arXiv subjects

Chunyi Zhang

Publications and source records attributed to Chunyi Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Jet-ISM Interaction and Multi-channel AGN Feedback in the Post-merger Galaxy 4C+29.30

4C+29.30 is a post-merger galaxy hosting a rejuvenated active galactic nucleus (AGN) with a complex multi-scale radio morphology, making it an ideal laboratory to study the interplay between different AGN feedback modes. We present a multi-wavelength analysis combining optical integral field spectroscopy (SDSS/MaNGA and CFHT/SITELLE) with radio continuum imaging (VLASS) to map the ionized gas kinematics and ionization structure across the galaxy. We uncover a galaxy-scale, biconical ionized gas outflow whose axis is misaligned by $\sim$26$^\circ$ from the radio jet. This outflow, characterized by broad line widths and Seyfert-like ionization, is mostly consistent with a radiatively driven wind from the central supermassive black hole, which is accreting at a relatively high Eddington ratio ($L_{\mathrm{bol}}/L_{\mathrm{Edd}} \gtrsim 0.1$). In contrast, the northern radio lobe clearly drives localized gas acceleration and increased velocity dispersion, indicative of jet-driven shocks interacting with the interstellar medium, consistent with previous X-ray findings. The coexistence of a radiatively driven galactic-scale outflow and a distinct, misaligned radio jet demonstrates that multiple AGN feedback channels can operate simultaneously within the same system, providing new evidence for the concurrent action of radiative and mechanical feedback.

astro-ph.GA↗

arXiv2026

Understanding the Density Maximum of Water with Machine Learned Potentials

After melting, at ambient pressure, the density of water continues to increase with temperature until it reaches a maximum around 4 {\deg}C. For nearly a century, this phenomenon has been qualitatively attributed to a mixture of ordered and disordered structures. Herein, we employ a deep neural network to train a machine learned (ML) interatomic potential for water using electronic structure data from advanced density functional theory. Notably, molecular dynamics simulations with the ML potential reproduce both the experimental water density anomaly and the thermal expansion coefficient. Detailed structural analysis of the computed hydrogen-bond network reveals that the density anomaly arises from an emergent liquid structure that retains nearly ideal tetrahedral coordination at short range but collapses at intermediate range. Our findings point to a more delicate mechanism causing the density maximum than the conventional picture, emphasizing the collective roles of structural orderings at different length scales.

physics.chem-ph↗

arXiv2026

fix pimd/langevin: An Efficient Implementation of Path Integral Molecular Dynamics in LAMMPS

Path integral molecular dynamics (PIMD), which maps a quantum particle onto a fictitious classical system of ring polymers and propagates the "beads" of this extended classical system using molecular dynamics, is widely used to capture nuclear quantum effects (NQEs) in molecular simulations. Accurate PIMD calculations typically require a large number of beads and are therefore computationally demanding. While software packages such as i-PI offer comprehensive PIMD functionality, the high efficiency of simulations driven by machine learning interatomic potentials, such as Deep Potential (DP), calls for more efficient PIMD implementations that fully exploit modern massively parallel supercomputers. Here we present fix pimd/langevin, an efficient PIMD implementation in LAMMPS that supports commonly used features and leverages the Message Passing Interface architecture of LAMMPS to achieve high computational efficiency. We demonstrate the usage and validate the correctness of our code using liquid water as a representative example, and provide a comprehensive overview of the supported features. Then we discuss several important technical aspects of the implementation. Using DP simulations of water as a benchmark, we show that our implementation achieves several-fold acceleration compared to i-PI. Finally, we report strong and weak scaling results that demonstrate the favorable parallel performance of our code.

