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The properties of nuclear matter at various densities is of great interest, as it is crucial for the understanding of the structure of nuclei and neutron stars, the dynamics of heavy ion collision (HIC), and neutron star mergers. HIC provides a unique opportunity to create nuclear matter with density away from the normal nuclear density (ρ0) in the terrestrial laboratory, however, the created nuclear matter only exists for a very short period and its properties cannot be measured directly. Usually, the properties of nuclear matter are deduced from the comparison of the event-average quantities between experimental measurements and transport model simulations. However, quantities in HIC can be obtained event-by-event both in experiments and in transport model simulations. These event-by-event data usually comprise a huge amount of data which encodes rich physical information, and at the same time, large fluctuations which can be taken as noises. Machine learning (ML) has been proven very powerful for the extraction of information from complex data in many branches of science. In this work, event-by-event observables from Au+Au and Sn+Sn collisions at intermediate energies are generated with the ultrarelativistic quantum molecular dynamics (UrQMD) model. Model parameters with different nuclear incompressibility K0 of isospin symmetric nuclear matter, the slope parameter L of nuclear symmetry energy, and the in-medium correction factor F of nucleon-nucleon cross section were used to generate labeled dataset. The light gradient boosting machine (LightGBM) which is a decision-tree-based algorithm was used to learn the labeled dataset. The advantages of LightGBM include faster training efficiency, low memory usage, higher accuracy, and ability to tackle large-scale data. It is found that the trained machine learning algorithm is able to infer K0 and L, and F from the event-by-event data. The mean absolute errors (MAE) which measure the average magnitude of the absolute differences between the predicted and true values of K0 and L, and F are 52 MeV, 29.6 MeV, and 0.08, respectively. These MAE values can be further reduced by using event-averaged data instead of event-by-event data, as fluctuations in the event-averaged data are largely suppressed. Furthermore, by using the feature_importance technology and shapley additive explanations (SHAP) which are two popular feature attribution methods to identify the most important features that drive predictions, features that have the greatest effect on the extraction of K0 and L, and F are found out. This work suggests that, with the help of ML, physical information can be extracted on the event-by-event basis, and it may offer a new paradigm to study the underlying physics in heavy ion collisions.

BackgroundElliptic flow (v2) is one of the most important observations for exploring the properties of nuclear matter using heavy-ion collisions. v2 is not only affected by dynamic processes but is also related to the Fermi momentum of the initial nucleus.PurposeThis study aims to quantitatively determine the effect of the initial Fermi momentum on the time evolution of v2.MethodsFirst, based on the Ultrarelativistic Quantum Molecular Dynamics (UrQMD) model, gold-gold (Au+Au) collisions at beam energies of 0.4A GeV and 0.8A GeV with impact parameter b = 6 fm were simulated. In the initial stage, three cases were considered: without Fermi momentum, with Fermi momentum, and with half-Fermi momentum. Then, by reverse tracing the nucleons that were emitted at mid-rapidity (|y0|

In the Formula Student Racing Car, the frame is a fundamental component that supports the body. The frame's anti-deformation ability will impact the four-wheel positioning parameters of the car, which subsequently affect the car's stability. The quality of the frame directly determines the power and efficiency of the racing car, making the frame crucial to the overall race performance. Therefore, researching lightweight frame design is particularly important to enhance frame performance and reduce its total weight. In this paper, based on the variable density method, the global topology optimization of the frame is carried out to obtain the distribution of the frame material, realize the efficient utilization of the material, and improve the torsional stiffness of the frame. Compared with the previous local topology optimization, the global topology optimization involves a wider range, and the results are more accurate. Based on the adjoint variable method, the sensitivity analysis of the frame is carried out to obtain the influence level of each design variable. The size of the frame is optimized according to the variables with high influence levels. After optimization, the total mass of the frame is reduced by 12.8%; the performance in terms of maximum displacement and maximum stress is improved; and the lightweight design of the frame is realized as a whole. [ABSTRACT FROM AUTHOR]

The ASY-EOS experiment at GSI laboratory measured the direct and elliptic flow of neutrons and light charged particles in the reaction 197Au+197 Au at 400 A MeV incident energy. The ratio of elliptic flow of neutrons with respect to that of the light charged particles was used as main experimental observable to probe the density dependence of the symmetry energy term of the nuclear equation of state. Results, obtained by comparison of the experimental data with the UrQMD model predictions, strongly support a moderately soft to linear density dependence of the symmetry energy at suprasaturation densities below 2ρ0.

