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Quantitative assessment for cognitive load and mental stress is very important in optimizing human-computer system designs to improve performance and efficiency. Traditional physiological measures, such as heart rate variation (HRV), blood pressure and electrodermal activity (EDA), are widely used but still have limitations in sensitivity, reliability and usability. In this study, we propose a novel photoplethysmogram-based stress induced vascular index (sVRI) to measure cognitive load and stress. We also provide the basic methodology and detailed algorithm framework. We employed a classic experiment with three levels of task difficulty and three stages of testing period to verify the new measure. Compared with the blood pressure, heart rate and HRV components recorded simultaneously, the sVRI reached the same level of significance on the effect of task difficulty/period as the most significant other measure. Our findings showed sVRI's potential as a sensitive, reliable and usable parameter.

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In this paper, we use (Yilanci et al. 2020 ) Fourier autoregressive distributed lag (ARDL) model to study the correlation between health expenditures, CO 2 emissions, and GDP fluctuations in BRICS countries from 2000 to 2019. The Fourier ARDL model has the function of bootstrap repeated simulation calculations, so that small samples can also achieve the advantages of finer inspection results. In this paper, we find that in the long term, Brazil and China are countries that both have cointegration relationships in health expenditure, CO 2 emissions, and economic growth. With CO 2 emissions as the dependent variable and health expenditure and economic growth as independent variables, in the short term, there is a negative causal relationship between India’s CO 2 emissions and health expenditure; other countries only show the relationship between CO 2 emissions, health expenditure, or economic growth one-way relationship. This paper also has some policy suggestions on health expenditures and CO 2 emissions in the BRICS countries at the end.

Air quality issue such as particulate matter pollution (PM 2.5 and PM 10) has become one of the biggest environmental problem in China. As one of the most important industrial base and economic core regions of China, Northeast China is facing serious air pollution problems in recent years, which has a profound impact on the health of local residents and atmospheric environment in some part of East Asia. Therefore, it is urgent to understand temporal-spatial characteristics of particles and analyze the causality factors. The results demonstrated that variation trend of particles was almost similar, the annual, monthly and daily distribution had their own characteristics. Particles decreased gradually from south to north, from west to east. Correlation analysis showed that wind speed was the most important factor affecting particles, and temperature, air pressure and relative humidity were key factors in some seasons. Path analysis showed that there was complex unidirectional causal relationship between particles and individual or combined effects, and NO 2 and CO were key factors affecting PM 2.5. The hot and cold areas changed little with the seasons. All the above results suggests that planning the industrial layout, adjusting industrial structure, joint prevention and control were necessary measure to reduce particles concentration. Air pollution has become one of the severe environmental problems in China. People in China have to cope with high levels of PM 2.5 (Particulate matter smaller than 2.5 micrometers in diameter). It not only induces the increase of low-visibility days 1-3 , but also penetrates lungs and does harm to respiration, cardiovascular, cerebral vascular and nervous systems 4-7. Studies show that atmospheric particulate matter is closely related to mortality for lots of causes 8-11. In addition, Particulate matter possibly has large influence on regional and global climate change 12-14 , ecosystems 15 , economic development 16 and so on. Thus, Chinese government has established air quality monitoring stations in many cities to monitor pollutants (including PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3) mass concentration, published the relative observed data online from January 2013. These air quality data played an important role on analyzing local air pollution situation and establishing air pollutant mass concentration prediction model 17. As previous studies have shown, pollutants sources emission, external transport, meteorological conditions and secondary generation of air pollution were important factors influencing atmospheric particulate matter mass concentration. In particular, meteorological conditions can diffuse, dilute and accumulate air pollutant mass concentration at a large extent. Secondary generation will aggravate air pollutants mass concentration 18,19. Therefore, the study of the distribution characteristics of air pollutants, the relationship between meteorological conditions and air pollutants mass concentration, as well as the relationship between different air pollutants...

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