physics.chem-ph↗

arXiv2025

Ab Initio Melting Properties of Water and Ice from Machine Learning Potentials

Liquid water exhibits several important anomalous properties in the vicinity of the melting temperature ($T_{\mathrm{m}}$) of ice Ih, including a higher density than ice and a density maximum at 4~$^{\circ}$C. Experimentally, an isotope effect on $T_{\mathrm{m}}$ is observed: the melting temperature of H$_2$O is approximately 4~K lower than that of D$_2$O. This difference can only be explained by nuclear quantum effects (NQEs), which can be accurately captured using path integral molecular dynamics (PIMD). Here we run PIMD simulations driven by Deep Potential (DP) models trained on data from density functional theory (DFT) based on SCAN, revPBE0-D3, SCAN0, and revPBE-D3 and a DP model trained on the MB-pol potential. We calculate the \tm of ice, the density discontinuity at melting, and the temperature of density maximum ($T_{\mathrm{dm}}$) of the liquid. We find that the model based on MB-pol agrees well with experiment. The models based on DFT incorrectly predict that NQEs lower $T_{\mathrm{m}}$. For the density discontinuity, SCAN and SCAN0 predict values close to the experimental result, while revPBE-D3 and revPBE0-D3 significantly underestimate it. Additionally, the models based on SCAN and SCAN0 correctly predict that the $T_{\mathrm{dm}}$ is higher than $T_{\mathrm{m}}$, while those based on revPBE-D3 and revPBE0-D3 predict the opposite. We attribute the deviations of the DFT-based models from experiment to the overestimation of hydrogen bond strength. Our results set the stage for more accurate simulations of aqueous systems grounded on DFT.

physics.chem-ph↗

arXiv2025

Assessment of First-Principles Methods in Modeling the Melting Properties of Water

First-principles simulations have played a crucial role in deepening our understanding of the thermodynamic properties of water, and machine learning potentials (MLPs) trained on these first-principles data widen the range of accessible properties. However, the capabilities of different first-principles methods are not yet fully understood due to the lack of systematic benchmarks, the underestimation of the uncertainties introduced by MLPs, and the neglect of nuclear quantum effects (NQEs). Here, we systematically assess first-principles methods by calculating key melting properties using path integral molecular dynamics (PIMD) driven by Deep Potential (DP) models trained on data from density functional theory (DFT) with SCAN, revPBE0-D3, SCAN0 and revPBE-D3 functionals, as well as from the MB-pol potential. We find that MB-pol is in qualitatively good agreement with the experiment in all properties tested, whereas the four DFT functionals incorrectly predict that NQEs increase the melting temperature. SCAN and SCAN0 slightly underestimate the density change between water and ice upon melting, but revPBE-D3 and revPBE0-D3 severely underestimate it. Moreover, SCAN and SCAN0 correctly predict that the maximum liquid density occurs at a temperature higher than the melting point, while revPBE-D3 and revPBE0-D3 predict the opposite behavior. Our results highlight limitations in widely used first-principles methods and call for a reassessment of their predictive power in aqueous systems.

physics.chem-ph↗

arXiv2025

The MALATANG survey: star formation, dense gas, and AGN feedback in NGC 1068

We aim to investigate the interplay between dense molecular gas, star formation, and active galactic nucleus (AGN) feedback in the luminous infrared galaxy (LIRG) NGC 1068 at sub-kiloparsec scales. We present the HCN (4-3) and HCO$^+$ (4-3) maps of NGC 1068, obtained with JCMT as part of the Mapping the dense molecular gas in the strongest star-forming galaxies (MALATANG) project, and perform spatially resolved analyses of their correlations with infrared luminosity and soft X-ray emission. Spatially resolved relations between the luminosities of infrared dust emission and dense molecular gas tracers ($L_{\rm IR}-L'_{\rm dense}$) are found to be nearly linear, without clear evidence of excess contributions from AGN activity. The spatially resolved X-ray emission ($L^{\rm gas}_{0.5-2\,\mathrm{keV}}$) displays a radially-dependent twofold correlation with the star formation rate (SFR), suggesting distinct gas-heating mechanisms between the galaxy center and the outer regions. A super-linear scaling is obtained in galactic center regions with SFR surface density ($\Sigma_{\rm SFR}$) $>$ 8.2 $\times$ 10$^{-6}$ $M_\odot$ yr$^{-1}$ kpc$^{-2}$: log($L^{\rm gas}_{0.5-2\,\mathrm{keV}}$/erg s$^{-1}$) = 2.2 log(SFR/$M_\odot$ yr$^{-1}$) + 39.1. We further found a statistically significant super-linear correlation ($\beta = 1.34$ $\pm$ 0.86) between $L^{\rm gas}_{0.5-2\,\mathrm{keV}}$/SFR and HCN(4-3)/CO(1-0) intensity ratio, whereas no such trend is seen for HCO$^+$(4-3)/CO(1-0) or CO(3-2)/CO(1-0). These findings indicate that AGN feedback does not dominate star formation regulation on sub-kiloparsec scales, and that the excitation of dense gas traced by HCN (4-3) may be more directly influenced by high-energy feedback processes compared to HCO$^+$ (4-3) and CO (3-2).

