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The “San Michele Arcangelo” historic trail (Aurunci Regional Park, central Italy), located along the southern slope of Mt. Altino, which is highly prone to rockfalls, is hiked every year by thousands of faithful on pilgrimage who are exposed to such kinds of instabilities. To contribute to a better understanding of the condition and evolution of such phenomena, providing a susceptibility scenario able to support the adoption of mitigation measures, a specific analysis was completed based on field and literature data. Three-dimensional virtual outcrop models were obtained through both photogrammetric and iPad Pro LiDAR surveys to derive geomechanical features of outcropping rocks and estimate potential detaching block volume. Possible mechanisms of detachment were then analyzed using the Markland test method. Susceptibility to rockfall propagation and block deposition was analyzed using GIS processing and rockfall propagation simulations based on deterministic/stochastic models. Such models consist of a combination of a deterministic algorithm able to simulate the physics of rockfall movement during propagation and a stochastic treatment of input parameters based on random sampling within defined interval. Two different rockfall simulations were compared in this study using the Rockyfor3D™ (ecorisQ Association) software and the newly developed RocFall3© (RocScience) code. The use of iPad Pro LiDAR survey provides accurate and high-resolution point clouds, with high speed of acquisition and real-time processing of data. Discontinuity sets identified from 3D models have represented the input for kinematic analyses of slopes, allowing to identify potential failure mechanisms among wedge and planar sliding or toppling. Rockfall simulations indicate the potential for rock blocks propagation and deposition over the whole study area. Comparing the results of rockfall simulations with the geomorphological map of the area it is evident the control exerted by the hydrographic network on rockfall propagation as demonstrated by the presence of screes and slope deposits along the main channels. Understanding the potential susceptibility to both propagation and deposition of rock blocks along the trail provides useful indication for the assessment of appropriate mitigation measures to realize for the safer touristic fruition of the site. [ABSTRACT FROM AUTHOR]

In recent decades, the increasing number and severity of wildfires have become a major concern in many regions of the world. Besides having direct consequences for forests, crops, and settlements, wildfires can have significant effects on the catchment's hydrology, altering their response to rainfall and promoting enhanced runoff and erosion. Increased runoff in burnt areas can result in catastrophic damage and loss of life from destructive debris flows and floods. Although observed in many regions of the world, post-wildfire debris flows/floods seem to prevail in the western USA and southern Europe. In Italy, post-wildfire debris flows/floods have been observed in a number of sectors of the Apennine range, and a total of 113 events occurred between 2001 and 2021 in the Campania region. A recent event occurred on August 24, 2020, in the Fosso delle Pescine catchment, on the southwestern slope of Mt. Omo in the L'Aquila municipality, hit by a severe wildfire at the beginning of the month. The consequences of the event on people and properties indicate the need for an evaluation of debris flow/flood hazard in both pre- and post-wildfire conditions. The analysis of these events may often be complex, requiring time and many input data. In this context, a simplified method of analysis which accounts for event initiation and propagation through a shallow water equation-based approach would represent a new and innovative solution for fast and preliminary debris flow/flood analyses. In this research, a reduced complexity analysis based on probability analysis of rainfall and 2D hydrodynamic modeling was carried out by estimating (i) wildfire extent and severity, (ii) sediment characteristics and availability, (iii) potential runoff induced by rainfall, and (iv) inundation by debris flow/flood. A debris flow/flood hazard model was developed on the basis of multiple return periods (10, 50, 100, and 300 years) for debris flow inundation scenarios derived by non-Newtonian 2D hydrodynamic modeling of debris flow hydrograph and qualitatively validated on the basis of the extent of alluvial fan deposits. Potential drawbacks of the model are related to the possible overestimation of surface runoff, related to the consideration of the whole catchment contributing area, and the limited volumetric sediment budget available at the headwater, which might not sustain a high return period event generation. A debris flow/flood event might originate from rainfall concentrated at the headwater of the catchment. [ABSTRACT FROM AUTHOR]

