Dynamical bar-mode instability in rotating and magnetized relativistic stars

We present three-dimensional simulations of the dynamical bar-mode instability in magnetized and differentially rotating stars in full general relativity. Our focus is on the effects that magnetic fields have on the dynamics and the onset of the instability. In particular, we perform ideal-magnetohydrodynamics simulations of neutron stars that are known to be either stable or unstable against the purely hydrodynamical instability, but to which a poloidal magnetic field in the range of $10^{14}$--$10^{16}$ G is superimposed initially. As expected, the differential rotation is responsible for the shearing of the poloidal field and the consequent linear growth in time of the toroidal magnetic field. The latter rapidly exceeds in strength the original poloidal one, leading to a magnetic-field amplification in the the stars. Weak initial magnetic fields, i.e. $\lesssim 10^{15}$ G, have negligible effects on the development of the dynamical bar-mode instability, simply braking the stellar configuration via magnetic-field shearing, and over a timescale for which we derived a simple algebraic expression. On the other hand, strong magnetic fields, i.e. $\gtrsim 10^{16}$ G, can suppress the instability completely, with the precise threshold being dependent also on the amount of rotation. As a result, it is unlikely that very highly magnetized neutron stars can be considered as sources of gravitational waves via the dynamical bar-mode instability.Comment: 18 pages, 13 figure

Bar-mode instability suppression in magnetized relativistic stars

We show that magnetic fields stronger than about $10^{15}$ G are able to suppress the development of the hydrodynamical bar-mode instability in relativistic stars. The suppression is due to a change in the rest-mass density and angular velocity profiles due to the formation and to the linear growth of a toroidal component that rapidly overcomes the original poloidal one, leading to an amplification of the total magnetic energy. The study is carried out performing three-dimensional ideal-magnetohydrodynamics simulations in full general relativity, superimposing to the initial (matter) equilibrium configurations a purely poloidal magnetic field in the range $10^{14}-10^{16}$ G. When the seed field is a few parts in $10^{15}$ G or above, all the evolved models show the formation of a low-density envelope surrounding the star. For much weaker fields, no effect on the matter evolution is observed, while magnetic fields which are just below the suppression threshold are observed to slow down the growth-rate of the instability.Comment: 6 pages, 4 figures, to appear on the proceedings of the 4th YRM (Trieste 2013

General-relativistic resistive magnetohydrodynamics in three dimensions: Formulation and tests

We present a new numerical implementation of the general-relativistic resistive magnetohydrodynamics (MHD) equations within the Whisky code. The numerical method adopted exploits the properties of implicit-explicit Runge-Kutta numerical schemes to treat the stiff terms that appear in the equations for large electrical conductivities. Using tests in one, two, and three dimensions, we show that our implementation is robust and recovers the ideal-MHD limit in regimes of very high conductivity. Moreover, the results illustrate that the code is capable of describing scenarios in a very wide range of conductivities. In addition to tests in flat spacetime, we report simulations of magnetized nonrotating relativistic stars, both in the Cowling approximation and in dynamical spacetimes. Finally, because of its astrophysical relevance and because it provides a severe testbed for general-relativistic codes with dynamical electromagnetic fields, we study the collapse of a nonrotating star to a black hole. We show that also in this case our results on the quasinormal mode frequencies of the excited electromagnetic fields in the Schwarzschild background agree with the perturbative studies within 0.7% and 5.6% for the real and the imaginary part of the l=1 mode eigenfrequency, respectively. Finally we provide an estimate of the electromagnetic efficiency of this process.Comment: 22 pages, 19 figure

