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The use of light detection and ranging technologies, i.e. terrestrial laser scanner (TLS), airborne laser scanner (ALS) and mobile laser scanner (MLS), together with the unmanned aerial vehicles digital photogrammetry (UAV-DP) and satellite data are proving to be fundamental tools to carry out reliable muographic measurement campaigns. The main purpose of this paper is to propose a workflow to correctly plan and exploit these types of data for muon radiography aims. To this end, a real case study is presented: searching for hidden tombs in the Etruscan necropolis of Palazzone (Umbria, Italy). A high-resolution digital elevation model (DEM) and three-dimensional models of the ground surface/sub-surface of the study area were created by merging data obtained using different survey methods to achieve the most accurate three-dimensional environment. Indeed, the simulated muon flux transmission used to infer relative transmission values, and the estimated density distribution, depends on the reliability of the three-dimensional reconstructed ground surface model. The aim of this study is to provide knowledge on the use of TLS and UAV-DP data and GPS-acquired points within the transmission-based muography process and how these data could improve or worsen the muon imaging results. Moreover, this study confirmed that muography applications require a multidisciplinary approach. [ABSTRACT FROM AUTHOR]

Campi Flegrei (CF) is an active and densely populated caldera in Southern Italy, which has manifested signs of significant unrest in the last 50 years. Due to the high volcanic risk, monitoring networks of the most sensitive unrest indicators have been implemented and improved over time. Precious database constituted by geophysical and geochemical data allowed the study of the caldera unrest phases. In this paper we retrace the caldera history in the time span 2000–2020 by analyzing displacement, seismicity and geochemical time series in a unified framework. To this end, Principal Component Analysis (PCA) was firstly applied only on geochemical data because of their compositional nature. The retrieved first three components were successively analyzed via PCA together with the geophysical and thermodynamical variables. Our results suggest that three independent processes relay on geochemical observations: a heating/pressurizing of the hydrothermal system, a process related to magmatic fluids injection at the hydrothermal system roots, and third process probably connected with a deeper magmatic dynamic. The actual volcano alert state seems mainly linked to the variation of the hydrothermal system activity. Our approach made it possible to explore the interrelation among observations of different nature highlighting the importance of the relative driving processes over time. [ABSTRACT FROM AUTHOR]

Worldwide, tropospheric ozone (O3) is a potential threat to wood production, but our understanding of O3 economic impacts on forests is still limited. To overcome this issue, we developed an approach for integrating O3 risk modelling and economic estimates, by using the Italian forests as a case study. Results suggested a significant impact of O3 expressed in terms of stomatal flux with an hourly threshold of uptake (Y = 1 nmol O3 m−2 leaf area s−1 to represent the detoxification capacity of trees), i.e. POD1. In 2005, the annual POD1 averaged over Italy was 20.4 mmol m−2 and the consequent potential damage ranged from 790.90 M€ to 2.85 B€ of capital value (i.e. 255–869 € ha−1, on average) depending on the interest rate. The annual damage ranged from 31.6 to 57.1 M€ (i.e. 10–17 € ha−1 per year, on average). There was also a 1.1% reduction in the profitable forest areas, i.e. with a positive Forest Expectation Value (FEV), with significant declines of the annual national wood production of firewood (− 7.5%), timber pole (− 7.4%), roundwood (− 5.0%) and paper mill (− 4.8%). Results were significantly different in the different Italian regions. We recommend our combined approach for further studies under different economic and phytoclimatic conditions. [ABSTRACT FROM AUTHOR]

Muon radiography (muography) is an imaging technique based on atmospheric muon absorption in matter that allows to obtain two and three-dimensional images of internal details of hidden objects or structures. The technique relies on atmospheric muon flux measurements performed around and underneath the object under examination. It is a non-invasive and passive technique and thus can be thought of as a valid alternative to common prospecting techniques used in archaeological, geological and civil security fields. This paper describes muon radiography measurements, in the context of archaeological and geological studies carried out at the Temperino mine (LI, Tuscany, Italy), for the search and three-dimensional visualisation of cavities. This mine has been exploited since Etruscan times until recently (1973), and is now an active tourist attraction with public access to the tunnels. Apart from the archaeological interest, the importance of mapping the cavities within this mine lies in identifying the areas where the extraction ores were found and also in the safety issues arising from the tourist presence inside the mine. The three-dimensional imaging is achieved with two different algorithms: one involving a triangulation of two or more measurements at different locations; the other, an innovative technique used here for the first time, is based on the back-projections of reconstructed muon tracks. The latter requires only a single muographic data tacking and is to be preferred in applications where more than one site location can be difficult to access. Finally the quality of the three-dimensional muographic imaging was evaluated by comparing the results with the laser scan profiles obtained for some known cavities within the Temperino mine. [ABSTRACT FROM AUTHOR]

