Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables

In this paper very simple nonparametric classification rule for mixtures of discrete and continuous random variables is described. It is based on the method of nearest neighbor proposed by Cover and Hart (1967). The bounds on the limit of the nearest neighbor rule risks are given. Both lower and upper bound depend on the Bayes risk and the loss function. Finally the method is compared with other existing methods on some practical data set.     

An Extreme Value Analysis of Pollutant Concentrations in Surface Soils Due to Atmospheric Deposition

Risk assessments often rely on deterministic models using long-term averages or “steady-state” values of input variables. Such models do not provide the information needed to estimate acute exposures. This study uses extreme value theory to examine the frequency and magnitude of daily pollutant concentrations in surface soils predicted at six U.S. locations. Concentrations are predicted using a deposition-leaching model and 50 years of historical precipitation data. A stochastic model also is used to generate 1000 years of precipitation data as modeling inputs for each location. The annual maximum concentrations at each site are fitted to a Gumbel type I distribution to estimate occurrence probability. For soluble pollutants, the predicted concentration varied substantially with precipitation, and the maximum daily concentrations exceeded annual averages by 4 to 8 times. Observed and synthetic precipitation data produced similar results at most study locations, though the synthetic data provided a slightly better fit to the Gumbel type I distribution. The precipitation model allows the generation of representative precipitation data that extend limited historical records. The extreme value analysis facilitates the evaluation of maximum pollutant concentrations, return periods, and other statistics that are important in evaluating acute exposures.     

Flood Risk Analysis Based on Information Diffusion Theory

Due to the fact that the flood data series of small drainage basins is relatively short, available data are often not sufficient for flood risk analysis. This presents the problem of risk analysis using very small data samples. One method that can be applied is to regard the available small samples as fuzzy information and optimize them using information diffusion technology to yield analytical results with greater reliability. In this article a risk analysis method based on information diffusion theory is applied to create a new flood risk analysis model. Application of the model is illustrated taking the Jinhuajiang and Qujiang drainage basins as examples. This is a new attempt at applying information diffusion theory in flood risk analysis. Computations based on this analytical flood risk model can yield an estimated flood damage value that is relatively accurate. This study indicates that the aforementioned model exhibits fairly stable analytical results, even when using a small set of sample data. The results also indicate that information diffusion technology is highly capable of extracting useful information and therefore improves system recognition accuracy. This method can be easily applied and the analytical results produced are easy to understand. Results are accurate enough to act as a guide in disaster situations.     

In Pursuit of Safety: One-Hundred Years of Toxicological Risk Assessment

The publication in 1962 of Rachel Carson's Silent Spring marks the mid-point in a century that saw, in its first half, the emergence of public health concerns related to human exposures to chemicals, and, in its second half, the emergence of public policies to deal with those concerns. Those policies made it imperative that the scientific community come to grips with the problem of identifying exposure levels not likely to cause harm. This problem was not significantly discussed within the scientific community until the 1950s, and well-described methods for practical solutions to it did not appear until the 1970s. An important report from the National Academy of Sciences, published in 1983 (Risk Assessment in the Federal Government), provided an analysis of these emerging methods, and recommended a useful framework for the assessment and management of risk. This framework remains central to public health and regulatory decision-making. A high-level perspective is offered on events leading to and following the 1983 report. The article describes early thinking about chemical toxicity and the scientific path that thinking followed through the 20th century, and to the present.     

Predator detection and avoidance by starlings under differing scenarios of predation risk

Practically all animals must find food while avoiding predators.An individual's perception of predation risk may depend on manyfactors, such as distance to refuge and group size, but it isunclear whether individuals respond to different factors ina similar manner. We tested whether flocks of foraging starlingsresponded in the same way to an increased perception of predationrisk by assessing three factors: (1) neighbor distances, (2)habitat obstruction, and (3) recent exposure to a predator.We found that in all three scenarios of increased risk, starlingsreduced their interscan intervals (food-searching bouts), whichincreased the frequency of their vigilance periods. We thenexamined how one of these factors, habitat obstruction, affectedescape speed by simulating an attack with a model predator.Starlings were slower to respond in visually obstructed habitats(long grass swards) and slower when they had their head downin obstructed habitats than when they had their head down inopen habitats. In addition, reaction times were quicker whenstarlings could employ their peripheral fields of vision. Ourresults demonstrate that different sources of increased riskcan generate similar behavioral responses within a species.The degree of visibility in the physical and social environmentaffects both the actual and perceived risk of predation.     

The significance of mycotoxins in the framework of assessing workplace related risks

Mycotoxins are fungal metabolite which may in some cases exhibit a high health hazard potential. Mycotoxins can show carcinogenic, mutagenic, toxic, teratogenic or immunotoxic effects. Mycotoxin exposure in the workplace may occur through inhalation and skin contact,e.g. during occupational handling of organic matter such as livestock feed, food products, or waste. Various studies suggest that both acute and chronic effects can occur, depending at least on the exposure level. The magnitude of the potential health risks associated with a respiratory or dermal intake of mycotoxins has largely remained unclear to date. However, according to the directive 2000/54/EC on biological agents and the corresponding German Biological Agents Ordinance, employers are also required to consider the potential hazards posed by toxic effects of biological agents when assessing workplace risks. The aim of this article, therefore, is to present some basis information that should facilitate an evaluation of the significance of mycotoxins in the context of assessing workplace risks. It also provides suggestions for occupational health and safety measures.     

Mass Collaboration in Earthquake Risk Management

The mechanism of mass collaboration in risk management was studied during the Sichuan earthquake under a Web-based “PeopleFinder” project, where information is contributed and shared among mass contributors. The case study is provided by a great earthquake that happened in Wenchuan County, Sichuan Province, of southwestern China at 2:28 p.m. on May 12, 2008. We witnessed and experienced the rescue and relief efforts for the great earthquake. In this article, two fundamental frameworks are developed to study the mechanism of mass collaboration. Mass collaboration is proven to be effective in a big public crisis such as the Sichuan earthquake.