Predicting Seismic Liquefaction Using Neural Networks

Neural networks have emerged as a powerful computational technique for modeling nonlinear multivariate relationships. The neural network is a product of artificial intelligence research. This paper examines the feasibility of using neural networks for assessing liquefaction potential, from actual field records. The paper starts with a brief overview of the basic architecture and concepts of neural networks. The application of the neural network methodology to evaluate seismic liquefaction potential is then presented

Fermi Surface Reconstruction in CeRh1−x_{1-x}Cox_{x}In5_{5}

The evolution of the Fermi surface of CeRh$_{1-x}$Co$_x$In$_5$ was studied as a function of Co concentration $x$ via measurements of the de Haas-van Alphen effect. By measuring the angular dependence of quantum oscillation frequencies, we identify a Fermi surface sheet with $f$-electron character which undergoes an abrupt change in topology as $x$ is varied. Surprisingly, this reconstruction does not occur at the quantum critical concentration $x_c$, where antiferromagnetism is suppressed to T=0. Instead we establish that this sudden change occurs well below $x_c$, at the concentration x ~ 0.4 where long range magnetic order alters its character and superconductivity appears. Across all concentrations, the cyclotron effective mass of this sheet does not diverge, suggesting that critical behavior is not exhibited equally on all parts of the Fermi surface.Comment: 4 pages, 4 figure

Estimation of diaphragm wall deflections for deep braced excavation in anisotropic clays using ensemble learning

This paper adopts the NGI-ADP soil model to carry out finite element analysis, based on which the effects of soft clay anisotropy on the diaphragm wall deflections in the braced excavation were evaluated. More than one thousand finite element cases were numerically analyzed, followed by extensive parametric studies. Surrogate models were developed via ensemble learning methods (ELMs), including the eXtreme Gradient Boosting (XGBoost), and Random Forest Regression (RFR) to predict the maximum lateral wall deformation (δhmax). Then the results of ELMs were compared with conventional soft computing methods such as Decision Tree Regression (DTR), Multilayer Perceptron Regression (MLPR), and Multivariate Adaptive Regression Splines (MARS). This study presents a cutting-edge application of ensemble learning in geotechnical engineering and a reasonable methodology that allows engineers to determine the wall deflection in a fast, alternative way

Open-ended evolution to discover analogue circuits for beyond conventional applications

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s10710-012-9163-8. Copyright @ Springer 2012.Analogue circuits synthesised by means of open-ended evolutionary algorithms often have unconventional designs. However, these circuits are typically highly compact, and the general nature of the evolutionary search methodology allows such designs to be used in many applications. Previous work on the evolutionary design of analogue circuits has focused on circuits that lie well within analogue application domain. In contrast, our paper considers the evolution of analogue circuits that are usually synthesised in digital logic. We have developed four computational circuits, two voltage distributor circuits and a time interval metre circuit. The approach, despite its simplicity, succeeds over the design tasks owing to the employment of substructure reuse and incremental evolution. Our findings expand the range of applications that are considered suitable for evolutionary electronics

Fermi-Surface Reconstruction in the Periodic Anderson Model

We study ground state properties of periodic Anderson model in a two-dimensional square lattice with variational Monte Carlo method. It is shown that there are two different types of quantum phase transition: a conventional antiferromagnetic transition and a Fermi-surface reconstruction which accompanies a change of topology of the Fermi surface. The former is induced by a simple back-folding of the Fermi surface while the latter is induced by localization of $f$ electrons. The mechanism of these transitions and the relation to the recent experiments on Fermi surface are discussed in detail.Comment: 8 pages, 7 figures, submitted to Journal of the Physical Society of Japa

Cosmic Ray Spectra in Nambu-Goldstone Dark Matter Models

We discuss the cosmic ray spectra in annihilating/decaying Nambu-Goldstone dark matter models. The recent observed positron/electron excesses at PAMELA and Fermi experiments are well fitted by the dark matter with a mass of 3TeV for the annihilating model, while with a mass of 6 TeV for the decaying model. We also show that the Nambu-Goldstone dark matter models predict a distinctive gamma-ray spectrum in a certain parameter space.Comment: 16 pages, 4 figure

Weblog patterns and human dynamics with decreasing interest

Weblog is the fourth way of network exchange after Email, BBS and MSN. Most bloggers begin to write blogs with great interest, and then their interests gradually achieve a balance with the passage of time. In order to describe the phenomenon that people's interest in something gradually decreases until it reaches a balance, we first propose the model that describes the attenuation of interest and reflects the fact that people's interest becomes more stable after a long time. We give a rigorous analysis on this model by non-homogeneous Poisson processes. Our analysis indicates that the interval distribution of arrival-time is a mixed distribution with exponential and power-law feature, that is, it is a power law with an exponential cutoff. Second, we collect blogs in ScienceNet.cn and carry on empirical studies on the interarrival time distribution. The empirical results agree well with the analytical result, obeying a special power law with the exponential cutoff, that is, a special kind of Gamma distribution. These empirical results verify the model, providing an evidence for a new class of phenomena in human dynamics. In human dynamics there are other distributions, besides power-law distributions. These findings demonstrate the variety of human behavior dynamics.Comment: 8 pages, 1 figure

Levels and variables associated with psychological distress during confinement due to the coronavirus pandemic in a community sample of Spanish adults

Due to the COVID-19 pandemic's consequences and the state of alarm, literature has shown that people worldwide have experienced severe stressors that have been associated with increased prevalence of emotional distress. In this study, we explored psychological distress (depression, anxiety, and somatization symptoms) using an online survey platform in a sample of 1,781 Spanish adults during the confinement due to COVID-19, relationships between distress and sleep problems, affect, pain, sleep, emotional regulation, gender, type of housing, history of psychopathology, and living alone during the confinement, and differences depending on demographic and psychological variables. Results showed that between 25% and 39% of the sample referred to clinically significant levels of distress. In addition, women showed higher levels of distress, negative affect, perception of pain, and cognitive reappraisal and lower levels of emotional suppression and sleep quality than men. A history of psychopathology, being younger, living alone or in a flat was associated with higher distress. Finally, the variables most strongly related to distress were negative and positive affect, levels of pain, sleep quality, and emotional suppression. Our results highlight the important role of emotional suppression, cognitive reappraisal, and loneliness and the impact of being a woman and younger in Spain during the COVID-19 pandemic. Therefore, it would be necessary to provide assessments of distress levels in these population groups and focus psychological preventive and therapeutic online interventions on expressing emotions and preventing loneliness