Inhomogeneous critical current in nanowire superconducting single-photon detectors

A superconducting thin film with uniform properties is the key to realize nanowire superconducting single-photon detectors (SSPDs) with high performance and high yield. To investigate the uniformity of NbN films, we introduce and characterize simple detectors consisting of short nanowires with length ranging from 100nm to 15{\mu}m. Our nanowires, contrary to meander SSPDs, allow probing the homogeneity of NbN at the nanoscale. Experimental results, endorsed by a microscopic model, show the strongly inhomogeneous nature of NbN films on the sub-100nm scale.Comment: 10 pages, 4 figure

On the potential of time delay neural networks to detect indirect coupling between time series

Determining the coupling between systems remains a topic of active research in the field of complex science. Identifying the proper causal influences in time series can already be very challenging in the trivariate case, particularly when the interactions are non-linear. In this paper, the coupling between three Lorenz systems is investigated with the help of specifically designed artificial neural networks, called time delay neural networks (TDNNs). TDNNs can learn from their previous inputs and are therefore well suited to extract the causal relationship between time series. The performances of the TDNNs tested have always been very positive, showing an excellent capability to identify the correct causal relationships in absence of significant noise. The first tests on the time localization of the mutual influences and the effects of Gaussian noise have also provided very encouraging results. Even if further assessments are necessary, the networks of the proposed architecture have the potential to be a good complement to the other techniques available in the market for the investigation of mutual influences between time serie

Ambient noise and ERT data provide insights into the structure of co-seismic rock avalanche deposits in Sichuan (China)

AbstractThe post-seismic history of the 2008 Mw7.9 Wenchuan earthquake shows that marginally stable deposits of large co-seismic landslide dams can pose persistent debris flow hazards for the downstream areas. Here, we combine analyses of single-station recordings of ambient noise with electrical resistivity tomography (ERT) surveys to explore the potential of drawing information on structure and geometry of the deposit of a large rock avalanche triggered by the Mw 7.9 2008 Wenchuan earthquake, which dammed the Yangjia stream in the Sichuan Province (China). The substantial thickness and heterogeneity of this kind of deposits limit the application of standard geophysical techniques, like active seismic surveys, which require highly energetic sources and long linear geophone arrays to reach adequate investigation depths. Passive single-station methods, relying on ambient noise recordings to determine site resonance properties, controlled by the contrast between soft surface layers and a stiffer substratum, offer the opportunity of investigating subsoil properties down to larger depths. In particular, we use a recently developed technique, which isolates the contribution of Rayleigh waves to ambient noise and draws information on sub-soil properties from the inversion of Rayleigh wave ellipticity curves plotted as function of frequency. In this framework, the ERT data can support the ellipticity curve inversion, typically affected by highly non-univocal solutions, by providing constraints for defining of the thickness of the uppermost surficial layers. The results allowed inferring the overlap of different layers within the 2008 rock avalanche deposit, as well as estimating lateral variations in their thickness and S-wave (Vs) velocities

NetCausality: A time-delayed neural network tool for causality detection and analysis

The analysis of causality between systems is still an important research activity, which finds application in several fields of science. The software presented is a new tool for causality detection and analysis between time series. The proposed technique is based on time-delayed neural networks (TDNN). The tool is developed in MATLAB and it comprises three main functions. The first one returns the total causality between two or more systems of equations. The second tool is used to find the ‘‘time horizon’’, id est the time delay at which the influence between the systems occurs. The last function is a causality feature detection to determine the time intervals, in which the mutual coupling is sufficiently strong to have a real influence on the target

Evaluation of the Spatiotemporal Epidemiological Modeler (STEM) during the recent COVID-19 pandemic

In early December 2019, some people in China were diagnosed with an unknown pneumonia in Wuhan, in the Hubei province. The responsible of the outbreak was identified in a novel human-infecting coronavirus which differs both from severe acute respiratory syndrome coronavirus and from Middle East respiratory syndrome coronavirus. The new coronavirus, officially named severe acute respiratory syndrome coronavirus 2 by the International Committee on Taxonomy of Viruses, has spread worldwide within few weeks. Only two vaccines have been approved by regulatory agencies and some others are under development. Moreover, effective treatments have not been yet identified or developed even if some potential molecules are under investigation. In a pandemic outbreak, when treatments are not available, the only method that contribute to reduce the virus spreading is the adoption of social distancing measures, like quarantine and isolation. With the intention of better managing emergencies like this, which are a great public health threat, it is important to dispose of predictive epidemiological tools that can help to understand both the virus spreading in terms of people infected, hospitalized, dead and recovered and the effectiveness of containment measures

