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Flow-like landslides are recognized to be the most destructive slope movements and to threaten human life. They may occur in several geological settings, as is the case of the pyroclastic soil covers resting on either igneous, or carbonate, bedrocks. The pyroclastic soil strata involved in these shallow landslides typically are partially saturated over the whole year. Intense rainfall, considered at hourly time scale, represents the triggering factor because it causes the decrease in matric suction and the consequent decrease in soil shear strength. However, it is recognized as the role of the hydromechanical slope behavior, the vegetation cover, and the geomorphological irregularities on shallow landslide hazard. These factors should be properly simulated in physically based predictive models of the failure onset to set up a reliable early warning system (EWS). This paper presents a new coupled thermohydraulic modeling of a pyroclastic soil cover in Campania, accounting for several slope factors that may predispose landslide activation, including the geomorphological local irregularities. The Mount Faito test site, in the Lattari Mountains (Southern Italy), has been adopted as a prototype slope for the geomorphological and hydromechanical scenarios of reference because of the extensive field and laboratory characterizations of the soil strata already available from previous studies. Once validated with these data, the numerical model has been used to estimate the slope response to critical rainfall scenarios. Such numerical estimations have been then compared to the instability predictions currently provided by empirical approaches, defined in terms of the intensity and duration of rainstorms threshold of shallow landslide activations. By comparison between the empirical and physically based approaches, the crucial role of antecedent slope hydraulic conditions and the geological setting for implementing reliable EWS, reducing false alarms, is proved. [ABSTRACT FROM AUTHOR]

Broad mountainous areas in the western Campania (southern Italy), where young pyroclastic deposits extensively outcrop, frequently experience rainfall-induced slope movements of different degrees of mobility, causing heavy damage and fatalities. Such landslides cannot be easily mitigated, and the implementation of physically based early warning systems is still not able to predict the post-failure evolution of slope movements and the exposed areas at risk. This paper is devoted to overcoming this limit. To this end, the mechanical characterization of pyroclastic soil, carried out through an extensive laboratory testing program, is presented and compared with those of two other ashy soils of different depositional mechanisms. The results show that the depositional mode influences soil properties; to begin with, it affects the unsaturated shear strength, whose intercept of cohesion is up to 5 kPa higher in ashes of flow deposition than in airfall ash deposits. The saturated undrained soil response allowed for the identification of different levels of susceptibility to the liquefaction of pyroclastic deposits, which is one of the main factors governing the post-failure evolution of landslides. Gathering all the acquired information, including saturated and unsaturated soil shear strength, permeability function, and water retention curves, into a soil database, it was possible to present all data under a unitary framework. Finally, the implementation of the proposed flowchart for a simplified assessment of post-failure evolution to be employed in regional early warning systems can enhance our knowledge of the areas at risk. [ABSTRACT FROM AUTHOR]

In this paper, integrating field surveys and literature data, an analysis of Ophioglossum lusitanicum plant communities and related 92/43/EEC habitats are reported for Italy. Two new syntaxa, Euphorbio exiguae-Ophioglossetum lusitanici ass. nova hoc loco and trifolietosum scabri subass. nova hoc loco of the Rumici bucephalophori-Ophioglossetum lusitanici were described in the Apulia and Campania regions.Both types of vegetation identified in Apulia, Campania, and Sicily regions represent two different aspects of the same priority habitat: "pseudo-steppe with grasses and annuals of the Thero-Brachypodietea" (habitat code 6220*). A phytosociological and ecological dataset of the literature and new field surveys highlighting the soil type as parameters affecting the vegetation cover of this small fernlike plant, with the Trachynion distachyae Rivas–Martínez, 1978 alliance on calcareous soils and Helianthemion guttati Br.-Bl. in Br.-Bl. et al., 1940 alliance on volcanic soils. Many species of other types of annual meadows have been identified within Ophioglossum communities due to the very small patches of land, where they have been found, and ecological conditions that facilitate this phenomenon of the transgression of other therophytes species. [ABSTRACT FROM AUTHOR]

Humans are mostly exposed to ionizing radiation through radon and its decay products. The results of indoor radon measurements in 39 kindergartens and primary schools in the Campania region of southern Italy are presented in this paper. The survey was carried out with CR-39 solid-state nuclear track detectors (SSNTDs). Radon concentrations were measured and ranged from 11 to 1416 Bq/m3, with a geometric mean of 77 Bq/m3 and a geometric standard deviation of 2. The findings revealed that 70% of the measures were below the WHO recommended level of 100 Bq/m3 and that 97 percent of the measurements were below the 300 Bq/m3 level set by Italian law. [ABSTRACT FROM AUTHOR]

