arXiv subjects

Zhihong Li

Publications and source records attributed to Zhihong Li.

12 recordsLinked to original sources

arXiv2026

Bidirectional Temporal Dynamics Modeling for EEG-based Driving Fatigue Recognition

Driving fatigue is a major contributor to traffic accidents and poses a serious threat to road safety. Electroencephalography (EEG) provides a direct measurement of neural activity, yet EEG-based fatigue recognition is hindered by strong non-stationarity and asymmetric neural dynamics. To address these challenges, we propose DeltaGateNet, a novel framework that explicitly captures Bidirectional temporal dynamics for EEG-based driving fatigue recognition. Our key idea is to introduce a Bidirectional Delta module that decomposes first-order temporal differences into positive and negative components, enabling explicit modeling of asymmetric neural activation and suppression patterns. Furthermore, we design a Gated Temporal Convolution module to capture long-term temporal dependencies for each EEG channel using depthwise temporal convolutions and residual learning, preserving channel-wise specificity while enhancing temporal representation robustness. Extensive experiments conducted under both intra-subject and inter-subject evaluation settings on the public SEED-VIG and SADT driving fatigue datasets demonstrate that DeltaGateNet consistently outperforms existing methods. On SEED-VIG, DeltaGateNet achieves an intra-subject accuracy of 81.89% and an inter-subject accuracy of 55.55%. On the balanced SADT 2022 dataset, it attains intra-subject and inter-subject accuracies of 96.81% and 83.21%, respectively, while on the unbalanced SADT 2952 dataset, it achieves 96.84% intra-subject and 84.49% inter-subject accuracy. These results indicate that explicitly modeling Bidirectional temporal dynamics yields robust and generalizable performance under varying subject and class-distribution conditions.

cs.OH↗

arXiv2026

High-Density Horizontal Arrays of Single-Chirality Carbon Nanotubes

Highly ordered high-density arrays of single-chirality single-walled carbon nanotubes (SWCNTs) are greatly desired for exploring the intrinsic anisotropic properties and collective performance of such 1-dimensional (1D) nanomaterials. Here we present a Marangoni flow-induced self-assembly (MISA) strategy to fabricate monolayered SWCNT arrays achieving a packing density of ~200 ${μm}^{-1}$ and a 2-dimensional order parameter ($S_{2\mathrm{D}}$) of ~0.95. Relying on its general compatibility with both organic and aqueous dispersions, we prepare single-chirality and enantiomer-pure SWCNT arrays from organic and aqueous dispersions resulting from the sorting processes. The anisotropic optical and electrical properties of the arrays are demonstrated by the polarization-dependent Rabi splitting as well as polarized near-infrared light emission and detection. With the great tolerance to solutions, substrates, and materials, as well as the feasibility and controllability, MISA shows great potential in the assembly of 1D nanomaterials.

cond-mat.mtrl-sci↗

arXiv2026

Restoring Heterogeneity in LLM-based Social Simulation: An Audience Segmentation Approach

Large Language Models (LLMs) are increasingly used to simulate social attitudes and behaviors, offering scalable "silicon samples" that can approximate human data. However, current simulation practice often collapses diversity into an "average persona," masking subgroup variation that is central to social reality. This study introduces audience segmentation as a systematic approach for restoring heterogeneity in LLM-based social simulation. Using U.S. climate-opinion survey data, we compare six segmentation configurations across two open-weight LLMs (Llama 3.1-70B and Mixtral 8x22B), varying segmentation identifier granularity, parsimony, and selection logic (theory-driven, data-driven, and instrument-based). We evaluate simulation performance with a three-dimensional evaluation framework covering distributional, structural, and predictive fidelity. Results show that increasing identifier granularity does not produce consistent improvement: moderate enrichment can improve performance, but further expansion does not reliably help and can worsen structural and predictive fidelity. Across parsimony comparisons, compact configurations often match or outperform more comprehensive alternatives, especially in structural and predictive fidelity, while distributional fidelity remains metric dependent. Identifier selection logic determines which fidelity dimension benefits most: instrument-based selection best preserves distributional shape, whereas data-driven selection best recovers between-group structure and identifier-outcome associations. Overall, no single configuration dominates all dimensions, and performance gains in one dimension can coincide with losses in another. These findings position audience segmentation as a core methodological approach for valid LLM-based social simulation and highlight the need for heterogeneity-aware evaluation and variance-preserving modeling strategies.

