Diffusive persistence on disordered lattices and random networks.

To better understand the temporal characteristics and the lifetime of fluctuations in stochastic processes in networks, we investigated diffusive persistence in various graphs. Global diffusive persistence is defined as the fraction of nodes for which the diffusive field at a site (or node) has not changed sign up to time t (or, in general, that the node remained active or inactive in discrete models). Here we investigate disordered and random networks and show that the behavior of the persistence depends on the topology of the network. In two-dimensional (2D) disordered networks, we find that above the percolation threshold diffusive persistence scales similarly as in the original 2D regular lattice, according to a power law P(t,L)∼t^{-θ} with an exponent θ≃0.186, in the limit of large linear system size L. At the percolation threshold, however, the scaling exponent changes to θ≃0.141, as the result of the interplay of diffusive persistence and the underlying structural transition in the disordered lattice at the percolation threshold. Moreover, studying finite-size effects for 2D lattices at and above the percolation threshold, we find that at the percolation threshold, the long-time asymptotic value obeys a power law P(t,L)∼L^{-zθ} with z≃2.86 instead of the value of z=2 normally associated with finite-size effects on 2D regular lattices. In contrast, we observe that in random networks without a local regular structure, such as Erdos-Rényi networks, no simple power-law scaling behavior exists above the percolation threshold.

Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends.

We study how public transportation data can inform the modeling of the spread of infectious diseases based on SIR dynamics. We present a model where public transportation data is used as an indicator of broader mobility patterns within a city, including the use of private transportation, walking etc. The mobility parameter derived from this data is used to model the infection rate. As a test case, we study the impact of the usage of the New York City subway on the spread of COVID-19 within the city during 2020. We show that utilizing subway transport data as an indicator of the general mobility trends within the city, and therefore as an indicator of the effective infection rate, improves the quality of forecasting COVID-19 spread in New York City. Our model predicts the two peaks in the spread of COVID-19 cases in NYC in 2020, unlike a standard SIR model that misses the second peak entirely.

Author Correction: Limits of Risk Predictability in a Cascading Alternating Renewal Process Model.

A correction to this article has been published and is linked from the HTML version of this paper. The error has not been fixed in the paper.

Evolution of threats in the global risk network.

With a steadily growing population and rapid advancements in technology, the global economy is increasing in size and complexity. This growth exacerbates global vulnerabilities and may lead to unforeseen consequences such as global pandemics fueled by air travel, cyberspace attacks, and cascading failures caused by the weakest link in a supply chain. Hence, a quantitative understanding of the mechanisms driving global network vulnerabilities is urgently needed. Developing methods for efficiently monitoring evolution of the global economy is essential to such understanding. Each year the World Economic Forum publishes an authoritative report on the state of the global economy and identifies risks that are likely to be active, impactful or contagious. Using a Cascading Alternating Renewal Process approach to model the dynamics of the global risk network, we are able to answer critical questions regarding the evolution of this network. To fully trace the evolution of the network we analyze the asymptotic state of risks (risk levels which would be reached in the long term if the risks were left unabated) given a snapshot in time; this elucidates the various challenges faced by the world community at each point in time. We also investigate the influence exerted by each risk on others. Results presented here are obtained through either quantitative analysis or computational simulations.

Limits of Risk Predictability in a Cascading Alternating Renewal Process Model.

Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, we propose the Cascading Alternating Renewal Process (CARP) to forecast interconnected global risks. However, assessments of the model's prediction precision are limited by lack of sufficient ground truth data. Here, we establish prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. We illustrate the approach on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. Using CARP, we also demonstrate estimation using a disparate dataset that also has dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. We conclude that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.

Time-resolved photoluminescence of Zn(OH)2 and its composites with graphite oxides.

Time-resolved photoluminescence is used to determine carrier recombination through radiative and nonradiative processes in zinc hydroxide Zn(OH)(2) and its porous composites with graphite oxide (GO). The decay times, measured by a streak camera, are found to be larger for zinc hydroxide (~1215±156 ps) than its composites (~976±81 ps for ZnGO-2 and 742±59 ps for ZnGO-5), but no significant changes in rise times (from 4.0 to 5.0 ps) are recorded. The dominant mechanism for the radiative process is attributed to free carrier recombination, while microporous networks present in these materials are found to be pathways for the nonradiative recombination process via multiphonon emission.

Structural and optical characterization of Zn(OH)2 and its composites with graphite oxides.

The optical properties of zinc (hydr)oxide and its porous composites with 2% and 5% graphite oxide (GO), thus forming ZnGO-2 and ZnGO-5, are investigated using reflectance spectroscopy and two-photon fluorescence (TPF) imaging. The bandgap energies for the Zn(OH)(2), ZnGO-2, and ZnGO-5 samples are determined to be in the range between 2.88 and 3.60 eV. The size of light-emitting regions (~from 4.5 to 45 µm) and pore size (~from 20 to 255 µm) are measured using the TPF imaging technique.