2023 has replaced 2016 as the warmest year on record since 1850, bringing us closer to the 1.5 oC limit set by the Paris Agreement. High temperatures increase the likelihood of extreme events, with heatwaves and drought being prominent among them. Climate change has led to a rise in the frequency of droughts, affecting countries that never experienced them. Assessing drought events is crucial and satellite data can provide significant assistance due to its large spatial coverage and continuous data supply. Based on the Infrared Atmospheric Sounder Interferometer (IASI), we designed a new Water Deficit Index (wdi) that we have already proven useful in detecting drought events. Unfortunately, infrared sensors such as IASI cannot penetrate thick cloud layers, so observations are blinded to surface emissions under cloudiness bringing sparse and not homogeneous distributed data over a given spatial region. To reconstruct a model of the field of interest for the entire surface on a regular grid mesh, interpolation techniques, and spatial statistics to deal with huge data sets are mandatory. In this paper, we exploited the capability of two machine learning algorithms, i.e. gradient boosting and random forest, in converting IASI L2 scattered data to a regular L3 grid. Specifically, we trained a model that can predict the wdi index over a 0.05o regular grid, using data from other sensors as a proxy together with vegetational products, soil indices, and territorial and geographic information as covariates. We applied the methodology over the Po Valley region, which experienced an intense drought in the last three years causing high vegetation and soil water stress. Overall, we found that these methods can yield good results and allow simultaneous regular grid conversion and downscaling.
Infrared radiative transfer, Vegetation and soil water stress, Drought, IASI, Surface Temperature, Dew point temperature, Machine Learning, Downscaling
Clustering univariate functional data is mostly based on projecting the curves onto an adequate basis and applying some distance or similarity models on the coefficients. The basis functions should be chosen depending on features of the function being estimated. Commonly used are Fourier, polynomial and splines, but these may not be well suited for curves that exhibit inhomogeneous behavior. Wavelets on the contrary are well suited for identifying highly discriminant local time and scale features, and are able to adapt to the data smoothness. In recent years, few methods, relying on wavelet-based similarity measures, have been proposed for clustering curves, observed on equidistant points. In this work, we present a non-equidistant design wavelet based method for non-parametrically estimating and clustering a large number of curves. The method consists of several crucial stages: fitting functional data by non-equispaced design wavelet regression, screening out nearly flat curves, denoising the remaining curves with wavelet thresholding, and finally clustering the denoised curves. Simulation studies compare our proposed method with some other functional clustering methods. The method is applied for clustering some real functional data profiles.
The Mediterranean basin is one of those areas where the impact of climate change is showing its most alarming consequences. Many regions in this area, both woodlands and croplands, have been suffering from droughts and water deficits due to the intense summer heatwaves of the last decades. Monitoring these phenomena is key to understanding how they are evolving and what could be done to mitigate their effects. Emissivity is a useful parameter in identifying the presence (or absence) of water. Surface and dew point temperatures are extremely useful not only in measuring the intensity of the heatwave but also in accounting for how much water content the surface is losing as humidity to the atmosphere. This paper presents a climatological study of Southern Italy’s water loss for the period 2015-2023 based on daily observations acquired by the Infrared Atmospheric Sounding Interferometer (IASI), mounted on top of EUMETSAT’s MetOp satellites. The Water Deficit Index (WDI) and the Emissivity Contrast Index (ECI) were estimated: monthly averages of each quantity were produced for the period of interest. Moreover, a validation with in situ measurements was conducted to better understand how these heatwave-induced droughts have been impacting the surface on different types of land covers.
climate change, remote sensing, droughts, heat waves, vegetation, water deficit, emissivity, land cover
: The brain-related phenotypes observed in 22q11.2 deletion syndrome (DS) patients are highly variable, and their origin is poorly understood. Changes in brain metabolism might contribute to these phenotypes, as many of the deleted genes are involved in metabolic processes, but this is unknown. This study shows for the first time that Tbx1 haploinsufficiency causes brain metabolic imbalance. We studied two mouse models of 22q11.2DS using mass spectrometry, nuclear magnetic resonance spectroscopy, and transcriptomics. We found that Tbx1 +/- mice and Df1/+ mice, with a multigenic deletion that includes Tbx1, have elevated brain methylmalonic acid, which is highly brain-toxic. Focusing on Tbx1 mutants, we found that they also have a more general brain metabolomic imbalance that affects key metabolic pathways, such as glutamine-glutamate and fatty acid metabolism. We provide transcriptomic evidence of a genotype-vitamin B12 treatment interaction. In addition, vitamin B12 treatment rescued a behavioural anomaly in Tbx1 +/- mice. Further studies will be required to establish whether the specific metabolites affected by Tbx1 haploinsufficiency are potential biomarkers of brain disease status in 22q11.2DS patients.
