Fabrication and Performance Evolution of AgNP Interdigitated Electrode Touch Sensor for Automotive Infotainment.

In the present scenario, a considerable assiduity is provided to develop novel human-machine interface technologies that rapidly outpace the capabilities of display technology in automotive industries. It is necessary to use a new cockpit design in conjunction with a fully automated driving environment in order to enhance the driving experience. It can create a seamless and futuristic dashboard for automotive infotainment application. In the present study, an endeavor was made to equip the In-vehicle bezels with printed capacitive sensors for providing superior sensing capabilities. Silver Nanoparticles based interdigitated pattern electrodes were formed over polycarbonate substrates to make printed capacitive sensors using screen printing process. The developed sensor was investigated to evaluate the qualitative and quantitative measures using direct and in-direct contact of touch. The proposed approach for sensors pattern and fabrication can highly impact on sensor performance in automotive infotainment application due to the excellent spatial interpolation with lower cost, light weight, and mechanical flexibility.

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia.

COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities.