Measuring the U.S.’s COVID-19 response performance is an extremely important challenge for health care policymakers. This study integrates Data Envelopment Analysis (DEA) with four different machine learning (ML) techniques to assess the efficiency and evaluate the U.S.’s COVID-19 response performance. First, DEA is applied to measure the efficiency of fifty U.S. states considering four inputs: number of tested, public funding, number of health care employees, number of hospital beds. Then, number of recovered from COVID-19 as a desirable output and number of confirmed COVID-19 cases as a undesirable output are considered. In the second stage, Classification and Regression Tree (CART), Boosted Tree (BT), Random Forest (RF), and Logistic Regression (LR) were applied to predict the COVID-19 response performance based on fifteen environmental factors, which were classified into social distancing, health policy, and socioeconomic measures. The results showed that 23 states were efficient with an average efficiency score of 0.97. Furthermore, BT and RF models produced the best prediction results and CART performed better than LR. Lastly, urban, physical inactivity, number of tested per population, population density, and total hospital beds per population were the most influential factors on efficiency.

Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues in the 2018 National Sample Survey of Registered Nurses dataset and predict nurse turnover using machine learning algorithms. Four machine learning algorithms, namely logistic regression, random forests, decision tree, and extreme gradient boosting, were applied to the SMOTE-enhanced dataset. The data were split into 80% training and 20% validation sets. Eighteen carefully selected variables from the database served as predictive features, and the machine learning model identified age, working hours, electric health record/electronic medical record, individual income, and job type as important features concerning nurse turnover. The study includes a performance comparison based on accuracy, precision, recall (sensitivity), F1-score, and AUC. In summary, the results demonstrate that SMOTE-enhanced random forests exhibit the most robust predictive power in the classical approach (with all 18 predictive variables) and an optimized approach (utilizing eight key predictive variables). Extreme gradient boosting, decision tree, and logistic regression follow in performance. Notably, age emerges as the most influential factor in nurse turnover, with working hours, electric health record/electronic medical record usability, individual income, and region also playing significant roles. This research offers valuable insights for healthcare researchers and stakeholders, aiding in selecting suitable machine learning algorithms for nurse turnover prediction.

Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues in the 2018 National Sample Survey of Registered Nurses (NSSRN) dataset and predict nurse turnover using machine learning (ML) algorithms. Four ML algorithms, namely logistic regression (LR), random forests (RF), decision tree (DT), and extreme gradient boosting (XGBoost), are applied to the SMOTE-enhanced dataset. The data is randomly split into an 80% training set and a 20% validation set. Eighteen carefully selected variables from the NSSRN database serve as predictive features, and the machine learning model identifies feature importance concerning nurse turnover. The study includes a performance comparison based on metrics such as Accuracy, Precision, Recall (Sensitivity), F1-score, and AUC. In summary, the results demonstrate that SMOTE-enhanced random forests (SMOTE_RT) exhibit the most robust predictive power, both in the classical approach (with all 18 predictive variables) and an optimized approach (utilizing eight key predictive variables). XGBoost, decision tree, and logistic regression follow in performance. Notably, age emerges as the most influential factor in nurse turnover, with working hours, EHR/EMR usability, individual income, and region also playing significant roles. This research offers valuable insights for healthcare researchers and stakeholders, aiding in selecting suitable ML algorithms for nurse turnover prediction.

Although asphalt mixtures can be applied to railway tracks due to their viscoelastic properties, caution is required, as their ductility and brittleness are highly sensitive to temperature variations. In recent years, interest in the application of asphalt in railway infrastructure has increased, driven by the development of modified mixtures and the broader availability of performance-enhancing additives. Additionally, evaluation methods for railway tracks should be adapted to account for the distinct loading mechanisms involved, which differ from those of conventional roadways. In this study, the comprehensive properties of asphalt binders, mixtures, and testing methods—including physical and engineering characteristics—were assessed to improve the performance of asphalt concrete layers for potential applications in railroad infrastructure. The results of this study indicate that (1) the higher the performance grade (PG), the higher the indirect tensile strength (ITS) value achieved by the 13 mm mixture using PG76-22, which is higher than that of the PG64-22 mixture. This indicates that higher PG grades and modification contribute to improved tensile strength, beneficial for upper layers subjected to dynamic railroad loads. (2) The tensile strength ratio (TSR) increased from the unmodified mixture to over 92% in mixtures containing crumb rubber modifier (CRM) and styrenic thermoplastic elastomer (STE), demonstrating enhanced durability under freeze–thaw conditions. (3) Wheel tracking test results showed that modified mixtures exhibited more than twice the rutting resistance compared to PG64-22. The 13 mm aggregate mixtures also generally performed better than the 19 mm mixtures, indicating reduced permanent deformation under repeated loading. (4) It was concluded that asphalt is a suitable material for railroads, as its overall characteristics comply with standard specifications.

This study examines change in operational efficiency and productivity across a balanced panel of 15 container ports for the years 2019 and 2020. Berth length, gantry cranes, and other cranes are inputs; high container throughput is desirable, and extended anchorage waiting time is undesirable. Waiting time is calculated as the unweighted mean of weekly port estimates published by the Asian Development Bank. A pooled convex variable-returns-to-scale directional distance function with weak disposability provides a common reference technology, and the VRS global Malmquist–Luenberger (GML) index compares the two years. The mean transformed efficiency score was 0.855 across 30 port-year observations, declining from 0.878 in 2019 to 0.833 in 2020. The arithmetic mean GML score was 0.951: one port improved, ten declined, and four remained unchanged. A throughput-only model and leave-one-port-out re-estimation show that waiting time treatment and influential reference ports affect individual comparisons. In particular, the omission of Shanghai or Tanjung Pelepas substantially changed some rankings and scores.