聚酰胺胺(PAMAM)树形分子因其具有多分支对称性、内部含有大小不一的空腔以及多官能团表面等一系列结构特性,在分析化学、催化剂、涂料和薄膜技术、生物医学等领域有广泛的应用前景.本文以二乙醇胺为核,采用单边发散法合成了0.5至2代的P...聚酰胺胺(PAMAM)树形分子因其具有多分支对称性、内部含有大小不一的空腔以及多官能团表面等一系列结构特性,在分析化学、催化剂、涂料和薄膜技术、生物医学等领域有广泛的应用前景.本文以二乙醇胺为核,采用单边发散法合成了0.5至2代的PAMAM树形分子,并对所合成的树形分子进行了1 H NMR和FT-IR结构表征.展开更多
Discretization based on rough set theory aims to seek the possible minimum number of the cut set without weakening the indiscemibility of the original decision system. Optimization of discretization is an NP-complete ...Discretization based on rough set theory aims to seek the possible minimum number of the cut set without weakening the indiscemibility of the original decision system. Optimization of discretization is an NP-complete problem and the genetic algorithm is an appropriate method to solve it. In order to achieve optimal discretization, first the choice of the initial set of cut set is discussed, because a good initial cut set can enhance the efficiency and quality of the follow-up algorithm. Second, an effective heuristic genetic algorithm for discretization of continuous attributes of the decision table is proposed, which takes the significance of cut dots as heuristic information and introduces a novel operator to maintain the indiscernibility of the original decision system and enhance the local research ability of the algorithm. So the algorithm converges quickly and has global optimizing ability. Finally, the effectiveness of the algorithm is validated through experiment.展开更多
Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user ...Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user behavior and relationship data, to predict user participation behavior and topic development trends. Firstly, for the complex factors of user behavior, three dynamic influence factor functions are defined, including individual, peer and community influence. These functions take timeliness into account using a time discretization method. Secondly, to determine laws of individual behavior and group behavior within a social topic, a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of randora field and Markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior, but also grasp the development trends of topics.展开更多
文摘聚酰胺胺(PAMAM)树形分子因其具有多分支对称性、内部含有大小不一的空腔以及多官能团表面等一系列结构特性,在分析化学、催化剂、涂料和薄膜技术、生物医学等领域有广泛的应用前景.本文以二乙醇胺为核,采用单边发散法合成了0.5至2代的PAMAM树形分子,并对所合成的树形分子进行了1 H NMR和FT-IR结构表征.
文摘Discretization based on rough set theory aims to seek the possible minimum number of the cut set without weakening the indiscemibility of the original decision system. Optimization of discretization is an NP-complete problem and the genetic algorithm is an appropriate method to solve it. In order to achieve optimal discretization, first the choice of the initial set of cut set is discussed, because a good initial cut set can enhance the efficiency and quality of the follow-up algorithm. Second, an effective heuristic genetic algorithm for discretization of continuous attributes of the decision table is proposed, which takes the significance of cut dots as heuristic information and introduces a novel operator to maintain the indiscernibility of the original decision system and enhance the local research ability of the algorithm. So the algorithm converges quickly and has global optimizing ability. Finally, the effectiveness of the algorithm is validated through experiment.
基金supported by the National Key Basic Research Program(973 program)of China(No.2013CB329606)National Science Foundation of China(Grant No.61272400)+2 种基金Science and Technology Research Program of the Chongqing Municipal Education Committee(No.KJ1500425)Wen Feng Foundation of CQUPT(No.WF201403)Chongqing Graduate Research And Innovation Project(No.CYS14146)
文摘Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user behavior and relationship data, to predict user participation behavior and topic development trends. Firstly, for the complex factors of user behavior, three dynamic influence factor functions are defined, including individual, peer and community influence. These functions take timeliness into account using a time discretization method. Secondly, to determine laws of individual behavior and group behavior within a social topic, a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of randora field and Markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior, but also grasp the development trends of topics.
