Modeled Estimates of Soil and Dust Ingestion Rates for Children

Daily soil/dust ingestion rates typically used in exposure and risk assessments are based on tracer element studies, which have a number of limitations and do not separate contributions from soil and dust. This article presents an alternate approach of modeling soil and dust ingestion via hand and object mouthing of children, using EPA's SHEDS model. Results for children 3 to <6 years old show that mean and 95th percentile total ingestion of soil and dust values are 68 and 224 mg/day, respectively; mean from soil ingestion, hand‐to‐mouth dust ingestion, and object‐to‐mouth dust ingestion are 41 mg/day, 20 mg/day, and 7 mg/day, respectively. In general, hand‐to‐mouth soil ingestion was the most important pathway, followed by hand‐to‐mouth dust ingestion, then object‐to‐mouth dust ingestion. The variability results are most sensitive to inputs on surface loadings, soil‐skin adherence, hand mouthing frequency, and hand washing frequency. The predicted total soil and dust ingestion fits a lognormal distribution with geometric mean = 35.7 and geometric standard deviation = 3.3. There are two uncertainty distributions, one below the 20th percentile and the other above. Modeled uncertainties ranged within a factor of 3–30. Mean modeled estimates for soil and dust ingestion are consistent with past information but lower than the central values recommended in the 2008 EPA Child‐Specific Exposure Factors Handbook. This new modeling approach, which predicts soil and dust ingestion by pathway, source type, population group, geographic location, and other factors, offers a better characterization of exposures relevant to health risk assessments as compared to using a single value.     

A simple hidden markov model for bayesian modeling with time dependent data

Consider a set of real valued observations collected over time. We pro¬pose a simple hidden Markow model for these realizations in which the the predicted distribution of the next future observation given the past is easily computed. The hidden or unobservable set of parameters is assumed to have a Markov structure of a special type. The model is quite flexible and can be used to incorporate different types of prior information in straightforward and sensible ways.     

Bayesian inference for smooth transition autoregressive (STAR) model: A prior sensitivity analysis

The main aim of this paper is to perform sensitivity analysis to the specification of prior distributions in a Bayesian analysis setting of STAR models. To achieve this aim, the joint posterior distribution of model order, coefficient, and implicit parameters in the logistic STAR model is first being presented. The conditional posterior distributions are then shown, followed by the design of a posterior simulator using a combination of Metropolis-Hastings, Gibbs Sampler, RJMCMC, and Multiple Try Metropolis algorithms, respectively. Following this, simulation studies and a case study on the prior sensitivity for the implicit parameters are being detailed at the end.     

The Self-Regulation of Gambling

Despite the prevalence of gambling world-wide, relatively few individuals become problem gamblers. Additionally many problem gamblers recover without professional assistance. The current study aim was to examine how individuals self-manage their gambling through (a) assessing frequency of use of a range of self-regulation strategies (b) examining how these strategies cluster and (c) exploring relationships between strategies, gambling frequency, amount spent and problem gambling severity. A sample of 303 gamblers was recruited, over-sampling for problem gamblers as assessed by the Problem Gambling Severity Index (PGSI) of the Canadian Problem Gambling Index (mean age 26.4 years, SD = 10.1 years; 119 males, 184 females; 238 social gamblers, 63 problem gamblers, 2 unclassified). They rated extent of usage of 27 gambling self-management techniques and completed the PGSI and other gambling measures. Factor analysis of items produced five factors, named Cognitive Approaches, Direct Action, Social Experience, Avoidance and Limit Setting. The relationships between these factors and key gambling variables were consistent with hypotheses that problem gamblers trying to reduce their gambling would be more likely to use the strategies than other gambler groups. The potential for developing the factors into a Gambling Self-regulation Measure was explored.     

HEAPING‐INDUCED BIAS IN REGRESSION‐DISCONTINUITY DESIGNS

This study uses Monte Carlo simulations to demonstrate that regression‐discontinuity designs arrive at biased estimates when attributes related to outcomes predict heaping in the running variable. After showing that our usual diagnostics may not be well suited to identifying this type of problem, we provide alternatives, and then discuss the usefulness of different approaches to addressing the bias. We then consider these issues in multiple non‐simulated environments. (JEL C21, C14, I12)     

Communicating about infectious disease threats: Insights from public health information officers

The public health communication challenges that arise in times of infectious disease threats (IDTs) were examined using the Risk Amplification through Media Spread (RAMS) Framework and in-depth phone interviews with 40 national, state, and local public health information officers (PIOs). Interviewees shared their experiences and insights related to how IDTs are communicated to the public, including the different types of traditional and social media used, how they develop and assess IDT messages, and their perceptions regarding the IDT risk amplification process. Theoretical and practical implications for health public relations and public health communication are discussed.     

RENEGING IN QUEUES REVISITED

Parkan and Warren [2] analyze the optimal reneging decision of a customer joining a G/M/1 queue with imperfect information about parameters of the system. Their Bayesian approach to the problem is incomplete and overstates the probability that the customer will renege, which is potentially costly to the server as well as to the customer.     

An Investigation of Scheduling Policies in a Dual-Constrained Manufacturing Cell*

In previous group scheduling studies, labor has essentially been ignored by assuming that enough labor is assigned to each machine in the cell. In reality, however, management usually does not have the resources to employ a laborer at each machine in the cell (i.e., machines need to share labor). Both labor scheduling and group scheduling heuristics need to be administered to manage the cell effectively. This study develops and examines scheduling procedures for a dual-constrained (i.e., machine and labor) manufacturing cell. Eleven decision rules are developed and tested under 16 different experimental conditions. The experimental factors used are interarrival time distribution, cell load, setup-to-run-time ratio, and transfer-to-run-time ratio. Results show that interarrival time distribution and cell load have the greatest impact on the performance of the cell. This suggests that effective production planning aimed at reducing job arrival variation and leveling the cell load can substantially improve cell performance. Among the experimental factors, setup and transfer-to-run-time ratio factors had the strongest influence on the rankings of the decision rules. These rankings were fairly robust across all experimental conditions and performance measures. The results also indicated that the inclusion of labor as a contraint in the cell had a significant impact on the performance of several group scheduling heuristics. Finally, it was shown that the best performing decision rules consider both transfer time and subfamily setup times.     

The Influence of Family and Community Social Capital on Educational Achievement*

Abstract It is increasingly recognized that families and communities are important in helping youths develop the knowledge and skills they need to obtain technologically sophisticated jobs, which are an emerging part of the global economy. In this paper we adopt social capital as a framework for examining the influence of family and community on promoting educational achievement among public school students. We explore more fully the role of community social capital in influencing educational performance beyond that attributed to family social capital. Using data from the National Education Longitudinal Survey (NELS), we find that both process and structural attributes of family social capital are key factors affecting high school students' educational achievement. Process and structural attributes of community social capital also help youths to excel, though they contribute less strongly to achievement. These findings suggest that policies designed to promote educational achievement must extend beyond the school and must seek to strengthen social capital in the family and the community.