陕西区域物流网络构建研究

区域物流网络是服务于区域经济活动的产物,是区域内外各种经济要素流通、聚集、扩散的基础和平台。构建区域物流网络要在明确物流网络服务对象的基础上,考虑区域地理形态、区域经济发展水平、区域物流发展水平三大影响因素,明确界定区域物流网络的节点、线路及联系。区域物流网络建设的重点是选择和确定物流节点,并要实现节点和线路的协调发展。本文提出了构建区域物流网络的基本思路,并以陕西省为研究对象,提出了陕西区域物流网络的＂3＋3＂层次结构,并对省域物流网络节点的选择及线路布局给出了具体建议。     

试论艺术教育的潜在课程及其育人功能

潜在课程的研究逐渐受到人们的关注.艺术教育的潜在课程是美的学习环境、美的教学方法、美的教师形象、美的人际关系、美的艺术作品等在潜移默化中给予学生的文化传递和教育影响.它在教育人、培养人的过程中具有独到的功能.     

工程训练中安全隐患的分析与预防

在高等教育工程训练教学中,对存在的安全隐患因素进行分析,通过对安全事故采取有效的预防措施,对提高教学质量,确保教学过程安全进行有很大帮助。     

Qualitative longitudinal analysis of symptoms in patients with primary and metastatic brain tumours

Summary.  Primary and metastatic brain tumour patients are treated with surgery, radiation therapy and chemotherapy. Such treatments often result in short- and long-term symptoms that impact cognitive, emotional and physical function. Therefore, understanding the transition of symptom burden over time is important for guiding treatment and follow-up of brain tumour patients with symptom-specific interventions. We describe the use of a hidden Markov model with person-specific random effects for the temporal pattern of symptom burden. Clinically relevant covariates are also incorporated in the analysis through the use of generalized linear models.     

Bayesian Inference and Forecasting in Dynamic Neural Networks with Fully Markov Switching ARCH Noises

We deal with one-layer feed-forward neural network for the Bayesian analysis of nonlinear time series. Noises are modeled nonlinearly and nonnormally, by means of ARCH models whose parameters are all dependent on a hidden Markov chain. Parameter estimation is performed by sampling from the posterior distribution via Evolutionary Monte Carlo algorithm, in which two new crossover operators have been introduced. Unknown parameters of the model also include the missing values which can occur within the observed series, so, considering future values as missing, it is also possible to compute point and interval multi-step-ahead predictions.     

Hidden Markov Mixture Autoregressive Models: Stability and Moments

This article introduces a parsimonious structure for mixture of autoregressive models, where the weighting coefficients are determined through latent random variables, as functions of all past observations. These latent variables follow a Markov model. We propose a dynamic programming algorithm for forecasting, which reduces the volume of calculations. We also derive limiting behavior of unconditional first moment of the process and an appropriate upper bound for the limiting value of the variance. Further more, we show convergence and stability of the second moment. Finally, we illustrate the efficacy of the proposed model by simulation.     

一种奇特的阐释现象：析索隐派红学之成因

在《红楼梦》问世迄今二百余年的复杂接受史中，索隐派的阐释活动构成了其重要的组成部分。这一脉虽屡经考证派等红学主流的严厉批判，然历二百年而绵延不绝，至上个世纪转加兴盛，这种奇特的阐释现象背后实有其深刻之学理动因。即1、以经史之眼观小说及民族主义的期待视野；2、《红楼梦》文本之特殊性提供的索隐空间；3、考证派自传说的缺陷。最后得出结论红学索隐派的兴起及不绝如缕，是《红楼梦》文本外部、内部各种合力推动的结果，自有其学理上的必然性。并由此可以预言，对《红楼梦》进行索隐式解读的阐释现象，在相当长的时间内还不会消亡。     

A comparison of network sampling designs for a hidden population of drug users: Random walk vs. respondent-driven sampling

Both random walk and respondent-driven sampling (RDS) exploit social networks and may reduce biases introduced by earlier methods for sampling from hidden populations. Although RDS has become much more widely used by social researchers than random walk (RW), there has been little discussion of the tradeoffs in choosing RDS over RW. This paper compares experiences of implementing RW and RDS to recruit drug users to a network-based study in Houston, Texas. Both recruitment methods were implemented over comparable periods of time, with the same population, by the same research staff. RDS methods recruited more participants with less strain on staff. However, participants recruited through RW were more forthcoming than RDS participants in helping to recruit members of their social networks. Findings indicate that, dependent upon study goals, researchers' choice of design may influence participant recruitment, participant commitment, and impact on staff, factors that may in turn affect overall study success.     

A hierarchical model for space–time surveillance data on meningococcal disease incidence

Summary. We describe a model-based approach to analyse space–time surveillance data on meningococcal disease. Such data typically comprise a number of time series of disease counts, each representing a specific geographical area. We propose a hierarchical formulation, where latent parameters capture temporal, seasonal and spatial trends in disease incidence. We then add—for each area—a hidden Markov model to describe potential additional (autoregressive) effects of the number of cases at the previous time point. Different specifications for the functional form of this autoregressive term are compared which involve the number of cases in the same or in neighbouring areas. The two states of the Markov chain can be interpreted as representing an 'endemic' and a 'hyperendemic' state. The methodology is applied to a data set of monthly counts of the incidence of meningococcal disease in the 94 départements of France from 1985 to 1997. Inference is carried out by using Markov chain Monte Carlo simulation techniques in a fully Bayesian framework. We emphasize that a central feature of our model is the possibility of calculating—for each region and each time point—the posterior probability of being in a hyperendemic state, adjusted for global spatial and temporal trends, which we believe is of particular public health interest.     

《卧虎藏龙》里的生态玄机：从禅宗公案看电影

由禅宗修行经验出发,强调无我、非二元性、无常性以及开放性的＂禅宗生态＂观,可以运用在电影文本分析上。以李安的《卧虎藏龙》作为个案讨论,《卧》里潜藏的公案结构挑战了传统以剧情为主的观看电影的模式。片中公案式的电影呈现与观看方式将观众从虚构的叙述时间里最终带回到充满生态性的当下时刻去。因此,《卧》可以被视为＂禅宗生态电影＂的范例。