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A coupled numerical approach that combines the WRF model and the Mike 3 (DHI) hydrodynamic model was developed and applied in two semi-enclosed basins in the Ionian Sea (Italy) to assess the wind-driven current. To gain a better understanding of how the sea current field can vary depending on meteorological data forcing, three different scenario were set up. The sensitivity of the sea current pattern was investigated as a function of the type of meteorological forcing and appreciating the differences in the results. The aims of this study are threefold. Firstly, we wish to define an ad hoc procedure to join the model-computed meteorological parameters in the hydrodynamic model. Secondly, we will investigate the feedback from the Mar Piccolo and Mar Grande basins in the Ionian Sea using fully coupled simulations and an uncoupled system where the atmospheric parameters are derived from a ground station. Finally, we will evaluate the results achieved by applying two scenarios of typical meteorological conditions to the study site. The model results highlighted the variability of sea currents depending on meteorological forcing. [ABSTRACT FROM AUTHOR]

A great deal of attention has been devoted to the analysis of particulate matter (PM) concentrations in various scenarios because of their negative effects on human health. Here, we investigate how meteorological conditions can affect PM concentrations in the peculiar case of the district of the city of Lecce in the Apulia region (Southern Italy), which is characterized by the highest tumor rate of the whole region despite the absence of nearby heavy industries. We present a unified machine learning framework which combines air quality and meteorological data, either measured on ground or forecast. Our findings show that the concentrations of PM10, PM2.5, NO2 and CO are significantly associated with the meteorological conditions and suggest that it is possible to predict air quality using either ground weather observations or weather forecasts. [ABSTRACT FROM AUTHOR]

In this work we propose a novel application of Partial Differential Equations (PDEs) inpainting techniques to two medical contexts. The first one concerning recovering of concentration maps for superparamagnetic nanoparticles, used as tracers in the framework of Magnetic Particle Imaging. The analysis is carried out by two set of simulations, with and without adding a source of noise, to show that the inpainted images preserve the main properties of the original ones. The second medical application is related to recovering data of corneal elevation maps in ophthalmology. A new procedure consisting in applying the PDEs inpainting techniques to the radial curvature image is proposed. The images of the anterior corneal surface are properly recovered to obtain an approximation error of the required precision. We compare inpainting methods based on second, third and fourth-order PDEs with standard approximation and interpolation techniques. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting mesoscale model (WRF) was used to simulate hourly 10m wind speed and direction over the city of Taranto, Apulia region (south-eastern Italy). This area is characterized by a large industrial complex including the largest European steel plant and is subject to a Regional Air Quality Recovery Plan. This plan constrains industries in the area to reduce by 10% the mean daily emissions by diffuse and point sources during specific meteorological conditions named wind days. According to the Recovery Plan, the Regional Environmental Agency ARPA-PUGLIA is responsible for forecasting these specific meteorological conditions with 72 h in advance and possibly issue the early warning. In particular, an accurate wind simulation is required. Unfortunately, numerical weather prediction models suffer from errors, especially for what concerns near-surface fields. These errors depend primarily on uncertainties in the initial and boundary conditions provided by global models and secondly on the model formulation, in particular the physical parametrizations used to represent processes such as turbulence, radiation exchange, cumulus and microphysics. In our work, we tried to compensate for the latter limitation by using different Planetary Boundary Layer (PBL) parameterization schemes. Five combinations of PBL and Surface Layer (SL) schemes were considered. Simulations are implemented in a real-time configuration since our intention is to analyze the same configuration implemented by ARPA-PUGLIA for operational runs; the validation is focused over a time range extending from 49 to 72 h with hourly time resolution. The assessment of the performance was computed by comparing the WRF model output with ground data measured at a weather monitoring station in Taranto, near the steel plant. After the analysis of the simulations performed with different PBL schemes, both simple (e.g. average) and more complex post-processing methods (e.g. weighted average, linear and nonlinear regression, and artificial neural network) are adopted to improve the performances with respect to the output of each single setup. The neural network approach comes out as the most promising method. [ABSTRACT FROM AUTHOR]

