Experimental investigation of biodynamic human body models subjected to whole-body vibration during a vehicle ride

In this study, responses of biodynamic human body models to whole-body vibration during a vehicle ride were investigated. Accelerations were acquired from three different body parts, such as the head, upper torso and lower torso, of 10 seated passengers during a car ride while two different road conditions were considered. The same multipurpose vehicle was used during all experiments. Additionally, by two widely used biodynamic models in the literature, a set of simulations were run to obtain theoretical accelerations of the models and were compared with those obtained experimentally. To sustain a quantified comparison between experimental and theoretical approaches, the root mean square acceleration and acceleration spectral density were calculated. Time and frequency responses of the models demonstrated that neither of the models showed the best prediction performance of the human body behaviour in all cases, indicating that further models are required for better prediction of the human body responses.     

What safety models and principles can be adapted and used in security science?

Engineering risk management is comprised of managing operational safety risks on the one hand and managing physical security risks on the other. Although some basic management principles are obviously the same for both safety and security, some important conceptual and calculation differences exist, as is explained in this paper. For instance, safety risk is usually calculated based on the scenarios’ consequences and likelihoods, while security needs to be determined by the assessment of vulnerability, the likelihood of attack and potential consequences. Nonetheless, there are also many similarities. Conceptual models, metaphors and principles that have been elaborated in the safety domain during the past century, many of them based on major accidents and their investigation, can easily be translated to the security domain. In the present study, we will explain how physical security should be seen in relation to safety, and what models and principles, derived from safety science, can be employed to manage the security aspects associated with physical threats.     

What is a static head measurement? Evaluating structural error and implications for regional groundwater modeling

Most groundwater modelers avoid using static heads measured from active production wells because they can introduce a bias into model calibration. However, in the deep confined Cambrian-Ordovician Sandstone Aquifer System in the Central Midcontinent of North America, dedicated observation wells are sparse and remote from areas of most concentrated pumping. As a result, in areas where drawdown is the greatest and modeling is most needed, only static heads from production wells are available for calibration. This paper evaluates two leading sources of discrepancies in using production well data, spatial and temporal structural error (S.E.). A simple Theis solution is used to evaluate the potential magnitude of spatial S.E. when calibrating a regional MODFLOW model with coarse cell resolution. Despite theoretical analyses indicating that spatial S.E. could be significant, statistical analysis of the model results suggests that temporal S.E. is dominant. Long (ranging over decades) or frequent (monthly) head datasets are key in understanding temporal S.E., to better capture water-level variability. In this study, the range in static head observations impacted estimates of the remaining time a well could extract water from the aquifer by 0.1 to 16.0 years. This uncertainty in future water supply is highly relevant to stakeholders and must be assessed in hydrographs depicting risk.     

Measuring direct and indirect treatment effects using safety performance intervention functions

Recent traffic safety research has advocated the use of the Full Bayes (FB) approach to conduct controlled before-after safety evaluations. The FB approach was shown to offer both methodological and data advantages. However, there is still a lack of complete understanding of the role that the model parameters play in the formulation of treatment effectiveness. This paper offers a novel approach to compute components related to direct and indirect treatment effects under a linear intervention model for the Safety Performance Function (SPF). The isolation of a component corresponding to the direct treatment effects enables the analyst to assess the effectiveness of the countermeasures apart from local (site-related) environmental factors. Two case studies are used to demonstrate the approach. The first case study is based on the results published from a previous safety evaluation in Iowa and the second uses a new data set from British Columbia based on the Insurance Corporation of British Columbia Road Improvement Program. The various direct and indirect components have yielded valuable insight into the effects of the model parameters on treatment impacts. Moreover, the calculation of the treatment effectiveness indices have been simplified by providing straightforward equations in terms of model parameters for the computation of their components without resorting to additional algorithms. More importantly, the results can have a significant impact on the economic evaluation of safety programs and countermeasures by allowing the calculation of collision modification factors (CMFs) that vary with time.     

