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Recent disruptions in transportation systems resulting from natural disasters, cyber accidents, and other factors clearly show the fragility of the airports and underscore the need for building resilience. This study introduces a comprehensive framework for evaluating the resilience of airport infrastructure, integrating critical functions and performance indicators in the context of specific missions that the airport needs to achieve. By focusing on the Dallas-Fort Worth International Airport (DFW) as a case study, the paper outlines a multi-criteria decision analysis (MCDA) methodology for identifying and assessing the critical functions of airports as well as their ability to recover and adapt under different threat scenarios including threat-agnostic situation. The methodology and its application to the DFW case study offer insights into the resilience of airport operations, highlighting key areas for improvement and the potential for policy intervention. This study provides a robust tool for airport administrators and policymakers to enhance infrastructure resilience through a detailed analysis and visualization of airport performance indicators, thereby contributing to the broader discourse on transportation system sustainability and disaster preparedness. 

The supply chains of semiconductors and integrated devices supports industry across all economic sectors. Globally, the supply chain is experiencing a variety of stressors and disruptions, with effects that cascade across the economy, causing product delays and enterprise losses. However, quantitative models that support an understanding of how stressors influence supply chain performance are needed. Here we show how stress testing can be used for assessing the impact of disruptions on supply chain performance metrics and for characterizing system resilience. We demonstrate a framework that utilizes discrete event simulation for stress testing the resilience of a semiconductor supply chain. Our results include a comparison of resilience curves with and without risk management countermeasures, showing the resilience-enhancing benefits of various supply chain management strategies such as maintaining safety stock and sourcing from multiple suppliers. Supply chain managers can utilize stress testing principles and methodologies to configure their supply chain and engage in practices that contribute to system resilience. 

Abstract Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Computing shortest paths is a straightforward task when the network of interest is fully known, and there are a plethora of computational algorithms for this purpose. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions and the Internet at the autonomous system level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks, where numbers of missing links exceed those of observable links. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security.