Modern ICT infrastructures, i.e., cyber-physical systems and critical infrastructures relying on interconnected IT (Information Technology)- and OT (Operational Technology)-based components and (sub-)systems, raise complex challenges in tackling security and safety issues. Nowadays, many security controls and mechanisms have been made available and exploitable to solve specific security needs, but, when dealing with very complex and multifaceted heterogeneous systems, a methodology is needed on top of the selection of each security control that will allow the designer/maintainer to drive her/his choices to build and keep the system secure as a whole, leaving the choice of the security controls to the last step of the system design/development. This paper aims at providing a comprehensive methodological approach to design and preliminarily implement an Open Platform Architecture (OPA) to secure the cyber-physical systems of critical infrastructures. Here, the Open Platform Architecture (OPA) depicts how an already existing or under-design target system (TS) can be equipped with technologies that are modern or currently under development, to monitor and timely detect possibly dangerous situations and to react in an automatic way by putting in place suitable countermeasures. A multifaceted use case (UC) that is able to show the OPA, starting from the security and safety requirements to the fully designed system, will be developed step by step to show the feasibility and the effectiveness of the proposed methodology.
Cybersecurity
Monitoring
Firewalling
Rule distribution
Slow DoS attack
Denial of service
Industrial security
Critical infrastructure protection
Security investments
Current research directions indicate that vehicles with autonomous capabilities will increase in traffic contexts. Starting from data analyzed in R. E. Stern et al. (2018), this paper shows the benefits due to the traffic control exerted by a unique autonomous vehicle circulating on a ring track with more than 20 human-driven vehicles. Considering different traffic experiments with high stop-and-go waves and using a general microscopic model for emissions, it was first proved that emissions reduces by about 25%. Then, concentrations for pollutants at street level were found by solving numerically a system of differential equations with source terms derived from the emission model. The results outline that ozone and nitrogen oxides can decrease, depending on the analyzed experiment, by about 10% and 30%, respectively. Such findings suggest possible management strategies for traffic control, with emphasis on the environmental impact for vehicular flows.
road traffic modeling, traffic waves, emissions, Nitrogen oxides, ozone production
The mathematical modeling of various real-life phenomena often leads to the formulation of positive and conservative Production-Destruction differential Systems (PDS). Here we address a general finite horizon Optimal Control Problem (OCP) for PDS and delve into the properties of its continuous-time solution. Leveraging the dynamic programming approach, we recast the OCP as a backward-in-time Hamilton-Jacobi-Bellman (HJB) equation, whose unique viscosity solution corresponds to the value function [1]. We then propose a parallel-in-space semi-Lagrangian approximation scheme for the HJB equation [3] and derive the optimal control in feedback form. Finally, to reconstruct the optimal trajectories of the controlled PDS, we employ unconditionally positive and conservative modified Patankar linear multistep methods [2]. [1] CRANDALL, M. G.; ISHII, H.; LIONS, P.-L. User’s guide to viscosity solutions of second order partial differential equations. Bull. Amer. Math. Soc., 1992, 27.1: 1-67. [2] IZZO, G.; MESSINA, E.; PEZZELLA, M.; VECCHIO, A. TITOLO DA ACCERTARE LUNEDì. In preparation. [3] FALCONE, M.; FERRETTI, R. Semi-Lagrangian approximation schemes for linear and Hamilton—Jacobi equations. SIAM, 2013.
Investigating the impact of the regularization parameter on EEG resting-state source reconstruction and functional connectivity using real and simulated data
Leone F.
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Caporali A.
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Pascarella A.
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Perciballi C.
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Maddaluno O.
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Basti A.
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Belardinelli P.
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Marzetti L.
