Incorporating Time‐Dose‐Response into Legionella Outbreak Models

A novel method was used to incorporate in vivo host–pathogen dynamics into a new robust outbreak model for legionellosis. Dose‐response and time‐dose‐response (TDR) models were generated for Legionella longbeachae exposure to mice via the intratracheal route using a maximum likelihood estimation approach. The best‐fit TDR model was then incorporated into two L. pneumophila outbreak models: an outbreak that occurred at a spa in Japan, and one that occurred in a Melbourne aquarium. The best‐fit TDR from the murine dosing study was the beta‐Poisson with exponential‐reciprocal dependency model, which had a minimized deviance of 32.9. This model was tested against other incubation distributions in the Japan outbreak, and performed consistently well, with reported deviances ranging from 32 to 35. In the case of the Melbourne outbreak, the exponential model with exponential dependency was tested against non‐time‐dependent distributions to explore the performance of the time‐dependent model with the lowest number of parameters. This model reported low minimized deviances around 8 for the Weibull, gamma, and lognormal exposure distribution cases. This work shows that the incorporation of a time factor into outbreak distributions provides models with acceptable fits that can provide insight into the in vivo dynamics of the host‐pathogen system.     

A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding

Floods are a natural hazard evolving in space and time according to meteorological and river basin dynamics, so that a single flood event can affect different regions over the event duration. This physical mechanism introduces spatio‐temporal relationships between flood records and losses at different locations over a given time window that should be taken into account for an effective assessment of the collective flood risk. However, since extreme floods are rare events, the limited number of historical records usually prevents a reliable frequency analysis. To overcome this limit, we move from the analysis of extreme events to the modeling of continuous stream flow records preserving spatio‐temporal correlation structures of the entire process, and making a more efficient use of the information provided by continuous flow records. The approach is based on the dynamic copula framework, which allows for splitting the modeling of spatio‐temporal properties by coupling suitable time series models accounting for temporal dynamics, and multivariate distributions describing spatial dependence. The model is applied to 490 stream flow sequences recorded across 10 of the largest river basins in central and eastern Europe (Danube, Rhine, Elbe, Oder, Waser, Meuse, Rhone, Seine, Loire, and Garonne). Using available proxy data to quantify local flood exposure and vulnerability, we show that the temporal dependence exerts a key role in reproducing interannual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin‐wide scale, while copulas allow the preservation of the spatial dependence of losses at weekly and annual time scales.     

A Stochastic Model to Assess the Effect of Meat Inspection Practices on the Contamination of the Pig Carcasses

The objective of meat inspection is to promote animal and public health by preventing, detecting, and controlling hazards originating from animals. With the improvements of sanitary level in pig herds, the hazards profile has shifted and the inspection procedures no longer target major foodborne pathogens (i.e., not risk based). Additionally, carcass manipulations performed when searching for macroscopic lesions can lead to cross‐contamination. We therefore developed a stochastic model to quantitatively describe cross‐contamination when consecutive carcasses are submitted to classic inspection procedures. The microbial hazard used to illustrate the model was Salmonella, the data set was obtained from Brazilian slaughterhouses, and some simplifying assumptions were made. The model predicted that due to cross‐contamination during inspection, the prevalence of contaminated carcass surfaces increased from 1.2% to 95.7%, whereas the mean contamination on contaminated surfaces decreased from 1 logCFU/cm² to ?0.87 logCFU/cm², and the standard deviations decreased from 0.65 to 0.19. These results are explained by the fact that, due to carcass manipulations with hands, knives, and hooks, including the cutting of contaminated lymph nodes, Salmonella is transferred to previously uncontaminated carcasses, but in small quantities. These small quantities can easily go undetected during sampling. Sensitivity analyses gave insight into the model performance and showed that the touching and cutting of lymph nodes during inspection can be an important source of carcass contamination. The model can serve as a tool to support discussions on the modernization of pig carcass inspection.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

