We present an isogeometric shell framework for the simulation of growth mechanics in thin biological tissues. Two alternative shell kinematic models are considered: a 7-DOF solid-shell model and a 3-DOF Kirchhoff–Love model. Growth at large strains is modeled via the multiplicative decomposition of the deformation gradient and both compressible and incompressible hyperelastic materials. The 7-DOF model naturally supports fully three-dimensional constitutive laws, while the 3-DOF model is generalized to growth problems through the introduction of tailored plane-stress enforcement strategies, numerical for compressible materials and analytical for incompressible ones. An isogeometric discretization based on cubic C2 NURBS, combined with a patch-wise reduced quadrature, provides high-order accuracy with approximately 2.25 surface integration points per element. A key contribution of this work is the closed-form derivation of all growth-induced tangent operators, enabling an efficient and robust arc-length path-following strategy for both shell theories, particularly suited to highly nonlinear regimes and instabilities. Numerical applications, with a focus on fascinating growth-induced buckling phenomena, demonstrate the robustness and versatility of the proposed simulation tool. The two variants of the 3-DOF model produce identical predictions for typical tissues properties. They remain in excellent agreement with those of the 7-DOF model and of a reference solid model in all the considered benchmarks.
Role-Based Access Control (RBAC) is the standard model for enterprise authorization. However, automated role assignment typically prioritizes administrative efficiency over security. Traditional approaches minimize the number of roles, often producing coarse-grained policies that violate the Principle of Least Privilege by bundling high-risk permissions with common permissions. In this paper, we propose a risk-aware role allocation framework that explicitly accounts for permission sensitivity. We formulate the problem as a submodular cost minimization task and introduce a non-linear penalty for high-risk permissions. To address the resulting NP-hard problem, we develop a scalable greedy role allocation algorithm that exploits submodularity. We also consider budget-constrained scenarios, ensuring effective risk reduction under limited administrative capacity. Experimental results show that our approach scales almost linearly, achieves near-optimal solutions, and effectively isolates high-risk permissions where traditional methods fail.
Adulteration of edible oils - particularly extra virgin olive oil (EVOO) - remains a major challenge for food quality and safety, owing to the difficulty of performing rapid, non-destructive and cost-effective inspections on sealed commercial products. This study introduces a portable ultrasonic system able to detect adulteration directly through standard commercial glass bottles, without sample extraction or destructive laboratory analysis. The method employs broadband linear chirp excitation (100 kHz-1 MHz) and a pulse-compression framework to retrieve high-SNR impulse responses from the weak through-glass ultrasonic transmission. A dedicated signal-processing procedure, based on the cumulative spectral energy and on a Boltzmann-function fit, is proposed in order to enhance the sensitivity to changes in composition. Experiments were carried out on EVOO adulterated with sunflower oil over the range 0%-50% v/v. As a proof of concept, a single sample was progressively adulterated and, at each level, five measurement sessions were acquired to assess the repeatability of the through-bottle interrogation. The proposed frequency-domain features are shown to separate pure EVOO from adulterated samples down to 2% v/v, with a session-to-session variability more than two orders of magnitude smaller than the corresponding feature shift, and to provide a clearer separation than the conventional time-of-flight approach. A cross-validated calibration indicates that the response is well behaved and monotonic in the low-adulteration regime that is most relevant to fraud detection, while quantification over the full 0%-50% range is limited by a non-monotonic behaviour of the acoustic response at intermediate concentrations. These results suggest that low-frequency broadband ultrasonics can provide an effective non-destructive tool for rapid in-bottle adulteration screening, with potential for integration into industrial quality-control workflows.
Hybrid finite elements have proved highly accurate in plasticity problems, particularly when the assumed stress interpolation satisfies the equilibrium equations a priori. However, enforcing plastic admissibility in these formulations requires element-level constitutive updates, typically involving constrained local optimisation problems whose cost increases with the number of stress parameters and control points.This work proposes an unsupervised physics-informed neural network surrogate to accelerate the element-level return mapping in incremental elastoplastic analyses based on hybrid finite element discretisations. The network is trained by embedding the governing plasticity conditions directly during the learning process and is used to approximate the plastically corrected stress parameters from an elastic trial stress state. The strategy is tested on a plane-stress hybrid element with a five-parameter equilibrated stress field, including additional inputs that describe distorted element configurations.The results show excellent agreement with standard element-level return-mapping solutions in terms of equilibrium paths, limit loads, and stress-field reconstruction and generally reduce the number of global equilibrium iterations in the tested examples. Overall, the proposed approach achieves speed-up factors ranging from two to eight, suggesting an effective route for accelerating hybrid finite element elastoplastic analyses while retaining accuracy.
Elasmobranchs are effective bioindicators of chemical contamination, integrating habitat use, trophic ecology, and longevity. We quantified 16 metals and metalloids by ICP-MS in the muscle of 81 individuals from six Mediterranean elasmobranch species spanning a benthic-to-pelagic gradient (Raja radula, Mustelus mustelus, Mustelus punctulatus, Myliobatis aquila, Dasyatis pastinaca, and Alopias vulpinus). Arsenic and iron dominated elemental profiles in benthic and demersal species; R. radula showed the highest As, Sr, Mn, and Ni concentrations, consistent with sustained sediment contact, while A. vulpinus (n = 3) showed markedly lower As and an aluminium-dominated profile. Kruskal-Wallis tests revealed significant interspecific differences for nine elements, and Principal Component Analysis explained 41.7% of variance, revealing a clear benthic-to-pelagic gradient. Model comparisons indicated that ecological guild, rather than species identity, better explained accumulation patterns. Mean concentrations of Pb and Cd were below EU maximum levels in all species; Pb exceeded the limit in one M. mustelus individual. These results support Mediterranean elasmobranchs as guildsensitive bioindicators and establish a baseline for ecotoxicological monitoring.
