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We introduce a comprehensive and robust theoretical framework and operational workflow that can be employed to enhance our understanding, modeling and management capability of complex heterogeneous large-scale groundwater systems. Our framework encapsulates key components such as the three-dimensional nature of groundwater flows, river–aquifer interactions, probabilistic reconstruction of three-dimensional spatial distributions of geomaterials and associated properties across the subsurface, multi-objective optimization for model parameter estimation through stochastic calibration, and informed global sensitivity analysis (GSA). By integrating these components, we effectively consider the inherent uncertainty associated with subsurface system characterizations as well as their interactions with surface waterbodies. The approach enables us to identify parameters impacting diverse system responses. By employing a coevolutionary optimization algorithm, we ensure efficient model parameterization, facilitating simultaneous and informed optimization of the defined objective functions. Additionally, estimation of parameter uncertainty naturally leads to quantification of uncertainty in system responses. The methodology is designed to increase our knowledge of the dynamics of large-scale groundwater systems. It also has the potential to guide future data acquisition campaigns through an informed global sensitivity analysis. We demonstrate the effectiveness of our proposed methodology by applying it to the largest groundwater system in Italy. We address the challenges posed by the characterization of the heterogeneous spatial distribution of subsurface attributes across large-scale three-dimensional domains upon incorporating a recent probabilistic hydrogeological reconstruction specific to the study case. The system considered faces multiple challenges, including groundwater contamination, seawater intrusion, and water scarcity. Our study offers a promising modeling strategy applicable to large-scale subsurface systems and valuable insights into groundwater flow patterns that can then inform effective system management. [ABSTRACT FROM AUTHOR]

This paper proposes a new multi-step approach for sensitivity assessment of surface runoff parameters. The procedure has been tested on a peri-urban basin in southern Italy, interested by intense urbanization. The basin has limited data about land characteristics, and nearby precipitation measurements are not available. Accordingly, rainfall events are defined based on depth-duration-frequency curve valid for the area. The main novelties of the work are to provide a general framework for assessing the influence of runoff parameters (i.e. depression storage and surface roughness) for a basin model in SWMM in relation to rain events of various intensity/duration, and to provide a ranking of crucial parameters significantly affecting peak discharge and total volume of the hydrograph, for an ungauged basin, by means the Fourier Amplitude Sensitivity Test (FAST). Results indicate the dependence on rainfall characteristics of the relative importance of the parameters describing the pervious and impervious areas. Notably, the peak discharge of the shortest considered event is influenced only by the two parameters of the impervious area, while the opposite holds for the longest rain event. The total runoff volume is mostly influenced by the depression storage of impervious areas, with the parameters of pervious areas becoming more influential for longer rain events. Results allow a clear interpretation of the modelled physical processes variability within the basin and their relationship with rainfall/areas features, thus providing useful insights for key parameter definition in other contexts and for other models. [ABSTRACT FROM AUTHOR]

In this paper the actual dynamic behavior of the civic Clock tower of Rotella, a little village in central Italy heavily damaged by the recent 2016 seismic sequence, is thoroughly investigated by means of a detailed numerical model built and calibrated using the experimental modal properties obtained through Ambient Vibration Tests. The goal is to update the uncertain parameters of two behavioral material models applied to the Finite Element Model (elastic moduli, mass densities, constraints, and boundary conditions) to minimize the discrepancy between experimental and numerical dynamic features. A sensitivity analysis was performed with the definition of a metamodel to reduce the computational strain and try to define the necessary parameters to use for the calibration process. Due to the high nonlinear dependency of the objective function of this optimization problem on the parameters, and the likely possibility to get trapped in local minima, a machine learning approach was meant. A fully automated Finite Element Model updating procedure based on genetic algorithms and global optimization is used, leading to tower uncertain parameters identification. The results allowed to create a reference numerical replica of the structure in its actual health state and to assess its dynamic performances allowing better control over their future evolution. [ABSTRACT FROM AUTHOR]

Clean hydrogen is a key pillar for the net zero economy which can be deployed by consistent utilization on heavy-duty transport. This study investigates a distributed green hydrogen infrastructure (DHI) for heavy-duty transportation consisting of on-site hydrogen production, storage, compression, and refueling systems in Italy. Two options for energy supply are analyzed: grid connection using green energy via Power Purchasing Agreements (PPAs) and direct connection to the photovoltaic field, respectively. Radiation data are representative of the three main Italian areas, namely South (Catania), Center (Roma), and North (Milano). The sensitivity analysis varies the PPA value between 50 €/MWh and 200 €/MWh, and the water electrolysis capacity factor between 20% and 100%. The study finds that the LCOH ranges from 7.4 €/kg H2 to 67.8 €/kg H2 for the first option, and 5.5 €/kg H2 to 27.5 €/kg H2 for the second option, with Southern Italy having the lowest LCOH due to higher solar irradiation. The research shows that a DHI can offer economic and technical benefits for heavy-duty mobility. However, the performance is highly influenced by external conditions, such as hydrogen demand and electricity prices. This study provides valuable insights into designing and operating a DHI for heavy-duty mobility, promoting a carbon-free society. • A novel approach for designing hydrogen infrastructure for heavy-duty mobility. • The parametric analysis provides insights into reducing the levelized cost of hydrogen. • The electrolyzer is the most expensive component, with 42% of the total cost. • Results suggest a strong potential for hydrogen in decarbonizing transportation. • External factors affect performance: demand for hydrogen and electricity price. [ABSTRACT FROM AUTHOR]

Quantitative assessment of dam-break flood hazard is central in dam emergency action planning and is typically performed deterministically. In structural failure of concrete or masonry dams the collapse is assumed to be total and instantaneous, with the reservoir level at the spillway crest level. A probabilistic method is here proposed based on a set of dam-break scenarios characterized by different breach widths and reservoir levels in order to provide an appraisal of uncertainties in flood hazard indicators. Each scenario is attributed a weight, defined as a conditional probability given a dam-break event. Probabilistic flood hazard and inundation maps are produced for the case study of the hypothetical collapse of the Mignano dam (River Arda, northern Italy), and a sampling-based global sensitivity analysis is performed. Dam-break flooding was simulated using a two-dimensional hydrodynamic model on a high-resolution mesh. The probabilistic maps inherently provide quantitative information on the uncertainty of dam-break flood hazard. [ABSTRACT FROM AUTHOR]

This work evaluated the remarkable influence of stereotomy on discrete methods adopted for the dynamic response of an ancient masonry structure. The historic building used as a case study is the Church of Santa Maria of Varano in Muccia, in the province of Macerata, Italy, which was exposed to the severe seismic sequence that hit Central Italy in 2016, namely the major events of Accumoli, Visso, and Norcia. The structure was modeled using the actual stereotomy of blocks and three hypothetical arrangements of blocks of the masonry walls to investigate the existing crack pattern and vulnerabilities of the church. Sensitivity analyses were performed using the nonsmooth contact dynamics and discrete-element methods. These novel approaches suitably reproduced complex behaviors, considering large displacements and complete block separations, which is useful for upcoming retrofitting works. [ABSTRACT FROM AUTHOR]

