Coronavirus disease (COVID-19) has evolved into a pandemic with many unknowns.  With a severe decline in international and inter-state travel, a model at the county level, as opposed to the state or country level, is needed. Existing approaches have a few drawbacks. Firstly, the data used is the number of COVID-19 positive cases instead of positivity. The former is a function of the number of tests carried out while the latter is normalized by the number of tests. Positivity gives a better picture of the spread of this pandemic as with time more tests are being administered. Secondly, the data used by models like SEIRD lacks information about the sentiment of people with respect to coronavirus. Thirdly, models that make use of social media posts might have too much noise. News sentiment, on the other hand, can capture long term effects of hidden variables like public policy, opinions of local doctors, and disobedience of state-wide mandates. The present study introduces a new AI model, viz., Sentiment Informed Timeseries Analyzing AI (SITALA), that has been trained on COVID-19 test positivity data and news sentiment from over 2750 news articles for the Harris county. The news sentiment was obtained using IBM Watson Discovery News. SITALA is inspired by Google-Wavenet architecture and makes use of TensorFlow. 

One avenue for efficient operation of an oil and gas plant is accurate predictions about the remaining useful life (RUL) of components used in oil and gas plants. The lubricant oil in bearing and gears needs replenishment from time to time to avoid component breakdown due to the increased presence of wear debris and friction between the sliding surfaces of bearings and gears. Traditionally, this oil change is carried out at pre-determined times, also know as preventive maintenance. This study explored the possibilities of employing machine learning to predict early failure behavior in sensor-instrumented tribosystems, resulting in predictive maintenance. Wear scar based RUL data from accelerated deterioration tests was used to train a multivariate convolutional neural network (CNN). The training accuracy of the model was above 99%, and the testing accuracy was above 95%. This work involved the model-free learning prediction of the remaining useful lifetime of ball bearing-type contacts as a function of key sensor input data (i.e., load, friction, temperature). This model can be deployed for in-field tribological machine elements to trigger automated maintenance without explicitly measuring the wear phenomenon. 

Summarized from Processes 2021, 9(6), 922