COSMO-SkyMed potential to detect and monitor Mediterranean maquis fires and regrowth: a pilot study in Capo Figari, Sardinia, Italy

Mediterranean maquis is a complex and widespread ecosystem in the region, intrinsically prone to fire. Many species have developed specific adaptation traits to cope with fire, ensuring resistance and resilience. Due to the recent changes in socio-economy and land uses, fires are more and more frequent in the urban-rural fringe and in the coastlines, both now densely populated. The detection of fires and the monitoring of vegetation regrowth is thus of primary interest for local management and for understanding the ecosystem dynamics and processes, also in the light of the recurrent droughts induced by climate change. Among the main objectives of the COSMO-SkyMed radar constellation mission there is the monitoring of environmental hazards; the very high revisiting time of this mission is optimal for post-hazard response activities. However, very few studies exploited such data for fire and vegetation monitoring. In this research, Cosmo-SkyMed is used in a Mediterranean protected area covered by maquis to detect the burnt area extension and to conduct a mid-term assessment of vegetation regrowth. The positive results obtained in this research highlight the importance of the very high-resolution continuous acquisitions and the multi-polarization information provided by COSMO-SkyMed for monitoring fire impacts on vegetation

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

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

The MEG detector for μ+→e+γ{\mu}+\to e+{\gamma} decay search

The MEG (Mu to Electron Gamma) experiment has been running at the Paul Scherrer Institut (PSI), Switzerland since 2008 to search for the decay \meg\ by using one of the most intense continuous $\mu^+$ beams in the world. This paper presents the MEG components: the positron spectrometer, including a thin target, a superconducting magnet, a set of drift chambers for measuring the muon decay vertex and the positron momentum, a timing counter for measuring the positron time, and a liquid xenon detector for measuring the photon energy, position and time. The trigger system, the read-out electronics and the data acquisition system are also presented in detail. The paper is completed with a description of the equipment and techniques developed for the calibration in time and energy and the simulation of the whole apparatus.Comment: 59 pages, 90 figure

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

Reynolds Number Effects at High Angles of Attack

Lessons learned from comparisons between ground-based tests and flight measurements for the high-angle-of-attack programs on the F-18 High Alpha Research Vehicle (HARV), the X-29 forward-swept wing aircraft, and the X-31 enhanced fighter maneuverability aircraft are presented. On all three vehicles, Reynolds number effects were evident on the forebodies at high angles of attack. The correlation between flight and wind tunnel forebody pressure distributions for the F-18 HARV were improved by using twin longitudinal grit strips on the forebody of the wind-tunnel model. Pressure distributions obtained on the X-29 wind-tunnel model at flight Reynolds numbers showed excellent correlation with the flight data up to alpha = 50 deg. Above (alpha = 50 deg. the pressure distributions for both flight and wind tunnel became asymmetric and showed poorer agreement, possibly because of the different surface finish of the model and aircraft. The detrimental effect of a very sharp nose apex was demonstrated on the X-31 aircraft. Grit strips on the forebody of the X-31 reduced the randomness but increased the magnitude of the asymmetry. Nose strakes were required to reduce the forebody yawing moment asymmetries and the grit strips on the flight test noseboom improved the aircraft handling qualities

Exploitation of TerraSAR-X Data for Land use/Land Cover Analysis Using Object-Oriented Classification Approach in the African Sahel Area, Sudan.

Recently, object-oriented classification techniques based on image segmentation approaches are being studied using high-resolution satellite images to extract various thematic information. In this study different types of land use/land cover (LULC) types were analysed by employing object-oriented classification approach to dual TerraSAR-X images (HH and HV polarisation) at African Sahel. For that purpose, multi-resolution segmentation (MRS) of the Definiens software was used for creating the image objects. Using the feature space optimisation (FSO) tool the attributes of the TerraSAR-X image were optimised in order to obtain the best separability among classes for the LULC mapping. The backscattering coefficients (BSC) for some classes were observed to be different for HH and HV polarisations. The best separation distance of the tested spectral, shape and textural features showed different variations among the discriminated LULC classes. An overall accuracy of 84 % with a kappa value 0.82 was resulted from the classification scheme, while accuracy differences among the classes were kept minimal. Finally, the results highlighted the importance of a combine use of TerraSAR-X data and object-oriented classification approaches as a useful source of information and technique for LULC analysis in the African Sahel drylands