Two kinetic models are applied to H atoms in capacitively coupled, radio-frequency H 2 discharges. A multicomponent particle-in-cell with Monte Carlo collisions kinetic model of the plasma phase, including several ionic species, electrons, molecules in specific internal states, is used to determine the atom source term in the whole discharge volume, while a Monte Carlo model is applied to H atoms. Using these models it is possible to treat the whole cycle of H atoms including production, slowing-down and development of the thermalized component and to account for the total atomic balance in the gas phase. The relevance of different atom production channels is quantitatively discussed, and results for the energy distribution and energy flux distribution on surfaces are given. Recommendations for H atom kinetics data are provided.

There are still open issues within the fluctuation theory of plasmas, in view of the difficulty of formulating adequate theoretical approaches and solving the related equations in particular regimes. A promising alternative approach is direct microphysical modeling based on first principles, as successfully applied to neutral rarefied fluids. Within this approach, the equations of motion of a large ensemble of charged particles are solved numerically while correlations are obtained from statistical analysis of the ensemble at different times. As a first step, in this work we validate the data analysis technique adopted in this numerical scheme for the case of an electron ensemble neglecting Coulomb interactions. The simulation results are compared with the analytical theory of 'natural' fluctuations for both unmagnetized and magnetized plasmas. For the latter, the derivations for arbitrary average distribution functions are presented.

Cs is the most well known catalyst used in negative ion sources for fast neutral beam generation employed in nuclear fusion, where the element is evaporated and deposited on Mo surfaces forming non permanent films. In this paper the interaction of Cs with Mo under conditions of interest for negative ion sources is studied using different methods. Cs-Mo potential has been characterized starting from high level electronic calculations for two atoms. Mo-Mo and Mo-Cs potentials are based on new fits of the literature data. Density functional theory calculations on a reduced cell are used to determine the adsorption energy of Cs on Mo for different sites. Good reproduction of experimental results, when available, is achieved (e.g. Mo crystal data, Cs 2 dissociation energy) and new results for the evaporation energy of Cs from Mo surfaces, CsMo dissociation energy, adatom geometry etc. are reported and tabulated. A functional expression of the Cs-Mo[0 0 1] interaction potential is proposed based on these ab-initio results. The use of this potential is illustrated by classical MD calculations for the morphology for Cs partial layers on Mo[0 0 1]. Calculations show that the interaction between Cs and the surface leads to peculiar morphology of Cs partial layers, to be considered in future studies of Cs role in negative ion sources as well as in the ongoing quest to alternative catalyzers.

The organized large-scale retail sector has been gradually establishing itself around the world, and has increased activities exponentially in the pandemic period. This modern sales system uses Data Mining technologies processing precious information to increase profit. In this direction, the extreme gradient boosting (XGBoost) algorithm was applied in an industrial project as a supervised learning algorithm to predict product sales including promotion condition and a multiparametric analysis. The implemented XGBoost model was trained and tested by the use of the Augmented Data (AD) technique in the event that the available data are not sufficient to achieve the desired accuracy, as for many practical cases of artificial intelligence data processing, where a large dataset is not available. The prediction was applied to a grid of segmented customers by allowing personalized services according to their purchasing behavior. The AD technique conferred a good accuracy if compared with results adopting the initial dataset with few records. An improvement of the prediction error, such as the Root Mean Square Error (RMSE) and Mean Square Error (MSE), which decreases by about an order of magnitude, was achieved. The AD technique formulated for large-scale retail sector also represents a good way to calibrate the training model.

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