Impact of Microbial Ecology of Meat and Poultry Products on Predictions from Exposure Assessment Scenarios for Refrigerated Storage

A novel extension of traditional growth models for exposure assessment of food-borne microbial pathogens was developed to address the complex interactions of competing microbial populations in foods. Scenarios were designed for baseline refrigeration and mild abuse of servings of chicken broiler and ground beef Our approach employed high-quality data for microbiology of foods at production, refrigerated storage temperatures, and growth kinetics of microbial populations in culture media. Simple parallel models were developed for exponential growth of multiple pathogens and the abundant and ubiquitous nonpathogenic indigenous microbiota. Monte Carlo simulations were run for unconstrained growth and growth with the density-dependent constraint based on the "Jameson effect," inhibition of pathogen growth when the indigenous microbiota reached 10(9) counts per serving. The modes for unconstrained growth of the indigenous microbiota were 10(8), 10(10), and 10(11) counts per serving for chicken broilers, and 10(7), 10(9) and 10(11) counts per serving for ground beef at respective sites for backroom, meat case, and home refrigeration. Contamination rates and likelihoods of reaching temperatures supporting growth of the pathogens in the baseline refrigeration scenario were rare events. The unconstrained exponential growth models appeared to overestimate L. monocytogenes growth maxima for the baseline refrigeration scenario by 1500-7233% (10(6)-10(7) counts/serving) when the inhibitory effects of the indigenous microbiota are ignored. The extreme tails of the distributions for the constrained models appeared to overestimate growth maxima 110% (10(4)-10(5) counts/serving) for Salmonella spp. and 108% (6 x 10(3) counts/serving) for E. coli O157:H7 relative to the extremes of the unconstrained models. The approach of incorporating parallel models for pathogens and the indigenous microbiota into exposure assessment modeling motivates the design of validation studies to test the modeling assumptions, consistent with the analytical-deliberative process of risk analysis.     

Consumer Deception or Unwarranted Product Disparagement? The Case of Lean,Finely Textured Beef

When celebrity chef Jamie Oliver used his television presence to denounce the inclusion of lean, finely textured beef (LFTB) in ground beef products sold to consumers and present in school lunches, a firestorm of protests ensued. Ultimately, processing plants were closed, employees laid off, school lunch programs were changed to exclude LFTB from their menus, and large retailers such as Costco and Walmart refused to sell products containing LFTB. Calls for mandatory labeling were proposed in Congress, the argument being that consumers had a right to know what was in the beef they were purchasing and feeding their families. Dubbed “pink slime” by its critics, LFTB is treated, processed beef renderings that are derived from trimmings that would otherwise be discarded or used for some inferior purpose such as animal food. This article chronicles the events surrounding the LFTB controversy, briefly reviews scientific evidence pertaining to its safety, and reviews public policy initiatives in the wake of public protests.     

Structure and Parameterization of Pharmacokinetic Models: Their Impact on Model Predictions

There has been an increasing interest in physiologically based pharmacokinetic (PBPK)models in the area of risk assessment. The use of these models raises two important issues: (1)How good are PBPK models for predicting experimental kinetic data? (2)How is the variability in the model output affected by the number of parameters and the structure of the model? To examine these issues, we compared a five-compartment PBPK model, a three-compartment PBPK model, and nonphysiological compartmental models of benzene pharmacokinetics. Monte Carlo simulations were used to take into account the variability of the parameters. The models were fitted to three sets of experimental data and a hypothetical experiment was simulated with each model to provide a uniform basis for comparison. Two main results are presented: (1)the difference is larger between the predictions of the same model fitted to different data se1ts than between the predictions of different models fitted to the dame data; and (2)the type of data used to fit the model has a larger effect on the variability of the predictions than the type of model and the number of parameters.     

Time‐Staged Overtime Staffing for Services with Updated Forecasts and Availabilities

This article develops a framework for staffing in a service environment when multiple opportunities exist for prescheduling overtime prior to the start of a shift. Demand forecasts improve as the shift approaches, while the availability of workers to be scheduled for overtime decreases. First, a single‐shift model is developed and used in computational studies to evaluate the benefits of time‐staged overtime staffing, which include slightly lower costs and significant reductions in unscheduled overtime and outside agents. A multishift model is then developed to consider constraints on consecutive hours worked and minimum rest intervals between shifts. A multishift computational study shows how the benefits of time‐staged overtime staffing depend on problem characteristics when interactions between shifts are considered. The article discusses how single‐shift and multishift models relate to each other and alternative ways the models may be used in practice, including decentralized open shift management and centralized overtime scheduling.     