Maps that attempt to predict landslide occurrences have essentially stayed the same since 1972. In fact, most of the geo-scientific efforts have been dedicated to improve the landslide prediction ability with models that have largely increased their complexity but still have addressed the same binary classification task. In other words, even though the tools have certainly changed and improved in 50 years, the geomorphological community addressed and still mostly addresses landslide prediction via data-driven solutions by estimating whether a given slope is potentially stable or unstable. This concept corresponds to the landslide susceptibility, a paradigm that neglects how many landslides may trigger within a given slope, how large these landslides may be and what proportion of the given slope they may disrupt. The landslide intensity concept summarized how threatening a landslide or a population of landslide in a study area may be. Recently, landslide intensity has been spatially modeled as a function of how many landslides may occur per mapping unit, something, which has later been shown to closely correlate to the planimetric extent of landslides per mapping unit. In this work, we take this observation a step further, as we use the relation between landslide count and planimetric extent to generate maps that predict the aggregated size of landslides per slope, and the proportion of the slope they may affect. Our findings suggest that it may be time for the geoscientific community as a whole, to expand the research efforts beyond the use of susceptibility assessment, in favor of more informative analytical schemes. In fact, our results show that landslide susceptibility can be also reliably estimated (AUC of 0.92 and 0.91 for the goodness-of-fit and prediction skill, respectively) as part of a Log-Gaussian Cox Process model, from which the intensity expressed as count per unit (Pearson correlation coefficient of 0.91 and 0.90 for the goodness-of-fit and prediction skill, respectively) can also be derived and then converted into how large a landslide or several coalescing ones may become, once they trigger and propagate downhill. This chain of landslide intensity, hazard and density may lead to substantially improve decision-making processes related to landslide risk. [ABSTRACT FROM AUTHOR]

Fallout volcanic deposits of Somma- Vesuvius (Campania, southern Italy), characterized by the presence of layers with contrasting textural and hydraulic properties, are frequently affected by shallow landslides during rainwater infiltration. The soils of the stratigraphic sequence present intra- particle pores, originated by the gases escaped during magma decompression in the volcanic conduit, thus are characterized by double porosity (i.e., intraparticle and interparticle pores), which is expected to affect their hydraulic behaviour, and to play a key role in rainwater infiltration through layered deposits. To understand the effect of double porosity on the hydraulic behaviour of the involved soils, controlled experiments have been carried out in an infiltration column. The experimental apparatus is provided with newly designed non-invasive Time Domain Reflectometry (TDR) probes, not buried in the investigated soil layers so as to minimize disturbance to the flow, allowing water content measurement during vertical flow processes. Specifically, transient flow experiments are carried out through reconstituted specimens of black scoriae and grey pumices, both loose pyroclastic granular soils from fallout deposits of Somma-Vesuvius, featuring double porosity with different pore size distributions, that were estimated by X-ray tomography and Mercury Intrusion Porosimetry. The experimental results highlight the effects of the double porosity and clearly indicate the different behaviour of the two soils during wetting and drying processes, mainly related to the different dimensions of intraparticle pores. [ABSTRACT FROM AUTHOR]

A key component of disaster management and infrastructure organization is predicting cumulative deformations caused by landslides. One of the critical points in predicting deformation is to consider the spatio-temporal relationships and interdependencies between the features, such as geological, geomorphological, and geospatial factors (predisposing factors). Using algorithms that create temporal and spatial connections is suggested in this study to address this important point. This study proposes a modified graph convolutional network (GCN) that incorporates a long and short-term memory (LSTM) network (GCN-LSTM) and applies it to the Moio della Civitella landslides (southern Italy) for predicting cumulative deformation. In our proposed deep learning algorithms (DLAs), two types of data are considered, the first is geological, geomorphological, and geospatial information, and the second is cumulative deformations obtained by permanent scatterer interferometry (PSI), with the first investigated as features and the second as labels and goals. This approach is divided into two processing strategies where: (a) Firstly, extracting the spatial interdependency between paired data points using the GCN regression model applied to velocity obtained by PSI and data depicting controlling predisposing factors; (b) secondly, the application of the GCN-LSTM model to predict cumulative landslide deformation (labels of DLAs) based on the correlation distance obtained through the first strategy and determination of spatio-temporal dependency. A comparative assessment of model performance illustrates that GCN-LSTM is superior and outperforms four different DLAs, including recurrent neural networks (RNNs), gated recurrent units (GRU), LSTM, and GCN-GRU. The absolute error between the real and predicted deformation is applied for validation, and in 92% of the data points, this error is lower than 4 mm. [ABSTRACT FROM AUTHOR]

