先秦的“法治”思想和实践考量

先秦法家在与儒家“礼治”的争论中提出“法治”思想,并与战国时期富国强兵的国家策略结合,变法图存的“法治”实践此起彼伏。其中最为成功的是秦国的商鞅变法,秦国由此迅速崛起并最终吞并六国,建立起统一的封建国家。秦帝国的建立是“法治”思想和实践的里程碑,大秦将集权专制和“法治”相互糅合,将法家重刑思想发展到极端,走向了崇尚暴力和滥刑滥杀的野蛮恐怖境地,导致天下怨叛,秦王朝二世而亡,君权至上最终将法治引向了集权专制的死胡同。     

救赎：寻求民族文化的出路——解读莫里森的《柏油孩子》

高速铁路具有高度集成、高精度的技术特点，运营过程中经受列车质量、速度、密度等多种因素影响，地理位置因素往往是影响铁路设备状态演变的决定性因素。采用网格化管理技术可将空间上连续分布的管理对象划分成较小的单元网格，有利于从空间位置角度研究管理对象状态的变化规律。随着信息系统技术、大数据技术的迅猛发展，基于位置而不是基于专业更符合高速铁路的管理需求，网格化管理技术给高速铁路管理带来了新的视角。     

体制变迁与供需失衡:铁路春运运力不足的结构性困境研究

铁路春运"一票难求"的表面原因是铁路运力不足,而运力不足的背后是深层次的社会问题,是人口流动背景下的春运客运需求激增、垄断体制下的铁路发展困境,以及因春节情结和公众选择偏好引发的放大效应等结构性困境。     

Simulation techniques for generalized Gaussian densities

This contribution deals with the Monte Carlo simulation of generalized Gaussian random variables. Such a parametric family of distributions has been proposed in many applications in science to describe physical phenomena and in engineering, and it seems to be also useful in modelling economic and financial data. For values of the shape parameter α within a certain range, the distribution presents heavy tails. In particular, the cases α=1/3 and α=1/2 are considered. For such values of the shape parameter, different simulation methods are assessed.     

试论环境约束下的江苏省重工业发展之路

本文对江苏省主要重工业污染排放情况进行了因素分解,考察了江苏省的地方政策在重工业行业污染控制方面的有效性,并分析了污染控制目标下的经济效益,以期为产业政策的调整提供依据。     

铁路总价承包合同管理存在的主要问题及对策

结合我国铁路总价承包合同执行现状，对铁路合同专用条款中存在的承发包双方法律地位不对等、工程款拨付比例不切实际、总承包风险费严重不足等主要不合理内容进行剖析，说明不合理的原因。对发包人处理正负量差的方式、工期调整产生的赶工费不能全额解决及部分变更设计在源头上得不到认可等在合同执行过程中存在的问题进行研究。简单分析产生不合理条款和合同执行不到位的主要原因。针对这些不合理条款，提出应对策略。     

党政机构微博在突发事件中传播效果的影响因素及策略

在分析铁路货运量预测方法的基础上，针对标准BP神经网络的不足，提出改进的BP神经网络预测模型。首先,利用动态陡度因子来改变激励函数的陡峭程度，以此来得到更好的激励函数响应特征以及更好的非线性表达能力；其次，利用附加动量因子，通过对以前经验的积累，既降低了神经网络对误差曲面的局部细节敏感特性，又较好的遏制了神经网络易于限于局部最小的缺陷；最后，采取改变学习率的方法，给定一个较大的学习率初始值，在学习的过程中学习率不断减小，网络最终趋于稳定。改进BP算法既可以得到更优的解，还能够缩短训练时间。利用全国铁路货运     

高铁媒体与旅游城市品牌传播——以京沪高铁为例

高速铁路的正式开通运行,为旅游城市品牌的传播开启了一个新的时代。本文以京沪高铁为例,阐述了我国城市品牌内涵及现状,通过高铁媒体传播途径优势的分析及旅游城市广告投放研究,论证了高铁媒体对旅游城市形象的提升及品牌的有效传播起着不可或缺的作用,并据此论证了高铁媒体对比其他大众媒体在旅游城市品牌传播过程中的显著优势。     

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.     

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring

A Bayesian statistical temporal‐prevalence‐concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch‐specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within‐batch and between‐batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.