Consorzio Roma Ricerche

Mountain regions in Europe are experiencing changes in temperature and precipitation patterns, as well as an increasing frequency and intensity of droughts. Periods of low precipitation reduce winter snow accumulation, while anomalous hot periods during spring and summer accelerate snowmelt, altering the water cycle and discharge patterns of mountain systems and their downstream areas. The combination of warmer and drier conditions increases water scarcity, reduces crop yields and decreases hydropower generation, with reductions in streamflow and groundwater recharge. Implementing high-resolution land surface models allow us to predict future conditions by capturing the complex behavior of flow paths and water storage across entire regions. Consequently, these models facilitate the study of future droughts and their impact on hydrological conditions within the catchment. We identify a number of droughts including snow droughts in the Central Apennines of Italy, and investigate their compounded effects on the hydrosphere, biosphere and pedosphere using a fully distributed, mechanistic land surface model (Tethys-Chloris) over two decades (from 2000 to 2020). Our model configuration resolves energy budgets and mass balances at an hourly timestep and 250 m resolution to simulate processes such as snowmelt, sublimation and plant transpiration. We force the model using a combination of stations and bias-corrected ERA5Land reanalysis data and evaluate it against streamflow measurements, snow depth, soil moisture and remote sensing products of snow-covered area and leaf area index. We analyze distributed simulations of snow depth, snow water equivalent, soil moisture, lateral subsurface water fluxes and surface temperature to obtain a highly resolved picture of the functioning of the mountain hydrological system. Our analysis shows how droughts produced by a reduction of snow accumulation, precipitation or warm temperatures produce runoff deficits and positive anomalies of evapotranspiration at high elevations. We focus in particular on the effect of evaporative fluxes in reducing water yields. Our findings indicate that warm periods lead to enhanced evapotranspiration at elevations between 1500 and 2500 m a.s.l. We also investigate the contrasting effect of snow on this so-called drought paradox, as snow provides water vital to plant functioning, but limits the growing season length. This phenomenon ultimately reduces downstream water availability in mountain regions, which impacts water security for mountain-dependent communities and ecosystems. As such, our study provides an entirely new understanding of the eco-hydrological functioning of the Central Apennines water system under drought stresses, and establishes a baseline of unprecedented resolution in time and space to predict the ecological and hydrological impacts of future droughts.

Modern Environmental and Earth sciences demand seamless integration across atmospheric, marine, terrestrial, and biodiversity data, and this process is often hindered by disciplinary silos. The ENVRI-Hub addresses this challenge directly by serving as the unified Virtual Research Environment (VRE) for Europe's Environmental Research Infrastructures. It moves beyond a simple data portal to function as an integrated platform where discovery, access, and analysis converge.The hub provides researchers with a centralised gateway to discover and access FAIRified research assets, enabled for cross-disciplinary work. Crucially, it enables these assets to be leveraged in situ through any VRE using a unified machine actionable API/toolset. , in order to support data analytics in scientific workflows. VREs will allow users to compose, execute, and share reproducible analytical pipelines - from accessing Essential Climate Variables (ECVs) to running complex AI analytics. This architecture not only streamlines the scientific process but also underpins applications like environmental Virtual Laboratories and foundational work for future applications like Digital Twins.This presentation will detail the ENVRI-Hub's technical architecture for enabling VRE support. We will demonstrate, through specific scientific use cases, how its Catalogue of Services and AI-powered Knowledge Base work synergistically to reduce data friction. The contribution will highlight how this integrated environment supports workflow builders in creating robust, cross-domain analyses, thereby accelerating scientific results and advancing collaborative, data-driven Environmental and Earth science.

