Cineca

Abstract The XRISM Resolve X-ray spectrometer makes it possible to gain detailed insights into the gas motions of the intracluster medium (ICM) of galaxy clusters. Current simulation studies focus mainly on statistical comparisons, making the comparison to the currently still small number of clusters difficult due to unknown selection effects. This study aims to bridge this gap, using simulated counterparts of Coma, Virgo, and Perseus from the SLOW constrained simulations. These clusters show excellent agreement in their properties and dynamical state with observations, thus providing an ideal testbed to understand the processes shaping the properties of the ICM. We find that the simulations match the order of the amount of turbulence for the three considered clusters, Coma being the most active, followed by Perseus, while Virgo is very relaxed. Typical turbulent velocities are a few hundred km s −1 , very close to observed values. The resulting turbulent pressure support is ≈1% for Virgo, ≈6% for Perseus, and ≈8% for Coma within the central 1%–2% of R 200 . Compared to previous simulations and observations, measured velocities and turbulent pressure support are on average lower, in line with XRISM findings, thus indicating the importance of selection effects.

Recent advances in artificial intelligence are transforming geosciences, with growing attention on explainable models to support both scientific research and practical adoption. Here, we present a general yet tailored pipeline for semantic segmentation of sedimentary facies from Holocene core images. We first identify the optimal model architecture, evaluating both convolutional neural networks and Transformer-based backbones, then perform a systematic patch-size ablation to better align with expert-level sedimentological reasoning. Finally, we enhance interpretability through saliency mapping with grad-CAM and predictive entropy, which highlight geologically meaningful regions and quantify model uncertainty. Our results demonstrate that an explainable AI framework not only takes a significant step forward in automatic facies prediction but also provides actionable insights into model behavior. This approach emphasizes the added value of explainable pipelines in critical domains of geosciences, where transparent decision-making is essential for bridging research and real-world applications.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-026-36765-y.

Abstract Assistive robotics holds promise for supporting therapeutic and educational interventions for autistic children, yet key questions remain regarding its practical benefits, limitations, and ethical implications. This study critically examines a 4-month therapeutic laboratory in which a humanoid robot (Pepper) was integrated into weekly sessions with four autistic children (ages 11–14, level 1 support needs). Through a mixed-methods approach, combining behavioural observations, standardised questionnaires, and thematic analysis of video recordings, we evaluated the robot’s role in fostering autonomy, communication, and engagement. Quantitative results showed limited improvements in functional autonomy, while qualitative findings highlighted the robot’s potential to stimulate curiosity, collaborative behaviours, and social participation, especially through unexpected modalities such as internet meme references. Challenges emerged around conversational rigidity, limited adaptability, and user frustration due to breakdowns in feedback and interaction flow. The study underscores the value of participatory, co-designed robotics in therapeutic contexts and offers design-oriented recommendations for developing more responsive, inclusive, and culturally adapted robot behaviours in child-centered interventions.

Abstract. We present the results of downscaling CMIP6 global climate projections to local scales for the Mediterranean and Italian regions, aiming to produce high-resolution climate information for the assessment of climate change signals, with a focus on precipitation extreme events. We performed hindcast (i.e., ERA5-driven) and historical simulations (driven by the MPI-ESM1-2-HR model) to simulate the present (1980–2014) and future (2015–2100) climate under three different emission scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5). For each experiment, a double-nesting approach is adopted to dynamically downscale global data to the regional domain of interest, firstly over the Europe (EURO) COordinated Regional climate Downscaling Experiment (CORDEX) domain, at a spatial resolution of 15 km, and then further refined (second nesting) over Italy and the northwestern Mediterranean at a resolution of 5 km, i.e., in the so-called gray zone (5–10 km), close to the convection-permitting (CP) limit. Besides validating the experimental protocol, this work potentially questions the need for climate simulations to always resort to deep convection parameterizations when spatial refinement is increased up to the limit of the CP scale, yet convective processes are still not explicitly resolved. Analyses of air temperature and precipitation are presented, with a focus on the spatial distribution of precipitation, its probability density function, and the statistics of extreme events for both current climate and far-end scenarios. By the end of the century for all the scenarios and seasons there is a projected general warming along with an intensification of the hydrological cycle over most of continental Europe and mean precipitation reduction over the Mediterranean region accompanied, over the Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the SSP5-8.5 scenario during autumn.

