Uncertainty in vulnerability of networks under attack.

This study builds conceptual explanations and empirical examinations of the vulnerability response of networks under attack. Two quantities of "vulnerability" and "uncertainty in vulnerability" are defined by scrutinizing the performance loss trajectory of networks experiencing attacks. Both vulnerability and uncertainty in vulnerability quantities are a function of the network topology and size. This is tested on 16 distinct topologies appearing in infrastructure, social, and biological networks with 8 to 26 nodes under two percolation scenarios exemplifying benign and malicious attacks. The findings imply (i) crossing path, tree, and diverging tail are the most vulnerable topologies, (ii) complete and matching pairs are the least vulnerable topologies, (iii) complete grid and complete topologies show the most uncertainty for vulnerability, and (iv) hub-and-spoke and double u exhibit the least uncertainty in vulnerability. The findings also imply that both vulnerability and uncertainty in vulnerability increase with an increase in the size of the network. It is argued that in networks with no undirected cycle and one undirected cycle, the uncertainty in vulnerability is maximal earlier in the percolation process. With an increase in the number of cycles, the uncertainty in vulnerability is accumulated at the end of the percolation process. This emphasizes the role of tailoring preparedness, response, and recovery phases for networks with different topologies when they might experience disruption.

A systematic exclusion induced by institutional ranking in engineering faculty hiring: Introducing a cycle of winners and losers.

This study empirically investigates exclusion induced by institutional ranking in engineering faculty hiring and introduces a cycle of winners and losers formed by privileging graduates of high-ranked institutions in the U.S. higher education system. We analyze and visualize academic origin (i.e., institutions faculty graduated from) and destination (i.e., institutions faculty are hired at) of 5,356 tenure-track faculty in four engineering disciplines of Chemical, Civil, Electrical, and Mechanical at the top 20 and bottom 20 of the top 100 engineering institutions according to the 2022 U.S. News & World Report. Our findings indicate that the hiring of engineering faculty in the U.S. higher education system is skewed in favor of graduates from high-ranked institutions, regardless of the discipline. Concerning each engineering discipline, 78% of electrical, 76% of chemical, 71% of mechanical, and 67% of civil engineering faculty of top 20 ranked institutions have academic origins in the top 20 ranked institutions. This hiring practice fosters inequalities by excluding qualified candidates and cementing the ranking system as the sole factor of academic quality. We bring attention to the pitfalls stemming from the exclusion in the U.S. higher education system, including (1) financial resources, (2) faculty and student resources, (3) selectivity and self-selection, and (4) geography. The cascading effect of the ranking practice is the unintended consequence of inaugurating a virtuous and vicious cycle, which creates a cycle of winners and losers that is difficult to break. High-ranked institutions easily dominate and maintain their ascendancy status in the ranking system as benefactors of the virtuous cycle. Low-ranked institutions are entrapped in the vicious cycle that makes it nearly impossible to (1) attract and retain both students and faculty, (2) secure external funding, (3) obtain resources for new programs, and (4) advance engineering research. Unless the U.S. higher education system is intent on squandering talent, confirming the belief that diversity is symbolic, and cementing the ranking system as the sole factor of academic quality, we recommend faculty hiring beyond the standard sociodemographic indicators and academic origins in hiring decisions. A proactive, open-minded, and neutral approach to the faculty selection process void of decision-making based on affinity should be the central tenet of the selection committee.

Recovery patterns and physics of the network.

In a progressively interconnected world, the loss of system resilience has consequences for human health, the economy, and the environment. Research has exploited the science of networks to explain the resilience of complex systems against random attacks, malicious attacks, and the localized attacks induced by natural disasters or mass attacks. Little is known about the elucidation of system recovery by the network topology. This study adds to the knowledge of network resilience by examining the nexus of recoverability and network topology. We establish a new paradigm for identifying the recovery behavior of networks and introduce the recoverability measure. Results indicate that the recovery response behavior and the recoverability measure are the function of both size and topology of networks. In small sized networks, the return to recovery exhibits homogeneous recovery behavior over topology, while the return shape is dispersed with an increase in the size of network. A network becomes more recoverable as connectivity measures of the network increase, and less recoverable as accessibility measures of network increase. Overall, the results not only offer guidance on designing recoverable networks, but also depict the recovery nature of networks deliberately following a disruption. Our recovery behavior and recoverability measure has been tested on 16 distinct network topologies. The relevant recovery behavior can be generalized based on our definition for any network topology recovering deliberately.

Choice of speed under compromised Dynamic Message Signs.

This study explores speed choice behavior of travelers under realistic and fabricated Dynamic Message Signs (DMS) content. Using web-based survey information of 4,302 participants collected by Amazon Mechanical Turk in the United States, we develop a set of multivariate latent-based ordered probit models participants. Results show female, African-Americans, drivers with a disability, elderly, and drivers who trust DMS are likely to comply with the fabricated messages. Drivers who comply with traffic regulations, have a good driving record, and live in rural areas, as well as female drivers are likely to slow down under fabricated messages. We highlight that calling or texting, taking picture, and tuning the radio are distracting activities leading drivers to slow down or stop under fictitious scenarios.

Using temporal detrending to observe the spatial correlation of traffic.

This empirical study sheds light on the spatial correlation of traffic links under different traffic regimes. We mimic the behavior of real traffic by pinpointing the spatial correlation between 140 freeway traffic links in a major sub-network of the Minneapolis-St. Paul freeway system with a grid-like network topology. This topology enables us to juxtapose the positive and negative correlation between links, which has been overlooked in short-term traffic forecasting models. To accurately and reliably measure the correlation between traffic links, we develop an algorithm that eliminates temporal trends in three dimensions: (1) hourly dimension, (2) weekly dimension, and (3) system dimension for each link. The spatial correlation of traffic links exhibits a stronger negative correlation in rush hours, when congestion affects route choice. Although this correlation occurs mostly in parallel links, it is also observed upstream, where travelers receive information and are able to switch to substitute paths. Irrespective of the time-of-day and day-of-week, a strong positive correlation is witnessed between upstream and downstream links. This correlation is stronger in uncongested regimes, as traffic flow passes through consecutive links more quickly and there is no congestion effect to shift or stall traffic. The extracted spatial correlation structure can augment the accuracy of short-term traffic forecasting models.