Within the ultrarelativistic quantum molecular dynamics (UrQMD) model, the effect of initial density fluctuations on cumulants of the net-proton multiplicity distribution in Au + Au Collisions at s N N = 7.7 GeV was investigated by varying the minimum distance d min between two nucleons in the initialization. It was found that the initial density fluctuations increased with the decrease of d min from 1.6 fm to 1.0 fm, and the influence of d min on the magnitude of the net-proton number fluctuation in a narrow pseudorapidity window ( Δ η ≤ 4) was negligible even if it indeed affected the density evolution during the collision. At a broad pseudorapidity window ( Δ η ≥ 4), the cumulant ratios were enlarged when the initial density fluctuations were increased with the smaller value of d min , and this enhancement was comparable to that observed in the presence of the nuclear mean-field potential. Moreover, the enhanced cumulants were more evident in collisions with a larger impact parameter. The present work demonstrates that the fingerprint of the initial density fluctuations on the cumulants in a broad pseudorapidity window is clearly visible, while it is not obvious as the pseudorapidity window becomes narrow. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play crucial roles in the development of essential hypertension (EH). Previously, we found that the expression of miR-1929-3p was decreased in C57BL/6 mice with hypertension induced by murine cytomegalovirus (MCMV). In this study, we explored the role of miR-1929-3p in hypertension myocardial remodeling in MCMV-infected mice. First, we measured MCMV DNA and host IgG and IgM after infection and determined the expression of miR-1929-3p and its target gene endothelin A receptor (Ednra) mRNA in the myocardium of mice. Then, we performed invasive blood pressure (BP) monitoring. Heart-to-body weight ratio (HW/BW%), along with mRNA levels of B-type natriuretic peptide (BNP) and beta myosin heavy chain (β-MHC), revealed myocardial remodeling. Hematoxylin/eosin and Masson's trichrome staining indicated morphological changes in the myocardium. Cardiac function was assessed via echocardiography. Moreover, MCMV-infected mice were injected with recombinant adeno-associated virus- (rAAV-) miR-1929-3p overexpression vector. Immunohistochemistry and western blotting showed the expression of Ednra and the activation of NOD-like receptor pyrin domain containing 3 (NLRP3) inflammasome. And enzyme-linked immunosorbent assay (ELISA) revealed the concentrations of endothelin-1 (ET-1), interleukin-1β (IL-1β), and interleukin-18 (IL-18). In this study, we found that decreased expression of miR-1929-3p in MCMV-infected mice induced high BP and further development of myocardial remodeling cardiac function injury through increased expression of Ednra. Strikingly, overexpression of miR-1929-3p ameliorated these pathological changes of the heart. The positive effect was shown to be associated with inhibition of NLRP3 inflammasome activation and decreased expression of key proinflammatory cytokine IL-1β. Collectively, these results indicate that miR-1929-3p overexpression may effectively alleviate EH myocardial remodeling by suppressing Ednra/NLRP3 inflammasome activation in MCMV-infected mice. [ABSTRACT FROM AUTHOR]

The impact parameter is one of the crucial physical quantities of heavy-ion collisions, and can affect obviously many observables at the final state, such as the multifragmentation and the collective flow. Usually, it cannot be measured directly in experiments but might be inferred from observables at the final state. Artificial intelligence has had great success in learning complex representations of data, which enables novel modeling and data processing approaches in physical sciences. In this article, we employ two of commonly used algorithms in the field of artificial intelligence, the convolutional neural networks (CNN) and light gradient boosting machine (LightBGM), to improve the accuracy of determining impact parameter by analyzing the proton spectra in transverse momentum and rapidity on the event-by-event basis. Au + Au collisions with the impact parameter of 0 ⩽ b ⩽ 10 fm at intermediate energies (Elab = 0.2–1.0 GeV/nucleon) are simulated with the ultrarelativistic quantum molecular dynamics model to generate the proton spectra data. It is found that the average difference between the true impact parameter and the estimated one can be smaller than 0.1 fm. The LightBGM algorithm shows an improved performance with respect to the CNN on the task in this work. By using the LightBGM's visualization algorithm, one can obtain the important feature map of the distribution of transverse momentum and rapidity, which may be helpful in inferring the impact parameter or centrality in heavy-ion experiments. [ABSTRACT FROM AUTHOR]