The complex relationship between personal sense of connection to animals and self‐reported proenvironmental behaviors by zoo visitors

The global biodiversity crisis requires an engaged citizenry that provides collective support for public policies and recognizes the consequences of personal consumption decisions. Understanding the factors that affect personal engagement in proenvironmental behaviors is essential for the development of actionable conservation solutions. Zoos and aquariums may be some of the only places where many people can explore their relations with wild animals and proenvironmental behaviors. Using a moderated‐mediation analysis of a survey of U.S. zoo and aquarium visitors (n = 3588), we explored the relationship between the sense of connection to animals and self‐reported engagement in proenvironmental behaviors related to climate change and how this relationship is affected by certainty that climate change is happening, level of concern about climate change, and perceptions of effectiveness in personally addressing climate change. We found a significant, directional relationship between sense of connection to animals and self‐reported proenvironmental behaviors. Political inclination within the conservative to liberal spectrum did not affect the relationship. We conclude that a personal sense of connection to animals may provide a foundation for educational and communication strategies to enhance involvement in proenvironmental actions.     

Minimum area requirements for an at‐risk butterfly based on movement and demography

Determining the minimum area required to sustain populations has a long history in theoretical and conservation biology. Correlative approaches are often used to estimate minimum area requirements (MARs) based on relationships between area and the population size required for persistence or between species’ traits and distribution patterns across landscapes. Mechanistic approaches to estimating MAR facilitate prediction across space and time but are few. We used a mechanistic MAR model to determine the critical minimum patch size (CMP) for the Baltimore checkerspot butterfly (Euphydryas phaeton), a locally abundant species in decline along its southern range, and sister to several federally listed species. Our CMP is based on principles of diffusion, where individuals in smaller patches encounter edges and leave with higher probability than those in larger patches, potentially before reproducing. We estimated a CMP for the Baltimore checkerspot of 0.7–1.5 ha, in accordance with trait‐based MAR estimates. The diffusion rate on which we based this CMP was broadly similar when estimated at the landscape scale (comparing flight path vs. capture‐mark‐recapture data), and the estimated population growth rate was consistent with observed site trends. Our mechanistic approach to estimating MAR is appropriate for species whose movement follows a correlated random walk and may be useful where landscape‐scale distributions are difficult to assess, but demographic and movement data are obtainable from a single site or the literature. Just as simple estimates of lambda are often used to assess population viability, the principles of diffusion and CMP could provide a starting place for estimating MAR for conservation.     

Herbicide Transport in Goodwater Creek Experimental Watershed: II. Long‐Term Research on Acetochlor,Alachlor, Metolachlor,and Metribuzin1

Lerch, R.N., E.J. Sadler, C. Baffaut, N.R. Kitchen, and K.A. Sudduth, 2010. Herbicide Transport in Goodwater Creek Experimental Watershed: II. Long‐Term Research on Acetochlor, Alachlor, Metolachlor, and Metribuzin. Journal of the American Water Resources Association (JAWRA) 1‐15. DOI: 10.1111/j.1752‐1688.2010.00504.x Abstract: Farmers in the Midwestern United States continue to be reliant on soil‐applied herbicides for weed control in crop production, and herbicide contamination of streams remains an environmental problem. The main objective of this study was to analyze trends in concentration and load of acetochlor, alachlor, metolachlor, and metribuzin in Goodwater Creek Experimental Watershed (GCEW) from 1992 to 2006. A secondary objective was to document the effects of best management practices (BMPs) implemented within GCEW on herbicide transport trends. Median relative herbicide loads, as a percent of applied, were 3.7% for metolachlor, 1.3% for metribuzin, 0.36% for acetochlor, and 0.18% for alachlor. The major decrease in alachlor use and increase in acetochlor use caused shifts in flow‐weighted concentrations that were observed over the entire concentration range. The smaller decrease in metolachlor use led to a consistent decreasing time trend only for the upper end of the concentration distribution. Metribuzin also showed moderate decreases in concentration with time since 1998. Annual loads were generally correlated to second quarter discharge. Despite extensive education efforts in the watershed, conservation BMPs within GCEW were mainly implemented to control erosion, and therefore had no discernable impact on reducing herbicide transport. Overall, changes in herbicide use and second quarter discharge had the greatest effect on trends in flow‐weighted concentration and annual load.     