The Impact of Social Amplification and Attenuation of Risk and the Public Reaction to Mad Cow Disease in Canada

Following the detection of bovine spongiform encephalopathy (BSE) in Canada, and subsequently in the United States, confidence in the safety of beef products remained high. Consumers actually increased their consumption of beef slightly after the news of an increased risk from mad cow disease, which has been interpreted as public support for beef farmers and confidence in government regulators. The Canadian public showed a markedly different reaction to the news of domestic BSE than the furious and panicked responses observed in the United Kingdom, Germany, and Japan. Using the social amplification of risk framework, we show that, while other countries displayed social amplification of risk, Canada experienced a social attenuation of risk. The attenuated reaction in Canada toward mad cow disease and increased human health risks from variant Creutzfeldt-Jakob disease (vCJD) was due to the social context at the time when BSE was discovered domestically. Mortality, morbidity, and psychosocial impacts resulting from other major events such as severe acute respiratory syndrome (SARS), West Nile virus (WNV), and the U.S.-Iraq war made the theoretical risks of BSE and vCJD a lower priority, reducing its concern as a risk issue.     

A Multifactorial Risk Prioritization Framework for Foodborne Pathogens

We develop a prioritization framework for foodborne risks that considers public health impact as well as three other factors (market impact, consumer risk acceptance and perception, and social sensitivity). Canadian case studies are presented for six pathogen‐food combinations: Campylobacter spp. in chicken; Salmonella spp. in chicken and spinach; Escherichia coli O157 in spinach and beef; and Listeria monocytogenes in ready‐to‐eat meats. Public health impact is measured by disability‐adjusted life years and the cost of illness. Market impact is quantified by the economic importance of the domestic market. Likert‐type scales are used to capture consumer perception and acceptance of risk and social sensitivity to impacts on vulnerable consumer groups and industries. Risk ranking is facilitated through the development of a knowledge database presented in the format of info cards and the use of multicriteria decision analysis (MCDA) to aggregate the four factors. Three scenarios representing different stakeholders illustrate the use of MCDA to arrive at rankings of pathogen‐food combinations that reflect different criteria weights. The framework provides a flexible instrument to support policymakers in complex risk prioritization decision making when different stakeholder groups are involved and when multiple pathogen‐food combinations are compared.     

Risk Perception of the "Mad Cow Disease" in France: Determinants and Consequences

Since 1996, when bovine spongiform encephalopathy (BSE) was assessed as a possible human transmissible disease, a variant of Creutzfeldt-Jakob disease (vCJD), French people have entered into a long period of fear and avoidance of beef and bovine byproducts, which produced an unprecedented collapse in the beef market. This article deals with the perceived risk of the "mad cow disease" (MCD) in the French general population. Two surveys were conducted on a representative sample of the adult population, the first one in 2000 during the peak of the crisis and the second one 13 months later in a quieter period. The main assumption we made was that changes in beef consumption are strongly related to the perceived risk of MCD, which we defined as people's cognitive and affective responses to hazard. Our objective was to identify the determinants and consequences of this perceived risk and to compare them in different sociopolitical contexts. The results issued from a bivariate and multivariate analysis show that: (i) the distribution of most of the variables significantly related to the perceived risk identified in the first survey had changed in the second survey, in relation with the reduction of worry and the resumption of national beef consumption; (ii) the propensity for self-protection through avoiding or ceasing beef eating was more related to feelings of worry than to subjective vCJD risk assessments; and (iii) the main determinant of less avoidance to beef products was the preference for beef, a feeling identified prior to emergence of the risk of MCD, remaining unchanged in various contexts.     

A COMPARATIVE ANALYSIS OF THE PREDICTIVE ABILITY OF ADAPTIVE FORECASTING,RE-ESTIMATION,AND RE-IDENTIFICATION USING BOX-JENKINS TIME-SERIES ANALYSIS ON QUARTERLY EARNINGS DATA

This paper assesses the predictive ability of the Box-Jenkins methodology when utilized in an on-going setting. Three procedures are utilized to update the original forecasts generated from the Box-Jenkins models: adaptive forecasting, re-estimation, and re-identification. The results indicate that constant monitoring of the structure and parameters of the time-series models are necessary through time. It appears that adaptive forecasting techniques are insufficient to update BJ time-series models when used in conjunction with quarterly earnings data. Re-estimation is recommended as each new observation becomes available. Re-identification procedures are recommended on a less frequent basis.     

Order Release in Automated Manufacturing Systems

Order release occurs when orders are released to the shop floor for processing. An order release mechanism (ORM) selectively releases orders to improve shop management and performance. This paper focuses on the question of how to select and develop better ORMs, providing guidelines for practice. We develop a number of propositions on how an ORM should be established and the impact of implementing an ORM. To test the validity of these propositions, we consider three real different automated manufacturing systems. For each system, ORMs are developed and implemented in simulation models. Analysis of the experimental results suggests that some propositions are true under all situations, whereas the degree of validity of others is dependent on variables like system type, and the levels of other design variables. We conjecture that all the propositions can be accepted when the volume of production is high. We use the analysis and the propositions to generate guidelines for practice and areas for future research.     