The quality of content and the attractiveness of an environmental website can create an environmentally friendly attitude before one visits a secured area. However, a website should be evaluated to ensure that its goal is met. For this reason, the websites of environmental content have been evaluated using a combination of AHP and PROMETHEE II. More specifically, the websites of environmental content that have been selected to be evaluated are the websites of the national parks of Italy. The main contribution of the particular paper is on comparing PROMETHEE II with three other common MCDM models (SAW, WPM, TOPSIS) and performing a sensitivity analysis to make the comparison more thorough. As a result, the conclusions drawn by this experiment involve the appropriateness of PROMETHEE II for the ranking of environmental websites as well as the robustness of the different MCDM models. The experiment revealed that the PROMETHEE II model was found to be very effective in ranking environmental websites and is the most robust model compared to the other ones. Furthermore, the evaluation of the websites of national parks in Italy revealed that the electronic presence of national parks is at an early stage. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID‐19) is a viral disease which is declared as a pandemic by WHO. This disease is posing a global threat, and almost every country in the world is now affected by this disease. Currently, there is no vaccine for this disease, and because of this, containing COVID‐19 is not an easy task. It is noticed that elderly people got severely affected by this disease specially in Europe. In the present paper, we propose and analyze a mathematical model for COVID‐19 virus transmission by dividing whole population in old and young groups. We find disease‐free equilibrium and the basic reproduction number (R0). We estimate the parameter corresponding to rate of transmission and rate of detection of COVID‐19 using real data from Italy and Spain by least square method. We also perform sensitivity analysis to identify the key parameters which influence the basic reproduction number and hence regulate the transmission dynamics of COVID‐19. Finally, we extend our proposed model to optimal control problem to explore the best cost‐effective and time‐dependent control strategies that can reduce the number of infectives in a specified interval of time. [ABSTRACT FROM AUTHOR]

• Recuperative ORC (RORC) presents a higher efficiency (+20 %) and power (+18 %) than ORC. • RORC works with a lower mass flow rate than ORC for a given evaporating temperature. • RORC needs a lower heat input thus increasing the operating time in absence of solar radiation. • The higher operating time involves a higher yearly electricity energy production (+37 %). • Hot water and coolant temperature play the most important effect on RORC performance. Organic Rankine Cycle -based microcogeneration systems that exploit solar sources to generate electricity and domestic hot water simultaneously are promising solutions for reducing CO 2 emissions in the residential energy-intensive sector. Such systems can be assisted by thermal energy storage reservoirs in which water is heated by solar energy and cooled by the direct use of thermal energy for domestic hot water production and acting as heat source of the power plant. An interesting plant layout optimisation involves the adoption of a recuperative heat exchanger to preheat the working fluid before it enters the heat recovery vapour generator, which is fed with the same working fluid leaving the expander. Despite the positive effect of recuperative heat exchanger adoption on the efficiency of the entire plant, the impact on electrical energy production at this microscale is not straightforward to assess as the heat source is represented by the water inside the reservoir and it is not a continuous high- or medium-temperature stream. The lack of experimental analyses in the literature on these applications makes this a debatable question. Thus, to fill this gap and quantitatively assess the benefits introduced by the adoption of a recuperative stage, a wide experimental comparison between recuperative and not-recuperative Organic Rankine Cycle-based power unit layout was performed. To support the experimental analysis, a comprehensive theoretical model of the Organic Rankine Cycle-based plant was developed and validated against experimental data. The experimental campaign demonstrated that the recuperative Organic Rankine Cycle-based unit required a lower working fluid flow rate for a given expander inlet temperature and pressure ratio. Consequently, a higher electrical power can be produced, requiring lower thermal power from a heat source. The reduction in the thermal power absorbed from the heat source, produced by a higher temperature incoming working fluid entering the Heat Recovery Vapor Generator, had a positive effect, allowing a longer-in-time working condition with a higher conversion efficiency intrinsically produced by the recuperative heat exchanger. Considering the annual electricity consumption of 2700 kWh of a family living in a city in central Italy, the contribution given by the recuperative Organic Rankine Cycle plant operating with a reservoir fed by 15 m2 of flat solar panels was 330 kWh, whereas without the recuperative stage, the production was 240 kWh. The cost of electricity saved justified the adoption of a recuperative heat exchanger. [ABSTRACT FROM AUTHOR]

In Oceania, North America and north‐western Europe, after decades of increase, cutaneous malignant melanoma (CMM) rates began to stabilise or decline before 2000. Anecdotal evidence suggests that the reversal of the incidence trend is extending to southern Europe. To obtain a formal confirmation, this nationwide study from Italy investigated the incidence trends by birth cohort. Twenty‐one local cancer registries covering a population of 15 814 455 provided incidence data for primary CMM registered between 1994 and 2013. Trends in age‐standardised rates were analysed using joinpoint regression models and age‐period‐cohort models. Age‐standardised incidence showed a consistent increase throughout the period (estimated annual percent change, 3.6 [95% confidence interval, 3.2‐4.0] among men and 2.5 [2.0‐3.1] among women). This pattern was confirmed by a sensitivity analysis with removal of low‐risk populations of southern Italy. The rates, however, showed a stabilisation or a decrease in men and women aged below 35. Using the cohort of 1949—the median cohort with respect to the number of cases for both genders—as a reference, the incidence rate ratio increased for successive cohorts born until 1973 (women) and 1975 (men), and subsequently tended to decline. For the most recent cohorts in both genders, the risk of disease returned to the level of the cohort of 1949. The changes observed in the latest generations can be interpreted as the earliest manifestations of a birth‐cohort‐dependent incidence decrease. Our study adds to previous data indicating that the reversal of the long‐term upward incidence trend of CMM is extending to southern Europe. What's new? After increasing for many years, malignant melanoma rates began to stabilise or decline in many regions before 2000. Is this also true for southern Europe? In the current study, the authors found that melanoma rates have also began to decline in Italy. The reversal of the long‐term, worldwide increase in melanoma risk seen in other Caucasian populations thus appears to extend to southern Europe. These results indicate that primary prevention strategies have been effective in modifying sun‐exposure habits in this region, and should continue to be reinforced via public health messaging. [ABSTRACT FROM AUTHOR]

The present study is a cradle-to-gate Life Cycle Assessment of a hypothetical production process of rigid polyurethane foam obtained from thistle seed oil, grown on the marginal soils of the Umbria region, Italy. Thistle is a particularly rustic crop, suitable for difficult soil and climatic conditions that allow its cultivation in the Mediterranean area on marginal lands and with only the water supply of the autumn-spring rains. Life Cycle Inventory was built on up-to-date inventory for thistle production, while experimental data for polyol and synthesis were obtained from literature, with reference to a pilot scale reactor. Energy recovery of lignocellulosic biomass and sensitivity analysis were also included in the study. Life Cycle Assessment was performed using SimaPro 9.0 and impacts were evaluated with the Impact2000+ method. Results show the overall impact of thistle oil including its final use for the production of polyurethane foam, and underline the important contribution as environmental benefit of the use of biomass leftovers in the agricultural phase. Moreover, results show that the major impacts of the rigid polyurethane foam production process were related to the synthesis of the foam itself. Among the previous phases constituting the process of synthesis of the polyol, the most impactful one was the cultivation; the oil processing stage, on the other hand, had a small global impact. The synthesis of the foam, taken individually, generated a greater impact than the entire synthesis process of the vegetable polyol starting from the cultivation of thistle. [ABSTRACT FROM AUTHOR]

