piu

The second study wants to study PIU ' influencing factors and construct the path analysis model .

研究二试图探讨病理性互联网使用的影响因素，并建构大中学生的PIU影响因素路径模型。

The behavior of pathological internet use seems related to the change of time cognitive structure with PIU individuals .

病理性互联网使用行为的出现可能与PIU个体时间认知结构改变相关，因此，考察PIU个体的时间认知加工特点很有必要。

The problems in the current research , methods to defend PIU and the future research field were also pointed out .

最后该文指出了本研究的实践意义与未来研究可以拓展的领域。

Objectives To study the relevant factors and countermeasures on the pathological internet use ( PIU ) among college students in Changsha .

目的了解长沙地区上网大学生中病理性互联网使用（PathologicalInternetUse，简称PIU）的影响因素，并针对影响因素提出防治对策。

Sex and grade are significant predictors of PIU .

性别和年级均是病理性互联网使用的有效预测变量。

In recent years , Internet Addiction or Pathological Internet Use ( PIU ) has received more and more concerns .

近年来网络成瘾现象受到了越来越多的关注，在研究中网络成瘾大多采用病理性互联网使用（PathologicalInternetUse，PIU）一词，主要强调对互联网的非理性或不当使用。

Neuroticism moderates the relationships between internet social , recreational , business service preference and PIU .

青少年神经质人格特征与互联网社交、娱乐和信息服务偏好在对PIU的影响上存在显著的交互作用，但与互联网交易服务偏好的交互作用不显著。

While not online , the PIU individuals have the same time cognition processing process with the non-PIU individual .

在非互联网使用条件下，PIU个体与非PIU个体存有相同的时间认知加工过程。

Many researchers considered the main causes of PIU were the extreme use of internet or misuse of some internets functions .

很多研究者认为互联网使用过度或不当是病理性使用互联网的主要原因。

The first study wants to study the college and middle school students ' Internet behaviors and the relations between PIU and personal details and Internet behaviors .

研究一试图了解大中学生的具体网络使用情况，以及不同的个人情况、网络使用情况与病理性使用间的关系。

The PIU rates are 0.8 % of higher primary school students and 1.7 % of the junior middle school students , which are lower than the existing studies .

小学高年级学生PIU发生率为0.8%，初中生PIU发生率为1.7%，低于已有的研究报告。

Objective To study the prevalence and the influenced factors of the pathological internet use ( PIU ), and to provide theoretical basis for PIU prevention and control .

目的了解高校上网大学生病理性互联网使用（PIU）的患病率及其影响因素，为预防和控制大学生PIU的发生提供理论依据。

Logistic regression analysis indicated that PIU was correlated to the emotional stability , negative events , social support and social utility , the differences had statistical meaning .

多因素回归分析显示：病理性网络使用组与对照组在情绪稳定性、负性生活事件、社会支持、社会支持利用度方面，差别有统计学意义。

Objective To explore the social and psychological risk factors of pathological internet use ( PIU ) among undergraduates , and to provide evidence for the prevention against PIU .

目的探讨影响大学生病理性网络使用（PIU）的社会心理危险因素，为大学生病理性网络使用的防治和干预提供依据。

Based on the past researches , this paper analyzes and reviews the PIU , s definition and measurement . Finally , how to conceptualize and measure PIU is discussed with the context of Chinese culture .

该文对病理性使用互联网已有研究的概念的界定与测量问题进行了分析与整合，讨论了在中国社会文化背景下如何界定与测量病理性使用互联网。

The application of common PIU control systems ,, neuron control system and expert control system in the rectification of vinyl chloride is introduced in brief , and the better usage effect of predicting function control system is introduced in detail .

简单介绍了常规PIU控制系统、神经元控制系统、专家控制系统在氯乙烯精馏工艺中的应用情况，详细介绍了应用效果较好的预测函数控制系统。

PIU and non-PIU Participants are selected at random in both experiments . In experiment ⅰ, we use network game task , operate the length of target duration , and investigate the difference of duration estimation between different individuals during online .

实验一分别选取PIU和非PIU被试，采用网络游戏任务，操纵目标时距的长度，考察不同个体在互联网使用条件下的时距估计差异。

One-factor analysis show that significant related factors that emerged as common correlates with PIU were sex , present live-condition approval , study pressure , bad love , the level of mental health condition , personality characteristic , self-esteem and social support etc.

单因素分析结果发现：性别、生活现状满意度、学习压力、不良爱好、心理健康状况、人格特质、自尊及社会支持等个人和社会相关因素对病理性网络使用有统计学意义。

So a view update algorithm called PIU algorithm which grounds on the dynamic data freshness priority is presented . But this algorithm only takes dynamic new-old extent of whole view data into account , thus there still exists a lot of limitations .

于是有人提出了根据动态的数据新鲜度优先级进行排队的视图更新算法PIU算法，但是这种算法仅仅考虑了视图整体数据的动态新旧度，即平均时间因素，所以仍存在许多缺陷。

Objective To investigate the distribution of IgG subclass of anti myeloperoxidase ( MPO ) antibodies , a kind of antineutrophil cytoplasmic autoantibody ( ANCA ), in sera from patients with propylthiouracil ( PIU ) induced vasculitis .

目的观察丙基硫氧嘧啶（PTU）引起的抗中性粒细胞胞浆抗体（ANCA）相关小血管炎患者活动期和缓解期血清中抗MPO抗体IgG亚型的分布并探讨其意义。

Base on this algorithm , an improved PIU algorithm which considers two other factors of user 's access probability and data complexity is presented to fetch up these shortages . And it effectively improves the user-oriented , real-time and consistency problem in view update of mobile database .

为了弥补这些缺陷，在这基础上，引入用户的访问概率和数据的复杂度两个因素，提出改进的PIU算法，有效地改善移动数据库视图更新的面向用户性、实时性和一致性。