The MESS of cosmological perturbations

We introduce two new effective quantities for the study of comoving curvature perturbations $\zeta$: the space dependent effective sound speed (SESS) and the momentum dependent effective sound speed (MESS) . We use the SESS and the MESS to derive a new set of equations which can be applied to any system described by an effective stress-energy-momentum tensor (EST), including multi-fields systems, supergravity and modified gravity theories. We show that this approach is completely equivalent to the standard one and it has the advantage of requiring to solve only one differential equation for $\zeta$ instead of a system, without the need of explicitly computing the evolution of entropy perturbations. The equations are valid for perturbations respect to any arbitrary flat spatially homogeneous background, including any inflationary and bounce model. As an application we derive the equation for $\zeta$ for multi-fields $KGB$ models and show that observed features of the primordial curvature perturbation spectrum are compatible with the effects of an appropriate local variation of the MESS in momentum space. The MESS is the natural quantity to parametrize in a model independent way the effects produced on curvature perturbations by multi-fields systems, particle production and modified gravity theories and could be conveniently used in the analysis of LSS observations, such as the ones from the upcoming EUCLID mission or CMB radiation measurements.Comment: We study the MESS of cosmological perturbations, version accepted in Physics Letters

Effects of particle production during inflation

The impact of particle production during inflation on the primordial curvature perturbation spectrum is investigated both analytically and numerically. We obtain an oscillatory behavior on small scales, while on large scales the spectrum is unaffected. The amplitude of the oscillations is proportional to the number of coupled fields, their mass, and the square of the coupling constant. The oscillations are due a discontinuity in the second time derivative of the inflaton, arising from a temporary violation of the slow-roll conditions. A similar effect on the power spectrum should be produced also in other inflationary models where the slow-roll conditions are temporarily violated.Comment: 7 pages, 5 figure

An Analytic Hierarchy Process for The Evaluation of Transport Policies to Reduce Climate Change Impacts

Transport is the sector with the fastest growth of greenhouse gases emissions, both in developed and in developing countries, leading to adverse climate change impacts. As the experts disagree on the occurrence of these impacts, by applying the analytic hierarchy process (AHP), we have faced the question on how to form transport policies when the experts have different opinions and beliefs. The opinions of experts have been investigated by a means of a survey questionnaire. The results show that tax schemes aiming at promoting environmental-friendly transport mode are the best policy. This incentives public and environmental-friendly transport modes, such as car sharing and car pooling.Analytic Hierarchy Process, Transport Policies, Climate Change

Large and strong scale dependent bispectrum in single field inflation from a sharp feature in the mass

We study an inflationary model driven by a single minimally coupled standard kinetic term scalar field with a step in its mass modeled by an Heaviside step function. We present an analytical approximation for the mode function of the curvature perturbation, obtain the power spectrum analytically and compare it with the numerical result. We show that, after the scale set by the step, the spectrum contains damped oscillations that are well described by our analytical approximation. We also compute the dominant contribution to the bispectrum in the equilateral and the squeezed limits and find new shapes. In the equilateral and squeezed limits the bispectrum oscillates and it has a linear growth envelope towards smaller scales. The bispectrum size can be large depending on the model parameters.Comment: 18 pages, 10 figures; v2: Plots and captions of figs. 7 and 10 correcte

Godel-type space-time metrics

A simple group theoretic derivation is given of the family of space-time metrics with isometry group SO(2,1) X SO(2) X R first described by Godel, of which the Godel stationary cosmological solution is the member with a perfect-fluid stress-energy tensor. Other members of the family are shown to be interpretable as cosmological solutions with a electrically charged perfect fluid and a magnetic field.Comment: Heavly rewritten respect to the orginal version, corrected some typos due to files transfer in the last submitted versio

Component-wise damage detection by neural networks and refined FEs training

Multilayer perceptrons are utilized in this work for vibration-based damage detection of multicomponent aerospace structures. A back-propagation algorithm is utilized along with Monte Carlo simulations and advanced structural theories for training Artificial Neural Networks (ANN’s), which are able to detect and classify local damages in structures given the natural frequencies and the associated vibrations modes. The latter ones are feed into the network in terms of Modal Assurance Criterion (MAC), which is a scalar representing the degree of consistency between undamaged and damaged modal vectors. Dataset and ANN training process is carried out by means of Carrera Unified Formulation (CUF), according to which refined finite elements with component-wise capabilities can be implemented in a hierarchical and unified manner. The proposed results demonstrate that CUF-trained ANNs can approximate complete mapping of the damage distribution, even in case of low damage intensities and local defects in localized components (stringers, spar caps, webs, etc.

