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In the context of climate change, the correct management of groundwater, which is strategic for meeting water needs, becomes essential. Groundwater modeling is particularly crucial for the sustainable and efficient management of groundwater. This manuscript provides different types of modeling according to data availability and features of three porous aquifer systems in Italy (Empoli, Magra, and Brenta systems). The models calibrated on robust time series enabled the performing of forecast simulations capable of representing the quantitative and qualitative response to expected climate regimes. For the Empoli aquifer, the process-based models highlighted the system’s ability to mitigate the effects of dry climate conditions thanks to its storage capability. The data-driven models concerning the Brenta foothill aquifer pointed out the high sensitivity of the system to climate extremes, thus suggesting the need for specific water management actions. The integrated data-driven/process-based approach developed for the Magra Valley aquifer remarked that the water quantity and quality effects are tied to certain boundary conditions over dry climate periods. This work shows that, for groundwater modeling, the choice of the suitable approach is mandatory, and it mainly depends on the specific aquifer features that result in different ways to be sensitive to climate. This manuscript also provides a novel outcome involving the integrated approach wherein it is a very efficient tool for forecasting modeling when boundary conditions, which significantly affect the behavior of such systems, are subjected to evolve under expected climate scenarios.

The AI Act has been recently proposed by the European Commission to regulate the use of AI in the EU, especially on high-risk applications, i.e. systems intended to be used as safety components in the management and operation of road traffic and the supply of water, gas, heating and electricity. On the other hand, IEC 61508, one of the most adopted international standards for safety-critical electronic components, seem to mostly forbid the use of AI in such systems. Given this conflict between IEC 61508 and the proposed AI Act, also stressed by the fact that IEC 61508 is not an harmonised European standard, with the present paper we study and analyse what is going to happen to industry after the entry into force of the AI Act. In particular, we focus on how the proposed AI Act might positively impact on the sustainability of critical infrastructures by allowing the use of AI on an industry where it was previously forbidden. To do so, we provide several examples of AI-based solutions falling under the umbrella of IEC 61508 that might have a positive impact on sustainability in alignment with the current long-term goals of the EU and the Sustainable Development Goals of the United Nations, i.e., affordable and clean energy, sustainable cities and communities., 10 pages, 2 tables

A new methodology for effective definition and efficient evaluation of dependability-related properties is proposed. The analysis targets the systems composed of a large number of components, each one modeled implicitly through high-level formalisms, such as stochastic Petri nets. Since the component models are implicit, the reward structure that characterizes the dependability properties has to be implicit as well. Therefore, we present a new formalism to specify those reward structures. The focus here is on component models that can be mapped to stochastic automata with one or several absorbing states so that the system model can be mapped to a stochastic automata network with one or several absorbing states. Correspondingly, the new reward structure defined on each component's model is mapped to a reward vector so that the dependability-related properties of the system are expressed through a newly introduced measure defined starting from those reward vectors. A simple, yet representative, case study is adopted to show the feasibility of the method.

Railway is currently envisioned as the most promising transportation system for both people and freight to reduce atmospheric emission and combat climate change. In this context, ensuring the energy efficiency of the railway systems is paramount in order to sustain their future expandability with minimum carbon footprint. Recent advancements in computing and communication technologies are expected to play a significant role to enable novel integrated control and management strategies in which heterogeneous data is exploited to noticeably increase energy efficiency. In this paper we focus on exploiting the convergence of heterogeneous information to improve energy efficiency of railway systems, in particular on the heating system for the railroad switches, one of the major energy intensive components. To this aim, we define new policies to efficiently manage the heating of these switches exploiting also external information such as weather and forecast data. In order to assess the performance of each strategy, a stochastic model representing the structure and operation of the railroad switch heating system and environmental conditions (both weather profiles and specific failure events) has been developed and exercised in a variety of representative scenarios. The obtained results allow to understand both strengths and limitations of each energy management policy, and serves as a useful support to make the choice of the best technique to employ to save on energy consumption, given the system conditions at hand.

This paper focuses on the generation of stochastic models for dependability and performability analysis, through mechanisms for the automatic replication of template models when identity of replicas cannot be anonymous. The major objective of this work is to support the modeler in selecting the most appropriate replication mechanism, given the characteristics of the system under analysis. To this purpose, three most used solutions to identity-aware replication are considered and a formal framework to allow representing them in a consistent way is first defined. Then, a comparison of their behavior is extensively carried out, with focus on efficiency, both from a theoretical perspective and from a quantitative viewpoint. For the latter, a specific implementation of the considered replication mechanisms in the Mobius modeling environment and a case study representative of realistic interconnected infrastructures are developed.