The collision centrality (or the impact parameter b) in heavy-ion experiments is inferred from final state observables, however, usually b is an input to theoretical calculations. The effects of different methods used for the centrality selection (i.e. total charged multiplicity Mch, total transverse kinetic energy of light charged particles , and the ratio of transverse-to-longitudinal kinetic energy ERAT) on the nuclear stopping and the collective flow are studied in Xe+ Sn collisions at INDRA energies (/nucleon). The study is conducted within the framework of the ultra-relativistic quantum molecular dynamics (UrQMD) model. It is found that the nuclear stopping power RE obtained from events binning with ERAT is larger than the experimental data as well as the results obtained using Mch and. At Ebeam = 50 MeV/nucleon, the elliptic flow from events selected with different methods spreads widely, especially for more central collisions. The elliptic flow from events sorted by ERAT is weakly dependent on the collision centrality. The difference in elliptic flow at mid-rapidity among different centrality filters steadily decreases with increasing beam energy and impact parameter. At 150 MeV/nucleon, the differences in directed and elliptic flows arising from different centrality filters vanish. [ABSTRACT FROM AUTHOR]

Abstract The effect of nuclear interactions on measurable net-proton number fluctuations in heavy ion collisions at the SIS18/GSI accelerator is investigated. The state of the art UrQMD model including interaction potentials is employed. It is found that the nuclear forces enhance the baryon number cumulants, as predicted from grand canonical thermodynamical models. The effect however is smeared out for proton number fluctuations due to iso-spin randomization and global baryon number conservation, which decreases the cumulant ratios. For a rapidity acceptance window larger than Δ y > 0.4 the effects of global baryon number conservation dominate and all cumulant ratios are significantly smaller than 1. [ABSTRACT FROM AUTHOR]

Background: The nuclear incompressibility ( K 0 ) plays a crucial role in understanding diverse phenomena in nuclear structure and reactions, as well as in astrophysics. Heavy-ion-collision measurements in combination with transport model simulations serve as important tools for extracting the nuclear incompressibility. However, uncertainties in transport models (or model dependence) partly affect the reliability of the extracted result. Purpose: In the present work, by using the recently measured data of rapidity-dependent flows, we constrain the incompressibility of nuclear matter and analyze the impact of model uncertainties on the obtained value. Method: The method is based on the newly updated version of the ultrarelativistic quantum molecular dynamics (UrQMD) model in which the Skyrme potential energy-density functional is introduced. Three different Skyrme interactions which give different incompressibilities varying from K 0 = 201 to 271 MeV are adopted. The incompressibility is deduced from the comparison of the UrQMD model simulations and the FOPI data for rapidity-dependent elliptic flow in Au + Au collisions at beam energies 0.4 A –1.0 A GeV. Results: The elliptic flow v 2 as a function of rapidity y 0 can be well described by a quadratic fit v 2 = v 20 + v 22 ⋅ y 0 2 . It is found that the quantity v 2 n defined by v 2 n = | v 20 | + | v 22 | is quite sensitive to the incompressibility K 0 and the in-medium nucleon–nucleon cross section, but not sensitive to the slope parameter L of the nuclear symmetry energy. Conclusions: With the FU3FP4 parametrization of the in-medium nucleon–nucleon cross section, an averaged K 0 = 220 ± 40 MeV is extracted from the v 2 n of free protons and deuterons. However, remaining systematic uncertainties, partly related to the choice of in-medium nucleon–nucleon cross sections, are of the same magnitude ( ± 40 MeV ). Overall, the rapidity dependent elliptic flow supports a soft symmetric-matter equation-of-state. [ABSTRACT FROM AUTHOR]

