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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|

Na3V2(PO4)3 (NVP), as a great potential cathode candidate for Na-ion batteries (NIBs), has attracted enormous interest due to its three-dimensional (3D) large open framework for convenient Na+ transport, yet its practical application is still limited by its inferior electron conductivity and sluggish Na+ diffusion kinetics. Herein, the tiny Cr doped hierarchical NVP micro-flower cathodes (i.e., Na3V2−xCrx(PO4)3@C, x ≤ 0.1), which are self-assembled with single-crystal nanoflake subunits in-situ coated with carbon nano-shell, are designed and fabricated via a scalable avenue. The optimized cathode, i.e., Na3V1.94Cr0.06(PO4)3@C (NVCP-6), was endowed with more electro-active Na(2) sites and higher electronic/ionic conductivity for efficient sodium storage. Benefiting from these competitive merits, the NVCP-6, when evaluated as a cathode towards NIBs, exhibits an ultrahigh-rate capability of 99.8 mAh·g−1 at 200 C and superior stability of 82.2% over 7300 cycles at 50 C. Furthermore, the NVCP-6 based full NIBs display remarkable electrochemical properties in terms of both high-rate capacities and long-duration cycling properties at different temperatures (−20–50 °C). The contribution, i.e., the design of "four ounces can move a thousand pounds", here will promote the practical industrial application of NVP towards advanced NIBs. [ABSTRACT FROM AUTHOR]

Our previous study demonstrated in vivo that mouse cytomegalovirus (MCMV) infection promoted vascular remodeling after downregulation of miR-1929-3p. This study aimed to investigate the role of miR-1929-3p/ETAR/NLRP3 pathway in mouse vascular smooth muscle cells (MOVAS) after MCMV infection. First, PCR was used to detect the success of the infection. Second, MOVAS were transfected with the miR-1929-3p mimic, inhibitor, and ETAR overexpressed adenovirus vector. Cell proliferation was detected using EdU, whereas apoptosis was detected using flow cytometry. The expression of miR-1929-3p and ETAR were detected using qRT-PCR. Western blot detected proteins of cell proliferation, apoptosis, and the NLRP3 inflammasome. Interleukin-1β and interleukin-18 were determined using ELISA. The results revealed that after 48 h, MCMV infection promoted the proliferation of MOVAS when the MOI was 0.01. MCMV infection increased ETAR by downregulating miR-1929-3p. The miR-1929-3p mimic reversed the proliferation and apoptosis, whereas the miR-1929-3p inhibitor promoted this effect. ETAR overexpression further promoted MCMV infection by downregulating miR-1929-3p-mediated proliferation and apoptosis. MCMV infection mediates the downregulation of miR-1929-3p and the upregulation of ETAR, which activates NLRP3 inflammasome. In conclusion, MCMV infection promoted the proliferation of MOVAS, possibly by downregulating miR-1929-3p, promoting the upregulation of the target gene ETAR and activating NLRP3 inflammasome. [ABSTRACT FROM AUTHOR]

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]

Within the framework of the Ultra-relativistic Quantum Molecular Dynamics model, the effects of resonance decay widths, in-medium nucleon-nucleon (in)elastic cross sections, and potentials on the pion Hanbury-Brown-Twiss (HBT) intensity interferometry in central Au+Au collisions at Ebeam = 1.23 GeV/nucleon are investigated. The results show that the pion emission source is visibly influenced by the Δ particles with small invariant masses, i.e., with long resonance lifetimes. Furthermore, the pion HBT radii are found to be insensitive to the nucleon-nucleon (in)elastic cross sections, while the measured rapidity distributions of the charged pions are sensitive to the nucleon-nucleon (in)elastic cross sections and can be much better reproduced by considering a reduction factor on the nucleon-nucleon inelastic cross sections. The observed charge-sign difference of the source radii, especially in outward and sideward directions, can be qualitatively explained by simulations with the potential contributions, especially including the Coulomb potential of mesons. The present work demonstrates that the potential updates play important roles in describing the measured HBT radii and duration-time-related radii ratios when understanding the dynamics in relativistic heavy-ion collisions at ∼GeV beam energies. [ABSTRACT FROM AUTHOR]

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]

