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BACKGROUND Detrital zircon U-Pb geochronology is an important tool for identifying sedimentary provenance and determining the maximum depositional age. The numbers of grains for detrital zircon provenance investigations using laser-ablation inductively coupled-plasma mass spectrometer (LA-ICP-MS) typically range from 60 to 120. In this range, age components are commonly not identified from the sample aliquot. In order to improve the reliability of provenance investigation, analysis of more grains (n≥300) or even the large-n aliquot with more than 1000 grains (n > 1000) are required. The emergence of large-n detrital zircon U-Pb geochronology is challenging the methods of data measurement, reduction and evaluation. OBJECTIVES To summarize the progress of measurement, data reduction and data evaluation of large-n detrital zircon U-Pb geochronology. METHODS By summarizing the method innovation of domestic and foreign literature. RESULTS Firstly, each measurement requires rapid acquisition of U and Pb isotope signals, which can be conducted by improving the transmission efficiency of aerosol. The "flat" signal acquisition time can be shortened or transformed to a "peak" signal mode for rapid measurement. Secondly, large-n data require efficient data reduction protocol or powerful software (e.g. iolite) to improve visualization and reduce the variability between inter-laboratory comparisons. For U-Pb data processing flow, several optimized methods are introduced for fractionation correction and propagating uncertainty. In addition, total integrated counts and linear regression correction are introduced to specially process "peak" signals. Thirdly, the new calculation method of U-Pb and Pb-Pb age discordance, such as using Aitchison concordia distance, makes data filtering more reasonable. Based on recent research progress, the future of automation and standardization of large-n detrital zircon U-Pb geochronology is discussed and advice on the selection of instruments and reduction software is provided. CONCLUSIONS In the future, the development of large-n detrital zircon U-Pb geochronology has great prospects, and will play a greater role in the study of provenance tracing and stratigraphic dating.

Objective: Trauma is a leading cause of death worldwide, with many incidents resulting in hemorrhage before the patient reaches the hospital. Despite advances in trauma care, the majority of deaths occur within the first three hours of hospital admission, offering a very limited window for effective intervention. Unfortunately, a significant increase in mortality from hemorrhagic trauma is primarily due to delays in hemorrhage control. Therefore, we propose a machine learning model to predict the need for urgent hemorrhage intervention. Methods: This study developed and validated an XGBoost-based machine learning model using data from the National Trauma Data Bank (NTDB) from 2017 to 2019. It focuses on demographic and clinical data from the initial hours following trauma for model training and validation, aiming to predict whether trauma patients require urgent hemorrhage intervention. Results: The XGBoost model demonstrated superior performance across multiple datasets, achieving an AUROC of 0.872 on the training set, 0.869 on the internal validation set, and 0.875 on the external validation set. The model also showed high sensitivity (77.8% on the external validation set) and specificity (82.1% on the external validation set), with an accuracy exceeding 81% across all datasets, highlighting its high reliability for clinical applications. Conclusions: Our study shows that the XGBoost model effectively predicts urgent hemorrhage interventions using data from the National Trauma Data Bank (NTDB). It outperforms other machine learning algorithms in accuracy and robustness across various datasets. These results highlight machine learning's potential to improve emergency responses and decision-making in trauma care. [ABSTRACT FROM AUTHOR]