astro-ph.GA↗

arXiv2025

Beyond the Hype: A Large-Scale Empirical Analysis of On-Chain Transactions in NFT Scams

Non-fungible tokens (NFTs) serve as a representative form of digital asset ownership and have attracted numerous investors, creators, and tech enthusiasts in recent years. However, related fraud activities, especially phishing scams, have caused significant property losses. There are many graph analysis methods to detect malicious scam incidents, but no research on the transaction patterns of the NFT scams. Therefore, to fill this gap, we are the first to systematically explore NFT phishing frauds through graph analysis, aiming to comprehensively investigate the characteristics and patterns of NFT phishing frauds on the transaction graph. During the research process, we collect transaction records, log data, and security reports related to NFT phishing incidents published on multiple platforms. After collecting, sanitizing, and unifying the data, we construct a transaction graph and analyze the distribution, transaction features, and interaction patterns of NFT phishing scams. We find that normal transactions on the blockchain accounted for 96.71% of all transactions. Although phishing-related accounts accounted for only 0.94% of the total accounts, they appeared in 8.36% of the transaction scenarios, and their interaction probability with normal accounts is significantly higher in large-scale transaction networks. Moreover, NFT phishing scammers often carry out fraud in a collective manner, targeting specific accounts, tend to interact with victims through multiple token standards, have shorter transaction cycles than normal transactions, and involve more multi-party transactions. This study reveals the core behavioral features of NFT phishing scams, providing important references for the detection and prevention of NFT phishing scams in the future.

cs.CR↗

arXiv2025

A Comprehensive Evaluation and Practice of System Penetration Testing

With the rapid advancement of information technology, the complexity of applications continues to increase, and the cybersecurity challenges we face are also escalating. This paper aims to investigate the methods and practices of system security penetration testing, exploring how to enhance system security through systematic penetration testing processes and technical approaches. It also examines existing penetration tools, analyzing their strengths, weaknesses, and applicable domains to guide penetration testers in tool selection. Furthermore, based on the penetration testing process outlined in this paper, appropriate tools are selected to replicate attack processes using target ranges and target machines. Finally, through practical case analysis, lessons learned from successful attacks are summarized to inform future research.

cs.CR↗

arXiv2025

Security Analysis of Ponzi Schemes in Ethereum Smart Contracts

The rapid advancement of blockchain technology has precipitated the widespread adoption of Ethereum and smart contracts across a variety of sectors. However, this has also given rise to numerous fraudulent activities, with many speculators embedding Ponzi schemes within smart contracts, resulting in significant financial losses for investors. Currently, there is a lack of effective methods for identifying and analyzing such new types of fraudulent activities. This paper categorizes these scams into four structural types and explores the intrinsic characteristics of Ponzi scheme contract source code from a program analysis perspective. The Mythril tool is employed to conduct static and dynamic analyses of representative cases, thereby revealing their vulnerabilities and operational mechanisms. Furthermore, this paper employs shell scripts and command patterns to conduct batch detection of open-source smart contract code, thereby unveiling the common characteristics of Ponzi scheme smart contracts.

cs.CR↗

arXiv2025

Global Hot Gas Excess in (U)LIRGs: Replicating Galactic Nuclei Scaling Relations between Diffuse X-ray Emission and Star Formation on Galaxy-Wide Scales

