Cascading Failures in Spatially-Embedded Random Networks

Cascading failures constitute an important vulnerability of interconnected systems. Here we focus on the study of such failures on networks in which the connectivity of nodes is constrained by geographical distance. Specifically, we use random geometric graphs as representative examples of such spatial networks, and study the properties of cascading failures on them in the presence of distributed flow. The key finding of this study is that the process of cascading failures is non-self-averaging on spatial networks, and thus, aggregate inferences made from analyzing an ensemble of such networks lead to incorrect conclusions when applied to a single network, no matter how large the network is. We demonstrate that this lack of self-averaging disappears with the introduction of a small fraction of long-range links into the network. We simulate the well studied preemptive node removal strategy for cascade mitigation and show that it is largely ineffective in the case of spatial networks. We introduce an altruistic strategy designed to limit the loss of network nodes in the event of a cascade triggering failure and show that it performs better than the preemptive strategy. Finally, we consider a real-world spatial network viz. a European power transmission network and validate that our findings from the study of random geometric graphs are also borne out by simulations of cascading failures on the empirical network.     

Network Science Based Quantification of Resilience Demonstrated on the Indian Railways Network

The structure, interdependence, and fragility of systems ranging from power-grids and transportation to ecology, climate, biology and even human communities and the Internet have been examined through network science. While response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science based quantitative framework for measuring, comparing and interpreting hazard responses as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario illustrate hazard responses and effectiveness of proposed recovery strategies. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of these strategies suggests that faster and more efficient recovery is possible through network centrality measures. Optimal recovery strategies may be different per hazard, per community within a network, and for different measures of partial recovery. In addition, topological characterization provides a means for interpreting the comparative performance of proposed recovery strategies. The methods can be directly extended to other Large-Scale Critical Lifeline Infrastructure Networks including transportation, water, energy and communications systems that are threatened by natural or human-induced hazards, including cascading failures. Furthermore, the quantitative framework developed here can generalize across natural, engineered and human systems, offering an actionable and generalizable approach for emergency management in particular as well as for network resilience in general.     

Optimal Allocation of Node Capacity in Cascade-Robustness Networks

The robustness of large scale critical infrastructures, which can be modeled as complex networks, is of great significance. One of the most important means to enhance robustness is to optimize the allocation of resources. Traditional allocation of resources is mainly based on the topology information, which is neither realistic nor systematic. In this paper, we try to build a framework for searching for the most favorable pattern of node capacity allocation to reduce the vulnerability to cascading failures at a low cost. A nonlinear and multi-objective optimization model is proposed and tackled using a particle swarm optimization algorithm (PSO). It is found that the network becomes more robust and economical when less capacity is left on the heavily loaded nodes and the optimized network performs better resisting noise. Our work is helpful in designing a robust economical network.     

Prediction of Cascading Failures in Spatial Networks

Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction can provide useful information to help control networks. However, for a large, gradually growing network with increasing complexity, it is often impractical to explore the behavior of a single node from the perspective of failure propagation. Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics. Therefore, in this study, we seek to predict the failure rates of nodes in a given group using machine-learning methods.We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance. The experimental results of a feedforward neural network (FNN), a recurrent neural network (RNN) and support vector regression (SVR) all show that these different models can accurately predict the similar behavior of nodes in a given group during cascading overload propagation.     

Abnormal Behavior in Cascading Dynamics with Node Weight

Considering a preferential selection mechanism of load destination, we introduce a new method to quantify initial load distribution and subsequently construct a simple cascading model. By attacking the node with the highest load, we investigate the cascading dynamics in some synthetic networks. Surprisingly, we observe that for several networks of different structural patterns, a counterintuitive phenomenon emerges if the highest load attack is applied to the system, i.e., investing more resources to protect every node in a network inversely makes the whole network more vulnerable. We explain this ability paradox by analyzing the micro-structural components of the underlying network and therefore reveals how specific structural patterns may influence the cascading dynamics. We discover that the robustness of the network oscillates as the capacity of each node increases. The conclusion of the paper may shed lights on future investigations to avoid the demonstrated ability paradox and subsequent cascading failures in real-world networks.     

