Calibration of spectra in presence of non-stationary background using unsupervised physics-informed deep learning

: Calibration is a key part of the development of a diagnostic. Standard approaches require the setting up of dedicated experiments under controlled conditions in order to find the calibration function that allows one to evaluate the desired information from the raw measurements. Sometimes, such controlled experiments are not possible to perform, and alternative approaches are required. Most of them aim at extracting information by looking at the theoretical expectations, requiring a lot of dedicated work and usually involving that the outputs are extremely dependent on some external factors, such as the scientist experience. This work presents a possible methodology to calibrate data or, more generally, to extract the information from the raw measurements by using a new unsupervised physics-informed deep learning methodology. The algorithm allows to automatically process the data and evaluate the searched information without the need for a supervised training by looking at the theoretical expectations. The method is examined in synthetic cases with increasing difficulties to test its potentialities, and it has been found that such an approach can also be used in very complex behaviours, where human-drive results may have huge uncertainties. Moreover, also an experimental test has been performed to validate its capabilities, but also highlight the limits of this method, which, of course, requires particular attention and a good knowledge of the analysed phenomena. The results are extremely interesting, and this methodology is believed to be applied to several cases where classic calibration and supervised approaches are not accessible

Alternative Detection of n = 1 Modes Slowing Down on ASDEX Upgrade

Disruptions in tokamaks are very often associated with the slowing down of magneto-hydrodynamic (MHD) instabilities and their subsequent locking to the wall. To improve the understanding of the chain of events ending with a disruption, a statistically robust and physically based criterion has been devised to track the slowing down of modes with toroidal mode numbers n = 1 and mostly poloidal mode numberm= 2, providing an alternative and earlier detection tool compared to simple threshold based indicators. A database of 370 discharges of axially symmetric divertor experiment—upgrade (AUG) has been studied and results compared with other indicators used in real time. The estimator is based on a weighted average value of the fast Fourier transform of the perturbed radial n = 1 magnetic field, caused by the rotation of the modes. The use of a carrier sinusoidal wave helps alleviating the spurious influence of non-sinusoidal magnetic perturbations induced by other instabilities like Edge localized modes (ELMs). The indicator constitutes a good candidate for further studies including machine learning approaches for mitigation and avoidance since, by deploying it systematically to evaluate the time instance for the expected locking, multi-machine databases can be populated. Furthermore, it can be thought as a contribution to a wider approach to dynamically tracking the chain of events leading to disruptions

Biological risk in Italian prisons: data analysis from the second to the fourth wave of COVID-19 pandemic

BackgroundThe management of COVID-19 in Italian prisons triggered considerable concern at the beginning of the pandemic due to numerous riots which resulted in inmate deaths, damages and prison breaks. The aim of this study is to shed some light, through analysis of the infection and relevant disease parameters, on the period spanning from the second to the fourth wave of the outbreak in Italy's prisons. MethodsReproductive number (Rt) and Hospitalisation were calculated through a Eulerian approach applied to differential equations derived from compartmental models. Comparison between trends was performed through paired t-test and linear regression analyses. ResultsThe infection trends (prevalence and Rt) show a high correlation between the prison population and the external community. Both the indices appear to be lagging 1 week in prison. The prisoners' Rt values are not statistically different from those of the general population. The hospitalisation trend of inmates strongly correlates with the external population's, with a delay of 2 weeks. The magnitude of hospitalisations in prison is less than in the external community for the period analysed. ConclusionsThe comparison with the external community revealed that in prison the infection prevalence was greater, although Rt values showed no significant difference, and the hospitalisation rate was lower. These results suggest that the consistent monitoring of inmates results in a higher infection prevalence while a wide vaccination campaign leads to a lower hospitalisation rate. All three indices demonstrate a lag of 1 or 2 weeks in prison. This delay could represent a useful time-window to strengthen planned countermeasures

Design of Miniaturized Sensors for a Mission-Oriented UAV Application: A New Pathway for Early Warning