Damage detection in composites by AI and high-order modelling surface-strain-displacement analysis

In the recent years, machine learning algorithms have been widely employed for structural health monitoring applications. As an example, Artificial Neu-ral Networks (ANN) could be useful in giving a precise and complete map-ping of damage distribution in a structure, including low-intensity or local-ized defects, which could be difficult to detected via traditional testing tech-niques. In this domain, Convolutional Neural Network (CNN) are employed in this work along with one-dimensional refined models based on the Carrera Unified formulation (CUF) for surface strain\displacement based damage detection in composite laminates. A layer-wise kinematic is adopted, while both an isotropic and orthotropic damage formulation is implemented. In de-tail, CUF-based finite element models have been exploited in combination with Monte Carlo simulations for the creation of a dataset of damage scenar-ios used for the training of the CNN. Therefore, the latter is fed with images of the strain or displacement field in a region of particular interest for each sample, which are subjected to the same boundary conditions. The trained ANN, given the strain\displacement mapping of an unknown structure, is therefore able to detect and classify all the damages within the structure, solving the so-called inverse problem

A Multi-Objective Approach to Optimize a Periodic Maintenance Policy

The present paper proposes a multi-objective approach to find out an optimal periodic maintenance policy for a repairable and stochastically deteriorating multi-component system over a finite time horizon. The tackled problem concerns the determination of the system elements to replace at each scheduled and periodical system inspection by ensuring the simultaneous minimization of both the expected total maintenance cost and the expected global system unavailability time. It is assumed that in the case of system elements failure they are instantaneously detected and repaired by means of minimal repair actions in order to rapidly restore the system. A non-linear integer mathematical programming model is developed to solve the treated multi-objective problem whereas the Pareto optimal frontier is described by the Lexicographic Goal Programming and the \u3b5-constraint methods. To explain the whole procedure a case study is solved and the related considerations are given

Decision Procedure for Entailment of Symbolic Heaps with Arrays

This paper gives a decision procedure for the validity of en- tailment of symbolic heaps in separation logic with Presburger arithmetic and arrays. The correctness of the decision procedure is proved under the condition that sizes of arrays in the succedent are not existentially bound. This condition is independent of the condition proposed by the CADE-2017 paper by Brotherston et al, namely, one of them does not imply the other. For improving efficiency of the decision procedure, some techniques are also presented. The main idea of the decision procedure is a novel translation of an entailment of symbolic heaps into a formula in Presburger arithmetic, and to combine it with an external SMT solver. This paper also gives experimental results by an implementation, which shows that the decision procedure works efficiently enough to use

Function Allocation between Automation and Human Pilot for Airborne Separation Assurance

Maintaining safe separation between aircraft is a key determinant of the airspace capacity to handle air transportation. With the advent of satellite-based surveillance, aircraft equipped with the needed technologies are now capable of maintaining awareness of their location in the airspace and sharing it with their surrounding traffic. As a result, concepts and cockpit automation are emerging to enable delegating the responsibility of maintaining safe separation from traffic to the pilot; thus increasing the airspace capacity by alleviating the limitation of the current non-scalable centralized ground-based system. In this paper, an analysis of allocating separation assurance functions to the human pilot and cockpit automation is presented to support the design of these concepts and technologies. A task analysis was conducted with the help of Petri nets to identify the main separation assurance functions and their interactions. Each function was characterized by three behavior levels that may be needed to perform the task: skill, rule and knowledge based levels. Then recommendations are made for allocating each function to an automation scale based on their behavior level characterization and with the help of Subject matter experts