• There are few examples of field survey data being utilized to test the effectiveness of UN SDG indicator 15.3.1. • The annual mean of NDVI shows some weakness in detecting land productivity dynamics. • Remote sensing coupled with a cloud-based computing platform may improve knowledge of the land productivity index. • The trend of maximum NDVI values appears more suitable for the assessing of land degradation processes. • Analysis of finer resolution satellite datasets is the best solution for regional/local contexts. Land degradation is a critical issue at a global level and its progressive increasing greatly reduces soil ecosystem services. In this context, the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, defined the Sustainable Development Goals (SDGs) and indicated some targets of particular interest for a territory to be integrated into short- and medium-term national programs. Target 15.3, which aims to end desertification and restore degraded lands, is currently monitored by indicator 15.3.1, measured as the combination of three sub-indicators (trends in land cover change, land productivity and carbon stocks) as suggested by the United Nations Convention to Combat Desertification (UNCCD), the custodian agency for the SDG indicator. In our opinion, this assessment shows some weakness that are generally caused by a lack of information from direct field observations. The greatest limitation regards land productivity dynamics linked to the NDVI trajectory adopted by the UNCCD methodological approach. For this reason, the paper proposes an alternative approach that consists of using annual maximum NDVI value assessments instead of annual mean values for trajectory calculation. To come to these conclusions, the study addresses a reliability assessment by using remote sensing techniques via the Google Earth Engine (GEE) and analysing the NDVI evolution over time at 450 locations spread around the Campania region (southern Italy). To this end, a customised Graphical User Interface (GUI) was built on the GEE platform and a Google Earth time slider tool was applied to visualize land cover changes which occurred at each location over a period of 18 years (2001–2018). The survey was carried out on MODIS and Landsat 7 collections and showed that the new approach had a better performance than the UNCCD approach (90 % vs 62 % of successful reliability tests, up to 96 % considering results from Landsat images). The application of maximum NDVI values to assess productivity dynamics spatially shows, with regard to UNCCD data, more than double the percentages of degraded and stable lands and a drastic reduction in improved areas within the Campania region. Overall, this innovative approach appears to agree more closely with ground truth and the use of finer resolution data is more suitable for investigating land degradation processes within a regional context. [ABSTRACT FROM AUTHOR]

Recent events, including the floods in central Europe in May 2021, have highlighted how climate change is giving rise to scenarios that were neither foreseen nor predictable. One problem this poses is the need to rethink the logic of various environmental constraints that are often based on return times of 20-50 years or 100-200. A single event does not change the statistical expectations for the recurrence of the event itself, but the recurrence of several extraordinary events in a few years is a clear indication of a changing trend. The prevention of the effects of such events is based on the definition of the areas at greater or lesser risk specifically based on the return times of the exceptional events, so it is foreseeable that a series of territorial plans, mostly carried out a few decades ago, will have to be updated or re-executed from scratch. These reworkings will be able to take advantage of the opensource software and open spatial data that have become available in the meantime, facilitating the entire process, and making it more open and shareable. In this paper we tested on a real case (the May 1999 pyroclastic flows in Campania, southern Italy) the actual possibility of implementing a model for forecasting such events using only open-source software and open data. It has been demonstrated that the entire process can be carried out using only open-source resources and it has been verified that the predictions of the hazard and risk model obtained with only input data prior to the event, give an output prediction that is significantly coincident with the events that actually occurred as documented by the authorities. [ABSTRACT FROM AUTHOR]

Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach's effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy. • We propose a multi-source time-series fusion methodology relying on Deep Learning. • We design a robust framework for daily 7-days-horizon predictions. • We assess the proposed approach on a real medical bookings dataset. [ABSTRACT FROM AUTHOR]

Cyanobacteria are ubiquitous photosynthetic microorganisms considered as important contributors to the formation of Earth's atmosphere and to the process of nitrogen fixation. However, they are also frequently associated with toxic blooms, named cyanobacterial harmful algal blooms (cyanoHABs). This paper reports on an unusual out-of-season cyanoHAB and its dynamics during the COVID-19 pandemic, in Lake Avernus, South Italy. Fast detection strategy (FDS) was used to assess this phenomenon, through the integration of satellite imagery and biomolecular investigation of the environmental samples. Data obtained unveiled a widespread Microcystis sp. bloom in February 2020 (i.e., winter season in Italy), which completely disappeared at the end of the following COVID-19 lockdown, when almost all urban activities were suspended. Due to potential harmfulness of cyanoHABs, crude extracts from the "winter bloom" were evaluated for their cytotoxicity in two different human cell lines, namely normal dermal fibroblasts (NHDF) and breast adenocarcinoma cells (MCF-7). The chloroform extract was shown to exert the highest cytotoxic activity, which has been correlated to the presence of cyanotoxins, i.e., microcystins, micropeptins, anabaenopeptins, and aeruginopeptins, detected by molecular networking analysis of liquid chromatography tandem mass spectrometry (LC-MS/MS) data. [ABSTRACT FROM AUTHOR]