Within the ultrarelativistic quantum molecular dynamics (UrQMD) model, by reversely tracing nucleons that are finally emitted at mid-rapidity (| y 0 | < 0.1) in the entire reaction process, the time evolution of elliptic flow (v 2) of these traced nucleons is studied in Au+Au collisions at beam energy of 0.4 GeV/nucleon with different impact parameters. The initial value of v 2 is positive and increases with impact parameter, it then decreases as time passes and tends to saturate at a negative value. It is found that nucleon-nucleon collisions always suppress the value of v 2 (enhance the out-of-plane emission), while the nuclear mean field potential effect is more complex, which depends on both impact parameter and reaction time. The most relevant density probed by v 2 of nucleons at mid-rapidity is found to be ∼ 60% of the maximum density reached during the collisions. [ABSTRACT FROM AUTHOR]

Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectation values of observables over all considered events are used in these approaches, however, observables can be obtained event-by-event both in experiments and transport model simulations. By using the light gradient boosting machine (LightGBM), a modern machine-learning algorithm, we present a framework for inferring the density-dependent nuclear symmetry energy from observables in heavy-ion collisions on the event-by-event analysis. The ultrarelativistic quantum molecular dynamics (UrQMD) model simulations are used as training data. The symmetry energy slope parameter extracted with LightGBM event-by-event from test data also by UrQMD has an average spread of approximately 30 MeV from the truth, and is found to be robust against variations in model parameters. In addition, LightGBM can identify features that have the greatest effect on the physics of interest, thereby offering valuable insights. Our study suggests that the present framework can be a powerful tool and may offer a new paradigm to study the underlying physics in heavy-ion collisions. [ABSTRACT FROM AUTHOR]

A systematic study of the in-medium correction factor (F) on nucleon-nucleon elastic cross section is performed within the Ultra-relativistic Quantum Molecular Dynamics (UrQMD) model. The effects of the beam energy dependence of F on the directed, elliptic flow and nuclear stopping in 197Au+197Au collisions with energy ranging from 0.09 to 0.8 A GeV are explored. It is found that the directed, elliptic flow and nuclear stopping at relatively low energies are very sensitive to F , and the sensitivity gradually weakens with increasing beam energy. The beam energy dependent in-medium correction factor F is deduced from the comparison of the excitation functions of the directed, elliptic flow and nuclear stopping between the calculated results and the FOPI experimental data. [ABSTRACT FROM AUTHOR]

A deep convolutional neural network (CNN) is developed to study symmetry energy (E sym (ρ)) effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of protons and neutrons in heavy-ion collisions. Supervised training is performed with labeled data-set from the ultrarelativistic quantum molecular dynamics (UrQMD) model simulation. It is found that, by using proton spectra on event-by-event basis as input, the accuracy for classifying the soft and stiff E sym (ρ) is about 60% due to large event-by-event fluctuations, while by setting event-summed proton spectra as input, the classification accuracy increases to 98%. The accuracies for 5-label (5 different E sym (ρ)) classification task are about 58% and 72% by using proton and neutron spectra, respectively. For the regression task, the mean absolute errors (MAE) which measure the average magnitude of the absolute differences between the predicted and actual L (the slope parameter of E sym (ρ)) are about 20.4 and 14.8 MeV by using proton and neutron spectra, respectively. Fingerprints of the density-dependent nuclear symmetry energy on the transverse momentum and rapidity distributions of protons and neutrons can be identified by convolutional neural network algorithm. [ABSTRACT FROM AUTHOR]

Initial fluctuation is one of the ingredients that washes fingerprints of the nuclear symmetry energy on observables in heavy-ion collisions. By artificially using the same initial nuclei in all collision events, the effect of the initial fluctuation on isospin-sensitive observables, e.g., the yield ratio of free neutrons with respect to protons N n / N p , 3 H/ 3 He yield ratio, the yield ratio between charged pions π − / π + , and the elliptic flow ratio or difference between free neutrons and protons v 2 n / v 2 p ( v 2 n - v 2 p ), are studied within the ultrarelativistic quantum molecular dynamics (UrQMD) model. In practice, Au + Au collisions with impact parameter b = 5 fm and beam energy E lab = 400 MeV/nucleon are calculated. It is found that the effect of the initialization on the yields of free protons and neutrons is small, while for the yield of pions, the directed and elliptic flows are found to be apparently influenced by the choice of initialization because of the strong memory effects. Regarding the isospin-sensitive observables, the effect of the initialization on N n / N p and 3 H/ 3 He is negligible, while π − / π + and v 2 n / v 2 p ( v 2 n - v 2 p ) display a distinct difference among different initializations. The fingerprints of symmetry energy on π − / π + and v 2 n / v 2 p can be either enhanced or reduced when different initializations are utilized. [ABSTRACT FROM AUTHOR]

