Polytechnic University of Turin

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Goal: The gold standard for detecting the presence of apneic events is a time and effort-consuming manual evaluation of type I polysomnographic recordings by experts, often not error-free. Such acquisition protocol requires dedicated facilities resulting in high costs and long waiting lists. The usage of artificial intelligence models assists the clinician's evaluation overcoming the aforementioned limitations and increasing healthcare quality. Methods: The present work proposes a machine learning-based approach for automatically recognizing apneic events in subjects affected by sleep apnea-hypopnea syndrome. It embraces a vast and diverse pool of subjects, the Wisconsin Sleep Cohort (WSC) database. Results: An overall accuracy of 87.2 $\pm$ 1.8% is reached for the event detection task, significantly higher than other works in literature performed over the same dataset. The distinction between different types of apnea was also studied, obtaining an overall accuracy of 62.9 $\pm$ 4.1%. Conclusions: The proposed approach for sleep apnea events recognition, validated over a wide pool of subjects, enlarges the landscape of possibilities for sleep apnea events recognition, identifying a subset of signals that improves State-of-the-art performance and guarantees simple interpretation.

Concrete is one of the most widely used materials in the world. Still, its production processes, energy consumption, and high use of raw materials make it one of the most environmentally harmful materials. This study aims to enhance the sustainability of concrete by reducing the amount of binder and incorporating secondary materials into the cementitious matrix. The binder reduction is achieved by using a foaming agent that creates a microporous matrix, significantly decreasing the volume of cement in the material. Additionally, reinforcing the material with sheep wool fiber not only improves its mechanical properties but also gives a new purpose to a commonly discarded secondary material. The research specifically seeks to identify the most effective treatments for sheep wool fiber (including non-treated, salt-treated, lime-treated, NaOH-treated, and surfactant-treated fibers), as well as the optimal fiber length (6, 12, and 20 mm) and content (4.5, 9, and 15 kg/m³) for ultralightweight foamed concrete in terms of mechanical strength. The findings demonstrate excellent compatibility between wool fibers and ultralightweight foamed concrete, with fiber-reinforced samples showing up to a 60% increase in flexural strength and up to a 50% increase in compressive strength. Among the various fiber treatments evaluated, surfactant-treated fibers yielded the best results.

Titanium dioxide nanotubes (TiO2 NTs) have been widely investigated in the past 20 years due to a variety of possible applications of this material. Indeed, their high surface area and tunable morphology can easily implement key features of TiO2, such as its biocompatibility and photo‐ and electrocatalytic properties. This combination makes TiO2 NTs perfect candidates for multifunctional applications ranging from biomedical application to sensing and energy devices. Herein, we present TiO2 NTs grown by anodic oxidation on top of a titanium foil in an ethylene glycol‐based electrolyte with NH4F. The as‐grown amorphous nanotubes were then subjected to annealing in a reducing atmosphere at different temperatures while maintaining their amorphicity. The morphological, physicochemical, and electronic properties were then thoroughly evaluated to assess their use in different fields, from energy storage devices to photo‐catalytical applications.

Propylene epoxidation in mild conditions using molecular O2 is a highly desirable reaction that represents a significant challenge in the field of heterogeneous catalysis for the synthesis of oxygenated organic compounds of industrial interest. In this work, CuxO/TiO2 composites with different mominal CuO loadings (in the range of 0.5–8.4 wt %) were used to promote the photocatalytic epoxidation of propylene with molecular oxygen under UV‐A irradiation in a fluidized bed system. The photocatalysts were prepared by a straightforward method consisting of thermal annealing of physical mixtures between copper acetate and sol‐gel‐derived TiO2. Different characterization techniques were employed to assess the influence of CuxO content on the physical‐chemical properties of the CuxO/TiO2 composites. The best combination in terms of propylene conversion and selectivity towards propylene oxide (18.1 % and 72 %, respectively) was obtained with CuxO/TiO2 at 1.1 wt % CuO, as shown by photocatalytic tests. The high propylene oxide selectivity is due to the ability of CuO in the CuxO/TiO2 composite to transform molecular O2 into hydrogen peroxide that, in turn, is able to directly oxidize propylene to propylene oxide. By using a UV‐A light intensity of 297.2 mW cm⁻², the propylene conversion and the epoxide yield were 31.5 and 22.2 %, respectively, significantly higher than that reported in the literature. Moreover, the energy consumption of the reaction system employed in this paper was significantly lower than that of photocatalytic systems studied in the literature dealing with selective propylene epoxidation.