Autonomous recovery in componentized Internet applications

In this paper we show how to reduce downtime of J2EE applications by rapidly and automatically recovering from transient and intermittent software failures, without requiring application modifications. Our prototype combines three application-agnostic techniques: macroanalysis for fault detection and localization, microrebooting for rapid recovery, and external management of recovery actions. The individual techniques are autonomous and work across a wide range of componentized Internet applications, making them well-suited to the rapidly changing software of Internet services. The proposed framework has been integrated with JBoss, an open-source J2EE application server. Our prototype provides an execution platform that can automatically recover J2EE applications within seconds of the manifestation of a fault. Our system can provide a subset of a system's active end users with the illusion of continuous uptime, in spite of failures occurring behind the scenes, even when there is no functional redundancy in the system.     

桂西南喀斯特季雨林木本植物的水力安全

桂西南喀斯特地区生物多样性丰富、特有种多, 同时也是石漠化问题较为严重的区域。由于该喀斯特地区土层浅薄、岩石裸露、表层储水能力差, 植物在干旱季节经常会受到水分胁迫。植物水力学特征不仅是探讨喀斯特地区植物的生理生态适应性的关键, 还能够为石漠化地区的植被恢复提供重要参考。该研究测定了桂西南喀斯特季雨林17种代表性木本植物(包括不同生活型、叶片习性和生境)的木质部脆弱性曲线、最低水势、叶片膨压丧失点和边材密度等水力性状, 结果发现: (1)喀斯特植物木质部导水率丧失50%时的水势值(P₅₀)的种间差异较大(-0.51- -2.51 MPa), 其中常绿种的抗栓塞能力比落叶种强; (2)喀斯特植物的木质部水力安全边界值(最低水势与P₅₀之间的差值)的均值为0.36 MPa, 说明喀斯特森林植物在自然最低水势状况下木质部发生栓塞的程度较高; 但是不同植物种间存在显著差异, 这可能与喀斯特峰丛洼地生境的复杂性以及物种不同的抗旱策略有关; (3)由于喀斯特植物水分适应机制的多样化, 导致木质部水力安全边界与叶片膨压丧失点、边材密度的相关性并不显著。在区域气候干热化的背景下, 结合喀斯特植物的栓塞脆弱性和长期水势监测(尤其极端干旱事件)分析它们的水力安全, 对预测未来喀斯特森林物种分布和群落动态具有重要的指示作用。     

A spatial multicriteria decision analysis for selecting priority sites for plant species restoration: a case study from the Chilean biodiversity hotspot

Developing conservation strategies to restore populations of threatened species has been signaled as an important task by the Convention on Biological Diversity 2011–2020 targets. Species are being threatened not only by habitat loss and fragmentation but increasingly by climate change. As resources for conservation are often limited, and restoration is among the most expensive conservation strategies, developing approaches that help in the prioritization of areas for restoration efforts is a critical task. In this study, we propose a spatial multicriteria decision analysis (SMCDA) framework for identifying potential areas for plant species restoration initiatives that can explicitly take into account future climatic change. As a way to show how the framework can be applied, we took advantage of freely available niche modeling software and geospatial information to identify regional‐scale priority areas for restoration of two threatened endemic tree species (i.e. Bielschmiedia miersii and Pouteria splendens) of the “Chilean Winter Rainfall‐Valdivian Forest” Hotspot. The SMCDA framework allowed us not only to identify priority areas for species restoration but also to analyze how different environmental conditions and land‐use types may affect the selection of areas for species restoration. Our analysis suggests that the inclusion of climate change is a key factor to assess the potential areas for species restoration because species may respond differentially to future climatic conditions. This framework is conceived to be used as a complementary approach to available landscape‐scale spatial conservation planning tools.     

