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Il contributo propone l’interpretazione del quadro di emergenza pandemica in termini di “paesaggio spezzato” come incontro con l’inatteso. Questa esperienza ha influito sul modo di porsi le domande più profonde sull’umano e sulla morte, con riflessi nell'attività dei professionisti educativi e dell’aiuto, The contribution proposes the interpretation of the emergency framework pandemic in terms of "broken landscape" as an encounter with the unexpected. This experience has influenced on the way to ask the deepest questions about the human and death, with impact in educational and help relationship work.

Il volume raccoglie il dialogo di docenti e ricercatori dei processi educativi e formativi intorno agli aspetti emozionali dell'educazione, alle latenze e agli impliciti del lavoro formativo ed educativo, The volume collects the dialogue of professors and researchers of educational and training processes on the emotional dimensions of human education, latencies and implicits of training and educational work

In questi anni le riflessioni e le pratiche che tematizzano il patrimonio culturale si sono arricchite di nuovi orizzonti, che ne rileggono sia i concetti e le categorie sia le modalità di fruizione e le finalità formative. L’attenzione di fondo con la quale si guarda alla promozione del patrimonio culturale sottende l’esigenza di allargare e innovare le categorie con le quali pensiamo e ci riferiamo ai beni culturali, ma anche di attuare profondi cambiamenti nella gestione e nella valorizzazione educativa di un patrimonio inteso come bene comune., In recent years the reflections and practices on cultural heritage have been enriched with new horizons for reinterpreting both the concepts and categories and the methods of fruition and educational purposes. The promotion of cultural heritage underlies the need to broaden and innovate the categories for thinking to the cultural heritage, but also to implement profound changes in the management and educational valorization of a heritage as a common good.

Il contributo a partire dal film "Io prima di te" affronta il tema della speranza nell'esperienza di malattia cronica nell'età adulta.

il contributo prende in esame gli effetti che la stagione della pandemia ha determinato nel riportare in primo piano il valore educativo del silenzio, di contro agli effetti pervasivi di una civiltà logorroica caratterizzata dal flusso ininterrotto delle comunicazioni e di un rumore di fondo che ostacola il processo di connessione con l'interiorità e con emozioni allo stato informe che chiedono di essere elaborate.

Metamorphic rocks are the records of plate tectonic processes whose reconstruction relies on correct estimates of the pressures and temperatures (P-T) experienced by these rocks through time. Unlike chemical geothermobarometry, elastic geobarometry does not rely on chemical equilibrium between minerals, so it has the potential to provide information on overstepping of reaction boundaries and to identify other examples of non-equilibrium behavior in rocks. Here we introduce a method that exploits the anisotropy in elastic properties of minerals to determine the unique P and T of entrapment from a single inclusion in a mineral host. We apply it to preserved quartz inclusions in garnet from eclogite xenoliths hosted in Yakutian kimberlites (Russia). Our results demonstrate that quartz trapped in garnet can be preserved when the rock reaches the stability field of coesite (the high-pressure and hightemperature polymorph of quartz) at 3 GPa and 850 °C. This supports a metamorphic origin for these xenoliths and sheds light on the mechanisms of craton accretion from a subducted crustal protolith. Furthermore, we show that interpreting P and T conditions reached by a rock from the simple phase identification of key inclusion minerals can be misleading.

Small-angle x-ray scattering tensor tomography provides three-dimensional information on the unresolved material anisotropic microarchitecture, which can be hundreds of times smaller than an image pixel. We develop a direct filtered back-projection method based on algebraic filters that enables rapid tensor-tomographic reconstructions and is a few orders of magnitude faster compared to established techniques, given the same computational resources. We demonstrate the accuracy of the method on experimental data for a fiber-reinforced material sample. The achieved acceleration may pave the way toward the investigation of multiple large samples as well as rapid control and feedback during in situ tensor-tomographic experiments, opening perspectives for the understanding of the fundamental link between functional material properties and microarchitecture.

