The Consiglio Nazionale delle Ricerche (CNR) or National Research Council, is the largest research council in Italy. As a public organisation, its remit is to support scientific and technological research. Its headquarters are in Rome.
Assessing data diversity and model fairness in machine learning (ML) requires access to sensitive demographic attributes, which are often unavailable due to privacy constraints. While several methods have been proposed to estimate these properties, the field lacks a unified and reproducible evaluation framework. To fill this gap, we introduce Proxy-based Assessment for Inclusion, Representation, and Equity (PAIRE), a standardized benchmark for evaluating Fairness and Diversity (FD) estimators that operate without individual-level sensitive attributes. Leveraging PAIRE, we evaluate state-of-the-art demographic estimators on binary classification (UCI Adult, 45k instances) and multiclass ranking (TREC Fair Ranking, 1.15M instances, 21 regions), measuring estimation accuracy and vulnerability to attribute inference. Advanced methods demonstrate superior diversity estimation, reducing estimation error by up to 81%. However, this performance can be inverted in fairness assessment, with naive counting-based methods achieving up to 41% lower error than advanced quantification-based estimators, highlighting that strong performance on direct prevalence estimation does not guarantee reliability for downstream fairness assessment. Finally, privacy attacks formalized with PAIRE highlight that aggregate demographic estimators can be exploited to infer individual sensitive attributes with high accuracy (F1macro>0.9). Overall, PAIRE establishes a challenging benchmark for attribute-unaware FD estimation, providing a holistic evaluation in sensitive applications.
Soil moisture (SM) is a key parameter for irrigation monitoring, scheduling, and supporting precision agriculture. In this study, we used Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data to estimate SM at high spatial resolution (20 m) in the Lower Chenab Canal Command (LCC) area of Punjab, Pakistan. To achieve this, we applied a semiempirical water cloud model (WCM) using both the VV and VH polarizations from SAR data. Additionally, two widely used machine learning (ML) models, random forest (RF) and support vector machines (SVM), were employed to estimate SM at the field scale. To assess model reliability and transferability, reference data from two field sites were split using two approaches: (1) stratified random sampling, with 50
The rapid growth of poly(lactic acid) (PLA) usage in packaging, food and additive manufacturing applications has led to a rising accumulation of PLA-containing waste streams. Efficient chemical recycling strategies capable of returning PLA to its monomer, lactic acid (LA), are crucial to closing the material loop and enabling a circular PLA economy. However, existing hydrolytic depolymerisation methods are still underdeveloped, typically requiring strongly basic conditions or soluble zinc catalysts that hampers sustainability and cost-effectiveness of the process. Herein, we demonstrate that readily available potassium carbonate and calcium carbonate function as mild, homogeneous and heterogeneous promoters, respectively, for the selective hydrolysis reaction of PLA in subcritical water. 100
Remote sensing is a valuable tool for creating site-specific maps to reduce the environmental impact of excessive nitrogen (N) fertilization and for predicting crop yield and quality in harvest planning and food security. The aim of this study was the application of remote sensing to improve N management and harvest assessment in bread wheat (Triticum aestivum L.). A wheat field experiment with four N levels and two water regimes was conducted in Central Spain over 2 years. Ground-truth measurements of biomass, plant N concentration, and nitrogen nutrition index (NNI) were collected at three growth stages, with grain yield and N concentration recorded at harvest. Close to the dates of the ground measurements, hyperspectral imagery was acquired covering the visible and near-infrared regions (400–850 nm) and part of the short-wave infrared (950–1750 nm) from an aircraft flying 300 m above the experiment. Sentinel-1 and Sentinel-2 imagery of the site was downloaded and processed. Vegetation indices extracted from the airborne imagery were tested to assess NNI and combined with satellite data by ensemble models (multiple linear regression, artificial neural network, random forest) to predict wheat traits at harvest. The canopy chlorophyll content index (CCCI) was the best proxy for crop N status, assessing NNI with root mean square error of 0.21. The NNI maps from aerial imagery reflected the spatial distribution of wheat N requirements and enabled identification of N-responsive and nonresponsive sites for yield at stem elongation and for grain N concentration at flowering. Visible and near-infrared regions provided reliable yield estimates, and bands from the red-edge and the short-wave infrared regions improved prediction of N-related crop traits. Models using hyperspectral imagery and Sentinel-2 data performed comparably. The findings highlight the effectiveness of hyperspectral and multispectral imagery for crop monitoring, N-fertilizer management, and harvest planning. Canopy chlorophyll content index (CCCI) correlates with nitrogen nutrition index (NNI). Crop N status maps based on NNI can guide N fertilizer recommendations. Yield response to N fertilization was assessed more accurately than grain quality response. Airborne hyperspectral and multispectral satellite sensors estimate wheat traits similarly at flowering. Assessing wheat N-related traits improves when SWIR and narrow red-edge bands are included. The data provided in this manuscript enable the creation of in-season N fertilizer application maps and the assessment of wheat yield and grain quality at harvest using sensors on aerial or satellite platforms. These maps reflect the spatial variability of the crop N status and are needed for site-specific N management at the field scale.
Poroelasticity describes the interaction of deformation and fluid flow in saturated porous media. A fully-mixed formulation of Biot's poroelasticity problem has the advantage of producing a better approximation of the Darcy velocity and stress field, as well as satisfying local mass and momentum conservation. In this work, we focus on a novel four-fields Virtual Element discretization of Biot's equations. The stress symmetry is strongly imposed in the definition of the discrete space, thus avoiding the use of an additional Lagrange multiplier. A complete a priori analysis is performed, showing the robustness of the proposed numerical method with respect to limiting material properties. The first order convergence of the lowest-order fully-discrete numerical method, which is obtained by coupling the spatial approximation with the backward Euler time-advancing scheme, is confirmed by a complete 3D numerical validation. A well known poroelasticity benchmark is also considered to assess the robustness properties and computational performance.
