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Wireless communications are typically subject to complex channel dynamics, requiring the transmission of pilot sequences to estimate and equalize such effects and correctly receive information bits. This is especially true in 6G non-terrestrial networks (NTNs) in low Earth orbit, where one end of the communication link orbits around the Earth at several kilometers per second, and a multi-carrier waveform, such as orthogonal frequency division multiplexing (OFDM), is employed. To minimize the pilot overhead, we remove pilot symbols every other OFDM slot and propose a channel predictor to obtain the channel frequency response (CFR) matrix in absence of pilots. The algorithm employs an encoder-decoder convolutional neural network and a long short-term memory layer, along with skip connections, to predict the CFR matrix on the upcoming slot based on the current one. We demonstrate the effectiveness of the proposed predictor through numerical simulations in tapped delay line channel models, highlighting the effective throughput improvement. We further assess the generalization capabilities of the model, showing minimal throughput degradation when testing under different Doppler spreads and in both line of sight (LoS) and non-LoS propagation conditions. Finally, we discuss computational-complexity-related aspects of the lightweight hybrid CNN-LSTM architecture., Comment: Submitted for publication

Faster-than-Nyquist (FTN) signaling aims at improving the spectral efficiency of wireless communication systems by exceeding the boundaries set by the Nyquist-Shannon sampling theorem. 50 years after its first introduction in the scientific literature, wireless communications have significantly changed, but spectral efficiency remains one of the key challenges. To adopt FTN signaling, inter-symbol interference (ISI) patterns need to be equalized at the receiver. Motivated by the pattern recognition capabilities of convolutional neural networks with skip connections, we propose such deep learning architecture for ISI equalization and symbol demodulation in FTN receivers. We investigate the performance of the proposed model considering quadrature phase shift keying modulation and low density parity check coding, and compare it to a set of benchmarks, including frequency-domain equalization, a quadratic-programming-based receiver, and an equalization scheme based on a deep neural network. We show that our receiver outperforms any benchmark, achieving error rates comparable to those in additive white Gaussian noise channel, and higher effective throughput, thanks to the increased spectral efficiency of FTN signaling. With a compression factor of 60% and code rate 3/4, the proposed model achieves a peak effective throughput of 2.5 Mbps at just 10dB of energy per bit over noise power spectral density ratio, with other receivers being limited by error floors due to the strong inter-symbol interference. To promote reproducibility in deep learning for wireless communications, our code is open source at the repository provided in the references., Comment: Submitted for publication

The $\beta$ decay of one-neutron halo nucleus $^{11}$Be was investigated using the Warsaw Optical Time Projection Chamber (OTPC) detector to measure $\beta$-delayed charged particles. The results of two experiments are reported. In the first one, carried out in LNS Catania, the absolute branching ratio for $\beta$-delayed $\alpha$ emission was measured by counting incoming $^{11}$Be ions stopped in the detector and the observed decays with the emission of $\alpha$ particle. The result of 3.27(46)\% is in good agreement with the literature value. In the second experiment, performed at the HIE-ISOLDE facility at CERN, bunches containing several hundreds of $^{11}$Be ions were implanted into the OTPC detector followed by the detection of decays with the emission of charged particles. The energy spectrum of $\beta$-delayed $\alpha$ particles was determined in the full energy range. It was analysed in the R-matrix framework and was found to be consistent with the literature. The best description of the spectrum was obtained assuming that the two $3/2^+$ and one $1/2^+$ states in $^{11}$B are involved in the transition. The search for $\beta$-delayed emission of protons was undertaken. Only the upper limit for the branching ratio for this process of $(2.2 \pm 0.6_{\rm stat} \pm 0.6_{\rm sys}) \times 10^{-6}$ could be determined. This value is in conflict with the result published in [Ayyad et al. Phys. Rev. Lett. 123, 082501 (2019)] but does agree with the limit reported in [Riisager et al., Eur. Phys. J. A (2020) 56:100], Comment: 16 pages, 18 figures, submitted to Phys. Rev. C