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Di Lorenzo G.
;
Betti V.
: Accurate EEG source localization is crucial for mapping resting-state network dynamics and it plays a key role in estimating source-level functional connectivity. However, EEG source estimation techniques encounter numerous methodological challenges, with a key one being the selection of the regularization parameter in minimum norm estimation. This choice is particularly intricate because the optimal amount of regularization for EEG source estimation may not align with the requirements of EEG connectivity analysis, highlighting a nuanced trade-off. In this study, we employed a methodological approach to determine the optimal regularization coefficient that yields the most effective reconstruction outcomes across all simulations involving varying signal-to-noise ratios for synthetic EEG signals. To this aim, we considered three resting state networks: the Motor Network, the Visual Network, and the Dorsal Attention Network. The performance was assessed using three metrics, at different regularization parameters: the Region Localization Error, source extension, and source fragmentation. The results were validated using real functional connectivity data. We show that the best estimate of functional connectivity is obtained using 10-2, while 10-1 has to be preferred when source localization only is at target.
Introduction: The formation and functioning of neural networks hinge critically on the balance between structurally homologous areas in the hemispheres. This balance, reflecting their physiological relationship, is fundamental for learning processes. In our study, we explore this functional homology in the resting state, employing a complexity measure that accounts for the temporal patterns in neurodynamics. Methods: We used Normalized Compression Distance (NCD) to assess the similarity over time, neurodynamics, of the somatosensory areas associated with hand perception (S1). This assessment was conducted using magnetoencephalography (MEG) in conjunction with Functional Source Separation (FSS). Our primary hypothesis posited that neurodynamic similarity would be more pronounced within individual subjects than across different individuals. Additionally, we investigated whether this similarity is influenced by hemisphere or age at a population level. Results: Our findings validate the hypothesis, indicating that NCD is a robust tool for capturing balanced functional homology between hemispheric regions. Notably, we observed a higher degree of neurodynamic similarity in the population within the left hemisphere compared to the right. Also, we found that intra-subject functional homology displayed greater variability in older individuals than in younger ones. Discussion: Our approach could be instrumental in investigating chronic neurological conditions marked by imbalances in brain activity, such as depression, addiction, fatigue, and epilepsy. It holds potential for aiding in the development of new therapeutic strategies tailored to these complex conditions, though further research is needed to fully realize this potential.
functional source separation
neurodynamics
normalized compression distance
resting state
temporal course of the neuronal electrical activity
In recent years, the use of techniques based on electromagnetic radiation as an investigative tool in the agri-food industry has grown considerably, and between them, the application of imaging and THz spectroscopy has gained significance in the field of food quality control. This study presents the development of an experimental setup operating in transmission mode within the frequency range of 18 to 40 GHz, which was specifically designed for assessing various quality parameters of hazelnuts. The THz measurements were conducted to distinguish between healthy and rotten hazelnut samples. Two different data analysis techniques were employed and compared: a traditional approach based on data matrix manipulation and curve fitting for parameter extrapolation, and the utilization of a Self-Organizing Map (SOM), for which we use a neural network commonly known as the Kohonen neural network, which is recognized for its efficacy in analyzing THz measurement data. The classification of hazelnuts based on their quality was performed using these techniques. The results obtained from the comparative analysis of coding efforts, analysis times, and outcomes shed light on the potential applications of each method. The findings demonstrate that THz spectroscopy is an effective technique for quality assessment in hazelnuts, and this research serves to clarify the suitability of each analysis technique.
A thorough experimental assessment of THz-TDS plasma diagnostic techniques for nuclear fusion applications
Teka G. G.
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Peng K.
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Alonzo M.
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Bombarda F.
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Koch-Dandolo C. L.
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Senni L.
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Taschin A.
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Zerbini M.
In this paper, the study of a plasma diagnostic system based on the THz time domain spectroscopy technique is presented. Such a system could potentially probe a large part of the electromagnetic spectrum currently covered by several other diagnostics in a single measurement. This feature, keeping in mind the basic requirements for plasma diagnostics in nuclear fusion experiments, such as robustness and hard environment applicability, as well as durability and low maintenance, makes the diagnostic of great interest. A conceptual design of the THz-TDS diagnostic has been developed, starting from the well-established classical microwave and far infrared plasma diagnostics landscape. The physical constraints and required instrumental characteristics have been studied and are described in detail here, together with the solutions available for each type of plasma measurement. Specific experimental laboratory tests of the different experimental configurations have been carried out, evaluating the capacity and potential of the novel diagnostic, together with the instrumental constraint, within the diagnostic parameter space.
