Early consensus management for non-ICU acute respiratory failure SARS-CoV-2 emergency in Italy : from ward to trenches

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Residual stress measurements on a deep rolled aluminum specimen through X-Ray Diffraction and Hole-Drilling, validated on a calibration bench

Residual stress measurements are notably affected by a high sensitivity to errors in input data. Measurements should then be presented together with an estimation of their accuracy. A common strategy is to carry out more measurements and/or to compare the results of different techniques. However, error contributions due to biases could be dangerously left unseen. In a previous work, the authors presented a calibration bench which can impose a known bending stress distribution on a specimen while simultaneously performing X-Ray Diffraction (XRD) or Hole-Drilling Method (HDM) residual stress measurements. Since the external load can freely be applied and removed, the superposition principle can be exploited to simultaneously identify either the reference bending stress distribution or the actual residual stress distribution, with the same experimental setup. A deep rolling treatment was measured and analyzed on the calibration bench with both XRD and HDM. First, residual stresses on the surface were evaluated with XRD measurements, then electrochemical material removal was performed to investigate stresses at higher depths. After that, HDM measurements were carried out and compared with the results of XRD. Both methods were also used to identify the known bending stresses, providing an additional validation of the residual stress results

Negation Detection for Robust Adverse Drug Event Extraction From Social Media Texts

Adverse Drug Event (ADE) extraction from user-generated content has gained popularity as a tool to aid researchers and pharmaceutical companies to monitor side effect of drugs in the wild. Automatic models can rapidly examine large collections of social media texts. However it is currently unknown if such models are robust in face of linguistic phenomena such as negation and speculation, which are pervasive across language varieties. We evaluate three state-of-the-art systems, showing their fragility against negation, and then we introduce two possible strategies to increase the robustness of these models: (i) a pipeline approach, using a specific component for negation detection; (ii) an augmentation of the dataset with artificially negated samples to further train the models. We show that both strategies bring significant increases in performance

Unsupervised anomaly detection in pressurized water reactor digital twins using autoencoder neural networks

Deep learning (DL), that is becoming quite popular for prediction and analysis of complex patterns in large amounts of data is used to investigate the safety behaviour of the nuclear plant items. This is achieved by using multiple layers of artificial neural networks to process and transform input data, allowing for the creation of highly accurate predictive models. Particularly to the aim the unsupervised machine learning approach and the digital twin concept in form of pressurized water reactor 2-loop simulator are used. This innovative methodology is based on neural network algorithm that makes capable to predict failures of plant structure, system, and components earlier than the activation of safety and emergency systems. Moreover, to match the objective of the study several scenarios of loss of cooling accident (LOCA) of different break size were simulated. To make the acquisition platform realistic, Gaussian noise was added to the input signals. The neural network has been fed by synthetic dataset provide by PCTRAN simulator and the efficiency in event identification was studied. Further, due to the very limited studies on the unsupervised anomaly detection by means of autoencoder neural networks applied for plant monitoring and surveillance, the methodology has been validated with experimental data from resonant test rig designed for fatigue testing of tubular components. The obtained results demonstrate the reliability and the efficiency of the methodology in detecting anomalous events prior the activation of safety system. Particularly, if the difference between the expected readings and the collected data goes beyond the predetermined threshold, then the anomalous event is identified, e.g., the model detected anomalies up to 38 min before the reactor scram intervention

Chasing Hypernyms in Vector Spaces with Entropy

In this paper, we introduce SLQS, a new entropy-based measure for the unsupervised identification of hypernymy and its directionality in Distributional Semantic Models (DSMs). SLQS is assessed through two tasks: (i.) identifying the hypernym in hyponym-hypernym pairs, and (ii.) discriminating hypernymy among various semantic relations. In both tasks, SLQS outperforms other state-of-the-art measures

Makrofauna Tanah Perkebunan Kelapa Sawit (Elais Guineensis Jacq) Di Lahan Gambut Dengan Pemberian Bahan Organik Pada Tinggi Muka Air Tanah Berbeda

This researchaims to know the species amountd, total individual, calculate population density (K) and relative density (KR) soil macrofauna in oil palm plantation (Elais guineensis Jacq) in peatland by giving organic matter in water level is different.This research are conducted by observation, sampling for soil macrofauna of data is determined by purposive random sampling method, and data soil macrofauna results to analyzed statistic descriptive. The results showed that the amountd species, total individual, population density and relative density of soil macrofauna were higher in water levels of 40-50 cm. The giving organic matter in the soil water level is different in the first month, giving palm fronds and Mucunna bracteata produces the number of species, the total individual, population density, and density of the soil macrofauna relative higher than that of oil palm empty fruit bunches,while in the third month the species, the total individual, population density, and density of the soil makorfauna ralatif higher in the provision of oil palm empty fruit bunches and palm fronds.In water levels same produce the amountd species, total individual, population density, and relative density of soil macrofauna higher ground in water levels of 40-50 cm except amountd species in the third month