The literature about landslide susceptibility mapping is rich of works focusing on improving or comparing the algorithms used for the modeling, but to our knowledge, a sensitivity analysis on the use of geological information has never been performed, and a standard method to input geological maps into susceptibility assessments has never been established. This point is crucial, especially when working on wide and complex areas, in which a detailed geological map needs to be reclassified according to more general criteria. In a study area in Italy, we tested different configurations of a random forest–based landslide susceptibility model, accounting for geological information with the use of lithologic, chronologic, structural, paleogeographic, and genetic units. Different susceptibility maps were obtained, and a validation procedure based on AUC (area under receiver-operator characteristic curve) and OOBE (out of bag error) allowed us to get to some conclusions that could be of help for in future landslide susceptibility assessments. Different parameters can be derived from a detailed geological map by aggregating the mapped elements into broader units, and the results of the susceptibility assessment are very sensitive to these geology-derived parameters; thus, it is of paramount importance to understand properly the nature and the meaning of the information provided by geology-related maps before using them in susceptibility assessment. Regarding the model configurations making use of only one parameter, the best results were obtained using the genetic approach, while lithology, which is commonly used in the current literature, was ranked only second. However, in our case study, the best prediction was obtained when all the geological parameters were used together. Geological maps provide a very complex and multifaceted information; in wide and complex area, this information cannot be represented by a single parameter: more geology-based parameters can perform better than one, because each of them can account for specific features connected to landslide predisposition. [ABSTRACT FROM AUTHOR]

Objective: To assess the potential impact of a melanoma screening programme, compared with usual care, on direct costs and life expectancy in the era of targeted drugs and cancer immunotherapy. Methods: Using a Whole Disease Model approach, a Markov simulation model with a time horizon of 25 years was devised to analyse the cost-effectiveness of a one-time, general practitioner-based melanoma screening strategy in the population aged over 20, compared with no screening. The study considered the most up-to-date drug therapy and was conducted from the perspective of the Veneto regional healthcare system within the Italian National Health Service. Only direct costs were considered. Sensitivity analyses, both one-way and probabilistic, were performed to identify the parameters with the greatest impact on cost-effectiveness, and to assess the robustness of our model. Results: Over a 25-year time horizon, the screening intervention dominated usual care. The probabilistic sensitivity analyses confirmed the robustness of these findings. The key drivers of the model were the proportion of melanomas detected by the screening procedure and the adherence of the target population to the screening programme. Conclusions: The screening programme proved to be a dominant option compared with usual care. These findings should prompt serious consideration of the design and implementation of a regional or national melanoma screening strategy within a National Health Service. [ABSTRACT FROM AUTHOR]

Stream gauge-based information is the foundation for many hydrological applications in a river basin including the aquatic-habitat conservation. A simple two-parameter model for routing streamflow depth (alternatively, stream–stage) hydrographs and estimating corresponding discharge hydrographs in river channels is proposed using the multilinear approach, based on Nash-type discrete-cascade model. The applicability of this model is investigated by extending its framework to the realm of compound cross-section trapezoidal channels for both in-bank and overbank flows by using 20 flood events of the Tiber River in the Umbria region of Central Italy, and subsequently comparing the simulated results with the corresponding simulations of the HEC-RAS (Hydrologic Engineering Center – River Analysis System) hydrodynamic model and observed flow depth hydrographs. The field application, comparative study, and uncertainty and sensitivity analysis of the results demonstrate that the proposed multilinear discrete Nash-cascade stage-hydrograph (MDNS) routing model has the potential for routing floods in real-world rivers and canal irrigation systems, especially in operational mode. [ABSTRACT FROM AUTHOR]

In measuring road safety, accident severity is a key concern. Crash severity prediction models inform researchers about the severity of a crash based on a variety of criteria. To date, an enormous amount of research has been done on crash severity, and several models have been suggested to forecast crash severity utilizing existing test data or simulated datasets created using regression or classification analysis. In this study, a new approach was developed to determine the level of road crash severity (LRCS) using a large amount of real-existing data (1627 cases) by applying machine learning methods to the roads of Calabria in southern Italy. This study has three main goals: building prediction models based on classification approaches with the highest accuracy; comparing the performance of two supervised learning methods, including artificial neural networks (ANN) and convolutional neural networks (CNN), for predicting the LRCS; as well as determining the role of each of the contributing parameters in the LRCS and presenting a ranking by performing a sensitivity analysis. Finally, based on the accuracy values, it has been found that there isn't a salient difference between the predicted models. But it should be noted that 68.4% accuracy for the testing dataset implies the CNN model was superior to the ANN model, which scored 61.74% accuracy. This is acceptable if the minor variation in modeling accuracy is desired. Also, the results of the sensitivity analysis showed that the number of vehicles and the road element had the highest and lowest effect rates on the LRCS, respectively. [ABSTRACT FROM AUTHOR]

We focus on the quantification of the probability of failure (PF) of an infiltration structure, of the kind that is typically employed for the implementation of low impact development strategies in urban settings. Our approach embeds various sources of uncertainty. These include (a) the mathematical models rendering key hydrological traits of the system and the ensuing model parametrization as well as (b) design variables related to the drainage structure. As such, we leverage on a rigorous multi-model Global Sensitivity Analysis framework. We consider a collection of commonly used alternative models to represent our knowledge about the conceptualization of the system functioning. Each model is characterized by a set of uncertain parameters. As an original aspect, the sensitivity metrics we consider are related to a single- and a multi-model context. The former provides information about the relative importance that model parameters conditional to the choice of a given model can have on PF. The latter yields the importance that the selection of a given model has on PF and enables one to consider at the same time all of the alternative models analyzed. We demonstrate our approach through an exemplary application focused on the preliminary design phase of infiltration structures serving a region in the northern part of Italy. Results stemming from a multi-model context suggest that the contribution arising from the adoption of a given model is key to the quantification of the degree of importance associated with each uncertain parameter. [Display omitted] • We present a novel framework to guide the design of a drainage structure. • Probability of Failure (PF) of a drainage structure is quantified. • Uncertainty on PF is assessed in a multi-model context. • Global Sensitivity Analysis allows quantifying relevance of model processes on PF. • Uncertainty on PF is mainly driven by the infiltration process, followed by runoff. [ABSTRACT FROM AUTHOR]

