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In the last years, an anomalously high spreading of West Nile virus (WNV) has been observed in Italy, with particularly high peaks of infections in southern Lazio, Campania and Veneto regions. The main disease vector for WNV is represented by Culex pipiens mosquitoes, which spread human infections through their bites. Here, we investigate WNV fever epidemic diffusion during summer season 2025 in Italy through a computational approach based on a quantum version of the Game of Life (GOL) cellular automaton model. Specifically, human dynamics evolves according to the GOL rules, while stochastic dynamics of disease vectors, i.e., mosquitoes, as well as their interaction with humans, simultaneously occur. We show that this model fits the curves of cumulative infected individuals with high accuracy, either at local and average-regional level, with only optimization of mosquito birth and removal rates parameters. Furthermore, leveraging model flexibility, we show that changes in model parameters values elucidate system response to environmental variations. For instance, we quantify, e.g., the impact of mosquito spreading containment measures or sudden mosquito increasing abundance due to

We propose a novel epidemiological model, referred to as SEIHRDV, for the numerical simulation of the COVID-19 epidemic, which we validate using data from Italy starting in September 2020. SEIHRDV features the following compartments: Susceptible (S), Exposed (E), Infectious (I), Healing (H), Recovered (R), Deceased (D) and Vaccinated (V). The model is age-stratified, as it considers the population split into 15 age groups. Moreover, it takes into account 7 different contexts of exposition to the infection (family, home, school, work, transport, leisure, other contexts), which impact on the transmission mechanism. Thanks to these features, the model can address the analysis of the epidemics and the efficacy of non-pharmaceutical interventions, as well as possible vaccination strategies and the introduction of the Green Pass, a containment measure introduced in Italy in 2021. By leveraging on the SEIHRDV model, we successfully analyzed epidemic trends during the COVID-19 outbreak from September 2020 to July 2021. The model proved instrumental in conducting comprehensive what-if studies and scenario analyses tailored to Italy and its regions. Furthermore, SEIHRDV facilitated accurate

We propose three spatial methods for estimating the full probability distribution of PM10 concentrations, with the ultimate goal of assessing air quality in Northern Italy. Moving beyond spatial averages and simple indicators, we adopt a distributional perspective to capture the complex variability of pollutant concentrations across space. The first proposed approach predicts class-based compositions via Fixed Rank Kriging; the second estimates multiple, non-crossing quantiles through a spatial regression with differential regularization; the third directly reconstructs full probability densities leveraging on both Fixed Rank Kriging and multiple quantiles spatial regression within a Simplicial Principal Component Analysis framework. These approaches are applied to daily PM10 measurements, collected from 2018 to 2022 in Northern Italy, to estimate spatially continuous distributions and to identify regions at risk of regulatory exceedance. The three approaches exhibit localized differences, revealing how modeling assumptions may influence the prediction of fine-scale pollutant concentration patterns. Nevertheless, they consistently agree on the broader spatial patterns of pollution.

Italy exhibits rich linguistic diversity across its territory due to the distinct regional languages spoken in different areas. Recent advances in self-supervised learning provide new opportunities to analyze Italy's linguistic varieties using speech data alone. This includes the potential to leverage representations learned from large amounts of data to better examine nuances between closely related linguistic varieties. In this study, we focus on automatically identifying the geographic region of origin of speech samples drawn from Italy's diverse language varieties. We leverage self-supervised learning models to tackle this task and analyze differences and similarities between Italy's regional languages. In doing so, we also seek to uncover new insights into the relationships among these diverse yet closely related varieties, which may help linguists understand their interconnected evolution and regional development over time and space. To improve the discriminative ability of learned representations, we evaluate several supervised contrastive learning objectives, both as pre-training steps and additional fine-tuning objectives. Experimental evidence shows that pre-trained self-