In light of the Russian invasion of Ukraine, this contribution to the special issue “Organizing European Security for yet another geopolitical era” argues that European defence should be considered a European public good. First, we develop a definition of European public good embedded in the political economy literature on public goods and state-building; then we discuss how to finance defence as a European public good, and we review public support for EU-level defence instruments.

Cardiovascular diseases, among which atherosclerosis is the most prevailing, are leading causes of death worldwide. Coronary artery bypass grafting (CABG) is one common surgical treatment. However, synthetic vascular grafts in small-diameter vessels are still limited due to their relatively high incidences of thrombosis and intimal hyperplasia. To overcome these limitations, advanced tissue-engineered vascular grafts (TEVGs) that possess characteristics similar to those of natural blood vessels and hold promise for better biocompatibility are desirable. In this study, heparinized polyurethane (PU)/polycaprolactone (PCL) composite nanofibers were fabricated by electrospinning for use as small-caliber TEVG scaffolds. Heparin was covalently attached onto PU/PCL (2:1 ratio) composite nanofibers through cross-linking chemistry. Scaffolds were systematically characterized with respect to physicochemical properties, heparin distribution uniformity, and in vitro biocompatibility. The results indicated that the incorporation of heparin into PU/PCL nanofibers enhanced the expression of markers associated with endothelialization and angiogenesis. Modified scaffolds exhibited better biocompatibility with increased cell proliferation and reduced platelet adhesion compared to pure PU scaffolds or neat blends PCL/PU ones. This work provides a promising method of improving results using small-diameter TEVGs by rational biochemical modifications for facilitating CABG and the development of other vascular replacement surgeries.

Societies are experiencing deep and intertwined structural changes that may unsettle perceptions European citizens have of their economic and employment security. In turn, such perceptions likely alter people’s political positions. For instance, those worried by labour market competition may prefer greater social protection to compensate for the accrued risk, or prefer more closed economies where external borders provide protection (or perceived protection). We develop expectations about how such distinct reactions can emerge from distinct labour-market risks of globalization, or automation, or migration. We test these expectations using a conjoint experiment in 13 European countries on European-level social policy. Results broadly corroborate our expectations on how different concerns about sources of labour market competition yield support for different features of European-level social policy.

In this paper, we present a kernel-based non-parametric approach to identifying stable multi-input multi-output linear systems in the presence of bounded noise affecting both the input and the output measurements. Firstly, we formulate the considered problem in terms of robust optimization techniques. Then, we show that the formulated robust optimization problem can be solved using semidefinite optimization. Since the involved optimization problem is computationally demanding, we also provide a result that allows the user to compute a bound on the approximation error introduced by considering reduced complexity models. We present some simulation examples to show the effectiveness of the proposed approach. Finally, we apply the proposed identification method to the dataset experimentally collected on a linear electronic filter.

The use of Shapley Values (SVs) to explain machine learning model predictions is established. Recent research efforts have been devoted to generating efficient Neural Network-based SVs estimates. However, the variability of the generated estimates, which depend on the selected data sampling, model, and training parameters, brings the reliability of such estimates into question. By leveraging the concept of Interval SVs, we propose to incorporate SVs uncertainty directly into the learning process. Specifically, we explain ensemble models composed of multiple predictors, each one generating potentially different outcomes. Unlike all existing approaches, the explainer design is tailored to Interval SVs learning instead of SVs only. We present three new Network-based explainers relying on different ISV paradigms, i.e., a Multi-Task Learning network inspired by the Shapley value's weighted least squares characterization and two Interval Shapley-Like Value Neural estimators. The experiments thoroughly evaluate the new approaches on ten benchmark datasets, looking for the best compromise between intervals' accuracy and explainers' efficiency.