• We simulate a steam reforming plant and perform a sensitivity analysis of the process. • We report actual data from a large scale SR plant and validate the model. • We design and simulate the RVPSA unit for carbon capture. • We evaluate the energetic assessment with and without carbon capture. Steam reforming (SR) of natural gas is the most widespread process to produce hydrogen. This paper presents a comprehensive simulation of an industrial plant for hydrogen production for the internal use in a refinery industrial complex based on the SR of different feedstocks. The reference plant is a 42,000 Nm3/h hydrogen production unit located in the South of Italy in the Refinery of Milazzo, Sicily. Hydrogen is produced from light gaseous hydrocarbons and purified by means of pressure swing adsorption technology. In the present work, a process simulation has been built on scientific basis and validated against field data at different feedstocks and operating conditions. The model correctly predicts the effect of relevant process parameters such as reformer feed pressure and temperature, steam to carbon ratio and plant load. In addition, the paper proposes the design of an industrial-scale rapid vacuum pressure swing adsorption (RVPSA) unit which was designed and simulated to capture carbon dioxide from the flue gas exiting the steam reformer. The RVPSA unit was integrated in the current plant enabling the concentration of CO 2 with a purity of 96.64%, an overall recovery of 90.84% and a specific energy consumption of 628.93 kJ/kg CO2 , thus meeting the requirements for transportation and geological storage. An energetic efficiency calculation was introduced to quantify the effectiveness of the hydrogen conversion process and to predict the effect of CO 2 capture and some process parameters to the overall H 2 production efficiency. [ABSTRACT FROM AUTHOR]

Featured Application: This work could contribute to evidence strengths and weaknesses of manual and automatic calibration of building dynamic simulation models leading at improving the quality of building retrofit solutions investigation. Energy reduction can benefit from the improvement of energy efficiency in buildings. For this purpose, simulation models can be used both as diagnostic and prognostic tools, reproducing the behaviour of the real building as accurately as possible. High modelling accuracy can be achieved only through calibration. Two approaches can be adopted—manual or automatic. Manual calibration consists of an iterative trial and error procedure that requires high skill and expertise of the modeler. Automatic calibration relies on mathematical and statistical methods that mostly use optimization algorithms to minimize the difference between measured and simulated data. This paper aims to compare a manual calibration procedure with an automatic calibration method developed by the authors, coupling dynamic simulation, sensitivity analysis and automatic optimization using IDA ICE, Matlab and GenOpt respectively. Differences, advantages and disadvantages are evidenced applying both methods to a dynamic simulation model of a real office building in Rome, Italy. Although both methods require high expertise from operators and showed good results in terms of accuracy, automatic calibration presents better performance and consistently helps with speeding up the procedure. [ABSTRACT FROM AUTHOR]

Predictors of decline in health in older populations have been investigated in multiple studies before. Most longitudinal studies of aging, however, assume that dropout at follow-up is ignorable (missing at random) given a set of observed characteristics at baseline. The objective of this study was to address non-ignorable dropout in investigating predictors of declining self-reported health (SRH) in older populations (50 years or older) in Sweden, the Netherlands, and Italy. We used the SHARE panel survey, and since only 2895 out of the original 5657 participants in the survey 2004 were followed up in 2013, we studied whether the results were sensitive to the expectation that those dropping out have a higher proportion of decliners in SRH. We found that older age and a greater number of chronic diseases were positively associated with a decline in self-reported health in the three countries studies here. Maximum grip strength was associated with decline in self-reported health in Sweden and Italy, and self-reported limitations in normal activities due to health problems were associated with decline in self-reported health in Sweden. These results were not sensitive to non-ignorable dropout. On the other hand, although obesity was associated with decline in a complete case analysis, this result was not confirmed when performing a sensitivity analysis to non-ignorable dropout. The findings, thereby, contribute to the literature in understanding the robustness of longitudinal study results to non-ignorable dropout while considering three different population samples in Europe. [ABSTRACT FROM AUTHOR]

Moving towards energy systems with high variable renewable energy shares requires a good understanding of the impacts of climate change on the energy penetration. To do so, most prior impact studies have considered climate projections available from Global Circulation Models (GCMs). Other studies apply sensitivity analyses on the climate variables that drive the system behavior to inform how much the system changes due to climate change. In the present work, we apply the Decision Scaling approach, a framework merging these two approaches, for analyzing a renewables-only scenario for the electric system of Northern Italy where the main renewable sources are solar and hydropower. Decision Scaling explores the system sensibility to a range of future plausible climate states. GCM projections are considered to estimate probabilities of the future climate states. We focus on the likely future energy mix within the region (25% of solar photovoltaic and 75% of hydropower). We also carry out a sensitivity analysis according to the storage capacity. The results show that run-of-the river power generation from this Alpine area is expected to increase although the average inflow decreases under climate change. They also show that the penetration rate is expected to increase for systems with storage capacity less than one month of average load and inversely for higher storage capacity. [ABSTRACT FROM AUTHOR]

The cereal sector plays a very significant socio-economic role in various areas of the Mediterranean because it generates income and employment both up and downstream of each crop cultivation. Considering the growing interest of the scientific world in agro-environmental themes and the green economy, apart the products by agro-food industry, cereals in particular, as well as their by-products need evaluating. However, for businessmen to re-use virtuously these by-products they must be motivated by environmental issues and perhaps even more by economic ones. So, several profit scenarios have been devised regarding cereal straw harvesting and management, starting from face-to-face surveys to define the current status quo. The results show that, currently, most farms work in break-even and they harvest the straw to safeguard the land and prevent it being illegally burned in the fields. The different scenarios achieved by sensitivity analyses show that the only farms who would keep positive profits, whatever the rise in costs, are those who have the straw harvested into large rectangular bales by sub-contractors. [ABSTRACT FROM AUTHOR]

• A novel Sensitivity Analysis (SA) method is proposed for Probabilistic Seismic Hazard Assessment (PSHA). • The method consists in a novel Bootstrapped Modularised Global Sensitivity Analysis (BMGSA). • The method is testes on a benchmark study. • Results are compared with a standard variance-based (GSA) method of literature. • The proposed BMGSA does not require direct accessibility to the PSHA code. Probabilistic Seismic Hazard Assessment (PSHA) evaluates the probability of exceedance of a given earthquake intensity threshold like the Peak Ground Acceleration, at a target site for a given exposure time. The stochasticity of the occurrence of seismic events is modelled by stochastic processes and the propagation of the earthquake wave in the soil is typically evaluated by empirical relationships called Ground Motion Prediction Equations. The large uncertainty affecting PSHA is quantified by defining alternative model settings and/or model parametrizations. In this work, we propose a novel Bootstrapped Modularised Global Sensitivity Analysis (BMGSA) method for identifying the model parameters most important for the uncertainty in PSHA, that consists in generating alternative artificial datasets by bootstrapping an available input-output dataset and aggregating the individual rankings obtained with the modularized method from each of those. The proposed method is tested on a realistic PSHA case study in Italy. The results are compared with a standard variance-based Global Sensitivity Analysis (GSA) method of literature. The novelty and strength of the proposed BMGSA method are both in the fact that its application only requires input-output data and not the use of a PSHA code for repeated calculations. [ABSTRACT FROM AUTHOR]

