On 27 February 2010, a magnitude M w = 8.8 earthquake occurred off the coast of Chile's Maule region causing substantial damage and loss of life. Ancestral tsunami knowledge from the 1960 event combined with education and evacuation exercises prompted most coastal residents to spontaneously evacuate after the earthquake. Many of the tsunami victims were tourists in coastal campgrounds. The international tsunami survey team (ITST) was deployed within days of the event and surveyed 800 km of coastline from Quintero to Mehuín and the Pacific Islands of Santa María, Mocha, Juan Fernández Archipelago, and Rapa Nui (Easter). The collected survey data include more than 400 tsunami flow depth, runup and coastal uplift measurements. The tsunami peaked with a localized runup of 29 m on a coastal bluff at Constitución. The observed runup distributions exhibit significant variations on local and regional scales. Observations from the 2010 and 1960 Chile tsunamis are compared.

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A simple and novel method to quantify adulterations of extra virgin olive oil (EVOO) with refined olive oil (ROO) and refined olive-pomace oil (ROPO) is described here. This method consists of calculating chaotic parameters (Lyapunov exponent, autocorrelation coefficients, and two fractal dimensions, CPs) from UV-vis scans of adulterated EVOO samples. These parameters have been successfully linearly correlated with the ROO or ROPO concentrations in 396 EVOO adulterated samples. By an external validation process, when the adulterating agent concentration is less than 10%, the integrated CPs/UV-vis model estimates the adulterant agent concentration with a mean correlation coefficient (estimated versus real concentration of low grade olive oil) greater than 0.97 and a mean square error of less than 1%. In light of these results, this detector is suitable not only to detect adulterations but also to measure impurities when, for instance, a higher grade olive oil is transferred to another storage tank in which lower grade olive oil was stored that had not been adequately cleaned.

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Self-organizing map (SOM) and learning vector quantification network (LVQ) models have been explored for the identification of edible and vegetable oils and to detect adulteration of extra virgin olive oil (EVOO) using the most common chemicals in these oils, viz. saturated fatty (mainly palmitic and stearic acids), oleic and linoleic acids. The optimization and validation processes of the models have been carried out using bibliographical sources, that is, a database for developing learning process and internal validation, and six other different databases to perform their external validation. The model's performances were analyzed by the number of misclassifications. In the worst of the cases, the SOM and LVQ models are able to classify more than the 94% of samples and detect adulterations of EVOO with corn, soya, sunflower, and hazelnut oils when their oil concentrations are higher than 10, 5, 5, and 10%, respectively.