Adaptive quasi-unsupervised detection of smoke plume by lidar

The early detection of fire is one of the possible applications of LiDAR techniques. The smoke generated by a fire is mainly compounded of CO2, H2O, particulate, and other combustion products, which involve the local variation of the scattering of the electromagnetic wave at specific wavelengths. The increases of the backscattering coefficient are transduced in peaks on the signal of the backscattering power recorded by the LiDAR system, located exactly where the smoke plume is, allowing not only the detection of a fire but also its localization. The signal processing of the LiDAR signals is critical in the determination of the performances of the fire detection. It is important that the sensitivity of the apparatus is high enough but also that the number of false alarms is small, in order to avoid the trigger of useless and expensive countermeasures. In this work, a new analysis method, based on an adaptive quasi-unsupervised approach was used to ensure that the algorithm is continuously updated to the boundary conditions of the system, such as the weather and experimental apparatus issues. The method has been tested on an experimental campaign of 227 pulses and the performances have been analyzed in terms of sensitivity and specificity

Viscosity and glass transition temperature of hydrous float glass

Viscosity of water-bearing float glass (0.03–4.87 wt% H2O) was measured in the temperature range of 573–1523 K and pressure range of 50–500 MPa using a parallel plate viscometer in the high viscosity range and the falling sphere method in the low viscosity range. Melt viscosity depends strongly on temperature and water content, but pressure up to 500 MPa has only minor influence. Consistent with previous studies on aluminosilicate compositions we found that the effect of dissolved water is most pronounced at low water content, but it is still noticeable at high water content. A new model for the calculation of the viscosities as a function of temperature and water content is proposed which describes the experimental data with a standard deviation of 0.22 log units. The depression of the glass tran- sition temperature Tg by dissolved water agrees reasonably well with the prediction by the model of Deubener [J. Deubener, R. Mu¨ ller, H. Behrens, G. Heide, J. Non-Cryst. Solids 330 (2003) 268]. Using water speciation measured by near-infrared spectroscopy we infer that although the effect of OH groups in reducing Tg is larger than that of H2O molecules, the difference in the contribution of both species is smaller than predicted by Deubener et al. (2003). Compared to alkalis and alkaline earth elements the effect of protons on glass fragility is small, mainly because of the relatively low concentration of OH groups (max. 1.5 wt% water dissolved as OH) in the glasses

A Multiphysics Ray Optics Model for the Propagation of Electromagnetic Waves in Plasmas and the Design of Laser-Based Diagnostics in Nuclear Fusion Reactors

Laser-based methods are widely used techniques for thermonuclear plasma diagnostics, since they can probe the internal of the plasma, being contactless and non-invasive. The interferometer, the polarimeter and Thomson scattering are the most widespread techniques, providing line-integral information of the electron density and the magnetic field (interferometer–polarimeter) and local information of the electron density and temperature (Thomson scattering). The design of the diagnostics is a fundamental step, which usually requires an iterative process to maximise the performances of the diagnostics and their durability. In the future reactors, such as ITER and DEMO, the working environment will be much challenging, due to the various electro-mechanical, thermal and nuclear loads which may affect the life of the components and degrade the performances of the diagnostics. This work aims to present the modelling of plasma interferometry, polarimetry and Thomson scattering applied to a ray optics code. The model, developed on the COMSOL Multiphysics software, can be easily interfaced with multiphysics problems, allowing the possibility to test the performances of the diagnostics in several conditions

A novel ultrafast-low-dose computed tomography protocol allows concomitant coronary artery evaluation and lung cancer screening

BACKGROUND:Cardiac computed tomography (CT) is often performed in patients who are at high risk for lung cancer in whom screening is currently recommended. We tested diagnostic ability and radiation exposure of a novel ultra-low-dose CT protocol that allows concomitant coronary artery evaluation and lung screening. METHODS: We studied 30 current or former heavy smoker subjects with suspected or known coronary artery disease who underwent CT assessment of both coronary arteries and thoracic area (Revolution CT, General Electric). A new ultrafast-low-dose single protocol was used for ECG-gated helical acquisition of the heart and the whole chest. A single IV iodine bolus (70-90 ml) was used. All patients with CT evidence of coronary stenosis underwent also invasive coronary angiography. RESULTS: All the coronary segments were assessable in 28/30 (93%) patients. Only 8 coronary segments were not assessable in 2 patients due to motion artefacts (assessability: 98%; 477/485 segments). In the assessable segments, 20/21 significant stenoses (> 70% reduction of vessel diameter) were correctly diagnosed. Pulmonary nodules were detected in 5 patients, thus requiring to schedule follow-up surveillance CT thorax. Effective dose was 1.3 ± 0.9 mSv (range: 0.8-3.2 mSv). Noteworthy, no contrast or radiation dose increment was required with the new protocol as compared to conventional coronary CT protocol. CONCLUSIONS:The novel ultrafast-low-dose CT protocol allows lung cancer screening at time of coronary artery evaluation. The new approach might enhance the cost-effectiveness of coronary CT in heavy smokers with suspected or known coronary artery disease