The project of the Visible Spectroscopy diagnostics for the Zeff radial profile measurement and for the divertor visible imaging spectroscopy, designed for the new tokamak DTT (Divertor Tokamak Test), is presented. To deal with the geometrical constraints of DTT and to minimize the diagnostics volume inside the access port, an integrated and compact solution hosting the two systems has been proposed. The Zeff radial profile will be evaluated from the Bremsstrahlung radiation measurement in the visible spectral range, acquiring light along ten Lines of Sight (LoS) in the upper part of the poloidal plane. The plasma emission will be focused on optical fibers, which will carry it to the spectroscopy laboratory. A second equipment, with a single toroidal LoS crossing the plasma centre and laying on the equatorial plane, will measure the average Zeff on a longer path, minimizing the incidental continuum spectrum contaminations by lines/bands emitted from the plasma edge. The divertor imaging system is designed to measure impurity and main gas influxes, to monitor the plasma position and kinetics of impurities, and to follow the plasma detachment evolution. The project aims at obtaining the maximum coverage of the divertor region. The collected light can be shared among different spectrometers and interferential filter devices placed outside the torus hall to easily change their setup. The system is composed of two telescopes, an upper and a lower one, allowing both a perpendicular and a tangential view of the DTT divertor region. This diagnostic offers a unique and compact solution designed to cope the demanding constraints of this next-generation tokamak fusion devices, integrating essential tools for wide-ranging impurity characterization and versatile investigation of divertor physics.
The study of materials for space exploration is one of the most interesting targets of international space agencies. An essential tool for realizing light junctions is epoxy adhesive (EA), which provides an elastic and robust material with a complex mesh of polymeric chains and crosslinks. In this work, a study of the structural and chemical modification of a commercial two-part flexible EA (3MTM Scotch-WeldTM EC-2216 B/A Gray), induced by 60Co gamma radiation, is presented. Combining different spectroscopic techniques, such as the spectroscopic Fourier transform infrared spectroscopy (FTIR), the THz time-domain spectroscopy (TDS), and the electron paramagnetic resonance (EPR), a characterization of the EA response in different regions of the electromagnetic spectrum is performed, providing valuable information about the structural and chemical properties of the polymers before and after irradiation. A simultaneous dissociation of polymeric chain and crosslinking formation is observed.The polymer is not subject to structural modification at an absorbed dose of 10 kGy, in which only transient free radicals are observed. Differently, between 100 and 500 kGy, a gradual chemical degradation of the samples is observed together with a broad and long-living EPR signal appearance. This study also provides a microscopic characterization of the material useful for the mechanism evaluation of system degradation.
We propose a non-standard numerical method for the solution of a system of integro-differential equations describing an epidemic of an infectious disease with behavioral changes in contact patterns. The method is constructed in order to preserve the key characteristics of the model, like the positivity of solutions, the existence of equilibria, and asymptotic behavior. We prove that the numerical solution converges to the exact solution as the step size h of the discretization tends to zero. Furthermore, the method is first-order accurate, meaning that the error in the discretization is O(h), it is linearly implicit, and it preserves all the properties of the continuous problem, unconditionally with respect to h. Numerical simulations show all these properties and confirm, also by means of a case-study, that the method provides correct qualitative information at a low computational cost
Numerous real-world phenomena involve the interplay between processes of production and decay or consumption and can be therefore modeled by positive and conservative Production-Destruction differential Systems (PDS). Patankar-type schemes are linearly implicit integrators specifically designed for PDS with the aim of retaining, with no restrictions on the stepsize, the positivity of the solution and the linear invariant of the system. In this work we extend the Patankar technique, already established for Runge-Kutta and deferred correction methods, to multistep schemes. As a result, we introduce the class of Modified Patankar Linear Multistep (MPLM) methods, for which a thorough investigation of the convergence is carried out. Furthermore, we design an embedding procedure for the computation of the Patankar weights and prove the high order of convergence of the resulting MPLM scheme. A comparative study on the simulation of selected test cases highlights the competitive performance of the MPLM methods with respect to other Patankar-type discretizations.