Consistent climate data and reliable sizing methods are fundamental for designing Technical Building Systems. This paper addresses both these aspects with reference to the Italian context analyzing 108 different locations. Regarding the climate data, a quality analysis through the application of rules and filters was performed on the locations. The results showed good data quality, with most sites having high percentages of usable material, although highlighting recursive criticalities in the detection of humidity and wind speed parameters. Regarding the sizing methods, a sensitivity analysis was carried on for the European standard EN ISO 15927-2 to evaluate its performances in selecting the Cooling Design Days in the 108 locations. The analysis highlighted great differences in terms of chosen Design Days when using different climatic parameters, showing at the same time a negligible influence of the wind speed on the selection process. Furthermore, a sizing process for a test building applied to the 108 locations was carried on by using the Cooling Design Days. The results highlighted that the Design Days obtained through the EN ISO method often give counterintuitive sizing outputs, even obtaining higher required powers for lower risk levels, the opposite of how it should be. This implies that a designer should be very careful in evaluating which parameters to use in the Design Day selection and if the output sizing powers obtained through them are reliable for its particular sizing process. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to evaluate the effectiveness of the application of 0.005% estriol gel to the vulvar vestibule in the management of postmenopausal dyspareunia.Study Design: Postmenopausal women with dyspareunia were enrolled in this study. Patients were instructed to use a fingertip to apply 0.25g of vaginal gel containing 25μg of estriol to the vulvar vestibule daily for three weeks and then twice weekly for up to 12 weeks.Results: Assessment of symptoms (dyspareunia and cotton swab test) and signs of vestibular atrophy were performed, and changes between baseline and weeks 3 and 12 were assessed. Adverse events were recorded. A total of 63 women were included. Of the 63, 59 (93.6%) completed the 12-week treatment period, and four dropped out for vestibular burning. Dyspareunia improved or was cured (score ≤1) by week 12 in 81.4% of patients. The patients also showed a statistically significant reduction in vestibular atrophy and cotton swab test at the end of treatment.Conclusions: Application of 0.005% estriol gel to the vulvar vestibule is effective in correcting menopausal coital pain. This suggests that reduction in sensory vestibular innervation sensitivity is likely to play a pivotal role in the relief of dyspareunia. One limitation of this study is the limited follow-up, but the therapy may be continued for as long as the patients are distressed by their symptoms without estrogen intervention. [ABSTRACT FROM AUTHOR]

Composite indicators (CIs) are a useful communication tool for conveying summary performance information in a relatively simple way. They are used widely in various sectors in public services. In Italy, they are also employed to allocate a quota of public funding to the universities. Despite increasing use, CIs value and benefit are still under debate and their suitability for the benchmark of universities is challenged by methodological issues and the multi-dimensional facet of higher education institutions. The issue addressed in the present work is whether the composite measure employed by the Italian Government is an accurate tool for evaluating, ranking and funding universities. For this purpose-after a revision of the indicators, weighting and aggregation methods put forward at the national level-CIs have been computed for the overall university performance, employing the indicators chosen at the national level and combining various weighting and normalisation schemes. The variation in the rankings assigned by the CIs to the given universities has been then explored to gauge its robustness. In the light of the results, some technical and analytical issues involved in the design of CIs and their policy implications are also discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Extensive survey on bell towers in Naples was carried out. • Parametric structural modelling of masonry bell towers was created. • A vulnerability index is defined as a function of a collapse measure. • Global sensitivity analysis highlighted influential parameters on seismic vulnerability. • A simplified model with six parameters can achieve good accuracy. The safeguard of historical heritage cannot disregard a rational assessment of the seismic vulnerability of the assets, especially in areas as Italy where high exposure and hazard may lead to significant risk. In this context, while established empirical methods based on observed damage after earthquakes are available for some constructional typologies as churches and palaces, large-scale approaches for masonry bell towers aimed at defining a vulnerability ranking for prioritisation of intervention are still missing or not sufficiently validated. In this paper, a novel simplified vulnerability model is formulated through sensitivity-informed parameter reduction of a more detailed mechanics-based assessment methodology. The framework is based on (a) a parametric representation of the bell tower, (b) a mechanical model providing a measure of collapse, (c) a vulnerability definition based on the measure of collapse. The variation ranges of the input parameters are calibrated against the results of the surveys carried out in the historical centre of Naples. A global sensitivity analysis is then used to identify the most important parameters governing the vulnerability of the structure and remove the least influential parameters. Vulnerability models based on different amount of input data are finally proposed, and their accuracy is assessed comparatively with the original mechanical model. The results show that good accuracy can be obtained by using six geometrical and typological parameters, while further increase in the quality of the prediction may be achieved with the introduction of additional information regarding materials and constructional features, which can only be acquired by means of more detailed surveys on-site. [ABSTRACT FROM AUTHOR]

A conceptual model related to a mountain aquifer that is characterized by a lack of data of hydrogeological parameters and boundary conditions, which were based on a single available observational dataset used for calibration, was studied using numerical models. For the first time, a preliminary spatial-temporal analysis has been applied to the study area in order to evaluate the real extension of the aquifer studied. The analysis was based on four models that were characterized by an increasing degree of complexity using a minimum of two zones and a maximum of five zones, which consequently increased the number of adjustable parameters from a minimum of 10 to a maximum of 22, calibrated using the parameter estimation code PEST. Statistical index and information criteria were calculated for each model, which showed comparable results; the information criteria indicated that the model with the low number of adjustable parameters was the optimal model. A comparison of the simulated and observed spring hydrographs showed a good shape correspondence but a general overestimation of the discharge, which indicated a good fit with the rainfall time series and a probably incorrect extension of the aquifer structure: the recharge contributes more than half of the total outflow at the springs but is not able to completely feed the springs. [ABSTRACT FROM AUTHOR]

This study examined investment–cash flow sensitivity in unconstrained and constrained firms from 1980 to 2010 in a sample of Italian manufacturing firms. Investment sensitivity to cash flow decreased over time, and financially constrained firms showed little difference in the decline compared with unconstrained firms. Last, investment sensitivity to cash flow increased during the financial crisis of 2008. [ABSTRACT FROM PUBLISHER]

The analysis of satisfaction, employability and economic perspectives after the Ph.D. in Italy has not received adequate attention in the past, especially in terms of comparison among universities. To analyze these aspects, in this paper we consider data from the survey ‘Statistica in TEma di Laureati e LAvoro’ on doctors who achieved the title on 2007, 2008 and 2009 [CILEA,Laureati STELLA, indagine occupazionale post-dottorato, dottori di ricerca 2007–2008, Tech. Rep., CILEA, Segrate, 2010; CILEA,Laureati STELLA, indagine occupazionale post-dottorato, dottori di ricerca 2008–2009, Tech. Rep., CILEA, Segrate, 2011]. To deal with the complex, multidimensional nature of the concept, we propose a flexible two-step procedure for the construction of a composite indicator, and make a first attempt to rank some Italian universities. In the first step, indicators for single dimensions are derived from cumulative link models with proportional odds. In the second step, aggregation through standard, ad hoc methods is proposed. [ABSTRACT FROM PUBLISHER]

Existing street lighting systems, in most of South Italy cities, are often inefficient due to the obsolescence of lamps and luminaires and of ineffective light control systems unable to implement efficient on-off and dimming strategies. Energy efficiency improvement, in street lighting systems, is often one of the key actions to be adopted by Public Administration in their Sustainable Energy Action Plan in the framework of the “Covenant of Majors” activities. As a task of FACTOR 20 project, a set of planning options has been analysed and proposed. Particularly, street lighting efficiency projects have been studied for representative case studies. A detailed survey of the public lighting systems, in Comiso, allowed represent current performance figures such us installed power, luminance and illuminance levels in roads categories, electricity consumption, switching and dimming schedules. A project of system upgrade has been elaborated. To do this, many lighting simulations, energy and economic assessments in three scenarios have been performed. The obtained results show that high improvements of the lighting quality are foreseeable together with large energy and economic saving. An economic sensitivity analysis, has shown how the performance can change. The proposed methodology can be applied in many similar South Italy cities. [ABSTRACT FROM AUTHOR]

