Simulation of UHE muons propagation for GEANT3

A simulation package for the transport of high energy muons has been developed. It has been conceived to replace the muon propagation software modules implemented in the detector simulation program GEANT3. Here we discuss the results achieved with our package and we check the agreement with numerical calculations up to 10**8 GeV.Comment: 21 pages, 6 figures, 1 Table. AMSTeX document, acknowledgments adde

Linear Theory of Electron-Plasma Waves at Arbitrary Collisionality

The dynamics of electron-plasma waves are described at arbitrary collisionality by considering the full Coulomb collision operator. The description is based on a Hermite-Laguerre decomposition of the velocity dependence of the electron distribution function. The damping rate, frequency, and eigenmode spectrum of electron-plasma waves are found as functions of the collision frequency and wavelength. A comparison is made between the collisionless Landau damping limit, the Lenard-Bernstein and Dougherty collision operators, and the electron-ion collision operator, finding large deviations in the damping rates and eigenmode spectra. A purely damped entropy mode, characteristic of a plasma where pitch-angle scattering effects are dominant with respect to collisionless effects, is shown to emerge numerically, and its dispersion relation is analytically derived. It is shown that such a mode is absent when simplified collision operators are used, and that like-particle collisions strongly influence the damping rate of the entropy mode.Comment: 23 pages, 10 figures, accepted for publication on Journal of Plasma Physic

A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables. These challenges are traditionally tackled by likelihood-free methods that use scientific simulators to generate datasets and reduce them to hand-designed, permutation-invariant summary statistics, often leading to inaccurate inference. In this work, we develop an exchangeable neural network that performs summary statistic-free, likelihood-free inference. Our framework can be applied in a black-box fashion across a variety of simulation-based tasks, both within and outside biology. We demonstrate the power of our approach on the recombination hotspot testing problem, outperforming the state-of-the-art.Comment: 9 pages, 8 figure

Visibility analysis of boundary layer transition

We study the transition to turbulence in a flat plate boundary layer by means of visibility analysis of velocity time-series extracted across the flow domain. By taking into account the mutual visibility of sampled values, visibility graphs are constructed from each time series. The latter are, thus, transformed into a geometrical object, whose main features can be explored using measures typical of network science that provide a reduced order representation of the underlying flow properties. Using these metrics, we observe the evolution of the flow from laminarity to turbulence and the effects exerted by the free-stream turbulence. Different from other methods requiring an extensive amount of spatiotemporal data (e.g., full velocity field) or a set of parameters and thresholds arbitrarily chosen by the user, the present network-based approach is able to identify the onset markers for transition by means of the streamwise velocity time-series alone. Published under an exclusive license by AIP Publishing

Seismic Response of Simply Supported Deck Bridges with Auxiliary Superelastic Devices

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Update on viral infections involving the central nervous system in pediatric patients

Infections of the central nervous system (CNS) are mainly caused by viruses, and these infections can be life-threatening in pediatric patients. Although the prognosis of CNS infections is often favorable, mortality and long-term sequelae can occur. The aims of this narrative review were to describe the specific microbiological and clinical features of the most frequent pathogens and to provide an update on the diagnostic approaches and treatment strategies for viral CNS infections in children. A literature analysis showed that the most common pathogens worldwide are enteroviruses, arboviruses, parechoviruses, and herpesviruses, with variable prevalence rates in different countries. Lumbar puncture (LP) should be performed as soon as possible when CNS infection is suspected, and cerebrospinal fluid (CSF) samples should always be sent for polymerase chain reaction (PCR) analysis. Due to the lack of specific therapies, the management of viral CNS infections is mainly based on supportive care, and empiric treatment against herpes simplex virus (HSV) infection should be started as soon as possible. Some researchers have questioned the role of acyclovir as an empiric antiviral in older children due to the low incidence of HSV infection in this population and observed that HSV encephalitis may be clinically recognizable beyond neonatal age. However, the real benefit-risk ratio of selective approaches is unclear, and further studies are needed to define appropriate indications for empiric acyclovir. Research is needed to find specific therapies for emerging pathogens. Moreover, the appropriate timing of monitoring neurological development, performing neuroimaging evaluations and investigating the effectiveness of rehabilitation during follow-up should be evaluated with long-term studies

Fluorescence and Hybrid Detection Aperture of the Pierre Auger Observatory

The aperture of the Fluorescence Detector (FD) of the Pierre Auger Observatory is evaluated from simulated events using different detector configurations: mono, stereo, 3-FD and 4-FD. The trigger efficiency has been modeled using shower profiles with ground impacts in the field of view of a single telescope and studying the trigger response (at the different levels) by that telescope and by its neighbours. In addition, analysis cuts imposed by event reconstruction have been applied. The hybrid aperture is then derived for the Auger final extension. Taking into account the actual Surface Detector (SD) array configuration and its trigger response, the aperture is also calculated for a typical configuration of the present phase.Comment: contribution to the 29th International Cosmic Ray Conference, Pune, India, 3-10 August 200

All optical implementation of a stochastic logic gate using a VCSEL with external optical injection

We perform numerical simulations of the dynamics of two VCSELs optically coupled in a master-slave configuration. We show that the interplay of nonlinearity and spontaneous emission noise can yield logic behavior, and the emergent outcome of such system is a reliable logic gate. Specifically in our case represents an all-optical logic gate, with the logic input encoded in the strength of the light injected in the slave laser by the master laser, and the fast and robust response decoded from the polarization state of the light emitted by the slave laser.Peer ReviewedPostprint (author’s final draft