Παιδεία, ελευθερία και πολιτική. Οι απόψεις του Ρουσσώ στον Αιμίλιο

Μέσα από τον Αιμίλιο, ο Ρουσσώ προσφέρει ένα ολόκληρο φάσμα της ανθρώπινης εμπειρίας από την προοπτική του συστήματος της «φυσικής καλοσύνης» του ανθρώπου εγγίζοντας πληθώρα ζητημάτων, όπως αυτά της ανθρώπινης ψυχολογίας, της ηθικής, της θρησκείας, των φύλων, της πολιτικής. Αρχικώς, θα παρουσιαστεί εν συντομία η δομή του έργου και θα σκιαγραφηθούν οι βασικές έννοιες του, ούτως ώστε να καταστεί ευκολότερη, αλλά και εναργέστερη η ανάλυση που θα ακολουθήσει. Έπειτα, στο κεντρικό μέρος της εργασίας θα επιχειρηθεί η, κατά την γράφουσα, κριτική επεξεργασία του έργου, ειδικά από την σκοπιά της ιδέας της ελευθερίας του πολίτη μέσα από το έργο του Ρουσσώ. Στόχος του ανά χείρας πονήματος είναι η, όσο κατά το δυνατόν, πληρέστερη παρουσίαση της οπτικής του συγγραφέα αναφορικά με τις έννοιες της παιδείας, της ελευθερίας και της πολιτικής, όπως αυτές ξεδιπλώνονται μέσα από τον Αιμίλιο, ένα από τα σημαντικότερα εκπαιδευτικά μυθιστορήματα του δυτικού πολιτισμού.Through Emile, Rousseau offers a whole range of human experience from the perspective of the system of human "natural goodness", touching on a variety of issues, such as those of human psychology, ethics, religion, gender, politics. Initially, the structure of the project will be briefly presented and its basic concepts will be outlined, in order to make the analysis that will follow easier, but also more active. Then, in the central part of the work, the critical elaboration of the work will be attempted, especially from the point of view of the idea of ​​the freedom of the citizen through the work of Rousseau

Opportunities for machine learning and artificial intelligence in national mapping agencies:enhancing ordnance survey workflow

National Mapping agencies (NMA) are frequently tasked with providing highly accurate geospatial data for a range of customers. Traditionally, this challenge has been met by combining the collection of remote sensing data with extensive field work, and the manual interpretation and processing of the combined data. Consequently, this task is a significant logistical undertaking which benefits the production of high quality output, but which is extremely expensive to deliver. Therefore, novel approaches that can automate feature extraction and classification from remotely sensed data, are of great potential interest to NMAs across the entire sector. Using research undertaken at Great Britain’s NMA; Ordnance Survey (OS) as an example, this paper provides an overview of the recent advances at an NMA in the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) based applications. Examples of these approaches are in automating the process of feature extraction and classification from remotely sensed aerial imagery. In addition, recent OS research in applying deep (convolutional) neural network architectures to image classification are also described. This overview is intended to be useful to other NMAs who may be considering the adoption of similar approaches within their workflows

Investigation of the Effects of a Novel NOX2 Inhibitor, GLX7013170, against Glutamate Excitotoxicity and Diabetes Insults in the Retina

Glutamate excitotoxicity and oxidative stress represent two major pathological mechanisms implicated in retinal disorders. In Diabetic Retinopathy (DR), oxidative stress is correlated to NADPH oxidase (NOX), a major source of Reactive Oxygen Species (ROS), and glutamate metabolism impairments. This study investigated the role of NOX2 and the novel NOX2 inhibitor, GLX7013170, in two models of a) retinal AMPA excitotoxicity [AMPA+GLX7013170 (10−4 M, intravitreally)] and b) early-stage DR paradigm (ESDR), GLX7013170: 14-day therapeutic treatment (topically, 20 μL/eye, 10 mg/mL (300 × 10−4 M), once daily) post-streptozotocin (STZ)-induced DR. Immunohistochemical studies for neuronal markers, nitrotyrosine, micro/macroglia, and real-time PCR, Western blot, and glutamate colorimetric assays were conducted. Diabetes increased NOX2 expression in the retina. NOX2 inhibition limited the loss of NOS-positive amacrine cells and the overactivation of micro/macroglia in both models. In the diabetic retina, GLX7013170 had no effect on retinal ganglion cell axons, but reduced oxidative damage, increased Bcl-2, reduced glutamate levels, and partially restored excitatory amino acid transporter (EAAT1) expression. These results suggest that NOX2 in diabetes is part of the triad, oxidative stress, NOX, and glutamate excitotoxicity, key players in the induction of DR. GLX7013170 is efficacious as a neuroprotective/anti-inflammatory agent and a potential therapeutic in retinal diseases, including ESDR

Opportunities for machine learning and artificial intelligence in a national mapping agency: a perspective on enhancing ordnance survey workflow

National Mapping agencies (NMA) are tasked with providing highly accurate geospatial data for a range of customers. This challenge has traditionally been met by combining remote sensing data gathering, field work and manual interpretation and processing of the data. This is a significant logistical undertaking which requires novel approaches to improve potential feature extraction from the available data. Using research undertaken at Great Britain’sNMA, Ordnance Survey (OS)as an example, this paper provides an overview of recent advances in the use of artificial intelligence (AI)to assist in improving feature classification from remotely sensed aerial imagery, describing research using high level neural network architecture to image classification that utilisesconvolutional neural network learning