Percolation on Networks with Conditional Dependence Group

Recently, the dependence group has been proposed to study the robustness of networks with interdependent nodes. A dependence group means that a failed node in the group can lead to the failures of the whole group. Considering the situation of real networks that one failed node may not always break the functionality of a dependence group, we study a cascading failure model that a dependence group fails only when more than a fraction β of nodes of the group fail. We find that the network becomes more robust with the increasing of the parameter β. However, the type of percolation transition is always first order unless the model reduces to the classical network percolation model, which is independent of the degree distribution of the network. Furthermore, we find that a larger dependence group size does not always make the networks more fragile. We also present exact solutions to the size of the giant component and the critical point, which are in agreement with the simulations well.     

An Assessment Framework for Optimal FMS Effectiveness

Equipment failures in an FMS are significant to performance and can lead to costly, incorrect decisions. Fortunately, effectiveness measurement techniques can be mapped to clever modeling frameworks to help predict, track, and then improve upon the FMS performability or mission effectiveness, and improve maintenance. This article provides sources and guidelines for efficient and effective FMS modeling, a framework for applying the modeling to predict the impact on customers from their point of view, and a method for tying it all together for improving the FMS effectiveness. It is not enough to simply examine the working and failed states of an FMS or even to calculate common reliability metrics. It is necessary to consider the FMS as a whole, and that system includes the needs of the customer and the business. It is also necessary to be purposeful about the measures of performance selected and to support the measures of effectiveness. In this article, we present: a framework for considering customer needs in the measures of effectiveness for FMS; modeling approaches for solving for effectiveness measures; and an example to show how to apply it to an FMS, to improve it or plan for meeting specific customer needs.     

On the Complexity of Life Cycle Inventory Networks: Role of Life Cycle Processes with Network Analysis

Determining the relevance and importance of a technosphere process or a cluster of processes in relation to the rest of the industrial network can provide insights into the sustainability of supply chains: those that need to be optimized or controlled/safeguarded. Network analysis (NA) can offer a broad framework of indicators to tackle this problem. In this article, we present a detailed analysis of a life cycle inventory (LCI) model from an NA perspective. Specifically, the network is represented as a directed graph and the “emergy” numeraire is used as the weight associated with the arcs of the network. The case study of a technological system for drinking water production is presented. We investigate the topological and structural characteristics of the network representation of this system and compare properties of its weighted and unweighted network, as well as the importance of nodes (i.e., life cycle unit processes). By identifying a number of advantages and limitations linked to the modeling complexity of such emergy‐LCI networks, we classify the LCI technosphere network of our case study as a complex network belonging to the scale‐free network family. The salient feature of this network family is represented by the presence of “hubs”: nodes that connect with many other nodes. Hub failures may imply relevant changes, decreases, or even breaks in the connectedness with other smaller hubs and nodes of the network. Hence, by identifying node centralities, we can rank and interpret the relevance of each node for its special role in the life cycle network.     

Defining and evaluating the ecological restoration economy

For decades, industry groups and many media outlets have propagated the notion that environmental protection is bad for business. However, missing from this public debate has been a detailed accounting of the U.S. economic output and employment that are created through conservation, restoration, and mitigation actions, which we call the “Restoration Economy.” In this paper, we review related literature, including 14 local and state‐level case studies of privately funded environmental restoration projects. We also review federal and state government programs that fund restoration throughout the United States, revealing the complex nature of this sector. We find growing evidence that the restoration industry not only protects public environmental goods but also contributes to national economic growth and employment, supporting as many as 33 jobs per $1 million invested, with an employment multiplier of between 1.48 and 3.8 (the number of jobs supported by every restoration job) and an output multiplier of between 1.6 and 2.59 (multiplier for total economic output from investments). The existing literature also shows that restoration investments lead to significant positive economic and employment impacts and appear to have particularly localized benefits, which can be attributed to the tendency for projects to employ local labor and materials. While these initial figures are promising, the extent of environmental restoration activities and benefits at a national level is not yet well understood. Our findings reveal the need for a methodological framework for more accurately and broadly estimating the size of the U.S. restoration sector and its impact on the U.S. economy.     

