pqr阶Cayley图的边-Hamilton性的一个注记

设G为限群，|G|=pqr,p,q,r为相异素数，M是G的一个生成集，作者证明了若M中含有p阶正规元，则Calyley图X（G，M）是边-Hamilton图     

图有分数因子的联结数和最小度条件

研究了图有分数因子的联结数和最小度条件．给出了一个图有分数1-因子的与联结数有关的充分必要条件．当k≥2时，给出了一个图有分数k-因子的两个充分条件，并证明了所得结果是最好可能的．     

主动数据库的规则终止性分析

提出一种基于进化图的规则终止性静态分析的方法，全面考虑了触发图、活化图和惰化图。这种基于进化图的分析方法比传统基于图的分析方法分析更为精确，之后提出了规则终止性分析算法并证明了其正确性。     

特殊的steinhaus图的同构问题

针对以双对称矩阵为邻接阵的steinhaus图．剖析它的邻接阵的结构，给出了这类图同构的判定条件。     

平图的transition多项式的Maple计算

将现有的计算方法改进得到了一种新的计算平图的transition多项式的方法。算法使用了圈置换的方法计算每个transition操作所产生的欧拉圈的数目。利用Maple软件编写出了该算法的程序,通过这个程序,可以实现任意一个平图的transition多项式的计算。     

简单正交多阵SOMA(4,n)的存在性

设整数k0,n≥2,简单正交多阵SOMA(k,n)是一个n×n的方阵A,它的每一个单元格包含kn-元集S的一个k-元子集且满足:1)S的每个元素在A的每一行和每一列恰好出现一次;2)S的每个2-元子集至多出现在A的一个单元格中.总结了SOMA(k,n)存在的最新结果,并且阐述了它和相互正交拉丁方之间的关系,同时通过直接构造的方法证明了n≥5,SOMA(4,n)的存在性,并且提出了进一步研究的问题.     

季节性应用复方丹参滴丸对单纯性高血压的疗效观察

目的探讨季节性应用复方丹参滴丸对单纯性高血压的影响,寻找出能够有效控制和预防单纯性高血压并发症发生的方法,提高治疗效果,提高患者生存质量的同时降低患者经济负担。方法对入选的单纯性高血压患者分成两组:治疗组和对照组。治疗组每年3～4月(春夏交替之际)及10～11月(秋冬交替之际)口服复方丹参滴丸+正常治疗),对照组采用正常治疗,即根据患者不同的情况,对患者进行个体化规范化的降压治疗;对治疗组和对照组进行2年随访,以其并发症和平均再住院次数为指标,进行统计学分析,以确定其临床实际意义。结果治疗组与对照组的高血压并发症的发生率比较,2年具有显著性差异(P0.05);两组平均再住院次数存在显著性差异(P0.05)。结论季节性应用复方丹参滴丸能够有效控制和预防单纯性高血压。     

The Self-Information Weighting-Based Node Importance Ranking Method for Graph Data

Due to their wide application in many disciplines, how to make an efficient ranking for nodes, especially for nodes in graph data, has aroused lots of attention. To overcome the shortcoming that most traditional ranking methods only consider the mutual influence between nodes but ignore the influence of edges, this paper proposes a self-information weighting-based method to rank all nodes in graph data. In the first place, the graph data are weighted by regarding the self-information of edges in terms of node degree. On this base, the information entropy of nodes is constructed to measure the importance of each node and in which case all nodes can be ranked. To verify the effectiveness of this proposed ranking method, we compare it with six existing methods on nine real-world datasets. The experimental results show that our method performs well on all of these nine datasets, especially for datasets with more nodes.     

Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph

An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfunctions and failures related to knowledge and data. In order to solve these problems, a fault diagnosis method based on the fusion of deep learning with a knowledge graph is proposed in this paper. Firstly, the knowledge rules of bearing data is used for entity extraction. Next, the multiscale optimized convolutional neural network (MOCNN) proposed in this paper is used for fault classification to achieve relationship extraction. Finally, the fault diagnosis graph of the bearing is constructed for fault-assisted decision-making as well as the detailed display of fault information. According to experiment analysis, the fault diagnosis model based on MOCNN proposed in this paper, which integrates the end-to-end convolutional neural network and the attention mechanism, still achieves an accuracy of 97.86% under the data set of 160 types of faults. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions and variable working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators, which is beneficial to practical use.     

就单摆问题引入大学物理教学新模式

单摆教学的过程中引入MATLAB数值计算的教学新模式,可以有效地改善大学物理教学的枯燥方式,有助于提高学生对大学物理课中抽象的、复杂的物理现象和规律的理解,也有助于促进对所学数学知识的实际应用.这种新教学模式是深化高等工程教育改革、加快新工科建设、促进创新型工科人才培养的有效手段.