Applying neighbourhood classification systems to natural hazards: a case study of Mt Vesuvius

The dynamic forces of urbanisation that characterised much of the 20th Century and still dominate population growth in developing countries have led to the increasing risk of natural hazards in cities around the world (Chester 2000, Pelling 2003). None of these physical dangers is more tangible than the threat volcanoes pose to the large populations living in close proximity. Vesuvius, a recognised decade volcano following the UN’s International Decade for Natural Disaster Reduction (IDNDR) has an estimated 550,000 people that live in areas susceptible to Pyroclastic Density Currents (PDC) (Barberi 2008) and a further 4 million at risk from ash fallout around the sprawling suburbs of Naples. Though quiescent since 1944, the prospect of a large eruption of Vesuvius presents a greater geophysical threat to the Campania region of Italy than perhaps ever before. With the Neopolitan region at risk from such an event, this paper proposes a new methodology for creating a Social Vulnerability Index (SoVi) using geodemographic classification systems. In this study, Experian’s MOSAIC Italy database is combined with geophysical risk boundaries to assess the overall vulnerability of the population around Vesuvius

Incorporating concepts of inequality and inequity into health benefits analysis

BACKGROUND: Although environmental policy decisions are often based in part on both risk assessment information and environmental justice concerns, formalized approaches for addressing inequality or inequity when estimating the health benefits of pollution control have been lacking. Inequality indicators that fulfill basic axioms and agree with relevant definitions and concepts in health benefits analysis and environmental justice analysis can allow for quantitative examination of efficiency-equality tradeoffs in pollution control policies. METHODS: To develop appropriate inequality indicators for health benefits analysis, we provide relevant definitions from the fields of risk assessment and environmental justice and consider the implications. We evaluate axioms proposed in past studies of inequality indicators and develop additional axioms relevant to this context. We survey the literature on previous applications of inequality indicators and evaluate five candidate indicators in reference to our proposed axioms. We present an illustrative pollution control example to determine whether our selected indicators provide interpretable information. RESULTS AND CONCLUSIONS: We conclude that an inequality indicator for health benefits analysis should not decrease when risk is transferred from a low-risk to high-risk person, and that it should decrease when risk is transferred from a high-risk to low-risk person (Pigou-Dalton transfer principle), and that it should be able to have total inequality divided into its constituent parts (subgroup decomposability). We additionally propose that an ideal indicator should avoid value judgments about the relative importance of transfers at different percentiles of the risk distribution, incorporate health risk with evidence about differential susceptibility, include baseline distributions of risk, use appropriate geographic resolution and scope, and consider multiple competing policy alternatives. Given these criteria, we select the Atkinson index as the single indicator most appropriate for health benefits analysis, with other indicators useful for sensitivity analysis. Our illustrative pollution control example demonstrates how these indices can help a policy maker determine control strategies that are dominated from an efficiency and equality standpoint, those that are dominated for some but not all societal viewpoints on inequality averseness, and those that are on the optimal efficiency-equality frontier, allowing for more informed pollution control policies

Single marker association analysis for unrelated samples

10.1007/978-1-61779-555-8_18Methods in Molecular Biology850347-35

JaSt: Fully Syntactic Detection of Malicious (Obfuscated) JavaScript

JavaScript is a browser scripting language initially created to enhance the interactivity of web sites and to improve their user-friendliness. However, as it offloads the work to the user's browser, it can be used to engage in malicious activities such as Crypto-Mining, Drive-by-Download attacks, or redirections to web sites hosting malicious software. Given the prevalence of such nefarious scripts, the anti-virus industry has increased the focus on their detection. The attackers, in turn, make increasing use of obfuscation techniques, so as to hinder analysis and the creation of corresponding signatures. Yet these malicious samples share syntactic similarities at an abstract level, which enables to bypass obfuscation and detect even unknown malware variants. In this paper, we present JaSt, a low-overhead solution that combines the extraction of features from the abstract syntax tree with a random forest classifier to detect malicious JavaScript instances. It is based on a frequency analysis of specific patterns, which are either predictive of benign or of malicious samples. Even though the analysis is entirely static, it yields a high detection accuracy of almost 99.5% and has a low false-negative rate of 0.54%