The cosmic 21-cm signal promises to revolutionize studies of the Epoch of Reionization (EoR). Radio interferometers are aiming for a preliminary, low signal-to-noise (S/N) detection of the 21-cm power spectrum. Cross-correlating 21-cm with galaxies will be especially useful in these efforts, providing both a sanity check for initial 21-cm detection claims and potentially increasing the S/N due to uncorrelated residual systematics. Here we self-consistently simulate large-scale (1 Gpc3) galaxy and 21-cm fields, computing their cross-power spectra for various choices of instruments and survey properties. We use 1080 h observations with SKA-low AA* and HERA-350 as our benchmark 21-cm observations. We create mock Lyman-α narrow-band, slitless and slit spectroscopic surveys, using benchmarks from instruments such as Subaru HyperSupremeCam, Roman grism, VLT MOONS, ELT MOSAIC, and JWST NIRCam. We forecast the resulting S/N of the galaxy–21-cm cross-power spectrum, varying for each pair of instruments the galaxy survey area, depth, and the 21-cm foreground contaminated region of Fourier space. We find that the highest S/N is achievable through slitless, wide-area spectroscopic surveys, with the proposed Roman HLS survey resulting in a ∼55σ (∼13σ) detection of the cross-power with 21-cm as observed with SKA-low AA* (HERA-350), for our fiducial model and assuming ∼500 sq. deg. of overlap. Narrow-band dropout surveys are unlikely to result in a detectable cross-power, due to their poor redshift localization. Slit spectroscopy can provide a high S/N detection of the cross-power for SKA-low AA* observations. Specifically, the planned MOONRISE survey with MOONS on the VLT can result in a ∼3σ detection, while a survey of comparable observing time using MOSAIC on the ELT can result in a ∼4σ detection. Our results can be used to guide survey strategies, facilitating the detection of the galaxy–21-cm cross-power spectrum.

<strong class="journal-contentHeaderColor">Abstract.</strong> We present the results of downscaling CMIP6 global climate projections to local scales for the Mediterranean and Italian regions, aiming to produce high-resolution climate information for the assessment of climate change signals, with a focus on precipitation extreme events. We performed hindcast (i.e. ERA5-driven) and historical simulations (driven by the MPI-ESM1-2-HR model) to simulate the present (1980&ndash;2014) and future (2015&ndash;2100) climate under three different emission scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5). For each experiment, a double nesting approach is adopted to dynamically downscale global data to the regional domain of interest, firstly over the Europe (EURO) CORDEX domain, at a spatial resolution of 15 km, and then further refined (second nesting) over Italy and north-western Mediterranean, at a resolution of 5 km, i.e. in the so-called gray-zone (5&ndash;10 km), close to the convection permitting (CP) limit. Besides validating the experimental protocol, this work potentially questions the need for climate simulations to always resort to deep-convection parameterizations when spatial refinement is increased up to the limit of the CP scale, yet convective processes are still not explicitly resolved. Analyses of the most relevant Essential Climate Variables (ECVs) are presented, with a focus on the spatial distribution of precipitation, its probability density function, and the statistics of extreme events, for both current climate and far-end scenarios. By the end of the century for all the scenarios and seasons there is a projected general warming along with an intensification of the hydrological cycle over most of the continental EU and mean precipitation reduction over the Mediterranean region accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the SSP5-8.5 scenario during autumn.