The Regionalization of National‐Scale SPARROW Models for Stream Nutrients1

Schwarz, Gregory E., Richard B. Alexander, Richard A. Smith, and Stephen D. Preston, 2011. The Regionalization of National‐Scale SPARROW Models for Stream Nutrients. Journal of the American Water Resources Association (JAWRA) 47(5):1151‐1172. DOI: 10.1111/j.1752‐1688.2011.00581.x Abstract: This analysis modifies the parsimonious specification of recently published total nitrogen (TN) and total phosphorus (TP) national‐scale SPAtially Referenced Regressions On Watershed attributes models to allow each model coefficient to vary geographically among three major river basins of the conterminous United States. Regionalization of the national models reduces the standard errors in the prediction of TN and TP loads, expressed as a percentage of the predicted load, by about 6 and 7%. We develop and apply a method for combining national‐scale and regional‐scale information to estimate a hybrid model that imposes cross‐region constraints that limit regional variation in model coefficients, effectively reducing the number of free model parameters as compared to a collection of independent regional models. The hybrid TN and TP regional models have improved model fit relative to the respective national models, reducing the standard error in the prediction of loads, expressed as a percentage of load, by about 5 and 4%. Only 19% of the TN hybrid model coefficients and just 2% of the TP hybrid model coefficients show evidence of substantial regional specificity (more than ±100% deviation from the national model estimate). The hybrid models have much greater precision in the estimated coefficients than do the unconstrained regional models, demonstrating the efficacy of pooling information across regions to improve regional models.     

Addressing onsite sampling in recreation site choice models

Independent experts and politicians have criticized statistical analyses of recreation behavior, which rely upon onsite samples due to their potential for biased inference. The use of onsite sampling usually reflects data or budgetary constraints, but can lead to two primary forms of bias in site choice models. First, the strategy entails sampling site choices rather than sampling individuals—a form of bias called endogenous stratification. Under these conditions, sample choices may not reflect the site choices of the true population. Second, exogenous attributes of the individuals sampled onsite may differ from the attributes of individuals in the population—the most common form in recreation demand is avidity bias. We propose addressing these biases by combining two the existing methods: Weighted Exogenous Stratification Maximum Likelihood estimation and propensity score estimation. We use the National Marine Fisheries Service's Marine Recreational Fishing Statistics Survey to illustrate methods of bias reduction, employing both simulated and empirical applications. We find that propensity score based weights can significantly reduce bias in estimation. Our results indicate that failure to account for these biases can overstate anglers' willingness to pay for improvements in fishing catch, but weighted models exhibit higher variance of parameter estimates and willingness to pay.     

Combining state and transition models with dynamic Bayesian networks

Bashari et al. (2009) propose combining state and transition models (STMs) with Bayesian networks for decision support tools where the focus is on modelling the system dynamics. There is already an extension of Bayesian networks - so-called dynamic Bayesian networks (DBNs) - for explicitly modelling systems that change over time, that has also been applied in ecological modelling. In this paper we propose a combination of STMs and DBNs that overcome some of the limitations of Bashari et al.’s approach including providing an explicit representation of the next state, while retaining its advantages, such an the explicit representation of transitions. We then show that the new model can be applied iteratively to predict into the future consistently with different time frames. We use Bashari et al.’s rangeland management problem as an illustrative case study. We present a comparative complexity analysis of the different approaches, based on the structure inherent in the problem being modelled. This analysis showed that any models that explicitly represent all the transitions only remain tractable when there are natural constraints in the domain. Thus we recommend modellers should analyse these aspects of their problem before deciding whether to use the framework.