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Background] The average transverse momentum of identified particle is an important observable in relativistic nucleus nucleus collision experiments. It can reflect the properties of hot matter created in the collisions and the characteristics of soft hadrons. [Purpose] It can also deepen our understanding and help to extract the information about the evolution of the collision system with systematic investigation. [Methods] By using the phenomenological linear and exponential functions, we studied the dependence of the average transverse momenta of identified particles produced at the midrapidity in relativistic nucleus nucleus collisions on the collision centrality, particle mass and the collision energy at given collision conditions, respectively, including the data from Au+Au, Cu+Cu, U+U collisions provided by the STAR Collaboration at the Relativistic Heavy Ion Collider (RHIC) and from Pb+Pb collisions provided by the ALICE Collaboration at the Large Hadron Collider (LHC). [Results & Conclusions] The systematic study shows that the good linear relationship between the average transverse momenta of identified particles and collision centrality is universal. Therefore, the collision centrality is a good physical quantity for studying the average transverse momenta of identified particles in relativistic nucleus nucleus collisions. It is also found that the fitting parameters of the linear function for the average transverse momentum with the collision centrality follow a power-law relationship with the collision energy, which is universal as well. For a given collision energy and collision centrality, it is found that the average transverse momenta of identified particles have a linear relationship with the particle mass qualitatively. Few data points of the identified particles deviate from the linear relationship which may be relevant with the details of their production mechanisms. Meanwhile, it is found that the average transverse momentum of a given identified particle follows an exponential relationship with the logarithm of the collision energy at a given collision centrality, which is also universal.

bstract [Background] X-ray Absorption Fine Structure (XAFS) is a vital technique for structural analysis, widely employed to investigate the oxidation state, coordination environment, and neighboring atom properties of amorphous materials and disordered systems. However, the complexity of XAFS spectra often requires interpretation by experienced researchers, which can still lead to inaccuracies. [Purpose] This study aims to use machine learning approaches to analyze XAFS data and predict the coordination number of absorbing atoms. [Methods] First, a dataset of 13,374 valid EXAFS spectra of fourth-period transition metal elements was sourced from the Materials Project database. Second, this data was utilized to train three machine learning models: neural networks, bagging models, and random forest models. Finally, these models were applied to predict the coordination numbers of the absorbing atoms in the spectra. [Results] The study achieved an average prediction accuracy of approximately 70%. Feature importance analysis revealed that data points within R < 3.0 were critical for predictions, consistent with the prominence of short-range atomic interactions in EXAFS theory. [Conclusions] This research enhances the efficiency and reliability of XAFS data analysis by improving model generalizability and interpretability.

Based on the Generalized reduced R-matrix theory, the RAC program (R-matrix analysis code) is used to analyze the experimental data of all the nuclear reaction channels related to the 5He system. The current calculations provide accurate and reliable evaluation data, and are in good agreement with the experimental data. In this work, self-consistent for each reaction evaluation data are obtained by multi-channel and multi-energy fitting, in particular, the error propagation theory of Generalized Least Squares is used to give the error of the evaluation data and the covariance matrix of the integral cross section. This R-matrix analysis for the 5He system has the following three features. First, for the first time, the error in the evaluation data of the T(d,n)4He reaction cross section and the covariance matrix of the integral cross section are given. Second, we use only one set of R-matrix parameters to depict the reaction cross section of each reaction channel of the 5He system for the whole energy region in our work. Third, in this evaluation, we have taken into account some of the latest measured experimental data, especially after 2000. The T(d,n)4He reaction cross section at 0.1 MeV and below has been carefully studied. The effect of different energy levels in T(d,n)4He has been analyzed, with the energy levels 3/2+ making a major contribution to cross section, and the role of S-wave and D-wave from 3/2- determines the lean forward trend of the angular distributions at 0.01 MeV - 0.1 MeV.

本文对基于Stokes迭代的稳态Boussinesq方程的多层混合有限元方法进行分析和讨论。 首先在网格尺寸为 $h_0$ 的粗网格上求解原问题,然后在网格尺寸为 $h_j(j=1,2,...J)$ 的细网格上求解一系列Stokes型的解耦线性问题。 在唯一性条件下,得到了多层有限元方法的数值解的稳定性和收敛性。 理论结果表明,当网格尺寸满足 $h_j=h^2_{j-1}(j=1,2,...J)$ 时,多层网格方法在 $H^1$ 范数下具有与一层方法相同的逼近精度。 最后,给出了一些数值结果,验证多层混合有限元方法的有效性。

针对软岩边坡蠕变效应开展系统性研究，建立软岩蠕变模型与锚索预应力损失模型的耦合分析框架，通过理论推导与数值模拟揭示岩石等效弹性模量的时效演化规律，并基于锚索预应力实测数据建立软岩蠕变参数反演方法。创新性地集成极限平衡理论与有限元分析技术，构建考虑预应力时变损失的路堑边坡稳定性综合评价体系。依托西南山区高速公路典型软岩路堑工程，结合十字锚固结构长达5年的预应力监测数据，定量预测边坡长期稳定状态并确立动态监测阈值。研究成果可为高边坡加固设计优化与全生命周期安全管控提供理论支撑，对保障交通基础设施安全运营具有重要工程价值。