Showing papers by "Scuola superiore di Catania published in 2013"

Graph Metrics for Temporal Networks

Abstract: Temporal networks, i.e., networks in which the interactions among a set of elementary units change over time, can be modelled in terms of time-varying graphs, which are time-ordered sequences of graphs over a set of nodes. In such graphs, the concepts of node adjacency and reachability crucially depend on the exact temporal ordering of the links. Consequently, all the concepts and metrics proposed and used for the characterisation of static complex networks have to be redefined or appropriately extended to time-varying graphs, in order to take into account the effects of time ordering on causality. In this chapter we discuss how to represent temporal networks and we review the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time. We then focus on temporal node-node distance, and we discuss how to characterise link persistence and the temporal small-world behaviour in this class of networks. Finally, we discuss the extension of classic centrality measures, including closeness, betweenness and spectral centrality, to the case of time-varying graphs, and we review the work on temporal motifs analysis and the definition of modularity for temporal graphs.

Graph Metrics for Temporal Networks

Abstract: Temporal networks, i.e., networks in which the interactions among a set of elementary units change over time, can be modelled in terms of time-varying graphs, which are time-ordered sequences of graphs over a set of nodes. In such graphs, the concepts of node adjacency and reachability crucially depend on the exact temporal ordering of the links. Consequently, all the concepts and metrics proposed and used for the characterisation of static complex networks have to be redefined or appropriately extended to time-varying graphs, in order to take into account the effects of time ordering on causality. In this chapter we discuss how to represent temporal networks and we review the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time. We then focus on temporal node–node distance, and we discuss how to characterise link persistence and the temporal small-world behaviour in this class of networks. Finally, we discuss the extension of classic centrality measures, including closeness, betweenness and spectral centrality, to the case of time-varying graphs, and we review the work on temporal motifs analysis and the definition of modularity for temporal graphs.

Self-assembled silver nanoparticles for plasmon-enhanced solar cell back reflectors: correlation between structural and optical properties

Abstract: The spectra of localized surface plasmon resonances (LSPRs) in self-assembled silver nanoparticles (NPs), prepared by solid-state dewetting of thin films, are discussed in terms of their structural properties. We summarize the dependences of size and shape of NPs on the fabrication conditions with a proposed structural-phase diagram. It was found that the surface coverage distribution and the mean surface coverage (SC) size were the most appropriate statistical parameters to describe the correlation between the morphology and the optical properties of the nanostructures. The results are interpreted with theoretical predictions based on Mie theory. The broadband scattering efficiency of LSPRs in the nanostructures is discussed towards application as plasmon-enhanced back reflectors in thin-film solar cells.

Evolution of correlation structure of industrial indices of U.S. equity markets.

Abstract: We investigate the dynamics of correlations present between pairs of industry indices of U.S. stocks traded in U.S. markets by studying correlation-based networks and spectral properties of the correlation matrix. The study is performed by using 49 industry index time series computed by K. French and E. Fama during the time period from July 1969 to December 2011, which spans more than 40 years. We show that the correlation between industry indices presents both a fast and a slow dynamics. The slow dynamics has a time scale longer than 5 years, showing that a different degree of diversification of the investment is possible in different periods of time. Moreover, we also detect a fast dynamics associated with exogenous or endogenous events. The fast time scale we use is a monthly time scale and the evaluation time period is a 3-month time period. By investigating the correlation dynamics monthly, we are able to detect two examples of fast variations in the first and second eigenvalue of the correlation matrix. The first occurs during the dot-com bubble (from March 1999 to April 2001) and the second occurs during the period of highest impact of the subprime crisis (from August 2008 to August 2009).

A Protein-Interaction Array Inside a Living Cell

Abstract: Cell phenotype is determined by protein network states that are maintained by the dynamics of multiple protein interactions.1 Fluorescence microscopy approaches that measure protein interactions in individual cells, such as by Forster resonant energy transfer (FRET), are limited by the spectral separation of fluorophores and thus are most suitable to analyze a single protein interaction in a given cell. However, analysis of correlations between multiple protein interactions is required to uncover the interdependence of protein reactions in dynamic signal networks. Available protein-array technologies enable the parallel analysis of interacting proteins from cell extracts, however, they can only provide a single snapshot of dynamic interaction networks. Moreover, because of the high level of variance from cell to cell in protein expression levels and reaction state, cell extracts only provide an average measure of protein interaction states and therefore the detection of the relations between proteins is blurred. As an intermediate step, a visual immunoprecipitation assay was developed that allowed direct observation of multiple, dynamic protein interactions on immobilized, distinguishable beads in cell extracts.2 A microstructuring approach allowed for analysis of the interaction of one naturally occurring receptor type with one of its interaction partners inside cells.3 To analyze multiple protein interactions inside a single living cell, multiple receptors must be arranged in a defined pattern to distinguish their identity. Herein, we developed a general strategy to generate protein arrays with multiple arbitrary bait proteins by way of artificial-receptor constructs at sub-cellular feature size and applied this technology to simultaneously measure two-protein interaction kinetics inside an individual living cell.

