S_m∨P_n和S_m∨C_n的边共色数

利用Lowell Beineke和Richard Ringeisen给出的边共色数的界,研究得到了Sm∨Pn和Sm∨Cn的边共色数.     

严格k—连通图的一个结构特征

本文证明了严格ｋ－连通图具有如下的结构特征，设ｕ是严格ｋ－连通图Ｇ的一个临界点，则ｕ在Ｇ中的邻点的集事与Ｇ－ｕ的任何一个片的交集是非空的，并由此得到一个从严格（ｋ－１）的连通图构造严格ｋ－连通图的方法。     

李型单群U3(q)的非交换图刻画

假设U3(q)是一李型单群,其中q=pn,p是素数,n是自然数.若▽(G)≌▽(U3(q)),则 G≌U3(q).从而,对于李型单群U3(q),AAM猜想是成立的.     

Analysis of Reliability of the Harary Graph

When the vertices fail independently with each other by a constant probability,the reliability of a sraph is defined to be the probability that the graph results from deleting thefailed venices together with the incidence edges is connected. The Harary graph,a class of graphswith the best connectivity is considered in this paper,the bound of it's reliability is obtained,and theasymptotic property of reliability is also analysed.     

基于深度学习的作物病虫害可视化知识图谱构建

针对作物病虫害领域存在实体关系交叉关联、多源异构数据聚合能力差、知识共享困难等问题，利用知识图谱以结构化的形式描述实体间复杂关系的优势，该研究提出了一种基于深度学习的作物病虫害知识图谱构建方法。该方法在领域本体的基础上，以一种与领域语料相适应的新标注模式实现实体和关系的联合抽取。将实体和关系抽取任务转化为序列标注问题，对实体和关系进行同步标注，有效提高标注效率；为了解决重叠关系抽取问题，直接对三元组建模而不是分别对实体和关系建模，通过标签匹配和映射即可获得三元组数据。利用来自转换器的双向编码器表征量（Bidirectional Encoder Representations from Transformers，BERT）-双向长短期记忆网络（Bi-directional Long-Short Term Memory，BiLSTM）+条件随机场（Conditional Random Field，CRF）端到端模型进行试验，结果表明效果优于基于普通标注方式的流水线方法和联合学习方法中的卷积神经网络（Convolutional Neural Networks，CNN）+BiLSTM+CRF、BiLSTM+CRF等经典模型，F1得分为91.34%。最后，将抽取到的知识存储到Neo4j图数据库中，直观地反映知识图谱的内部结构，实现知识可视化和知识推理。该研究构建的知识图谱可为作物病虫害智能问答系统、推荐系统、智能搜索等下游应用提供高质量的知识库基础。     

麦蚜取食行为特殊刺探电位波形图(EPG)研究初报

研究了麦长管蚜(Sitobionavenae.Fab.)、禾谷缢管蚜(RhopalosiphumPadi)在不同小麦上的刺探行为,发现了一些特殊的刺探电位波形(EPG),即点G波,E0波和两种F波,并就其可能的生物学意义进行了探讨。初步认为,点G波是蚜虫在刺探途径阶段(C波阶段)的试探性主动吸食过程;E0波是蚜虫口针即将到达韧皮部时短暂的判断、休息和适应过程;一种F波是蚜虫在木质部主动吸食受阻的一种过渡波;另一种F波与蚜虫口针在植物组织中遇阻后的重复回撤与刺探行为有关。结果表明,EPG波形不仅有其稳定性,同时也有其多样性。     

两个新的正整数分拆恒等式

正整数的分拆与许多计数问题有着密切的关系,并且关于正整数的分拆产生了许多重要的恒等式,但很多正整数的分拆恒等式常以有序分拆或无序分拆单方面讨论。将正整数的有序分拆和无序分拆联系起来,给出了两个新的与正整数的有序分拆和无序分拆相关的恒等式,并利用组合方法给出了证明。     

在Autocad平台建立具有GIS特征的森林图形系统

该文从森林图形库的概念和基本特征出发 ,完成森林图形库图层的标准设计和森林图形中小班多边形的封闭 .通过对森林图形的图元访问 ,实现森林图形”归一化” .在”归一化”森林图形上 ,实现其与资源数据库互访的动态管理     

满足某些性质的树因子

设Ｇ是一个图ｆ是定义在Ｖ（Ｇ）上的非负整数函数，Ｇ的一个支撑子图Ｆ称为Ｇ的一个ｆ因子，若对任意的ｖ∈Ｖ（Ｇ），有ｄＦ（ｖ）＝ｆ（ｖ）．若Ｔ是一个连通无圈图，则称Ｔ是一棵树。在本文中，我们对满足某些性质的树因子进行了研究，分别给出了一棵树存在ｆ－因子过指定的一条边或不过指定一条边的充要条件。     

Network thinking in riverscape conservation – A graph-based approach

Graph theoretic approaches have received increased interest recently in landscape planning and conservation in the terrestrial realm, because these approaches facilitate the effective modelling of connectivity among habitats. We examined whether basic principles of graph theory can be extended to other ecosystems. Specifically, we demonstrate how a network-based context can be used for enhancing the more effective conservation of riverine systems. We first show how to use graph theoretic techniques to model riverscapes at the segment level. Then we use a real stream network (Zagyva river basin, Hungary) to examine the topological importance of segments in maintaining riverscape connectivity, using betweenness centrality, a commonly used network measure. Using the undirected graph model of this riverscape, we then prioritize segments for conservation purpose. We examine the value of each of the 93 segments present in the Zagyva river basin by considering the conservation value of local fish assemblages, connectivity and the size of the habitat patches. For this purpose we use the ‘integral index of connectivity’, a recently advocated habitat availability index. Based on the results the selection of the most valuable habitat segments can be optimized depending on conservation resources. Because of their inherent advantage in the consideration of connectivity relationships, we suggest that network analyses offer a simple, yet effective tool for searching for key segments (or junctions) in riverscapes for conservation and environmental management. Further, although the joint consideration of aquatic and terrestrial networks is challenging, the extension of network analyses to freshwater systems may facilitate the more effective selection of priority areas for conservation in continental areas.