A partire da una rilettura del film, piuttosto recente, "Io prima di te", il contributo si focalizza di due visioni della speranza di cui sono portatori i due protagonisti del film, una giovane donna e un giovane uomo, quest'ultimo colpito improvvisamente da paralisi a causa di un investimento stradale. Di qui, la riflessione sul concetto di cura

Time-resolved X-ray tomographic microscopy is an invaluable technique to investigate dynamic processes in 3D for extended time periods. Because of the limited signal-to-noise ratio caused by the short exposure times and sparse angular sampling frequency, obtaining quantitative information through post-processing remains challenging and requires intensive manual labor. This severely limits the accessible experimental parameter space and so, prevents fully exploiting the capabilities of the dedicated time-resolved X-ray tomographic stations. Though automatic approaches, often exploiting iterative reconstruction methods, are currently being developed, the required computational costs typically remain high. Here, we propose a highly efficient reconstruction and classification pipeline (SIRT-FBP-MS-D-DIFF) that combines an algebraic filter approximation and machine learning to significantly reduce the computational time. The dynamic features are reconstructed by standard filtered back-projection with an algebraic filter to approximate iterative reconstruction quality in a computationally efficient manner. The raw reconstructions are post-processed with a trained convolutional neural network to extract the dynamic features from the low signal-to-noise ratio reconstructions in a fully automatic manner. The capabilities of the proposed pipeline are demonstrated on three different dynamic fuel cell datasets, one exploited for training and two for testing without network retraining. The proposed approach enables automatic processing of several hundreds of datasets in a single day on a single GPU node readily available at most institutions, so extending the possibilities in future dynamic X-ray tomographic investigations.

Synchrotron X-ray tomography enables the examination of the internal structure of materials at submicron spatial resolution and subsecond temporal resolution. Unavoidable experimental constraints can impose dose and time limits on the measurements, introducing noise in the reconstructed images. Convolutional neural networks (CNNs) have emerged as a powerful tool to remove noise from reconstructed images. However, their training typically requires collecting a dataset of paired noisy and high-quality measurements, which is a major obstacle to their use in practice. To circumvent this problem, methods for CNN-based denoising have recently been proposed that require no separate training data beyond the already available noisy reconstructions. Among these, the Noise2Inverse method is specifically designed for tomography and related inverse problems. To date, applications of Noise2Inverse have only taken into account 2D spatial information. In this paper, we expand the application of Noise2Inverse in space, time, and spectrum-like domains. This development enhances applications to static and dynamic micro-tomography as well as X-ray diffraction tomography. Results on real-world datasets establish that Noise2Inverse is capable of accurate denoising and enables a substantial reduction in acquisition time while maintaining image quality.

Many of the grand challenges in volcanic and magmatic research are focused on understanding the dynamics of highly heterogeneous systems and the critical conditions that enable magmas to move or eruptions to initiate. From the formation and development of magma reservoirs, through propagation and arrest of magma, to the conditions in the conduit, gas escape, eruption dynamics, and beyond into the environmental impacts of that eruption, we are trying to define how processes occur, their rates and timings, and their causes and consequences. However, we are usually unable to observe the processes directly. Here we give a short synopsis of the new capabilities and highlight the potential insights that in situ observation can provide. We present the XRheo and Pele furnace experimental apparatus and analytical toolkit for the in situ X-ray tomography-based quantification of magmatic microstructural evolution during rheological testing. We present the first 3D data showing the evolving textural heterogeneity within a shearing magma, highlighting the dynamic changes to microstructure that occur from the initiation of shear, and the variability of the microstructural response to that shear as deformation progresses. The particular shear experiments highlighted here focus on the effect of shear on bubble coalescence with a view to shedding light on both magma transport and fragmentation processes. The XRheo system is intended to help us understand the microstructural controls on the complex and non-Newtonian evolution of magma rheology, and is therefore used to elucidate the many mobilization, transport, and eruption phenomena controlled by the rheological evolution of a multi-phase magmatic flows. The detailed, in situ characterization of sample textures presented here therefore represents the opening of a new field for the accurate parameterization of dynamic microstructural control on rheological behavior.

Tomographic X-ray microscopy beamlines at synchrotron light sources worldwide have pushed the achievable time-resolution for dynamic 3-dimensional structural investigations down to a fraction of a second, allowing the study of quickly evolving systems. The large data rates involved impose heavy demands on computational resources, making it difficult to readily process and interrogate the resulting volumes. The data acquisition is thus performed essentially blindly. Such a sequential process makes it hard to notice problems with the measurement protocol or sample conditions, potentially rendering the acquired data unusable, and it keeps the user from optimizing the experimental parameters of the imaging task at hand. We present an efficient approach to address this issue based on the real-time reconstruction, visualisation and on-the-fly analysis of a small number of arbitrarily oriented slices. This solution, requiring only a single additional computing workstation, has been implemented at the TOMCAT beamline of the Swiss Light Source. The system is able to process multiple sets of slices per second, thus pushing the reconstruction throughput on the same level as the data acquisition. This enables the monitoring of dynamic processes as they occur and represents the next crucial step towards adaptive feedback control of time-resolved in situ tomographic experiments.