cs.CY↗

arXiv2026

Impact of Nuclear Reaction Rate Uncertainties on Type I X-ray Burst Nucleosynthesis: A Monte Carlo Study

Type I X-ray bursts are thermonuclear flashes on the surface of accreting neutron stars, involving hundreds of nuclei and thousands of reactions with larger uncertainties in reaction rate. To investigate the impact of nuclear reaction rate uncertainties on type I X-ray burst nucleosynthesis, comprehensive Monte Carlo simulations were performed with temperature-independent and -dependent variations in reaction rates using the REACLIB and STARLIB libraries, respectively. A total of 1,711 $(p, γ)$, $(p, α)$, $(α, p)$, and $(α, γ)$ reaction rates are varied simultaneously along with their inverse reactions via detailed balance. For the first time, it has been found that Monte Carlo sampling with larger perturbations to these reaction rates may lead to multi-peak abundance distributions for certain isotopes, such as $^{64}$Zn and $^{55}$Co. These multi-peak structures arise not only from coupled reactions but also from single reactions in some cases. Our studies also confirm previously identified key reactions and provide more robust lists that deserve priority consideration in future studies.

astro-ph.HE↗

arXiv2026

Impact of Nuclear Reaction Rates on Calcium Production in Population III Stars: A Global Analysis

We investigate the sensitivity of calcium production to nuclear reaction rates of a 40 solar-mass Population III star using 1D multi-zone stellar models. A comprehensive nuclear reaction network was constructed, and all $(p,γ)$ and $(p,α)$ reaction rates were individually varied by a factor of 10 up and down, identifying 13 preliminary key reactions for calcium production. To propagate the reaction rate uncertainties on calcium production, two sets of Monte Carlo simulations were performed for these key reactions: one adopting STARLIB reaction rates and the other incorporating updated rates from recent experimental data and evaluations. Our results show that Monte Carlo simulations using the updated rates show good agreement with the observed calcium abundance of the extremely iron-poor star SMSS J031300.36-670839.3 within the 68% confidence interval predicted by the models. In contrast, the observed calcium abundance lies marginally outside the 68% C.I. when using the STARLIB rates. Spearman rank-order correlation analysis and SHAP values show that the $(p,γ)$ and $(p,α)$ reactions of F18 and F19 exhibit strong coupled effects on calcium production. These reaction-rate uncertainties need to be reduced to constrain the stellar model predictions. Our study provides insights for future nuclear physics experiments aimed at reducing reaction rate uncertainties in the nucleosynthesis of Population III Stars. Additionally, comparisons between 20 solar-mass and 40 solar-mass Population III stellar models confirm that the latter, with updated reaction rates, is more capable of reproducing the observed Ca abundance and [Ca/Mg] ratio.

astro-ph.SR↗

arXiv2024

Integrative Pan-Cancer Analysis of RNMT: a Potential Prognostic and Immunological Biomarker

Background: RNA guanine-7 methyltransferase (RNMT) is one of the main regulators of N7-methylguanosine, and the deregulation of RNMT correlated with tumor development and immune metabolism. However, the specific function of RNMT in pan-cancer remains unclear. Methods: RNMT expression in different cancers was analyzed using multiple databases, including Cancer Cell Line Encyclopedia (CCLE), Genotype-Tissue Expression Project (GTEx), and The Cancer Genome Atlas (TCGA). Cox regression analysis and Kaplan-Meier analysis were used to estimate the correlation of RNMT expression to prognosis. The data was also used to research the relationship between RNMT expression and common immunoregulators, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase (DNMT). Additionally, the cBioPortal website was used to evaluate the characteristics of RNMT alteration. The TISDB database was used to obtain the expression of different subtypes. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the association between RNMT and tumor immune infiltration. Gene set enrichment analysis (GSEA) was used to identify the relevant pathways. Results: RNMT was ubiquitously highly expressed across cancers and survival analysis revealed that its expression was highly associated with the clinical prognosis of various cancer types. Remarkably, RNMT participates in immune regulation and plays a crucial part in the tumor microenvironment. A positive association was found between RNMT expression and six immune cell types expression in colon adenocarcinoma, kidney renal clear cell carcinoma, and liver hepatocellular carcinoma. Moreover, RNMT expression was highly associated with immunoregulators in most cancer types, and correlated to TMB, MSI, MMR, and DNMT. Finally, GSEA indicated that RNMT may correlate with tumor immunity.