Railways monitoring and control are currently performed by different heterogeneous vertical systems working in isolation without or with limited cooperation among them. Such configuration, widely adopted in practical deployments today, is in contrast with the integrated vision of systems that are at the foundation of the smart-city concept. In order to overcome the current fractured ecosystem that monitors and controls railways functionalities, the adoption of a novel integrated approach is mandatory to create an all-in-one railway system. To this aim, new IoT-based communication technologies, like wireless or Power Line Communication technologies, are considered the main enablers to integrate in a very rapid and easy manner existing vertical systems. In this work, we analyse the architecture of future railways systems based on a mix of wireless and Power Line Communication technologies. In our analysis, we aim at studying possible failure management strategies on rail-road switches to improve the level of reliability, crucial requirement for systems that demand maximum resiliency as they manage a critical function of the infrastructure. In particular, we propose a set of solutions aimed at detecting and handling network and sensor failures to ensure continuity in the execution of the basic control functions. The proposed approach is evaluated by means of simulations and demonstrated to be effective in ensuring a good level of performance even when failures occur.

FeedForward Deep Neural Networks (DNNs) robustness is a relevant property to study, since it allows to establish whether the classification performed by DNNs is vulnerable to small perturbations in the provided input, and several verification approaches have been developed to assess such robustness degree. Recently, an approach has been introduced to evaluate forward robustness, based on symbolic computations and designed for the ReLU activation function. In this paper, a generalization of such a symbolic approach for the widely adopted LeakyReLU activation function is developed. A preliminary numerical campaign, briefly discussed in the paper, shows interesting results.

Awareness and efforts to moderate energy consumption, desirable from both economical and environmental perspectives, are nowadays increasingly pursued. However, when critical sectors are addressed, energy saving should be cautiously tackled, so to not impair stringent dependability properties such contexts typically require. This is the case of the railway transportation system, which is the critical infrastructure this paper focuses on. For this system category, the attitude has been typically to neglect efficient usage of energy sources, motivated by avoiding to put dependability in danger. The new directives, both at national and international level, are going to change this way of thinking. Our study intends to be a useful support to careful energy consumption. In particular, a refined stochastic modeling framework is offered, tailored to the railroad switch heating system, through which analyses can be performed to understand the sophisticated dynamics between the system (both the cyber and physical components) and the surrounding weather conditions.

Many studies in neuropsychology have highlighted that expert musicians, who started learning music in childhood, present structural differences in their brains with respect to non-musicians. This indicates that early music learning affects the development of the brain. Also, musicians' neuronal activity is different depending on the played instrument and on the expertise. This difference can be analysed by processing electroencephalographic (EEG) signals through Artificial Intelligence models. This paper explores the feasibility to build an automatic model that distinguishes violinists from pianists based only on their brain signals. To this aim, EEG signals of violinists and pianists are recorded while they play classical music pieces and an Artificial Neural Network is trained through a cloud computing platform to build a binary classifier of segments of these signals. Our model has the best classification performance on 20 seconds EEG segments, but this performance depends on the involved musicians' expertise. Also, the brain signals of a cellist are demonstrated to be more similar to violinists' signals than to pianists' signals. In summary, this paper demonstrates that distinctive information is present in the two types of musicians' brain signals, and that this information can be detected even by an automatic model working with a basic EEG equipment.

Many foothill zones in Italy contain aquifer systems of strategic interest for water supplying, especially for drinking purposes (Doveri et al., 2016). The hydrogeological features in these environments generally promote the infiltration of both local rainfall and seepage from stream water originating in mountain catchments. On the other hand, because of their linkage with mountain areas, these systems are characterized by significant sensitivity towards the meteo-climatic variations and changes. To cope with climate change, a high level of knowledge of this type of aquifer should be reached in order to address the water management issues. This work focus on the aquifer system extending in the foothill plain located in the Piedmont region (NW Italy), between the Western Alps and the Torino Hill (Piana et al., 2017). Taking into account previous studies (De Luca et al., 2014 and references therein) and datasets from monitoring activities institutionally performed by the Environmental Protection Agency of Piedmont Region (ARPA Piemonte), the study examined the geological, hydrogeological and hydraulic-hydrodynamic features of the aquifer, as well as the chemistry of groundwater. This comprehensive approach steered the definition of the aquifer system conceptual model as well as the evolution of groundwater quantity and the chemical quality of groundwater. The statistical analysis performed on datasets highlighted some trends over the lasts two decades, both for groundwater quantity and quality. In particular, a significant sensitivity to meteo-climate conditions was pointed out by high confidence trends of piezometric level decrease occurred over both the periods 2004-2008 and 2010-2017, because of the respective rainfall decrease patterns. The rainy year 2009, which interposed itself between the two relatively dry periods, promoted sharp increases in terms of piezometric levels and concentrations of some chemical compounds in groundwater, thus pointing out as the hydrologic extremes can lead to the accumulation of nutrients and salinity in the unsaturated zone of the aquifer and their successive concentrate release in groundwater.

