植物的象征手法在颐和园中的运用

在中国古典园林中,植物因能改善环境、又令人赏心悦目而成为园林要素之一,同时,植物还是表达造园者思想和意志的重要载体,承载了多元的精神内涵,并被赋予一定的象征意义。植物的象征意义能创造园林意境,提升园林艺术的感染力。以中国古典园林颐和园为例,通过实地调查及文献资料分析,归纳了植物的象征手法在颐和园中的总体运用,揭示了颐和园中植物配植的文化内涵。结果表明,造园者通过运用植物的象征意义寄托美好愿望。这给现代景观设计以启示,即通过运用古典园林艺术精髓,以期形成具有自然与人文兼容并蓄的可持续发展的创新景观。     

世界核电布局走向及对我国核电布局的启示

论文从福岛核电站外部自然事件的地震、海啸叠加超设计基准引起的7级核事故,提出了核电布局、选址对核电站安全的重要性;分析了世界核电分布的地区、国家、堆型、发电的现状,揭示了未来核电发展布局的走向;阐明了核电站布局、选址的八大原则;重点探讨了世界各国滨海与滨河（沿海与内陆）核电站布局,并以法国实例介绍了法国内陆核电站布局状况;最后,深入探讨了我国核电站布局,认为,沿海、东部是我国核电布局的重点,我国建成、在建的45座核电机组均布局在沿海,为滨海电站,东部的黑龙江、吉林、辽宁、江苏、山东、福建、广东、广西均可建滨河、滨湖核电站;中部崛起急需核电,是我国内陆核电站的重点,是我国核电大国、核电强国的标志;西部重点发展水能、风能、太阳能,而核电、火电作为基本负荷电源,支撑上述清洁能源的发展,有几个省市需要独立发展核电.     

Robust Structural Equations for Designing and Monitoring Strategic International Facility Networks

Using predictive global sensitivity analysis, we develop a structural equations model to abstract from the details of a large‐scale mixed integer program (MIP) to capture essential design trade‐offs of global manufacturing and distribution networks. We provide a conceptual framework that describes a firm's network structure along three dimensions: market focus, plant focus, and network dispersion. Normalized dependent variables are specified that act as proxies for a company's placement into our conceptual network classification via the calculation of just a few key independent variables. We provide robust equation sets for eight cost structure clusters. Many different product types could be classified into one of these groups, which would allow managers to use the equations directly without needing to run the MIP for themselves. Our numerical tests suggest that the formulas representing the network structure drivers—economies of scale, complexity costs, transportation costs, and tariffs—may be sufficient for managers to design their strategic network structures, and perhaps more importantly, to monitor them over time to detect potential need for adjustment.     

Decision Making in Evidence‐Based Practice: Science and Art

In this article the author reviews problems in the implementation of evidence‐based practices (EBP) and concludes that these derive not only from numerous practical concerns, but also from fundamental epistemological issues. These include an antiquated understanding of the scientific method, involving types of naive inductivism and rationalism. The central argument of this article is that recent developments in the field of naturalistic decision making provide a critical framework for understanding the problems in the implementation of EBP, as well as possible avenues for their resolution. Developments that are reviewed include the use of heuristics, decision trees, case‐based reasoning, as well as an example of a generalized model of expert decision making. Such models increasingly combine situational awareness with critical thinking and metacognitive capacities. They promise to more effectively focus the application of rational and empirical problem‐solving methods, such as EBP, on the most complex problems.     

突发事件下的医院应急群决策模型研究*

针对突发事件下的医院应急管理问题,进行了医院应急群决策模型研究。考虑到医院应急决策者心理行为特征的偏向性,将病人应急方案的选择设计成一个以医院决策者信息与病人集结信息之间距离最小为目标的非线性规划模型。根据病人的伤重程度和应急资源使用度的损益值,获得医院应急情景下的综合价值,根据累积前景理论,通过计算区间概率,获得医院应急情景下不同应急方式的累积前景值。使用模拟植物生长算法求解,莸得病人的最优集结信息。案例分析表明,该决策过程可以使得突发事件下医院应急管理方案达到最优。     

The Role of Classification Trees and Expert Knowledge in Building Bayesian Networks: A Case Study in Medicine

In clinical research an early and prompt detection of the risk class of a new patient may really play a crucial role in determining the effectiveness of the treatment and, consequently, achieving a satisfying prognosis of the patient's chances. There exists a number of popular rule-based algorithms for classification, whose performances are very attractive whenever data of large number of patients are available. However, when datasets only include data of a few hundred patients, the most common approaches give unstable results and developing effective decision-support systems become scientifically challenging. Since rules can be derived from different models as well as expert knowledge resources, each of them having its advantages and weaknesses, this article suggests a “hybrid” approach to address the classification problem when the number of patients is too small to effectively use a single technique only. The hybrid strategy was applied to a case study and its predictive performance was compared with performances of each single approach: due to the seriousness of a misclassification of high-risk patients, special attention was paid on the specificity. The results show that the hybrid strategy outperforms each single strategy involved.     

专业课教师素质与教学质量

在高等学校推行教学评估,全面提高教学质量的社会大背景下,全方位提高专业教师的素质,充分激发学生学习热情,提高教育质量是我们目前急需解决的课题。本文结合植物保护专业的培养目标,阐述了植物病害流行学课教师应具备的素质以及植物保护专业提高教学质量的方法与途径。     

The Rise of Statistical Phylogenetics

This paper provides a review of the many applications of statistics within the field of phylogenetics, that is, the study of evolutionary history. The reader is assumed to be a statistician rather than a phylogeneticist, so some background is given on what phylogenetics is, along with a brief history of different approaches to phylogenetic inference. The latter half of the paper focuses on a series of open statistical problems in the field with the aim of encouraging more statisticians to engage with this fascinating area of research.     

A Note on Distinguishing Random Trees Populations

ABSTRACT

Several new results are presented for a class of univariate distributions for which the maximum likelihood estimate of the population mean is the sample mean. It is shown that the convolution of any two such distributions also belongs to this class of functions. It is also shown that the marginal distribution for the sample mean captures all of the Fisher information for the population mean contained in the full distribution. Parameters orthogonal to the mean are found for special cases of these distributions. If the distribution is conditioned on the sample mean, the conditional distribution depends on the parameters only through parameters orthogonal to the mean.     

Detection of multiple change-points in multivariate data

The statistical analysis of change-point detection and estimation has received much attention recently. A time point such that observations follow a certain statistical distribution up to that point and a different distribution – commonly of the same functional form but different parameters after that point – is called a change-point. Multiple change-point problems arise when we have more than one change-point. This paper develops a method for multivariate normally distributed data to detect change-points and estimate within-segment parameters using maximum likelihood estimation.