The Mediterranean Sea is often affected by tropical-like cyclones, which cause heavy rainfall, strong winds, storm surges and flooding. The accurate classification of precipitation into convective and stratiform within these systems is essential for understanding storm dynamics and improving predictive models. In this study, we developed a deep learning approach based on U-Net architecture to classify convective and stratiform precipitations during Mediterranean cyclones using the Global Precipitation Measurement (GPM) IMERG product. We derived physically consistent labels for training through an exponential distribution-based thresholding of rainfall intensities. The trained U-Net model effectively reproduced the spatial structure of convective rainbands and surrounding stratiform regions within the cyclone structure. In addition to validating the convective precipitation detection using brightness temperature satellite observations and ERA5 reanalysis, we also incorporated pluviometer records. These ground-based measurements confirmed the model’s strong capability to identify areas affected by convective precipitation. This study demonstrates the potential of integrating a physics-based approach with deep learning for high-resolution characterization of precipitation in Mediterranean cyclones. While the segmentation of convective precipitation alone does not directly quantify coastal hazard, these results provide essential input layers for downstream coastal-impact assessments. In particular, the high-resolution identification of convective rainfall can be integrated into hydrological and hydraulic models (e.g., HEC-RAS or similar) to simulate surface runoff, flash-flood dynamics, and related coastal impacts under a changing climate.

Abstract Collaboration and research productivity are widely recognized as interconnected, yet the directionality of this relationship remains debated. This study examines the interplay between collaborative structures and research productivity in two scientific domains: Life Sciences (LS) and Physics and Engineering (PE), using a Bayesian network approach. By analyzing both publication-based and project-based collaborations, we observe distinct domain-specific mechanisms. In LS, the relationship between collaboration and research productivity is more variable: Degree Centrality in publications shows the strongest association with performance, but in some years Closeness Centrality mediates this link by capturing how proximity to knowledge flows temporarily amplifies the effect of direct collaborative ties. By contrast, in PE, Degree Centrality consistently emerges as the strongest and most stable driver of scientific output. The what-if analysis further shows that in PE, higher productivity increases the ability of universities to attract firm partners in EU-funded projects, whereas in LS it primarily reinforces academic collaborations. These findings indicate that collaboration within publication networks tends to play a leading role in shaping research productivity, while research productivity appears to have a stronger influence on collaboration patterns within project-based networks. This mutual reinforcement is shaped by disciplinary and temporal factors, highlighting the importance of tailoring collaboration strategies to specific scientific domains.

Abstract L-Glutamate (L-Glu) is the major excitatory neurotransmitter in the central nervous system and plays a key role in neuronal communication, energy metabolism, and cellular development. However, excessive glutamatergic transmission can induce excitotoxicity, leading to neuronal damage and death. Beyond its physiological role, L-Glu, commonly used in the food industry as monosodium glutamate (MSG), has raised safety concerns due to its potential adverse effects, highlighting the importance of L-Glu detection in biological and food samples. In this work, we investigate the binding interactions between the glutamate-binding protein (GluB) from Corynebacterium glutamicum and L-Glu under different pH conditions using fluorescence correlation spectroscopy (FCS). GluB was labeled with CF488 and CF647 dyes, and fluorescence fluctuations were analyzed in the absence and in the presence of L-Glu. Steady-state fluorescence measurements were conducted on the unlabeled GluB, supporting FCS. They revealed pH-dependent structural changes of GluB, with conformational rearrangements at acidic pH and partial denaturation at alkaline conditions. At pH 8.0, GluB displayed a stable conformation and a measurable response to L-Glu binding. Moreover, experiments performed on the near-infrared labeled GluB-CF647 suggested a potential applicability of GluB for living cell studies.

We investigate the behaviour of a lattice model based on the ideas of random sequential adsorption and diffusional relaxation, for the compaction of a granular system subject to random perturbation and gravity. The lattice is composed by a given number of horizontal layers where non overlapping particles diffuse. Besides diffusion within its own layer a particle suffers a downfall to the layer below whenever there is enough space there. We restrict at first to the case of one-dimensional layers, and observe, both numerically and by a mean field approximation, an algebraic growth of the particle density that becomes slower and slower as the particle size increases with respect to the lattice spacing, both numerically and by a mean field approximation. We then consider a modification of the model that allows for a nearly exact treatment in the limit of low densities, and show that the same algebraic decay takes place in the general d dimensional case suggesting a logarithmic-like behaviour in the continuum limit.

