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In many fields of science, various types of models are available to describe phenomena, observations and the results of experiments. In the last decades, given the enormous advances of information gathering technologies, also machine learning techniques have been systematically deployed to extract models from the large available databases. However, regardless of their origins, no universal criterion has been found so far to select the most appropriate model given the data. A unique solution is probably a chimera, particularly in applications involving complex systems. Consequently, in this work a utility-based approach is advocated. However, the solutions proposed are not purely subjective but all based on “objective” criteria, rooted in the properties of the data, to preserve generality and to allow comparative assessments of the results. Several methods have been developed and tested, to improve the discrimination capability of basic Bayesian and information theoretic criteria. Both the quality of the fits and the evaluation of model complexity are aspects addressed by the advances proposed. The competitive advantages of the individual alternatives, for both cross sectional data and time series, are clearly identified, together with their most appropriate fields of application. Particular attention has been paid to ensure that the proposed improvements of the criteria are easy to implement in practice, in both confirmatory and exploratory settings. Extensive numerical tests have been performed to support the conceptual and theoretical considerations.

Background: The aim of this research is to assess the predictive accuracy of the Infectious Diseases Seeker (IDS) – an innovative tool for prompt identification of the causative agent of infectious diseases during outbreaks – when field epidemiological data collected from a novel outbreak of unknown origin are analysed by the tool. For this reason, it has been taken into account the novel coronavirus disease (COVID-19) outbreak, which began in China at the end of December 2019, has rapidly spread around the globe, and it has led to a public health emergency of international concern (PHEIC), declared to the 30th of January 2020 by the World Health Organization (WHO). Methods: The IDS takes advantage of an off-line database, built before the COVID-19 pandemic, which represents a pivotal characteristic for working without an internet connection. The software has been tested using the epidemiological data available in different and progressive stages of the COVID-19 outbreak. As a comparison, the results of the tests performed using the epidemiological data from the Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) epidemic in 2002 and Middle East Respiratory Syndrome coronavirus (MERS-CoV) epidemic in 2012, are shown. Results: The overall outcomes provided by the software are comforting, as a matter of the fact that IDS has identified with a good accuracy the SARS and MERS epidemics (over 90%), while, as expected, it has not provided erroneous and equivocal readings after the elaboration COVID-19 epidemic data. Conclusions: Even though IDS has not recognized the COVID-19 epidemic, it has not given to the end user a false result and wrong interpretation, as expected by the developers. For this reason, IDS reveals itself as useful software to identify a possible epidemic or outbreak. Thus, the intention of developers is to plan, once the software will be released, dedicated updates and upgrades of the database (e.g., SARS-CoV-2) in order to keep this tool increasingly useful and applicable to reality.

In many areas of research, from neurobiology to nuclear fusion, which investigate complex dynamical systems involving numerous, different and interconnected physical quantities, the application of advanced analysis tools based on Chaos theory and Information theory has provided significant improvements both by supporting theoretical models and by highlighting hidden relationships between quantities characterizing the observed phenomena. The present article is therefore devoted to the analysis of synchronization experiments in magnetically controlled plasmas at JET, involving a ‘target’ quantity (sawteeth) and a ‘driver’ (modulated injected radio frequency) one. The typical approach to such analysis would start from assuming that the coupling between driver and target is free from external influences. However, since sawteeth can occur naturally and constitute a confounding factor in the analysis, by relaxing such an assumption a more realistic description of the system can emerge. The use of the conditional joint recurrence plots, complemented by the conditional transfer entropy, has provided further evidences supporting the effective influence of the pacing on the fast ions populations of both the minority and of the main plasma species in H-mode plasmas. The results highlight specific physical factors affecting the efficiency of the pacing and are in agreement with modelling estimates. The analysis performed then paves the way for future studies on more recent DT pulses performed at JET, and on data from other synchronization experiments.

The analysis of causality between systems is still an important research activity, which finds application in several fields of science. The software presented is a new tool for causality detection and analysis between time series. The proposed technique is based on time-delayed neural networks (TDNN). The tool is developed in MATLAB and it comprises three main functions. The first one returns the total causality between two or more systems of equations. The second tool is used to find the “time horizon”, id est the time delay at which the influence between the systems occurs. The last function is a causality feature detection to determine the time intervals, in which the mutual coupling is sufficiently strong to have a real influence on the target.