Since memristor was found, it has shown great application potential in neuromorphic computing. Currently, most neural networks based on memristors deploy the special analog characteristics of memristor. However, owing to the limitation of manufacturing process, non-ideal characteristics such as non-linearity, asymmetry, and inconsistent device periodicity appear frequently and definitely, therefore, it is a challenge to employ memristor in a massive way. On the contrary, a binary neural network (BNN) requires its weights to be either +1 or −1, which can be mapped by digital memristors with high technical maturity. Upon this, a highly robust BNN inference accelerator with binary sigmoid activation function is proposed. In the accelerator, the inputs of each network layer are either +1 or 0, which can facilitate feature encoding and reduce the peripheral circuit complexity of memristor hardware. The proposed two-column reference memristor structure together with current controlled voltage source (CCVS) circuit not only solves the problem of mapping positive and negative weights on memristor array, but also eliminates the sneak current effect under the minimum conductance status. Being compared to the traditional differential pair structure of BNN, the proposed two-column reference scheme can reduce both the number of memristors and the latency to refresh the memristor array by nearly 50%. The influence of non-ideal factors of memristor array such as memristor array yield, memristor conductance fluctuation, and reading noise on the accuracy of BNN is investigated in detail based on a newly memristor circuit model with non-ideal characteristics. The experimental results demonstrate that when the array yield α ≥ 5%, or the reading noise σ ≤ 0.25, a recognition accuracy greater than 97% on the MNIST data set is achieved. [ABSTRACT FROM AUTHOR]

It is shown that the inclusion of hadronic interactions, and in particular nuclear potentials, in simulations of heavy ion collisions at the SPS energy range can lead to obvious correlations of protons. These correlations contribute significantly to an intermittency analysis as performed at the NA61 experiment. The beam energy and system size dependence is studied by comparing the resulting intermittency index for heavy ion collisions of different nuclei at beam energies of 40 A , 80 A and 150 A GeV. The resulting intermittency index from our simulations is similar to the reported values of the NA61 collaboration, if nuclear interactions are included. The observed apparent intermittency signal is the result of the correlated proton pairs with small relative transverse momentum Δ p t , which would be enhanced by hadronic potentials, and this correlation between the protons is slightly influenced by the coalescence parameters and the relative invariant four-momentum q i n v cut. [ABSTRACT FROM AUTHOR]

The elliptic flow ratio of neutrons versus protons in heavy-ion collisions (HICs) is one of the important probes to constrain the nuclear symmetry energy, however measuring the flow of neutrons with high precision is a great challenge for experimental techniques. In this work, the elliptic flow of protons is studied and it is found that the rapidity at which the sign of v 2 changes from negative to positive is sensitive to the density dependence of the symmetry energy. By comparing the existing FOPI experimental data for proton flows to the calculations with the ultrarelativistic quantum molecular dynamics (UrQMD) model, the slope parameter of the nuclear symmetry energy is extracted to be L 0 = 43 ± 20 MeV at 95% confidence level. This is in good agreement with many recent results from investigations of nuclear structure properties and also partly overlaps with the recent result of the ASY-EOS experiment. [ABSTRACT FROM AUTHOR]

Within two different frameworks of isospin-dependent transport model, effect of nuclear symmetry energy at supradensities on the isospin-fractionation (IsoF) was investigated. With positive/negative symmetry potential at supradensities (i.e., values of symmetry energy increase/decrease with density above saturation density), for energetic nucleons, the value of neutron to proton ratio of free nucleons is larger/smaller than that of bound nucleon fragments. Compared with extensively studied quantitative observables of nuclear symmetry energy, the normal or abnormal isospin-fractionation of energetic nucleons can be a qualitative probe of nuclear symmetry energy at supradensities. [ABSTRACT FROM AUTHOR]