Targeted drug delivery to the deep lung improves therapeutic outcomes, but respiratory system variability complicates drug spray design. Numerical simulations offer insights for individualized treatments but are computationally intensive, highlighting the need for surrogate models for real-time deposition prediction. This study comprehensively explores the multi-task predictive capability of regression models, including Linear regression (LR), Bayesian regression (BR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), XGBoost, and CatBoost, for predicting total and regional deposition rates of inhaled particles in airway. A training dataset is obtained from well-validated CFD simulations with realistic human airway model using Euler-Lagrangian method. The results indicate that LR, BR, and SVM yield unsatisfactory predictive accuracy, with average R² values in range of 0.21 to 0.73. Comparatively, BPNN and decisiontree-based models show great potential in predicting total deposition rate in the upper and central airway. However, for regional deposition rate prediction, BPNN did not consistently yield high accuracy, particularly for oral deposition (R² = 0.538). Comparatively, XGBoost emerges as optimal model, achieving an R² approximately close to 1 on both the training and testing datasets, with predictive errors within the range of ±0.5. The overall results demonstrate that decision-tree-based models, particularly XGBoost, have superior performance in accurately predicting both total and regional deposition rates of inhaled particles within airway. Despite limitations like geometry complexity and data quantity, the workflow developed in this study is expected to pave the way for future research integrating ML models into drug delivery device design and evaluation.

Temporally compound heatwaves (CHWs), two consecutive heatwaves (HWs) with an intermittent cool break between them, are projected to occur more frequently under a warming globe. However, their spatiotemporal characteristics and interaction with urban heat island (UHI) are unexplored at the continental scale. Using observational data from over 2000 ground-based stations over China, we find that CHWs constitute an increasing portion of HW hazard from 1961 to 2021. The increasing trend is especially evident when using the daily minimum temperature to define hot days, suggesting an aggravated thermal environment at night. Urban-rural contrast of CHW trends illustrates that urbanization contributes substantially to the increased frequency of CHWs in cities, especially in southern China. Results show that mean UHI intensity (UHII) tends to weaken under HW and CHW conditions, which correlates with increased pressure and reduced precipitation. During CHW events, UHII reduces during cool break due to enhanced evaporative cooling in urban areas under precipitation. The interaction between UHI and HW is subject to change with background climate, which is positive for dry regions and negative for wet regions. This study provides insights into CHW evolution over mainland China and demonstrates the need for heat mitigation strategies under climate change.