Enrico Fermi Center for Study and Research

Ecosystems face intensifying threats from climate change, overexploitation, and other human pressures, emphasizing the urgent need to identify keystone species and vulnerable ones. While established network-based measures often rely on a single metric to quantify a species’ relevance, they overlook how organisms can be both carbon providers and consumers, thus playing a dual role in food webs. Here, we introduce a novel approach that assigns each species two complementary scores—an importance index quantifying their centrality as carbon source and a predatory index capturing their vulnerability. We show that species with high importance index are more likely to trigger co-extinctions upon removal, while high-robustness index species typically endure until later stages of collapse, in line with their broader prey ranges. On the other hand, low robustness index species are the most vulnerable and susceptible to extinctions. Tested on multiple food webs, our method outperforms traditional degree-based analyses and competes effectively with eigenvector-based approaches, while also providing additional insights. This scalable and data-driven approach, relying solely on interaction data, provides a cost-effective tool that complements expert classifications for prioritizing conservation efforts. • Introduces two indices (importance & robustness) for species roles in food webs. • Bidimensional framework identifies keystone and vulnerable species in ecosystems. • Outperforms single-metric approaches by capturing non-linear interactions. • Scales to complex webs with a data-driven approach, complementing expert knowledge. • Prioritizes conservation efforts by quantifying species’ ecological impact.

GEMpix detector is a proportional triple-GEM chamber with Timepix1 quad readout. The present work introduces a GEMpix in side-on configuration with interchangeable cathode that allow the insertion of a target material inside the chamber. It has been conceived for the measurement and discrimination of light charged particles produced by neutron induced reactions. The main goal is the study of neutron cross sections for some elements of interest in different research fields like nuclear fusion, nuclear astrophysics, nuclear medicine (dosimetry, neutron therapy, radioisotope production), and nuclear energy applications. The paper shows the first characterization tests performed with a LiF-coated Alumina target on the HOTNES facility, a thermal neutron source peaked at about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$25 \text{meV}. \text{LiF}$</tex> is enriched at 95.6 % with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${ }^{6} \text{Li}$</tex> and interaction with thermal neutrons produces alpha particles and tritons. By applying a dedicated track analysis, the sideon GEMpix showed a clear discrimination between these two particles for energies higher than 200 keV. This represents an optimal result that demonstrated the capability of this new GEMpix to identify light charged particles at low energies that are typically forbidden to solid-state detectors.

ABSTRACT This paper provides an exploratory analysis of the relationship between critical raw materials (CRMs) and environmental technologies. Using text mining techniques to parse and analyse patent data, we explore: (i) the dependence of green technologies on CRMs; (ii) the countries that are more reliant on CRMs in their green patent portfolios; and (iii) the connections between major users and suppliers of CRMs. Framed in the context of recent policy debates on the viability of the sustainable transition, our study points to the criticalities of both the evolution of green technology and of the spatial distribution of demand and supply of CRMs.

Recent beam test results are presented for the prototype detector of the ePIC-dRICH forward particle-identification system at the EIC. A large-area prototype SiPM readout plane consisting of a total of up to 2048 3×3 mm2 sensors has been constructed. The photodetector prototype is modular and based on a novel EIC-driven photodetection unit developed by INFN, which integrates 256 SiPM pixel sensors, cooling and TDC electronics in a volume of ∼5 × 5 × 14 cm3. Several modules have been built and successfully tested with particle beams at CERN-PS in October 2023 and in May 2024. The data have been collected with a complete chain of front-end and readout electronics based on the ALCOR chip, developed by INFN Torino.

Abstract The spin-statistics theorem leads to Pauli’s Exclusion Principle (PEP), the basis of the stability of matter and many other phenomena relevant to physics, astrophysics, cosmology, and even biology. Possible violations of the PEP (PEPV) have been searched for since its inception; they may come from various Beyond Standard Model descriptions, including NonCommutative Quantum Gravity models and extensions of the Quantum Field Theory, which must follow the Messiah-Greenberg Super-Selection (MGSS) rule and lead to the Quon description of the spin-statistic symmetries: the parastatstics. Efforts to search for a PEPV are not new; however, they have typically measured those cases without respecting MGSS. VIP (VIolation of the Pauli exclusion principle) first and its successor VIP-2, both sited in the “Gran Sasso underground laboratory,” aimed to set the most stringent limit for the non-Paulian transition following the MGSS rule. After two years of data with VIP-2 and better modeling of the electron-atom interaction, we present the most stringent upper limit of the probability of PEP violation for this case (β 2/2 &lt; 2.47×10−43), thus completing the state-of-the-art of all possible PEPV cases, and the consequent constraint to the Quon description: 1+q &lt; 4.94×10−43

Motivated by the expectation that relativistic symmetries might acquire quantum features in Quantum Gravity, we take the first steps towards a theory of ''Doubly'' Quantum Mechanics, a modification of Quantum Mechanics in which the geometrical configurations of physical systems, measurement apparata, and reference frame transformations are themselves quantized and described by ''geometry'' states in a Hilbert space. We develop the formalism for spin-12 measurements by promoting the group of spatial rotations SU(2) to the quantum group SUq(2) and generalizing the axioms of Quantum Theory in a covariant way. As a consequence of our axioms, the notion of probability becomes a self-adjoint operator acting on the Hilbert space of geometry states, hence acquiring novel non-classical features. After introducing a suitable class of semi-classical geometry states, which describe near-to-classical geometrical configurations of physical systems, we find that probability measurements are affected, in these configurations, by intrinsic uncertainties stemming from the quantum properties of SUq(2). This feature translates into an unavoidable fuzziness for observers attempting to align their reference frames by exchanging qubits, even when the number of exchanged qubits approaches infinity, contrary to the standard SU(2) case.

Scale invariance profoundly influences the dynamics and structure of complex systems, spanning from critical phenomena to network architecture. Here, we propose a precise definition of scale-invariant networks by leveraging the concept of a constant entropy-loss rate across scales in a renormalization-group coarse-graining setting. This framework enables us to differentiate between scale-free and scale-invariant networks, revealing distinct characteristics within each class. Furthermore, we offer a comprehensive inventory of genuinely scale-invariant networks, both natural and artificially constructed, demonstrating, e.g., that the human connectome exhibits notable features of scale invariance. Our findings open new avenues for exploring the scale-invariant structural properties crucial in biological and sociotechnological systems.

Studying how we explore the world in search of novelties is key to understand the mechanisms that can lead to new discoveries. Previous studies analyzed novelties in various exploration processes, defining them as the first appearance of an element. However, novelties can also be generated by combining what is already known. We hence define higher-order novelties as the first time two or more elements appear together, and we introduce higher-order Heaps’ exponents as a way to characterize their pace of discovery. Through extensive analysis of real-world data, we find that processes with the same pace of discovery, as measured by the standard Heaps’ exponent, can instead differ at higher orders. We then propose to model an exploration process as a random walk on a network in which the possible connections between elements evolve in time. The model reproduces the empirical properties of higher-order novelties, revealing how the network we explore changes over time along with the exploration process.