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Diesel particulate filter (DPF) is an advanced technology adopted in diesel vehicles to control particulate matter emissions. However, particulates get accumulated in the DPF and have to be regenerated periodically to reduce the backpressure. Passive regeneration can be done with the aid of catalytic coating on the DPF, for the oxidation of soot. In the present work, Ceria which is an excellent catalyst has been coated over the DPF as nanofibers to ensure passive regeneration at a lower temperature, which aids in the continuous regeneration without soot accumulation. The cerium oxide nanofibers were synthesized by hydrothermal synthesis and characterized. The ceria nanofibers were coated on FeCrAl alloy by dip-coating method, and the regeneration studies were done by Thermogravimetric analysis (TGA) and CO2 emission analysis. TGA analysis shows that the ceria nanofibers ignite the soot oxidation at a lower temperature of 300 °C. CO2 emission analysis shows that the onset temperature of regeneration for the coated sample is 60 °C lesser than that of the uncoated sample. The pressure drop across the DPF for the coated and non-coated sample was found to be almost the same. The prototype of the DPF was tested in a single-cylinder diesel engine. Emission studies show a reduction in opacity up to 10% for the coated DPF as compared to uncoated DPF. Thus the rate of accumulation of soot in the DPF will be decreased, resulting in the increased mileage as the interval of the post-injection for regeneration can be increased.

Purpose The purpose of this study is to develop an efficient prediction model using vital signs and standard medical score systems, which predicts the clinical severity level of the patient in advance based on the quick sequential organ failure assessment (qSOFA) medical score method. Design/methodology/approach To predict the clinical severity level of the patient in advance, the authors have formulated a training dataset that is constructed based on the qSOFA medical score method. Further, along with the multiple vital signs, different standard medical scores and their correlation features are used to build and improve the accuracy of the prediction model. It is made sure that the constructed training set is suitable for the severity level prediction because the formulated dataset has different clusters each corresponding to different severity levels according to qSOFA score. Findings From the experimental result, it is found that the inclusion of the standard medical scores and their correlation along with multiple vital signs improves the accuracy of the clinical severity level prediction model. In addition, the authors showed that the training dataset formulated from the temporal data (which includes vital signs and medical scores) based on the qSOFA medical scoring system has the clusters which correspond to each severity level in qSOFA score. Finally, it is found that RAndom k-labELsets multi-label classification performs better prediction of severity level compared to neural network-based multi-label classification. Originality/value This paper helps in identifying patient' clinical status.