Nanometer-sized clusters are often targeted due to their potential applications as nanoreactors or storage/delivery devices. One route to assemble and stabilize finite structures consists in imparting directional bonding patterns between the nanoparticles. When only a portion of the particle surface is able to form an inter-particle bond, finite-size aggregates such as micelles and vesicles may form. Building on this approach, we combine particle shape anisotropy with the directionality of the bonding patterns and investigate the combined effect of particle elongation and surface patchiness on the low density assembly scenario. To this aim, we study the assembly of tip-functionalised Janus hard spherocylinder by means of Monte Carlo simulations. By exploring the effects of changing the interaction strength and range at different packing fractions, we highlight the role played by shape and bond anisotropy on the emerging aggregates (micelles, vesicles, elongated micelles and lamellae). We observe that shape anisotropy plays a crucial role in suppressing phases that are typical to spherical Janus nanoparticles and that a careful tuning of the interaction parameters allows to promote the formation of spherical micelles. These finite-size spherical clusters composed of elongated particles might offer more interstitials and larger surface areas than those offered by micelles of spherical or almost-spherical units, thus enhancing their storage and catalytic properties.

Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58.4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on an temporally-independent cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by the area under the curve (AUC) was the primary outcome. Overall, median AI-CAC score was 35 (0-388) and 28.9% patients had no AI-CAC. AUC of the AI-CAC model to identify a CAC>0 was 0.90 in the internal validation cohort and 0.77 in the external validation cohort. Sensitivity was consistently above 92% in both cohorts. In the overall cohort (n=540), among patients with AI-CAC=0, a single ASCVD event occurred, after 4.3 years. Patients with AI-CAC>0 had significantly higher Kaplan Meier estimates for ASCVD events (13.5% vs. 3.4%, log-rank=0.013). Conclusion. The AI-CAC model seems to accurately detect subclinical atherosclerosis on chest x-ray with elevated sensitivity, and to predict ASCVD events with elevated negative predictive value. Adoption of the AI-CAC model to refine CV risk stratification or as an opportunistic screening tool requires prospective evaluation., Comment: Submitted to European Heart Journal - Cardiovascular Imaging Added also the additional material 44 pages (30 main paper, 14 additional material), 14 figures (5 main manuscript, 9 additional material)

Patient triage plays a crucial role in healthcare, ensuring timely and appropriate care based on the urgency of patient conditions. Traditional triage methods heavily rely on human judgment, which can be subjective and prone to errors. Recently, a growing interest has been in leveraging artificial intelligence (AI) to develop algorithms for triaging patients. This paper presents the development of a novel algorithm for triaging patients. It is based on the analysis of patient data to produce decisions regarding their prioritization. The algorithm was trained on a comprehensive data set containing relevant patient information, such as vital signs, symptoms, and medical history. The algorithm was designed to accurately classify patients into triage categories through rigorous preprocessing and feature engineering. Experimental results demonstrate that our algorithm achieved high accuracy and performance, outperforming traditional triage methods. By incorporating computer science into the triage process, healthcare professionals can benefit from improved efficiency, accuracy, and consistency, prioritizing patients effectively and optimizing resource allocation. Although further research is needed to address challenges such as biases in training data and model interpretability, the development of AI-based algorithms for triaging patients shows great promise in enhancing healthcare delivery and patient outcomes.

The study of the equation-of-state (EoS) describing the properties of nuclear matter away from the normal conditions is a relevant and intriguing topic of modern nuclear physics. The last decades have witnessed a substantial experimental progress in derivation of the symmetric matter term of the EoS and of the so-called symmetry energy for the asymmetric matter, especially at densities below the saturation point. But it is only in recent years that the opening of the multi-messenger astronomy era, triggered by detection of gravitational waves due to the neutron star mergers, has renewed and enlarged the interest in high-density EoS, being the main ingredient for determining the structure and properties of neutron stars. In this paper we review our knowledge obtained from heavy-ion collisions up to the 1 GeV/nucleon regime, on the EoS above nuclear saturation density. Special emphasis is given on the still few results on symmetry energy at high densities and their interconnections with multi-messenger astronomy findings., Comment: 79 pages, 25 figures

The interplay between Machine Learning (ML) and Constrained Optimization (CO) has recently been the subject of increasing interest, leading to a new and prolific research area covering (e.g.) Decision Focused Learning and Constrained Reinforcement Learning. Such approaches strive to tackle complex decision problems under uncertainty over multiple stages, involving both explicit (cost function, constraints) and implicit knowledge (from data), and possibly subject to execution time restrictions. While a good degree of success has been achieved, the existing methods still have limitations in terms of both applicability and effectiveness. For problems in this class, we propose UNIFY, a unified framework to design a solution policy for complex decision-making problems. Our approach relies on a clever decomposition of the policy in two stages, namely an unconstrained ML model and a CO problem, to take advantage of the strength of each approach while compensating for its weaknesses. With a little design effort, UNIFY can generalize several existing approaches, thus extending their applicability. We demonstrate the method effectiveness on two practical problems, namely an Energy Management System and the Set Multi-cover with stochastic coverage requirements. Finally, we highlight some current challenges of our method and future research directions that can benefit from the cross-fertilization of the two fields.