Reinterpreting the development of extensive air showers initiated by nuclei and photons

Abstract: Ultra-high energy cosmic rays (UHECRs) interacting with the atmosphere generate extensive air showers (EAS) of secondary particles. The depth corresponding to the maximum development of the shower, $\Xmax$, is a well-known observable for determining the nature of the primary cosmic ray which initiated the cascade process. In this paper, we present an empirical model to describe the distribution of $\Xmax$ for EAS initiated by nuclei, in the energy range from $10^{17}$ eV up to $10^{21}$ eV, and by photons, in the energy range from $10^{17}$ eV up to $10^{19.6}$ eV. Our model adopts the generalized Gumbel distribution motivated by the relationship between the generalized Gumbel statistics and the distribution of the sum of non-identically distributed variables in dissipative stochastic systems. We provide an analytical expression for describing the $\Xmax$ distribution for photons and for nuclei, and for their first two statistical moments, namely $\langle \Xmax\rangle$ and $\sigma^{2}(\Xmax)$. The impact of the hadronic interaction model is investigated in detail, even in the case of the most up-to-date models accounting for LHC observations. We also briefly discuss the differences with a more classical approach and an application to the experimental data based on information theory.

Luminometric sub-nanoliter droplet-to-droplet array (LUMDA) and its application to drug screening by phase I metabolism enzymes

Abstract: Here we show the fabrication of the Luminometric Sub-nanoliter Droplet-to-droplet Array (LUMDA chip) by inkjet printing. The chip is easy to be implemented and allows for a multiplexed multi-step biochemical assay in sub-nanoliter liquid spots. This concept is here applied to the integral membrane enzyme CYP3A4, i.e. the most relevant enzymatic target for phase I drug metabolism, and to some structurally-related inhibitors.

Reinterpreting the development of extensive air showers initiated by nuclei and photons

Abstract: Ultra-high energy cosmic rays (UHECRs) interacting with the atmosphere generate extensive air showers (EAS) of secondary particles. The depth corresponding to the maximum development of the shower, Xmax, is a well-known observable for determining the nature of the primary cosmic ray which initiated the cascade process. In this paper, we present an empirical model to describe the distribution of Xmax for EAS initiated by nuclei, in the energy range from 1017 eV up to 1021 eV, and by photons, in the energy range from 1017 eV up to 1019.6 eV. Our model adopts the generalized Gumbel distribution motivated by the relationship between the generalized Gumbel statistics and the distribution of the sum of non-identically distributed variables in dissipative stochastic systems. We provide an analytical expression for describing the Xmax distribution for photons and for nuclei, and for their first two statistical moments, namely Xmax and σ2(Xmax). The impact of the hadronic interaction model is investigated in detail, even in the case of the most up-to-date models accounting for LHC observations. We also briefly discuss the differences with a more classical approach and an application to the experimental data based on information theory.

Applications of Temporal Graph Metrics to Real-World Networks

Abstract: Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.

Applications of Temporal Graph Metrics to Real-World Networks

Abstract: Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.

Peptide nucleic acid molecular beacons for the detection of PCR amplicons in droplet-based microfluidic devices

Abstract: The use of droplet-based microfluidics and peptide nucleic acid molecular beacons for the detection of polymerase chain reaction (PCR)-amplified DNA sequences within nanoliter-sized droplets is described in this work. The nanomolar–attomolar detection capabilities of the method were preliminarily tested by targeting two different single-stranded DNA sequences from the genetically modified Roundup Ready soybean and the Olea europaea genomes and detecting the fluorescence generated by peptide nucleic acid molecular beacons with fluorescence microscopy. Furthermore, the detection of 10 nM solutions of PCR amplicon of DNA extracted from leaves of O. europaea L. encapsulated in nanoliter-sized droplets was performed to demonstrate that peptide nucleic acid molecular beacons can discriminate O. europaea L. cultivar species carrying different single-nucleotide polymorphisms.