Risk Assessment of Escherichia coli O157 Illness from Consumption of Hamburgers in the United States Made from Australian Manufacturing Beef

We analyze the risk of contracting illness due to the consumption in the United States of hamburgers contaminated with enterohemorrhagic Escherichia coli (EHEC) of serogroup O157 produced from manufacturing beef imported from Australia. We have used a novel approach for estimating risk by using the prevalence and concentration estimates of E. coli O157 in lots of beef that were withdrawn from the export chain following detection of the pathogen. For the purpose of the present assessment an assumption was that no product is removed from the supply chain following testing. This, together with a number of additional conservative assumptions, leads to an overestimation of E. coli O157‐associated illness attributable to the consumption of ground beef patties manufactured only from Australian beef. We predict 49.6 illnesses (95%: 0.0–148.6) from the 2.46 billion hamburgers made from 155,000 t of Australian manufacturing beef exported to the United States in 2012. All these illness were due to undercooking in the home and less than one illness is predicted from consumption of hamburgers cooked to a temperature of 68 °C in quick‐service restaurants.     

Bargaining without a Common Prior—An Immediate Agreement Theorem

In sequential bargaining models without outside options, each player's bargaining power is ultimately determined by which player will make an offer and when. This paper analyzes a sequential bargaining model in which players may hold different beliefs about which player will make an offer and when. Excessive optimism about making offers in the future can cause delays in agreement. The main result states that, despite this, if players will remain sufficiently optimistic for a sufficiently long future, then in equilibrium they will agree immediately. This result is also extended to other canonical models of optimism.     

An Experimental Study of Belief Learning Using Elicited Beliefs

This paper investigates belief learning. Unlike other investigators who have been forced to use observable proxies to approximate unobserved beliefs, we have, using a belief elicitation procedure (proper scoring rule), elicited subject beliefs directly. As a result we were able to perform a more direct test of the proposition that people behave in a manner consistent with belief learning. What we find is interesting. First to the extent that subjects tend to “belief learn,” the beliefs they use are the stated beliefs we elicit from them and not the “empirical beliefs” posited by fictitious play or Cournot models. Second, we present evidence that the stated beliefs of our subjects differ dramatically, both quantitatively and qualitatively, from the type of empirical or historical beliefs usually used as proxies for them. Third, our belief elicitation procedures allow us to examine how far we can be led astray when we are forced to infer the value of parameters using observable proxies for variables previously thought to be unobservable. By transforming a heretofore unobservable into an observable, we can see directly how parameter estimates change when this new information is introduced. Again, we demonstrate that such differences can be dramatic. Finally, our belief learning model using stated beliefs outperforms both a reinforcement and EWA model when all three models are estimated using our data.     

EDUCATION

This paper illustrates how multivariate spectral analysis can be used to analyze complex, dynamic, business systems. Important temporal aspects of multiple time series are captured in the frequency domain by the coherence, phase, and gain statistics. A computer simulation of a time domain model produces sample results which demonstrate how the timing relations between two series can be reversed when the effects common to both series are partialled out. Finally, an empirical multivariate analysis of beef prices is used to determine the timing relations among prices at different levels of the production-distribution system.     

The Application of Neural Networks and a Qualitative Response Model to the Auditor's Going Concern Uncertainty Decision*

An auditor gives a going concern uncertainty opinion when the client company is at risk of failure or exhibits other signs of distress that threaten its ability to continue as a going concern. The decision to issue a going concern opinion is an unstructured task that requires the use of the auditor's judgment. In cases where judgment is required, the auditor may benefit from the use of statistical analysis or other forms of decision models to support the final decision. This study uses the generalized reduced gradient (GRG2) optimizer for neural network learning, a backpropagation neural network, and a logit model to predict which firms would receive audit reports reflecting a going concern uncertainty modification. The GRG2 optimizer has previously been used as a more efficient optimizer for solving business problems. The neural network model formulated using GRG2 has the highest prediction accuracy of 95 percent. It performs best when tested with a small number of variables on a group of data sets, each containing 70 observations. While the logit procedure fails to converge when using our eight variable model, the GRG2 based neural network analysis provides consistent results using either eight or four variable models. The GRG2 based neural network is proposed as a robust alternative model for auditors to support their assessment of going concern uncertainty affecting the client company.     

