Search results for " prediction"
showing 10 items of 366 documents
Theoretical Determination of the pK a Values of Betalamic Acid Related to the Free Radical Scavenger Capacity: Comparison Between Empirical and Quant…
2015
Health benefits of dietary phytochemicals have been suggested in recent years. Among 1000s of different compounds, Betalains, which occur in vegetables of the Cariophyllalae order (cactus pear fruits and red beet), have been considered because of reducing power and potential to affect redox-modulated cellular processes. The antioxidant power of Betalains is strictly due to the dissociation rate of the acid moieties present in all the molecules of this family of phytochemicals. Experimentally, only the pK a values of betanin were determined. Recently, it was evidenced it was evidenced as the acid dissociation, at different environmental pHs, affects on its electron-donating capacity, and fur…
Prediction Models for Age-at-Death Estimates for Calves, Using Unfused Epiphyses and Diaphyses
2013
For cattle (Bos taurus), age estimations using dental criteria before the eruption of the first molar (3-8months) have large error margins. This hampers archaeozoological investigation into perinatal mortality or the putative slaughtering of very young calves for milk exploitation. Previous ageing methods for subjuveniles have focused on the length of unfused bones, but it is rarely possible to use them because they are restricted to foetuses and because of the fragmentation of bones. This paper presents new age prediction models based on length, breadth and depth of post cranial bones produced from a dataset of modern calves (n=27). This reference collection was compiled from material of k…
Exploiting deep learning algorithms and satellite image time series for deforestation prediction
2022
In recent years, we have witnessed the emergence of Deep Learning (DL) methods, which have led to enormous progress in various fields such as automotive driving, computer vision, medicine, finances, and remote sensing data analysis. The success of these machine learning methods is due to the ever-increasing availability of large amounts of information and the computational power of computers. In the field of remote sensing, we now have considerable volumes of satellite images thanks to the large number of Earth Observation (EO) satellites orbiting the planet. With the revisit time of satellites over an area becoming shorter and shorter, it will probably soon be possible to obtain daily imag…
Biodegradability Prediction of Fragrant Molecules by Molecular Topology
2016
Biodegradability is a key property in the development of safer fragrances. In this work we present a green methodology for its preliminary assessment. The structure of various fragrant molecules is characterized by computing a large set of topological indices. Those relevant to biodegradability are selected by means of a hybrid stepwise selection method to build a linear classifier. This model is compared with a more complex artificial neural network trained with the indices previously found. After validation, the models show promise for time and cost reduction in the development of new, safer fragrances. The methodology presented could easily be adapted to many quasi-big data problems in R…
A Review of Kernel Methods in Remote Sensing Data Analysis
2011
Kernel methods have proven effective in the analysis of images of the Earth acquired by airborne and satellite sensors. Kernel methods provide a consistent and well-founded theoretical framework for developing nonlinear techniques and have useful properties when dealing with low number of (potentially high dimensional) training samples, the presence of heterogenous multimodalities, and different noise sources in the data. These properties are particularly appropriate for remote sensing data analysis. In fact, kernel methods have improved results of parametric linear methods and neural networks in applications such as natural resource control, detection and monitoring of anthropic infrastruc…
An Application of Hybrid Models in Credit Scoring
2000
The predictive capability of parametric and non-parametric models in solving problems related to financial classification has been widely proved in empirical research carried out in the financial field, particulary in problems like bond rating, bankruptcy prediction and credit scoring. However, recently, it has been shown that a combination of different models generally reduces the prediction error, so that the best alternative to consider may not be a specific model but a combination of them. In this paper, we study hybrid systems based on the aggregation of individual (parametric and nonparametric) models. Our hybrids are built by using both parametric and non parametric models as the sys…
Mixed-Phase Clouds: Progress and Challenges
2017
Mixed-phase clouds represent a three-phase colloidal system consisting of water vapor, ice particles, and coexisting supercooled liquid droplets. Mixed-phase clouds are ubiquitous in the troposphere, occurring at all latitudes from the polar regions to the tropics. Because of their widespread nature, mixed-phase processes play critical roles in the life cycle of clouds, precipitation formation, cloud electrification, and the radiative energy balance on both regional and global scales. Yet, in spite of many decades of observations and theoretical studies, our knowledge and understanding of mixed-phase cloud processes remains incomplete. Mixed-phase clouds are notoriously difficult to represe…
Comparative assessment of RAMS and WRF short-term forecasts over Eastern Iberian Peninsula using various in-situ observations, remote sensing product…
2018
The Regional Atmospheric Modeling System (RAMS) and the Weather Research and Forecasting (WRF) mesoscale models are being used for weather and air quality studies as well as forecasting tools in Numerical Weather Prediction (NWP) systems. In the current study, we perform a comparative assessment of these models under distinct typical atmospheric conditions, classified according to the dominant wind flow and cloudiness, over Eastern Iberian Peninsula. This study is focused on the model representation of key physical processes in terms of meteorology and surface variables during a 7-days period in summer 2011. The hourly outputs produced by these two models are compared not only with observed…
Aviation Contrail Cirrus and Radiative Forcing Over Europe During 6 Months of COVID‐19
2021
Abstract The COVID‐19 pandemic led to a 72% reduction of air traffic over Europe in March–August 2020 compared to 2019. Modeled contrail cover declined similarly, and computed mean instantaneous radiative contrail forcing dropped regionally by up to 0.7 W m−2. Here, model predictions of cirrus optical thickness and the top‐of‐atmosphere outgoing longwave and reflected shortwave irradiances are tested by comparison to Meteosat‐SEVIRI‐derived data. The agreement between observations and modeled data is slightly better when modeled contrail cirrus contributions are included. The spatial distributions and diurnal cycles of the differences in these data between 2019 and 2020 are partially caused…
Gaussian Process Sensitivity Analysis for Oceanic Chlorophyll Estimation
2017
Source at https://doi.org/10.1109/JSTARS.2016.2641583. Gaussian process regression (GPR) has experienced tremendous success in biophysical parameter retrieval in the past years. The GPR provides a full posterior predictive distribution so one can derive mean and variance predictive estimates, i.e., point-wise predictions and associated confidence intervals. GPR typically uses translation invariant covariances that make the prediction function very flexible and nonlinear. This, however, makes the relative relevance of the input features hardly accessible, unlike in linear prediction models. In this paper, we introduce the sensitivity analysis of the GPR predictive mean and variance functions…