Pressure induced magnetic phase separation in La0.75_{0.75}Ca0.25_{0.25}MnO3_{3} manganite

The pressure dependence of the Curie temperature T$_{C}(P)$ in La$_{0.75}$Ca$_{0.25}$MnO$_{3}$ was determined by neutron diffraction up to 8 GPa, and compared with the metallization temperature T$_{IM}(P)$ \cite{irprl}. The behavior of the two temperatures appears similar over the whole pressure range suggesting a key role of magnetic double exchange also in the pressure regime where the superexchange interaction is dominant. Coexistence of antiferromagnetic and ferromagnetic peaks at high pressure and low temperature indicates a phase separated regime which is well reproduced with a dynamical mean-field calculation for a simplified model. A new P-T phase diagram has been proposed on the basis of the whole set of experimental data.Comment: 5 pages, 4 figure

The emotional contagion in children with autism spectrum disorder

Studies of the last decade have demonstrated that children with Autism Spectrum Disorder (ASD) showed difficulties in language, social and relational areas, but they had also impairment in the mechanisms of embodied simulation, namely the imitative behaviors that allow the body to give an experiential meaning to own and other’s emotions. The identification of this specific emotional response in ASD children, also defined as emotional contagion, allows to move the therapeutic focus from reducing the behavioral symptomatic expressions of the child to promoting the expression of his ability of emotional regulation. The aim of this study was to investigate the presence of emotional contagion in 53 ASD children aged between 22 and 66 months, through the Test of emotional contagion and verify the presence of compromised emotional contagion areas. Our findings have shown that the severity of the disorder is closely related to the inability of the child to respond to the emotional stimuli, regardless from cognitive abilities, and that emotion to which children responded most frequently was happiness, while the one who responded less was anger

From the emotional integration to the cognitive construction: the developmental approach of Turtle Project in children with autism spectrum disorder

Background: Children with autism spectrum disorder show a deficit in neurobiological processes. This deficit hinders the development of intentional behavior and appropriate problem-solving, leading the child to implement repetitive and stereotyped behaviors and to have difficulties in reciprocal interactions, empathy and in the development of a theory of mind. The objective of this research is to verify the effectiveness of a relationship-based approach on the positive evolution of autistic symptoms. Method: A sample of 80 children with autism spectrum disorder was monitored during the first four years of therapy, through a clinical diagnostic assessment at the time of intake and then in two follow-up. Results: The results showed that through the Autism Diagnostic Observation Schedule it is possible to assess the socio-relational key elements on which the therapy is based. There was evidence, in fact, of significant improvements after two and four years of therapy, both for children with severe autistic symptoms and for those in autistic spectrum. Conclusions: Socio-relational aspects represent the primary element on which work in therapy with autistic children and can be considered as indicators of a positive evolution and prognosis that will produce improvements even in the cognitive are

Ergodicity breaking in strong and network-forming glassy system

The temperature dependence of the non-ergodicity factor of vitreous GeO$_2$, $f_{q}(T)$, as deduced from elastic and quasi-elastic neutron scattering experiments, is analyzed. The data are collected in a wide range of temperatures from the glassy phase, up to the glass transition temperature, and well above into the undercooled liquid state. Notwithstanding the investigated system is classified as prototype of strong glass, it is found that the temperature- and the $q$-behavior of $f_{q}(T)$ follow some of the predictions of Mode Coupling Theory. The experimental data support the hypothesis of the existence of an ergodic to non-ergodic transition occurring also in network forming glassy systems

Electron beam transfer line design for plasma driven Free Electron Lasers

Plasma driven particle accelerators represent the future of compact accelerating machines and Free Electron Lasers are going to benefit from these new technologies. One of the main issue of this new approach to FEL machines is the design of the transfer line needed to match of the electron-beam with the magnetic undulators. Despite the reduction of the chromaticity of plasma beams is one of the main goals, the target of this line is to be effective even in cases of beams with a considerable value of chromaticity. The method here explained is based on the code GIOTTO [1] that works using a homemade genetic algorithm and that is capable of finding optimal matching line layouts directly using a full 3D tracking code.Comment: 9 Pages, 4 Figures. A related poster was presented at EAAC 201

Ultrahigh brightness electron beams by plasma-based injectors for driving all-optical free-electron lasers

