10,013 research outputs found
Smoothed Boundary Method for Solving Partial Differential Equations with General Boundary Conditions on Complex Boundaries
In this article, we describe an approach for solving partial differential
equations with general boundary conditions imposed on arbitrarily shaped
boundaries. A function that has a prescribed value on the domain in which a
differential equation is valid and smoothly but rapidly varying values on the
boundary where boundary conditions are imposed is used to modify the original
differential equations. The mathematical derivations are straight forward, and
generically applicable to a wide variety of partial differential equations. To
demonstrate the general applicability of the approach, we provide four
examples: (1) the diffusion equation with both Neumann and Dirichlet boundary
conditions, (2) the diffusion equation with surface diffusion, (3) the
mechanical equilibrium equation, and (4) the equation for phase transformation
with additional boundaries. The solutions for a few of these cases are
validated against corresponding analytical and semi-analytical solutions. The
potential of the approach is demonstrated with five applications:
surface-reaction diffusion kinetics with a complex geometry,
Kirkendall-effect-induced deformation, thermal stress in a complex geometry,
phase transformations affected by substrate surfaces, and a self-propelling
droplet.Comment: A better smooth algorithm has been developed and tested, will soon
replace Eq. 58 in page 16. We have also developed a level-set moving boundary
SBM method, and it will replace the Navier-Stokes-Cahn-Hilliard type domain
parameter tracking method in Section 5.
Estimating the Impacts of Climate Change on Mortality in OECD Countries
The major contribution of this study is to combines both climatic and macroeconomic factors simultaneously in the estimation of mortality using the capital city of 22 OECD countries from the period 1990 to 2008. The empirical results provide strong evidences that higher income and a lower unemployment rate could reduce mortality rates, while the increases in precipitation and temperature variation have significantly positive impacts on the mortality rates. The effects of changing average temperature on mortality rates in summer and winter are asymmetrical and also depend on the location. Combining the future climate change scenarios with the estimation outcomes show that mortality rates in OECD countries in 2100 will be increased by 3.77% to 5.89%.Climate change; mortality; panel data model
Modeling the Effect of Oil Price on Global Fertilizer Prices
The main purpose of this paper is to evaluate the effect of crude oil price on global fertilizer prices in both the mean and volatility. The endogenous structural breakpoint unit root test, the autoregressive distributed lag (ARDL) model, and alternative volatility models, including the generalized autoregressive conditional heteroskedasticity (GARCH) model, Exponential GARCH (EGARCH) model, and GJR model, are used to investigate the relationship between crude oil price and six global fertilizer prices. Weekly data for 2003-2008 for the seven price series are analyzed. The empirical results from ARDL show that most fertilizer prices are significantly affected by the crude oil price, which explains why global fertilizer prices reached a peak in 2008. We also find that that the volatility of global fertilizer prices and crude oil price from March to December 2008 are higher than in other periods, and that the peak crude oil price caused greater volatility in the crude oil price and global fertilizer prices. As volatility invokes financial risk, the relationship between oil price and global fertilizer prices and their associated volatility is important for public policy relating to the development of optimal energy use, global agricultural production, and financial integration.Volatility; Global fertilizer price; Crude oil price; Non-renewable fertilizers; Structural breakpoint unit root test
Revisiting the problem of audio-based hit song prediction using convolutional neural networks
Being able to predict whether a song can be a hit has impor- tant
applications in the music industry. Although it is true that the popularity of
a song can be greatly affected by exter- nal factors such as social and
commercial influences, to which degree audio features computed from musical
signals (whom we regard as internal factors) can predict song popularity is an
interesting research question on its own. Motivated by the recent success of
deep learning techniques, we attempt to ex- tend previous work on hit song
prediction by jointly learning the audio features and prediction models using
deep learning. Specifically, we experiment with a convolutional neural net-
work model that takes the primitive mel-spectrogram as the input for feature
learning, a more advanced JYnet model that uses an external song dataset for
supervised pre-training and auto-tagging, and the combination of these two
models. We also consider the inception model to characterize audio infor-
mation in different scales. Our experiments suggest that deep structures are
indeed more accurate than shallow structures in predicting the popularity of
either Chinese or Western Pop songs in Taiwan. We also use the tags predicted
by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP
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