Background: Human papillomavirus (HPV) plays a role in the development of benign and malign neoplasms in both sexes. The Italian recommendations for HPV vaccines consider only females. The BEST II study (Bayesian modelling to assess the Effectiveness of a vaccination Strategy to prevent HPV-related diseases) evaluates 1) the cost-effectiveness of immunization strategies targeting universal vaccination compared with cervical cancer screening and female-only vaccination and 2) the economic impact of immunization on various HPV-induced diseases.Objective: The objective of this study was to evaluate whether female-only vaccination or universal vaccination is the most cost-effective intervention against HPV.Methods: We present a dynamic Bayesian Markov model to investigate transmission dynamics in cohorts of females and males in a follow-up period of 55 years. We assumed that quadrivalent vaccination (against HPV 16, 18, 6, and 11) is available for 12-year-old individuals. The model accounts for the progression of subjects across HPV-induced health states (cervical, vaginal, vulvar, anal, penile, and head/neck cancer as well as anogenital warts). The sexual mixing is modeled on the basis of age-, sex-, and sexual behavioral-specific matrices to obtain the dynamic force of infection.Results: In comparison to cervical cancer screening, universal vaccination results in an incremental cost-effectiveness ratio of €1,500. When universal immunization is compared with female-only vaccination, it is cost-effective with an incremental cost-effectiveness ratio of €11,600. Probabilistic sensitivity analysis shows a relatively large amount of parameter uncertainty, which interestingly has, however, no substantial impact on the decision-making process. The intervention being assessed seems to be associated with an attractive cost-effectiveness profile.Conclusions: Universal HPV vaccination is found to be a cost-effective choice when compared with either cervical cancer screening or female-only vaccination within the Italian context. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are becoming a relevant electricity source, characterised by a growing trend in the last years. This paper analyses the economic feasibility of investments in industrial PV systems of different sizes (200 kW, 400 kW, 1 MW, and 5 MW), in the absence of subsidies, and in a mature market (Italy). The selected indicators for this kind of assessment are net present value (NPV) and discounted payback time (DPBT). Furthermore, the environmental advantage in comparison to fossil sources of energy is evaluated through the reduction of carbon dioxide emissions (ERcd). Finally, a sensitivity analysis on critical variables (percentage of self-consumed energy, average annual insolation rate, annual electricity purchase price, annual electricity sale price, unitary investment cost and opportunity cost) is conducted. Results highlight the strategic role of self-consumption in a market characterised by an absence of public policy incentives and the presence of interesting economic opportunities for industrial applications. [ABSTRACT FROM AUTHOR]

Animal monitoring is a key stage in the process of understanding individual behaviour and social dynamics that characterize populations and species. Recent technological advances allow to collect large data sets on animal movement. Using such data to compute activity patterns is very challenging without expensive field surveys, video analyses and/or accelerometer sensors. We introduce here a new tool (UABE: Unsupervised Animal Behaviour Examiner) that makes use of a set of methodologies (if-then-else rules, sensitivity analyses, Markov chains) to interpret GPS data in behavioural terms under worst case conditions, i.e. in absence of ancillary data. The UABE is halfway between unsupervised inductive (clustering or segmentation) and deductive (expert-based) methods. As a case study, we applied the UABE to the investigation of the red-footed falcons' activity at nest at the two largest breeding colonies in Italy during the chick-rearing period. By using accurate GPS data-loggers, we tracked four adults in June–July 2019 and 2020, and collected 5840 GPS points. Activity at nest occupied one fourth of the red-footed falcons' day, with substantial differences between months and between daytime and night-time. On an hourly basis, clear temporal patterns of activity at nest emerged. Through the combined use of frequency and continuity of the studied behaviour, the UABE also allowed to raise plausible hypotheses for the reasons behind birds' activity at nest at specific times of day. The UABE supports the creation of unsupervised ethograms of both stationary (e.g., standing at nest or roost, hovering/perching) and nonstationary (e.g., flight, walking) activity patterns. It can be applied to any species regardless of the tracking instrument used. [Display omitted] • UABE is a new method to deduct behavioural states from GPS data. • It is halfway between unsupervised inductive and deductive methods. • It combines frequency and continuity of labelled data to interpret animal behaviour. • It makes use of if-then-else rules, sensitivity analyses and Markov chains. • It can equally be applied to any species regardless of the tracking instrument used. [ABSTRACT FROM AUTHOR]

A wide program of structural assessment has been carried out by Politecnico di Milano on the historic bell tower of the church Chiesa Collegiata in Arcisate (Varese, northern Italy). Within this context, the first part of the article summarizes the results obtained from the application of the stochastic subspace identification method to ambient response data collected in two dynamic tests, carried out in June 2007 and June 2008. Next presented is the vibration-based methodology developed for the calibration of a three-dimensional finite element model of the tower, consisting in the successive application of systematic manual tuning, sensitivity analysis, and simple system identification algorithm. [ABSTRACT FROM PUBLISHER]

We analysed the sensitivity of a Case-2 bio-optical model where the water reflectance is computed as a function of concentrations of three optical water quality parameters (WQPs) of three Italian lakes (Garda, Mantua and Trasimeno) and their specific absorption and backscattering coefficients. The modelled reflectance is computed based on the spectral characteristics of three optical sensors, on-board Landsat-8, Sentinel-2 and Sentinel-3. The variance-based analysis was able to quantify the lake-dependence for all (50,000 runs) the simulated reflectance. The results confirmed that Sentinel-3 water reflectance is sensitive to WQPs in all the trophic conditions investigated. [ABSTRACT FROM AUTHOR]

Offshore production system is predicted to increase in the near future driven by the lack of coastal space and lower environmental impacts. The aim of this paper has been to evaluate the economic performance of offshore production system respect to inshore one, by comparing net present value (NPV), discounted payback time (DPBT) and internal rate of return (IRR) of two Italian mariculture farms that produce European sea bass. Results showed a better economic profitability of offshore farm, even if sensitivity analysis revealed that financial indicators of both aquaculture production systems have been very sensitive to market condition changes. So, offshore production system could represent an opportunity for fish farmers to increase their profitability, obtaining a more sustainable production and avoiding possible conflicts with other human activities in coastal areas. [ABSTRACT FROM AUTHOR]

