Semiconvergence analysis of the randomized row iterative method and its extended variants

The row iterative method is popular in solving the large‐scale ill‐posed problems due to its simplicity and efficiency. In this work we consider the randomized row iterative (RRI) method to tackle this issue. First, we present the semiconvergence analysis of RRI method for the overdetermined and inconsistent system, and derive upper bounds for the noise error propagation in the iteration vectors. To achieve a least squares solution, we then propose an extended version of the RRI (ERRI) method, which in fact can converge in expectation to the solution of the overdetermined or underdetermined, consistent or inconsistent systems. Finally, some numerical examples are given to demonstrate the convergence behaviors of the RRI and ERRI methods for these types of linear system.     

“联用技术及元素形态”国际课程探索与实践*

充分利用线上教学的优势,对化学院本科生开设了“联用技术及元素形态”国际课程,避免了传统教学中组织、协调外籍/外地专家资源过程中必要的各种消耗,极大程度地整合教学资源、改善教学效果。在该课程的探索与实践过程中,学生们对痕量元素形态及基于等离子体质谱的各种联用技术产生了极大的兴趣,激发了他们的主动学习热情;教师之间及师生之间的沟通趋向更简单、更灵活、更实时,为后续线上线下混合式国际课程建设提供了良好的基础和借鉴。     

求解矩阵方程AXB+CXD=F参数迭代法的最优参数分析

本文针对求矩阵方程AXB+CXD=F唯一解的参数迭代法，分析当矩阵A，B，C，D均是Hermite正（负）定矩阵时，迭代矩阵的特征值表达式，给出了最优参数的确定方法，并提出了相应的加速算法.     

Some extensions of the operator entropy type inequalities

In this paper, we define the generalised relative operator entropy and investigate some of its properties such as subadditivity and homogeneity. As application of our result, we obtain the information inequality. In continuation, we establish some reverses of the operator entropy inequalities under certain conditions by using the Mond–Pe?ari? method.     

甲烷-二氧化碳重整制合成气与现代分析技术应用于探究性化工专业实验的设计与探索

以甲烷-二氧化碳重整制合成气为实例，设计探究性实验，将合成气的制备和现代分析技术应用于化工专业实验的教学实践中以提高学生的创新和实践能力。实验包括催化剂的制备，催化剂的性能评价和催化剂的表征等3大部分。采用工业最常用的浸渍法制备含有不同助剂的Ni/X/γ-Al2O3（X为Co，Fe，MgO，CeO2）催化剂，以甲烷-二氧化碳重整反应评价其催化性能，并采用XRD、H2-TPR、BET和TG对催化剂的微观结构进行表征。结合催化剂的性能评价结果和表征结果，探讨不同助剂对镍基催化剂性能的改善效果及机制。通过开设该实验，可以让学生了解化工学科的前沿知识以及现代分析技术的基本原理和用途，掌握专业的实验操作、数据处理和谱图绘制方法，提高学生的专业素养和综合能力。     

Force-field parametrization based on radial and energy distribution functions

We propose a novel force-field-parametrization procedure that fits the parameters of potential functions in a manner that the pair distribution function (DF) of molecules derived from candidate parameters can reproduce the given target DF. Conventionally, approaches to minimize the difference between the candidate and target DFs employ radial DFs (RDF). RDF itself has been reported to be insufficient for uniquely identifying the parameters of a molecule. To overcome the weakness, we introduce energy DF (EDF) as a target DF, which describes the distribution of the pairwise energy of molecules. We found that the EDF responds more sensitively to a small perturbation in the pairwise potential parameters and provides better fitting accuracy compared to that of RDF. These findings provide valuable insights into a wide range of coarse graining methods, which determine parameters using information obtained from a higher-level calculation than that of the developed force field. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.     

Binary neutron stars gravitational wave detection based on wavelet packet analysis and convolutional neural networks

This work investigates the detection of binary neutron stars gravitational wave based on convolutional neural network(CNN).To promote the detection performance and efficiency,we proposed a scheme based on wavelet packet(WP)decomposition and CNN.The WP decomposition is a time-frequency method and can enhance the discriminant features between gravitational wave signal and noise before detection.The CNN conducts the gravitational wave detection by learning a function mapping relation from the data under being processed to the space of detection results.This function-mapping-relation style detection scheme can detection efficiency significantly.In this work,instrument effects are con-sidered,and the noise are computed from a power spectral density(PSD)equivalent to the Advanced LIGO design sensitivity.The quantitative evaluations and comparisons with the state-of-art method matched filtering show the excellent performances for BNS gravitational wave detection.On efficiency,the current experiments show that this WP-CNN-based scheme is more than 960 times faster than the matched filtering.     

Determination of cobalt ion based on polyethyleneimine modified silver nanoclustersEI北大核心CSCD

PolyethyleneiminePEImodified silver nanoclustersAg NCswere synthesized through chemical reduction method by using PEI as stabilizerAgNO3 as silve source and ascorbic acid as reducing agentand it was applied to the detection of cobalt ions. It was found that the fluorescence of the silver nanoclusters was quenched by Co2+ for the aggregation of PEI-Ag NCs caused by the coordination between cobalt ion and amino group on polyethyleneimine. Under the optimized experimental conditionsthe fluorescence quenching degree of PEI-Ag NCs was piecewise linear with the concentration of cobalt ion in the range of 8.30×10-7-4.17×10-4 mol/L and the detection limit was 0.41 μµmol/L. © 2022, Youke Publishing Co.,Ltd. All rights reserved.     

Stokes equations under nonlinear slip boundary conditions coupled with the heat equation: A priori error analysis

In this work, we consider the heat equation coupled with Stokes equations under threshold type boundary condition. The conditions for existence and uniqueness of the weak solution are made clear. Next we formulate the finite element problem, recall the conditions of its solvability, and study its convergence by making use of Babuska–Brezzi's conditions for mixed problems. Third we formulate an Uzawa's type iterative algorithm that separates the fluid from heat conduction, study its feasibility, and convergence. Finally the theoretical findings are validated by numerical simulations.     

Some optimizations on detecting gravitational wave using convolutional neural network

This work investigates the problem of detecting gravitational wave (GW) events based on simulated damped sinusoid signals contaminated with white Gaussian noise. It is treated as a classification problem with one class for the interesting events. The proposed scheme consists of the following two successive steps: decomposing the data using a wavelet packet, representing the GW signal and noise using the derived decomposition coefficients; and determining the existence of any GW event using a convolutional neural network (CNN) with a logistic regression output layer. The characteristic of this work is its comprehensive investigations on CNN structure, detection window width, data resolution, wavelet packet decomposition and detection window overlap scheme. Extensive simulation experiments show excellent performances for reliable detection of signals with a range of GW model parameters and signal-to-noise ratios. While we use a simple waveform model in this study, we expect the method to be particularly valuable when the potential GW shapes are too complex to be characterized with a template bank.