In recent decades, the increasing threats associated with Chemical and Radiological (CR) agents prompted the development of new tools to detect and collect samples without putting in danger first responders inside contaminated areas. A particularly promising branch of these technological developments relates to the integration of different detectors and sampling systems with Unmanned Aerial Vehicles (UAV). The adoption of this equipment may bring significant benefits for both military and civilian implementations. For instance, instrumented UAVs could be used in support of specialist military teams such as Sampling and Identification of Biological, Chemical and Radiological Agents (SIBCRA) team, tasked to perform sampling in contaminated areas, detecting the presence of CR substances in field and then confirming, collecting and evaluating the effective threats. Furthermore, instrumented UAVs may find dual-use application in the civil world in support of emergency teams during industrial accidents and in the monitoring activities of critical infrastructures. Small size drones equipped with different instruments for detection and collection of samples may enable, indeed, several applications, becoming a tool versatile and easy to use in different fields, and even featuring equipment normally utilized in manual operation. The authors hereby present the design of miniaturized sensors for a mission-oriented UAV application and the preliminary results from an experimental campaign performed in 2020

Development and performance testing of a miniaturized multi-sensor system combining MOX and PID for potential UAV application in TIC, VOC and CWA dispersion scenarios

The development of a tool to reduce the exposure of personnel in case of inten- tional or accidental toxic chemicals dispersion scenarios opens the field to new operational perspectives in the domain of operator safety and of critical infrastructure monitoring. The use of two sensors with different operating principles, metal oxide and photo-ionization detector, allows to confirm the presence of specific classes of chemicals in a contaminated area. All instruments are expected to be integrated into the payload of an unmanned aerial vehicle (UAV) and used for different purposes such as critical infrastructure surveillance focused on the volatile organic chemical and chemical warfare agents (CWA) detection and the post-incident of contamination level monitoring. In this paper, the authors presented the hardware set-up implemented and the test realized with CWAs simulants and will discuss the results obtained presenting advantages and disadvantages of this system in an application such as a UAV for the detection of chemical substances

The free license codes as decision support system (DSS) for the emergency planning to simulate radioactive releases in case of accidents in the new generation energy plants

The radiological risk is related to a wide range of activities, beginning with the medical and military ones and including those connected to the industrial and research activities such as nuclear fusion. A valid tool to predict the consequences of the accidents and reduce the risk is represented by computing systems that allow modeling the evolution of a possible release of radioactive materials over time and space. In addition to proprietary codes there are free license codes, like Hot-Spot, that allow providing a set of tools to simulate diffusion in case of accidents involving radioactive materials and analyze the safety and security of the facilities in which the radioactive material is manipulated. The case studies scenario’s consists in two simulations accidents scenario the first to biomass plant and the second at nuclear fission plant. The simulation of the radioactive contamination have been conducted with the code HOT SPOT, a free license code. The results of the simulation and data discussion will be presented in this work by the authors

Experimental measurements of pressure, temperature and dust velocities in case of LOVA: Comparisons with a multiphase numerical model

The production of dust inside the nuclear fusion power plants is one of the safety issues of this technology. Dust is generated because of plasma-material interactions and subsequently it deposits in the bottom regions of the TOKAMAK. In case of a Loss Of Vacuum Accident (LOVA), the dust may be resuspended, threatening the functioning and the safety of these reactors. A deep study of this phenomenon is required to develop countermeasures and to improve the safety of this promising way to produce energy. The authors have studied the fluid dynamics of these accidents with a scaled experiment, called STARDUST-Upgrade. Optical techniques have been implemented to measure dust resuspension and diffusion properties, such as velocity vectors and resuspension rate. This work shows the results obtained with a new numerical model able to take into account also the dispersed phase (dust). The software uses the Euler-Euler approach, a Schiller-Naumann resistance model, and a k-ε turbulence model. The dust used is tungsten dust, that has been placed close to the inlet valve in both cases (numerical and experimental). The numerical results are analysed and compared with the experimental ones and the main agreements and differences are highlighted. The results show good accordance with the velocity vectors of dust, while the resuspension rate is overestimated in the numerical case because of the absence of adhesion and cohesion forces between dust particles and walls. This analysis is the starting point for the evolution and completion of a numerical model suitable for dust resuspension in case of LOVAs