Model experimental study on the mechanism of collapse induced by leakage of underground pipeline.

The evolution and mechanism of ground collapse caused by underground water pipeline leakage have become increasingly significant as more urban areas experience collapses. Based on the principle of similarity, and considering the engineering context of road collapses in Anqing City, Anhui Province, this study designed a 3 m × 2 m × 2 m rupture-collapse model test device. Digital Image Correlation (DIC) technology was employed to investigate the erosion process and collapse mechanisms caused by underground pipeline leakage. The results indicate that groundwater seepage provides the driving force for collapses, combined with the migration space provided by defects, collectively triggering the collapses. When groundwater seepage is minimal, the cohesive forces between soil particles maintain soil stability. As groundwater seepage increases, the soil particle framework is eroded, leading to soil structure destabilization and collapse initiation. The depth of collapse significantly influences stress evolution: stress evolution intensity beneath and above the collapse pit is positively correlated with the distance from the collapse pit bottom, but negatively correlated with the distance from the defect. The research provides insights for the early warning and management of ground collapse.

Reservoir temperature prediction based on characterization of water chemistry data-case study of western Anatolia, Turkey.

Reservoir temperature estimation is crucial for geothermal studies, but traditional methods are complex and uncertain. To address this, we collected 83 sets of water chemistry and reservoir temperature data and applied four machine learning algorithms. These models considered various input factors and underwent data preprocessing steps like null value imputation, normalization, and Pearson coefficient calculation. Cross-validation addressed data volume issues, and performance metrics were used for model evaluation. The results revealed that our machine learning models outperformed traditional fluid geothermometers. All machine learning models surpassed traditional methods. The XGBoost model, based on the F-3 combination, demonstrated the best prediction accuracy with an R2 of 0.9732, while the Bayesian ridge regression model using the F-4 combination had the lowest performance with an R2 of 0.8302. This study highlights the potential of machine learning for accurate reservoir temperature prediction, offering geothermal professionals a reliable tool for model selection and advancing our understanding of geothermal resources.

Machine learning-based anomaly detection of groundwater microdynamics: case study of Chengdu, China.

Detection of subsurface hydrodynamic anomalies plays a significant role in groundwater resource management and environmental monitoring. In this paper, based on data from the groundwater level, atmospheric pressure, and precipitation in the Chengdu area of China, a method for detecting outliers considering the factors affecting groundwater levels is proposed. By analyzing the factors affecting groundwater levels in the monitoring site and eliminating them, simplified groundwater data is obtained. Applying sl-Pauta (self-learning-based Pauta), iForest (Isolated Forest), OCSVM (One-Class SVM), and KNN to synthetic data with known outliers, testing and evaluating the effectiveness of 4 technologies. Finally, the four methods are applied to the detection of outliers in simplified groundwater levels. The results show that in the detection of outliers in synthesized data, the OCSVM method has the best detection performance, with a precision rate of 88.89%, a recall rate of 91.43%, an F1 score of 90.14%, and an AUC value of 95.66%. In the detection of outliers in simplified groundwater levels, a qualitative analysis of the displacement data within the field of view indicates that the outlier detection performance of iForest and OCSVM is better than that of KNN. The proposed method for considering the factors affecting groundwater levels can improve the efficiency and accuracy of detecting outliers in groundwater level data.

Three-Dimensional Culture Decreases the Angiogenic Ability of Mouse Macrophages.

Macrophages play important roles in angiogenesis; however, previous studies on macrophage angiogenesis have focused on traditional 2D cultures. In this study, we established a 3D culture system for macrophages using collagen microcarriers and assessed the effect of 3D culture on their angiogenic capabilities. Macrophages grown in 3D culture displayed a significantly different morphology and arrangement under electron microscopy compared to those grown in 2D culture. Tube formation assays and chick embryo chorioallantoic membrane assays further revealed that 3D-cultured macrophages were less angiogenic than those in 2D culture. Whole-transcriptome sequencing showed that nearly 40% of genes were significantly differently expressed, including nine important angiogenic factors of which seven had been downregulated. In addition, the expression of almost all genes related to two important angiogenic pathways was decreased in 3D-cultured macrophages, including the two key angiogenic factors, VEGFA and ANG2. Together, the findings of our study improve our understanding of angiogenesis and 3D macrophage culture in tissues, and provide new avenues and methods for future research on macrophages.