Double pulses and cascades above 2 PeV in IceCube

IceCube collaboration has seen an unexpected population of high energy neutrinos compatible with an astrophysical origin. We consider two categories of events that can help to diagnose cosmic neutrinos: double pulse, that may allow us to clearly discriminate the cosmic component of tau neutrinos; cascades with deposited energy above 2 PeV, including events produced by electron antineutrinos at the Glashow resonance, that can be used to investigate the neutrino production mechanisms. We show that one half of the double pulse signal is due to the neutrinos spectral region already probed by IceCube. By normalizing to HESE data, we find that 10 more years are required to obtain 90% probability to observe a double pulse. The cascades above 2 PeV provide us a sensitive probe of the high energy tail of the neutrino spectrum and are potentially observable, but even in this case, the dependence on type of the source is mild. In fact we find that pp or p{\gamma} mechanisms give a difference in the number of cascades above 2 PeV of about 25 % that can be discriminated at 2{\sigma} in about 50 years of data taking.Comment: 20 pages, 7 figures, accepted for publication in EPJ

High Voltage System for the Generation of High Energy X-rays: synchronization and improvement

The x-ray (3 keV - 25 keV) are produced by high voltage discharge applied inside plasma source interaction chamber by focusing a laser beam. The control system is based on a high voltage power supply and an LC-inverter circuit with Thyratron, the generation of the trigger signal increasing the efficiency of the system. Analysis of the system, a possible layout with solid-state element and preliminary results of synchronization between high voltage discharge and Laser beam Nd-YAG are presented and discussed

ADAGSS: Automatic Dataset Generation for Semantic Segmentation

A common issue in medical deep learning research is the creation of dataset for training the neural networks. Medical data collection is also tied-up by privacy laws and even if a lot of medical data are available, often their elaboration can be time demanding. This problem can be avoided using neural networks architectures that can achieve a good predicting precision with few images (e.g. U-Net). In the case of semantic segmentation, the dataset generation is even more cumbersome since it requires the creation of segmentation masks manually. Some automatic ground-truth creation techniques may be employed like filtering, thresholding and Self Organized Maps1 (SOM). These automatic methods can be very powerful and useful, but they always have a bottle-neck phase: data validation. Due to algorithm reliability (that sometimes can fail), data needs to be validated manually before they can be included in a dataset for training. In this work, we propose a method to automatize this phase by moving manual intervention to an easier task: instead of creating masks and then validate them manually, we train a convolutional neural network to classify segmentation quality. Therefore, the validation is performed automatically. An initial manual phase is still required, but the classification task requires a smaller number of elements in the dataset that will feed a network employed for classification. After this phase, similar dataset creations will require less effort. This procedure is based on the fact that to obtain a high classification precision, fewer data are required than the data that are needed to obtain high precision in semantic segmentation. High classification score, can automatize validation procedure in dataset creation, being able to discard failure case in dataset creation. Being able to produce bigger dataset in less time can led to higher precision in semantic segmentation

Telematic solutions in plastic surgery during COVID-19 pandemic: Liability issues and risk management

During the COVID-19 pandemic, surgical elective procedures were stopped in our plastic surgery unit. Limitations for consultations and for follow-up of previous surgical procedures were imposed in order to minimize the risk of contagion in waiting rooms and outpatient clinics. We have identified telemedicine as an alternative way to follow patients during the lockdown. Nevertheless, we have experienced different difficulties. We have not had the possibility to use a secure teleconferencing software. In our unit we had not technological devices. Surgeons in our department were not able to use remote video technology for patient management. Guidelines for an appropriate selection of patients which could be served via telemedicine had to be created.Telemedicine must be regulated by healthcare organizations for legal, ethical, medico-legal and risk management aspects.Even if we have experienced an important need to use telematic solutions during the COVID-19 lockdown, liability and risk management issues has greatly limited this possibility in our unit. The need of telemedicine in the time of COVID-19 pandemic has encouraged us to implement future virtual encounters in order to reduce unnecessary in-person visits by taking into consideration all legal, ethical and medico-legal aspects

Functionally graded plate fracture analysis using the field boundary element method

This paper describes the Field Boundary Element Method (FBEM) applied to the fracture analysis of a 2D rectangular plate made of Functionally Graded Material (FGM) to calculate Mode I Stress Intensity Factor (SIF). The case study of this Field Boundary Element Method is the transversely isotropic plane plate. Its material presents an exponential variation of the elasticity tensor depending on a scalar function of position, i.e., the elastic tensor results from multiplying a scalar function by a constant taken as a reference. Several examples using a parametric representation of the structural response show the suitability of the method that constitutes a Stress Intensity Factor evaluation of Functionally Graded Materials plane plates even in the case of more complex geometries