基于产业集群的企业重点投资地区决策的仿真

用多Agent仿真技术,研究了基于产业集群的台资IT企业在大陆重点投资地区的决策问题。提出了针对这一问题的、将集群竞争力评价的GEM模型、多Agent仿真技术及演化博弈相结合的思路与方法。仿真模型对集群的研究方法以及多Agent的应用领域有拓展意义,而仿真发现与结论可为政府制定有关政策提供参考。     

消费者网络购物感知风险概念及测量模型研究

感知风险在消费者行为研究中是一个重要的变量,它对消费者信息处理及购物决策产生重要影响.随着网络购物的兴起,消费者的信息搜寻和购买决策过程都发生了重大的改变.本文对消费者网络购物感知风险进行了界定,提出感知风险可以由五个具体的网络购物感知风险维度构成,并在此基础上开发了15个项目作为测量指标.论文还对消费者网络购物感知风险不同结构的模型进行了探索比较,根据拟合程度和简约特点界定了消费者网络购物感知风险的概念和测量模型.     

A Systematic Uncertainty Analysis of an Evaluative Fate and Exposure Model

Multimedia fate and exposure models are widely used to regulate the release of toxic chemicals, to set cleanup standards for contaminated sites, and to evaluate emissions in life-cycle assessment. CalTOX, one of these models, is used to calculate the potential dose, an outcome that is combined with the toxicity of the chemical to determine the Human Toxicity Potential (HTP), used to aggregate and compare emissions. The comprehensive assessment of the uncertainty in the potential dose calculation in this article serves to provide the information necessary to evaluate the reliability of decisions based on the HTP A framework for uncertainty analysis in multimedia risk assessment is proposed and evaluated with four types of uncertainty. Parameter uncertainty is assessed through Monte Carlo analysis. The variability in landscape parameters is assessed through a comparison of potential dose calculations for different regions in the United States. Decision rule uncertainty is explored through a comparison of the HTP values under open and closed system boundaries. Model uncertainty is evaluated through two case studies, one using alternative formulations for calculating the plant concentration and the other testing the steady state assumption for wet deposition. This investigation shows that steady state conditions for the removal of chemicals from the atmosphere are not appropriate and result in an underestimate of the potential dose for 25% of the 336 chemicals evaluated.     

并行工程产品开发过程定量化建模与计划制订

由于现有的一些并行工程定量化模型中修改设计微循环的细节特征描述得还不够充分 ,本文提出了一种新的基于产品 -工艺设计活动对网络的定量化模型来描述并行工程产品开发过程 ,并且给出了产品 -工艺设计活动对平均持续时间与产品或工艺设计活动资源占用率的计算方法 .在此基础上 ,将并行工程产品开发过程的计划制订问题建模成一个有资源约束的项目调度问题 .与其它此类问题不同的是 ,在本文中 ,分配给产品开发项目的各类资源的数量不是事先给定的 ,而是与最优产品开发计划一起获得的 ,所以这是一个资源分配与计划制订的集成优化问题 .本文提出了一种新的基于分枝定界的算法来解决此问题并引入一个启发式规则来提高算法的搜索效率     

供应链管理理论与方法

进入九十年代以来,供应链管理成为增强企业竞争力最重要方法之一。本文介绍了供应链管理的基本思想,说明了供应链管理产生的理论与现实背景,介绍了实施供应链管理的基本过程,分析了为辅助供应链管理的模型,并说明了供应链管理对我国企业管理的借鉴作用。     

决策支持系统的自适应建模研究及应用实例分析

在进行决策的过程中 ,建立正确的系统模型是解决问题的关键 .由于多方面的原因 ,有时要针对研究对象选用适用的模型来进行分析是很困难的 .本文根据遗传算法在程序设计中的应用 ,探讨了基于遗传算法的自适应建模方法 ,并对实际应用中自适应建模的算法存在的问题提出了改进方法 .最后应用自适应建模方法对全国的集装箱与外贸数据进行了建模分析 ,应用实例表明自适应建模方法对于解决难以选择合适的模型进行建模分析时是很有效的