This study focuses on the validation of high-resolution regional reanalyses to understand their effectiveness in reproducing precipitation patterns over Italy, a climate change hotspot characterized by coastal sea-land interaction and complex orography. Nine reanalysis products were evaluated, with the ECMWF global reanalysis ERA5 serving as a benchmark. These included both European (COSMO-REA6, CERRA) and Italy-specific (BOLAM, MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA\_IT) datasets, using different models and parametrizations. The inter-comparison involved determining the effective resolution of daily precipitation fields using wavelet techniques and assessing intense precipitation statistics through frequency distributions. In-situ observations and observational gridded datasets were used to independently validate reanalysis precipitation fields. The capability of reanalyses to depict daily precipitation patterns was assessed, highlighting a maximum radius of precipitation misplacement of about 15 km, with notably lower skills during summer. An overall overestimation of precipitation was identified in the reanalysis climatological fields over the Po Valley and the Alps, where

Italy is characterized by a one-of-a-kind linguistic diversity landscape in Europe, which implicitly encodes local knowledge, cultural traditions, artistic expressions and history of its speakers. However, most local languages and dialects in Italy are at risk of disappearing within few generations. The NLP community has recently begun to engage with endangered languages, including those of Italy. Yet, most efforts assume that these varieties are under-resourced language monoliths with an established written form and homogeneous functions and needs, and thus highly interchangeable with each other and with high-resource, standardized languages. In this paper, we introduce the linguistic context of Italy and challenge the default machine-centric assumptions of NLP for Italy's language varieties. We advocate for a shift in the paradigm from machine-centric to speaker-centric NLP, and provide recommendations and opportunities for work that prioritizes languages and their speakers over technological advances. To facilitate the process, we finally propose building a local community towards responsible, participatory efforts aimed at supporting vitality of languages and dialects of Italy.

The availability of affordable and high-quality childcare services has become a significant concern in recent years. Such services can facilitate the balance between work and family life, increasing participation in the workforce and promoting gender equality. Furthermore, childcare can also help address the issue of decreasing fertility rates by making it more affordable for parents to have children while maintaining their careers. This is critical, especially for countries that are facing ultralow fertility rates like Italy. The Italian government has included within the recovery and resilience plan financed with Next Generations EU funds an unprecedented investment in order to increase the supply of children's education services and make it more equitably distributed across the country. In this article, we estimate groups of spatial areas with similar structures in terms of coverage (availability of childcare services at the municipality level), public expenditure rates in childcare, as well as other socio-demographic and economic factors, such as female employment, education, and grandparent rates. Our empirical findings confirm how Italy is characterized by a large number of "

Women remain underrepresented in the labour market. Although significant advancements are being made to increase female participation in the workforce, the gender gap is still far from being bridged. We contribute to the growing literature on gender inequalities in the labour market, evaluating the potential of the LinkedIn estimates to monitor the evolution of the gender gaps sustainably, complementing the official data sources. In particular, assessing the labour market patterns at a subnational level in Italy. Our findings show that the LinkedIn estimates accurately capture the gender disparities in Italy regarding sociodemographic attributes such as gender, age, geographic location, seniority, and industry category. At the same time, we assess data biases such as the digitalisation gap, which impacts the representativity of the workforce in an imbalanced manner, confirming that women are under-represented in Southern Italy. Additionally to confirming the gender disparities to the official census, LinkedIn estimates are a valuable tool to provide dynamic insights; we showed an immigration flow of highly skilled women, predominantly from the South. Digital surveillance of gender

We present here the East Asia to Italy Nearly Global VLBI (EATING VLBI) project. How this project started and the evolution of the international collaboration between Korean, Japanese, and Italian researchers to study compact sources with VLBI observations is reported. Problems related to the synchronization of the very different arrays and technical details of the telescopes involved are presented and discussed. The relatively high observation frequency (22 and 43 GHz) and the long baselines between Italy and East Asia produced high-resolution images. We present example images to demonstrate the typical performance of the EATING VLBI array. The results attracted international researchers and the collaboration is growing, now including Chinese and Russian stations. New in progress projects are discussed and future possibilities with a larger number of telescopes and a better frequency coverage are briefly discussed herein.