Patankar-type schemes, Positivity-preserving, High order, Conservativity, Linear multistep methods
Divertor Tokamak Test facility project: status of design and implementation
Romanelli F.
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Abate D.
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Acampora E.
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Agguiaro D.
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Agnello R.
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Agostinetti P.
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Agostini M.
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Aimetta A.
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Albanese R.
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Alberti G.
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Albino M.
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Alessi E.
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Almaviva S.
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Alonzo M.
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Ambrosino R.
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Andreoli P.
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Angelone M.
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Angelucci M.
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Angioni C.
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Angrisani Armenio A.
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Antonini P.
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Aprile D.
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Apruzzese G.
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Aquilini M.
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Aragone G.
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Arena P.
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Ariola M.
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Artaserse G.
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Aucone L.
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Augieri A.
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Auriemma F.
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Ayllon Guerola J.
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Badodi N.
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Baiocchi B.
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Balbinot L.
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Baldacchini C.
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Balestri A.
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Barberis T.
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Barone G.
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Barucca L.
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Baruzzo M.
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Begozzi S.
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Belardi V.
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Belli F.
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Belpane A.
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Beone F.
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Bertolami S.
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Bianucci S.
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Bifaretti S.
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Bigioni S.
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Bin W.
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Boccali P.
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Boeswirth B.
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Bogazzi E.
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Bojoi R.
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Bollanti S.
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Bolzonella T.
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Bombarda F.
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Bonan M.
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Bonanomi N.
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Bonaventura A.
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Boncagni L.
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Bonesso M.
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Bonfiglio D.
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Bonifetto R.
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Bonomi D.
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Borgogno D.
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Borzone T.
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Botti S.
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Boz E.
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Braghin F.
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Brena M.
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Brezinsek S.
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Brombin M.
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Bruschi A.
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Buonocore S.
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Buratti P.
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Buratti P.
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Busi D.
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Calabro G.
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Caldora M.
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Calvo G.
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Camera G.
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Campana G.
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Candela S.
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Candela V.
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Cani F.
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Cantone L.
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Capaldo F.
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Cappello S.
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Caponero M.
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Carchella S.
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Cardinali A.
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Carnevale D.
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Carraro L.
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Carrelli C.
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Casalegno V.
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Casiraghi I.
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Castaldo C.
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Castaldo A.
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Castro G.
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Carpignano A.
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Causa F.
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Cavazzana R.
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Cavedon M.
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Cavenago M.
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Cecchini M.
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Ceccuzzi S.
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Celentano G.
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Celona L.
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Centioli C.
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Centomani G. V.
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Cesaroni S.
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Chiariello A. G.
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Chomicz R.
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Cianfarani C.
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Cichocki F.
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Cinque M.
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Cioffi A.
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Ciotti M.
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Cipriani M.
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Ciufo S.
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Claps V.
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Claps G.
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Coccorese V.
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Coccorese D.
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Colangeli A.
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Coltella T.
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Consoli F.
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Cordella F.
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Corradini D.
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Costa O.
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Crea F.
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Cremona A.
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Crescenzi F.
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Crisanti F.
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Cristofari G.
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Croci G.
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Cucchiaro A.
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D'Ambrosio D.
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Dal Molin M.
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Dalla Palma M.
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Dane F.
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Day C.
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De Angeli M.
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De Leo V.
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De Luca R.
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De Marchi E.
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De Marzi G.
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De Masi G.
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De Nardi E.
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De Piccoli C.
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De Sano G.
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De Santis M.
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De Tommasi G.
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Del Nevo A.
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Delfino A.
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Della Corte A.
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Deodati P.
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Desiderati S.
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Di Ferdinando E.
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Di Florio M. G.
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Di Gironimo G.
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Di Grazia L. E.
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Di Marzo V.
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Di Paolo F.
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Di Pietro E.
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Di Prinzio M.
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Di Zenobio A.
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Dima R.
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Domenichelli A.
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Doria A.
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Dose G.
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Dubbioso S.
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Dulla S.
