Limitations in the Use of the Equivalent Diameter

This paper deals with the inaccuracy assessment of the friction pressure loss estimation based on Darcy formula combined with an equivalent hydraulic diameter and a friction factor valid for circular pipes when applied to a square rod bundle. The assessment has been done by comparing the analytical and semi-empirical predictions with two different CFD codes results: CFX and NEPTUNE_CFD. Two different analytical approaches have been considered: the whole-bundle and sub-channel approaches, both for laminar and turbulent flow conditions. Looking at results, it is reasonable to assume that an error in the range of 11% - 23% is likely when using equivalent diameter in the laminar regime. In the case of turbulent regime, the equivalent diameter works better and the error is in the range between a few percent and ~12%

Neural network multispectral satellite images classification of volcanic ash plumes in a cloudy scenario

This work shows the potential use of neural networks in the characterization of eruptive events monitored by satellite, through fast and automatic classification of multispectral images. The algorithm has been developed for the MODIS instrument and can easily be extended to other similar sensors. Six classes have been defined paying particular attention to image regions that represent the different surfaces that could possibly be found under volcanic ash clouds. Complex cloudy scenarios composed by images collected during the Icelandic eruptions of the Eyjafjallajökull (2010) and Grimsvötn (2011) volcanoes have been considered as test cases. A sensitivity analysis on the MODIS TIR and VIS channels has been performed to optimize the algorithm. The neural network has been trained with the first image of the dataset, while the remaining data have been considered as independent validation sets. Finally, the neural network classifier’s results have been compared with maps classified with several interactive procedures performed in a consolidated operational framework. This comparison shows that the automatic methodology proposed achieves a very promising performance, showing an overall accuracy greater than 84%, for the Eyjafjallajökull event, and equal to 74% for the Grimsvötn event

Volcanic ash detection and retrievals using MODIS data by means of neural networks

Volcanic ash clouds detection and retrieval represent a key issue for aviation safety due to the harming effects on aircraft. A lesson learned from the recent Eyjafjallajokull eruption is the need to obtain accurate and reliable retrievals on a real time basis. <br><br> In this work we have developed a fast and accurate Neural Network (NN) approach to detect and retrieve volcanic ash cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) data in the Thermal InfraRed (TIR) spectral range. Some measurements collected during the 2001, 2002 and 2006 Mt. Etna volcano eruptions have been considered as test cases. <br><br> The ash detection and retrievals obtained from the Brightness Temperature Difference (BTD) algorithm are used as training for the NN procedure that consists in two separate steps: ash detection and ash mass retrieval. The ash detection is reduced to a classification problem by identifying two classes: "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. A segmentation procedure has also been tested to remove the false ash pixels detection induced by the presence of high meteorological clouds. The segmentation procedure shows a clear advantage in terms of classification accuracy: the main drawback is the loss of information on ash clouds distal part. <br><br> The results obtained are very encouraging; indeed the ash detection accuracy is greater than 90%, while a mean RMSE equal to 0.365 t km<sup>−2</sup> has been obtained for the ash mass retrieval. Moreover, the NN quickness in results delivering makes the procedure extremely attractive in all the cases when the rapid response time of the system is a mandatory requirement

Unmet needs in ANCA-associated vasculitis: Physicians’ and patients’ perspectives

In recent years, clinical research has increased significantly and therapies for antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis have improved. However, there are still unanswered questions and unmet needs about AAV patients. The purpose of this review is to examine the frontiers of research related to emerging biomarkers eventually predicting relapse, and new therapeutic approaches, not to mention new quality of life assessment tools. Identifying predictors of relapse may help optimize therapeutic strategies, minimize disease recurrence, and reduce treatment-related side effects. In addition, it is important to recognize that patients may suffer long-term consequences of the disease and its treatment, which, although life-saving, is often associated with significant side effects. Our goal, therefore, is to highlight what has been achieved, the pitfalls, and what still needs to be done, comparing the views of physicians and patients

Volcanic ash retrieval from IR multispectral measurements by means of Neural Networks

The lesson learned from the recent Icelandic Eyjafjallajokull volcanic eruption is the need to obtain accurate near real time retrievals in order to sample the phenomenon evolution. In particular, because of the harming effects of fine volcanic ash particles on aircrafts, the real time tracking of volcanic clouds is a key issue for aviation safety. The current mostly utilized procedure for the ash retrievals is based on the Brightness Temperature Difference (BTD) algorithm, using the 11 and 12 micron channels measurements and radiative transfer model computation. This latter requires many input parameters and is time consuming, preventing the utilization during the crisis phases. In this work a fast and accurate Neural Network (NN) approach has been developed to detect and retrieve volcanic ash cloud properties using multispectral IR measurements. The exploited data come from the Moderate Resolution Imaging Spectroradiometer (MODIS) acquired over Mt. Etna volcano during the 2001, 2002 and 2006 eruptive events. The procedure consists in two separate steps that uses the three MODIS channels 28, 31 and 32: the detection and the ash retrievals. The detection is reduced to a classification problem. In this context several classes can be individuated, such as free sea surface, meteorological clouds, and ash plume. To maintain the solution of the problem as easy as possible we have simplified the scenario identifying only two classes on the MODIS images: 'ash' and 'no ash' pixels. This approach is coherent with the philosophy of this work in which the time passed to obtain the result is a stringent factor. For the ash mass retrieval, the trained network replicates the model. In fact, in order to have a network able to learn a behavior and to represent it through a functional approximation, it is necessary to provide appropriate information by an ensemble of examples. These latter can be obtained from a model if a direct measure is not available. In this work the results obtained with the BTD procedure have been considered. The results obtained from the entire procedure are encouraging, indeed the confusion matrix for the test set has an accuracy greater than 90%. Moreover the ash mass retrieval shows a good agreement with that achieved by BTD procedure