Welding occurs during transport and deposition of volcanic particles in diverse settings, including pyroclastic density currents, volcanic conduits, and jet engines. Welding rate influences hazard-relevant processes, and is sensitive to water concentration in the melt. We characterize welding of fragments of crystal-free, water-supersaturated rhyolitic glass at high temperature using in-situ synchrotron-source X-ray tomography. Continuous measurement of evolving porosity and pore-space geometry reveals that porosity decays to a percolation threshold of 1–3 vol.%, at which bubbles become isolated and welding ceases. We develop a new mathematical model for this process that combines sintering and water diffusion, which fits experimental data without requiring empirically-adjusted parameters. A key advance is that the model is valid for systems in which welding is driven by confining pressure, surface tension, or a combination of the two. We use the model to constrain welding timescales in a wide range of volcanic settings. We find that volcanic systems span the regime divide between capillary welding in which surface tension is important, and pressure welding in which confining pressure is important. Our model predicts that welding timescales in nature span seconds to years and that this is dominantly dependent on the particle viscosity or the evolution of this viscosity during particle degassing. We provide user-friendly tools, written in Python™ and in Excel®, to solve for the evolution of porosity and dissolved water concentration during welding for user-defined initial conditions.

Magnetic mineral inclusions, as iron oxides or sulfides, occur quite rarely in natural diamonds. Nonetheless, they represent a key tool not only to unveil the conditions of formation of host diamonds, but also to get hints about the paleointensity of the geomagnetic field present at times of the Earth's history otherwise not accessible. This possibility is related to their capability to carry a remanent magnetization dependent on their magnetic history. However, comprehensive experimental studies on magnetic inclusions in diamonds have been rarely reported so far. Here we exploit X-ray diffraction, Synchrotron-based X-ray Tomographic Microscopy and Alternating Field Magnetometry to determine the crystallographic, morphological and magnetic properties of ferrimagnetic Fe-oxides entrapped in diamonds coming from Akwatia (Ghana). We exploit the methodology to estimate the natural remanence of the inclusions, associated to the Earth's magnetic field they experienced, and to get insights on the relative time of formation between host and inclusion systems. Furthermore, from the hysteresis loops and First Order Reversal Curves we determine qualitatively the anisotropy, size and domain state configuration of the magnetic grains constituting the inclusions.

Super-deep diamonds (SDDs) are those that form at depths between ~300 and ~1000 km in Earth's mantle. They compose only 1% of the entire diamond population but play a pivotal role in geology, as they represent the deepest direct samples from the interior of our planet. Ferropericlase, (Mg,Fe)O, is the most abundant mineral found as inclusions in SDDs and, when associated with low-Ni enstatite, which is interpreted as retrogressed bridgmanite, is considered proof of a lower-mantle origin. As this mineral association in diamond is very rare, the depth of formation of most ferropericlase inclusions remains uncertain. Here we report geobarometric estimates based on both elasticity and elastoplasticity theories for two ferropericlase inclusions, not associated with enstatite, from a single Brazilian diamond. We obtained a minimum depth of entrapment of 15.7 (±2.5) GPa at 1830 (±45) K (~450 [±70] km depth), placing the origin of the diamond-inclusion pairs at least near the upper mantle-transition zone boundary and confirming their super-deep origin. Our analytical approach can be applied to any type of mineral inclusion in diamond and is expected to allow better insights into the depth distribution and origin of SDDs.

There is a widening gap between the fast advancement of computational methods for tomographic reconstruction and their successful implementation in production software at various synchrotron facilities. This is due in part to the lack of readily available instrument datasets and phantoms representative of real materials for validation and comparison of new numerical methods. Recent advancements in detector technology have made sub-second and multi-energy tomographic data collection possible (Gibbs et al 2015 Sci. Rep. 5 11824), but have also increased the demand to develop new reconstruction methods able to handle in situ (Pelt and Batenburg 2013 IEEE Trans. Image Process. 22 5238-51) and dynamic systems (Mohan et al 2015 IEEE Trans. Comput. Imaging 1 96-111) that can be quickly incorporated in beamline production software (Gürsoy et al 2014 J. Synchrotron Radiat. 21 1188-93). The x-ray tomography data bank, tomoBank, provides a repository of experimental and simulated datasets with the aim to foster collaboration among computational scientists, beamline scientists, and experimentalists and to accelerate the development and implementation of tomographic reconstruction methods for synchrotron facility production software by providing easy access to challenging datasets and their descriptors.