In this paper we deal with the logistic wavelets introduced in \cite{RF}. We modify them by multiplying by appropriate coefficients so that their norm in the space $L^{2}(R)$ is equal to 1. We calculate the normalization coefficients using the Grosset-Veselov formula \cite{GV}, Eulerian numbers and Bernoulli numbers. Then we apply the logistic wavelets to model of the first wave of Covid-19 deaths in Italy in 2020. This example shows that even asymmetric and skewed data can be modeled, with high accuracy, by a sum of logistic functions.

Using firm-level data collected by Statistics Italy for 2008, 2011, and 2015, we examine the Triple-Helix synergy among geographical and size distributions of firms, and the NACE codes attributed to these firms, at the different levels of regional and national government. At which levels is innovation-systemness indicated? The contributions of regions to the Italian innovation system have increased, but synergy generation between regions and supra-regionally has remained at almost 45%. As against the statistical classification of Italy into twenty regions or into Northern, Central, and Southern Italy, the greatest synergy is retrieved by considering the country in terms of Northern and Southern Italy as two sub-systems, with Tuscany included as part of Northern Italy. We suggest that separate innovation strategies should be developed for these two parts of the country. The current focus on regions for innovation policies may to some extent be an artifact of the statistics and EU policies. In terms of sectors, both medium- and high-tech manufacturing (MHTM) and knowledge-intensive services (KIS) are proportionally integrated in the various regions.

The novel Coronavirus disease (COVID-19) is a severe respiratory infection that officially occurred in Wuhan, China, in December 2019. In late February, the disease began to spread quickly across the world, causing serious health, social, and economic emergencies. This paper aims to forecast the short to medium-term incidence of COVID-19 epidemic through the medium of an autoregressive integrated moving average (ARIMA) model, applied to Italy, Russia, and the USA The analysis is carried out on the number of new daily confirmed COVID-19 cases, collected by Worldometer website. The best ARIMA models are Italy (4,2,4), Russia (1,2,1), and the USA (6,2,3). The results show that: i) ARIMA models are reliable enough when new daily cases begin to stabilize; ii) Italy, the USA, and Russia reached the peak of COVID-19 infections in mid-April, mid-May, and late May, respectively; and iii) Russia and the USA will require much more time than Italy to drop COVID-19 cases near zero. This may suggest the importance of the application of quick and effective lockdown measures, which have been relatively stricter in Italy. Therefore, even if the results should be interpreted with caution, ARIMA mode

In this letter we study the temporal evolution of the Sars-Cov-2 in Italy. The time window of the real data is between February 24 and March 25. After we upgrade the data until April 1.We perform the analysis with 4 different model and we think that the best candidate to describe correctly the italian situation is a generalized Logistic equation. We use two coupled differential equations that describe the evolution of the severe infected and the deaths. We have done this choice, because in Italy the pharyngeal swabs are made only to severe infected and so we have no information about asymptomatic people. An important observation is that the virus spreads between Regions with some delay; so we suggest that a different analysis region by region would be more sensible than that on the whole Italy. In particular the region Lombardia has a behaviour very fast with respect to the other ones. We show the behaviour of the total deaths and the total severe infected for Italy and five regions: Lombardia, Emilia Romagna, Veneto, Piemonte, Toscana. Finally we do an analysis of the peak and an estimation of how many lifes have been saved with the LockDown.

An integrated and widespread road system, like the one built during the Roman Empire in Italy, plays an important role today in facilitating the construction of new infrastructure. This paper investigates the historical path of Roman roads as main determinant of both motorways and railways in the country. The empirical analysis shows how the modern Italian transport infrastructure followed the path traced in ancient times by the Romans in constructing their roads. Being paved and connecting Italy from North to South, consular trajectories lasted in time, representing the starting physical capital for developing the new transport networks.

Climate extreme events are constantly increasing. What is the effect of these potentially catastrophic events on insurance demand in Italy, with particular reference to the economic activities? Extreme precipitation events over most of the midlatitude land masses and over wet tropical regions will very likely become more intense and more frequent by the end of this century, as global mean surface temperature increases. If we look to Italy, examination of the precipitation time series shows a sensitive and highly significant decrease in the total number of precipitation events in Italy, with a trend of events intense dissimilar as regards to low and high intensity, with a decline of firsts and an increase of seconds. The risk related to hydrological natural disasters is in Italy one of the most important problem for both damage and number of victims. How evolves the ability to pay for damages, with a view to safeguarding work and economic activities, and employment protection?