Safety of Biologic-DMARDs in Rheumatic Musculoskeletal Disorders: A Population-Based Study over the First Two Waves of COVID-19 Outbreak

This study aims to explore disease patterns of coronavirus disease (COVID-19) in patients with rheumatic musculoskeletal disorders (RMD) treated with immunosuppressive drugs in comparison with the general population. The observational study considered a cohort of RMD patients treated with biologic drugs or small molecules from September 2019 to November 2020 in the province of Udine, Italy. Data include the assessment of both pandemic waves until the start of the vaccination, between February 2020 and April 2020 (first), and between September 2020 and November 2020 (second). COVID-19 prevalence in 1051 patients was 3.5% without significant differences compared to the general population, and the course of infection was generally benign with 2.6% mortality. A small percentage of COVID-19 positive subjects were treated with low doses of steroids (8%). The most used treatments were represented by anti-TNF agents (65%) and anti-IL17/23 agents (16%). More than two-thirds of patients reported fever, while gastro-intestinal symptoms were recorded in 27% of patients and this clinical involvement was associated with longer swab positivity. The prevalence of COVID-19 in RMD patients has been confirmed as low in both waves. The benign course of COVID-19 in our patients may be linked to the very low number of chronic corticosteroids used and the possible protective effect of anti-TNF agents, which were the main class of biologics herein employed. Gastro-intestinal symptoms might be a predictor of viral persistence in immunosup-pressed patients. This finding could be useful to identify earlier COVID-19 carriers with uncommon symptoms, eventually eligible for antiviral drugs

Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

The accurate automatic volcanic cloud detection by means of satellite data is a challenging task and of great concern for both scientific community and stakeholder due to the well-known issues generated by a strong eruption event in relation to aviation safety and health impact. In this context, machine learning techniques applied to recent spaceborne sensors acquired data have shown promising results in the last years. This work focuses on the application of a neural network based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. The classification of the clouds and of the other surfaces composing the scene is also carried out. The neural network has been trained with MODIS (MODerate resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallaj&ouml;kull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the events comparable latitudes foster the robustness of the approach, which allows overcoming the lack in SLSTR products collected in previous mid-high latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared with RGB visual inspection and BTD (Brightness Temperature Difference) procedure. Moreover, the comparison between the ash cloud obtained by neural network and a plume mask manually generated for the specific SLSTR considered images, shows significant agreement. Thus, the proposed approach allows an automatic image classification during eruption events, which it is also considerably faster than time-consuming manually algorithms (e.g. find the best BTD product-specific threshold). Furthermore, the whole image classification indicates an overall reliability of the algorithm, in particular for meteo-clouds recognition and discrimination from volcanic clouds. Finally, the results show that the NN developed for the SLSTR nadir view is able to properly classify also the SLSTR oblique view images.</p

Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

Accurate automatic volcanic cloud detection by means of satellite data is a challenging task and is of great concern for both the scientific community and aviation stakeholders due to well-known issues generated by strong eruption events in relation to aviation safety and health impacts. In this context, machine learning techniques applied to satellite data acquired from recent spaceborne sensors have shown promising results in the last few years. This work focuses on the application of a neural-network-based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. A classification of meteorological clouds and of other surfaces comprising the scene is also carried out. The neural network has been trained with MODIS (Moderate Resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallajökull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the comparable latitudes of the eruptions permit an extension of the approach to SLSTR, thereby overcoming the lack in Sentinel-3 products collected in previous mid- to high-latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared to RGB visual inspection and BTD (brightness temperature difference) procedures. Moreover, the comparison between the ash cloud obtained by the neural network (NN) and a plume mask manually generated for the specific SLSTR images considered shows significant agreement, with an F-measure of around 0.7. Thus, the proposed approach allows for an automatic image classification during eruption events, and it is also considerably faster than time-consuming manual algorithms. Furthermore, the whole image classification indicates the overall reliability of the algorithm, particularly for recognition and discrimination between volcanic clouds and other objects.</p

Management of mixed cryoglobulinemia with rituximab: evidence and consensus-based recommendations from the Italian Study Group of Cryoglobulinemia (GISC)

Cryoglobulinemic vasculitis (CV) or mixed cryoglobulinemic syndrome (MCS) is a systemic small-vessel vasculitis characterized by the proliferation of B-cell clones producing pathogenic immune complexes, called cryoglobulins. It is often secondary to hepatitis C virus (HCV), autoimmune diseases, and hematological malignancies. CV usually has a mild benign clinical course, but severe organ damage and life-threatening manifestations can occur. Recently, evidence in favor of rituximab (RTX), an anti-CD 20 monoclonal antibody, is emerging in CV: nevertheless, questions upon the safety of this therapeutic approach, especially in HCV patients, are still being issued and universally accepted recommendations that can help physicians in MCS treatment are lacking. A Consensus Committee provided a prioritized list of research questions to perform a systematic literature review (SLR). A search was made in Medline, Embase, and Cochrane library, updated to August 2021. Of 1227 article abstracts evaluated, 27 studies were included in the SLR, of which one SLR, 4 RCTs, and 22 observational studies. Seventeen recommendations for the management of mixed cryoglobulinemia with rituximab from the Italian Study Group of Cryoglobulinemia (GISC) were developed to give a valuable tool to the physician approaching RTX treatment in CV