Variability and Uncertainty Analysis of the Cross‐Contamination Ratios of Salmonella During Pork Cutting

The transfer ratio of bacteria from one surface to another is often estimated from laboratory experiments and quantified by dividing the expected number of bacteria on the recipient surface by the expected number of bacteria on the donor surface. Yet, the expected number of bacteria on each surface is uncertain due to the limited number of colonies that are counted and/or samples that can be analyzed. The expected transfer ratio is, therefore, also uncertain and its estimate may exceed 1 if real transfer is close to 100%. In addition, the transferred fractions vary over experiments but it is unclear, using this approach, how to combine uncertainty and variability into one estimate for the transfer ratio. A Bayesian network model was proposed that allows the combination of uncertainty within one experiment and variability over multiple experiments and prevents inappropriate values for the transfer ratio. Model functionality was shown using data from a laboratory experiment in which the transfer of Salmonella was determined from contaminated pork meat to a butcher's knife, and vice versa. Recovery efficiency of bacteria from both surfaces was also determined and accounted for in the analysis. Transfer ratio probability distributions showed a large variability, with a mean value of 0.19 for the transfer of Salmonella from pork meat to the knife and 0.58 for the transfer of Salmonella from the knife to pork meat. The proposed Bayesian model can be used for analyzing data from similar study designs in which uncertainty should be combined with variability.     

牡蛎壳污染的治理思路及资源化综合利用对策——以福建省惠安县为例

通过对惠安县牡蛎生产及牡蛎壳污染情况的调查,分析了牡蛎壳污染的主要危害,提出了牡蛎壳综合治理的基本思路,即强化管理、集中储埋、鼓励利用;以及资源化利用的对策建议:(1)积极探索牡蛎壳资源化综合开发利用途径;(2)抓紧制定中长期开发利用计划。     

现代礼仪文化在民航服务中的实践探析

民航空乘人员的仪表形象、服务态度、服务技巧、言谈举止等不仅代表着航空公司,还代表整个民航的尊严与形象,其职业礼仪素养直接影响着民航的服务质量,关系到民航未来的发展.     

Predicting psychological ripple effects: the role of cultural identity, in-group/out-group identification, and attributions of blame in crisis communication

Incidents of intentional food contamination can produce ripple effects in consumers such as reduced trust and increased anxiety. In their postcrisis communication, food companies often direct the blame at the perpetrator in an effort to mitigate potential losses and regain consumer trust. The attempt to placate consumers may, in itself, potentially create psychological ripple effects in message readers. This study examined the interacting influence of two message characteristics: identity of the perpetrator of the crime (in-group/out-group membership), and the attribution of blame (reason why the perpetrator committed the crime), with message receiver characteristic (cultural identity) on psychological ripple effects such as blame, trust, anxiety, and future purchase intention. Results indicated that although group membership of the perpetrator was not significant in predicting outcomes for the organization, the attribution communicated in the message was. American message receivers blamed the organization more and trusted it less when personal dispositional attributions were made about the perpetrator. Asian message receivers blamed the organization more and trusted it less when situational attributions were made about the perpetrator. Lowered trust in the company and increased anxiety correlated with lower purchase intent for both American and Asian message receivers. Implications for crisis message design are discussed.     

从生存基础到力动之源——朱子哲学中的“气”论思想

理气观是朱子哲学的核心,但其理气关系不仅在于凸显天理的超越性,同时也反显着气的实在性。因此,对于朱子的理气观,不仅需要从理的角度进行理解,同时也需要从气的角度进行解读。从理气关系的角度看,气始终充当着一切存在之前提基础的作用;而从生物之具到人的生存基础,就是朱子哲学中的气在宇宙天道与人生世界中的两种不同的表现。但无论是生物之具还是生存基础抑或是宇宙发展的力动之源,气既是作为所有存在之前提基础出现的,同时也代表着人所必须超越、驾驭与主宰的对象;而从生存基础到力动之源,则充分展现了从自然到天性这一人既生存其中,同时又不得不时时面对的世界。     