We studied the generation of low emittance high current monoenergetic beams from plasma waves driven by ultrashort laser pulses, in view of achieving beam brightness of interest for free-electron laser (FEL) applications. The aim is to show the feasibility of generating nC charged beams carrying peak currents much higher than those attainable with photoinjectors, together with comparable emittances and energy spread, compatibly with typical FEL requirements. We identified two regimes: the first is based on a laser wakefield acceleration plasma driving scheme on a gas jet modulated in areas of different densities with sharp density gradients. The second regime is the so-called bubble regime, leaving a full electron-free zone behind the driving laser pulse: with this technique peak currents in excess of 100 kA are achievable. We have focused on the first regime, because it seems more promising in terms of beam emittance. Simulations carried out using VORPAL show, in fact, that in the first regime, using a properly density modulated gas jet, it is possible to generate beams at energies of about 30 MeV with peak currents of 20 kA, slice transverse emittances as low as 0.3 mm mrad, and energy spread around 0.4%. These beams break the barrier of 10^{18}  A/(mm mrad)^{2} in brightness, a value definitely above the ultimate performances of photoinjectors, therefore opening a new range of opportunities for FEL applications. A few examples of FELs driven by such kind of beams injected into laser undulators are finally shown. The system constituted by the electron beam under the effect of the electromagnetic undulator has been named AOFEL (for all optical free-electron laser)

Identifying Galaxy Mergers in Observations and Simulations with Deep Learning

Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any differences between these two classifications. With one of the main difficulties of merger studies being the lack of a truth sample, we can use our method to test biases in visually identified merger catalogues. A convolutional neural network architecture was developed and trained in two ways: one with observations from SDSS and one with simulated galaxies from EAGLE, processed to mimic the SDSS observations. The SDSS images were also classified by the simulation trained network and the EAGLE images classified by the observation trained network. The observationally trained network achieves an accuracy of 91.5% while the simulation trained network achieves 65.2% on the visually classified SDSS and physically classified EAGLE images respectively. Classifying the SDSS images with the simulation trained network was less successful, only achieving an accuracy of 64.6%, while classifying the EAGLE images with the observation network was very poor, achieving an accuracy of only 53.0% with preferential assignment to the non-merger classification. This suggests that most of the simulated mergers do not have conspicuous merger features and visually identified merger catalogues from observations are incomplete and biased towards certain merger types. The networks trained and tested with the same data perform the best, with observations performing better than simulations, a result of the observational sample being biased towards conspicuous mergers. Classifying SDSS observations with the simulation trained network has proven to work, providing tantalizing prospects for using simulation trained networks for galaxy identification in large surveys.Comment: Submitted to A&A, revised after first referee report. 20 pages, 22 figures, 14 tables, 1 appendi

Estimating the generation interval from the incidence rate, the optimal quarantine duration and the efficiency of fast switching periodic protocols for COVID‑19

The transmissibility of an infectious disease is usually quantified in terms of the reproduction number Rt representing, at a given time, the average number of secondary cases caused by an infected individual. Recent studies have enlightened the central role played by w(z), the distribution of generation times z, namely the time between successive infections in a transmission chain. In standard approaches this quantity is usually substituted by the distribution of serial intervals, which is obtained by contact tracing after measuring the time between onset of symptoms in successive cases. Unfortunately, this substitution can cause important biases in the estimate of Rt . Here we present a novel method which allows us to simultaneously obtain the optimal functional form of w(z) together with the daily evolution of Rt , over the course of an epidemic. The method uses, as unique information, the daily series of incidence rate and thus overcomes biases present in standard approaches. We apply our method to one year of data from COVID-19 officially reported cases in the 21 Italian regions, since the first confirmed case on February 2020. We find that w(z) has mean value z ≃ 6 days with a standard deviation a ≃ 1 day, for all Italian regions, and these values are stable even if one considers only the first 10 days of data recording. This indicates that an estimate of the most relevant transmission parameters can be already available in the early stage of a pandemic. We use this information to obtain the optimal quarantine duration and to demonstrate that, in the case of COVID-19, post-lockdown mitigation policies, such as fast periodic switching and/or alternating quarantine, can be very efficient

The Role of Digital Technologies in Improving Energy Efficiency at Logistics Facilities: A State-of-the-Art

The logistics industry is facing increasing challenges that have been further amplified by recent disruptions. In this context, warehouses have been playing an ever-crucial role. They have been transitioning from simple storage centres into high-functional facilities where several and heterogeneous processes are performed to guarantee efficiency and service level fulfilment. These dramatic changes have often made them highly energy intensive. To cope with these changes, a wide array of digital technologies is now available and has started to be gradually introduced by companies at their logistics facilities to reduce energy consumption and improve the environmental sustainability of warehousing operations. Nevertheless, on the academic side, although a mounting number of papers have been found addressing the adoption of digital technologies at logistics facilities with an energy efficiency perspective, a clear overview of the solutions in place and their impact on warehousing processes has been largely neglected so far. This contribution aims at addressing this research gap by offering a state-of-the-art of the role of digital technologies in improving energy efficiency at logistics facilities. The study is based on a systematic review approach performed by means of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol. The research is part of a broader Italian funded PNRR Research project “Centro Nazionale per la Mobilità Sostenibile” (MOST) – Spoke 10 “Sustainable Logistics”. Results indicate that the impact of digital technologies on warehouse processes is still underexamined, and research has mainly focused on specific technical issues or single warehousing processes rather than providing a holistic approach. The study provides a comprehensive framework offering guidance for technology implementation. Implications are discussed and streams for future investigation are identified