This paper explored whether air pollutants influenced acute aortic dissection (AAD) incidence in a moderately polluted area. A total of 494 AAD patients’ data from 2013 to 2016 were analyzed. The results showed that AAD had the strongest associations with $P{M}_{10}$ , $S{O}_2$ , $N{O}_2$ , $CO$ , and $O_3$ on the day before an AAD incident (lag1) and with $P{M}_{2.5}$ two days before an incident (lag2) in single-pollutant model. In the three-pollutant model, $P{M}_{10}$ was associated with the highest risk of adverse effects (RR = 1.37, 95% CI: 1.22, 1.53), whereas $P{M}_{2.5}$ was associated with the lowest risk (RR = .83, 95% CI: .79, .88). Both $P{M}_{2.5}$ and $P{M}_{10}$ were affected by season, and $S{O}_2$ was significantly different between heating and non-heating seasons as well. This study revealed significant associations between short-term $P{M}_{2.5}$ , $P{M}_{10}$ , and $S{O}_2$ exposure and daily AAD incidence, showing that $P{M}_{10}$ and $S{O}_2$ were strong predictors of AAD incidence in a moderately polluted area.

There is limited evidence concerning the association between air pollution and different outpatient visits in moderately polluted areas. This paper investigates the effects of moderate-level air pollution on outpatient visits associated with six categories of clinic department. We analyzed a total of 1,340,791 outpatient visits for the pediatric, respiratory, ear-nose-throat (ENT), cardiovascular, ophthalmology, and orthopedics departments from January 2016 to December 2018. A distributed lag nonlinear model was used to analyze the associations and was fitted and stratified by age and season (central heating season and nonheating season). We found ${\mathrm{SO}}_2$ had the largest effect on pediatrics visits (RR = 1.105 (95%CI: 1.090, 1.121)). Meanwhile, ${\mathrm{PM}}_{2.5}$ and ${\mathrm{SO}}_2$ had greater effects on ENT visits for people under 50 years old. The results showed a strong association between ${\mathrm{O}}_3$ and cardiovascular outpatient visits in the nonheating season (RR = 1.273, 95% CI: 1.189,1.358). The results showed every 10 $\mathsf{\mu }\mathrm{g}/{\mathrm{m}}^3$ increase in ${\mathrm{SO}}_2$ was associated with a lower number of respiratory outpatient visits. Significant different associations were observed in ${\mathrm{PM}}_{2.5}$, ${\mathrm{NO}}_2$, CO, and ${\mathrm{O}}_3$ on ophthalmology visits between the heating and nonheating seasons. Although no significant association has been found in existing studies, our findings showed ${\mathrm{PM}}_{2.5}$ and ${\mathrm{NO}}_2$ were significantly related to orthopedic outpatient visits for people under 60 (RR = 1.063 (95%CI: 1.032, 1.095), RR = 1.055 (95%CI: 1.011, 1.101)). This study also found that the effect-level concentrations of air pollutants for some clinic departments were lower than the national standards, which means that people should also pay more attention when the air quality is normal.

We have used a two-dimensional charge transport model to study edge effects in photorefractive thin films. Our result shows the presence of a large surface charge layer that causes saturation of trap-limited-field in the large fringe spacing limit. Quadratic electro-optic materials were used as an example to quantitatively study the contribution of the surface-charge-layer field to the total photorefractive grating. We clearly demonstrate that the device performance can be dominated by either surface charge or bulk trap charges.

Background: The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. Objective: The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. Methods: We first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis. Results: Our proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t198=–8.722, P<.001; model 2, t198=–5.000, P<.001, model 3, t198=–1.882, P=.06; model 4, t198=–4.644, P<.001; model 5, t198=–4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t198=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks. Conclusions: Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.

On the basis of Time-Trigger Protocol (TTP), Time Triggered Ethernet (TTE) inherits the characteristics of the popularity, maturity and flexibility of traditional Ethernet technology, so as to support the comprehensive transmission of various services with different properties, high reliability and high performance in the same network. For the precise global clock synchronization to ensure the service quality of high priority services, TTE has gradually been widely used in aerospace, rail transit, automotive vehicle driving and other fields. Traffic scheduling is a key issue of TTE technology to meet the deterministic network performance. In this paper, network calculus theory is applied to the performance analysis of schedule partition and schedule porosity method with different ratios of Time-Triggered (TT) and Rate-Constraint (RC) traffics. We focus on the latency of RC frames under the condition that the TT traffic transmission requirements are firstly met. The simulation results justify the analysis validation. It is shown that neither of two methods is not always work well for ever, which it depends on the amount of TT traffics in the total traffics.

BACKGROUND The SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored. OBJECTIVE The main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources. METHODS We first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis. RESULTS Our proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t198=–8.722, P<.001; model 2, t198=–5.000, P<.001, model 3, t198=–1.882, P=.06; model 4, t198=–4.644, P<.001; model 5, t198=–4.488, P<.001). In Hubei province, our proposed model's forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t198=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks. CONCLUSIONS Our approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.