We discuss the efficient computation of performance, reliability, and availability measures for Markov chains; these metrics — and the ones obtained by combining them, are often called performability measures. We show that this computational problem can be recasted as the evaluation of a bilinear form induced by appropriate matrix functions, and thus solved by leveraging the fast methods available for this task. We provide a comprehensive analysis of the theory required to translate the problem from the language of Markov chains to the one of matrix functions. The advantages of this new formulation are discussed, and it is shown that this setting allows to easily study the sensitivities of the measures with respect to the model parameters. Numerical experiments confirm the effectiveness of our approach; the tests we have run show that we can outperform the solvers available in state of the art commercial packages on a representative set of large scale examples.

Yearly water balance of the Piedmont Alpine zone

Many foothill zones in Italy contain aquifer systems of strategic interest for water supplying, especially for drinking purposes (Doveri et al., 2016). The hydrogeological features in these environments generally promote the infiltration of both local rainfall and stream water originating in mountain catchments. On the other hand, because of their linkage with mountain areas, these systems are characterized by significant sensitivity towards the meteo-climatic variations and changes. Hence, a high level of knowledge of the foothill aquifers should be reached for addressing the water management issues by real technical approaches and management actions. This work focus on the aquifer system extending in the foothill plain located in the Piedmont region (NW Italy), between the Western Alps and the Torino Hill (Piana et al. 2017). This area is of scientific interest, since it represents the hydrogeological "transition zone" between the Western Po Plain and the southern Piedmont Plain aquifer systems (Irace et al., 2010). Furthermore, the interest towards these aquifers is tied to the reliance on their groundwater resource for drinking water supply, which is performed by SMAT (Società Metropolitana Acque Torino). In close cooperation with SMAT, which also co-founded the project, and taking also into account previous studies (De Luca et al., 2014; De Luca and Osella, 2014) and datasets from monitoring activities institutionally performed by the Environmental Protection Agency of Piedmont Region (ARPA Piemonte), the study examined the geological, hydrogeological and hydraulic-hydrodynamic features of the aquifer, as well as the chemistry of groundwater. Furthermore, some preliminary isotopic analyses were performed. The aquifer recharge estimated through a hydrologic balance approach by Brussolo et al. (2018 - in this conference) was also taken into account. This comprehensive approach steered the definition of the aquifer system conceptual model, comprising the kind of rocks hosting groundwater and their hydraulic properties, the arrangement of groundwater flow, the seasonal evolution of groundwater quantity and the chemical quality of groundwater. Furthermore, the statistical analysis performed on datasets from monitoring stations highlighted some trends over decades. The main results can be summarized as follows: - the system consists of a multilayer that has a phreatic aquifer overlying a succession of impermeable and permeable layers, in which groundwater flow generally occurs in confined to semi-confined conditions; -the phreatic aquifer has thicknesses between 30 and 80 m and it is hosted in gravel, sandy gravel and pebbles of fluvial and fluvioglacial origin (Pleistocene-Holocene deposits). The hydraulic conductivity ranges from 5 * 10-4 m/s to 5 * 10-3 m/s; -the confined to semi-confined sub-system mainly develops within the Pliocene continental deposits, which consist of coarse-grained sands and gravels, alternating to peat-rich clayey and silty deposits. The average hydraulic conductivity of this system ranges from 4*10-5 to 4*10-4 m/s; -the aquifer system is fed by infiltration of local rainfall, river seepage and transfers of groundwater that originate in upland zones. The outputs of groundwater is for exploitation by wells and for drainage operated by rivers, in the lower part of the plain. Generally, the groundwater flow of the phreatic aquifer is separated from that of the confined to semi-confined sub-system. Nevertheless, over wide zones the shallow aquifer and the uppermost permeable layers of the deeper system result in continuity (or separated by weak thicknesses of clayey deposits), thus making possible the mixing of groundwater between the two sub-systems. The main groundwater flow paths occur from northwest to southeast and from west to east; -groundwater are mainly of the Ca(Mg)-HCO3 type with a relatively low salinity for both unconfined and confined aquifers. Nevertheless, the phreatic aquifer hosts groundwater with a relatively higher salinity and, in some zones, characterized by increasing of Cl and SO4 contents. Hence, a certain impact from anthropic activities seems to be possible. In this terms the confined to semi-confined sub-system appears effectively more protected, even though the higher salinity and Cl contents observed in a few deeper wells suggest the possibility of connection with the shallower aquifer.

