2,181 research outputs found
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
A-priori Validation of Subgrid-scale Models for Astrophysical Turbulence
We perform a-priori validation tests of subgrid-scale (SGS) models for the
turbulent transport of momentum, energy and passive scalars. To this end, we
conduct two sets of high-resolution hydrodynamical simulations with a
Lagrangian code: an isothermal turbulent box with rms Mach number of 0.3, 2 and
8, and the classical wind tunnel where a cold cloud traveling through a hot
medium gradually dissolves due to fluid instabilities. Two SGS models are
examined: the eddy diffusivity (ED) model wildly adopted in astrophysical
simulations and the "gradient model" due to Clark et al. (1979). We find that
both models predict the magnitude of the SGS terms equally well (correlation
coefficient > 0.8). However, the gradient model provides excellent predictions
on the orientation and shape of the SGS terms while the ED model predicts
poorly on both, indicating that isotropic diffusion is a poor approximation of
the instantaneous turbulent transport. The best-fit coefficient of the gradient
model is in the range of [0.16, 0.21] for the momentum transport, and the
turbulent Schmidt number and Prandtl number are both close to unity, in the
range of [0.92, 1.15].Comment: ApJ accepted; analysis code available at
https://github.com/huchiayu/Lapriori.j
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
Improved Antireflection Properties of an Optical Film Surface with Mixing Conical Subwavelength Structures
Based on finite difference time domain method, an optical film surface with mixing conical subwavelength structures is numerically investigated to improve antireflection property. The mixing conical subwavelength structure is combined with the pure periodic conical subwavelength structures and the added small conical structures in the gap between the pure periodic conical subwavelength structures. The antireflection properties of two types of subwavelength structures with different aspect ratios in spectral range of 400–800 nm are analyzed and compared. It is shown that, for the mixing type, the average reflectance is decreased and the variances of the reflectance are evidently smaller. When the added structure with a better aspect ratio exists, the average reflectance of the surface can be below 0.30%. Obviously, the antireflection properties of the optical film surface with mixing conical subwavelength structures can be improved
PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation
Fall accidents are critical issues in an aging and aged society. Recently,
many researchers developed pre-impact fall detection systems using deep
learning to support wearable-based fall protection systems for preventing
severe injuries. However, most works only employed simple neural network models
instead of complex models considering the usability in resource-constrained
mobile devices and strict latency requirements. In this work, we propose a
novel pre-impact fall detection via CNN-ViT knowledge distillation, namely
PreFallKD, to strike a balance between detection performance and computational
complexity. The proposed PreFallKD transfers the detection knowledge from the
pre-trained teacher model (vision transformer) to the student model
(lightweight convolutional neural networks). Additionally, we apply data
augmentation techniques to tackle issues of data imbalance. We conduct the
experiment on the KFall public dataset and compare PreFallKD with other
state-of-the-art models. The experiment results show that PreFallKD could boost
the student model during the testing phase and achieves reliable F1-score
(92.66%) and lead time (551.3 ms)
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
WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories
Our research focuses on solving the zero-shot text classification problem in
NLP, with a particular emphasis on innovative self-training strategies. To
achieve this objective, we propose a novel self-training strategy that uses
labels rather than text for training, significantly reducing the model's
training time. Specifically, we use categories from Wikipedia as our training
set and leverage the SBERT pre-trained model to establish positive correlations
between pairs of categories within the same text, facilitating associative
training. For new test datasets, we have improved the original self-training
approach, eliminating the need for prior training and testing data from each
target dataset. Instead, we adopt Wikipedia as a unified training dataset to
better approximate the zero-shot scenario. This modification allows for rapid
fine-tuning and inference across different datasets, greatly reducing the time
required for self-training. Our experimental results demonstrate that this
method can adapt the model to the target dataset within minutes. Compared to
other BERT-based transformer models, our approach significantly reduces the
amount of training data by training only on labels, not the actual text, and
greatly improves training efficiency by utilizing a unified training set.
Additionally, our method achieves state-of-the-art results on both the Yahoo
Topic and AG News datasets
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