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.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

USING FIELD SCALE MODELS TO PREDICT PEAK FLOWS ON AGRICULTURAL WATERSHEDS1

The goal of this study was to develop a methodology for generating storm hydrographs at a watershed scale based on daily runoff estimates from a field scale model. The methodology was evaluated on a small agricultural watershed using the ADAPT field scale process model. A comparison of observed and predicted peak flows for 11 of the largest events that occurred in a three year period gave r² values of 0.84, 0.82, and 0.81 when the watershed was subdivided into 1, 5, and 10 sub watersheds. However, all other statistical measures improved when the watershed was subdivided into at least five sub watersheds. Guidelines need to be developed on the use of the procedure but it first needs to be evaluated on several watersheds that exhibit a range in sizes, land uses, slopes, and soil properties.     

Light-limited population dynamics of phytoplankton: modeling light and depth effects

Light has received very less attention as a selective factor, probably because the changing vertical light gradient makes light a rather complex factor when compared with the nutrient competition. In our study we modified an analytically tractable model based upon the Beer–Lambert law. The study aimed to predict the outcome of phytoplankton competition for light, on the basis of field measurements. When we considered competition theory, Fragilaria was found to be more competitive than other genera. The overall result shows that the trends flow in the following manner; Fragilaria > Gomphonema > Diatoma > Amphora > Achnanthes.     

Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude –0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude –0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude –0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude –0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at –0.3769.     

Sensitivity of normal theory methods to model misspecification in the calculation of upper confidence limits on the risk function for continuous responses

Normal theory procedures for calculating upper confidence limits (UCL) on the risk function for continuous responses work well when the data come from a normal distribution. However, if the data come from an alternative distribution, the application of the normal theory procedures may lead serious over- or under-coverage depending upon the alternative distribution. In this paper we conduct simulation studies to investigate the sensitivity of three normal theory UCL procedures to departures from normality. Data from several gamma, reciprocal gamma, and lognormal distributions are considered. The normal theory procedures are applied to both the raw data and the log-transformed data.     

Sequential extraction of copper from soils and relationships with copper in maize

We studied copper uptake by maize grown on soils that have been contaminated with CuSO₄. In soil the total copper level ranged from 24 to 135 mg kg^–1. The copper distribution in soil fractions was assessed by sequential extraction, showing that anthropogenic copper is mainly concentrated in oxides fractions. The copper concentration of maize at the maturity stage reached values from 36.3 to 65.9 mg kg^–1 compared to copper levels usually found in non-contaminated crops (5–30 mg kg^–1). Here we demonstrate that copper can be accumulated by maize and that copper concentration in maize can be predicted by equations including copper concentration of soil fractions.     

MODELING WATER UTILITY FINANCIAL PERFORMANCE1

ABSTRACT: The purpose of this paper is to present a theory and model for assessing the financial health of public water systems. Using financial information from 25 water utilities in Georgia, the paper seeks to identify the causal relationships between the financial performance of a water utility and its fiscal position. The need for a theoretical understanding of water utility financial health is the result of the increasingly stringent performance requirements under the Safe Drinking Water Act (SDWA). The issue has become particularly important for small water systems that will be exposed to significant financial demands. A set of financial ratios were developed and tested in a model that was based on liquid asset theory. The model contained five variables designed to account for the size of liquid assets, current debt, cash-flow, and the level of expenses. The variables fit the need of water utilities: to provide an adequate level of operation and maintenance to meet current and future system needs as well as SDWA standards.     

Zeolite formation from coal fly ash and heavy metal ion removal characteristics of thus-obtained Zeolite X in multi-metal systems

Zeolitic materials have been prepared from coal fly ash as well as from a SiO₂–Al₂O₃ system upon NaOH fusion treatment, followed by subsequent hydrothermal processing at various NaOH concentrations and reaction times. During the preparation process, the starting material initially decomposed to an amorphous form, and the nucleation process of the zeolite began. The carbon content of the starting material influenced the formation of the zeolite by providing an active surface for nucleation. Zeolite A (Na-A) was transformed into zeolite X (Na-X) with increasing NaOH concentration and reaction time. The adsorption isotherms of the obtained Na-X based on the characteristics required to remove heavy ions such as Ni²⁺, Cu²⁺, Cd²⁺ and Pb²⁺ were examined in multi-metal systems. Thus obtained experimental data suggests that the Langmuir and Freundlich models are more accurate compared to the Dubinin–Kaganer–Radushkevich (DKR) model. However, the sorption energy obtained from the DKR model was helpful in elucidating the mechanism of the sorption process. Further, in going from a single- to multi-metal system, the degree of fitting for the Freundlich model compared with the Langmuir model was favored due to its basic assumption of a heterogeneity factor. The Extended-Langmuir model may be used in multi-metal systems, but gives a lower value for equilibrium sorption compared with the Langmuir model.     

Household water use behavior: An integrated model

Water authorities are dealing with the challenge of ensuring that there is enough water to meet demand in the face of drought, population growth and predictions of reduced supply due to climate change. In order to develop effective household demand management programs, water managers need to understand the factors that influence household water use. Following an examination and re-analysis of current water consumption behavioral models we propose a new model for understanding household water consumption. We argue that trust plays a role in household water consumption, since people will not save water if they feel others are not minimizing their water use (inter-personal trust). Furthermore, people are less likely to save water if they do not trust the water authority (institutional trust). This paper proposes that to fully understand the factors involved in determining household water use the impact of trust on water consumption needs investigation.