On the Weyl transverse frames in type I spacetimes

We apply a covariant and generic procedure to obtain explicit expressions of the transverse frames that a type I spacetime admits in terms of an arbitrary initial frame. We also present a simple and general algorithm to obtain the Weyl scalars $\Psi_2^T$, $\Psi_0^T$ and $\Psi_4^T$ associated with these transverse frames. In both cases it is only necessary to choose a particular root of a cubic expression.Comment: 12 pages, submitted to Gen. Rel. Grav. (6-3-2004

Angular dependence of magnetoresistivity in c-oriented MgB2 thin film

The anisotropy of MgB2 is still under debate: its value, strongly dependent on the sample and on the measuring method, ranges between 1.2 and 13. In this work we present our results on a MgB2 c-oriented superconducting thin film. To evaluate the anisotropy, we followed two different approaches. Firstly, magnetoresistivity was measured as a function of temperature at selected magnetic fields applied both parallel and perpendicular to the c-axis; secondly, we measured magnetoresistivity at selected temperatures and magnetic fields, varying the angle q between the magnetic field and the c-axis. The anisotropy estimated from the ratio between the upper critical fields parallel and perpendicular to the c-axis and the one obtained in the framework of the scaling approach within the anisotropic Ginzburg-Landau theory are different but show a similar trend in the temperature dependence. The obtained results are compared and discussed in the light of the two-band nature of MgB2. A comparison between critical fields in thin films and single crystal is also performed.Comment: 13 pages, 4 figures, European Physical Journal B in pres

Full-vector analysis of a realistic photonic crystal fiber

We analyze the guiding problem in a realistic photonic crystal fiber using a novel full-vector modal technique, a biorthogonal modal method based on the nonselfadjoint character of the electromagnetic propagation in a fiber. Dispersion curves of guided modes for different fiber structural parameters are calculated along with the 2D transverse intensity distribution of the fundamental mode. Our results match those achieved in recent experiments, where the feasibility of this type of fiber was shown.Comment: 3 figures, submitted to Optics Letter

Simbol-X Background Minimization: Mirror Spacecraft Passive Shielding Trade-Off Study

The present work shows a quantitative trade-off analysis of the Simbol-X Mirror Spacecraft (MSC) passive shielding, in the phase space of the various parameters: mass budget, dimension, geometry, and composition. A simplified physical (and geometrical) model of the sky screen, implemented by means of a GEANT4 simulation, has been developed to perform a performance-driven mass optimization and evaluate the residual background level on Simbol-X focal plane.Comment: 3 pages, 6 figures, to appear in the proceedings of the second Simbol-X International Symposium "Simbol-X - Focusing on the Hard X-ray Universe", AIP Conf. Proc. Series, P. Ferrando and J. Rodriguez ed

Smart balancing of E-scooter sharing systems via deep reinforcement learning: a preliminary study

Nowadays, micro-mobility sharing systems have become extremely popular. Such systems consist in fleets of dockless electric vehicles which are deployed in cities, and used by citizens to move in a more ecological and flexible way. Unfortunately, one of the issues related to such technologies is its intrinsic load imbalance, since users can pick up and drop off the electric vehicles where they prefer. In this paper we present ESB-DQN, a multi-agent system for E-Scooter Balancing (ESB) based on Deep Reinforcement Learning where agents are implemented as Deep Q-Networks (DQN). ESB-DQN offers suggestions to pick or return e-scooters in order to make the fleet usage and sharing as balanced as possible, still ensuring that the original plans of the user undergo only minor changes. The main contributions of this paper include a careful analysis of the state of the art, an innovative customer-oriented rebalancing strategy, the integration of state-of-the-art libraries for deep Reinforcement Learning into the existing ODySSEUS simulator of mobility sharing systems, and preliminary but promising experiments that suggest that our approach is worth further exploration