A doubly reduced approximation for the solution to PDEs based on a domain truncation and a reduced basis method: Application to Navier–Stokes equations

Elise Grosjean

doi:10.1016/j.compfluid.2026.107199

Leveraging machine learning to predict employee turnover in the service industry

2026-08-03

0

PRE

AI

K

Kavitha Haldorai

W

Woo Gon Kim

R

R.L. Fernando Garcia

J

Jun Li *

DOI：10.1016/j.ijhm.2026.104859

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Abstract

En 中文

• Availability and quality of community support are leading predictors of turnover. • Physical ergonomics significantly influences turnover. • Recognition from supervisors reduces turnover through social exchange processes. • Resilience lowers turnover, supporting conservation of resources theory. • Logistic regression achieved the highest predictive accuracy.

Keywords:

Employee turnover

Machine learning

Predictive modeling

Hospitality workforce

Community resources

Human resource information system

Leveraging machine learning to predict employee turnover in the service industry

Abstract

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