Polytechnic University of Turin

The Divertor Tokamak Test (DTT) facility is a challenging high-field and compact tokamak, currently under construction at the Frascati ENEA Research Center. Its purpose is to significantly contribute to the exploration, design and assessment of systems intended for the management of heat exhaust. Its superconducting magnetic system consists of 18 Toroidal Field (TF) coils, 6 Poloidal Field (PF) coils and a Central Solenoid (CS). This work is focused on the superconducting praying-hands joint, manufactured by ASG-Superconductors for the DTT TF coils terminations and for the connection among the adjacent double-pancakes constituting the TF winding pack. The joint has been tested and fully characterized by dedicated 2-weeks cryogenic tests in the SULTAN facility at Swiss Plasma Centre (SPC, EPFL) in November 2023. The tests included 3000 EM cycles at the maximum foreseen operative load of 8.1 T - 21 kA and two Warm-Up-Cool-Down (WUCD) cycles, while reference DC measurements were done in normal and reversed polarity up to 42.5 kA and to 4 T, before, during and after cyclic loading and WUCDs. Assessment of AC losses, performed through bipolar sinusoidal pulsing at frequencies up to 2 Hz, and pressure drop measurements as a function of He mass flow rate, were conducted on the virgin sample and after full cyclic loading. The Minimum Quench Energy (MQE) has also been measured at different temperatures under nominal current conditions. All the conducted tests confirmed the efficacy and goodness of the joint design, with its resistance at self-field consistently remaining below the nominal allowable value of 2 nω.

Recurrent catheter‐associated urinary tract infections (CAUTIs) in catheterized patients, increase their morbidity and hospital stay at substantial costs for healthcare systems. Hence, novel and efficient strategies for mitigating CAUTIs are needed. In this work, a bio‐based nanocomposite coating is engineered with bactericidal, antibiofilm, and antioxidant properties on commercial silicone catheters using a combined ultrasound/nanoparticles (NPs) driven coating approach. This approach integrates citronellal‐loaded lauryl gallate NPs (CLG_NPs), as both antimicrobial and structural elements, with chitosan (CS), in a substrate‐independent sonochemical coating process. The hybrid CS/CLG_NPs coating shows pH‐dependent citronellal release, strong antibacterial activity toward the common CAUTI pathogens Escherichia coli and Staphylococcus aureus, alongside strong antioxidant activity, and biocompatibility to fibroblast and keratinocytes. Moreover, the nano‐enabled coating significantly mitigated bacterial biofilm formation after a week in a simulated human bladder environment, outperforming the commercially‐available silicone catheters. These results underscore the potential of the novel biopolymer nanocomposites obtained by ultrasound coating technology, offering a straightforward antimicrobial/antibiofilm solution for indwelling medical devices.

In this paper we discuss the approximation of the spectrum of the Steklov eigenvalue problem, by using the well known Hybrid High-Order (HHO) method. The analysis developed in this work is partially based on the existing literature about the HHO method for the Laplacian eigenvalue problem. As usual with HHO methods, we are able to eliminate the volume unknowns, by introducing a suitable discrete solver operator. This allows us to numerically solve on the skeleton of the mesh, reducing the computational cost. The a priori error analysis lets us to prove optimal convergence rates for the eigenvalues and the eigenfunctions, when the latter are smooth enough. Numerical examples that confirm our theoretical findings are provided.

Background Spinal cord compression in patients with vertebral metastases often requires surgical decompression with spinal fixation. Recent studies reported increased implant failures due to mechanical complications, raising concerns about current clinical practices. Long-segment fixation ( Lf ) is commonly employed to enhance mechanical stability and reduce the severity of pedicle screw failure. The study investigates how the number of vertebral levels involved in fixation affects the loads on pedicle screw anchorages in a fatigue-related displacement domain. Method Using a rigid-flexible multibody approach, a non-linear T12–S1 model was employed to simulate two fixation types following L3 posterior decompression surgery: Lf spanning two levels above and below the decompression site (L1, L2, L4, and L5) and a short-segment fixation ( Sf ) involving only adjacent vertebrae. Internal reactions at the rod-pedicle screw anchorages were estimated in terms of pullout, shear forces, and bending moments. The range of motion analysed (flexion: 22°, extension: 8°, lateral bending: 12°, axial rotation: 5°) was confined to the “Cone of Economy”, representing a small-displacement volume where loads are assumed cyclically exchanged. Results Lf exhibited up to fivefold higher reactions than Sf , with a heterogeneous shear force distribution: middle screws appeared shielded, while extremity screws were overloaded (~400 N, comparable to experimental fatigue strength). Pullout forces remained within safe limits (< 150 N). Conclusions The rigid-flexible multibody approach effectively estimated internal loads in the implant-spine constructs under dynamic conditions. The findings highlight the long-term implications of Lf , demonstrating that involving more vertebral levels triggers adverse loads on pedicle screws, potentially compromising implant durability.

