吉林大学化学学院研究生课程思政的多维度构建与探索*

研究生课程思政的建设影响着研究生的培养质量,关乎着能否实现立德树人这一人才培养的根本任务。介绍了吉林大学化学学院立足学科特点,在研究生课程思政建设方面取得成功的经验,并对未来的课程思政建设方向进行了探讨。     

Rapid qualitative profiling and quantitative analysis of Juglandis Mandshuricae Cortex and seven flavonoids by ultra-high performance liquid chromatography–quadrupole/orbitrap high-resolution mass spectrometry

Juglandis Mandshuricae Cortex is the bark of Juglans mandshurica Maxim., which has been used as a folk medicine plant in China and India. In this study, an ultra-high performance liquid chromatography–quadrupole/orbitrap high-resolution mass spectrometry method was developed to clarify and quantify the chemical profiling of Juglandis Mandshuricae Cortex rapidly. A total of 113 compounds were characterized. Among them, seven flavonoids were simultaneously quantified in 15 min, including myricetin, myricetrin, taxifolin, kaempferol, quercetin, quercitrin, and naringenin. The method was validated for accuracy, precision, and the limits of detection and quantification. All calibration curves showed a good linear relationship (r > 0.9990) within test ranges. The intra- and inter-day relative standard deviations were less than 2.16%. Accuracy validation showed that the recovery was between 95.6 and 101.3% with relative standard deviation values below 2.85%. The validated method was successfully applied to determine the contents of seven flavones in Juglandis Mandshuricae Cortex from seven sources and the contents of these places were calculated respectively. This method provides a theoretical basis for further developing the medicinal value of Juglandis Mandshuricae Cortex.     

On the mean field limit of the Random Batch Method for interacting particle systems

The Random Batch Method proposed in our previous work(Jin et al.J Comput Phys,2020)is not only a numerical method for interacting particle systems and its mean-field limit,but also can be viewed as a model of the particle system in which particles interact,at discrete time,with randomly selected mini-batch of particles.In this paper,we investigate the mean-field limit of this model as the number of particles N→∞.Unlike the classical mean field limit for interacting particle systems where the law of large numbers plays the role and the chaos is propagated to later times,the mean field limit now does not rely on the law of large numbers and the chaos is imposed at every discrete time.Despite this,we will not only justify this mean-field limit(discrete in time)but will also show that the limit,as the discrete time intervalτ→0,approaches to the solution of a nonlinear Fokker-Planck equation arising as the mean-field limit of the original interacting particle system in the Wasserstein distance.     

Terahertz Dual-Band Near-Zero Effective Index Metamaterial Based on Double-Sided Metal Microstructure

Journal of Russian Laser Research - The zero-refractive-index metamaterials have excellent electromagnetic properties, which provide new ideas and methods to realize the control of electromagnetic...     

基于图像特征检测和匹配的无人船航行姿态角提取方法

计算机视觉的飞速发展, 使得采用视觉技术辅助无人船航行成为可能. 在无人船巡航过程中, 获取船体航向是航行控制的必备基础. 特征匹配是无人船相关视觉技术中的重要部分, 是目标识别和定位等功能的关键步骤. 获取无人船运动姿态的基本步骤是对图像前后帧进行有效的特征提取和匹配. 针对水域环境中的图像静态特征提取速度慢、精度低的问题, 本文提出一种图像匹配方法以求取无人船的航行姿态角. 首先对图像预处理, 并对有效区域进行特征提取. 其次, 设计一种基于描述子相似度的初始特征匹配策略. 再其次, 筛选特征匹配对, 优化模型参数. 最后, 通过前后帧旋转矩阵计算航行姿态角. 实验表明, 该方法能有效提取无人船的航行姿态角.     

利用图像法巧解一类均匀带电双圆环问题

给出了单个均匀带电圆环在轴线上各点产生的场强随距离变化的关系式和极值条件以及图像并分析其特点,利用图像叠加法对有关两个均匀带电圆环在轴线上产生电场的问题进行巧妙解答.     

n-APR tilting and $$\tau $$ τ -mutations

Journal of Algebraic Combinatorics - APR tilts for path algebra kQ can be realized as the mutation of the quiver Q in $${\mathbb Z}Q$$ with respect to the translation. In this paper, we show that...     

Investigation of Molecular Diffusion at Block Copolymer Thin Films Using Maximum Entropy Method-Based Fluorescence Correlation Spectroscopy and Single Molecule Tracking

Fluorescence correlation spectroscopy (FCS) has been widely used to investigate molecular diffusion behavior in various samples. The use of the maximum entropy method (MEM) for FCS data analysis provides a unique means to determine multiple distinct diffusion coefficients without a priori assumption of their number. Comparison of the MEM-based FCS method (MEM-FCS) with another method will reveal its utility and advantage as an analytical tool to investigate diffusion dynamics. Herein, we measured diffusion of fluorescent probes doped into nanostructured thin films using MEM-FCS, and validated the results with single molecule tracking (SMT) data. The efficacy of the MEM code employed was first demonstrated by analyzing simulated FCS data for systems incorporating one and two diffusion modes with broadly distributed diffusion coefficients. The MEM analysis accurately afforded the number of distinct diffusion modes and their mean diffusion coefficients. These results contrasted with those obtained by fitting the simulated data to conventional two-component and anomalous diffusion models, which yielded inaccurate estimates of the diffusion coefficients. Subsequently, the MEM analysis was applied to FCS data acquired from hydrophilic dye molecules incorporated into microphase-separated polystyrene-block-poly(ethylene oxide) (PS-b-PEO) thin films characterized under a water-saturated N₂ atmosphere. The MEM analysis revealed distinct fast and slow diffusion components attributable to molecules diffusing on the film surface and inside the film, respectively. SMT studies of the same materials yielded trajectories for mobile molecules that appear to follow the curved PEO microdomains. Diffusion coefficients obtained from the SMT data were consistent with those obtained for the slow diffusion component detected by MEM-FCS. These results highlight the utility of MEM-FCS and SMT for gaining complementary information on molecular diffusion processes in heterogeneous material systems.

Graphical Abstract

     

On Minimal Asymptotic Bases

Mathematical Notes - Let $$\mathbb N$$ denote the set of all nonnegative integers, and let $$A\subseteq\mathbb N$$ . Let $$h,n\in\mathbb N$$ , $$h\ge 2$$ and $$r_h(A,n)=\#\{(a_1,\dots,a_h)\in...