In recent decades, developing countries have experienced an increase in the impact of natural disasters due to ongoing climate change and the sustained expansion of urban areas. The intrinsic vulnerability of settlements, due to poverty and poor governance, as well as the lack of tools for urban occupation planning and mitigation protocols, has made such impacts particularly severe. Cuenca (Ecuador) is a significant example of a city that in recent decades has experienced considerable population growth (i.e. exposure) and an associated increase in loss due to landslide occurrence. Despite such effects, updated urban planning tools are absent, so an evaluation of multitemporal exposure to landslides and related risks is required. In this perspective, a potential urban planning tool is presented based on updated data depicting the spatial distribution of landslides and their predisposing factors, as well as population change between 2010 and 2020. In addition, a multitemporal analysis accounting for changes in exposure between 2010 and 2020 and an estimation of relative landside risk was carried out. Due to the absence of spatially distributed population data, energy supply contract data have been used as a proxy of the population. The results show that the current higher exposure and related relative risk are estimated for parishes (parroquias) located in the southern sector of the study area (i.e. Turi, Santa Ana, Tarqui, Nulti, Baños and Paccha). Moreover, the exposure multitemporal analysis indicates that most parishes located in the hilly areas bounding the city centre (i.e. Sayausi, San Joaquin, Tarqui, Sidcay, Baños, Ricaurte, Paccha and Chiquintad) are experiencing sustained population growth and will be potentially exposed to an increased risk with a consistently growing trend. The obtained relative risk map can be considered a valuable tool for guiding land planning, land management, occupation restriction and early warning strategy adoption in the area. The methodological approach used, which accounts for landslide susceptibility and population variation through proxy data analysis, has the potential to be applied in a similar context of growing population cities in low- to mid-income countries, where data usually needed for a comprehensive landslide risk analysis are non-existing or only partially available. [ABSTRACT FROM AUTHOR]

Landslides represent a severe geohazard in many countries. The availability of inventories depicting the spatial and temporal distribution of landslides is crucial for assessing landslide susceptibility and risk for territorial planning or investigating landscape evolution. Nevertheless, these inventories are usually affected by limitations due to their nonpublic availability and inhomogeneities in characterization and mapping. Such problems are fully recognizable by the analysis of the multiple landslide inventories of the Campania region, which is one of the Italian regions with the highest exposure to landslide hazard and risk. On this basis, a revised Landslide Inventory of the Campania region (LaICa), resulting from the processing of multiple existing landslide inventories, has been reconstructed. It aims to (i) provide a new geodatabase that is able to overcome issues derived from the coexistence of multiple inventories and (ii) provide a methodological paradigm able to support the reorganization of existing official inventories. The implication of LaICa, with its 83,284 records, will possibly improve the assessment of landslide susceptibility and then reassess the related risk. Measurement(s) landslide Technology Type(s) Data Set [ABSTRACT FROM AUTHOR]

In this paper, we aim to define a procedure of flood hazard assessment applicable to large river basins in which flood events can be induced/sustained by the full basin area or by fractions of the total area as functions of the extent of the triggering precipitation event. The proposed procedure is based on a combined approach accounting for (1) the reconstruction of intensity–duration–frequency curves expressing the magnitude in terms of intensity for multiple return periods; (2) the application of the soil conservation service method for runoff estimation from a selected rainfall scenario considering some characteristics of the basin (i.e. soil type, land use/treatment, surface condition, and antecedent moisture conditions); (3) 2D hydrodynamic modelling conducted by the HEC-RAS model using runoff hydrographs as hydrological input data; (4) the reconstruction of flood hazard maps by overlaying multiple inundation maps depicting flood extent for different return periods. To account for the variability in the extent of the triggering precipitation event and the resulting input hydrograph, multiple contributing areas are considered. The procedure is tested at the archaeological site of Sybaris in southern Italy, which is periodically involved in flood events of variable magnitude. The obtained results highlight that the variable extent of the floodable area is strongly conditioned by the extent of the contributing area and return period, as expected. The archaeological site is always involved in the simulated flooding process, except for the smallest contributing area for which only a 300-year event involves this part of the site. Our findings may be useful for developing and supporting flood risk management plans in the area. The developed procedure might be easily exported and tested in other fluvial contexts in which evaluations of multiple flood hazard scenarios, due to the basin geometry and extent, are needed. [ABSTRACT FROM AUTHOR]