The effect of growth pressure on the properties of β-Ga2 O3 thin films epitaxial on c-plane sapphire substrate with 6° oblique cut angle by plasma-assisted molecular beam epitaxy were systematically studied. All of the epitaxial films present (201) orientated single-crystalline structure with smooth surface morphology. In addition, the crystalline quality and growth rate increase gradually with the increase of the growth pressure, as demonstrated by X-ray diffraction and scanning electron microscopy characterizations. According to the X-ray photoelectron spectroscopy measurement results, the percentage of oxygen vacancy and Ga3+ oxidation states decrease and increase, respectively, with increasing growth pressure, resulting in the increment of atomic ratio of O to Ga. Moreover, through the calculation of Tauc formula and Urbach tail model, the results show that the optical band gap of the films increase from 4.94 eV to 5.00 eV, while Urbach energy decreases from 0.47 eV to 0.32 eV. These results suggest that the crystal quality and optical property of β-Ga2O3 were improved by increasing the growth pressure. [ABSTRACT FROM AUTHOR]

Electrocatalytic hydrogen production is hindered by the high cost or low efficiency of electrocatalysts. In this work, we synthesized a highly dispersed CoP on 3-dimensional ordered mesoporous FeP (CoP@3DOM-FeP) for efficient electrocatalytic hydrogen evolution reaction (HER). The uniform surface decoration of CoP on a 3D porous iron framework is achieved by delicately embedding ZIF-67 in polystyrene (PS) nanospheres, followed by the removal of templates and chemical vapor deposition (CVD) of phosphorus. Owing to the well dispersed active site CoP and the excellent 3D FeP substrate with high porosity and conductivity, the present CoP@3DOM-FeP demonstrated high efficiency in the electrocatalytic HER, with overpotentials as low as of 67.2, 71.1 and 76.5 mV at a current density of 10 mA cm−2 in alkaline, neutral, and acidic electrolytes. Moreover, low Tafel slopes ranging from 66.3–77.5 mV dec−1 of the as-prepared electrocatalyst indicate that the Volmer–Heyrovsky mechanism dominated the surface hydrogen generation process with fast electron transfer kinetics. The developed nano-engineering strategy for surface decoration of active sites on a 3D porous substrate enables an extremely low amount of cobalt usage but a maximized exposure of active sites. Hence, this work not only proposes a cost-effective, efficient, and robust cobalt-based electrocatalyst in all-pH HER, but also paves the way for developing next-generation nano-engineered electrocatalysts. [ABSTRACT FROM AUTHOR]

The ecological resistance to the invasion of alien species has been extensively researched at the within‐trophic level; however, the effect of cross‐trophic interactions remains largely unknown. The red imported fire ant (RIFA, Solenopsis invicta) is a globally invasive species, and a cross‐trophic interaction study between RIFAs and plants may provide a more comprehensive understanding and management of its invasion. We hypothesized that the diversity of trees, which are the primary producers supporting terrestrial ecosystems, could suppress the invasion of omnivorous RIFAs by regulating food resources (e.g. plants and arthropods) and the microenvironment (e.g. soil microclimate) as well as enhancing competition with other carnivorous ants. To test this hypothesis, we investigated RIFA abundance and mound numbers across tree species richness gradients in a subtropical plantation forest. Using structural equation models, we discovered that tree species richness had contrasting effects on RIFA abundance and mound numbers. RIFA abundance directly decreased with higher tree species richness because of the dilution effect (i.e. lower abundance of preferred tree species). In contrast, RIFA mound numbers indirectly increased with tree species richness via an increase in arthropod abundance (excluding ants) (i.e. more ample food resources for RIFA). Moreover, warmer soil temperatures decreased RIFA abundance, whereas other carnivorous ant species richness and abundance negatively affected RIFA abundance and mound numbers. Synthesis and applications: Our findings demonstrate that the ecological resistance to RIFA invasion is influenced by tree species richness in a complex manner; specifically, the relative strengths of the effects of tree species characteristics, native competitors and environmental conditions are likely to impact the power of ecological resistance. This highlights the importance of cross‐trophic interactions underlying the resistance of complex ecosystems to alien species invasion; therefore, maintaining diverse tree communities in controlled systems (e.g. suburban and urban green spaces) should be viewed as a potential strategy for controlling RIFA invasion. [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]