Back in December 2019, the novel coronavirus disease 2019 (Covid-19) started rapidly spreading worldwide, especially in Italy that was among the most affected countries. The geographical distribution of air pollution and Covid-19 mortality in Italy suggested atmospheric pollution as a worsening factor of severe Covid-19 health outcomes. The present nationwide ecological study focused on all 107 Italian territorial areas, aiming to assess the potential association between Particulate Matter concentration, less than 2.5 μm in diameter (exposure), and Covid-19 mortality rate (outcome) throughout 2020, by looking at 28 potential confounders. A potential positive association between exposure and outcome was observed when performing a multivariate regression analysis with a Negative Binomial model, suggesting that an increase of 1 μg/m3 in the exposure is associated with an increase of 9.0% (95% CI: 6.5%–11.6%) in the average Covid-19 mortality rate, conditional on all 28 potential confounders. A sensitivity analysis, based on the E-value, shows that a hypothetical unmeasured confounder would have to be associated with both PM 2.5 concentration and Covid-19 mortality rate by a rate ratio of at least 1.40-fold each to explain away the exposure-outcome association, conditional on all 28 covariates included in the main analysis model. Moreover, the Observed Covariate E-value (OCE) was reported to provide a contextualization of the E-value on the observed covariates included in the study. The OCE sensitivity analysis shows that a set of unknown confounders similar in size and magnitude to the set of the considered climatic factors could potentially explain away the estimated exposure-outcome association. Consequently, the role of climatic factors in the Covid-19 pandemic is worth of further investigation. [ABSTRACT FROM AUTHOR]

This paper presents a case study of a railway timetable optimization for the very dense Simplon corridor, a major railway connection in the Alps between Switzerland and Italy. The key to deal with the complexity of this scenario is the use of a novel aggregation-disaggregation method. Starting from a detailed microscopic representation as it is used in railway simulation, the data is transformed by an automatic procedure into a less detailed macroscopic representation, that is sufficient for the purpose of capacity planning and amenable to state-of-the-art integer programming optimization methods. This macroscopic railway network is saturated with trains. Finally, the optimized timetable is re-transformed to the microscopic level in such a way that it can be operated without any conflicts among the train paths. Using this micro-macro aggregation-disaggregation approach in combination with integer programming methods, it becomes for the first time possible to generate a profit maximal and conflict free timetable for the complete Simplon corridor over an entire day by a simultaneous optimization of all trains requests. In addition, this also allows us to undertake a sensitivity analysis of various problem parameters. [ABSTRACT FROM AUTHOR]

The study introduces a map of the long-term changes in the level of land sensitivity to degradation for Italy, presented at a scale of 1:1,300,000. According to the Environmental Sensitive Area (ESA) approach, the national territory was divided into different classes of land sensitivity by assigning a score based on the observed changes in four themes (climate, soil, vegetation and land management) related to land degradation processes. The observed increase in the level of land sensitivity was rapid and spatially heterogeneous and reflects the decrease in land quality mainly due to human-derived landscape transformations. Maps illustrating the change in land sensitivity over a sufficiently long time period should be developed at country scale as a contribution to monitoring and dissemination of scientific results in the framework of the ‘Zero Net’ Land Degradation strategy introduced by the United Nations Convention to Combat Desertification. [ABSTRACT FROM AUTHOR]

The chipping operation is an important component of harvesting systems producing biomass and pulp chips. This paper aimed to develop a valid model to predict the productivity of chipping as part of these operations. Over a number of years more than 200 different time studies were conducted on chipping operations in Italy and Australia. Multiple regressions and backward stepwise data analysis methods were applied to develop a productivity prediction equation, considering the following variables: machine power (kW), piece size (m3), crew size, harvesting method, species, tree part, wood condition, wood lay-out, chipping type, propulsion, feeding method, point of chipping, season, location of chip discharge, country (Italy or Australia) and type of operation (biomass chip operation or pulp chip operation). The final productivity model included machine power, average piece size, location of chip discharge and type of operation as significant variables. The internal validation test was conducted using five witness samples from Italy and Australia, which confirmed the validity at α=0.05. Additional international case studies from North America, South America, and central and northern Europe were used to test the accuracy of the model, in which 15 studies confirmed the model's validity and two failed to pass the test. [ABSTRACT FROM AUTHOR]

Abstract: Several studies have investigated the barriers to industrial energy efficiency, but few have focused on the most effective means (drivers) to promote the adoption of energy-efficient technologies and practices. In this respect, Small and Medium-sized Enterprises (SMEs) have been completely overlooked despite interesting consequences for their overall energy consumption and their concurrent low levels of adoption of energy-efficiency measures. Starting from insights garnered from the extant literature on the drivers of industrial energy efficiency, this paper presents an empirical investigation of 71 Italian manufacturing SMEs through a multiple case-study approach. The research highlights the importance of allowances or public financing for energy efficiency interventions, as well as the importance of external pressures such as increases in energy prices and the introduction or increasing of fees on both resources consumed and on emissions of pollutants. Moreover, enterprises look favourably upon energy-efficient technologies which are able to provide long-term benefits, evidence of their willingness to adopt seemingly radical solutions when these are able to improve their long-term competitiveness. Other drivers considered as strategic for increasing energy efficiency are the presence within the company of people with great ambition and entrepreneurial mind and the management sensitivity to the issue. This paper also provides a preliminary analysis of how factors such as firm size, sector, supply chain complexity, and innovation characteristics are or might be able to significantly affect drivers toward the adoption of energy-efficient technologies. [Copyright &y& Elsevier]

Smaller glaciers (<0.5 km2) react quickly to environmental changes and typically show a large scatter in their individual response. Accounting for these ice bodies is essential for assessing regional glacier change, given their high number and contribution to the total loss of glacier area in mountain regions. However, studying small glaciers using traditional techniques may be difficult or not feasible, and assessing their current activity and dynamics may be problematic. In this paper, we present an integrated approach for characterizing the current behaviour of a small, avalanche-fed glacier at low altitude in the Italian Alps, combining geomorphological, geophysical and high-resolution geodetic surveying with a terrestrial laser scanner. The glacier is still active and shows a detectable mass transfer from the accumulation area to the lower ablation area, which is covered by a thick debris mantle. The glacier owes its existence to the local topo-climatic conditions, ensured by high rock walls which enhance accumulation by delivering avalanche snow and reduce ablation by providing topographic shading and regulating the debris budget of the glacier catchment. In the last several years the glacier has displayed peculiar behaviour compared with most glaciers of the European Alps, being close to equilibrium conditions in spite of warm ablation seasons. Proportionally small relative changes have also occurred since the Little Ice Age maximum. Compared with the majority of other Alpine glaciers, we infer for this glacier a lower sensitivity to air temperature and a higher sensitivity to precipitation, associated with important feedback from increasing debris cover during unfavourable periods. [ABSTRACT FROM AUTHOR]

Abstract: This paper conducts an empirical investigation of the finance–growth nexus at firm level. We exploit a large panel of Italian manufacturing firms observed over the period 1998–2003 to jointly assess the impact of cash flow and leverage on corporate growth measured in terms of employees and sales. We tackle problems of endogeneity, unobservable heterogeneity and persistence by using a system GMM estimator fully developed by Blundell and Bond (1998). We find an inverted U-shaped relationship between debt and growth: at low levels, leverage exerts a positive influence on growth and yet there appears to be a negative relationship between growth and debt-exposure for fragile firms, i.e. highly leveraged ones. This finding is consistent with the idea that debt initially enables firms to broaden their financing options and provides additional resources to growth. Nevertheless, once debt-exposure reaches a certain threshold, liquidity and debt overhang effects prevail and negatively affect firm expansion. Highly leveraged firms are also endowed with relatively lower levels of internal cash flow and exhibit higher growth-cash flow sensitivity. We conclude that this latter result can be interpreted as evidence of the existence of financial constraints. [Copyright &y& Elsevier]