In recent years, the study of anthropogenic sinkholes in densely urbanized areas has attracted the attention of both researchers and land management entities. The city of Naples (Italy) has been frequently affected by processes generating such landforms in the last decades: for this reason, an update of the sinkhole inventory and a preliminary susceptibility estimation are proposed in this work. Starting from previous data, not modified since 2010, a total of 270 new events occurred in the period February 2010–June 2021 were collected through the examination of online newspapers, local daily reports, council chronicle news and field surveys. The final consistence of the updated inventory is of 458 events occurred between 1880 and 2021, distributed through time with an increasing trend in frequency. Spatial analysis of sinkholes indicates a concentration in the central sector of the city, corresponding to its ancient and historic centre, crossed by a dense network of underground tunnels and cavities. Cavity-roof collapse is confirmed as one of the potential genetic types, along with processes related to rainfall events and service lines damage. A clear correlation between monthly rainfall and the number of triggered sinkholes was identified. Finally, a preliminary sinkhole susceptibility assessment, carried out by Frequency Ratio method, confirms the central sector of city as that most susceptible to sinkholes and emphasizes the predisposing role of service lines, mostly in the outermost areas of the city. [ABSTRACT FROM AUTHOR]

Thickness and stratigraphic settings of soils covering slopes potentially control susceptibility to initiation of rainfall-induced shallow landslides due to their local effect on slope hydrological response. Notwithstanding the relevance of the assessment of hazard to shallow landsliding at a distributed scale by approaches based on a coupled modelling of slope hydrological response and slope stability, the spatial variability of soil thickness and stratigraphic settings are factors poorly considered in the literature. Under these premises, this paper advances the well-known case study of rainfall-induced shallow landslides involving ash-fall pyroclastic soils covering the peri-Vesuvian mountains (Campania, southern Italy). In such a unique geomorphological setting, the soil covering is formed by alternating loose ash-fall pyroclastic deposits and paleosols, with high contrasts in hydraulic conductivity and total thickness decreasing as the slope angle increases, thus leading to the establishment of lateral flow and an increase of pore water pressure in localised sectors of the slope where soil horizon thickness is less. In particular, we investigate the effects, on hillslope hydrological regime and slope stability, of irregular bedrock topography, spatial variability of soil thickness and vertical hydraulic heterogeneity of soil horizons, by using a coupled three-dimensional hydrological and a probabilistic infinite slope stability model. The modelling is applied on a sample mountain catchment, located on Sarno Mountains (Campania, southern Italy), and calibrated using physics-based rainfall thresholds derived from the literature. The results obtained under five simulated constant rainfall intensities (2.5, 5, 10, 20 and 40 mm h−1) show an increase of soil pressure head and major failure probability corresponding to stratigraphic and morphological discontinuities, where a soil thickness reduction occurs. The outcomes obtained from modelling match the hypothesis of the formation of lateral throughflow due to the effect of intense rainfall, which leads to the increase of soil water pressure head and water content, up to values of near-saturation, in narrow zones of the slope, such as those of downslope reduction of total soil thickness and pinching out of soil horizons. The approach proposed can be conceived as a further advance in the comprehension of slope hydrological processes at a detailed scale and their effects on slope stability under given rainfall and antecedent soil hydrological conditions, therefore in predicting the most susceptible areas to initiation of rainfall-induced shallow landslides and the related I-D rainfall thresholds. Results obtained demonstrate the occurrence of a slope hydrological response depending on the spatial variability of soil thickness and leading to focus slope instability in specific slope sectors. The approach proposed is conceived to be potentially exportable to other slope environments for which a spatial modelling of soil thickness would be possible. [ABSTRACT FROM AUTHOR]

