Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

The spatial implications of the functional proximity deriving from air passenger flows between European metropolitan urban regions

Until recently the traditional spatial configuration of the European geography was based upon the core-periphery model. The ‘pentagon’, broadly defined as lying between London, Paris, Milan, Munich and Hamburg, was seen as the core area characterised by having the highest concentration of economic development in the European Union (EU), with the remainder of the European territory viewed as peripheral, albeit to varying degrees. In a number of cases such peripheral areas equated with clear regional disparities. The elaboration of the European Spatial Development Perspective (ESDP) (CEC, European spatial development perspective, towards balanced and sustainable development in the territory of the European Union, 1999) challenged this core-periphery model. European spatial planning policies, aimed at encouraging social and economic, and with ever increasing importance, territorial cohesion, seek amongst other aspects to encourage the development of a balanced and polycentric urban system. This paper adopts a network analysis approach to the analysis of air passenger flows between some 28 principal European metropolitan urban regions. The evaluation of these flows contributes to an enhanced comprehension of the spatial dynamics of the European metropolitan territory which goes beyond that deriving from the more standard analyses of the individual components of the urban system. Several indicators are used, deriving from gravitational modelling techniques, to analyse the complexity of the air passenger flows. A multidimensional scaling (MDS) technique is introduced in order to interpret and visualise the resulting spatial configuration and positioning of the different metropolitan centres within the conceptual European ‘space of air passenger flows’, thereby contrasting with the more traditional map-based geographical image of Europe, based upon Cartesian coordinates.

Observation of gravity waves in a boreal forest

In this paper we report the results of the analysis of two 60-min wave events that occurred in a boreal aspen forest during the 1994 BOREAS (Boreal Ecosystems-Atmosphere Study) field experiment. High frequency wind and temperature data were provided by three 3-D sonic anemometer/thermometers and fourteen fine-wire thermocouples positioned within and above the forest. Wave phase speeds, estimated from information revealed by spectral analysis and linear plane wave equations, are 2.2 and 1.3 m s^-1 for the two events. The wavelengths are 130 m and 65 m respectively and are much larger than the vertical wave displacements. There is strong evidence from the present analysis and from the literature supporting our postulate that these waves are generated by shear instability. We propose that wind shear near the top of the stand is often large enough to reduce the gradient Richardson number below the critical value of 0.25 and thus is able to trigger the instability. When external conditions are favorable, the instability will grow into waves.     

Role of Wavelets in the Physical and Statistical Modelling of Complex Geological Processes

— Today, wavelets are recognized to have a wide range of useful properties that allow them to treat effectively multifacet problems, such as data compression, scale-localization analysis, feature extraction, statistics, numerical simulation, visualization, and communication. Second-generation wavelets represent a recent improvement of the wavelet algorithm, allowing for greater flexibility in the spatial domain and other computational advantages. We will show how these wavelets can be employed to extract large-scale coherent structures from (1) three-dimensional turbulent flows and (2) high Rayleigh number thermal convection. We will discuss the concept of modelling via decomposition into coherent and incoherent fields, taking into account the effect of the incoherent field via statistical modelling. We will discuss wavelet properties and how they can be utilized and integrated in handling large-scale problems in earthquake physics and other nonlinear phenomena in the geosciences.     

ISA model: A constitutive model for soils with yield surface in the intergranular strain space

In this article, a new constitutive model for soils is proposed. It is formulated by means of plasticity, but in contrast to the precedent works, it presents a yield function describing a surface within the intergranular strain space. This latter is a state variable providing information of the recent strain history. An expression for the plastic strain rate has been proposed to guarantee the stress rate continuity. Under the application of medium or large strain amplitudes, the constitutive equation becomes independent of the intergranular strain and delivers a mathematical structure similar to some Karlsruhe hypoplastic models. Some simulations of monotonic and cyclic triaxial test are provided to evaluate and analyze the model performance. Copyright © 2015 John Wiley & Sons, Ltd.     