Hot ionized interstellar medium interlinks star formation and stellar feedback processes, redistributing energy, momentum, and material throughout galaxies. We use X-ray data from $Chandra$ to extract the hot gas emission from 78 of the most luminous infrared-selected galaxies in the local Universe. In the extreme star-forming environments, the intrinsic thermal X-ray luminosity of hot gas ($L_{\rm 0.5 - 2\,keV}^{\rm gas}$) shows a significant excess over the predictions of the standard linear $L_{\rm X}$$-$SFR relation for most objects with very high star formation rates (SFRs). The contribution of active galactic nuclei (AGNs) appears to have little impact on the global hot gas luminosity. For galaxies with SFR $\gt$ 50 ${M_{\rm \odot}}\,\,{\rm yr^{-1}}$, the Bayesian analysis gives a super-linear relation of ${\rm log}(L_{\rm 0.5-2\,keV}^{\rm gas} /{\rm erg\,s^{-1}})=1.34\,{\rm log}({\rm SFR}/{M_{\rm \odot}}\,{\rm yr^{-1}})+39.82$, similar to that found in the central regions of normal spiral galaxies. These results suggest a scenario in which the merger of galaxies delivers substantial amounts of gas, triggering intense star formation in both the nuclear region and the galactic disk, and ultimately enhancing the global thermal X-ray emission. The ratio of the apparent thermal luminosity in the 0.5$-$2 keV band ($L_{\rm 0.5 - 2\,keV}^{\rm appar}$) to $L_{\rm 0.5 - 2\,keV}^{\rm gas}$ shows statistically significant negative correlations with the intrinsic column density ($N_{\rm H}$) and SFR. Moreover, in contrast to the luminosity ratio, SFR shows a moderate positive correlation with intrinsic $N_{\rm H}$. This suggests that the correlation between $L_{\rm 0.5 - 2\,keV}^{\rm appar}$/$L_{\rm 0.5 - 2\,keV}^{\rm gas}$ and SFR may be driven by the underlying $L_{\rm 0.5 - 2\,keV}^{\rm appar}$/$L_{\rm 0.5 - 2\,keV}^{\rm gas}$$-$$N_{\rm H}$ and SFR$-$$N_{\rm H}$ relations.

astro-ph.GA↗

arXiv2025

Simulations of dielectric permittivity of water by Machine Learned Potentials with long-range Coulombic interactions

The dielectric permittivity of liquid water is a fundamental property that underlies its distinctive behaviors in numerious physical, biological, and chemical processes. Within a machine learning framework, we present a unified approach to compute the dielectric permittivity of water, systematically incorporating various electric boundary conditions. Our method employs a long-range-inclusive deep potential trained on data from hybrid density functional theory calculations. Dielectric response is evaluated using an auxiliary deep neural network that predicts the centers of maximally localized Wannier functions. We investigate three types of electric boundary conditions--metallic, insulating, and Kirkwood-Frohlich--to assess their influence on correlated dipole fluctuations and dielectric relaxation dynamics. In particular, we demonstrate a consistent methodology for computing the Kirkwood correlation factor, correlation length, and dielectric permittivity under each boundary condition, where long-range electrostatics play a critical role. This work establishes a robust and generalizable machine-learning framework for modeling the dielectric properties of polar liquids under diverse electrostatic environments.

cond-mat.soft↗

arXiv2025

DoS Attacks and Defense Technologies in Blockchain Systems: A Hierarchical Analysis

Blockchain technology is widely used in various fields due to its ability to provide decentralization and trustless security. This is a fundamental understanding held by many advocates, but it is misunderstood, leading participants to fail to recognize the limitations of the security that blockchain can provide. Among all current network attacks, Denial of Service (DoS) attacks pose significant threats due to their ease of execution and destructive potential. This paper, based on the blockchain architecture hierarchy, categorizes and organizes existing DoS attacks, with a focus on explaining the principles and methods of contract layer and consensus layer DoS attacks. Furthermore, this paper comprehensively analyzes and compares commonly used detection methods and defense technologies, which will contribute to strengthening the security and stability of blockchain systems and promoting further innovation and application of blockchain systems.

cs.CR↗

arXiv2025

Diffuse X-Ray-emitting Gas in the Central Region of Star-Forming Galaxies

The interstellar medium of galaxies, with temperatures reaching several million degrees, provides a pivotal perspective for understanding the physical and chemical properties of star formation, galactic evolution, and their associated feedback mechanisms. We use archival data from $Chandra$ observations to extract the diffuse X-ray emission from 23 nearby star-forming galaxies and study its correlation with star formation activity in the central region of these galaxies. The surface brightness profile of each galaxy presents a sharp decrease in the central region of $\thicksim$0.3$-$2 kpc and then varies slowly outside this range. Compared to the global relation between the diffuse thermal X-ray luminosity from hot gas ($L_{\rm 0.5 - 2\,keV}^{\rm gas}$) and the star formation rate (SFR), we found a super-linear relation of ${\rm log}(L_{\rm 0.5-2\,keV}^{\rm gas} /{\rm erg\,s^{-1}})=1.34\,{\rm log}({\rm SFR}/{M_{\rm \odot}}\,{\rm yr^{-1}})+40.15$ for the center of these sample galaxies. This result suggests that more intense stellar feedback is associated with stronger star formation activity in the central region of star-forming galaxies, where more energy output from supernovae (SNe) and stellar winds is converted into X-ray flux. Furthermore, the slope of the $L_{\rm 0.5 - 2\,keV}^{\rm gas}$$-$SFR relation anticorrelates with spatial scale in the galactic central region. This indicates that the characteristics of central hot gas emission are gradually averaged over larger areas. The diffuse X-ray luminosity also shows a good correlation with molecular gas, stellar mass, and mid-plane pressure traced by the baryonic mass, although these relations show relatively large scatter.

astro-ph.GA↗

arXiv2025

Hybrid Wi-Fi/PDR Indoor Localization with Fingerprint Matching

Indoor position technology has become one of the research highlights in the Internet of Things (IoT), but there is still a lack of universal, low-cost, and high-precision solutions. This paper conducts research on indoor position technology based on location fingerprints and proposes a practical hybrid indoor positioning system. In this experiment, the location fingerprint database is established by using RSS signal in the offline stage, the location algorithm is improved and innovated in the online stage. The weighted k-nearest neighbor algorithm is used for location fingerprint matching and pedestrian dead reckoning technology is used for trajectory tracking. This paper designs and implements an indoor position system that performs the functions of data collection, positioning, and position tracking. Through the test, it is found that it can meet the requirements of indoor positioning.

cs.NI↗

arXiv2025

Spectral Similarity Masks Structural Diversity at Hydrophobic Water Interfaces

The air-water and graphene-water interfaces represent quintessential examples of the liquid-gas and liquid-solid boundaries, respectively. While the sum-frequency generation (SFG) spectra of these interfaces exhibit certain similarities, a consensus on their signals and interpretations has yet to be reached. Leveraging deep learning, we accessed fully first-principles SFG spectra for both systems, addressing recent experimental discrepancies. Despite both interfaces exhibiting microscopically hydrophobic characteristics, our findings reveal that similarities in SFG signals do not translate into comparable interfacial microscopic properties. Instead, graphene-water and air-water interfaces exhibit fundamental differences in SFG-active thicknesses, hydrogen-bonding networks, and dynamic diffusion behavior. These distinctions underscore the stronger confinements imposed by the solid-liquid interface compared with the weaker constraints of the gas-liquid interface.

physics.comp-ph↗

arXiv2025

Fire and Ice in the Whirlpool: Spatially Resolved Scaling Relations between X-ray Emitting Hot Gas and Cold Molecular Gas in M51

The cold and hot interstellar medium (ISM) in star forming galaxies resembles the reservoir for star formation and associated heating by stellar winds and explosions during stellar evolution, respectively. We utilize data from deep $Chandra$ observations and archival millimeter surveys to study the interconnection between these two phases and the relation to star formation activities in M51 on kiloparsec scales. A sharp radial decrease is present in the hot gas surface brightness profile within the inner 2 kpc of M51. The ratio between the total infrared luminosity ($L_{\rm IR}$) and the hot gas luminosity ($L_{\rm 0.5 - 2\,keV}^{\rm gas}$) shows a positive correlation with the galactic radius in the central region. For the entire galaxy, a twofold correlation is revealed in the $L_{\rm 0.5 - 2\,keV}^{\rm gas}$${-}$$L_{\rm IR}$ diagram, where $L_{\rm 0.5 - 2\,keV}^{\rm gas}$ sharply increases with $L_{\rm IR}$ in the center but varies more slowly in the disk. The best fit gives a steep relation of ${\rm log}(L_{\rm 0.5-2\,keV}^{\rm gas} /{\rm erg\,s^{-1}})=1.82\,{\rm log}(L_{\rm IR} /{L_{\rm \odot}})+22.26$ for the center of M51. The similar twofold correlations are also found in the $L_{\rm 0.5 - 2\,keV}^{\rm gas}$${-}$molecular line luminosity ($L^\prime_{\rm gas}$) relations for the four molecular emission lines CO(1-0), CO(2-1), HCN(1-0), and HCO$^+$(1-0). We demonstrate that the core-collapse supernovae (SNe) are the primary source of energy for heating gas in the galactic center of M51, leading to the observed steep $L_{\rm 0.5 - 2\,keV}^{\rm gas}$${-}$$L_{\rm IR}$ and $L_{\rm 0.5 - 2\,keV}^{\rm gas}$${-}$$L^\prime_{\rm gas}$ relations, as their X-ray radiation efficiencies ($\eta$ $\equiv$ $L_{\rm 0.5 - 2\,keV}^{\rm gas}$/$\dot{E}_\mathrm{SN}$) increase with the star formation rate surface densities, where $\dot{E}_\mathrm{SN}$ is the SN mechanical energy input rate.