In the last decades, measurements of spallation, fragmentation and Coulex induced fission reactions in inverse kinematics have provided valuable data to accurately investigate the fission dynamics and nuclear structure at large deformations of a large variety of stable and non-stable heavy nuclei. To go a step further, we propose now to induce fission by the use of quasi-free (p,2p) scattering reactions in inverse kinematics, which allows us to reconstruct the excitation energy of the compound fissioning system by using the four-momenta of the two outgoing protons. Therefore, this new approach might permit to correlate the excitation energy with the charge and mass distributions of the fission fragments and with the fission probabilities, given for the first time direct access to the simultaneous measurement of the fission yield dependence on temperature and fission barrier heights of exotic heavy nuclei, respectively. The first experiment based on this methodology was realized recently at the GSI/FAIR facility and a detailed description of the experimental setup is given here., Comment: 4 pages, 15th International Conference on Nuclear Data for Science and Technology (ND2022)

Starting from experimental studies on alpha-clustering in excited self-conjugate nuclei (from $^{16}$O to $^{28}$Si), temperature and density conditions for such a clustering are determined. Measured temperatures have been found in the range of 5.5 - 6.0 MeV whereas density values of 0.3 - 0.4 times the saturation density are deduced, i.e., 0.046 to 0.062 $fm^{-3}$. Such a density domain is also predicted by constrained self-consistent mean field calculations. These results constitute a benchmark for alpha clustering from self-conjugate nuclei in relation to descriptions of stellar evolution and supernovae., Comment: 7 pages, 3 figures. arXiv admin note: text overlap with arXiv:1602.05827, arXiv:1603.01114

We study the electron-loss-to-continuum (ELC) cusp experimentally and theoretically by comparing the ionization of U$^{89+}$ projectiles in collisions with N$_2$ and Xe targets, at a beam energy of 75.91 MeV/u. The coincidence measurement between the singly ionized projectile and the energy of the emitted electron is used to compare the shape of the ELC cusp at weak and strong perturbations. A significant energy shift for the centroid of the electron cusp is observed for the heavy target of Xe as compared to the light target of N$_2$. Our results provide a stringent test for fully relativistic calculations of double-differential cross sections performed in the first-order approximation and in the continuum-distorted-wave approach.

Determination of the high density behavior of the symmetry energy through the simultaneous measurement of elliptic flow excitation functions of neutrons, protons and light clusters is proposed. The elliptic flow developed in relativistic heavy ion collisions has been proven theoretically and experimentally to have a unique sensitivity and robustness in probing the symmetry energy up to around $2 \rho_{o}$. The knowledge of the density dependence of the symmetry energy in a broad range of densities will provide a missing link for astrophysical predictions of the neutron star mass--radius relation. In particular, the data colud provide tighter constraints on the slope parameter L and entirely new limits on $K_{sym}$, the currently poorly constrained symmetry energy curvature parameter.

A detailed and updated review is given of De Filippo's Non-unitary $4$-th Derivative Gravity and its Newtonian limit, by pointing out the crucial role of non-unitarity in addressing transition to classicality and specifically localization of macroscopic bodies, microscopic foundation of the second law of thermodynamics, measurement problem; furthermore it provides a quantum field theory of gravity possibly not only renormalizable but even finite, with a cancelation mechanism analogous to supersymmetric field theories where cancelations are due to superpartners whereas here to negative energy fields. Finally this non-unitary proposal addresses the longstanding black hole information loss problem and this according to an unorthodox view at variance with the mainstream endeavors to save unitarity at the expense of changing General Relativity in vague unspecified ways. Last but not least motivations and conceptual framework are given, as the author could not present them in his first papers written in a hurry since he was aware that in a little time he would be unable to use pc keyboard or to write on paper due to the progressing of motor neuron disease.

