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.     

An aromatase inhibitor and tamoxifen decrease plasma levels of o,p'-DDT and its metabolites in Nile tilapia (Oreochromis niloticus)

The objective of this study was to determine if tamoxifen or an aromatase inhibitor (4-hydroxyandrostenedione; 4-OHA) affected plasma concentrations of o,p' -DDT and its metabolites, o,p'-DDD and o,p'-DDE, in mature tilapia. Male and female tilapia were injected 6 times intraperitoneally with o,p'-DDT (40 mg/kg) alone or in combination with 4-OHA (2 mg/kg) over a 12 day period. An additional group of male fish was injected with tamoxifen (5 mg/kg) plus o,p'-DDT. At the end of the treatment period, plasma samples were extracted and analyzed by GC/ECD. Females injected only with o,p'-DDT had significantly higher levels of o,p'-DDT compared with males. Interestingly, females and males treated concomitantly with o,p'-DDT and 4-OHA or tamoxifen had significantly lower concentrations of plasma o,p'-DDT (about 50%) compared with fish treated with only o,p'-DDT. These initial results suggest that an interaction between endocrine-active compounds occurs in vivo in tilapia and may involve alterations in metabolism of o,p'-DDT.     

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.     

Interactive discovery of sequential patterns in time series of wind data

Wind speed and direction vary over space and time due to the interactions between different pressures and temperature gradients within the atmospheric layers. Near the earth’s surface, these interactions are modulated by topography and artificial structures. Hence, characterizing wind behaviour over large areas and long periods is a complex but essential task for various energy-related applications. In this study, we present a novel approach to discover wind patterns by integrating sequential pattern mining and interactive visualization techniques. The approach relies on the use of the Linear time Closed pattern Miner sequence algorithm in conjunction with a time sliding window that allows the discovery of all sequential patterns present in the data. These patterns are then visualized using integrated 2D and 3D coordinated multiple views and visually explored to gain insight into the characteristics of the wind from a spatial, temporal and attribute (type of wind pattern) point of view. This proposed approach is used to analyse 10 years of hourly wind speed and direction data for 29 weather stations in the Netherlands. The results show that there are 15 main sequential patterns in the data. The spatial task shows that weather stations located in the same region do not necessarily experience similar wind pattern. For within the selected time interval, similar wind patterns can be observed in different stations and in the same station at different times of occurrence. The attribute task discovered that the repetitive occurrences of chosen pattern indicate as regular wind behaviour at different weather stations that persisted continuously over time. The results of these tasks show that the proposed interactive discovery facilitates the understanding of wind dynamics in space and time.     

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.     

内蒙古鄂尔多斯高原古冰缘遗迹科学考察研究进展

冰缘遗迹(特别是冷生楔形构造及融冻褶皱)是重建古气候及第四纪晚期多年冻土环境的重要证据.内蒙古鄂尔多斯高原是我国北方地区冰缘现象最为发育的地区之一.为准确了解鄂尔多斯高原冰缘遗迹类型及其分布特征、区域冻土演化历史等,中国科学院西北生态环境资源研究院和荷兰自由大学共同组成科研小组,于2018年5—6月组织了"鄂尔多斯高原...