astro-ph.GA↗

arXiv2024

Unveiling hole-facilitated amorphisation in pressure-induced phase transformation of silicon

Pressure-induced phase transformation occurs during silicon (Si) wafering processes. \b{eta}-tin (Si-II) phase is formed at high pressures, followed by the transformation to Si-XII, Si-III or/and amorphous Si ({\alpha}-Si) phases during the subsequent decompression. While the imposed pressure and its release rate are known to dictate the phase transformation of Si, the effect of charge carriers are ignored. Here, we experimentally unveil that the increased hole concentration facilitates the amorphization in the pressure-induced phase transformation of Si. The underlying mechanism is elucidated by the theoretical calculations based on machine-learning interatomic potentials. The hole-facilitated amorphization is also experimentally confirmed to occur in the indented Ge, GaAs or SiC. We discover that hole concentration is another determining factor for the pressure-induced phase transformations of the industrially important semiconductors.

cond-mat.mtrl-sci↗

arXiv2024

Sub-kiloparsec scaling relations between hot gas, dense gas and star formation rate in five nearby star-forming galaxies

Based on the newly acquired dense gas observations from the JCMT MALATANG survey and X-ray data from Chandra, we explore the correlation between hot gas and HCN $J=4 \rightarrow 3$, HCO$^+\ J=4 \rightarrow 3$ emission for the first time at sub-kiloparsec scale of five nearby star-forming galaxies, namely M82, M83, IC 342, NGC 253, and NGC 6946. We find that both HCN $J=4 \rightarrow 3$ and HCO$^+\ J=4 \rightarrow 3$ line luminosity show a statistically significant correlation with the 0.5${-}$2 keV X-ray emission of the diffuse hot gas ($L_{\rm 0.5 - 2\,keV}^{\rm gas}$). The Bayesian regression analysis gives the best fit of ${\rm log}(L_{\rm 0.5-2\,keV}^{\rm gas} /{\rm erg\,s^{-1}})=2.39\,{\rm log}(L'_{\rm HCN(4-3)} /{\rm K\,km\,s^{-1}\,pc^{2}})+24.83$ and ${\rm log}(L_{\rm 0.5-2\,keV}^{\rm gas} /{\rm erg\,s^{-1}})=2.48\,{\rm log}(L'_{\rm HCO^{+}(4-3)} /{\rm K\,km\,s^{-1}\,pc^{2}})+23.84$, with dispersion of $\thicksim$0.69 dex and 0.54 dex, respectively. At the sub-kiloparsec scale, we find that the power-law index of the $L_{\rm 0.5 - 2\,keV}^{\rm gas}$ ${-}$ star formation rate (SFR) relation is ${\rm log}(L_{\rm 0.5-2\,keV}^{\rm gas} /{\rm erg\,s^{-1}})=1.80\,{\rm log} ({\rm SFR} /M_\odot\,{\rm yr}^{-1})+39.16$, deviated from previous linear relations at global scale. This implies that the global property of hot gas significantly differs from individual resolved regions, which is influenced by the local physical conditions close to the sites of star formation.

astro-ph.GA↗