q-bio.GN↗

arXiv2023

FlowHON: Representing Flow Fields Using Higher-Order Networks

Flow fields are often partitioned into data blocks for massively parallel computation and analysis based on blockwise relationships. However, most of the previous techniques only consider the first-order dependencies among blocks, which is insufficient in describing complex flow patterns. In this work, we present FlowHON, an approach to construct higher-order networks (HONs) from flow fields. FlowHON captures the inherent higher-order dependencies in flow fields as nodes and estimates the transitions among them as edges. We formulate the HON construction as an optimization problem with three linear transformations. The first two layers correspond to the node generation and the third one corresponds to edge estimation. Our formulation allows the node generation and edge estimation to be solved in a unified framework. With FlowHON, the rich set of traditional graph algorithms can be applied without any modification to analyze flow fields, while leveraging the higher-order information to understand the inherent structure and manage flow data for efficiency. We demonstrate the effectiveness of FlowHON using a series of downstream tasks, including estimating the density of particles during tracing, partitioning flow fields for data management, and understanding flow fields using the node-link diagram representation of networks.

cs.LG↗

arXiv2023

Measurement of $^{19}$F($p$,$γ$)$^{20}$Ne reaction suggests CNO break-out in first stars

The origin of calcium production in the first stars (Pop III stars), which formed out of the primordial matter of the Big Bang, and their fates, remain most fascinating mysteries in astrophysics. Advanced nuclear burning and supernovae were thought to be the dominant source of the Ca production seen in all stars. Here we report on a qualitatively different path to Ca production through break-out from the "warm" carbon-nitrogen-oxygen (CNO) cycle. We extend direct measurement of the $^{19}$F($p$, $γ$)$^{20}$Ne break-out reaction down to an unprecedentedly low energy point of 186 keV and discover a key resonance at 225 keV. In the domain of astrophysical interest, at around 0.1 giga kelvin, this thermonuclear $^{19}$F($p$,$γ$)$^{20}$Ne rate is up to a factor of 7.4 larger than the previous recommended rate. Our stellar models show a stronger break-out during stellar hydrogen burning than thought before, and may reveal the nature of Ca production in Pop III stars imprinted on the oldest known ultra-iron poor star, SMSS0313-6708. This result from the China Jinping Underground Laboratory, the deepest laboratory in the world, offering an environment with extremely low cosmic-ray induced background, has far-reaching implications on our understanding of how the first stars evolve and die. Our rate showcases the impact that faint Pop III star supernovae can have on the nucleosynthesis observed in the oldest known stars and first galaxies, key mission targets of the James Webb Space Telescope.

astro-ph.SR↗

arXiv2022

MSTGD:A Memory Stochastic sTratified Gradient Descent Method with an Exponential Convergence Rate

The fluctuation effect of gradient expectation and variance caused by parameter update between consecutive iterations is neglected or confusing by current mainstream gradient optimization algorithms.Using this fluctuation effect, combined with the stratified sampling strategy, this paper designs a novel \underline{M}emory \underline{S}tochastic s\underline{T}ratified Gradient Descend(\underline{MST}GD) algorithm with an exponential convergence rate. Specifically, MSTGD uses two strategies for variance reduction: the first strategy is to perform variance reduction according to the proportion p of used historical gradient, which is estimated from the mean and variance of sample gradients before and after iteration, and the other strategy is stratified sampling by category. The statistic \ $\bar{G}_{mst}$\ designed under these two strategies can be adaptively unbiased, and its variance decays at a geometric rate. This enables MSTGD based on $\bar{G}_{mst}$ to obtain an exponential convergence rate of the form $λ^{2(k-k_0)}$($λ\in (0,1)$,k is the number of iteration steps,$λ$ is a variable related to proportion p).Unlike most other algorithms that claim to achieve an exponential convergence rate, the convergence rate is independent of parameters such as dataset size N, batch size n, etc., and can be achieved at a constant step size.Theoretical and experimental results show the effectiveness of MSTGD

