Magic is a property of a quantum state that characterizes its deviation from a stabilizer state, serving as a useful resource for achieving universal quantum computation e.g., within schemes that use Clifford operations. In this work, we study magic, as quantified by the stabilizer Renyi entropy, in a class of models known as generalized Rokhsar-Kivelson systems, i.e., Hamiltonians that allow a stochastic matrix form (SMF) decomposition. The ground state wavefunctions of these systems can be written explicitly throughout their phase diagram, and their properties can be related to associated classical statistical mechanics problems, thereby allowing powerful analytical and numerical approaches that are not usually available in conventional quantum many body settings. As a result, we are able to express the SRE in terms of wave function coefficients that can be understood as a free energy difference of related classical problems. We apply this insight to a range of quantum many body SMF Hamiltonians, which affords us to study numerically the SRE of large high-dimensional systems, and in some cases to obtain analytical results. We observe that the behaviour of the SRE is relatively featureless across quantum phase transitions in these systems, although it is indeed singular (in its first or higher order derivative, depending on the nature of the transition). On the contrary, we find that the maximum of the SRE generically occurs at a cusp away from the quantum critical point, where the derivative suddenly changes sign. Furthermore, we compare the SRE and the logarithm of overlaps with specific stabilizer states, asymptotically realised in the ground state phase diagrams of these systems. We find that they display strikingly similar behaviors, which in turn establish rigorous bounds on the min-relative entropy of magic.

Functionalization of graphene with dimethyl acetylenedicarboxylate is achieved through a microwave-assisted Diels-Alder reaction. The physical, chemical, and electrochemical properties of the modified sheets are thoroughly investigated by complementary characterization techniques. Density Functional Theory calculations are employed to examine the functionalization mechanism and to highlight the role of defects such as epoxide bridges introduced in graphene during exfoliation. Our findings provide valuable insights into the development of efficient and cost-effective methods for large-scale production of high-quality graphene-based materials. Specifically, the electrochemical properties of anode materials containing functionalized graphene are evaluated for Li-ion electrochemical energy storage devices, demonstrating excellent electrochemical reversibility and rate capability. The cyclic voltammetry analysis reveals material stabilization after a few cycles, resulting in a coulombic efficiency of up to 95 % and a discharge capacity of 162.3 mA·h·g−1. The galvanostatic cycling test indicates that the material electrode retains 57 % of its initial capacity at a C-rate of 10C, indicating high-power capability. These promising results position organic modified graphene as a potential material for Li-ion capacitors, with a specific capacity that aligns with the last intercalation stage capacity at a lower potential. Overall, the study's findings offer significant contributions to the advancement of graphene-based materials in energy storage applications.

The Internet of Things (IoT) is a key enabler for critical systems, but IoT devices are increasingly targeted by cyberattacks due to their diffusion and hardware and software limitations. This calls for designing and evaluating new effective approaches for protecting IoT systems at the network level. While recent proposals based on machine- and deep-learning provide effective solutions to the problem of attack-traffic classification, their adoption is severely challenged by the amount of labeled traffic they require to train the classification models. In fact, this results in the need for collecting and labeling large amounts of malicious traffic, which may be hindered by the nature of the malware possibly generating little and hard-to-capture network activity. To tackle this challenge, we adopt few-shot learning approaches for attack-traffic classification, with the objective to improve detection performance for attack classes with few labeled samples. We leverage advanced deep-learning architectures to perform feature extraction and provide an extensive empirical study—using recent and publicly available datasets—comparing the performance of an ample variety of solutions based on different learning paradigms, and exploring a number of design choices in depth (impact of embedding function, number of classes of attacks, or number of attack samples). In comparison to non-few-shot baselines, we achieve a relative improvement in the F1-score ranging from 8% to 27%.

The area surrounding the Po River, known as the Po Valley, provides a central contribution in the economy of Italy and is highly devoted to agriculture. Recently it has been hit by multiple droughts, among which the exceptional event of summer 2022 is considered the worst dry period of the past 200 years. In the near future, the frequency of such exceptional events is predicted to rise; thus, a deep knowledge of the past droughts that hit the area, the variables used to characterize the events, the impacts they caused and the mitigation strategies adopted to deal with dry periods is of the utmost importance for policy definitions and planning. This study maps the scientific literature published from 2000 to February 2024 on the topic of drought in the Po Valley using the Scopus and Web of Science databases. Overall, 44 articles have been identified and grouped in three main classes: event identification and characterization, impact analysis and management strategies. The main gaps found in the collected papers are the lack of evaluations of the impacts of drought events on human health, hydroelectric energy production and tourism. Furthermore, comprehensive drought management and planning in the area is never addressed in the considered articles. The mentioned aspects deserve more attention, especially the development of drought management plans and policies and the evaluation of their effectiveness.