In this paper, we present the recent advances in the computation of the Dirac-Kohn-Sham (DKS) method of the BERTHA code. We show here that the simple underlined structure of the FORTRAN code also favors efficient porting of the code to GPUs, leading to a particularly efficient hybrid CPU/GPU implementation (OpenMP/OpenACC), where the most computationally intensive part for DKS matrix evaluation (three-center two-electron integrals evaluated via the McMurchie-Davidson scheme) is efficiently offloaded to the GPU via compiler directives based on the OpenACC programming model. This scheme in combination with the use of a linear algebra library optimized for GPUs (cuBLAS, cuSOLVER) significantly accelerates the DKS calculations. In addition, the low-level integral kernel developed here at FORTRAN level was used to port our real-time DKS (RT-TDDKS) implementation based on Python (PyBERTHART) for the utilization of the GPU. The results obtained on the new Tier-0 EuroHPC supercomputer (LEONARDO) of the CINECA Supercomputing Centre with a single NVIDIA A100 card are very satisfactory. We achieve a speedup up to 30 for Au16 in a single-point DKS energy calculation and up to 10 for the Au8 systems in an RT-TDDKS calculation, compared to our OpenMP (i.e., CPU only) parallel implementation (with 32 cores). The approach presented here is very general and, to our knowledge, represents the first port of a Python API to GPUs based on a FORTRAN kernel for the evaluation of two-electron integrals. The implementation is currently limited to the use of a single GPU accelerator, but future paths to an actual exascale implementation are discussed.

<strong class="journal-contentHeaderColor">Abstract.</strong> We present the results of downscaling CMIP6 global climate projections to local scales for the Mediterranean and Italian regions, aiming to produce high-resolution climate information for the assessment of climate change signals, with a focus on precipitation extreme events. We performed hindcast (i.e. ERA5-driven) and historical simulations (driven by the MPI-ESM1-2-HR model) to simulate the present (1980&ndash;2014) and future (2015&ndash;2100) climate under three different emission scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5). For each experiment, a double nesting approach is adopted to dynamically downscale global data to the regional domain of interest, firstly over the Europe (EURO) CORDEX domain, at a spatial resolution of 15 km, and then further refined (second nesting) over Italy and north-western Mediterranean, at a resolution of 5 km, i.e. in the so-called gray-zone (5&ndash;10 km), close to the convection permitting (CP) limit. Besides validating the experimental protocol, this work potentially questions the need for climate simulations to always resort to deep-convection parameterizations when spatial refinement is increased up to the limit of the CP scale, yet convective processes are still not explicitly resolved. Analyses of the most relevant Essential Climate Variables (ECVs) are presented, with a focus on the spatial distribution of precipitation, its probability density function, and the statistics of extreme events, for both current climate and far-end scenarios. By the end of the century for all the scenarios and seasons there is a projected general warming along with an intensification of the hydrological cycle over most of the continental EU and mean precipitation reduction over the Mediterranean region accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the SSP5-8.5 scenario during autumn.

Large-scale computing clusters have been the basis of scientific progress for several decades and have now become a commodity fuelling the AI revolution. Dark Silicon, energy efficiency, power consumption, and hot spots are no longer looming threats of an Information and Communication Technologies (ICT) niche but are today the limiting factor of the capability of the entire human society and a contributor to global carbon emissions. However, from the end user, system administrators, and system integrator perspective, handling and optimising the system for these constraints is not straightforward due to the elevated degree of fragmentation in the software tools and interfaces which handles the power management in high-performance computing (HPC) clusters. In this paper, we present the REGALE Library. It is the result of a collaborative effort in the EU EuroHPC JU REGALE project, which aims to effectively materialize the HPC PowerStack initiative, providing a single layer of communication among different power management tools, libraries, and software. The proposed framework is based on the data distribution service (DDS) and real-time publish–subscribe (RTPS) protocols and FastDDS as their implementation. This enables the various actors in the ecosystem to communicate and exchange messages without any further modification inside their implementation. In this paper, we present the blueprint, functionality tests, and performance and scalability evaluation of the DDS implementation currently used in the REGALE Library in the HPC context.