed that there is in fact a known relationship between surface winds and sea surface temperature [22]. Geopotential height depends on sea level pressure, which explains the similarity in their clusters. We also observe distinctive latitudinal bands, likely a result of the interplay between the wind belts that make up the global atmospheric circulation and these two pressure-related variables. Interestingly, it was shown in [33] that the tropics and extra-tropics consist of two separate networks with fundamentally different properties, which validates the presence of distinct communities in these regions. Finally, vertical wind speed looks unlike any other variable as it does not form geographically cohesive clusters. But this behavior is not surprising due to its involvement in convection, a highly localized activity that remains one of the most difficult atmospheric processes to model [9].
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Microkinetic Modeling of Complex Reaction Networks Using Automated Network Generation

Microkinetic Modeling of Complex Reaction Networks Using Automated Network Generation

2.1 Network generation and analysis: a review 7 thereby enabling quick structure manipulation. Adjacency matrix is the most common representation format owing to its simplicity. An adjacency matrix “M” of a molecule is a square matrix containing connectivity and bond order information between every two atoms. Thus, M(i,j) = 0 implies that the i th and j th atoms are not connected while a positive nonzero value would indicate the strength of the bond (1 is a single bond, 2 is a double bond, etc). The diagonal values indicate the number of unpaired electrons in the atoms. The Bond-electron matrix, therefore, is an adjacency matrix. Third, an in- ternal representation of reaction rules that can be applied iteratively on the molecules is required. A common representation scheme is to employ a matrix for reaction rules “R” proposed by Dugundji and Ugi [37] and later used in other tools such as NETGEN [34], BNICE [38, 39, 40, 41]. Baltanas & Froment [42] used a Boolean matrix to represent molecules for generation of networks for modeling paraffin cracking and isomerization on bifunctional catalysts. The Boolean matrix is similar to the adjacency matrix; how- ever, bonds of a higher order (e.g. double bonds) and information on charges (such as +1 for carbenium ions) are stored separately. This method, therefore, is similar to that of Dugundji and Ugi [37]. Transformations in RDL [35] and RDL++ [43], on the other hand, are input by the user as English-language-like statements describing changes in the charge/ bonding of atoms participating in the reaction rule which get directly ap- plied on the internal graph description of molecules. Fourth, all network generators have a generation scheme that iteratively applies the reaction rules to all input and generated molecules so that the resultant network is exhaustive. The scheme should ensure that all possible reactions of a given set of reactants are generated corresponding to that reaction rule. Faulon and Sault [44] describe such a generation scheme as deterministic network generation.
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