Nanoengineering of Hybrid Lightweight Cellulosic Fibre Foams for better Flame Resistance

We studied the flame propagation and combustion properties of a lightweight fibrous foam produced from a layered double hydroxides (LDH) modified thermomechanical pulp fibres. The in situ synthesis of Mg-Al LDH with pulp fibres was engineered to include both micron and nano-sized particles. The method allowed loading the fibres with LDH up to 34% (w/w). Observed pyrolytic effects included 60% reduction in CO2 production rate, and similar reductions in peak heat release rate (PHRR) and in amount of soot during the oxidative pyrolysis. The in situ synthesised LDH particles shielded the fibres from external heat by reducing the rate of oxidation and liberation of volatile gases. Effective charring was observed at the interphase of LDH nanoparticles and organic material.</p

Assessing Trustworthy AI in times of COVID-19. Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients

Abstract—The paper's main contributions are twofold: to demonstrate how to apply the general European Union’s High-Level Expert Group’s (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare; and to investigate the research question of what does “trustworthy AI” mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient’s lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia (Italy) since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses socio-technical scenarios to identify ethical, technical and domain-specific issues in the use of the AI system in the context of the pandemic.</p

Search for neutral MSSM Higgs bosons decaying to a pair of tau leptons in pp collisions

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Measurement of top quark–antiquark pair production in association with a W or Z boson in pp collisions at √s=8 TeV

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Measurement of prompt J/ψ pair production in pp collisions at √s = 7 Tev

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Study of hadronic event-shape variables in multijet final states in pp collisions at √s=7 TeV

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Constraints on parton distribution functions and extraction of the strong coupling constant from the inclusive jet cross section in pp collisions at √s=7 TeV

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Searches for electroweak production of charginos, neutralinos, and sleptons decaying to leptons and W, Z, and Higgs bosons in pp collisions at 8 TeV

Peer reviewe