Evidence of human activities during the Middle to Upper Palaeolithic transition is well represented from rock_shelters, caves and open_air sites across Italy. Over the past decade, both the revision of taphonomic processes affecting archaeological faunal assemblages and new zooarchaeological studies have allowed archaeologists to better understand subsistence strategies and cultural behaviors attributed to groups of Neandertal and modern humans living in the region. This work presents the preliminary results of a 5 years research programme (ERC n. 724046_SUCCESS) and offers a state_of_the_art synthesis of archaeological faunal assemblages including mammals and birds uncovered in Italy between 50 and 35 ky ago. The present data were recovered in primary Late Mousterian, Uluzzian, and Protoaurignacian stratigraphic contexts from Northern Italy (Grotta di Fumane, Riparo del Broion, Grotta Maggiore di San Bernardino, Grotta del Rio Secco, Riparo Bombrini), and Southern Italy (Grotta di Castelcivita, Grotta della Cala, Grotta del Cavallo, and Riparo l'Oscurusciuto). The available Number of Identified Specimens (NISP) is analysed through intra- and inter-site comparisons at a regional sc

A sequence of earthquakes occurred between the end of August 2016 and the end of October 2016 in Central Italy causing significant damage and major disruption in a wide area. The sequence of events is composed of five events with magnitude between Mw 5.5 to 6.5. As a consequence, numerous residential buildings in the affected area was not particularly resistant to the shaking, resulting in the collapse and heavy damage. With a particular focus on masonry infilled steel frames, this paper evaluates the seismic performance of an infilled moment-resisting steel frame located in Amatrice, Central Italy, which suffered significant damage during the August 2016 Central Italy earthquake. The aim is to investigate the effect of the masonry infill to the seismic performance of the building. The three-dimensional (3D) frame building is modeled using the Opensees software, where the beam and column elements are modeled by using a nonlinear hinge model and the infill is idealized as diagonal struts with nonlinear hysteretic behavior. Nonlinear static and dynamic analyses are performed for both bare and infill frames in order to assess the effect of the masonry infill on the overall seismic res

We analyze the data about casualties in Italy in the period 01/01/2015 to 30/09/2020 released by the Italian National Institute of Statistics (ISTAT). The data exhibit a clear sinusoidal behavior, whose fit allows for a robust subtraction of the baseline trend of casualties in Italy, with a surplus of mortality in correspondence to the flu epidemics in winter and to the hottest periods in summer. While these peaks are symmetric in shape, the peak in coincidence with the COVID-19 pandemics is asymmetric and more pronounced. We fit the former with a Gaussian function and the latter with a Gompertz function, in order to quantify number of casualties, the duration and the position of all causes of excess deaths. The overall quality of the fit to the data turns out to be very good. We discuss the trend of casualties in Italy by different classes of ages and for the different genders. We finally compare the data-subtracted casualties as reported by ISTAT with those reported by the Italian Department for Civil Protection (DPC) relative to the deaths directly attributed to COVID-19, and we discuss the differences.

The dataset described in this paper contains daily data about COVID-19 cases that occurred in Italy over the period from Jan. 28, 2020 to March 20, 2021, divided into ten age classes of the population, the first class being 0-9 years, the tenth class being 90 years and over. The dataset contains eight columns, namely: date (day), age class, number of new cases, number of newly hospitalized patients, number of patients entering intensive care, number of deceased patients, number of recovered patients, number of active infected patients. This data has been officially released for research purposes by the Italian authority for COVID-19 epidemiologic surveillance (Istituto Superiore di Sanità - ISS), upon formal request by the authors, in accordance with the Ordonnance of the Chief of the Civil Protection Department n. 691 dated Aug. 4 2020. A separate file contains the numerosity of the population in each age class, according to the National Institute of Statistics (ISTAT) data of the resident population of Italy as of Jan. 2020. This data has potential use, for instance, in epidemiologic studies of the effects of the COVID-19 contagion in Italy, in mortality analysis by age class, an