The present paper further develops a coastal vulnerability index formulation (CVI) previously proposed by the authors by integrating a new variable and redefining three variables to improve the suitability of the index for low-lying coasts. Eleven variables are divided into three typological groups: geological, hydro-physical process and vegetation. The geological variables are: geomorphology, shoreline erosion/accretion rates, coastal slope, emerged beach width, and dune. The hydro-physical process group includes: river discharge, sea-level change, mean significant wave height and mean tide range. The vegetation variables are: vegetation behind the back-beach and coverage of Posidonia oceanica. The index was applied to a stretch of the Ionian coast in the province of Crotone in the Calabria region (Southern Italy), and a vulnerability map was produced. A geography information system (GIS) platform was used to better process the data. For the case study area, the most influential variables are shoreline erosion/accretion rates, coastal slope, emerged beach width, dune, vegetation behind the back-beach, and coverage of Posidonia oceanica. The most vulnerable transects are those near urban areas characterized by the absence of dunes and vegetation. Statistical and sensitivity analyses were performed, and the proposed CVI was compared with the previous formulation proposed by the authors and with two other CVI methods present in the literature. [ABSTRACT FROM AUTHOR]

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention. [ABSTRACT FROM AUTHOR]

Due to the SARS-CoV-2 outbreak, wearing a disposable face mask has become a worldwide daily routine, not only for medical operators or specialized personnel, but also for common people. Notwithstanding the undeniable positive effect in reducing the risk of virus transmission, it is important to understand if a prolonged usage of the same face mask can have effectiveness on filtering capability and potential health consequences. To this aim, we present three investigations. A survey, carried out in central Italy, offers an overview of the distorted public awareness of face mask usage. A functional study shows how prolonged wearing leads to substantial drops in humid air filtration efficiency. Finally, a morphological analysis reports the proliferation of fungal or bacteria colonies inside an improperly used mask. Our study highlights therefore that wearing a face mask is really beneficial only if it is used correctly. [ABSTRACT FROM AUTHOR]

Quantitative evaluation of the human corneal grafts stored in the tissue banks is usually limited to endothelial cell density and central thickness. Swept-source OCT (SS-OCT) is capable of measuring the central curvatures of the corneal tissue prepared for transplantation without loss of sterileness, providing insights on its refractive state. The aim of the paper is to compare in vitro SS-OCT measurements with pre-excision values. Hand-held keratometry and ultrasound pachymetry was performed on 22 corneas before excision of corneoscleral button and insertion in the vial with Eusol-C solution (AlchimiaS.r.l, Nicolò, Italy). After 12 to 36 h of hypothermic storage the corneas were examined within the vials with custom built SS-OCT system maintaining a sterile environment. The anterior and posterior central curvatures, and central corneal thickness (CCT) were measured. Rotation of the corneoscleral button was controlled by making a 6-o'clock mark during excision. Mean pre-excision CCT was 626.45 ± 28.71 µm and 468.05 ± 52.96 µm when measured with SS OCT (r = 0.55; p < 0.001). Respective values for average keratometry were 7.74 ± 0.39 mm and 7.92 ± 0.57 mm (r = 0.6; p = 0.22). Although high differences were observed in corneal thickness, keratometric radius of curvature at the flat (r = 0.42; p < 0.001) and steep (r = 0.62; p = 0.014) meridian of the anterior corneal surface, as well as corneal anterior astigmatism (r = 0.3; p < 0.001), showed good correlation with pre-excision values. SS-OCT is capable of providing quantitative evaluation of the human corneal grafts in hypothermic storage. Good correlation between curvature measurements before excision and during banking in the vial indicates its clinical utility. [ABSTRACT FROM AUTHOR]