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Duran I.
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Eboli M.
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Elitropi M.
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Emanuelli E.
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Esposito B.
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Ettorre P.
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Fabbri C.
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Fabbri F.
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Fadone M.
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Faggiano M. M.
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Falcioni F.
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Falessi M. V.
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Fanale F.
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Fanelli P.
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Fassina A.
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Fassina A.
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Favaretto M.
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Favero G.
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Ferraris M.
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Ferrazza F.
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Ferretti C.
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Ferro A.
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Ferron N.
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Fiamozzi Zignani C.
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Figini L.
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Filippi F.
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Filippini M.
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Fimiani A.
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Fincato M.
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Fiorenza F.
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Fiorucci D.
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Flammini D.
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Flora F.
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Fonnesu N.
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Franz P.
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Fusco V.
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Fusco P.
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Gaio E.
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Galdon Quiroga J.
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Gobbin M.
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Granucci G.
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Grasso D.
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Grasso T.
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Grazioso S.
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Greuner H.
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Griva G.
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Grosso G.
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Guerini S.
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Gunn J. P.
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Hauer V.
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Hidalgo Salaverri J.
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Hoppe M.
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Houry M.
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Hoelzl M.
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Iaboni A.
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Iafrati M.
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Iaiunese A.
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Imbriani V.
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Koechl F.
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Kryzhanovskyy A.
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Laguardia L.
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Lampasi D. A.
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Lanchi C.
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Lanzotti F.
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Lanzotti A.
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Laquaniti M.
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Leone F.
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Li J.
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Libe M.
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Lisanti F.
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Liuzza D.
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Locati F.
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Lombroni R.
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Lorenzini R.
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Lorusso P.
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Lotto L.
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Loureiro J.
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Lucca F.
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Luda Di Cortemiglia T.
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Manca G.
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Mandala P.
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Mandolesi B.
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Mandrile F.
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Manduchi G.
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Manganelli M.
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Mantica P.
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Marchiori G.
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Marconato N.
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Marelli G.
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Mariani A.
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Marin A.
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Marinari R.
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Marinelli M.
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Marino F.
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Marino P.
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Marocco D.
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Marsilio R.
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Martelli E.
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Martin P.
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Martinelli F.
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Martini G.
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Martone R.
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Marucci A.
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Marzullo D.
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Masala V.
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Mascali D.
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Masi A.
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Mastrostefano S.
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Mattei M.
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Mauro G.
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Mauro S.
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Meineri C.
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Melaragni L.
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Mele A.
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Meller P.
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Meloni S.
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Menicucci I.
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Messina G.
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Mezi L.
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Micheletti M.
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Migliori S.
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Milanesio D.
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Milazzo F.
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Milazzo R.
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Mirizzi F.
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Monarca D.
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Monti C.
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Moriani A.
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Morici L.
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Moro A.
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Moro A.
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Moro F.
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Mosetti P.
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Mozzillo R.
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Murari A.
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Muraro A.
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Murra D.
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Muscente P.
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Musumeci S.
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Muzzi L.
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Nallo G. F.
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Napoli F.
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Nardon E.
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Naselli E.
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Neu R.
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Nocente M.
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Notazio M.
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Nowak S.
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Ocello E.
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Oliva A.
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Orsetti V.
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Orsini A.
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Ortino M.
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Ottavi M.
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Paccagnella G.
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Pacella D.
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Pagani I.
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Paganucci N.
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Pagliaro A.
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Palazzolo V.
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Palermo M.
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Palomba S.
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Panza F.
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Paoletti D.
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Parisi M.
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Pasqualotto R.
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Passarello S.
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Passoni M.
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Patton T.
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An overview is presented of the progress since 2021 in the construction and scientific programme preparation of the Divertor Tokamak Test (DTT) facility. Licensing for building construction has been granted at the end of 2021. Licensing for Cat. A radiologic source has been also granted in 2022. The construction of the toroidal field magnet system is progressing. The prototype of the 170 GHz gyrotron has been produced and it is now under test on the FALCON facility. The design of the vacuum vessel, the poloidal field coils and the civil infrastructures has been completed. The shape of the first DTT divertor has been agreed with EUROfusion to test different plasma and exhaust scenarios: single null, double null, X-divertor and negative triangularity plasmas. A detailed research plan is being elaborated with the involvement of the EUROfusion laboratories.