Background: Several technologies for rapid molecular identification of pathogens are currently available, jointly with monitoring tools (i.e., web-based surveillance tools, infectious diseases modelers, and epidemic intelligence methods), they represent important components for timely outbreak detection and identification of the involved pathogen. The application of these approaches is usually feasible and effective when performed by healthcare professionals with specific expertise and skills and when data and resources are easily accessible. Contrariwise, in the field situation where healthcare workers or first responders from heterogeneous competences can be asked to investigate an outbreak of unknown origin, a simple and suitable tool for rapid agent identification and appropriate outbreak management is highly needed. Most especially when time is limited, available data are incomplete, and accessible infrastructure and resources are inadequate. The use of a prompt, user-friendly, and accessible tool able to rapidly recognize an infectious disease outbreak and with high sensitivity and precision may be a game-changer to support emergency response and public health investigations. Methods: This paper presents the work performed to implement and test an innovative tool for prompt identification of infectious diseases during outbreaks, called Infectious Diseases Seeker (IDS). IDS is a standalone software that runs on the most common operative systems. It has been built by integrating a database containing an interim set of 60 different disease causative agents and COVID-19 data and is able to work in an off-line mode without requiring a network connection. Results: IDS has been applied in a real and complex scenario in terms of concomitant infectious diseases (yellow fever, COVID-19, and Lassa fever), as can be in the second part of 2020 in Nigeria. The outcomes have allowed inferring that yellow fever (YF), and not Lassa fever, was affecting the area under investigation. Conclusions: Our result suggests that a tool like IDS could be valuable for the quick and easy identification and discrimination of infectious disease outbreaks even when concurrent outbreaks occur, like for the case study of YF and COVID-19 pandemic in Nigeria.

The advancements in the field of robotics, specifically in the aerial robotics, combined with technological improvements of the capability of drones, have increased dramatically the use of these devices as a valuable tool in a wide range of applications. From civil to commercial and military area, the requirements in the emerging application for monitoring complex scenarios that are potentially dangerous for operators give rise to the need of a more powerful and sophisticated approach. This work aims at proposing the use of swarm drones to increase plume detection, tracking and source declaration for chemical releases. The several advantages which this technology may lead to this research and application fields are investigated, as well as the research and technological activities to be performed to make swarm drones efficient, reliable, and accurate.

Laser-based methods are widely used techniques for thermonuclear plasma diagnostics, since they can probe the internal of the plasma, being contactless and non-invasive. The interferometer, the polarimeter and Thomson scattering are the most widespread techniques, providing line-integral information of the electron density and the magnetic field (interferometer&ndash, polarimeter) and local information of the electron density and temperature (Thomson scattering). The design of the diagnostics is a fundamental step, which usually requires an iterative process to maximise the performances of the diagnostics and their durability. In the future reactors, such as ITER and DEMO, the working environment will be much challenging, due to the various electro-mechanical, thermal and nuclear loads which may affect the life of the components and degrade the performances of the diagnostics. This work aims to present the modelling of plasma interferometry, polarimetry and Thomson scattering applied to a ray optics code. The model, developed on the COMSOL Multiphysics software, can be easily interfaced with multiphysics problems, allowing the possibility to test the performances of the diagnostics in several conditions.

The work investigates refractory metals (bulk W, W produced via plasma spraying, W-1% La2O3 and Mo) of interest as plasma facing materials in future nuclear fusion reactors. They have been irradiated by a single Nd:YAG laser pulse to simulate the effects of transient thermal loads of high energy occurring in a tokamak under operative conditions and then examined by SEM observations. In all the materials the laser pulse induces a crater in the central area of laser spot surrounded by a ridge due to movement of molten metal while in a more external area a network of cracks is observed. Diameter and depth of the crater, ablated volume and morphological features of the surrounding area exhibit differences depending on the specific metal, its physical and microstructural characteristics which affect vaporization, melting and heat propagation from the irradiated spot.