The robustness of a network of ecological networks to habitat loss

There have been considerable advances in our understanding of the tolerance of species interaction networks to sequential extinctions of plants and animals. However, communities of species exist in a mosaic of habitats, and the vulnerability of habitats to anthropogenic change varies. Here, we model the cascading effects of habitat loss, driven by plant extinctions, on the robustness of multiple animal groups. Our network is constructed from empirical observations of 11 animal groups in 12 habitats on farmland. We simulated sequential habitat removal scenarios: randomly; according to prior information; and with a genetic algorithm to identify best‐ and worst‐case permutations of habitat loss. We identified two semi‐natural habitats (waste ground and hedgerows together comprising < 5% of the total area of the farm) as disproportionately important to the integrity of the overall network. Our approach provides a new tool for network ecologists and for directing the management and restoration of multiple‐habitat sites.     

Human Initiated Cascading Failures in Societal Infrastructures

In this paper, we conduct a systematic study of human-initiated cascading failures in three critical inter-dependent societal infrastructures due to behavioral adaptations in response to a crisis. We focus on three closely coupled socio-technical networks here: (i) cellular and mesh networks, (ii) transportation networks and (iii) mobile call networks. In crises, changes in individual behaviors lead to altered travel, activity and calling patterns, which influence the transport network and the loads on wireless networks. The interaction between these systems and their co-evolution poses significant technical challenges for representing and reasoning about these systems. In contrast to system dynamics models for studying these interacting infrastructures, we develop interaction-based models in which individuals and infrastructure elements are represented in detail and are placed in a common geographic coordinate system. Using the detailed representation, we study the impact of a chemical plume that has been released in a densely populated urban region. Authorities order evacuation of the affected area, and this leads to individual behavioral adaptation wherein individuals drop their scheduled activities and drive to home or pre-specified evacuation shelters as appropriate. They also revise their calling behavior to communicate and coordinate among family members. These two behavioral adaptations cause flash-congestion in the urban transport network and the wireless network. The problem is exacerbated with a few, already occurring, road closures. We analyze how extended periods of unanticipated road congestion can result in failure of infrastructures, starting with the servicing base stations in the congested area. A sensitivity analysis on the compliance rate of evacuees shows non-intuitive effect on the spatial distribution of people and on the loading of the base stations. For example, an evacuation compliance rate of 70% results in higher number of overloaded base stations than the evacuation compliance rate of 90%.     

Design and Analysis of Bioenergy Networks

This article presents a new methodology for designing industrial networks and analyzing them dynamically from the standpoint of sustainable development. The approach uses a combination of optimization and simulation tools. Assuming "top-down" overarching control of the network, we use global dynamic optimization to determine which evolutionary pathways are preferred in terms of economic, social, and environmental performance. Considering the autonomy of network entities and their actions, we apply agent-based simulation to analyze how the network actually evolves. These two perspectives are integrated into a powerful multiscale modeling framework for evaluating the consequences of new policy instruments or different business strategies aimed at stimulating sustainable development as well as identifying optimal leverage points for improved performance of the network in question. The approach is demonstrated for a regional network of interdependent organizations deploying a set of bioenergy technologies within a developing-economy context.     

Restoration Enhances Wetland Biodiversity and Ecosystem Service Supply,but Results Are Context-Dependent: A Meta-Analysis