The use of light as a catalytic prompt for the synthesis of industrial relevant compounds is widely explored in the past years, with a special consideration over the hydrogen evolution reaction (HER). However, semiconductors for heterogeneous photocatalysis suffer from fast charge recombination and, consequently, low solar-to-hydrogen efficiency. These drawbacks can be mitigated by coupling photocatalysts with an external circuit that can physically separate the photogenerated charge carriers (electrons and holes). For this reason, photoelectrochemical (PEC) production of hydrogen is under the spotlight as promising green and sustainable technique and widely investigated in numerous publications. However, considering that a significant fraction of the hydrogen produced is used for reduction processes, the development of PEC devices for direct in situ hydrogenation can address the challenges associated with hydrogen storage and distribution. This Perspective aims at highlighting the fundamental aspects of HER from PEC systems, and how these can be harnessed toward the implementation of suitable settings for the hydrogenation of organic compounds of industrial value.

The significant role of bridges in transportation networks requires continuous improvement of bridge management strategies, which has been even more exacerbated by the recent bridge collapses worldwide. This study proposes a framework for the prioritization and preliminary risk assessment of existing bridges subjected to seismic and traffic hazards. The proposed method adopts quantitative expressions to define risk in a simplified manner and, consequently, the priority associated with a certain bridge within a bridge stock. The three primary components of risk are considered: vulnerability, exposure, and hazard, which are specified according to the characteristics of the bridge, site, and road network. The bridge state of conservation is accounted for in the vulnerability component through average and critical defect condition indicators. The proposed method is implemented in a case-study of thirty real bridges in northern Italy. The impact of defects on vulnerability and priority ranking of bridges, the assumptions for defects condition and each subcomponent’s contribution to risk classification are discussed. The prioritization results are presented in absolute and normalized formats, with the latter providing a clearer view of the bridges’ relative priority within the stock. Finally, the proposed method’s findings are compared with the current Italian qualitative guidelines for bridge management.

The high-pressure and high-temperature thermodynamic properties of rhodium (up to 2000 K and 300 GPa) are presented using the first principle approach within the quasi-harmonic approximation. The thermal Helmholtz free energy includes the contribution of both phonon vibrations and electronic excitations. The performance of three popular exchange-correlation functionals—local density approximation [Perdew et al., Phys. Rev. B 23, 5048 (1981)], Perdew–Burke–Ernzerhof generalized gradient approximation (PBE) [Perdew et al., Phys. Rev. Lett. 77, 3865 (1996)], PBE modified for dense solids [Perdew et al., Phys. Rev. Lett. 100, 136406 (2008)] are shown. The simulated thermal expansion coefficient, isobaric heat capacity, mode-Grüneisen parameter, thermodynamic average Grüneisen parameter, and bulk modulus are compared with the available experimental and theoretical reports. The contribution of thermal electronic excitations to the obtained thermodynamic parameters is significant at low pressure and high temperatures, except in bulk modulus, where it is small. The pressure-dependent elastic constant coefficient (Cij) and the Debye temperature are computed at 0 K. The Pugh ratio calculated from Cij indicates that rhodium undergoes brittle to ductile transitions at an average pressure of 7.45 GPa.

Jet-milling is a particle engineering process widely adopted in industrial manufacturing to reduce the particle size of powders. Computational Fluid Dynamics simulations (CFD) coupled with Discrete Element Modelling (DEM) proved to be a valuable tool to tackle the complexity and the non-linearity of particle breakage and classification occurring in mills. To date however, they have been employed to address only single aspects of process design being unable to reproduce it completely. The coupled CFD-DEM simulations presented in this work are for the first time capable of simultaneously describe particle fragmentation, particle-gas interaction and classification. Through coarse-graining, realistic amounts of powder can be simulated allowing to demonstrate/study how the hold-up mass slows down the milling gas affecting classification and thus the milling performance. Bottlenecks/limitations of model and methodology are critically examined to understand what is currently preventing us from creating a digital twin of the milling process.