A Novel Method to Estimate the Damage Severity Using Spatial Wavelets and Local Regularity Algorithm

To improve the uniformity of the flow field and the poor axial velocity in the chamber of Si3N4 dry granulation, the influence of geometric parameters at open turbine combined structure on the flow field distribution is studied. The Euler–Euler gas-solid two-phase flow model is established and the physical model of dry granulation chamber under the combined structure is simplified. Under the same radial structure, the volume distribution and velocity field of Si3N4 particles in the granulation chamber with a different number and angle of the axial structure at the open turbine are analyzed by the CFD method. The influence of the axial structure at the open turbine on the flow field distribution of Si3N4 particles under different geometric parameters is compared. The results show that the axial structure of the open turbine in the granulation chamber is the most uniform when the number of blades is 6 and the inclination angle is [Formula: see text], and the circulating flow of the upper and lower parts of Si3N4 powder is strong.

show abstract

Predicting Average Wait-Time of COVID-19 Test Results and Efficacy Using Machine Learning Algorithms

Hijry

Olawoyin

Edwards

et al. 2021

IJIEOM

View full text Add to dashboard Cite

Due to the rising number of confirmed positive tests, the global impact of COVID-19 continues to grow. This can be attributed to the long wait times patients face to receive COVID-19 test results. During these lengthy waiting periods, people become anxious, especially those who are not experiencing early COVID-19 symptoms. This study aimed to develop models that predict waiting times for COVID-19 test results based on different factors such as testing facility, result interpretation, and date of test. Several machine learning algorithms were used to predict average waiting times for COVID-19 test results and to find the most accurate model. These algorithms include neural network, support vector regression, K-nearest neighbor regression, and more. COVID-19 test result waiting times were predicted for 54,730 patients recorded during the pandemic across 171 hospitals and 14 labs. To examine and evaluate the model’s accuracy, different measurements were applied such as root mean squared and R-Squared. Among the eight proposed models, the results showed that decision tree regression performed the best for predicting COVID-19 test results waiting times. The proposed models could be used to prioritize testing for COVID-19 and provide decision makers with the proper prediction tools to prepare against possible threats and consequences of future COVID-19 waves.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Novel Method to Estimate the Damage Severity Using Spatial Wavelets and Local Regularity Algorithm

Cited by 3 publications

References 23 publications

A review on failure mechanisms and analysis of multidirectional laminates

A review on failure mechanisms and analysis of multidirectional laminates

Effect of geometric parameters at open turbine combined structure on flow field distribution of dry granulation for Si3N4 powder

Predicting Average Wait-Time of COVID-19 Test Results and Efficacy Using Machine Learning Algorithms

Contact Info

Product

Resources

About