Air pollution can increase the risk of respiratory diseases, enhancing the susceptibility to viral and bacterial infections. Some studies suggest that small air particles facilitate the spread of viruses and also of the new coronavirus, besides the direct person-to-person contagion. However, the effects of the exposure to particulate matter and other contaminants on SARS-CoV-2 has been poorly explored. Here we examined the possible reasons why the new coronavirus differently impacted on Italian regional and provincial populations. With the help of artificial intelligence, we studied the importance of air pollution for mortality and positivity rates of the SARS-CoV-2 outbreak in Italy. We discovered that among several environmental, health, and socio-economic factors, air pollution and fine particulate matter (PM2.5), as its main component, resulted as the most important predictors of SARS-CoV-2 effects. We also found that the emissions from industries, farms, and road traffic - in order of importance - might be responsible for more than 70% of the deaths associated with SARS-CoV-2 nationwide. Given the major contribution played by air pollution (much more important than other health and socio-economic factors, as we discovered), we projected that, with an increase of 5–10% in air pollution, similar future pathogens may inflate the epidemic toll of Italy by 21–32% additional cases, whose 19–28% more positives and 4–14% more deaths. Our findings, demonstrating that fine-particulate (PM2.5) pollutant level is the most important factor to predict SARS-CoV-2 effects that would worsen even with a slight decrease of air quality, highlight that the imperative of productivity before health and environmental protection is, indeed, a short-term/small-minded resolution. Image 1 • Air quality is linked to respiratory diseases and may facilitate microbial infections. • The prolonged exposure to air pollution can affect SARS-CoV-2 outbreak. • We found that air quality is the best predictor for SARS-CoV-2 mortality in Italy. • A prominent role is played by PM2.5 and emissions from industries and farms. • A worsening of air quality may inflate the death toll of similar future pandemics. Short summary : Artificial intelligence reveals that, in Italy, air pollution mainly due to fine particulate matter produced by industries, farms, and road traffic may severely affect the susceptibility to respiratory infections caused by SARS-CoV-like pathogens. [ABSTRACT FROM AUTHOR]

Remote sensing images find application in several different domains, such as land cover or land usage observation, environmental monitoring, and urbanization. This latter field has recently witnessed an interesting development with the use of remote sensing for infrastructural monitoring. In this work, we present an analysis of Sentinel-1 images, which were used to monitor the Italian provinces of Bologna and Modena located at the Emilia Region Apennines foothill. The goal of this study was the development of a machine learning-based detection system to monitor the deterioration of public aqueduct infrastructures based on Persistent Scatterer Interferometry (PSI). We evaluated the land deformation over a temporal range of five years; these series feed a k-means clustering algorithm to separate the pixels of the region according to different deformation patterns. Furthermore, we defined the critical areas as those areas where different patterns collided or overlapped. The proposed approach provides an informative tool for the structural health monitoring of underground infrastructures. [ABSTRACT FROM AUTHOR]

In recent years, a number of different procedures have been proposed for segmentation of remote sensing images, basing on spectral information. Model-based and machine learning strategies have been investigated in several studies. This work presents a comprehensive overview and an unbiased comparison of the most adopted segmentation strategies: Support Vector Machines (SVM), Random Forests, Neural networks, Sen2Cor, FMask and MAJA. We used a training set for learning and two different independent sets for testing. The comparison accounted for 135 images acquired from 54 different worldwide sites. We observed that machine learning segmentations are extremely reliable when the training and test are homogeneous. SVM performed slightly better than other methods. In particular, when using heterogeneous test data, SVM remained the most accurate segmentation method while state-of-the-art model-based methods such as MAJA and FMask obtained better sensitivity and precision, respectively. Therefore, even if each method has its specific advantages and drawbacks, SVM resulted in a competitive option for remote sensing applications. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-expression network analysis with the study of Shannon entropy of the betweenness. We analyzed the publicly available GSE1297 dataset, achieved from the GEO database in NCBI, containing hippocampal gene expression of 9 control and 22 AD human subjects. Co-expressed genes were clustered into different communities. Two communities of interest (composed by 72 and 39 genes) were found by calculating the correlation coefficient between communities and clinical features. The detected communities resulted stable, replicated on two independent datasets and mostly enriched in pathways closely associated with neuro-degenative diseases. A comparison between our findings and other module detection techniques showed that the detected communities were more related to AD phenotype. Lastly, the hub genes within the two communities of interest were identified by means of a centrality analysis and a bootstrap procedure. The communities of the hub genes presented even stronger correlation with clinical features. These findings and further explorations on the detected genes could shed light on the genetic aspects related with physiological aspects of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer. [ABSTRACT FROM AUTHOR]