Superconductivity in Ropes of Single-Walled Carbon Nanotubes

We report measurements on ropes of Single Walled Carbon Nanotubes (SWNT) in low-resistance contact to non-superconducting (normal) metallic pads, at low voltage and at temperatures down to 70 mK. In one sample, we find a two order of magnitude resistance drop below 0.55 K, which is destroyed by a magnetic field of the order of 1T, or by a d.c. current greater than 2.5 microA. These features strongly suggest the existence of superconductivity in ropes of SWNT.Comment: Accepted for publication in Phys. Rev. Let

Superconductivity in Ropes of Single-Walled Carbon Nanotubes

We report measurements on ropes of Single Walled Carbon Nanotubes (SWNT) in low-resistance contact to non-superconducting (normal) metallic pads, at low voltage and at temperatures down to 70 mK. In one sample, we find a two order of magnitude resistance drop below 0.55 K, which is destroyed by a magnetic field of the order of 1T, or by a d.c. current greater than 2.5 microA. These features strongly suggest the existence of superconductivity in ropes of SWNT.Comment: Accepted for publication in Phys. Rev. Let

Co-infection and ICU-acquired infection in COIVD-19 ICU patients: a secondary analysis of the UNITE-COVID data set

Background: The COVID-19 pandemic presented major challenges for critical care facilities worldwide. Infections which develop alongside or subsequent to viral pneumonitis are a challenge under sporadic and pandemic conditions; however, data have suggested that patterns of these differ between COVID-19 and other viral pneumonitides. This secondary analysis aimed to explore patterns of co-infection and intensive care unit-acquired infections (ICU-AI) and the relationship to use of corticosteroids in a large, international cohort of critically ill COVID-19 patients.Methods: This is a multicenter, international, observational study, including adult patients with PCR-confirmed COVID-19 diagnosis admitted to ICUs at the peak of wave one of COVID-19 (February 15th to May 15th, 2020). Data collected included investigator-assessed co-infection at ICU admission, infection acquired in ICU, infection with multi-drug resistant organisms (MDRO) and antibiotic use. Frequencies were compared by Pearson's Chi-squared and continuous variables by Mann-Whitney U test. Propensity score matching for variables associated with ICU-acquired infection was undertaken using R library MatchIT using the "full" matching method.Results: Data were available from 4994 patients. Bacterial co-infection at admission was detected in 716 patients (14%), whilst 85% of patients received antibiotics at that stage. ICU-AI developed in 2715 (54%). The most common ICU-AI was bacterial pneumonia (44% of infections), whilst 9% of patients developed fungal pneumonia; 25% of infections involved MDRO. Patients developing infections in ICU had greater antimicrobial exposure than those without such infections. Incident density (ICU-AI per 1000 ICU days) was in considerable excess of reports from pre-pandemic surveillance. Corticosteroid use was heterogenous between ICUs. In univariate analysis, 58% of patients receiving corticosteroids and 43% of those not receiving steroids developed ICU-AI. Adjusting for potential confounders in the propensity-matched cohort, 71% of patients receiving corticosteroids developed ICU-AI vs 52% of those not receiving corticosteroids. Duration of corticosteroid therapy was also associated with development of ICU-AI and infection with an MDRO.Conclusions: In patients with severe COVID-19 in the first wave, co-infection at admission to ICU was relatively rare but antibiotic use was in substantial excess to that indication. ICU-AI were common and were significantly associated with use of corticosteroids

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

Development of a Twin Model for Real-time Detection of Fall Hazards

The Architecture, Engineering and Construction (AEC) industry is still one of the most hazardous industries in the world. Researchers impute this trend to many factors such as the separation between the phases of safety planning and project execution, implicit safety issues and, most of all, the dynamic and complex nature of construction projects. Several studies show that the AEC industry could greatly benefit of latest advances in Information and Communication Technologies (ICTs) to develop tools contributing to safety management. A digital twin of the construction site, which is automatically instantiated and updated by real-time collected data, can run fast forward simulations in order to pro-actively support activities and forecast dangerous scenarios. In this paper, the twin model of the Digital Construction Capability Centre (DC3) at the Polytechnic University of Marche (UNIVPM) is developed and run as a mock-up, thanks to the adoption of a serious game engine. This mock-up is able to mirror all the relevant features of a job site during the execution of works from a safety-wide perspective. In such a scenario, virtual avatars randomly explore the construction site in order to detect accessible, unprotected and risky workspaces at height, while warning the safety inspector in case additional safety measures are needed

Development of digital twin models supporting ambient assisted living

World population aging requires finding solutions to improve independent living options. Ambient Assisted Living (AAL) is making step forward developing services supporting the elderly, but the implementation of predictive environments is still far away. Besides, the emerging Digital Twin (DT) concept has begun to shape the first cognitive environments that integrate users into assessments, improving efficiency, prevention, and prediction of likely events through realtime AI computing. This paper aims to provide a prototype of a Cognitive Building framework based on DT models that develop high-level knowledge to achieve real-time Scenario Awareness and offer appropriate AAL services once anomalies are detected

Lumped parameter models for energy auditing of existing buildings

The energy audit of existing buildings is usually a time consuming and expensive process, due to efforts required for data collection and modelling of audited objects. However, the modelling phase might be made less demanding, thanks to the development of reduced models, conceived in the form of lumped parameters models. They are shown very suitable because they require a reduced number of input data to be described. According to relevant literature, the detailed physical models take long time and they are often too expensive; they are adopted when a very detailed assessment is necessary. However, when preliminary energy audits of buildings can be made, reduced order models, usually defined as grey-box models, showed their reliability to achieve a suitable description of the thermal response of buildings in a short time. Thus, they are more cost-effective. The thermal parameters of the simplified model are usually extracted in real time; this allows to estimate the thermal response of a building in its current state, whose information can be reused to make predictions about its expected behaviour. The purpose of this work is to survey on an empirical procedure for deriving a lumped parameter model by means of measurements collected in a relatively short time period. In this way, owners and managers of real estates would be allowed to perform fast and cheap preliminary assessment on the opportunity to implement energy renovation actions. A first test performed in the machine laboratory of our department at the Universita Politecnica delle Marche for experimentally estimating thermal parameters will be described and results will be presented