stat.ML↗

arXiv2019

An Integrated Model for User Innovation Knowledge Based on Super-network

Online user innovation communities are becoming a promising source of user innovation knowledge and creative users. With the purpose of identifying valuable innovation knowledge and users, this study constructs an integrated super-network model, i.e., User Innovation Knowledge Super-Network (UIKSN), to integrate fragmented knowledge, knowledge fields, users and posts in an online community knowledge system. Based on the UIKSN, the core innovation knowledge, core innovation knowledge fields, core creative users, and the knowledge structure of individual users were identified specifically. The findings help capture the innovation trends of products, popular innovations and creative users, and makes contributions on mining, and integrating and analyzing innovation knowledge in community based innovation theory.

cs.SI↗

arXiv2012

Determination of astrophysical 12N(p,g)13O reaction rate from the 2H(12N, 13O)n reaction and its astrophysical implications

The evolution of massive stars with very low-metallicities depends critically on the amount of CNO nuclides which they produce. The $^{12}$N($p$,\,$γ$)$^{13}$O reaction is an important branching point in the rap-processes, which are believed to be alternative paths to the slow 3$α$ process for producing CNO seed nuclei and thus could change the fate of massive stars. In the present work, the angular distribution of the $^2$H($^{12}$N,\,$^{13}$O)$n$ proton transfer reaction at $E_{\mathrm{c.m.}}$ = 8.4 MeV has been measured for the first time. Based on the Johnson-Soper approach, the square of the asymptotic normalization coefficient (ANC) for the virtual decay of $^{13}$O$_\mathrm{g.s.}$ $\rightarrow$ $^{12}$N + $p$ was extracted to be 3.92 $\pm$ 1.47 fm$^{-1}$ from the measured angular distribution and utilized to compute the direct component in the $^{12}$N($p$,\,$γ$)$^{13}$O reaction. The direct astrophysical S-factor at zero energy was then found to be 0.39 $\pm$ 0.15 keV b. By considering the direct capture into the ground state of $^{13}$O, the resonant capture via the first excited state of $^{13}$O and their interference, we determined the total astrophysical S-factors and rates of the $^{12}$N($p$,\,$γ$)$^{13}$O reaction. The new rate is two orders of magnitude slower than that from the REACLIB compilation. Our reaction network calculations with the present rate imply that $^{12}$N($p,\,γ$)$^{13}$O will only compete successfully with the $β^+$ decay of $^{12}$N at higher ($\sim$two orders of magnitude) densities than initially predicted.

nucl-ex↗

arXiv2010

Neutron spectroscopic factors of 7Li and astrophysical 6Li(n,g)7Li reaction rates

Angular distributions of the 7Li(6Li,6Li)7Li elastic scattering and the 7Li(6Li,7Li_{g.s.})6Li, 7Li(6Li,7Li*_{0.48})6Li transfer reactions at Ec.m. = 23.7 MeV were measured with the Q3D magnetic spectrograph. The optical potential of 6Li+7Li was obtained by fitting the elastic scattering differential cross sections. Based on the distorted wave Born approximation (DWBA) analysis, spectroscopic factors of 7Li=6Li+n were determined to be 0.73 +- 0.05 and 0.90 +- 0.09 for the ground and first exited states in 7Li, respectively. Using the spectroscopic factors, the cross sections of the 6Li(n,g)7Li direct neutron capture reactions and the astrophysical 6Li(n,g)7Li reaction rates were derived.

nucl-ex↗