Detection and determination of groundwater contamination plume using time-lapse electrical resistivity tomography (ERT) method

Abstract Protection of water resources from contamination and detection of the contaminants and their treatments are among the essential issues in the management of water resources. In this work, the time-lapse electrical resistivity tomography (ERT) surveys were conducted along 7 longitudinal lines in the downstream of the Latian dam in Jajrood (Iran), in order to detect the contamination resulting from the direct injection of a saltwater solution in to the saturated zone in the area. To investigate the pollutant quantities affecting the resistivity of this zone, the temperature and electrical conductivity measurement were carried out using a self-recording device during 20 days (before and after the injection). The results obtained from the selfrecording device measurements and ERT surveys indicated that in addition to the salt concentration changes in water, the resistivity changes in the saturated zone were dependent on other factors such as the lithology and absorption of contaminants by the subsurface layers. Furthermore, the expansion of contamination toward the geological trend, sedimentation, and groundwater flow direction of the area were shown

A Multi-Solver Scheme for Viscous Flows Using Adaptive Cartesian Grids and Meshless Grid Communication

This work concerns the development of an adaptive multi-solver approach for CFD simulation of viscous flows. Curvilinear grids are used near solid bodies to capture boundary layers, and stuctured adaptive Cartesian grids are used away from the body to fill the majority of the computational domain. An edge-based meshless scheme is used in the interface region to connnect the near-body and off-body codes. We show that the combination of a body-fitted grid near the surface coupled with an adaptive Cartesian grid system away from the surface leads to a highly efficient scheme with sharp feature resolution. The use of a meshless flow solver to interface the body-fitted and Cartesian grid systems leads to seamless grid communication without many of the complexities inherent in traditional Chimera overset grid interpolation schemes. The hierarchical structure of the nested Cartesian grids may be exploited to achieve multigrid convergence for steady problems and for use in dual-time stepping algorithms for unsteady problems. Results of two-dimensional steady airfoil calculations are presented. I

An experimental study of the intrinsic stability of random forest variable importance measures

BACKGROUND: The stability of Variable Importance Measures (VIMs) based on random forest has recently received increased attention. Despite the extensive attention on traditional stability of data perturbations or parameter variations, few studies include influences coming from the intrinsic randomness in generating VIMs, i.e. bagging, randomization and permutation. To address these influences, in this paper we introduce a new concept of intrinsic stability of VIMs, which is defined as the self-consistence among feature rankings in repeated runs of VIMs without data perturbations and parameter variations. Two widely used VIMs, i.e., Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) are comprehensively investigated. The motivation of this study is two-fold. First, we empirically verify the prevalence of intrinsic stability of VIMs over many real-world datasets to highlight that the instability of VIMs does not originate exclusively from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. Second, through Spearman and Pearson tests we comprehensively investigate how different factors influence the intrinsic stability. RESULTS: The experiments are carried out on 19 benchmark datasets with diverse characteristics, including 10 high-dimensional and small-sample gene expression datasets. Experimental results demonstrate the prevalence of intrinsic stability of VIMs. Spearman and Pearson tests on the correlations between intrinsic stability and different factors show that #feature (number of features) and #sample (size of sample) have a coupling effect on the intrinsic stability. The synthetic indictor, #feature/#sample, shows both negative monotonic correlation and negative linear correlation with the intrinsic stability, while OOB accuracy has monotonic correlations with intrinsic stability. This indicates that high-dimensional, small-sample and high complexity datasets may suffer more from intrinsic instability of VIMs. Furthermore, with respect to parameter settings of random forest, a large number of trees is preferred. No significant correlations can be seen between intrinsic stability and other factors. Finally, the magnitude of intrinsic stability is always smaller than that of traditional stability. CONCLUSION: First, the prevalence of intrinsic stability of VIMs demonstrates that the instability of VIMs not only comes from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. This finding gives a better understanding of VIM stability, and may help reduce the instability of VIMs. Second, by investigating the potential factors of intrinsic stability, users would be more aware of the risks and hence more careful when using VIMs, especially on high-dimensional, small-sample and high complexity datasets

Space Charge at Nanoscale: Probing Injection and Dynamic Phenomena Under Dark/Light Configurations by Using KPFM and C-AFM

International audienc

Can understanding reward help illuminate anhedonia?

Purpose of review: The goal of this paper is to examine how reward processing might help us understand the symptom of anhedonia. Recent findings: There are extensive reviews exploring the relationship between responses to rewarding stimuli and depression. These often include a discussion on anhedonia and how this might be underpinned in particular by dysfunctional reward processing. However, there is no specific consensus on whether studies to date have adequately examined the various sub-components of reward processing or how these might relate in turn to various aspects of anhedonia symptoms. Summary: The approach to understanding the symptom of anhedonia should be to examine all the sub-components of reward processing at the subjective and objective behavioural and neural level, with well validated tasks that can be replicated. Investigating real life experiences of anhedonia and how theses might be predicted by objective lab measures is also needed in future research