HERMES: Simulating the propagation of ultra-high energy cosmic rays

Abstract: The study of ultra-high energy cosmic rays (UHECR) at Earth cannot prescind from the study of their propagation in the Universe. In this paper, we present HERMES, the ad hoc Monte Carlo code we have developed for the realistic simulation of UHECR propagation. We discuss the modeling adopted to simulate the cosmology, the magnetic fields, the interactions with relic photons and the production of secondary particles. In order to show the potential applications of HERMES for astroparticle studies, we provide an estimation of the surviving probability of UHE protons, the GZK horizons of nuclei and the all-particle spectrum observed at Earth in different astrophysical scenarios. Finally, we show the expected arrival direction distribution of UHECR produced from nearby candidate sources. A stable version of HERMES will be released in the next future for public use together with libraries of already propagated nuclei to allow the community to perform mass composition and energy spectrum analysis with our simulator.

Influence of cosmological models on the GZK horizon of ultrahigh energy protons

Abstract: We investigate how the density of baryonic and cold dark matter, the density of dark energy and the value of the Hubble parameter at the present time influence the propagation of ultrahigh energy protons in the nearby Universe. We take into account energy losses in the cosmic microwave radiation, the only one relevant for protons above 1018 eV, and we explore the dependence of the Greisen–Zatsepin–Kuz’min (GZK) horizon on the cosmology. We investigate several cosmological scenarios, from matter dominated to energy dominated ones, and we consider the impact of uncertainties in the Hubble parameter in a Λ-cold dark matter (ΛCDM) Universe, estimated from recent observations, on the GZK horizon. The impact of the (unknown) extragalactic magnetic field on our study is discussed, as well as possible probes of the Hubble parameter attainable by current and future experiments.

HERMES: Simulating the Propagation of Ultra-High Energy Cosmic Rays

Abstract: The study of ultra-high energy cosmic rays (UHECR) at Earth cannot prescind from the study of their propagation in the Universe. In this paper, we present HERMES, the \emph{ad hoc} Monte Carlo code we have developed for the realistic simulation of UHECR propagation. We discuss the modeling adopted to simulate the cosmology, the magnetic fields, the interactions with relic photons and the production of secondary particles. In order to show the potential applications of HERMES for astroparticle studies, we provide an estimation of the surviving probability of UHE protons, the GZK horizons of nuclei and the all-particle spectrum observed at Earth in different astrophysical scenarios. Finally, we show the expected arrival direction distribution of UHECR produced from nearby candidate sources. A stable version of HERMES will be released in the next future for public use together with libraries of already propagated nuclei to allow the community to perform mass composition and energy spectrum analysis with our simulator.

Recent progress in low-order density matrix theory of inhomogeneous electron liquids by exact solution of two- and four-electron model atoms

Abstract: Three aspects of low-order density matrix (DM) theory will be reviewed, following some brief comments on analogies with, and differences from, density functional theory (DFT). First, the local energy equation, involving first-to-third order DMs, will be set out and applied exactly to model spin-compensated two-electron atoms. Explicit relations known between low-order DMs for harmonic confinement and arbitrary interparticle interactions will be reported for such model atoms. Second, the March–Young proposal for use variationally, satisfying N-representability, will be set out for spin-free systems such as a four-electron model in the quintet state. Third, the equation of motion for the correlated 1DM is summarised, and brief comments are made on its application to (a) the He atom and (b) crystalline Si. Finally, a model two-electron atom with Coulomb confinement plus an s-wave Coulomb repulsion modified by a δ-function radial correlation is reported as an exactly solvable example.

Co-evolution of networks and quantum dynamics: a generalization of preferential attachment

Abstract: We propose a model of network growth in which the network is co-evolving together with the dynamics of a quantum mechanical system, namely a quantum walk taking place over the network. The model naturally generalizes the Barabasi-Albert model of preferential attachment and has a rich set of tunable parameters, such as the initial conditions of the dynamics or the interaction of the system with its environment. We show that the model produces networks with two-modal power-law degree distributions, super-hubs, finite clustering coefficient, small-world behaviour and non-trivial degree-degree correlations.

EleCa: a Monte Carlo code for the propagation of extragalactic photons at ultra- high energy

Abstract: Ultra high energy photons, above 10 17 – 10 18 eV , can interact with the extragalactic background radiation leading to the development of electromagnetic cascades. A Monte Carlo code to simulate the electromagnetic cascades initiated by high-energy photons and electrons is presented. Results from simulations and their impact on the predicted flux at Earth are discussed in different astrophysical scenarios.

Co-evolution of networks and quantum dynamics: a generalization of preferential attachment

Abstract: We propose a model of network growth in which the network is co-evolving together with the dynamics of a quantum mechanical system, namely a quantum walk taking place over the network. The model naturally generalizes the Barabasi–Albert model of preferential attachment and it has a rich set of tunable parameters, such as the initial conditions of the dynamics or the interaction of the system with its environment. We show that the model produces networks with two-modal power-law degree distributions, super-hubs, finite clustering coefficient, small-world behaviour and non-trivial degree–degree correlations.