Wetlands are valuable ecosystems because they harbor a huge biodiversity and provide key services to societies. When natural or human factors degrade wetlands, ecological restoration is often carried out to recover biodiversity and ecosystem services (ES). Although such restorations are routinely performed, we lack systematic, evidence-based assessments of their effectiveness on the recovery of biodiversity and ES. Here we performed a meta-analysis of 70 experimental studies in order to assess the effectiveness of ecological restoration and identify what factors affect it. We compared selected ecosystem performance variables between degraded and restored wetlands and between restored and natural wetlands using response ratios and random-effects categorical modeling. We assessed how context factors such as ecosystem type, main agent of degradation, restoration action, experimental design, and restoration age influenced post-restoration biodiversity and ES. Biodiversity showed excellent recovery, though the precise recovery depended strongly on the type of organisms involved. Restored wetlands showed 36% higher levels of provisioning, regulating and supporting ES than did degraded wetlands. In fact, wetlands showed levels of provisioning and cultural ES similar to those of natural wetlands; however, their levels of supporting and regulating ES were, respectively, 16% and 22% lower than in natural wetlands. Recovery of biodiversity and of ES were positively correlated, indicating a win-win restoration outcome. The extent to which restoration increased biodiversity and ES in degraded wetlands depended primarily on the main agent of degradation, restoration actions, experimental design, and ecosystem type. In contrast, the choice of specific restoration actions alone explained most differences between restored and natural wetlands. These results highlight the importance of comprehensive, multi-factorial assessment to determine the ecological status of degraded, restored and natural wetlands and thereby evaluate the effectiveness of ecological restorations. Future research on wetland restoration should also seek to identify which restoration actions work best for specific habitats.     

Linking landscape-level change to habitat quality: an evaluation of restoration actions on the freshwater habitat of spring-run Chinook salmon

1. Conservation planning is often hampered by the lack of causal quantitative links between landscape characteristics, restoration actions and habitat conditions that impact the status of imperilled species. Here we present a first step toward linking actions on the landscape to the population status of endangered stream‐type Chinook salmon (Oncorhynchus tshawytscha). 2. We developed relationships between land use, landscape characteristics and freshwater habitat of spring Chinook salmon in the Wenatchee River basin. Available data allowed us to find relationships that described water temperatures at several life stages (prespawning, egg incubation and summer rearing) and substratum characteristics, including fine sediments, cobble and embeddedness. Predictors included altitude, gradient, mean annual precipitation, total and riparian forest cover, road density, impervious surface and alluvium. We used a model averaging approach to account for parameter and model selection uncertainty. Key predictors were total forest cover and impervious surface area for prespawning and summer rearing temperatures; precipitation and stream gradients were important predictors of the percent of fine sediments in stream substrata. 3. We estimated habitat conditions using these relationships in three alternative landscape scenarios: historical, no restoration and one that included a set of restoration actions from local conservation planning. We found that prespawning and summer temperatures were estimated to be slightly higher historically relative to current conditions in dry sparsely forested areas, but lower in some important Chinook salmon spawning and rearing areas and lower in those locations under the restoration scenario. Fine sediments were lower in the historical scenario and were reduced as a consequence of restoration actions in two areas currently unoccupied by Chinook salmon that contain reaches with some potential for high quality spawning and rearing. Cobble and embeddedness in general were predicted to be higher historically and changed little as a result of restoration actions relative to current conditions. 4. This modelling framework converts suites of restoration actions into changes in habitat condition, thereby enabling restoration planners to evaluate alternative combinations of proposed actions. It also provides inputs to models linking habitat conditions to population status. This approach represents a first step in estimating impacts of restoration strategies, and can provide key information for conservation managers and planners.     

The contribution of latent human failures to the breakdown of complex systems

Several recent accidents in complex high-risk technologies had their primary origins in a variety of delayed-action human failures committed long before an emergency state could be recognized. These disasters were due to the adverse conjunction of a large number of causal factors, each one necessary but singly insufficient to achieve the catastrophic outcome. Although the errors and violations of those at the immediate human-system interface often feature large in the post-accident investigations, it is evident that these 'front-line' operators are rarely the principal instigators of system breakdown. Their part is often to provide just those local triggering conditions necessary to manifest systemic weaknesses created by fallible decisions made earlier in the organizational and managerial spheres. The challenge facing the human reliability community is to find ways of identifying and neutralizing these latent failures before they combine with local triggering events to breach the system's defences. New methods of risk assessment and risk management are needed if we are to achieve any significant improvements in the safety of complex, well-defended, socio-technical systems. This paper distinguishes between active and latent human failures and proposes a general framework for understanding the dynamics of accident causation. It also suggests ways in which current methods of protection may be enhanced, and concludes by discussing the unusual structural features of 'high-reliability' organizations.