We present a numerical study of mixing and reaction efficiency in closed domains. In particular we focus our attention on laminar flows. In the case of inert transport the mixing properties of the flows strongly depend on the details of the Lagrangian transport. We also study the reaction efficiency. Starting with a little spot of product we compute the time needed to complete the reaction in the container. We found that the reaction efficiency is not strictly related to the mixing properties of the flow. In particular, reaction acts as a "dynamical regulator".

[1] Rivers experience a wide range of discharges. It is nowadays acknowledged that is not realistic to assume that the morphology of a river is influenced by only a single formative discharge. Rather, it is the full range of flows that are able to move sediments and erode banks that affect the fluvial morphology. Thus, the channel morphology emerges from the interactions between different competent discharges. A goal that has still not been completely achieved in geomorphology is the understanding of the role of discharge variability on river morphological processes. In this paper, we present the results of an experimental investigation concerning the impact of the sequencing of two competent discharges on a self-forming pseudomeandering pattern. The inception of the pattern, the bar dynamics, and the bend erosion are investigated. A comparison of the experiments performed with steady and unsteady discharges has indicated the key role of the discharge variability in promoting and sustaining the pseudomeandering channel. These experimental findings shed light on some important morphological processes (bar deformation, low-flow channel incision, and triggering of the bend inception) that are affected by discharge variations to a great extent, in agreement with some field studies and conceptual models.

A new approach for the profiling of movable sediment beds in laboratory experiments is presented. It couples a triangulation laser sensor and an ultrasonic level transmitter, and allows a non‐intrusive, fast and accurate measurement of bed topography without stopping the experimental runs. The distortion of the laser beam due to the refraction at the water surface is corrected by contemporaneously measuring the elevation of the water surface through the ultrasonic level transmitter and taking advantage of geometrical relations involving the water depth, distance of the sensors from the water surface, and the angles that the emitted laser beam forms with the vertical before and after refraction. Several tests, under either still‐ or flowing‐water conditions, as well as increasing/decreasing water surface elevation, were carried out to evaluate the accuracy of the measurements. These tests indicate that good‐quality measurements are obtained for flow depths in the range 0 < D < 60 mm, typical of morphodynamic laboratory experiments. Finally, two relevant applications to movable bed experiments carried out under either lagoonal or fluvial conditions are presented that show the effectiveness of the proposed profiling technique. Copyright © 2012 John Wiley & Sons, Ltd.

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The Cherenkov Telescope Array (CTA) is a worldwide project aimed at building the nextgeneration ground-based gamma-ray observatory. CTA will be composed of two arrays of telescopes of different sizes, one each in the Northern and Southern Hemispheres, to achieve a full sky-coverage and a ten-fold improvement in sensitivity over an unprecedented energy range extending from 20 GeV to 300 TeV. Within the CTA project, the Italian National Institute for Astrophysics (INAF) is developing an end-to-end prototype of the CTA Small-Size Telescopes with a dual-mirror (SST-2M) Schwarzschild-Couder configuration. The prototype, named ASTRI SST-2M, is located at the INAF "M.C. Fracastoro" observing station in Serra La Nave (Mt. Etna, Sicily) and is currently in the scientific and performance validation phase. A mini-array of (at least) nine ASTRI telescopes has been then proposed to be deployed at the Southern CTA site, by means of a collaborative effort carried out by institutes from Italy, Brazil, and South-Africa. The CTA/ASTRI team is developing an end-to-end software package for the reduction of the raw data acquired with both ASTRI SST-2M prototype and mini-array, with the aim of actively contributing to the global ongoing activities for the official data handling system of the CTA observatory. The group is also undertaking a massive Monte Carlo simulation data production using the detector Monte Carlo software adopted by the CTA consortium. Simulated data are being used to validate the simulation chain and evaluate the ASTRI SST-2M prototype and mini-array performance. Both activities are also carried out in the framework of the European H2020-ASTERICS (Astronomy ESFRI and Research Infrastructure Cluster) project. A data archiving system, for both ASTRI SST-2M prototype and mini-array, has been also developed by the CTA/ASTRI team, as a testbed for the scientific archive of CTA. The archive system provides the data access at different user levels and for different use cases, each one with a customized data organization. A dedicated framework to access, browse and download data produced by the ASTRI telescopes has been developed within a scientific gateway utility. In this contribution, we present the main components of the ASTRI data handling systems and report the status of their development.

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The interaction of gamma rays and cosmic rays with the Earth's atmosphere initiate air showers that, in turn, induce the emission of Cherenkov photons detectable by ground-based Imaging Atmospheric Cherenkov Telescopes (IACTs). Any data analysis software for gamma-ray astronomy with IACTs requires an essential component to discriminate the nature of the primary particle, as well as to reconstruct its energy and arrival direction. In this field, the standard reconstruction approach is to use supervised machine learning techniques, mostly based on decision trees or Random Forest, which build models by training on simulated data using image and stereoscopic parameters as input features. This approach can be overcome by deep learning techniques, directly operating on pixelated camera images recorded by the array telescopes as input to models. In this way, all available information per each shower image can potentially be exploited for reconstruction, without relying solely on derived parameters. We evaluated some deep learning techniques on Monte Carlo simulated data of the ASTRI Mini-Array, an array of nine dual-mirror 4-m class IACTs under deployment at the Observatorio del Teide (Tenerife, Spain), sensitive to gamma-ray radiation in the 1-200 TeV energy range. In this contribution we present how deep learning algorithms such as convolutional neural networks can be used to reconstruct events acquired by the ASTRI Mini-Array; we will first describe the analysis work flow and introduce the architectures, and then compare the performance obtained with the new reconstruction methods with that of standard method.

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