An X-ray Diffraction pattern consists of the relevant information (the signal) and the noisy background. Under the assumption that they behave as the components of a two-dimensional mixture (bicomponent fluid) having slightly different physical properties related to the density-gradients, a Lattice Boltzmann Method is applied to disentangle the two different diffusive dynamics. The solution is numerically stable, computationally not demanding and, moreover, it provides an efficient increase of the signal-to-noise ratio for patterns blurred by poissonian noise and affected by collection data anomalies (fiber-like samples, experimental setup, etc.). The model has been succesfully applied to different resolution images. Ke
Cyber risk is a significant concern for all types of businesses. The consequences of a cyber attack can be quite severe. Investing in security to mitigate the impact of such risks is a crucial task, both in terms of the frequency and the severity of cyber incidents. In this paper, we propose a practical application of the Gordon and Loeb model, thereby suggesting a methodology to estimate risk exposure and reconsidering some investment evaluation metrics. Our findings strongly support the claim that maximizing the expected net benefit of an investment solely at the optimal level is not sufficient for sound decision-making. On the contrary, incorporating metrics that evaluate the benefit in relation to risk and consider worst-case scenarios offers deeper insights
cyber risk, security economics, security investments, risk exposure, Gordon-Loeb model
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Tokamak à configuration variable (TCV), recently celebrating 30 years of near-continual operation, continues in its missions to advance outstanding key physics and operational scenario issues for ITER and the design of future power plants such as DEMO. The main machine heating systems and operational changes are first described. Then follow five sections: plasma scenarios. ITER Base-Line (IBL) discharges, triangularity studies together with X3 heating and N2 seeding. Edge localised mode suppression, with a high radiation region near the X-point is reported with N2 injection with and without divertor baffles in a snowflake configuration. Negative triangularity (NT) discharges attained record, albeit transient, βN ∼ 3 with lower turbulence, higher low-Z impurity transport, vertical stability and density limits and core transport better than the IBL. Positive triangularity L-Mode linear and saturated ohmic confinement confinement saturation, often-correlated with intrinsic toroidal rotation reversals, was probed for D, H and He working gases. H-mode confinement and pedestal studies were extended to low collisionality with electron cyclotron heating obtaining steady state electron iternal transport barrier with neutral beam heating (NBH), and NBH driven H-mode configurations with off-axis co-electron cyclotron current drive. Fast particle physics. The physics of disruptions, runaway electrons and fast ions (FIs) was developed using near-full current conversion at disruption with recombination thresholds characterised for impurity species (Ne, Ar, Kr). Different flushing gases (D2, H2) and pathways to trigger a benign disruption were explored. The 55 kV NBH II generated a rich Alfvénic spectrum modulating the FI fas ion loss detector signal. NT configurations showed less toroidal Alfvén excitation activity preferentially affecting higher FI pitch angles. Scrape-off layer and edge physics. gas puff imaging systems characterised turbulent plasma ejection for several advanced divertor configurations, including NT. Combined diagnostic array divertor state analysis in detachment conditions was compared to modelling revealing an importance for molecular processes. Divertor physics. Internal gas baffles diversified to include shorter/longer structures on the high and/or low field side to probe compressive efficiency. Divertor studies concentrated upon mitigating target power, facilitating detachment and increasing the radiated power fraction employing alternative divertor geometries, optimised X-point radiator regimes and long-legged configurations. Smaller-than-expected improvements with total flux expansion were better modelled when including parallel flows. Peak outer target heat flux reduction was achieved (>50%) for high flux-expansion geometries, maintaining core performance (H98 > 1). A reduction in target heat loads and facilitated detachment access at lower core densities is reported. Real-time control. TCV’s real-time control upgrades employed MIMO gas injector control of stable, robust, partial detachment and plasma β feedback control avoiding neoclassical tearing modes with plasma confinement changes. Machine-learning enhancements include trajectory tracking disruption proximity and avoidance as well as a first-of-its-kind reinforcement learning-based controller for the plasma equilibrium trained entirely on a free-boundary simulator. Finally, a short description of TCV’s immediate future plans will be given.
