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Barrera, J., & Lagos, G. (2020). Limit distributions of the upper order statistics for the Levy-frailty Marshall-Olkin distribution. Extremes, 23, 603–628.
Abstract: The Marshall-Olkin (MO) distribution is considered a key model in reliability theory and in risk analysis, where it is used to model the lifetimes of dependent components or entities of a system and dependency is induced by “shocks” that hit one or more components at a time. Of particular interest is the Levy-frailty subfamily of the Marshall-Olkin (LFMO) distribution, since it has few parameters and because the nontrivial dependency structure is driven by an underlying Levy subordinator process. The main contribution of this work is that we derive the precise asymptotic behavior of the upper order statistics of the LFMO distribution. More specifically, we consider a sequence ofnunivariate random variables jointly distributed as a multivariate LFMO distribution and analyze the order statistics of the sequence asngrows. Our main result states that if the underlying Levy subordinator is in the normal domain of attraction of a stable distribution with index of stability alpha then, after certain logarithmic centering and scaling, the upper order statistics converge in distribution to a stable distribution if alpha> 1 or a simple transformation of it if alpha <= 1. Our result can also give easily computable confidence intervals for the last failure times, provided that a proper convergence analysis is carried out first.
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Dragano, M. A., Seccatore, J., Cardu, M., Marin, T., & Bettencourt, J. (2019). Influence of blasting charges and delays on the energy consumption of mechanical crushing. REM, 72(2).
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Espinoza, C., Seccatore, J., & Herrera, M. (2020). Chilean artisanal mining: a gambling scenario. REM, 73(2).
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Lopatin. (2023). Interannual Variability of Remotely Sensed Phenology Relates to Plant Communities. IEEE Geosci. Remote. Sens. Lett., 20, 2502405.
Abstract: Vegetation phenology is considered an essential biological indicator in understanding the behavior of ecosystems and how they respond to environmental cues. However, the potential of interannual variations of remotely sensed phenology signals to differentiate plant types remains poorly understood, especially in understudied systems with highly heterogeneous landscapes such as wetlands. This study presents a case study in a San Francisco Bay area marsh that investigates the usefulness of interannual variation, defined as the root-mean-square error of enhanced vegetation index (EVI) measurements against a fitted phenology curve, at the beginning, middle, and end of the growing season as indicators of plant types. The study found that altitude above sea level and certain land surface phenology metrics, such as the day-of-the-year of the end of the season, the mid-autumn day, and the greening rate before the summer peak, were significantly related to these interannual variation trends. These results indicate that a detailed time-series analysis at the beginning and end of growing seasons may enhance large-scale wetland characterization. Overall, the findings of this study contribute to our understanding of vegetation phenology and provide a framework for more accurate wetland classification in future studies.
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Lopatin, J. (2023). Estimation of Foliar Carotenoid Content Using Spectroscopy Wavelet-Based Vegetation Indices. IEEE Geosci. Remote. Sens. Lett., 20, 2500405.
Abstract: The plant carotenoid (Car) content plays a crucial role in the xanthophyll cycle and provides essential information on the physiological adaptations of plants to environmental stress. Spectroscopy data are essential for the nondestructive prediction of Car and other traits. However, Car content estimation is still behind in terms of accuracy compared to other pigments, such as chlorophyll (Chl). Here, I examined the potential of using the continuous wavelet transform (CWT) on leaf reflectance data to create vegetation indices (VIs). I compared six CWT mother families and six scales and selected the best overall dataset using random forest (RF) regressions. Using a brute-force approach, I created wavelet-based VIs on the best mother family and compared them against established Car reflectance-based VIs. I found that wavelet-based indices have high linear sensitivity to the Car content, contrary to typical nonlinear relationships depicted by the reflectance-based VIs. These relations were theoretically contrasted with the synthetic data created using the PROSPECT-D radiative transfer model. However, the best selection of wavelength bands in wavelet-based VIs varies greatly depending on the spectral characteristics of the input data before the transformation.
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Roman, M. T. (2023). Mid-Infrared Observations of the Giant Planets. Remote Sensing, 15(7), 1811.
Abstract: The mid-infrared spectral region provides a unique window into the atmospheric temperature, chemistry, and dynamics of the giant planets. From more than a century of mid-infrared remote sensing, progressively clearer pictures of the composition and thermal structure of these atmospheres have emerged, along with a greater insight into the processes that shape them. Our knowledge of Jupiter and Saturn has benefitted from their proximity and relatively warm temperatures, while the details of colder and more distant Uranus and Neptune are limited as these planets remain challenging targets. As the timeline of observations continues to grow, an understanding of the temporal and seasonal variability of the giant planets is beginning to develop with promising new observations on the horizon.
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Seccatore, J. (2019). A review of the benefits for comminution circuits offered by rock blasting. REM, 72(1).
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Seccatore, J., Gonzalez, P., & Herrera, M. (2020). Peculiarities of drilling and blasting in underground small-scale mines. REM, 73(3).
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Tolorza, V., Poblete-Caballero, D., Banda, D., Little, C., Leal, C., & Galleguillos, M. (2022). An operational method for mapping the composition of post-fire litter. Remote Sens. Lett., 13(5), 511–521.
Abstract: Recent increase in the frequency and spatial extent of wildfires motivates the quick recognition of the affected soil properties over large areas. Digital Soil Mapping is a valuable approach to map soil attributes based on remote sensing and field observations. We predicted the spatial distribution of post-fire litter composition in a 40,600 ha basin burned on the 2017 wildfire of Chile. Remotely sensed data of topography, vegetation structure and spectral indices (SI) were used as predictors of random forest (RF) models. Litter sampled in 60 hillslopes after the fire provided training and validation data. Predictors selected by the Variable Selection Using Random Forests (VSURF) algorithm resulted in models for litter composition with acceptable accuracy (coefficient of determination, R (2) = 0.51-0.64, Normalized Root Mean Square Error, NRMSE = 16.9-22.1, percentage bias, pbias = -0.35%-0.5%). Modelled litter parameters decrease in concentration respect to the degree of burn severity, and the pre-fire biomass. Because pre-fire vegetation was conditioned by land cover and by a previous (2 years old) wildfire event, our results highlight the cumulative effect of severe wildfires in the depletion of litter composition.
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