Flushing of Contaminated Homogenous and Heterogenous Aquifers

Flushing is one of the most common and important remediation technologies to deal with the contaminated aquifers. However, the present solutions used to calculate contaminant flushing are sometimes too complicated to apply in engineering practice for quick screening purposes. This thesis uses the finite-element COMSOL Multiphysics as a platform to obtain a new solution to describe the flushing process on the homogeneous aquifers, which has been verified to be reliable. This thesis will investigate two kinds of heterogeneity in which: 1) the flow direction is parallel to; or 2) perpendicular to the two-zone interface. To analyze how the transport properties of the zones of these with contrasting hydraulic properties will impact on the contaminant transport during the flushing process, one or more parameter values were changed, while the other parameter values were kept constant. Then the predicted breakthrough curves (BTCs) were compared with each other. Through the analysis of a series of hypothetical scenarios, the results of this thesis are summarized as follows: 1) For the case when the flow direction is perpendicular to the interface of two different heterogeneous zones, the order of heterogeneous aquifers in series will not affect the flushing results, and the properties (dispersivity, porosity, and retardation factor) of heterogeneity in series can be homogenized with the arithmetic mean. Furthermore, when the averaged dispersivity increases along the flowpath from the up-gradient to the down-gradient zones, the BTC will decline slower. When the averaged porosity or retardation factor is increasing, it takes less time to flush out the same amount of contaminants, but does not affect the decline rate of the BTC; 2) For the case when the flow direction is parallel with the interface of two different heterogeneous zones, a greater difference of porosities of two zones will lead to a greater mass flux between two zones; a greater difference of transverse dispersivities will lead to a greater mass flux between the two zones; and the difference of thickness of two zones will not affect the results. This research is expected to fill the gap of flushing model on homogeneous and heterogeneous aquifers

Driver’s Shy Away Effect in Urban Extra-Long Underwater Tunnel

For urban extra-long underwater tunnels, the obstacle space formed by the tunnel walls on both sides has an impact on the driver\u27s driving. The aim of this study is to investigate the shy away characteristics of drivers in urban extra-long underwater tunnels. Using trajectory offset and speed data obtained from real vehicle tests, the driving behaviour at different lanes of an urban extra-long underwater tunnel was investigated, and a theory of shy away effects and indicators of sidewall shy away deviation for quantitative analysis were proposed. The results show that the left-hand lane has the largest offset and driving speed from the sidewall compared to the other two lanes. In the centre lane there is a large fluctuation in the amount of deflection per 50 seconds of driving, increasing the risk of two-lane collisions. When the lateral clearances are increased from 0.5 m to 2.19 m on the left and 1.29 m on the right, the safety needs of drivers can be better met. The results of this study have implications for improving traffic safety in urban extra-long underwater tunnels and for the improvement of tunnel traffic safety facilities

Interpreting mechanism of Synergism of drug combinations using attention based hierarchical graph pooling

The synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations. The major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusion of AI models un-transparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in the real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, Self-Attention based Node and Edge pool (henceforth SANEpool), that can compute the attention score (importance) of nodes and edges based on the node features and the graph topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. We evaluate SANEpool on molecular networks formulated by genes from 46 core cancer signaling pathways and drug combinations from NCI ALMANAC drug combination screening data. The experimental results indicate that 1) SANEpool can achieve the current state-of-art performance among other popular graph neural networks; and 2) the sub-molecular network detected by SANEpool are self-explainable and salient for identifying synergistic drug combinations

Mip-Splatting: Alias-free 3D Gaussian Splatting

Recently, 3D Gaussian Splatting has demonstrated impressive novel view synthesis results, reaching high fidelity and efficiency. However, strong artifacts can be observed when changing the sampling rate, \eg, by changing focal length or camera distance. We find that the source for this phenomenon can be attributed to the lack of 3D frequency constraints and the usage of a 2D dilation filter. To address this problem, we introduce a 3D smoothing filter which constrains the size of the 3D Gaussian primitives based on the maximal sampling frequency induced by the input views, eliminating high-frequency artifacts when zooming in. Moreover, replacing 2D dilation with a 2D Mip filter, which simulates a 2D box filter, effectively mitigates aliasing and dilation issues. Our evaluation, including scenarios such a training on single-scale images and testing on multiple scales, validates the effectiveness of our approach.Comment: Project page: https://niujinshuchong.github.io/mip-splatting