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The pandemic of coronavirus disease 2019 (COVID-19) caused by syndrome-coronavirus-2 (SARS-CoV-2) has been found rapid and large-scale diagnosis to spread across the communities. The risk of infectious airborne aerosol transmission has been an area of increasing concern. In this regard, a selective optic detection of SARS-CoV-2 aerosol is highly desirable, which can actively collect the surrounding air samples and provide real-time feedbacks to users. In this work, we developed an integrated Air Quality Monitoring System (iAQMS) that enables real-time SARS-CoV-2 aerosol detection with high sensitivity. The detection device, inspired by localized surface plasmon resonance (LSPR), was designed to measure the intensity changes of light going through our biochips in which gold nanoparticles (AuNP) combined with antibodies were exposed to airflow containing virus aerosols. We found that the variation of the measured photocurrent after 350 seconds of exposure to virus aerosols is a function of virus particle concentrations. By incorporating the air collection device and the optic detection device into a handheld sized device that can communicate with a smart-phone through Bluetooth serial, we demonstrate real-time and sensitive detection of SARS-CoV-2 virus aerosols with a concentration dynamic range of 0.00001 - 0.1 pfu/uL. Because of its sensitivity and reliability, our device can also be used for detection of other virus aerosols by simply switching the antibodies combined to the AuNP in the biochips.

We report the largest and most diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 78 genome-wide-significant (P

Reinforcement learning has become one of the most trending subjects in the recent decade. It has seen applications in various fields such as robot manipulations, autonomous driving, path planning, computer gaming, etc. We accomplished three tasks during the course of this project. Firstly, we studied the Q-learning algorithm for tabular environments and applied it successfully to an OpenAi Gym environment, Taxi. Secondly, we gained an understanding of and implemented the deep Q-network algorithm for Cart-Pole environment. Thirdly, we also studied the application of reinforcement learning in autonomous driving and its combination with safety check constraints (safety controllers). We trained a rough autonomous driving agent using highway-gym environment and explored the effects of various environment configurations like reward functions on the agent training performance., Comment: 32 pages in total, 7 figures, 3 appendices, 5 tables