This paper addresses the generation of stochastic models for dependability and performability analysis of complex systems, through automatic replication of template models inside the Möbius modeling framework. The proposed solution is tailored to systems composed by large populations of similar non-anonymous components, loosely interconnected with each other (as typically encountered in the electrical or transportation sectors). The approach is based on models that define channels shared among replicas, used to exchange the values of each state variable of a replica with the other replicas that need to use them. The goal is to improve the performance of simulation based solvers with respect to the existing state-sharing approach, when employed in the modeling of the addressed class of systems. Simulation results for the time overheads induced by both channel-sharing and state-sharing approaches for different system scenarios are presented and discussed. They confirm the expected gain in efficiency of the proposed channel-sharing approach in the addressed system context.

Diplomazia2 is framed within the program "Science for Diplomacy: Multi-disciplinary Training Program", and it is the result of closer and more mature collaboration between the Directorate General for Cooperation on Development of the Ministry for Foreign Affairs and International Cooperation (MAECI), the Italian Agency for Cooperation on Development (AICS) and the National Research Council (CNR). The implementation of joint projects such as DIPLOMAzia2 highlights the effectiveness of the diplomacy-science partnership as the driving force behind the achievement of relevant goals jointly pursued by the international community.

This paper addresses the generation of stochastic models for dependability and performability analysis of complex systems, through automatic replication of template models. The proposed solution is tailored to systems composed by large populations of similar non-anonymous components, interconnected with each other according to a variety of topologies. A new efficient replication technique is presented and its implementation is discussed. The goal is to improve the performance of simulation solvers with respect to standard approaches, when employed in the modeling of the addressed class of systems, in particular for loosely interconnected system components (as typically encountered in the electrical or transportation sectors). Effectiveness of the new technique is demonstrated by comparison with a state of the art alternative solution on a representative case study.

Jacobian-free Newton-Raphson methods are general purpose iterative non-linear system solvers. The need to solve non-linear systems is ubiquitous throughout computational physics [1] and Jacobian-free Newton-Raphson methods can offer scalability, super-linear convergence and applicability. In fact, applications span from discretized PDEs [2] to power-flow problems [3]. The focus of this article is on Inexact-Newton-Krylov [2] and Quasi-Inverse-Newton [4] methods. For both of them, we prove analytically that the initial ordering of the equations can have a great impact on the numerical solution, as well as on the number of iterations to reach the solution. We also present numerical results obtained from a simple but representative case study, to quantify the impact of initial equations ordering on a concrete scenario.

Smart grids provide services at the basis of a number of application sectors, several of which are critical from the perspective of human life, environment or financials. It is therefore paramount to be assisted by technologies able to analyze the smart grid behavior in critical scenarios, e.g. where cyber malfunctions or grid disruptions occur. In this paper, we present a stochastic modelling framework to quantitatively assess representative indicators of the resilience and quality of service of the distribution grid, in presence of accidental faults and malicious attacks. The results from the performed analysis can be exploited to understand the dynamics of failures and to identify potential system vulnerabilities, against which appropriate countermeasures should be developed. The features of the proposed analysis framework are discussed, pointing out the strong non-linearity of the involved physics, the developed solutions to deal with control actions and the definition of indicators under analysis. A case study based on a real-world network is also presented.

The electric power system is among the most critical infrastructures, being a support for many of them and for key resource sectors. In fact, the complex, digital world around us requires electric power for fundamental aspects of most business and consumer activities. Therefore, it is paramount to assure the correct power supply. Following the increasing trend in electricity generation from renewable sources, pushed by the need to meet sustainable energy goals in many countries, more sophisticated control strategies are being adopted to regulate the operation of the electric power system. Analyzing their effectiveness and ability to face operation in critical scenarios, such as in presence of failures, is certainly a relevant aspect to investigate. In this paper we focus on the medium voltage grid control and adopt astochastic modeling framework appropriate to analyze voltage controlstrategies in presence of selected failure scenarios. The impact ofthe addressed failures on the ability of the control to satisfy voltage requirements is assessed in a real-world reference grid through indicators representative of the resilienceshown by the analyzed system. Variability in both generation and loads is covered, to reflectrealistic contexts where the addressed system is called to operate.

The multidisciplinary approach (geological and geomorphological studies, structural analysis, geoelectrical and seismic surveys, borehole data) utilized in this research allowed the definition of a reliable geological model of the Torrio landslide, one of the largest mass movements of the whole Northern Apennines occurring in the Aveto valley (Emilia-Romagna Region) and that determined the inclusion of the Torrio old villages in the “List of inhabited centres to be transferred”. The results indicate that the Torrio mega-landslide is actually composed by three superposed main landslide bodies characterized by different genetic mechanisms, timing of emplacement and state of activity, but all showing deep sliding surfaces (from 30–40 m up to 250 m). This study represents a considerable improvement for the objective of achieving the landslide risk assessment mapping of the territory surrounding the Torrio inhabited centre, according to the Emilia-Romagna Region legislation.