The growth in the number and types of cloud-based services offered to IT customers is supported by the constant entry of new actors in the market and the consolidation of disruptive technologies such as AI, Big Data and Micro-services. From the customer’s perspective, in a market landscape where the cloud offer is highly diversified due to the presence of multiple competing service providers, picking the service that best accommodate their specific needs is a critical challenge. Once the choice is made, so called “cloud orchestration tools” (orchestrators) are required to take care of the customer application’s life-cycle. While big players offer their customers proprietary orchestrators, in the literature quite a number of open-source initiatives have launched multi-cloud orchestrators capable of transparently managing applications on top of the most representative cloud platforms. In this paper, we propose TORCH, a TOSCA-based framework for the deployment and orchestration of cloud applications, both classical and containerised, on multiple cloud providers. The framework assists the cloud customer in defining application requirements by using standard specification models. Unlike other multi-cloud orchestrators, adopts a strategy that separates the provisioning workflow from the actual invocation of proprietary cloud services API. The main benefit is the possibility to add support to any cloud platforms at a very low implementation cost. In the paper, we present a prototypal implementation of TORCH and showcase its interaction with two different container-based cluster platforms. Preliminary performance tests conducted on a small-scale test-bed confirm the potential of TORCH. [ABSTRACT FROM AUTHOR]

Statistical landslide susceptibility mapping is a topic in complete and constant evolution, especially since the introduction of machine learning (ML) methods. A new methodological approach is here presented, based on the ensemble of artificial neural network, generalized boosting model and maximum entropy ML algorithms. Such approach has been tested in the Monterosso al Mare area, Cinque Terre National Park (Northern Italy), severely hit by landslides in October 2011, following an extraordinary precipitation event, which caused extensive damage at this World Heritage site. Thirteen predisposing factors were selected and assessed according to the main characteristics of the territory and through variance inflation factor, whilst a database made of 260 landslides was adopted. Four different Ensemble techniques were applied, after the averaging of 300 stand-alone methods, each one providing validation scores such as ROC (receiver operating characteristics)/AUC (area under curve) and true skill statistics (TSS). A further model performance evaluation was achieved by assessing the uncertainty through the computation of the coefficient of variation (CV). Ensemble modelling thus showed improved reliability, testified by the higher scores, by the low values of CV and finally by a general consistency between the four Ensemble models adopted. Therefore, the improved reliability of Ensemble modelling confirms the efficacy and suitability of the proposed approach for decision-makers in land management at local and regional scales. [ABSTRACT FROM AUTHOR]

Purpose: Blunt aortic injuries (BAI) have historically been considered an indication for emergent surgical intervention. Nevertheless, the observation that the outcome of the concomitant traumatic injuries has a major impact on prognosis and the rise of thoracic endovascular aortic repair (TEVAR) as an effective therapy for BAI have significantly changed in recent years the treatment algorithm of this condition. Our objective was to identify findings associated with the aortic injury which would be the best predictor of prognosis, with the objective of guiding the decision-making process for selecting the optimal timing of aortic repair. Methods: We reviewed blunt aortic injuries from 3 Level I Trauma Centers from July 2008 to December 2016. We analyzed overall and BAI-related 30-day mortality in relation to: hemodynamics, timing of treatment, TEVAR vs open repair, and aortic injury grade as defined by the Society for Vascular Surgery. Based on computed tomographic angiography (CT scan) imaging, we selected the radiologic aortic findings most indicative of high mortality risk, which we defined as "Radiographic Severe Injury" (RSI): (1) total/partial aortic transection, (2) active contrast extravasation, or (3) the association of 2 of more of the following: contained contrast extravasation > 10 mm, periaortic hematoma, and/or mediastinal hematoma with thickness > 10 mm, or significant left pleural effusion. Results: Of a total of 76 consecutive patients, 50 (66%) underwent immediate repair, 24 (31%) delayed aortic repair, and 2 (3%) died prior to repair. 58 patients (76%) had TEVAR, while 16 (24%) had open repair. Overall mortality was 18% and BAI-related mortality was 13%. In BAI-related mortalities, 70% of patients had RSI. Patients with high risk of overall mortality had hypotension and tachycardia (SBP < 100, HR ≥ 100), high ISS, and required vasopressors. Factors only associated with BAI-related mortality included RSI. Conclusion: CT scan findings suggestive of RSI are predictive of mortality associated with BAI. Radiologic assessment of the severity of the aortic injury with characterization for the presence of RSI may represent the key factors to determine the optimal timing of treatment of the aortic injury and guide the overall treatment strategy. Level of evidence: IV. [ABSTRACT FROM AUTHOR]