Makrofauna Tanah Perkebunan Kelapa Sawit (Elais Guineensis Jacq) Di Lahan Gambut Dengan Pemberian Bahan Organik Pada Tinggi Muka Air Tanah Berbeda

This researchaims to know the species amountd, total individual, calculate population density (K) and relative density (KR) soil macrofauna in oil palm plantation (Elais guineensis Jacq) in peatland by giving organic matter in water level is different.This research are conducted by observation, sampling for soil macrofauna of data is determined by purposive random sampling method, and data soil macrofauna results to analyzed statistic descriptive. The results showed that the amountd species, total individual, population density and relative density of soil macrofauna were higher in water levels of 40-50 cm. The giving organic matter in the soil water level is different in the first month, giving palm fronds and Mucunna bracteata produces the number of species, the total individual, population density, and density of the soil macrofauna relative higher than that of oil palm empty fruit bunches,while in the third month the species, the total individual, population density, and density of the soil makorfauna ralatif higher in the provision of oil palm empty fruit bunches and palm fronds.In water levels same produce the amountd species, total individual, population density, and relative density of soil macrofauna higher ground in water levels of 40-50 cm except amountd species in the third month

Helmet CPAP to Treat Acute Hypoxemic Respiratory Failure in Patients with COVID-19 : a Management Strategy Proposal

Since the beginning of March 2020, the coronavirus disease 2019 (COVID-19) pandemic has caused more than 13,000 deaths in Europe, almost 54% of which has occurred in Italy. The Italian healthcare system is experiencing a stressful burden, especially in terms of intensive care assistance. In fact, the main clinical manifestation of COVID-19 patients is represented by an acute hypoxic respiratory failure secondary to bilateral pulmonary infiltrates, that in many cases, results in an acute respiratory distress syndrome and requires an invasive ventilator support. A precocious respiratory support with non-invasive ventilation or high flow oxygen should be avoided to limit the droplets' air-dispersion and the healthcare workers' contamination. The application of a continuous positive airway pressure (CPAP) by means of a helmet can represent an effective alternative to recruit diseased alveolar units and improve hypoxemia. It can also limit the room contamination, improve comfort for the patients, and allow for better clinical assistance with long-term tolerability. However, the initiation of a CPAP is not free from pitfalls. It requires a careful titration and monitoring to avoid a delayed intubation. Here, we discuss the rationale and some important considerations about timing, criteria, and monitoring requirements for patients with COVID-19 respiratory failure requiring a CPAP treatment

Fatigue fracture surface investigations with a 3D optical profiler

In this paper a set of specimens, used for the critical distance determination, are investigated with a non-contact 3D optical profiler. The fatigue fracture surfaces of both plain and V-notched specimens, under axial (mode I) and torsional (mode III) loadings are observed, investigating steel 42CrMo4+QT and aluminium alloy 7075-T6. The fatigue fracture profiles are compared to be previously obtained critical distances, both for mode I and mode III. The stage I to stage II transition was found at a smaller size than the axial critical distance, for the steel, while for the torsional load a local plateau at the nucleation was observed. The fracture surface of the axial loading was instead much irregular at the scale of the mode I critical distance, for the aluminium alloy, resembling a not concluded stage I, while again a relatively flat surface was observed for the mode III loading

The effect of a multidisciplinary approach for smoking cessation in patients with Crohn's disease : results from an observational cohort study

Introduction: Cigarette smoking is the most important risk factor for Crohn's disease (CD). The effectiveness of smoking cessation programs (SCPs) in patients with CD is still poorly understood. Methods: This was a retrospective, observational, single-centre, cohort study of 136 active smokers with mean age 55 years (SD=11), 58% males, including 27 (19.8%) patients with CD who entered the multidisciplinary SCP of the Luigi Sacco University Hospital of Milan from January 2017 through January 2019. A pulmonologist was responsible for the clinical and pharmacological management, while a psychiatrist and a psychologist conducted the counselling and assessed the motivation to quit, anxiety and depression using the Brief Psychiatric Rating Scale (BPRS) and the nicotine dependence with the Fagerstr\uf6m test. Patients were defined as quitters after 12 months. Results: Demographic and clinical characteristics, and Fagerstr\uf6m score, did not differ in patients with and without CD. At baseline, patients with CD had a higher BPRS (median: 27, IQR: 22-32; vs 25 and 22-28.5; p=0.03), and a lower motivation to quit score (median: 10, IQR: 9-13; vs 14 and 12-15; p<0.001). After 12 months, the quitting rate of smokers with CD was significantly lower (14.8% vs 36.7%; p<0.022) and the chance of quitting was negatively associated with the baseline BPRS (r=-0.256; p<0.003). Varenicline and nicotine replacement therapy tended to be less effective in patients with CD. Conclusions: The lower efficacy of SCPs in patients with CD might be secondary to a higher prevalence of anxiety and depression. Psychological issue recognition and support should be enhanced to increase SCP effectiveness in CD