Unconventional scenarios with hazardous radioactive levels are expected as consequences of accidents in the industrial sector of the nuclear energy production or following intentional releases of radioactive materials for terrorist purposes (dirty bombs, indoor contaminations, etc.). Nowadays, the need to balance the high standards of safety and security through an effective detection network is a matter of paramount importance. In this work, the authors’ challenge has been to design, realize and test a low-cost gamma detection and spectroscopy system which may be used in unmanned vehicles in general and/or drones with low payload capabilities. The designed platform may be used to carry out mapping or localization operations in order to reduce the risk factor for first responders or for the population affected by radiological and nuclear events. In this paper, the design process of a gamma ray detection and spectroscopy system based on affordable and commercially available technologies is presented along with the results of our ongoing characterization of the prototype.

In recent years, the techniques of the exact sciences have been applied to the analysis of increasingly complex and non-linear systems. The related uncertainties and the large amounts of data available have progressively shown the limits of the traditional hypothesis driven methods, based on first principle theories. Therefore, a new approach of data driven theory formulation has been developed. It is based on the manipulation of symbols with genetic computing and it is meant to complement traditional procedures, by exploring large datasets to find the most suitable mathematical models to interpret them. The paper reports on the vast amounts of numerical tests that have shown the potential of the new techniques to provide very useful insights in various studies, ranging from the formulation of scaling laws to the original identification of the most appropriate dimensionless variables to investigate a given system. The application to some of the most complex experiments in physics, in particular thermonuclear plasmas, has proved the capability of the methodology to address real problems, even highly nonlinear and practically important ones such as catastrophic instabilities. The proposed tools are therefore being increasingly used in various fields of science and they constitute a very good set of techniques to bridge the gap between experiments, traditional data analysis and theory formulation.

The rapid detection of ongoing outbreak - and the identification of causative pathogen - is pivotal for the early recognition of public health threats. The emergence and re-emergence of infectious diseases are linked to several determinants, both human factors - such as population density, travel, and trade - and ecological factors - like climate change and agricultural practices. Several technologies are available for the rapid molecular identification of pathogens [e.g. real-time polymerase chain reaction (PCR)], and together with on line monitoring tools of infectious disease activity and behaviour, they contribute to the surveillance system for infectious diseases. Web-based surveillance tools, infectious diseases modelling and epidemic intelligence methods represent crucial components for timely outbreak detection and rapid risk assessment. The study aims to integrate the current prevention and control system with a prediction tool for infectious diseases, based on regression analysis, to support decision makers, health care workers, and first responders to quickly and properly recognise an outbreak. This study has the intention to develop an infectious disease regressive prediction tool working with an off-line database built with specific epidemiological parameters of a set of infectious diseases of high consequences. The tool has been developed as a first prototype of a software solution called Infectious Diseases Seeker (IDS) and it had been established in two main steps, the database building stage and the software implementation stage (MATLAB® environment). The IDS has been tested with the epidemiological data of three outbreaks occurred recently: severe acute respiratory syndrome epidemic in China (2002-2003), plague outbreak in Madagascar (2017) and the Ebola virus disease outbreak in the Democratic Republic of Congo (2018). The outcomes are promising and they reveal that the software has been able to recognize and characterize these outbreaks. The future perspective about this software regards the developing of that tool as a useful and user-friendly predictive tool appropriate for first responders, health care workers, and public health decision makers to help them in predicting, assessing and contrasting outbreaks.

The increasingly sophisticated investigations of complex systems require more robust estimates of the correlations between the measured quantities. The traditional Pearson correlation coefficient is easy to calculate but sensitive only to linear correlations. The total influence between quantities is, therefore, often expressed in terms of the mutual information, which also takes into account the nonlinear effects but is not normalized. To compare data from different experiments, the information quality ratio is, therefore, in many cases, of easier interpretation. On the other hand, both mutual information and information quality ratio are always positive and, therefore, cannot provide information about the sign of the influence between quantities. Moreover, they require an accurate determination of the probability distribution functions of the variables involved. As the quality and amount of data available are not always sufficient to grant an accurate estimation of the probability distribution functions, it has been investigated whether neural computational tools can help and complement the aforementioned indicators. Specific encoders and autoencoders have been developed for the task of determining the total correlation between quantities related by a functional dependence, including information about the sign of their mutual influence. Both their accuracy and computational efficiencies have been addressed in detail, with extensive numerical tests using synthetic data. A careful analysis of the robustness against noise has also been performed. The neural computational tools typically outperform the traditional indicators in practically every respect.