SH evaluation in Po Plain region is revisited with a view to: ( $$a)$$ updating available seismic source and ground motion attenuation models with recent data and testing the sensitivity of the results, ( $$b)$$ exploiting available non-stationary models to check their predictive performance at particular sites and, ( $$c)$$ handling the ground motion hazard generated by composite fault systems. Update of area source models and GMPEs does not strongly affect SH evaluated at representative sites, while usefulness of predictions derived from non-stationary models needs to be assessed on a site-by-site basis. A hybrid (deterministic-probabilistic) approach seems to be a promising tool for assessing site-specific hazard contributed by composite sources. [ABSTRACT FROM AUTHOR]

The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The assessment of the degree of "spatial hazard" or "susceptibility" was carried out by the spatial prediction regardless of the return time of the events. The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network. The method described in this paper is a novel technique based on a neuro-fuzzy system. It is able to train data like neural network and it is able to shape and control uncertain and complex systems like a fuzzy system. This methodology allows us to derive susceptibility maps of the study area. These data are obtained from thematic maps representing the parameters responsible for the instability of the slopes. The parameters used in the analysis are: plan curvature, elevation (DEM), angle and aspect of the slope, lithology, fracture density, kinematic hazard index of planar and wedge sliding and toppling. Moreover, this method is characterized by the network training which uses a training matrix, consisting of input and output training data, which determine the landslide susceptibility. The neuro-fuzzy method was integrated to a sensitivity analysis in order to overcome the uncertainty linked to the used membership functions. The method was compared to the landslide inventory map and was validated by applying three methods: a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a SCAI method. The developed neuro-fuzzy method showed a good performance in the determination of the landslide susceptibility map. [ABSTRACT FROM AUTHOR]

Abstract: We studied the effect of prospective climate change upon crop yield, and related water footprint of maize (Zea mays L.) for a relevant case study area in the Po valley of Northern Italy. To simulate maize production we used a cropping system simulation model CropSyst, which we set up and validated by way of crop yield data during 2001–2010. We then calculated the present water footprint (green, blue) of maize in the area, defined as the absolute and specific (per kg yield) amount of water evapotranspired during growing season, under three irrigation scenarios, namely (i) no irrigation, (ii) manual irrigation at fixed dates, and (iii) automatic irrigation on demand. We then evaluated the effects of prospective climate change upon maize production until mid-century (2045–2054), and we quantified the water footprint therein. We considered climate variations with focus upon temperature, precipitation, and CO2. First, we assessed maize yield and water footprint sensitivity to potential changes of these weather variables. We then fed the maize yield model with properly downscaled climate projections (storyline A2, business as usual) from global circulation models (GCM), included within the board of the Intergovernmental Panel of Climate Change, IPCC, and with those from a local scenario LOC, obtained by projecting recently observed local climate trends (1975–2010). Under the worst, more likely future scenarios of increasing temperature and decreasing precipitation, crop yield decreased and water footprint, especially blue, increased, due to increased evapotranspiration, higher irrigation demand, and lower final yield. Increase of CO2, albeit possibly increasing water use efficiency, seemed not to affect the water footprint noticeably. A possible increase of precipitation as projected by some GCMs, may partly make up for the increase of temperature, especially under a no, or little irrigation scenario, further diminishing the blue water footprint. Uncertainty in future precipitation has the greatest impact in scenarios projecting maize yield and water footprint. Our study provides hints as to how one can (i) evaluate the amount of water required to cultivate maize or other crops, and virtually traded when such crops are sold or bought, (ii) evaluate the impact of climate change upon water footprint and virtual water trade, and (iii) benchmark objectively adaptation strategies for agricultural systems with an eye on least water consumption. [Copyright &y& Elsevier]

Numerical runout models are important tools for predicting the potential downstream impacts of tailings dam breaches that generate tailings flows, which is a crucial step in emergency preparedness and planning, and risk management. Most existing runout models were originally developed for the analysis of water floods or flow-like landslides (e.g. debris flows). In this study, we back-analyze two well-documented historical tailings dam breaches (1985 Stava, Italy and 1994 Merriespruit, South Africa) using four numerical models (DAN3D, MADflow, FLO-2D and FLOW-3D). The main objective of this multi-case, multi-model benchmarking exercise is to identify collective opportunities to adapt these types of models and associated modelling methods to tailings dam breach runout applications. Comparing best-fit simulation results, we find that all four models are capable of reproducing the bulk behaviour of the real events; however, (i) multiple sets of rheological parameters may produce very similar output results, (ii) the best-fit input parameter combinations are non-transferable between models and inconsistent with independently measured rheological properties of stored tailings, and (iii) choosing an appropriate set requires sufficient understanding of material rheological properties and expert judgment. Using a systematic sensitivity analysis with the First-Order Second-Moment (FOSM) approach, we also find that each model is sensitive to different input parameters, although the total released volume is among the main high-influence parameters in every scenario. We conclude that more case study back-analyses are needed to enhance our understanding of these sensitivities and develop better guidance on the use of these types of numerical models for tailings flow runout prediction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

A patient self-rated 19-item wearing-off questionnaire (WOQ-19) was shown to be a potent screening tool for wearing-off (WO) in patients with Parkinson's disease. As part of the early detection of wearing-off in Parkinson's disease study, we preliminarily performed a linguistic and a psychometric validation of an Italian version of the WOQ-19. Moreover, we assessed the sensitivity and specificity of the WOQ-19 in the office-based settings. The psychometric validation study included assessment of discriminant ability, internal consistency and test-retest reliability. The intraclass correlation coefficient (ICC) and the Cronbach's Alpha were assessed. The WOQ-19 sensitivity and specificity were calculated in respect to the physician assessment of WO, taken as the gold standard. The Italian version of WOQ-19 was found easy to understand by ≥80% of the patients. The number of WO-related symptoms detected by WOQ-19 was 7.5 ± 3.2 in patients with WO and 1.6 ± 2.6 in patients without WO ( p < 0.0001). Cronbach's alpha was equal to 0.868 for the total number of WO symptoms. The ICC value was equal to 0.858 for WO-related symptoms as a whole. The diagnostic ability study recruited 207 patients: for a WOQ-19 cut-off ≥2, the sensitivity was equal to 0.881 and the specificity was 0.674. This is the first validation of WOQ-19 in Italian, with excellent linguistic validity and psychometric properties. WOQ-19 was confirmed to be reliable in WO detection. [ABSTRACT FROM AUTHOR]

Diverting waste from landfill is one of the basic priorities on improving the use of resources and reducing the environmental impacts of waste management. In order to achieve this goal it is necessary to limit the amount of materials sent to final disposal and promote energy recovery. In Italy the use of recycling is registering a growing trend but the recourse to landfill is still too high with respect to European Commission targets. The aim of the paper was to analyse the financial and economic benefits that energy recovery could produce by diverting waste from landfills in an Italian region, as landfilling cannot be a solution in the long term because of its finite capacity and for various other ecological reasons. A sensitivity analysis on the critical variables of this plan and a risk analysis are also provided. [ABSTRACT FROM AUTHOR]