This paper presents and frames the results of the recent book The creative response: knowledge and innovation by Antonelli and Colombelli (2023). The book combines the advances of the economics of knowledge and innovation, implementing the Schumpeterian notion of creative response to understand the determinants and the effects of the rate and direction of technological and organizational change and its variance across time and space, firms and industries. The notion of creative response, introduced by Joseph Schumpeter in the essay “The creative response in economic history” published in 1947 by The Journal of Economic History , can be regarded as the synthesis of his life-long work on innovation. It provides an inclusive framework that enables to highlight the crucial role of knowledge in assessing the rate and direction of technological change and to clarify that no innovation is possible without the generation of new knowledge, while the generation of new knowledge augments the chances of innovation but does not yield automatically the introduction of innovation. Firms thus are faced with a number of strategic decisions to make the creative response possible. The position paper elaborates the analytical core of the notion of creative response and articulates its implications for economic policy and strategic management.

The article is related to mitigation of magnetic field emitted by a 10/0.4 kV substation located in a primary school in Belgrade, Serbia. During the first testing in the classroom located directly above the substation, the measured values of magnetic flux density significantly exceeded the reference level of 40 μT prescribed by Serbian legislation, and it was concluded that the field levels at the transformer rated load could exceed the reference level of 100 μT prescribed by European Council Recommendation 1999/519/EC. For that reason, the 0.4 kV busbars located near the ceiling of the substation were removed and replaced with cables that were placed on the floor of the substation. The testing was repeated and the measured values as well as the values at the rated load were lower than 40 μT. However, above the locations where the cables were connected to the transformer and to the 0.4 kV switchboard, the field levels were still higher than in the rest of the classroom. The focus of this article is to analyze different solutions based on passive shielding for the reduction of magnetic field in these two areas. Seven solutions based on conductive shields with different geometries are analyzed. The substation and the shields are modeled by using appropriate software tools to determine which shield is the most effective. In the case of Solution 7, the lowest field values were obtained. The highest value of magnetic flux density in this case was 6.95 μT at the transformer rated load.

This study investigates the movement of moisture and associated heat loss from the sock to the microclimate and external environment in mountain footwear. As it is not how much sweat is produced, but how much of it can evaporate, a thorough study was carried out to analyse the various contributions to heat exchange in this context. The tests were performed on a thermal manikin with a 100% cotton sock loaded with different amounts of water (0g, 5g, 15g, and 30g) at 10°C and 50% RH. Long-term tests were carried out with the maximum amount of water loaded (30g) to observe the different phases of heat and mass transfer until complete evaporation of all moisture (both from the socks and from the boots). In addition, evaporation tests were performed under semi-isothermal conditions (Tamb = Tman = Tsocks) at 34°C and 12% RH (same vapour pressure as in the cold tests) to isolate the evaporative contribution without any conductive, convective or condensing heat loss component. The actual effect of the evaporation was compared with the theoretical estimates from the mass-loss method. It was found that heat loss by evaporation was overestimated as a fraction of water evaporated from the socks underwent condensation in the boots, which does not contribute to cooling the system and returns heat to the system.

The accuracy of bibliometric databases in classifying document types (DTs)—such as research articles , conference proceedings , reviews , short notes , letters , book chapters , etc.—is crucial for the academic community, as bibliometric indicators may significantly influence research funding, decision-making, and academic reputation. This study presents a semi-automated methodology to assess the accuracy of DT classification in bibliometric databases, such as Scopus and Web of Science (WoS). The methodology can handle large document volumes and adapt to different DT categories without predefined correspondences. The first phase of the methodology automatically identifies discrepancies in DT classifications between Scopus and WoS, in order to find potentially misclassified documents; the second phase involves manually analyzing these documents to confirm and attribute classification errors. The methodology is applied to a sample of several tens of thousands of papers from the teaching staff of two major universities in Turin (Italy). The results show overall error rates of approximately 2.7% for Scopus and 2.3% for WoS. The paper also analyzes the most common types of errors found in both databases, providing an interpretation of these inaccuracies and some insights for possible improvements in the quality of these databases.