Pulse shape discrimination, to distinguish between neutrons and gamma rays, is a very important classification task in thermonuclear fusion. Gaussian Mixture Models and probabilistic Support Vector Machines have been applied to hundreds of thousands of pulses obtained with a counter based on the NE213 liquid scintillator. The results of the two completely independent mathematical methods are in very good agreement, the maximum discrepancy being of the order of 2%. The achieved classification also shows an excellent value for the figure of merit, a Mahalanobis type of distance, implemented to quantify statistically the separation between the two particle distributions. These two machine learning tools provide also the probability of each example being a neutron or a gamma ray, allowing more detailed studies of the distribution of pulses. The proposed methodology therefore clearly outperforms previous techniques in practically all aspects of the classification.

Notwithstanding the efforts exerted over many years, disruptions remain a major impediment on the route to a magnetic confinement reactor of the Tokamak type. Machine learning predictors, relying on adaptive strategies, have recently proved to achieve very good performance. Even if their last generation implement a "from scratch" approach to learning, i.e. they can start predicting after the first example of each class (safe and disruptive), it would be extremely useful to profit from the experience of previous devices, when new machines come on online, to reduce excessive errors at the beginning of the learning process. In this paper, adaptive predictors, based on ensemble classifiers, have been operated on a series of AUG campaigns and then they have been deployed on several JET campaigns with the ILW, all together covering more than order of magnitude in plasma current. The criteria to normalise the signals and to translate the parameters of the predictors from one device to the other are discussed in detail. With regard to mitigation, the overall performance, both in terms of success rate and false alarms, are quite positive (98% success rate and only 1.9% false alarm rate). Encouraging results have also been obtained for prevention (94.2% success rate and only 7.7 % false alarm rate), by providing as inputs to the classifiers appropriate profile indicators. Even if they require significant refinements, adaptive predictors, capable of capitalising on the experience of smaller devices, have therefore become a serious candidate for deployment in the next generation of machines.

Plasma polarimetry is a diagnostic technique used in nuclear fusion reactors to measure the line integral of some plasma parameters, such as the electron density and the magnetic field, and constrain, analyse and validate the equilibrium models. Despite the strong link between the plasma properties and light polarisation propagation, the interpretation of plasma polarimetry remains complex and sometimes uncertain. The type 1 approximation is the most common hypothesis used to link the polarisation effects, such as the Faraday rotation and the Cotton Mouton phase shift, with the plasma properties (electron density and magnetic fields). However, this approximation is valid only in specific conditions, which depend on both the plasma configuration and initial polarisation of the electromagnetic wave. Moreover, the uncertainty propagations of these measurements are affected by regions where their values are too high to guarantee accurate values, making the measurement unsuitable. The need to measure an unsteady physical environment, which goes from the condition of no plasma to the flat top of the discharge, makes the setting of the measurement more constrained. The aim of this work is to analyse, both analytically and numerically, the behaviours of the interpretability and uncertainty propagation of polarisation measurements, in order to give a detailed and the most general as possible description of these issues, ensuring an easier, more performant and reliable understanding and design of plasma polarimetry. The results will show that an input linear polarisation around 45° degree is the most suitable for plasma polarimetry when type 1 approximation is adopted and which the choice of the laser wavelength governs the performances of the polarimeter.

The world has changed. The global crisis related to the reduction of energy fossil resources, the reduction of potable water resources and the war for the control of energy sources are part of the causes which can lead to an intentional CBRNe (Chemical, Biological, Radiological, Nuclear, and explosive) event together with the potential misuse of CBRNe agents, once considered to be solely the concern of the military, now needs to be considered also in the public domain. This kind of events could also be the consequence of an unintentional release of substances (i.e., an accident of a truck containing a Toxic Industrial Chemical), or of natural events like a tsunami or an earthquake. Thus, the high percentage of risk connected to their occurrence is clear. The aim of this paper is to analyze the CBRNe risk scenario and propose a standardized CBRNe joint approach for the establishment of “CBRNe technical tables” composed by cluster Entities from around Europe to manage the production of technologies, research, education and training activities in order to: (1) establish and maintain a roster of capabilities and facilities, and (2) organize to share expertise, and (3) plan to pool resources. The authors will show the results in the “Italian case study” as a potential pilot project analyzing the strengths and weaknesses to propose new ideas to improve it.

