Magneto-acoustic waves in sunspots from observations and numerical simulations

We study the propagation of waves from the photosphere to the chromosphere of sunspots. From time series of cospatial Ca II H (including its line blends) intensity spectra and polarimetric spectra of Si I 1082.7 nm and He I 1083.0 nm we retrieve the line-of-sight velocity at several heights. The analysis of the phase difference and amplification spectra shows standing waves for frequencies below 4 mHz and propagating waves for higher frequencies, and allows us to infer the temperature and height where the lines are formed. Using these observational data, we have constructed a model of sunspot, and we have introduced the velocity measured with the photospheric Si I 1082.7 nm line as a driver. The numerically propagated wave pattern fits reasonably well with the observed using the lines formed at higher layers, and the simulations reproduce many of the observed features. The observed waves are slow MHD waves propagating longitudinally along field lines.Comment: proceedings of GONG 2010/SOHO 24 meeting, June 27 - July 2, 2010, Aix-en-Provence, Franc

Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model

Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time applications or massive real world networks. Thus, approximation techniques have been developed and used to compute the measures in such scenarios. In this paper, we demonstrate and analyze the use of neural network learning algorithms to tackle such task and compare their performance in terms of solution quality and computation time with other techniques from the literature. Our work offers several contributions. We highlight both the pros and cons of approximating centralities though neural learning. By empirical means and statistics, we then show that the regression model generated with a feedforward neural networks trained by the Levenberg-Marquardt algorithm is not only the best option considering computational resources, but also achieves the best solution quality for relevant applications and large-scale networks. Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models, Machine Learning, Regression ModelComment: 8 pages, 5 tables, 2 figures, version accepted at IJCNN 2018. arXiv admin note: text overlap with arXiv:1810.1176

Divergence of the Thermal Conductivity in Uniaxially Strained Graphene

We investigate the effect of strain and isotopic disorder on thermal transport in suspended graphene by equilibrium molecular dynamics simulations. We show that the thermal conductivity of unstrained graphene, calculated from the fluctuations of the heat current at equilibrium is finite and converges with size at finite temperature. In contrast, the thermal conductivity of strained graphene diverges logarithmically with the size of the models, when strain exceeds a relatively large threshold value of 2%. An analysis of phonon populations and lifetimes explains the divergence of the thermal conductivity as a consequence of changes in the occupation of low-frequency out-of-plane phonons and an increase in their lifetimes due to strain.Comment: 6 pages, 7 figures. Accepted for publication in Physical Review

Scattering in the energy space for Boussinesq equations

In this note we show that all small solutions in the energy space of the generalized 1D Boussinesq equation must decay to zero as time tends to infinity, strongly on slightly proper subsets of the space-time light cone. Our result does not require any assumption on the power of the nonlinearity, working even for the supercritical range of scattering. No parity assumption on the initial data is needed

Fleet Prognosis with Physics-informed Recurrent Neural Networks

Services and warranties of large fleets of engineering assets is a very profitable business. The success of companies in that area is often related to predictive maintenance driven by advanced analytics. Therefore, accurate modeling, as a way to understand how the complex interactions between operating conditions and component capability define useful life, is key for services profitability. Unfortunately, building prognosis models for large fleets is a daunting task as factors such as duty cycle variation, harsh environments, inadequate maintenance, and problems with mass production can lead to large discrepancies between designed and observed useful lives. This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists have the chance to use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads). A simple numerical experiment is used to present the main features of the proposed physics-informed recurrent neural network for damage accumulation. The test problem consist of predicting fatigue crack length for a synthetic fleet of airplanes subject to different mission mixes. The model is trained using full observation inputs (far-field loads) and very limited observation of outputs (crack length at inspection for only a portion of the fleet). The results demonstrate that our proposed hybrid physics-informed recurrent neural network is able to accurately model fatigue crack growth even when the observed distribution of crack length does not match with the (unobservable) fleet distribution.Comment: Data and codes (including our implementation for both the multi-layer perceptron, the stress intensity and Paris law layers, the cumulative damage cell, as well as python driver scripts) used in this manuscript are publicly available on GitHub at https://github.com/PML-UCF/pinn. The data and code are released under the MIT Licens

Export growth and diversification : the case of Peru

The rapid growth of exports since the early 1990s is a central feature in the extraordinary rise of Peru's economy in recent years. This study puts a lens on this export growth episode, with special attention to two issues. The first one is the role of international price levels as well as export volumes in explaining this growth. The second one is whether Peru has seen a diversification of its exports during this growth episode. The empirical analysis finds that although the increase in international mineral prices has exerted a significant impact in recent years, much of the growth of Peru's export revenues has also been related to an increase in volumes. This finding applies to traditional and non- traditional exports, although the importance of volumes is more predominant for the latter. The analysis does not reveal a trend toward greater diversification of Peru's exports since 1993. On the contrary, some of the evidence suggests that the rises in price and volumes in the mining components could be leading to greater concentration. Nonetheless, there is a clear trend toward diversification among non-traditional exports due to the significant emergence of new export products in recent years.Economic Theory&Research,Achieving Shared Growth,Agribusiness&Markets,Markets and Market Access,Tax Law