The growing availability of affordable Virtual Reality (VR) hardware and the increasing interest in the Metaverse are driving the expansion of Social VR (SVR) platforms. These platforms allow users to embody avatars in immersive social virtual environments, enabling real-time interactions using consumer devices. Beyond merely replicating real-life social dynamics, SVR platforms offer opportunities to surpass real-world constraints by augmenting these interactions. One example of such augmentation is Artificial Facial Mimicry (AFM), which holds significant potential to enhance social experiences. Mimicry, the unconscious imitation of verbal and non-verbal behaviors, has been shown to positively affect human-agent interactions, yet its role in avatar-mediated human-to-human communication remains under-explored. AFM presents various possibilities, such as amplifying emotional expressions, or substituting one emotion for another to better align with the context. Furthermore, AFM can address the limitations of current facial tracking technologies in fully capturing users' emotions. To investigate the potential benefits of AFM in SVR, an automated AM system was developed. This system provides AFM, along with other kinds of head mimicry (nodding and eye contact), and it is compatible with consumer VR devices equipped with facial tracking. This system was deployed within a test-bench immersive SVR application. A between-dyads user study was conducted to assess the potential benefits of AFM for interpersonal communication while maintaining avatar behavioral naturalness, comparing the experiences of pairs of participants communicating with AFM enabled against a baseline condition. Subjective measures revealed that AFM improved interpersonal closeness, aspects of social attraction, interpersonal trust, social presence, and naturalness compared to the baseline condition. These findings demonstrate AFM's positive impact on key aspects of social interaction and highlight its potential applications across various SVR domains.

The Zero Degree Calorimeters (ZDC) of the ALICE experiment at the LHC were designed to characterize the event and monitor the luminosity in heavy-ion collisions. In order to fully exploit the potential offered by the LHC increased luminosity in Run 3, while preserving the time and charge resolution performance, the ZDC readout system was upgraded to allow the acquisition of all collisions in self-triggered mode without dead time. The presence of electromagnetic dissociation (EMD) processes makes the ZDC operating conditions extremely challenging, raising the readout rate for the channels of the most exposed calorimeters up to 1.4 Mevents/s, compared to an hadronic rate of about 50 Kevents/s sustained by all other detectors. The new acquisition chain is based on a commercial 12 bit digitizer with a sampling rate of 1 GSps, assembled on an FPGA Mezzanine Card. The signals produced by the ZDC channels are digitized, the samples are processed through an FPGA that, thanks to a custom trigger algorithm, flags for readout the relevant portion of the waveform and extracts information such as timing, baseline average and event rate. The system is fully integrated with the ALICE data taking infrastructure and acquired physics data during the 2023 LHC heavy-ion data taking. The architecture of the new readout system, the auto trigger strategy, and the ZDC performance during the 2023 Pb–Pb collisions are presented.

The design of interfaces between nanostructured electrodes and advanced electrolytes is critical for realizing advanced electrochemical double‐layer capacitors (EDLCs) that combine high charge‐storage capacity, high‐rate capability, and enhanced safety. Toward this goal, this work presents a novel and sustainable approach for fabricating ionogel‐based electrodes using a renewed slurry casting method, in which the solvent is replaced by the ionic liquid (IL), namely 1‐ethyl‐3‐methylimidazolium bis(fluorosulfonyl)imide (EMIFSI). This method avoids time‐consuming and costly electrolyte‐filling steps by integrating the IL directly into the electrode during slurry preparation, while improving the rate capability of EDLCs based on pure non‐flammable ILs. The resulting ionogel electrodes demonstrate exceptional electrolyte accessibility and enable the production of symmetric EDLCs with high energy density (over 30 Wh kg⁻¹ based on electrode material weight) and high‐rate performance. These EDLCs could operate at temperatures up to 180 °C, far exceeding the limitations of traditional EDLCs based on organic electrolytes (e. g., 1 M TEABF4 in acetonitrile, up to 65 °C). Ionogel‐type EDLCs exhibit remarkable long‐term stability, retaining 88 % specific capacity after 10000 galvanostatic charge/discharge cycles at 10 A g⁻¹ and demonstrating superior retention compared to conventional EDLCs (50 %), while also maintaining 92.4 % energy density during 100 h floating tests at 2.7 V. These electrochemical properties highlight their potential for robust performance under demanding conditions. This study highpoints the practical potential of ionogel‐based electrodes to advance IL‐based EDLC technology, paving the way for next‐generation energy storage devices with high‐temperature and high‐voltage operational capabilities.

