成人获得性维生素K依赖性凝血因子缺乏症伴多部位出血1例

患者,男,62岁,因“反复牙龈出血,血尿3月余,腹痛,便血3 d。“为主诉入院。患者缘于入院3月余前无明显诱因始出现反复牙龈出血,偶伴血尿,无尿频、尿急、尿痛;也无发热、畏冷;曾在当地治疗,好转后出院,入院3 d前无明显诱因出现腹痛,脐周明显,伴排稀水便,初为黑色,后为鲜红色,2~3次/d,无恶心、呕吐,也无返酸、嗳气。查体:体温36.2℃、脉搏96次/MIn、呼吸20次/MIn、血

A Lexical-chain Based Filtering Model And Application

随着因特网的迅猛发展，大量的信息朝着人们扑面而来，如何才能得到用户真正需求的信息已成为越来越突出的问题。对文本进行过滤是人们经常采用的一种文本管理方法。 本文提出了一个基于词汇链的自然语言文本自动过滤模型，该模型以《WordNet》为主要的概念关系知识源，把文本中的词按照关系组成词汇链的形式，来表示文本内容，从而利用文本的这些词汇链进行文本过滤。该模型概述如下：文本过滤系统分为训练模块和过滤模块；不论是训练集中的文本还是测试集中的文本，我们都对其中的关键词间的关系进行分类，并按照某种关系将文本的关键词集聚起来形成词汇链以表示文本。系统开始工作后，首先利用训练集提供的文本进行学习，逐步精确到用...With the rapid growth of Internet, lots of information surges toward us. It has been an urgent problem on how to manage all the information we have gotten. Text Filtering(TF) is an important method which is been generally used to deal with this problem by people. This paper presents a new automatic natural language text fitering module based on lexical chain. This module uses WordNet as the main...学位：工学硕士院系专业：计算机与信息工程学院计算机科学系_计算机应用技术学号：20002800

A Text Filtering Module Based on Concept-expanded

该文在介绍文本过滤的背景及向量空间模型的同时,提出了基于语义词典对用户模板进行扩充的文本过滤模型,该模型首先对文本进行分析,把文本表示成向量空间中的向量形式,在形成用户初始模板之后,对用户模板进行同义词扩充,形成扩充后的用户模板,以此模板来进行文本过滤。在用户反馈的基础上,自适应地修改该模板,以适应用户变化的需求及改善系统过滤性能。实验表明,这样的确可以提高系统覆盖面,提高系统效率。In this paper we first give some information about the text filtering and VSM(Vector Support Machine),then we introduce a model that build a concept-expanded-based profile.We use this concept-expanded profile to sift the information which may be of the user's interest.In the model,the profile is represented as a vector in the vector space.We use the synsets in WordNet to expand the profile automatically.The enrichment of profile with semantically-related terms can enhance recall,as it permits matching relevant text that could not contain any of the old profile terms.A filtering system should be able to adapt to user's interest changes,so we automatically modify the user model to recognize the changes.Experimental results show that the methods can improve the text filtering performance.国家863高技术研究发展计划项目(编号:2001AA114110);; 福建省科技计划重点项目(编号:2001H023

A Text Filtering Module Based on Lexical Chain

在介绍文本过滤的背景及传统基于关键词的向量空间方法不足之处的同时,引入了词汇链的概念,提出了基于词汇链表示文本的文本过滤模型,该模型首先对文本进行分析,把文本表示成词汇链的形式,在形成用户初始模板之后,以此模板来进行文本过滤。在用户反馈的基础上,自适应地修改该模板,以适应用户变化的需求及改善系统过滤性能,实验表明,这样的确可以提高系统精度。In this paper we first give some information about the text filtering and the defects in VSM(Vector Support Machine),then we introduce the concept of lexical chain,give a model that build a profile based on lexical chain.We first analyse the text,then express the text with lexical chain.We use this lexicalchained profile to sift the information which may be of the user's interest.A filtering system should be able to adapt to user's interest changes,so we automatically modify the user model to recognize the changes.Experimental results show that the methods can improve the text filtering performance.国家"863"计划项目(2001AA114110);; 福建省科技计划重点项目(2001H023

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies