In this study, we explored machine learning approaches for predictive diagnosis using surface-enhanced Raman scattering (SERS), applied to the detection of COVID-19 infection in biological samples. To do this, we utilized SERS data collected from 20 patients at the University of Maryland Baltimore School of Medicine. As a preprocessing step, the positive-negative labels are obtained using Polymerase Chain Reaction (PCR) testing. First, we compared the performance of linear and nonlinear dimensionality techniques for projecting the high-dimensional Raman spectra to a low-dimensional space where a smaller number of variables defines each sample. The appropriate number of reduced features used was obtained by comparing the mean accuracy from a 10-fold cross-validation. Finally, we employed Gaussian process (GP) classification, a probabilistic machine learning approach, to correctly predict the occurrence of a negative or positive sample as a function of the low-dimensional space variables. As opposed to providing rigid class labels, the GP classifier provides a probability (ranging from zero to one) that a given sample is positive or negative. In practice, the proposed framework can be used to provide high-throughput rapid testing, and a follow-up PCR can be used for confirmation in cases where the model’s uncertainty is unacceptably high.

Nanofluids are solid-liquid composites which show higher convective heat transfer performance than conventional heat transfer fluids. However, most of the nanoparticles used are metallic oxides which are known to be toxic both to the environment and humans. Hence, the study of bionanomaterial to which the environment is naturally exposed is an important study. These biomaterials are the additives to the base fluid. Mango leaves-water nanofluid is the nanofluid being studied under laminar flow conditions in a horizontal pipe. The multi-phase mixture model was used to simulate the nanofluid behavior. ANSYS Fluent 18.2 finite volume commercial code was used to discretize and solve the governing equations of flow and energy with residuals set to 10-6 for each governing equation. The Semi-Implicit method for pressure linked equations algorithm [SIMPLE] was used for pressure-velocity coupling. It was observed that the local heat transfer coefficient always decreased with the axis location. A 12% increase of the average heat transfer coefficient was observed for 3% volume fraction of mango leaves-water nanofluid in comparison to the base fluid. Hence there are great prospects for the use of these fluids as heat transfer fluids it being superior to the base fluid in terms of heat transfer characteristics.