This manuscript presents the first and simplest ever‐reported electrical cell, which leverages one memristor on Edge of Chaos to reproduce the three‐bifurcation cascade, marking the entire life cycle from birth to extinction via All‐to‐None effect of an electrical spike, also referred to as Action Potential, across axon membranes under monotonic modulation in the net synaptic current, as predicted by the Hodgkin‐Huxley neuron model, yet through half the number of degrees of freedom relative to the bio‐plausible mathematical description, which earned the two American luminaries Hodgkin and Huxley a Nobel prize in Medicine and Physiology in 1961. Fig. 1 (a) Fourth‐order Hodgkin‐Huxley neuron circuit model. (b) Bifurcation diagram of the model under a monotonic decrease in the net synaptic current iin = Iin for Cm = 1 µF. The fourth‐order Hodgkin‐Huxley Ordinary Differential Equation (ODE) system undergoes a Supercritical, a Subcritical, and a Saddle‐Node Limit Cycle Bifurcation when the monotonically‐decreasing DC input current Iin respectively attains the values 154.529 µA, 9.77003 µA, and 6.25567 µA. (c) The first ever‐reported and simplest possible three‐element second‐order bio‐mimetic circuit capable to reproduce the three‐bifurcation cascade occurring in the fourth‐order Hodgkin‐Huxley neuron model under monotonic synaptic current sweep. (d) Bifurcation diagram of the proposed bio‐inspired circuit under a monotonic increase in the respective input current i = I for C = 5 nF. The second‐order Hodgkin‐Huxley neuristor ODE system undergoes a Supercritical, a Subcritical, and a Saddle‐Node Limit Cycle Bifurcation when the monotonically‐increasing DC input current Iin respectively attains the values 2.136 mA, 17.960 mA, and 18.1379189 mA. image

The Proceedings of the ACM on Networking (PACMNET) series showcases top-tier research in emerging computer networks and their applications. We welcome submissions introducing new technologies, innovative experiments, creative applications of networking technologies, and fresh insights gained through analysis. Supported by the ACM Special Interest Group on Communications and Computer Networks (SIGCOMM), the journal is backed by a distinguished Editorial Board composed of leading researchers in the field. This issue begins the third volume of PACMNET. It features 6 articles, all submitted by the June 2024 deadline when 121 submissions in total were received. Each submission underwent a thorough review process involving over 80 Editors, coordinated by two Associate Editors. In the initial phase, every article received a minimum of three reviews. Following an online discussion, roughly half of the submissions were rejected, while the other half advanced to a second review phase. In this phase, Editors produced at least two additional reviews per article. After further discussion and remote Editors' meeting, 8 articles were given one-shot major revision. The same Editors reviewed the revised version the Authors prepared, and 6 out of 8 articles were finally selected. These 6 articles appear in this issue. Topics include network support for large language models and deep learning, security, and wireless networking. All papers include a thorough set of experiments to validate the proposed solutions. From a methodological perspective, machine learning and artificial intelligence-based solutions are becoming central in developing novel networking solutions. We want to express our gratitude to all those who contributed to this issue of PACMNET, especially the Authors for submitting their finest work and the Associate Editors for offering valuable feedback in their reviews and engaging in the discussions. Our thanks also go to the SIGCOMM Executive Committee Chair and the CoNEXT Steering Committee members for their continued support and guidance, providing essential suggestions and insights throughout the article selection process.