The energetic limitsof Photosystem II (PSII) photochemical reactivityrequired reconsideration after the discovery of far-red light acclimationresponses in cyanobacteria. Insights into PSII functionality followingthe inclusion of the red-shifted Chlorophylls d and f can be obtained by extending the current knowledge onspectroscopic and structural properties of its reaction center (RC).The photoinduced triplet states, which represent selective endogenousprobes, were therefore investigated in far-red adapted PSII by magneticresonance techniques. Zero-field splitting tensor analysis combinedwith spin-polarization dynamics arising from radical pair recombinationunambiguously identifies an intrinsically low-energy-absorbing chlorophyllparticipating in charge separation reactions. The triplet-minus-singlet (T–S) spectrum associated with therecombination triplet state, obtained by microwave selection, showeda sharp 725 nm bleaching demonstrating the dominant involvement ofthis red-shifted chlorophyll in the lowest RC exciton. Moreover, spectralsimulations provided strong evidence in favor of its localizationat the ChlD1 position, making it the most likely site ofprimary photochemistry.

Fault zones in carbonate rocks exhibit distinct microstructural fabrics that develop different microstructures with increasing deformation, going from the outer zone towards the fault core. These fabrics can be effectively characterized using X-ray micro-computed tomography (XRμCT), a powerful imaging technique that supports a wide range of analyses, from morphometric measurements (e.g., pore size distribution, fractures orientation) to digital rock physics (i.e., virtual experiments on 3D volumes). However, the need for an automated, user-independent tool to classify these microstructures is crucial for large-scale studies. Furthermore, a fully quantitative classification of fault rock fabrics provides valuable insights into the extent and nature of deformation within these rocks. In this study, we present a deep learning-based supervised neural network designed to automate the classification of fault rock microstructures. This system offers rapid, quantitative, and scalable analysis of XRμCT data, facilitating the identification and classification of fabrics of brittle fault limestone rocks with high precision. The network was trained and validated on purpose collected datasets representing specific fabrics, then it was successfully used on different limestone fault rocks collected from the same area or obtained from the literature. The results show that the software can reliably classify fault rock fabrics affected by brittle deformation into three primary categories, each representing a distinct stage of deformation: fractured limestone, breccia, and cataclasite. The network assigns identification probabilities to each image, which can then be visualized in a ternary diagram for intuitive comparison and interpretation. This classification system streamlines fabric analysis and provides a quantitative measure of the degree of deformation within the rock. This automated classification tool paves the way for advanced studies on the anisotropic properties of fault rocks, enabling high-throughput analysis and enhancing our understanding of fault zone mechanics. • Machine learning was used to identify fault rock microstructures • Deep neural networks can quantitatively characterize fault rock textures • The presented microstructure classification approach proved fast and versatile.

Operator precedence languages (OPLs) enjoy the local parsability property, which means that a code fragment enclosed within a pair of markers playing the role of parentheses can be parsed with no knowledge of its external context. This property has been exploited to build parallel parsers for languages formalized as OPLs. It has been observed, however, that when the syntax trees of sentences have a linear substructure, parsing must necessarily proceed sequentially, making it ineffective to split such a subtree into chunks to be processed in parallel. This inconvenience derives from the hypothesis that the equality precedence relation cannot be cyclic, which has been so far assumed by most literature on OPLs. This hypothesis was motivated by the need to keep the mathematical notation as simple as possible, although it caused a discrepancy between the expressive power of operator precedence grammars and other formalisms defining OPLs such as operator precedence automata, monadic second order logic and operator precedence expressions, which do not assume acyclicity. We present an enriched version of operator precedence grammars, called cyclic , that allows for a simplified version of regular expressions in the right hand sides of grammar rules. For this class of operator precedence grammars the acyclicity hypothesis of the equality precedence relation is no more needed to guarantee the algebraic properties of the generated languages. The expressive power of the cyclic grammars is now fully equivalent to that of other formalisms defining OPLs. As a result, cyclic operator precedence grammars produce unranked syntax trees and sentences with flat unbounded substructures that can be naturally partitioned into chunks suitable for parallel parsing.