Ionic exchange tests have been performed on superficial wastewaters to remove ammonia using a volcanic zeolitized rock from Lazio Region (Central Italy). The zeolitite (natural zeolite) is characterized by chabazite, phillipsite and minor amounts of sanidine, leucite and analcime. After preliminary column experiments in laboratory focused to determine the saturation time of the zeolitite, a pilot plant was built up on a little water course near the area of San Giustino channel (Abruzzo Region, Central Italy). Wastewaters, characterized by starting ammonia value ranging between 5 and 120 mg/l, were filtered with a zeolitic bed. The first experimental results indicate a positive ammonia reduction of about 80–90% and, in all cases, NH4+ concentration values under the EU law limits. A main purpose of this paper is to evidence that most of studies published on uptake of ammonia by means of zeolitite lead with clinoptilolite-dominant zeolitite despite the large and best performance of phillipsite-chabazite zeolites (up to 61–79% improvement of ammonia uptake). Last but not least, a large number of published studies are of difficult comparison because of poor characterization of the zeolitite used. [ABSTRACT FROM AUTHOR]

This study was designed to explore learning experiences of university students with dyslexia and factors that could contribute to their success in the university career. Although, great efforts have been made to diagnose dyslexia and to mitigate its effects at primary and secondary school, little has been done at the university level in particular in the Italian context. Indeed in the university context, the availability and possibility to use of support tools, that enable the student to achieve educational success, is still not sufficiently adequate. In this paper we used bivariate association tests and cluster analysis, in order to identify the most suitable compensatory tools and support strategies that can facilitate the students' performance in higher education. The data were obtained through the voluntary participation of Italian students, enrolled in a bachelor degree course, with certified diagnosis of dyslexia. Six groups of students were identified from the cluster analysis, defining specific support tools and learning strategies for each group. Furthermore, through the creation of these six groups, it was possible to describe "profiles" that highlight the risk factors (late diagnosis) and-or protection factors (such as associations, support from friends and family) in analyzing the academic career of students with dyslexia. Therefore, starting from these data, through artificial intelligence it will be possible to identify and suggest study methodologies and create specific support tools for each student that can enable her/him to achieve educational success in her/his academic career. [ABSTRACT FROM AUTHOR]

European countries struggled to fight against the second and the third waves of the COVID-19 pandemic, as the Test-Trace-Isolate (TTI) strategy widely adopted over the summer and early fall 2020 failed to contain the spread of the disease effectively. This paper sheds light on the effectiveness of such a strategy in two European countries (Spain and Italy) by analysing data from June to December 2020, collected via a large-scale online citizen survey with 95,251 and 43,393 answers in Spain and Italy, respectively. Our analysis describes several weaknesses in each of the three pillars of the TTI strategy: Test, Trace, and Isolate. We find that 40% of respondents had to wait more than 48 hours to obtain coronavirus tests results, while literature has shown that a delay of more than one day might make tracing all cases inefficient. We also identify limitations in the manual contact tracing capabilities in both countries, as only 29% of respondents in close contact with a confirmed infected individual reported having been contact traced. Moreover, our analysis shows that more than 45% of respondents report being unable to self-isolate if needed. We also analyse the mitigation strategies deployed to contain the second wave of coronavirus. We find that these interventions were particularly effective in Italy, where close contacts were reduced by more than 20% in the general population. Finally, we analyse the participants' perceptions about the coronavirus risk associated with different daily activities. We observe that they are often gender- and age-dependent, and not aligned with the actual risk identified by the literature. This finding emphasises the importance of deploying public-health communication campaigns to debunk misconceptions about SARS-CoV-2. Overall, our work illustrates the value of online citizen surveys to quickly and efficiently collect large-scale population data to support and evaluate policy decisions to combat the spread of infectious diseases, such as coronavirus. [ABSTRACT FROM AUTHOR]

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI's application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks. [ABSTRACT FROM AUTHOR]