Mental and neurological disorders significantly impact global health. This systematic review examines the use of artificial intelligence (AI) techniques to automatically detect these conditions using electroencephalography (EEG) signals. Guided by Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA), we reviewed 74 carefully selected studies published between 2013 and August 2024 that used machine learning (ML), deep learning (DL), or both of these two methods to detect neurological and mental health disorders automatically using EEG signals. The most common and most prevalent neurological and mental health disorder types were sourced from major databases, including Scopus, Web of Science, Science Direct, PubMed, and IEEE Xplore. Epilepsy, depression, and Alzheimer's disease are the most studied conditions that meet our evaluation criteria, 32, 12, and 10 studies were identified on these topics, respectively. Conversely, the number of studies meeting our criteria regarding stress, schizophrenia, Parkinson's disease, and autism spectrum disorders was relatively more average: 6, 4, 3, and 3, respectively. The diseases that least met our evaluation conditions were one study each of seizure, stroke, anxiety diseases, and one study examining Alzheimer's disease and epilepsy together. Support Vector Machines (SVM) were most widely used in ML methods, while Convolutional Neural Networks (CNNs) dominated DL approaches. DL methods generally outperformed traditional ML, as they yielded higher performance using huge EEG data. We observed that the complex decision process during feature extraction from EEG signals in ML‐based models significantly impacted results, while DL‐based models handled this more efficiently. AI‐based EEG analysis shows promise for automated detection of neurological and mental health conditions. Future research should focus on multi‐disease studies, standardizing datasets, improving model interpretability, and developing clinical decision support systems to assist in the diagnosis and treatment of these disorders.

The threat stemming from the use of vehicles as a weapon in urban environments may be mitigated by employing properly designed protective structures such as bollards, street furniture or landscaping options. In order to assess the performance of a barrier resistance to a vehicle impact, the initial step involves characterizing the load on the barrier. To this aim, two recently developed generic vehicle models are utilized to conduct numerical simulations of vehicle impacts on a security barrier. Various impact configurations are examined and compared based on force-time functions. In addition to comparing the impact loadings in terms of peak forces, comparisons are also done in terms of equivalent static loads, determined by computing the dynamic load factors (DLF). The study provides new insights into the characterization of vehicle impact loads on security barriers, which could improve current engineering practices in the field.

Hard carbon (HC) has significant potential as anode material for both Li‐ion and Na‐ion batteries; however, its commercialization is hindered by challenges such as poor rate capability and low initial Coulombic efficiency (ICE). Although polymeric binders constitute a small fraction of the overall electrode composition, they play a crucial role in influencing the electrochemical performance. Here, this study introduces a novel dual composite binder, combining polyacrylic acid (PAA) and polyvinyl butyral (PVB). The interaction between the COOH groups in PAA and the OH groups in PVB via hydrogen bonding prompts a cohesive polymer network resulting in electrodes exhibiting superior rate capability and high ICE in both Li‐ion and Na‐ion laboratory‐scale cells, surpassing the performance of those with other binders tested. After optimizing the formulations by using commercial PVB, we demonstrate for the first time the use of recycled PVB, sourced from laminated glass waste, to address the lack of end‐of‐life programs for this material, which often ends up in landfills. Repurposing PVB waste for battery applications tackles waste management issues and contributes to innovative development of advanced, green battery materials in a circular economy approach, thus paving the way for novel waste‐to‐energy solutions combining high‐performance with socio‐economical and environmental benefits.

Self‐organized criticality (SOC) has attracted large interest as a key property for the optimization of information processing in biological neural systems. Inspired by this synergy, nanoscale self‐organizing devices are demonstrated to emulate critical dynamics due to their complex nature, proving to be ideal candidates for the hardware implementation of brain‐inspired unconventional computing paradigms. However, controlling the emerging critical dynamics and understanding its relationship with computing capabilities remains a challenge. Here, it is shown that memristive nanowire networks (NWNs) can be programmed in a critical state through appropriate electrical stimulation. Furthermore, multiterminal electrical characterization reveals that network areas can establish spatial interactions endowing local critical dynamics. The impact of such tunable and local dynamics versus the information processing in the network is experimentally analyzed through in materia implementation of nonlinear transformation (NLT) tasks, in the framework of reservoir computing. As for brain where cortical areas are specialized for a certain function, it is demonstrated that the computing performance of nanowire networks rely on the response of reduced subsets of outputs, which may show critical dynamics or not, depending on the specificity of the task. Such brain‐like behavior can lead to neuromorphic systems based on self‐organizing networks with reduced hardware complexity by exploiting their local and specialized behavior.