RESUMEN
Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic and profoundly affects almost all people around the world. Thus, many automatic diagnosis methods based on computed tomography (CT) images have been proposed to reduce the workload of radiologists. Most of the existing methods focus on the in-domain predictions, i.e., the training and testing have similar distributions, which is impractical in real-world situations, since the CT images can be collected from different devices and in different hospitals. To improve the diagnosis performance of COVID-19 for both in-domain and out-of-domain data, this paper proposes a spectrum and style transformation framework for omni-domain COVID-19 diagnosis. To achieve this, we first present a spectrum transform module, which helps to discover the discriminating features of each domain to recognize the in-domain data. Then, we formulate a cross-domain stylization module, which learns the cross-domain knowledge to enhance the model generalization capability to deal with out-of-domain data. Moreover, our framework is a plug-and-play module that can be easily integrated into existing deep models. We evaluate our framework on four COVID-19 datasets and show our method consistently improves the diagnosis performance of various methods on both in-domain and out-of-domain data.
RESUMEN
The COVID-19 pandemic disrupted almost all spares of global social, psychological, and economic life. The emergence of various variants and corresponding variations in daily infection asymmetrically influenced economic indicators. This study extends the existing literature by exploring the hedging potential of crude oil, carbon efficiency index of green firms, and bitcoin during this pandemic. This objective is realized by employing the recently advanced rolling window multiple correlation of Polanco-Martínez (2020). This approach is based on the new p-value corrected method, which has advantages over other correlation methods. The sample observations are based on daily data from 1/22/2020 to 12/20/2021. In the bivariate case, we find a significant positive correlation between COVID-19 and CEI, while a negative impact is observed between COVID-19 and WTI. Similarly, we observe a significant and nonlinear association between COVID-19 and BTC. However, our findings show positive and significant correlations among variables in the multivariate case. The overall findings show that CEI and BTC can be safe havens for investors during this worse pandemic. The study's robust findings can be used to derive important policy implications worldwide during the COVID-19 pandemic.
RESUMEN
We provide first evidence of the multiscale comovement of correlations between the S&P 500 VIX and the VIXs of Amazon, Apple, Google, Goldman Sachs, and IBM. Using grey correlation and wavelet analysis on daily data (July 2011 - September 2021), the dynamics of grey-based correlations vary across scales and depend on the fluctuation intensity of the medium time–frequency domains. The lead–lag relationships of VIX correlations are inconclusive about the dominant periodicity, although some evidence of weekly and monthly periodicity emerges. The pandemic affects the dynamics and lead-lag relationships. Such indications are useful for trading strategies and market-timing decisions.
RESUMEN
Background: Unintentional falls seriously threaten the life and health of people in China. This study aimed to assess the long-term trends of mortality from unintentional falls in China and to examine the age-, period-, and cohort-specific effects behind them. Methods: This population-based multiyear cross-sectional study of Chinese people aged 0-84 years was a secondary analysis of the mortality data of fall injuries from 1990 to 2019, derived from the Global Burden of Disease Study 2019. Age-standardized mortality rates of unintentional falls by year, sex, and age group were used as the main outcomes and were analyzed within the age-period-cohort framework. Results: Although the crude mortality rates of unintentional falls for men and women showed a significant upward trend, the age-standardized mortality rates for both sexes only increased slightly. The net drift of unintentional fall mortality was 0.13% (95% CI, -0.04 to 0.3%) per year for men and -0.71% (95% CI, -0.96 to -0.46%) per year for women. The local drift values for both sexes increased with age group. Significant age, cohort, and period effects were found behind the mortality trends of the unintentional falls for both sexes in China. Conclusions: Unintentional falls are still a major public health problem that disproportionately threatens the lives of men and women in China. Efforts should be put in place urgently to prevent the growing number of fall-related mortality for men over 40 years old and women over 70 years old. Gains observed in the recent period, relative risks (RRs), and cohort RRs may be related to improved healthcare and better education.