A pesar de la reconocida importancia de los incendios forestales, la investigación sobre modelación de ocurrencia de incendios es escasa en el Hemisferio Sur, y en particular en Chile. Se estudió el efecto de la heterogeneidad del paisaje en la ocurrencia de incendios forestales en Chile Central. Se ajustó un modelo que incluye variables climáticas, topográficas, de actividad humana y cobertura del suelo. La probabilidad de ocurrencia aumentó con una mayor temperatura mínima y estacionalidad de la precipitación, y disminuyó con una mayor distancia a las ciudades. El modelo clasificó correctamente un 65% de los datos de validación. Las predicciones sugieren que un 38% del área de estudio tiene muy alta probabilidad de ocurrencia de incendios, la cual aumenta bajo un simulado escenario futuro. El modelo predictivo debería ser utilizado como una herramienta para orientar aspectos urgentes de protección y prevención, lo cual debe continuar siendo discutido y evaluado.

The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson–Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model’s predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress–strain behavior of Ti64 alloy and three virtual materials.

As diseases caused by solar radiation have gained great prominence, several methods to prevent them have been developed. Among the most common, the use of sunscreens is customary and accessible. The application of theoretical methods has helped to design new compounds with therapeutic and protective functions. Natural compounds with described photoprotective potential properties (3-O-methylquercetin, gallic acid, aloin, catechin, quercetin, and resveratrol) were selected to perform theoretical studies. Computational methods were applied to predict their absorption spectra, using DFT and TD-DFT methods with functional B3LYP/6−311+g(d,p) basis sets and methanol (IEFPCM) as a solvent. The main electronic transitions of the compounds were evaluated by observing whether the differences in HOMO and LUMO energies that absorb in the UV range are UVA (320–400 nm), UVB (290–320 nm), or UVC (100–290 nm). Experimental validation was carried out for EMC, quercetin, and resveratrol, demonstrating the consistency of the computational method. Results obtained suggest that resveratrol is a candidate for use in sunscreens. The study provided relevant information about the in silico predictive power of natural molecules with the potential for use as photoprotective adjuvants, which may result in fewer time and resource expenditures in the search for photoprotective compounds.

Climate change scenarios are computed on a large scale, not accounting for local variations presented in historical data and related to human scale. Based on historical records, we validate a baseline (1962–1990) and correct the bias of A2 and B2 regional projections for the end of twenty-first century (2070–2100) issued from a high resolution dynamical downscaled (using PRECIS mesoscale model, hereinafter DGF-PRECIS) of Hadley GCM from the IPCC 3rd Assessment Report (TAR). This is performed for the Araucanía Region (Chile; 37°–40°S and 71°–74°W) using two different bias correction methodologies. Next, we study high-resolution precipitations to find monthly patterns such as seasonal variations, rainfall months, and the geographical effect on these two scenarios. Finally, we compare the TAR projections with those from the recent Assessment Report 5 (AR5) to find regional precipitation patterns and update the Chilean `projection. To show the effects of climate change projections, we compute the rainfall climatology for the Araucanía Region, including the impact of ENSO cycles (El Niño and La Niña events). The corrected climate projection from the high-resolution dynamical downscaled model of the TAR database (DGF-PRECIS) show annual precipitation decreases: B2 (−19.19 %, −287 ± 42 mm) and A2 (−43.38 %, −655 ± 27.4 mm per year. Furthermore, both projections increase the probability of lower rainfall months (lower than 100 mm per month) to 64.2 and 72.5 % for B2 and A2, respectively.