29,068 research outputs found

    The three-dimensional structure of sunspots II. The moat flow at two different heights

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    Many sunspots are surrounded by a radial outflow called the moat flow. We investigate the moat flow at two different heights of the solar atmosphere for a sunspot whose magnetic properties were reported in the first paper of this series. We use two simultaneous time series taken with the Transition Region And Coronal Explorer (TRACE) in white light and in the UV at 170 nm. The field-of-view is centered on the small sunspot NOAA 10886 located near disk center. Horizontal velocities are derived by applying two different local correlation tracking techniques. Outflows are found everywhere in the moat. In the inner moat, the velocities from the UV series are larger than those from white light, whereas in the outer part of the moat we find the converse result. The results imply that the white light velocities represent a general outflow of the quiet sun plasma in the moat, while UV velocities are dominated by small bright points that move faster than the general plasma flow.Comment: Manuscript accepted by Astronomy & Astrophysic

    An evolutionary modelling approach to predicting stress-strain behaviour of saturated granular soils

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    Purpose: To develop a unified framework for modelling triaxial deviator stress - axial strain and volumetric strain – axial strain behaviour of granular soils with the ability to predict the entire stress paths, incrementally, point by point, in deviator stress versus axial strain and volumetric strain versus axial strain spaces using an evolutionary-based technique based on a comprehensive set of data directly measured from triaxial tests without pre-processing. 177 triaxial test results acquired from literature were used to develop and validate the models. Models aimed not only to be capable of capturing and generalising the complicated behaviour of soils but also to explicitly remain consistent with expert knowledge available for such behaviour. Methodology: Evolutionary polynomial regression was used to develop models to predict stress - axial strain and volumetric strain – axial strain behaviour of granular soils. EPR integrates numerical and symbolic regression to perform evolutionary polynomial regression. The strategy uses polynomial structures to take advantage of favourable mathematical properties. EPR is a two-stage technique for constructing symbolic models. It initially implements evolutionary search for exponents of polynomial expressions using a genetic algorithm (GA) engine to find the best form of function structure, secondly it performs a least squares regression to find adjustable parameters, for each combination of inputs (terms in the polynomial structure). Findings: EPR-based models were capable of generalizing the training to predict the behaviour of granular soils under conditions that have not been previously seen by EPR in the training stage. It was shown that the proposed EPR models outperformed ANN and provided closer predictions to the experimental data cases. The entire stress paths for the shearing behaviour of granular soils using developed model predictions were created with very good accuracy despite error accumulation. Parametric study results revealed the consistency of developed model predictions, considering roles of various contributing parameters, with physical and engineering understandings of the shearing behaviour of granular soils. Originality/Value: In this paper, an evolutionary-based data-mining method was implemented to develop a novel unified framework to model the complicated stress-strain behaviour of saturated granular soils. The proposed methodology overcomes the drawbacks of artificial neural network-based models with black box nature by developing accurate, explicit, structured and user-friendly polynomial models, and enabling the expert user to obtain a clear understanding of the system

    Statistical Inferences for Polarity Identification in Natural Language

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    Information forms the basis for all human behavior, including the ubiquitous decision-making that people constantly perform in their every day lives. It is thus the mission of researchers to understand how humans process information to reach decisions. In order to facilitate this task, this work proposes a novel method of studying the reception of granular expressions in natural language. The approach utilizes LASSO regularization as a statistical tool to extract decisive words from textual content and draw statistical inferences based on the correspondence between the occurrences of words and an exogenous response variable. Accordingly, the method immediately suggests significant implications for social sciences and Information Systems research: everyone can now identify text segments and word choices that are statistically relevant to authors or readers and, based on this knowledge, test hypotheses from behavioral research. We demonstrate the contribution of our method by examining how authors communicate subjective information through narrative materials. This allows us to answer the question of which words to choose when communicating negative information. On the other hand, we show that investors trade not only upon facts in financial disclosures but are distracted by filler words and non-informative language. Practitioners - for example those in the fields of investor communications or marketing - can exploit our insights to enhance their writings based on the true perception of word choice

    Rheological effects in the linear response and spontaneous fluctuations of a sheared granular gas

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    The decay of a small homogeneous perturbation of the temperature of a dilute granular gas in the steady uniform shear flow state is investigated. Using kinetic theory based on the inelastic Boltzmann equation, a closed equation for the decay of the perturbation is derived. The equation involves the generalized shear viscosity of the gas in the time-dependent shear flow state, and therefore it predicts relevant rheological effects beyond the quasi-elastic limit. A good agreement is found when comparing the theory with molecular dynamics simulation results. Moreover, the Onsager postulate on the regression of fluctuations is fulfilled

    Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics

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    First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.Web of Science421049
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