The production of dust inside the nuclear fusion power plants is one of the safety issues of this technology. Dust is generated because of plasma-material interactions and subsequently it deposits in the bottom regions of the TOKAMAK. In case of a Loss Of Vacuum Accident (LOVA), the dust may be resuspended, threatening the functioning and the safety of these reactors. A deep study of this phenomenon is required to develop countermeasures and to improve the safety of this promising way to produce energy. The authors have studied the fluid dynamics of these accidents with a scaled experiment, called STARDUST-Upgrade. Optical techniques have been implemented to measure dust resuspension and diffusion properties, such as velocity vectors and resuspension rate. This work shows the results obtained with a new numerical model able to take into account also the dispersed phase (dust). The software uses the Euler-Euler approach, a Schiller-Naumann resistance model, and a k-e turbulence model. The dust used is tungsten dust, that has been placed close to the inlet valve in both cases (numerical and experimental). The numerical results are analysed and compared with the experimental ones and the main agreements and differences are highlighted. The results show good accordance with the velocity vectors of dust, while the resuspension rate is overestimated in the numerical case because of the absence of adhesion and cohesion forces between dust particles and walls. This analysis is the starting point for the evolution and completion of a numerical model suitable for dust resuspension in case of LOVAs.

Polarimetry exploits the optical activity and birefringent properties of thermonuclear plasmas to calculate some important quantities for their control like the line-integrated electron density and magnetic field distribution. The Joint European Torus (JET) far infrared polarimeter shares the same probing laser beams of the interferometer, with eight channels, four vertical and four lateral. While the vertical channels were already optimised to provide accurate measurements of the Faraday rotation angle, Cotton–Mouton (CM) phase shift and ellipticity, the lateral channels had been only optimised for Faraday rotation angle alone. By setting the initial polarisation angle of the lateral channel at zero degrees the CM effect is minimised, the ellipticity is almost zero, and the CM phase shift angle is impossible to measure. During the recent JET experimental campaign (C38 in 2019–2020), the input polarisation for the lateral channels was altered for a class of pulses to analyse the possibility to measure the CM phase shift angle and the ellipticity, and, more important, to assess if it is possible to provide information of line-integrated electron density using the lateral channels of JET polarimetry in an ITER-like configuration. As a note, ITER will have only tangential channels with reflectors buried deep inside the first wall, so this setup was truly an ITER-like configuration. The results clearly show huge improvements, which can be achieved by just changing the input polarisation. Moreover, the analysis of the measurements shows that the polarimetric measurements have a systematic error, which is probably due to the effect of refraction and to the in-vessel mirrors, which was only partially taken into account during the calibration phase before the plasma. Thus, a new calibration method was developed and the results presented on a statistical basis. It has been demonstrated that, varying the input polarisation of the polarimeter and using the new calibration method, it is possible to measure the line-integrated electron density, using the CM phase shift (or the ellipticity) of the lateral channels, with good accuracy with respect to the electron density measured by the interferometer that was considered the reference.

Many pharmaceutical industries all around the world are facing the problem of dust mobilization during the productive process of medicines. This mobilization can be dangerous for the safety of the operators working in the factory and for the safety of the factory itself. It is therefore necessary to develop predictive models to simulate and forecast dust mobilization. The Quantum Electronics and Plasma Physics (QEP) Research Group of the University of Rome Tor Vergata has developed a facility to experimentally replicate dust mobilization in different critical conditions in an enclosed environment. The measurements performed with diagnostics available in the facility, provide the boundary conditions to run numerical simulations and to validate mobilization models . Even if the initial field of application of this novel facility is dust mobilization is nuclear fusion, the methodology developed can be used for the medicine industry, for the agribusiness and others. The authors will present the experimental and numerical results discussing new applications.

In recent years, the publication of the studies on the transmissibility in mammals of the H5N1 influenza virus and synthetic genomes has triggered heated and concerned debate within the community of scientists on biological dual-use research; these papers have raised the awareness that, in some cases, fundamental research could be directed to harmful experiments, with the purpose of developing a weapon that could be used by a bioterrorist. Here is presented an overview regarding the dual-use concept and its related international agreements which underlines the work of the Australia Group (AG) Export Control Regime. It is hoped that the principles and activities of the AG, that focuses on export control of chemical and biological dual-use materials, will spread and become well known to academic researchers in different countries, as they exchange biological materials (i.e. plasmids, strains, antibodies, nucleic acids) and scientific papers. To this extent, and with the aim of drawing the attention of the scientific community that works with yeast to the so called Dual-Use Research of Concern, this article reports case studies on biological dual-use research and discusses a synthetic biology applied to the yeast Saccharomyces cerevisiae, namely the construction of the first eukaryotic synthetic chromosome of yeast and the use of yeast cells as a factory to produce opiates. Since this organism is considered harmless and is not included in any list of biological agents, yeast researchers should take simple actions in the future to avoid the sharing of strains and advanced technology with suspicious individuals.