Carbon fluxes through major phytoplankton groups during the spring bloom and post-bloom in the Northwestern Mediterranean Sea

The carbon flux through major phytoplankton groups, defined by their pigment markers, was estimated in two contrasting conditions of the Northwestern Mediterranean open ocean ecosystem: the spring bloom and post-bloom situations (hereafter Bloom and Post-bloom, respectively). During Bloom, surface chlorophyll a (Chl a) concentration was higher and dominated by diatoms (53% of Chl a), while during Post-bloom Synechococcus (42%) and Prymnesiophyceae (29%) became dominant. The seawater dilution technique, coupled to high pressure liquid chromatography (HPLC) analysis of pigments and flow cytometry (FCM), was used to estimate growth and grazing rates of major phytoplankton groups in surface waters. Estimated growth rates were corrected for photoacclimation based on FCM-detected changes in red fluorescence per cell. Given the 30% average decrease in the pigment content per cell between the beginning and the end of the incubations, overlooking photoacclimation would have resulted in a 0.40 d^?1 underestimation of phytoplankton growth rates. Corrected average growth rates (μ_o) were 0.90±0.20 (SD) and 0.40±0.14 d^?1 for Bloom and Post-bloom phytoplankton, respectively. Diatoms, Cryptophyceae and Synechococcus were identified as fast-growing groups and Prymnesiophyceae and Prasinophyceae as slow-growing groups across Bloom and Post-bloom conditions. The higher growth rate during Bloom was due to dominance of phytoplankton groups with higher growth rates than those dominating in Post-bloom. Average grazing rates (m) were 0.58±0.20 d^?1 (SD) and 0.31±0.07 d^?1. The proportion of phytoplankton growth consumed by microzooplankton grazing (m/μ_o) tended to be lower in Bloom (0.69±0.34) than in Post-bloom (0.80±0.08). The intensity of nutrient limitation experienced by phytoplankton indicated by μ_o/μ_n (where μ_n is the nutrient-amended growth rate), was similar during Bloom (0.78) and Post-bloom (0.73). Primary production from surface water (PP) was estimated with ¹⁴C incubations. A combination of PP and Chl a synthesis rate yielded C/Chl a ratios of 34±21 and 168±75 (g:g) for Bloom and Post-bloom, respectively. Transformation of group-specific Chl a fluxes into carbon equivalents confirmed the dominant role of diatoms during Bloom and Synechococcus and Prymnesiophyceae during Post-bloom.     

On the simulation of multidimensional cyclic loading with intergranular strain

A sample of soil is subjected to multidimensional cyclic loading when two or three principal components of the stress or strain tensor are simultaneously controlled to perform a repetitive path. These paths are very useful to evaluate the performance of models simulating cyclic loading. In this article, an extension of an existing constitutive model is proposed to capture the behavior of the soil under this type of loading. The reference model is based on the intergranular strain anisotropy concept and therefore incorporates an elastic locus in terms of a strain amplitude. In order to evaluate the model performance, a modified triaxial apparatus able to perform multidimensional cyclic loading has been used to conduct some experiments with a fine sand. Simulations of the extended model with multidimensional loading paths are carefully analyzed. Considering that many cycles are simulated (\(N>30\)), some additional simulations have been performed to quantify and analyze the artificial accumulation generated by the (hypo-)elastic component of the model. At the end, a simple boundary value problem with a cyclic loading as boundary condition is simulated to analyze the model response.     

Rainfall thresholds for the triggering of landslides considering previous soil moisture conditions (Asturias,NW Spain)

Given its geological and climatic conditions and its rugged orography, Asturias is one of the most landslide prone areas in the North of Spain. Most of the landslides occur during intense rainfall episodes. Thus, precipitation is considered the main triggering factor in the study area, reaching average annual values of 960 mm. Two main precipitation patterns are frequent: (i) long-lasting periods of moderate rainfall during autumn and winter and (ii) heavy short rainfall episodes during spring and early summer. In the present work, soil moisture conditions in the locations of 84 landslides are analysed during two rainfall episodes, which represent the most common precipitation patterns: October–November 2008 and June 2010. Empirical data allowed the definition of available water capacity percentages of 99–100% as critical soil moisture conditions for the landslide triggering. Intensity-duration rainfall thresholds were calculated for each episode, considering the periods with sustained high soil moisture levels before the occurrence of each analysed landslide event. For this purpose, data from daily water balance models and weather stations were used. An inverse relationship between the duration of the precipitation and its intensity, consistent with published intensity-duration thresholds, was observed, showing relevant seasonal differences.