Simulation of the December 2017 Flood on the Enza River using a 2D SWE code Coupled with a Levee Breach Erosion Model

The levee breach that occurred on the Enza River (Italy) on December 12th 2017 and the resulting flood are simulated with a GPU-accelerated 2D SWE code, where a simple erosion model was implemented to describe the breach evolution in detail

Sub-wavelength surface IR imaging of soft-condensed matter

Outlined here is a technique for sub-wavelength infrared surface imaging performed using a phase matched optical parametric oscillator laser and an atomic force microscope as the detection mechanism. The technique uses a novel surface excitation illumination approach to perform simultaneously chemical mapping and AFM topography imaging with an image resolution of 200 nm. This method was demonstrated by imaging polystyrene micro-structures

Conjoined lumbosacral nerve roots: observations on three cases and review of the literature

Lumbosacral nerve root anomalies are a rare group of congenital anatomical anomalies. Various types of anomalies of the lumbosacral nerve roots have been documented in the available international literature. Generally speaking, these anomalies may consist of a bifid, conjoined structure, of a transverse course or of a characteristic anastomized appearance. Firstly described as an incidental finding during autopsies or surgical procedures performed for lumbar disk herniations and often asymptomatic, lumbosacral nerve root anomalies have been more frequently described in the last years due to the advances made in radiological diagnosis (metrizamide myelography and CT, MRI). Our study comprised three patients with conjoined lumbosacral nerve roots, representing 0.25% of a total of 1200 patients who underwent lumbosacral CT/MRI procedures in the Addolorata Hospital and in the Service of Neuroradiology of the University of Rome "La Sapienza" during the last three years (March 2001-March 2004). We report our experience with three cases of conjoined lumbosacral nerve roots and analyze the most important literature on this topic. MR imaging is a better diagnostic procedure (in comparison to CT) for the differentiation of nerve root anomalies and, in particular, coronal sections furnish a precise definition of the profile of the conjoined/enlarged rootlets. In fact, the accurate information derived from MRI of multiple planes may be priceless for the preoperative and diagnostic evaluation of lumbosacral nerve root anomalies

Role of land set-up systems on soil (Physicochemical) conditions

Land reclamation and drainage networks represent one of the most ancient human modifications of the Italian soilscape, where tailored land set-up systems were developed in agro-and forest-ecosystems in three millennia of man’s activity. Most of once manually maintained land settings are currently scarcely working or even disappeared because of the cost needed for their mainte-nance and the advent of mechanization that have simplified the field organization. The scarce attention to the soil experienced in the last decades, has accelerated soil erosion and flooding events, which entailed high costs in terms of money and human lives, but also caused reduction of soil thickness, water holding capacity, and fertility. In view of a sustainable agriculture, it is mandatory to assess the role of land set-up systems, which for centuries have been key in protecting soil from erosion, but also in increasing soil fertility. Such an effort cannot be made without considering the different pedo-climatic conditions and land uses of the Italian ter-ritory, which is different with respect to the past because of the multiple transformations made to favour the mechanization of agriculture. In this review we discuss the main effect of Italian land settings on the soilscape and on soil physicochemical condi-tions. Since land settings were developed centuries ago, detailed information about their effect on specific soil parameters is scarce in the scientific literature; thus, in some case, we provide information gathered in places where land set-up systems are still present. mm

Poor sleep quality may independently predict suicidal risk in COVID-19 survivors: a 2-year longitudinal study

Objective: Multiple symptoms of psychiatric, neurological, and physical illnesses may be part of Post-COVID conditions and may pose COVID-19 survivors a high suicidal risk. Accordingly, we aimed to study factors contributing to suicidal risk in Post COVID-19 patients. Method: Consecutive patients with post COVID-19 conditions were followed for 2 years at the University Hospital of Ferrara at baseline (T0), 6 (T1), 12 (T2), and 24 (T3) months. Demographics, and clinical data for all patients included: disease severity, hospital length of stay, comorbidity, clinical complications, sleep quality, cognitive complaints, anxiety and stress-related symptoms, depressive symptoms, and suicidal ideation. Results: The final sample included 81 patients with post COVID survivors. The mean age was 64 + 10,6 years, 35,8% were females, 65,4% had medical comorbidities, and 69,1% had WHO severe form of COVID forms. At T0 more than 90% of patients showed poor sleep quality, 59.3% reported moderate/severe depressive symptoms, and 51.% experienced anxiety, 25.9% experienced post-traumatic stress symptoms. At T0 suicidal ideation, interested 6.1% and at T3 it increased to 7.4%. In the regression analysis, suicidal ideation at baseline was best predicted by poor sleep quality (O.R. 1.71, p=0.044) and, after 2 years, suicidal ideation was best predicted by poor sleep quality experienced at baseline (OR 67.3, p=0.001). Conclusions: Poor sleep quality may play as an independent predictor of suicidal risk in post-COVID survivors. Evaluating and targeting sleep disturbances in COVID survivors is important to prevent the consequences of disrupted sleep in mental health

Reconhecimento Off-line de Voz Contínuo para Dispositivos Móveis: Uma Análise Comparativa de Métricas de Avaliação

Abstract. Speech recognition is a form of accessibility used to perform tasks with hands and eyes free, and this is advantageous regardless of the user type. Current APIs make implementation easy, but they have limitations because depend on Internet connectivity and, are often proprietary software. The proposed solution to resolve these limitations is the development of an off-line continuous speech recognition system. The best techniques selected in a systematic review were implemented using libraries. This work presents a comparative analysis of the evaluation metrics obtained for each library

Accurate deep neural network inference using computational phase-change memory

In-memory computing is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. Crossbar arrays of resistive memory devices can be used to encode the network weights and perform efficient analog matrix-vector multiplications without intermediate movements of data. However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the analog resistive memory devices will not result in significant loss of accuracy. Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in no appreciable accuracy loss when transferring weights to in-memory computing hardware based on phase-change memory (PCM). We also propose a compensation technique that exploits the batch normalization parameters to improve the accuracy retention over time. We achieve a classification accuracy of 93.7% on the CIFAR-10 dataset and a top-1 accuracy on the ImageNet benchmark of 71.6% after mapping the trained weights to PCM. Our hardware results on CIFAR-10 with ResNet-32 demonstrate an accuracy above 93.5% retained over a one day period, where each of the 361,722 synaptic weights of the network is programmed on just two PCM devices organized in a differential configuration.Comment: This is a pre-print of an article accepted for publication in Nature Communication