We have further extended our compartmental model describing the spread of the infection in Italy. As in our previous work, the model assumes that the time evolution of the observable quantities (number of people still positive to the infection, hospitalized and fatalities cases, healed people, and total number of people that has contracted the infection) depends on average parameters, namely people diffusion coefficient, infection cross-section, and population density. The model provides information on the tight relationship between the variation of the reported infection cases and a well-defined observable physical quantity: the average number of people that lie within the daily displacement area of any single person. With respect to our previous paper, we have extended the analyses to several regions in Italy, characterized by different levels of restrictions and we have correlated them to the diffusion coefficient. Furthermore, the model now includes self-consistent evaluation of the reproduction index, effect of immunization due to vaccination, and potential impact of virus variants on the dynamical evolution of the outbreak. The model fits the epidemic data in Italy, and allows us to strictly relate the time evolution of the number of hospitalized cases and fatalities to the change of people mobility, vaccination rate, and appearance of an initial concentration of people positives for new variants of the virus. [ABSTRACT FROM AUTHOR]

Design ground motion intensities determine the actions for which structures are checked, in the conventional approach of seismic codes, not to fail the target performances. On the other hand, due to inherent characteristics of probabilistic seismic hazard analysis (PSHA), it is expected that site-specific design intensity based on PSHA is exceeded in the epicentral area of moderate-to-high magnitude earthquakes. In the context of regional seismic loss assessment and of the evolution of seismic codes from the regulator perspective, it is useful to gather insights about the extent of the zone around the earthquake source where code-conforming structures are expected to be systematically exposed to seismic actions larger than those accounted for in design. To assess such areal extent based on empirical evidence is the scope of the study presented in the paper. To this aim, peak ground acceleration ShakeMap data for Italian earthquakes from 2008 to 2020 were compared to the current design intensities in the same areas for which the maps are available. This allowed, first, to develop simple semi-empirical models of the exceedance area versus the magnitude of the earthquakes. Second, it allowed to model the probability that an earthquake of given magnitude causes exceedance of the design intensity via logistic regressions. Coupling the first and second class of models provides an approximation of the expected exceedance (logarithmic) area upon occurrence of an earthquake of given magnitude. Such an area can be of several thousand square kilometers for earthquakes occurring relatively frequently in countries such as Italy. [ABSTRACT FROM AUTHOR]

Social media play a key role in shaping citizens' political opinion. According to the Eurobarometer, the percentage of EU citizens employing online social networks on a daily basis has increased from 18% in 2010 to 48% in 2019. The entwinement between social media and the unfolding of political dynamics has motivated the interest of researchers for the analysis of users online behavior—with particular emphasis on group polarization during debates and echo-chambers formation. In this context, semantic aspects have remained largely under-explored. In this paper, we aim at filling this gap by adopting a two-steps approach. First, we identify the discursive communities animating the political debate in the run up of the 2018 Italian Elections as groups of users with a significantly-similar retweeting behavior. Second, we study the mechanisms that shape their internal discussions by monitoring, on a daily basis, the structural evolution of the semantic networks they induce. Above and beyond specifying the semantic peculiarities of the Italian electoral competition, our approach innovates studies of online political discussions in two main ways. On the one hand, it grounds semantic analysis within users' behaviors by implementing a method, rooted in statistical theory, that guarantees that our inference of socio-semantic structures is not biased by any unsupported assumption about missing information; on the other, it is completely automated as it does not rest upon any manual labelling (either based on the users' features or on their sharing patterns). These elements make our method applicable to any Twitter discussion regardless of the language or the topic addressed. [ABSTRACT FROM AUTHOR]

Older adults are the main victims of the novel COVID-19 coronavirus outbreak and elderly in Long Term Care Facilities (LTCFs) are severely hit in terms of mortality. This paper presents a quantitative study of the impact of COVID-19 outbreak in Italy during first stages of the epidemic, focusing on the effects on mortality increase among older adults over 80 and its correlation with LTCFs. The study of growth patterns shows a power-law scaling regime for the first stage of the pandemic with an uneven behaviour among different regions as well as for the overall mortality increase according to the different impact of COVID-19. However, COVID-19 incidence rate does not fully explain the differences of mortality impact in older adults among different regions. We define a quantitative correlation between mortality in older adults and the number of people in LTCFs confirming the tremendous impact of COVID-19 on LTCFs. In addition a correlation between LTCFs and undiagnosed cases as well as effects of health system dysfunction is also observed. Our results confirm that LTCFs did not play a protective role on older adults during the pandemic, but the higher the number of elderly people living in LTCFs the greater the increase of both general and COVID-19 related mortality. We also observed that the handling of the crises in LTCFs hampered an efficient tracing of COVID-19 spread and promoted the increase of deaths not directly attributed to SARS-CoV-2. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]