On the Effects of Capacity Agent on Market Equilibrium

We consider a setting of two firms and one capacity agent. Each firm serves a primary market, and the capacity agent sustains a common market to draw demand for capacity from the external firms. The firms can partner with the capacity agent under her contract to serve the common market. When they use the common market mainly as an outlet for their unused capacities, the capacity agent will only specify a variable fee for each capacity unit deployed through her, and prefer to partner with one firm in most circumstances. When the firms adjust capacities to accommodate the businesses created by serving the common market, the capacity agent will specify a lump‐sum payment and a variable fee, and will be more likely to incentivize only one firm to partner with her, when the common market is sufficiently large or the demands in the common and primary markets are strongly correlated. She will always use a fixed fee to extract, while not necessarily all, the profit gains to the firms serving the common market, but will use a variable fee only when partnering with both firms. The key results are robust with respect to market configuration and contract type.     

The Performance of MRP Purchase Lot-Size Procedures Under Actual Multiple Purchase Discount Conditions

Several heuristic procedures for purchase lot sizing in material requirements planning (MRP) systems were tested with actual data from manufacturing companies. Information provided by the companies for each purchased item included the estimated requirements and costs, the price discount structure from the vendor, and the actual company ordering policy. Simulation tests for each purchased item involved comparisons of several purchase lot-size procedures from the research literature along with the actual procedure used by the company providing the data. Results indicate that one of the heuristic lot-size procedures from the literature consistently outperformed the company policies as well as all other models tested. Another noteworthy result is that, in some cases, the actual company order policy was more cost-effective than some of the models from the research literature.     

Managing a Remanufacturing System with Random Yield: Properties,Observations, and Heuristics

We study a remanufacturing system that involves the ordering of a serviceable product and the remanufacturing of multiple types of returned products (cores) into the serviceable product. In addition to random demand for the serviceable product and random returned quantities of different types of cores in each time period, the remanufacturing yield of each type of core is also uncertain. By analyzing a multi‐period stochastic dynamic program, we derive several properties of the optimal ordering/remanufacturing policy. In addition to some insights, these properties can be used to reduce the search effort of the optimal policy. We also demonstrate that some existing results derived from related models no longer hold in remanufacturing systems with random yield. Recognizing the optimal ordering/remanufacturing policy is highly complex, we examine three simple heuristics that can be efficiently solved and implemented in practice. Among these three heuristics, our numerical analysis suggests that the heuristic that captures most of the yield uncertainty and future system evolvement as well as some of the properties of the optimal ordering/remanufacturing policy outperforms the other two heuristics.     

Sensitivity Analysis of a Two-Dimensional Probabilistic Risk Assessment Model Using Analysis of Variance

This article demonstrates application of sensitivity analysis to risk assessment models with two-dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used as a test bed. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Among many available sensitivity analysis methods, analysis of variance (ANOVA) is evaluated in comparison to commonly used methods based on correlation coefficients. In a two-dimensional risk model, the identification of key controllable inputs that can be prioritized with respect to risk management is confounded by uncertainty. However, as shown here, ANOVA provided robust insights regarding controllable inputs most likely to lead to effective risk reduction despite uncertainty. ANOVA appropriately selected the top six important inputs, while correlation-based methods provided misleading insights. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error. For the selected sample size, differences in F values of 60% or more were associated with clear differences in rank order between inputs. Sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.     

基于合作博弈模型的公共河流水资源分配方案研究

水资源的稀缺性使得公共河流的水资源分配问题中存在着各种冲突与矛盾。为了确定公共河流流经的各用水主体之间分配水资源的合理方案，本文建立具有外部性的合作博弈模型来分析公共河流流经的各用水主体之间在竞争与合作并存情况下的水资源分配问题。使用动态博弈的方法来确定联盟之间通过竞争产生的均衡水资源分配量，然后在各联盟之内通过Nash协商的方式来分配联盟的均衡水资源分配量，比较各种方案下用水主体产生的总效用，进而得到公共河流用水主体之间竞争与合作并存时的最优水资源分配方案和各用水主体之间形成联盟的具体形式。研究表明：受到外部性环境的影响，公共河流用水主体之间部分合作可能比完全合作产生更大的总效用，合作与竞争并存时的最优分配方案优于完全合作时的最优分配方案。     

Identification of Standard Auction Models

This paper presents new identification results for models of first–price, second–price, ascending (English), and descending (Dutch) auctions. We consider a general specification of the latent demand and information structure, nesting both private values and common values models, and allowing correlated types as well as ex ante asymmetry. We address identification of a series of nested models and derive testable restrictions enabling discrimination between models on the basis of observed data. The simplest model—symmetric independent private values—is nonparametrically identified even if only the transaction price from each auction is observed. For richer models, identification and testable restrictions may be obtained when additional information of one or more of the following types is available: (i) the identity of the winning bidder or other bidders; (ii) one or more bids in addition to the transaction price; (iii) exogenous variation in the number of bidders; (iv) bidder–specific covariates. While many private values (PV) models are nonparametrically identified and testable with commonly available data, identification of common values (CV) models requires stringent assumptions. Nonetheless, the PV model can be tested against the CV alternative, even when neither model is identified.