指人名词充当主语的名词性谓语句

名词直接充当谓语是汉语所特有的现象,我们称这种句式为名词性谓语,亦即“ＮＰ１—ＮＰ２。”句式。并非所有的名词性词语都能直接作谓语句,所以,本文着重讨论ＮＰ２所特有的结构、意义类型及其述谓性特征,从而对“ＮＰ１－ＮＰ２”句式作出一定的描写和解释。     

帕金森病患者住院费用相关因素分析

为分析并探讨帕金森病患者住院费用的影响因素,该研究选取2009年1月至2015年12月在南京医科大学第一附属医院帕金森病住院患者320例,其中接受脑深部电刺激器（DBS）置入手术患者37例,非手术患者283例,根据H&Y（Holehn &Yahr）分级、年龄以及合并感染情况等对住院费用及住院时间进行分析比较。结果发现,非手术组帕金森患者年龄、合并感染情况及H&Y分级与总住院费用及住院时间显著相关。接受DBS手术组患者住院费用较晚期非手术患者显著增高（t=7.40,P<0.001）。高龄、合并感染及帕金森病晚期增加非手术帕金森病患者住院费用和住院时间。     

新媒体环境下高校思想政治教育创新平台建设研究——基于对高校官方微信公众平台的实证分析

移动互联时代,国内高校面临的传播环境正在发生深刻的变化,思想政治教育工作也因此面临着机遇与挑战并存的现实.在新形势下,高校官方微信公众平台的建设对于思想政治教育工作是一种有益的探索和尝试.高校思想政治教育工作者应积极利用微信媒体平台,把握功能定位与受众需求,坚持内容与形式并重,明确议题和价值的框架建构,注重团队培养与资源整合,建设创新型的育人平台,进一步提高思想政治教育的时效性.     

基于自组网技术的燃气无线抄表系统研究

为解决现阶段燃气无线抄表系统数据采集中无线网络不稳定、路由协议复杂、自修复能力薄弱等问题，提出并设计了一种基于cc1101无线传送模块的树形拓扑结构无线自组网系统。系统中主要对数据采集层路由协议和硬件实现进行了研究和设计，分析了该燃气无线抄表系统与传统燃气无线抄表系统的不同之处，设计了该系统数据采集层网络传输协议和总体结构。无线节点建在穿透力强的433 MHz的频率下进行通信，保证了系统自组网的稳定快速。     

双线自闭区段客货列车速度比对扣除系数的影响分析

目前扣除系数的计算方法主要为分析法与模拟法，扣除系数的常见影响因素有客货列车速度比、区段均衡度、追踪间隔时分等。通过模拟法进行不同客货列车速度比下的运行图铺画，得出客货列车速度比对扣除系数的影响规律分析。实验表明：随着客货速度比的增加，扣除系数将逐渐变大，据此建议在相关双线客货混跑区段，选取合适的客货速度比，以减小对运行图通过能力的影响     

固定效应部分线性变系数面板模型的快速有效估计

本文将最小二乘支持向量机(LSSVM) 和二次推断函数法(QIF) 相结合，为个体内具有相关结构的固定效应部分线性变系数面板模型提供了一种新的快速估计方法；在一定的正则条件下，论证了参数估计量的渐近正态性和非参数估计量的收敛速度；采用Monte Carlo模拟考察了估计方法在有限样本下的表现并将估计技术应用于现实数据分析。该方法不仅保证了估计的有效性和统计推断力，而且程序运行速度得到较大幅度提升。     

Stochastic properties of the mixed accelerated hazard models

The accelerated hazard model in survival analysis assumes that the covariate effect acts the time scale of the baseline hazard rate. In this paper, we study the stochastic properties of the mixed accelerated hazard model since the covariate is considered basically unobservable. We build dependence structure between the population variable and the covariate, and also present some preservation properties. Using some well-known stochastic orders, we compare two mixed accelerated hazards models arising out of different choices of distributions for unobservable covariates or different baseline hazard rate functions.     

Robust Bayesian nonlinear mixed‐effects modeling of time to positivity in tuberculosis trials

Early phase 2 tuberculosis (TB) trials are conducted to characterize the early bactericidal activity (EBA) of anti‐TB drugs. The EBA of anti‐TB drugs has conventionally been calculated as the rate of decline in colony forming unit (CFU) count during the first 14 days of treatment. The measurement of CFU count, however, is expensive and prone to contamination. Alternatively to CFU count, time to positivity (TTP), which is a potential biomarker for long‐term efficacy of anti‐TB drugs, can be used to characterize EBA. The current Bayesian nonlinear mixed‐effects (NLME) regression model for TTP data, however, lacks robustness to gross outliers that often are present in the data. The conventional way of handling such outliers involves their identification by visual inspection and subsequent exclusion from the analysis. However, this process can be questioned because of its subjective nature. For this reason, we fitted robust versions of the Bayesian nonlinear mixed‐effects regression model to a wide range of TTP datasets. The performance of the explored models was assessed through model comparison statistics and a simulation study. We conclude that fitting a robust model to TTP data obviates the need for explicit identification and subsequent “deletion” of outliers but ensures that gross outliers exert no undue influence on model fits. We recommend that the current practice of fitting conventional normal theory models be abandoned in favor of fitting robust models to TTP data.