This paper presents a framework for integrating Low-Earth Orbit (LEO) platforms with Non-Terrestrial Networks (NTNs) in the emerging 6G communication landscape. Our work applies the Mega-Constellation Services in Space (MCSS) paradigm, leveraging LEO mega-constellations’ expansive coverage and capacity, designed initially for terrestrial devices, to serve platforms in lower LEO orbits. Results show that this approach overcomes the limitation of sporadic and time-bound satellite communication links, a challenge not fully resolved by available Ground Station Networks and Data Relay Systems.We contribute three key elements: ( i ) a detailed MCSS evaluation framework employing Monte Carlo simulations to assess space user links and distributions; ( ii ) a novel Space User Terminal (SUT) design optimized for MCSS, using different configurations and 5G New Radio Adaptive Coding and Modulation; ( iii ) extensive results demonstrating MCSS’s substantial improvement over existing Ground Station Networks and Data Relay Systems, motivating its role in the upcoming 6G NTNs. The space terminal, incorporating a multi-system, multi-orbit, and software-defined architecture, can handle Terabit-scale daily data volumes and minute-scale latencies. It offers a compact, power-efficient solution for properly integrating LEO platforms as space internet nodes.

Electric Vehicles (EVs) provide an alternative to traditional mobility and a sustainable means of transportation. As a result, electric vehicle sales are increasing across Europe, prompting researchers to wonder about the impact of EVs on smart grids. The proposed framework simulates users’ activities, highly characterising individual behaviour using Time Use Survey (TUS) data to estimate EV usage and consumption. Then, for each trip, the routes between origin and destination are determined, simulating in separate modules i) the driving behaviour, ii) the motion of the EV and its discharge considering spatial data and iii) the charge considering users’ preference. Thanks to the spatial information openly available, it is possible to characterise the simulation and improve EV consumption estimation. Different scenarios are analysed to demonstrate the versatility of the proposed framework by exploiting its modularity. The individuals’ heterogeneity is considered by using an agent-oriented approach. Furthermore, the simulation proceeds on a time-step basis to enable the use of the simulator in a co-simulation environment for future purposes, such as the integration of power networks. The results indicate that achieving a high realism with limited, i.e. containing scarce data for the problem under study, is feasible, enabling researchers to make informed decisions about future mobility.

Advanced structural design approaches should consider the economic and technological benefits offered by the structural applications of fibre-reinforced concrete. In this framework, it is important to highlight how the ductility of fibre-reinforced concrete structures is strongly dependent on the fibre volume fraction together with the structural size. This crucial coupling induces two reverse ductile-to-brittle transitions in the mechanical response of fibre-reinforced and hybrid-reinforced concrete elements: by increasing the characteristic size of the structure, an increase in its load-bearing capacity can be observed together with a decrease in its plastic rotation capacity. These size-scale effects can be taken into account by an effective fracture mechanics approach represented by the Updated Bridged Crack Model (UBCM), which can provide significant improvements in current Standards and regulations on fibre-reinforced concrete structures.

Science should drive policies and regulations to ensure a sustainable (environmentally, socially, and economically) green transition to a Net-Zero / Net-Negative circular economy. Since 2015, which saw COP21 in Paris, Net Zero has been a global target that must be rapidly accompanied by a Net Negative strategy to mitigate climate change. Accordingly, biochar's role as a durable carbon removal method is gaining attention and increasing. In this work, we discuss the durability of the carbon in biochar and the need for analytical techniques to support stakeholders on a project level. The different ecologically relevant groups of carbon forms contained in biochar are presented, and possible project-based methods to assess the quality and durability of the product versus the regulatory requirements for the permanence of carbon removals are summarized. Biochar is today one of the CDR technologies with the highest technology readiness level (TRL 8–9) that can ensure permanent removals for time frames relevant to climate change mitigation projects, combined with co-benefits that are gaining relevance in terms of mitigating climate impacts in agricultural soils.

Self‐healing materials solutions and rapid prototyping approaches are actively searched to improve the safety and the production processes of batteries at the gigascale. Here, a self‐reparable polymer electrolyte designed into 3D‐printable ink formulation for digital light processing is shown. For this purpose, covalent adaptable networks containing hindered urea dynamic bonds end‐capped with photopolymerizable methacrylate groups are designed and investigated in terms of dynamicity and self‐healing properties. Electrochemical performance of the electrolytes is tested and compared with a commercially available benchmark, showing in all cases superior electrolyte uptake, ionic conductivities, and full specific capacity recovery after being cut in operando. This work brings the first self‐healable and 3D‐photoprinted electrolyte system for lithium batteries, at once ensuring safety, performance, and upscalability; the concept is also exploitable in lithium‐mediated ammonia electrosynthesis.

A review on the classical Plateau problem is presented. Then, the state of the art about the Kirchhoff-Plateau problem is illustrated as well as some possible future directions of research.