Refined Structure of Metastable Ice XVII from Neutron Diffraction Measurements

The structure of the recently identified metastable ice XVII, obtained by release of hydrogen from the C$_0$ D$_2$O-H$_2$ compound (filled ice), has been accurately measured by neutron powder diffraction. The diffraction pattern is indexed with a hexagonal cell and can be refined with space group $P6_122$ so to obtain accurate values of the oxygen and deuterium positions. The values of the lattice constants at three temperatures between 25 to 100 K are reported, and their behavior is compared with that of ice Ih. Ice XVII is a porous solid that, if exposed to H$_2$ gas, may adsorb a substantial amount of it. Monitoring this effect at a constant temperature of 50 K, we have observed that the two lattice constants show opposite behavior, $a$ increases and $c$ decreases, with the volume showing a linear increase. At temperatures higher than 130 K the metastability of this form of porous ice is lost and the sample transforms into ice Ih

Quantum calculation of inelastic neutron scattering spectra of a hydrogen molecule inside a nanoscale cavity based on rigorous treatment of the coupled translation-rotation dynamics

We present a quantum methodology for the calculation of the inelastic neutron scattering (INS) spectra of an ${\mathrm{H}}_{2}$ molecule confined in a nanoscale cavity. Our approach incorporates the coupled five-dimensional translation-rotation (TR) energy levels and wave functions of the guest molecule. The computed INS spectra are highly realistic and reflect in full the complexity of the coupled TR dynamics on the anisotropic potential energy surfaces of the confining environment. Utilizing this methodology, we simulate the INS spectra of $p$- and $o$-H${}_{2}$ in the small cage of the structure II clathrate hydrate and compare them with the experimental data

Fetal MRI: what’s new? A short review

Introduction: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been a major global health problem since December 2019. This work aimed to investigate whether pregnant women's mild and moderate SARS-CoV-2 infection was associated with microstructural and vascular changes in the placenta observable in vivo by Intravoxel Incoherent Motion (IVIM) at different gestational ages (GA). Methods: This was a retrospective, nested case-control of pregnant women during the SARS-CoV-2 pandemic (COVID-19 group, n = 14) compared to pre-pandemic healthy controls (n = 19). MRI IVIM protocol at 1.5T was constituted of diffusion-weighted (DW) images with TR/TE = 3100/76 ms and 10 b-values (0,10,30,50,75,100,200,400,700,1000s/mm2). Differences between IVIM parameters D (diffusion), and f (fractional perfusion) quantified in the two groups were evaluated using the ANOVA test with Bonferroni correction and linear correlation between IVIM metrics and GA, COVID-19 duration, the delay time between a positive SARS-CoV-2 test and MRI examination (delay-time exam+) was studied by Pearson-test. Results: D was significantly higher in the COVID-19 placentas compared to that of the age-matched healthy group (p < 0.04 in fetal and p < 0.007 in maternal site). No significant difference between f values was found in the two groups suggesting no-specific microstructural damage with no perfusion alteration (potentially quantified by f) in mild/moderate SARS-Cov-2 placentas. A significant negative correlation was found between D and GA in the COVID-19 placentas whereas no significant correlation was found in the control placentas reflecting a possible accelerated senescence process due to COVID-19. Discussion: We report impaired microstructural placental development during pregnancy and the absence of perfusion-IVIM parameter changes that may indicate no perfusion changing through microvessels and microvilli in the placentas of pregnancies with mild/moderate SARS-Cov-2 after reaching negativity

Notulae to the Italian flora of algae, bryophytes, fungi and lichens: 14

In this contribution, new data concerning bryophytes, fungi and lichens of the Italian flora are presented. It includes new records and confirmations for the algal genus Chara, for the bryophyte genera Bryum, Grimmia, Cephaloziella, Hypnum, Nogopterium, Physcomitrium, Polytrichastrum, Rhynchostegiella, Saelania, and Schistostega, the fungal genera Cortinarius, Lentinellus, Omphalina, and Xerophorus, and the lichen genera Acarospora, Agonimia, Candelariella, Cladonia, Graphis, Gyalolechia, Hypogymnia, Lichinella, Megalaria, Nephroma, Ochrolechia, Opegrapha, Peltigera, Placidium, Ramalina, Rhizoplaca, Ropalospora, Strangospora, Toniniopsis, Usnea, and Zahlbrucknerell

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Random Wave-Induced Momentary Liquefaction around Rubble Mound Breakwaters with Submerged Berms

The effects of submerged berms in attenuating the momentary liquefaction beneath rubble mound breakwaters under regular waves were investigated in a recent study. The present work aims to investigate the momentary liquefaction probabilities around and beneath breakwaters with submerged berms under random waves. The interaction between waves and breakwaters with submerged berms has been simulated through a phase-resolving numerical model. The soil response to the seabed pressure induced by random waves has been investigated using a poro-elastic soil solver. For three different breakwater configurations, the liquefaction depths under random wave conditions have been compared with those cases under representative regular waves. In the present study, the offshore spectral wave height (Hm0) and the peak period (Tp) of irregular waves are used as representative regular wave parameters. Results reveal the importance of considering random waves for a safe estimation of the momentary liquefaction probability. Indication about the minimum number of random waves, which is required to properly catch the liquefaction occurrences, has been also addressed.publishedVersio