Public View of Public Health Emergencies Based on Artificial Intelligence Data.

In the current environment where the network and the real society are intertwined, the network public view of public emergencies has involved in reality and altered the ecology of communal public views in China. A new online court of influence has been created, and it affected the trend of events. As the main type of public emergencies, public health emergencies are directly related to people's health and life insurance. Therefore, the public often pays special attention. At present, correct media guidance plays an irreplaceable and important role in calming people's hearts and stabilizing social order. If news and public view are left unchecked, it is likely to cause panic among the people. However, in reality, public view research has always been a research object that is difficult to intelligentize and quantify. Based on such a realistic background, the article conducts a research on public view of public health emergencies based on artificial intelligence data analysis. This study designs an expert system for network public view and optimizes the algorithm for the key problem: SFC deployment. Finally, the system was put into real news and public opinion research on new coronavirus epidemic prevention, and experimental tests were carried out. The experimental results have shown that in the new coronavirus incident, the nuclear leakage incident, and the epidemic prevention policy, the data obtained by the public through the Internet are 50%, 68.06%, and 64.35%, respectively. For the system function in this study, both ICSO and IPSO are far better than the optimization results of CSO and PSO. For most of the test functions, IPSO is better than ICSO's optimization results, which better fulfills the needs of the research content. This study will make an in-depth analysis of the evolution process of online public opinion on public emergencies from the macro-, meso-, and micro-perspectives, in order to analyze the dissemination methods and internal evolution mechanism of various public emergencies of online public opinion, which provides countermeasures and suggestions for the government to guide and manage network public opinion.

Ultrafast Detection and Discrimination of Methanol Gas Using a Polyindole-Embedded Substrate Integrated Waveguide Microwave Sensor.

The fast and sensitive detection of methanol gas using cost-effective sensors in the industry is a significant issue to be addressed. Herein, a polyindole (PIn)-deposited substrate integrated waveguide (SIW) has been introduced to perform quantitative and qualitative methanol gas sensing with quick response and recovery time at room temperature. First, PIn is synthesized and deposited in the microwell etched at the intensified electric field region of the microwave-based cavity resonator, which gives a sensing response through variation of PIn's high-frequency conductivity and dielectric property caused by the adsorption and desorption of methanol gas. Second, an enhanced filling factor and high Q factor have been attained using the proposed microwell etched SIW structure, which exhibits high sensitivity in terms of frequency shift (3.33 kHz/ppm), amplitude shift (0.005 dB/ppm), bandwidth broadening (3.66 kHz/ppm), and loaded Q factor (10.60 Q value/ppm). Third, the gas measurement results reveal excellent long-term stability with a relative standard deviation (RSD) of 0.02% for 7 days, excellent repeatability with an RSD of 0.004%, and desired response and recovery time of 95 and 120 s, respectively. The results indicate that the proposed microwave sensor has great potential to achieve high sensitivity and fast response toward methanol gas molecules at room temperature.