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.
Polymer nanocomposites incorporating electrically conductive nanostructured fillers are increasingly investigated as multifunctional materials capable of addressing functional requirements across a broad range of applications, from biomedical devices to industrial load-bearing materials and structures. Recent studies have suggested that this class of nanocomposites can be designed to exhibit electrically activated healing, restoring electrical percolation and mechanical integrity. This research focuses on this peculiar self-healing process, evaluating both its general applicability, even to polymers whose molecular structures were not designed to be "healed", and its effectiveness in restoring electrical, mechanical, and structural continuity in polymer nanocomposites. The restoration occurs through a sort of migration of nanoparticles with attached polymer chains toward the damaged region, thereby restoring the functional and molecular nanocomposite architecture. The electric-field-assisted nanofiller redistribution in the damaged region following restoration is experimentally verified by Tunneling Atomic Force Microscopy (AFM-TUNA). The recovery of the mechanical integrity of the polymer nanocomposites after the electro-healing process is further evaluated through dedicated airtightness tests performed before and after damage and subsequent healing. To enable electro-healing, multiwalled carbon nanotubes (MWCNTs) were used as conductive nanofillers. Three polymer matrices, poly(ethylene-co-methacrylic acid) (EMAAc), thermoplastic vulcanizate polypropylene (TVPP), and acrylonitrile butadiene styrene (ABS), were selected to investigate the generality of electro-healing behavior and to assess its potential applicability across diverse material systems and application fields. The results highlight that electro-healing can repair damage initiation up to spatial scales of micrometers or several millimeters and can be successfully applied to load-bearing composite structures.
Abstract Fiber-reinforced polymer (FRP) bars used as internal reinforcement in concrete structures have gained renewed interest in recent years, primarily due to their high durability, particularly in aggressive environmental conditions. The adoption of FRP bars as an alternative to steel reinforcement effectively mitigates corrosion-related deterioration; however, it also raises concerns regarding the reliability and safety assessment of nonmetallic reinforcement systems. In recent years, substantial efforts have been devoted to developing and implementing design provisions for FRP-reinforced concrete members. In this context, the qualification of FRP bars in accordance with standardized procedures is essential for the reliable determination of partial safety factors within the semiprobabilistic framework adopted by modern design codes. Based on an extensive database of tensile test results on glass FRP straight bars, this study presents a comprehensive reliability-based assessment of partial safety factors. Material partial safety factors are derived using a code-based procedure. Both uncertainties in material properties and uncertainties related to resistance modeling and geometric variability are considered. Two alternative approaches to the treatment of these uncertainties are investigated: one employing separate partial factors for material and model uncertainties, and the other adopting a single resistance factor. The results contribute to the ongoing development of reliability-consistent design provisions for FRP-reinforced concrete structures and support the calibration of resistance safety factors for FRP bars within a European code–oriented framework.
We develop and analyze a conforming Virtual Element Method on general polygonal meshes for the two-dimensional generalized Stokes equations. This parameter-dependent model provides a continuous transition between the classical Stokes problem and Darcy flow through the viscosity parameter E. Our discretization employs enhanced virtual element spaces for the velocity, in which the normal trace on each edge is approximated by polynomials of degree one higher than those used for the tangential trace. We prove that the method is inf-sup stable with respect to the parameter E. We provide the a priori error analysis in two different e-weighted norms. In the first case, the a priori error analysis yields an optimal-order estimate for the velocity in a natural E-weighted norm and a pressure estimate in the L2 norm also reveals the E-dependency of the constants that appear in the priori analysis. In the second case, the weighted velocity estimate is uniform with respect to E, which ensures robust approximation across the full Stokes-Darcy transition regime. We assess the performance of the method on several numerical tests using different families of polygonal meshes, including triangular, quadrilateral, Voronoi, and non-convex meshes.
In this work we present a novel discrete collocation method for the numerical solution of Fredholm integral equations of the second kind in the space of continuous functions equipped with the uniform norm. The method is based on a rational interpolation scheme recently developed within the general framework of reproducing kernel Hilbert spaces. This rational approximation has no real poles, interpolates the target function at arbitrary Jacobi nodes and exhibits uniformly bounded Lebesgue constants. Moreover, it converges uniformly for all continuous functions at a rate at least equal to that of the best uniform polynomial approximation. These interesting properties are inherited by the resulting numerical method, for which stability, convergence and good conditioning are established under minimal assumptions on the integral kernel. A series of numerical experiments confirm the theoretical findings and indicate that, in the presence of particularly challenging kernels, the proposed approach provides a robust and effective alternative to Nyström-type methods.
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.