Nowadays, the monitoring of air in the environment is crucial to prevent chemical poisoning and disease onsets. Among the several techniques used to monitor the environment, the Differential Absorption LIDAR (DIAL) method has a very interesting approach. It is a remote technique able to give linear, areal or volumetric information. The range can be very large (more than 2 km) and the response is fast near-real-time. On the other hand, the classical DIAL approach is limited to the measurement of only one chemical and it is affected by the influence of other chemicals, which makes the sensitivity and specificity of the method low. In this work, the authors describe the principal results of the project TELEMACO where the authors developed a multiwavelength-based DIAL demonstrator able to perform the measurement of several hazardous chemicals in the environment and to increase the accuracy of the technique. TELEMACO can scan 66 wavelength lines and, at the moment, it can identify and measure the concentration of 24 chemicals included in the database. This database can be extended with new measurements. The experimental apparatus is described as well as its functioning. Then, the most relevant tests performed to validate the functioning of TELEMACO are shown and discussed. The potentialities and the limits of this promising technology are analysed.

The development and improvement of techniques to monitor off-normal concentrations of chemicals in the atmosphere are crucial to guarantee human and environmental health, safety, and security. An interesting technique for use in research activities is the differential absorption lidar (DIAL); an improvement of the lidar technique able to provide information about the concentration of chemicals in the atmosphere. This work is focused on the use of DIAL, using a multiwavelength approach to increase the accuracy of gas concentration measurements in the atmosphere. The authors perform the uncertainty propagation analysis of this method, and highlight the advantages and the limits of this technique. Then, they applied this multiwavelength technique to preliminary DIAL measurements of ammonia in the atmosphere using three couples of laser wavelengths. The measurements are performed over a traffic area and are compared with water vapour measurements. A strong correlation between ammonia and water has been found a symptom that both chemicals belong to the exhaust gases of vehicles.

Accidents in spent nuclear fuel reprocessing plants are a critical issue for the safety of people, of operators and the environment, as well as for the security of the plants. The purpose of this work is to demonstrate the possibility to use a free license code to simulate radiological diffusion after an accident in these particular plants, in order to obtain a model which allows identifying escape routes for the people potentially involved in the fallout. The authors performed a benchmark analysis of the data collected by the IAEA during the radiological accident of Tomsk. These data were further used to simulate general worst case scenarios. Numerical values of Total Effective Dose Equivalent (TEDE) generated in each “worst case scenario” where compared to the ICRP (International Commission on Radiological Protection) dose limits for acute exposition to radiation, in order to identify information on evacuation, sheltering and iodine prophylaxis in case of radionuclides release in conditions comparable to those analyzed. The authors achieved a good match between the numerical and field-results, giving a solid background to the worst case scenario simulations and allowing proposing a methodology for safety and security analysis with free license codes.

The aim of this work is to simulate a Loss of Vacuum Accident (LOVA) in the STARDUST (Small Tank for Aerosol Removal and DUST)-UPGRADE facility. These events are one of the major safety concerns in Tokamaks, since they can cause the mobilization and the dispersion of radioactive dust contained in a fusion reactor. The first step in the study of a LOVA event is the estimation, by means of numerical simulations, of the pressurization transient in the vacuum chamber. The STARDUST-UPGRADE facility, which has a cylindrical shape, is considered as a case study. An air inlet is located in a radial position with respect to the facility, so the numerical domain is symmetric and, therefore, only a half of it has been considered in the simulation. A time-dependent mass flow rate is imposed at the inlet, in a range consistent with experimental estimates. The simulation takes 20 s and the attention is focused on the mean pressure value over time and on the Mach number distribution. The results are presented and discussed in the perspective of simulating LOVAs in ITER (International Thermonuclear Experimental Reactor).