Landslides are among the most important and frequent natural calamities that cause severe socio-economic and human losses. After earthquakes, landslides are responsible for the greatest number of casualties and the largest amount of damage to man-made structures. On average, southern Italy is affected by a high spatial density of landslides due to its complex geological setting, which often predisposes it to slope instability phenomena under both natural and anthropogenic influences. Structurally complex formations are widespread in the southern Apennines and are characterized by high heterogeneity and very poor mechanical properties. Thus, these formations represent one of the main factors contributing to the predisposition of slopes to landsliding. In this paper, landslide-induced damage was investigated and analyzed in an area within the municipality of Agnone (Molise region), which is affected by a complex landslide that involves a structurally complex formation. The approaches used were based on six different methods that have previously been described in the literature, and a comparison of the results was made. Data regarding the damage, which consists largely of cracks observed in buildings and at the ground, were compiled through field surveys. The results were critically analyzed to note the advantages and constraints of each classification scheme. The aim of the work was to apply and compare different approaches in order to test the best and most accurate procedures for assessing damage due to landslides at the scale of individual buildings as well as to provide an objective assessment of the degree of landslide damage to structures and facilities. [ABSTRACT FROM AUTHOR]

Significant advances in medical imaging have been made in the past decades, enabling physicians to reach high precision in diagnosing patients' diseases by means of sophisticated imaging tools. However, the use of sophisticated tools is limited by the high costs and, in some cases, by the utilization of ionizing radiation, which have both great impact on the economy of a nation and on citizens' health, respectively. Guidelines have been published among countries to provide physicians with structured rules to be followed to suggest the correct imaging technique, suiting better the diagnostic question and avoiding inappropriate imaging requests. The COLLABORADI is a research project that addressed the phenomenon of inappropriate imaging prescriptions in Sicily (Italy) and proposed the design and implementation of a clinical decision support system to help physicians to set up the most appropriate diagnostic route for their patients. The aim of this paper is to describe the characteristics of the COLLABORADI software and its potential impact in diminishing inappropriate imaging. [ABSTRACT FROM AUTHOR]

The number of both service providers operating in the cloud market and customers consuming cloud-based services is constantly increasing, proving that the cloud computing paradigm has successfully delivered its potential. Nevertheless, the unceasing growth of the cloud market is posing hard challenges on its participants. On the provider side, the capability of orchestrating resources in order to maximise profits without failing customers' expectations is a matter of concern. On the customer side, the efficient resource selection from a plethora of similar services advertised by a multitude of providers is an open question. In such a multi-cloud landscape, several research initiatives advocate the employment of software frameworks (namely, cloud resource orchestration frameworks - CROFs) capable of orchestrating the heterogeneous resources offered by a multitude of cloud providers in a way that best suits the customer's need. The objective of this paper is to provide the reader with a systematic review and comparison of the most relevant CROFs found in the literature, as well as to highlight the multi-cloud computing open issues that need to be addressed by the research community in the near future. [ABSTRACT FROM AUTHOR]

With the wide adoption of cloud computing across technology industries and research institutions, an ever-growing interest in cloud orchestration frameworks has emerged over the past few years. These orchestration frameworks enable the automated provisioning and decommissioning of cloud applications in a timely and efficient manner, but they offer limited or no support for application management. While management functionalities, such as configuring, monitoring and scaling single components, can be directly covered by cloud providers and configuration management tools, holistic management features, such as backing up, testing and updating multiple components, cannot be automated using these approaches. In this paper, we propose a concept to automatically generate executable holistic management workflows based on the TOSCA standard. The practical feasibility of the approach is validated through a prototype implementation and a case study., Competing Interests: Conflict of interestThe authors declare that they have no competing interests., (© The Author(s) 2023.)