Large-scale Bayesian spatial modelling of air pollution for policy support

The potential effects of air pollution are a major concern both in terms of the environment and in relation to human health. In order to support environmental policy, there is a need for accurate measurements of the concentrations of pollutants at high geographical resolution over large regions. However, within such regions, there are likely to be areas where the monitoring information will be sparse and so methods are required to accurately predict concentrations. Set within a Bayesian framework, models are developed which exploit the relationships between pollution and geographical covariate information, such as land use, climate and transport variables together with spatial structure. Candidate models are compared based on their ability to predict a set of validation sites. The chosen model is used to perform large-scale prediction of nitrogen dioxide at a 1×1 km resolution for the entire EU. The models allow probabilistic statements to be made with regard to the levels of air pollution that might be experienced in each area. When combined with population data, such information can be invaluable in informing policy by indicating areas for which improvements may be given priority.     

Poisson Regression and the Case-Crossover Design: Similarities and Differences

Poisson regression and case-crossover are frequently used methods to estimate transient risks of environmental exposures such as particulate air pollution on acute events such as mortality. Roughly speaking, a case-crossover design results from a Poisson regression by conditioning on the total number of failures. We show that the case-crossover design is somewhat more generally applicable than Poisson regression. Stratification in the case-crossover design is analogous to Poisson regression with dummy variables, or to a marked Poisson regression. Poisson regression makes it possible to express case-crossover likelihood functions as multinomial likelihoods without making reference to cases, controls, or matching. This derivation avoids the counterintuitive notion of basing inferences on exposures that occur post-failure.     

Improving The Efficiency of The Case-Crossover Design

The case-crossover design has been used by many researchers to study the transient effect of an exposure on the risk of a rare outcome. In a case-crossover design, only cases are sampled and each case will act as his/her own control. The time of failure acts as the case and non failure times act as the controls. Case-crossover designs have frequently been used to study the effect of environmental exposures on rare diseases or mortality. Time trends and seasonal confounding may be present in environmental studies and thus need to be controlled for by the sampling design. Several sampling methods are available for this purpose. In time-stratified sampling, disjoint strata of equal size are formed and the control times within the case stratum are used for comparison. The random semi-symmetric sampling design randomly selects a control time for comparison from two possible control times. The fixed semi-symmetric sampling design is a modified version of the random semi-symmetric sampling design that removes the random selection. Simulations show that the fixed semi-symmetric sampling design improves the variance of the random semi-symmetric sampling estimator by at least 35% for the exposures we studied. We derive expressions for the asymptotic variance of risk estimators for these designs, and show, that while the designs are not theoretically equivalent, in many realistic situations, the random semi-symmetric sampling design has similar efficiency to a time-stratified sampling design of size two and the fixed semi-symmetric sampling design has similar efficiency to a time-stratified sampling design of size three.     

Robustness of bayesian factor analysis estimates

This paper presents the result of a study of the robustness of posterior estimators of the factor loading matrix, the factor scores, and the disturbance covariance matrix (the main model parameters) in a Bayesian factor analysis with respect to variations in the values of the parameters of their prior distributions (the hyperparameter). We adopt the ε - contamination model of Berger and Berliner(1986) to generate prior distributions whose hyper-paramters reflects small variations in the elements of the uncontaminated hyperparameters, and we use directional derivatives to examine the variation of the uncontaminated estimators with respect to changes in the values of the hyperparameters, in the directions of the main model parameters. Several matrix norms are used to measure the closeness of the resulting values. We illustrate the results with a numerical example.     

具有混合边界条件的地下水污染模型的特征有限元方法

本文研究了地下水污染理论中的一类带混合边界条件的非线性耦合方程组的特征有限元方法，构造模型的特征有限元全离散格式，且利用椭圆投影，建立了计算格式在Ｈ＾１模下的最佳阶误差估计，并给出了数值算例及结果分析。