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Recent theoretical advances related to time-variant water age in hydrologic systems have opened the door to a new method that probes water mixing and selection behaviors using StorAge Selection (SAS) functions. In this study, SAS functions were applied to investigate storage, water mixing behaviors, and nitrate (NO3-) export regimes in a tile-drained corn-soybean rotation field in the Midwestern United States. The natural abundance stable nitrogen and oxygen isotopes of tile drainage NO3- were also measured to provide constraints on biogeochemical NO3- transformations. The SAS functions calibrated using chloride measurements at tile drain outlets revealed a strong young water preference during tile discharge generation. The use of a time-variant SAS function for tile discharge generated unique water age dynamics that reveals an inverse storage effect driven by activation of preferential flow paths and mechanically explains the observed variations in NO3- isotopes. Combining the water age estimates with NO3- isotope fingerprinting delineated NO3- export dynamics at the tile-drain scale, where a lack of strong contrast in NO3- concentration across the soil profile results in chemostatic NO3- export regimes. For the first time, NO3- isotopes were embedded into a water age-based transport model to model reactive NO3- transport under transient conditions. Results from this modeling study provided a proof-of-concept for the potential of coupled water age modeling and NO3- isotope analysis in elucidating complex mechanisms that control the coupled water and NO3- transport. Further integration of water age theory and NO3- isotope biogeochemistry is expected to significantly improve reactive NO3- transport modeling.

Pipeline transportation is widely used in industrial production and daily life. In order to reduce the waste of resources and economic losses caused by pipeline leakage, effective pipeline leakage detection and localization technology is particularly important. Among the many leakage detection methods, the model-based method for pipeline leakage detection and localization is widely used. However, the key to the method is how to obtain an accurate and reliable pipeline model to ensure and improve the detection accuracy. This paper proposes a novel method to obtain a reliable pipeline-mechanism model that fused data and mechanism models based on Bayesian theory. Moreover, in the process of Bayesian fusion, the complexity and calculations in the mechanism models were greatly reduced by establishing a surrogate model. After that, the multidimensional posterior distribution was sampled by the Markov chain Monte Carlo-differential evolution adaptive metropolis (ZS) (MCMC-DREAM (ZS)) algorithm, and the uncertainty in the model was updated to obtain a reliable pipeline-mechanism model. Subsequently, the pipeline resistance coefficient, which could be calculated based on the reliable pipeline-mechanism model, was proposed as an indicator for detecting whether the pipeline leaked or not. Finally, the pipeline leak model was used to determine the location of the leak. The reliable pipeline-mechanism model was applied in an experimental device to validate its performance. The results showed that the proposed method improved the accuracy and reliability of the mechanism model, and, in addition, the leakage could be accurately located.

Raman spectroscopy has numerous advantages as a means of analyzing materials and is widely used in petrochemical, material, food, biological, medical, and other fields. Its analysis process is fast, nondestructive, and requires no prepreparation. Meanwhile, the research on applying machine learning methods in Raman spectral recognition is becoming increasingly popular. In this study, an end-to-end deep learning method called deep residual shrinkage-VGG (DRS-VGG) is proposed, which is able to match Raman spectral features with model structure and reduces the reliance on feature engineering. The addition of identity shortcut and soft thresholding in the model eliminates redundant signals to achieve end-to-end spectral identification. The effectiveness of the proposed model is verified in three subsets of the RRUFF Raman database and bacterial Raman dataset from different perspectives without data augmentation, and the recognition accuracy is 97.84%, 92.81%, and 95.08%, respectively. Compared with other methods, the proposed DRS-VGG model achieved a significant improvement in speed or accuracy. The model's understanding of the spectra is visualized by the gradient-weighted class activation mapping (Grad-CAM), which explains the excellent classification performance. Additionally, the weight pruning technique is used to achieve model compression and improve recognition accuracy by shrinking the weights and fine-tuning the biases.