Identification of biological indicators for human exposure toxicology in smart cities based on public health data and deep learning.

With the acceleration of urbanization, the risk of urban population exposure to environmental pollutants is increasing. Protecting public health is the top priority in the construction of smart cities. The purpose of this study is to propose a method for identifying toxicological biological indicators of human exposure in smart cities based on public health data and deep learning to achieve accurate assessment and management of exposure risks. Initially, the study used a network of sensors within the smart city infrastructure to collect environmental monitoring data, including indicators such as air quality, water quality, and soil pollution. Using public health data, a database containing information on types and concentrations of environmental pollutants has been established. Convolutional neural network was used to recognize the pattern of environmental monitoring data, identify the relationship between different indicators, and build the correlation model between health indicators and environmental indicators. Identify biological indicators associated with environmental pollution exposure through training optimization. Experimental analysis showed that the prediction accuracy of the model reached 93.45%, which could provide decision support for the government and the health sector. In the recognition of the association pattern between respiratory diseases, cardiovascular diseases and environmental exposure factors such as PM2.5 and SO2, the fitting degree between the model and the simulation value reached more than 0.90. The research design model can play a positive role in public health and provide new decision-making ideas for protecting public health.

Reciprocal polarization imaging of optical activity in reflection.

We present reciprocal polarization imaging for the optical activity of chiral media in reflection geometry. The method is based on the reciprocal polar decomposition of backscattering Mueller matrices accounting for the reciprocity of light waves in forward and backward scattering paths. Anisotropic depolarization is introduced to gain sensitivity to optical activity in backscattering. Experiments with glucose solutions show that while the Lu-Chipman decomposition of the backscattering Mueller matrices produces erroneous results, reciprocal polarization imaging correctly retrieves the optical activity of chiral media. The recovered optical rotation agrees with that obtained in the forward geometry and increases linearly with the concentration and thickness of the chiral media. The potential for in vivo glucose monitoring based on optical activity sensing using reciprocal polarization imaging is then discussed.