The societal impact of traffic is a long-standing and complex problem. We focus on the estimation of ozone production due to vehicular traffic. For this, we couple a system of conservation laws for vehicular traffic, an emission model, and a system of partial differential equations for the main reactions leading to ozone production and diffusion. The second-order model for traffic is obtained by choosing a special velocity function for a Collapsed Generalized Aw-Rascle-Zhang model and is tuned on NGSIM data. On the other side, the system of partial differential equations describes the main chemical reactions of $\mathrm{NO_x}$ gases with a source term provided by a general emission model applied to the output of the traffic model. We analyze the ozone impact of various traffic scenarios and describe the effect of traffic light timing. The numerical tests show the negative effect of vehicles restarts on $\mathrm{NO_x}$ emissions, suggesting to increase the length of the green phase of traffic lights to reduce them., Comment: 27 pages, 13 figures

The quest for a microscopic foundation of Thermodynamics is addressed within the Nonunitary Newtonian Gravity model through the study of a specific closed system, namely a three-dimensional harmonic nanocrystal. A numerical calculation of the nanocrystal von Neumann entropy as a function of time is performed, showing a sharp monotonic increase, followed by a stabilization at late times. This behavior is consistent with the emergence of a micro-canonical ensemble within the initial energy levels, signaling, in this way, the establishment of a non-unitary gravity-induced thermal equilibrium., Comment: 2 figures, 8 pages. arXiv admin note: text overlap with arXiv:1801.02500

Method: To examine signatures of this alpha-condensation, a compound nucleus reaction using 160, 280, and 400 MeV 16O beams impinging on a carbon target was used to investigate the 12C(16O,7a) reaction. This permits a search for near-threshold states in the alpha-conjugate nuclei up to 24Mg. Results: Events up to an alpha-particle multiplicity of 7 were measured and the results were compared to both an Extended Hauser-Feshbach calculation and the Fermi break-up model. The measured multiplicity distribution exceeded that predicted from a sequential decay mechanism and had a better agreement with the multi-particle Fermi break-up model. Examination of how these 7 alpha final states could be reconstructed to form 8Be and 12C(0_2+) showed a quantitative difference in which decay modes were dominant compared to the Fermi break-up model. No new states were observed in 16O, 20Ne, and 24Mg due to the effect of the N-alpha penetrability suppressing the total alpha-particle dissociation decay mode. Conclusion: The reaction mechanism for a high energy compound nucleus reaction can only be described by a hybrid of sequential decay and multi-particle breakup. Highly alpha-clustered states were seen which did not originate from simple binary reaction processes. Direct investigations of near-threshold states in N-alpha systems are inherently impeded by the Coulomb barrier prohibiting the observation of states in the N-alpha decay channel. No evidence of a highly clustered 15.1 MeV state in 16O was observed from (28Si*,12C(0_2+))16O(0_6+) when reconstructing the Hoyle state from 3 alpha-particles. Therefore, no experimental signatures for alpha-condensation were observed.

Nuclear astrophysics, the union of nuclear physics and astronomy, went through an impressive expansion during the last twenty years. This could be achieved thanks to milestone improvements in astronomical observations, cross section measurements, powerful computer simulations and much refined stellar models. Italian groups are giving quite important contributions to every domain of nuclear astrophysics, sometimes being the leaders of worldwide unique experiments. In this paper we will discuss the astrophysical scenarios where nuclear astrophysics plays a key role and we will provide detailed descriptions of the present and future of the experiments on nuclear astrophysics which belong to the scientific programme of INFN (the National Institute for Nuclear Physics in Italy)., Comment: Added paragraphs 5.6 and 7.2. Paragraph 7.3 (former 7.2) revised. Accepted for publication in La Rivista del Nuovo Cimento

Semiclassical Gravity replaces the energy momentum tensor $T_{\mu \nu}$ with its expectation value $\langle T_{\mu \nu}\rangle$ in Einstein equations, this making the Einstein tensor $G_{\mu \nu}$ a classical variable thus avoiding the quantization of the gravitational field. Jointly with the Copenhagen interpretation of QM, Semiclassical Gravity gives rise to cases of Superluminal Communication. It is shown in a specific instance that a model of Nonunitary Newtonian Gravity avoids Superluminal Communication by decohering linear superpositions of macroscopically distinct states, namely Schrodinger's cats.