Cascading dominates large-scale disruptions in transport over complex networks

The core functionality of many socio-technical systems, such as supply chains, (inter) national trade and human mobility, concern transport over large geographically-spread complex networks. The dynamical intertwining of many heterogeneous operational elements, agents and locations are oft-cited generic factors to make these systems prone to large-scale disruptions: initially localised perturbations amplify and spread over the network, leading to a complete standstill of transport. Our level of understanding of such phenomena, let alone the ability to anticipate or predict their evolution in time, remains rudimentary. We approach the problem with a prime example: railways. Analysing spreading of train delays on the network by building a physical model, supported by data, reveals that the emergence of large-scale disruptions rests on the dynamic interdependencies among multiple ‘layers’ of operational elements (resources and services). The interdependencies provide pathways for the so-called delay cascading mechanism, which gets activated when, constrained by local unavailability of on-time resources, already-delayed ones are used to operate new services. Cascading locally amplifies delays, which in turn get transported over the network to give rise to new constraints elsewhere. This mechanism is a rich addition to some well-understood ones in, e.g., epidemiological spreading, or the spreading of rumours and opinions over (contact) networks, and stimulates rethinking spreading dynamics on complex networks. Having these concepts built into the model provides it with the ability to predict the evolution of large-scale disruptions in the railways up to 30-60 minutes up front. For transport systems, our work suggests that possible alleviation of constraints as well as a modular operational approach would arrest cascading, and therefore be effective measures against large-scale disruptions

A reliable ensemble based approach to semi-supervised learning

Semi-supervised learning (SSL) methods attempt to achieve better classification of unseen data through the use of unlabeled data than can be achieved by learning from the available labeled data alone. Most SSL methods require the user to familiarize themselves with novel, complex concepts and to ensure the underlying assumptions made by these methods match the problem structure, or they risk a decrease in predictive performance. In this paper, we present the reliable semi-supervised ensemble learning (RESSEL) method, which exploits unlabeled data by using it to generate diverse classifiers through self-training and combines these classifiers into an ensemble for prediction. Our method functions as a wrapper around a supervised base classifier and refrains from introducing additional problem dependent assumptions. We conduct experiments on a number of commonly used data sets to prove its merit. The results show RESSEL improves significantly upon the supervised alternatives, provided that the base classifier which is used is able to produce adequate probability-based rankings. It is shown that RESSEL is reliable in that it delivers results comparable to supervised learning methods if this requirement is not met, while the method also broadens the range of good parameter values. Furthermore, RESSEL is demonstrated to outperform existing self-labeled wrapper approaches

Discontinuous evolution of the structure of stretching polycrystalline graphene

Polycrystalline graphene has an inherent tendency to buckle, i.e., develop out-of-plane, three-dimensional structure. A force applied to stretch a piece of polycrystalline graphene influences the out-of-plane structure. Even if the graphene is well relaxed, this happens in nonlinear fashion: Occasionally, a tiny increase in stretching force induces a significant displacement, in close analogy to avalanches, which in turn can create vibrations in the surrounding medium. We establish this effect in computer simulations: By continuously changing the strain, we follow the displacements of the carbon atoms that turn out to exhibit a discontinuous evolution. Furthermore, the displacements exhibit a hysteretic behavior upon the change from low to high stress and back. These behaviors open up another direction in studying dynamical elasticity of polycrystalline quasi-two-dimensional systems, and in particular the implications on their mechanical and thermal properties

Super slowing down in the bond-diluted Ising model

In models in statistical physics, the dynamics often slows down tremendously near the critical point. Usually, the correlation time τ at the critical point increases with system size L in power-law fashion: τ ∼ L z , which defines the critical dynamical exponent z . We show that this also holds for the two-dimensional bond-diluted Ising model in the regime p > p c , where p is the parameter denoting the bond concentration, but with a dynamical critical exponent z ( p ) which shows a strong p dependence. Moreover, we show numerically that z ( p ) , as obtained from the autocorrelation of the total magnetization, diverges when the percolation threshold p c = 1 / 2 is approached: z ( p ) − z ( 1 ) ∼ ( p − p c ) − 2 . We refer to this observed extremely fast increase of the correlation time with size as super slowing down. Independent measurement data from the mean-square deviation of the total magnetization, which exhibits anomalous diffusion at the critical point, support this result

Reducing societal impacts of SARS-CoV-2 interventions through subnational implementation

To curb the initial spread of SARS-CoV-2, many countries relied on nation-wide implementation of non-pharmaceutical intervention measures, resulting in substantial socio-economic impacts. Potentially, subnational implementations might have had less of a societal impact, but comparable epidemiological impact. Here, using the first COVID-19 wave in the Netherlands as a case in point, we address this issue by developing a high-resolution analysis framework that uses a demographically stratified population and a spatially explicit, dynamic, individual contact-pattern based epidemiology, calibrated to hospital admissions data and mobility trends extracted from mobile phone signals and Google. We demonstrate how a subnational approach could achieve similar level of epidemiological control in terms of hospital admissions, while some parts of the country could stay open for a longer period. Our framework is exportable to other countries and settings, and may be used to develop policies on subnational approach as a better strategic choice for controlling future epidemics

One-parametric bifurcation analysis of data-driven car-following models

In this study, an equation-free method is used to perform bifurcation analyses of various artificial neural network (ANN) based car-following models. The ANN models were trained on Multiple Car Following (MCF) model output data (ANN-m) and field data (ANN-r). The ANN-m model could capture the behaviour of the MCF model in quite detail. A bifurcation analysis, using the circuit length L as parameter, for the ANN-m model leads to good results if the training data set from the MCF model is sufficiently diverse, namely that it incorporates data from a wide range of vehicle densities that encompass the stable free-flow and the stable jam-flow regimes. The ANN-r model is in general able to capture the feature of traffic jams when a car takes headway and velocity of itself and of the two cars ahead as input. However, the traffic flow of the ANN-r model is more regular in comparison to the field data. It is possible to construct a partial bifurcation diagram in L for the ANN-r using the equation-free method and it is found that the flow changes stability due to a subcritical Hopf bifurcation

Structural dynamics of polycrystalline graphene

The exceptional properties of the two-dimensional material graphene make it attractive for multiple functional applications, whose large-area samples are typically polycrystalline. Here, we study the mechanical properties of graphene in computer simulations and connect these to the experimentally relevant mechanical properties. In particular, we study the fluctuations in the lateral dimensions of the periodic simulation cell. We show that over short timescales, both the area A and the aspect ratio B of the rectangular periodic box show diffusive behavior under zero external field during dynamical evolution, with diffusion coefficients DA and DB that are related to each other. At longer times, fluctuations in A are bounded, while those in B are not. This makes the direct determination of DB much more accurate, from which DA can then be derived indirectly. We then show that the dynamic behavior of polycrystalline graphene under external forces can also be derived from DA and DB via the Nernst-Einstein relation. Additionally, we study how the diffusion coefficients depend on structural properties of the polycrystalline graphene, in particular, the density of defects